diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 72140e5e..61c6ebf1 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -34,7 +34,7 @@ concurrency: env: # default Python version to use for checks that do not require multiple versions - DEFAULT_PYTHON_VERSION: '3.8' + DEFAULT_PYTHON_VERSION: '3.10' IDAES_CONDA_ENV_NAME_DEV: idaes-examples-dev PYTEST_ADDOPTS: "--color=yes" @@ -62,9 +62,10 @@ jobs: fail-fast: false matrix: python-version: - - '3.8' - '3.9' - '3.10' + - '3.11' + - '3.12' os: - linux - win64 diff --git a/.readthedocs.yml b/.readthedocs.yml index db0be9c9..017e3d60 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -3,7 +3,7 @@ version: 2 build: os: ubuntu-20.04 tools: - python: "3.8" + python: "3.10" jobs: pre_build: # Set conf vars and update the Sphinx configuration (conf.py) diff --git a/idaes_examples/browse.py b/idaes_examples/browse.py index 2a779efb..c49dfd8c 100644 --- a/idaes_examples/browse.py +++ b/idaes_examples/browse.py @@ -4,9 +4,6 @@ # stdlib from importlib import resources -if not hasattr(resources, "files"): - # importlib.resources.files() added in Python 3.9 - import importlib_resources as resources import json import logging from logging.handlers import RotatingFileHandler diff --git a/idaes_examples/mod/hda/hda_ideal_VLE.py b/idaes_examples/mod/hda/hda_ideal_VLE.py index 9498ec7c..b6c3207e 100644 --- a/idaes_examples/mod/hda/hda_ideal_VLE.py +++ b/idaes_examples/mod/hda/hda_ideal_VLE.py @@ -15,33 +15,41 @@ Benzene-Toluene-o-Xylene system. """ -# Import Python libraries -import logging # Import Pyomo libraries -from pyomo.environ import Constraint, Expression, log, NonNegativeReals,\ - Var, Set, Param, sqrt, log10, units as pyunits -from pyomo.opt import TerminationCondition +from pyomo.environ import ( + Constraint, + Expression, + log, + NonNegativeReals, + Var, + Set, + Param, + sqrt, + log10, + units as pyunits, +) from pyomo.util.calc_var_value import calculate_variable_from_constraint +from pyomo.common.config import ConfigValue # Import IDAES cores -from idaes.core import (declare_process_block_class, - MaterialFlowBasis, - PhysicalParameterBlock, - StateBlockData, - StateBlock, - MaterialBalanceType, - EnergyBalanceType, - Component, - LiquidPhase, - VaporPhase) +from idaes.core import ( + declare_process_block_class, + MaterialFlowBasis, + PhysicalParameterBlock, + StateBlockData, + StateBlock, + MaterialBalanceType, + EnergyBalanceType, + Component, + LiquidPhase, + VaporPhase, +) from idaes.core.util.constants import Constants as const -from idaes.core.util.initialization import (fix_state_vars, - revert_state_vars, - solve_indexed_blocks) +from idaes.core.util.initialization import fix_state_vars, solve_indexed_blocks +from idaes.core.initialization import InitializerBase from idaes.core.util.misc import add_object_reference -from idaes.core.util.model_statistics import degrees_of_freedom, \ - number_unfixed_variables +from idaes.core.util.model_statistics import number_unfixed_variables from idaes.core.util.misc import extract_data from idaes.core.solvers import get_solver import idaes.core.util.scaling as iscale @@ -51,14 +59,90 @@ _log = idaeslog.getLogger(__name__) +class HDAInitializer(InitializerBase): + """ + Initializer for HDA Property package. + + """ + + CONFIG = InitializerBase.CONFIG() + CONFIG.declare( + "solver", + ConfigValue(default=None, domain=str, description="Initialization solver"), + ) + CONFIG.declare( + "solver_options", + ConfigValue(default=None, description="Initialization solver options"), + ) + + def initialization_routine(self, blk): + init_log = idaeslog.getInitLogger( + blk.name, self.config.output_level, tag="properties" + ) + solve_log = idaeslog.getSolveLogger( + blk.name, self.config.output_level, tag="properties" + ) + + # Set solver + solver = get_solver(self.config.solver, self.config.solver_options) + + # --------------------------------------------------------------------- + # If present, initialize bubble and dew point calculations + for k in blk.keys(): + if hasattr(blk[k], "eq_temperature_dew"): + calculate_variable_from_constraint( + blk[k].temperature_dew, blk[k].eq_temperature_dew + ) + + if hasattr(blk[k], "eq_pressure_dew"): + calculate_variable_from_constraint( + blk[k].pressure_dew, blk[k].eq_pressure_dew + ) + + init_log.info_high( + "Initialization Step 1 - Dew and bubble points " "calculation completed." + ) + + # --------------------------------------------------------------------- + # If flash, initialize T1 and Teq + for k in blk.keys(): + if blk[k].config.has_phase_equilibrium and not blk[k].config.defined_state: + blk[k]._t1.value = max( + blk[k].temperature.value, blk[k].temperature_bubble.value + ) + blk[k]._teq.value = min(blk[k]._t1.value, blk[k].temperature_dew.value) + + init_log.info_high( + "Initialization Step 2 - Equilibrium temperature " " calculation completed." + ) + + # --------------------------------------------------------------------- + # Initialize flow rates and compositions + free_vars = 0 + for k in blk.keys(): + free_vars += number_unfixed_variables(blk[k]) + if free_vars > 0: + try: + with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc: + res = solve_indexed_blocks(solver, [blk], tee=slc.tee) + except: + res = None + else: + res = None + + init_log.info("Initialization Complete") + + return res + + @declare_process_block_class("HDAParameterBlock") class HDAParameterData(PhysicalParameterBlock): CONFIG = PhysicalParameterBlock.CONFIG() def build(self): - ''' + """ Callable method for Block construction. - ''' + """ super(HDAParameterData, self).build() self._state_block_class = IdealStateBlock @@ -72,284 +156,304 @@ def build(self): self.Vap = VaporPhase() # List of components in each phase (optional) - self.phase_comp = {"Liq": self.component_list, - "Vap": self.component_list} + self.phase_comp = {"Liq": self.component_list, "Vap": self.component_list} # List of phase equilibrium index self.phase_equilibrium_idx = Set(initialize=[1, 2, 3, 4]) - self.phase_equilibrium_list = \ - {1: ["benzene", ("Vap", "Liq")], - 2: ["toluene", ("Vap", "Liq")], - 3: ["hydrogen", ("Vap", "Liq")], - 4: ["methane", ("Vap", "Liq")]} + self.phase_equilibrium_list = { + 1: ["benzene", ("Vap", "Liq")], + 2: ["toluene", ("Vap", "Liq")], + 3: ["hydrogen", ("Vap", "Liq")], + 4: ["methane", ("Vap", "Liq")], + } # Thermodynamic reference state - self.pressure_ref = Param(mutable=True, - default=101325, - units=pyunits.Pa, - doc='Reference pressure') - self.temperature_ref = Param(mutable=True, - default=298.15, - units=pyunits.K, - doc='Reference temperature') + self.pressure_ref = Param( + mutable=True, default=101325, units=pyunits.Pa, doc="Reference pressure" + ) + self.temperature_ref = Param( + mutable=True, default=298.15, units=pyunits.K, doc="Reference temperature" + ) # Source: The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid - pressure_crit_data = {'benzene': 48.9e5, - 'toluene': 41e5, - 'hydrogen': 12.9e5, - 'methane': 46e5 - } + pressure_crit_data = { + "benzene": 48.9e5, + "toluene": 41e5, + "hydrogen": 12.9e5, + "methane": 46e5, + } self.pressure_crit = Param( self.component_list, within=NonNegativeReals, - mutable=False, + mutable=True, units=pyunits.Pa, initialize=extract_data(pressure_crit_data), - doc='Critical pressure') + doc="Critical pressure", + ) # Source: The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid - temperature_crit_data = {'benzene': 562.2, - 'toluene': 591.8, - 'hydrogen': 33.0, - 'methane': 190.4 - } + temperature_crit_data = { + "benzene": 562.2, + "toluene": 591.8, + "hydrogen": 33.0, + "methane": 190.4, + } self.temperature_crit = Param( self.component_list, within=NonNegativeReals, - mutable=False, + mutable=True, units=pyunits.K, initialize=extract_data(temperature_crit_data), - doc='Critical temperature') + doc="Critical temperature", + ) # Source: The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid - mw_comp_data = {'benzene': 78.1136E-3, - 'toluene': 92.1405E-3, - 'hydrogen': 2.016e-3, - 'methane': 16.043e-3} - - self.mw_comp = Param(self.component_list, - mutable=False, - units=pyunits.kg/pyunits.mol, - initialize=extract_data(mw_comp_data), - doc="molecular weight") + mw_comp_data = { + "benzene": 78.1136e-3, + "toluene": 92.1405e-3, + "hydrogen": 2.016e-3, + "methane": 16.043e-3, + } + + self.mw_comp = Param( + self.component_list, + mutable=True, + units=pyunits.kg / pyunits.mol, + initialize=extract_data(mw_comp_data), + doc="molecular weight", + ) # Constants for liquid densities # Source: Perry's Chemical Engineers Handbook # - Robert H. Perry (Cp_liq) - dens_liq_data = {('benzene', '1'): 1.0162, - ('benzene', '2'): 0.2655, - ('benzene', '3'): 562.16, - ('benzene', '4'): 0.28212, - ('toluene', '1'): 0.8488, - ('toluene', '2'): 0.26655, - ('toluene', '3'): 591.8, - ('toluene', '4'): 0.2878, - ('hydrogen', '1'): 5.414, - ('hydrogen', '2'): 0.34893, - ('hydrogen', '3'): 33.19, - ('hydrogen', '4'): 0.2706, - ('methane', '1'): 2.9214, - ('methane', '2'): 0.28976, - ('methane', '3'): 190.56, - ('methane', '4'): 0.28881} + dens_liq_data = { + ("benzene", "1"): 1.0162, + ("benzene", "2"): 0.2655, + ("benzene", "3"): 562.16, + ("benzene", "4"): 0.28212, + ("toluene", "1"): 0.8488, + ("toluene", "2"): 0.26655, + ("toluene", "3"): 591.8, + ("toluene", "4"): 0.2878, + ("hydrogen", "1"): 5.414, + ("hydrogen", "2"): 0.34893, + ("hydrogen", "3"): 33.19, + ("hydrogen", "4"): 0.2706, + ("methane", "1"): 2.9214, + ("methane", "2"): 0.28976, + ("methane", "3"): 190.56, + ("methane", "4"): 0.28881, + } self.dens_liq_param_1 = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in dens_liq_data.items() if j == '1'}, + mutable=True, + initialize={c: v for (c, j), v in dens_liq_data.items() if j == "1"}, doc="Parameter 1 to compute liquid densities", - units=pyunits.kmol*pyunits.m**-3 + units=pyunits.kmol * pyunits.m**-3, ) self.dens_liq_param_2 = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in dens_liq_data.items() if j == '2'}, + mutable=True, + initialize={c: v for (c, j), v in dens_liq_data.items() if j == "2"}, doc="Parameter 2 to compute liquid densities", - units=pyunits.dimensionless + units=pyunits.dimensionless, ) self.dens_liq_param_3 = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in dens_liq_data.items() if j == '3'}, + mutable=True, + initialize={c: v for (c, j), v in dens_liq_data.items() if j == "3"}, doc="Parameter 3 to compute liquid densities", - units=pyunits.K + units=pyunits.K, ) self.dens_liq_param_4 = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in dens_liq_data.items() if j == '4'}, + mutable=True, + initialize={c: v for (c, j), v in dens_liq_data.items() if j == "4"}, doc="Parameter 4 to compute liquid densities", - units=pyunits.dimensionless + units=pyunits.dimensionless, ) # Boiling point at standard pressure # Source: Perry's Chemical Engineers Handbook # - Robert H. Perry (Cp_liq) - bp_data = {('benzene'): 353.25, - ('toluene'): 383.95, - ('hydrogen'): 20.45, - ('methane'): 111.75} + bp_data = { + ("benzene"): 353.25, + ("toluene"): 383.95, + ("hydrogen"): 20.45, + ("methane"): 111.75, + } self.temperature_boil = Param( - self.component_list, - mutable=False, - units=pyunits.K, - initialize=extract_data(bp_data), - doc="Pure component boiling points at standard pressure") + self.component_list, + mutable=True, + units=pyunits.K, + initialize=extract_data(bp_data), + doc="Pure component boiling points at standard pressure", + ) # Constants for specific heat capacity, enthalpy # Sources: The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid # Perry's Chemical Engineers Handbook # - Robert H. Perry (Cp_liq) - cp_ig_data = {('Liq', 'benzene', '1'): 1.29E5, - ('Liq', 'benzene', '2'): -1.7E2, - ('Liq', 'benzene', '3'): 6.48E-1, - ('Liq', 'benzene', '4'): 0, - ('Liq', 'benzene', '5'): 0, - ('Vap', 'benzene', '1'): -3.392E1, - ('Vap', 'benzene', '2'): 4.739E-1, - ('Vap', 'benzene', '3'): -3.017E-4, - ('Vap', 'benzene', '4'): 7.130E-8, - ('Vap', 'benzene', '5'): 0, - ('Liq', 'toluene', '1'): 1.40E5, - ('Liq', 'toluene', '2'): -1.52E2, - ('Liq', 'toluene', '3'): 6.95E-1, - ('Liq', 'toluene', '4'): 0, - ('Liq', 'toluene', '5'): 0, - ('Vap', 'toluene', '1'): -2.435E1, - ('Vap', 'toluene', '2'): 5.125E-1, - ('Vap', 'toluene', '3'): -2.765E-4, - ('Vap', 'toluene', '4'): 4.911E-8, - ('Vap', 'toluene', '5'): 0, - ('Liq', 'hydrogen', '1'): 0, # 6.6653e1, - ('Liq', 'hydrogen', '2'): 0, # 6.7659e3, - ('Liq', 'hydrogen', '3'): 0, # -1.2363e2, - ('Liq', 'hydrogen', '4'): 0, # 4.7827e2, # Eqn 2 - ('Liq', 'hydrogen', '5'): 0, - ('Vap', 'hydrogen', '1'): 2.714e1, - ('Vap', 'hydrogen', '2'): 9.274e-3, - ('Vap', 'hydrogen', '3'): -1.381e-5, - ('Vap', 'hydrogen', '4'): 7.645e-9, - ('Vap', 'hydrogen', '5'): 0, - ('Liq', 'methane', '1'): 0, # 6.5708e1, - ('Liq', 'methane', '2'): 0, # 3.8883e4, - ('Liq', 'methane', '3'): 0, # -2.5795e2, - ('Liq', 'methane', '4'): 0, # 6.1407e2, # Eqn 2 - ('Liq', 'methane', '5'): 0, - ('Vap', 'methane', '1'): 1.925e1, - ('Vap', 'methane', '2'): 5.213e-2, - ('Vap', 'methane', '3'): 1.197e-5, - ('Vap', 'methane', '4'): -1.132e-8, - ('Vap', 'methane', '5'): 0} + cp_ig_data = { + ("Liq", "benzene", "1"): 1.29e5, + ("Liq", "benzene", "2"): -1.7e2, + ("Liq", "benzene", "3"): 6.48e-1, + ("Liq", "benzene", "4"): 0, + ("Liq", "benzene", "5"): 0, + ("Vap", "benzene", "1"): -3.392e1, + ("Vap", "benzene", "2"): 4.739e-1, + ("Vap", "benzene", "3"): -3.017e-4, + ("Vap", "benzene", "4"): 7.130e-8, + ("Vap", "benzene", "5"): 0, + ("Liq", "toluene", "1"): 1.40e5, + ("Liq", "toluene", "2"): -1.52e2, + ("Liq", "toluene", "3"): 6.95e-1, + ("Liq", "toluene", "4"): 0, + ("Liq", "toluene", "5"): 0, + ("Vap", "toluene", "1"): -2.435e1, + ("Vap", "toluene", "2"): 5.125e-1, + ("Vap", "toluene", "3"): -2.765e-4, + ("Vap", "toluene", "4"): 4.911e-8, + ("Vap", "toluene", "5"): 0, + ("Liq", "hydrogen", "1"): 0, # 6.6653e1, + ("Liq", "hydrogen", "2"): 0, # 6.7659e3, + ("Liq", "hydrogen", "3"): 0, # -1.2363e2, + ("Liq", "hydrogen", "4"): 0, # 4.7827e2, # Eqn 2 + ("Liq", "hydrogen", "5"): 0, + ("Vap", "hydrogen", "1"): 2.714e1, + ("Vap", "hydrogen", "2"): 9.274e-3, + ("Vap", "hydrogen", "3"): -1.381e-5, + ("Vap", "hydrogen", "4"): 7.645e-9, + ("Vap", "hydrogen", "5"): 0, + ("Liq", "methane", "1"): 0, # 6.5708e1, + ("Liq", "methane", "2"): 0, # 3.8883e4, + ("Liq", "methane", "3"): 0, # -2.5795e2, + ("Liq", "methane", "4"): 0, # 6.1407e2, # Eqn 2 + ("Liq", "methane", "5"): 0, + ("Vap", "methane", "1"): 1.925e1, + ("Vap", "methane", "2"): 5.213e-2, + ("Vap", "methane", "3"): 1.197e-5, + ("Vap", "methane", "4"): -1.132e-8, + ("Vap", "methane", "5"): 0, + } self.cp_ig_1 = Param( self.phase_list, self.component_list, - mutable=False, - initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == '1'}, + mutable=True, + initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == "1"}, doc="Parameter 1 to compute Cp_comp", - units=pyunits.J/pyunits.mol/pyunits.K + units=pyunits.J / pyunits.mol / pyunits.K, ) self.cp_ig_2 = Param( self.phase_list, self.component_list, - mutable=False, - initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == '2'}, + mutable=True, + initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == "2"}, doc="Parameter 2 to compute Cp_comp", - units=pyunits.J/pyunits.mol/pyunits.K**2 + units=pyunits.J / pyunits.mol / pyunits.K**2, ) self.cp_ig_3 = Param( self.phase_list, self.component_list, - mutable=False, - initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == '3'}, + mutable=True, + initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == "3"}, doc="Parameter 3 to compute Cp_comp", - units=pyunits.J/pyunits.mol/pyunits.K**3 + units=pyunits.J / pyunits.mol / pyunits.K**3, ) self.cp_ig_4 = Param( self.phase_list, self.component_list, - mutable=False, - initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == '4'}, + mutable=True, + initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == "4"}, doc="Parameter 4 to compute Cp_comp", - units=pyunits.J/pyunits.mol/pyunits.K**4 + units=pyunits.J / pyunits.mol / pyunits.K**4, ) self.cp_ig_5 = Param( self.phase_list, self.component_list, - mutable=False, - initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == '5'}, + mutable=True, + initialize={(p, c): v for (p, c, j), v in cp_ig_data.items() if j == "5"}, doc="Parameter 5 to compute Cp_comp", - units=pyunits.J/pyunits.mol/pyunits.K**5 + units=pyunits.J / pyunits.mol / pyunits.K**5, ) # Source: The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid # fitted to Antoine form # H2, Methane from NIST webbook - pressure_sat_coeff_data = {('benzene', 'A'): 4.202, - ('benzene', 'B'): 1322, - ('benzene', 'C'): -38.56, - ('toluene', 'A'): 4.216, - ('toluene', 'B'): 1435, - ('toluene', 'C'): -43.33, - ('hydrogen', 'A'): 3.543, - ('hydrogen', 'B'): 99.40, - ('hydrogen', 'C'): 7.726, - ('methane', 'A'): 3.990, - ('methane', 'B'): 443.0, - ('methane', 'C'): -0.49} + pressure_sat_coeff_data = { + ("benzene", "A"): 4.202, + ("benzene", "B"): 1322, + ("benzene", "C"): -38.56, + ("toluene", "A"): 4.216, + ("toluene", "B"): 1435, + ("toluene", "C"): -43.33, + ("hydrogen", "A"): 3.543, + ("hydrogen", "B"): 99.40, + ("hydrogen", "C"): 7.726, + ("methane", "A"): 3.990, + ("methane", "B"): 443.0, + ("methane", "C"): -0.49, + } self.pressure_sat_coeff_A = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in pressure_sat_coeff_data.items() if j == 'A'}, + mutable=True, + initialize={ + c: v for (c, j), v in pressure_sat_coeff_data.items() if j == "A" + }, doc="Parameter A to compute saturated pressure", - units=pyunits.dimensionless + units=pyunits.dimensionless, ) self.pressure_sat_coeff_B = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in pressure_sat_coeff_data.items() if j == 'B'}, + mutable=True, + initialize={ + c: v for (c, j), v in pressure_sat_coeff_data.items() if j == "B" + }, doc="Parameter B to compute saturated pressure", - units=pyunits.K + units=pyunits.K, ) self.pressure_sat_coeff_C = Param( self.component_list, - mutable=False, - initialize={c: v for (c, j), v in pressure_sat_coeff_data.items() if j == 'C'}, + mutable=True, + initialize={ + c: v for (c, j), v in pressure_sat_coeff_data.items() if j == "C" + }, doc="Parameter C to compute saturated pressure", - units=pyunits.K + units=pyunits.K, ) # Source: The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid - dh_vap = {'benzene': 3.387e4, - 'toluene': 3.8262e4, - 'hydrogen': 0, - 'methane': 0} + dh_vap = {"benzene": 3.387e4, "toluene": 3.8262e4, "hydrogen": 0, "methane": 0} - self.dh_vap = Param(self.component_list, - mutable=False, - units=pyunits.J/pyunits.mol, - initialize=extract_data(dh_vap), - doc="heat of vaporization") + self.dh_vap = Param( + self.component_list, + mutable=True, + units=pyunits.J / pyunits.mol, + initialize=extract_data(dh_vap), + doc="heat of vaporization", + ) # Set default scaling factors self.set_default_scaling("flow_mol", 1e3) @@ -377,45 +481,52 @@ def define_metadata(cls, obj): """Define properties supported and units.""" obj.add_properties( { - 'flow_mol': {'method': None}, - 'flow_mol_phase_comp': {'method': None}, - 'mole_frac_comp': {'method': None}, - 'temperature': {'method': None}, - 'pressure': {'method': None}, - 'flow_mol_phase': {'method': None}, - 'dens_mol_phase': {'method': '_dens_mol_phase'}, - 'pressure_sat': {'method': '_pressure_sat'}, - 'mole_frac_phase_comp': {'method': '_mole_frac_phase'}, - 'energy_internal_mol_phase_comp': { - 'method': '_energy_internal_mol_phase_comp'}, - 'energy_internal_mol_phase': { - 'method': '_energy_internal_mol_phase'}, - 'enth_mol_phase_comp': {'method': '_enth_mol_phase_comp'}, - 'enth_mol_phase': {'method': '_enth_mol_phase'}, - 'entr_mol_phase_comp': {'method': '_entr_mol_phase_comp'}, - 'entr_mol_phase': {'method': '_entr_mol_phase'}, - 'temperature_bubble': {'method': '_temperature_bubble'}, - 'temperature_dew': {'method': '_temperature_dew'}, - 'pressure_bubble': {'method': '_pressure_bubble'}, - 'pressure_dew': {'method': '_pressure_dew'}, - 'fug_phase_comp': {'method': '_fug_phase_comp'}, - } + "flow_mol": {"method": None}, + "flow_mol_phase_comp": {"method": None}, + "mole_frac_comp": {"method": None}, + "temperature": {"method": None}, + "pressure": {"method": None}, + "flow_mol_phase": {"method": None}, + "dens_mol_phase": {"method": "_dens_mol_phase"}, + "pressure_sat": {"method": "_pressure_sat"}, + "mole_frac_phase_comp": {"method": "_mole_frac_phase"}, + "energy_internal_mol_phase_comp": { + "method": "_energy_internal_mol_phase_comp" + }, + "energy_internal_mol_phase": {"method": "_energy_internal_mol_phase"}, + "enth_mol_phase_comp": {"method": "_enth_mol_phase_comp"}, + "enth_mol_phase": {"method": "_enth_mol_phase"}, + "entr_mol_phase_comp": {"method": "_entr_mol_phase_comp"}, + "entr_mol_phase": {"method": "_entr_mol_phase"}, + "temperature_bubble": {"method": "_temperature_bubble"}, + "temperature_dew": {"method": "_temperature_dew"}, + "pressure_bubble": {"method": "_pressure_bubble"}, + "pressure_dew": {"method": "_pressure_dew"}, + "fug_phase_comp": {"method": "_fug_phase_comp"}, + } ) obj.define_custom_properties( { # Enthalpy of vaporization - 'dh_vap': {'method': '_dh_vap', "units": obj.derived_units.ENERGY_MOLE}, + "dh_vap": {"method": "_dh_vap", "units": obj.derived_units.ENERGY_MOLE}, # Entropy of vaporization - 'ds_vap': {'method': '_ds_vap', "units": obj.derived_units.ENTROPY_MOLE}, + "ds_vap": { + "method": "_ds_vap", + "units": obj.derived_units.ENTROPY_MOLE, + }, + } + ) + + obj.add_default_units( + { + "time": pyunits.s, + "length": pyunits.m, + "mass": pyunits.kg, + "amount": pyunits.mol, + "temperature": pyunits.K, } ) - - obj.add_default_units({'time': pyunits.s, - 'length': pyunits.m, - 'mass': pyunits.kg, - 'amount': pyunits.mol, - 'temperature': pyunits.K}) class _IdealStateBlock(StateBlock): @@ -424,394 +535,317 @@ class _IdealStateBlock(StateBlock): whole, rather than individual elements of indexed Property Blocks. """ - def initialize(blk, state_args={}, state_vars_fixed=False, - hold_state=False, outlvl=idaeslog.NOTSET, - solver=None, optarg=None): + default_initializer = HDAInitializer + + def fix_initialization_states(blk): """ - Initialization routine for property package. - Keyword Arguments: - state_args : Dictionary with initial guesses for the state vars - chosen. Note that if this method is triggered - through the control volume, and if initial guesses - were not provided at the unit model level, the - control volume passes the inlet values as initial - guess.The keys for the state_args dictionary are: - - flow_mol_phase_comp : value at which to initialize - phase component flows - pressure : value at which to initialize pressure - temperature : value at which to initialize temperature - outlvl : sets output level of initialization routine - * 0 = no output (default) - * 1 = return solver state for each step in routine - * 2 = include solver output information (tee=True) - optarg : solver options dictionary object (default=None) - 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. - solver : str indicating which 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 variables 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 - release_state method + Fixes state variables for state blocks. + Returns: - If hold_states is True, returns a dict containing flags for - which states were fixed during initialization. + None """ - init_log = idaeslog.getInitLogger(blk.name, outlvl, tag="properties") - solve_log = idaeslog.getSolveLogger(blk.name, outlvl, tag="properties") - - # Fix state variables if not already fixed - if state_vars_fixed is False: - flags = fix_state_vars(blk, state_args) - - 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.") - # Set solver - opt = get_solver(solver, optarg) - - # --------------------------------------------------------------------- - # If present, initialize bubble and dew point calculations - for k in blk.keys(): - if hasattr(blk[k], "eq_temperature_dew"): - calculate_variable_from_constraint(blk[k].temperature_dew, - blk[k].eq_temperature_dew) - - if hasattr(blk[k], "eq_pressure_dew"): - calculate_variable_from_constraint(blk[k].pressure_dew, - blk[k].eq_pressure_dew) - - init_log.info_high("Initialization Step 1 - Dew and bubble points " - "calculation completed.") - - # --------------------------------------------------------------------- - # If flash, initialize T1 and Teq - for k in blk.keys(): - if (blk[k].config.has_phase_equilibrium and - not blk[k].config.defined_state): - blk[k]._t1.value = max(blk[k].temperature.value, - blk[k].temperature_bubble.value) - blk[k]._teq.value = min(blk[k]._t1.value, - blk[k].temperature_dew.value) - - init_log.info_high("Initialization Step 2 - Equilibrium temperature " - " calculation completed.") - - # --------------------------------------------------------------------- - # Initialize flow rates and compositions - # TODO : This will need to be generalised more when we move to a - # modular implementation - for k in blk.keys(): - # Deactivate equilibrium constraints, as state is fixed - if hasattr(blk[k], 'equilibrium_constraint'): - blk[k].equilibrium_constraint.deactivate() - - free_vars = 0 - for k in blk.keys(): - free_vars += number_unfixed_variables(blk[k]) - if free_vars > 0: - try: - with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc: - res = solve_indexed_blocks(opt, [blk], tee=slc.tee) - except: - res = None - else: - res = None - - for k in blk.keys(): - # Reactivate equilibrium constraints - if hasattr(blk[k], 'equilibrium_constraint'): - blk[k].equilibrium_constraint.activate() + # Fix state variables + fix_state_vars(blk) - # --------------------------------------------------------------------- - # Return state to initial conditions - if state_vars_fixed is False: - if hold_state is True: - return flags - else: - blk.release_state(flags) + # Also need to deactivate sum of mole fraction constraint + for k in blk.values(): + if not k.config.defined_state: + k.equilibrium_constraint.deactivate() - init_log.info("Initialization Complete") - def release_state(blk, flags, outlvl=0): - ''' - Method to release state variables fixed during initialization. - 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 - ''' - init_log = idaeslog.getInitLogger(blk.name, outlvl, tag="properties") - if flags is None: - init_log.debug("No flags passed to release_state().") - return - - # Unfix state variables - revert_state_vars(blk, flags) - - init_log.info_high("State Released.") - - -@declare_process_block_class("IdealStateBlock", - block_class=_IdealStateBlock) +@declare_process_block_class("IdealStateBlock", block_class=_IdealStateBlock) class IdealStateBlockData(StateBlockData): """An example property package for ideal VLE.""" def build(self): """Callable method for Block construction.""" - super(IdealStateBlockData, self).build() + super().build() # Add state variables self.flow_mol_phase_comp = Var( - self._params.phase_list, - self._params.component_list, - initialize=0.5, - units=pyunits.mol/pyunits.s, - bounds=(1e-12, 100), - doc='Phase-component molar flow rates') - - self.pressure = Var(initialize=101325, - bounds=(100000, 1000000), - units=pyunits.Pa, - domain=NonNegativeReals, - doc='State pressure') - self.temperature = Var(initialize=298.15, - units=pyunits.K, - bounds=(298, 1000), - domain=NonNegativeReals, - doc='State temperature') + self._params.phase_list, + self._params.component_list, + initialize=0.5, + units=pyunits.mol / pyunits.s, + bounds=(1e-12, 100), + doc="Phase-component molar flow rates", + ) + + self.pressure = Var( + initialize=101325, + bounds=(100000, 1000000), + units=pyunits.Pa, + domain=NonNegativeReals, + doc="State pressure", + ) + self.temperature = Var( + initialize=298.15, + units=pyunits.K, + bounds=(298, 1000), + domain=NonNegativeReals, + doc="State temperature", + ) # Add supporting variables def flow_mol_phase(b, p): - return sum(b.flow_mol_phase_comp[p, j] - for j in b._params.component_list) - self.flow_mol_phase = Expression(self._params.phase_list, - rule=flow_mol_phase, - doc='Phase molar flow rates') + return sum(b.flow_mol_phase_comp[p, j] for j in b._params.component_list) + + self.flow_mol_phase = Expression( + self._params.phase_list, rule=flow_mol_phase, doc="Phase molar flow rates" + ) def flow_mol(b): - return sum(b.flow_mol_phase_comp[p, j] - for j in b._params.component_list - for p in b._params.phase_list) - self.flow_mol = Expression(rule=flow_mol, - doc='Total molar flowrate') + return sum( + b.flow_mol_phase_comp[p, j] + for j in b._params.component_list + for p in b._params.phase_list + ) + + self.flow_mol = Expression(rule=flow_mol, doc="Total molar flowrate") def mole_frac_phase_comp(b, p, j): - return b.flow_mol_phase_comp[p, j]/b.flow_mol_phase[p] + return b.flow_mol_phase_comp[p, j] / b.flow_mol_phase[p] + self.mole_frac_phase_comp = Expression( - self._params.phase_list, - self._params.component_list, - rule=mole_frac_phase_comp, - doc='Phase mole fractions') + self._params.phase_list, + self._params.component_list, + rule=mole_frac_phase_comp, + doc="Phase mole fractions", + ) def mole_frac_comp(b, j): - return (sum(b.flow_mol_phase_comp[p, j] - for p in b._params.phase_list) / b.flow_mol) - self.mole_frac_comp = Expression(self._params.component_list, - rule=mole_frac_comp, - doc='Mixture mole fractions') + return ( + sum(b.flow_mol_phase_comp[p, j] for p in b._params.phase_list) + / b.flow_mol + ) + + self.mole_frac_comp = Expression( + self._params.component_list, + rule=mole_frac_comp, + doc="Mixture mole fractions", + ) # Reaction Stoichiometry - add_object_reference(self, "phase_equilibrium_list_ref", - self._params.phase_equilibrium_list) + add_object_reference( + self, "phase_equilibrium_list_ref", self._params.phase_equilibrium_list + ) - if (self.config.has_phase_equilibrium and - self.config.defined_state is False): + if self.config.has_phase_equilibrium and self.config.defined_state is False: # Definition of equilibrium temperature for smooth VLE self._teq = Var( - initialize=self.temperature.value, - units=pyunits.K, - doc='Temperature for calculating phase equilibrium') - self._t1 = Var(initialize=self.temperature.value, - units=pyunits.K, - doc='Intermediate temperature for calculating Teq') - - self.eps_1 = Param(default=0.01, - units=pyunits.K, - mutable=True, - doc='Smoothing parameter for Teq') - self.eps_2 = Param(default=0.0005, - units=pyunits.K, - mutable=True, - doc='Smoothing parameter for Teq') + initialize=self.temperature.value, + units=pyunits.K, + doc="Temperature for calculating phase equilibrium", + ) + self._t1 = Var( + initialize=self.temperature.value, + units=pyunits.K, + doc="Intermediate temperature for calculating Teq", + ) + + self.eps_1 = Param( + default=0.01, + units=pyunits.K, + mutable=True, + doc="Smoothing parameter for Teq", + ) + self.eps_2 = Param( + default=0.0005, + units=pyunits.K, + mutable=True, + doc="Smoothing parameter for Teq", + ) # PSE paper Eqn 13 def rule_t1(b): - return b._t1 == 0.5*( - b.temperature + b.temperature_bubble + - sqrt((b.temperature-b.temperature_bubble)**2 + - b.eps_1**2)) + return b._t1 == 0.5 * ( + b.temperature + + b.temperature_bubble + + sqrt((b.temperature - b.temperature_bubble) ** 2 + b.eps_1**2) + ) + self._t1_constraint = Constraint(rule=rule_t1) # PSE paper Eqn 14 # TODO : Add option for supercritical extension def rule_teq(b): - return b._teq == 0.5*(b._t1 + b.temperature_dew - - sqrt((b._t1-b.temperature_dew)**2 + - b.eps_2**2)) + return b._teq == 0.5 * ( + b._t1 + + b.temperature_dew + - sqrt((b._t1 - b.temperature_dew) ** 2 + b.eps_2**2) + ) + self._teq_constraint = Constraint(rule=rule_teq) def rule_tr_eq(b, i): return b._teq / b._params.temperature_crit[i] + self._tr_eq = Expression( - self._params.component_list, - rule=rule_tr_eq, - doc='Component reduced temperatures') + self._params.component_list, + rule=rule_tr_eq, + doc="Component reduced temperatures", + ) def rule_equilibrium(b, i): return b.fug_phase_comp["Liq", i] == b.fug_phase_comp["Vap", i] + self.equilibrium_constraint = Constraint( - self._params.component_list, rule=rule_equilibrium) + self._params.component_list, rule=rule_equilibrium + ) -# ----------------------------------------------------------------------------- -# Property Methods + # ----------------------------------------------------------------------------- + # Property Methods def _dens_mol_phase(self): - self.dens_mol_phase = Var(self._params.phase_list, - initialize=1.0, - units=pyunits.mol*pyunits.m**-3, - doc="Molar density") + self.dens_mol_phase = Var( + self._params.phase_list, + initialize=1.0, + units=pyunits.mol * pyunits.m**-3, + doc="Molar density", + ) def rule_dens_mol_phase(b, p): - if p == 'Vap': + if p == "Vap": return b._dens_mol_vap() else: return b._dens_mol_liq() - self.eq_dens_mol_phase = Constraint(self._params.phase_list, - rule=rule_dens_mol_phase) + + self.eq_dens_mol_phase = Constraint( + self._params.phase_list, rule=rule_dens_mol_phase + ) def _energy_internal_mol_phase_comp(self): self.energy_internal_mol_phase_comp = Var( - self._params.phase_list, - self._params.component_list, - units=pyunits.J/pyunits.mol, - doc="Phase-component molar specific internal energies") + self._params.phase_list, + self._params.component_list, + units=pyunits.J / pyunits.mol, + doc="Phase-component molar specific internal energies", + ) def rule_energy_internal_mol_phase_comp(b, p, j): - if p == 'Vap': - return b.energy_internal_mol_phase_comp[p, j] == \ - b.enth_mol_phase_comp[p, j] - \ - const.gas_constant*(b.temperature - - b._params.temperature_ref) + if p == "Vap": + return b.energy_internal_mol_phase_comp[p, j] == b.enth_mol_phase_comp[ + p, j + ] - const.gas_constant * (b.temperature - b._params.temperature_ref) else: - return b.energy_internal_mol_phase_comp[p, j] == \ - b.enth_mol_phase_comp[p, j] + return ( + b.energy_internal_mol_phase_comp[p, j] + == b.enth_mol_phase_comp[p, j] + ) + self.eq_energy_internal_mol_phase_comp = Constraint( self._params.phase_list, self._params.component_list, - rule=rule_energy_internal_mol_phase_comp) + rule=rule_energy_internal_mol_phase_comp, + ) def _energy_internal_mol_phase(self): self.energy_internal_mol_phase = Var( self._params.phase_list, - units=pyunits.J/pyunits.mol, - doc='Phase molar specific internal energies') + units=pyunits.J / pyunits.mol, + doc="Phase molar specific internal energies", + ) def rule_energy_internal_mol_phase(b, p): return b.energy_internal_mol_phase[p] == sum( - b.energy_internal_mol_phase_comp[p, i] * - b.mole_frac_phase_comp[p, i] - for i in b._params.component_list) + b.energy_internal_mol_phase_comp[p, i] * b.mole_frac_phase_comp[p, i] + for i in b._params.component_list + ) + self.eq_energy_internal_mol_phase = Constraint( - self._params.phase_list, - rule=rule_energy_internal_mol_phase) + self._params.phase_list, rule=rule_energy_internal_mol_phase + ) def _enth_mol_phase_comp(self): self.enth_mol_phase_comp = Var( - self._params.phase_list, - self._params.component_list, - initialize=7e5, - units=pyunits.J/pyunits.mol, - doc='Phase-component molar specific enthalpies') + self._params.phase_list, + self._params.component_list, + initialize=7e5, + units=pyunits.J / pyunits.mol, + doc="Phase-component molar specific enthalpies", + ) def rule_enth_mol_phase_comp(b, p, j): - if p == 'Vap': + if p == "Vap": return b._enth_mol_comp_vap(j) else: return b._enth_mol_comp_liq(j) + self.eq_enth_mol_phase_comp = Constraint( - self._params.phase_list, - self._params.component_list, - rule=rule_enth_mol_phase_comp) + self._params.phase_list, + self._params.component_list, + rule=rule_enth_mol_phase_comp, + ) def _enth_mol_phase(self): self.enth_mol_phase = Var( - self._params.phase_list, - initialize=7e5, - units=pyunits.J/pyunits.mol, - doc='Phase molar specific enthalpies') + self._params.phase_list, + initialize=7e5, + units=pyunits.J / pyunits.mol, + doc="Phase molar specific enthalpies", + ) def rule_enth_mol_phase(b, p): return b.enth_mol_phase[p] == sum( - b.enth_mol_phase_comp[p, i] * - b.mole_frac_phase_comp[p, i] - for i in b._params.component_list) - self.eq_enth_mol_phase = Constraint(self._params.phase_list, - rule=rule_enth_mol_phase) + b.enth_mol_phase_comp[p, i] * b.mole_frac_phase_comp[p, i] + for i in b._params.component_list + ) + + self.eq_enth_mol_phase = Constraint( + self._params.phase_list, rule=rule_enth_mol_phase + ) def _entr_mol_phase_comp(self): self.entr_mol_phase_comp = Var( - self._params.phase_list, - self._params.component_list, - units=pyunits.J/pyunits.mol/pyunits.K, - doc='Phase-component molar specific entropies') + self._params.phase_list, + self._params.component_list, + units=pyunits.J / pyunits.mol / pyunits.K, + doc="Phase-component molar specific entropies", + ) def rule_entr_mol_phase_comp(b, p, j): - if p == 'Vap': + if p == "Vap": return b._entr_mol_comp_vap(j) else: return b._entr_mol_comp_liq(j) + self.eq_entr_mol_phase_comp = Constraint( - self._params.phase_list, - self._params.component_list, - rule=rule_entr_mol_phase_comp) + self._params.phase_list, + self._params.component_list, + rule=rule_entr_mol_phase_comp, + ) def _entr_mol_phase(self): self.entr_mol_phase = Var( - self._params.phase_list, - units=pyunits.J/pyunits.mol/pyunits.K, - doc='Phase molar specific enthropies') + self._params.phase_list, + units=pyunits.J / pyunits.mol / pyunits.K, + doc="Phase molar specific enthropies", + ) def rule_entr_mol_phase(b, p): return b.entr_mol_phase[p] == sum( - b.entr_mol_phase_comp[p, i] * - b.mole_frac_phase_comp[p, i] - for i in b._params.component_list) - self.eq_entr_mol_phase = Constraint(self._params.phase_list, - rule=rule_entr_mol_phase) - -# ----------------------------------------------------------------------------- -# General Methods + b.entr_mol_phase_comp[p, i] * b.mole_frac_phase_comp[p, i] + for i in b._params.component_list + ) + + self.eq_entr_mol_phase = Constraint( + self._params.phase_list, rule=rule_entr_mol_phase + ) + + # ----------------------------------------------------------------------------- + # General Methods def get_material_flow_terms(self, p, j): """Create material flow terms for control volume.""" if not self.is_property_constructed("material_flow_terms"): try: + def rule_material_flow_terms(blk, p, j): return blk.flow_mol_phase_comp[p, j] + self.material_flow_terms = Expression( self.params.phase_list, self.params.component_list, - rule=rule_material_flow_terms + rule=rule_material_flow_terms, ) except AttributeError: self.del_component(self.material_flow_terms) @@ -825,11 +859,12 @@ def get_enthalpy_flow_terms(self, p): """Create enthalpy flow terms.""" if not self.is_property_constructed("enthalpy_flow_terms"): try: + def rule_enthalpy_flow_terms(blk, p): return blk.flow_mol_phase[p] * blk.enth_mol_phase[p] + self.enthalpy_flow_terms = Expression( - self.params.phase_list, - rule=rule_enthalpy_flow_terms + self.params.phase_list, rule=rule_enthalpy_flow_terms ) except AttributeError: self.del_component(self.enthalpy_flow_terms) @@ -839,13 +874,14 @@ def get_material_density_terms(self, p, j): """Create material density terms.""" if not self.is_property_constructed("material_density_terms"): try: + def rule_material_density_terms(b, p, j): - return self.dens_mol_phase[p] * \ - self.mole_frac_phase_comp[p, j] + return self.dens_mol_phase[p] * self.mole_frac_phase_comp[p, j] + self.material_density_terms = Expression( self.params.phase_list, self.params.component_list, - rule=rule_material_density_terms + rule=rule_material_density_terms, ) except AttributeError: self.del_component(self.material_density_terms) @@ -859,12 +895,12 @@ def get_enthalpy_density_terms(self, p): """Create energy density terms.""" if not self.is_property_constructed("enthalpy_density_terms"): try: + def rule_energy_density_terms(b, p): - return (self.dens_mol_phase[p] * - self.energy_internal_mol_phase[p]) + return self.dens_mol_phase[p] * self.energy_internal_mol_phase[p] + self.energy_density_terms = Expression( - self.params.phase_list, - rule=rule_energy_density_terms + self.params.phase_list, rule=rule_energy_density_terms ) except AttributeError: self.del_component(self.energy_density_terms) @@ -881,20 +917,23 @@ def get_material_flow_basis(b): def define_state_vars(self): """Define state vars.""" - return {"flow_mol_phase_comp": self.flow_mol_phase_comp, - "temperature": self.temperature, - "pressure": self.pressure} + return { + "flow_mol_phase_comp": self.flow_mol_phase_comp, + "temperature": self.temperature, + "pressure": self.pressure, + } # Property package utility functions def calculate_bubble_point_temperature(self, clear_components=True): - """"To compute the bubble point temperature of the mixture.""" + """ "To compute the bubble point temperature of the mixture.""" if hasattr(self, "eq_temperature_bubble"): # Do not delete components if the block already has the components clear_components = False - calculate_variable_from_constraint(self.temperature_bubble, - self.eq_temperature_bubble) + calculate_variable_from_constraint( + self.temperature_bubble, self.eq_temperature_bubble + ) return self.temperature_bubble.value @@ -904,14 +943,15 @@ def calculate_bubble_point_temperature(self, clear_components=True): self.del_component(self.temperature_bubble) def calculate_dew_point_temperature(self, clear_components=True): - """"To compute the dew point temperature of the mixture.""" + """ "To compute the dew point temperature of the mixture.""" if hasattr(self, "eq_temperature_dew"): # Do not delete components if the block already has the components clear_components = False - calculate_variable_from_constraint(self.temperature_dew, - self.eq_temperature_dew) + calculate_variable_from_constraint( + self.temperature_dew, self.eq_temperature_dew + ) return self.temperature_dew.value @@ -923,14 +963,15 @@ def calculate_dew_point_temperature(self, clear_components=True): self.del_component(self.temperature_dew) def calculate_bubble_point_pressure(self, clear_components=True): - """"To compute the bubble point pressure of the mixture.""" + """ "To compute the bubble point pressure of the mixture.""" if hasattr(self, "eq_pressure_bubble"): # Do not delete components if the block already has the components clear_components = False - calculate_variable_from_constraint(self.pressure_bubble, - self.eq_pressure_bubble) + calculate_variable_from_constraint( + self.pressure_bubble, self.eq_pressure_bubble + ) return self.pressure_bubble.value @@ -942,14 +983,13 @@ def calculate_bubble_point_pressure(self, clear_components=True): self.del_component(self.pressure_bubble) def calculate_dew_point_pressure(self, clear_components=True): - """"To compute the dew point pressure of the mixture.""" + """ "To compute the dew point pressure of the mixture.""" if hasattr(self, "eq_pressure_dew"): # Do not delete components if the block already has the components clear_components = False - calculate_variable_from_constraint(self.pressure_dew, - self.eq_pressure_dew) + calculate_variable_from_constraint(self.pressure_dew, self.eq_pressure_dew) return self.pressure_dew.value @@ -960,42 +1000,55 @@ def calculate_dew_point_pressure(self, clear_components=True): self.del_component(self._p_sat_dewP) self.del_component(self.pressure_dew) -# ----------------------------------------------------------------------------- -# Bubble and Dew Points -# Ideal-Ideal properties allow for the simplifications below -# Other methods require more complex equations with shadow compositions + # ----------------------------------------------------------------------------- + # Bubble and Dew Points + # Ideal-Ideal properties allow for the simplifications below + # Other methods require more complex equations with shadow compositions -# For future work, propose the following: -# Core class writes a set of constraints Phi_L_i == Phi_V_i -# Phi_L_i and Phi_V_i make calls to submethods which add shadow compositions -# as needed + # For future work, propose the following: + # Core class writes a set of constraints Phi_L_i == Phi_V_i + # Phi_L_i and Phi_V_i make calls to submethods which add shadow compositions + # as needed def _temperature_bubble(self): - self.temperature_bubble = Param(initialize=33.0, - units=pyunits.K, - doc="Bubble point temperature") + self.temperature_bubble = Param( + initialize=33.0, units=pyunits.K, doc="Bubble point temperature" + ) def _temperature_dew(self): - self.temperature_dew = Var(initialize=298.15, - units=pyunits.K, - doc="Dew point temperature") + self.temperature_dew = Var( + initialize=298.15, units=pyunits.K, doc="Dew point temperature" + ) def rule_psat_dew(b, j): - return 1e5*pyunits.Pa*10**(b._params.pressure_sat_coeff_A[j] - - b._params.pressure_sat_coeff_B[j] / - (b.temperature_dew + - b._params.pressure_sat_coeff_C[j])) + return ( + 1e5 + * pyunits.Pa + * 10 + ** ( + b._params.pressure_sat_coeff_A[j] + - b._params.pressure_sat_coeff_B[j] + / (b.temperature_dew + b._params.pressure_sat_coeff_C[j]) + ) + ) try: # Try to build expression - self._p_sat_dewT = Expression(self._params.component_list, - rule=rule_psat_dew) + self._p_sat_dewT = Expression( + self._params.component_list, rule=rule_psat_dew + ) def rule_temp_dew(b): - return b.pressure * sum(b.mole_frac_comp[i] / - b._p_sat_dewT[i] - for i in ['toluene', 'benzene']) \ - - 1 == 0 + return ( + b.pressure + * sum( + b.mole_frac_comp[i] / b._p_sat_dewT[i] + for i in ["toluene", "benzene"] + ) + - 1 + == 0 + ) + self.eq_temperature_dew = Constraint(rule=rule_temp_dew) except AttributeError: # If expression fails, clean up so that DAE can try again later @@ -1005,31 +1058,44 @@ def rule_temp_dew(b): self.del_component(self._p_sat_dewT) def _pressure_bubble(self): - self.pressure_bubble = Param(initialize=1e8, - units=pyunits.Pa, - doc="Bubble point pressure") + self.pressure_bubble = Param( + initialize=1e8, units=pyunits.Pa, doc="Bubble point pressure" + ) def _pressure_dew(self): - self.pressure_dew = Var(initialize=298.15, - units=pyunits.Pa, - doc="Dew point pressure") + self.pressure_dew = Var( + initialize=298.15, units=pyunits.Pa, doc="Dew point pressure" + ) def rule_psat_dew(b, j): - return 1e5*pyunits.Pa*10**(b._params.pressure_sat_coeff_A[j] - - b._params.pressure_sat_coeff_B[j] / - (b.temperature + - b._params.pressure_sat_coeff_C[j])) + return ( + 1e5 + * pyunits.Pa + * 10 + ** ( + b._params.pressure_sat_coeff_A[j] + - b._params.pressure_sat_coeff_B[j] + / (b.temperature + b._params.pressure_sat_coeff_C[j]) + ) + ) try: # Try to build expression - self._p_sat_dewP = Expression(self._params.component_list, - rule=rule_psat_dew) + self._p_sat_dewP = Expression( + self._params.component_list, rule=rule_psat_dew + ) def rule_pressure_dew(b): - return b.pressure_dew * \ - sum(b.mole_frac_comp[i] / b._p_sat_dewP[i] - for i in ['toluene', 'benzene']) \ - - 1 == 0 + return ( + b.pressure_dew + * sum( + b.mole_frac_comp[i] / b._p_sat_dewP[i] + for i in ["toluene", "benzene"] + ) + - 1 + == 0 + ) + self.eq_pressure_dew = Constraint(rule=rule_pressure_dew) except AttributeError: # If expression fails, clean up so that DAE can try again later @@ -1038,179 +1104,198 @@ def rule_pressure_dew(b): self.del_component(self.pressure_dew) self.del_component(self._p_sat_dewP) -# ----------------------------------------------------------------------------- -# Liquid phase properties + # ----------------------------------------------------------------------------- + # Liquid phase properties def _dens_mol_liq(b): - return b.dens_mol_phase['Liq'] == 1e3*sum( - b.mole_frac_phase_comp['Liq', j] * - b._params.dens_liq_param_1[j] / - b._params.dens_liq_param_2[j] ** - (1 + (1-b.temperature / - b._params.dens_liq_param_3[j]) ** - b._params.dens_liq_param_4[j]) - for j in ['benzene', 'toluene']) + return b.dens_mol_phase["Liq"] == 1e3 * sum( + b.mole_frac_phase_comp["Liq", j] + * b._params.dens_liq_param_1[j] + / b._params.dens_liq_param_2[j] + ** ( + 1 + + (1 - b.temperature / b._params.dens_liq_param_3[j]) + ** b._params.dens_liq_param_4[j] + ) + for j in ["benzene", "toluene"] + ) def _fug_phase_comp(self): def fug_phase_comp_rule(b, p, i): if p == "Liq": - if i in ['hydrogen', 'methane']: - return b.mole_frac_phase_comp['Liq', i] + if i in ["hydrogen", "methane"]: + return b.mole_frac_phase_comp["Liq", i] else: - return b.pressure_sat[i] * b.mole_frac_phase_comp['Liq', i] + return b.pressure_sat[i] * b.mole_frac_phase_comp["Liq", i] else: - if i in ['hydrogen', 'methane']: + if i in ["hydrogen", "methane"]: return 1e-6 else: - return b.mole_frac_phase_comp['Vap', i] * b.pressure - + return b.mole_frac_phase_comp["Vap", i] * b.pressure + self.fug_phase_comp = Expression( self._params.phase_list, self._params.component_list, - rule=fug_phase_comp_rule + rule=fug_phase_comp_rule, ) def _pressure_sat(self): - self.pressure_sat = Var(self._params.component_list, - initialize=101325, - units=pyunits.Pa, - doc="Vapor pressure") + self.pressure_sat = Var( + self._params.component_list, + initialize=101325, + units=pyunits.Pa, + doc="Vapor pressure", + ) def rule_P_sat(b, j): - return ((log10(b.pressure_sat[j]/pyunits.Pa*1e-5) - - b._params.pressure_sat_coeff_A[j]) * - (b._teq + b._params.pressure_sat_coeff_C[j])) == \ - -b._params.pressure_sat_coeff_B[j] - self.eq_pressure_sat = Constraint(self._params.component_list, - rule=rule_P_sat) + return ( + ( + log10(b.pressure_sat[j] / pyunits.Pa * 1e-5) + - b._params.pressure_sat_coeff_A[j] + ) + * (b._teq + b._params.pressure_sat_coeff_C[j]) + ) == -b._params.pressure_sat_coeff_B[j] + + self.eq_pressure_sat = Constraint(self._params.component_list, rule=rule_P_sat) def _enth_mol_comp_liq(b, j): - return b.enth_mol_phase_comp['Liq', j] * 1E3 == \ - ((b._params.cp_ig_5['Liq', j] / 5) * - (b.temperature**5 - b._params.temperature_ref**5) - + (b._params.cp_ig_4['Liq', j] / 4) * - (b.temperature**4 - b._params.temperature_ref**4) - + (b._params.cp_ig_3['Liq', j] / 3) * - (b.temperature**3 - b._params.temperature_ref**3) - + (b._params.cp_ig_2['Liq', j] / 2) * - (b.temperature**2 - b._params.temperature_ref**2) - + b._params.cp_ig_1['Liq', j] * - (b.temperature - b._params.temperature_ref)) + return b.enth_mol_phase_comp["Liq", j] * 1e3 == ( + (b._params.cp_ig_5["Liq", j] / 5) + * (b.temperature**5 - b._params.temperature_ref**5) + + (b._params.cp_ig_4["Liq", j] / 4) + * (b.temperature**4 - b._params.temperature_ref**4) + + (b._params.cp_ig_3["Liq", j] / 3) + * (b.temperature**3 - b._params.temperature_ref**3) + + (b._params.cp_ig_2["Liq", j] / 2) + * (b.temperature**2 - b._params.temperature_ref**2) + + b._params.cp_ig_1["Liq", j] * (b.temperature - b._params.temperature_ref) + ) def _entr_mol_comp_liq(b, j): - return b.entr_mol_phase_comp['Liq', j] * 1E3 == ( - ((b._params.cp_ig_5['Liq', j] / 4) * - (b.temperature**4 - b._params.temperature_ref**4) - + (b._params.cp_ig_4['Liq', j] / 3) * - (b.temperature**3 - b._params.temperature_ref**3) - + (b._params.cp_ig_3['Liq', j] / 2) * - (b.temperature**2 - b._params.temperature_ref**2) - + b._params.cp_ig_2['Liq', j] * - (b.temperature - b._params.temperature_ref) - + b._params.cp_ig_1['Liq', j] * - log(b.temperature / b._params.temperature_ref)) - - const.gas_constant * - log(b.mole_frac_phase_comp['Liq', j]*b.pressure / - b._params.pressure_ref)) - -# ----------------------------------------------------------------------------- -# Vapour phase properties + return b.entr_mol_phase_comp["Liq", j] * 1e3 == ( + ( + (b._params.cp_ig_5["Liq", j] / 4) + * (b.temperature**4 - b._params.temperature_ref**4) + + (b._params.cp_ig_4["Liq", j] / 3) + * (b.temperature**3 - b._params.temperature_ref**3) + + (b._params.cp_ig_3["Liq", j] / 2) + * (b.temperature**2 - b._params.temperature_ref**2) + + b._params.cp_ig_2["Liq", j] + * (b.temperature - b._params.temperature_ref) + + b._params.cp_ig_1["Liq", j] + * log(b.temperature / b._params.temperature_ref) + ) + - const.gas_constant + * log( + b.mole_frac_phase_comp["Liq", j] * b.pressure / b._params.pressure_ref + ) + ) + + # ----------------------------------------------------------------------------- + # Vapour phase properties def _dens_mol_vap(b): - return b.pressure == (b.dens_mol_phase['Vap'] * - const.gas_constant * - b.temperature) + return b.pressure == ( + b.dens_mol_phase["Vap"] * const.gas_constant * b.temperature + ) def _dh_vap(self): # heat of vaporization - add_object_reference(self, "dh_vap", - self._params.dh_vap) + add_object_reference(self, "dh_vap", self._params.dh_vap) def _ds_vap(self): # entropy of vaporization = dh_Vap/T_boil # TODO : something more rigorous would be nice - self.ds_vap = Var(self._params.component_list, - initialize=86, - units=pyunits.J/pyunits.mol/pyunits.K, - doc="Entropy of vaporization") + self.ds_vap = Var( + self._params.component_list, + initialize=86, + units=pyunits.J / pyunits.mol / pyunits.K, + doc="Entropy of vaporization", + ) def rule_ds_vap(b, j): - return b.dh_vap[j] == (b.ds_vap[j] * - b._params.temperature_boil[j]) - self.eq_ds_vap = Constraint(self._params.component_list, - rule=rule_ds_vap) + return b.dh_vap[j] == (b.ds_vap[j] * b._params.temperature_boil[j]) + + self.eq_ds_vap = Constraint(self._params.component_list, rule=rule_ds_vap) def _enth_mol_comp_vap(b, j): - return b.enth_mol_phase_comp['Vap', j] == b.dh_vap[j] + \ - ((b._params.cp_ig_5['Vap', j] / 5) * - (b.temperature**5 - b._params.temperature_ref**5) - + (b._params.cp_ig_4['Vap', j] / 4) * - (b.temperature**4 - b._params.temperature_ref**4) - + (b._params.cp_ig_3['Vap', j] / 3) * - (b.temperature**3 - b._params.temperature_ref**3) - + (b._params.cp_ig_2['Vap', j] / 2) * - (b.temperature**2 - b._params.temperature_ref**2) - + b._params.cp_ig_1['Vap', j] * - (b.temperature - b._params.temperature_ref)) + return b.enth_mol_phase_comp["Vap", j] == b.dh_vap[j] + ( + (b._params.cp_ig_5["Vap", j] / 5) + * (b.temperature**5 - b._params.temperature_ref**5) + + (b._params.cp_ig_4["Vap", j] / 4) + * (b.temperature**4 - b._params.temperature_ref**4) + + (b._params.cp_ig_3["Vap", j] / 3) + * (b.temperature**3 - b._params.temperature_ref**3) + + (b._params.cp_ig_2["Vap", j] / 2) + * (b.temperature**2 - b._params.temperature_ref**2) + + b._params.cp_ig_1["Vap", j] * (b.temperature - b._params.temperature_ref) + ) def _entr_mol_comp_vap(b, j): - return b.entr_mol_phase_comp['Vap', j] == ( - b.ds_vap[j] + - ((b._params.cp_ig_5['Vap', j] / 4) * - (b.temperature**4 - b._params.temperature_ref**4) - + (b._params.cp_ig_4['Vap', j] / 3) * - (b.temperature**3 - b._params.temperature_ref**3) - + (b._params.cp_ig_3['Vap', j] / 2) * - (b.temperature**2 - b._params.temperature_ref**2) - + b._params.cp_ig_2['Vap', j] * - (b.temperature - b._params.temperature_ref) - + b._params.cp_ig_1['Vap', j] * - log(b.temperature / b._params.temperature_ref)) - - const.gas_constant * - log(b.mole_frac_phase_comp['Vap', j]*b.pressure / - b._params.pressure_ref)) + return b.entr_mol_phase_comp["Vap", j] == ( + b.ds_vap[j] + + ( + (b._params.cp_ig_5["Vap", j] / 4) + * (b.temperature**4 - b._params.temperature_ref**4) + + (b._params.cp_ig_4["Vap", j] / 3) + * (b.temperature**3 - b._params.temperature_ref**3) + + (b._params.cp_ig_3["Vap", j] / 2) + * (b.temperature**2 - b._params.temperature_ref**2) + + b._params.cp_ig_2["Vap", j] + * (b.temperature - b._params.temperature_ref) + + b._params.cp_ig_1["Vap", j] + * log(b.temperature / b._params.temperature_ref) + ) + - const.gas_constant + * log( + b.mole_frac_phase_comp["Vap", j] * b.pressure / b._params.pressure_ref + ) + ) def calculate_scaling_factors(self): # Get default scale factors super().calculate_scaling_factors() is_two_phase = len(self._params.phase_list) == 2 - sf_flow = iscale.get_scaling_factor( - self.flow_mol, default=1, warning=True) - sf_T = iscale.get_scaling_factor( - self.temperature, default=1, warning=True) - sf_P = iscale.get_scaling_factor( - self.pressure, default=1, warning=True) + sf_flow = iscale.get_scaling_factor(self.flow_mol, default=1, warning=True) + sf_T = iscale.get_scaling_factor(self.temperature, default=1, warning=True) + sf_P = iscale.get_scaling_factor(self.pressure, default=1, warning=True) if self.is_property_constructed("_teq"): iscale.set_scaling_factor(self._teq, sf_T) if self.is_property_constructed("_teq_constraint"): iscale.constraint_scaling_transform( - self._teq_constraint, sf_T, overwrite=False) + self._teq_constraint, sf_T, overwrite=False + ) if self.is_property_constructed("_t1"): iscale.set_scaling_factor(self._t1, sf_T) if self.is_property_constructed("_t1_constraint"): iscale.constraint_scaling_transform( - self._t1_constraint, sf_T, overwrite=False) + self._t1_constraint, sf_T, overwrite=False + ) if self.is_property_constructed("_mole_frac_pdew"): iscale.set_scaling_factor(self._mole_frac_pdew, 1e3) iscale.constraint_scaling_transform( - self._sum_mole_frac_pdew, 1e3, overwrite=False) + self._sum_mole_frac_pdew, 1e3, overwrite=False + ) if self.is_property_constructed("total_flow_balance"): iscale.constraint_scaling_transform( - self.total_flow_balance, sf_flow, overwrite=False) + self.total_flow_balance, sf_flow, overwrite=False + ) if self.is_property_constructed("component_flow_balances"): for i, c in self.component_flow_balances.items(): if is_two_phase: s = iscale.get_scaling_factor( - self.mole_frac_comp[i], default=1, warning=True) + self.mole_frac_comp[i], default=1, warning=True + ) s *= sf_flow iscale.constraint_scaling_transform(c, s, overwrite=False) else: s = iscale.get_scaling_factor( - self.mole_frac_comp[i], default=1, warning=True) + self.mole_frac_comp[i], default=1, warning=True + ) iscale.constraint_scaling_transform(c, s, overwrite=False) if self.is_property_constructed("dens_mol_phase"): @@ -1220,37 +1305,37 @@ def calculate_scaling_factors(self): if self.is_property_constructed("dens_mass_phase"): for p, c in self.eq_dens_mass_phase.items(): sf = iscale.get_scaling_factor( - self.dens_mass_phase[p], default=1, warning=True) + self.dens_mass_phase[p], default=1, warning=True + ) iscale.constraint_scaling_transform(c, sf, overwrite=False) if self.is_property_constructed("enth_mol_phase"): for p, c in self.eq_enth_mol_phase.items(): sf = iscale.get_scaling_factor( - self.enth_mol_phase[p], default=1, warning=True) + self.enth_mol_phase[p], default=1, warning=True + ) iscale.constraint_scaling_transform(c, sf, overwrite=False) if self.is_property_constructed("enth_mol"): - sf = iscale.get_scaling_factor( - self.enth_mol, default=1, warning=True) + sf = iscale.get_scaling_factor(self.enth_mol, default=1, warning=True) sf *= sf_flow - iscale.constraint_scaling_transform( - self.eq_enth_mol, sf, overwrite=False) + iscale.constraint_scaling_transform(self.eq_enth_mol, sf, overwrite=False) if self.is_property_constructed("entr_mol_phase"): for p, c in self.eq_entr_mol_phase.items(): sf = iscale.get_scaling_factor( - self.entr_mol_phase[p], default=1, warning=True) + self.entr_mol_phase[p], default=1, warning=True + ) iscale.constraint_scaling_transform(c, sf, overwrite=False) if self.is_property_constructed("entr_mol"): - sf = iscale.get_scaling_factor( - self.entr_mol, default=1, warning=True) + sf = iscale.get_scaling_factor(self.entr_mol, default=1, warning=True) sf *= sf_flow - iscale.constraint_scaling_transform( - self.eq_entr_mol, sf, overwrite=False) + iscale.constraint_scaling_transform(self.eq_entr_mol, sf, overwrite=False) if self.is_property_constructed("gibbs_mol_phase"): for p, c in self.eq_gibbs_mol_phase.items(): sf = iscale.get_scaling_factor( - self.gibbs_mol_phase[p], default=1, warning=True) + self.gibbs_mol_phase[p], default=1, warning=True + ) iscale.constraint_scaling_transform(c, sf, overwrite=False) diff --git a/idaes_examples/mod/surrogates/AR_training_methods.py b/idaes_examples/mod/surrogates/AR_training_methods.py index 69d9cd48..5e62695f 100644 --- a/idaes_examples/mod/surrogates/AR_training_methods.py +++ b/idaes_examples/mod/surrogates/AR_training_methods.py @@ -224,7 +224,7 @@ def train_load_surrogates(retrain=False) -> Set[SurrType]: # Train surrogates using Keras - if os.path.exists('keras_surrogate/') and retrain is False: + if os.path.exists('keras_surrogate/keras_model.keras') and retrain is False: # surrogates folder already exists, skip training # we will load the object into the flowsheet later pass @@ -263,7 +263,7 @@ def train_load_surrogates(retrain=False) -> Set[SurrType]: # Train surrogate (calls optimizer on neural network and solves # for weights) model.compile(loss=loss, optimizer=optimizer, metrics=metrics) - mcp_save = tf.keras.callbacks.ModelCheckpoint('.mdl_wts.hdf5', + mcp_save = tf.keras.callbacks.ModelCheckpoint('.mdl_wts.keras', save_best_only=True, monitor='val_loss', mode='min') @@ -281,7 +281,7 @@ def train_load_surrogates(retrain=False) -> Set[SurrType]: input_bounds=input_bounds, input_scaler=input_scaler, output_scaler=output_scaler) - keras_surrogate.save_to_folder('keras_surrogate') + keras_surrogate.save_to_folder(keras_folder_name="keras_surrogate", keras_model_name="keras_model") trained_surr.add(SurrType.KERAS) diff --git a/idaes_examples/mod/tut/visualizer_tutorial.py b/idaes_examples/mod/tut/visualizer_tutorial.py index 8622c69e..0bcd0a71 100644 --- a/idaes_examples/mod/tut/visualizer_tutorial.py +++ b/idaes_examples/mod/tut/visualizer_tutorial.py @@ -48,6 +48,8 @@ from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.solvers import get_solver +from idaes.core.util.exceptions import InitializationError try: import idaes_ui @@ -289,7 +291,13 @@ def initialize_model(m: ConcreteModel) -> ConcreteModel: seq.set_guesses_for(m.fs.H101.inlet, tear_guesses) def initialize_unit(unit): - unit.initialize(outlvl=idaeslog.DEBUG) + try: + initializer = unit.default_initializer() + initializer.initialize(unit, output_level=idaeslog.INFO) + except InitializationError: + solver=get_solver() + solver.solve(unit) + seq.run(m, initialize_unit) return m 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 ea0e1dd1..a8b9ba91 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -234,6 +234,7 @@ "from idaes.core.util.initialization import propagate_state\n", "from idaes.core.solvers import get_solver\n", "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", "\n", "# Import idaes logger to set output levels\n", "import idaes.logger as idaeslog" @@ -737,7 +738,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -952,7 +961,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [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", + "2024-08-28 18:38:14 [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", @@ -1002,7 +1046,15 @@ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1061,7 +1113,15 @@ "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)" @@ -1078,7 +1138,20 @@ "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)" @@ -1110,7 +1183,7 @@ " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", " (0, \"Liq\", \"methane\"): 1e-5,\n", " },\n", - " \"temperature\": {0: 303},\n", + " \"temperature\": {0: 303.2},\n", " \"pressure\": {0: 350000},\n", "}\n", "\n", @@ -1122,7 +1195,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit." + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." ] }, { @@ -1132,7 +1205,15 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " # Try initializing using default initializer, \n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1148,7 +1229,132 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1167,7 +1373,91 @@ "cell_type": "code", "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 8.42e+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 6.41e+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 2.24e-08 4.99e-01 -3.8 5.92e-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......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+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) = 0.011\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", @@ -1215,7 +1505,617 @@ "cell_type": "code", "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [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", @@ -1314,7 +2214,101 @@ "cell_type": "code", "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.64e-03 -3.8 2.00e-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......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+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) = 0.083\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': 0.2022566795349121}], '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)" ] @@ -1348,7 +2342,26 @@ "cell_type": "code", "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\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", @@ -1390,7 +2403,16 @@ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427596.73056805483\n", + "14704.740184467111\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1411,7 +2433,40 @@ "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()" ] @@ -1427,7 +2482,40 @@ "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()" ] @@ -1448,7 +2536,25 @@ "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", @@ -1755,7 +2861,117 @@ "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.48e-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 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-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) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1789,7 +3005,26 @@ "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\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", @@ -1831,7 +3066,16 @@ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "408883.5314830889\n", + "29956.3669431971\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1853,7 +3097,23 @@ "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1883,13 +3143,6 @@ "\n", "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1909,7 +3162,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb index ff03b6e7..2983e6ec 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb @@ -1,7592 +1,2889 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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 Simulation and Optimization\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an 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", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. 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", - "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", - "\n", - "![](HDA_flowsheet_distillation.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Translator block\n", - "\n", - "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", - "\n", - "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "- Flash\n", - "- Separator (splitter) \n", - "- PressureChanger\n", - "- Translator (to switch from one property package to another)\n", - "- TrayColumn (distillation column)\n", - "- CondenserType (Type of the overhead condenser: complete or partial)\n", - "- TemperatureSpec (Temperature specification inside the condenser)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " CSTR,\n", - " Flash,\n", - " Translator,\n", - ")\n", - "\n", - "from idaes.models_extra.column_models import TrayColumn\n", - "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Utility tools to put together the flowsheet and calculate the degrees of freedom\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 idaes.core.solvers import get_solver\n", - "import idaes.core.util.scaling as iscale\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction packages\n", - "\n", - "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "\n", - "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Pyomo Concrete Model to contain the problem\n", - "m = ConcreteModel()\n", - "\n", - "# Add a steady state flowsheet block to the model\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now add the thermophysical and reaction packages to the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Property package for benzene, toluene, hydrogen, methane mixture\n", - "m.fs.BTHM_params = HDAParameterBlock()\n", - "\n", - "# Property package for the benzene-toluene mixture\n", - "m.fs.BT_params = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", - ")\n", - "\n", - "# Reaction package for the HDA reaction\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.BTHM_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the mixer M101 to the flowsheet\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.BTHM_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "# Adding the heater H101 to the flowsheet\n", - "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.BTHM_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = CSTR(\n", - " property_package=m.fs.BTHM_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the flash tank F101 to the flowsheet\n", - "m.fs.F101 = Flash(\n", - " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", - ")\n", - "\n", - "# Adding the splitter S101 to the flowsheet\n", - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", - ")\n", - "\n", - "# Adding the compressor C101 to the flowsheet\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.BTHM_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remark\n", - "\n", - "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Add translator block to convert between property packages\n", - "m.fs.translator = Translator(\n", - " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translator block constraints\n", - "\n", - "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", - "\n", - "For this process, five constraints are required based on the state variables used in the outgoing process.\n", - "\n", - "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", - "- Temperature of the inlet and outlet streams must be the same.\n", - "- Pressure of the inlet and outgoing streams must be the same\n", - "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", - "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", - "m.fs.translator.eq_total_flow = Constraint(\n", - " expr=m.fs.translator.outlet.flow_mol[0]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - ")\n", - "\n", - "# Add constraint: Outlet temperature = Inlet temperature\n", - "m.fs.translator.eq_temperature = Constraint(\n", - " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", - "m.fs.translator.eq_pressure = Constraint(\n", - " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Remaining constraints on the translator block\n", - "\n", - "# Add constraint: Benzene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "# Add constraint: Toluene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet\n", - "m.fs.H102 = Heater(\n", - " property_package=m.fs.BT_params,\n", - " has_pressure_change=True,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet_distillation.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", - "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Appending additional constraints to the model\n", - "\n", - "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", - "\n", - "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", - "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", - "\n", - "We add the constraint to the model as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the conversion variables using 'Var'\n", - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "# Append the constraint to the model\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions and Initializing the flowsheet\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H101\n", - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for the reactor R101 to the following conditions:\n", - "
    \n", - "
  • `conversion` = 0.75
    • \n", - "
  • `heat_duty` = 0
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "m.fs.R101.conversion.fix(0.75)\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "\n", - "# Fix the pressure drop in the flash F101\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the split fraction of the 'purge' stream from S101\n", - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "\n", - "# Fix the pressure of the outlet from the compressor C101\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H102\n", - "m.fs.H102.outlet.temperature.fix(375)\n", - "\n", - "# Fix the pressure drop in the heater H102\n", - "m.fs.H102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:18 [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", - "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", - "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.H101)\n", - "iscale.calculate_scaling_factors(m.fs.R101)\n", - "iscale.calculate_scaling_factors(m.fs.F101)\n", - "iscale.calculate_scaling_factors(m.fs.H102)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# Todo: Check the degrees of freedom\n", - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization\n", - "\n", - "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "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" - ] - } - ], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", - "\n", - "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:19 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.translator.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:20 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:22 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:23 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Wegstein failed to converge in 3 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:24 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "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" - ] - }, - { - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "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 2.24e-08 5.91e-02 -3.8 1.38e-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......: 1.5042546731871284e+04 1.5042546731871284e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042546731871284e+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) = 0.010\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", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add distillation column \n", - "\n", - "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", - "\n", - "In the following, we will\n", - "- Add the distillation column \n", - "- Connect it to the heater \n", - "- Add the necessary equality constraints\n", - "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", - "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", - "- Scale the control volume heat variables to help convergence\n", - "- Initialize the distillation block.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:25 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:26 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:26 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:26 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:26 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:26 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:26 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:27 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:28 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:28 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:28 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:28 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - 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Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an 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", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. 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", + "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", + "\n", + "![](HDA_flowsheet_distillation.png)\n", + "\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translator block\n", + "\n", + "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", + "\n", + "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "- Flash\n", + "- Separator (splitter) \n", + "- PressureChanger\n", + "- Translator (to switch from one property package to another)\n", + "- TrayColumn (distillation column)\n", + "- CondenserType (Type of the overhead condenser: complete or partial)\n", + "- TemperatureSpec (Temperature specification inside the condenser)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " CSTR,\n", + " Flash,\n", + " Translator,\n", + ")\n", + "\n", + "from idaes.models_extra.column_models import TrayColumn\n", + "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility tools to put together the flowsheet and calculate the degrees of freedom\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 idaes.core.solvers import get_solver\n", + "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction packages\n", + "\n", + "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "\n", + "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Pyomo Concrete Model to contain the problem\n", + "m = ConcreteModel()\n", + "\n", + "# Add a steady state flowsheet block to the model\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now add the thermophysical and reaction packages to the flowsheet." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Property package for benzene, toluene, hydrogen, methane mixture\n", + "m.fs.BTHM_params = HDAParameterBlock()\n", + "\n", + "# Property package for the benzene-toluene mixture\n", + "m.fs.BT_params = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", + ")\n", + "\n", + "# Reaction package for the HDA reaction\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.BTHM_params\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the mixer M101 to the flowsheet\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.BTHM_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "# Adding the heater H101 to the flowsheet\n", + "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.BTHM_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = CSTR(\n", + " property_package=m.fs.BTHM_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the flash tank F101 to the flowsheet\n", + "m.fs.F101 = Flash(\n", + " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", + ")\n", + "\n", + "# Adding the splitter S101 to the flowsheet\n", + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", + ")\n", + "\n", + "# Adding the compressor C101 to the flowsheet\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.BTHM_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remark\n", + "\n", + "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Add translator block to convert between property packages\n", + "m.fs.translator = Translator(\n", + " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translator block constraints\n", + "\n", + "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", + "\n", + "For this process, five constraints are required based on the state variables used in the outgoing process.\n", + "\n", + "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", + "- Temperature of the inlet and outlet streams must be the same.\n", + "- Pressure of the inlet and outgoing streams must be the same\n", + "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", + "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", + "m.fs.translator.eq_total_flow = Constraint(\n", + " expr=m.fs.translator.outlet.flow_mol[0]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + ")\n", + "\n", + "# Add constraint: Outlet temperature = Inlet temperature\n", + "m.fs.translator.eq_temperature = Constraint(\n", + " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", + "m.fs.translator.eq_pressure = Constraint(\n", + " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Remaining constraints on the translator block\n", + "\n", + "# Add constraint: Benzene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "# Add constraint: Toluene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet\n", + "m.fs.H102 = Heater(\n", + " property_package=m.fs.BT_params,\n", + " has_pressure_change=True,\n", + " has_phase_equilibrium=True,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n" - ] + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet_distillation.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", + "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appending additional constraints to the model\n", + "\n", + "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", + "\n", + "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", + "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", + "\n", + "We add the constraint to the model as shown below." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the conversion variables using 'Var'\n", + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "# Append the constraint to the model\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions and Initializing the flowsheet\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H101\n", + "m.fs.H101.outlet.temperature.fix(600)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for the reactor R101 to the following conditions:\n", + "
    \n", + "
  • `conversion` = 0.75
    • \n", + "
  • `heat_duty` = 0
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "m.fs.R101.conversion.fix(0.75)\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101\n", + "m.fs.R101.heat_duty.fix(0)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "\n", + "# Fix the pressure drop in the flash F101\n", + "m.fs.F101.deltaP.fix(0)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the split fraction of the 'purge' stream from S101\n", + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "\n", + "# Fix the pressure of the outlet from the compressor C101\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n" - ] + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H102\n", + "m.fs.H102.outlet.temperature.fix(375)\n", + "\n", + "# Fix the pressure drop in the heater H102\n", + "m.fs.H102.deltaP.fix(-200000)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [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", + "2024-08-28 18:38:14 [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", + "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", + "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.H101)\n", + "iscale.calculate_scaling_factors(m.fs.R101)\n", + "iscale.calculate_scaling_factors(m.fs.F101)\n", + "iscale.calculate_scaling_factors(m.fs.H102)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "# Todo: Check the degrees of freedom\n", + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization\n", + "\n", + "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "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" + ] + } + ], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", + "\n", + "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303.2},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " # Try initializing using default initializer, \n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], + "source": [ + "seq.run(m, function)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 8.42e+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 6.41e+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 2.24e-08 4.99e-01 -3.8 5.92e-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......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+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) = 0.011\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", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add distillation column \n", + "\n", + "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", + "\n", + "In the following, we will\n", + "- Add the distillation column \n", + "- Connect it to the heater \n", + "- Add the necessary equality constraints\n", + "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", + "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", + "- Scale the control volume heat variables to help convergence\n", + "- Initialize the distillation block.\n", + "\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [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", + " number_of_trays=10,\n", + " feed_tray_location=5,\n", + " condenser_type=CondenserType.totalCondenser,\n", + " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", + " property_package=m.fs.BT_params,\n", + ")\n", + "\n", + "# Connect the outlet from the heater H102 to the distillation column\n", + "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", + "\n", + "# Add the necessary equality constraints\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "\n", + "# Propagate the state\n", + "propagate_state(m.fs.s11)\n", + "\n", + "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", + "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", + "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", + "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", + "\n", + "# set scaling factors\n", + "# Set scaling factors for heat duty\n", + "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.D101)\n", + "\n", + "# Initialize the distillation column\n", + "m.fs.D101.initialize(outlvl=idaeslog.INFO)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute capital and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Expression to compute the total cooling cost\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", + " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", + ")\n", + "\n", + "# Expression to compute the total heating cost\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", + " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", + ")\n", + "\n", + "# Expression to compute the total operating cost\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")\n", + "\n", + "# Expression to compute the total capital cost\n", + "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the entire flowsheet" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.64e-03 -3.8 2.00e-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......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+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) = 0.083\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': 0.2022566795349121}], '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)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n" - ] + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\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", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "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" + ] + } + ], + "source": [ + "m.fs.R101.report()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "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" + ] + } + ], + "source": [ + "m.fs.F101.report()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "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" + ] + } + ], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:33 [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", - " number_of_trays=10,\n", - " feed_tray_location=5,\n", - " condenser_type=CondenserType.totalCondenser,\n", - " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", - " property_package=m.fs.BT_params,\n", - ")\n", - "\n", - "# Connect the outlet from the heater H102 to the distillation column\n", - "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", - "\n", - "# Add the necessary equality constraints\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "\n", - "# Propagate the state\n", - "propagate_state(m.fs.s11)\n", - "\n", - "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", - "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", - "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", - "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", - "\n", - "# set scaling factors\n", - "# Set scaling factors for heat duty\n", - "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.D101)\n", - "\n", - "# Initialize the distillation column\n", - "m.fs.D101.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute capital and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Expression to compute the total cooling cost\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", - " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", - ")\n", - "\n", - "# Expression to compute the total heating cost\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", - " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", - ")\n", - "\n", - "# Expression to compute the total operating cost\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")\n", - "\n", - "# Expression to compute the total capital cost\n", - "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve the entire flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] }, { - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 outlet temperature\n", + "- F101 outlet temperature\n", + "- H102 outlet temperature\n", + "- Condenser pressure\n", + "- reflux ratio\n", + "- boilup ratio\n" + ] }, { - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.conversion.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.D101.condenser.condenser_pressure.unfix()\n", + "m.fs.D101.condenser.reflux_ratio.unfix()\n", + "m.fs.D101.reboiler.boilup_ratio.unfix()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 63, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature.unfix()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 900] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - H102 outlet temperature [350, 400] K\n", + " - D101 condenser pressure [101325, 150000] Pa\n", + " - D101 reflux ratio [0.1, 5]\n", + " - D101 boilup ratio [0.1, 5]" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Set bounds on the temperature of the outlet from H101\n", + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)\n", + "\n", + "# Set bounds on the temperature of the outlet from R101\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(900)\n", + "\n", + "# Set bounds on the volume of the reactor R101\n", + "m.fs.R101.volume[0].setlb(0)\n", + "\n", + "# Set bounds on the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "\n", + "# Set bounds on the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature[0].setlb(350)\n", + "m.fs.H102.outlet.temperature[0].setub(400)\n", + "\n", + "# Set bounds on the pressure inside the condenser\n", + "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", + "m.fs.D101.condenser.condenser_pressure.setub(150000)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \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...: 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 1.27e-04 7.49e+06 -3.8 5.68e-08 - 1.00e+00 2.50e-01h 3\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 0.0000000e+00 9.51e-05 5.62e+06 -3.8 1.19e-06 - 1.00e+00 2.50e-01h 3\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 5.6320546602153890e+06 5.6320546602153890e+06\n", - "Constraint violation....: 2.9103830456733704e-11 9.5061208412516862e-05\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 5.6320546602153890e+06\n", - "\n", - "\n", - "Number of objective function evaluations = 18\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 18\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 10\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.010\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", + "m.fs.D101.condenser.reflux_ratio.setub(5)\n", + "\n", + "# Todo: Set bounds on the boilup ratio\n", + "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", + "m.fs.D101.reboiler.boilup_ratio.setub(5)" + ] }, { - "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': 0.08404064178466797}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.4707525308\n", - "operating cost = $ 427596.7305680824\n", - "capital cost = $ 14704.74018444835\n", - "\n", - "Distillate flowrate = 0.16196898920642744 mol/s\n", - "Benzene purity = 89.49161665800828 %\n", - "Residue flowrate = 0.1051500712068829 mol/s\n", - "Toluene purity = 43.32260291055269 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.16193848360187 %\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", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "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()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "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()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "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" - ] - } - ], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 outlet temperature\n", - "- F101 outlet temperature\n", - "- H102 outlet temperature\n", - "- Condenser pressure\n", - "- reflux ratio\n", - "- boilup ratio\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.conversion.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.D101.condenser.condenser_pressure.unfix()\n", - "m.fs.D101.condenser.reflux_ratio.unfix()\n", - "m.fs.D101.reboiler.boilup_ratio.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 900] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - H102 outlet temperature [350, 400] K\n", - " - D101 condenser pressure [101325, 150000] Pa\n", - " - D101 reflux ratio [0.1, 5]\n", - " - D101 boilup ratio [0.1, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Set bounds on the temperature of the outlet from H101\n", - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)\n", - "\n", - "# Set bounds on the temperature of the outlet from R101\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(900)\n", - "\n", - "# Set bounds on the volume of the reactor R101\n", - "m.fs.R101.volume[0].setlb(0)\n", - "\n", - "# Set bounds on the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "\n", - "# Set bounds on the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature[0].setlb(350)\n", - "m.fs.H102.outlet.temperature[0].setub(400)\n", - "\n", - "# Set bounds on the pressure inside the condenser\n", - "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", - "m.fs.D101.condenser.condenser_pressure.setub(150000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", - "m.fs.D101.condenser.reflux_ratio.setub(5)\n", - "\n", - "# Todo: Set bounds on the boilup ratio\n", - "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", - "m.fs.D101.reboiler.boilup_ratio.setub(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that the overhead loss of benzene from F101 <= 20%\n", - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(\n", - " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "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" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure that the overhead loss of benzene from F101 <= 20%\n", + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 69, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(\n", + " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.48e-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 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-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) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\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", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\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...: 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" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " 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" - ] + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " 15 4.4899127e+05 4.83e+00 9.06e+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 6.41e-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 3.65e-07 1.29e-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 3.73e-08 3.38e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842628671e+02 4.3883989842628670e+05\n", - "Dual infeasibility......: 3.3796318887468276e-07 3.3796318887468276e-04\n", - "Constraint violation....: 2.9103830456733704e-11 3.7252902984619141e-08\n", - "Complementarity.........: 9.0909948039808002e-09 9.0909948039808004e-06\n", - "Overall NLP error.......: 9.0909948039808002e-09 3.3796318887468276e-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) = 0.076\n", - "Total CPU secs in NLP function evaluations = 0.017\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.8984262867\n", - "operating cost = $ 408883.53148308955\n", - "capital cost = $ 29956.366943197143\n", - "\n", - "Distillate flowrate = 0.17999999002639888 mol/s\n", - "Benzene purity = 98.99999900049087 %\n", - "Residue flowrate = 0.10851616424263709 mol/s\n", - "Toluene purity = 15.676178086212413 %\n", - "\n", - "Conversion = 93.38705916369456 %\n", - "\n", - "Overhead benzene loss in F101 = 17.340617931156157 %\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key Takeaways\n", + "\n", + "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", + "\n", + "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", + "\n", + "\n", + "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." + ] } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.9232042951961 K\n", - "\n", - "R101 outlet temperature = 790.3655425698863 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.7414339952852 K\n" - ] + ], + "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": "3.10.13" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Key Takeaways\n", - "\n", - "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", - "\n", - "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", - "\n", - "\n", - "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "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_exercise.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb index 410b7c88..65ccb95f 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 @@ -61,7 +61,7 @@ "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", @@ -220,6 +220,7 @@ "from idaes.core.util.initialization import propagate_state\n", "from idaes.core.solvers import get_solver\n", "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", "\n", "# Import idaes logger to set output levels\n", "import idaes.logger as idaeslog" @@ -656,7 +657,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -840,7 +849,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [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", + "2024-08-28 18:38:14 [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", @@ -921,7 +965,15 @@ "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)" @@ -938,7 +990,20 @@ "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)" @@ -970,7 +1035,7 @@ " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", " (0, \"Liq\", \"methane\"): 1e-5,\n", " },\n", - " \"temperature\": {0: 303},\n", + " \"temperature\": {0: 303.2},\n", " \"pressure\": {0: 350000},\n", "}\n", "\n", @@ -982,7 +1047,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit." + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." ] }, { @@ -992,7 +1057,15 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " # Try initializing using default initializer, \n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1008,7 +1081,132 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1027,7 +1225,91 @@ "cell_type": "code", "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 8.42e+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 6.41e+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 2.24e-08 4.99e-01 -3.8 5.92e-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......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+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) = 0.011\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", @@ -1059,7 +1341,617 @@ "cell_type": "code", "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [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", @@ -1144,7 +2036,101 @@ "cell_type": "code", "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.64e-03 -3.8 2.00e-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......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+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) = 0.083\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': 0.2022566795349121}], '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)" ] @@ -1162,7 +2148,26 @@ "cell_type": "code", "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\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", @@ -1207,7 +2212,40 @@ "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()" ] @@ -1223,7 +2261,40 @@ "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()" ] @@ -1244,7 +2315,25 @@ "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", @@ -1504,7 +2593,117 @@ "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.48e-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 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-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) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1522,7 +2721,26 @@ "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\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", @@ -1567,7 +2785,23 @@ "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1597,13 +2831,6 @@ "\n", "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1623,9 +2850,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.13" } }, "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 1412322e..a401e9b8 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 @@ -61,7 +61,7 @@ "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", @@ -234,6 +234,7 @@ "from idaes.core.util.initialization import propagate_state\n", "from idaes.core.solvers import get_solver\n", "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", "\n", "# Import idaes logger to set output levels\n", "import idaes.logger as idaeslog" @@ -737,7 +738,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -938,7 +947,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [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", + "2024-08-28 18:38:14 [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", @@ -988,7 +1032,15 @@ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1033,7 +1085,15 @@ "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)" @@ -1050,7 +1110,20 @@ "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)" @@ -1082,7 +1155,7 @@ " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", " (0, \"Liq\", \"methane\"): 1e-5,\n", " },\n", - " \"temperature\": {0: 303},\n", + " \"temperature\": {0: 303.2},\n", " \"pressure\": {0: 350000},\n", "}\n", "\n", @@ -1094,7 +1167,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit." + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." ] }, { @@ -1104,7 +1177,15 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " # Try initializing using default initializer, \n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1120,7 +1201,132 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1139,7 +1345,91 @@ "cell_type": "code", "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 8.42e+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 6.41e+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 2.24e-08 4.99e-01 -3.8 5.92e-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......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+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) = 0.011\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", @@ -1171,7 +1461,617 @@ "cell_type": "code", "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for 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fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [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", @@ -1256,7 +2156,101 @@ "cell_type": "code", "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.64e-03 -3.8 2.00e-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......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+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) = 0.083\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': 0.2022566795349121}], '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)" ] @@ -1274,7 +2268,26 @@ "cell_type": "code", "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\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", @@ -1319,7 +2332,40 @@ "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()" ] @@ -1335,7 +2381,40 @@ "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()" ] @@ -1356,7 +2435,25 @@ "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", @@ -1663,7 +2760,117 @@ "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.48e-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 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-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) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1681,7 +2888,26 @@ "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\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", @@ -1726,7 +2952,23 @@ "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1756,13 +2998,6 @@ "\n", "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1782,9 +3017,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb index 1468da16..b035cc2c 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb @@ -61,7 +61,7 @@ "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", @@ -221,6 +221,7 @@ "from idaes.core.util.initialization import propagate_state\n", "from idaes.core.solvers import get_solver\n", "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", "\n", "# Import idaes logger to set output levels\n", "import idaes.logger as idaeslog" @@ -672,7 +673,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -871,7 +880,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [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", + "2024-08-28 18:38:14 [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", @@ -908,7 +952,15 @@ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -967,7 +1019,15 @@ "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)" @@ -984,7 +1044,20 @@ "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)" @@ -1016,7 +1089,7 @@ " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", " (0, \"Liq\", \"methane\"): 1e-5,\n", " },\n", - " \"temperature\": {0: 303},\n", + " \"temperature\": {0: 303.2},\n", " \"pressure\": {0: 350000},\n", "}\n", "\n", @@ -1028,7 +1101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit." + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." ] }, { @@ -1038,7 +1111,15 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " # Try initializing using default initializer, \n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1054,7 +1135,132 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1073,7 +1279,91 @@ "cell_type": "code", "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 8.42e+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 6.41e+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 2.24e-08 4.99e-01 -3.8 5.92e-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......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+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) = 0.011\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", @@ -1121,7 +1411,617 @@ "cell_type": "code", "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for 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fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [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", @@ -1220,7 +2120,101 @@ "cell_type": "code", "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.64e-03 -3.8 2.00e-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......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+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) = 0.083\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': 0.2022566795349121}], '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)" ] @@ -1254,7 +2248,26 @@ "cell_type": "code", "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\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", @@ -1296,7 +2309,16 @@ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427596.73056805483\n", + "14704.740184467111\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1317,7 +2339,40 @@ "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()" ] @@ -1333,7 +2388,40 @@ "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()" ] @@ -1354,7 +2442,25 @@ "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", @@ -1619,7 +2725,117 @@ "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.48e-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 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-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) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1653,7 +2869,26 @@ "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\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", @@ -1695,7 +2930,16 @@ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "408883.5314830889\n", + "29956.3669431971\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1717,7 +2961,23 @@ "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1747,13 +3007,6 @@ "\n", "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1773,9 +3026,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb index 1412322e..a401e9b8 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb @@ -61,7 +61,7 @@ "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", @@ -234,6 +234,7 @@ "from idaes.core.util.initialization import propagate_state\n", "from idaes.core.solvers import get_solver\n", "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", "\n", "# Import idaes logger to set output levels\n", "import idaes.logger as idaeslog" @@ -737,7 +738,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -938,7 +947,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [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", + "2024-08-28 18:38:14 [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", @@ -988,7 +1032,15 @@ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1033,7 +1085,15 @@ "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)" @@ -1050,7 +1110,20 @@ "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)" @@ -1082,7 +1155,7 @@ " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", " (0, \"Liq\", \"methane\"): 1e-5,\n", " },\n", - " \"temperature\": {0: 303},\n", + " \"temperature\": {0: 303.2},\n", " \"pressure\": {0: 350000},\n", "}\n", "\n", @@ -1094,7 +1167,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit." + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." ] }, { @@ -1104,7 +1177,15 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " # Try initializing using default initializer, \n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1120,7 +1201,132 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1139,7 +1345,91 @@ "cell_type": "code", "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 8.42e+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 6.41e+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 2.24e-08 4.99e-01 -3.8 5.92e-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......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+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) = 0.011\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", @@ -1171,7 +1461,617 @@ "cell_type": "code", "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for 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fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [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", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [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", @@ -1256,7 +2156,101 @@ "cell_type": "code", "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.64e-03 -3.8 2.00e-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......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+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) = 0.083\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': 0.2022566795349121}], '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)" ] @@ -1274,7 +2268,26 @@ "cell_type": "code", "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\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", @@ -1319,7 +2332,40 @@ "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()" ] @@ -1335,7 +2381,40 @@ "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()" ] @@ -1356,7 +2435,25 @@ "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", @@ -1663,7 +2760,117 @@ "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. 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 6.48e-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 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-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) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1681,7 +2888,26 @@ "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\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", @@ -1726,7 +2952,23 @@ "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1756,13 +2998,6 @@ "\n", "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1782,9 +3017,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py index 83c8d23e..94e29ac1 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py @@ -52,6 +52,7 @@ import idaes.core.util.scaling as iscale from pyomo.util.check_units import assert_units_consistent from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.exceptions import InitializationError # Import costing methods - classes, heaters, vessels, compressors, columns from idaes.models.costing.SSLW import ( @@ -216,7 +217,13 @@ def hda_with_flash(tee=True): seq.set_guesses_for(m.fs.H101.inlet, tear_guesses) def function(unit): - unit.initialize(outlvl=outlvl) + try: + initializer = unit.default_initializer() + initializer.initialize(unit, output_level=idaeslog.INFO) + except InitializationError: + solver=get_solver() + solver.solve(unit) + seq.run(m, function) @@ -433,7 +440,12 @@ def hda_with_distillation(tee=True): seq.set_guesses_for(m.fs.H101.inlet, tear_guesses) def function(unit): - unit.initialize(outlvl=outlvl) + try: + initializer = unit.default_initializer() + initializer.initialize(unit, output_level=idaeslog.INFO) + except InitializationError: + solver=get_solver() + solver.solve(unit) seq.run(m, function) diff --git a/idaes_examples/notebooks/docs/surrogates/.mdl_wts.keras b/idaes_examples/notebooks/docs/surrogates/.mdl_wts.keras new file mode 100644 index 00000000..15c3cfb5 Binary files /dev/null and b/idaes_examples/notebooks/docs/surrogates/.mdl_wts.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb index a2b493ba..d325174c 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb @@ -223,7 +223,7 @@ " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(\n", " keras_surrogate,\n", @@ -531,7 +531,7 @@ " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(\n", " keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb index 939c494d..a890d44a 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb @@ -1,3497 +1,638 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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": [ - "# ML/AI Best Practices: \"Selecting Surrogate Model Form/Size for Optimization\"\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "In this notebook we demonstrate the use of model and solver statistics to select the best surrogate model. For this purpose we trained (offline) different models with ALAMO, PySMO for three basis forms, and TensorFlow Keras. The surrogates are imported into the notebook, and the IDAES flowsheet is constructed and solved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the ALAMO, PySMO and Keras surrogate trainers, and compares key indicators of model performance. In this notebook, IPOPT will be run with statistics using ALAMO, PySMO Polynomial, PySMO RBF, PySMO Kriging and Keras surrogate models to assess each model type for flowsheet integration and tractability." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Problem Statement \n", - "\n", - "Within the context of a larger Natural Gas Fuel Cell (NGFC) system, the autothermal reformer unit is used to generate syngas from air, steam, and natural gas. Two input variables are considered for this example (reformer bypass fraction and fuel to steam ratio). The reformer bypass fraction (also called internal reformation percentage) plays an important role in the syngas final composition and it is typically controlled in this process. The fuel to steam ratio is an important variable that affects the final syngas reaction and heat duty required by the reactor. The syngas is then used as fuel by a solid-oxide fuel cell (SOFC) to generate electricity and heat. \n", - "\n", - "The autothermal reformer is typically modeled using the IDAES Gibbs reactor and this reactor is robust once it is initialized; however, the overall model robustness is affected due to several components present in the reaction, scaling issues for the largrangean multipliers, and Gibbs free energy minimization formulation. Substituting rigorously trained and validated surrogates in lieu of rigorous unit model equations increases the robustness of the problem.\n", - "\n", - "### 2.1. Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "### 2.2. Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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", + "###############################################################################" ] - }, - "execution_count": 2, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\best_practices_optimization_doc_4_0.png" - } - }, - "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(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Previous Jupyter Notebooks demonstrated the workflow to import data, train surrogate models using [ALAMO](alamo/alamo_flowsheet_optimization_src_doc.md), [PySMO](pysmo/pysmo_flowsheet_optimization_src_doc.md) and Keras, and develop IDAES's validation plots. To keep this notebook simple, this notebook simply loads the surrogate models trained off line.\n", - "\n", - "Note that the training/loading method includes a \"retrain\" argument in case the user wants to retrain all surrogate models. Since the retrain method runs ALAMO, PySMO (Polynomial, Radial Basis Functions, and Kriging basis types) and Keras, it takes about an 1 hr to complete the training for all models.\n", - "\n", - "Each run will overwrite the serialized JSON files for previously trained surrogates if retraining is enforced. To retrain individual surrogates, simply delete the desired JSON before running this notebook (for Keras, delete the folder `keras_surrogate/`)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading existing surrogate models and training missing models.\n", - "Any training output will print below; otherwise, models will be loaded without any further output.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dkgun\\miniconda3\\envs\\idaes-examples-py311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", - " return bound(*args, **kwds)\n" - ] - } - ], - "source": [ - "from idaes_examples.mod.surrogates.AR_training_methods import (\n", - " train_load_surrogates,\n", - " SurrType,\n", - ")\n", - "\n", - "trained_surr = train_load_surrogates(retrain=False)\n", - "# setting retrain to True will take ~ 1 hour to run, best to load if possible\n", - "# setting retrain to False will only generate missing surrogates (only if JSON/folder doesn't exist)\n", - "# this method trains surrogates and serializes to JSON\n", - "# The return value is a set of surrogate types (instances of SurrType) that were trained\n", - "\n", - "# imports to capture long output\n", - "from io import StringIO\n", - "import sys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This step builds an IDAES flowsheet and imports the surrogate model objects. As shown in the prior three examples, a single model object accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component. While the serialization method and file structure differs slightly between the ALAMO, PySMO and Keras Python Wrappers, the three are imported similarly into IDAES flowsheets as shown below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build IDAES Flowsheet\n", - "\n", - "This method builds an instance of the IDAES flowsheet model and solves the flowsheet using IPOPT. The method allows users to select a case and the surrogate model type to be used (i.e., alamo, pysmo, keras). The case argument consists of a list with values for the input variables (in this order, bypass split fraction and natural gas to steam ratio). Then the method fixes the input variables values to solve a square problem with IPOPT. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import IDAES and Pyomo libraries\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value, Var, Constraint, Set\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "\n", - "def build_flowsheet(case, surrogate_type: SurrType = None):\n", - " print(case, \" \", surrogate_type.value)\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C3H8,\n", - " m.fs.C4H10,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # call correct PySMO object to use below (will let us avoid nested switches)\n", - "\n", - " # capture long output from loading surrogates (don't need to print it)\n", - " stream = StringIO()\n", - " oldstdout = sys.stdout\n", - " sys.stdout = stream\n", - "\n", - " if surrogate_type == SurrType.ALAMO:\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - " elif SurrType.is_pysmo(\n", - " surrogate_type\n", - " ): # surrogate is one of the three pysmo basis options\n", - " surrogate = PysmoSurrogate.load_from_file(\n", - " surrogate_type.value + \"_surrogate.json\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " else:\n", - " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", - "\n", - " # revert to standard output\n", - " sys.stdout = oldstdout\n", - "\n", - " # fix input values and solve flowsheet\n", - " m.fs.bypass_frac.fix(case[0])\n", - " m.fs.ng_steam_ratio.fix(case[1])\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " try: # attempt to solve problem\n", - " results = solver.solve(m, tee=True)\n", - " except: # retry solving one more time\n", - " results = solver.solve(m, tee=True)\n", - "\n", - " return (\n", - " value(m.fs.steam_flowrate),\n", - " value(m.fs.reformer_duty),\n", - " value(m.fs.C2H6),\n", - " value(m.fs.CH4),\n", - " value(m.fs.H2),\n", - " value(m.fs.O2),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Model Size/Form Comparison\n", - "\n", - "As mentioned above, as part of best practices the IDAES ML/AI demonstration includes the analysis of model/solver statistics and performance to determine the best surrogate model, including model size, model form, model trainer, etc. This section provides the rigorous analysis of solver performance comparing different surrogate models (ALAMO, PySMO polynomial, PysMO RBF, and PySMO Kriging).\n", - "\n", - "To obtain the results, we run the flowsheet for ten different simulation cases for each surrogate model type. Since the simulation cases are obtained from the training data set we can compare model performance (absolute error of measurement vs predicted output values)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.373913 1.073684] pysmo_kriging\n", - "2023-11-02 10:24:52 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.69e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.69e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.373913 1.073684] pysmo_rbf\n", - "2023-11-02 10:24:53 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.69e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.69e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.373913 1.073684] pysmo_poly\n", - "2023-11-02 10:24:53 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.69e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.11e-16 0.00e+00 -1.0 1.69e+04 - 1.00e+00 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....: 1.1102230246251565e-16 1.1102230246251565e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.394203 0.936842] pysmo_kriging\n", - "2023-11-02 10:24:53 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.42e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.42e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.394203 0.936842] pysmo_rbf\n", - "2023-11-02 10:24:53 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.42e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.42e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.394203 0.936842] pysmo_poly\n", - "2023-11-02 10:24:53 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.43e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.43e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.789855 0.978947] pysmo_kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:24:53 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 8.29e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 8.29e+02 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.789855 0.978947] pysmo_rbf\n", - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 8.31e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 8.31e+02 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.789855 0.978947] pysmo_poly\n", - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 8.29e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 8.29e+02 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.191304 0.936842] pysmo_kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.18e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.191304 0.936842] pysmo_rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.18e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.191304 0.936842] pysmo_poly\n", - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.11e-16 0.00e+00 -1.0 2.18e+04 - 1.00e+00 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....: 1.1102230246251565e-16 1.1102230246251565e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\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) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.231884 0.863158] pysmo_kriging\n", - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.90e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.90e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.231884 0.863158] pysmo_rbf\n", - "2023-11-02 10:24:54 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.90e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.90e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.231884 0.863158] pysmo_poly\n", - "2023-11-02 10:24:55 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.90e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.90e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.150725 0.978947] pysmo_kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:24:55 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.41e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.41e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.150725 0.978947] pysmo_rbf\n", - "2023-11-02 10:24:55 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.41e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.41e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.150725 0.978947] pysmo_poly\n", - "2023-11-02 10:24:56 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.41e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.41e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.728986 0.8 ] pysmo_kriging\n", - "2023-11-02 10:24:56 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.84e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.84e+03 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.728986 0.8 ] pysmo_rbf\n", - "2023-11-02 10:24:56 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.82e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.82e+03 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.728986 0.8 ] pysmo_poly\n", - "2023-11-02 10:24:57 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 1.83e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.83e+03 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.1 1.052632] pysmo_kriging\n", - "2023-11-02 10:24:57 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.76e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.76e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.1 1.052632] pysmo_rbf\n", - "2023-11-02 10:24:57 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.76e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.76e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.1 1.052632] pysmo_poly\n", - "2023-11-02 10:24:58 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.76e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.76e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.181159 0.915789] pysmo_kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:24:58 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.18e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.181159 0.915789] pysmo_rbf\n", - "2023-11-02 10:24:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.18e+04 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.181159 0.915789] pysmo_poly\n", - "2023-11-02 10:24:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ML/AI Best Practices: \"Selecting Surrogate Model Form/Size for Optimization\"\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "In this notebook we demonstrate the use of model and solver statistics to select the best surrogate model. For this purpose we trained (offline) different models with ALAMO, PySMO for three basis forms, and TensorFlow Keras. The surrogates are imported into the notebook, and the IDAES flowsheet is constructed and solved." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 2.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.11e-16 0.00e+00 -1.0 2.18e+04 - 1.00e+00 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....: 1.1102230246251565e-16 1.1102230246251565e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the ALAMO, PySMO and Keras surrogate trainers, and compares key indicators of model performance. In this notebook, IPOPT will be run with statistics using ALAMO, PySMO Polynomial, PySMO RBF, PySMO Kriging and Keras surrogate models to assess each model type for flowsheet integration and tractability." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.668116 1.073684] pysmo_kriging\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Problem Statement \n", + "\n", + "Within the context of a larger Natural Gas Fuel Cell (NGFC) system, the autothermal reformer unit is used to generate syngas from air, steam, and natural gas. Two input variables are considered for this example (reformer bypass fraction and fuel to steam ratio). The reformer bypass fraction (also called internal reformation percentage) plays an important role in the syngas final composition and it is typically controlled in this process. The fuel to steam ratio is an important variable that affects the final syngas reaction and heat duty required by the reactor. The syngas is then used as fuel by a solid-oxide fuel cell (SOFC) to generate electricity and heat. \n", + "\n", + "The autothermal reformer is typically modeled using the IDAES Gibbs reactor and this reactor is robust once it is initialized; however, the overall model robustness is affected due to several components present in the reaction, scaling issues for the largrangean multipliers, and Gibbs free energy minimization formulation. Substituting rigorously trained and validated surrogates in lieu of rigorous unit model equations increases the robustness of the problem.\n", + "\n", + "### 2.1. Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "### 2.2. Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:24:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "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(\"AR_PFD.png\"))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 5.48e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.48e+03 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training Surrogates" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.668116 1.073684] pysmo_rbf\n", - "2023-11-02 10:25:00 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previous Jupyter Notebooks demonstrated the workflow to import data, train surrogate models using [ALAMO](alamo/alamo_flowsheet_optimization_src_doc.md), [PySMO](pysmo/pysmo_flowsheet_optimization_src_doc.md) and Keras, and develop IDAES's validation plots. To keep this notebook simple, this notebook simply loads the surrogate models trained off line.\n", + "\n", + "Note that the training/loading method includes a \"retrain\" argument in case the user wants to retrain all surrogate models. Since the retrain method runs ALAMO, PySMO (Polynomial, Radial Basis Functions, and Kriging basis types) and Keras, it takes about an 1 hr to complete the training for all models.\n", + "\n", + "Each run will overwrite the serialized JSON files for previously trained surrogates if retraining is enforced. To retrain individual surrogates, simply delete the desired JSON before running this notebook (for Keras, delete the folder `keras_surrogate/`)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 5.49e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.49e+03 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from idaes_examples.mod.surrogates.AR_training_methods import (\n", + " train_load_surrogates,\n", + " SurrType,\n", + ")\n", + "\n", + "trained_surr = train_load_surrogates(retrain=False)\n", + "# setting retrain to True will take ~ 1 hour to run, best to load if possible\n", + "# setting retrain to False will only generate missing surrogates (only if JSON/folder doesn't exist)\n", + "# this method trains surrogates and serializes to JSON\n", + "# The return value is a set of surrogate types (instances of SurrType) that were trained\n", + "\n", + "# imports to capture long output\n", + "from io import StringIO\n", + "import sys" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.668116 1.073684] pysmo_poly\n", - "2023-11-02 10:25:00 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Build and Run IDAES Flowsheet" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 13\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 13\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\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 5.49e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.49e+03 - 1.00e+00 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....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\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) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - } - ], - "source": [ - "# Import Auto-reformer training data\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "csv_data = pd.read_csv(r\"reformer-data.csv\") # 2800 data points\n", - "\n", - "# extracting 10 data points out of 2800 data points, randomly selecting 10 cases to run\n", - "case_data = csv_data.sample(n=10)\n", - "\n", - "# selecting columns that correspond to Input Variables\n", - "inputs = np.array(case_data.iloc[:, :2])\n", - "\n", - "# selecting columns that correspond to Output Variables\n", - "cols = [\"Steam_Flow\", \"Reformer_Duty\", \"C2H6\", \"CH4\", \"H2\", \"O2\"]\n", - "outputs = np.array(case_data[cols])\n", - "\n", - "# For results comparison with minimum memory usage we will extract the values to plot on each pass\n", - "# note that the entire model could be returned and saved on each loop if desired\n", - "\n", - "# create empty dictionaries so we may easily index results as we save them\n", - "# for convenience while plotting, each output variable has its own dictionary\n", - "# indexed by (case number, trainer type)\n", - "# trainers = [\"alamo\", \"pysmo_poly\", \"pysmo_rbf\", \"pysmo_krig\", \"keras\"]\n", - "# temporarily remove keras\n", - "trainers = list(trained_surr - {SurrType.KERAS})\n", - "\n", - "cases = range(len(inputs))\n", - "steam_flow_error = {}\n", - "reformer_duty_error = {}\n", - "conc_C2H6 = {}\n", - "conc_CH4 = {}\n", - "conc_H2 = {}\n", - "conc_O2 = {}\n", - "\n", - "# run flowsheet for each trainer and save results\n", - "i = 0\n", - "for case in inputs: # each case is a value pair (bypass_frac, ng_steam_ratio)\n", - " i = i + 1\n", - " for trainer in trainers:\n", - " [sf, rd, eth, meth, hyd, oxy,] = build_flowsheet(\n", - " case, surrogate_type=trainer\n", - " )\n", - " steam_flow_error[(i, trainer)] = abs(\n", - " (sf - value(outputs[i - 1, 0])) / value(outputs[i - 1, 0])\n", - " )\n", - " reformer_duty_error[(i, trainer)] = abs(\n", - " (rd - value(outputs[i - 1, 1])) / value(outputs[i - 1, 1])\n", - " )\n", - " conc_C2H6[(i, trainer)] = abs(eth - value(outputs[i - 1, 2]))\n", - " conc_CH4[(i, trainer)] = abs(meth - value(outputs[i - 1, 3]))\n", - " conc_H2[(i, trainer)] = abs(hyd - value(outputs[i - 1, 4]))\n", - " conc_O2[(i, trainer)] = abs(oxy - value(outputs[i - 1, 5]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize these results by plotting a graph for each of the quantities above, creating a data series for each surrogate trainer. Some data series may overlay if values are identical for all cases:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step builds an IDAES flowsheet and imports the surrogate model objects. As shown in the prior three examples, a single model object accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component. While the serialization method and file structure differs slightly between the ALAMO, PySMO and Keras Python Wrappers, the three are imported similarly into IDAES flowsheets as shown below." ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\best_practices_optimization_doc_15_0.png" - } - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build IDAES Flowsheet\n", + "\n", + "This method builds an instance of the IDAES flowsheet model and solves the flowsheet using IPOPT. The method allows users to select a case and the surrogate model type to be used (i.e., alamo, pysmo, keras). The case argument consists of a list with values for the input variables (in this order, bypass split fraction and natural gas to steam ratio). Then the method fixes the input variables values to solve a square problem with IPOPT. 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", 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" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import IDAES and Pyomo libraries\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value, Var, Constraint, Set\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "\n", + "def build_flowsheet(case, surrogate_type: SurrType = None):\n", + " print(case, \" \", surrogate_type.value)\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C3H8,\n", + " m.fs.C4H10,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # call correct PySMO object to use below (will let us avoid nested switches)\n", + "\n", + " # capture long output from loading surrogates (don't need to print it)\n", + " stream = StringIO()\n", + " oldstdout = sys.stdout\n", + " sys.stdout = stream\n", + "\n", + " if surrogate_type == SurrType.ALAMO:\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " elif surrogate_type == SurrType.KERAS:\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " elif SurrType.is_pysmo(\n", + " surrogate_type\n", + " ): # surrogate is one of the three pysmo basis options\n", + " surrogate = PysmoSurrogate.load_from_file(\n", + " surrogate_type.value + \"_surrogate.json\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " else:\n", + " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", + "\n", + " # revert to standard output\n", + " sys.stdout = oldstdout\n", + "\n", + " # fix input values and solve flowsheet\n", + " m.fs.bypass_frac.fix(case[0])\n", + " m.fs.ng_steam_ratio.fix(case[1])\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " try: # attempt to solve problem\n", + " results = solver.solve(m, tee=True)\n", + " except: # retry solving one more time\n", + " results = solver.solve(m, tee=True)\n", + "\n", + " return (\n", + " value(m.fs.steam_flowrate),\n", + " value(m.fs.reformer_duty),\n", + " value(m.fs.C2H6),\n", + " value(m.fs.CH4),\n", + " value(m.fs.H2),\n", + " value(m.fs.O2),\n", + " )" ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\best_practices_optimization_doc_15_2.png" - } - }, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Mole fraction predictions displayed with absolute error:\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Model Size/Form Comparison\n", + "\n", + "As mentioned above, as part of best practices the IDAES ML/AI demonstration includes the analysis of model/solver statistics and performance to determine the best surrogate model, including model size, model form, model trainer, etc. This section provides the rigorous analysis of solver performance comparing different surrogate models (ALAMO, PySMO polynomial, PysMO RBF, and PySMO Kriging).\n", + "\n", + "To obtain the results, we run the flowsheet for ten different simulation cases for each surrogate model type. Since the simulation cases are obtained from the training data set we can compare model performance (absolute error of measurement vs predicted output values)." + ] }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "csv_data = pd.read_csv(r\"reformer-data.csv\") # 2800 data points\n", + "\n", + "# extracting 10 data points out of 2800 data points, randomly selecting 10 cases to run\n", + "case_data = csv_data.sample(n=10)\n", + "\n", + "# selecting columns that correspond to Input Variables\n", + "inputs = np.array(case_data.iloc[:, :2])\n", + "\n", + "# selecting columns that correspond to Output Variables\n", + "cols = [\"Steam_Flow\", \"Reformer_Duty\", \"C2H6\", \"CH4\", \"H2\", \"O2\"]\n", + "outputs = np.array(case_data[cols])\n", + "\n", + "# For results comparison with minimum memory usage we will extract the values to plot on each pass\n", + "# note that the entire model could be returned and saved on each loop if desired\n", + "\n", + "# create empty dictionaries so we may easily index results as we save them\n", + "# for convenience while plotting, each output variable has its own dictionary\n", + "# indexed by (case number, trainer type)\n", + "# trainers = [\"alamo\", \"pysmo_poly\", \"pysmo_rbf\", \"pysmo_krig\", \"keras\"]\n", + "# temporarily remove keras\n", + "trainers = list(trained_surr - {SurrType.KERAS})\n", + "\n", + "cases = range(len(inputs))\n", + "steam_flow_error = {}\n", + "reformer_duty_error = {}\n", + "conc_C2H6 = {}\n", + "conc_CH4 = {}\n", + "conc_H2 = {}\n", + "conc_O2 = {}\n", + "\n", + "# run flowsheet for each trainer and save results\n", + "i = 0\n", + "for case in inputs: # each case is a value pair (bypass_frac, ng_steam_ratio)\n", + " i = i + 1\n", + " for trainer in trainers:\n", + " [sf, rd, eth, meth, hyd, oxy,] = build_flowsheet(\n", + " case, surrogate_type=trainer\n", + " )\n", + " steam_flow_error[(i, trainer)] = abs(\n", + " (sf - value(outputs[i - 1, 0])) / value(outputs[i - 1, 0])\n", + " )\n", + " reformer_duty_error[(i, trainer)] = abs(\n", + " (rd - value(outputs[i - 1, 1])) / value(outputs[i - 1, 1])\n", + " )\n", + " conc_C2H6[(i, trainer)] = abs(eth - value(outputs[i - 1, 2]))\n", + " conc_CH4[(i, trainer)] = abs(meth - value(outputs[i - 1, 3]))\n", + " conc_H2[(i, trainer)] = abs(hyd - value(outputs[i - 1, 4]))\n", + " conc_O2[(i, trainer)] = abs(oxy - value(outputs[i - 1, 5]))" ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\best_practices_optimization_doc_15_4.png" - } - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize these results by plotting a graph for each of the quantities above, creating a data series for each surrogate trainer. Some data series may overlay if values are identical for all cases:" ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\best_practices_optimization_doc_15_5.png" - } - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "# create figure/axes for each plot sequentially, plotting each trainer as a separate data series\n", + "\n", + "# Steam Flow Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " sf = [steam_flow_error[(i, j)] for (i, j) in steam_flow_error if j == trainer]\n", + " ax.plot(cases, sf, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Relative Error\")\n", + "ax.set_title(\"Steam Flow Prediction\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# Reformer Duty Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " rd = [reformer_duty_error[(i, j)] for (i, j) in reformer_duty_error if j == trainer]\n", + " ax.plot(cases, rd, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Relative Error\")\n", + "ax.set_title(\"Reformer Duty Prediction\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# C2H6 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " eth = [conc_C2H6[(i, j)] for (i, j) in conc_C2H6 if j == trainer]\n", + " ax.plot(cases, eth, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"C2H6 Mole Fraction Prediction (O(1E-2))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"Mole fraction predictions displayed with absolute error:\")\n", + "print()\n", + "\n", + "# CH4 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " meth = [conc_CH4[(i, j)] for (i, j) in conc_CH4 if j == trainer]\n", + " ax.plot(cases, meth, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"CH4 Mole Fraction Prediction (O(1E-1))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# H2 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " hyd = [conc_H2[(i, j)] for (i, j) in conc_H2 if j == trainer]\n", + " ax.plot(cases, hyd, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"H2 Mole Fraction Prediction (O(1E-1))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# O2 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " oxy = [conc_O2[(i, j)] for (i, j) in conc_O2 if j == trainer]\n", + " ax.plot(cases, oxy, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"O2 Mole Fraction Prediction (O(1E-20))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()" ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\best_practices_optimization_doc_15_6.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "# create figure/axes for each plot sequentially, plotting each trainer as a separate data series\n", - "\n", - "# Steam Flow Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " sf = [steam_flow_error[(i, j)] for (i, j) in steam_flow_error if j == trainer]\n", - " ax.plot(cases, sf, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Relative Error\")\n", - "ax.set_title(\"Steam Flow Prediction\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# Reformer Duty Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " rd = [reformer_duty_error[(i, j)] for (i, j) in reformer_duty_error if j == trainer]\n", - " ax.plot(cases, rd, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Relative Error\")\n", - "ax.set_title(\"Reformer Duty Prediction\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# C2H6 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " eth = [conc_C2H6[(i, j)] for (i, j) in conc_C2H6 if j == trainer]\n", - " ax.plot(cases, eth, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"C2H6 Mole Fraction Prediction (O(1E-2))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "print()\n", - "print(\"Mole fraction predictions displayed with absolute error:\")\n", - "print()\n", - "\n", - "# CH4 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " meth = [conc_CH4[(i, j)] for (i, j) in conc_CH4 if j == trainer]\n", - " ax.plot(cases, meth, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"CH4 Mole Fraction Prediction (O(1E-1))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# H2 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " hyd = [conc_H2[(i, j)] for (i, j) in conc_H2 if j == trainer]\n", - " ax.plot(cases, hyd, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"H2 Mole Fraction Prediction (O(1E-1))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# O2 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " oxy = [conc_O2[(i, j)] for (i, j) in conc_O2 if j == trainer]\n", - " ax.plot(cases, oxy, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"O2 Mole Fraction Prediction (O(1E-20))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.3 Comparing Surrogate Optimization\n", - "Extending this analysis, we will run a single optimization scenario for each surrogate model and compare results. As in previous examples detailing workflows for [ALAMO](alamo_flowsheet_optimization_src_doc.md), [PySMO](pysmo_flowsheet_optimization_src_doc.md) and [Keras](keras_flowsheet_optimization_src_doc.md), we will optimize hydrogen production while restricting nitrogen below 34 mol% in the product stream." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Import additional Pyomo libraries\n", - "from pyomo.environ import Objective, maximize\n", - "\n", - "\n", - "def run_optimization(surrogate_type=None):\n", - " print(surrogate_type)\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C3H8,\n", - " m.fs.C4H10,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # call correct PySMO object to use below (will let us avoid nested switches)\n", - "\n", - " # capture long output from loading surrogates (don't need to print it)\n", - " stream = StringIO()\n", - " oldstdout = sys.stdout\n", - " sys.stdout = stream\n", - "\n", - " if surrogate_type == SurrType.ALAMO:\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - " elif SurrType.is_pysmo(\n", - " surrogate_type\n", - " ): # surrogate is one of the three pysmo basis options\n", - " surrogate = PysmoSurrogate.load_from_file(\n", - " surrogate_type.value + \"_surrogate.json\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " else:\n", - " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", - "\n", - " # revert to standard output\n", - " sys.stdout = oldstdout\n", - "\n", - " # unfix input values and add the objective/constraint to the model\n", - " m.fs.bypass_frac.unfix()\n", - " m.fs.ng_steam_ratio.unfix()\n", - " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " try: # attempt to solve problem\n", - " results = solver.solve(m, tee=True)\n", - " except: # retry solving one more time\n", - " results = solver.solve(m, tee=True)\n", - "\n", - " return inputs, outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SurrType.PYSMO_KRG\n", - "2023-11-02 10:25:04 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 39\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 3\n", - "\n", - "Total number of variables............................: 15\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 0\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 -0.0000000e+00 4.20e+01 2.20e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -2.2210575e-01 8.98e+00 1.39e-02 -1.7 6.93e+02 - 9.41e-01 1.00e+00h 1\n", - " 2 -3.1543427e-01 4.63e+02 3.09e-02 -2.5 6.13e+03 - 9.54e-01 1.00e+00h 1\n", - " 3 -3.2648096e-01 1.67e+03 1.99e-02 -2.5 1.85e+04 - 5.27e-01 4.45e-01h 1\n", - " 4 -3.2734223e-01 2.21e+02 6.68e-03 -2.5 9.12e+03 - 1.00e+00 1.00e+00h 1\n", - " 5 -3.2603609e-01 3.46e+01 3.14e-04 -2.5 2.48e+01 - 1.00e+00 1.00e+00h 1\n", - " 6 -3.2609557e-01 2.53e-01 3.18e-06 -2.5 1.40e+02 - 1.00e+00 1.00e+00h 1\n", - " 7 -3.3096545e-01 6.96e+01 4.78e-03 -3.8 3.81e+03 - 9.20e-01 1.00e+00h 1\n", - " 8 -3.3154563e-01 3.39e+00 3.75e-04 -3.8 1.11e+03 - 1.00e+00 1.00e+00h 1\n", - " 9 -3.3138393e-01 5.32e-01 1.27e-05 -3.8 3.19e+02 - 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", - " 10 -3.3139248e-01 4.61e-04 6.78e-08 -3.8 1.24e+01 - 1.00e+00 1.00e+00h 1\n", - " 11 -3.3167648e-01 1.35e-01 7.35e-04 -5.7 1.71e+02 - 1.00e+00 9.75e-01h 1\n", - " 12 -3.3168623e-01 7.84e-04 7.02e-09 -5.7 1.52e+01 - 1.00e+00 1.00e+00h 1\n", - " 13 -3.3168990e-01 3.58e-05 2.76e-09 -8.6 1.64e+00 - 1.00e+00 1.00e+00h 1\n", - " 14 -3.3168990e-01 4.38e-09 2.72e-13 -8.6 2.99e-03 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 14\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: -3.3168990316632091e-01 -3.3168990316632091e-01\n", - "Dual infeasibility......: 2.7227776253254969e-13 2.7227776253254969e-13\n", - "Constraint violation....: 1.5548231598831156e-11 4.3801264837384224e-09\n", - "Complementarity.........: 2.5059042947426033e-09 2.5059042947426033e-09\n", - "Overall NLP error.......: 2.5059042947426033e-09 4.3801264837384224e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 15\n", - "Number of objective gradient evaluations = 15\n", - "Number of equality constraint evaluations = 15\n", - "Number of inequality constraint evaluations = 15\n", - "Number of equality constraint Jacobian evaluations = 15\n", - "Number of inequality constraint Jacobian evaluations = 15\n", - "Number of Lagrangian Hessian evaluations = 14\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", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Comparing Surrogate Optimization\n", + "Extending this analysis, we will run a single optimization scenario for each surrogate model and compare results. As in previous examples detailing workflows for [ALAMO](alamo_flowsheet_optimization_src_doc.md), [PySMO](pysmo_flowsheet_optimization_src_doc.md) and [Keras](keras_flowsheet_optimization_src_doc.md), we will optimize hydrogen production while restricting nitrogen below 34 mol% in the product stream." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "SurrType.PYSMO_RBF\n", - "2023-11-02 10:25:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Import additional Pyomo libraries\n", + "from pyomo.environ import Objective, maximize\n", + "\n", + "\n", + "def run_optimization(surrogate_type=None):\n", + " print(surrogate_type)\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C3H8,\n", + " m.fs.C4H10,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # call correct PySMO object to use below (will let us avoid nested switches)\n", + "\n", + " # capture long output from loading surrogates (don't need to print it)\n", + " stream = StringIO()\n", + " oldstdout = sys.stdout\n", + " sys.stdout = stream\n", + "\n", + " if surrogate_type == SurrType.ALAMO:\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " elif surrogate_type == SurrType.KERAS:\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " elif SurrType.is_pysmo(\n", + " surrogate_type\n", + " ): # surrogate is one of the three pysmo basis options\n", + " surrogate = PysmoSurrogate.load_from_file(\n", + " surrogate_type.value + \"_surrogate.json\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " else:\n", + " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", + "\n", + " # revert to standard output\n", + " sys.stdout = oldstdout\n", + "\n", + " # unfix input values and add the objective/constraint to the model\n", + " m.fs.bypass_frac.unfix()\n", + " m.fs.ng_steam_ratio.unfix()\n", + " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " try: # attempt to solve problem\n", + " results = solver.solve(m, tee=True)\n", + " except: # retry solving one more time\n", + " results = solver.solve(m, tee=True)\n", + "\n", + " return inputs, outputs" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 39\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 3\n", - "\n", - "Total number of variables............................: 15\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 0\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 -0.0000000e+00 4.58e+01 2.28e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -2.2187269e-01 1.44e+01 1.33e-02 -1.7 6.80e+02 - 9.44e-01 1.00e+00h 1\n", - " 2 -3.1833654e-01 6.49e+02 1.65e-02 -2.5 6.42e+03 - 9.23e-01 1.00e+00h 1\n", - " 3 -3.2904216e-01 1.98e+03 3.27e-03 -2.5 1.61e+04 - 6.11e-01 5.97e-01h 1\n", - " 4 -3.2728772e-01 2.07e+02 3.54e-03 -2.5 8.41e+03 - 1.00e+00 1.00e+00h 1\n", - " 5 -3.2617335e-01 1.27e+01 3.00e-04 -2.5 7.83e+02 - 1.00e+00 1.00e+00h 1\n", - " 6 -3.2612431e-01 5.10e-03 1.28e-06 -2.5 4.03e+01 - 1.00e+00 1.00e+00h 1\n", - " 7 -3.3094622e-01 5.19e+01 4.80e-03 -3.8 3.78e+03 - 9.21e-01 1.00e+00h 1\n", - " 8 -3.3154557e-01 3.78e+00 1.51e-03 -3.8 1.17e+03 - 1.00e+00 9.89e-01h 1\n", - " 9 -3.3138015e-01 4.26e-01 1.61e-05 -3.8 3.24e+02 - 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", - " 10 -3.3138995e-01 5.21e-04 8.48e-08 -3.8 1.39e+01 - 1.00e+00 1.00e+00h 1\n", - " 11 -3.3167385e-01 1.35e-01 8.06e-04 -5.7 1.76e+02 - 1.00e+00 9.73e-01h 1\n", - " 12 -3.3168390e-01 1.87e-04 4.24e-08 -5.7 1.52e+01 - 1.00e+00 1.00e+00h 1\n", - " 13 -3.3168758e-01 3.70e-05 1.36e-09 -8.6 1.69e+00 - 1.00e+00 1.00e+00h 1\n", - " 14 -3.3168758e-01 8.00e-10 5.07e-14 -8.6 3.09e-03 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 14\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: -3.3168758389150360e-01 -3.3168758389150360e-01\n", - "Dual infeasibility......: 5.0701311326148040e-14 5.0701311326148040e-14\n", - "Constraint violation....: 2.9648061330219216e-12 8.0035533756017685e-10\n", - "Complementarity.........: 2.5059042476427779e-09 2.5059042476427779e-09\n", - "Overall NLP error.......: 2.5059042476427779e-09 2.5059042476427779e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 15\n", - "Number of objective gradient evaluations = 15\n", - "Number of equality constraint evaluations = 15\n", - "Number of inequality constraint evaluations = 15\n", - "Number of equality constraint Jacobian evaluations = 15\n", - "Number of inequality constraint Jacobian evaluations = 15\n", - "Number of Lagrangian Hessian evaluations = 14\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", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create list objects to store data, run optimization\n", + "results = {}\n", + "for trainer in trainers:\n", + " inputs, outputs = run_optimization(trainer)\n", + " for var in inputs:\n", + " results[(var.name, trainer)] = value(var)\n", + " for var in outputs:\n", + " results[(var.name, trainer)] = value(var)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "SurrType.PYSMO_PLY\n", - "2023-11-02 10:25:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# print results as a table\n", + "df_index = []\n", + "for var in inputs:\n", + " df_index.append(var.name)\n", + "for var in outputs:\n", + " df_index.append(var.name)\n", + "df_cols = trainers\n", + "\n", + "df = pd.DataFrame(index=df_index, columns=df_cols)\n", + "for i in df_index:\n", + " for j in df_cols:\n", + " df[j][i] = results[(i, j)]\n", + "\n", + "df # display results table" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 39\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 3\n", - "\n", - "Total number of variables............................: 15\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 13\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 0\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 -0.0000000e+00 5.35e+01 3.24e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -2.2227750e-01 1.30e+01 1.45e-02 -1.7 6.89e+02 - 9.38e-01 1.00e+00h 1\n", - " 2 -3.1492876e-01 5.64e+02 3.41e-02 -2.5 6.01e+03 - 9.71e-01 1.00e+00h 1\n", - " 3 -3.2714017e-01 2.01e+03 1.99e-02 -2.5 1.39e+04 - 6.00e-01 7.01e-01h 1\n", - " 4 -3.2662426e-01 2.91e+02 5.57e-03 -2.5 7.48e+03 - 1.00e+00 1.00e+00h 1\n", - " 5 -3.2618524e-01 1.47e+01 1.24e-04 -2.5 4.56e+02 - 1.00e+00 1.00e+00h 1\n", - " 6 -3.2612971e-01 4.24e-02 9.16e-07 -2.5 8.41e+01 - 1.00e+00 1.00e+00h 1\n", - " 7 -3.3096494e-01 8.05e+01 4.63e-03 -3.8 3.74e+03 - 9.24e-01 1.00e+00h 1\n", - " 8 -3.3151074e-01 7.83e+00 1.23e-04 -3.8 1.13e+03 - 1.00e+00 1.00e+00h 1\n", - " 9 -3.3138534e-01 5.65e-01 1.09e-05 -3.8 2.55e+02 - 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", - " 10 -3.3139357e-01 1.08e-03 3.11e-08 -3.8 1.28e+01 - 1.00e+00 1.00e+00h 1\n", - " 11 -3.3167896e-01 1.27e-01 8.04e-04 -5.7 1.96e+02 - 1.00e+00 9.72e-01h 1\n", - " 12 -3.3168859e-01 1.17e-03 2.28e-08 -5.7 1.38e+01 - 1.00e+00 1.00e+00h 1\n", - " 13 -3.3169228e-01 3.87e-07 1.13e-09 -8.6 1.95e+00 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 13\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: -3.3169227859422723e-01 -3.3169227859422723e-01\n", - "Dual infeasibility......: 1.1339429495425323e-09 1.1339429495425323e-09\n", - "Constraint violation....: 1.4249529091406254e-09 3.8666985346935689e-07\n", - "Complementarity.........: 3.9898113808548269e-09 3.9898113808548269e-09\n", - "Overall NLP error.......: 3.9898113808548269e-09 3.8666985346935689e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 14\n", - "Number of objective gradient evaluations = 14\n", - "Number of equality constraint evaluations = 14\n", - "Number of inequality constraint evaluations = 14\n", - "Number of equality constraint Jacobian evaluations = 14\n", - "Number of inequality constraint Jacobian evaluations = 14\n", - "Number of Lagrangian Hessian evaluations = 13\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "# create list objects to store data, run optimization\n", - "results = {}\n", - "for trainer in trainers:\n", - " inputs, outputs = run_optimization(trainer)\n", - " for var in inputs:\n", - " results[(var.name, trainer)] = value(var)\n", - " for var in outputs:\n", - " results[(var.name, trainer)] = value(var)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SurrType.PYSMO_KRGSurrType.PYSMO_RBFSurrType.PYSMO_PLY
fs.bypass_frac0.10.10.1
fs.ng_steam_ratio1.1246241.1243051.126451
fs.steam_flowrate1.2265391.2261531.228592
fs.reformer_duty39082.34592539062.17708939131.26643
fs.AR0.0041070.0041070.004107
fs.C2H60.0005180.0005450.000519
fs.C3H80.0001150.0001190.000114
fs.C4H100.0000650.0000680.000065
fs.CH40.0161930.0169910.0162
fs.CO0.1048370.1048560.104419
fs.CO20.0535350.0535280.053561
fs.H20.331690.3316880.331692
fs.H2O0.1489510.1489180.149056
fs.N20.340.340.34
fs.O20.00.0-0.0
\n", - "
" - ], - "text/plain": [ - " SurrType.PYSMO_KRG SurrType.PYSMO_RBF SurrType.PYSMO_PLY\n", - "fs.bypass_frac 0.1 0.1 0.1\n", - "fs.ng_steam_ratio 1.124624 1.124305 1.126451\n", - "fs.steam_flowrate 1.226539 1.226153 1.228592\n", - "fs.reformer_duty 39082.345925 39062.177089 39131.26643\n", - "fs.AR 0.004107 0.004107 0.004107\n", - "fs.C2H6 0.000518 0.000545 0.000519\n", - "fs.C3H8 0.000115 0.000119 0.000114\n", - "fs.C4H10 0.000065 0.000068 0.000065\n", - "fs.CH4 0.016193 0.016991 0.0162\n", - "fs.CO 0.104837 0.104856 0.104419\n", - "fs.CO2 0.053535 0.053528 0.053561\n", - "fs.H2 0.33169 0.331688 0.331692\n", - "fs.H2O 0.148951 0.148918 0.149056\n", - "fs.N2 0.34 0.34 0.34\n", - "fs.O2 0.0 0.0 -0.0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + ], + "metadata": { + "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": "3.8.16" } - ], - "source": [ - "# print results as a table\n", - "df_index = []\n", - "for var in inputs:\n", - " df_index.append(var.name)\n", - "for var in outputs:\n", - " df_index.append(var.name)\n", - "df_cols = trainers\n", - "\n", - "df = pd.DataFrame(index=df_index, columns=df_cols)\n", - "for i in df_index:\n", - " for j in df_cols:\n", - " df[j][i] = results[(i, j)]\n", - "\n", - "df # display results table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb index 90a84df6..e8a1a7c0 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb @@ -223,7 +223,7 @@ " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(\n", " keras_surrogate,\n", @@ -531,7 +531,7 @@ " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(\n", " keras_surrogate,\n", @@ -635,4 +635,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb index 174e9648..ce8cfb48 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb @@ -223,7 +223,7 @@ " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(\n", " keras_surrogate,\n", @@ -531,7 +531,7 @@ " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", " m.fs.surrogate = SurrogateBlock()\n", " m.fs.surrogate.build_model(\n", " keras_surrogate,\n", @@ -635,4 +635,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/keras_model.keras b/idaes_examples/notebooks/docs/surrogates/keras_surrogate/keras_model.keras new file mode 100644 index 00000000..15c3cfb5 Binary files /dev/null and b/idaes_examples/notebooks/docs/surrogates/keras_surrogate/keras_model.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.data-00000-of-00001 b/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.data-00000-of-00001 deleted file mode 100644 index 29d80d2b..00000000 Binary files a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.data-00000-of-00001 and /dev/null differ diff --git a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.index b/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.index deleted file mode 100644 index 7cb3e1df..00000000 Binary files a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.index and /dev/null differ diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/.mdl_wts.keras b/idaes_examples/notebooks/docs/surrogates/omlt/.mdl_wts.keras new file mode 100644 index 00000000..55b0068b Binary files /dev/null and b/idaes_examples/notebooks/docs/surrogates/omlt/.mdl_wts.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb index 955e84d1..16436978 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb @@ -250,7 +250,7 @@ "# 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_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", ")\n", "history = model.fit(\n", " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", @@ -268,7 +268,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")\n", + "keras_surrogate.save_to_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", "\n", "# revert back to normal output capture\n", "sys.stdout = oldstdout\n", @@ -411,7 +411,7 @@ "\n", "# create the Pyomo/IDAES block that corresponds to the surrogate\n", "# Keras\n", - "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", "m.fs.surrogate = SurrogateBlock()\n", "m.fs.surrogate.build_model(\n", " keras_surrogate,\n", @@ -523,4 +523,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb index 9fd28b4c..603e227a 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb @@ -1,2919 +1,526 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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": [ - "# Autothermal Reformer Flowsheet Optimization with OMLT (TensorFlow Keras) Surrogate Object\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the OMLT package utilizing TensorFlow Keras neural networks. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Problem Statement \n", - "\n", - "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", - "\n", - "## 2.1. Main Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "## 2.2. Main Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\omlt\\keras_flowsheet_optimization_doc_3_0.png" - } - }, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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": [ + "# Autothermal Reformer Flowsheet Optimization with OMLT (TensorFlow Keras) Surrogate Object\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the OMLT package utilizing TensorFlow Keras neural networks. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Statement \n", + "\n", + "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", + "\n", + "## 2.1. Main Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "## 2.2. Main Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "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(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training and Validating Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import the required Python, Pyomo and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " SolverFactory,\n", + " value,\n", + " Var,\n", + " Constraint,\n", + " Set,\n", + " Objective,\n", + " maximize,\n", + ")\n", + "from pyomo.common.timing import TicTocTimer\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 (\n", + " KerasSurrogate,\n", + " save_keras_json_hd5,\n", + " load_keras_json_hd5,\n", + ")\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core import FlowsheetBlock\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": [ + "## 3.1 Importing Training and Validation Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", + "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:]\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", + ") # seed=100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Training Surrogates with TensorFlow Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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: relu, sigmoid, ***tanh***\n", + "- Optimizer: ***Adam***, RMSprop, SGD\n", + "- Number of hidden layers: 1, ***2***, 4\n", + "- Number of neurons per layer: 10, 20, ***40***\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": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# capture long output (not required to use surrogate API)\n", + "from io import StringIO\n", + "import sys\n", + "\n", + "stream = StringIO()\n", + "oldstdout = sys.stdout\n", + "sys.stdout = stream\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 2, 40\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))\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_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(\n", + " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", + ")\n", + "\n", + "# save model to JSON and create callable surrogate object\n", + "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\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_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", + "\n", + "# revert back to normal output capture\n", + "sys.stdout = oldstdout\n", + "\n", + "# display first 50 lines and last 50 lines of output\n", + "celloutput = stream.getvalue().split(\"\\n\")\n", + "for line in celloutput[:50]:\n", + " print(line)\n", + "print(\".\")\n", + "print(\".\")\n", + "print(\".\")\n", + "for line in celloutput[-50:]:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Visualizing surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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 fit the data. Then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "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": [ + "## 3.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "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": [ + "# 4. IDAES Flowsheet Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build an IDAES flowsheet and import the surrogate model object. A single Keras neural network model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create the IDAES model and flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "# create flowsheet input variables\n", + "m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + ")\n", + "m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + ")\n", + "\n", + "# create flowsheet output variables\n", + "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + "# create input and output variable object lists for flowsheet\n", + "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + "outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C4H10,\n", + " m.fs.C3H8,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + "]\n", + "\n", + "# create the Pyomo/IDAES block that corresponds to the surrogate\n", + "# Keras\n", + "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", + "m.fs.surrogate = SurrogateBlock()\n", + "m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + ")\n", + "\n", + "# fix input values and solve flowsheet\n", + "m.fs.bypass_frac.fix(0.5)\n", + "m.fs.ng_steam_ratio.fix(1)\n", + "\n", + "solver = SolverFactory(\"ipopt\")\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print some model results:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", + "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", + "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", + "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", + "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", + "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", + "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", + "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", + "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", + "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", + "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", + "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", + "print(\"Mole Fraction O2 = \", value(m.fs.O2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Optimizing the Autothermal Reformer\n", + "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", + "\n", + "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# unfix input values and add the objective/constraint to the model\n", + "m.fs.bypass_frac.unfix()\n", + "m.fs.ng_steam_ratio.unfix()\n", + "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + "# solve the model\n", + "tmr = TicTocTimer()\n", + "status = solver.solve(m, tee=True)\n", + "solve_time = tmr.toc(\"solve\")\n", + "\n", + "# print and check results\n", + "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", + "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", + "print(\"Model status: \", status)\n", + "print(\"Solve time: \", solve_time)\n", + "for var in inputs:\n", + " print(var.name, \": \", value(var))\n", + "for var in outputs:\n", + " print(var.name, \": \", value(var))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "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(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training and Validating Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import the required Python, Pyomo and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "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", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " SolverFactory,\n", - " value,\n", - " Var,\n", - " Constraint,\n", - " Set,\n", - " Objective,\n", - " maximize,\n", - ")\n", - "from pyomo.common.timing import TicTocTimer\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 (\n", - " KerasSurrogate,\n", - " save_keras_json_hd5,\n", - " load_keras_json_hd5,\n", - ")\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core import FlowsheetBlock\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": [ - "## 3.1 Importing Training and Validation Datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dkgun\\miniconda3\\envs\\idaes-examples-py311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", - " return bound(*args, **kwds)\n" - ] + ], + "metadata": { + "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": "3.8.16" } - ], - "source": [ - "# Import Auto-reformer training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", - "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:]\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", - ") # seed=100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Training Surrogates with TensorFlow Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "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: relu, sigmoid, ***tanh***\n", - "- Optimizer: ***Adam***, RMSprop, SGD\n", - "- Number of hidden layers: 1, ***2***, 4\n", - "- Number of neurons per layer: 10, 20, ***40***\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": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dkgun\\miniconda3\\envs\\idaes-examples-py311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", - " saving_api.save_model(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.3816 - mae: 0.5276 - mse: 0.3816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 1s 79ms/step - loss: 0.3703 - mae: 0.5194 - mse: 0.3703 - val_loss: 0.3230 - val_mae: 0.4945 - val_mse: 0.3230\n", - "Epoch 2/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.3274 - mae: 0.4777 - mse: 0.3274\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.3078 - mae: 0.4684 - mse: 0.3078 - val_loss: 0.2686 - val_mae: 0.4450 - val_mse: 0.2686\n", - "Epoch 3/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.2678 - mae: 0.4329 - mse: 0.2678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 22ms/step - loss: 0.2556 - mae: 0.4217 - mse: 0.2556 - val_loss: 0.2235 - val_mae: 0.3991 - val_mse: 0.2235\n", - "Epoch 4/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.2350 - mae: 0.4022 - mse: 0.2350\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.2136 - mae: 0.3798 - mse: 0.2136 - val_loss: 0.1862 - val_mae: 0.3568 - val_mse: 0.1862\n", - "Epoch 5/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.1953 - mae: 0.3559 - mse: 0.1953\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.1792 - mae: 0.3424 - mse: 0.1792 - val_loss: 0.1557 - val_mae: 0.3193 - val_mse: 0.1557\n", - "Epoch 6/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.1581 - mae: 0.3172 - mse: 0.1581\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 24ms/step - loss: 0.1512 - mae: 0.3100 - mse: 0.1512 - val_loss: 0.1303 - val_mae: 0.2857 - val_mse: 0.1303\n", - "Epoch 7/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.1335 - mae: 0.2904 - mse: 0.1335\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.1286 - mae: 0.2829 - mse: 0.1286 - val_loss: 0.1099 - val_mae: 0.2583 - val_mse: 0.1099\n", - "Epoch 8/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.1202 - mae: 0.2749 - mse: 0.1202\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.1108 - mae: 0.2615 - mse: 0.1108 - val_loss: 0.0935 - val_mae: 0.2381 - val_mse: 0.0935\n", - "Epoch 9/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.1021 - mae: 0.2497 - mse: 0.1021\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0969 - mae: 0.2445 - mse: 0.0969 - val_loss: 0.0810 - val_mae: 0.2227 - val_mse: 0.0810\n", - "Epoch 10/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0953 - mae: 0.2431 - mse: 0.0953\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0870 - mae: 0.2324 - mse: 0.0870 - val_loss: 0.0717 - val_mae: 0.2123 - val_mse: 0.0717\n", - "Epoch 11/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0818 - mae: 0.2266 - mse: 0.0818\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 23ms/step - loss: 0.0795 - mae: 0.2228 - mse: 0.0795 - val_loss: 0.0650 - val_mae: 0.2041 - val_mse: 0.0650\n", - "Epoch 12/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0744 - mae: 0.2109 - mse: 0.0744\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0745 - mae: 0.2165 - mse: 0.0745 - val_loss: 0.0599 - val_mae: 0.1972 - val_mse: 0.0599\n", - "Epoch 13/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0674 - mae: 0.2048 - mse: 0.0674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0703 - mae: 0.2108 - mse: 0.0703 - val_loss: 0.0565 - val_mae: 0.1925 - val_mse: 0.0565\n", - "Epoch 14/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0756 - mae: 0.2171 - mse: 0.0756\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 24ms/step - loss: 0.0670 - mae: 0.2056 - mse: 0.0670 - val_loss: 0.0534 - val_mae: 0.1879 - val_mse: 0.0534\n", - "Epoch 15/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0709 - mae: 0.2180 - mse: 0.0709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0640 - mae: 0.2005 - mse: 0.0640 - val_loss: 0.0506 - val_mae: 0.1828 - val_mse: 0.0506\n", - "Epoch 16/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0443 - mae: 0.1687 - mse: 0.0443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0611 - mae: 0.1949 - mse: 0.0611 - val_loss: 0.0477 - val_mae: 0.1767 - val_mse: 0.0477\n", - "Epoch 17/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0589 - mae: 0.1894 - mse: 0.0589\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 24ms/step - loss: 0.0582 - mae: 0.1889 - mse: 0.0582 - val_loss: 0.0454 - val_mae: 0.1711 - val_mse: 0.0454\n", - "Epoch 18/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0636 - mae: 0.1930 - mse: 0.0636\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0557 - mae: 0.1833 - mse: 0.0557 - val_loss: 0.0436 - val_mae: 0.1659 - val_mse: 0.0436\n", - "Epoch 19/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0483 - mae: 0.1658 - mse: 0.0483\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0533 - mae: 0.1778 - mse: 0.0533 - val_loss: 0.0418 - val_mae: 0.1601 - val_mse: 0.0418\n", - "Epoch 20/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0401 - mae: 0.1556 - mse: 0.0401\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 24ms/step - loss: 0.0514 - mae: 0.1727 - mse: 0.0514 - val_loss: 0.0403 - val_mae: 0.1546 - val_mse: 0.0403\n", - "Epoch 21/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0536 - mae: 0.1683 - mse: 0.0536\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0496 - mae: 0.1681 - mse: 0.0496 - val_loss: 0.0385 - val_mae: 0.1487 - val_mse: 0.0385\n", - "Epoch 22/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0435 - mae: 0.1560 - mse: 0.0435\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 20ms/step - loss: 0.0479 - mae: 0.1630 - mse: 0.0479 - val_loss: 0.0370 - val_mae: 0.1437 - val_mse: 0.0370\n", - "Epoch 23/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0517 - mae: 0.1663 - mse: 0.0517\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 19ms/step - loss: 0.0464 - mae: 0.1589 - mse: 0.0464 - val_loss: 0.0359 - val_mae: 0.1404 - val_mse: 0.0359\n", - "Epoch 24/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0471 - mae: 0.1616 - mse: 0.0471\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0449 - mae: 0.1550 - mse: 0.0449 - val_loss: 0.0347 - val_mae: 0.1372 - val_mse: 0.0347\n", - "Epoch 25/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 0.0414 - mae: 0.1462 - mse: 0.0414\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 0.0433 - mae: 0.1511 - mse: 0.0433 - val_loss: 0.0335 - val_mae: 0.1347 - val_mse: 0.0335\n", - ".\n", - ".\n", - ".\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.1661e-04 - mae: 0.0080 - mse: 1.1661e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.3688e-04 - mae: 0.0084 - mse: 1.3688e-04 - val_loss: 9.2093e-05 - val_mae: 0.0068 - val_mse: 9.2093e-05\n", - "Epoch 977/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.3928e-04 - mae: 0.0085 - mse: 1.3928e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.3427e-04 - mae: 0.0085 - mse: 1.3427e-04 - val_loss: 8.4304e-05 - val_mae: 0.0067 - val_mse: 8.4304e-05\n", - "Epoch 978/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.0357e-04 - mae: 0.0081 - mse: 1.0357e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.3646e-04 - mae: 0.0087 - mse: 1.3646e-04 - val_loss: 9.1711e-05 - val_mae: 0.0071 - val_mse: 9.1711e-05\n", - "Epoch 979/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.3587e-04 - mae: 0.0084 - mse: 1.3587e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.3732e-04 - mae: 0.0087 - mse: 1.3732e-04 - val_loss: 8.2200e-05 - val_mae: 0.0067 - val_mse: 8.2200e-05\n", - "Epoch 980/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.7129e-04 - mae: 0.0094 - mse: 1.7129e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.2526e-04 - mae: 0.0082 - mse: 1.2526e-04 - val_loss: 1.0392e-04 - val_mae: 0.0074 - val_mse: 1.0392e-04\n", - "Epoch 981/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.1030e-04 - mae: 0.0075 - mse: 1.1030e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.3412e-04 - mae: 0.0082 - mse: 1.3412e-04 - val_loss: 8.1917e-05 - val_mae: 0.0065 - val_mse: 8.1917e-05\n", - "Epoch 982/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.2036e-04 - mae: 0.0083 - mse: 1.2036e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.3123e-04 - mae: 0.0085 - mse: 1.3123e-04 - val_loss: 8.0986e-05 - val_mae: 0.0067 - val_mse: 8.0986e-05\n", - "Epoch 983/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.5498e-04 - mae: 0.0090 - mse: 1.5498e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.2668e-04 - mae: 0.0084 - mse: 1.2668e-04 - val_loss: 8.8135e-05 - val_mae: 0.0069 - val_mse: 8.8135e-05\n", - "Epoch 984/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.5492e-04 - mae: 0.0090 - mse: 1.5492e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.2770e-04 - mae: 0.0082 - mse: 1.2770e-04 - val_loss: 8.4017e-05 - val_mae: 0.0066 - val_mse: 8.4017e-05\n", - "Epoch 985/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 9.4717e-05 - mae: 0.0074 - mse: 9.4717e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 26ms/step - loss: 1.2767e-04 - mae: 0.0081 - mse: 1.2767e-04 - val_loss: 7.7592e-05 - val_mae: 0.0064 - val_mse: 7.7592e-05\n", - "Epoch 986/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.0524e-04 - mae: 0.0075 - mse: 1.0524e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 12ms/step - loss: 1.2811e-04 - mae: 0.0085 - mse: 1.2811e-04 - val_loss: 7.8276e-05 - val_mae: 0.0066 - val_mse: 7.8276e-05\n", - "Epoch 987/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.2713e-04 - mae: 0.0084 - mse: 1.2713e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 11ms/step - loss: 1.2328e-04 - mae: 0.0082 - mse: 1.2328e-04 - val_loss: 9.2079e-05 - val_mae: 0.0071 - val_mse: 9.2079e-05\n", - "Epoch 988/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.3256e-04 - mae: 0.0082 - mse: 1.3256e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.2592e-04 - mae: 0.0081 - mse: 1.2592e-04 - val_loss: 7.5439e-05 - val_mae: 0.0064 - val_mse: 7.5439e-05\n", - "Epoch 989/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 8.0360e-05 - mae: 0.0070 - mse: 8.0360e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.2697e-04 - mae: 0.0086 - mse: 1.2697e-04 - val_loss: 8.1591e-05 - val_mae: 0.0068 - val_mse: 8.1591e-05\n", - "Epoch 990/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.1743e-04 - mae: 0.0082 - mse: 1.1743e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.2365e-04 - mae: 0.0082 - mse: 1.2365e-04 - val_loss: 8.8448e-05 - val_mae: 0.0067 - val_mse: 8.8448e-05\n", - "Epoch 991/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.0918e-04 - mae: 0.0075 - mse: 1.0918e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.2486e-04 - mae: 0.0079 - mse: 1.2486e-04 - val_loss: 8.3745e-05 - val_mae: 0.0065 - val_mse: 8.3745e-05\n", - "Epoch 992/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.0088e-04 - mae: 0.0076 - mse: 1.0088e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.2131e-04 - mae: 0.0082 - mse: 1.2131e-04 - val_loss: 7.8312e-05 - val_mae: 0.0066 - val_mse: 7.8312e-05\n", - "Epoch 993/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 9.0210e-05 - mae: 0.0075 - mse: 9.0210e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 16ms/step - loss: 1.2277e-04 - mae: 0.0083 - mse: 1.2277e-04 - val_loss: 7.5934e-05 - val_mae: 0.0064 - val_mse: 7.5934e-05\n", - "Epoch 994/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.2529e-04 - mae: 0.0084 - mse: 1.2529e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 10ms/step - loss: 1.1942e-04 - mae: 0.0082 - mse: 1.1942e-04 - val_loss: 7.9994e-05 - val_mae: 0.0067 - val_mse: 7.9994e-05\n", - "Epoch 995/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.4463e-04 - mae: 0.0086 - mse: 1.4463e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 6ms/step - loss: 1.1739e-04 - mae: 0.0079 - mse: 1.1739e-04 - val_loss: 8.5978e-05 - val_mae: 0.0066 - val_mse: 8.5978e-05\n", - "Epoch 996/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.2477e-04 - mae: 0.0077 - mse: 1.2477e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 8ms/step - loss: 1.1918e-04 - mae: 0.0078 - mse: 1.1918e-04 - val_loss: 7.7405e-05 - val_mae: 0.0062 - val_mse: 7.7405e-05\n", - "Epoch 997/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.3463e-04 - mae: 0.0084 - mse: 1.3463e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 10ms/step - loss: 1.1708e-04 - mae: 0.0080 - mse: 1.1708e-04 - val_loss: 7.7886e-05 - val_mae: 0.0064 - val_mse: 7.7886e-05\n", - "Epoch 998/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.0271e-04 - mae: 0.0075 - mse: 1.0271e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 9ms/step - loss: 1.1993e-04 - mae: 0.0079 - mse: 1.1993e-04 - val_loss: 7.7599e-05 - val_mae: 0.0065 - val_mse: 7.7599e-05\n", - "Epoch 999/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.3346e-04 - mae: 0.0085 - mse: 1.3346e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 17ms/step - loss: 1.1611e-04 - mae: 0.0081 - mse: 1.1611e-04 - val_loss: 7.2370e-05 - val_mae: 0.0064 - val_mse: 7.2370e-05\n", - "Epoch 1000/1000\n", - "\r", - "1/3 [=========>....................] - ETA: 0s - loss: 1.4569e-04 - mae: 0.0092 - mse: 1.4569e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 15ms/step - loss: 1.1746e-04 - mae: 0.0083 - mse: 1.1746e-04 - val_loss: 8.1321e-05 - val_mae: 0.0068 - val_mse: 8.1321e-05\n", - "\n" - ] - } - ], - "source": [ - "# capture long output (not required to use surrogate API)\n", - "from io import StringIO\n", - "import sys\n", - "\n", - "stream = StringIO()\n", - "oldstdout = sys.stdout\n", - "sys.stdout = stream\n", - "\n", - "# selected settings for regression (best fit from options above)\n", - "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 2, 40\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))\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_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", - ")\n", - "history = model.fit(\n", - " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", - ")\n", - "\n", - "# save model to JSON and create callable surrogate object\n", - "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\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\")\n", - "\n", - "# revert back to normal output capture\n", - "sys.stdout = oldstdout\n", - "\n", - "# display first 50 lines and last 50 lines of output\n", - "celloutput = stream.getvalue().split(\"\\n\")\n", - "for line in celloutput[:50]:\n", - " print(line)\n", - "print(\".\")\n", - "print(\".\")\n", - "print(\".\")\n", - "for line in celloutput[-50:]:\n", - " print(line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Visualizing surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "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 fit the data. Then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "1/3 [=========>....................] - ETA: 0s" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "3/3 [==============================] - 0s 0s/step\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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zB2PGjMG6devQpEkT8XOVSoX169djyZIl6NevH8LCwrBx40YcO3YMP/74IwDgu+++w/nz5/HJJ5+gc+fOGDx4MObNm4eVK1eK01dr1qxBYGAgFi9ejLZt2yI+Ph7/+c9/sHTpUvFaS5YswXPPPYfx48cjJCQEa9asgbOzMzZs2GDeh0FERFQLa0zj6NevHzp16oSdO3cCKKvCvXz5cpw7dw6bN2/GwYMHMW3aNABAz549kZSUBLlcLo6MvfrqqwCA4uJizJs3D7/88gu++uorZGVlYdy4cWa7j5pYRdAUFxeHqKgoDBgwQOvztLQ0FBcXa33epk0btGjRAqmpqQCA1NRUdOjQAV5eXmKbyMhI5Ofn49y5c2KbiueOjIwUz1FUVIS0tDStNnZ2dhgwYIDYpqLCwkLk5+drvYiIiBqyNm3aICsrCwCQkJCAvn37IiAgAP369cPbb7+Nzz//HEDZCnCFQgGJRAJvb294e3ujcePGAIBnn30WgwcPRqtWrdCjRw8sX74c3377Le7cuWOp2xJZPGjatm0bTp48iQULFlQ6lpOTA5lMVilRzMvLCzk5OWKb8gGT5rjmWE1t8vPz8ffff+PWrVsoLS2tso3mHBUtWLAACoVCfPn7++t+00RERPWQIAjidNuBAwfQv39/NG/eHK6urhg7diyUSiXu3btX4znS0tIQHR2NFi1awNXVFY888giAsvxlS7No0HTlyhW8/PLL2LJlCxwdHS3ZFb3NmDEDKpVKfF25csXSXSIiIrKoCxcuIDAwEFlZWRgyZAg6duyIHTt2IC0tDStXrgRQ85Th3bt3ERkZCblcji1btuDEiRP48ssva/2euVg0ETwtLQ03btxAaGio+FlpaSl++OEHJCcnY9++fSgqKkJeXp7WaNP169fFBDRvb+9Kq9w0q+vKt6m44u769euQy+VwcnKCVCqFVCqtso3mHBU5ODjAwcHBsBuvha4l9Vl6n4iIrMXBgwdx5swZTJkyBWlpaVCr1Vi8eLG4EbFmak5DJpNV2tbk4sWLUCqVWLhwoTiD8/PPP5vnBnRg0aCpf//+OHPmjNZn48ePR5s2bTB9+nT4+/vD3t4eKSkpGD58OAAgIyMDf/75JyIiIgAAEREReOedd3Djxg0xs37//v2Qy+UICQkR2+zZs0frOvv37xfPIZPJEBYWhpSUFAwbNgxA2a7IKSkpiI+PN9n9V8fDwwPx8fGsCE5ERFapsLAQOTk5KC0txfXr17F3714sWLAAQ4YMwTPPPIOzZ8+iuLgYK1asQHR0NI4ePYo1a9ZonSMgIAB37txBSkoKOnXqBGdnZ7Ro0QIymQwrVqzApEmTcPbsWcybN89Cd1mZRafnXF1d0b59e62Xi4sLPDw80L59eygUCkyYMAGJiYk4dOgQ0tLSMH78eERERKBHjx4AgIEDByIkJARjx47FL7/8gn379mHWrFmIi4sTR4ImTZqE33//HdOmTcPFixexatUqfP7555gyZYrYl8TERKxbtw6bN2/GhQsXMHnyZNy9e1dcEmluHh4e8PHxgY+PD0pLfXDxYtmfms8YMBERkaXs3bsXPj4+CAgIwKBBg3Do0CEsX74c//d//wepVIpOnTphyZIlePfdd9G+fXts2bKlUu5yz549MWnSJIwYMQLNmjXDokWL0KxZM2zatAnbt29HSEgIFi5ciPfff99Cd1mZRBAEwdKdKK9Pnz7o3LkzkpKSAJQVt3zllVfw6aeforCwEJGRkVi1apXWtNkff/yByZMn4/Dhw3BxcUFMTAwWLlyIRo3+HUg7fPgwpkyZgvPnz8PPzw+zZ8+utIQxOTkZ7733HnJyctC5c2csX74c4eHhOvU7Pz8fCoUCKpUKcrm8zs9BY/16IDYWUKsBOztg7VpgwgSjnZ6IiCzk/v37yMzMRGBgoN55vZau02RranrW+vz+trqgyVaZImi6ehVo2bIsYNKQSoGsLMDPzyiXICIiC6lL0ARwY3d9GCtospqK4FTZb79pB0wAUFoKXLrEoImIqKFjQGR+Fq/TRNVr3bpsSq48qRQIDrZMf4iIiBoyBk1WzM+vLIdJKi17L5UCH3zAUSYiIiJL4PSclZswAYiMLJuSCw5mwERERGQpDJpsgJ8fgyUiIiJL4/QcERERkQ4YNBERERHpgEETERERkQ4YNDUQSqUS2dnZ1b6USqWlu0hERITDhw9DIpEgLy9P5+8EBASIO4mYEhPBGwCW2yciImMZN24cNm/ejOeff77SJrxxcXFYtWoVYmJisGnTJst00IQYNNUj1ZXUv3Xrlk7fr6kcPxERkYa/vz+2bduGpUuXwsnJCUDZViVbt25FixYtLNw70+H0XD2hGU1au3ZtpdfOnTst3T0iIqpHQkND4e/vr/X7ZefOnWjRogW6dOkiflZYWIiXXnoJnp6ecHR0xEMPPYQTJ05onWvPnj144IEH4OTkhL59+yIrK6vS9Y4cOYKHH34YTk5O8Pf3x0svvYS7d++a7P6qw6CpnuAoERFRw3X1KnDoUNmf5vLss89i48aN4vsNGzZg/PjxWm2mTZuGHTt2YPPmzTh58iSCg4MRGRmJ3NxcAMCVK1fw5JNPIjo6Gunp6Zg4cSJee+01rXNcvnwZgwYNwvDhw3H69Gl89tlnOHLkCOLj401/kxUwaCIiIrJh69cDLVsC/fqV/bl+vXmu+/TTT+PIkSP4448/8Mcff+Do0aN4+umnxeN3797F6tWr8d5772Hw4MEICQnBunXr4OTkhPX/dHL16tUICgrC4sWL8eCDD2LMmDEYN26c1nUWLFiAMWPGICEhAa1bt0bPnj2xfPlyfPTRR7h//755bvYfzGkiIiKyUVevArGxgFpd9l6tBp5/vmz7LVPvJNGsWTNERUVh06ZNEAQBUVFRaNq0qXj88uXLKC4uRq9evcTP7O3t0b17d1y4cAEAcOHCBYSHh2udNyIiQuv9L7/8gtOnT2PLli3iZ4IgQK1WIzMzE23btjXF7VWJQVMDpFK5IjfXA+7uSigUBZbuDhERGei33/4NmDRKS8v2KzXH9lvPPvusOE22cuVKk1zjzp07eP755/HSSy9VOmbupHMGTQ3MyZNd8M03QyAIdpBI1IiO3oXQ0FOW7hYRERmgdWvAzk47cJJKyzZ4N4dBgwahqKgIEokEkZGRWseCgoIgk8lw9OhRtGzZEgBQXFyMEydOICEhAQDQtm1bfP3111rf+/HHH7Xeh4aG4vz58wg2103VgDlN9ZRK5YrMzACoVK5an2kCJgAQBDt8880QsY1MJrNIX4mIyDB+fsDatWWBElD25wcfmG+Td6lUigsXLuD8+fOQajrxDxcXF0yePBlTp07F3r17cf78eTz33HO4d+8eJkyYAACYNGkSfvvtN0ydOhUZGRnYunVrpfpO06dPx7FjxxAfH4/09HT89ttv+L//+z+LJIJzpKkeqm40KTfXQwyYNATBDr16xaBPH7CwJRGRDZowoSyH6dKlshEmcwVMGnK5vNpjCxcuhFqtxtixY1FQUICuXbti3759aNKkCYCy6bUdO3ZgypQpWLFiBbp374758+fj2WefFc/RsWNHfP/995g5cyYefvhhCIKAoKAgjBgxwuT3VpFEEATB7Feth/Lz86FQKKBSqWr8C2QqmjpNKpUrkpIStIIjiUSNhIQkAMCyZVOgVkvEY1IpkJVl/v/IiIgauvv37yMzMxOBgYFwdHS0dHfqtZqetT6/vzk9V094eHggPj4ePXvGVDuaNHNmDNaulVhsGJeIiMiWcXquHvHw8ECPHlUnBYaHe8DDw/LDuERERLaKI031jC5JgX5+QJ8+DJiIiIj0wZGmeoijSURERMbHoMlKKZXKGveTk8lkNa528/NjsEREZAu4Hsv0jPWMGTRZIc1KuNrEx8ezTAARkY3S1DUqKiqCk5OThXtTv2kGISrWktIXgyYrVNMIkyHtiIjI+jRq1AjOzs64efMm7O3tYWfHNGNTUKvVuHnzJpydndGoUd3CHgZNVK26ThESEVH1JBIJfHx8kJmZiT/++MPS3anX7Ozs0KJFC0gkktob14BBE1WJU4RERKYnk8nQunVrzhyYmEwmM8pIHoMmqhKnCImIzMPOzo4VwW0EJ1CtkEqlqvC+8ua7VbUjIiIi0+FIkxUqLi4Wf65u892K7QDT5iCpVK7IzfWAu7sSCkWBQecgIiKyZQyarJhK5SoGTEDZHnLffDMEQUGXKgUupsxBqilwIyIiaig4PWeFNEsic3M9qtx8NzfXXasdYLocpOoCt4pThURERPUdgyYr5ObmBgBwd1dCIlFrHZNI1HB3z9VqZ0q1BW5EREQNBYMmK6ZQFCA6ehcATeBUNjVmzpyi2gI3IiKihoJBkw3Q1OLSpyZXdSvudCWTyQD8G7hpAidNTpMmcNO0IyIiqu+YCG7F9EkEL88YidseHh6Ij48Xc6DmzLmJrKxGCAgoga9vNwDdWBGciIgaFAZNVkgzelNTPpFCUaBVp+nWrVsADA+0qlI+IPLxAcLCDLodIiKieoFBkxXSjPJkZZXg448FqNX/zsvZ2QliPtFnn31W6bu1BVpERERkGOY0WSkPDw+EhXlh7VoJpNKyz6RSYNEiVY3BT22J28xBIiIiMoxEEATB0p2oD/Lz86FQKKBSqSCXy4167qtXgUuXgOBgQCrNxtq1a2tsf/JkF+zaFQ21WgKpVMC776owevTfzEEiIiKqQJ/f35yeswF+fmUvAMjO1j5W1fYmoaGnMGdOOAoKvBAcLIGfnxsAN3N2mYiIqN5h0GTDalol5+urho+PhTtIRERUjzCnyUZxexMiIiLzsmjQtHr1anTs2BFyuRxyuRwRERH49ttvxeN9+vSBRCLRek2aNEnrHH/++SeioqLg7OwMT09PTJ06FSUlJVptDh8+jNDQUDg4OCA4OBibNm2q1JeVK1ciICAAjo6OCA8Px/Hjx01yz8ai7/YmSqUS2dnZ1b6USqXR+lZfr0VERA2bRafn/Pz8sHDhQrRu3RqCIGDz5s14/PHHcerUKbRr1w4A8Nxzz+Gtt94Sv+Ps7Cz+XFpaiqioKHh7e+PYsWPIzs7GM888A3t7e8yfPx8AkJmZiaioKEyaNAlbtmxBSkoKJk6cCB8fH0RGRgIoW7qfmJiINWvWIDw8HElJSYiMjERGRgY8PT3N+ER0p1klVz5wqm57E6VSieTk5FrPGR8fX+dEcUteq6r8LmNdi4iIyKJBU3R0tNb7d955B6tXr8aPP/4oBk3Ozs7w9vau8vvfffcdzp8/jwMHDsDLywudO3fGvHnzMH36dLz55puQyWRYs2YNAgMDsXjxYgBA27ZtceTIESxdulQMmpYsWYLnnnsO48ePBwCsWbMGu3fvxoYNG/Daa6+Z6vYNUnF7k4o5TVVtb6Kp6l0bXdvpc47qAhljX6um/C5jXIuIiMhqEsFLS0uxfft23L17FxEREeLnW7ZswSeffAJvb29ER0dj9uzZ4mhTamoqOnToAC8vL7F9ZGQkJk+ejHPnzqFLly5ITU3FgAEDtK4VGRmJhIQEAGW/UNPS0jBjxgzxuJ2dHQYMGIDU1NRq+1tYWIjCwkLxfX5+fp3uX1fG2N6kukDG2IyxnYsujFkFnYiIqDoWD5rOnDmDiIgI3L9/H40bN8aXX36JkJAQAMDo0aPRsmVL+Pr64vTp05g+fToyMjKwc+dOAEBOTo5WwARAfJ+Tk1Njm/z8fPz999+4ffs2SktLq2xz8eLFavu9YMECzJ07t243b6C6bG9SHwMZVkEnIiJzsHjQ9OCDDyI9PR0qlQpffPEFYmJi8P333yMkJASxsbFiuw4dOsDHxwf9+/fH5cuXERQUZMFeAzNmzEBiYqL4Pj8/H/7+/hbsUe3MEcjk5eUBqD2QycvLg4+RaiLok99FRERkKIuXHJDJZAgODkZYWBgWLFiATp06YdmyZVW2DQ8PBwBcunQJAODt7Y3r169rtdG81+RBVddGLpfDyckJTZs2hVQqrbJNdblUAODg4CCu+tO8rJ2+K+4MoVm5WNt2LhVXONaFJr9Lc72K+V1ERETGYPGgqSK1Wq2VK1Reeno6AIgjFBEREThz5gxu3Lghttm/fz/kcrk4xRcREYGUlBSt8+zfv1/Mm5LJZAgLC9Nqo1arkZKSopVbVR/UFsgYk0JRgI4dTwPQ7NIjoGPH0yYLZEJDTyEhIQkxMZuQkJBkkilHIiJq2CwaNM2YMQM//PADsrKycObMGcyYMQOHDx/GmDFjcPnyZcybNw9paWnIysrC119/jWeeeQa9e/dGx44dAQADBw5ESEgIxo4di19++QX79u3DrFmzEBcXBwcHBwDApEmT8Pvvv2PatGm4ePEiVq1ahc8//xxTpkwR+5GYmIh169Zh8+bNuHDhAiZPnoy7d++Kq+nqC3OOyKhUrjh9uiMAyT+fSHD6dEeTFt9UKAoQGPgHR5iIiMgkLJrTdOPGDTzzzDPIzs6GQqFAx44dsW/fPjz66KO4cuUKDhw4gKSkJNy9exf+/v4YPnw4Zs2aJX5fKpVi165dmDx5MiIiIuDi4oKYmBituk6BgYHYvXs3pkyZgmXLlsHPzw8ffvihWG4AAEaMGIGbN29izpw5yMnJQefOnbF3795KyeG2qnz5gdDQUwgKuoTcXHe4u+dqBRjl2xnK3t4eQO05TZp2daFrf41xX0RERBJBEITam1Ft9Nkl2RKUSmWN9YpqK1Ogq+zsbKxduxYqlSuSkhIqJWcnJCRBoShAbGysURLBzXVfRERUP+nz+9viq+fIPMwdONRWfNNYGBAREZG5MGgik6lpKpCIiMjWMGgio6qYP6RQFFQZLDHPiIiIbA2DJjKqitu8VIV5RkREZIsYNJHRMSAiIqL6yOqKWxIRERFZIwZNRERERDpg0ERERESkAwZNRERERDpg0ERERESkAwZNREZ29Spw6FDZn0REVH+w5ABROeX3srt2zQ6ZmY0QGFgCX181gOprTGm+t3WrE6ZNU0CtlsDOTsCiRSqMHv03a1MREdUDDJqI/qFUKpGcnAwAOHmyS6V980JDTwEA4uPjtQIgzff+3aRYAgBQqyWYOlWOv/7aAIWioNL3iIjItnB6jugfmhEmlcpVDJgAQBDs8M03Q6BSuWq1q/i93FwP8TsagmCH3Fz3Kr9HRES2hUETUQW1BT/VcXdXQiJRa30mkajh7p5r9D4SEZH5cXqOjM7QvCBroQl+ygdOugQ/CkUBoqN3VZrWq2rDYiIisj0MmsioDM0LsiZ1CX5CQ08hKOgScnPd4e6ey4CJiKgeYdBERlVbXlBQ0CUoFAVWn99Tl+BHoShgsEREVA8xaCKTqCkvyFYCCgY/RERUHhPBySSYFE1ERPUNgyYyCU1ekCZwsoWkaJlMZlA7Q79HRES2hdNzZDK2lhTt4eGB+Pj4GvOtqlr5Z+j3iIjItjBoIpOytbwgQwMbBkRERPUfp+eIiIiIdMCgiYyK+T1ERFRfcXqO6uzqVeC334DWrQE/P93ze7S/Z8YOExERGYBBExlEs1XK1q1OmDZNAbVaAjs7AYsWqTB6dFG1ic+a7y1enFfF9/5mwjQREVktBk2kN81WKSqVK5KSEiAIEgCAWi3B1Kly/PXXBigUBZW2SjH0e0RERNaAOU2kN83UW01Vv8u3q+v3iIiIrAGDJjKYoVW/WS2ciIhsEYMmMpihVb+tqVr41avAoUNlf9anaxERkfExp4nqxNCq35asFl5zErtpktHXrwdiYwG1GrCzA9auBSZMMOoliIjIxBg0UZ0ZWvXbEtXCzZ2MrlQqkZVVgthYT6jVmmsBzz8voHPnGwgIaMSkdyIiG8HpObIYlcoVmZkBUKlczXZNcyajawK0FSv2igGTRmmpBCtWfIvk5GQolco6X4uIiEyPI01kESdPdsE33wyBINiJOU2hoafMdn1NMnr5wMnYyeiawKu2a3G1IBGRbeBIE+nN0K1S7t27B6BshEkTMAFlIzzffDNEHHHStDMlcyajW1PiOxERGY4jTaQ3Dw/dt0opz9nZGUDNU2MKRYHYztTMmYxuycR3IiIyDgZNZJC6JC+bY2pMV+ZMRrdE4jsRERkPp+fI7DhdRUREtogjTWQRnK4iIiJbw6CJLMYS01WGJrETERExaKIGxdAkdkMwQCMiql8YNFGDY64K3OYM0IiIyPQYNJHZNMSRFwZERET1B4MmMhuOvBARkS2zaMmB1atXo2PHjpDL5ZDL5YiIiMC3334rHr9//z7i4uLg4eGBxo0bY/jw4bh+/brWOf78809ERUXB2dkZnp6emDp1KkpKSrTaHD58GKGhoXBwcEBwcDA2bdpUqS8rV65EQEAAHB0dER4ejuPHj5vknhs6Dw8P+Pj4VPtiwERERNbKokGTn58fFi5ciLS0NPz888/o168fHn/8cZw7dw4AMGXKFHzzzTfYvn07vv/+e1y7dg1PPvmk+P3S0lJERUWhqKgIx44dw+bNm7Fp0ybMmTNHbJOZmYmoqCj07dsX6enpSEhIwMSJE7Fv3z6xzWeffYbExES88cYbOHnyJDp16oTIyEjcuHHDfA+DiIiIrJtgZZo0aSJ8+OGHQl5enmBvby9s375dPHbhwgUBgJCamioIgiDs2bNHsLOzE3JycsQ2q1evFuRyuVBYWCgIgiBMmzZNaNeundY1RowYIURGRorvu3fvLsTFxYnvS0tLBV9fX2HBggU691ulUgkABJVKpd8NExERkcXo8/vbaiqCl5aWYtu2bbh79y4iIiKQlpaG4uJiDBgwQGzTpk0btGjRAqmpqQCA1NRUdOjQAV5eXmKbyMhI5Ofni6NVqampWufQtNGco6ioCGlpaVpt7OzsMGDAALFNVQoLC5Gfn6/1IiIiovrL4kHTmTNn0LhxYzg4OGDSpEn48ssvERISgpycHMhkMri5uWm19/LyQk5ODgAgJydHK2DSHNccq6lNfn4+/v77b9y6dQulpaVVttGcoyoLFiyAQqEQX/7+/gbdP1nW1avAoUNlfxIREdXE4kHTgw8+iPT0dPz000+YPHkyYmJicP78eUt3q1YzZsyASqUSX1euXLF0l0hP69cDLVsC/fqV/bl+vaV7RERE1sziJQdkMhmCg4MBAGFhYThx4gSWLVuGESNGoKioCHl5eVqjTdevX4e3tzcAwNvbu9IqN83quvJtKq64u379OuRyOZycnCCVSiGVSqtsozlHVRwcHODg4GDYTZNFKZVKZGWVIDbWE2q1BACgVgPPPy+gc+cbCAhoxFV8RERUicVHmipSq9UoLCxEWFgY7O3tkZKSIh7LyMjAn3/+iYiICABAREQEzpw5o7XKbf/+/ZDL5QgJCRHblD+Hpo3mHDKZDGFhYVpt1Go1UlJSxDZUfyiVSiQnJ2PFir1iwKRRWirBihXfIjk5GUql0kI9JCIia2XRkaYZM2Zg8ODBaNGiBQoKCrB161YcPnwY+/btg0KhwIQJE5CYmAh3d3fI5XK8+OKLiIiIQI8ePQAAAwcOREhICMaOHYtFixYhJycHs2bNQlxcnDgKNGnSJCQnJ2PatGl49tlncfDgQXz++efYvXu32I/ExETExMSga9eu6N69O5KSknD37l2MHz/eIs+FTEdTWNPdXQmJRI3yayEkEjXc3XO12hEREWkYHDQFBATg2Wefxbhx49CiRQuDznHjxg0888wzyM7OhkKhQMeOHbFv3z48+uijAIClS5fCzs4Ow4cPR2FhISIjI7Fq1Srx+1KpFLt27cLkyZMREREBFxcXxMTE4K233hLbBAYGYvfu3ZgyZQqWLVsGPz8/fPjhh4iMjBTbjBgxAjdv3sScOXOQk5ODzp07Y+/evZWSw8nyrl4FfvsNaN0a8PMz/DwKRQGio3fhm2+GQBDsIJGoER29CwpFgfE6S0RE9YpEEATBkC8mJSVh06ZNOHv2LPr27YsJEybgiSeeaLB5Pvn5+VAoFFCpVJDL5ZbuTr20fj0QG1uWf2RnB6xdC0yYoN85srOzsXbtWvG9SuWK3Fx3uLvnagVMsbGxKC31MUqARkRE1kuf398G5zQlJCQgPT0dx48fR9u2bfHiiy/Cx8cH8fHxOHnypKGnJarS1av/BkyAJnG77qUCFIoCBAb+UWmEaetWJ66sIyIiLXVOBA8NDcXy5ctx7do1vPHGG/jwww/RrVs3dO7cGRs2bICBA1lEIqVSiR9/VIoBk0ZpKfDTT0qjJ22rVK6YNk1RIUATkJZ2vU7XYk0oIiLbVuegqbi4GJ9//jmGDh2KV155BV27dsWHH36I4cOH4/XXX8eYMWOM0U9qoDSr3Y4d2wyJRDtqkkjUOHp0s9FXu+XmehhtZZ1SqUR2djYWL85Dy5bCPyNXAhYvzkN2djZX6RER2RCDE8FPnjyJjRs34tNPP4WdnR2eeeYZLF26FG3atBHbPPHEE+jWrZtROkoNk2YVW22J28Zc7WaslXWagE+lckVSUgIEQVMTSoKpU+X4668NUCgKEB8fz7pQREQ2wOCgqVu3bnj00UexevVqDBs2DPb29pXaBAYGYuTIkXXqIJFGaOgpBAVdqjJxW1cymazWNsZaWacJrnJzPVBxm0dBsENurjsUigKWNyAishEGB02///47WrZsWWMbFxcXbNy40dBLEFWiUBTUqSyAh4cH4uPjqwxUbt26hZ07dwIwToCm4e6uBCAA+HfKr/zIFRER2QaDg6a+ffvixIkTlaYV8vLyEBoait9//73OnSMyBV2nwuoaoGnTDpr0WR9hrNpURERUNwYngmdlZaG0tLTS54WFhfjrr7/q1Cmi+iAvLw9A2fRc5f/UyqbnyrcrjwnkRETWR++Rpq+//lr8WbPdiUZpaSlSUlIQEBBglM4R2bKSkhIAtSeWa9ppMIGciMg66R00DRs2DAAgkUgQExOjdcze3h4BAQFYvHixUTpHVB/om1jOBHIiIuukd9Ck/qfiX2BgIE6cOIGmTZsavVNEGrqsdtOnnaWuZUhieW0jVEREZF4GJ4JnZmYasx9EVapptZuGTCYzyjSVsa9VsQxHdYnlVZXr0LS3hU2FmahORA2FwUHTW2+9VePxOXPmGHpqIi3mzNsx5rXK5/sZ2s6YpQ80jBnkGGMTZSIiW2Fw0PTll19qvS8uLkZmZiYaNWqEoKAgBk1ERmKM0gdKpRJFRUXYutXpn331JLCzE7BokQqjR/+t92idUqlEVlYJYmM9xS1nNHv0de58AwEBjZikTkT1jsFB06lTpyp9lp+fj3HjxuGJJ56oU6eIyHiMvRpPc77MzACo1dqLQTR79AUG/sHVfURU79R5w97y5HI55s6di9mzZxvztEQ2yZxJ7DXRZTVe+Xa6nk+TqF6eIXv0VXT1KnDoUNmfRETWxOCRpuqoVCqoVCpjn5bI5hiaWG6qYMvYq/FMkajOHCkismYGB03Lly/Xei8IArKzs/Hxxx9j8ODBde4YUX1gyPSUqVYMmiLIMVaiOnOkiMgWGBw0LV26VOu9nZ0dmjVrhpiYGMyYMaPOHSNqyMoHCMZc7WaK1Xh1TVRnjhQR2QrWaSKyYqaYrjLuRsR1VzFHqrrpQ1ZAJyJLq1MiuCAIuHXrFjcPJTIypVKJtLTriI0V8E8RfnG6Ki3ter38b04zfahJLrfWYp5E1HAZNNKUk5ODadOm4euvv0ZBQdk/aHK5HE888QQWLFgALy8vo3aSqCFpyNNVppg+JCIyFr2Dpvz8fPTs2RN37tzB+PHj0aZNGwiCgPPnz+PTTz/FkSNHcPLkSTRu3NgU/SWq94w9XWXs1XimLqVgbdOHREQaegdNy5Ytg1Qqxblz59CsWTOtY7NmzUKvXr2wfPlyvP7660brJFFDZKzVbsZejWfO/QAr4j53RGRJegdNu3fvxuuvv14pYAIAT09PzJgxA+vWrWPQRGQExpquMnYAY4lpwbItYFjDiYgsR+9E8F9//RU9e/as9njPnj2RkZFRp04R0b8UigIEBv7RoKesVCrXf/bMK3tf35Piicg66R005efnw83Nrdrjbm5uyM/Pr0ufiKgB0SX3KTfXQyx6qaFJik9OTmbgRERmoff0nCAIsLOrPtaSSCQQBKFOnSKihqOmHKlbt25h586drOFERFbBoKDpgQcegEQiqfY4EZE+asuRMsUWMERE+tI7aNq4caMp+kFE/zD1kn5bxRpORGRpegdNMTExtTcq59NPP8XQoUPh4uKi76WIGiRLLum3dqzhRESWZPDec7p6/vnnER4ejlatWpn6UkT1RkMMiIiIrF2d9p7TBXOciIiIqD4wedBEREREVB8waCIiq2XJpPirV4FDh8r+JCICzJDTRERkKEslxa9fD8TGcssWItLGoImI9KZUKsVA5to1O2RmNkJgYAl8fcv2OTFmIGPupPirV/8NmADNli1AZCQ3CSZq6AwKmkpLS3H06FF07Nixxi1VAKBly5awt7c35DJEZIWUSiWSk5MBACdPdqlUcDI09BQAID4+3uZWASqVSvz4I6BWa/e7tBT46SclnJy4spGoITMop0kqlWLgwIG4fft2rW3Pnj0Lf39/Qy5DRFZIM8KkUrmKARMACIIdvvlmCFQqV612tkITDB47thkSiVrrmESixtGjm7nPHVEDZ3AiePv27fH7778bsy9EZENycz209oIDygKn3Fx3C/WobjRBnmbLFk3gVHHLFlsLBonIeAzOaXr77bfx6quvYt68eQgLC6tU8Vsul9e5c0RkvWrbRNeWccsWIqqKwUHTY489BgAYOnSo1ua9giBAIpGgtLS07r0jIqtV3zfR5ZYtRFSRwUHToUOHjNkPIrIReXl5Wu81Rf8rFv/Py8uDj4+PeTpFRGQGBgdNjzzyiDH7QUQ2oqSkBMC/ieD/pkaWJYIHBV2CQlEgtiMiqi/qVBH8f//7H55++mn07NkTf/31FwDg448/xpEjR4zSOSKyXvUtEZyIqDYGB007duxAZGQknJyccPLkSRQWFgIAVCoV5s+fr9M5FixYgG7dusHV1RWenp4YNmwYMjIytNr06dMHEolE6zVp0iStNn/++SeioqLg7OwMT09PTJ06tdL/yz18+DBCQ0Ph4OCA4OBgbNq0qVJ/Vq5ciYCAADg6OiI8PBzHjx/X44kQNQyaumuaRPDyyieCsz4bEdU3BgdNb7/9NtasWYN169Zp/ePYq1cvnDx5UqdzfP/994iLi8OPP/6I/fv3o7i4GAMHDsTdu3e12j333HPIzs4WX4sWLRKPlZaWIioqCkVFRTh27Bg2b96MTZs2Yc6cOWKbzMxMREVFoW/fvkhPT0dCQgImTpyIffv2iW0+++wzJCYm4o033sDJkyfRqVMnREZG4saNG4Y+IqJ6SaFQ/PNnzUvzNe1shSX3uSMi2yARhIrpm7pxdnbG+fPnERAQAFdXV/zyyy9o1aoVfv/9d4SEhOD+/ft6n/PmzZvw9PTE999/j969ewMoG2nq3LkzkpKSqvzOt99+iyFDhuDatWvw8vICAKxZswbTp0/HzZs3IZPJMH36dOzevRtnz54Vvzdy5Ejk5eVh7969AIDw8HB069ZNrHSsVqvh7++PF198Ea+99lqtfc/Pz4dCoYBKpWK5BbJqV68Cv/0GtG5t2LYg2dnZWLt2rfhepXKtcml+bGysyRPBjb2dS/nzVcUU+9wRkWXp8/vb4ERwb29vXLp0CQEBAVqfHzlyBK1atTLonCqVCgDg7q6dE7FlyxZ88skn8Pb2RnR0NGbPng1nZ2cAQGpqKjp06CAGTAAQGRmJyZMn49y5c+jSpQtSU1MxYMAArXNGRkYiISEBQFmxurS0NMyYMUM8bmdnhwEDBiA1NbXKvhYWFopTkkDZQyeyVppgYOtWJ0ybpoBaLYGdnYBFi1QYPfrvOgUDllqab4rtXBgQEVFNDA6annvuObz88svYsGEDJBIJrl27htTUVLz66quYPXu23udTq9VISEhAr1690L59e/Hz0aNHo2XLlvD19cXp06cxffp0ZGRkYOfOnQCAnJwcrYAJgPg+Jyenxjb5+fn4+++/cfv2bZSWllbZ5uLFi1X2d8GCBZg7d67e90lkbprgQqVyRVJSAgShrK6aWi3B1Kly/PXXBigUBTa3V1xt27loVvGxgjcRGYvBQdNrr70GtVqN/v374969e+jduzccHBzw6quv4sUXX9T7fHFxcTh79myllXexsbHizx06dICPjw/69++Py5cvIygoyNDu19mMGTOQmJgovs/Pz+cee2SVNEFDTavd9AkurC33p7b7IiIyFoODJolEgpkzZ2Lq1Km4dOkS7ty5g5CQEDRu3Fjvc8XHx2PXrl344Ycf4FdLkkV4eDgA4NKlSwgKCoK3t3elVW7Xr18HUDaFqPlT81n5NnK5HE5OTpBKpZBKpVW20ZyjIgcHBzg4OOh+k0QWZqxtTzw8PBAfH281uT/1eTsXIrIudarTBJT94xgSEoLu3bvrHTAJgoD4+Hh8+eWXOHjwIAIDA2v9Tnp6OgCICaYRERE4c+aM1iq3/fv3Qy6XIyQkRGyTkpKidZ79+/cjIiJCvIewsDCtNmq1GikpKWIbIltX22o3fXh4eMDHx6falzmn+Yx5X0RENTF4pOn+/ftYsWIFDh06hBs3bkCt1q7XokvZgbi4OGzduhX/93//B1dXVzEHSaFQwMnJCZcvX8bWrVvx2GOPwcPDA6dPn8aUKVPQu3dvdOzYEQAwcOBAhISEYOzYsVi0aBFycnIwa9YsxMXFiSNBkyZNQnJyMqZNm4Znn30WBw8exOeff47du3eLfUlMTERMTAy6du2K7t27IykpCXfv3sX48eMNfUREVsfSG9Eae7WbhqXvi4gaBoODpgkTJuC7777Df/7zH3Tv3l1r015drV69GkBZWYHyNm7ciHHjxkEmk+HAgQNiAOPv74/hw4dj1qxZYlupVIpdu3Zh8uTJiIiIgIuLC2JiYvDWW2+JbQIDA7F7925MmTIFy5Ytg5+fHz788ENERkaKbUaMGIGbN29izpw5yMnJQefOnbF3795KyeFEtq4+rXYrjxvsEpGpGRw07dq1C3v27EGvXr0MvnhtJaL8/f3x/fff13qeli1bYs+ePTW26dOnD06dOlVjm/j4eMTHx9d6PSLSH1e7EZGtMzinqXnz5nB1dTVmX4ioATDWnnXWtoqPiOo/g0eaFi9ejOnTp2PNmjVo2bKlMftEREZkbcFFfV3FR0T1n8FBU9euXXH//n20atUKzs7OlTbnzM3lcl8ia2BtwYVmtVvFnCZDV/EREZmLwUHTqFGj8Ndff2H+/Pnw8vIyKBGciMzD2oILrnYjIltkcNB07NgxpKamolOnTsbsDxE1EFztRkS2xuBE8DZt2uDvv/82Zl+IiIiIrJbBQdPChQvxyiuv4PDhw1AqlcjPz9d6ERGVZ20J6URE+pIItRVLqoadXVm8VTGXSRAESCQSlJaW1r13NiQ/Px8KhQIqlQpyudzS3SGySuUrgleFq92IyNz0+f1tcE7ToUOHDP0qETVQDIiIyJYZFDQVFxfjrbfewpo1a9C6dWtj94mIbJip9pcjIrI0g4Ime3t7nD592th9ISIbZ+r95YiILMng6bmnn34a69evx8KFC43ZHyKyYdxfTnfM7yKyPQYHTSUlJdiwYQMOHDiAsLAwuLi4aB1fsmRJnTtHRLappv3lWJtJe0QOKAsyc3M94O6u1Ho+FUfkOPVJZFkGB01nz55FaGgoAODXX3/VOsbq4EQNm7H2l6uvyo8w1TSNWb4dpz6JLI+r54jI6Iy5v1x9Vts0Znmc+iSyPIODpvKuXr0KAPDz8zPG6YioHuD+crUzZBqTU59ElmNwRXC1Wo233noLCoUCLVu2RMuWLeHm5oZ58+ZBrVYbs49EZKMUigIEBv7BX+bV0ExjllfbNKYh3yEi4zA4aJo5cyaSk5OxcOFCnDp1CqdOncL8+fOxYsUKzJ4925h9JCKqlzTTmJogSJdpTEO+Q0TGYfD03ObNm/Hhhx9i6NCh4mcdO3ZE8+bN8cILL+Cdd94xSgeJyHZwfzn9GTKNyalPIsswOGjKzc1FmzZtKn3epk0b5OZymJioIfLw8EB8fDzrD+lJoSjQO/Ax5DtEVDcGB02dOnVCcnIyli9frvV5cnIyOnXqVOeOEZFtYkBUO47IEdkmg4OmRYsWISoqCgcOHEBERAQAIDU1FVeuXMGePXuM1kEiovrGkBE5BlpElicRBEEw9MvXrl3DypUrcfHiRQBA27Zt8cILL8DX19doHbQV+fn5UCgUUKlUkMvllu4OEdVD3HqFyPj0+f2tV9D05JNPYtOmTZDL5fjoo48wYsQIODg41LnD9QGDJiLbx6CEqOExWdAkk8nwxx9/wMfHB1KpFNnZ2fD09Kxzh+sDBk1Etq3ifnDV4TYlRPWLPr+/9cppatOmDWbMmIG+fftCEAR8/vnn1V7gmWee0efUREQWpev2I/VpmxKOrBHpR6+gac2aNUhMTMTu3bshkUgwa9asKjfnlUgkDJqIyKapVK7IzfWAu7uyXi7trziyVt39cmSN6F96BU09e/bEjz/+CACws7PDr7/+yuk5Iqp3Tp7sUmmz4dDQU5bullGVH2Gq6X7r08gaUV0ZvI1KZmYmmjVrZsy+EBFZnErlKgYQQNlmuN98MwQqlauFe2YaDe1+ierC4DpNLVu2xP/+9z988MEHuHz5Mr744gs0b94cH3/8MQIDA/HQQw8Zs59ERGaRm+shBhAagmCH3Fx3q56mMzQ/yVbvl8gSDA6aduzYgbFjx2LMmDE4deoUCgsLAQAqlQrz589ngUsisknu7kpIJGqtQEIiUcPd3Xq3h6rLyr/a7vfWrVu4ds0OmZmNEBhYAl/fso2CmSRODZHB03Nvv/021qxZg3Xr1sHe3l78vFevXjh58qRROkdEZG4KRQGio3dBIikLDjQ5PtY86lJxhEmlckVmZkClKbaqRqJqu99ZszLRrVsz/Pe/HujWrRkmTTqBtWvXIjk5GUql0kR3RGSdDB5pysjIQO/evSt9rlAokJeXV5c+ERGZXfntR0JDTyEo6BJyc93h7p6rFTBZ+zYlhiSxV3e/1eU7BQVdgkJRwCRxanAMDpq8vb1x6dIlBAQEaH1+5MgRtGrVqq79IiIyK0P2g7M2tQU5NVEoCiq1Yb4TkTaDg6bnnnsOL7/8MjZs2ACJRIJr164hNTUVr7zyCubMmWPMPhIRmYU1B0S60CfI0WXEzBbzu4hMyeCg6bXXXoNarUb//v1x79499O7dGw4ODpg6dSomTpxozD4SEZEO9AlyahpZu3XrFnbu3CnmO1Wc7uMoEzVUBgdNEokEM2fOxNSpU3Hp0iXcuXMHISEh+OCDDxAYGIicnBxj9pOIiGqhb5Cjy8haTfldRA2N3kFTYWEh3nzzTezfv18cWRo2bBg2btyIJ554AlKpFFOmTDFFX4mIrI617d9miiCnqnwnooZI76Bpzpw5+OCDDzBgwAAcO3YM//3vfzF+/Hj8+OOPWLx4Mf773/9CKpWaoq9ERFalLvWRjKliflJ1QY61r/wjsnZ6B03bt2/HRx99hKFDh+Ls2bPo2LEjSkpK8Msvv1S5eS8RUX2l65J7Uy/NN3TlX3WjZCqVSqfrMgijhkbvoOnq1asICwsDALRv3x4ODg6YMmUKAyYiIgvSdyRL11Gyp556Cm5ubpU+t/byC0SmoHfQVFpaqvX/Lho1aoTGjRsbtVNERLZIpXJFbq4H3N2VVp8DpOvol5ubG3x8fEzcGyLboHfQJAgCxo0bBwcHBwDA/fv3MWnSJLi4uGi127lzp3F6SERkAwypxE1EtkXvoCkmJkbr/dNPP220zhAR2aK6VOK2FrY0SkZkKXoHTRs3bjRFP4iIbJatbzfCUTIi3djV3sR0FixYgG7dusHV1RWenp4YNmwYMjIytNrcv38fcXFx8PDwQOPGjTF8+HBcv35dq82ff/6JqKgoODs7w9PTE1OnTkVJSYlWm8OHDyM0NBQODg4IDg7Gpk2bKvVn5cqVCAgIgKOjI8LDw3H8+HGj3zMR1T+aStzl2cp2I9WNkqlUrhbuGZH1sWjQ9P333yMuLg4//vgj9u/fj+LiYgwcOBB3794V20yZMgXffPMNtm/fju+//x7Xrl3Dk08+KR4vLS1FVFQUioqKcOzYMWzevBmbNm3S2v8uMzMTUVFR6Nu3L9LT05GQkICJEydi3759YpvPPvsMiYmJeOONN3Dy5El06tQJkZGRuHHjhnkeBhHZHM2iGE0lbk3gVLEStzUvza9plIyItEkEQRAs3QmNmzdvwtPTE99//z169+4NlUqFZs2aYevWrfjPf/4DALh48SLatm2L1NRU9OjRA99++y2GDBmCa9euwcvLCwCwZs0aTJ8+HTdv3oRMJsP06dOxe/dunD17VrzWyJEjkZeXh7179wIAwsPD0a1bN3EJrlqthr+/P1588UW89tprtfY9Pz8fCoUCKpUKcrnc2I+GiKxU+VpH167ZISurEQICSuDrWxZAWevS/OzsbKxduxYqlSuSkhIq7VeXkJAEhaIAsbGxXD1H9Zo+v78N3nvOFDQF1dzdy/4fTlpaGoqLizFgwACxTZs2bdCiRQsxaEpNTUWHDh3EgAkAIiMjMXnyZJw7dw5dunRBamqq1jk0bRISEgCULb1NS0vDjBkzxON2dnYYMGAAUlNTq+xrYWEhCgsLxff5+fl1u3kisknlAyIfH+CfMna1svT2KxVHyarbr86aR8mIzM1qgia1Wo2EhAT06tUL7du3BwDk5ORAJpNVKqzm5eUlbgick5OjFTBpjmuO1dQmPz8ff//9N27fvo3S0tIq21y8eLHK/i5YsABz58417GaJqEEz9/Yr1QVoI0aMQHFxMZ56qhHmzLlZbpSsG4BuVjtKRmQpVhM0xcXF4ezZszhy5Iilu6KTGTNmIDExUXyfn58Pf39/C/aIiGyFObdf0SdACwvTLUCy9CgZkaVYRdAUHx+PXbt24YcffoCfn5/4ube3N4qKipCXl6c12nT9+nV4e3uLbSquctOsrivfpuKKu+vXr0Mul8PJyQlSqRRSqbTKNppzVOTg4CAW+CQislbGDtCsZZNiIkuw6Oo5QRAQHx+PL7/8EgcPHkRgYKDW8bCwMNjb2yMlJUX8LCMjA3/++SciIiIAABEREThz5ozWKrf9+/dDLpcjJCREbFP+HJo2mnPIZDKEhYVptVGr1UhJSRHbEBGR4UGYUqlEdnZ2tS+lUmmK7hIZlUVHmuLi4rB161b83//9H1xdXcUcJIVCAScnJygUCkyYMAGJiYlwd3eHXC7Hiy++iIiICPTo0QMAMHDgQISEhGDs2LFYtGgRcnJyMGvWLMTFxYkjQZMmTUJycjKmTZuGZ599FgcPHsTnn3+O3bt3i31JTExETEwMunbtiu7duyMpKQl3797F+PHjzf9giKhBqe/VuDk6RfWFRYOm1atXAwD69Omj9fnGjRsxbtw4AMDSpUthZ2eH4cOHo7CwEJGRkVi1apXYViqVYteuXZg8eTIiIiLg4uKCmJgYvPXWW2KbwMBA7N69G1OmTMGyZcvg5+eHDz/8EJGRkWKbESNG4ObNm5gzZw5ycnLQuXNn7N27t1JyOBGRMTWEatzmzOEiMiWLBk26lIhydHTEypUrsXLlymrbtGzZEnv27KnxPH369MGpUzX/QxQfH4/4+Pha+0REZAyW2LPO2KNa9X2UjKg8q0gEJyJqiMy9Z52xR7UMPV91gdatW7eqbM/VeGQtGDQREZmZpmCkZs+6itW4NXvWGbOwZG2jWlUFLDUFK4aOktUUaO3cubPa7zHfiawBgyYiIjPz8PBAfHw8ioqK0Lx5PqZPV6C0VAKpVMC77+Zj9OhRRhtd0QRetY1qVRewVBesGDJKVpfpSOY7kTVg0EREZAGaQOSVV4ARI4BLl4DgYAn8/NwAuBn1OvHx8cjKKsHHHwtQqyXisfKjWtWpGKzUZZTM3NORRMbGoImIyML8/MpepuLh4QEPD2DtWuD554HSUkAqFRAVtUvvYKUuo2S1BVoAE8vJujFoIiJqICZMACIjy0a1XF1vYNcuw5LA9R0l03Vz4IZQfoFsG4MmIqIGRDOqlZ2tNur5alJ+dAqA1ubAMlkgdu48ZZHyC0T6YtBEREQmnxYrP13n4wOEhZX9rAnemO9EtoBBExFRA2cN02K65DsRWZpFN+wlIiLLqm5aTKVyNcv1K+Y7SSRlI08V852MWbOKyFAcaSIiaoB0rd9krGBFqVRWW2tpxIgREAQBsbFuWvlOvr7dAHRjRXCyGhJBlw3gqFb5+flQKBRQqVSQy+WW7g4RUa2USiWyskrQvbunVv0mqVTATz/dQEBAI6MEK0qlEsnJybW2Y9VvsgR9fn9zeo6IqIHy8PBAWJgX1q6VQCot+0wqBT74QIKwMC+jBTC6VvNm1W+ydpyeIyJq4MrXbwoONm2hTSJbxqCJiIhMXpWcqD7g9BwRERGRDhg0EREREemAQRMREZmVSuWKzMwAs9WCIjIW5jQREZHZWEP1cSJDcaSJiIhMSlMgs7bq46z6TdaOxS2NhMUtiYiqp1QqcegQ8N//Vq799MUXSvTpA73rQtVUZRwAK4mTTvT5/c3pOSIiMjkPDw/06AHY2QFq9b+fS6VAeLgH9I1tWGWcLIHTc0REZBZ+fsDatahQfdyw+lCsMk6WwJEmIiIyG1YfJ1vGoImIiMzKWquPM0eKasOgiYiIGjzmSJEumNNEREQNHnOkSBcMmoiIyOaxyjiZA6fniIjIprHKOJkLR5qIiMjm3Lt3D0DtVcY17fTFkSuqCkeaiIjI5jg7OwMAcnM9xIBJQxDskJvrDoWiQGynj5pGrm7dulXld7iyrmFg0ERERDbL3V0JiUStFThJJGq4u+cadL7qRq6Cgi5BoSjAzp07q/0uV9bVf5yeIyIim6VQFCA6ehckkrK9WTQjQwpFgUHnq2nkqjbXrl1Ddna21kupVBrUD7JOHGkiIiKbFhp6CkFBl5Cb6w5391yDAiaZTAagbiNX1Y1CcQSq/mDQRERENk+hKDB4dAko21A4Pj4eRUVFaN48H9OnK1BaKoFUKmD27GsADD83azvVHwyaiIjI5qlUrsjN9YC7u9Lg4EkzGvTKK8CIEZr98SSQSqVYu9a41yLbxKCJiIhsjkqlEn+uabWbSqWCj4+P3ucvvz9edva/n9d8LQZT9R2DJiIisjmCIACofbWbpp0x1HSty5eDWWCzAeDqOSIisjlubm4Aal/tpmlnDNVd68oVvxoLbFL9waCJiIhslma1W3l1qdNUlYor6ypeC5AYXKaAbAuDJiIislnGrtNUFc3KuqlTR+G99/IhlZZN+dnZCYiO3gV//ysmD9zIOjCniYiIbJox6jTVpqqVdR4eedi5syxvKTp6V6WcJk0/NCNVZPsYNBERUT0hMctV/l1Z1wS+vvFiHaY5c24iK6sRAgJK4OvbDUA37klXzzBoIiIim1ZTGQBTKx8Q+fgAYWFmuSxZCHOaiIjI5mimvKorA6BZucapMTImiwZNP/zwA6Kjo+Hr6wuJRIKvvvpK6/i4ceMgkUi0XoMGDdJqk5ubizFjxkAul8PNzQ0TJkzAnTt3tNqcPn0aDz/8MBwdHeHv749FixZV6sv27dvRpk0bODo6okOHDtizZ4/R75eIiIxDk5zds2dMlSvXevWKMeqeb0qlstJmvKbYmNdc1yHDWHR67u7du+jUqROeffZZPPnkk1W2GTRoEDZu3Ci+d3Bw0Do+ZswYZGdnY//+/SguLsb48eMRGxuLrVu3AgDy8/MxcOBADBgwAGvWrMGZM2fw7LPPws3NDbGxsQCAY8eOYdSoUViwYAGGDBmCrVu3YtiwYTh58iTat29vorsnIqK68PDwQI8egJ0doC63eE0qBcLDPWCsVCKlUonk5ORa29U1SDPXdchwFg2aBg8ejMGDB9fYxsHBAd7e3lUeu3DhAvbu3YsTJ06ga9euAIAVK1bgsccew/vvvw9fX19s2bIFRUVF2LBhA2QyGdq1a4f09HQsWbJEDJqWLVuGQYMGYerUqQCAefPmYf/+/UhOTsaaNWuMeMdERGRMfn7A2rXA888DpaVlAdMHH/y7BYo+lEpllZvr3rp1S6fv13VjXl2/zw2ALcfqE8EPHz4MT09PNGnSBP369cPbb78tRtipqalwc3MTAyYAGDBgAOzs7PDTTz/hiSeeQGpqKnr37q01rx0ZGYl3330Xt2/fRpMmTZCamorExESt60ZGRlaaLiyvsLAQhYWF4vv8/Hwj3TEREeljwgQgMlKzwa7hAZMuozzUsFl1IvigQYPw0UcfISUlBe+++y6+//57DB48GKWlpQCAnJwceHp6an2nUaNGcHd3R05OjtjGy8tLq43mfW1tNMersmDBAigUCvHl7+9ft5slIiKD+fkBffoYFjAB1jl6o1K5IjMzgNuxWBGrHmkaOXKk+HOHDh3QsWNHBAUF4fDhw+jfv78FewbMmDFDa3QqPz+fgRMRERmFJcsoUPWseqSpolatWqFp06a4dOkSAMDb2xs3btzQalNSUoLc3FwxD8rb2xvXr1/XaqN5X1ub6nKpgLJcK7lcrvUiIiKqq9rKKJDl2FTQdPXqVSiVSvj4+AAAIiIikJeXh7S0NLHNwYMHoVarER4eLrb54YcfUFxcLLbZv38/HnzwQTRp0kRsk5KSonWt/fv3IyIiwtS3RERENsbU02a5uR7cANhKWXR67s6dO+KoEQBkZmYiPT0d7u7ucHd3x9y5czF8+HB4e3vj8uXLmDZtGoKDgxEZGQkAaNu2LQYNGoTnnnsOa9asQXFxMeLj4zFy5Ej4+voCAEaPHo25c+diwoQJmD59Os6ePYtly5Zh6dKl4nVffvllPPLII1i8eDGioqKwbds2/Pzzz1i7dq15HwgREVkFlcoVubkecHdXau1lp8+0WXWr8TSq22LF3V0JiUStFThxA2DrYNGg6eeff0bfvn3F95ocoZiYGKxevRqnT5/G5s2bkZeXB19fXwwcOBDz5s3TqtW0ZcsWxMfHo3///rCzs8Pw4cOxfPly8bhCocB3332HuLg4hIWFoWnTppgzZ45YbgAAevbsia1bt2LWrFl4/fXX0bp1a3z11Ves0URE1ABVFxhVN20WFHQJCkWB1iptQ2ouab6vUBRwA2ArJREEQbB0J+qD/Px8KBQKqFQq5jcREdmY7OxsrF27FiqVK5KSEiqN8iQkJCE31wObN8dU+u4XXyjRp4/2PnSa89UmNjZWTDkBtEenrl2zK7cBcFn1Tm4AbHz6/P626tVzRERE5qAZvakpn8jdXQk7OwFqtUQ8Zuzq49wA2LoxaCIiogZPs5ddVlYJPv64YmAk4MUXByMgoBHatZMYpfp4dQzNgyLzYNBERESEssDJw6OqbVkkCAsrK4BsjOrj1eHec9aPQRMREVE5tQVGfn76B0vVrcYrj3vPWT8GTURERBUYEhhVh9W96w+bKm5JRERkS1jdu37hSBMREZGR6bIar2Jtp4qqm9K7detWtddkrpNpsU6TkbBOExERladUKpGVVYLu3T0rrcb76acbCAhoVG1tJ0On9J566im4ublV+pwBVfVYp4mIiMjCdFmNV5XaKo/X5PPPP6/2GFfd1R2DJiIiIhPStUxBXl4egNqn9AzFVXd1x6CJiIjIxHRZjXfnzh0Aum3YW1MJA13KG5BhGDQRERFZAUdHRwC1b9hbU74TyxuYFoMmIiIiKxMaegqenjm4cqUF/P3/hJ9fNoCa850AGJwLRbph0ERERGRlTp7sgq+/HoKycopqDB1aNmJUU74TIDFJLhT9i8UtiYiIrIhK5VouYAIAO3z9dVlBTHd3JQDtSkGafCdNLlRVx8g4GDQRERFZkStX/FH517MdrlzRZJJrB02aaouaXChN4FQxF0qzOo8Mx+k5IiIiG5Gb64GqAirNFFxo6CkEBV1Cbq473N1ztablJBIJjEGpVIrlC65ds0NmZiMEBpbA17csWKvPhTQZNBEREVmBRo3KfiX7+19B2WjSv0GORKKGv/9V8eeayhH882ml8ysUijr3UalUIjk5GUDNK/XqayFNTs8RERFZAU9PTwBl02xDh35T5TRbbVNwJ092QVJSAjZvjkFSUgJOnuxi1D5qRphq24jY0EKaV68Chw6V/WmNONJERERkBTw8PBAfHy8GHHPm3ERWViMEBJTA2bk1Pv+8bBSnuim4umy/oi9TVC1fvx6IjQXUasDOrmz7mQkTjNFb42HQREREZCXKT2n5+ABhYZp3XloBVXm3bt3Czp07jRrIlM9bqngtQLeq5fq4evXfgAko+/P558u2n6mtkro5MWgiIiKyAbXlCBkrkCmftwRUvS2LZpqwfC2p8tOE+vrtt38DJo3S0rL9+hg0ERERkVHVtv2KrsqPMNW2LYtEUlbyoOLCPJVKBR8fH52v2bp12ZRc+cBJKi3b4NiaMGgiIiKyYTKZTPy5ppID5dvpoi5bthQXF+t8HaVSCam0CIsWOWH6dAVKSyWQSgW8+64KUunfUCqtp4QBgyYiIiIbVjGBvCqG1E6qy5Ytt2/fRlra9VprOFWcCnzpJVcx4LtzpwBr15Z9bi0lDBg0ERER2ThTBBS15UjVdGzx4jx8802zWms4VQz0NGUVKjK0hIGxsU4TERERiVQqFYCyAKZjx9P4d9sWAR07nq61XpSpajhZA440ERERkUiTj6RSueL06Y74t7q4BKdPd0S/fgdr3LLFFDWcrAWDJiIiIqpEl+Cnquk0Y9dwsiacniMiIiKRZg88TfBTni7BT21bvdgyjjQRERGRyM3NDUDd6j7VVPrAUFevlhXBbN3acgUvGTQRERFRlWoKfp588kk0bdpUfK/ZzkWjupVwhti61QnTpll+XzoGTURERFSt6oKfpk2b6lX1uyq6FNxUqVzx1lsKq9iXjkETERER1ZmuFcfLt6tYmPPaNbtKBTF/+skZS5dq79NiqX3pGDQRERGRyJDgBzC8Mrnm/fr1QGys9hTcsGFK+Pvfh52dHGr1v4GTVCrA1fUGlMpGZq0ULhEEQai9GdUmPz8fCoUCKpUKcrnc0t0hIiIymFKpNPq2LDW5ehVo0aJs818NiURAQsJSKBQFNW4cXNctVvT5/c2RJiIiItJiztEbpVKJ3bslEAR3rc8FQYIrV/ygUFyoMSHdnBXGWaeJiIiILEKzYe+BAyk6fkNSexMT4kgTERERWcSNGzcAAP7+VwCooT2Wo4a//1UAqHF6zpw40kREREQWUVJSAqCsrMHQobtQFjgBgBpDh+q2AbA5caSJiIiILM4WNgBm0ERERERWwdo3AOb0HBEREVkta9oAmCNNREREZNVqKjlw7949s/WDQRMRERFZver2wHN2djZbHyw6PffDDz8gOjoavr6+kEgk+Oqrr7SOC4KAOXPmwMfHB05OThgwYAB+++03rTa5ubkYM2YM5HI53NzcMGHCBNy5c0erzenTp/Hwww/D0dER/v7+WLRoUaW+bN++HW3atIGjoyM6dOiAPXv2GP1+iYiI6F/29vaW7oJeLBo03b17F506dcLKlSurPL5o0SIsX74ca9aswU8//QQXFxdERkbi/v37YpsxY8bg3Llz2L9/P3bt2oUffvgBsbGx4vH8/HwMHDgQLVu2RFpaGt577z28+eabWLt2rdjm2LFjGDVqFCZMmIBTp05h2LBhGDZsGM6ePWu6myciImrgmjVrZuku6MVq9p6TSCT48ssvMWzYMABlo0y+vr545ZVX8OqrrwIAVCoVvLy8sGnTJowcORIXLlxASEgITpw4ga5duwIA9u7di8ceewxXr16Fr68vVq9ejZkzZyInJ0fcXPC1117DV199hYsXLwIARowYgbt372LXrl1if3r06IHOnTtjzZo1OvWfe88RERHpr7p97m7duoWdO3fW+v3Y2Fj4+PgYfH19fn9b7eq5zMxM5OTkYMCAAeJnCoUC4eHhSE1NBQCkpqbCzc1NDJgAYMCAAbCzs8NPP/0ktundu7fWbsyRkZHIyMjA7du3xTblr6Npo7lOVQoLC5Gfn6/1IiIiIv14eHjAx8en0qtp06aW7lolVhs05eTkAAC8vLy0Pvfy8hKP5eTkwNPTU+t4o0aN4O7urtWmqnOUv0Z1bTTHq7JgwQIoFArx5e/vr+8tEhERkQ2x2qDJ2s2YMQMqlUp8XblyxdJdIiIiIhOy2qDJ29sbAHD9+nWtz69fvy4e8/b2Fjf70ygpKUFubq5Wm6rOUf4a1bXRHK+Kg4MD5HK51ouIiIiMo3xajTHaGYPV1mkKDAyEt7c3UlJS0LlzZwBlyVo//fQTJk+eDACIiIhAXl4e0tLSEBYWBgA4ePAg1Go1wsPDxTYzZ85EcXGxuLRx//79ePDBB9GkSROxTUpKChISEsTr79+/HxEREWa6WyIiIirPw8MD8fHxVSaJa8hkMnh4eJitTxYNmu7cuYNLly6J7zMzM5Geng53d3e0aNECCQkJePvtt9G6dWsEBgZi9uzZ8PX1FVfYtW3bFoMGDcJzzz2HNWvWoLi4GPHx8Rg5ciR8fX0BAKNHj8bcuXMxYcIETJ8+HWfPnsWyZcuwdOlS8bovv/wyHnnkESxevBhRUVHYtm0bfv75Z62yBERERGRe5gyIdCJY0KFDhwQAlV4xMTGCIAiCWq0WZs+eLXh5eQkODg5C//79hYyMDK1zKJVKYdSoUULjxo0FuVwujB8/XigoKNBq88svvwgPPfSQ4ODgIDRv3lxYuHBhpb58/vnnwgMPPCDIZDKhXbt2wu7du/W6F5VKJQAQVCqVfg+BiIiILEaf399WU6fJ1rFOExERke2pF3WaiIiIiKwJgyYiIiIiHTBoIiIiItIBgyYiIiIiHTBoIiIiItIBgyYiIiIiHTBoIiIiItKB1W6jYms05a7y8/Mt3BMiIiLSleb3ti5lKxk0GUlBQQEAwN/f38I9ISIiIn0VFBRAoVDU2IYVwY1ErVbj2rVrcHV1hUQi0fv7+fn58Pf3x5UrVxp0RXE+h3/xWZThcyjD5/AvPosyfA5l6vocBEFAQUEBfH19YWdXc9YSR5qMxM7ODn5+fnU+j1wub9B/+TX4HP7FZ1GGz6EMn8O/+CzK8DmUqctzqG2ESYOJ4EREREQ6YNBEREREpAMGTVbCwcEBb7zxBhwcHCzdFYvic/gXn0UZPocyfA7/4rMow+dQxpzPgYngRERERDrgSBMRERGRDhg0EREREemAQRMRERGRDhg0EREREemAQZMZrVy5EgEBAXB0dER4eDiOHz9ebdtz585h+PDhCAgIgEQiQVJSkvk6amL6PId169bh4YcfRpMmTdCkSRMMGDCgxva2Rp9nsXPnTnTt2hVubm5wcXFB586d8fHHH5uxt6ajz3Mob9u2bZBIJBg2bJhpO2gm+jyHTZs2QSKRaL0cHR3N2FvT0vfvRF5eHuLi4uDj4wMHBwc88MAD2LNnj5l6azr6PIc+ffpU+jshkUgQFRVlxh6bhr5/H5KSkvDggw/CyckJ/v7+mDJlCu7fv1/3jghkFtu2bRNkMpmwYcMG4dy5c8Jzzz0nuLm5CdevX6+y/fHjx4VXX31V+PTTTwVvb29h6dKl5u2wiej7HEaPHi2sXLlSOHXqlHDhwgVh3LhxgkKhEK5evWrmnhufvs/i0KFDws6dO4Xz588Lly5dEpKSkgSpVCrs3bvXzD03Ln2fg0ZmZqbQvHlz4eGHHxYef/xx83TWhPR9Dhs3bhTkcrmQnZ0tvnJycszca9PQ91kUFhYKXbt2FR577DHhyJEjQmZmpnD48GEhPT3dzD03Ln2fg1Kp1Pr7cPbsWUEqlQobN240b8eNTN/nsGXLFsHBwUHYsmWLkJmZKezbt0/w8fERpkyZUue+MGgyk+7duwtxcXHi+9LSUsHX11dYsGBBrd9t2bJlvQma6vIcBEEQSkpKBFdXV2Hz5s2m6qLZ1PVZCIIgdOnSRZg1a5Ypumc2hjyHkpISoWfPnsKHH34oxMTE1IugSd/nsHHjRkGhUJipd+al77NYvXq10KpVK6GoqMhcXTSLuv4bsXTpUsHV1VW4c+eOqbpoFvo+h7i4OKFfv35anyUmJgq9evWqc184PWcGRUVFSEtLw4ABA8TP7OzsMGDAAKSmplqwZ+ZljOdw7949FBcXw93d3VTdNIu6PgtBEJCSkoKMjAz07t3blF01KUOfw1tvvQVPT09MmDDBHN00OUOfw507d9CyZUv4+/vj8ccfx7lz58zRXZMy5Fl8/fXXiIiIQFxcHLy8vNC+fXvMnz8fpaWl5uq20Rnj38v169dj5MiRcHFxMVU3Tc6Q59CzZ0+kpaWJU3i///479uzZg8cee6zO/eGGvWZw69YtlJaWwsvLS+tzLy8vXLx40UK9Mj9jPIfp06fD19dX6z8gW2Tos1CpVGjevDkKCwshlUqxatUqPProo6burskY8hyOHDmC9evXIz093Qw9NA9DnsODDz6IDRs2oGPHjlCpVHj//ffRs2dPnDt3ziibh1uKIc/i999/x8GDBzFmzBjs2bMHly5dwgsvvIDi4mK88cYb5ui20dX138vjx4/j7NmzWL9+vam6aBaGPIfRo0fj1q1beOihhyAIAkpKSjBp0iS8/vrrde4PgyayGQsXLsS2bdtw+PDhepXwqg9XV1ekp6fjzp07SElJQWJiIlq1aoU+ffpYumtmUVBQgLFjx2LdunVo2rSppbtjUREREYiIiBDf9+zZE23btsUHH3yAefPmWbBn5qdWq+Hp6Ym1a9dCKpUiLCwMf/31F9577z2bDZrqav369ejQoQO6d+9u6a6Y3eHDhzF//nysWrUK4eHhuHTpEl5++WXMmzcPs2fPrtO5GTSZQdOmTSGVSnH9+nWtz69fvw5vb28L9cr86vIc3n//fSxcuBAHDhxAx44dTdlNszD0WdjZ2SE4OBgA0LlzZ1y4cAELFiyw2aBJ3+dw+fJlZGVlITo6WvxMrVYDABo1aoSMjAwEBQWZttMmYIx/I+zt7dGlSxdcunTJFF00G0OehY+PD+zt7SGVSsXP2rZti5ycHBQVFUEmk5m0z6ZQl78Td+/exbZt2/DWW2+ZsotmYchzmD17NsaOHYuJEycCADp06IC7d+8iNjYWM2fOhJ2d4ZlJzGkyA5lMhrCwMKSkpIifqdVqpKSkaP0/xfrO0OewaNEizJs3D3v37kXXrl3N0VWTM9bfCbVajcLCQlN00Sz0fQ5t2rTBmTNnkJ6eLr6GDh2Kvn37Ij09Hf7+/ubsvtEY4+9DaWkpzpw5Ax8fH1N10ywMeRa9evXCpUuXxAAaAH799Vf4+PjYZMAE1O3vxPbt21FYWIinn37a1N00OUOew7179yoFRpqAWqjrdrt1TiUnnWzbtk1wcHAQNm3aJJw/f16IjY0V3NzcxCXCY8eOFV577TWxfWFhoXDq1Cnh1KlTgo+Pj/Dqq68Kp06dEn777TdL3YJR6PscFi5cKMhkMuGLL77QWkpbUFBgqVswGn2fxfz584XvvvtOuHz5snD+/Hnh/fffFxo1aiSsW7fOUrdgFPo+h4rqy+o5fZ/D3LlzhX379gmXL18W0tLShJEjRwqOjo7CuXPnLHULRqPvs/jzzz8FV1dXIT4+XsjIyBB27doleHp6Cm+//balbsEoDP1v46GHHhJGjBhh7u6ajL7P4Y033hBcXV2FTz/9VPj999+F7777TggKChKeeuqpOveFQZMZrVixQmjRooUgk8mE7t27Cz/++KN47JFHHhFiYmLE95mZmQKASq9HHnnE/B03Mn2eQ8uWLat8Dm+88Yb5O24C+jyLmTNnCsHBwYKjo6PQpEkTISIiQti2bZsFem18+jyHiupL0CQI+j2HhIQEsa2Xl5fw2GOPCSdPnrRAr01D378Tx44dE8LDwwUHBwehVatWwjvvvCOUlJSYudfGp+9zuHjxogBA+O6778zcU9PS5zkUFxcLb775phAUFCQ4OjoK/v7+wgsvvCDcvn27zv2QCEJdx6qIiIiI6j/mNBERERHpgEETERERkQ4YNBERERHpgEETERERkQ4YNBERERHpgEETERERkQ4YNBERERHpgEETEZENGjduHIYNG2bpbhA1KAyaiMioxo0bB4lEIr48PDwwaNAgnD592tJdM4ry96Z5PfTQQya7XlZWFiQSCdLT07U+X7ZsGTZt2mSy6xJRZQyaiMjoBg0ahOzsbGRnZyMlJQWNGjXCkCFDLN0to9m4caN4f9nZ2fj666+rbFdcXGyyPigUCri5uZns/ERUGYMmIjI6BwcHeHt7w9vbG507d8Zrr72GK1eu4ObNm+jXrx/i4+O12t+8eRMymUzcyTwgIADz5s3DqFGj4OLigubNm2PlypVa31myZAk6dOgAFxcX+Pv744UXXsCdO3fE43/88Qeio6PRpEkTuLi4oF27dtizZw8A4Pbt2xgzZgyaNWsGJycntG7dGhs3btT5/tzc3MT78/b2hru7uzgi9Nlnn+GRRx6Bo6MjtmzZAqVSiVGjRqF58+ZwdnZGhw4d8Omnn2qdT61WY9GiRQgODoaDgwNatGiBd955BwAQGBgIAOjSpQskEgn69OkDoPL0XGFhIV566SV4enrC0dERDz30EE6cOCEeP3z4MCQSCVJSUtC1a1c4OzujZ8+eyMjI0Pm+iRo6Bk1EZFJ37tzBJ598guDgYHh4eGDixInYunUrCgsLxTaffPIJmjdvjn79+omfvffee+jUqRNOnTqF1157DS+//DL2798vHrezs8Py5ctx7tw5bN68GQcPHsS0adPE43FxcSgsLMQPP/yAM2fO4N1330Xjxo0BALNnz8b58+fx7bff4sKFC1i9ejWaNm1qlPvV9PXChQuIjIzE/fv3ERYWht27d+Ps2bOIjY3F2LFjcfz4cfE7M2bMwMKFC8V+bd26FV5eXgAgtjtw4ACys7Oxc+fOKq87bdo07NixA5s3b8bJkycRHByMyMhI5ObmarWbOXMmFi9ejJ9//hmNGjXCs88+a5T7JmoQ6rzlLxFROTExMYJUKhVcXFwEFxcXAYDg4+MjpKWlCYIgCH///bfQpEkT4bPPPhO/07FjR+HNN98U37ds2VIYNGiQ1nlHjBghDB48uNrrbt++XfDw8BDfd+jQQeuc5UVHRwvjx4836P4ACI6OjuL9ubi4CF9++aWQmZkpABCSkpJqPUdUVJTwyiuvCIIgCPn5+YKDg4Owbt26Kttqznvq1Cmtz2NiYoTHH39cEARBuHPnjmBvby9s2bJFPF5UVCT4+voKixYtEgRBEA4dOiQAEA4cOCC22b17twBA+Pvvv/V5BEQNFkeaiMjo+vbti/T0dKSnp+P48eOIjIzE4MGD8ccff8DR0RFjx47Fhg0bAAAnT57E2bNnMW7cOK1zREREVHp/4cIF8f2BAwfQv39/NG/eHK6urhg7diyUSiXu3bsHAHjppZfw9ttvo1evXnjjjTe0EtEnT56Mbdu2oXPnzpg2bRqOHTum1/0tXbpUvL/09HQ8+uij4rGuXbtqtS0tLcW8efPQoUMHuLu7o3Hjxti3bx/+/PNPAMCFCxdQWFiI/v3769WH8i5fvozi4mL06tVL/Mze3h7du3fXemYA0LFjR/FnHx8fAMCNGzcMvjZRQ8KgiYiMzsXFBcHBwQgODka3bt3w4Ycf4u7du1i3bh0AYOLEidi/fz+uXr2KjRs3ol+/fmjZsqXO58/KysKQIUPQsWNH7NixA2lpaWLOU1FRkXiN33//HWPHjsWZM2fQtWtXrFixAgDEAG7KlCm4du0a+vfvj1dffVXn63t7e4v3FxwcDBcXF617L++9997DsmXLMH36dBw6dAjp6emIjIwU++nk5KTzdY3B3t5e/FkikQAoy6kiotoxaCIik5NIJLCzs8Pff/8NAOjQoQO6du2KdevWYevWrVXm1fz444+V3rdt2xYAkJaWBrVajcWLF6NHjx544IEHcO3atUrn8Pf3x6RJk7Bz50688sorYtAGAM2aNUNMTAw++eQTJCUlYe3atca8ZdHRo0fx+OOP4+mnn0anTp3QqlUr/Prrr+Lx1q1bw8nJSUyCr0gmkwEoG7GqTlBQEGQyGY4ePSp+VlxcjBMnTiAkJMRId0JEjSzdASKqfwoLC5GTkwOgbKVacnIy7ty5g+joaLHNxIkTER8fDxcXFzzxxBOVznH06FEsWrQIw4YNw/79+7F9+3bs3r0bABAcHIzi4mKsWLEC0dHROHr0KNasWaP1/YSEBAwePBgPPPAAbt++jUOHDolB15w5cxAWFoZ27dqhsLAQu3btEo8ZW+vWrfHFF1/g2LFjaNKkCZYsWYLr16+LwYyjoyOmT5+OadOmQSaToVevXrh58ybOnTuHCRMmwNPTE05OTti7dy/8/Pzg6OgIhUKhdQ0XFxdMnjwZU6dOhbu7O1q0aIFFixbh3r17mDBhgknui6gh4kgTERnd3r174ePjAx8fH4SHh+PEiRPYvn27uFweAEaNGoVGjRph1KhRcHR0rHSOV155BT///DO6dOmCt99+G0uWLEFkZCQAoFOnTliyZAneffddtG/fHlu2bMGCBQu0vl9aWoq4uDi0bdsWgwYNwgMPPIBVq1YBKBu9mTFjBjp27IjevXtDKpVi27ZtJnkWs2bNQmhoKCIjI9GnTx94e3tXquQ9e/ZsvPLKK5gzZw7atm2LESNGiHlGjRo1wvLly/HBBx/A19cXjz/+eJXXWbhwIYYPH46xY8ciNDQUly5dwr59+9CkSROT3BdRQyQRBEGwdCeIqOHJyspCUFAQTpw4gdDQUK1jAQEBSEhIQEJCgmU6R0RUBU7PEZFZFRcXQ6lUYtasWejRo0elgImIyFpxeo6IzOro0aPw8fHBiRMnKuUhWdr8+fPRuHHjKl+DBw+2dPeIyMI4PUdE9I/c3NxKFbQ1nJyc0Lx5czP3iIisCYMmIiIiIh1weo6IiIhIBwyaiIiIiHTAoImIiIhIBwyaiIiIiHTAoImIiIhIBwyaiIiIiHTAoImIiIhIBwyaiIiIiHTw/1qTwgfIumOGAAAAAElFTkSuQmCC", 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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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", 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", 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", 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", 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", 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", 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+/fZbvPjii1i0aBGCg4Nr7Y+tra3WNfUlkUgMMqVmLHv27MGPP/6If/3rXzhy5AgqKyuxcOFCWFhUDXZu3rxZo76NjY3WpyrOnDmDX3/9FQkJCWL4PHz4cON0gIiITMrevXuxf/9+jbIZM2Zgz54W8POrCk8HDhipcf/PqNN5NjY28Pb2RnZ2tlhWWVmJ7Oxs+Pr66jzH19dXoz4A7N69W6yvUCjg4uKiUaekpAQ5OTlineTkZBw/fhzHjh3DsWPHxEXLaWlpWLBggXjevn37MGLECHz44YeYNGmSYTptBsrKyqBWq3HlyhUcPXoUH3zwAUaNGoUXXngBwcHB6NKlC+7fv4+lS5fiwoULWLduHVJSUjSu4e7ujjt37iA7Oxs3b97E3bt30bFjR9jY2IjnZWRk4P333zdSL4mIyFji4uK0AlRsbCxatGiBhARApYJRp/FEgpFt2rRJsLW1FVJTU4VTp04JkyZNEhwdHQW1Wi0IgiCMHTtWCA8PF+t///33gpWVlZCUlCScPn1aiI2NFaytrYUff/xRrJOQkCA4OjoK27dvF06cOCGMGjVKUCgUwu+//66zDfn5+QIAITc3Vyzbs2ePYG9vL0RERAjXrl0TX7/++qvefSsuLhYACMXFxRrlv//+u3Dq1Kka22PKQkJCBAACAMHKykpo166doFQqhdWrVwsVFRVivY8//liQy+WCnZ2d4O/vL6xdu1YAINy+fVus89Zbbwlt27YVAAixsbGCIAjChg0bBHd3d8HW1lbw9fUVMjIytH43TVlT/t0TETW0y5cvC3PnztV4ffvttxp1tm8XBF/fqq8Npaa/348y+hYHALBs2TJ89NFHUKvV8PT0RHJyMnx8fAAAgwcPhru7u7j/EFC12WZUVBQKCgrg4eGBxMRE/P3vfxePC4KA2NhYrFy5EkVFRRgwYAA++eQTdO3aVef9CwoKoFAoNLY4CA0N1ZjWqzZo0CDs27dPr37VZYsDah74uyci0i0uLk6rbPr06ZDJZI3eFn23ODCJEGWuGKLoUfzdExFp0xWgvLxixYXjjb2FQZPYJ4qIiIiarzNnziAtLU2rPDY2Fn5+f6x9MtY+UI/DEEVERESNTtfo0zvvvIO2bdsiIwO4dQvo3t24Wxg8jtGfnUdERETNi64AFRsbi++/bws/P2D2bCAvD2jd2nRHoQCORBEREVEjUalU2LVrl0aZRCJBTEwMAIjbF3TvDvj6mvYoFMAQRURERI1A1+jT/v3vYtq01uL34eEw2mLy+mCIIiIiogalK0Bt3BiLvLyqXcfT0qpCU/WrqeCaKCIiImoQmzZtqnH90/8/rx737pnI7uP1wJEoIiIiMjhd4emZZ6bj449l8PICPvywagG5RGL6a59qwpEoMkmhoaEICAgQvx88eDCmT5/+RNc0xDWIiKh2giDUOPr08ccyjb2fzpwBTp9uWlN4D+NIFNXJw4/Dsba2RseOHREcHIzIyEhYWTXcf07btm2DtbW1XnX37duHIUOG4Pbt23B0dKzXNYiIqO4+/fRTqNVqrfKsrFh4eWkuHDcHDFFUZ8OGDcOaNWtQVlaGnTt34u2334a1tTUiIiI06pWXl8PGxsYg92zTpo1JXIOIiHTTNfo0Y8YM/O1vLcTRpwMHmu6oky6czqM6s7W1hYuLCzp16oTJkydDqVQiIyNDnIJbsGABXF1d0a1bNwDApUuX8I9//AOOjo5o06YNRo0ahYKCAvF6FRUVCAsLg6OjI9q2bYvZs2fj0Uc6PjoVV1ZWhjlz5sDNzQ22trbo0qULVq1ahYKCAgwZMgQA0Lp1a0gkEoSGhuq8xu3btxEcHIzWrVvD3t4ew4cPx9mzZ8XjqampcHR0RFZWFnr06IEWLVpg2LBhuHbtmlhn37596NevHxwcHODo6Ii//OUv+OWXXwz0kyYiMn0VFRU1Tt+1aNEC4eFNY8+n+mCIoidmZ2eH8vJyAEB2djby8vKwe/du7NixA/fv34e/vz9atmyJ7777Dt9//70YRqrPWbhwIVJTU7F69Wr873//w61bt/Cf//yn1nsGBwdj48aNSE5OxunTp/Hpp5+iRYsWcHNzw9atWwEAeXl5uHbtGpYsWaLzGqGhoTh8+DAyMjKgUqkgCAL+/ve/4/79+2Kdu3fvIikpCevWrcP+/ftx8eJFzJw5EwDw4MEDBAQEYNCgQThx4gRUKhUmTZoESfVHToiIzFxcXBzmz5+vVR4bGyv+75EjzW8Eqhqn86jeBEFAdnY2srKyMHXqVNy4cQMODg74/PPPxWm8L7/8EpWVlfj888/FcLFmzRo4Ojpi3759+Nvf/obFixcjIiICL7/8MgAgJSUFWVlZNd73559/xubNm7F7924olUoAwNNPPy0er562a9++vcaaqIedPXsWGRkZ+P777+Hn5wcAWL9+Pdzc3JCeno5XX30VAHD//n2kpKSgc+fOAKqe6zRv3jwAVU/5Li4uxgsvvCAe79GjR91/kERETZCu0afk5HCsWWNrhNYYB0eizEBGBuDnV/W1MezYsQMtWrSAVCrF8OHDERgYiLlz5wIAevXqpbEO6vjx4zh37hxatmyJFi1aoEWLFmjTpg3u3buH8+fPo7i4GNeuXYOPj494jpWVFfr06VPj/Y8dOwZLS0sMGjSo3n04ffo0rKysNO7btm1bdOvWDadPnxbL7O3txYAEAHK5HNevXwdQFdZCQ0Ph7++PF198EUuWLNGY6iMiMkfl5eU6A1RWVizWrLE1yxGnmnAkygxUP2uo+iOjDW3IkCFYsWIFbGxs4OrqqvGpPAcHB426d+7cgbe3N9avX691nXbt2tXr/nZ2dvU6rz4e/TSfRCLRWK+1Zs0avPvuu8jMzERaWhqioqKwe/du9O/fv9HaSETUWHSFJ6Bq+u6hGbxmgyNRZqCxF+05ODigS5cu6Nix42O3NXj22Wdx9uxZtG/fHl26dNF4yWQyyGQyyOVy5OTkiOc8ePAAR44cqfGavXr1QmVlJb799ludx6tHwioqKmq8Ro8ePfDgwQON+/7666/Iy8vDn/70p1r79CgvLy9ERETgwIED6NmzJzZs2FCn84mImgJdASoyMlJj/VNzwxBlBkx50d4///lPODk5YdSoUfjuu++Qn5+Pffv24d1338Xly5cBANOmTUNCQgLS09Nx5swZTJkyBUVFRTVe093dHSEhIRg/fjzS09PFa27evBkA0KlTJ0gkEuzYsQM3btzAnTt3tK7h4eGBUaNGYeLEifjf//6H48eP4/XXX0eHDh0watQovfqWn5+PiIgIqFQq/PLLL9i1axfOnj3LdVFEZFbu3r1b46fvmvveewxR1KDs7e2xf/9+dOzYES+//DJ69OiBCRMm4N69e2jVqhWAqn1Exo4di5CQEPj6+qJly5Z46aWXar3uihUrMHr0aEyZMgXdu3fHxIkTUVpaCgDo0KED4uLiEB4eDmdnZ7zzzjs6r7FmzRp4e3vjhRdegK+vLwRBwM6dO/V+U7C3t8eZM2fwyiuvoGvXrpg0aRLefvttvPnmm3X4CRERma64uDh89NFHWuXNefTpYRLh0Q15yGBKSkogk8lQXFwsBgYAuHfvHvLz86FQKCCVSo3YQmps/N0TUVOha/QpOjoaFhbmP/5S09/vR3FhOREREQGo+pT3kiW3MHDgUq1jHH3SxhBFREREAIDc3DgMHKhdXl7OAKULQxQRERHpnL6LiYnhUxhqwRBFRETUjK1fn49z59ZqlXP67vEYooyIa/qbH/7OiciU1LR5ppcXA5Q+GKKMoPoj9Hfv3m3U3bfJ+O7evQtAeyd0IqLGlJFRtf7pUV5esSa556CpYogyAktLSzg6OorPYLO3t+ecs5kTBAF3797F9evX4ejoCEtLS2M3iYiaqYMHDyI392utck7f1R1DlJG4uLgAgBikqHlwdHQUf/dERI2N03eGxRBlJBKJBHK5HO3bt8f9+/eN3RxqBNbW1hyBIiKjqenRLVR/DFFGZmlpyT+sRETUYHbu3IlDhw5plTNAPTmGKCIiIjNV0/QdA5RhMEQRERGZkYwMICEB8Pfn9F1DM4mnCC5fvhzu7u6QSqXw8fHBwYMHa62/ZcsWdO/eHVKpFL169cLOnTs1jguCgJiYGMjlctjZ2UGpVOLs2bM6r1VWVgZPT09IJBIcO3ZM49iJEyfw3HPPQSqVws3NDYmJiU/UTyIiooa2e/c6BqhGYvQQlZaWhrCwMMTGxuLo0aPo3bs3/P39a/zU2oEDBxAUFIQJEyYgNzcXAQEBCAgIwMmTJ8U6iYmJSE5ORkpKCnJycuDg4AB/f3/cu3dP63qzZ8+Gq6urVnlJSQn+9re/oVOnTjhy5Ag++ugjzJ07FytXrjRc54mIiAwoLi4OTk4XNMocHR0ZoBqIRDDyFso+Pj7o27cvli1bBgCorKyEm5sbpk6divDwcK36gYGBKC0txY4dO8Sy/v37w9PTEykpKRAEAa6urpgxYwZmzpwJACguLoazszNSU1MxZswY8byvv/4aYWFh2Lp1K5555hnk5ubC09MTALBixQq89957UKvVsLGxAQCEh4cjPT0dZ86c0atvJSUlkMlkKC4uRqtWrer18yEiIqoNp+8MT9+/30YdiSovL8eRI0egVCrFMgsLCyiVSqhUKp3nqFQqjfoA4O/vL9bPz8+HWq3WqCOTyeDj46NxzcLCQkycOBHr1q2Dvb29zvsMHDhQDFDV98nLy8Pt27fr12EiIiID++GHJAYoIzHqwvKbN2+ioqICzs7OGuXOzs41jvao1Wqd9dVqtXi8uqymOoIgIDQ0FG+99Rb69OmDgoICnfdRKBRa16g+1rp1a61zysrKUFZWJn5fUlKisw9ERESGEBcXB1tbzbKuXbsiKCjIOA1qZprlp/OWLl2K3377DREREQa9bnx8fI0fJyUiIjIkbp5pfEadznNycoKlpSUKCws1ygsLC2t8NIaLi0ut9au/1lZnz549UKlUsLW1hZWVFbp06QIA6NOnD0JCQmq9z8P3eFRERASKi4vF16VLl2r/ARAREdVRXFwcA5SJMGqIsrGxgbe3N7Kzs8WyyspKZGdnw9fXV+c5vr6+GvUBYPfu3WJ9hUIBFxcXjTolJSXIyckR6yQnJ+P48eM4duwYjh07Jm6RkJaWhgULFoj32b9/v8YjWXbv3o1u3brpnMoDAFtbW7Rq1UrjRUREVF8ZGYCfX9VXQPfok6+vLwOUkRh9Oi8sLAwhISHo06cP+vXrh8WLF6O0tBTjxo0DAAQHB6NDhw6Ij48HAEybNg2DBg3CwoULMWLECGzatAmHDx8Wtx6QSCSYPn065s+fDw8PDygUCkRHR8PV1RUBAQEAgI4dO2q0oUWLFgCAzp0746mnngIAvPbaa4iLi8OECRMwZ84cnDx5EkuWLMGiRYsa48dCRESEhARApar6mpvL0SdTY/QQFRgYiBs3biAmJgZqtRqenp7IzMwUF3FfvHgRFhZ/DJj5+flhw4YNiIqKQmRkJDw8PJCeno6ePXuKdWbPno3S0lJMmjQJRUVFGDBgADIzMyGVSvVul0wmw65du/D222/D29sbTk5OiImJwaRJkwzXeSIiohpkZAC3bgFz5/LRLabK6PtEmTPuE0VERPXl56d776fhw4ejX79+RmhR86Hv32+jj0QRERGRNu79ZPoYooiIiExAbTuPAwxQpoghioiIyATMng0EBWkHqMDAQHTv3t0ILaLHYYgiIiIyMkEQEBQ0T6uco0+mjSGKiIjICDh91/QxRBERETWy996rClAxMdoBavz48XBzczNCq6iuGKKIiIga2Sef3EdMzAda5Rx9aloYooiIiBpRXFwcpk/XLmeAanqM+uw8IiIic6bPs++mTp3KANVEcSSKiIiogVQ/+27hwlLk5iZpHWd4atoYooiIiBpIeLjuBwcDDFDmgCGKiIiogegKUDNnzoSDg4MRWkOGxhBFRERkYLdu3cLSpUu1yjn6ZF4YooiIiAxI1+JxgAHKHDFEERERGYiuAPXee+/Byop/bs0Rf6tERERP6NKlS1i9erVWOUefzBtDFBER0RPg9F3zxRBFRERUT7oCVExMDCQSiRFaQ42NIYqIiKiOfvrpJ/z73//WKufoU/PCEEVERFQHnL6jagxRREREetIVoBiemi+GKCIiosc4cOAAdu/erVXOANW8MUQRERHVgtN3VBOGKCIioodkZAAJCTU/PJjhiaoxRBERET0kIQFo2/Yr5OYe1TrGAEUPszB2A4iIiIwtIwPw86v66u8fhz59GKDo8TgSRUREzV5CAqBScfqO6oYhioiImr2XX14Df/+LWuUMUFQbhigiImp2Hrd43NLSElFRUUZoGTUlDFFERNTscPqODIEhioiImp3hwxPg71+mVc4ARXXBEEVERM1C9RSev7/26JOrqysmTpxohFZRU8YQRUREzUJNAYqjT1RfRt8navny5XB3d4dUKoWPjw8OHjxYa/0tW7age/fukEql6NWrF3bu3KlxXBAExMTEQC6Xw87ODkqlEmfPntWoM3LkSHTs2BFSqRRyuRxjx47F1atXNepkZWWhf//+aNmyJdq1a4dXXnkFBQUFBukzERE1vIf3foqLi2OAIoMzaohKS0tDWFgYYmNjcfToUfTu3Rv+/v64fv26zvoHDhxAUFAQJkyYgNzcXAQEBCAgIAAnT54U6yQmJiI5ORkpKSnIycmBg4MD/P39ce/ePbHOkCFDsHnzZuTl5WHr1q04f/48Ro8eLR7Pz8/HqFGjMHToUBw7dgxZWVm4efMmXn755Yb7YRARkUHVtni8d+/eDFD0xCSCIAjGurmPjw/69u2LZcuWAQAqKyvh5uaGqVOnIjw8XKt+YGAgSktLsWPHDrGsf//+8PT0REpKCgRBgKurK2bMmIGZM2cCAIqLi+Hs7IzU1FSMGTNGZzsyMjIQEBCAsrIyWFtb49///jeCgoJQVlYGC4uqnPnVV19h1KhRYh19lJSUQCaTobi4GK1atarTz4aIiJ5MRgY/fUf1o+/fb6ONRJWXl+PIkSNQKpV/NMbCAkqlEiqVSuc5KpVKoz4A+Pv7i/Xz8/OhVqs16shkMvj4+NR4zVu3bmH9+vXw8/MTw5G3tzcsLCywZs0aVFRUoLi4GOvWrYNSqaw1QJWVlaGkpETjRUREjS8uLo4Bihqc0ULUzZs3UVFRAWdnZ41yZ2dnqNVqneeo1epa61d/1eeac+bMgYODA9q2bYuLFy9i+/bt4jGFQoFdu3YhMjIStra2cHR0xOXLl7F58+Za+xQfHw+ZTCa+3Nzcaq1PRESGFxenHZ6GDh3KAEUGZ/SF5cYya9Ys5ObmYteuXbC0tERwcDCqZzbVajUmTpyIkJAQHDp0CN9++y1sbGwwevRo1Db7GRERgeLiYvF16dKlxuoOERFBd4CKjY3Fc889Z4TWkLkz2hYHTk5OsLS0RGFhoUZ5YWEhXFxcdJ7j4uJSa/3qr4WFhZDL5Rp1PD09te7v5OSErl27okePHnBzc8MPP/wAX19fLF++HDKZDImJiWL9L7/8Em5ubsjJyUH//v11ts/W1ha2trb6/QCIiOiJPO7RLQCn76hhGW0kysbGBt7e3sjOzhbLKisrkZ2dDV9fX53n+Pr6atQHgN27d4v1FQoFXFxcNOqUlJQgJyenxmtW3xeoWtMEAHfv3hUXlFeztLTUqEtERMZRvXXBnDk1f/pu9OjRDFDU4Iy62WZYWBhCQkLQp08f9OvXD4sXL0ZpaSnGjRsHAAgODkaHDh0QHx8PAJg2bRoGDRqEhQsXYsSIEdi0aRMOHz6MlStXAgAkEgmmT5+O+fPnw8PDAwqFAtHR0XB1dUVAQAAAICcnB4cOHcKAAQPQunVrnD9/HtHR0ejcubMYtEaMGIFFixZh3rx5CAoKwm+//YbIyEh06tQJXl5ejf+DIiIiUfXWBd26CZg7d57WcYYnaixGDVGBgYG4ceMGYmJioFar4enpiczMTHFh+MWLFzVGhPz8/LBhwwZERUUhMjISHh4eSE9PR8+ePcU6s2fPRmlpKSZNmoSioiIMGDAAmZmZkEqlAAB7e3ts27YNsbGxKC0thVwux7BhwxAVFSVOxQ0dOhQbNmxAYmIiEhMTYW9vD19fX2RmZsLOzq4Rf0JERPQoTt+RqTDqPlHmjvtEEREZnq7F4+PGjUPHjh2N0BoyR/r+/eaz84iIqEl48OABFixYoFXO0ScyFoYoIiIyebpGnwAGKDIuhigiIjJpugLUlClT0K5dOyO0hugPDFFERGSSfv/9d439+qpx9IlMBUMUERGZHE7fUVPAEEVERCZFV4CaOXMmHBwcjNAaopoxRBERkUm4ffs2kpOTtco5+kSmiiGKiIiMjtN31BQxRBERkVHpClCRkZGwtrY2QmuI9McQRURERnH58mWsWrVKq5yjT9RUMEQREVGj4/QdmQOGKCIialS6AlR0dLTGA+eJmgKGKCIiahRnzpxBWlqaVjlHn6ipYogiIqIGx+k7MkcMUURE1KB0BSiGJzIHDFFERNQgcnJykJmZqVXOAEXmgiGKiIgMIiMDSEgAwsOB3FxO35H5Y4giIiKDSEgAVCrdAYrhicwRQxQRERnE669/DX//g1rlDFBkrhiiiIjoifHTd9QcMUQREdET4afvqLliiCIionpZt24dLly4oFXOAEXNBUMUERHVGafviBiiiIiojjh9R1SFIYqIiPQyd24SJJJSrXIGKGquGKKIiOix4uLiIJFolrVv3x6TJ082ToOITECdQ5SlpSWuXbuG9u3ba5T/+uuvaN++PSoqKgzWOCIiMj5O3xHpVucQJQiCzvKysjLY2Ng8cYOIiMg0cPE4Ue30DlHJyckAAIlEgs8//xwtWrQQj1VUVGD//v3o3r274VtIRESNTleAatPGE1OnjjJCa4hMk94hatGiRQCqRqJSUlJgaWkpHrOxsYG7uztSUlIM30IiImpUnL4j0o/eISo/Px8AMGTIEGzbtg2tW7dusEYREVHj4/QdUd3UeU3U3r17G6IdRERkRLoC1NChQ/Hcc88ZoTVETUOdQ9T48eNrPb569ep6N4aIiBofp++I6seirifcvn1b43X9+nXs2bMH27ZtQ1FRUZ0bsHz5cri7u0MqlcLHxwcHDx6stf6WLVvQvXt3SKVS9OrVCzt37tQ4LggCYmJiIJfLYWdnB6VSibNnz2rUGTlyJDp27AipVAq5XI6xY8fi6tWrWtdJSkpC165dYWtriw4dOmDBggV17h8RkamKi4tjgCJ6AnUeifrPf/6jVVZZWYnJkyejc+fOdbpWWloawsLCkJKSAh8fHyxevBj+/v7Iy8vT2ocKAA4cOICgoCDEx8fjhRdewIYNGxAQEICjR4+iZ8+eAIDExEQkJyfjiy++gEKhQHR0NPz9/XHq1ClIpVIAVeu6IiMjIZfLceXKFcycOROjR4/GgQMHxHtNmzYNu3btQlJSEnr16oVbt27h1q1bdeofEZGp0hWeXn75ZfTq1csIrSFqmiRCTRs/1VFeXh4GDx6Ma9eu6X2Oj48P+vbti2XLlgGoCmNubm6YOnUqwsPDteoHBgaitLQUO3bsEMv69+8PT09PpKSkQBAEuLq6YsaMGZg5cyYAoLi4GM7OzkhNTcWYMWN0tiMjIwMBAQEoKyuDtbU1Tp8+jT//+c84efIkunXrVpcfg4aSkhLIZDIUFxejVatW9b4OEZGhCIKAefPmaZVz9InoD/r+/a7zdF5Nzp8/jwcPHuhdv7y8HEeOHIFSqfyjMRYWUCqVUKlUOs9RqVQa9QHA399frJ+fnw+1Wq1RRyaTwcfHp8Zr3rp1C+vXr4efnx+sra0BAF999RWefvpp7NixAwqFAu7u7njjjTceOxJVVlaGkpISjRcRkamIi4tjgCIyoDpP54WFhWl8LwgCrl27hv/+978ICQnR+zo3b95ERUUFnJ2dNcqdnZ1x5swZneeo1Wqd9dVqtXi8uqymOtXmzJmDZcuW4e7du+jfv7/G6NaFCxfwyy+/YMuWLVi7di0qKirwr3/9C6NHj8aePXtq7FN8fHyNHxEmIjImXe9NISEhcHd3b/zGEJmJOo9E5ebmarxOnDgBAFi4cCEWL15s6PY1mFmzZiE3Nxe7du2CpaUlgoODxUfaVFZWoqysDGvXrsVzzz2HwYMHY9WqVdi7dy/y8vJqvGZERASKi4vF16VLlxqrO0REOlVUVNS4eJwBiujJGG2fKCcnJ1haWqKwsFCjvLCwEC4uLjrPcXFxqbV+9dfCwkLI5XKNOp6enlr3d3JyQteuXdGjRw+4ubnhhx9+gK+vL+RyOaysrNC1a1exfo8ePQAAFy9erHGdlK2tLWxtbfXoPRFRw8rIAHJzuXkmUUOq95qo69ev47vvvsN3332H69ev1/l8GxsbeHt7Izs7WyyrrKxEdnY2fH19dZ7j6+urUR8Adu/eLdZXKBRwcXHRqFNSUoKcnJwar1l9X6BqTRMA/OUvf8GDBw9w/vx5sc7PP/8MAOjUqVNduklEZBS6AtSUKVMYoIgMqM4jUSUlJXj77bexceNGMXxYWloiMDAQy5cvh0wm0/taYWFhCAkJQZ8+fdCvXz8sXrwYpaWlGDduHAAgODgYHTp0QHx8PICqbQcGDRqEhQsXYsSIEdi0aRMOHz6MlStXAqh6OPL06dMxf/58eHh4iFscuLq6IiAgAACQk5ODQ4cOYcCAAWjdujXOnz+P6OhodO7cWQxaSqUSzz77LMaPH4/FixejsrISb7/9Nv76179qjE4REZmasrIyJCQkaJUzPBEZXp1D1MSJE5Gbm4v//ve/YuhQqVSYNm0a3nzzTWzatEnvawUGBuLGjRuIiYmBWq2Gp6cnMjMzxYXhFy9ehIXFH4Nlfn5+2LBhA6KiohAZGQkPDw+kp6eLe0QBwOzZs1FaWopJkyahqKgIAwYMQGZmprhHlL29PbZt24bY2FiUlpZCLpdj2LBhiIqKEqfiLCws8NVXX2Hq1KkYOHAgHBwcMHz4cCxcuLCuPy4iokbDZ98RNa467xPl4OCArKwsDBgwQKP8u+++w7Bhw1BaWmrQBjZl3CeKiBqLrgA1c+ZMODg4GKE1RE2bvn+/6zwS1bZtW51TdjKZDK1bt67r5YiI6AmUlJRg0aJFWuUcfSJqeHUOUVFRUQgLC8O6devET8Op1WrMmjUL0dHRBm8gERHpxuk7IuOq83Sel5cXzp07h7KyMnTs2BFA1dolW1tbeHh4aNQ9evSo4VraBHE6j4gaiq4AFRERARsbGyO0hsi8NNh03qhRoyCRSJ6ocUREVD+FhYVISUnRKufoE1HjM9gDiEkbR6KIyJA4fUfUOBpsJOrpp5/GoUOH0LZtW43yoqIiPPvss7hw4ULdW0tERLXSFaCio6M1toEhosZV5xBVUFCAiooKrfKysjJcvnzZII0iIqIq58+fx5dffqlVztEnIuPTO0RlZGSI/zsrK0tjm4OKigpkZ2dDoVAYtnVERM0Yp++ITJveIar6sSkSiQQhISEax6ytreHu7s4dvYmIDERXgIqJieEHe4hMiN4hqvo5eQqFAocOHYKTk1ODNYqIqLk6duwYtm/frlWelRULDkARmZY6r4nKz89viHYQETV7NU3fZWXFIjy8kRtDRI9V5xA1b968Wo/HxMTUuzFERM2VrgBVvfaJI1BEpqnOIeo///mPxvf3799Hfn4+rKys0LlzZ4YoIqI6yMzMRE5OjlY5F48Tmb46h6jc3FytspKSEoSGhuKll14ySKOIiJqDmqbvNm7k+ieipsAgu7S1atUKcXFxfAAxEZGedAUoL69YZGXFIjHRCA0iojqr80hUTYqLi1FcXGyoyxERmaW0tDScOXNGq7x6+m7kyMZuERHVV51DVHJyssb3giDg2rVrWLduHYYPH26whhERmRtunklkXuocohYtWqTxvYWFBdq1a4eQkBBEREQYrGFEROakpuk7jjwRNV3cJ4qIqAF9+umnUKvVWuVz58bC15fTd0RNWb3WRBUVFeHcuXMAgC5dusDR0dGQbSIiMgu6Rp86dOiA9u3fgK8vuIEmURNXpxBVUFCAt99+G1lZWRAEAUDVs/SGDRuGZcuWwd3dvSHaSETU5NS2eSbAESgic6B3iLp06RL69+8Pa2trvP/+++jRowcA4NSpU1ixYgV8fX1x6NAhPPXUUw3WWCIiUzdv3jzxH5kP4+JxIvMjEXT9v12HCRMm4Ny5c8jKyoJUKtU49vvvv2PYsGHw8PDA559/3iANbYpKSkogk8lQXFyMVq1aGbs5RNTAdI0+9e7dGwEBAY3fGCKqN33/fus9EpWZmYm0tDStAAUAdnZ2eP/99zFmzJj6tZaIqIl73PQdEZkfvUPUzZs3a13z9PTTT+PWrVuGaBMRUZPBvZ+Imi+9H/sil8tx6tSpGo+fPHkSLi4uBmkUEVFToCtA7d6tRFYWAxRRc6B3iAoICMDMmTNx48YNrWPXr1/HnDlzOO9PRM2GrgBVXh6Lysq/cOsComZC74Xlt2/fho+PD9RqNV5//XV0794dgiDg9OnT2LBhA1xcXPDDDz+gTZs2Dd3mJoMLy4nMT03Td9WbZx440MgNIiKDM/jC8tatWyMnJweRkZHYtGkTioqKAACOjo547bXX8MEHHzBAEZFZ0xWgAgIC8Msvvbl5JlEzpPdI1MMEQRCn9dq1aweJRGLwhpkDjkQRmQdBEDBv3jytci4eJzJPBh+JephEIkH79u3r3TgioqaCn74joprUK0QRETUHugJUcHAwFAqFEVpDRKZG70/nERE1F5WVlToDVFZWLH78kQGKiKqYRIhavnw53N3dIZVK4ePjg4MHD9Zaf8uWLejevTukUil69eqFnTt3ahwXBAExMTGQy+Wws7ODUqnE2bNnNeqMHDkSHTt2hFQqhVwux9ixY3H16lWd9zt37hxatmwJR0fHJ+onEZm+uLg4vP/++1rlSUmxUKmAhAQjNIqITJLRQ1RaWhrCwsIQGxuLo0ePonfv3vD398f169d11j9w4ACCgoIwYcIE5ObmIiAgAAEBATh58qRYJzExEcnJyUhJSUFOTg4cHBzg7++Pe/fuiXWGDBmCzZs3Iy8vD1u3bsX58+cxevRorfvdv38fQUFBeO655wzfeSIyKbpGnyZPnoysrFjcuQO0aMFP4BHRH/T6dF5ycrLeF3z33Xfr1AAfHx/07dsXy5YtA1A1jO7m5oapU6ciXMe7VWBgIEpLS7Fjxw6xrH///vD09ERKSgoEQYCrqytmzJiBmTNnAgCKi4vh7OyM1NTUGp/vl5GRgYCAAJSVlcHa2losnzNnDq5evYrnn38e06dPF7d20Ac/nUfUNJSVlSFBxxBT9eLxjIyqEajwcGDkyMZuHRE1NoN+Om/RokV63VQikdQpRJWXl+PIkSOIiIgQyywsLKBUKqFSqXSeo1KpEBYWplHm7++P9PR0AEB+fj7UajWUSqV4XCaTwcfHByqVSmeIunXrFtavXw8/Pz+NALVnzx5s2bIFx44dw7Zt2x7bn7KyMpSVlYnfl5SUPPYcIjIufT59N3IkwxMRadMrROXn5zfIzW/evImKigo4OztrlDs7O+PMmTM6z1Gr1Trrq9Vq8Xh1WU11qs2ZMwfLli3D3bt30b9/f43RrV9//RWhoaH48ssv9R5Fio+Pr/ENmYhMj67/v4aFhaFly5ZGaA0RNTX1XhNVXl6OvLw8PHjwwJDtaVSzZs1Cbm4udu3aBUtLSwQHB6N6dnPixIl47bXXMHDgQL2vFxERgeLiYvF16dKlhmo6ET2B3377TWeA8vKKhb9/S2RkGKFRRNTk1HmfqLt372Lq1Kn44osvAAA///wznn76aUydOhUdOnTQuY6pJk5OTrC0tERhYaFGeWFhIVxcXHSe4+LiUmv96q+FhYWQy+UadTw9PbXu7+TkhK5du6JHjx5wc3PDDz/8AF9fX+zZswcZGRlISkoCUPWJv8rKSlhZWWHlypUYP368VttsbW1ha2urd/+JqPHVNn3n5wfxE3icviOix6nzSFRERASOHz+Offv2QSqViuVKpRJpaWl1upaNjQ28vb2RnZ0tllVWViI7Oxu+vr46z/H19dWoDwC7d+8W6ysUCri4uGjUKSkpQU5OTo3XrL4vAHFNk0qlwrFjx8TXvHnz0LJlSxw7dgwvvfRSnfpJRKZBV4AKDw8X1z+Fh4PPwCMivdV5JCo9PR1paWno37+/xjPznnnmGZw/f77ODQgLC0NISAj69OmDfv36YfHixSgtLcW4ceMAVO0O3KFDB8THxwMApk2bhkGDBmHhwoUYMWIENm3ahMOHD2PlypUAqha3T58+HfPnz4eHhwcUCgWio6Ph6uqKgIAAAEBOTg4OHTqEAQMGoHXr1jh//jyio6PRuXNnMWj16NFDo52HDx+GhYUFevbsWec+EpFx3bhxA5988olW+aOPbuECciKqizqHqBs3buh8bl5paWm9HkQcGBiIGzduICYmBmq1Gp6ensjMzBQXhl+8eBEWFn8MmPn5+WHDhg2IiopCZGQkPDw8kJ6erhFuZs+ejdLSUkyaNAlFRUUYMGAAMjMzxZEze3t7bNu2DbGxsSgtLYVcLsewYcMQFRXF6TgiM8Nn3xFRQ9Frn6iHDRw4EK+++iqmTp2Kli1b4sSJE1AoFJg6dSrOnj2LzMzMhmprk8N9ooiMS1eAio6O1viHGRHRowy6T9TDPvjgAwwfPhynTp3CgwcPsGTJEpw6dQoHDhzAt99++0SNJiIyhPz8fKxdu1arnKNPRGRIdf7n2IABA3Ds2DE8ePAAvXr1wq5du9C+fXuoVCp4e3s3RBuJiPQWFxfHAEVEjaLO03mkP07nETUuXdN3MTEx9VqvSUTNl0Gn8+ry+BKGBSJqbCdOnMB//vMfrXKOPhFRQ9IrRDk6Our9L7mKioonahARUV3w03dEZCx6hai9e/eK/7ugoADh4eEIDQ0V91RSqVT44osvxL2ciIgag64AxfBERI2lzmuinn/+ebzxxhsICgrSKN+wYQNWrlyJffv2GbJ9TRrXRBE1jP3792v8464aAxQRGYK+f7/r/Ok8lUqFPn36aJX36dMHBw8erOvliIjqJC4uTmeAysqK5YODiahR1TlEubm54bPPPtMq//zzz+Hm5maQRhER6VLT9F1WVqz44GAiosZS5802Fy1ahFdeeQVff/01fHx8AAAHDx7E2bNnsXXrVoM3kIho+/btOHbsmFb5ww8OTkjgg4OJqHHVa5+oy5cv45NPPsGZM2cAVD2s96233uJI1CO4JoroyfHTd0TU2PT9+83NNhsQQxTRk+Gn74jIGBrs2XkAUFRUhFWrVuH06dMAgGeeeQbjx4+HTCarX2uJiB7y+eef48qVK1rlDFBEZErqvLD88OHD6Ny5MxYtWoRbt27h1q1b+Pjjj9G5c2ccPXq0IdpIRM1IXFycVoBydnZmgCIik1Pn6bznnnsOXbp0wWeffQYrq6qBrAcPHuCNN97AhQsXsH///gZpaFPE6TyiuuH0HRGZggZbE2VnZ4fc3Fx0795do/zUqVPo06cP7t69W78WmyGGKCL9LFy4EHfu3NEqZ4AiImNosDVRrVq1wsWLF7VC1KVLl9CyZcu6t5SImjVdo089e/bEK6+8YoTWEBHpr84hKjAwEBMmTEBSUhL8/PwAAN9//z1mzZql9SgYIqLacPqOiJqyOoeopKQkSCQSBAcH48GDBwAAa2trTJ48GQncLpiI9MC9n4jIHNR7n6i7d+/i/PnzAIDOnTvD3t7eoA0zB1wTRaRNV4AaMmQIBg4ciIyMP3YeHznSCI0jIgI32zQJDFFEmh43fefnB6hUgK8vcOBAY7aMiOgPBl9YPn78eL3qrV69Wt9LElEzoe/0HZ+BR0RNid4hKjU1FZ06dYKXlxc4eEVE+tIVoF555RVcuNATfn6aU3cjR3Iaj4iaDr1D1OTJk7Fx40bk5+dj3LhxeP3119GmTZuGbBsRNWGCIGDevHla5dWjT5MmVU3dJSQwOBFR06T3Y1+WL1+Oa9euYfbs2fjqq6/g5uaGf/zjH8jKyuLIFBFpiIuLqzFAZWRUrX0aMqRq7ROn7oioqar3wvJffvkFqampWLt2LR48eICffvoJLVq0MHT7mjQuLKfmSNf0XWhoKDp16gSAi8eJyPTp+/e7zg8gFk+0sIBEIoEgCKioqKjvZYjITFRWVtb46bvqAAVUjTxxBIqIzEGdNtssKyvDtm3bsHr1avzvf//DCy+8gGXLlmHYsGGwsKh3HiOiJq4um2dy8TgRmQu9Q9SUKVOwadMmuLm5Yfz48di4cSOcnJwasm1E1AToClBTpkxBu3btjNAaIqLGo/eaKAsLC3Ts2BFeXl6QSCQ11tu2bZvBGtfUcU0UmbP79+/jgw8+0Crno1uIqKkz+GabwcHBtYYnImo++Ow7IqI6brZJRKQrQM2YMYOfziWiZsckVoMvX74c7u7ukEql8PHxwcGDB2utv2XLFnTv3h1SqRS9evXCzp07NY4LgoCYmBjI5XLY2dlBqVTi7NmzGnVGjhyJjh07QiqVQi6XY+zYsbh69ap4fN++fRg1ahTkcjkcHBzg6emJ9evXG67TRE3M77//rjNAeXnF4m9/a4GMDCM0iojIiIweotLS0hAWFobY2FgcPXoUvXv3hr+/P65fv66z/oEDBxAUFIQJEyYgNzcXAQEBCAgIwMmTJ8U6iYmJSE5ORkpKCnJycuDg4AB/f3/cu3dPrDNkyBBs3rwZeXl52Lp1K86fP4/Ro0dr3OfPf/4ztm7dihMnTmDcuHEIDg7Gjh07Gu6HQWSi4uLikJiYqFUeGxuLhIQ/dh4nImpO6r3ZpqH4+Pigb9++WLZsGYCqvWbc3NwwdepUhOvYSCYwMBClpaUaYaZ///7w9PRESkoKBEGAq6srZsyYgZkzZwIAiouL4ezsjNTUVIwZM0ZnOzIyMhAQEICysjJYW1vrrDNixAg4Ozvr/ZBlLiwnc6Br9OnPf47ARx/ZiHs9VT80mFsXEJE5aPDNNg2hvLwcR44cgVKpFMssLCygVCqhUql0nqNSqTTqA4C/v79YPz8/H2q1WqOOTCaDj49Pjde8desW1q9fDz8/vxoDFFAVxvi8QGouioqKatw886OPbKBSAf/8Z1XZgQMMUETU/Bg1RN28eRMVFRVwdnbWKHd2doZardZ5jlqtrrV+9Vd9rjlnzhw4ODigbdu2uHjxIrZv315jWzdv3oxDhw5h3LhxNdYpKytDSUmJxouoKYqLi8OSJUu0yqs/fRceDrRoAdy5w2k8Imq+jL4myphmzZqF3Nxc7Nq1C5aWlggODtb5MOW9e/di3Lhx+Oyzz/DMM8/UeL34+HjIZDLx5ebm1pDNJ2oQukafoqOjNbYvGDkSWL+ej28houatTo99MTQnJydYWlqisLBQo7ywsBAuLi46z3Fxcam1fvXXwsJCyOVyjTqenp5a93dyckLXrl3Ro0cPuLm54YcffoCvr69Y59tvv8WLL76IRYsWITg4uNb+REREICwsTPy+pKSEQYqaDLVajU8//VSrvKa9n/j4FiJq7ow6EmVjYwNvb29kZ2eLZZWVlcjOztYIMg/z9fXVqA8Au3fvFusrFAq4uLho1CkpKUFOTk6N16y+L1A1JVdt3759GDFiBD788ENMmjTpsf2xtbVFq1atNF5ETUFcXFydAhQRERl5JAoAwsLCEBISgj59+qBfv35YvHgxSktLxbVHwcHB6NChA+Lj4wEA06ZNw6BBg7Bw4UKMGDECmzZtwuHDh7Fy5UoAgEQiwfTp0zF//nx4eHhAoVAgOjoarq6uCAgIAADk5OTg0KFDGDBgAFq3bo3z588jOjoanTt3FoPW3r178cILL2DatGl45ZVXxPVUNjY2XFxOZkXX9F1MTAyfUEBE9BhGD1GBgYG4ceMGYmJioFar4enpiczMTHFh+MWLF2Fh8ceAmZ+fHzZs2ICoqChERkbCw8MD6enp6Nmzp1hn9uzZKC0txaRJk1BUVIQBAwYgMzMTUqkUAGBvb49t27YhNjYWpaWlkMvlGDZsGKKiomBrawsA+OKLL3D37l3Ex8eLAQ4ABg0ahH379jXCT4aoYV26dEnndh0cfSIi0o/R94kyZ9wnikwVn31HRFQzgz+AmIjMQ017PwFARgY3ziQi0lez3uKAqDnJy8urNUAB4CNciIjqgCGKqBmIi4vDpk2bNMrat28PL69Y+PlBfHhweDj3fiIi0hfXRDUgrokiU1Db6JOfX9XIk69v1aNbiIiIa6KImr3jx48jPT1dq/zh6bvw8D/WQBERUd0wRBGZIV2jT56enhg1apRGGXcdJyKqP4YoIjOjK0B5ecUiIQGQSBiaiIgMhSGKyEwcPHgQX3/9tVZ5bGysuPYpIYEhiojIUBiiiMyArtGn5557DkOHDgXAtU9ERA2BIYqoidMVoLKyYuHl9cf3XPtERGR4DFFETdSePXvw3XffaZVnZcVy6o6IqBEwRBE1QbpGn0aOHAkvLy94eXHqjoioMTBEETUxj3t0C6fuiIgaB0MUURORnp6O48ePa5U/HKCIiKjxMEQRNQG6Rp+CgoLQtWtXI7SGiIgAPoCYyOTVNH1XHaAyMqDxEGEiImocHIkiMlFffPEFCgoKtMofnb5LSOBGmkRExsAQRWSCdI0+jR8/Hm5ublrl3EiTiMg4GKKITMzjPn33KH4aj4jIOBiiiExEcnIybt++rVXOT98REZkmLiwnMgFxcXFaAWrKlCnw8orlonEiIhPFEEVkRIIg1Dh9165dO41F40REZFo4nUdkJLrCE6A5fcdF40REposhisgIdAWof/3rX2jVqpVGGReNExGZLoYookZUUVGB+fPna5Vz8TgRUdPDEEXUSPSZviMioqaDIYqoEegKULNnz4adnZ0RWkNERIbAEEXUgMrLyxEfH69VztEnIqKmjyGKqIFw+o6IyLwxRBE1AF0BKjIyEtbW1kZoDRERNQSGKCIDKi0tRVJSklY5R5+IiMwPQxSRgXD6joioeWGIIjIAXQEqOjoaFhZ8shIRkbkyiXf45cuXw93dHVKpFD4+Pjh48GCt9bds2YLu3btDKpWiV69e2Llzp8ZxQRAQExMDuVwOOzs7KJVKnD17VqPOyJEj0bFjR0ilUsjlcowdOxZXr17VqHPixAk899xzkEqlcHNzQ2JiomE6TGajqKioxmffMUAREZk3o7/Lp6WlISwsDLGxsTh69Ch69+4Nf39/XL9+XWf9AwcOICgoCBMmTEBubi4CAgIQEBCAkydPinUSExORnJyMlJQU5OTkwMHBAf7+/rh3755YZ8iQIdi8eTPy8vKwdetWnD9/HqNHjxaPl5SU4G9/+xs6deqEI0eO4KOPPsLcuXOxcuXKhvthUJMSFxeHJUuWaJRZW1tz+o6IqJmQCIIgGLMBPj4+6Nu3L5YtWwYAqKyshJubG6ZOnYpwHU9dDQwMRGlpKXbs2CGW9e/fH56enkhJSYEgCHB1dcWMGTMwc+ZMAEBxcTGcnZ2RmpqKMWPG6GxHRkYGAgICUFZWBmtra6xYsQLvvfce1Go1bGxsAADh4eFIT0/HmTNn9OpbSUkJZDIZiouLtZ6JRk2brtGnmJgYSCQSI7SGiIgMSd+/30YdiSovL8eRI0egVCrFMgsLCyiVSqhUKp3nqFQqjfoA4O/vL9bPz8+HWq3WqCOTyeDj41PjNW/duoX169fDz89P/Ai6SqXCwIEDxQBVfZ+8vDzcvn1b53XKyspQUlKi8SLzcv369Rqn7xigiIiaF6OGqJs3b6KiogLOzs4a5c7OzlCr1TrPUavVtdav/qrPNefMmQMHBwe0bdsWFy9exPbt2x97n4fv8aj4+HjIZDLx5ebmprMeNU1xcXFYsWKFRlm7du04fUdE1EwZfU2UMc2aNQu5ubnYtWsXLC0tERwcjCeZ3YyIiEBxcbH4unTpkgFbS8aka/TJyysWU6ZMMUJriIjIFBh1iwMnJydYWlqisLBQo7ywsBAuLi46z3Fxcam1fvXXwsJCyOVyjTqenp5a93dyckLXrl3Ro0cPuLm54YcffoCvr2+N93n4Ho+ytbWFra3tY3pNTcnly5exatUqrXKOPhERkVFHomxsbODt7Y3s7GyxrLKyEtnZ2fD19dV5jq+vr0Z9ANi9e7dYX6FQwMXFRaNOSUkJcnJyarxm9X2BqnVN1ffZv38/7t+/r3Gfbt26oXXr1nXsKTVFcXFxWgGqd+/eDFBERATABDbbDAsLQ0hICPr06YN+/fph8eLFKC0txbhx4wAAwcHB6NChA+Lj4wEA06ZNw6BBg7Bw4UKMGDECmzZtwuHDh8WtByQSCaZPn4758+fDw8MDCoUC0dHRcHV1RUBAAAAgJycHhw4dwoABA9C6dWucP38e0dHR6Ny5sxi0XnvtNcTFxWHChAmYM2cOTp48iSVLlmDRokWN/0OiRlfT4nEiIqJqRg9RgYGBuHHjBmJiYqBWq+Hp6YnMzExxEffFixc1Ni308/PDhg0bEBUVhcjISHh4eCA9PR09e/YU68yePRulpaWYNGkSioqKMGDAAGRmZkIqlQIA7O3tsW3bNsTGxqK0tBRyuRzDhg1DVFSUOB0nk8mwa9cuvP322/D29oaTkxNiYmIwadKkRvzpUGM7e/YsNmzYoFXOAEVERI8y+j5R5oz7RDUtukafBgwYgOeff94IrSEiImPR9++30UeiiEwBp++IiKiuGKKoWTt16hS2bNmiVe7lxQBFRES1Y4iiZkvX6NOLL76IZ5991gitISKipoYhipolTt8REdGTYoiiZuXIkSMaD6+uxgBFRER1xRBFzYau0afAwEB0797dCK0hIqKmjiGKmgVO3xERkaExRJFZ279/P/bu3atVzgBFRERPiiGKzJau0afQ0FB06tTJCK0hIiJzwxBFZonTd0RE1NAYosis7Ny5E4cOHdIqZ4AiIiJDY4gis6Fr9GnSpEmQy+VGaA0REZk7higyC5y+IyKixsYQRU3ajh07cOTIEa1yBigiImpoDFHUZOkafZo6dSratGljhNYQEVFzY2HsBhDVlSAIOgNUVlYsAxQRETUajkRRk7Jp0ybk5eVplWdlxSI83AgNIiKiZoshipoMXaNPM2fOhIODA7gEioiIGhtDFJm8yspKvP/++1rlXDxORETGxBBFJi0tLQ1nzpzRKmeAIiIiY2OIIpOla/ouPDwctra2RmgNERGRJoYoMjkPHjzAggULtMq9vGLB/ERERKaCIYpMyurVq3Hp0iWNstu3n8KSJRPg6wuMHGmkhhERET2CIYpMQkYGkJurPX333nvvYedOKxw8CG5hQEREJoUhiozu/v37yM39QKu8evH4yJEcgSIiItPDEEVGtW7dOly4cEGjrEePHvjHP/5hpBYRERHphyGKjEbXp++io6NhYcGnERERkeljiKJGd+/ePXz44Yda5dz7iYiImhKGKGpUKSkpKCws1CgbPHgwBg0aZKQWERER1Q9DFDUaXdN3MTExkEgkRmgNERHRk+HiE6qTjAzAz6/qq77u3LmjM0DFxsYyQBERUZPFkSiqk4QEQKWq+qrPtgNJSUkoLS3VKBs2bBh8fHwaqIVERESNgyGK6iQ8vCpA6bPxZU2jT0RERObA6NN5y5cvh7u7O6RSKXx8fHDw4MFa62/ZsgXdu3eHVCpFr169sHPnTo3jgiAgJiYGcrkcdnZ2UCqVOHv2rHi8oKAAEyZMgEKhgJ2dHTp37ozY2FiUl5drXCcrKwv9+/dHy5Yt0a5dO7zyyisoKCgwWL+bkoen8EaOBA4cqH0Uqri4mAGKiIjMnlFDVFpaGsLCwhAbG4ujR4+id+/e8Pf3x/Xr13XWP3DgAIKCgjBhwgTk5uYiICAAAQEBOHnypFgnMTERycnJSElJQU5ODhwcHODv74979+4BAM6cOYPKykp8+umn+Omnn7Bo0SKkpKQgMjJSvEZ+fj5GjRqFoUOH4tixY8jKysLNmzfx8ssvN+wPxEQ9PIX3OHFxcVi8eLFGWUBAAAMUERGZHYkgCIKxbu7j44O+ffti2bJlAIDKykq4ublh6tSpCNcxXxQYGIjS0lLs2LFDLOvfvz88PT2RkpICQRDg6uqKGTNmYObMmQCqRkWcnZ2RmpqKMWPG6GzHRx99hBUrVog7Z//73/9GUFAQysrKxI0fv/rqK4waNQplZWWwtrbWq38lJSWQyWQoLi5Gq1at9P/BmJiMjD+m8GobgeLoExERmQN9/34bbSSqvLwcR44cgVKp/KMxFhZQKpVQqVQ6z1GpVBr1AcDf31+sn5+fD7VarVFHJpPBx8enxmsCVUGrTZs24vfe3t6wsLDAmjVrUFFRgeLiYqxbtw5KpbLWAFVWVoaSkhKNlzl43BTezZs3GaCIiKjZMVqIunnzJioqKuDs7KxR7uzsDLVarfMctVpda/3qr3W55rlz57B06VK8+eabYplCocCuXbsQGRkJW1tbODo64vLly9i8eXOtfYqPj4dMJhNfbm5utdZvbPXZnuBx4uLisHz5co2yMWPGMEAREZHZM/rCcmO6cuUKhg0bhldffRUTJ04Uy9VqNSZOnIiQkBAcOnQI3377LWxsbDB69GjUNvsZERGB4uJi8XXp0qXG6Ibe6rK2SR81jT5169bNMDcgIiIyYUbb4sDJyQmWlpZajwApLCyEi4uLznNcXFxqrV/9tbCwEHK5XKOOp6enxnlXr17FkCFD4Ofnh5UrV2ocW758OWQyGRITE8WyL7/8Em5ubsjJyUH//v11ts/W1ha2tra19Nq46rI9QW2uXr2Kzz77TKuco09ERNScGG0kysbGBt7e3sjOzhbLKisrkZ2dDV9fX53n+Pr6atQHgN27d4v1FQoFXFxcNOqUlJQgJydH45pXrlzB4MGD4e3tjTVr1oiLx6vdvXtXq8zS0lJsY1Olz/YEjxMXF6cVoIKDgxmgiIio2THqdF5YWBg+++wzfPHFFzh9+jQmT56M0tJSjBs3DkDVH+eIiAix/rRp05CZmYmFCxfizJkzmDt3Lg4fPox33nkHACCRSDB9+nTMnz8fGRkZ+PHHHxEcHAxXV1cEBAQA+CNAdezYEUlJSbhx4wbUarXGmqkRI0bg0KFDmDdvHs6ePYujR49i3Lhx6NSpE7y8vBrvB2Riapq+UygURmgNERGRcRl1x/LAwEDcuHEDMTExUKvV8PT0RGZmprgw/OLFixojQn5+ftiwYQOioqIQGRkJDw8PpKeno2fPnmKd2bNno7S0FJMmTUJRUREGDBiAzMxMSKVSAFUjV+fOncO5c+fw1FNPabSner3T0KFDsWHDBiQmJiIxMRH29vbw9fVFZmYm7OzsGvrHYnKuXbumNeUJcPqOiIiaN6PuE2XuzGGfqJUrV+LatWsaZZMnT0b79u2N1CIiIqKGpe/fbz47j2rEvZ+IiIhqxhBFWgoLC5GSkqJR1rVrVwQFBRmpRURERKaHIYo0LFmyBEVFRRplYWFhaNmypXEaREREZKIYokjE6TsiIiL9MUQRLl++jFWrVmmUeXp6YtSoUUZqERERkeljiGrmEhISUFZWplE2a9Ys2NvbG6lFRERETQNDVDPG6TsiIqL6Y4hqhgoKCvDFF19olPn4+GDYsGFGahEREVHTwxDVzOgafQoPDzfpBycTERGZIoaoZkIQBMybN0+rnNN3RERE9cMQ1QzoevbdoEGDMHjwYOM0iIiIyAwwRDVBGRlAQgIQHg6MHFl73XXr1uHChQsaZZGRkbC2tm7AFhIREZk/C2M3gOouIQFQqaq+PiwjA/Dzq/oqCALi4uI0ApSdnR1iY2MZoIiIiAyAI1FNUHj4HyNRD6sOVytWXEJu7mqNY0FBQejatWsjtpKIiMi8SQRBEIzdCHNVUlICmUyG4uJitGrVqsHvl5EB7Nv3GWSyqxrlUVFRsLS0bPD7ExERmQN9/35zJMpMCIKA3Nx5kMn+KGvdujXeffdd4zWKiIjIjDFEmYHbt28jOTlZo2zs2LF4+umnjdQiIiIi88cQ1cTl5OQgMzNToyw6OhoWFvzMABERUUNiiGqiBEHA4sWLUVJSIpaNGDECffr0MWKriIiImg+GqCbo119/xbJlyzTKpk+fDtnDC6KIiIioQTFENUEPByhnZ2e8+eabkEgkRmwRERFR88MQ1QT17t0bx48fx6hRo+Dp6Wns5hARETVL3CeqATX2PlFERET05PT9+82PcBERERHVA0MUERERUT0wRBERERHVA0MUERERUT0wRBERERHVA0MUERERUT0wRBERERHVA0MUERERUT0wRBERERHVA0MUERERUT0YPUQtX74c7u7ukEql8PHxwcGDB2utv2XLFnTv3h1SqRS9evXCzp07NY4LgoCYmBjI5XLY2dlBqVTi7Nmz4vGCggJMmDABCoUCdnZ26Ny5M2JjY1FeXq51naSkJHTt2hW2trbo0KEDFixYYLiOExERUZNm1BCVlpaGsLAwxMbG4ujRo+jduzf8/f1x/fp1nfUPHDiAoKAgTJgwAbm5uQgICEBAQABOnjwp1klMTERycjJSUlKQk5MDBwcH+Pv74969ewCAM2fOoLKyEp9++il++uknLFq0CCkpKYiMjNS417Rp0/D5558jKSkJZ86cQUZGBvr169dwPwwiIiJqUoz6AGIfHx/07dsXy5YtAwBUVlbCzc0NU6dORXh4uFb9wMBAlJaWYseOHWJZ//794enpiZSUFAiCAFdXV8yYMQMzZ84EABQXF8PZ2RmpqakYM2aMznZ89NFHWLFiBS5cuAAAOH36NP785z/j5MmT6NatW737xwcQExERNT0m/wDi8vJyHDlyBEql8o/GWFhAqVRCpVLpPEelUmnUBwB/f3+xfn5+PtRqtUYdmUwGHx+fGq8JVAWtNm3aiN9/9dVXePrpp7Fjxw4oFAq4u7vjjTfewK1bt2rtU1lZGUpKSjReREREZJ6sjHXjmzdvoqKiAs7Ozhrlzs7OOHPmjM5z1Gq1zvpqtVo8Xl1WU51HnTt3DkuXLkVSUpJYduHCBfzyyy/YsmUL1q5di4qKCvzrX//C6NGjsWfPnhr7FB8fj7i4OK1yhikiIqKmo/rv9uMm64wWokzBlStXMGzYMLz66quYOHGiWF5ZWYmysjKsXbsWXbt2BQCsWrUK3t7eyMvLq3GKLyIiAmFhYRrX/9Of/gQ3N7eG7QgREREZ3G+//QaZTFbjcaOFKCcnJ1haWqKwsFCjvLCwEC4uLjrPcXFxqbV+9dfCwkLI5XKNOp6enhrnXb16FUOGDIGfnx9WrlypcUwul8PKykoMUADQo0cPAMDFixdrDFG2trawtbUVv2/RogVOnTqFP/3pT7h06ZJZr4sqKSmBm5ub2fcTYF/NUXPpJ8C+mqvm0tfG6qcgCPjtt9/g6upaaz2jhSgbGxt4e3sjOzsbAQEBAKpGgLKzs/HOO+/oPMfX1xfZ2dmYPn26WLZ79274+voCABQKBVxcXJCdnS2GppKSEuTk5GDy5MniOVeuXMGQIUPg7e2NNWvWwMJCc2nYX/7yFzx48ADnz59H586dAQA///wzAKBTp05699HCwgIdOnQAALRq1cqs/8Ou1lz6CbCv5qi59BNgX81Vc+lrY/SzthGoakadzgsLC0NISAj69OmDfv36YfHixSgtLcW4ceMAAMHBwejQoQPi4+MBVG07MGjQICxcuBAjRozApk2bcPjwYXEkSSKRYPr06Zg/fz48PDygUCgQHR0NV1dXMahduXIFgwcPRqdOnZCUlIQbN26I7akeyVIqlXj22Wcxfvx4LF68GJWVlXj77bfx17/+VWN0ioiIiJovo4aowMBA3LhxAzExMVCr1fD09ERmZqa4MPzixYsao0R+fn7YsGEDoqKiEBkZCQ8PD6Snp6Nnz55indmzZ6O0tBSTJk1CUVERBgwYgMzMTEilUgBVI1fnzp3DuXPn8NRTT2m0p3oBmYWFBb766itMnToVAwcOhIODA4YPH46FCxc29I+EiIiImgqBGtS9e/eE2NhY4d69e8ZuSoNqLv0UBPbVHDWXfgoC+2qumktfTa2fRt1sk4iIiKipMvqz84iIiIiaIoYoIiIionpgiCIiIiKqB4YoIiIionpgiHrI8uXL4e7uDqlUCh8fHxw8eLDW+lu2bEH37t0hlUrRq1cv7Ny5U+O4IAiIiYmBXC6HnZ0dlEolzp49Kx4vKCjAhAkToFAoYGdnh86dOyM2Nhbl5eVa10lKSkLXrl1ha2uLDh06YMGCBWbZ16ysLPTv3x8tW7ZEu3bt8Morr6CgoKBJ9RUARo4ciY4dO0IqlUIul2Ps2LG4evWqRp0TJ07gueeeg1QqhZubGxITE5+on6ba13379mHUqFGQy+VwcHCAp6cn1q9fb3b9fNi5c+fQsmVLODo6PlE/AdPtqzm8L+nbV0O/Lxmjn9XKysrg6ekJiUSCY8eOaRwzl/ekajX11aDvSUb5TKAJ2rRpk2BjYyOsXr1a+Omnn4SJEycKjo6OQmFhoc7633//vWBpaSkkJiYKp06dEqKiogRra2vhxx9/FOskJCQIMplMSE9PF44fPy6MHDlSUCgUwu+//y4IgiB8/fXXQmhoqJCVlSWcP39e2L59u9C+fXthxowZGveaOnWq0K1bN2H79u3ChQsXhMOHDwu7du0yu75euHBBsLW1FSIiIoRz584JR44cEQYOHCh4eXk1qb4KgiB8/PHHgkqlEgoKCoTvv/9e8PX1FXx9fcXjxcXFgrOzs/DPf/5TOHnypLBx40bBzs5O+PTTT82urwsWLBCioqKE77//Xjh37pywePFiwcLCQvjqq6/Mqp/VysvLhT59+gjDhw8XZDJZvfrYFPpqDu9L+vTV0O9LxupntXfffVcYPny4AEDIzc0Vy83pPelxfTXkexJD1P/r16+f8Pbbb4vfV1RUCK6urkJ8fLzO+v/4xz+EESNGaJT5+PgIb775piAIglBZWSm4uLgIH330kXi8qKhIsLW1FTZu3FhjOxITEwWFQiF+f+rUKcHKyko4c+ZMvfqli6n2dcuWLYKVlZVQUVEhlmVkZAgSiUQoLy+vWyf/n6n0dfv27Rr9+OSTT4TWrVsLZWVlYp05c+YI3bp1q3sn/5+p9lWXv//978K4ceP06tejTL2fs2fPFl5//XVhzZo1TxyiTLWv5vy+9GhfDf2+ZMx+7ty5U+jevbvw008/aQULc3tPqq2vutT3PYnTeQDKy8tx5MgRKJVKsczCwgJKpRIqlUrnOSqVSqM+APj7+4v18/PzoVarNerIZDL4+PjUeE0AKC4uRps2bcTvv/rqKzz99NPYsWMHFAoF3N3d8cYbb+DWrVtm11dvb29YWFhgzZo1qKioQHFxMdatWwelUglra+sm29dbt25h/fr18PPzE/uhUqkwcOBA2NjYaNwnLy8Pt2/fNqu+6vLo715fpt7PPXv2YMuWLVi+fHmd+/YoU+6rub4v6eqrId+XjNnPwsJCTJw4EevWrYO9vb3O+5jLe9Lj+qpLfd+TGKIA3Lx5ExUVFeLjZqo5OztDrVbrPEetVtdav/prXa557tw5LF26FG+++aZYduHCBfzyyy/YsmUL1q5di9TUVBw5cgSjR4+uWyf/nyn3VaFQYNeuXYiMjIStrS0cHR1x+fJlbN68uW6d/H/G7uucOXPg4OCAtm3b4uLFi9i+fftj7/PwPerClPv6qM2bN+PQoUPiMzLrwpT7+euvvyI0NBSpqakGeTCqKffV3N6XauurId+XjNVPQRAQGhqKt956C3369KnTfR6+R12Ycl8f9STvSQxRJuLKlSsYNmwYXn31VUycOFEsr6ysRFlZGdauXYvnnnsOgwcPxqpVq7B3717k5eUZscX1V1Nf1Wo1Jk6ciJCQEBw6dAjffvstbGxsMHr0aPG5hk3JrFmzkJubi127dsHS0hLBwcFNsh/60Leve/fuxbhx4/DZZ5/hmWeeMUJLn0xt/Zw4cSJee+01DBw40MitNIza+mpu70u19dUc3peWLl2K3377DREREcZuSoOra1+f9D2JIQqAk5MTLC0tUVhYqFFeWFgIFxcXnee4uLjUWr/6qz7XvHr1KoYMGQI/Pz+sXLlS45hcLoeVlRW6du0qlvXo0QNA1QOa68qU+7p8+XLIZDIkJibCy8sLAwcOxJdffons7Gzk5OQ0ub46OTmha9eu+Otf/4pNmzZh586d+OGHH2q9z8P3qAtT7mu1b7/9Fi+++CIWLVqE4ODgOvex+j6m2s89e/YgKSkJVlZWsLKywoQJE1BcXAwrKyusXr3arPpqbu9LtfXVkO9Lxurnnj17oFKpYGtrCysrK3Tp0gUA0KdPH4SEhNR6n4fvYS59rWaI9ySGKAA2Njbw9vZGdna2WFZZWYns7Gz4+vrqPMfX11ejPgDs3r1brK9QKODi4qJRp6SkBDk5ORrXvHLlCgYPHgxvb2+sWbMGFhaav5K//OUvePDgAc6fPy+W/fzzzwCATp06mVVf7969q1VmaWkptrEp9fVR1e0vKysT77N//37cv39f4z7dunVD69at69hT0+4rUPWR4hEjRuDDDz/EpEmT6ty/aqbcT5VKhWPHjomvefPmoWXLljh27Bheeukls+qrOb0vPerRvhryfclY/UxOTsbx48fF/zartw1IS0sTt6Uwl/ckffoKGO49iZ/O+3+bNm0SbG1thdTUVOHUqVPCpEmTBEdHR0GtVguCIAhjx44VwsPDxfrff/+9YGVlJSQlJQmnT58WYmNjdX4U09HRUdi+fbtw4sQJYdSoURofxbx8+bLQpUsX4fnnnxcuX74sXLt2TXxVq6ioEJ599llh4MCBwtGjR4XDhw8LPj4+wl//+lez62t2drYgkUiEuLg44eeffxaOHDki+Pv7C506dRLu3r3bZPr6ww8/CEuXLhVyc3OFgoICITs7W/Dz8xM6d+4sPnm8qKhIcHZ2FsaOHSucPHlS2LRpk2Bvb//EHyc2xb7u2bNHsLe3FyIiIjR+77/++qtZ9fNRhvh0nqn21Vzel/Tpq6Hfl4zRz0fl5+drfWLNXN6T9OmrId+TGKIesnTpUqFjx46CjY2N0K9fP+GHH34Qjw0aNEgICQnRqL9582aha9eugo2NjfDMM88I//3vfzWOV1ZWCtHR0YKzs7Nga2srPP/880JeXp54fM2aNQIAna+HXblyRXj55ZeFFi1aCM7OzkJoaGi9/wCZel83btwoeHl5CQ4ODkK7du2EkSNHCqdPn25SfT1x4oQwZMgQoU2bNoKtra3g7u4uvPXWW8Lly5c1rnP8+HFhwIABgq2trdChQwchISHhifppqn0NCQnR+XsfNGiQWfXzUYYIUabcV3N4X9K3r4Z+X2rsfj5KV7AQBPN4T9Knr4Z8T5IIQhNZGUdERERkQrgmioiIiKgeGKKIiIiI6oEhioiIiKgeGKKIiIiI6oEhioiIiKgeGKKIiIiI6oEhioiIiKgeGKKIiIiI6oEhioioFiqVCpaWlhgxYoRGeUFBASQSifhq06YNBg0ahO+++85ILSWixsYQRURUi1WrVmHq1KnYv38/rl69qnX8m2++wbVr17B//364urrihRde0HrKPBGZJ4YoIqIa3LlzB2lpaZg8eTJGjBiB1NRUrTpt27aFi4sLevbsicjISPGp8kRk/hiiiIhqsHnzZnTv3h3dunXD66+/jtWrV6Omx43+/vvvWLt2LQDAxsamMZtJREZiZewGEBGZqlWrVuH1118HAAwbNgzFxcX49ttvMXjwYLGOn58fLCwscPfuXQiCAG9vbzz//PNGajERNSaORBER6ZCXl4eDBw8iKCgIAGBlZYXAwECsWrVKo15aWhpyc3OxdetWdOnSBampqbC2tjZGk4mokXEkiohIh1WrVuHBgwdwdXUVywRBgK2tLZYtWya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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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69fHgwQPcvXsXnp6e5R7v4eEB4J+lHooMGTIE77//PhYuXAhHR0ecPHkSZ86cwffff69zjGVhzY4B+CidEds9BLb/NzeArUKBed0bwEfpbOLIiIjMi6ma/du1a4fTp0/j2LFjiI6OxqBBg9CjRw9cunQJOTk5ePXVV1G5cmXp8fXXXyM1NbXU850/fx5hYWFqc8JEREQgOzsb169fl7Y1bdq0xOc/W+vi5eWFSpUqSYlO0baiZRM0iTE0NFSzgimFEE//QLrOedO1a1fY2triu+++A/C0U3S7du2kZi9DYc2OgfRsVhOt63ngyp0cBFSvxESHiKgEZTX7G/J908XFBXXq1AEArF27Fo0aNcKaNWvQoEEDAMDu3bvh6+ur9hxHR0e9XLck9vb20s9Fo8SepVAopD4t2dnZFY6xtOtp6vz583Bzc5OWBrGxsZESoCIVmeHYwcEBAwYMQHx8PLp3747Nmzfj888/10uMZWGyY0A+SmcmOUREZTCHZn8bGxtMnjwZY8aMwcWLF+Ho6IirV6+qNQeVJygoCN9++y2EEFLtx9GjR+Hq6qrTiK2SBAcHaxWjtm7duoXNmzeja9euUnOch4eH1AQHAFlZWUhLS1N7nr29fYl9fIYMGYIGDRpg+fLlePLkCbp3727YGwCbsYiIyITMpdn/nXfega2tLVatWoVx48Zh9OjRWL9+PVJTU3Hy5EksXboU69evL/X5w4cPx7Vr1/Dhhx/ijz/+wPfff4/p06djzJgxeu946+rqqlWMFSGEQEZGBtLT03H+/HmsXbsW4eHhUCqViIuLk45r3749NmzYgP/97384c+YMoqOjiy3SGRAQgISEBGRkZODvv/+WtgcFBaFly5aYMGECevfuLS32aUis2SEiIpMyh2Z/Ozs7xMTEYMGCBUhLS4OHhwdiY2Nx+fJluLu74+WXX8bkyZNLfb6vry/27NmD8ePHo1GjRqhatSoGDx6MKVOmGCTe2bNnaxxjRWRlZcHHxwcKhQJubm548cUXER0djY8++ghubm7ScZMmTUJaWhreeOMNKJVKzJ49u1jNzmeffYYxY8Zg9erV8PX1xZUrV6R9RUPa33vvPZ3irSiFeL7RzQplZWVBqVRCpVKp/TGJiKhkjx49QlpaGgIDA+Hk5GTqcMjCzJ49G9u3b8fvv/9e7rFlvdYq+vnNZiwiIiIyiuzsbJw9exbLli3Dhx9+aLTrMtkhIiKyAi+99JLaUPVnH5s2bTJKDDExMWjatCnatm1rtCYsgH12iIiIrMKePXtKHR7u5eVllBjWrVtnkoVTmewQERFZAX9/f1OHYDJsxiIiIq1xjAsZmj5eY0x2iIhIY0Uz/D67EjeRIRS9xp6fVVoTbMYiIiKN2drawt3dXVqvqVKlSjqvm0T0LCEEcnJycOvWLbi7uxebtFATTHaIiEgrRatZFyU8RIbw/Mrp2mCyQ0REWlEoFPDx8YGnp2eFFoEk0pS9vb1ONTpFmOwQEZFObG1t9fKBRGQoJu2gXFBQgKlTpyIwMBDOzs6oXbs2Zs+erdbzWgiBadOmwcfHB87OzoiMjMSff/6pdp579+6hb9++cHNzg7u7OwYPHozs7Gxj3w4RERGZIZMmO/Pnz8eKFSuwbNkynD9/HvPnz8eCBQuwdOlS6ZgFCxZgyZIlWLlyJY4dOwYXFxdERUXh0aNH0jF9+/ZFSkoKDhw4gF27duHnn3/GsGHDTHFLREREZGZMuhDoG2+8AS8vL6xZs0ba1qNHDzg7O2Pjxo0QQqBGjRoYO3Ysxo0bBwBQqVTw8vLCunXr0KtXL5w/fx7BwcFITk5GaGgoAGDv3r14/fXXcf36ddSoUaPcOLgQKBERkeWxiIVAw8PDkZCQgIsXLwIAfvvtNxw5cgSdOnUCAKSlpSEjIwORkZHSc5RKJVq0aIGkpCQAQFJSEtzd3aVEBwAiIyNhY2ODY8eOlXjdvLw8ZGVlqT2IiIhInkzaQXnixInIyspC/fr1YWtri4KCAsydOxd9+/YFAGRkZAAovmaHl5eXtC8jIwOenp5q++3s7FC1alXpmOfFxsZi5syZ+r4dIiIiMkMmrdn5z3/+g02bNmHz5s04efIk1q9fj08//RTr16836HUnTZoElUolPa5du2bQ6xEREZHpmLRmZ/z48Zg4cSJ69eoFAAgJCcFff/2F2NhYREdHS5MIZWZmwsfHR3peZmYmGjduDODppFbPT2j15MkT3Lt3r9RJiBwdHeHo6GiAOyIiIiJzY9KanZycHNjYqIdga2uLwsJCAEBgYCC8vb2RkJAg7c/KysKxY8cQFhYGAAgLC8P9+/dx4sQJ6ZhDhw6hsLAQLVq0MMJdEBERkTkzac1Oly5dMHfuXNSsWRMvvfQSTp06hYULF+K9994D8HR2zlGjRmHOnDmoW7cuAgMDMXXqVNSoUQNdu3YFAAQFBaFjx44YOnQoVq5cifz8fMTExKBXr14VGolFRERE8mbSZGfp0qWYOnUqhg8fjlu3bqFGjRr417/+hWnTpknHfPzxx3j48CGGDRuG+/fvo1WrVti7dy+cnJykYzZt2oSYmBh06NABNjY26NGjB5YsWWKKWyIiIiIzY9J5dswF59khIiKyPBYxzw4RERGRoTHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNEREQGk67KRWLqHaSrck0Wg53JrkxERESyti35KibtOINCAdgogNjuIejZrKbR42DNDhEREelduipXSnQAoFAAk3ecNUkND5MdIiIi0ru0Ow+lRKdIgRC4cifH6LEw2SEiIiK9C6zuAhuF+jZbhQIB1SsZPRYmO0RERKR3PkpnxHYPga3iacZjq1BgXvcG8FE6Gz0WdlAmIiIig+jZrCZa1/PAlTs5CKheySSJDsBkh4iIiAzIR+lssiSnCJuxiIiISNaY7BAREZGsmTzZuXHjBvr164dq1arB2dkZISEh+PXXX6X9QghMmzYNPj4+cHZ2RmRkJP7880+1c9y7dw99+/aFm5sb3N3dMXjwYGRnZxv7VoiIiMgMmTTZ+fvvvxEREQF7e3v88MMPOHfuHD777DNUqVJFOmbBggVYsmQJVq5ciWPHjsHFxQVRUVF49OiRdEzfvn2RkpKCAwcOYNeuXfj5558xbNgwU9wSERERmRmFEEKUf5hhTJw4EUePHsX//ve/EvcLIVCjRg2MHTsW48aNAwCoVCp4eXlh3bp16NWrF86fP4/g4GAkJycjNDQUALB37168/vrruH79OmrUqFFuHFlZWVAqlVCpVHBzc9PfDRIREZHBVPTz26Q1Ozt37kRoaCjeeecdeHp6okmTJli9erW0Py0tDRkZGYiMjJS2KZVKtGjRAklJSQCApKQkuLu7S4kOAERGRsLGxgbHjh0r8bp5eXnIyspSexAREZE8mTTZuXz5MlasWIG6deti3759+OCDDzBy5EisX78eAJCRkQEA8PLyUnuel5eXtC8jIwOenp5q++3s7FC1alXpmOfFxsZCqVRKDz8/P33fGhEREZkJkyY7hYWFePnllzFv3jw0adIEw4YNw9ChQ7Fy5UqDXnfSpElQqVTS49q1awa9HhEREZmOSZMdHx8fBAcHq20LCgrC1atXAQDe3t4AgMzMTLVjMjMzpX3e3t64deuW2v4nT57g3r170jHPc3R0hJubm9qDiIiI5MmkyU5ERAQuXLigtu3ixYvw9/cHAAQGBsLb2xsJCQnS/qysLBw7dgxhYWEAgLCwMNy/fx8nTpyQjjl06BAKCwvRokULI9wFERERpatykZh6B+mqXFOHUoxJl4sYPXo0wsPDMW/ePLz77rs4fvw4vvzyS3z55ZcAAIVCgVGjRmHOnDmoW7cuAgMDMXXqVNSoUQNdu3YF8LQmqGPHjlLzV35+PmJiYtCrV68KjcQiIiIi3WxLvopJO86gUAA2CiC2ewh6Nqtp6rAkJh16DgC7du3CpEmT8OeffyIwMBBjxozB0KFDpf1CCEyfPh1ffvkl7t+/j1atWmH58uWoV6+edMy9e/cQExOD//73v7CxsUGPHj2wZMkSVK5cuUIxcOg5ERGRdtJVuYiIO4TCZ7IJW4UCRya2M/iaWBX9/DZ5smMOmOwQERFpJzH1DvqsLj7Vy5ahLRFWu5pBr20R8+wQERGRZQus7gIbhfo2W4UCAdUrmSagEjDZISIiIq35KJ0R2z0EtoqnGY+tQoF53RsYvAlLEybtoExERESWr2ezmmhdzwNX7uQgoHols0p0ACY7REREpAc+SmezS3KKsBmLiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlnTOtkJCAjArFmzcPXqVX3GQ0RERKRXWic7o0aNwo4dO1CrVi28+uqr2Lp1K/Ly8vQZGxEREZHOdEp2Tp8+jePHjyMoKAgffvghfHx8EBMTg5MnT+ozRiIiIiKtKYQQQh8nys/Px/LlyzFhwgTk5+cjJCQEI0eOxKBBg6BQKMo/gQlVdIl4IiIiMh8V/fzWeW2s/Px8fPfdd4iPj8eBAwfQsmVLDB48GNevX8fkyZNx8OBBbN68WdfLEBEREWlF62Tn5MmTiI+Px5YtW2BjY4MBAwZg0aJFqF+/vnRMt27d0KxZM70ESkRERKQNrZOdZs2a4dVXX8WKFSvQtWtX2NvbFzsmMDAQvXr10ilAIiIiIl1onexcvnwZ/v7+ZR7j4uKC+Ph4bS9BREREpDOtR2O1a9cOd+/eLbb9/v37qFWrlk5BEREREemL1snOlStXUFBQUGx7Xl4ebty4oVNQRERERPqicTPWzp07pZ/37dsHpVIp/V5QUICEhAQEBAToJTgiIiIiXWmc7HTt2hUAoFAoEB0drbbP3t4eAQEB+Oyzz/QSHBEREZGuNE52CgsLATwdaZWcnIzq1avrPSgiIiIifdF6NFZaWpo+4yAiIiILlq7KRdqdhwis7gIfpbOpw1GjdbIza9asMvdPmzZN21MTERGRBdmWfBWTdpxBoQBsFEBs9xD0bFbT1GFJtF4bq0mTJmq/5+fnIy0tDXZ2dqhdu7ZFLQbKtbGIiIi0k67KRUTcIRQ+k03YKhQ4MrGdwWt4DL421qlTp0q86MCBA9GtWzdtT0tEREQWJO3OQ7VEBwAKhMCVOzlm05yl9Tw7JXFzc8PMmTMxdepUfZ6WiIiIzFRgdRfYKNS32SoUCKheyTQBlUCvyQ4AqFQqqFQqfZ+WiIiIzJCP0hmx3UNgq3ia8dgqFJjXvYHZ1OoAOjRjLVmyRO13IQTS09OxYcMGdOrUSefAiIiIyDL0bFYTret54MqdHARUr2RWiQ6gQwflwMBAtd9tbGzg4eGB9u3bY9KkSXB1ddVLgMbADspERESWx+AdlDnPDhEREVkCnfrsCCFw586dElc/JyIiIjIHWiU7GRkZGDBgAKpUqQIvLy94enqiSpUqeO+995CZmanvGImIiIi0pnEzVlZWFsLDw5GdnY1Bgwahfv36EELg3Llz2LJlC44cOYKTJ0+icuXKhoiXiIiISCMaJzuff/45bG1tkZKSAg8PD7V9U6ZMQUREBJYsWYLJkyfrLUgiIiIibWncjLV7925Mnjy5WKIDAJ6enpg0aRL++9//6iU4IiIisgzpqlwkpt5BuirX1KEUo3Gyc/HiRYSHh5e6Pzw8HBcuXNApKGtX1gvGnF9MRESa4nuaPGxLvoqIuEPos/oYIuIOYVvyVVOHpEarPjvu7u6l7nd3d0dWVpYuMVm1slaONfdVZYmINMH3NHlIV+VKf0cAKBTA5B1n0bqeh9lMLqhxzY4QAjY2pT9NoVBAy3kKrV5pL5h0VW6Z+4iILA3f0+SjrIVAAfOovdO4ZkcIgXr16kGhUJS6n7RT1gtGQJj9qrJERBVlCStlU8UULQT67N+zaCFQc6m90zjZiY+PN0QchLJfMADK3EdEZElcHGxL3F7JQe/rU5OBFS0EOnnHWRQIIS0ECsBsmrc0Tnaio6M1On7Lli1488034eLioumlrE5pL5iiF0VZ+4iILMnDxwUlbs95XGjkSEgfSloINDH1jtnU3mm9NlZF/etf/0KLFi1Qq1YtQ19KFspaOdbcV5UlIqqo8mqyyfL4KJ3VPpfM6W9s8PpC9uHRnI/SGWG1q5WYzJS1j4jIUhTVZNv+X/9P1lbLjzn9jQ1es0NERFQS1lbLn7n8jZnsEBGRyTzf9EHyYw5/Y3Z7JyIiIlljskNGYw4TSxERkfXRqhmroKAAR48eRcOGDctcOgIA/P39YW9vr81lSEbMZWIpIiKyPlrV7Nja2uK1117D33//Xe6xZ8+ehZ+fnzaXIZngtPBERGRKWjdjNWjQAJcvX9ZnLCRT5a2bQkREZEhaJztz5szBuHHjsGvXLqSnpyMrK0vtQVSkaGKpZ3HyMCIiMhaF0HLWv2dXPn92UVAhBBQKBQoKSp4K3BxlZWVBqVRCpVLBzc3N1OHI0rbkq8WWumCfHSIi0kVFP7+1nmfn8OHD2j6VrJC5TCxFRETWR+tkp02bNvqMg6yAOUwsRURE1keneXb+97//oV+/fggPD8eNGzcAABs2bMCRI0f0EhwRERGRrrROdr799ltERUXB2dkZJ0+eRF5eHgBApVJh3rx5eguQiIiISBc6jcZauXIlVq9erTZpYEREBE6ePKmX4IiIiIh0pXWyc+HCBbRu3brYdqVSifv37+sSExEREZHeaJ3seHt749KlS8W2HzlyBLVq1dIpKCIiIiJ90TrZGTp0KD766CMcO3YMCoUCN2/exKZNmzBu3Dh88MEH+oyRiIhkigsEkzFoPfR84sSJKCwsRIcOHZCTk4PWrVvD0dER48aNw4cffqjPGImIyEykq3KRduchAqu76DyVBBcIJmPRegblIo8fP8alS5eQnZ2N4OBgVK5cWV+xGQ1nUCYiKp8+k5N0VS4i4g6prZtnq1DgyMR2nI+LKszgMygXcXBwQHBwsK6nISIiM5auypUSHQAoFMDkHWfRup6HVslJWQsE65Ls6LPmieRD62Tn0aNHWLp0KQ4fPoxbt26hsLBQbT+HnxMRyYe+k5OiBYKfr9nRZYFgNotRabTuoDx48GAsWLAA/v7+eOONN/DWW2+pPbQRFxcHhUKBUaNGSdsePXqEESNGoFq1aqhcuTJ69OiBzMxMteddvXoVnTt3RqVKleDp6Ynx48fjyZMn2t4aERE9pyg5eZYuyYmP0hmx3UNg+38LSRctEKxtbUxpNU/s+EyADjU7u3btwp49exAREaGXQJKTk7Fq1So0bNhQbfvo0aOxe/dubN++HUqlEjExMejevTuOHj0KACgoKEDnzp3h7e2NxMREpKenY8CAAbC3t+dMzkRGxiYE+SpKTibvOIsCIXROTgD9LhBsqGYxkgetkx1fX1+4urrqJYjs7Gz07dsXq1evxpw5c6TtKpUKa9aswebNm9G+fXsAQHx8PIKCgvDLL7+gZcuW2L9/P86dO4eDBw/Cy8sLjRs3xuzZszFhwgTMmDEDDg4OeomRiMrGJgT502dyUkRfCwRXpFmMybj10roZ67PPPsOECRPw119/6RzEiBEj0LlzZ0RGRqptP3HiBPLz89W2169fHzVr1kRSUhIAICkpCSEhIfDy8pKOiYqKQlZWFlJSUkq8Xl5eHrKystQeRKQ9NiFYDx+lM8JqVzO7ZKG8ZrFtyVcREXcIfVYfQ0TcIWxLvmrKcMnItK7ZCQ0NxaNHj1CrVi1UqlRJbX0sALh3716FzrN161acPHkSycnJxfZlZGTAwcEB7u7uatu9vLyQkZEhHfNsolO0v2hfSWJjYzFz5swKxUdE5TOnJgR+e7depdU86XskGVkerZOd3r1748aNG5g3bx68vLygUCjKf9Jzrl27ho8++ggHDhyAk5OTtqFobNKkSRgzZoz0e1ZWFvz8/Ix2fSK5McTIGm2wKY1KahYzp2ScTEPrZCcxMRFJSUlo1KiR1hc/ceIEbt26hZdfflnaVlBQgJ9//hnLli3Dvn378PjxY9y/f1+tdiczMxPe3t4Anq7Rdfz4cbXzFo3WKjrmeY6OjnB0dNQ6biJSZ4jOq5rit3cqjbkk42Q6Wic79evXR26ubu3xHTp0wJkzZ9S2DRo0CPXr18eECRPg5+cHe3t7JCQkoEePHgCerrZ+9epVhIWFAQDCwsIwd+5c3Lp1C56engCAAwcOwM3NjZMdEhmRITqvaoLf3qk05pCMk2lpnezExcVh7NixmDt3LkJCQor12anIsguurq5o0KCB2jYXFxdUq1ZN2j548GCMGTMGVatWhZubGz788EOEhYWhZcuWAIDXXnsNwcHB6N+/PxYsWICMjAxMmTIFI0aMYO0NkZHpa2SNNqz52zv7KZXP1Mk4mZbWyU7Hjh0BPK2deZYQAgqFAgUFBbpF9n8WLVoEGxsb9OjRA3l5eYiKisLy5cul/ba2tti1axc++OADhIWFwcXFBdHR0Zg1a5Zerk9ElsFav72zn1LFmTIZJ9PSeiHQn376qcz9bdq00SogU+BCoETyka7KtZpv71xMk6ydQRcCzc/Px6xZs7By5UrUrVtX6yDljlXLRMZnTd/e2U+JqGK0Snbs7e3x+++/6zsWWWHVMhEZmjX3UyLShNYzKPfr1w9r1qzRZyyywdlkicgY9L2YJpFcad1B+cmTJ1i7di0OHjyIpk2bwsXFRW3/woULdQ7OUrFqmYiMhaOMiMqndbJz9uxZaTLAixcvqu3TZjZlOWHVMhEZkzX1UyLShtbJzuHDh/UZh6xY6xBYIiIic6R1svOs69evAwBeeOEFfZxOFli1bFocCUdEREW07qBcWFiIWbNmQalUwt/fH/7+/nB3d8fs2bNRWFiozxgtlo/SGWG1q/HD1si2JV9FRNwh9Fl9DBFxh7At+aqpQyIiIhPSumbnk08+wZo1axAXF4eIiAgAwJEjRzBjxgw8evQIc+fO1VuQRBXFxSDNF2vbiMhUtE521q9fj6+++gpvvvmmtK1hw4bw9fXF8OHDmeyQSXAknHnivFNEZEpaN2Pdu3cP9evXL7a9fv36uHfvnk5BEWmraCTcszgSrnTpqlwkpt4x6BxQnHeKiExN62SnUaNGWLZsWbHty5YtQ6NGjXQKikhbnGSt4ozVt6ms2jYiImPQuhlrwYIF6Ny5Mw4ePIiwsDAAQFJSEq5du4Y9e/boLUCi0pTWB4Qj4cpnzL5NnHeKiExN65qdNm3a4OLFi+jWrRvu37+P+/fvo3v37rhw4QJeeeUVfcZIVEx5tRIcCVc2Y9a2sLaNiExNo5qd7t27Y926dXBzc8PXX3+Nnj17siMyGR1HXOnO2LUtrG0jIlPSqGZn165dePjwIQBg0KBBUKlUBgmKqCym6ANijI68xmSK2hbWthGRqWhUs1O/fn1MmjQJ7dq1gxAC//nPf+Dm5lbisQMGDNBLgETPM3athLbDps19XhnWthCRtVAIIUT5hz2VmJiIMWPGIDU1Fffu3YOrq2uJi34qFAqLGn6elZUFpVIJlUpVavJG5mVb8tVia48ZYt6WdFUuIuIOFUusjkxsV2ZywHlliIgMr6Kf3xrV7ISHh+OXX34BANjY2ODixYvw9PTULVIiLRirVkKbSQrZp4iIyLxoPfQ8LS0NHh4e+oyFSCM+SmeDJw+B1V2gAPBsvqMAymwy4yzOZAjm3ixKZM60Hnru7++PI0eOoF+/fggLC8ONGzcAABs2bMCRI0f0FqAlk1unVvo/xVtu1XAWZ9I3Lm5LpButk51vv/0WUVFRcHZ2xqlTp5CXlwcAUKlUmDdvnt4CtFR8c5KHtDsP8XynNiFQ5sivopFORQmPjQKcV4a0xuU2iHSndbIzZ84crFy5EqtXr4a9vb20PSIiAidPntRLcJaKb07yoUstTVHX/4oPASAqjsttEOlO62TnwoULaN26dbHtSqUS9+/f1yUmi8c3J/nQZj6aomS36CUgwGSXtMdmUdNidwR50LqDsre3Ny5duoSAgAC17UeOHEGtWrV0jcuicS0geenZrCbqe7si+crfaBZQBY38qpR5PDsokz4VJdzPT7XA15LhcQoJ+dA62Rk6dCg++ugjrF27FgqFAjdv3kRSUhLGjh2LadOm6TNGi8M3J3nR9A3PUMkuR+NYL04AaXycQkJetE52Jk6ciMLCQnTo0AE5OTlo3bo1HB0dMX78eAwZMkSfMVokvjnJgzZveIZIdvkNk4wx1QL9gzW08qJ1sqNQKPDJJ59g/PjxuHTpErKzsxEcHIxVq1YhMDAQGRkZ+ozTIvHNyfJp+4anz2SX3zCJjI/dEeRF4w7KeXl5mDRpEkJDQxEREYE9e/YgODgYKSkpePHFF/H5559j9OjRhoiVyOh06Ryqr4Uv2eGdyPhMsVguGY7GNTvTpk3DqlWrEBkZicTERLzzzjsYNGgQfvnlF3z22Wd45513YGtra4hYiYzOHPpflTiLs6LsWZyJSHfsjiAfGic727dvx9dff40333wTZ8+eRcOGDfHkyRP89ttvJS4KSmTpzPINj3P3EBkFuyPIg8bJzvXr19G0aVMAQIMGDeDo6IjRo0cz0SFZM+UbXomzOAPsKElEVEEa99kpKCiAg4OD9LudnR0qV66s16CI6B+cVI6ISDca1+wIITBw4EA4OjoCAB49eoT3338fLi4uasft2LFDPxGS2eF8L8ZlDv2GiIgsmcbJTnR0tNrv/fr101swZP4434tpmGW/ISIiC6EQgssUZmVlQalUQqVSwc3NzdThmK10VS4i4g4Vm3fiyMR2/PAlIiKjq+jnt9YLgZL14XwvRERkiZjsUIWxoywREVkiJjtUYZxRlEhduioXial3kK7KNXUoRFQGrdfGIuvEjrJET7GzPpHlYM0OaUxfaz4RWarSFmdlDQ+ReWKyQ/R/2CRBFcXO+paJ/+PWi81YRGCTBGmmqLP+89MwsLO++eL/uHVjzQ5ZPTZJkKbYWd+y8H+cWLNDFktfy1aU1STBDy8qDTvrWw7+jxOTHbJI+qySZpMEactH6cwPSwvA/3FiM5YZYie6sum7SppNEmSNrOl9hv/jxJodM8NOdOUzRJU0myTImljj+wz/x60ba3bMCDvRVYyhlq3g/EFkDaz5fYb/49aLyY4Z4dwdFcMqaSLt8X2GrBGbscwIO9FVHKukibTD9xmyRqzZMSOssdAMq6SJNMf3GbJGCiGEKP8wecvKyoJSqYRKpYKbm5upw0G6Kpc1FkRkUHyfITmo6Oc3m7HMEOfuICJD4/uMYelr0lPSDyY7Vo7/kERE+mWNQ/vNHZMdK8Z/SCKSG1N/gSttaH/reh78QmlCTHasFP8hiUhuzOELHNfhMk8cjWWlONcGAda1ZADJm7lMlmioSU9JN0x2rEBJH2j8h6RtyVcREXcIfVYfQ0TcIWxLvmrqkIi0Zi5f4Di03zyxGUvmSqvWLfqHnLzjLAqE0Ogf0tRt4hVhCTGaEpsxSW7MabJETnpqfpjsyFh5H2ja/EOaQ5t4eSwhRlNjvwKSG12+wBkqHv4vmQ8mOzJWkQ80Tf4hLaE2wBJiNAfm9C2YSF9Yo0KlYZ8dGdN3vxxzaRMviyXEaA6KvgUXvT5sFGC/ApIFLiNDJWGyI2P67ihnCZ2aLSFGc1K0WAwXjSGiInIcpcm1sWB+a2Ppmz7XwNmWfLVYm7i59YexhBhNLV2Vi4i4Q8WasY5MbMdvxERWzNL6PHJtLJLos6OcJbSJW0KMpsYOykT0PDn3eWSyYyKWPDTaEkYZWEKMpsQOykT0PDl/CWKfHRPgZG5kapz4zLTk2CeCLJ+c+zyaNNmJjY1Fs2bN4OrqCk9PT3Tt2hUXLlxQO+bRo0cYMWIEqlWrhsqVK6NHjx7IzMxUO+bq1avo3LkzKlWqBE9PT4wfPx5Pnjwx5q1UmLlMaU7Us1lNHJnYDluGtsSRie3Mul1eTvhlh8yVnL8EmbQZ66effsKIESPQrFkzPHnyBJMnT8Zrr72Gc+fOwcXFBQAwevRo7N69G9u3b4dSqURMTAy6d++Oo0ePAgAKCgrQuXNneHt7IzExEenp6RgwYADs7e0xb948U95eieRcTUiWh819xiXnPhEkD3Lt82hWo7Fu374NT09P/PTTT2jdujVUKhU8PDywefNmvP322wCAP/74A0FBQUhKSkLLli3xww8/4I033sDNmzfh5eUFAFi5ciUmTJiA27dvw8HBodzrGnM0FkfBEFmvxNQ76LP6WLHtW4a2RFjtaiaIiMiyVfTz26z67KhUKgBA1apVAQAnTpxAfn4+IiMjpWPq16+PmjVrIikpCQCQlJSEkJAQKdEBgKioKGRlZSElJaXE6+Tl5SErK0vtYSxyriYkorLJuU8EkTkzm9FYhYWFGDVqFCIiItCgQQMAQEZGBhwcHODu7q52rJeXFzIyMqRjnk10ivYX7StJbGwsZs6cqec7qDi5VhMSUdnMbf0mImthNsnOiBEjcPbsWRw5csTg15o0aRLGjBkj/Z6VlQU/Pz+DX/dZ7CtBZJ34ZYfI+Mwi2YmJicGuXbvw888/44UXXpC2e3t74/Hjx7h//75a7U5mZia8vb2lY44fP652vqLRWkXHPM/R0RGOjo56vgsioorhlx0i4zJpnx0hBGJiYvDdd9/h0KFDCAwMVNvftGlT2NvbIyEhQdp24cIFXL16FWFhYQCAsLAwnDlzBrdu3ZKOOXDgANzc3BAcHGycGyEiIiKzZdKanREjRmDz5s34/vvv4erqKvWxUSqVcHZ2hlKpxODBgzFmzBhUrVoVbm5u+PDDDxEWFoaWLVsCAF577TUEBwejf//+WLBgATIyMjBlyhSMGDGCtTdERGTVLHm2fn0y6dBzhUJR4vb4+HgMHDgQwNNJBceOHYstW7YgLy8PUVFRWL58uVoT1V9//YUPPvgAP/74I1xcXBAdHY24uDjY2VUsl5P7QqBEVD5+KFg3Of79LW1RT21U9PPbrObZMRUmO0TWzRo+FKh0cvz7W8ucbhY5zw4RkbFxCRfrZqi/v6nXPytrtn5rZBajsYiITIVLuFg3Q/z9y6spMkaTWdEEls/X7FjrBJas2SEiq8ZZja2bvv/+5dUUGWshWM7Wr441O0RUjBw7a5ZGDrMaW9PfS9/0/fcvr/nImAvBcgLLfzDZISI1cuysWR5L/lAwp7+XpSZd+vz7l9V8ZIomU05g+RSTHSKSlFYFb6hvnubEEj8UzOnvZU5Jlzb09fcvr6aI/WhMg8kOEUlM8c3TUmsDzIG5dK42p6TLHPRsVhP1vV2RfOVvNAuogkZ+VQDIo8nUUjHZISKJsUdwWHptgKkFVneBAsCz+Y4CMHpNgbkkXeairNe1uTSZWtuXDI7GIiJJRUZw6Gv+EM5vYyAlT0xvUBzR9o+KvK59lM4Iq13NZEmGsUaEmRPW7BCRmrK+eeqzJoa1AbpLu/MQzxUhhIDRy5DNM/8w99e1tTY5MtkhomJK6qyp7zdJTnqmO1OUYWnNH+bSPGNq5v66NvdkzFDYjEUlMvVU52R+9D39PCc9052xy7C85g9TN8+YA3N/XVtrkyMXAgUXAn0eO41SSQy1sGC6KtfqawN0VVYZ6qsjqrUsLKkv5vy63pZ8tViTo6W+x1f085vNWKTGWttzqXyG6pdhifPbmJvSypB9rEzHnF/X1tjkyGSH1PANjcpijW+Slop9rCyPMYeDm3MyZghMdkgN39CoPNb2Jmmp9P3FhSOuDIvdBwyLyQ6p4RsakTwY4osLa/YMwxTdB6xtUkEmO1QM39CILB/7WFkOY3cfsMZaJCY7VCK+oRFZPn5xMU/P16oYs/uAtQ5CYbJDRCRj/OJiXkqrVTFW9wFrHYTCZIeIiMgIyqpVMVYtnLUOQuEMykREREZQ3izkxpiB2txneDYU1uwQEREZgbnUqlhjXy7W7BAREelZSesLmlOtirWtY8aaHSIiIj0qa2i3NdaqmAPW7FgYrkZORGS+SuuE/HwNjzXVqpgD1uxYEGucCIqIyJJY69Buc8eaHQtRkW8LRERkWkWdkJ9lDUO7zR2THQtR3pBFIiIyPXPqhEz/YDOWhTCXIYtERFQ2dkI2P6zZsRD8tkBEZDnYCdm8sGbHgvDbAhERkeaY7FgYLupHZD6eX72ayNzwNfoUkx0iIi1wKggyd3yN/oN9doiINMSpIMjc8TWqjskOEZGGOBUEmTu+RtUx2SEi0hAnjiNzx9eoOiY7REQa4lQQZO74GlWnEEKI8g+Tt6ysLCiVSqhUKri5uZk6HCKyEOmqXE4FQWZN7q/Rin5+czQWEZGWDDEVBIcKkz5xupKnmOwQkaxYcrLAocJEhsFkh4hkw5KThdKGCreu52FxSRuRuWEHZSKSBUufV4RDhYkMh8kOEcmCpScLHCpMZDhMdohIFiw9WeBQYSLDYZ8dIpKFomRh8o6zKBDCIpOFns1qonU9D1kPFSYyBSY7RCQbckgWOFSYSP+Y7JDJWfJQYTI/TBaI6HlMdsikLHmoMBGRPvGLn+Ew2SGT4bwiRERP8YufYXE0FpmMpQ8VJiLSB0ufI8oSMNkhk7H0ocJERPrAL36Gx2SHTIbzihAR8YufMbDPDpmUHIYKExHpQg5zRJk7JjtkchwqTETWjl/8DIvJDhERkRngFz/DYZ8dKlG6KheJqXc4GoCIiCwea3aoGM73QEREcsKaHVLD+R6IiEhumOyQGs73QEREcsNkh9RwvgciIpIbJjukhhP9ERGR3LCDMhXD+R6IiEhOmOxQiTjfAxERyQWbsYiIiEjWmOwQERGRrDHZISIiIlljskNERESyJptk54svvkBAQACcnJzQokULHD9+3NQhWTSujUVERHIhi2Rn27ZtGDNmDKZPn46TJ0+iUaNGiIqKwq1bt0wdmkXalnwVEXGH0Gf1MUTEHcK25KumDomIiEhrskh2Fi5ciKFDh2LQoEEIDg7GypUrUalSJaxdu9bUoVkcro1FRERyY/HJzuPHj3HixAlERkZK22xsbBAZGYmkpKQSn5OXl4esrCy1Bz3FtbGIiEhuLD7ZuXPnDgoKCuDl5aW23cvLCxkZGSU+JzY2FkqlUnr4+fkZI1SLwLWxiIhIbiw+2dHGpEmToFKppMe1a9dMHZLZ4NpYREQkNxa/XET16tVha2uLzMxMte2ZmZnw9vYu8TmOjo5wdHQ0RngWiWtjERGRnFh8zY6DgwOaNm2KhIQEaVthYSESEhIQFhZmwsgsm4/SGWG1qzHRISIii2fxNTsAMGbMGERHRyM0NBTNmzfH4sWL8fDhQwwaNMjUoREREZGJySLZ6dmzJ27fvo1p06YhIyMDjRs3xt69e4t1WiYiIiLroxBCiPIPk7esrCwolUqoVCq4ubmZOhwiIiKqgIp+flt8nx0iIiKisjDZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWZDGDsq6K5lXMysoycSRERERUUUWf2+XNj8xkB8CDBw8AAH5+fiaOhIiIiDT14MEDKJXKUvdzuQg8XSX95s2bcHV1hUKh0Pj5WVlZ8PPzw7Vr16x6uQmWw1Msh3+wLJ5iOTzFcvgHy+IpXctBCIEHDx6gRo0asLEpvWcOa3YA2NjY4IUXXtD5PG5ublb9oi3CcniK5fAPlsVTLIenWA7/YFk8pUs5lFWjU4QdlImIiEjWmOwQERGRrDHZ0QNHR0dMnz4djo6Opg7FpFgOT7Ec/sGyeIrl8BTL4R8si6eMVQ7soExERESyxpodIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2amAL774AgEBAXByckKLFi1w/PjxUo9NSUlBjx49EBAQAIVCgcWLFxsvUCPQpCxWr16NV155BVWqVEGVKlUQGRlZ5vGWRJNy2LFjB0JDQ+Hu7g4XFxc0btwYGzZsMGK0hqVJWTxr69atUCgU6Nq1q2EDNBJNymHdunVQKBRqDycnJyNGaziavh7u37+PESNGwMfHB46OjqhXrx727NljpGgNS5OyaNu2bbHXhEKhQOfOnY0YsWFo+ppYvHgxXnzxRTg7O8PPzw+jR4/Go0ePdAtCUJm2bt0qHBwcxNq1a0VKSooYOnSocHd3F5mZmSUef/z4cTFu3DixZcsW4e3tLRYtWmTcgA1I07Lo06eP+OKLL8SpU6fE+fPnxcCBA4VSqRTXr183cuT6pWk5HD58WOzYsUOcO3dOXLp0SSxevFjY2tqKvXv3Gjly/dO0LIqkpaUJX19f8corr4i33nrLOMEakKblEB8fL9zc3ER6err0yMjIMHLU+qdpOeTl5YnQ0FDx+uuviyNHjoi0tDTx448/itOnTxs5cv3TtCzu3r2r9no4e/assLW1FfHx8cYNXM80LYdNmzYJR0dHsWnTJpGWlib27dsnfHx8xOjRo3WKg8lOOZo3by5GjBgh/V5QUCBq1KghYmNjy32uv7+/rJIdXcpCCCGePHkiXF1dxfr16w0VolHoWg5CCNGkSRMxZcoUQ4RnVNqUxZMnT0R4eLj46quvRHR0tCySHU3LIT4+XiiVSiNFZzyalsOKFStErVq1xOPHj40VotHo+j6xaNEi4erqKrKzsw0VolFoWg4jRowQ7du3V9s2ZswYERERoVMcbMYqw+PHj3HixAlERkZK22xsbBAZGYmkpCQTRmZ8+iiLnJwc5Ofno2rVqoYK0+B0LQchBBISEnDhwgW0bt3akKEanLZlMWvWLHh6emLw4MHGCNPgtC2H7Oxs+Pv7w8/PD2+99RZSUlKMEa7BaFMOO3fuRFhYGEaMGAEvLy80aNAA8+bNQ0FBgbHCNgh9vF+uWbMGvXr1gouLi6HCNDhtyiE8PBwnTpyQmrouX76MPXv24PXXX9cpFi4EWoY7d+6goKAAXl5eatu9vLzwxx9/mCgq09BHWUyYMAE1atRQe+FbGm3LQaVSwdfXF3l5ebC1tcXy5cvx6quvGjpcg9KmLI4cOYI1a9bg9OnTRojQOLQphxdffBFr165Fw4YNoVKp8OmnnyI8PBwpKSl6WZTYFLQph8uXL+PQoUPo27cv9uzZg0uXLmH48OHIz8/H9OnTjRG2Qej6fnn8+HGcPXsWa9asMVSIRqFNOfTp0wd37txBq1atIITAkydP8P7772Py5Mk6xcJkh4wiLi4OW7duxY8//iibjpiacHV1xenTp5GdnY2EhASMGTMGtWrVQtu2bU0dmtE8ePAA/fv3x+rVq1G9enVTh2NSYWFhCAsLk34PDw9HUFAQVq1ahdmzZ5swMuMqLCyEp6cnvvzyS9ja2qJp06a4ceMG/v3vf1t0sqOrNWvWICQkBM2bNzd1KEb3448/Yt68eVi+fDlatGiBS5cu4aOPPsLs2bMxdepUrc/LZKcM1atXh62tLTIzM9W2Z2Zmwtvb20RRmYYuZfHpp58iLi4OBw8eRMOGDQ0ZpsFpWw42NjaoU6cOAKBx48Y4f/48YmNjLTrZ0bQsUlNTceXKFXTp0kXaVlhYCACws7PDhQsXULt2bcMGbQD6eJ+wt7dHkyZNcOnSJUOEaBTalIOPjw/s7e1ha2srbQsKCkJGRgYeP34MBwcHg8ZsKLq8Jh4+fIitW7di1qxZhgzRKLQph6lTp6J///4YMmQIACAkJAQPHz7EsGHD8Mknn8DGRrveN+yzUwYHBwc0bdoUCQkJ0rbCwkIkJCSofSuzBtqWxYIFCzB79mzs3bsXoaGhxgjVoPT1migsLEReXp4hQjQaTcuifv36OHPmDE6fPi093nzzTbRr1w6nT5+Gn5+fMcPXG328JgoKCnDmzBn4+PgYKkyD06YcIiIicOnSJSnpBYCLFy/Cx8fHYhMdQLfXxPbt25GXl4d+/foZOkyD06YccnJyiiU0Rcmw0GUpT526N1uBrVu3CkdHR7Fu3Tpx7tw5MWzYMOHu7i4NE+3fv7+YOHGidHxeXp44deqUOHXqlPDx8RHjxo0Tp06dEn/++aepbkFvNC2LuLg44eDgIL755hu1IZUPHjww1S3ohablMG/ePLF//36Rmpoqzp07Jz799FNhZ2cnVq9ebapb0BtNy+J5chmNpWk5zJw5U+zbt0+kpqaKEydOiF69egknJyeRkpJiqlvQC03L4erVq8LV1VXExMSICxcuiF27dglPT08xZ84cU92C3mj7v9GqVSvRs2dPY4drMJqWw/Tp04Wrq6vYsmWLuHz5sti/f7+oXbu2ePfdd3WKg8lOBSxdulTUrFlTODg4iObNm4tffvlF2temTRsRHR0t/Z6WliYAFHu0adPG+IEbgCZl4e/vX2JZTJ8+3fiB65km5fDJJ5+IOnXqCCcnJ1GlShURFhYmtm7daoKoDUOTsnieXJIdITQrh1GjRknHenl5iddff12cPHnSBFHrn6avh8TERNGiRQvh6OgoatWqJebOnSuePHli5KgNQ9Oy+OOPPwQAsX//fiNHalialEN+fr6YMWOGqF27tnBychJ+fn5i+PDh4u+//9YpBoUQutQLEREREZk39tkhIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BARGdHAgQPRtWtXU4dBZFWY7BARgKcfwgqFQnpUq1YNHTt2xO+//27q0PTi2XsrerRq1cpg17ty5QoUCgVOnz6ttv3zzz/HunXrDHZdIiqOyQ4RSTp27Ij09HSkp6cjISEBdnZ2eOONN0wdlt7Ex8dL95eeno6dO3eWeFx+fr7BYlAqlXB3dzfY+YmoOCY7RCRxdHSEt7c3vL290bhxY0ycOBHXrl3D7du30b59e8TExKgdf/v2bTg4OEirGgcEBGD27Nno3bs3XFxc4Ovriy+++ELtOQsXLkRISAhcXFzg5+eH4cOHIzs7W9r/119/oUuXLqhSpQpcXFzw0ksvYc+ePQCAv//+G3379oWHhwecnZ1Rt25dxMfHV/j+3N3dpfvz9vZG1apVpRqYbdu2oU2bNnBycsKmTZtw9+5d9O7dG76+vqhUqRJCQkKwZcsWtfMVFhZiwYIFqFOnDhwdHVGzZk3MnTsXABAYGAgAaNKkCRQKBdq2bQugeDNWXl4eRo4cCU9PTzg5OaFVq1ZITk6W9v/4449QKBRISEhAaGgoKlWqhPDwcFy4cKHC901k7ZjsEFGJsrOzsXHjRtSpUwfVqlXDkCFDsHnzZuTl5UnHbNy4Eb6+vmjfvr207d///jcaNWqEU6dOYeLEifjoo49w4MABab+NjQ2WLFmClJQUrF+/HocOHcLHH38s7R8xYgTy8vLw888/48yZM5g/fz4qV64MAJg6dSrOnTuHH374AefPn8eKFStQvXp1vdxvUaznz59HVFQUHj16hKZNm2L37t04e/Yshg0bhv79++P48ePScyZNmoS4uDgprs2bN8PLywsApOMOHjyI9PR07Nixo8Trfvzxx/j222+xfv16nDx5EnXq1EFUVBTu3bundtwnn3yCzz77DL/++ivs7Ozw3nvv6eW+iayCTsuIEpFsREdHC1tbW+Hi4iJcXFwEAOHj4yNOnDghhBAiNzdXVKlSRWzbtk16TsOGDcWMGTOk3/39/UXHjh3VztuzZ0/RqVOnUq+7fft2Ua1aNen3kJAQtXM+q0uXLmLQoEFa3R8A4eTkJN2fi4uL+O6770RaWpoAIBYvXlzuOTp37izGjh0rhBAiKytLODo6itWrV5d4bNF5T506pbb92ZXes7Ozhb29vdi0aZO0//Hjx6JGjRpiwYIFQgghDh8+LACIgwcPSsfs3r1bABC5ubmaFAGR1WLNDhFJ2rVrh9OnT+P06dM4fvw4oqKi0KlTJ/z1119wcnJC//79sXbtWgDAyZMncfbsWQwcOFDtHGFhYcV+P3/+vPT7wYMH0aFDB/j6+sLV1RX9+/fH3bt3kZOTAwAYOXIk5syZg4iICEyfPl2tg/QHH3yArVu3onHjxvj444+RmJio0f0tWrRIur/Tp0/j1VdflfaFhoaqHVtQUIDZs2cjJCQEVatWReXKlbFv3z5cvXoVAHD+/Hnk5eWhQ4cOGsXwrNTUVOTn5yMiIkLaZm9vj+bNm6uVGQA0bNhQ+tnHxwcAcOvWLa2vTWRNmOwQkcTFxQV16tRBnTp10KxZM3z11Vd4+PAhVq9eDQAYMmQIDhw4gOvXryM+Ph7t27eHv79/hc9/5coVvPHGG2jYsCG+/fZbnDhxQurT8/jxY+kaly9fRv/+/XHmzBmEhoZi6dKlACAlXqNHj8bNmzfRoUMHjBs3rsLX9/b2lu6vTp06cHFxUbv3Z/373//G559/jgkTJuDw4cM4ffo0oqKipDidnZ0rfF19sLe3l35WKBQAnvYZIqLyMdkholIpFArY2NggNzcXABASEoLQ0FCsXr0amzdvLrHfyC+//FLs96CgIADAiRMnUFhYiM8++wwtW7ZEvXr1cPPmzWLn8PPzw/vvv48dO3Zg7NixUrIFAB4eHoiOjsbGjRuxePFifPnll/q8ZcnRo0fx1ltvoV+/fmjUqBFq1aqFixcvSvvr1q0LZ2dnqXP28xwcHAA8rSEqTe3ateHg4ICjR49K2/Lz85GcnIzg4GA93QkR2Zk6ACIyH3l5ecjIyADwdOTTsmXLkJ2djS5dukjHDBkyBDExMXBxcUG3bt2KnePo0aNYsGABunbtigMHDmD79u3YvXs3AKBOnTrIz8/H0qVL0aVLFxw9ehQrV65Ue/6oUaPQqVMn1KtXD3///TcOHz4sJUvTpk1D06ZN8dJLLyEvLw+7du2S9ulb3bp18c033yAxMRFVqlTBwoULkZmZKSUhTk5OmDBhAj7++GM4ODggIiICt2/fRkpKCgYPHgxPT084Oztj7969eOGFF+Dk5ASlUql2DRcXF3zwwQcYP348qlatipo1a2LBggXIycnB4MGDDXJfRNaINTtEJNm7dy98fHzg4+ODFi1aIDk5Gdu3b5eGTQNA7969YWdnh969e8PJyanYOcaOHYtff/0VTZo0wZw5c7Bw4UJERUUBABo1aoSFCxdi/vz5aNCgATZt2oTY2Fi15xcUFGDEiBEICgpCx44dUa9ePSxfvhzA09qSSZMmoWHDhmjdujVsbW2xdetWg5TFlClT8PLLLyMqKgpt27aFt7d3sZmPp06dirFjx2LatGkICgpCz549pX40dnZ2WLJkCVatWoUaNWrgrbfeKvE6cXFx6NGjB/r374+XX34Zly5dwr59+1ClShWD3BeRNVIIIYSpgyAiy3HlyhXUrl0bycnJePnll9X2BQQEYNSoURg1apRpgiMiKgGbsYioQvLz83H37l1MmTIFLVu2LJboEBGZKzZjEVGFHD16FD4+PkhOTi7Wz8bU5s2bh8qVK5f46NSpk6nDIyITYzMWEVm8e/fuFZtxuIizszN8fX2NHBERmRMmO0RERCRrbMYiIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGs/X+x+X5shbnhZwAAAABJRU5ErkJggg==", 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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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", 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", 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", 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", 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", 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I6RSWVUgBqlaNECgqu2yfCrkJu/ZEBQQEwNPTE6WlpWbLS0tL6xxcawUHBzdYvvbf0tJShISEmJWJiYkx2+7MmTMYNGgQ+vbti/fff/+G9Y2Pj8fWrVvrXe/l5QUvL68b7qcxZe4tlr59eKiAtGFRGNG7naLPuWnTJrRo0cJsWUPXbFq3bh327t2LlStXAgDOnTuH8vJydOvWzWL5bt26QQiBgoICxMXFNV7FiYhcVESALzxUMAtSnioVwgN4PUIl2bUnSq1WIzY2FtnZ2dIyk8mE7OxsJCQkWNwmISHBrDwAbN26VSofERGB4OBgszJGoxF5eXlm+/ztt98wcOBAxMbGYtWqVVbd0PbAgQNmwcze6uu+VbpHatCgQThw4IDZ48MPP7RYdvv27Rg/fjw++OAD3HbbbWbrhBAWtyEiItuEaL2RNiwKnv+7jp6nSoXXh/XgUJ7C7H7bl2nTpmHcuHHo1asX4uLisGTJElRUVGD8+PEAgLFjx6JNmzZIS0sD8Mf8msTERLz11lsYPHgw1q5dix9//FHqSVKpVJg6dSpee+01dO7cGREREZg9ezZCQ0ORnJwM4P8CVPv27fHmm2/i3LlzUn1qe7JWr14NtVqNnj17AgA2bNiAjz76qN6wYA8Ndd8q+Yfj6+uLTp06mS07ffp0nXLff/89HnroIbzzzjsYO3astDwwMBD+/v44evSoxf0fPXoUKpWqznMQEVH9RvRuhwFdAlFUdhnhAT4MUE3A7iFqxIgROHfuHObMmQO9Xo+YmBhs3rxZmhheXFxs1kvUt29frFmzBrNmzcLMmTPRuXNnbNy4ET169JDKvPzyy6ioqMDEiRNRXl6O/v37Y/PmzdBoNAD+6LkqKChAQUEB2rZta1af63tHFixYgF9//RXNmjVDZGQkMjMz8eijjyr5ctjEkbtvd+zYgQcffBBvvPEGJk6caLbOw8MDw4cPxyeffIL58+ebDd1WVlbivffeg06ns3i2JBER1S9E683w1IRUgmMqijEajdBqtTAYDPDz85OWV1VVobCwEBEREVKwkytzbzFmbjiEGiGk7lsl50Q9+eSTKC8vx8aNG82W79ixA4MGDcKFCxewf/9+PPjgg5gyZQpeeOEFqYxarZaC0e+//474+Hh4e3tj0aJF6NGjBwoLCzFr1iz88ssvyM3NRYcOHSzWoTFfPyIioj+r7/j9Z3bviaKb44jdt6tXr8bly5eRlpYmDcMCQGJiInbs2AEAuPXWW/HDDz9g/vz5mDRpEvR6PVq1aoX7778fH3/8Mdq1U3ZyPBER0c1iT5SCmqInyh3x9SMiIiVZ2xPl9teJIiIiIpKDIYqIiIhIBoYoIiIiIhkYooiIiIhkYIiyI87pl4evGxEROQKGKDto3rw5AODyZd4YUo7q6moAgKenp51rQkRE7ozXibIDT09P+Pv74+zZswAAHx8fqP53vyNqmMlkwrlz5+Dj44NmzfjrS0RE9sOjkJ3U3uqkNkiR9Tw8PNCuXTsGTyIisiuGKDtRqVQICQlB69atcfXqVXtXx6mo1Wqz+ykSERHZA0OUnXl6enJuDxERkRPi13kiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkcIkQtX74c4eHh0Gg0iI+Px549exosv379ekRGRkKj0SAqKgpZWVlm64UQmDNnDkJCQuDt7Y2kpCQcP35cWl9UVIQJEyYgIiIC3t7e6NixI1JTU1FdXW22n59//hl33nknNBoNwsLCsGjRosZrNBE5tRJDJXJOlKHEUGnvqhCRndg9RGVmZmLatGlITU3Fvn37EB0dDZ1Oh7Nnz1osn5OTg1GjRmHChAnYv38/kpOTkZycjEOHDkllFi1ahKVLlyI9PR15eXnw9fWFTqdDVVUVAODYsWMwmUxYuXIlDh8+jHfeeQfp6emYOXOmtA+j0Yh7770X7du3R35+PhYvXoy5c+fi/fffV/YFISKHl7m3GP0WbsPoD/LQb+E2ZO4ttneViMgOVEIIYc8KxMfHo3fv3nj33XcBACaTCWFhYXj++eeRkpJSp/yIESNQUVGBTZs2Scv69OmDmJgYpKenQwiB0NBQvPjii5g+fToAwGAwICgoCBkZGRg5cqTFeixevBgrVqzAyZMnAQArVqzAq6++Cr1eD7VaDQBISUnBxo0bcezYMavaZjQaodVqYTAY4OfnZ/2LQkQOq8RQiX4Lt8F03Senp0qFXSmDEKL1tl/FiKjRWHv8tmtPVHV1NfLz85GUlCQt8/DwQFJSEnJzcy1uk5uba1YeAHQ6nVS+sLAQer3erIxWq0V8fHy9+wT+CFqtWrUye54BAwZIAar2eX755RdcuHDB4j6uXLkCo9Fo9iAi11JYVmEWoACgRggUlV22T4WIyG7sGqLKyspQU1ODoKAgs+VBQUHQ6/UWt9Hr9Q2Wr/3Xln0WFBRg2bJlmDRp0g2f5/rn+LO0tDRotVrpERYWZrEcETmviABfeKjMl3mqVAgP8LFPhYjIbuw+J8refvvtN9x333147LHH8PTTT9/UvmbMmAGDwSA9Tp061Ui1JCJHEaL1RtqwKHiq/khSnioVXh/Wg0N5RG6omT2fPCAgAJ6enigtLTVbXlpaiuDgYIvbBAcHN1i+9t/S0lKEhISYlYmJiTHb7syZMxg0aBD69u1bZ8J4fc9z/XP8mZeXF7y8vCyuIyLXMaJ3OwzoEoiisssID/BhgCJyU3btiVKr1YiNjUV2dra0zGQyITs7GwkJCRa3SUhIMCsPAFu3bpXKR0REIDg42KyM0WhEXl6e2T5/++03DBw4ELGxsVi1ahU8PMxfioSEBOzcuRNXr141e56uXbuiZcuW8htNRC4hROuNhI63MkARuTNhZ2vXrhVeXl4iIyNDHDlyREycOFH4+/sLvV4vhBBizJgxIiUlRSq/e/du0axZM/Hmm2+Ko0ePitTUVNG8eXNx8OBBqczChQuFv7+/+PLLL8XPP/8shg4dKiIiIkRlZaUQQojTp0+LTp06ibvvvlucPn1alJSUSI9a5eXlIigoSIwZM0YcOnRIrF27Vvj4+IiVK1da3TaDwSAACIPBcLMvExERETURa4/fdh3OA/64ZMG5c+cwZ84c6PV6xMTEYPPmzdIk7uLiYrNeor59+2LNmjWYNWsWZs6cic6dO2Pjxo3o0aOHVObll19GRUUFJk6ciPLycvTv3x+bN2+GRqMB8EePUkFBAQoKCtC2bVuz+oj/XfFBq9Viy5YtmDx5MmJjYxEQEIA5c+Zg4sSJSr8kRERE5ATsfp0oV8brRBERETkfp7hOFBEREZGzYogiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZ7B6ili9fjvDwcGg0GsTHx2PPnj0Nll+/fj0iIyOh0WgQFRWFrKwss/VCCMyZMwchISHw9vZGUlISjh8/blbm73//O/r27QsfHx/4+/tbfB6VSlXnsXbt2ptqKxEREbkOu4aozMxMTJs2Dampqdi3bx+io6Oh0+lw9uxZi+VzcnIwatQoTJgwAfv370dycjKSk5Nx6NAhqcyiRYuwdOlSpKenIy8vD76+vtDpdKiqqpLKVFdX47HHHsNf//rXBuu3atUqlJSUSI/k5ORGaTcRERE5P5UQQtjryePj49G7d2+8++67AACTyYSwsDA8//zzSElJqVN+xIgRqKiowKZNm6Rlffr0QUxMDNLT0yGEQGhoKF588UVMnz4dAGAwGBAUFISMjAyMHDnSbH8ZGRmYOnUqysvL6zyXSqXCF198cVPByWg0QqvVwmAwwM/PT/Z+iIiIqOlYe/y2W09UdXU18vPzkZSU9H+V8fBAUlIScnNzLW6Tm5trVh4AdDqdVL6wsBB6vd6sjFarRXx8fL37bMjkyZMREBCAuLg4fPTRR7hR3rxy5QqMRqPZg4iIiFxTM3s9cVlZGWpqahAUFGS2PCgoCMeOHbO4jV6vt1her9dL62uX1VfGWvPnz8ddd90FHx8fbNmyBc8++ywuXbqEF154od5t0tLSMG/ePJueh4iIiJyT3UKUo5s9e7b0/549e6KiogKLFy9uMETNmDED06ZNk342Go0ICwtTtJ5ERERkH3YbzgsICICnpydKS0vNlpeWliI4ONjiNsHBwQ2Wr/3Xln1aKz4+HqdPn8aVK1fqLePl5QU/Pz+zBxEREbkmu4UotVqN2NhYZGdnS8tMJhOys7ORkJBgcZuEhASz8gCwdetWqXxERASCg4PNyhiNRuTl5dW7T2sdOHAALVu2hJeX103th4iIiFyDXYfzpk2bhnHjxqFXr16Ii4vDkiVLUFFRgfHjxwMAxo4dizZt2iAtLQ0AMGXKFCQmJuKtt97C4MGDsXbtWvz44494//33AfxxRt3UqVPx2muvoXPnzoiIiMDs2bMRGhpqdpZdcXExzp8/j+LiYtTU1ODAgQMAgE6dOqFFixb4+uuvUVpaij59+kCj0WDr1q14/fXXpTP+iIiIiCDsbNmyZaJdu3ZCrVaLuLg48cMPP0jrEhMTxbhx48zKr1u3TnTp0kWo1Wpx2223iW+++cZsvclkErNnzxZBQUHCy8tL3H333eKXX34xKzNu3DgBoM5j+/btQggh/v3vf4uYmBjRokUL4evrK6Kjo0V6erqoqamxqW0Gg0EAEAaDwabtiIiIyH6sPX7b9TpRro7XiSIiInI+Dn+dKCIiIiJnxhBFREREJANDFBEREZEMDFFEREREMjBEEREREcnAEEVEREQkA0MUERERkQwMUUREREQyMEQRERERycAQRURERCQDQxQRERGRDAxRRERERDIwRBERERHJwBBFREREJANDFBEREZEMzeRuWF5ejoKCAgBAp06d4O/v31h1IiIiInJ4NvdEFRUVYfDgwQgICEB8fDzi4+MREBCABx98EEVFRQpUkYiIiMjx2NQTderUKfTp0wfNmzfHggUL0K1bNwDAkSNHsGLFCiQkJGDv3r1o27atIpUlIiIichQqIYSwtvCECRNQUFCAb7/9FhqNxmxdZWUl7rvvPnTu3Bkffvhho1fUGRmNRmi1WhgMBvj5+dm7OkRERGQFa4/fNvVEbd68GZmZmXUCFAB4e3tjwYIFGDlypO21JSIiInIyNs2JKisrQ3h4eL3rO3TogPPnz99snYiIiIgcnk0hKiQkBEeOHKl3/aFDhxAcHHzTlSIiIiJydDaFqOTkZEyfPh3nzp2rs+7s2bN45ZVXkJyc3Fh1IyIiInJYNk0sv3DhAuLj46HX6/HEE08gMjISQggcPXoUa9asQXBwMH744Qe0atVKyTo7DU4sJyIicj6KTCxv2bIl8vLyMHPmTKxduxbl5eUAAH9/f4wePRqvv/46AxQRERG5BZt6oq4nhJCG9QIDA6FSqRq1Yq6APVFERETOR5GeqOupVCq0bt1a7uZERERETs3m275kZWXhqaeewssvv4yjR4+arbtw4QLuuuuuRqscERERkaOyKUStWbMGQ4YMgV6vR25uLu644w588skn0vrq6mp8//33jV5JIiIiIkdj03De4sWL8fbbb+OFF14AAKxbtw5/+ctfUFVVhQkTJihSQSIiIiJHZFOIOn78OB566CHp5+HDhyMwMBBDhgzB1atX8fDDDzd6BYmIiIgckU0hys/PD6WlpYiIiJCWDRo0CJs2bcKDDz6I06dPN3oFiYiIiByRTXOi4uLi8O9//7vO8sTERHz99ddYsmRJY9WLiIiIyKHZFKL+9re/QaPRWFw3cOBAfP311xg7dmyjVIyIiIjIkdl0sU2j0WhVOV5Y8g+82CYREZHzUeRim/7+/lZdmbympsaW3RIRERE5HZtC1Pbt26X/CyHwwAMP4MMPP0SbNm0avWJEREREjsymEJWYmGj2s6enJ/r06YMOHTo0aqWIiIiIHJ3Nt30hIiIiIoYoIiIiIlluOkRZM9GciIiIyNXYNCdq2LBhZj9XVVXhmWeega+vr9nyDRs23HzNiIiIiByYTSFKq9Wa/fzEE080amWIiIiInIVNIWrVqlVK1YOIiIjIqXBiOREREZEMDFFEREREMjBEuYESQyVyTpShxFBp76oQERG5DJvmRJHzydxbjBkbDsIkAA8VkDYsCiN6t7N3tYiIiJwee6JcWImhUgpQAGASwMwNh9gjRURE1AgYolxYYVmFFKBq1QiBorLL9qkQERGRC2GIcmERAb7w+NMF5T1VKoQH+NinQkRERC6EIcqFhWi9kTYsCp7/uzWPp0qF14f1QIjW2841IyIicn6cWO7iRvRuhwFdAlFUdhnhAT4MUERERI2EIcoNhGi9GZ6IiIgaGYfziIiIiGRgiCIiIiKSgSGKiIiISAa7h6jly5cjPDwcGo0G8fHx2LNnT4Pl169fj8jISGg0GkRFRSErK8tsvRACc+bMQUhICLy9vZGUlITjx4+blfn73/+Ovn37wsfHB/7+/hafp7i4GIMHD4aPjw9at26Nl156CdeuXbupthIREZHrsGuIyszMxLRp05Camop9+/YhOjoaOp0OZ8+etVg+JycHo0aNwoQJE7B//34kJycjOTkZhw4dksosWrQIS5cuRXp6OvLy8uDr6wudToeqqiqpTHV1NR577DH89a9/tfg8NTU1GDx4MKqrq5GTk4PVq1cjIyMDc+bMadwXgIiIiJyXsKO4uDgxefJk6eeamhoRGhoq0tLSLJYfPny4GDx4sNmy+Ph4MWnSJCGEECaTSQQHB4vFixdL68vLy4WXl5f49NNP6+xv1apVQqvV1lmelZUlPDw8hF6vl5atWLFC+Pn5iStXrljdPoPBIAAIg8Fg9TZERERkX9Yev+3WE1VdXY38/HwkJSVJyzw8PJCUlITc3FyL2+Tm5pqVBwCdTieVLywshF6vNyuj1WoRHx9f7z7re56oqCgEBQWZPY/RaMThw4fr3e7KlSswGo1mDyIiInJNdgtRZWVlqKmpMQsqABAUFAS9Xm9xG71e32D52n9t2actz3P9c1iSlpYGrVYrPcLCwqx+TmdWYqhEzoky3tiYiIjcit0nlruSGTNmwGAwSI9Tp07Zu0qKy9xbjH4Lt2H0B3not3AbMvcW27tKRERETcJuISogIACenp4oLS01W15aWorg4GCL2wQHBzdYvvZfW/Zpy/Nc/xyWeHl5wc/Pz+zhykoMlZix4SBM4o+fTQKYueEQe6SIiMgt2C1EqdVqxMbGIjs7W1pmMpmQnZ2NhIQEi9skJCSYlQeArVu3SuUjIiIQHBxsVsZoNCIvL6/efdb3PAcPHjQ7S3Dr1q3w8/ND9+7drd6Pqyssq5ACVK0aIVBUdtk+FSIiImpCdr133rRp0zBu3Dj06tULcXFxWLJkCSoqKjB+/HgAwNixY9GmTRukpaUBAKZMmYLExES89dZbGDx4MNauXYsff/wR77//PgBApVJh6tSpeO2119C5c2dERERg9uzZCA0NRXJysvS8xcXFOH/+PIqLi1FTU4MDBw4AADp16oQWLVrg3nvvRffu3TFmzBgsWrQIer0es2bNwuTJk+Hl5dWkr5EjiwjwhYcKZkHKU6VCeIBPg9uVGCpRWFaBiABf3tOPiIicll1D1IgRI3Du3DnMmTMHer0eMTEx2Lx5szSJu7i4GB4e/9dZ1rdvX6xZswazZs3CzJkz0blzZ2zcuBE9evSQyrz88suoqKjAxIkTUV5ejv79+2Pz5s3QaDRSmTlz5mD16tXSzz179gQAbN++HQMHDoSnpyc2bdqEv/71r0hISICvry/GjRuH+fPnK/2SOJUQrTfShkVh5oZDqBECnioVXh/Wo8FglLm3WBoC9FABacOiMKJ3uyasNRERUeNQCSHEjYuRHEajEVqtFgaDwaXnR5UYKlFUdhnhAT4NBqgSQyX6LdxWp+dqV8og9kgREZHDsPb4zbPzqFEI3DiLcw4VERG5ErsO55Hzs2V4Tu4cKiIiIkfEniiSzdZLHNTOofJUqQDAqjlUREREjoo9USRbQ8Nz9QWjEb3bYUCXQKvmUBERETkyhiiSTe7wXIjWm+GJiIicHofzSDYOzxERkTtjTxTdFA7PERGRu2KIopvG4TkiInJHHM4jIiIikoEhioiIGlRiqETOibJ6L19C5K44nEdERPXi/S6J6seeKCIissjWC+oSuRuGKCIisoj3uyRqGEMUOTTOxSCyn9oL6l6P97skR+EIxwfOiSKHxbkYRPZVe0HdmRsOoUYIXlCXHIajHB9UQghx42Ikh9FohFarhcFggJ+fn72r41RKDJXot3BbnVvK7EoZxA9woiZWYqjkBXVJcT+duoA9RecRF94K0WEt6y3XFMcHa4/f7IkihyTn5sZEpAxeUJeU9uK6A/h832/Sz4/c0QZvDY+xWNaRjg+cE0UOiXMxiIjcw0+nLpgFKAD4fN9v+OnUBYvlHen4wBBFDok3NyYicg97is5bXP5jkeUQ5UjHBw7nkcPizY2JiFxfXHgri8t7hdc/L8pRjg8MUeTQOBeDiMi1RYe1xCN3tKkzJ6qhyeWAYxwfGKKIiIjIrt4aHoOxCe3xY9EF9ApvecMA5SgYooiIiMjuosOcJzzV4sRyIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiokZXYqhEzokylBgq7V0VxfDeeURERNSoMvcWY8aGgzAJwEMFpA2Lwoje7exdrUbHnigiIiJqNCWGSilAAYBJADM3HHLJHimGKCIiImo0hWUVUoCqVSMEisou26dCCmKIIiIiokYTEeALD5X5Mk+VCuEBPvapkIIYooiIiKjRhGi9kTYsCp6qP5KUp0qF14f1QIjW2841a3ycWE5ERESNakTvdhjQJRBFZZcRHuDjkgEKYIgiIiIiBYRovV02PNXicB4RERGRDAxRRERERDIwRBERERHJwBBFREREJANDFBEREZEMDFFEREREMjBEEZFTcIc7whORc+F1oojI4bnLHeGJyLmwJ4qI7MLaniVXuCM8e9GIXBN7ooioydnSs9TQHeEbuhpyiaEShWUViAjwtetVk9mLRuS62BNFRE3K1p4lOXeEz9xbjH4Lt2H0B3not3AbMvcWN1b1bSK3F409V0TOgSGKSEE8GNbVUM+SJbbeEd6Rhv9sbSvgOAGQiG6Mw3lECuEwjmW1PUvXh4sb9SzZckd4ucN/SrC1rfUFwAFdAl3+Rq5Ezog9UUQKcKTeEEdja8/S9dsldLz1huXkDP8pxda2yum5IiL7YU8UkQIcqTeklqNMtAZs61myVW1wmbnhEGqEsDqkKcWWtsrppSMi+3GInqjly5cjPDwcGo0G8fHx2LNnT4Pl169fj8jISGg0GkRFRSErK8tsvRACc+bMQUhICLy9vZGUlITjx4+blTl//jwef/xx+Pn5wd/fHxMmTMClS5ek9UVFRVCpVHUeP/zwQ+M1nFyWI/WGAI47z0ZA3LiQDCN6t8OulEH49Ok+2JUyyO7DqNb2osntpSMi+7B7iMrMzMS0adOQmpqKffv2ITo6GjqdDmfPnrVYPicnB6NGjcKECROwf/9+JCcnIzk5GYcOHZLKLFq0CEuXLkV6ejry8vLg6+sLnU6Hqqoqqczjjz+Ow4cPY+vWrdi0aRN27tyJiRMn1nm+7777DiUlJdIjNja28V8EcjmOdDBsqqFFWybRN0Wosza4OBpHC4BEVD+VEEKZr4JWio+PR+/evfHuu+8CAEwmE8LCwvD8888jJSWlTvkRI0agoqICmzZtkpb16dMHMTExSE9PhxACoaGhePHFFzF9+nQAgMFgQFBQEDIyMjBy5EgcPXoU3bt3x969e9GrVy8AwObNm/HAAw/g9OnTCA0NRVFRESIiIrB//37ExMTIapvRaIRWq4XBYICfn5+sfZBzKzFU2jRkpcSQW86JMoz+IK/O8k+f7oOEjrc2ynPYMom+xFCJfgu31Rmy2pUyyOkCDxG5JmuP33btiaqurkZ+fj6SkpKkZR4eHkhKSkJubq7FbXJzc83KA4BOp5PKFxYWQq/Xm5XRarWIj4+XyuTm5sLf318KUACQlJQEDw8P5OWZH2yGDBmC1q1bo3///vjqq68abM+VK1dgNBrNHuTebOkNUap3RumhRVt7ujh52vXx0h7kLuwaosrKylBTU4OgoCCz5UFBQdDr9Ra30ev1DZav/fdGZVq3bm22vlmzZmjVqpVUpkWLFnjrrbewfv16fPPNN+jfvz+Sk5MbDFJpaWnQarXSIyws7EYvAREAZYfclB5atDUUOdp8MWpcjjr/jkgJPDuvHgEBAZg2bZr0c+/evXHmzBksXrwYQ4YMsbjNjBkzzLYxGo0MUjfJkc4oU5LSZ/MpeTacrWeUOdrZc67AUf5OeJ0rcjd2DVEBAQHw9PREaWmp2fLS0lIEBwdb3CY4OLjB8rX/lpaWIiQkxKxM7dym4ODgOhPXr127hvPnz9f7vMAf87e2bt1a73ovLy94eXnVu55s404Xq5R7arstB88QrbciBzI5oUjJUOduHOnvxBEv7UGkJLsO56nVasTGxiI7O1taZjKZkJ2djYSEBIvbJCQkmJUHgK1bt0rlIyIiEBwcbFbGaDQiLy9PKpOQkIDy8nLk5+dLZbZt2waTyYT4+Ph663vgwAGzYEbKcbeLVYZovfFwzzZmy5J7hjZ44HGkYRM5Z5Q569lzjsTR/k44VEvuxu7DedOmTcO4cePQq1cvxMXFYcmSJaioqMD48eMBAGPHjkWbNm2QlpYGAJgyZQoSExPx1ltvYfDgwVi7di1+/PFHvP/++wAAlUqFqVOn4rXXXkPnzp0RERGB2bNnIzQ0FMnJyQCAbt264b777sPTTz+N9PR0XL16Fc899xxGjhyJ0NBQAMDq1auhVqvRs2dPAMCGDRvw0Ucf4cMPP2ziV8jxKTGU4G7faEsMlfhi/29myzbuP4Ppuq4W29sUwya2vq9K9XRR/Rzt74RDteRu7B6iRowYgXPnzmHOnDnQ6/WIiYnB5s2bpYnhxcXF8PD4vw6zvn37Ys2aNZg1axZmzpyJzp07Y+PGjejRo4dU5uWXX0ZFRQUmTpyI8vJy9O/fH5s3b4ZGo5HKfPLJJ3juuedw9913w8PDA4888giWLl1qVrcFCxbg119/RbNmzRAZGYnMzEw8+uijCr8izkWpoQRHvXKzUnNPbD0YKn3wdKQhIqqfI/6dcKiWmoojzAW0+3WiXJmrXydK6ev9ZO4trvON1p4HciWDha2vpZKvPa/j5Fwc7e9EaY5w4CT7U/qLnrXHb7v3RJHzcuYzymyl9PCZrcMgSg6bONoQETXMkf5OlMYeUgIc6yxQhiiSrSmGEhxlnk1TBAtbD4ZKHTwdcYiIGuYofydKcqQDJ9mXI33Rs/u988h5OdL94ZTWVGcd2XrGmhJnuLnT+3oznPmq3M5Yd17pnmo50lmg7Imim+IuQwnudtaRu7yvcjnzsJKz1p09pFTLkT6PObFcQa4+sdwd2XpDYXI9zjzx3pnrDrjfJHpqmJKfx5xYTrLx7Jf6ucPck6birL9njjQfw1bOXHeAPaRkzhE+jxmiyIyzdvWTc3Hm3zNnHlZy5rrXcoQDJ1EtTiwniaPdQoJck7P/njnzxHtnrjuRI2JPlBuwdtjE2bv6XYGzDnHZwhV+z5x5WMmZ60725Q6fT7ZiiHJxtgybuEJXvzNz5iEuW7jK75kjDSvxPoekNHf5fLIVh/NcmK3DJuzqtx9nH+KyhTv+nil5XabMvcXot3AbRn+Qh34LtyFzb3GjPwe5N3f6fLIVe6JcmJxhE3b124crDHHZQs7vmdJDCUrtX+l7LvIq3qQ0d/t8sgVDlAuTO2zCrv6m5ypDXLaw5fdM6aEEpfavdMjhwY2agjt+PlmLw3kuzB2HTZwV36v6KT2UoOT+lb5ViSPd/sJVOOMtcZTGz6f6sSfKxXF4znko/V4565k1Sve2KLl/pb/BO9LtL1wBJ0/Xj8cSyxii3ACH55yHUu+VMx8clA4iSu6/KUIOD26Ng/PLbozHkroYoohcnLMfHOQGEWt73pQOOk0Rcnhwu3mcX0ZyMEQRuThXODjYGkRs7XlTOugw5Dg+Tp62P2eccsAQReTiXOXgYG0QkdvzxqDj3ji/zL6cdcoBQxSRi3O3g4Mr9LyRfXB+mX0485QDhigiNzCidztEBt+CvUUX0Du8JaLDWtq7SopxlZ43sg/2SDY9Z/7iw+tEEbmBzL3FePi9HLz2zVE8/F6OS98ahNe0IXIuzny9M5UQQty4GMlhNBqh1WphMBjg5+dn7+qQBY42kVGJ+pQYKtFv4bY6PTO7UgY5RJuVUmKo5LAMkZPI3FtcZ8qBPedEWXv85nAeuS1Hm8ioVH2cuav8ZnBYhsh5OOt8NA7nkVtytLuSK1kfZ+4qJyL3EaL1RkLHW50mQAEMUeSmlL6nWS1r78OlZH04R4iISBkcziO31BRncGXuLUbK5wchAKgALHyk/uE5pevjrF3lRK7G0eZh0s1hTxS5JaV7Z0oMlVKAAgABIOXzg/X2SDVFb5EzdpUTuZLMvcXot3AbRn+Qh34Lt7n0WbLugj1R5LaU7J35seg8/nzaqwCQX3QBD0Zbfh459eG3WiLn4MwXlKT6MUSRW1PqDC6VSlXP8sarj6OdXUhE9XPXs2RdHYfziBQQ274l/pyXVCrgjvaNc6VwRzu7kIgaxrNkXRNDFJECQrTeWPhIlPSh6aECFg6LarRvnE11diGRu7H2jFpbyzfVWbK21p9uDofziBSi5Jwr3h+OqPHZOkRua3mlz5LlEH/TY08UkYKUOiOO136ipuQOvRu2DpHXnoF7ffmUDfWfgVtLqc8EDvHbB3uiiJwUr/1ETcFdejdsnfid/+uFumfgCmDfrxcw+PbG+1u09gxcTly3D4YoIifG+8PZjztcXsKdTsu3dYhciD9HqNrljVcnWwIsh/jtg8N5RGQXzjxE5C4XTXSnExhsHSLvFd6q7hm4AGLD7XMGLof47YM9UUTU5Jx5iIi9M67bu2HLEHntGbgzPj8IE/7okUh7pGnOwK3vOTjE3/QYooioSTl7CHGnuSe1vRszNxxCjRBu0bthyxC5I56ByyH+psUQRURNytlDCHtnHP89akpKhRZ3DLDOiCGKiJqUs4cQdzy4sXfDPpw9wLrDyRcMUUROzBk/pFwhhDj7wc3ROOPvcVNx1gDrzPMebaES9Z2nSTfNaDRCq9XCYDDAz8/P3tUhF+PsH1IlhkqGEHL632OlOWPALDFUot/CbXV6m3elDHKaNlh7/GZPFDU5Z/xQcDTOPjkbcN5v2NR4muL32NbPG0f6fHLWgOns8x5twRDlhBzpj9xWzvqh4Gjc6UOKXJfSv8dK3wtPSc78RcnZ5z3aghfbdDLOfJE/3tup8dR+SF3PVT+kyHXJ+T229iKtcu6F50ifT858oVN3uvAne6KciDN/MwHYe9KYXGFyNpGtv8e29BTZ+nnjaJ9Pzt6b4y4nXzBEORFH+yO3la/a0+JyHzU7ROVwlw8pcm3W/h7b+iXS1hAiN7QoNb3CEb8o2dpWd5j3yBDlRJz9m0lFdY3F5ZerTU1cE9fhDh9S5Pqs+T229UukrSFETmhReg6VI31RcqT5Yo6EIcqJOOI3E1s4ewgEnHtSP5Ezk/P5YWsIsaV8U02vcIQvSs4+lURJDFFOxpG+mdjK2UMgv4kR2Y/czw9bQ4i15Z19eoUt3KmttmKIckKO8M1ELmcNgfwmRmR/cj4/lOo9doWedWu5U1ttxRm91ORCtN5I6HirU4UPZz7dmMjRWXvZAsC2zw8lLwnjTqfxu1NbbcWeKCIr8JsYkTKUGiZvit5jZ+1Zl8Od2moL9kRRHbZ8K3QX/CZG1PiUvMBlU/UeO2PPulzu1FZrsSeKzHDydP1G9G6HyOBbsLfoAnqHt0R0WEt7V4nIqSk5YZm9x9QU2BNFEke77YGjydxbjIffy8Fr3xzFw+/lONUtd4gckZK3L2LvMTUFhwhRy5cvR3h4ODQaDeLj47Fnz54Gy69fvx6RkZHQaDSIiopCVlaW2XohBObMmYOQkBB4e3sjKSkJx48fNytz/vx5PP744/Dz84O/vz8mTJiAS5cumZX5+eefceedd0Kj0SAsLAyLFi1qnAY3MWuH5zh5un4MmESNT+mgM6J3O+xKGYRPn+6DXSmD2KtOjc7uISozMxPTpk1Damoq9u3bh+joaOh0Opw9e9Zi+ZycHIwaNQoTJkzA/v37kZycjOTkZBw6dEgqs2jRIixduhTp6enIy8uDr68vdDodqqqqpDKPP/44Dh8+jK1bt2LTpk3YuXMnJk6cKK03Go2499570b59e+Tn52Px4sWYO3cu3n//feVeDAXYcnYKb2pbPwZMImUoHXQ4j4eUpBJCiBsXU058fDx69+6Nd999FwBgMpkQFhaG559/HikpKXXKjxgxAhUVFdi0aZO0rE+fPoiJiUF6ejqEEAgNDcWLL76I6dOnAwAMBgOCgoKQkZGBkSNH4ujRo+jevTv27t2LXr16AQA2b96MBx54AKdPn0ZoaChWrFiBV199FXq9Hmq1GgCQkpKCjRs34tixY1a1zWg0QqvVwmAwwM/P76ZeJzlKDJXot3BbnTkBu1IGNXiDzz9fzI7f3uS9lkRE5JysPX7btSequroa+fn5SEpKkpZ5eHggKSkJubm5FrfJzc01Kw8AOp1OKl9YWAi9Xm9WRqvVIj4+XiqTm5sLf39/KUABQFJSEjw8PJCXlyeVGTBggBSgap/nl19+wYULFyzW7cqVKzAajWYPe5LTe8Lub8s4v4KIiP7MrmfnlZWVoaamBkFBQWbLg4KC6u3t0ev1Fsvr9Xppfe2yhsq0bt3abH2zZs3QqlUrszIRERF19lG7rmXLumdmpaWlYd68efU3uInJPTvFma+IriReJ4WIiK5n9zlRrmTGjBkwGAzS49SpU3atD3tPGh/nVxARUS279kQFBATA09MTpaWlZstLS0sRHBxscZvg4OAGy9f+W1paipCQELMyMTExUpk/T1y/du0azp8/b7YfS89z/XP8mZeXF7y8vOptrz2w94SIiEgZdu2JUqvViI2NRXZ2trTMZDIhOzsbCQkJFrdJSEgwKw8AW7dulcpHREQgODjYrIzRaEReXp5UJiEhAeXl5cjPz5fKbNu2DSaTCfHx8VKZnTt34urVq2bP07VrV4tDeY6MvSdEREQKEHa2du1a4eXlJTIyMsSRI0fExIkThb+/v9Dr9UIIIcaMGSNSUlKk8rt37xbNmjUTb775pjh69KhITU0VzZs3FwcPHpTKLFy4UPj7+4svv/xS/Pzzz2Lo0KEiIiJCVFZWSmXuu+8+0bNnT5GXlyd27dolOnfuLEaNGiWtLy8vF0FBQWLMmDHi0KFDYu3atcLHx0esXLnS6rYZDAYBQBgMhpt5iYiIiKgJWXv8tnuIEkKIZcuWiXbt2gm1Wi3i4uLEDz/8IK1LTEwU48aNMyu/bt060aVLF6FWq8Vtt90mvvnmG7P1JpNJzJ49WwQFBQkvLy9x9913i19++cWszO+//y5GjRolWrRoIfz8/MT48ePFxYsXzcr89NNPon///sLLy0u0adNGLFy40KZ2MUQRERE5H2uP33a/TpQrs/d1ooiIiMh2TnGdKCIiIiJnxRBFREREJANDFBEREZEMDFFEREREMjBEEREREcnAEEVEREQkA0MUERERkQwMUUREREQy2PUGxK6u9jqmRqPRzjUhIiIia9Uet290PXKGKAVdvHgRABAWFmbnmhAREZGtLl68CK1WW+963vZFQSaTCWfOnMEtt9wClUrVaPs1Go0ICwvDqVOnXPJ2Mq7ePsD12+jq7QNcv41sn/Nz9TYq2T4hBC5evIjQ0FB4eNQ/84k9UQry8PBA27ZtFdu/n5+fS/5h1HL19gGu30ZXbx/g+m1k+5yfq7dRqfY11ANVixPLiYiIiGRgiCIiIiKSgSHKCXl5eSE1NRVeXl72rooiXL19gOu30dXbB7h+G9k+5+fqbXSE9nFiOREREZEM7IkiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiHIQy5cvR3h4ODQaDeLj47Fnz54Gyy9ZsgRdu3aFt7c3wsLC8Le//Q1VVVU3tU8lNXb75s6dC5VKZfaIjIxUuhn1sqV9V69exfz589GxY0doNBpER0dj8+bNN7XPptDYbXSk93Dnzp146KGHEBoaCpVKhY0bN95wmx07duCOO+6Al5cXOnXqhIyMjDplHOU9VKJ9zvz+lZSUYPTo0ejSpQs8PDwwdepUi+XWr1+PyMhIaDQaREVFISsrq/ErbyUl2piRkVHnPdRoNMo04AZsbd+GDRtwzz33IDAwEH5+fkhISMC3335bp5zSf4MMUQ4gMzMT06ZNQ2pqKvbt24fo6GjodDqcPXvWYvk1a9YgJSUFqampOHr0KP75z38iMzMTM2fOlL1PJSnRPgC47bbbUFJSIj127drVFM2pw9b2zZo1CytXrsSyZctw5MgRPPPMM3j44Yexf/9+2ftUmhJtBBznPayoqEB0dDSWL19uVfnCwkIMHjwYgwYNwoEDBzB16lQ89dRTZh/ijvQeKtE+wHnfvytXriAwMBCzZs1CdHS0xTI5OTkYNWoUJkyYgP379yM5ORnJyck4dOhQY1bdakq0Efjjat/Xv4e//vprY1XZJra2b+fOnbjnnnuQlZWF/Px8DBo0CA899FDTf44Ksru4uDgxefJk6eeamhoRGhoq0tLSLJafPHmyuOuuu8yWTZs2TfTr10/2PpWkRPtSU1NFdHS0IvW1la3tCwkJEe+++67ZsmHDhonHH39c9j6VpkQbHek9vB4A8cUXXzRY5uWXXxa33Xab2bIRI0YInU4n/exo72GtxmqfM79/10tMTBRTpkyps3z48OFi8ODBZsvi4+PFpEmTbrKGN6+x2rhq1Sqh1WobrV6Nxdb21erevbuYN2+e9HNT/A2yJ8rOqqurkZ+fj6SkJGmZh4cHkpKSkJuba3Gbvn37Ij8/X+qWPHnyJLKysvDAAw/I3qdSlGhfrePHjyM0NBQdOnTA448/juLiYuUaUg857bty5UqdLnNvb2/pW7wjvX9y63OjNtZyhPdQjtzcXLPXAwB0Op30ejjae2irG7WvlrO+f9aw9jVwdpcuXUL79u0RFhaGoUOH4vDhw/aukiwmkwkXL15Eq1atADTd3yBDlJ2VlZWhpqYGQUFBZsuDgoKg1+stbjN69GjMnz8f/fv3R/PmzdGxY0cMHDhQGu6Ss0+lKNE+AIiPj0dGRgY2b96MFStWoLCwEHfeeScuXryoaHv+TE77dDod3n77bRw/fhwmkwlbt27Fhg0bUFJSInufSlKijYDjvIdy6PV6i6+H0WhEZWWlw72HtrpR+wDnfv+sUd9r4Azvn7W6du2Kjz76CF9++SU+/vhjmEwm9O3bF6dPn7Z31Wz25ptv4tKlSxg+fDiApvscZYhyQjt27MDrr7+O9957D/v27cOGDRvwzTffYMGCBfauWqOwpn33338/HnvsMdx+++3Q6XTIyspCeXk51q1bZ8eaW+cf//gHOnfujMjISKjVajz33HMYP348PDxc58/RmjY683tIfP9cQUJCAsaOHYuYmBgkJiZiw4YNCAwMxMqVK+1dNZusWbMG8+bNw7p169C6desmfe5mTfpsVEdAQAA8PT1RWlpqtry0tBTBwcEWt5k9ezbGjBmDp556CgAQFRWFiooKTJw4Ea+++qqsfSpFifZZChv+/v7o0qULCgoKGr8RDZDTvsDAQGzcuBFVVVX4/fffERoaipSUFHTo0EH2PpWkRBstsdd7KEdwcLDF18PPzw/e3t7w9PR0qPfQVjdqnyXO9P5Zo77XwBneP7maN2+Onj17OtV7uHbtWjz11FNYv3692dBdU32Ous5XXyelVqsRGxuL7OxsaZnJZEJ2djYSEhIsbnP58uU6QcLT0xMAIISQtU+lKNE+Sy5duoQTJ04gJCSkkWpunZt5rTUaDdq0aYNr167h888/x9ChQ296n0pQoo2W2Os9lCMhIcHs9QCArVu3Sq+Ho72HtrpR+yxxpvfPGnJeA2dXU1ODgwcPOs17+Omnn2L8+PH49NNPMXjwYLN1TfY32GhT1Em2tWvXCi8vL5GRkSGOHDkiJk6cKPz9/YVerxdCCDFmzBiRkpIilU9NTRW33HKL+PTTT8XJkyfFli1bRMeOHcXw4cOt3qezt+/FF18UO3bsEIWFhWL37t0iKSlJBAQEiLNnzzp8+3744Qfx+eefixMnToidO3eKu+66S0RERIgLFy5Yvc+mpkQbHek9vHjxoti/f7/Yv3+/ACDefvttsX//fvHrr78KIYRISUkRY8aMkcqfPHlS+Pj4iJdeekkcPXpULF++XHh6eorNmzdLZRzpPVSifc78/gkhpPKxsbFi9OjRYv/+/eLw4cPS+t27d4tmzZqJN998Uxw9elSkpqaK5s2bi4MHDzZp22op0cZ58+aJb7/9Vpw4cULk5+eLkSNHCo1GY1amqdjavk8++UQ0a9ZMLF++XJSUlEiP8vJyqUxT/A0yRDmIZcuWiXbt2gm1Wi3i4uLEDz/8IK1LTEwU48aNk36+evWqmDt3rujYsaPQaDQiLCxMPPvss2YHqBvts6k1dvtGjBghQkJChFqtFm3atBEjRowQBQUFTdgic7a0b8eOHaJbt27Cy8tL3HrrrWLMmDHit99+s2mf9tDYbXSk93D79u0CQJ1HbZvGjRsnEhMT62wTExMj1Gq16NChg1i1alWd/TrKe6hE+5z9/bNUvn379mZl1q1bJ7p06SLUarW47bbbxDfffNM0DbJAiTZOnTpV+v0MCgoSDzzwgNi3b1/TNeo6trYvMTGxwfK1lP4bVAlRz/gIEREREdWLc6KIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIjeyY8cOqFQqlJeX27sqRE6PIYqIFPHkk09CpVJh4cKFZss3btwIlUol/SyEwAcffICEhAT4+fmhRYsWuO222zBlyhSrb4R6+fJlzJgxAx07doRGo0FgYCASExPx5ZdfSmXCw8OxZMmSRmmb0mpfO5VKhebNmyMiIgIvv/wyqqqqbNrPwIEDMXXqVLNlffv2RUlJCbRabSPWmMg9MUQRkWI0Gg3eeOMNXLhwweJ6IQRGjx6NF154AQ888AC2bNmCI0eO4J///Cc0Gg1ee+01q57nmWeewYYNG7Bs2TIcO3YMmzdvxqOPPorff/+9MZvTpO677z6UlJTg5MmTeOedd7By5Uqkpqbe9H7VajWCg4PNgiwRydSoN5EhIvqfcePGiQcffFBERkaKl156SVr+xRdfiNqPnk8//VQAEF9++aXFfZhMJqueS6vVioyMjHrXW7rPVq3//Oc/on///kKj0Yi2bduK559/Xly6dEla///+3/8TsbGxokWLFiIoKEiMGjVKlJaWSutr7/m1efNmERMTIzQajRg0aJAoLS0VWVlZIjIyUtxyyy1i1KhRoqKiwqr2jBs3TgwdOtRs2bBhw0TPnj2ln8vKysTIkSNFaGio8Pb2Fj169BBr1qwx28ef21xYWCjV9/p7UX722Weie/fuQq1Wi/bt24s333zTqnoSuTv2RBGRYjw9PfH6669j2bJlOH36dJ31n376Kbp27YohQ4ZY3N7a3pLg4GBkZWXh4sWLFtdv2LABbdu2xfz581FSUoKSkhIAwIkTJ3DffffhkUcewc8//4zMzEzs2rULzz33nLTt1atXsWDBAvz000/YuHEjioqK8OSTT9Z5jrlz5+Ldd99FTk4OTp06heHDh2PJkiVYs2YNvvnmG2zZsgXLli2zqj1/dujQIeTk5ECtVkvLqqqqEBsbi2+++QaHDh3CxIkTMWbMGOzZswcA8I9//AMJCQl4+umnpTaHhYXV2Xd+fj6GDx+OkSNH4uDBg5g7dy5mz56NjIwMWXUlciv2TnFE5Jqu703p06eP+Mtf/iKEMO+JioyMFEOGDDHbbsqUKcLX11f4+vqKNm3aWPVc33//vWjbtq1o3ry56NWrl5g6darYtWuXWZn27duLd955x2zZhAkTxMSJE82W/ec//xEeHh6isrLS4nPt3btXABAXL14UQvxfT9R3330nlUlLSxMAxIkTJ6RlkyZNEjqdzqr2jBs3Tnh6egpfX1/h5eUlAAgPDw/x2WefNbjd4MGDxYsvvij9nJiYKKZMmWJW5s89UaNHjxb33HOPWZmXXnpJdO/e3aq6Erkz9kQRkeLeeOMNrF69GkePHr1h2VdffRUHDhzAnDlzcOnSJav2P2DAAJw8eRLZ2dl49NFHcfjwYdx5551YsGBBg9v99NNPyMjIQIsWLaSHTqeDyWRCYWEhgD96ah566CG0a9cOt9xyCxITEwEAxcXFZvu6/fbbpf8HBQXBx8cHHTp0MFt29uxZq9oDAIMGDcKBAweQl5eHcePGYfz48XjkkUek9TU1NViwYAGioqLQqlUrtGjRAt9++22det3I0aNH0a9fP7Nl/fr1w/Hjx1FTU2PTvojcDUMUESluwIAB0Ol0mDFjhtnyzp0745dffjFbFhgYiE6dOqF169Y2PUfz5s1x55134pVXXsGWLVswf/58LFiwANXV1fVuc+nSJUyaNAkHDhyQHj/99BOOHz+Ojh07oqKiAjqdDn5+fvjkk0+wd+9efPHFFwBQZ7/NmzeX/l97Vt31VCoVTCaT1e3x9fVFp06dEB0djY8++gh5eXn45z//Ka1fvHgx/vGPf+CVV17B9u3bceDAAeh0ugbbS0SNq5m9K0BE7mHhwoWIiYlB165dpWWjRo3C6NGj8eWXX2Lo0KGN+nzdu3fHtWvXUFVVBbVaDbVaXadn5Y477sCRI0fQqVMni/s4ePAgfv/9dyxcuFCaT/Tjjz82aj2t4eHhgZkzZ2LatGkYPXo0vL29sXv3bgwdOhRPPPEEAMBkMuG///0vunfvLm1nqc1/1q1bN+zevdts2e7du9GlSxd4eno2fmOIXAh7ooioSURFReHxxx/H0qVLpWUjR47Eo48+ipEjR2L+/PnIy8tDUVERvv/+e2RmZlp9EB84cCBWrlyJ/Px8FBUVISsrCzNnzsSgQYPg5+cH4I/rRO3cuRO//fYbysrKAACvvPIKcnJy8Nxzz+HAgQM4fvw4vvzyS2liebt27aBWq7Fs2TKcPHkSX3311Q2HCJXy2GOPwdPTE8uXLwfwRy/e1q1bkZOTg6NHj2LSpEkoLS012yY8PFx6TcvKyiz2hL344ovIzs7GggUL8N///herV6/Gu+++i+nTpzdJu4icGUMUETWZ+fPnmx3IVSoVMjMzsWTJEmRlZeHuu+9G165d8Ze//AVhYWHYtWuXVfvV6XRYvXo17r33XnTr1g3PP/88dDod1q1bZ/bcRUVF6NixIwIDAwH8MY/p+++/x3//+1/ceeed6NmzJ+bMmYPQ0FAAfwwtZmRkYP369ejevTsWLlyIN998sxFfEes1a9YMzz33HBYtWoSKigrMmjULd9xxB3Q6HQYOHIjg4GAkJyebbTN9+nR4enqie/fuCAwMtDhf6o477sC6deuwdu1a9OjRA3PmzMH8+fMtnoFIROZUQghh70oQERERORv2RBERERHJwBBFRA7v+ksQ/Pnxn//8x97Vs0lxcXGD7bH1EgVEZD8cziMih9fQjYjbtGkDb2/vJqzNzbl27RqKiorqXR8eHo5mzXjiNJEzYIgiIiIikoHDeUREREQyMEQRERERycAQRURERCQDQxQRERGRDAxRRERERDIwRBERERHJwBBFREREJANDFBEREZEM/x+RJfi4saexAgAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\omlt\\keras_flowsheet_optimization_doc_15_70.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "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": [ - "## 3.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "1/1 [==============================] - ETA: 0s" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "1/1 [==============================] - 0s 12ms/step\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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3kS2OaFG9EkJItwJ//PFHDB48GG3btoWjoyPGjBmDwsJC3Lhxo9pjZGZmIjIyEu3bt4ejoyMGDBgAADh37pzF8yciooavqMhVKrLKCSFHUZFLvefCQovqVW5uLnx9fXHmzBkMHToUjz76KLZs2YLMzEysWLECQPW3M0tKSqDRaODk5IQvvvgChw4dwrffflvjfkRE1Hy4uBRCJjP9lrtMZoSLS1G958JCi+rN3r17ceTIEYwYMQKZmZkwGo1YtGgR+vXrh86dO+PChQsm8QqFAgaDwaTtxIkTKCwsxPz58/H444+ja9eu0gOhiYioeVMoFAAAtfo6IiO3S8WWTGZEZOR26bZheVx9aBCF1ooVK+Dj4wOVSoWQkBAcPHiw2vjNmzeja9euUKlU6NmzJ3bu3GmyXQiBGTNmwNPTE3Z2dggPD8epU6ek7WfOnEFMTAx8fX1hZ2eHTp06YebMmRVGRA4fPozHH38cKpUK3t7eSEpKMjuX5qq0tBRarRb//e9/kZWVhffffx/Dhw/H0KFDMXbsWPj5+eH27dtYtmwZfv/9d3z22WdITk42OYaPjw+Ki4uRmpqKK1eu4MaNG2jfvj0UCoW039atWzFnzhwrXSURETUkrq6uiIuLQ2xsLJKT++LQocv4+utCHDp0GcnJfREbG1uvi5UCDaDQ2rRpExISEjBz5kxkZWWhV69e0Gg0VY5SpKWlYfTo0YiJiUF2djaioqIQFRWFo0ePSjFJSUlYunQpkpOTkZGRAQcHB2g0Gty6dQvA3VERo9GIlStX4tixY/jwww+RnJyMf/zjH9Ix9Ho9nn76aXTo0AGZmZlYuHAhZs2ahVWrVpmVS3O1a9cueHp6wsfHB0OGDMG+ffuwdOlSfP/997CxsUGvXr2wePFiLFiwAI888gi++OILJCYmmhwjLCwMr732GkaOHIk2bdogKSkJbdq0QUpKCjZv3ozu3btj/vz5+OCDD6x0lURE1NC4urrC09MTnp6eCApyx4gRrggKcpfa6rPIAgCZEELU6xnvExISgr59+0prPhmNRnh7e+P111/H1KlTK8SPHDkSJSUl2L59u9TWr18/BAQEIDk5GUIIeHl5YdKkSdJyADqdDu7u7khJScGoUaMqzWPhwoX45JNP8PvvvwMAPvnkE7zzzjvQarXSEOPUqVPx3Xff4cSJE7XKpSZ6vR5qtRo6nQ5OTk4m227duoX8/Hz4+vpCpVLVeKz7WXsdrcbmYfubiIjqR0N4lmF1n9/3s+ryDmVlZcjMzMS0adOkNrlcjvDwcKSnp1e6T3p6OhISEkzaNBoNvvvuOwBAfn4+tFotwsPDpe1qtRohISFIT0+vstDS6XRwcfnz2wjp6el44oknTO7jajQaLFiwAFevXkWrVq1qzOV+paWlKC0tld7r9fpK4+pC+fCptX8ZiYiI6kpjHESwaqF15coVGAwGuLu7m7S7u7tLo0b302q1lcZrtVppe3lbVTH3y8vLw7Jly0xuQWm1Wvj6+lY4Rvm2Vq1a1ZjL/RITEzF79uxKt1lCQ/klIyIiqgsNcTHumlh9jpa1/fe//8WQIUPw3HPP4ZVXXrHouaZNmwadTie9/vjjD4uej4iIqCnT6RyRn+8Dnc7R2qlUyaojWq1bt4aNjQ0KCgpM2gsKCiqsBl7Ow8Oj2vjynwUFBfD09DSJCQgIMNnvwoULGDRoEMLCwkwmuVd3nnvPUVMu91MqlVAqlZVuIyIiotrLyuotrf5evnxDYGC2tdOqwKojWgqFAkFBQUhNTZXajEYjUlNTERoaWuk+oaGhJvEAsGfPHine19cXHh4eJjF6vR4ZGRkmx/zvf/+LgQMHIigoCOvWrYNcbtoVoaGh+Omnn3D79m2T83Tp0gWtWrWqVS5ERERU96p6xE5DHNmy+q3DhIQErF69GuvXr0dubi4mTJiAkpISjBs3DgAwduxYk8nyb775Jnbt2oVFixbhxIkTmDVrFn799VfExcUBAGQyGeLj4zF37lxs3boVR44cwdixY+Hl5YWoqCgAfxZZ7du3xwcffIDLly9Dq9WazK164YUXoFAoEBMTg2PHjmHTpk346KOPTCa/15QLERER1b2G9Iidmlj9odIjR47E5cuXMWPGDGi1WgQEBGDXrl3SJPNz586ZjDaFhYVhw4YNePfdd/GPf/wD/v7++O677/DII49IMVOmTEFJSQliY2Nx7do19O/fH7t27ZK+tr9nzx7k5eUhLy8P7dq1M8mnfLULtVqN3bt3Y+LEiQgKCkLr1q0xY8YMxMbGmpULERER1a3yR+zcW2xZ6xE7NbH6OlrNmSXX0SLzsL+JiBq+ixcvSnOqq5ujFRsbazJPu641mnW0iIiIiGrr3rUtAwOz0alTHoqKXODiUiQ9x/D+OGtjoUWNzv79+zFo0CBcvXoVzs7OtdrHx8cH8fHxiI+Pt2huRERkOY1xMW6rT4anpufll1+GTCbDa6+9VmHbxIkTIZPJ8PLLL9d/YkRE1Ojd+yzDyl4NqcgCWGiRhXh7e2Pjxo24efOm1Hbr1i1s2LAB7du3t2JmRERE9YeFFllEYGAgvL298c0330ht33zzDdq3b4/evXtLbaWlpXjjjTfg5uYGlUqF/v3749ChQybH2rlzJzp37gw7OzsMGjQIZ86cqXC+n3/+GY8//jjs7Ozg7e2NN954AyUlJRa7PiIiotpgodVMnD8P7Nt392d9+dvf/oZ169ZJ79euXSutj1ZuypQp2LJlC9avX4+srCz4+flBo9GgqOjuV3T/+OMP/OUvf0FkZCRycnIwfvx4TJ061eQYp0+fxpAhQzBixAgcPnwYmzZtws8//8z1zIiIyOpYaDUDa9YAHToATz559+eaNfVz3pdeegk///wzzp49i7Nnz+LAgQN46aWXpO0lJSX45JNPsHDhQjzzzDPo3r07Vq9eDTs7O6z5/0l+8skn6NSpExYtWoQuXbrgxRdfrDC/KzExES+++CLi4+Ph7++PsLAwLF26FP/85z9x69at+rlYIiKiSvBbh03c+fNAbCxgNN59bzQCr74KaDTAfWu11rk2bdogIiICKSkpEEIgIiICrVu3lrafPn0at2/fxmOPPSa12draIjg4GLm5uQCA3NxchISEmBz3/kcc/fbbbzh8+DC++OILqU0IAaPRiPz8fHTr1s0Sl0dERFQjFlpN3KlTfxZZ5QwGIC/P8oUWcPf2YfktvBUrVljkHMXFxXj11VfxxhtvVNjGifdERGRNLLSaOH9/QC43LbZsbAA/v/o5/5AhQ1BWVgaZTAaNRmOyrVOnTlAoFDhw4AA6dOgAALh9+zYOHTokrXfVrVs3bN261WS/X375xeR9YGAgjh8/Dr/6uigiIqJa4hytJq5dO2DVqrvFFXD358qV9TOadfd8NsjNzcXx48dhU57E/+fg4IAJEybgrbfewq5du3D8+HG88soruHHjBmJiYgAAr732Gk6dOoW33noLJ0+exIYNG5CSkmJynLfffhtpaWmIi4tDTk4OTp06he+//56T4YmIyOo4otUMxMTcnZOVl3d3JKu+iqxy1T0Hav78+TAajRgzZgyuX7+OPn364IcffkCrVq0A3L31t2XLFvzv//4vli1bhuDgYLz//vv429/+Jh3j0Ucfxf/93//hnXfeweOPPw4hBDp16oSRI0da/NqIiIiqw4dKWxEfKt1wsL+JiKi2zHmoNG8dEhEREVkICy0iIiIiC2GhRURERGQhLLSIiIiILISFVgPH7yrUD/YzERFZAgutBsrW1hYAcOPGDStn0jyU93N5vxMREdUFrqPVQNnY2MDZ2RmXLl0CANjb20Mmk1k5q6ZHCIEbN27g0qVLcHZ2rrCoKhER0cNgodWAeXh4AIBUbJHlODs7S/1NRERUV1hoNWAymQyenp5wc3PD7du3rZ1Ok2Vra8uRLCIisggWWo2AjY0NCwEiIqJGiJPhiYiIiCyEhRYRERGRhbDQIiIiIrIQFlpEREREFsJCi4iIiMhCWGgRERERWQgLLSIiIiIL4TpaREREVOcKCwtRVlZW5XaFQgFXV9d6zMg6WGgRERFRnSosLMTy5ctrjIuLi2vyxRZvHRIREVGdqm4k60HiGjOrF1orVqyAj48PVCoVQkJCcPDgwWrjN2/ejK5du0KlUqFnz57YuXOnyXYhBGbMmAFPT0/Y2dkhPDwcp06dMomZN28ewsLCYG9vD2dn5wrnSElJgUwmq/RV/oDn/fv3V7pdq9U+XIcQERFRk2HVQmvTpk1ISEjAzJkzkZWVhV69ekGj0UjFzP3S0tIwevRoxMTEIDs7G1FRUYiKisLRo0elmKSkJCxduhTJycnIyMiAg4MDNBoNbt26JcWUlZXhueeew4QJEyo9z8iRI3Hx4kWTl0ajwYABA+Dm5mYSe/LkSZO4+7cTERFR8yUTQghrnTwkJAR9+/aV7uMajUZ4e3vj9ddfx9SpUyvEjxw5EiUlJdi+fbvU1q9fPwQEBCA5ORlCCHh5eWHSpEmYPHkyAECn08Hd3R0pKSkYNWqUyfFSUlIQHx+Pa9euVZvn5cuX0bZtW6xZswZjxowBcHdEa9CgQbh69Wqlo2K1odfroVarodPp4OTk9EDHICIiamguXryIVatW1RgXGxsLT0/Pesiobpnz+W21Ea2ysjJkZmYiPDz8z2TkcoSHhyM9Pb3SfdLT003iAUCj0Ujx+fn50Gq1JjFqtRohISFVHrM2/vnPf8Le3h5//etfK2wLCAiAp6cnnnrqKRw4cKDa45SWlkKv15u8iIiIqOmyWqF15coVGAwGuLu7m7S7u7tXOc9Jq9VWG1/+05xj1saaNWvwwgsvwM7OTmrz9PREcnIytmzZgi1btsDb2xsDBw5EVlZWlcdJTEyEWq2WXt7e3g+cExERUWOh0zkiP98HOp2jtVOpd1zeoQbp6enIzc3FZ599ZtLepUsXdOnSRXofFhaG06dP48MPP6wQW27atGlISEiQ3uv1ehZbRETUpGVl9ca2bUMhhBwymRGRkdsRGJht7bTqjdVGtFq3bg0bGxsUFBSYtBcUFMDDw6PSfTw8PKqNL/9pzjFr8umnnyIgIABBQUE1xgYHByMvL6/K7UqlEk5OTiYvIiKipkahUAC4O5JVXmQBgBBybNs2VBrZKo9ryqxWaCkUCgQFBSE1NVVqMxqNSE1NRWhoaKX7hIaGmsQDwJ49e6R4X19feHh4mMTo9XpkZGRUeczqFBcX46uvvkJMTEyt4nNychrlpD4iIqK65Orqiri4OISFRUtFVjkh5HjssehmsVgpYOVbhwkJCYiOjkafPn0QHByMJUuWoKSkBOPGjQMAjB07Fm3btkViYiIA4M0338SAAQOwaNEiREREYOPGjfj111+lbzbIZDLEx8dj7ty58Pf3h6+vL6ZPnw4vLy9ERUVJ5z137hyKiopw7tw5GAwG5OTkAAD8/PzQsmVLKW7Tpk24c+cOXnrppQq5L1myBL6+vujRowdu3bqFTz/9FHv37sXu3bst1FtERESNh6urK/r1A+RywGj8s93GBggJcUUzqLHuEla2bNky0b59e6FQKERwcLD45ZdfpG0DBgwQ0dHRJvFfffWV6Ny5s1AoFKJHjx5ix44dJtuNRqOYPn26cHd3F0qlUgwePFicPHnSJCY6OloAqPDat2+fSVxoaKh44YUXKs17wYIFolOnTkKlUgkXFxcxcOBAsXfvXrOuXafTCQBCp9OZtR8REVFj8emnQtjYCAHc/fnpp9bO6OGZ8/lt1XW0mjuuo0VERM3B+fNAXh7g5we0a2ftbB6eOZ/f/NYhERERWVS7dk2jwHoQVn/WIREREVFTxUKLiIiIyEJYaBERERFZCAstIiIiIgthoUVERERkISy0iIiIiCyEhRYRERGRhbDQIiIiIrIQFlpEREREFsJCi4iIiMhCWGgRERERWQgLLSIiIiILYaFFREREZCEstIiIiIgshIUWERERkYWw0CIiIiKyEBZaRERERBbSwtoJEBERkfUUFhairKysyu0KhQKurq71mFHTwkKLiIiomSosLMTy5ctrjIuLi2Ox9YB465CIiKiZqm4k60HiqCIWWkREREQWwkKLiIiIyEJYaBERERFZCAstIiIiIgthoUVEREQAAJ3OEfn5PtDpHK2dSpPB5R2IiIgIWVm9sW3bUAghh0xmRGTkdgQGZls7rUaPI1pERETNlEKhAHB3JKu8yAIAIeTYtm2oNLJVHkfmkwkhhLWTaK70ej3UajV0Oh2cnJysnQ4RETVDhYWF2LcPeO65iguSfv11IQYOBBcrvY85n9+8dUhERNSMubq6ol8/QC4HjMY/221sgJAQV7DGeji8dUhERNTMtWsHrFp1t7gC7v5cufJuOz0cjmgRERERYmIAjQbIywP8/Fhk1RUWWkRERATgbnHFAqtuWf3W4YoVK+Dj4wOVSoWQkBAcPHiw2vjNmzeja9euUKlU6NmzJ3bu3GmyXQiBGTNmwNPTE3Z2dggPD8epU6dMYubNm4ewsDDY29vD2dm50vPIZLIKr40bN5rE7N+/H4GBgVAqlfDz80NKSorZ109ERERNl1ULrU2bNiEhIQEzZ85EVlYWevXqBY1Gg0uXLlUan5aWhtGjRyMmJgbZ2dmIiopCVFQUjh49KsUkJSVh6dKlSE5ORkZGBhwcHKDRaHDr1i0ppqysDM899xwmTJhQbX7r1q3DxYsXpVdUVJS0LT8/HxERERg0aBBycnIQHx+P8ePH44cffni4TiEiIqKmQ1hRcHCwmDhxovTeYDAILy8vkZiYWGn8888/LyIiIkzaQkJCxKuvviqEEMJoNAoPDw+xcOFCafu1a9eEUqkUX375ZYXjrVu3TqjV6krPBUB8++23VeY+ZcoU0aNHD5O2kSNHCo1GU+U+99PpdAKA0Ol0td6HiIiIrMucz2+rjWiVlZUhMzMT4eHhUptcLkd4eDjS09Mr3Sc9Pd0kHgA0Go0Un5+fD61WaxKjVqsREhJS5TGrM3HiRLRu3RrBwcFYu3YtxD1LjtWUS2VKS0uh1+tNXkRERNR0WW0y/JUrV2AwGODu7m7S7u7ujhMnTlS6j1arrTReq9VK28vbqoqprffeew9PPvkk7O3tsXv3bvz9739HcXEx3njjjWpz0ev1uHnzJuzs7CocMzExEbNnzzYrDyIiImq8+K3DKkyfPl36c+/evVFSUoKFCxdKhdaDmDZtGhISEqT3er0e3t7eD5UnERERNVxWu3XYunVr2NjYoKCgwKS9oKAAHh4ele7j4eFRbXz5T3OOWVshISE4f/48SktLq83Fycmp0tEsAFAqlXBycjJ5ERERUdNltUJLoVAgKCgIqampUpvRaERqaipCQ0Mr3Sc0NNQkHgD27Nkjxfv6+sLDw8MkRq/XIyMjo8pj1lZOTg5atWoFpVJZq1yIiIiIrHrrMCEhAdHR0ejTpw+Cg4OxZMkSlJSUYNy4cQCAsWPHom3btkhMTAQAvPnmmxgwYAAWLVqEiIgIbNy4Eb/++itWrVoF4O7aV/Hx8Zg7dy78/f3h6+uL6dOnw8vLy2RphnPnzqGoqAjnzp2DwWBATk4OAMDPzw8tW7bEtm3bUFBQgH79+kGlUmHPnj14//33MXnyZOkYr732GpYvX44pU6bgb3/7G/bu3YuvvvoKO3bsqJ/OIyIioobP8l+CrN6yZctE+/bthUKhEMHBweKXX36Rtg0YMEBER0ebxH/11Veic+fOQqFQiB49eogdO3aYbDcajWL69OnC3d1dKJVKMXjwYHHy5EmTmOjoaAGgwmvfvn1CCCH+9a9/iYCAANGyZUvh4OAgevXqJZKTk4XBYDA5zr59+0RAQIBQKBSiY8eOYt26dWZdO5d3ICIianzM+fyWCXHPmgVUr/R6PdRqNXQ6HedrERERNRLmfH5b/RE8RERERE0VCy0iIiIiC2GhRURERGQhLLSIiIiILISFFhEREZGFsNAiIiIishAWWkREREQWwkKLiIiIyEJYaBERERFZCAstIiIiIgthoUVERERkISy0iIiIiCyEhRYRERGRhbDQIiIiIrIQFlpEREREFsJCi4iIiMhCWlg7ASIioqaksLAQZWVlVW5XKBRwdXWtx4zImlhoERER1ZHCwkIsX768xri4uDgWW80Ebx0SERHVkftHsnQ6R+Tn+0Cnc6w2jpoujmgRERFZQFZWb2zbNhRCyCGTGREZuR2BgdnWTovqGUe0iIiI6phO5ygVWQAghBzbtg2tMLJFTR8LLSIiojpWVOQqFVnlhJCjqMjFShmRtbDQIiIiqmMuLoWQyYwmbTKZES4uRVbKiKyFhRYREVEdU6uvIzJyu1Rslc/RUquvWzkzqm+cDE9ERGQBgYHZ6NQpD0VFLnBxKWKR1Uyx0CIiIqojCoXC5L1afb3SAuv+OGq6WGgRERHVEVdXV8TFxXFleJKw0CIiIjJDbR6x4+npWY8ZUUPGQouIiKiW7n/Ejk7niKIiV7i4FJrcIuQjdqgcCy0iIqJaunckq7qV3/mIHSrH5R2IiIjMxJXfqbZYaBEREZmJK79TbbHQIiIiMhNXfqfasnqhtWLFCvj4+EClUiEkJAQHDx6sNn7z5s3o2rUrVCoVevbsiZ07d5psF0JgxowZ8PT0hJ2dHcLDw3Hq1CmTmHnz5iEsLAz29vZwdnaucI7ffvsNo0ePhre3N+zs7NCtWzd89NFHJjH79++HTCar8NJqtQ/WEURE1Ghw5XeqLatOht+0aRMSEhKQnJyMkJAQLFmyBBqNBidPnoSbm1uF+LS0NIwePRqJiYkYOnQoNmzYgKioKGRlZeGRRx4BACQlJWHp0qVYv349fH19MX36dGg0Ghw/fhwqlQrA3UmKzz33HEJDQ7FmzZoK58nMzISbmxs+//xzeHt7Iy0tDbGxsbCxsUFcXJxJ7MmTJ+Hk5CS9ryxvIiJqerjyO9WGTAghrHXykJAQ9O3bV/qqrNFohLe3N15//XVMnTq1QvzIkSNRUlKC7du3S239+vVDQEAAkpOTIYSAl5cXJk2ahMmTJwMAdDod3N3dkZKSglGjRpkcLyUlBfHx8bh27VqNuU6cOBG5ubnYu3cvgLsjWoMGDcLVq1crHRWrDb1eD7VaDZ1OZ1KsERFRw3Tx4kWsWrWqxrjY2FiupdWEmfP5bbVbh2VlZcjMzER4ePifycjlCA8PR3p6eqX7pKenm8QDgEajkeLz8/Oh1WpNYtRqNUJCQqo8Zm3pdDq4uFSc5BgQEABPT0889dRTOHDgQLXHKC0thV6vN3kREVHjUdtH5/ARO1TOarcOr1y5AoPBAHd3d5N2d3d3nDhxotJ9tFptpfHl86LKf1YX8yDS0tKwadMm7NixQ2rz9PREcnIy+vTpg9LSUnz66acYOHAgMjIyEBgYWOlxEhMTMXv27AfOg4iIrIuP2CFzccHSGhw9ehTDhw/HzJkz8fTTT0vtXbp0QZcuXaT3YWFhOH36ND788EN89tlnlR5r2rRpSEhIkN7r9Xp4e3tbLnkiIqpzLKLIHFa7ddi6dWvY2NigoKDApL2goAAeHh6V7uPh4VFtfPlPc45ZnePHj2Pw4MGIjY3Fu+++W2N8cHAw8vLyqtyuVCrh5ORk8iIiIqKmy2qFlkKhQFBQEFJTU6U2o9GI1NRUhIaGVrpPaGioSTwA7NmzR4r39fWFh4eHSYxer0dGRkaVx6zKsWPHMGjQIERHR2PevHm12icnJ4eTH4mIiEhi1VuHCQkJiI6ORp8+fRAcHIwlS5agpKQE48aNAwCMHTsWbdu2RWJiIgDgzTffxIABA7Bo0SJERERg48aN+PXXX6VvgMhkMsTHx2Pu3Lnw9/eXlnfw8vJCVFSUdN5z586hqKgI586dg8FgQE5ODgDAz88PLVu2xNGjR/Hkk09Co9EgISFBmt9lY2ODNm3aAACWLFkCX19f9OjRA7du3cKnn36KvXv3Yvfu3fXUe0RERNTgCStbtmyZaN++vVAoFCI4OFj88ssv0rYBAwaI6Ohok/ivvvpKdO7cWSgUCtGjRw+xY8cOk+1Go1FMnz5duLu7C6VSKQYPHixOnjxpEhMdHS0AVHjt27dPCCHEzJkzK93eoUMH6RgLFiwQnTp1EiqVSri4uIiBAweKvXv3mnXtOp1OABA6nc6s/YiIiMh6zPn8tuo6Ws0d19EiIrKOwsJCfnOQHpg5n9/81iERETUrhYWF0kLZAKDTOaKoyBUuLoUmq7vHxcWx2KKHxkKLiIialXtHsrKyemPbtqEQQi49rzAwMLtCHNGDsvpDpYmIiKxBp3OUiiwAEEKObduGQqdztHJm1JSw0CIiomapqMhVKrLKCSFHUVHFx60RPSizC63t27djxowZ0nP99u7di2effRZDhgyp1YM2iYiIGgIXl0LIZEaTNpnMCBeXIitlRE2RWYXWypUr8T//8z/YuXMnnn32WXz++eeIiopC27Zt4ePjg/j4eHz00UeWypWIiKjOqNXXERm5XSq2yudo3TshnuhhmTUZfunSpfj444/xyiuvYN++fXj22WexaNEi/P3vfwcA9OvXD0lJSXjzzTctkiwREVFdCgzMRqdOeSgqcoGLSxGLLKpzZo1o5efnQ6PRAAAGDRoEg8GAJ554Qto+cOBAnD17tm4zJCIisiC1+jp8fc+yyCKLMKvQcnV1lQqpCxcu4M6dOzh37py0/ezZs3Bx4SRCIiJquBQKRZ3GEVXHrFuHw4cPR0xMDKKjo7F161aMHTsWkyZNglwuh0wmw1tvvYWnn37aUrkSERE9NFdXV8TFxXFleKoXZhVaCxYsQFlZGTZu3IiwsDAsW7YMS5cuxfDhw3H79m0MGDBAegA0ERFRQ8UiiupLnTzr8NatW7h9+zYcHbnImzn4rEMiIqLGp96fdahSqaBSqeriUERERERNhlmFVkJCQq3iFi9e/EDJEBERETUlZhVa2dnZJu9//vlnBAUFwc7OTmqTyWR1kxkRERFRI2dWobVv3z6T946OjtiwYQM6duxYp0kRERERNQV8qDQRERGRhbDQIiIiIrIQFlpEREREFmLWHK3Dhw+bvBdC4MSJEyguLjZpf/TRRx8+MyIiIqJGzqwFS8sftVPZLuXtMpkMBoOhTpNsqrhgKRERUeNjsQVL8/PzHyoxIiIioubErEKrQ4cOlsqDiIiIqMkxezK8EAL5+fm4c+cOAKCsrAybNm3CP//5T1y5cqXOEyQiIiJqrMwa0Tp58iQ0Gg3++OMPdOzYEbt378Zzzz2HEydOQAgBe3t7pKWlwd/f31L5EhERETUaZo1ovf322+jVqxdycnIwdOhQREREoF27drh69SqKiooQGhqK9957z1K5EhERETUqZn3r0M3NDbt370ZAQABKSkrg6OiIn376Cf379wcApKWlYfTo0Th79qzFEm5K+K1DIiKixsecz2+zRrSKi4vh4uICAHBwcICDgwM8PT2l7d7e3igoKHiAlImIiIiaHrMKLS8vL5w7d056n5SUBDc3N+n95cuX0apVq7rLjoiIiKgRM6vQCg8Px4kTJ6T3EyZMgKOjo/R+9+7dCAwMrLvsiIiIiBoxs+Zo1eTMmTNQKpUmtxOpapyjRURE1PhYbI7W3r170b17d+j1+grbdDodIiIikJeXZ162RETU5BUWFuLixYtVvgoLC62dIpFFmLWO1pIlS/DKK69UWr2p1Wq8+uqrWLx4MR5//PE6S5CIiBq3wsJCLF++vMa4uLg4uLq61kNGRPXHrBGt3377DUOGDKly+9NPP43MzEyzElixYgV8fHygUqkQEhKCgwcPVhu/efNmdO3aFSqVCj179sTOnTtNtgshMGPGDHh6esLOzg7h4eE4deqUScy8efMQFhYGe3t7ODs7V3qec+fOISIiAvb29nBzc8Nbb70lrYZfbv/+/QgMDIRSqYSfnx9SUlLMunYiouagrKysTuOIGhOzCq2CggLY2tpWub1Fixa4fPlyrY+3adMmJCQkYObMmcjKykKvXr2g0Whw6dKlSuPL1+mKiYlBdnY2oqKiEBUVhaNHj0oxSUlJWLp0KZKTk5GRkQEHBwdoNBrcunVLiikrK8Nzzz2HCRMmVHoeg8GAiIgIlJWVIS0tDevXr0dKSgpmzJghxeTn5yMiIgKDBg1CTk4O4uPjMX78ePzwww+1vn4iouZIp3NEfr4PdDrHmoOJGjthho4dO4pvv/22yu1btmwRvr6+tT5ecHCwmDhxovTeYDAILy8vkZiYWGn8888/LyIiIkzaQkJCxKuvviqEEMJoNAoPDw+xcOFCafu1a9eEUqkUX375ZYXjrVu3TqjV6grtO3fuFHK5XGi1Wqntk08+EU5OTqK0tFQIIcSUKVNEjx49TPYbOXKk0Gg0NVz1n3Q6nQAgdDpdrfchImpsLly4IGbNmiVmzZolhg37XshkBgEIIZMZxLBh30vbLly4YO1UiWrFnM9vs0a0nn32WUyfPt1kdKjczZs3MXPmTAwdOrRWxyorK0NmZibCw8OlNrlcjvDwcKSnp1e6T3p6ukk8AGg0Gik+Pz8fWq3WJEatViMkJKTKY1Z1np49e8Ld3d3kPHq9HseOHatVLpUpLS2FXq83eRERNRc6nSO2bRuK8o8eIeTYtm0oR7aoSTOr0Hr33XdRVFSEzp07IykpCd9//z2+//57LFiwAF26dEFRURHeeeedWh3rypUrMBgMJsUMALi7u0Or1Va6j1arrTa+/Kc5xzTnPPeeo6oYvV6PmzdvVnrcxMREqNVq6eXt7V3rnIiIGruiIlfc/+97IeQoKnKxUkZElmfWtw7d3d2RlpaGCRMmYNq0aRD/fwkumUwGjUaDFStWVCg+6E/Tpk1DQkKC9F6v17PYIqJmw8WlEDKZ0aTYksmMcHEpsmJWRJZlVqEFAB06dMDOnTtx9epV5OXlQQgBf39/sx+907p1a9jY2FR4NmJBQQE8PDwq3cfDw6Pa+PKfBQUFJoumFhQUICAgoNa5eXh4VPj2Y/l57z1XZbk4OTnBzs6u0uMqlUoolcpa50FE1JSo1dcRGbldun0okxkRGbkdavV1a6dGZDFm3Tq8V6tWrdC3b18EBwc/0PMNFQoFgoKCkJqaKrUZjUakpqYiNDS00n1CQ0NN4gFgz549Uryvry88PDxMYvR6PTIyMqo8ZlXnOXLkiMm3H/fs2QMnJyd07969VrkQEdFdCoVC+nNgYDbi45cgOjoF8fFLEBiYXWkcUZNh8an51di4caNQKpUiJSVFHD9+XMTGxgpnZ2fp235jxowRU6dOleIPHDggWrRoIT744AORm5srZs6cKWxtbcWRI0ekmPnz5wtnZ2fx/fffi8OHD4vhw4cLX19fcfPmTSnm7NmzIjs7W8yePVu0bNlSZGdni+zsbHH9+nUhhBB37twRjzzyiHj66adFTk6O2LVrl2jTpo2YNm2adIzff/9d2Nvbi7feekvk5uaKFStWCBsbG7Fr165aXz+/dUhEzcWVK1fEhQsXqnxduXLF2ikS1Zo5n99WLbSEEGLZsmWiffv2QqFQiODgYPHLL79I2wYMGCCio6NN4r/66ivRuXNnoVAoRI8ePcSOHTtMthuNRjF9+nTh7u4ulEqlGDx4sDh58qRJTHR0tABQ4bVv3z4p5syZM+KZZ54RdnZ2onXr1mLSpEni9u3bJsfZt2+fCAgIEAqFQnTs2FGsW7fOrGtnoUVERNT4mPP5XacPlSbz8KHSREREjY/FHipNRERERLXHQouIiIjIQlhoEREREVmI2etoERFR01RYWIiysrIqtysUCri6utZjRkSNHwstIiJCYWEhli9fXmNcXFwciy0iM/DWIRERmSzQXBdxRHQXCy0iIsKdO3fqNI6I7mKhRURERGQhLLSIiIiILISFFhERVXD+vCfS0vrh/HlPa6dC1KjxW4dERGTi22+H47ffegGQARDo1es3/M//fG/ttIgaJY5oERGR5Px5z3uKLACQ4bffenFki+gBsdAiIiLY2toCAM6d64A/i6xyMvzxR3uTOCKqHRZaRESENm3aAADatz8LQNy3VcDb+5xJHBHVDudoERERXF1dERcXh7KyMuh0N7F5sx3K52g999xNvPdeJB/BQ/QAWGgREREASEXUV18Bhw4BBw4Ajz0mQ9++9gDsrZscUSPFQouIiCro2/fui4geDudoEREREVkICy0iIiIiC2GhRURERGQhLLSIiIiILISFFhEREZGFsNAiIiIishAWWkREREQWwkKLiIiIyEJYaBERERFZCAstIiIiIgthoUVERERkISy0iIiIiCyED5UmIrKCwsJClJWVVbldoVDA1dW1HjMiIktgoUVEVM8KCwuxfPnyGuPi4uJYbBE1crx1SERUz6obyXqQOCJquFhoEREREVlIgyi0VqxYAR8fH6hUKoSEhODgwYPVxm/evBldu3aFSqVCz549sXPnTpPtQgjMmDEDnp6esLOzQ3h4OE6dOmUSU1RUhBdffBFOTk5wdnZGTEwMiouLpe2zZs2CTCar8HJwcJBiUlJSKmxXqVR10CNERETUFFi90Nq0aRMSEhIwc+ZMZGVloVevXtBoNLh06VKl8WlpaRg9ejRiYmKQnZ2NqKgoREVF4ejRo1JMUlISli5diuTkZGRkZMDBwQEajQa3bt2SYl588UUcO3YMe/bswfbt2/HTTz8hNjZW2j558mRcvHjR5NW9e3c899xzJvk4OTmZxJw9e7aOe4iIiIgaK5kQQlgzgZCQEPTt21eaGGo0GuHt7Y3XX38dU6dOrRA/cuRIlJSUYPv27VJbv379EBAQgOTkZAgh4OXlhUmTJmHy5MkAAJ1OB3d3d6SkpGDUqFHIzc1F9+7dcejQIfTp0wcAsGvXLjz77LM4f/48vLy8Kpz3t99+Q0BAAH766Sc8/vjjAO6OaMXHx+PatWsPdO16vR5qtRo6nQ5OTk4PdAwianwuXryIVatW1RgXGxsLT0/PesiIiMxhzue3VUe0ysrKkJmZifDwcKlNLpcjPDwc6enple6Tnp5uEg8AGo1Gis/Pz4dWqzWJUavVCAkJkWLS09Ph7OwsFVkAEB4eDrlcjoyMjErP++mnn6Jz585SkVWuuLgYHTp0gLe3N4YPH45jx45Veb2lpaXQ6/UmLyIiImq6rFpoXblyBQaDAe7u7ibt7u7u0Gq1le6j1WqrjS//WVOMm5ubyfYWLVrAxcWl0vPeunULX3zxBWJiYkzau3TpgrVr1+L777/H559/DqPRiLCwMJw/f77S3BMTE6FWq6WXt7d3pXFE1LzodI7Iz/eBTudo7VSIqI5xHa1a+Pbbb3H9+nVER0ebtIeGhiI0NFR6HxYWhm7dumHlypWYM2dOheNMmzYNCQkJ0nu9Xs9ii6gZUigU0p+zsnpj27ahEEIOmcyIyMjtCAzMrhBHRI2TVQut1q1bw8bGBgUFBSbtBQUF8PDwqHQfDw+PauPLfxYUFJjMbSgoKEBAQIAUc/9k+zt37qCoqKjS83766acYOnRohVGy+9na2qJ3797Iy8urdLtSqYRSqaz2GETU9Lm6uiIuLg5nztzBe++5QQgZAEAIOXbsiMSMGSHw8WnBxUqJmgCr3jpUKBQICgpCamqq1GY0GpGammoyUnSv0NBQk3gA2LNnjxTv6+sLDw8Pkxi9Xo+MjAwpJjQ0FNeuXUNmZqYUs3fvXhiNRoSEhJgcOz8/H/v27atw27AyBoMBR44c4eRVIqqRq6sr9Hp3GI0yk3aDQYbr191ZZBE1FcLKNm7cKJRKpUhJSRHHjx8XsbGxwtnZWWi1WiGEEGPGjBFTp06V4g8cOCBatGghPvjgA5GbmytmzpwpbG1txZEjR6SY+fPnC2dnZ/H999+Lw4cPi+HDhwtfX19x8+ZNKWbIkCGid+/eIiMjQ/z888/C399fjB49ukJ+7777rvDy8hJ37typsG327Nnihx9+EKdPnxaZmZli1KhRQqVSiWPHjtXq2nU6nQAgdDpdrfuLiJqOP/4QQi4XAvjzZWNzt52IGi5zPr+tPkdr5MiRuHz5MmbMmAGtVouAgADs2rVLuk137tw5yOV/DryFhYVhw4YNePfdd/GPf/wD/v7++O677/DII49IMVOmTEFJSQliY2Nx7do19O/fH7t27TJZTPSLL75AXFwcBg8eDLlcjhEjRmDp0qUmuRmNRqSkpODll1+GjY1NhdyvXr2KV155BVqtFq1atUJQUBDS0tLQvXv3uu4mImqgHubh0O3aAatWAa++ChgMgI0NsHLl3XYiahqsvo5Wc8Z1tIgat7p6OPT580BeHuDnxyKLqDEw5/Pb6iNaRESN1eXLl2sdV12h1a4dCyyipoqFFhFRNaq7NVjVo8Lud/v27bpMiYgaERZaRERVqO2tQSKiqlj9odJERA1VbUesynGFdyK6H0e0iIiqcOfOnVrHZmX1xtatQ3H3369GDBv25wrvRNR8cUSLiKiWqhqx0ukc7ymyAECOrVuHcmSLiDiiRURUG9U9k/CPP7xR8d+tcvzxRzuo1bn1nisRNRwc0SIiqsHdEatICHH3r0wh5Ni2rfYjVi1a8N+0RM0VCy0iohrcHbEyfSahEHdHrADA2/sPAKZrP8tkRnh7nwcAuLm51UeaRNQA8Z9ZREQPKSqqD9q21WHKFDWMRhnkcoGkJD1eeGF0tY/gIaKmj4UWEVEVbG1tAZSPWBlhehPAdMRq0iRnjBxZ/igdGdq1cwbgXL8JE1GDw0KLiJq8B33wc5s2bQAAavV1DBu2vcLyDWr1dZM4PkqHiO7Hh0pbER8qTWR5D/vg53uLtAsX5DhzpgV8fO7Ay8sIoOoijYiaLj5Umojo/6tuJKs2cfcWUZ6eQFBQnaRFRM0Ev3VIREREZCEstIioWeHzCImoPvHWIRE1G9Wt7k5EZAkc0SKiZkGnc5SKLMD81d2JiB4ECy0iahaKilylIqucEHIUFblYKSMiag5YaBFRs+DiUgiZzGjSJpMZ4eJSZKWMiKg5YKFFRE2aQqEAcHfR0cjI7VKxVT5Hq3zR0fI4IqK6xAVLrYgLlhLVDy46SkR1iQuWEhHdg4uOEpG18NYhERERkYWw0CIiIiKyEBZaRERERBbCQouIiIjIQlhoEREREVkICy0iIiIiC2GhRURERGQhLLSIiIiILISFFhEREZGFsNAiIiIispAGUWitWLECPj4+UKlUCAkJwcGDB6uN37x5M7p27QqVSoWePXti586dJtuFEJgxYwY8PT1hZ2eH8PBwnDp1yiSmqKgIL774IpycnODs7IyYmBgUFxdL28+cOQOZTFbh9csvv5iVCxERETVfVi+0Nm3ahISEBMycORNZWVno1asXNBoNLl26VGl8WloaRo8ejZiYGGRnZyMqKgpRUVE4evSoFJOUlISlS5ciOTkZGRkZcHBwgEajwa1bt6SYF198EceOHcOePXuwfft2/PTTT4iNja1wvh9//BEXL16UXkH3PCStNrkQERFRMyasLDg4WEycOFF6bzAYhJeXl0hMTKw0/vnnnxcREREmbSEhIeLVV18VQghhNBqFh4eHWLhwobT92rVrQqlUii+//FIIIcTx48cFAHHo0CEp5l//+peQyWTiv//9rxBCiPz8fAFAZGdnV5l7TbnURKfTCQBCp9PVKp6IiIisz5zPb6uOaJWVlSEzMxPh4eFSm1wuR3h4ONLT0yvdJz093SQeADQajRSfn58PrVZrEqNWqxESEiLFpKenw9nZGX369JFiwsPDIZfLkZGRYXLsYcOGwc3NDf3798fWrVvNyuV+paWl0Ov1Ji8iIiJquqxaaF25cgUGgwHu7u4m7e7u7tBqtZXuo9Vqq40v/1lTjJubm8n2Fi1awMXFRYpp2bIlFi1ahM2bN2PHjh3o378/oqKiTIqtmnK5X2JiItRqtfTy9vauNI6IiIiahhbWTqChat26NRISEqT3ffv2xYULF7Bw4UIMGzbsgY45bdo0k2Pq9XoWW0RERE2YVUe0WrduDRsbGxQUFJi0FxQUwMPDo9J9PDw8qo0v/1lTzP2T7e/cuYOioqIqzwsAISEhyMvLq3Uu91MqlXBycjJ5ERERUdNl1UJLoVAgKCgIqampUpvRaERqaipCQ0Mr3Sc0NNQkHgD27Nkjxfv6+sLDw8MkRq/XIyMjQ4oJDQ3FtWvXkJmZKcXs3bsXRqMRISEhVeabk5MDT0/PWudCREREzVw9TM6v1saNG4VSqRQpKSni+PHjIjY2Vjg7OwutViuEEGLMmDFi6tSpUvyBAwdEixYtxAcffCByc3PFzJkzha2trThy5IgUM3/+fOHs7Cy+//57cfjwYTF8+HDh6+srbt68KcUMGTJE9O7dW2RkZIiff/5Z+Pv7i9GjR0vbU1JSxIYNG0Rubq7Izc0V8+bNE3K5XKxdu9asXKrDbx0SERE1PuZ8flu90BJCiGXLlon27dsLhUIhgoODxS+//CJtGzBggIiOjjaJ/+qrr0Tnzp2FQqEQPXr0EDt27DDZbjQaxfTp04W7u7tQKpVi8ODB4uTJkyYxhYWFYvTo0aJly5bCyclJjBs3Tly/fl3anpKSIrp16ybs7e2Fk5OTCA4OFps3b66Qe025VIeFFhERUeNjzue3TAghrDum1nzp9Xqo1WrodDrO1yIiImokzPn8tvrK8ERERERNFQstIiIiIgthoUVERERkISy0iIiIiCyEhRYRERGRhbDQIiIiIrIQFlpEREREFsJCi4iIiMhCWGgRERERWUgLaydARNUrLCxEWVlZldsVCgVcXV3rMSMiIqotFlpEDVhhYSGWL19eY1xcXByLLSKiBoi3DokasOpGsh4kjoiI6hcLLSIiIiILYaFFREREZCEstIiIiIgshIUWERERkYWw0CIiIiKyEBZaRI2ITueI/Hwf6HSO1k6FiIhqgetoETVgCoVC+nNWVm9s2zYUQsghkxkRGbkdgYHZFeKIiKjhkAkhhLWTaK70ej3UajV0Oh2cnJysnQ41UIWFhThz5g6Cg91gNMqkdhsbgYyMS/DxacHFSomI6pE5n9+8dUjUwLm6ukKvdzcpsgDAYJDh+nV3FllERA0YCy2iRsDfH5Df93+rjQ3g52edfIiIqHZYaBE1Au3aAatW3S2ugLs/V668205ERA0XJ8MTNRIxMYBGA+Tl3R3JYpFFRNTwsdAiakTatWOBRUTUmPDWIREREZGFsNAiIiIishAWWkREREQWwkKLiIiIyEI4GZ6anMLCQpSVlVW5XaFQcJFPIiKqFyy0qEkpLCzE8uXLa4yLi4tjsUVERBbHW4fUpFQ3kvUgcURERA+DhRYRERGRhTSIQmvFihXw8fGBSqVCSEgIDh48WG385s2b0bVrV6hUKvTs2RM7d+402S6EwIwZM+Dp6Qk7OzuEh4fj1KlTJjFFRUV48cUX4eTkBGdnZ8TExKC4uFjavn//fgwfPhyenp5wcHBAQEAAvvjiC5NjpKSkQCaTmbxUKtVD9gYRERE1FVYvtDZt2oSEhATMnDkTWVlZ6NWrFzQaDS5dulRpfFpaGkaPHo2YmBhkZ2cjKioKUVFROHr0qBSTlJSEpUuXIjk5GRkZGXBwcIBGo8GtW7ekmBdffBHHjh3Dnj17sH37dvz000+IjY01Oc+jjz6KLVu24PDhwxg3bhzGjh2L7du3m+Tj5OSEixcvSq+zZ8/WcQ8RERFRYyUTQghrJhASEoK+fftKE5iNRiO8vb3x+uuvY+rUqRXiR44ciZKSEpOCp1+/fggICEBycjKEEPDy8sKkSZMwefJkAIBOp4O7uztSUlIwatQo5Obmonv37jh06BD69OkDANi1axeeffZZnD9/Hl5eXpXmGhERAXd3d6xduxbA3RGt+Ph4XLt27YGuXa/XQ61WQ6fTwcnJ6YGOQaYuXryIVatW1RgXGxsLT0/PesiIiIiaGnM+v606olVWVobMzEyEh4dLbXK5HOHh4UhPT690n/T0dJN4ANBoNFJ8fn4+tFqtSYxarUZISIgUk56eDmdnZ6nIAoDw8HDI5XJkZGRUma9Op4OLi4tJW3FxMTp06ABvb28MHz4cx44dq3L/0tJS6PV6kxcRERE1XVZd3uHKlSswGAxwd3c3aXd3d8eJEycq3Uer1VYar9Vqpe3lbdXFuLm5mWxv0aIFXFxcpJj7ffXVVzh06BBWrlwptXXp0gVr167Fo48+Cp1Ohw8++ABhYWE4duwY2lXy5N/ExETMnj270uM3R1zvioiImjquo1UL+/btw7hx47B69Wr06NFDag8NDUVoaKj0PiwsDN26dcPKlSsxZ86cCseZNm0aEhISpPd6vR7e3t6WTb6BstR6VwqFwuS9TueIoiJXuLgUQq2+XmUcERGRJVi10GrdujVsbGxQUFBg0l5QUAAPD49K9/Hw8Kg2vvxnQUGByRycgoICBAQESDH3T7a/c+cOioqKKpz3//7v/xAZGYkPP/wQY8eOrfZ6bG1t0bt3b+Tl5VW6XalUQqlUVnuM5sJS6125uroiLi4OZWVl2LDBDu+9p4bRKINcLpCUpMMLL9zkSBkREdUbq87RUigUCAoKQmpqqtRmNBqRmppqMlJ0r9DQUJN4ANizZ48U7+vrCw8PD5MYvV6PjIwMKSY0NBTXrl1DZmamFLN3714YjUaEhIRIbfv370dERAQWLFhg8o3EqhgMBhw5coSTrK3M1dUVBoMnpkxxhtEoAwAYjTK8/bYzDAZPFllERFRvrH7rMCEhAdHR0ejTpw+Cg4OxZMkSlJSUYNy4cQCAsWPHom3btkhMTAQAvPnmmxgwYAAWLVqEiIgIbNy4Eb/++qv0TTOZTIb4+HjMnTsX/v7+8PX1xfTp0+Hl5YWoqCgAQLdu3TBkyBC88sorSE5Oxu3btxEXF4dRo0ZJ3zjct28fhg4dijfffBMjRoyQ5m4pFAppQvx7772Hfv36wc/PD9euXcPChQtx9uxZjB8/vj67kCpx6hRgNJq2GQxAXh5QyfQ5IiIii7B6oTVy5EhcvnwZM2bMgFarRUBAAHbt2iVNZj937hzk8j8H3sLCwrBhwwa8++67+Mc//gF/f3989913eOSRR6SYKVOmoKSkBLGxsbh27Rr69++PXbt2mSwm+sUXXyAuLg6DBw+GXC7HiBEjsHTpUmn7+vXrcePGDSQmJkpFHgAMGDAA+/fvBwBcvXoVr7zyCrRaLVq1aoWgoCCkpaWhe/fuluouqiV/f0AuNy22bGwAPz/r5URERM2P1dfRas6a8zpa9bHe1Zo1wKuv3h3JsrEBVq4EYmIe6FBEREQScz6/rT6iRWQpMTGARnP3dqGfH28ZEhFR/WOhRU1au3YssIiIyHqs/qxDap4qW+8qP98HOp1jtXFERESNCUe0yCq43hURETUHnAxvRc15Mny58+eBDh0qfjvwzBne8iMiooap0TxUmqi69a6IiIgaOxZaZFXl613di+tdERFRU8E5Wk1IYWGh9GzACxfkyM9vAV/fO/Dyujtk1BDnPLVrB6xaVXG9K942JCKipoCFVhNRWFiI5cuXAwCysnpj27ahEEIOmcyIyMjtCAzMBgDExcU1uGKL610REVFTxUKriSgfydLpHKUiCwCEkGPbtqHo1CkPavV1Ka6h4XpXRETUFHGOVhNTVOQqFVnlhJCjqMjFShkRERE1Xyy0mhgXl0LIZKZf45PJjHBxKbJSRkRERM0XC60mRq2+jsjI7VKxVT5HS62+buXMiIiImh/O0WqCAgOz0alTHoqKXODiUsQii4iIyEpYaDVRavV1FlhERERWxluHRERERBbCQquJUCgUdRpHRERED4+3DpsIV1dXxMXFVbtOVkNcGZ6IiKgpY6HVhLCIIiIialh465CIiIjIQlhoEREREVkICy0iIiIiC2GhRURERGQhLLSIiIiILISFFhEREZGFsNAiIiIishAWWkREREQWwkKLiIiIyEK4MrwVCSEAAHq93sqZEBERUW2Vf26Xf45Xh4WWFV2/fh0A4O3tbeVMiIiIyFzXr1+HWq2uNkYmalOOkUUYjUZcuHABjo6OkMlkFbbr9Xp4e3vjjz/+gJOTkxUybPjYRzVjH9WMfVQ99k/N2Ec1a0p9JITA9evX4eXlBbm8+llYHNGyIrlcjnbt2tUY5+Tk1Oh/KS2NfVQz9lHN2EfVY//UjH1Us6bSRzWNZJXjZHgiIiIiC2GhRURERGQhLLQaMKVSiZkzZ0KpVFo7lQaLfVQz9lHN2EfVY//UjH1Us+baR5wMT0RERGQhHNEiIiIishAWWkREREQWwkKLiIiIyEJYaBERERFZCAstK1uxYgV8fHygUqkQEhKCgwcPVhl77NgxjBgxAj4+PpDJZFiyZEn9JWpF5vTR6tWr8fjjj6NVq1Zo1aoVwsPDq41vKszpo2+++QZ9+vSBs7MzHBwcEBAQgM8++6wes7UOc/roXhs3boRMJkNUVJRlE7Qyc/onJSUFMpnM5KVSqeoxW+sw93fo2rVrmDhxIjw9PaFUKtG5c2fs3LmznrK1DnP6aODAgRV+j2QyGSIiIuox43ogyGo2btwoFAqFWLt2rTh27Jh45ZVXhLOzsygoKKg0/uDBg2Ly5Mniyy+/FB4eHuLDDz+s34StwNw+euGFF8SKFStEdna2yM3NFS+//LJQq9Xi/Pnz9Zx5/TG3j/bt2ye++eYbcfz4cZGXlyeWLFkibGxsxK5du+o58/pjbh+Vy8/PF23bthWPP/64GD58eP0kawXm9s+6deuEk5OTuHjxovTSarX1nHX9MrePSktLRZ8+fcSzzz4rfv75Z5Gfny/2798vcnJy6jnz+mNuHxUWFpr8Dh09elTY2NiIdevW1W/iFsZCy4qCg4PFxIkTpfcGg0F4eXmJxMTEGvft0KFDsyi0HqaPhBDizp07wtHRUaxfv95SKVrdw/aREEL07t1bvPvuu5ZIr0F4kD66c+eOCAsLE59++qmIjo5u0oWWuf2zbt06oVar6ym7hsHcPvrkk09Ex44dRVlZWX2laHUP+3fRhx9+KBwdHUVxcbGlUrQK3jq0krKyMmRmZiI8PFxqk8vlCA8PR3p6uhUzazjqoo9u3LiB27dvw8XFxVJpWtXD9pEQAqmpqTh58iSeeOIJS6ZqNQ/aR++99x7c3NwQExNTH2lazYP2T3FxMTp06ABvb28MHz4cx44dq490reJB+mjr1q0IDQ3FxIkT4e7ujkceeQTvv/8+DAZDfaVdr+ri7+s1a9Zg1KhRcHBwsFSaVsFCy0quXLkCg8EAd3d3k3Z3d3dotVorZdWw1EUfvf322/Dy8jL5n78pedA+0ul0aNmyJRQKBSIiIrBs2TI89dRTlk7XKh6kj37++WesWbMGq1evro8UrepB+qdLly5Yu3Ytvv/+e3z++ecwGo0ICwvD+fPn6yPlevcgffT777/j66+/hsFgwM6dOzF9+nQsWrQIc+fOrY+U693D/n198OBBHD16FOPHj7dUilbTwtoJEFnK/PnzsXHjRuzfv79ZTNQ1h6OjI3JyclBcXIzU1FQkJCSgY8eOGDhwoLVTs7rr169jzJgxWL16NVq3bm3tdBqk0NBQhIaGSu/DwsLQrVs3rFy5EnPmzLFiZg2H0WiEm5sbVq1aBRsbGwQFBeG///0vFi5ciJkzZ1o7vQZnzZo16NmzJ4KDg62dSp1joWUlrVu3ho2NDQoKCkzaCwoK4OHhYaWsGpaH6aMPPvgA8+fPx48//ohHH33Ukmla1YP2kVwuh5+fHwAgICAAubm5SExMbJKFlrl9dPr0aZw5cwaRkZFSm9FoBAC0aNECJ0+eRKdOnSybdD2qi7+LbG1t0bt3b+Tl5VkiRat7kD7y9PSEra0tbGxspLZu3bpBq9WirKwMCoXCojnXt4f5PSopKcHGjRvx3nvvWTJFq+GtQytRKBQICgpCamqq1GY0GpGammryL8Xm7EH7KCkpCXPmzMGuXbvQp0+f+kjVaurq98hoNKK0tNQSKVqduX3UtWtXHDlyBDk5OdJr2LBhGDRoEHJycuDt7V2f6VtcXfwOGQwGHDlyBJ6enpZK06oepI8ee+wx5OXlSUU6APznP/+Bp6dnkyuygIf7Pdq8eTNKS0vx0ksvWTpN67D2bPzmbOPGjUKpVIqUlBRx/PhxERsbK5ydnaWvSY8ZM0ZMnTpVii8tLRXZ2dkiOztbeHp6ismTJ4vs7Gxx6tQpa12CxZnbR/PnzxcKhUJ8/fXXJl8bvn79urUuweLM7aP3339f7N69W5w+fVocP35cfPDBB6JFixZi9erV1roEizO3j+7X1L91aG7/zJ49W/zwww/i9OnTIjMzU4waNUqoVCpx7Ngxa12CxZnbR+fOnROOjo4iLi5OnDx5Umzfvl24ubmJuXPnWusSLO5B/z/r37+/GDlyZH2nW29YaFnZsmXLRPv27YVCoRDBwcHil19+kbYNGDBAREdHS+/z8/MFgAqvAQMG1H/i9cicPurQoUOlfTRz5sz6T7wemdNH77zzjvDz8xMqlUq0atVKhIaGio0bN1oh6/plTh/dr6kXWkKY1z/x8fFSrLu7u3j22WdFVlaWFbKuX+b+DqWlpYmQkBChVCpFx44dxbx588SdO3fqOev6ZW4fnThxQgAQu3fvrudM649MCCGsNJhGRERE1KRxjhYRERGRhbDQIiIiIrIQFlpEREREFsJCi4iIiMhCWGgRERERWQgLLSIiIiILYaFFREREZCEstIiImomXX34ZUVFR1k6DqFlhoUVEVvfyyy9DJpNJL1dXVwwZMgSHDx+2dmp14t5rK3/179/fYuc7c+YMZDIZcnJyTNo/+ugjpKSkWOy8RFQRCy0iahCGDBmCixcv4uLFi0hNTUWLFi0wdOhQa6dVZ9atWydd38WLF7F169ZK427fvm2xHNRqNZydnS12fCKqiIUWETUISqUSHh4e8PDwQEBAAKZOnYo//vgDly9fxpNPPom4uDiT+MuXL0OhUCA1NRUA4OPjgzlz5mD06NFwcHBA27ZtsWLFCpN9Fi9ejJ49e8LBwQHe3t74+9//juLiYmn72bNnERkZiVatWsHBwQE9evTAzp07AQBXr17Fiy++iDZt2sDOzg7+/v5Yt25dra/P2dlZuj4PDw+4uLhII0+bNm3CgAEDoFKp8MUXX6CwsBCjR49G27ZtYW9vj549e+LLL780OZ7RaERSUhL8/PygVCrRvn17zJs3DwDg6+sLAOjduzdkMhkGDhwIoOKtw9LSUrzxxhtwc3ODSqVC//79cejQIWn7/v37IZPJkJqaij59+sDe3h5hYWE4efJkra+bqLljoUVEDU5xcTE+//xz+Pn5wdXVFePHj8eGDRtQWloqxXz++edo27YtnnzySalt4cKF6NWrF7KzszF16lS8+eab2LNnj7RdLpdj6dKlOHbsGNavX4+9e/diypQp0vaJEyeitLQUP/30E44cOYIFCxagZcuWAIDp06fj+PHj+Ne//oXc3Fx88sknaN26dZ1cb3muubm50Gg0uHXrFoKCgrBjxw4cPXoUsbGxGDNmDA4ePCjtM23aNMyfP1/Ka8OGDXB3dwcAKe7HH3/ExYsX8c0331R63ilTpmDLli1Yv349srKy4OfnB41Gg6KiIpO4d955B4sWLcKvv/6KFi1a4G9/+1udXDdRs2Dtp1oTEUVHRwsbGxvh4OAgHBwcBADh6ekpMjMzhRBC3Lx5U7Rq1Ups2rRJ2ufRRx8Vs2bNkt536NBBDBkyxOS4I0eOFM8880yV5928ebNwdXWV3vfs2dPkmPeKjIwU48aNe6DrAyBUKpV0fQ4ODuLbb78V+fn5AoBYsmRJjceIiIgQkyZNEkIIodfrhVKpFKtXr640tvy42dnZJu3R0dFi+PDhQgghiouLha2trfjiiy+k7WVlZcLLy0skJSUJIYTYt2+fACB+/PFHKWbHjh0CgLh586Y5XUDUbHFEi4gahEGDBiEnJwc5OTk4ePAgNBoNnnnmGZw9exYqlQpjxozB2rVrAQBZWVk4evQoXn75ZZNjhIaGVnifm5srvf/xxx8xePBgtG3bFo6OjhgzZgwKCwtx48YNAMAbb7yBuXPn4rHHHsPMmTNNJuNPmDABGzduREBAAKZMmYK0tDSzru/DDz+Uri8nJwdPPfWUtK1Pnz4msQaDAXPmzEHPnj3h4uKCli1b4ocffsC5c+cAALm5uSgtLcXgwYPNyuFep0+fxu3bt/HYY49Jbba2tggODjbpMwB49NFHpT97enoCAC5duvTA5yZqTlhoEVGD4ODgAD8/P/j5+aFv37749NNPUVJSgtWrVwMAxo8fjz179uD8+fNYt24dnnzySXTo0KHWxz9z5gyGDh2KRx99FFu2bEFmZqY0h6usrEw6x++//44xY8bgyJEj6NOnD5YtWwYAUtH3v//7v7hw4QIGDx6MyZMn1/r8Hh4e0vX5+fnBwcHB5NrvtXDhQnz00Ud4++23sW/fPuTk5ECj0Uh52tnZ1fq8dcHW1lb6s0wmA3B3jhgR1YyFFhE1SDKZDHK5HDdv3gQA9OzZE3369MHq1auxYcOGSucJ/fLLLxXed+vWDQCQmZkJo9GIRYsWoV+/fujcuTMuXLhQ4Rje3t547bXX8M0332DSpElSoQcAbdq0QXR0ND7//HMsWbIEq1atqstLlhw4cADDhw/HSy+9hF69eqFjx474z3/+I2339/eHnZ2d9EWA+ykUCgB3R8aq0qlTJygUChw4cEBqu337Ng4dOoTu3bvX0ZUQUQtrJ0BEBNz9BpxWqwVw9xt+y5cvR3FxMSIjI6WY8ePHIy4uDg4ODvif//mfCsc4cOAAkpKSEBUVhT179mDz5s3YsWMHAMDPzw+3b9/GsmXLEBkZiQMHDiA5Odlk//j4eDzzzDPo3Lkzrl69in379kmF2owZMxAUFIQePXqgtLQU27dvl7bVNX9/f3z99ddIS0tDq1atsHjxYhQUFEgFkEqlwttvv40pU6ZAoVDgsccew+XLl3Hs2DHExMTAzc0NdnZ22LVrF9q1aweVSgW1Wm1yDgcHB0yYMAFvvfUWXFxc0L59eyQlJeHGjRuIiYmxyHURNUcc0SKiBmHXrl3w9PSEp6cnQkJCcOjQIWzevFlamgAARo8ejRYtWmD06NFQqVQVjjFp0iT8+uuv6N27N+bOnYvFixdDo9EAAHr16oXFixdjwYIFeOSRR/DFF18gMTHRZH+DwYCJEyeiW7duGDJkCDp37oyPP/4YwN1RomnTpuHRRx/FE088ARsbG2zcuNEiffHuu+8iMDAQGo0GAwcOhIeHR4UV3adPn45JkyZhxowZ6NatG0aOHCnNm2rRogWWLl2KlStXwsvLC8OHD6/0PPPnz8eIESMwZswYBAYGIi8vDz/88ANatWplkesiao5kQghh7SSIiGrjzJkz6NSpEw4dOoTAwECTbT4+PoiPj0d8fLx1kiMiqgRvHRJRg3f79m0UFhbi3XffRb9+/SoUWUREDRVvHRJRg3fgwAF4enri0KFDFeZVWdv777+Pli1bVvp65plnrJ0eEVkZbx0SET2EoqKiCiupl7Ozs0Pbtm3rOSMiakhYaBERERFZCG8dEhEREVkICy0iIiIiC2GhRURERGQhLLSIiIiILISFFhEREZGFsNAiIiIishAWWkREREQWwkKLiIiIyEL+H6tX+6GtnZUSAAAAAElFTkSuQmCC", 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", 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Ke6BsbW3rvF+qrOI4894zIiKqS2YLUa1atYJCoUBeXp7e9Ly8PLi5uVW5jJubW431FT8NWWdNzp8/j8GDByM6OhrvvvvuA+utrKykgTVrM8AmL+HVDx5nIiIyBbOFKKVSicDAQCQnJ0vTdDodkpOTERwcXOUywcHBevUAsH//fqne29sbbm5uejUFBQVITU2tdp3VOXfuHJ588klERUVhwYIFBi1LREREdU+tViMnJ6fal1qtrtd+zDrEQWxsLKKiotCrVy/06dMHy5cvR3FxMcaPHw8AeOGFF9CmTRvExcUBAF5//XUMGDAAS5cuRVhYGBITE3HixAmsXbsWQPkZhylTpmD+/Pnw9fWVhjjw8PBARESEtN0rV64gPz8fV65cgVarRXp6OgDAx8cHLVq0wNmzZzFo0CCEhoYiNjZWup9KoVCgdevW9XeAiIiICEB5gKr4Nn9NYmJi6u1+Y7PeEzVq1CgsWbIEs2bNgp+fH9LT07F3717pxvArV64gJydHqu/Xrx8SEhKwdu1a9OzZE1999RW++eYbaYwoAJg2bRomT56M6Oho9O7dG0VFRdi7d680RhQAzJo1C/7+/pg9ezaKiorg7+8Pf39/nDhxAgDw1Vdf4caNG9i8eTPc3d2lV+/evevpyDRc48aNg0wmg0wmg6WlJVxdXfHUU09hw4YN0Ol0tV5PfHx8lQOdEhERVaW0tLRO6+qCWceJauqqG2fizp07yMrKgre3t164M4Rara7xD0WpVJokiY8bNw55eXnYuHEjtFot8vLysHfvXsTFxeGJJ57Azp07YWHx4BOchozR9bDq4ngTEZF55eTkSFeeahIdHf3QQ9o0+HGiyHjmPqVpZWUl3ajfpk0bBAQEoG/fvhg8eDDi4+MxceJELFu2DBs3bsR///tfODk5ITw8HIsWLUKLFi3www8/SJdsK276nj17NubMmYPPP/8cH330ETIzM2FnZ4dBgwZh+fLlVY5gT0REZE4c4qARaoinNAcNGoSePXtix44dAMoHTl2xYgXOnTuHTZs24eDBg5g2bRqA8suyy5cvh4ODg3QzYMUI82VlZZg3bx5+/vlnfPPNN8jOzsa4cePqbT+IiIhqi2eiqM507twZp0+fBgBMmTJFmu7l5YX58+fjlVdewccffwylUgmVSgWZTFZp6IkXX3xR+r1Dhw5YsWKFdG9bixYt6mU/iIiIaoNnoqjOCCGky3MHDhzA4MGD0aZNG9jb22Ps2LFQq9W4fft2jetIS0tDeHg42rVrB3t7ewwYMABA+ZcMiIiIGhKGKKozGRkZ8Pb2RnZ2NoYNG4YePXpg+/btSEtLw+rVqwHUfImxuLgYoaGhcHBwwJYtW3D8+HF8/fXXD1yOiIiaH43GHllZXtBo7M3WAy/nUZ04ePAgzpw5gzfeeANpaWnQ6XRYunQp5PLynP7ll1/q1SuVSmi1Wr1pFy5cgFqtxsKFC+Hp6QkA0rATRETUvCmVSun3kyf9sWvXMAghh0ymQ3h4EgICTlWqMzWGKDJYSUkJcnNzKw1xMGzYMLzwwgs4e/YsysrKsHLlSoSHh+PIkSNYs2aN3jq8vLxQVFSE5ORk9OzZE7a2tmjXrh2USiVWrlyJV155BWfPnsW8efPMtJdERNSQODs7IyYmBtnZd/Heey4Qovz2ESHk2L07HLNmBcHLy6LeBtoEeDmPjLB37164u7vDy8sLQ4YMwaFDh7BixQp8++23UCgU6NmzJ5YtW4YPPvgAjz76KLZs2SKNOl+hX79+eOWVVzBq1Ci0bt0aixYtQuvWrREfH49t27aha9euWLhwIZYsWWKmvSQioobG2dkZBQWu0On0n4mq1cpQWOharwEK4GCbJmWqwTbNPU5UY8PBNomIGo8HDSZ986Y1/Pxa4t6HZCgUQHY20LZt3fTAwTabsIpTmuYYsZyIiMhUanuSYNmyqZg6tQW02vIA9emndRegDMEQ1UgxIBERUVNT229iP/dcIUaObIFLlwAfH/MEKIAhioiIiBqhtm3NF54q8MZyIiIiIiMwRBEREREZgSGKiIiIGqSGMCp5TXhPFBERETU4NY1K3lDwTBQRERE1KBqNvRSggPJRyXftGtbgzkgxRBEREVGDUPHcu/x8ZylAVRBCjvx8J706c2OIogblhx9+gEwmw61bt2q9jJeXF5YvX26ynoiIqH5UDCY9efIQyOX6D1RRKAQmTx7aoJ7GwRBFBhk3bhxkMhleeeWVSvMmTZoEmUyGcePG1X9jRETUJDg7OyMw0BVr18qgUJRPKx+VXIbAwPp/Pl5NGKLIYJ6enkhMTMQff/whTbtz5w4SEhLQrl07M3ZGRERNxYQJ5c/DO3So/OeECebuqDKGKDJYQEAAPD09sWPHDmnajh070K5dO/j7+0vTSkpK8Nprr8HFxQXW1tZ4/PHHcfz4cb117dmzB506dYKNjQ2efPJJZGdnV9re4cOH8cQTT8DGxgaenp547bXXUFxcbLL9IyKihqFtW2DgQPOPTF4dhqgm4Nq18qR+7Vr9bfPFF1/Exo0bpfcbNmzA+PHj9WqmTZuG7du3Y9OmTTh58iR8fHwQGhqK/Px8AMDVq1fx7LPPIjw8HOnp6Zg4cSKmT5+ut47Lly9jyJAhGDlyJE6fPo2tW7fi8OHDiImJMf1OEhER1YAhqpFbvx5o3x4YNKj85/r19bPd559/HocPH8avv/6KX3/9FUeOHMHzzz8vzS8uLsYnn3yCxYsXY+jQoejatSvWrVsHGxsbrP//Jj/55BN07NgRS5cuxSOPPIIxY8ZUup8qLi4OY8aMwZQpU+Dr64t+/fphxYoV+Oyzz3Dnzp362VkiIqIqcLDNRuzaNSA6GtDpyt/rdMDLLwOhoaY/9dm6dWuEhYUhPj4eQgiEhYWhVatW0vzLly+jrKwMjz32mDTN0tISffr0QUZGBgAgIyMDQUFBeusNDg7We//zzz/j9OnT2LJlizRNCAGdToesrCx06dLFFLtHRET0QAxRjdjFi38GqApaLXDpUv1cP37xxRely2qrV682yTaKiorw8ssv47XXXqs0jzexExGROTFENWK+voBcrh+kFArAx6d+tj9kyBCUlpZCJpMhNDRUb17Hjh2hVCpx5MgRtG/fHgBQVlaG48ePY8qUKQCALl26YOfOnXrL/fTTT3rvAwICcP78efjU104RERHVEu+JasTatgXWrsV942jU37cYFAoFMjIycP78eSgqmvh/dnZ2ePXVV/HWW29h7969OH/+PF566SXcvn0bE/7/e6qvvPIKLl68iLfeeguZmZlISEhAfHy83nrefvttHD16FDExMUhPT8fFixfx7bff8sZyIiIyO56JauQmTCi/B+rSpfIzUPX9NVAHB4dq5y1cuBA6nQ5jx45FYWEhevXqhX379qFly5YAyi/Hbd++HW+88QZWrlyJPn364P3338eLL74oraNHjx7497//jXfeeQdPPPEEhBDo2LEjRo0aZfJ9IyIiqolMCCEeXEbGKCgogEqlgkaj0Qsbd+7cQVZWFry9vWFtbW3GDpsHHm8iIjJEdZ/f9+PlPCIiIiIjMEQRERERGYEhioiIiMgIDFFERERERmCIMiPe018/eJyJiMgUGKLMwNLSEgBw+/ZtM3fSPFQc54rjTkREVBc4TpQZKBQKODo64vr16wAAW1tbyGQyM3fV9AghcPv2bVy/fh2Ojo6VBgQlIiJ6GAxRZuLm5gYAUpAi03F0dJSONxERUV1hiDITmUwGd3d3uLi4oKyszNztNFmWlpY8A0VERCbBEGVmCoWCH/JERESNEG8sJyIiIjICQxQRERGRERiiiIiIiIzAEEVERERkBIYoIiIiIiMwRBEREREZgSGKiIiIyAgcJ4qIiIgMolarUVpaWu18pVIJZ2fneuzIPBiiiIiIqNbUajVWrVr1wLqYmJgmH6R4OY+IiIhqraYzUMbUNWZmD1GrV6+Gl5cXrK2tERQUhGPHjtVYv23bNnTu3BnW1tbo3r079uzZozdfCIFZs2bB3d0dNjY2CAkJwcWLF/VqFixYgH79+sHW1haOjo5VbufKlSsICwuDra0tXFxc8NZbb+Hu3bsPta9ERETUdJg1RG3duhWxsbGYPXs2Tp48iZ49eyI0NBTXr1+vsv7o0aOIjIzEhAkTcOrUKURERCAiIgJnz56VahYtWoQVK1ZgzZo1SE1NhZ2dHUJDQ3Hnzh2pprS0FH/729/w6quvVrkdrVaLsLAwlJaW4ujRo9i0aRPi4+Mxa9asuj0ARERE1GjJhBDCXBsPCgpC7969pWurOp0Onp6emDx5MqZPn16pftSoUSguLkZSUpI0rW/fvvDz88OaNWsghICHhwemTp2KN998EwCg0Wjg6uqK+Ph4jB49Wm998fHxmDJlCm7duqU3/bvvvsOwYcPw22+/wdXVFQCwZs0avP3227hx4waUSmWt9q+goAAqlQoajQYODg61Pi5EREQNVU5ODtauXfvAuujoaLi7u9dDR3Wvtp/fZjsTVVpairS0NISEhPzZjFyOkJAQpKSkVLlMSkqKXj0AhIaGSvVZWVnIzc3Vq1GpVAgKCqp2ndVtp3v37lKAqthOQUEBzp07V+1yJSUlKCgo0HsRERFR02S2EHXz5k1otVq9oAIArq6uyM3NrXKZ3NzcGusrfhqyTkO2c+82qhIXFweVSiW9PD09a71NIiIialzMfmN5UzJjxgxoNBrpdfXqVXO3RERERCZithDVqlUrKBQK5OXl6U3Py8uDm5tblcu4ubnVWF/x05B1GrKde7dRFSsrKzg4OOi9iIiImpLa3hdc27rGzGyDbSqVSgQGBiI5ORkREREAym8sT05ORkxMTJXLBAcHIzk5GVOmTJGm7d+/H8HBwQAAb29vuLm5ITk5GX5+fgDKbw5LTU2t9pt41W1nwYIFuH79OlxcXKTtODg4oGvXrobvLBERURPh7OyMmJgYjlgOM49YHhsbi6ioKPTq1Qt9+vTB8uXLUVxcjPHjxwMAXnjhBbRp0wZxcXEAgNdffx0DBgzA0qVLERYWhsTERJw4cUL6loBMJsOUKVMwf/58+Pr6wtvbGzNnzoSHh4cU1IDyMaDy8/Nx5coVaLVapKenAwB8fHzQokULPP300+jatSvGjh2LRYsWITc3F++++y4mTZoEKyurej1GREREDc29AenaNeDiRcDXF2jb1oxNmYMws5UrV4p27doJpVIp+vTpI3766Sdp3oABA0RUVJRe/Zdffik6deoklEql6Natm9i9e7fefJ1OJ2bOnClcXV2FlZWVGDx4sMjMzNSriYqKEgAqvQ4dOiTVZGdni6FDhwobGxvRqlUrMXXqVFFWVmbQvmk0GgFAaDQag5YjIiJqDP71LyHkciGA8p//+pe5O6obtf38Nus4UU0dx4kiIqKm6to1oH17QKf7c5pCAWRnN/4zUg1+nCgiIiJqvC5e1A9QAKDVApcumacfc2CIIiIiIoP5+gLy+1KEQgH4+JinH3NgiCIiIiKDtW0LrF1bHpyA8p+fftr4L+UZwqzfziMiIqLGa8IEIDS0/BKej0/zClAAQxQRERE9hLZtm194qsDLeURERERGYIgiIiIiMgJDFBEREZERGKKIiIiIjMAQRURERGQEhigiIiIiIzBEERERERmBIYqIiIjICAxRREREREZgiCIiIiIyAkMUERERkREYooiIiIiMwBBFREREZAQLczdAREREpqFWq1FaWlrtfKVSCWdn53rsqGlhiCIiImqC1Go1Vq1a9cC6mJgYBikj8XIeERFRE1TTGShj6qgyhigiIiIiIzBEERERERmBIYqIiIjICAxRREREREZgiCIiIiIyAkMUERERkREYooiIiJogpVJZp3VUGQfbJCIiaoKcnZ0RExMjjQP1229yZGVZwNv7Ljw8dAA4YvnDYogiIiJqoioC0vr1QHQ0oNMBcjmwdi0wYYKZm2sCeDmPiIioCbt27c8ABZT/fPnl8un0cBiiiIiImrCLF/8MUBW0WuDSJfP005QwRBERETVhvr7ll/DupVAAPj7m6acpYYgiIiJqwtq2Lb8HSqEof69QAJ9+Wj6dHg5vLCciImriJkwAQkPLL+H5+DBA1RWGKCIiomagbVuGp7rGy3lERERERmCIIiIiIjICQxQRERGRERiiiIiIiIzAEEVERERkBIYoIiIiIiMwRBEREREZgSGKiIiIyAgMUURERERGYIgiIiIiMgJDFBEREZERGKKIiIiIjMAQRURERGQEhigiIiIiIzBEERERERnB7CFq9erV8PLygrW1NYKCgnDs2LEa67dt24bOnTvD2toa3bt3x549e/TmCyEwa9YsuLu7w8bGBiEhIbh48aJeTX5+PsaMGQMHBwc4OjpiwoQJKCoq0qvZt28f+vbtC3t7e7Ru3RojR45EdnZ2newzERERNX5mDVFbt25FbGwsZs+ejZMnT6Jnz54IDQ3F9evXq6w/evQoIiMjMWHCBJw6dQoRERGIiIjA2bNnpZpFixZhxYoVWLNmDVJTU2FnZ4fQ0FDcuXNHqhkzZgzOnTuH/fv3IykpCT/++COio6Ol+VlZWRgxYgQGDRqE9PR07Nu3Dzdv3sSzzz5ruoNBREREjYswoz59+ohJkyZJ77VarfDw8BBxcXFV1j/33HMiLCxMb1pQUJB4+eWXhRBC6HQ64ebmJhYvXizNv3XrlrCyshJffPGFEEKI8+fPCwDi+PHjUs13330nZDKZ+N///ieEEGLbtm3CwsJCaLVaqWbnzp1CJpOJ0tLSWu+fRqMRAIRGo6n1MkRERGRetf38NtuZqNLSUqSlpSEkJESaJpfLERISgpSUlCqXSUlJ0asHgNDQUKk+KysLubm5ejUqlQpBQUFSTUpKChwdHdGrVy+pJiQkBHK5HKmpqQCAwMBAyOVybNy4EVqtFhqNBp9//jlCQkJgaWlZ7T6VlJSgoKBA70VERERNk9lC1M2bN6HVauHq6qo33dXVFbm5uVUuk5ubW2N9xc8H1bi4uOjNt7CwgJOTk1Tj7e2N77//Hv/85z9hZWUFR0dHXLt2DV9++WWN+xQXFweVSiW9PD09a6wnIiKixsvsN5Y3RLm5uXjppZcQFRWF48eP49///jeUSiX++te/QghR7XIzZsyARqORXlevXq3HromIiKg+WZhrw61atYJCoUBeXp7e9Ly8PLi5uVW5jJubW431FT/z8vLg7u6uV+Pn5yfV3H/j+t27d5Gfny8tv3r1aqhUKixatEiq2bx5Mzw9PZGamoq+fftW2Z+VlRWsrKwetOtERETUBJjtTJRSqURgYCCSk5OlaTqdDsnJyQgODq5ymeDgYL16ANi/f79U7+3tDTc3N72agoICpKamSjXBwcG4desW0tLSpJqDBw9Cp9MhKCgIAHD79m3I5fqHRqFQSD0SERFduwYcOlT+k5qp+rnPvWqJiYnCyspKxMfHi/Pnz4vo6Gjh6OgocnNzhRBCjB07VkyfPl2qP3LkiLCwsBBLliwRGRkZYvbs2cLS0lKcOXNGqlm4cKFwdHQU3377rTh9+rQYMWKE8Pb2Fn/88YdUM2TIEOHv7y9SU1PF4cOHha+vr4iMjJTmJycnC5lMJubOnSt++eUXkZaWJkJDQ0X79u3F7du3a71//HYeEVHT9K9/CSGXCwGU//zXv8zdEdWl2n5+mzVECSHEypUrRbt27YRSqRR9+vQRP/30kzRvwIABIioqSq/+yy+/FJ06dRJKpVJ069ZN7N69W2++TqcTM2fOFK6ursLKykoMHjxYZGZm6tWo1WoRGRkpWrRoIRwcHMT48eNFYWGhXs0XX3wh/P39hZ2dnWjdurUYPny4yMjIMGjfGKKIiJqWmzdvihMncoVcrhOAkF4KhU6cOJErbt68ae4WqQ7U9vNbJkQNd0rTQykoKIBKpYJGo4GDg4O52yEiooegVquxatUqZGV5YdOmqErzo6Li4e39K2JiYuDs7GyGDqmu1Pbzm9/OIyIiqoXS0lIAgJOTGjKZ/v2xMpkOTk75enXU9DFEERERGUClKkR4eJIUpGQyHcLDk6BSFZq5M6pvZhvigIiIqLEKCDiFjh0vIT/fCU5O+QxQzRRDFBERkRFUqkKGp2aOl/OIiIiIjMAQRURERGQEhigiIqIqcERyehCGKCIiovusXw+0bw8MGlT+c/368seV1UZt66jx42CbJsTBNomIGp9r18qD072PSlUogOxswMZGXeM4UEqlkgNtNgG1/fzmt/OIiIj+n1qtxk8/ATqdfhDSaoHUVDUGDgTc3d3N0xw1OLycR0REhD8f63L06KYqRyQ/cmQTVq1aBbVabaYOqaFhiCIiIsKfj2t50IjkfKwLVeDlPCIiovtwRHKqDYYoIiKiKnBEcnoQXs4jIiIiMgJDFBEREZERGKKIiIiIjMAQRURERGQEhigiIiLwsS5kOH47j4iICICzszNiYmL4WBeqNYYoIiKi/8eARIbg5TwiIiIiIzBEERFRk3btGnDoUPlPorrEEEVERE3W+vVA+/bAoEHlP9evN3dH1JQwRBERUZN07RoQHQ3oyp8jDJ0OePllnpGiumPUjeXHjh1DSkoKcnNzAQBubm4IDg5Gnz596rQ5IiIiY6jVavz0E6DT6d8ortUCqalq2NjwJnJ6eAaFqOvXr2PkyJE4cuQI2rVrB1dXVwBAXl4e3njjDTz22GPYvn07XFxcTNIsERHRg6jVaqxatQoajT1ksikQ4s+LLjKZDkeObMLZs4WIiYlhkKKHYtDlvH/84x/QarXIyMhAdnY2UlNTkZqaiuzsbGRkZECn02HSpEmm6pWIiOiBKsZ5UqkKER6eBJms/HqeTKZDeHgSVKpCvToiYxl0Jmrfvn348ccf8cgjj1Sa98gjj2DFihUYOHBgXfVGRET0UAICTqFjx0vIz3eCk1O+FKCI6oJBIcrKygoFBQXVzi8sLISVldVDN0VERFRXVKpChicyCYMu540aNQpRUVH4+uuv9cJUQUEBvv76a4wfPx6RkZF13iQRERFRQ2PQmahly5ZBp9Nh9OjRuHv3rvQQxtLSUlhYWGDChAlYsmSJSRolIiIiakgMvpz3ySef4IMPPkBaWpreEAeBgYFwcHAwSZNEREREDY1R40Q5ODjgySefrOteiIiIiBqNOh2xPC8vD++9915drpKIiMggFbea1FUdUXVkQghRVyv7+eefERAQAK1WW1erbNQKCgqgUqmg0Wh4qZOIqB6p1eoax4FSKpUcaJOqVdvPb4Mu550+fbrG+ZmZmYasjoiIyCQYkKg+GBSi/Pz8IJPJUNXJq4rpMpmszpojIiIiaqgMClFOTk5YtGgRBg8eXOX8c+fOITw8vE4aIyIiImrIDApRgYGB+O2339C+ffsq59+6davKs1RERERETY1BIeqVV15BcXFxtfPbtWuHjRs3PnRTRERERA1dnX47j/Tx23lERESNT20/v+t0nCgiIiKi5sKgy3mxsbG1qlu2bJlRzRARERE1FgaFqFOnTum9P3z4MAIDA2FjYyNN4xAHRERE1BwYFKIOHTqk997e3h4JCQno0KFDnTZFRERE1NDxnigiIiIiIzBEERERERmBIYqIiIjICA/1AGIhBC5cuICioiK96T169Hj4zoiIiIgaMIPORPn5+cHf3x9+fn7w8/PD7du3MWzYML3p/v7+BjWwevVqeHl5wdraGkFBQTh27FiN9du2bUPnzp1hbW2N7t27Y8+ePXrzhRCYNWsW3N3dYWNjg5CQEFy8eFGvJj8/H2PGjIGDgwMcHR0xYcKESkFQCIElS5agU6dOsLKyQps2bbBgwQKD9o2IiIiaLoPORGVlZdXpxrdu3YrY2FisWbMGQUFBWL58OUJDQ5GZmQkXF5dK9UePHkVkZCTi4uIwbNgwJCQkICIiAidPnsSjjz4KAFi0aBFWrFiBTZs2wdvbGzNnzkRoaCjOnz8Pa2trAMCYMWOQk5OD/fv3o6ysDOPHj0d0dDQSEhKkbb3++uv4/vvvsWTJEnTv3h35+fnIz8+v0/0nIiKiRkyYUZ8+fcSkSZOk91qtVnh4eIi4uLgq65977jkRFhamNy0oKEi8/PLLQgghdDqdcHNzE4sXL5bm37p1S1hZWYkvvvhCCCHE+fPnBQBx/Phxqea7774TMplM/O9//5NqLCwsxIULFx5q/zQajQAgNBrNQ62HiIiI6k9tP7/r5MbyQYMG4ddffzVomdLSUqSlpSEkJESaJpfLERISgpSUlCqXSUlJ0asHgNDQUKk+KysLubm5ejUqlQpBQUFSTUpKChwdHdGrVy+pJiQkBHK5HKmpqQCAXbt2oUOHDkhKSoK3tze8vLwwceLEB56JKikpQUFBgd6LiIiImiaDLuft3Lmzyuk//vgjkpKS4OnpCQAYPnz4A9d18+ZNaLVauLq66k13dXXFhQsXqlwmNze3yvrc3FxpfsW0mmruv1RoYWEBJycnqea///0vfv31V2zbtg2fffYZtFot3njjDfz1r3/FwYMHq92nuLg4zJ0790G7TkRERE2AQSEqIiICMpkMQohK8yZPngyg/LEvWq22brozE51Oh5KSEnz22Wfo1KkTAGD9+vUIDAxEZmYmHnnkkSqXmzFjht7zBQsKCqRgSURERE2LQZfzQkNDMXToUOTm5kKn00kvhUKBs2fPQqfT1TpAtWrVCgqFAnl5eXrT8/Ly4ObmVuUybm5uNdZX/HxQzfXr1/Xm3717F/n5+VKNu7s7LCwspAAFAF26dAEAXLlypdp9srKygoODg96LiKg5uXYNOHSo/CdRU2dQiPruu+8wePBg9OrVC0lJSQ+1YaVSicDAQCQnJ0vTdDodkpOTERwcXOUywcHBevUAsH//fqne29sbbm5uejUFBQVITU2VaoKDg3Hr1i2kpaVJNQcPHoROp0NQUBAA4LHHHsPdu3dx+fJlqeaXX34BALRv3/5hdpuIqMlRq9XIycnB0qW30L69wKBBQPv2AkuX3kJOTg7UarW5WyQyDWPuWj916pTo2rWriI6OFsXFxcLCwkKcO3fO4PUkJiYKKysrER8fL86fPy+io6OFo6OjyM3NFUIIMXbsWDF9+nSp/siRI8LCwkIsWbJEZGRkiNmzZwtLS0tx5swZqWbhwoXC0dFRfPvtt+L06dNixIgRwtvbW/zxxx9SzZAhQ4S/v79ITU0Vhw8fFr6+viIyMlKar9VqRUBAgOjfv784efKkOHHihAgKChJPPfWUQfvHb+cRUVN38+ZNMWfOHPHGG0uFTKYVgJBeMplWvPHGUjFnzhxx8+ZNc7dKVGsm/Xaen58fTpw4AZlMBj8/vyrvkaqNUaNGYcmSJZg1axb8/PyQnp6OvXv3SjeGX7lyBTk5OVJ9v379kJCQgLVr16Jnz5746quv8M0330hjRAHAtGnTMHnyZERHR6N3794oKirC3r17pTGiAGDLli3o3LkzBg8ejGeeeQaPP/441q5dK82Xy+XYtWsXWrVqhf79+yMsLAxdunRBYmKiUftJRNRUlZaWAgDy851x/0eKEHLk5zvp1RE1JTJhbAL6fzt37sShQ4cwY8aMKgfIbM4KCgqgUqmg0Wh4fxQRNUk5OTlYu3YtNBp7LF8+RS9IyWQ6TJmyHCpVIaKjo+Hu7m7GTolqr7af3wadiTp48CC6du2qN/7R8OHD8eGHH8LKygrdunXDf/7zH+O7JiKiRkmlKkR4eBJkMh2A8gAVHp4ElarQzJ0RmY5BQxwsX74cL730UpWpTKVS4eWXX8ayZcvwxBNP1FmDRETUOAQEnELHjpeQn+8EJ6d8Bihq8gw6E/Xzzz9jyJAh1c5/+umn9b71RkREzYtKVQhv718ZoKhZMChE5eXlwdLSstr5FhYWuHHjxkM3RURERNTQGRSi2rRpg7Nnz1Y7//Tp07xxkIiIiJoFg0LUM888g5kzZ+LOnTuV5v3xxx+YPXs2hg0bVmfNERFRw6ZUKuu0jqgxMWiIg7y8PAQEBEChUCAmJkZ6htyFCxewevVqaLVanDx5stIDgJsrDnFARM2BWq2ucRwopVIJZ2fneuyI6OHU9vPboG/nubq64ujRo3j11VcxY8YMaZBNmUyG0NBQrF69mgGKiKiZYUCi5sqgEAWUPztuz549+P3333Hp0iUIIeDr64uWLVuaoj8iIiKiBsngEFWhZcuW6N27d132QkRERNRoGPXsPCIiIqLmjiGKiIiIyAhGX84jIqLGhd+iI6pbDFFERM2AWq3GqlWrHlgXExPDIEVUS7ycR0TUDFy/fl3vvUZjj6wsL2g09jXWEVH1eCaKiKgZuHv3rvT7yZP+2LlzGMr/O1qH4cOTEBBwqlIdEdWMZ6KIiJoRjcb+ngAFAHLs3Dms0hkpInowhigiombk6lVPVP6nX46rV9uaox2iRo0hioiIiMgIDFFERM2Ip+dVAPrPnZfJdPD0vGaehogaMYYoIqJmRKUqxPDhuyCT6QCUB6jw8CSoVIVm7oyo8eG384iImgFLS0vp94CAU+jY8RLy853g5JSvF6DurSOimjFEERE1A61bt9Z7r1IVVnn26f46IqoeQxQRUTPg7OyMmJgYPvaFqA4xRBERNRMMSER1izeWExERERmBIYqIiIjICAxRREREREZgiCIiIiIyAkMUERERkREYooiIiIiMwBBFREREZASGKCIiIiIjMEQRERERGYEhioiIiMgIDFFERERERmCIIiIiIjICH0BMRGQCarUapaWl1c5XKpV8IDBRI8cQRURUx9RqNVatWvXAupiYGAYpokaMl/OIiOpYTWegjKkjooaJIYqIiIjICAxRREREREZgiCIiIiIyAkMUERERkREYooiITEyjsUdWlhc0Gntzt0JEdYhDHBARmdDJk/7YtWsYhJBDJtMhPDwJAQGnzN0WEdUBnokiIqpjSqUSQPkZqIoABQBCyLFr1zDpjFRFHRE1TjIhhDB3E01VQUEBVCoVNBoNHBwczN0OEdUjtVqNQ4eAv/2t8mCaX32lxsCB4ECbRA1UbT+/eTmPiMgEnJ2d0bcvIJcDOt2f0xUKICjIGcxPRI0fL+cREZlI27bA2rXlwQko//npp+XTiajx45koIiITmjABCA0FLl0CfHwYoIiakgZxJmr16tXw8vKCtbU1goKCcOzYsRrrt23bhs6dO8Pa2hrdu3fHnj179OYLITBr1iy4u7vDxsYGISEhuHjxol5Nfn4+xowZAwcHBzg6OmLChAkoKiqqcnuXLl2Cvb09HB0dH2o/iahxuXz5Ms6cOVPt6/Lly7VaT9u2wMCBDFBETY3Zz0Rt3boVsbGxWLNmDYKCgrB8+XKEhoYiMzMTLi4uleqPHj2KyMhIxMXFYdiwYUhISEBERAROnjyJRx99FACwaNEirFixAps2bYK3tzdmzpyJ0NBQnD9/HtbW1gCAMWPGICcnB/v370dZWRnGjx+P6OhoJCQk6G2vrKwMkZGReOKJJ3D06FHTHxAiahAuX76MzZs3S+81Gnvk5zvDyUkNlapQmv7888+jY8eO5miRiMzM7N/OCwoKQu/evbFq1SoAgE6ng6enJyZPnozp06dXqh81ahSKi4uRlJQkTevbty/8/PywZs0aCCHg4eGBqVOn4s033wQAaDQauLq6Ij4+HqNHj0ZGRga6du2K48ePo1evXgCAvXv34plnnsG1a9fg4eEhrfvtt9/Gb7/9hsGDB2PKlCm4detWrfeN384jarzOnDmDHTt2ACgf62nnzmEoP3mvw/Dhf4719Oyzz6J79+7ma5SI6lxtP7/NejmvtLQUaWlpCAkJkabJ5XKEhIQgJSWlymVSUlL06gEgNDRUqs/KykJubq5ejUqlQlBQkFSTkpICR0dHKUABQEhICORyOVJTU6VpBw8exLZt27B69epa7U9JSQkKCgr0XkTUcKnVauTk5FT5+v333wGUn4H6M0ABgBw7dw7j6ONEZN7LeTdv3oRWq4Wrq6vedFdXV1y4cKHKZXJzc6usz83NleZXTKup5v5LhRYWFnBycpJq1Go1xo0bh82bN9f6LFJcXBzmzp1bq1oiMi+1Wi2dAQeqv1x39aonKv/3phxXr7aFSpVRP80SUYNk9nuiGqqXXnoJf//739G/f/9aLzNjxgzExsZK7wsKCuDp6WmK9ojoIV2/fl36nY9mISJjmPVyXqtWraBQKJCXl6c3PS8vD25ublUu4+bmVmN9xc8H1dz7DygA3L17F/n5+VLNwYMHsWTJElhYWMDCwgITJkyARqOBhYUFNmzYUGVvVlZWcHBw0HsRUcN09+5dABWX68KrfTSLp+dVAPq3jspkOnh6XqvXfomo4TFriFIqlQgMDERycrI0TafTITk5GcHBwVUuExwcrFcPAPv375fqvb294ebmpldTUFCA1NRUqSY4OBi3bt1CWlqaVHPw4EHodDoEBQUBKL9vKj09XXq99957sLe3R3p6Ov7yl7/UzQEgIrMrv1wn05smRPnlOgBQqQoxfPguyGTlw45XnKm695IfETVPZr+cFxsbi6ioKPTq1Qt9+vTB8uXLUVxcjPHjxwMAXnjhBbRp0wZxcXEAgNdffx0DBgzA0qVLERYWhsTERJw4cQJr164FAMhkMkyZMgXz58+Hr6+vNMSBh4cHIiIiAABdunTBkCFD8NJLL2HNmjUoKytDTEwMRo8eLX0zr0uXLnp9njhxAnK5XBpGgYiaj4CAU+jY8RLy853g5JTPAEVEABpAiBo1ahRu3LiBWbNmITc3F35+fti7d690Y/iVK1cgl/95wqxfv35ISEjAu+++i3/+85/w9fXFN998oxdupk2bhuLiYkRHR+PWrVt4/PHHsXfvXmmMKADYsmULYmJiMHjwYMjlcowcORIrVqyovx0nogah/HKdDvon5itfrlOpCqsMT7a2tibtj4gaLrOPE9WUcZwoooartuNA9erVC+3atatyHba2thxok6gJqu3nt9nPRBERmVtNl+vatWvHwTSJqEoMUUTULFlaWuq9r+5y3f11REQVGKKIqNFTq9UoLS2tdr5SqYSzs7PetNatW9dq3bWtI6LmhyGKiBq12o48HhMToxeknJ2dERMTY3D4IiKqwBBFRI3avSGoppHHqwpLDEhE9DDMOtgmEVFd0WjspQAFVB55nIiorjFEEVGTkJ/vLAWoCkLIkZ/vZKaOiKipY4gioibByUktPZqlgkymg5NTvpk6IqKmjiGKiJoElaoQ4eFJfMYdEdUb3lhORE0Gn3FHRPWJIYqImpTqBs0kIqprvJxHRI2aUqms0zoiotrimSgiatQ4aCYRmQtDFBE1egxIRGQOvJxHREREZASGKCIiIiIjMEQRERERGYEhioiIiMgIDFFERERERmCIIiIiIjICQxQRERGRERiiiIiIiIzAEEVERERkBIYoIiIiIiMwRBEREREZgSGKiIiIyAgMUURERERGYIgiIiIiMgJDFBEREZERGKKIiIiIjMAQRURERGQEhigiIiIiIzBEERERERmBIYqIiIjICAxRREREREZgiCIiIiIyAkMUERERkREYooiIiIiMwBBFREREZASGKCIiIiIjMEQRERERGYEhioiIiMgIDFFERERERrAwdwNEzZlarUZpaWm185VKJZydneuxIyIiqi2GKCIzUavVWLVq1QPrYmJiGKSIiBogXs4jMpOazkAZU0dERPWLIYqIiIjICAxRRA2ERmOPrCwvaDT25m6FiIhqgfdEETUAJ0/6Y9euYRBCDplMh/DwJAQEnDJ3W0REVAOeiSIyM43GXgpQACCEHLt2DeMZKSKiBq5BhKjVq1fDy8sL1tbWCAoKwrFjx2qs37ZtGzp37gxra2t0794de/bs0ZsvhMCsWbPg7u4OGxsbhISE4OLFi3o1+fn5GDNmDBwcHODo6IgJEyagqKhImv/DDz9gxIgRcHd3h52dHfz8/LBly5a622mi/5ef7ywFqApCyJGf72SmjoiIqDbMHqK2bt2K2NhYzJ49GydPnkTPnj0RGhqK69evV1l/9OhRREZGYsKECTh16hQiIiIQERGBs2fPSjWLFi3CihUrsGbNGqSmpsLOzg6hoaG4c+eOVDNmzBicO3cO+/fvR1JSEn788UdER0frbadHjx7Yvn07Tp8+jfHjx+OFF15AUlKS6Q4GNUtOTmrIZDq9aTKZDk5O+WbqiIiIakMmhBDmbCAoKAi9e/eWxsvR6XTw9PTE5MmTMX369Er1o0aNQnFxsV6Y6du3L/z8/LBmzRoIIeDh4YGpU6fizTffBABoNBq4uroiPj4eo0ePRkZGBrp27Yrjx4+jV69eAIC9e/fimWeewbVr1+Dh4VFlr2FhYXB1dcWGDRtqtW8FBQVQqVTQaDRwcHAw6LhQ03fvOFE13RPFcaKIiOpXbT+/zXpjeWlpKdLS0jBjxgxpmlwuR0hICFJSUqpcJiUlBbGxsXrTQkND8c033wAAsrKykJubi5CQEGm+SqVCUFAQUlJSMHr0aKSkpMDR0VEKUAAQEhICuVyO1NRU/OUvf6ly2xqNBl26dKl2f0pKSlBSUiK9LygoqH7nqdlzdnZGTEyMNA7UrFk3kJ1tAS+vu/Dw6A2gN0csJyJqwMwaom7evAmtVgtXV1e96a6urrhw4UKVy+Tm5lZZn5ubK82vmFZTjYuLi958CwsLODk5STX3+/LLL3H8+HF8+umn1e5PXFwc5s6dW+18ovvdG5Dc3YHAQDM2Q0REBjH7PVGNwaFDhzB+/HisW7cO3bp1q7ZuxowZ0Gg00uvq1av12CURERHVJ7OGqFatWkGhUCAvL09vel5eHtzc3Kpcxs3Nrcb6ip8Pqrn/xvW7d+8iPz+/0nb//e9/Izw8HB9++CFeeOGFGvfHysoKDg4Oei8iIiJqmswaopRKJQIDA5GcnCxN0+l0SE5ORnBwcJXLBAcH69UDwP79+6V6b29vuLm56dUUFBQgNTVVqgkODsatW7eQlpYm1Rw8eBA6nQ5BQUHStB9++AFhYWH44IMP9L65R0RERARhZomJicLKykrEx8eL8+fPi+joaOHo6Chyc3OFEEKMHTtWTJ8+Xao/cuSIsLCwEEuWLBEZGRli9uzZwtLSUpw5c0aqWbhwoXB0dBTffvutOH36tBgxYoTw9vYWf/zxh1QzZMgQ4e/vL1JTU8Xhw4eFr6+viIyMlOYfPHhQ2NraihkzZoicnBzppVara71vGo1GABAajeZhDhERERHVo9p+fps9RAkhxMqVK0W7du2EUqkUffr0ET/99JM0b8CAASIqKkqv/ssvvxSdOnUSSqVSdOvWTezevVtvvk6nEzNnzhSurq7CyspKDB48WGRmZurVqNVqERkZKVq0aCEcHBzE+PHjRWFhoTQ/KipKAKj0GjBgQK33iyGKiIio8ant57fZx4lqyjhOFBERUeNT289vfjuPiIiIyAgMUURERERGYIgiIiIiMoJZRywnMoZarZYelVIVPiqFiIjqA0MUNSr3PrS3JnxoLxERmRov51GjUtMZKGPqiIiIjMUQRY2aRmOPrCwvaDT25m6FiIiaGV7Oo0br5El/7No1DELIIZPpEB6ehICAU+Zui4iImgmeiaJGSaOxlwIUAAghx65dw3hGioiI6g1DFDVK+fnOUoCqIIQc+flOZuqIiIiaG4YoapScnNSQyXR602QyHZyc8s3UERERNTe8J4pMwtRjOalUhQgPT6p0T5RKVWj0OomIiAzBEEV1zpRjOSmVSun3gIBT6NjxEvLzneDklK8XoO6tIyIiMgWGKKpz95+B0mjskZ/vDCcntV7QMWYsJ2dnZ8TExHDEciIiMjuGKDIpUwxDwIBEREQNAW8sJ5PhMARERNSUMUSRyXAYAiIiasoYoshkOAwBERE1ZQxRZDIVwxBUBCkOQ0BERE0Jbywnk6ppGAIiIqLGjCGK6tz9YzSpVIVVhieO5URERI0ZQxTVOY7lREREzQFDFJkEAxIRETV1vLGciIiIyAgMUURERERG4OW8RkKtVvMeIyIiogaEIaoRUKvVWLVq1QPrYmJiGKSIiIjqCS/nNQL3n4HSaOyRleVV6Rl0NZ2pIiIiorrFM1GNzMmT/tJDfStGAA8IOGXutoiIiJodnolqRDQaeylAAeUP8921a1ilM1JERERkegxRjUh+vrMUoCoIIUd+vpOZOiIiImq+GKIaEScntfQw3woymQ5OTvlm6oiIiKj5YohqRFSqQoSHJ0lBquKeKD7Ul4iIqP7xxvJGJiDgFDp2vIT8fCc4OeUzQBEREZkJQ1QjoFQq9d6rVIVVhqf764iIiMh0GKIaAWdnZ8TExHDEciIiogaEIaqRYEAiIiJqWHhjOREREZERGKKIiIiIjMAQRURERGQEhigiIiIiIzBEERERERmBIYqIiIjICAxRREREREZgiCIiIiIyAkMUERERkRE4YrkJCSEAAAUFBWbuhIiIiGqr4nO74nO8OgxRJlRYWP6QYE9PTzN3QkRERIYqLCyESqWqdr5MPChmkdF0Oh1+++032NvbQyaT6c0rKCiAp6cnrl69CgcHBzN12LDxGD0Yj9GD8Rg9GI/Rg/EY1aypHR8hBAoLC+Hh4QG5vPo7n3gmyoTkcjnatm1bY42Dg0OT+IMzJR6jB+MxejAeowfjMXowHqOaNaXjU9MZqAq8sZyIiIjICAxRREREREZgiDITKysrzJ49G1ZWVuZupcHiMXowHqMH4zF6MB6jB+MxqllzPT68sZyIiIjICDwTRURERGQEhigiIiIiIzBEERERERmBIYqIiIjICAxRJrR69Wp4eXnB2toaQUFBOHbsWLW1586dw8iRI+Hl5QWZTIbly5fXX6NmZMgxWrduHZ544gm0bNkSLVu2REhISI31TYUhx2jHjh3o1asXHB0dYWdnBz8/P3z++ef12K15GHKM7pWYmAiZTIaIiAjTNtgAGHKM4uPjIZPJ9F7W1tb12G39M/Rv6NatW5g0aRLc3d1hZWWFTp06Yc+ePfXUrXkYcowGDhxY6W9IJpMhLCysHjuuB4JMIjExUSiVSrFhwwZx7tw58dJLLwlHR0eRl5dXZf2xY8fEm2++Kb744gvh5uYmPvzww/pt2AwMPUZ///vfxerVq8WpU6dERkaGGDdunFCpVOLatWv13Hn9MfQYHTp0SOzYsUOcP39eXLp0SSxfvlwoFAqxd+/eeu68/hh6jCpkZWWJNm3aiCeeeEKMGDGifpo1E0OP0caNG4WDg4PIycmRXrm5ufXcdf0x9PiUlJSIXr16iWeeeUYcPnxYZGVliR9++EGkp6fXc+f1x9BjpFar9f5+zp49KxQKhdi4cWP9Nm5iDFEm0qdPHzFp0iTpvVarFR4eHiIuLu6By7Zv375ZhKiHOUZCCHH37l1hb28vNm3aZKoWze5hj5EQQvj7+4t3333XFO01CMYco7t374p+/fqJf/3rXyIqKqrJhyhDj9HGjRuFSqWqp+7Mz9Dj88knn4gOHTqI0tLS+mrR7B7236IPP/xQ2Nvbi6KiIlO1aBa8nGcCpaWlSEtLQ0hIiDRNLpcjJCQEKSkpZuys4aiLY3T79m2UlZXBycnJVG2a1cMeIyEEkpOTkZmZif79+5uyVbMx9hi99957cHFxwYQJE+qjTbMy9hgVFRWhffv28PT0xIgRI3Du3Ln6aLfeGXN8du7cieDgYEyaNAmurq549NFH8f7770Or1dZX2/WqLv69Xr9+PUaPHg07OztTtWkWDFEmcPPmTWi1Wri6uupNd3V1RW5urpm6aljq4hi9/fbb8PDw0Ps/dlNi7DHSaDRo0aIFlEolwsLCsHLlSjz11FOmbtcsjDlGhw8fxvr167Fu3br6aNHsjDlGjzzyCDZs2IBvv/0Wmzdvhk6nQ79+/XDt2rX6aLleGXN8/vvf/+Krr76CVqvFnj17MHPmTCxduhTz58+vj5br3cP+e33s2DGcPXsWEydONFWLZmNh7gaIjLFw4UIkJibihx9+aPI3vBrK3t4e6enpKCoqQnJyMmJjY9GhQwcMHDjQ3K2ZXWFhIcaOHYt169ahVatW5m6nwQoODkZwcLD0vl+/fujSpQs+/fRTzJs3z4ydNQw6nQ4uLi5Yu3YtFAoFAgMD8b///Q+LFy/G7Nmzzd1eg7N+/Xp0794dffr0MXcrdY4hygRatWoFhUKBvLw8vel5eXlwc3MzU1cNy8McoyVLlmDhwoU4cOAAevToYco2zcrYYySXy+Hj4wMA8PPzQ0ZGBuLi4ppkiDL0GF2+fBnZ2dkIDw+Xpul0OgCAhYUFMjMz0bFjR9M2Xc/q4t8jS0tL+Pv749KlS6Zo0ayMOT7u7u6wtLSEQqGQpnXp0gW5ubkoLS2FUqk0ac/17WH+hoqLi5GYmIj33nvPlC2aDS/nmYBSqURgYCCSk5OlaTqdDsnJyXr/ddecGXuMFi1ahHnz5mHv3r3o1atXfbRqNnX1d6TT6VBSUmKKFs3O0GPUuXNnnDlzBunp6dJr+PDhePLJJ5Geng5PT8/6bL9e1MXfkVarxZkzZ+Du7m6qNs3GmOPz2GOP4dKlS1IAB4BffvkF7u7uTS5AAQ/3N7Rt2zaUlJTg+eefN3Wb5mHuO9ubqsTERGFlZSXi4+PF+fPnRXR0tHB0dJS+Jjx27Fgxffp0qb6kpEScOnVKnDp1Sri7u4s333xTnDp1Sly8eNFcu2Byhh6jhQsXCqVSKb766iu9r84WFhaaaxdMztBj9P7774vvv/9eXL58WZw/f14sWbJEWFhYiHXr1plrF0zO0GN0v+bw7TxDj9HcuXPFvn37xOXLl0VaWpoYPXq0sLa2FufOnTPXLpiUocfnypUrwt7eXsTExIjMzEyRlJQkXFxcxPz58821CyZn7P/PHn/8cTFq1Kj6brfeMESZ0MqVK0W7du2EUqkUffr0ET/99JM0b8CAASIqKkp6n5WVJQBUeg0YMKD+G69Hhhyj9u3bV3mMZs+eXf+N1yNDjtE777wjfHx8hLW1tWjZsqUIDg4WiYmJZui6fhlyjO7XHEKUEIYdoylTpki1rq6u4plnnhEnT540Q9f1x9C/oaNHj4qgoCBhZWUlOnToIBYsWCDu3r1bz13XL0OP0YULFwQA8f3339dzp/VHJoQQZjoJRkRERNRo8Z4oIiIiIiMwRBEREREZgSGKiIiIyAgMUURERERGYIgiIiIiMgJDFBEREZERGKKIiIiIjMAQRUTUBIwbNw4RERHmboOoWWGIIiKTGjduHGQymfRydnbGkCFDcPr0aXO3Vifu3beK1+OPP26y7WVnZ0MmkyE9PV1v+kcffYT4+HiTbZeIKmOIIiKTGzJkCHJycpCTk4Pk5GRYWFhg2LBh5m6rzmzcuFHav5ycHOzcubPKurKyMpP1oFKp4OjoaLL1E1FlDFFEZHJWVlZwc3ODm5sb/Pz8MH36dFy9ehU3btzAoEGDEBMTo1d/48YNKJVK6anxXl5emDdvHiIjI2FnZ4c2bdpg9erVesssW7YM3bt3h52dHTw9PfGPf/wDRUVF0vxff/0V4eHhaNmyJezs7NCtWzfs2bMHAPD7779jzJgxaN26NWxsbODr64uNGzfWev8cHR2l/XNzc4OTk5N0xmjr1q0YMGAArK2tsWXLFqjVakRGRqJNmzawtbVF9+7d8cUXX+itT6fTYdGiRfDx8YGVlRXatWuHBQsWAAC8vb0BAP7+/pDJZBg4cCCAypfzSkpK8Nprr8HFxQXW1tZ4/PHHcfz4cWn+Dz/8AJlMhuTkZPTq1Qu2trbo168fMjMza73fRM0dQxQR1auioiJs3rwZPj4+cHZ2xsSJE5GQkICSkhKpZvPmzWjTpg0GDRokTVu8eDF69uyJU6dOYfr06Xj99dexf/9+ab5cLseKFStw7tw5bNq0CQcPHsS0adOk+ZMmTUJJSQl+/PFHnDlzBh988AFatGgBAJg5cybOnz+P7777DhkZGfjkk0/QqlWrOtnfil4zMjIQGhqKO3fuIDAwELt378bZs2cRHR2NsWPH4tixY9IyM2bMwMKFC6W+EhIS4OrqCgBS3YEDB5CTk4MdO3ZUud1p06Zh+/bt2LRpE06ePAkfHx+EhoYiPz9fr+6dd97B0qVLceLECVhYWODFF1+sk/0mahbM/QRkImraoqKihEKhEHZ2dsLOzk4AEO7u7iItLU0IIcQff/whWrZsKbZu3Sot06NHDzFnzhzpffv27cWQIUP01jtq1CgxdOjQare7bds24ezsLL3v3r273jrvFR4eLsaPH2/U/gEQ1tbW0v7Z2dmJr7/+WmRlZQkAYvny5Q9cR1hYmJg6daoQQoiCggJhZWUl1q1bV2VtxXpPnTqlNz0qKkqMGDFCCCFEUVGRsLS0FFu2bJHml5aWCg8PD7Fo0SIhhBCHDh0SAMSBAwekmt27dwsA4o8//jDkEBA1WzwTRUQm9+STTyI9PR3p6ek4duwYQkNDMXToUPz666+wtrbG2LFjsWHDBgDAyZMncfbsWYwbN05vHcHBwZXeZ2RkSO8PHDiAwYMHo02bNrC3t8fYsWOhVqtx+/ZtAMBrr72G+fPn47HHHsPs2bP1bmx/9dVXkZiYCD8/P0ybNg1Hjx41aP8+/PBDaf/S09Px1FNPSfN69eqlV6vVajFv3jx0794dTk5OaNGiBfbt24crV64AADIyMlBSUoLBgwcb1MO9Ll++jLKyMjz22GPSNEtLS/Tp00fvmAFAjx49pN/d3d0BANevXzd620TNCUMUEZmcnZ0dfHx84OPjg969e+Nf//oXiouLsW7dOgDAxIkTsX//fly7dg0bN27EoEGD0L59+1qvPzs7G8OGDUOPHj2wfft2pKWlSfdMlZaWStv473//i7Fjx+LMmTPo1asXVq5cCQBSoHvjjTfw22+/YfDgwXjzzTdrvX03Nzdp/3x8fGBnZ6e37/davHgxPvroI7z99ts4dOgQ0tPTERoaKvVpY2NT6+3WBUtLS+l3mUwGoPyeLCJ6MIYoIqp3MpkMcrkcf/zxBwCge/fu6NWrF9atW4eEhIQq78v56aefKr3v0qULACAtLQ06nQ5Lly5F37590alTJ/z222+V1uHp6YlXXnkFO3bswNSpU6UQBwCtW7dGVFQUNm/ejOXLl2Pt2rV1ucuSI0eOYMSIEXj++efRs2dPdOjQAb/88os039fXFzY2NtJN9fdTKpUAys9oVadjx45QKpU4cuSINK2srAzHjx9H165d62hPiMjC3A0QUdNXUlKC3NxcAOXfhFu1ahWKiooQHh4u1UycOBExMTGws7PDX/7yl0rrOHLkCBYtWoSIiAjs378f27Ztw+7duwEAPj4+KCsrw8qVKxEeHo4jR45gzZo1estPmTIFQ4cORadOnfD777/j0KFDUgibNWsWAgMD0a1bN5SUlCApKUmaV9d8fX3x1Vdf4ejRo2jZsiWWLVuGvLw8KdxYW1vj7bffxrRp06BUKvHYY4/hxo0bOHfuHCZMmAAXFxfY2Nhg7969aNu2LaytraFSqfS2YWdnh1dffRVvvfUWnJyc0K5dOyxatAi3b9/GhAkTTLJfRM0Rz0QRkcnt3bsX7u7ucHd3R1BQEI4fP45t27ZJX88HgMjISFhYWCAyMhLW1taV1jF16lScOHEC/v7+mD9/PpYtW4bQ0FAAQM+ePbFs2TJ88MEHePTRR7FlyxbExcXpLa/VajFp0iR06dIFQ4YMQadOnfDxxx8DKD+7M2PGDPTo0QP9+/eHQqFAYmKiSY7Fu+++i4CAAISGhmLgwIFwc3OrNNL4zJkzMXXqVMyaNQtdunTBqFGjpPuULCwssGLFCnz66afw8PDAiBEjqtzOwoULMXLkSIwdOxYBAQG4dOkS9u3bh5YtW5pkv4iaI5kQQpi7CSKi7OxsdOzYEcePH0dAQIDePC8vL0yZMgVTpkwxT3NERFXg5TwiMquysjKo1Wq8++676Nu3b6UARUTUUPFyHhGZ1ZEjR+Du7o7jx49Xuo/J3N5//320aNGiytfQoUPN3R4RmRkv5xERVSM/P7/SCN8VbGxs0KZNm3ruiIgaEoYoIiIiIiPwch4RERGRERiiiIiIiIzAEEVERERkBIYoIiIiIiMwRBEREREZgSGKiIiIyAgMUURERERGYIgiIiIiMsL/AWzNppSv6kyNAAAAAElFTkSuQmCC", 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qJRERNXZl90C1aNHCwj1pHsqOc23uPbNYiGrVqhVUKhVyc3NN2nNzc+Hu7l7hOu7u7lXWl/2syTarcvr0aQwZMgTR0dF4880371pvY2MjTazJCTaJiEgOXsIzj7o4zhYLUWq1GoGBgUhOTpbajEYjkpOTERwcXOE6wcHBJvUAsGfPHqnex8cH7u7uJjWFhYVIS0urdJuVOXXqFB544AFERUVh/vz5NVqXiIhqTqfTITs7u9KXTqezdBeJTFh0ioO4uDhERUWhd+/e6Nu3LxISElBcXIyJEycCAJ588km0adMG8fHxAIAXXngBISEheO+99xAWFoZ169bhyJEjWLlyJYDSVBkbG4u33noLfn5+0hQHnp6eiIiIkPZ76dIl5Ofn49KlSzAYDDh+/DgAoGPHjmjZsiVOnjyJwYMHQ6vVIi4uTrqfSqVSoXXr1uY7QEREzYROp5O+qV2VmJgY3i9EDYZF74kaPXo0Fi1ahJkzZ8Lf3x/Hjx/Hrl27pBvDL126hOzsbKm+f//+SEpKwsqVK9GzZ098+eWX2LJlizRHFAC8+uqrmDp1KqKjo9GnTx9cvXoVu3btkuaIAoCZM2eiV69emDVrFq5evYpevXqhV69eOHLkCADgyy+/xJ9//onPP/8cHh4e0qtPnz5mOjJERM1LSUlJndaReU2YMAEKhQIKhQLW1tZwc3PDgw8+iE8++QRGo7Ha20lMTKxwEuyGyqLzRDV1nCeKiKh6srOzpasKVYmOjq7VV9IbsuvXryMzMxM+Pj4m//CvLp1OV2XIVKvV9XYWb8KECcjNzcXq1athMBiQm5uLXbt2IT4+HgMGDMDXX38NK6u7X/yqyfyNtVXV8W7w80QRERFR3WgIl0NtbGykL3G1adMGAQEB6NevH4YMGYLExEQ8/fTTWLx4MVavXo3ffvsNzs7OCA8Px8KFC9GyZUvs379fup2n7KbvWbNmYfbs2fjss8/wwQcf4OzZs7C3t8fgwYORkJBQ4dNNzIlTHBARETVyDfVy6ODBg9GzZ09s3rwZQOmk2kuWLMGpU6ewZs0a7N27F6+++iqA0lt2EhIS4OjoKH2ZoOzpIzdv3sS8efPw888/Y8uWLcjKysKECRPMOpaK8EwUERER1ZsuXbrgxIkTAIDY2Fip3dvbG2+99RaeffZZfPjhh1Cr1dBoNFAoFOWmJXrqqaekP3fo0AFLliyR7ntu2bKlWcZREZ6JIiIionojhJAuz3333XcYMmQI2rRpAwcHB4wfPx46nQ7Xrl2rchvp6ekIDw9Hu3bt4ODggJCQEAClX0CzJIYoIiJqcPR6B2RmekOvd7B0V6iWMjIy4OPjg6ysLAwbNgz33nsvNm3ahPT0dCxfvhxA1ZcZi4uLodVq4ejoiC+++AKHDx/GV199ddf1zIGX84iIyOLUarX056NHe2HbtmEQQgmFwojw8O0ICDhWro4avr179+KXX37Biy++iPT0dBiNRrz33ntQKkvP4WzYsMGkXq1Ww2AwmLSdOXMGOp0OCxYsgJeXFwBIUxJZGkMUERFZnIuLC2JiYpCVdQtz57pCiNLLP0IosWNHOGbODIK3txUn2mzAbty4gZycnHJTHAwbNgxPPvkkTp48iZs3b2Lp0qUIDw/HwYMHsWLFCpNteHt74+rVq0hOTkbPnj3RokULtGvXDmq1GkuXLsWzzz6LkydPYt68eRYapSleziMiogbBxcUFhYVuMBpNn2lmMChQVOTGANXA7dq1Cx4eHvD29sbQoUOxb98+LFmyBFu3boVKpULPnj2xePFivPPOO7jnnnvwxRdfSE8kKdO/f388++yzGD16NFq3bo2FCxeidevWSExMxMaNG9GtWzcsWLAAixYtstAoTXGyzXrEyTaJqLmSO/Hj5ctA+/bA7ZNcq1RAVhbQtm09dLQBqc1kmw1hnqjGhpNtEhFRg1ObD/S2bYGVK4HJkwGDoTRAffxx0w9QtVV2OdRSM5Y3VwxRRERUp2o78eOkSYBWC5w/D3TsyABVXQxI5scQRUREDU7btgxP1PDxxnIiIiIiGRiiiIiIiGRgiCIionrF2cepqeI9UUREVG+qmn2cqLHjmSgiIqoXer2DFKCA0tnHt20bxjNS1GQwRBERUZ0qe75dfr6LFKDKCKFEfr6zSR1RY8UQRUREdaps4sepU4dCqTR9KIZKJTB16sOcOZtqZP/+/VAoFCgoKKj2Ot7e3khISKi3PgEMUUREVA9cXFwQGOiGlSsVUKlK20pnH1cgMJDPwWtqJkyYAIVCgWeffbbcsilTpkChUGDChAnm71g9Y4giIqJ6M2lS6XPv9u0r/TlpkqV7RPXFy8sL69atw99//y21Xb9+HUlJSWjXrp0Fe1Z/GKKIiKhetW0LDBrEGcibuoCAAHh5eWHz5s1S2+bNm9GuXTv06tVLartx4waef/55uLq6wtbWFvfffz8OHz5ssq2dO3eiU6dOsLOzwwMPPICsrKxy+ztw4AAGDBgAOzs7eHl54fnnn0dxcXG9ja8iDFFERERNzOXLpWf/Ll82736feuoprF69Wvr9k08+wcSJE01qXn31VWzatAlr1qzB0aNH0bFjR2i1WuTn5wMAfv/9dzz66KMIDw/H8ePH8fTTT2PatGkm27hw4QKGDh2KkSNH4sSJE1i/fj0OHDiAmJiY+h/kbRiiiIiImpBVq4D27YHBg0t/rlplvn0/8cQTOHDgAC5evIiLFy/i4MGDeOKJJ6TlxcXF+Oijj/Duu+/i4YcfRrdu3fDvf/8bdnZ2WPX/Hf3oo4/g6+uL9957D507d8a4cePK3U8VHx+PcePGITY2Fn5+fujfvz+WLFmCTz/9FNevXzfbeDnZJhERURNx+TIQHQ0YjaW/G43A5MmAVmuey6mtW7dGWFgYEhMTIYRAWFgYWrVqJS2/cOECbt68ifvuu09qs7a2Rt++fZGRkQEAyMjIQFBQkMl2g4ODTX7/+eefceLECXzxxRdSmxACRqMRmZmZ6Nq1a30MrxyGKCIioibi3Ln/BagyBgNw/rz57kl76qmnpMtqy5cvr5d9XL16FZMnT8bzzz9fbpk5b2JniCIiImoi/PwApdI0SKlUQMeO5uvD0KFDUVJSAoVCAa1Wa7LM19cXarUaBw8eRPv27QEAN2/exOHDhxEbGwsA6Nq1K77++muT9X766SeT3wMCAnD69Gl0NOfAKsB7ooiIiJqItm2BlStxx9xc5v1mpEqlQkZGBk6fPg1VWUf+n729PZ577jm88sor2LVrF06fPo1nnnkG165dw6T/n//i2Wefxblz5/DKK6/g7NmzSEpKQmJiosl2XnvtNaSkpCAmJgbHjx/HuXPnsHXrVrPfWM4zUURERE3IpEml90CdP196BsoSU0s4OjpWumzBggUwGo0YP348ioqK0Lt3b+zevRv/+Mc/AJRejtu0aRNefPFFLF26FH379sXbb7+Np556StrGvffei++//x5vvPEGBgwYACEEfH19MXr06Hof2+0UQghx9zKSo7CwEBqNBnq9vso3FBER0fXr15GZmQkfHx/Y2tpaujtNXlXHu7qf37ycR0RERCQDQxQRERGRDAxRRERERDIwRBERERHJwBBFRETUgPD7XuZRF8eZIYqIiKgBsLa2BgBcu3bNwj1pHsqOc9lxl4PzRBERETUAKpUKTk5OuHLlCgCgRYsWUCgUFu5V0yOEwLVr13DlyhU4OTmVmxC0JhiiiIiIGgh3d3cAkIIU1R8nJyfpeMvFEEVERNRAKBQKeHh4wNXVFTdv3rR0d5osa2vrWp2BKsMQRURE1MCoVKo6+ZCn+sUby4mIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAbOE0VERESy6XQ6lJSUVLpcrVbDxcXFjD0yH4YoIiIikkWn02HZsmV3rYuJiWmSQYqX84iIiEiWqs5AyalrbCweopYvXw5vb2/Y2toiKCgIhw4dqrJ+48aN6NKlC2xtbdGjRw/s3LnTZLkQAjNnzoSHhwfs7OwQGhqKc+fOmdTMnz8f/fv3R4sWLeDk5FThfi5duoSwsDC0aNECrq6ueOWVV3Dr1q1ajZWIiIiaDouGqPXr1yMuLg6zZs3C0aNH0bNnT2i12kqfXp2SkoLIyEhMmjQJx44dQ0REBCIiInDy5EmpZuHChViyZAlWrFiBtLQ02NvbQ6vV4vr161JNSUkJHn/8cTz33HMV7sdgMCAsLAwlJSVISUnBmjVrkJiYiJkzZ9btASAiIqJGSyGEEJbaeVBQEPr06SNdTzUajfDy8sLUqVMxbdq0cvWjR49GcXExtm/fLrX169cP/v7+WLFiBYQQ8PT0xEsvvYSXX34ZAKDX6+Hm5obExESMGTPGZHuJiYmIjY1FQUGBSfs333yDYcOG4Y8//oCbmxsAYMWKFXjttdfw559/Qq1WV2t8hYWF0Gg00Ov1cHR0rPZxISIiagyys7OxcuXKu9ZFR0fDw8PDDD2qG9X9/LbYmaiSkhKkp6cjNDT0f51RKhEaGorU1NQK10lNTTWpBwCtVivVZ2ZmIicnx6RGo9EgKCio0m1Wtp8ePXpIAapsP4WFhTh16lSl6924cQOFhYUmLyIiImqaLBai8vLyYDAYTIIKALi5uSEnJ6fCdXJycqqsL/tZk23WZD+376Mi8fHx0Gg00svLy6va+yQiIqLGxeI3ljcl06dPh16vl16///67pbtERERE9cRiIapVq1ZQqVTIzc01ac/NzYW7u3uF67i7u1dZX/azJtusyX5u30dFbGxs4OjoaPIiIiJqqqp7j3B16xobi022qVarERgYiOTkZERERAAovbE8OTkZMTExFa4THByM5ORkxMbGSm179uxBcHAwAMDHxwfu7u5ITk6Gv78/gNKbw9LS0ir9Jl5l+5k/fz6uXLkCV1dXaT+Ojo7o1q1bzQdLRETUBLm4uCAmJoYzlltCXFwcoqKi0Lt3b/Tt2xcJCQkoLi7GxIkTAQBPPvkk2rRpg/j4eADACy+8gJCQELz33nsICwvDunXrcOTIEembAQqFArGxsXjrrbfg5+cHHx8fzJgxA56enlJQA0rngMrPz8elS5dgMBhw/PhxAEDHjh3RsmVLPPTQQ+jWrRvGjx+PhQsXIicnB2+++SamTJkCGxsbsx4jIiKihuz2gHT5MnDuHODnB7Rta8FOmYuwsKVLl4p27doJtVot+vbtK3766SdpWUhIiIiKijKp37Bhg+jUqZNQq9Wie/fuYseOHSbLjUajmDFjhnBzcxM2NjZiyJAh4uzZsyY1UVFRAkC51759+6SarKws8fDDDws7OzvRqlUr8dJLL4mbN2/WaGx6vV4AEHq9vkbrERERNTb/+Y8QSqUQQOnP//zH0j2Sr7qf3xadJ6qp4zxRRETUHFy+DLRvDxiN/2tTqYCsrMZ5RqrBzxNFRERETcO5c6YBCgAMBuD8ecv0x1wYooiIiKhW/PwA5R2JQqUCOna0TH/MhSGKiIiIaqVtW2DlytLgBJT+/PjjxnkpryYs+u08IiIiahomTQK02tJLeB07Nv0ABTBEERERUR1p27Z5hKcyvJxHREREJANDFBEREZEMDFFEREREMjBEEREREcnAEEVEREQkA0MUERERkQwMUUREREQyMEQRERERycAQRURERCQDQxQRERGRDAxRRERERDIwRBERERHJwBBFREREJIOVpTtAREREVB06nQ4lJSWVLler1XBxcTFbfxiiiIiIqMHT6XRYtmzZXetiYmLMFqR4OY+IiIgavKrOQMmpqwsMUUREREQyMEQRERERycAQRURERCQDQxQRERGRDAxRRERERDIwRBERERHJwBBFREREDZ5ara7TurrAyTaJiIiowXNxcUFMTIw0D9QffyiRmWkFH59b8PQ0AuCM5UREREQVKgtIq1YB0dGA0QgolcDKlcCkSebvDy/nERERUaNx+fL/AhRQ+nPy5NJ2c2OIIiIiokbj3Ln/BagyBgNw/rz5+8IQRURERI2Gn1/pJbzbqVRAx47m7wtDFBERETUabduW3gOlUpX+rlIBH39c2m5uvLGciIiIGpVJkwCttvQSXseOlglQAEMUERERNUJt21ouPJXh5TwiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSweIhavny5fD29oatrS2CgoJw6NChKus3btyILl26wNbWFj169MDOnTtNlgshMHPmTHh4eMDOzg6hoaE4d+6cSU1+fj7GjRsHR0dHODk5YdKkSbh69apJze7du9GvXz84ODigdevWGDlyJLKysupkzERERNT4WTRErV+/HnFxcZg1axaOHj2Knj17QqvV4sqVKxXWp6SkIDIyEpMmTcKxY8cQERGBiIgInDx5UqpZuHAhlixZghUrViAtLQ329vbQarW4fv26VDNu3DicOnUKe/bswfbt2/HDDz8gOjpaWp6ZmYkRI0Zg8ODBOH78OHbv3o28vDw8+uij9XcwiIiIqHERFtS3b18xZcoU6XeDwSA8PT1FfHx8hfWjRo0SYWFhJm1BQUFi8uTJQgghjEajcHd3F++++660vKCgQNjY2Ii1a9cKIYQ4ffq0ACAOHz4s1XzzzTdCoVCI//73v0IIITZu3CisrKyEwWCQar7++muhUChESUlJtcen1+sFAKHX66u9DhEREVlWdT+/LXYmqqSkBOnp6QgNDZXalEolQkNDkZqaWuE6qampJvUAoNVqpfrMzEzk5OSY1Gg0GgQFBUk1qampcHJyQu/evaWa0NBQKJVKpKWlAQACAwOhVCqxevVqGAwG6PV6fPbZZwgNDYW1tXWlY7px4wYKCwtNXkRERNQ0WSxE5eXlwWAwwM3NzaTdzc0NOTk5Fa6Tk5NTZX3Zz7vVuLq6miy3srKCs7OzVOPj44Nvv/0Wr7/+OmxsbODk5ITLly9jw4YNVY4pPj4eGo1Genl5eVVZT0RERI2XxW8sb4hycnLwzDPPICoqCocPH8b3338PtVqNxx57DEKIStebPn069Hq99Pr999/N2GsiIiIyJytL7bhVq1ZQqVTIzc01ac/NzYW7u3uF67i7u1dZX/YzNzcXHh4eJjX+/v5SzZ03rt+6dQv5+fnS+suXL4dGo8HChQulms8//xxeXl5IS0tDv379KuyfjY0NbGxs7jZ0IiIiagIsdiZKrVYjMDAQycnJUpvRaERycjKCg4MrXCc4ONikHgD27Nkj1fv4+MDd3d2kprCwEGlpaVJNcHAwCgoKkJ6eLtXs3bsXRqMRQUFBAIBr165BqTQ9NCqVSuqjpV2+DOzbV/qTiIiILMQ897lXbN26dcLGxkYkJiaK06dPi+joaOHk5CRycnKEEEKMHz9eTJs2Tao/ePCgsLKyEosWLRIZGRli1qxZwtraWvzyyy9SzYIFC4STk5PYunWrOHHihBgxYoTw8fERf//9t1QzdOhQ0atXL5GWliYOHDgg/Pz8RGRkpLQ8OTlZKBQKMWfOHPHrr7+K9PR0odVqRfv27cW1a9eqPb76+Hbef/4jhFIpBFD68z//qbNNExERkaj+57dFQ5QQQixdulS0a9dOqNVq0bdvX/HTTz9Jy0JCQkRUVJRJ/YYNG0SnTp2EWq0W3bt3Fzt27DBZbjQaxYwZM4Sbm5uwsbERQ4YMEWfPnjWp0el0IjIyUrRs2VI4OjqKiRMniqKiIpOatWvXil69egl7e3vRunVrMXz4cJGRkVGjsdVliMrLyxNHjuQIpdIoACG9VCqjOHIkR+Tl5dV6H0RERFT9z2+FEFXcKU21UlhYCI1GA71eD0dHR9nb0el0WLZsGTIzvbFmTVS55VFRifDxuYiYmBi4uLjUpstERETNXnU/v/ntvEagpKQEAODsrINCYXpPlkJhhLNzvkkdERER1T+GqEZEoylCePh2KUgpFEaEh2+HRlNk4Z4RERE1Pxab4oDkCQg4Bl/f88jPd4azcz4DFBERkYUwRDVCGk0RwxMREZGF8XIeERERkQwMUUREREQyMEQRUa1wBn0iaq4YohoBtVpdp3VEdWXVKqB9e2Dw4NKfq1ZZukdERObDyTbrUV1NtgmUTrhZ1TxQarWaE22SWV2+XBqcbn+cpEoFZGUBbdtarFtERLVW3c9vfjuvkWBAooZEp9Php58Ao9H0fWkwAGlpOtjZ8T1LRE0fL+cRUY2UPYYoJWVNhTPoHzy4BsuWLYNOp7NQD4mIzIMhiohqpOyy8t1m0OdjiIioqePlPCKSjTPoE1FzxhBFRLXCGfSJqLni5TwiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCKqET6GiIioFL+dR0Q14uLigpiYGD6GiIiaPYYoIqoxBiQiIl7OIyIiIpKFIYqIGo3Ll4F9+0p/EhFZGkMUETUKq1YB7dsDgweX/ly1ytI9IqLmjiGKiBq8y5eB6GjAWPqsYxiNwOTJPCNFRJYl68byQ4cOITU1FTk5OQAAd3d3BAcHo2/fvnXaOSIinU6Hn34CjEbTm9kNBiAtTQc7O97oTkSWUaMQdeXKFYwcORIHDx5Eu3bt4ObmBgDIzc3Fiy++iPvuuw+bNm2Cq6trvXSWiJoXnU6HZcuWQa93gEIRCyH+d/JcoTDi4ME1OHmyCDExMQxSRGR2Nbqc969//QsGgwEZGRnIyspCWloa0tLSkJWVhYyMDBiNRkyZMqW++kpEzUzZXFQaTRHCw7dDoSi9nqdQGBEevh0aTZFJHRGROdXoTNTu3bvxww8/oHPnzuWWde7cGUuWLMGgQYPqqm9ERJKAgGPw9T2P/HxnODvnSwGKiMhSahSibGxsUFhYWOnyoqIi2NjY1LpTREQV0WiKGJ6IqMGo0eW80aNHIyoqCl999ZVJmCosLMRXX32FiRMnIjIyss47SURERNTQ1OhM1OLFi2E0GjFmzBjcunVLesBoSUkJrKysMGnSJCxatKheOkpERETUkNT4ct5HH32Ed955B+np6SZTHAQGBsLR0bFeOklERETU0MiaJ8rR0REPPPBAXfeFiIiIqNGo0xnLc3NzMXfu3LrcJBE1Y2W3DNRVHRFRXVIIIURdbeznn39GQEAADAZDXW2yUSssLIRGo4Fer+elTiKZdDpdlfNAqdVqTrRJRHWqup/fNbqcd+LEiSqXnz17tiabIyK6KwYkImqoahSi/P39oVAoUNHJq7J2hUJRZ50jIiIiaqhqFKKcnZ2xcOFCDBkypMLlp06dQnh4eJ10jIiIiKghq1GICgwMxB9//IH27dtXuLygoKDCs1RERERETU2NQtSzzz6L4uLiSpe3a9cOq1evrnWniIiIiBq6Ov12Hpnit/OIiIgan+p+ftfpPFFEREREzUWNLufFxcVVq27x4sWyOkNERETUWNQoRB07dszk9wMHDiAwMBB2dnZSG6c4ICIiouagRiFq3759Jr87ODggKSkJHTp0qNNOERERETV0vCeKiIiISAaGKCIiIiIZGKKIiIiIZKjVA4iFEDhz5gyuXr1q0n7vvffWvmdEREREDViNzkT5+/ujV69e8Pf3h7+/P65du4Zhw4aZtPfq1atGHVi+fDm8vb1ha2uLoKAgHDp0qMr6jRs3okuXLrC1tUWPHj2wc+dOk+VCCMycORMeHh6ws7NDaGgozp07Z1KTn5+PcePGwdHREU5OTpg0aVK5ICiEwKJFi9CpUyfY2NigTZs2mD9/fo3GRkRERE1Xjc5EZWZm1unO169fj7i4OKxYsQJBQUFISEiAVqvF2bNn4erqWq4+JSUFkZGRiI+Px7Bhw5CUlISIiAgcPXoU99xzDwBg4cKFWLJkCdasWQMfHx/MmDEDWq0Wp0+fhq2tLQBg3LhxyM7Oxp49e3Dz5k1MnDgR0dHRSEpKkvb1wgsv4Ntvv8WiRYvQo0cP5OfnIz8/v07HT0RERI2YsKC+ffuKKVOmSL8bDAbh6ekp4uPjK6wfNWqUCAsLM2kLCgoSkydPFkIIYTQahbu7u3j33Xel5QUFBcLGxkasXbtWCCHE6dOnBQBx+PBhqeabb74RCoVC/Pe//5VqrKysxJkzZ2o1Pr1eLwAIvV5fq+0QERGR+VT387tObiwfPHgwLl68WKN1SkpKkJ6ejtDQUKlNqVQiNDQUqampFa6TmppqUg8AWq1Wqs/MzEROTo5JjUajQVBQkFSTmpoKJycn9O7dW6oJDQ2FUqlEWloaAGDbtm3o0KEDtm/fDh8fH3h7e+Ppp5++65moGzduoLCw0ORFRERETVONLud9/fXXFbb/8MMP2L59O7y8vAAAw4cPv+u28vLyYDAY4ObmZtLu5uaGM2fOVLhOTk5OhfU5OTnS8rK2qmruvFRoZWUFZ2dnqea3337DxYsXsXHjRnz66acwGAx48cUX8dhjj2Hv3r2Vjik+Ph5z5sy529CJiIioCahRiIqIiIBCoYAQotyyqVOnAih97IvBYKib3lmI0WjEjRs38Omnn6JTp04AgFWrViEwMBBnz55F586dK1xv+vTpJs8XLCwslIIlERERNS01upyn1Wrx8MMPIycnB0ajUXqpVCqcPHkSRqOx2gGqVatWUKlUyM3NNWnPzc2Fu7t7heu4u7tXWV/28241V65cMVl+69Yt5OfnSzUeHh6wsrKSAhQAdO3aFQBw6dKlSsdkY2MDR0dHkxdRY3H5MrBvX+lPIiK6uxqFqG+++QZDhgxB7969sX379lrtWK1WIzAwEMnJyVKb0WhEcnIygoODK1wnODjYpB4A9uzZI9X7+PjA3d3dpKawsBBpaWlSTXBwMAoKCpCeni7V7N27F0ajEUFBQQCA++67D7du3cKFCxekml9//RUA0L59+9oMm6hB0el0yM7OxnvvFaB9e4HBg4H27QXee68A2dnZ0Ol0lu4iEVHDJeeu9WPHjolu3bqJ6OhoUVxcLKysrMSpU6dqvJ1169YJGxsbkZiYKE6fPi2io6OFk5OTyMnJEUIIMX78eDFt2jSp/uDBg8LKykosWrRIZGRkiFmzZglra2vxyy+/SDULFiwQTk5OYuvWreLEiRNixIgRwsfHR/z9999SzdChQ0WvXr1EWlqaOHDggPDz8xORkZHScoPBIAICAsTAgQPF0aNHxZEjR0RQUJB48MEHazQ+fjuPGrK8vDwxe/Zs8eKL7wmFwiAAIb0UCoN48cX3xOzZs0VeXp6lu0pEZFb1+u08f39/HDlyBAqFAv7+/hXeI1Udo0ePxqJFizBz5kz4+/vj+PHj2LVrl3Rj+KVLl5CdnS3V9+/fH0lJSVi5ciV69uyJL7/8Elu2bJHmiAKAV199FVOnTkV0dDT69OmDq1evYteuXdIcUQDwxRdfoEuXLhgyZAgeeeQR3H///Vi5cqW0XKlUYtu2bWjVqhUGDhyIsLAwdO3aFevWrZM1TqKGqKSkBACQn++CO/8qEEKJ/HxnkzoiIjKlEHIT0P/7+uuvsW/fPkyfPr3CCTKbs8LCQmg0Guj1et4fRQ1OdnY2Vq5cCb3eAQkJsSZBSqEwIjY2ARpNEaKjo+Hh4WHBnhIRmVd1P79rdCZq79696Natm8n8R8OHD8f7778PGxsbdO/eHT/++KP8XhOR2Wk0RQgP3w6FwgigNECFh2+HRlNk4Z4RETVsNZriICEhAc8880yFqUyj0WDy5MlYvHgxBgwYUGcdJKL6FxBwDL6+55Gf7wxn53wGKCKiaqjRmaiff/4ZQ4cOrXT5Qw89ZPKtNyJqPDSaIvj4XGSAIiKqphqFqNzcXFhbW1e63MrKCn/++WetO0VERETU0NUoRLVp0wYnT56sdPmJEyd4AyoRERE1CzUKUY888ghmzJiB69evl1v2999/Y9asWRg2bFiddY6I6o9ara7TOiKi5qZGUxzk5uYiICAAKpUKMTEx0jPkzpw5g+XLl8NgMODo0aPlHgDcXHGKA2rodDpdlfNAqdVquLi4mLFHRESWV93P7xp9O8/NzQ0pKSl47rnnMH36dGmSTYVCAa1Wi+XLlzNAETUiDEhERPLVKEQBpc+O27lzJ/766y+cP38eQgj4+fnhH//4R330j4iIiKhBqnGIKvOPf/wDffr0qcu+EBERETUasp6dR0RERNTcMUQRERERySD7ch7RnfhNLyIiak4YoqhO6HQ6LFu27K51MTExDFL1hCGWiMi8GKKoTlT14S2njmrmzhCr1zsgP98Fzs46k2fhMcQSEdUdhiiqF5V9iFP9uD2cHj3aC9u2DYMQSigURoSHb0dAwLFydUREVDsMUVTnqvoQp/ql1ztIxx4AhFBi27Zh8PU9zzBLRFTH+O08qlOVfYjr9Q4W7lnzkJ/vIh37MkIokZ/vbKEeERE1XQxRVKf4IW5Zzs46KBRGkzaFwghn53wL9YiIqOliiKI6xQ9xy9JoihAevl36b1B2OZWX8oiI6h7viaI6VfYhfuc9UfwQN5+AgGPw9T2P/HxnODvn89gTEdUThiiqE2q1WvpzVR/it9dR/dFoihieiIjqGUMU1QkXFxfExMRwskcLqW44ZYglIqo7CiGEsHQnmqrCwkJoNBro9Xo4OjpaujvUxHHGciKiulHdz2+eiSJqIhiQiIjMi9/OIyIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZOADiJs4nU6HkpKSSper1Wo+uJaIiEgGhqgmTKfTYdmyZXeti4mJYZAiIiKqIV7Oa8KqOgMlp46IiIj+hyGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIaoZ0esdkJnpDb3ewdJdISIiavQ4xUEzcfRoL2zbNgxCKKFQGBEevh0BAccs3S0iIqJGi2eimjC1Wg2g9AxUWYACACGU2LZtmHRGqqyOiIiIqk8hhBCW7kRTVVhYCI1GA71eD0dHR4v0QafTYd8+4PHHy0+m+eWXOgwaBE60SUREdJvqfn7zcl4T5+Lign79AKUSMBr/165SAUFBLmB+IiIikoeX85qBtm2BlStLgxNQ+vPjj0vbiYiISB6eiWomJk0CtFrg/HmgY0cGKCIiotpqEGeili9fDm9vb9ja2iIoKAiHDh2qsn7jxo3o0qULbG1t0aNHD+zcudNkuRACM2fOhIeHB+zs7BAaGopz586Z1OTn52PcuHFwdHSEk5MTJk2ahKtXr1a4v/Pnz8PBwQFOTk61GqeltW0LDBrEAEVEDZNOp0N2dnalL51O16j3R02Pxc9ErV+/HnFxcVixYgWCgoKQkJAArVaLs2fPwtXVtVx9SkoKIiMjER8fj2HDhiEpKQkRERE4evQo7rnnHgDAwoULsWTJEqxZswY+Pj6YMWMGtFotTp8+DVtbWwDAuHHjkJ2djT179uDmzZuYOHEioqOjkZSUZLK/mzdvIjIyEgMGDEBKSkr9HxAiomZIp9Nh2bJl0u96vQPy813g7KyDRlMktcfExNTJl2Hu3F9l6mp/1DRZ/Nt5QUFB6NOnj/RmNhqN8PLywtSpUzFt2rRy9aNHj0ZxcTG2b98utfXr1w/+/v5YsWIFhBDw9PTESy+9hJdffhkAoNfr4ebmhsTERIwZMwYZGRno1q0bDh8+jN69ewMAdu3ahUceeQSXL1+Gp6entO3XXnsNf/zxB4YMGYLY2FgUFBRUe2wN4dt5RESNQXZ2NlauXAmg6nntoqOj4eHhUaf7AyoPbXW1P2pcGsW380pKSpCeno7p06dLbUqlEqGhoUhNTa1wndTUVMTFxZm0abVabNmyBQCQmZmJnJwchIaGSss1Gg2CgoKQmpqKMWPGIDU1FU5OTlKAAoDQ0FAolUqkpaXhn//8JwBg79692LhxI44fP47NmzffdTw3btzAjRs3pN8LCwvvfhCIGiGdToeSkpJKl6vVav7rnWSpbF47X9/zJuGmLnEyYpLLoiEqLy8PBoMBbm5uJu1ubm44c+ZMhevk5ORUWJ+TkyMtL2urqubOS4VWVlZwdnaWanQ6HSZMmIDPP/+82meR4uPjMWfOnGrVEjVW5r7sQuU15RCbn+8iBagyQiiRn+9cLyHKEqGNmg6L3xPVUD3zzDMYO3YsBg4cWO11pk+fbnKWrLCwEF5eXvXRPSKLuf3Du6p/wVf1IU/yNfUQ6+ysg0JhNAlSCoURzs759bI/c4c2alos+u28Vq1aQaVSITc316Q9NzcX7u7uFa7j7u5eZX3Zz7vVXLlyxWT5rVu3kJ+fL9Xs3bsXixYtgpWVFaysrDBp0iTo9XpYWVnhk08+qbBvNjY2cHR0NHkRNVV3e5wQ1Y87Q2xCQizWrIlCQkIsjh7tVWFdY6LRFCE8fDsUitLZgcvCeX0FmrLQdrv6DG3UtFg0RKnVagQGBiI5OVlqMxqNSE5ORnBwcIXrBAcHm9QDwJ49e6R6Hx8fuLu7m9QUFhYiLS1NqgkODkZBQQHS09Olmr1798JoNCIoKAhA6b1Xx48fl15z586Fg4MDjh8/Lt0zRdScVfUveKp/TTnEBgQcQ2xsAqKiEhEbm1Cv9yeZO7RR02Lxy3lxcXGIiopC79690bdvXyQkJKC4uBgTJ04EADz55JNo06YN4uPjAQAvvPACQkJC8N577yEsLAzr1q3DkSNHpG9ZKBQKxMbG4q233oKfn580xYGnpyciIiIAAF27dsXQoUPxzDPPYMWKFbh58yZiYmIwZswY6Zt5Xbt2NennkSNHoFQqpWkUiJo7c192IVNN/TKURlNktnEEBByDr+955Oc7w9k5v0kcPzIPi4eo0aNH488//8TMmTORk5MDf39/7Nq1S7ox/NKlS1Aq//cXRf/+/ZGUlIQ333wTr7/+Ovz8/LBlyxaTcPPqq6+iuLgY0dHRKCgowP33349du3ZJc0QBwBdffIGYmBgMGTIESqUSI0eOxJIlS8w3cKJGruxf8HfeE8UPIPNoaiFWrVbXaV1Nt1NZaKur/VHTZPF5opoyzhNFTVHF8+uU/xc859epH+aeT8mczP2tw6b8LUeqnUYxTxQRNX7mvOxCppraZShzBxYGJKothigiqhFzX3ahqjHEElkOQxQR1YiLiwtiYmJ4GcRCzB1iecmLqHK8J6oe8Z4oIqoP5go2TX1iT6LK8J4oIqImylyBhbPTE1XNopNtEhFRw9eUJ/Ykqg2GKCIiqhJnpyeqGEMUERFVic+XI6oYQxQREVWJz5cjqhhvLCciortqahN7EtUFhigiIqoWTuxJZIqX84iIqEKcnZ6oajwTRUREFeLs9ERVY4giIqJKMSARVY6X84iIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGRgiCIiIiKSgSGKiIiISAaGKCIiIiIZGKKIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiIiIiGRiiiIiIiGSwsnQHiIiIqHnR6XQoKSmpdLlarYaLi4sZeyQPQxQRERGZjU6nw7Jly+5aFxMT0+CDFC/nERERkdlUdQZKTp0lMUQRERERycAQRURERBaj1zsgM9Mber2DpbtSY7wnioiIiCzi6NFe2LZtGIRQQqEwIjx8OwICjlm6W9XGM1FERERkdnq9gxSgAEAIJbZtG9aozkg1iBC1fPlyeHt7w9bWFkFBQTh06FCV9Rs3bkSXLl1ga2uLHj16YOfOnSbLhRCYOXMmPDw8YGdnh9DQUJw7d86kJj8/H+PGjYOjoyOcnJwwadIkXL16VVq+f/9+jBgxAh4eHrC3t4e/vz+++OKLuhs0ERFRM5af7yIFqDJCKJGf72yhHtWcxUPU+vXrERcXh1mzZuHo0aPo2bMntFotrly5UmF9SkoKIiMjMWnSJBw7dgwRERGIiIjAyZMnpZqFCxdiyZIlWLFiBdLS0mBvbw+tVovr169LNePGjcOpU6ewZ88ebN++HT/88AOio6NN9nPvvfdi06ZNOHHiBCZOnIgnn3wS27dvr7+DQURE1Ew4O+ugUBhN2hQKI5yd8y3Uo5pTCCGEJTsQFBSEPn36SHNGGI1GeHl5YerUqZg2bVq5+tGjR6O4uNgkzPTr1w/+/v5YsWIFhBDw9PTESy+9hJdffhkAoNfr4ebmhsTERIwZMwYZGRno1q0bDh8+jN69ewMAdu3ahUceeQSXL1+Gp6dnhX0NCwuDm5sbPvnkk2qNrbCwEBqNBnq9Ho6OjjU6LkRERE3R7fNEVXVPlCXniaru57dFbywvKSlBeno6pk+fLrUplUqEhoYiNTW1wnVSU1MRFxdn0qbVarFlyxYAQGZmJnJychAaGiot12g0CAoKQmpqKsaMGYPU1FQ4OTlJAQoAQkNDoVQqkZaWhn/+858V7luv16Nr166VjufGjRu4ceOG9HthYWHlgyciImqGXFxcEBMTI80DNXPmn8jKsoK39y14evYB0IczlldHXl4eDAYD3NzcTNrd3Nxw5syZCtfJycmpsD4nJ0daXtZWVY2rq6vJcisrKzg7O0s1d9qwYQMOHz6Mjz/+uNLxxMfHY86cOZUuJyIiIpgEJA8PIDDQgp2pBYvfE9UY7Nu3DxMnTsS///1vdO/evdK66dOnQ6/XS6/ff//djL0kIiIic7JoiGrVqhVUKhVyc3NN2nNzc+Hu7l7hOu7u7lXWl/28W82dN67funUL+fn55fb7/fffIzw8HO+//z6efPLJKsdjY2MDR0dHkxcRERE1TRYNUWq1GoGBgUhOTpbajEYjkpOTERwcXOE6wcHBJvUAsGfPHqnex8cH7u7uJjWFhYVIS0uTaoKDg1FQUID09HSpZu/evTAajQgKCpLa9u/fj7CwMLzzzjsm39wjIiIigrCwdevWCRsbG5GYmChOnz4toqOjhZOTk8jJyRFCCDF+/Hgxbdo0qf7gwYPCyspKLFq0SGRkZIhZs2YJa2tr8csvv0g1CxYsEE5OTmLr1q3ixIkTYsSIEcLHx0f8/fffUs3QoUNFr169RFpamjhw4IDw8/MTkZGR0vK9e/eKFi1aiOnTp4vs7GzppdPpqj02vV4vAAi9Xl+bQ0RERERmVN3Pb4uHKCGEWLp0qWjXrp1Qq9Wib9++4qeffpKWhYSEiKioKJP6DRs2iE6dOgm1Wi26d+8uduzYYbLcaDSKGTNmCDc3N2FjYyOGDBkizp49a1Kj0+lEZGSkaNmypXB0dBQTJ04URUVF0vKoqCgBoNwrJCSk2uNiiCIiImp8qvv5bfF5opoyzhNFRETU+FT385vfziMiIiKSgSGKiIiISAaGKCIiIiIZLDpjOREREVF16XQ66XExFTH342IYooiIiKjBu/3BxVUx54OLeTmPiIiIGryqzkDJqasLDFFERETU6Oj1DsjM9IZe72CxPvByHhERETUqR4/2wrZtwyCEEgqFEeHh2xEQcMzs/eCZKCIiImo09HoHKUABgBBKbNs2zCJnpBiiiIiIqNHIz3eRAlQZIZTIz3c2e18YooiIiKjRcHbWQaEwmrQpFEY4O+ebvS8MUURERNRoaDRFCA/fLgWpsnuiNJois/eFN5YTERFRg3ft2jXpzwEBx+Drex75+c5wds43CVC319U3nokiIiKiBq9FixYVtCqqWVc/eCaKiIiIGhVOcUBERERUQ5zigIiIiEgGTnFAREREJAOnOCAiIiKSgVMcEBEREclU1RQH5sQQRURERA2eWq02+V2jKaowPN1ZV58YooiIiKjBc3FxQUxMDEpKSiqtUavVcHFxMVufGKKIiIioUTBnQKoO3lhOREREJANDFBEREZEMvJxH1IzpdLoGdX8BEVFjwhBF1EzpdDosW7bsrnUxMTEMUkREFeDlPKJm6s4zUHq9AzIzvcs9f6qqM1VERM0Zz0QRUYN5IjoRUWPCM1FEzVxDeiI6EVFjwhBF1Mw1pCeiExE1JgxRRM1cQ3oiOhFRY8IQRdTMNaQnohMRNSa8sZyIGswT0YmIGhOGKKJmqiE+EZ2IqDFhiCJqphriE9GJiBoThiiiZowBiYhIPt5YTkRERCQDQxQRERGRDAxRRERERDIwRBERERHJwBBFREREJANDFBEREZEMDFFEREREMjBEEREREcnAEEVEREQkA2csr0dCCABAYWGhhXtCRERE1VX2uV32OV4Zhqh6VFRU+jBXLy8vC/eEiIiIaqqoqAgajabS5Qpxt5hFshmNRvzxxx9wcHCAQqGo1bYKCwvh5eWF33//HY6OjnXUw8aluR+D5j5+gMcA4DEAeAwAHgOgfo+BEAJFRUXw9PSEUln5nU88E1WPlEol2rZtW6fbdHR0bLb/w5Rp7seguY8f4DEAeAwAHgOAxwCov2NQ1RmoMryxnIiIiEgGhigiIiIiGRiiGgkbGxvMmjULNjY2lu6KxTT3Y9Dcxw/wGAA8BgCPAcBjADSMY8Aby4mIiIhk4JkoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigLWb58Oby9vWFra4ugoCAcOnSoyvqEhAR07twZdnZ28PLywosvvojr169Ly2fPng2FQmHy6tKlS30Po1Zqcgxu3ryJuXPnwtfXF7a2tujZsyd27dpVq202BHV9DBrb++CHH35AeHg4PD09oVAosGXLlruus3//fgQEBMDGxgYdO3ZEYmJiuZrG8j6oj/E39fdAdnY2xo4di06dOkGpVCI2NrbCuo0bN6JLly6wtbVFjx49sHPnzrrvfB2pj2OQmJhY7n1ga2tbPwOoAzU9Bps3b8aDDz6I1q1bw9HREcHBwdi9e3e5uvr+u4AhygLWr1+PuLg4zJo1C0ePHkXPnj2h1Wpx5cqVCuuTkpIwbdo0zJo1CxkZGVi1ahXWr1+P119/3aSue/fuyM7Oll4HDhwwx3BkqekxePPNN/Hxxx9j6dKlOH36NJ599ln885//xLFjx2Rv09Lq4xgAjet9UFxcjJ49e2L58uXVqs/MzERYWBgeeOABHD9+HLGxsXj66adN/vJsTO+D+hg/0LTfAzdu3EDr1q3x5ptvomfPnhXWpKSkIDIyEpMmTcKxY8cQERGBiIgInDx5si67Xmfq4xgApTN53/4+uHjxYl11uc7V9Bj88MMPePDBB7Fz506kp6fjgQceQHh4uPk/EwSZXd++fcWUKVOk3w0Gg/D09BTx8fEV1k+ZMkUMHjzYpC0uLk7cd9990u+zZs0SPXv2rJf+1oeaHgMPDw+xbNkyk7ZHH31UjBs3TvY2La0+jkFjex/cDoD46quvqqx59dVXRffu3U3aRo8eLbRarfR7Y3sflKmr8Tf198DtQkJCxAsvvFCufdSoUSIsLMykLSgoSEyePLmWPax/dXUMVq9eLTQaTZ31y5xqegzKdOvWTcyZM0f63Rx/F/BMlJmVlJQgPT0doaGhUptSqURoaChSU1MrXKd///5IT0+XTkP+9ttv2LlzJx555BGTunPnzsHT0xMdOnTAuHHjcOnSpfobSC3IOQY3btwodyrazs5O+he2nG1aUn0cgzKN5X0gR2pqqskxAwCtVisds8b2Pqipu42/TFN+D1RHdY9TU3f16lW0b98eXl5eGDFiBE6dOmXpLtUbo9GIoqIiODs7AzDf3wUMUWaWl5cHg8EANzc3k3Y3Nzfk5ORUuM7YsWMxd+5c3H///bC2toavry8GDRpkcjkvKCgIiYmJ2LVrFz766CNkZmZiwIABKCoqqtfxyCHnGGi1WixevBjnzp2D0WjEnj17sHnzZmRnZ8vepiXVxzEAGtf7QI6cnJwKj1lhYSH+/vvvRvc+qKm7jR9o+u+B6qjsODWF90B1de7cGZ988gm2bt2Kzz//HEajEf3798fly5ct3bV6sWjRIly9ehWjRo0CYL7PBIaoRmD//v14++238eGHH+Lo0aPYvHkzduzYgXnz5kk1Dz/8MB5//HHce++90Gq12LlzJwoKCrBhwwYL9rzufPDBB/Dz80OXLl2gVqsRExODiRMnQqlsPm/h6hyDpv4+oLvje4AAIDg4GE8++ST8/f0REhKCzZs3o3Xr1vj4448t3bU6l5SUhDlz5mDDhg1wdXU1676bzydQA9GqVSuoVCrk5uaatOfm5sLd3b3CdWbMmIHx48fj6aefRo8ePfDPf/4Tb7/9NuLj42E0Gitcx8nJCZ06dcL58+frfAy1JecYtG7dGlu2bEFxcTEuXryIM2fOoGXLlujQoYPsbVpSfRyDijTk94Ec7u7uFR4zR0dH2NnZNbr3QU3dbfwVaWrvgeqo7Dg1hfeAXNbW1ujVq1eTex+sW7cOTz/9NDZs2GBy6c5cfxcwRJmZWq1GYGAgkpOTpTaj0Yjk5GQEBwdXuM61a9fKnXFRqVQAAFHJow+vXr2KCxcuwMPDo456XnfkHIMytra2aNOmDW7duoVNmzZhxIgRtd6mJdTHMahIQ34fyBEcHGxyzABgz5490jFrbO+Dmrrb+CvS1N4D1SHnODV1BoMBv/zyS5N6H6xduxYTJ07E2rVrERYWZrLMbH8X1Nkt6lRt69atEzY2NiIxMVGcPn1aREdHCycnJ5GTkyOEEGL8+PFi2rRpUv2sWbOEg4ODWLt2rfjtt9/Et99+K3x9fcWoUaOkmpdeekns379fZGZmioMHD4rQ0FDRqlUrceXKFbOPrzpqegx++uknsWnTJnHhwgXxww8/iMGDBwsfHx/x119/VXubDU19HIPG9j4oKioSx44dE8eOHRMAxOLFi8WxY8fExYsXhRBCTJs2TYwfP16q/+2330SLFi3EK6+8IjIyMsTy5cuFSqUSu3btkmoa0/ugPsbf1N8DQgipPjAwUIwdO1YcO3ZMnDp1Slp+8OBBYWVlJRYtWiQyMjLErFmzhLW1tfjll1/MOrbqqo9jMGfOHLF7925x4cIFkZ6eLsaMGSNsbW1NahqSmh6DL774QlhZWYnly5eL7Oxs6VVQUCDVmOPvAoYoC1m6dKlo166dUKvVom/fvuKnn36SloWEhIioqCjp95s3b4rZs2cLX19fYWtrK7y8vMS//vUvkw/P0aNHCw8PD6FWq0WbNm3E6NGjxfnz5804opqryTHYv3+/6Nq1q7CxsREuLi5i/Pjx4r///W+NttkQ1fUxaGzvg3379gkA5V5l446KihIhISHl1vH39xdqtVp06NBBrF69utx2G8v7oD7G3xzeAxXVt2/f3qRmw4YNolOnTkKtVovu3buLHTt2mGdAMtTHMYiNjZX+H3BzcxOPPPKIOHr0qPkGVUM1PQYhISFV1pep778LFEJUcj2IiIiIiCrFe6KIiIiIZGCIIiIiIpKBIYqIiIhIBoYoIiIiIhkYooiIiIhkYIgiIiIikoEhioiIiEgGhigiomZk//79UCgUKCgosHRXiBo9higiqhcTJkyAQqHAggULTNq3bNkChUIh/S6EwL///W8EBwfD0dERLVu2RPfu3fHCCy9U+2Gp165dw/Tp0+Hr6wtbW1u0bt0aISEh2Lp1q1Tj7e2NhISEOhlbfSs7dgqFAtbW1vDx8cGrr76K69ev12g7gwYNQmxsrElb//79kZ2dDY1GU4c9JmqeGKKIqN7Y2trinXfewV9//VXhciEExo4di+effx6PPPIIvv32W5w+fRqrVq2Cra0t3nrrrWrt59lnn8XmzZuxdOlSnDlzBrt27cJjjz0GnU5Xl8Mxq6FDhyI7Oxu//fYb3n//fXz88ceYNWtWrberVqvh7u5uEmSJSKY6fYgMEdH/i4qKEsOGDRNdunQRr7zyitT+1VdfibK/etauXSsAiK1bt1a4DaPRWK19aTQakZiYWOnyip6zVebHH38U999/v7C1tRVt27YVU6dOFVevXpWWf/rppyIwMFC0bNlSuLm5icjISJGbmystL3vm165du4S/v7+wtbUVDzzwgMjNzRU7d+4UXbp0EQ4ODiIyMlIUFxdXazxRUVFixIgRJm2PPvqo6NWrl/R7Xl6eGDNmjPD09BR2dnbinnvuEUlJSSbbuHPMmZmZUn9vf/bml19+Kbp16ybUarVo3769WLRoUbX6SdTc8UwUEdUblUqFt99+G0uXLsXly5fLLV+7di06d+6M4cOHV7h+dc+WuLu7Y+fOnSgqKqpw+ebNm9G2bVvMnTsX2dnZyM7OBgBcuHABQ4cOxciRI3HixAmsX78eBw4cQExMjLTuzZs3MW/ePPz888/YsmULsrKyMGHChHL7mD17NpYtW4aUlBT8/vvvGDVqFBISEpCUlIQdO3bg22+/xdKlS6s1njudPHkSKSkpUKvVUtv169cRGBiIHTt24OTJk4iOjsb48eNx6NAhAMAHH3yA4OBgPPPMM9KYvby8ym07PT0do0aNwpgxY/DLL79g9uzZmDFjBhITE2X1lahZsXSKI6Km6fazKf369RNPPfWUEML0TFSXLl3E8OHDTdZ74YUXhL29vbC3txdt2rSp1r6+//570bZtW2FtbS169+4tYmNjxYEDB0xq2rdvL95//32TtkmTJono6GiTth9//FEolUrx999/V7ivw4cPCwCiqKhICPG/M1HfffedVBMfHy8AiAsXLkhtkydPFlqttlrjiYqKEiqVStjb2wsbGxsBQCiVSvHll19WuV5YWJh46aWXpN9DQkLECy+8YFJz55mosWPHigcffNCk5pVXXhHdunWrVl+JmjOeiSKievfOO+9gzZo1yMjIuGvtG2+8gePHj2PmzJm4evVqtbY/cOBA/Pbbb0hOTsZjjz2GU6dOYcCAAZg3b16V6/38889ITExEy5YtpZdWq4XRaERmZiaA0jM14eHhaNeuHRwcHBASEgIAuHTpksm27r33XunPbm5uaNGiBTp06GDSduXKlWqNBwAeeOABHD9+HGlpaYiKisLEiRMxcuRIabnBYMC8efPQo0cPODs7o2XLlti9e3e5ft1NRkYG7rvvPpO2++67D+fOnYPBYKjRtoiaG4YoIqp3AwcOhFarxfTp003a/fz8cPbsWZO21q1bo2PHjnB1da3RPqytrTFgwAC89tpr+PbbbzF37lzMmzcPJSUlla5z9epVTJ48GcePH5deP//8M86dOwdfX18UFxdDq9XC0dERX3zxBQ4fPoyvvvoKAMpt19raWvpz2bfqbqdQKGA0Gqs9Hnt7e3Ts2BE9e/bEJ598grS0NKxatUpa/u677+KDDz7Aa6+9hn379uH48ePQarVVjpeI6paVpTtARM3DggUL4O/vj86dO0ttkZGRGDt2LLZu3YoRI0bU6f66deuGW7du4fr161Cr1VCr1eXOrAQEBOD06dPo2LFjhdv45ZdfoNPpsGDBAul+oiNHjtRpP6tDqVTi9ddfR1xcHMaOHQs7OzscPHgQI0aMwBNPPAEAMBqN+PXXX9GtWzdpvYrGfKeuXbvi4MGDJm0HDx5Ep06doFKp6n4wRE0Iz0QRkVn06NED48aNw5IlS6S2MWPG4LHHHsOYMWMwd+5cpKWlISsrC99//z3Wr19f7Q/xQYMG4eOPP0Z6ejqysrKwc+dOvP7663jggQfg6OgIoHSeqB9++AH//e9/kZeXBwB47bXXkJKSgpiYGBw/fhznzp3D1q1bpRvL27VrB7VajaVLl+K3337D119/fddLhPXl8ccfh0qlwvLlywGUnsXbs2cPUlJSkJGRgcmTJyM3N9dkHW9vb+mY5uXlVXgm7KWXXkJycjLmzZuHX3/9FWvWrMGyZcvw8ssvm2VcRI0ZQxQRmc3cuXNNPsgVCgXWr1+PhIQE7Ny5E0OGDEHnzp3x1FNPwcvLCwcOHKjWdrVaLdasWYOHHnoIXbt2xdSpU6HVarFhwwaTfWdlZcHX1xetW7cGUHof0/fff49ff/0VAwYMQK9evTBz5kx4enoCKL20mJiYiI0bN6Jbt25YsGABFi1aVIdHpPqsrKwQExODhQsXori4GG+++SYCAgKg1WoxaNAguLu7IyIiwmSdl19+GSqVCt26dUPr1q0rvF8qICAAGzZswLp163DPPfdg5syZmDt3boXfQCQiUwohhLB0J4iIiIgaG56JIiIiIpKBIYqIGrzbpyC48/Xjjz9auns1cunSpSrHU9MpCojIcng5j4gavKoeRNymTRvY2dmZsTe1c+vWLWRlZVW63NvbG1ZW/OI0UWPAEEVEREQkAy/nEREREcnAEEVEREQkA0MUERERkQwMUUREREQyMEQRERERycAQRURERCQDQxQRERGRDAxRRERERDL8Hw51/eUiJFEbAAAAAElFTkSuQmCC", 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", 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", 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", 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ARqNBTk5OqftISEhAQEAAmjVrBmtra/j5+QEArl69avT6l4YBiaiWu3vXTgpHhYSQ4+5dWxPViIhqi3PnzqF58+ZITU3FoEGD0LFjR2zduhUJCQmIjo4GUPptv+zsbKjVatjY2OCbb77Bb7/9hh9++OGJ21UF3mIjqqWUSiUAwNZWA5lMpxeSZDIdbG3v6pUjIiqP//73v/jjjz8wdepUJCQkQKfTYenSpZDLC/7WfPfdd3rllUol8vPz9ZadP38eGo0GCxcuhIuLCwDgxIkTVdOAJ2BAIqql7OzsEBwcjLy8PDRpkokPPlAhP18GhULg008zMWLEcM6kTURlkpubi/T0dL3H/MPDwzFo0CCMGjUKZ86cwaNHjxAZGYmAgAAcPnwYq1ev1tuHm5sb7t+/j7i4OHh5ecHKygrNmjWDUqlEZGQkJk6ciDNnzmDBggUmaqU+3mIjqsXs7Ozg5OSEd99tiNRUGfbvB1JTZXj33YZwcnJiOCKiMomNjYWTkxPc3NzQv39/7N+/H5999hl27NgBhUIBLy8vLFu2DJ9++inat2+Pb775BuHh4Xr76NmzJyZOnIigoCDY29tj0aJFsLe3x4YNG7Blyxa0bdsWCxcuxJIlS0zUSn0yIYQwdSVqoszMTKhUKmi1WtjY2Ji6OkREVI09fPgQKSkpaN68OSwsLMq9vannQappSjvfZf38NvkttujoaCxevBjp6enw8vJCZGQkunfvXmzZP//8E7Nnz0ZCQgKuXLmC5cuX6z1WCAC//PILFi9ejISEBKSlpeGHH35AYGCgXhkhBObMmYMvvvgC9+7dw7PPPotVq1bBw8PDSK0kIiKquMdvmZeEt8wrl0lvsW3evBmhoaGYM2cOTp48CS8vL6jVaty6davY8jk5OWjRogUWLlxYZB6FQtnZ2fDy8pJGzxdn0aJF+Oyzz7B69WocO3YM9evXh1qtxsOHDyulXURERJWt8JZ5SS+Go8pl0ltsPj4+6Natm9RtqNPp4OLigsmTJ2P69Omlbuvm5oaQkJAiPUiPk8lkRXqQhBBwdnbGu+++K83iqdVq4eDggA0bNmDYsGFlqjtvsRERUVk97S02Kp/KuMVmsh6kvLw8JCQkwN/f/5/KyOXw9/dHfHy80Y6bkpKC9PR0veOqVCr4+PiUetzc3FxkZmbqvYiIiKh2MllAunPnDvLz8+Hg4KC33MHBAenp6UY7buG+y3vc8PBwqFQq6VU4XwMRERHVPnzMv4zCwsKg1Wql17Vr10xdJSIiIjISkwWkRo0aQaFQICMjQ295RkZGiQOwK0Phvst7XHNzc9jY2Oi9iIiIqHYyWUBSKpXw9vZGXFyctEyn0yEuLg6+vr5GO27z5s3h6Oiod9zMzEwcO3bMqMclIiKimsOk8yCFhoZi9OjR6Nq1K7p3746IiAhkZ2djzJgxAIBRo0ahSZMm0myceXl5OHv2rPTzjRs3kJiYiAYNGqBly5YAgPv37+PixYvSMVJSUpCYmAhbW1s0a9YMMpkMISEh+Oijj+Dh4YHmzZtj1qxZcHZ2LjJfEhEREdVRwsQiIyNFs2bNhFKpFN27dxdHjx6V1vn5+YnRo0dL71NSUgSAIi8/Pz+pzP79+4st8/h+dDqdmDVrlnBwcBDm5ubihRdeEElJSeWqt1arFQCEVqutaNOJiKiOePDggTh79qx48OCBqatS7RR+bv/1119l3sbV1VUsX768xPWlne+yfn6bfJB2cHAwrly5gtzcXBw7dgw+Pj7SugMHDmDDhg3Sezc3NwghirwOHDgglenTp0+xZR7fj0wmw/z585Geno6HDx9i3759aNWqVRW0loiIqGZ58803IZPJMHHixCLrJk2aBJlMhjfffLPqK2ZkJg9IREREVL25uLhg06ZNePDggbTs4cOHiImJQbNmzUxYM+NhQCIiIqJSdenSBS4uLti2bZu0bNu2bWjWrBk6d+4sLcvNzcU777yDxo0bw8LCAs899xx+++03vX3t2bMHrVq1gqWlJZ5//nmkpqYWOd6hQ4fQq1cvWFpawsXFBe+88w6ys7ON1r7iMCARERHVMNevA/v3F/xbVd566y2sX79eer9u3TrpoapC77//PrZu3YqNGzfi5MmTaNmyJdRqNe7evQsAuHbtGgYPHoyAgAAkJibi7bffLvLVYpcuXUL//v0xZMgQnD59Gps3b8ahQ4cQHBxs/EY+hgGJiIioBlm7FnB1Bfr2Lfh37dqqOe7rr7+OQ4cO4cqVK7hy5QoOHz6M119/XVqfnZ2NVatWYfHixRgwYADatm2LL774ApaWllj7v0quWrUK7u7uWLp0KVq3bo2RI0cWGb8UHh6OkSNHIiQkBB4eHujZsyc+++wzfPnll1X6pfImfcyfiIiIyu76dWD8eECnK3iv0wETJgBqNdC0qXGPbW9vj4EDB2LDhg0QQmDgwIFo1KiRtP7SpUt49OgRnn32WWmZmZkZunfvjnPnzgEAzp07p/cwFoAicxD+/vvvOH36NL755htpmRACOp0OKSkpaNOmjTGaVwQDEhERUQ2RnPxPOCqUnw9cvGj8gAQU3GYrvNUVHR1tlGPcv38fEyZMwDvvvFNkXVUOCGdAIiIiqiE8PAC5XD8kKRTA/+ZKNrr+/fsjLy8PMpkMarVab527uzuUSiUOHz4MV1dXAMCjR4/w22+/ISQkBADQpk0b7Ny5U2+7o0eP6r3v0qULzp49K00AbSocg0RERFRDNG0KrFlTEIqAgn8//7xqeo8KjqfAuXPncPbsWSgKK/E/9evXx7///W9MmzYNsbGxOHv2LMaNG4ecnByMHTsWADBx4kQkJydj2rRpSEpKQkxMjN48hQDwwQcf4MiRIwgODkZiYiKSk5OxY8eOKh+kzR4kIiKiGmTs2IIxRxcvFvQcVVU4KlTal7UvXLgQOp0Ob7zxBrKystC1a1f89NNPeOaZZwAU3CLbunUrpk6disjISHTv3h2ffPIJ3nrrLWkfHTt2xMGDB/Hhhx+iV69eEELA3d0dQUFBRm/b42RCCFGlR6wlMjMzoVKpoNVqS71YiIiIHj58iJSUFDRv3hwWFhamrk6tV9r5LuvnN2+xERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIREVEV4XNRVaMyzjMDEhERkZGZmZkBAHJyckxck7qh8DwXnveK4DxIRERERqZQKNCwYUPcunULAGBlZQWZTGbiWtU+Qgjk5OTg1q1baNiwYZHJLMuDAYmIiKgKODo6AoAUksh4GjZsKJ3vimJAIiIiqgIymQxOTk5o3LgxHj16ZOrq1FpmZmZP1XNUiAGJiIioCikUikr5ACfj4iBtIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSLXE9evA/v0F/xIREdHTYUCqwTQaDdLS0rB06T24ugr07Qu4ugosXXoPaWlp0Gg0pq4iERFRjcTvYquhNBoNoqKioNVaIyIiBELIAAA6nQzTptngxo11UKmyEBwcDDs7OxPXloiIqGZhD1INlZeXBwC4e9cOQuj/GoWQ4+5dW71yREREVHYMSDWcra0GMplOb5lMpoOt7V0T1YiIiKjmqxYBKTo6Gm5ubrCwsICPjw+OHz9eYtk///wTQ4YMgZubG2QyGSIiIiq0zz59+kAmk+m9Jk6cWJnNqhIqVRYCAnZLIUkm0yEgYDdUqiwT14yIiKjmMvkYpM2bNyM0NBSrV6+Gj48PIiIioFarkZSUhMaNGxcpn5OTgxYtWuC1117D1KlTn2qf48aNw/z586X3VlZWld/AKtClyym4u1/E3bu2sLW9y3BERET0lEzeg7Rs2TKMGzcOY8aMQdu2bbF69WpYWVlh3bp1xZbv1q0bFi9ejGHDhsHc3Pyp9mllZQVHR0fpZWNjU+ntqyoqVRaaN7/CcERERFQJTBqQ8vLykJCQAH9/f2mZXC6Hv78/4uPjjb7Pb775Bo0aNUL79u0RFhaGnJycEvebm5uLzMxMvRcRERHVTia9xXbnzh3k5+fDwcFBb7mDgwPOnz9v1H2OGDECrq6ucHZ2xunTp/HBBx8gKSkJ27ZtK3a/4eHhmDdvXoXqRERERDWLyccgmcr48eOlnzt06AAnJye88MILuHTpEtzd3YuUDwsLQ2hoqPQ+MzMTLi4uVVLX4iiVykotR0RERP8waUBq1KgRFAoFMjIy9JZnZGTA0dGxSvfp4+MDALh48WKxAcnc3LzEMU+mYGdnh+Dg4FLnOVIqlZwkkoiIqAJMOgZJqVTC29sbcXFx0jKdToe4uDj4+vpW6T4TExMBAE5OThU6rinY2dnBycmpxBfDERERUcWY/BZbaGgoRo8eja5du6J79+6IiIhAdnY2xowZAwAYNWoUmjRpgvDwcAAFg7DPnj0r/Xzjxg0kJiaiQYMGaNmyZZn2eenSJcTExOCll16CnZ0dTp8+jalTp6J3797o2LGjCc4CERERVScmD0hBQUG4ffs2Zs+ejfT0dHTq1AmxsbHSIOurV69CLv+no+vmzZvo3Lmz9H7JkiVYsmQJ/Pz8cODAgTLtU6lUYt++fVJwcnFxwZAhQzBz5syqazgRERFVWzIhhDB1JWqizMxMqFQqaLXaGj1/EhERUV1S1s9vk08USURERFTdMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiAwxIRERERAYYkIiIiIgMMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQqt+vXgf37C/4lIiKqjRiQqFzWrgVcXYG+fQv+XbvW1DUiIiKqfAxIVGbXrwPjxwM6XcF7nQ6YMIE9SbUZewuJqK5iQKIy0Wg0OHpUI4WjQvn5wLFjGmg0GtNUjCqdRqNBWloali69B1dX8b/eQoGlS+8hLS2Nv2siqhPqmboCVP1pNBpERUVBq7WGTBYCIf7J1TKZDocPb8SZM1kIDg6GnZ2dCWtKT+vx33VERAiEkAEAdDoZpk2zwY0b66BS8XdNRLUfe5DoifLy8gAAKlUWAgJ2QyYr6EaSyXQICNgNlSpLrxzVXIW/w7t37fSCMAAIIcfdu7Z65YiIaiv2IFG5dOlyCu7uF3H3ri1sbe9K4YhqF1tbDWQyXZHeQlvbuyasFRFR1WEPEpWbSpWF5s2vMBzVYk/qLSQiqu3Yg0RExWJvIRHVZQxIRFQilSqLwYiI6iTeYiMiIiIywIBEREREZIABiZ5IqVRWajmqvvi7JiIqIBNCCFNXoibKzMyESqWCVquFjY2NqatjdBqNptS5b5RKJScOrCX4uyai2qysn98cpE1lwg/EuoO/ayIi3mIjIiIiKoIBiYiIiKqV69eB/fsL/jUVBiQiIiKqNtauBVxdgb59C/5du9Y09WBAIiIiomrh+nVg/HhAV/AtR9DpgAkTTNOTxIBEREREJqfRaHD0qEYKR4Xy84FjxzTQaDRVWp9qEZCio6Ph5uYGCwsL+Pj44Pjx4yWW/fPPPzFkyBC4ublBJpMhIiKiQvt8+PAhJk2aBDs7OzRo0ABDhgxBRkZGZTaLiIiIykCj0SAqKgpHjmyUviS7kEymw+HDGxEVFVWlIcnkAWnz5s0IDQ3FnDlzcPLkSXh5eUGtVuPWrVvFls/JyUGLFi2wcOFCODo6VnifU6dOxa5du7BlyxYcPHgQN2/exODBg43SRiIiIipZ4dxrKlUWAgJ2SyFJJtMhIGC39J2Qpc3RVtlMPlGkj48PunXrhqioKACATqeDi4sLJk+ejOnTp5e6rZubG0JCQhASElKufWq1Wtjb2yMmJgavvvoqAOD8+fNo06YN4uPj0aNHjyfWu65NFElERGQsaWlpWLNmjfReq7XG3bu2sLW9q/eF2ePHj4eTk9NTHausn98m7UHKy8tDQkIC/P39pWVyuRz+/v6Ij4832j4TEhLw6NEjvTKenp5o1qxZicfNzc1FZmam3ouIiIiMRWbSo5t0Ju07d+4gPz8fDg4OessdHBxw/vx5o+0zPT0dSqUSDRs2LFImPT292P2Gh4dj3rx5FaoTERERlc3Jk52xa9cgCCGXbrF16XKqyuth8jFINUVYWBi0Wq30unbtmqmrREREVKtotdZSOAIAIeTYtWsQtFrrKq+LSQNSo0aNoFAoijw9lpGRUeIA7MrYp6OjI/Ly8nDv3r0yH9fc3Bw2NjZ6LyIiInp6hZ/Hd+/aSeGokBBy3L1rq1euKpg0ICmVSnh7eyMuLk5aptPpEBcXB19fX6Pt09vbG2ZmZnplkpKScPXq1Qofl4iIiCrm77//BgDY2mqKfczf1vauXrmqYNIxSAAQGhqK0aNHo2vXrujevTsiIiKQnZ2NMWPGAABGjRqFJk2aIDw8HEDBIOyzZ89KP9+4cQOJiYlo0KABWrZsWaZ9qlQqjB07FqGhobC1tYWNjQ0mT54MX1/fMj3BRmRK168DycmAhwfQtKmpa0NE9PTMzMwA/POYv+EYpMIn2QrLVQWTB6SgoCDcvn0bs2fPRnp6Ojp16oTY2FhpkPXVq1chl//T0XXz5k107txZer9kyRIsWbIEfn5+OHDgQJn2CQDLly+HXC7HkCFDkJubC7VajZUrV1ZNo4kqaO3af6bhl8uBNWuAsWNNXSuqTBqNRprr5eZNOVJS6qF587/h7Fzwf9VKpRJ2dnaVflwGbzIllUol/dylyym4u18s9jH/x8sZm8nnQaqpOA8SVSWNRoPU1L/RvXtj6HT/PPqqUAgcO3YLbm71jPKhSVWrcDZhoPQneYKDgyvl910YxmJiLPH++yrodDLI5QKLFmkxYsQDo4UxIkOG8yCVpCrnQTJ5DxIRla7wQzMlxQ063Wi9dfn5MkRG/gfNm1+ptA9NMp3CnqOSnuRxd78IlSqrUmYTLryutFprRESEQIiC4K3TyTBtmg1u3FgHlSqL1xXVWXzMn6iaK/wwfNLgxaqcgp+M60lP8lSGwuvlScfidUV1FQMSUQ3xpO8ootrjSWG4ph6LqCbhLTaiGqS0wYtUezzpSZ6aeiyikiiVykotVxkYkIhqGJUqix9edUBVhmEGbzI1Ozs7BAcHl3pLt6ofGmBAIiKqpqoyDDN4k6lVt4cBOAaJiIiIjOb6dWD//oJ/axIGJCKiaqI6jsMgqgiNRoO0tDQsXXoPrq4CffsCrq4CS5feQ1paGjQajamr+ES8xUZUzfFDs+6oynEYvK7IWGrLHFsMSETVXHUcvEjGU1W/R15XZCxlmWOrsiY8NSYGJKIagB9SZAy8rsiYCufYejwk1aQ5tjgGiYiIiCpdTZ/clj1IREREZBQ1eY4tBiQiIiIympo6xxZvsREREREZYEAiIiIiMsCARFRH1NTZbKl24vVYe9WWObY4BomoDli7Fhg/HtDpALkcWLMGGDvW1LWiukaj0SAvLw8xMZZ4/30VdDoZ5HKBRYu0GDHiAeddqiVqyxxbMiGEMHUlaqLMzEyoVCpotVrY2NiYujpExdJoNEhN/RvduzeGTieTlisUAseO3YKbW71q/0eKaoeisyvrz40TEhJRI2ZXppqvrJ/fvMVGVEsVfiBFRsbqhSMAyM+XITLyP4iKiqoR34lENV9ZZld+vByRqTEgEdVShR80hbPZPu7x2Wz5gURV6UnXI1F1wYBEVMvV9NlsqXbh9Ug1BQdpE9UBNXk2W6p9eD1STcCARFRH1NTZbKl2qsrr8fp1IDkZ8PAAmjatkkNSLcBbbEREVOtoNBqkpaVh6dJ7cHUV6NsXcHUVWLr0HtLS0vhwAj0Re5CIiKhWKTqlQMFTnDqdDNOm2eDGjXWcUoCeiD1IRERkdFU5uzKnFKDKwB6kaob3yqmy1Jbp/ql2MMXsyoVTChhOSskpBagsGJCqAU6/T8ZQW6b7p9qjqq+1wikFdu0aBCHknFKAyoUBycR4r5yMidcM1XWcUoAqigHJxMpyr1ylyuK9ciKiCuIUF1QRHKRdTXD6fSIiouqDAama4PT7RERE1QdvsVUjvFdORPT0+AQnVYZqEZCio6OxePFipKenw8vLC5GRkejevXuJ5bds2YJZs2YhNTUVHh4e+PTTT/HSSy9J6zMyMvDBBx/g559/xr1799C7d29ERkbCw8NDKtOnTx8cPHhQb78TJkzA6tWrK7+B5cB75URET4dPcFJlMPktts2bNyM0NBRz5szByZMn4eXlBbVajVu3bhVb/siRIxg+fDjGjh2LU6dOITAwEIGBgThz5gwAQAiBwMBAXL58GTt27MCpU6fg6uoKf39/ZGdn6+1r3LhxSEtLk16LFi0yenuJiMj47Ozs4OTkVOKL4YiexOQBadmyZRg3bhzGjBmDtm3bYvXq1bCyssK6deuKLb9ixQr0798f06ZNQ5s2bbBgwQJ06dIFUVFRAIDk5GQcPXoUq1atQrdu3dC6dWusWrUKDx48wLfffqu3LysrKzg6OkovGxsbo7eXiIiIqj+TBqS8vDwkJCTA399fWiaXy+Hv74/4+Phit4mPj9crDwBqtVoqn5ubCwCwsLDQ26e5uTkOHTqkt90333yDRo0aoX379ggLC0NOTk6Jdc3NzUVmZqbeqzLwXjkREVH1Y9IxSHfu3EF+fj4cHBz0ljs4OOD8+fPFbpOenl5s+fT0dACAp6cnmjVrhrCwMHz++eeoX78+li9fjuvXryMtLU3aZsSIEXB1dYWzszNOnz6NDz74AElJSdi2bVuxxw0PD8e8efOeprnF4r1yIiKi6qdaDNKuTGZmZti2bRvGjh0LW1tbKBQK+Pv7Y8CAARBCSOXGjx8v/dyhQwc4OTnhhRdewKVLl+Du7l5kv2FhYQgNDZXeZ2ZmwsXFpVLqzPBDRERUvZg0IDVq1AgKhQIZGRl6yzMyMuDo6FjsNo6Ojk8s7+3tjcTERGi1WuTl5cHe3h4+Pj7o2rVriXXx8fEBAFy8eLHYgGRubg5zc/Myt42IiIhqLpOOQVIqlfD29kZcXJy0TKfTIS4uDr6+vsVu4+vrq1ceAPbu3VtseZVKBXt7eyQnJ+PEiRN45ZVXSqxLYmIiAMDJyakCLSEiIqLaxOS32EJDQzF69Gh07doV3bt3R0REBLKzszFmzBgAwKhRo9CkSROEh4cDAKZMmQI/Pz8sXboUAwcOxKZNm3DixAmsWbNG2ueWLVtgb2+PZs2a4Y8//sCUKVMQGBiIfv36AQAuXbqEmJgYvPTSS7Czs8Pp06cxdepU9O7dGx07dqz6k0BERETViskDUlBQEG7fvo3Zs2cjPT0dnTp1QmxsrDQQ++rVq5DL/+no6tmzJ2JiYjBz5kzMmDEDHh4e2L59O9q3by+VSUtLQ2hoKDIyMuDk5IRRo0Zh1qxZ0nqlUol9+/ZJYczFxQVDhgzBzJkzq67hREREVG3JxOMjl6nMMjMzoVKpoNVqOX8SERFRDVHWz2+TTxRJREREVN0wIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkYEKBaQtW7Zg8ODBaN++Pdq3b4/Bgwfj+++/r+y6EREREZlEuQKSTqdDUFAQgoKCcPbsWbRs2RItW7bEn3/+iaCgIAwbNgycVomIiIhqunLNpL1ixQrs27cPO3fuxKBBg/TW7dy5E2PGjMGKFSsQEhJSmXUkIiIiqlLl6kFav349Fi9eXCQcAcDLL7+MRYsWYd26dZVWOSIiIiJTKFdASk5Ohr+/f4nr/f39kZyc/NSVIiIiIjKlcgUkS0tL3Lt3r8T1mZmZsLCweNo6EVEddf06sH9/wb9ERKZUroDk6+uLVatWlbg+Ojoavr6+T10pIqo7NBoN0tLSsHTpPbi6CvTtC7i6Cixdeg9paWnQaDSmriIR1UHlGqT94Ycfok+fPtBoNHjvvffg6ekJIQTOnTuHpUuXYseOHdi/f7+x6kpEtYxGo0FUVBS0WmtERIRACBkAQKeTYdo0G9y4sQ4qVRaCg4NhZ2dn4toSUV1SroDUs2dPbN68GePHj8fWrVv11j3zzDP49ttv8eyzz1ZqBYmo9srLywMA3L1rByH0O7SFkOPuXVuoVFlSOSKiqlKugAQA//rXv6BWq/HTTz9JA7JbtWqFfv36wcrKqtIrSES1n62tBjKZTi8kyWQ62NreNWGtiKguK1dA+u9//4vg4GAcPXoU//rXv/TWabVatGvXDqtXr0avXr0qtZJEVLupVFkICNiNXbsGQQg5ZDIdAgJ2Q6XKMnXViKiOKldAioiIwLhx42BjY1NknUqlwoQJE7Bs2TIGJCIqty5dTsHd/SLu3rWFre1dhiMiMqlyBaTff/8dn376aYnr+/XrhyVLljx1pYjIdDQajTTm5+ZNOVJS6qF587/h7KwDACiVSqMNmFapshiMiKhaKFdAysjIgJmZWck7q1cPt2/ffupKEZFpFD5VBgAnT3YucsurS5dTAMCnyoio1ivXPEhNmjTBmTNnSlx/+vRpODk5PXWliMg0CnuOtFprKRwBBU+U7do1CFqttV45IqLaqlwB6aWXXsKsWbPw8OHDIusePHiAOXPmFPs9bURUs5T22H1lUiqVlVqOiKiylOsW28yZM7Ft2za0atUKwcHBaN26NQDg/PnziI6ORn5+Pj788EOjVJSIqk5VPXZvZ2eH4ODgUnukjDnmiYioJOUKSA4ODjhy5Aj+/e9/IywsDEIIAIBMJoNarUZ0dDQcHByMUlEiqjpV+dg9ww8RVUflnijS1dUVe/bswV9//YWLFy9CCAEPDw8888wzxqgfEZkIH7snorqs3AGp0DPPPINu3bpVZl2IqJrhY/dEVFeVa5A2ERERUV3AgEREEj5VRkRUoMK32Iio9uFTZUREBRiQiEgPww8REW+xERERERXBgERERERkgAGJiIiIyAADEhEREZGBahGQoqOj4ebmBgsLC/j4+OD48eOllt+yZQs8PT1hYWGBDh06YM+ePXrrMzIy8Oabb8LZ2RlWVlbo378/kpOT9co8fPgQkyZNgp2dHRo0aIAhQ4YgIyOj0ttGRERENY/JA9LmzZsRGhqKOXPm4OTJk/Dy8oJarcatW7eKLX/kyBEMHz4cY8eOxalTpxAYGIjAwECcOXMGACCEQGBgIC5fvowdO3bg1KlTcHV1hb+/P7Kzs6X9TJ06Fbt27cKWLVtw8OBB3Lx5E4MHD66SNhMREVH1JhOF3zhrIj4+PujWrRuioqIAADqdDi4uLpg8eTKmT59epHxQUBCys7Oxe/duaVmPHj3QqVMnrF69GhcuXEDr1q1x5swZtGvXTtqno6MjPvnkE7z99tvQarWwt7dHTEwMXn31VQDA+fPn0aZNG8THx6NHjx5PrHdmZiZUKhW0Wi1sbGwq41QQERGRkZX189ukPUh5eXlISEiAv7+/tEwul8Pf3x/x8fHFbhMfH69XHgDUarVUPjc3FwBgYWGht09zc3McOnQIAJCQkIBHjx7p7cfT0xPNmjUr8bi5ubnIzMzUexEREVHtZNKAdOfOHeTn58PBwUFvuYODA9LT04vdJj09vdTyhUEnLCwMf/31F/Ly8vDpp5/i+vXrSEtLk/ahVCrRsGHDMh83PDwcKpVKerm4uFSkyURERFQDmHwMUmUzMzPDtm3bcOHCBdja2sLKygr79+/HgAEDIJdXvLlhYWHQarXS69q1a5VYayIiIqpOTPpVI40aNYJCoSjy9FhGRgYcHR2L3cbR0fGJ5b29vZGYmAitVou8vDzY29vDx8cHXbt2lfaRl5eHe/fu6fUilXZcc3NzmJubV6SZREREVMOYtAdJqVTC29sbcXFx0jKdToe4uDj4+voWu42vr69eeQDYu3dvseVVKhXs7e2RnJyMEydO4JVXXgFQEKDMzMz09pOUlISrV6+WeFwiIiKqO0z+ZbWhoaEYPXo0unbtiu7duyMiIgLZ2dkYM2YMAGDUqFFo0qQJwsPDAQBTpkyBn58fli5dioEDB2LTpk04ceIE1qxZI+1zy5YtsLe3R7NmzfDHH39gypQpCAwMRL9+/QAUBKexY8ciNDQUtra2sLGxweTJk+Hr61umJ9iIiIiodjN5QAoKCsLt27cxe/ZspKeno1OnToiNjZUGYl+9elVv7FDPnj0RExODmTNnYsaMGfDw8MD27dvRvn17qUxaWhpCQ0ORkZEBJycnjBo1CrNmzdI77vLlyyGXyzFkyBDk5uZCrVZj5cqVVdNoIiIiqtZMPg9STcV5kIiIiGqeGjEPEhEREVF1xIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZKBaBKTo6Gi4ubnBwsICPj4+OH78eKnlt2zZAk9PT1hYWKBDhw7Ys2eP3vr79+8jODgYTZs2haWlJdq2bYvVq1frlenTpw9kMpnea+LEiZXeNiIiIqp5TB6QNm/ejNDQUMyZMwcnT56El5cX1Go1bt26VWz5I0eOYPjw4Rg7dixOnTqFwMBABAYG4syZM1KZ0NBQxMbG4uuvv8a5c+cQEhKC4OBg7Ny5U29f48aNQ1pamvRatGiRUdtKRERENYNMCCFMWQEfHx9069YNUVFRAACdTgcXFxdMnjwZ06dPL1I+KCgI2dnZ2L17t7SsR48e6NSpk9RL1L59ewQFBWHWrFlSGW9vbwwYMAAfffQRgIIepE6dOiEiIqJC9c7MzIRKpYJWq4WNjU2F9kFERERVq6yf3ybtQcrLy0NCQgL8/f2lZXK5HP7+/oiPjy92m/j4eL3yAKBWq/XK9+zZEzt37sSNGzcghMD+/ftx4cIF9OvXT2+7b775Bo0aNUL79u0RFhaGnJycSmwdERER1VT1THnwO3fuID8/Hw4ODnrLHRwccP78+WK3SU9PL7Z8enq69D4yMhLjx49H06ZNUa9ePcjlcnzxxRfo3bu3VGbEiBFwdXWFs7MzTp8+jQ8++ABJSUnYtm1bscfNzc1Fbm6u9D4zM7Pc7SUiIqKawaQByVgiIyNx9OhR7Ny5E66urvjll18wadIkODs7S71P48ePl8p36NABTk5OeOGFF3Dp0iW4u7sX2Wd4eDjmzZtXZW0gIiIi0zHpLbZGjRpBoVAgIyNDb3lGRgYcHR2L3cbR0bHU8g8ePMCMGTOwbNkyBAQEoGPHjggODkZQUBCWLFlSYl18fHwAABcvXix2fVhYGLRarfS6du1amdtJRERENYtJA5JSqYS3tzfi4uKkZTqdDnFxcfD19S12G19fX73yALB3716p/KNHj/Do0SPI5fpNUygU0Ol0JdYlMTERAODk5FTsenNzc9jY2Oi9iIiIqHYy+S220NBQjB49Gl27dkX37t0RERGB7OxsjBkzBgAwatQoNGnSBOHh4QCAKVOmwM/PD0uXLsXAgQOxadMmnDhxAmvWrAEA2NjYwM/PD9OmTYOlpSVcXV1x8OBBfPnll1i2bBkA4NKlS4iJicFLL70EOzs7nD59GlOnTkXv3r3RsWNH05wIIiIiqjZMHpCCgoJw+/ZtzJ49G+np6ejUqRNiY2OlgdhXr17V6w3q2bMnYmJiMHPmTMyYMQMeHh7Yvn072rdvL5XZtGkTwsLCMHLkSNy9exeurq74+OOPpYkglUol9u3bJ4UxFxcXDBkyBDNnzqzaxhMREVG1ZPJ5kGoqzoNERERU89SIeZCIiIiIqiMGJCIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkQEGJCIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkQEGJCIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkQEGJCIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkQEGJCIiIiIDDEhEREREBqpFQIqOjoabmxssLCzg4+OD48ePl1p+y5Yt8PT0hIWFBTp06IA9e/borb9//z6Cg4PRtGlTWFpaom3btli9erVemYcPH2LSpEmws7NDgwYNMGTIEGRkZFR624iIiKjmMXlA2rx5M0JDQzFnzhycPHkSXl5eUKvVuHXrVrHljxw5guHDh2Ps2LE4deoUAgMDERgYiDNnzkhlQkNDERsbi6+//hrnzp1DSEgIgoODsXPnTqnM1KlTsWvXLmzZsgUHDx7EzZs3MXjwYKO3l4iIiKo/mRBCmLICPj4+6NatG6KiogAAOp0OLi4umDx5MqZPn16kfFBQELKzs7F7925pWY8ePdCpUyepl6h9+/YICgrCrFmzpDLe3t4YMGAAPvroI2i1Wtjb2yMmJgavvvoqAOD8+fNo06YN4uPj0aNHjyfWOzMzEyqVClqtFjY2Nk91DoiIiKhqlPXz26Q9SHl5eUhISIC/v7+0TC6Xw9/fH/Hx8cVuEx8fr1ceANRqtV75nj17YufOnbhx4waEENi/fz8uXLiAfv36AQASEhLw6NEjvf14enqiWbNmJR43NzcXmZmZei8iIiKqnUwakO7cuYP8/Hw4ODjoLXdwcEB6enqx26Snpz+xfGRkJNq2bYumTZtCqVSif//+iI6ORu/evaV9KJVKNGzYsMzHDQ8Ph0qlkl4uLi7lbS4RERHVECYfg2QMkZGROHr0KHbu3ImEhAQsXboUkyZNwr59+yq8z7CwMGi1Wul17dq1SqwxERERVSf1THnwRo0aQaFQFHl6LCMjA46OjsVu4+joWGr5Bw8eYMaMGfjhhx8wcOBAAEDHjh2RmJiIJUuWwN/fH46OjsjLy8O9e/f0epFKO665uTnMzc0r2lQiIiKqQUzag6RUKuHt7Y24uDhpmU6nQ1xcHHx9fYvdxtfXV688AOzdu1cq/+jRIzx69AhyuX7TFAoFdDodgIIB22ZmZnr7SUpKwtWrV0s8LhERERmPRqNBWlpaiS+NRlOl9TFpDxJQ8Ej+6NGj0bVrV3Tv3h0RERHIzs7GmDFjAACjRo1CkyZNEB4eDgCYMmUK/Pz8sHTpUgwcOBCbNm3CiRMnsGbNGgCAjY0N/Pz8MG3aNFhaWsLV1RUHDx7El19+iWXLlgEAVCoVxo4di9DQUNja2sLGxgaTJ0+Gr69vmZ5gIyIiosqj0Wikp9lLExwcDDs7uyqoUTUISEFBQbh9+zZmz56N9PR0dOrUCbGxsdJA7KtXr+r1BvXs2RMxMTGYOXMmZsyYAQ8PD2zfvh3t27eXymzatAlhYWEYOXIk7t69C1dXV3z88ceYOHGiVGb58uWQy+UYMmQIcnNzoVarsXLlyqprOBEREQEoeKq9MstVBpPPg1RTcR4kIiKiypGWlibdCSrN+PHj4eTk9FTHqhHzIBERERFVRwxIRERERAYYkIiIiIgMMCARERFRtaLVWiMlxQ1arbXJ6mDyp9iIiIiICp082Rm7dg2CEHLIZDoEBOxGly6nqrwe7EEiIiIik1IqlQAKeo4KwxEACCHHrl2DpJ6kwnJVgY/5VxAf8yciIqo8Go0G+/cDr71WdCLI77/XoE8fVMokkWX9/OYtNiIiIjI5Ozs79OgByOXA/74ZDACgUAA+Pnaoogm0JbzFRkRERNVC06bAmjUFoQgo+PfzzwuWVzX2IBEREVG1MXYsoFYDFy8CLVuaJhwBDEhERERUzTRtarpgVIi32IiIiIgMMCARERERGWBAIiIiIjLAgERERERkgIO0iYiIqFgajQZ5eXklrlcqlZUyeWN1xIBERERERWg0GkRFRT2xXHBwcK0MSbzFRkREREWU1nNUkXI1DQMSERERkQEGJCIiIiIDHINERETlUpcH7lLdwYBERERlVtcH7lLdwVtsRERUZnV94C7VHQxIRERERAYYkIiIiKgIpVJZqeVqGo5BIiIioiLs7OwQHBws3S69eVOOlJR6aN78bzg76wDU7gH5DEhERERUrMLws3YtMH48oNMBcjmwZg0wdqyJK2dkvMVGREREJbp+/Z9wBBT8O2FCwfLajAGJiIiISpSc/E84KpSfD1y8aJr6VBUGJCIiKjPDAblarTVSUtyg1VqXWo5qLg+Pgttqj1MogJYtTVOfqsIxSEREVGaPD9yNibHE/Pkq6HQyyOUCixZpMWLEg1o9cLcuatq0YMzRhAkFPUcKBfD55wXLazOZEEKYuhI1UWZmJlQqFbRaLWxsbExdHSKiKnX9OuDqqn/rRaEAUlNr/wdnXXX9esFttZYta/bvuKyf3+xBIiKicittXEpN/vCkkjVtWrd+txyDRERE5VZXx6VQ3VEtAlJ0dDTc3NxgYWEBHx8fHD9+vNTyW7ZsgaenJywsLNChQwfs2bNHb71MJiv2tXjxYqmMm5tbkfULFy40SvuIiGqbwnEpCkXB+7KMS9FoNEhLSyvxpdFoqqbyRGVg8ltsmzdvRmhoKFavXg0fHx9ERERArVYjKSkJjRs3LlL+yJEjGD58OMLDwzFo0CDExMQgMDAQJ0+eRPv27QEAaWlpetv85z//wdixYzFkyBC95fPnz8e4ceOk99bW+k9hEBFRycaOBdTqso1L0Wg0iIqKeuI+g4ODOcCbqgWTD9L28fFBt27dpP9wdDodXFxcMHnyZEyfPr1I+aCgIGRnZ2P37t3Ssh49eqBTp05YvXp1sccIDAxEVlYW4uLipGVubm4ICQlBSEhIherNQdpERGWXlpaGNWvWPLHc+PHj4eTkVAU1orqqrJ/fJr3FlpeXh4SEBPj7+0vL5HI5/P39ER8fX+w28fHxeuUBQK1Wl1g+IyMDP/74I8YWMyf6woULYWdnh86dO2Px4sX4+++/S6xrbm4uMjMz9V5ERERUO5n0FtudO3eQn58PBwcHveUODg44f/58sdukp6cXWz49Pb3Y8hs3boS1tTUGDx6st/ydd95Bly5dYGtriyNHjiAsLAxpaWlYtmxZsfsJDw/HvHnzyto0IiIiqsFMPgbJ2NatW4eRI0fCwsJCb3loaKj0c8eOHaFUKjFhwgSEh4fD3Ny8yH7CwsL0tsnMzISLi4vxKk5EREQmY9KA1KhRIygUCmRkZOgtz8jIgKOjY7HbODo6lrn8r7/+iqSkJGzevPmJdfHx8cHff/+N1NRUtG7dush6c3PzYoMTERER1T4mHYOkVCrh7e2tN3hap9MhLi4Ovr6+xW7j6+urVx4A9u7dW2z5tWvXwtvbG15eXk+sS2JiIuRyebFPzhEREVHdYvJbbKGhoRg9ejS6du2K7t27IyIiAtnZ2RgzZgwAYNSoUWjSpAnCw8MBAFOmTIGfnx+WLl2KgQMHYtOmTThx4kSRpyMyMzOxZcsWLF26tMgx4+PjcezYMTz//POwtrZGfHw8pk6ditdffx3PPPOM8RtNRERE1ZrJA1JQUBBu376N2bNnIz09HZ06dUJsbKw0EPvq1auQPzZda8+ePRETE4OZM2dixowZ8PDwwPbt26U5kApt2rQJQggMHz68yDHNzc2xadMmzJ07F7m5uWjevDmmTp2qN8aIiIgqj1Kp1Huv1Vrj7l072NpqoFJllViOyFRMPg9STcV5kIiIykej0SAvLw8xMZZ4/30VdDoZ5HKBRYu0GDHiAZRKJSeJJKMr6+c3A1IFMSAREZXf9euAq6v+F90qFEBqat36IlQynRoxUSQREdUtycn64QgA8vMLvq6EqDphQCIioirj4QHIDT55FIqC73Ijqk4YkIiIqMo0bQqsWVMQioCCfz//nLfXqPox+VNsRERUt4wdC6jVBbfVWrZkOKLqiQGJiIiqXNOmDEZUvfEWGxEREZEBBiQiIiIiAwxIRERERAYYkIiIiIgMMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiAwxIRERERAb4XWwVJIQAAGRmZpq4JkRERFRWhZ/bhZ/jJWFAqqCsrCwAgIuLi4lrQkREROWVlZUFlUpV4nqZeFKEomLpdDrcvHkT1tbWkMlkT7WvzMxMuLi44Nq1a7CxsamkGtYsPAc8BwDPAcBzAPAc1PX2A8Y9B0IIZGVlwdnZGXJ5ySON2INUQXK5HE2bNq3UfdrY2NTZ/xgK8RzwHAA8BwDPAcBzUNfbDxjvHJTWc1SIg7SJiIiIDDAgERERERlgQKoGzM3NMWfOHJibm5u6KibDc8BzAPAcADwHAM9BXW8/UD3OAQdpExERERlgDxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgGUl0dDTc3NxgYWEBHx8fHD9+vNTyERERaN26NSwtLeHi4oKpU6fi4cOH0vq5c+dCJpPpvTw9PY3djAorT/sfPXqE+fPnw93dHRYWFvDy8kJsbOxT7bM6qOxzUNOugV9++QUBAQFwdnaGTCbD9u3bn7jNgQMH0KVLF5ibm6Nly5bYsGFDkTI16Towxjmo7ddBWloaRowYgVatWkEulyMkJKTYclu2bIGnpycsLCzQoUMH7Nmzp/IrX0mMcQ42bNhQ5DqwsLAwTgMqQXnPwbZt2/Diiy/C3t4eNjY28PX1xU8//VSknDH/HjAgGcHmzZsRGhqKOXPm4OTJk/Dy8oJarcatW7eKLR8TE4Pp06djzpw5OHfuHNauXYvNmzdjxowZeuXatWuHtLQ06XXo0KGqaE65lbf9M2fOxOeff47IyEicPXsWEydOxL/+9S+cOnWqwvs0NWOcA6DmXAMAkJ2dDS8vL0RHR5epfEpKCgYOHIjnn38eiYmJCAkJwdtvv633R7GmXQfGOAdA7b4OcnNzYW9vj5kzZ8LLy6vYMkeOHMHw4cMxduxYnDp1CoGBgQgMDMSZM2cqs+qVxhjnACiYZfrx6+DKlSuVVeVKV95z8Msvv+DFF1/Enj17kJCQgOeffx4BAQFV+7kgqNJ1795dTJo0SXqfn58vnJ2dRXh4eLHlJ02aJPr27au3LDQ0VDz77LPS+zlz5ggvLy+j1Leylbf9Tk5OIioqSm/Z4MGDxciRIyu8T1MzxjmoSdeAIQDihx9+KLXM+++/L9q1a6e3LCgoSKjVaul9TbsOHldZ56C2XweP8/PzE1OmTCmyfOjQoWLgwIF6y3x8fMSECROesobGV1nnYP369UKlUlVavapSec9BobZt24p58+ZJ743994A9SJUsLy8PCQkJ8Pf3l5bJ5XL4+/sjPj6+2G169uyJhIQEqWvw8uXL2LNnD1566SW9csnJyXB2dkaLFi0wcuRIXL161XgNqaCKtD83N7dI17ClpaX0f8UV2acpGeMcFKoJ10BFxcfH650zAFCr1dI5q2nXQUU86RwUqs3XQVmU9TzVdvfv34erqytcXFzwyiuv4M8//zR1lYxGp9MhKysLtra2AKrm7wEDUiW7c+cO8vPz4eDgoLfcwcEB6enpxW4zYsQIzJ8/H8899xzMzMzg7u6OPn366N1i8/HxwYYNGxAbG4tVq1YhJSUFvXr1QlZWllHbU14Vab9arcayZcuQnJwMnU6HvXv3Ytu2bUhLS6vwPk3JGOcAqDnXQEWlp6cXe84yMzPx4MGDGncdVMSTzgFQ+6+DsijpPNWW66AsWrdujXXr1mHHjh34+uuvodPp0LNnT1y/ft3UVTOKJUuW4P79+xg6dCiAqvlcYECqBg4cOIBPPvkEK1euxMmTJ7Ft2zb8+OOPWLBggVRmwIABeO2119CxY0eo1Wrs2bMH9+7dw3fffWfCmleOFStWwMPDA56enlAqlQgODsaYMWMgl9edy7Ms56A2XwNUdrwOCAB8fX0xatQodOrUCX5+fti2bRvs7e3x+eefm7pqlS4mJgbz5s3Dd999h8aNG1fZcevOJ1AVadSoERQKBTIyMvSWZ2RkwNHRsdhtZs2ahTfeeANvv/02OnTogH/961/45JNPEB4eDp1OV+w2DRs2RKtWrXDx4sVKb8PTqEj77e3tsX37dmRnZ+PKlSs4f/48GjRogBYtWlR4n6ZkjHNQnOp6DVSUo6NjsefMxsYGlpaWNe46qIgnnYPi1LbroCxKOk+15TqoCDMzM3Tu3LnWXQebNm3C22+/je+++07vdlpV/D1gQKpkSqUS3t7eiIuLk5bpdDrExcXB19e32G1ycnKK9JYoFAoAgCjhq/Lu37+PS5cuwcnJqZJqXjkq0v5CFhYWaNKkCf7++29s3boVr7zyylPv0xSMcQ6KU12vgYry9fXVO2cAsHfvXumc1bTroCKedA6KU9uug7KoyHmq7fLz8/HHH3/Uquvg22+/xZgxY/Dtt99i4MCBeuuq5O9BpQz1Jj2bNm0S5ubmYsOGDeLs2bNi/PjxomHDhiI9PV0IIcQbb7whpk+fLpWfM2eOsLa2Ft9++624fPmy+Pnnn4W7u7sYOnSoVObdd98VBw4cECkpKeLw4cPC399fNGrUSNy6davK2/ck5W3/0aNHxdatW8WlS5fEL7/8Ivr27SuaN28u/vrrrzLvs7oxxjmoSdeAEEJkZWWJU6dOiVOnTgkAYtmyZeLUqVPiypUrQgghpk+fLt544w2p/OXLl4WVlZWYNm2aOHfunIiOjhYKhULExsZKZWradWCMc1DbrwMhhFTe29tbjBgxQpw6dUr8+eef0vrDhw+LevXqiSVLlohz586JOXPmCDMzM/HHH39UadvKyhjnYN68eeKnn34Sly5dEgkJCWLYsGHCwsJCr0x1Ut5z8M0334h69eqJ6OhokZaWJr3u3bsnlTH23wMGJCOJjIwUzZo1E0qlUnTv3l0cPXpUWufn5ydGjx4tvX/06JGYO3eucHd3FxYWFsLFxUX83//9n96HY1BQkHBychJKpVI0adJEBAUFiYsXL1Zhi8qnPO0/cOCAaNOmjTA3Nxd2dnbijTfeEDdu3CjXPqujyj4HNe0a2L9/vwBQ5FXY7tGjRws/P78i23Tq1EkolUrRokULsX79+iL7rUnXgTHOQV24Door7+rqqlfmu+++E61atRJKpVK0a9dO/Pjjj1XToAowxjkICQmR/jtwcHAQL730kjh58mTVNaqcynsO/Pz8Si1fyJh/D2RClHAPh4iIiKiO4hgkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRUS1x4MAByGQy3Lt3z9RVIarxGJCIqNzefPNNyGQyLFy4UG/59u3bIZPJpPdCCHzxxRfw9fWFjY0NGjRogHbt2mHKlCll/lLNnJwchIWFwd3dHRYWFrC3t4efnx927NghlXFzc0NERESltM3YCs+dTCaDmZkZmjdvjvfffx8PHz4s13769OmDkJAQvWU9e/ZEWloaVCpVJdaYqG5iQCKiCrGwsMCnn36Kv/76q9j1QgiMGDEC77zzDl566SX8/PPPOHv2LNauXQsLCwt89NFHZTrOxIkTsW3bNkRGRuL8+fOIjY3Fq6++Co1GU5nNqVL9+/dHWloaLl++jOXLl+Pzzz/HnDlznnq/SqUSjo6OeiGViCqo0r60hIjqjNGjR4tBgwYJT09PMW3aNGn5Dz/8IAr/rHz77bcCgNixY0ex+9DpdGU6lkqlEhs2bChxfXHf2VTo119/Fc8995ywsLAQTZs2FZMnTxb379+X1n/55ZfC29tbNGjQQDg4OIjhw4eLjIwMaX3h90fFxsaKTp06CQsLC/H888+LjIwMsWfPHuHp6Smsra3F8OHDRXZ2dpnaM3r0aPHKK6/oLRs8eLDo3Lmz9P7OnTti2LBhwtnZWVhaWor27duLmJgYvX0YtjklJUWq7+Pf4/j999+Ltm3bCqVSKVxdXcWSJUvKVE+iuo49SERUIQqFAp988gkiIyNx/fr1Iuu//fZbtG7dGi+//HKx25e1l8PR0RF79uxBVlZWseu3bduGpk2bYv78+UhLS0NaWhoA4NKlS+jfvz+GDBmC06dPY/PmzTh06BCCg4OlbR89eoQFCxbg999/x/bt25Gamoo333yzyDHmzp2LqKgoHDlyBNeuXcPQoUMRERGBmJgY/Pjjj/j5558RGRlZpvYYOnPmDI4cOQKlUikte/jwIby9vfHjjz/izJkzGD9+PN544w0cP34cALBixQr4+vpi3LhxUptdXFyK7DshIQFDhw7FsGHD8Mcff2Du3LmYNWsWNmzYUKG6EtUppk5oRFTzPN4L0qNHD/HWW28JIfR7kDw9PcXLL7+st92UKVNE/fr1Rf369UWTJk3KdKyDBw+Kpk2bCjMzM9G1a1cREhIiDh06pFfG1dVVLF++XG/Z2LFjxfjx4/WW/frrr0Iul4sHDx4Ue6zffvtNABBZWVlCiH96kPbt2yeVCQ8PFwDEpUuXpGUTJkwQarW6TO0ZPXq0UCgUon79+sLc3FwAEHK5XHz//felbjdw4EDx7rvvSu/9/PzElClT9MoY9iCNGDFCvPjii3plpk2bJtq2bVumuhLVZexBIqKn8umnn2Ljxo04d+7cE8t++OGHSExMxOzZs3H//v0y7b937964fPky4uLi8Oqrr+LPP/9Er169sGDBglK3+/3337FhwwY0aNBAeqnVauh0OqSkpAAo6GEJCAhAs2bNYG1tDT8/PwDA1atX9fbVsWNH6WcHBwdYWVmhRYsWestu3bpVpvYAwPPPP4/ExEQcO3YMo0ePxpgxYzBkyBBpfX5+PhYsWIAOHTrA1tYWDRo0wE8//VSkXk9y7tw5PPvss3rLnn32WSQnJyM/P79c+yKqaxiQiOip9O7dG2q1GmFhYXrLPTw8kJSUpLfM3t4eLVu2ROPGjct1DDMzM/Tq1QsffPABfv75Z8yfPx8LFixAXl5eidvcv38fEyZMQGJiovT6/fffkZycDHd3d2RnZ0OtVsPGxgbffPMNfvvtN/zwww8AUGS/ZmZm0s+FT589TiaTQafTlbk99evXR8uWLeHl5YV169bh2LFjWLt2rbR+8eLFWLFiBT744APs378fiYmJUKvVpbaXiCpXPVNXgIhqvoULF6JTp05o3bq1tGz48OEYMWIEduzYgVdeeaVSj9e2bVv8/fffePjwIZRKJZRKZZEekS5duuDs2bNo2bJlsfv4448/oNFosHDhQmn8zokTJyq1nmUhl8sxY8YMhIaGYsSIEbC0tMThw4fxyiuv4PXXXwcA6HQ6XLhwAW3btpW2K67Nhtq0aYPDhw/rLTt8+DBatWoFhUJR+Y0hqkXYg0RET61Dhw4YOXIkPvvsM2nZsGHD8Oqrr2LYsGGYP38+jh07htTUVBw8eBCbN28u8wd0nz598PnnnyMhIQGpqanYs2cPZsyYgeeffx42NjYACuZB+uWXX3Djxg3cuXMHAPDBBx/gyJEjCA4ORmJiIpKTk7Fjxw5pkHazZs2gVCoRGRmJy5cvY+fOnU+8bWcsr732GhQKBaKjowEU9L7t3bsXR44cwblz5zBhwgRkZGTobePm5iad0zt37hTbg/Xuu+8iLi4OCxYswIULF7Bx40ZERUXhvffeq5J2EdVkDEhEVCnmz5+v9yEtk8mwefNmREREYM+ePXjhhRfQunVrvPXWW3BxccGhQ4fKtF+1Wo2NGzeiX79+aNOmDSZPngy1Wo3vvvtO79ipqalwd3eHvb09gIJxQwcPHsSFCxfQq1cvdO7cGbNnz4azszOAgtt9GzZswJYtW9C2bVssXLgQS5YsqcQzUnb16tVDcHAwFi1ahOzsbMycORNdunSBWq1Gnz594OjoiMDAQL1t3nvvPSgUCrRt2xb29vbFjk/q0qULvvvuO2zatAnt27fH7NmzMX/+/GKf1CMifTIhhDB1JYiIiIiqE/YgERERERlgQCIik3r8MXzD16+//mrq6pXL1atXS21PeR/TJyLT4S02IjKp0r60tkmTJrC0tKzC2jydv//+G6mpqSWud3NzQ716fHiYqCZgQCIiIiIywFtsRERERAYYkIiIiIgMMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiA/8PIALA0zF657wAAAAASUVORK5CYII=", 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", 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", 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d4UeSDMNPGzf2ZyQIAFAhGYbhvqS1ZcsW3XHHHapfv75CQ0M1ZMgQORwOXbx4schjpKenKykpSQ0bNlRoaKh69OghSTp27Fi51389BCAvycwMd4effIbhp8xMu48qAgCgcPv371fjxo119OhR9e/fX61bt9a6deuUnp6uhQsXSir6styFCxeUmJiosLAwvf3229q1a5fee++9637OW7gEVs6sVqskyW53yGJxeYQgi8Uluz3Tox0AAL7297//XV9++aWefvpppaeny+Vy6ZVXXpGf3+XfYf/93//t0d5qtSovL89j2zfffCOHw6GZM2cqOjpakvSvf/3LOydQDASgchYeHq7k5GTl5OSofv0sPfusTXl5Fvn7G5o1K0sPPDCYlaABAD6TnZ2tkydPetwGn5qaqv79+2vo0KHat2+fcnNzNX/+fCUlJWn79u1avHixxzFiYmJ0/vx5paWlqU2bNqpevboaNmwoq9Wq+fPn67HHHtO+ffs0ffp0H51lQVwC84Lw8HBFRUVp3LiaOnrUoq1bpaNHLRo3rqaioqIIPwAAn9m8ebOioqIUExOjPn36aOvWrZo3b57++te/yt/fX23atNGcOXM0a9Ys3XrrrXr77beVmprqcYwuXbroscce08CBA1WnTh3Nnj1bderU0fLly/XOO++oZcuWmjlzpl5++WUfnWVBFsMwDF8XUdFkZWXJZrPJ6XQqLCzM1+UAACqwS5cu6ciRI2rcuLGCgoJK/HlfrwNU2RTV3yX5/c0lMAAAfOjKqRKFYapE2SMAAQDgY4Qb72MOEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAKBfbtm2TxWLRzz//XOzPxMTEaO7cueVWUz4CEAAAJjV8+HBZLBY99thjBfaNHj1aFotFw4cP935hXlAhAtDChQsVExOjoKAgderUSTt37iy07fr16xUfH6+aNWsqJCREbdu21cqVKwtt/9hjj8lisXglTQIAUNlER0drzZo1+uWXX9zbLl26pNWrV6thw4Y+rKx8+TwArV27VikpKZo6dap2796tNm3aKDExUadPn75me7vdrsmTJ2vHjh3au3evRowYoREjRuijjz4q0Pa9997T559/rnr16pX3aQAAUCnFxcUpOjpa69evd29bv369GjZsqHbt2rm3ZWdna8yYMapbt66CgoLUtWtX7dq1y+NYH374oW666SYFBwfr9ttv19GjRwt836effqpu3bopODhY0dHRGjNmjC5cuFBu51cYnwegOXPm6NFHH9WIESPUsmVLLV68WNWrV9dbb711zfY9e/bUPffcoxYtWig2NlZPPfWUWrdurU8//dSj3Q8//KAnn3xSb7/9tgICArxxKgAA3LDjx6WtWy//6S0PP/ywli1b5n7/1ltvacSIER5tJkyYoHXr1mnFihXavXu3mjZtqsTERGVmZkqSvv/+e/3+979XUlKSMjIyNHLkSE2cONHjGIcPH1afPn107733au/evVq7dq0+/fRTJScnl/9JXsWnASgnJ0fp6elKSEhwb/Pz81NCQoJ27Nhx3c8bhqG0tDQdOHBA3bt3d293uVwaMmSIxo8fr1tuueW6x8nOzlZWVpbHCwAAb1u6VGrUSOrV6/KfS5d653sfeughffrpp/ruu+/03Xffafv27XrooYfc+y9cuKBFixbppZdeUt++fdWyZUstWbJEwcHBWvp/RS5atEixsbF65ZVXdPPNN+vBBx8sMH8oNTVVDz74oMaOHatmzZqpS5cumjdvnv785z/r0qVL3jnZ/+PTp8GfPXtWeXl5ioiI8NgeERGhb775ptDPOZ1O1a9fX9nZ2fL399frr7+u3r17u/fPmjVL1apV05gxY4pVR2pqqp5//vnSnQQAAGXg+HFp1CjJ5br83uWS/vAHKTFRatCgfL+7Tp066tevn5YvXy7DMNSvXz/Vrl3bvf/w4cPKzc3Vbbfd5t4WEBCgjh07av/+/ZKk/fv3q1OnTh7H7dy5s8f7//3f/9XevXv19ttvu7cZhiGXy6UjR46oRYsW5XF61+TTAFRaoaGhysjI0Pnz55WWlqaUlBQ1adJEPXv2VHp6ul577TXt3r1bFoulWMebNGmSUlJS3O+zsrIUHR1dXuUDAFDAwYO/hZ98eXnSoUPlH4Cky5fB8i9FLVy4sFy+4/z58/rDH/5wzQEKb0+49mkAql27tvz9/XXq1CmP7adOnVJkZGShn/Pz81PTpk0lSW3bttX+/fuVmpqqnj176p///KdOnz7t0ZF5eXkaN26c5s6de80JWYGBgQoMDCybkwIAoBSaNZP8/DxDkL+/9H+/7spdnz59lJOTI4vFosTERI99sbGxslqt2r59uxo1aiRJys3N1a5duzR27FhJUosWLbRhwwaPz33++ece7+Pi4vT111+7f4f7kk/nAFmtVrVv315paWnubS6XS2lpaQWGzYricrmUnZ0tSRoyZIj27t2rjIwM96tevXoaP378Ne8UAwCgImjQQHrzzcuhR7r85xtveGf05/L3+Wv//v36+uuv5Z9fxP8JCQnR448/rvHjx2vz5s36+uuv9eijj+rixYt65JFHJF1edubgwYMaP368Dhw4oNWrV2v58uUex3n22Wf12WefKTk5WRkZGTp48KD++te/+mQStM8vgaWkpGjYsGGKj49Xx44dNXfuXF24cME9+3zo0KGqX7++UlNTJV2erxMfH6/Y2FhlZ2frww8/1MqVK7Vo0SJJUnh4uMLDwz2+IyAgQJGRkbr55pu9e3IAAJTAI49cnvNz6NDlkR9vhZ98YWFhhe6bOXOm+yajc+fOKT4+Xh999JFq1aol6fIlrHXr1unpp5/W/Pnz1bFjR7344ot6+OGH3cdo3bq1/vGPf2jy5Mnq1q2bDMNQbGysBg4cWO7ndjWfB6CBAwfqzJkzmjJlik6ePKm2bdtq8+bN7onRx44dk5/fbwNVFy5c0BNPPKHjx48rODhYzZs316pVq3zSeQAAlLUGDbwXfK4eobna+++/7/57UFCQ5s2bp3nz5hXavn///urfv7/Htqtvp+/QoYM+/vjjQo9xrakq5cFiGIbhlW+qRLKysmSz2eR0OotMwwAAXLp0SUeOHFHjxo0VFBTk63KqvKL6uyS/v32+ECIAAIC3EYAAAIDpEIAAAIDpEIAAACgDTKn1jrLqZwIQAAA3IP+B2xcvXvRxJeaQ3883+qBzn98GDwBAZebv76+aNWvq9OnTkqTq1asX+1FMKD7DMHTx4kWdPn1aNWvWLLBYY0kRgAAAuEH5j2/KD0EoPzVr1izycVnFRQACAOAGWSwWRUVFqW7dusrNzfV1OVVWQEDADY/85CMAAQBQRvz9/cvsFzTKF5OgAQCA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAUKTjx6WtWy//CQBAVUEAQgEOh0MnTpzQK6/8rEaNDPXqJTVqZOiVV37WiRMn5HA4fF0iAAA3hEdhwIPD4dCCBQvkdIZq7tyxMozLTzR2uSwaPz5MP/zwlmy2c0pOTlZ4eLiPqwUAoHQYAYKHnJwcSVJmZrgMw/PHwzD8lJlp92gHAEBlRADCNdntDlksLo9tFotLdnumjyoCAKDsEIBwTTbbOSUlbXKHIIvFpaSkTbLZzvm4MgAAbhxzgFCouLg9io09pMxMu+z2TMIPAKDKIAChSDbbuXIJPsePSwcPSs2aSQ0alPnhAQAoEpfA4DXcXg8AqCgYAYJXcHs9AKAiYQQIHqxWa5m2y8ft9QCAioQRIHgIDw9XcnJykUHEarWWepQm//b6K0MQt9cDALyNAIQCyvMSVP7t9Rs39pdh+HF7PQDAJwhA8DpurwcA+BoBCD5RXrfXAwBQHEyCBgAApkMAAgAApkMAgleU1+31AACUBnOA4BXlfXs9AAAlQQCC1xBuAAAVBZfAAACA6RCAAACA6RCAgDJw/Li0devlPwEAFR8BCLhBS5dKjRpJvXpd/nPpUl9XBAC4HgIQcAOOH5dGjZJcrsvvXS7pD39gJAgAKjoCEHADDh78Lfzky8uTDh3yTT0AgOIhAAGl5HA4FBZ2Sn5+hsd2f39DoaGn5HA4fFQZAOB6WAcIKAWHw6EFCxZIkvr3b6eNG/vLMPxksbjUr98mbdq0R5KUnJzM+kcAUAERgIBSuHJF67i4PYqNPaTMTLvs9kyPp9wXtfI1AMB3CEBAGbDZznkEHwBAxcYcIAAAYDoEIAAAYDoEIAAAYDoEIAAoBI84AaouAhAAXAOPOAGqNgIQUApWq7VM26HicDgcSk8/pVGjjKsecWIoPZ0FLoGqgtvggVIIDw9XcnJykev8WK1WFkGsZPIXuDxyJEYu1zCPfXl5Fs2f/zc1bvwdC1wCVQABCCglfgFWPfmB1m53yGJxyTB+GyS3WFyy2zM92gGovCrEJbCFCxcqJiZGQUFB6tSpk3bu3Flo2/Xr1ys+Pl41a9ZUSEiI2rZtq5UrV7r35+bm6tlnn1WrVq0UEhKievXqaejQofrxxx+9cSoAfKCsJyvbbOeUlLRJFsvla2AWi0tJSZtY7BKoQnw+ArR27VqlpKRo8eLF6tSpk+bOnavExEQdOHBAdevWLdDebrdr8uTJat68uaxWqzZt2qQRI0aobt26SkxM1MWLF7V7924999xzatOmjX766Sc99dRTGjBggP71r3/54AwBlAeHw6GcnBytXh2sCRNscrks8vMzNHu2Uw888MsNX4Is6hEnACo/i2EYxvWblZ9OnTqpQ4cO7gdLulwuRUdH68knn9TEiROLdYy4uDj169dP06dPv+b+Xbt2qWPHjvruu+/UsGHD6x4vKytLNptNTqdTYWFhxT8ZAF6RP1fH6QzV3LljC1yqGjt2rmy2cyWeq3PixAm9+eab1203atQoRUVFlap2AOWnJL+/fXoJLCcnR+np6UpISHBv8/PzU0JCgnbs2HHdzxuGobS0NB04cEDdu3cvtJ3T6ZTFYlHNmjWvuT87O1tZWVkeLwAVV/4cnMzMcI/wI0mG4afMTLtHOwC4mk8D0NmzZ5WXl6eIiAiP7RERETp58mShn3M6napRo4asVqv69eun+fPnq3fv3tdse+nSJT377LMaPHhwoWkwNTVVNpvN/YqOji79SQHwmvzJyle6crIyABSmQkyCLqnQ0FBlZGRo165deuGFF5SSkqJt27YVaJebm6v7779fhmFo0aJFhR5v0qRJcjqd7tf3339fjtUDuJbSTGRmsjKA0vLpJOjatWvL399fp06d8th+6tQpRUZGFvo5Pz8/NW3aVJLUtm1b7d+/X6mpqerZs6e7TX74+e677/T3v/+9yGuBgYGBCgwMvLGTAVBqS5dKo0ZdXnDQz096803pkUeK99mynKzMApeAefg0AFmtVrVv315paWm6++67JV2eBJ2Wlqbk5ORiH8flcik7O9v9Pj/8HDx4UFu3bmW9FqCCcjgcOnr0V40aVVcul0XSb6sut217WjEx1Yr1/1+b7VyZjPqwwCVgHj6/DT4lJUXDhg1TfHy8OnbsqLlz5+rChQsaMWKEJGno0KGqX7++UlNTJV2erxMfH6/Y2FhlZ2frww8/1MqVK92XuHJzc3Xfffdp9+7d2rRpk/Ly8tzziex2O//lBlQQFXXVZcINYA4+D0ADBw7UmTNnNGXKFJ08eVJt27bV5s2b3ROjjx07Jj+/36YqXbhwQU888YSOHz+u4OBgNW/eXKtWrdLAgQMlST/88IM2bNgg6fLlsStt3brV4zIZAN9h1WUAvuTzACRJycnJhV7yunpy84wZMzRjxoxCjxUTEyMfL20EoATyJzJv3NhfhuFXrInMzNUBcKMqRAACYG4lnch89VydH3/005Ej1dS48a+qV+/yHWHM1QFQFAIQgAqhtBOZC3sUBgAUhQAEoNIp+CiM/DvILBo/Pkw//PBWqR6FAcA8KuVCiADMjUdhALhRBCAAlRaPwgBQWgQgAD5RFndy8SgMAKXFHCAAPlFWqy6X5aMwAJgHAQiAz5TVBOWyehQGAPPgEhgAADAdAhAAADAdAhCASqckE6iPH5e2bpWOHy/nogBUKswBAlDpXG8CtdPplGEYWr7cXxMmGAVWieYxGQAIQAAqpcICjMPh0Nq1a1klGkCRuAQGoEphlWgAxcEIEIAqyW53SHLJ87/zKucq0Q6H44bXSwLgiQAEoAqzeL6zFNKsAst/8Gs+pzNUmZnhstsdHmsfcUkPKBkCEIAqxel0Srp8CezqAJR/CcxmOyen06moqCgfVFgyV4787N7dThs39pdh+Lkf+xEXt6dAOwDXxxwgAFVKbm6upOs/KDW/XWXhdIa6w490Ocxt3NhfTmeojysDKicCEIAqqao9KPV6k7oBlAyXwABUWVXpQan5I1pXhqArR7QAlAwjQACqlGrVPP+7zmY7p8aNvysQfq5uV9FVtREtwNcq178AAHAdNWvWLNN2FUlVGtECfI0ABACViM12juADlAEugQFABVaSB78CKD5GgABUKVUtMFzvwa8SK0EDpUEAAlClEBgAFAcBCECVU5XCDY/CAMoHAQgAKjAehQGUDyZBA0AlwKMwgLJFAAKASoBHYQBliwAEAJXA9R7uCqBkCEAAUAnwKAygbDEJGgAqCR6FAZQdAhAAVCI8CgMoG1wCA4AKrKqtbA1UFIwAAUAFxsrWQPkgAAFABUe4Acoel8AAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpcBs8AJiUw+FgfSGYFgEIAEzI4XBowYIF122XnJxMCEKVxCUwADChq0d+nM5QHTkSI6cztMh2QFXBCBAAmNzu3e20cWN/GYafLBaXkpI2KS5uj6/LAsoVI0AAYGJOZ6g7/EiSYfhp48b+BUaCgKqGAAQAJpaZGe4OP/kMw0+ZmXYfVQR4BwEIAEzMbnfIYnF5bLNYXLLbM31UEeAdBCAAMDGb7ZySkja5Q1D+HCCb7ZyPKwPKF5OgAcDk4uL2KDb2kDIz7bLbMwk/MAUCEACYkNVq9Xhvs527ZvC5uh1QVRCAAMCEwsPDlZyczErQMC0CEACYFOEGZlYhJkEvXLhQMTExCgoKUqdOnbRz585C265fv17x8fGqWbOmQkJC1LZtW61cudKjjWEYmjJliqKiohQcHKyEhAQdPHiwvE8DAABUEj4PQGvXrlVKSoqmTp2q3bt3q02bNkpMTNTp06ev2d5ut2vy5MnasWOH9u7dqxEjRmjEiBH66KOP3G1mz56tefPmafHixfriiy8UEhKixMREXbp0yVunBQAAKjCLYRiGLwvo1KmTOnTo4H4on8vlUnR0tJ588klNnDixWMeIi4tTv379NH36dBmGoXr16mncuHF65plnJElOp1MRERFavny5Bg0adN3jZWVlyWazyel0KiwsrPQnBwAAvKYkv799OgKUk5Oj9PR0JSQkuLf5+fkpISFBO3bsuO7nDcNQWlqaDhw4oO7du0uSjhw5opMnT3oc02azqVOnTsU6JgAAqPp8Ogn67NmzysvLU0REhMf2iIgIffPNN4V+zul0qn79+srOzpa/v79ef/119e7dW5J08uRJ9zGuPmb+vqtlZ2crOzvb/T4rK6tU5wMAACqHSnkXWGhoqDIyMnT+/HmlpaUpJSVFTZo0Uc+ePUt1vNTUVD3//PNlWyQAAKiwfHoJrHbt2vL399epU6c8tp86dUqRkZGFfs7Pz09NmzZV27ZtNW7cON13331KTU2VJPfnSnLMSZMmyel0ul/ff//9jZwWAACo4HwagKxWq9q3b6+0tDT3NpfLpbS0NHXu3LnYx3G5XO5LWI0bN1ZkZKTHMbOysvTFF18UeszAwECFhYV5vAAAQNXl80tgKSkpGjZsmOLj49WxY0fNnTtXFy5c0IgRIyRJQ4cOVf369d0jPKmpqYqPj1dsbKyys7P14YcfauXKlVq0aJEkyWKxaOzYsZoxY4aaNWumxo0b67nnnlO9evV09913++o0AQBABeLzADRw4ECdOXNGU6ZM0cmTJ9W2bVtt3rzZPYn52LFj8vP7baDqwoULeuKJJ3T8+HEFBwerefPmWrVqlQYOHOhuM2HCBF24cEGjRo3Szz//rK5du2rz5s0KCgry+vkBAICKx+frAFVErAMEAEDlU2nWAQIAAPCFEgegDz/8UCNHjtSECRMKrNXz008/qVevXmVWHAAAQHkoUQBavXq1BgwYoJMnT2rHjh1q166d3n77bff+nJwc/eMf/yjzIgEAAMpSiSZBv/TSS5ozZ47GjBkjSfrv//5vPfzww7p06ZIeeeSRcikQAACgrJUoAB08eFBJSUnu9/fff7/q1KmjAQMGKDc3V/fcc0+ZFwgAAFDWShSAwsLCdOrUKTVu3Ni97fbbb9emTZvUv39/HT9+vMwLBAAAKGslmgPUsWNH/e1vfyuwvUePHtq4caPmzp1bVnUBAACUmxIFoKeffrrQxQR79uypjRs3aujQoWVSGAAAQHlhIcRrYCFEAAAqn5L8/i7RHCA/Pz9ZLJYi21gsFv36668lOSwAAIBXlSgAvffee4Xu27Fjh+bNmyeXy3XDRQEAAJSnEgWgu+66q8C2AwcOaOLEidq4caMefPBB/elPfyqz4gAAAMpDqZ8F9uOPP+rRRx9Vq1at9OuvvyojI0MrVqxQo0aNyrI+AACAMleiESBJcjqdevHFFzV//ny1bdtWaWlp6tatW3nUBgAwGYfDoZycnEL3W61WhYeHe7EiVFUlCkCzZ8/WrFmzFBkZqb/85S/XvCQGAEBpOBwOLViw4LrtkpOTCUG4YSUKQBMnTlRwcLCaNm2qFStWaMWKFddst379+jIpDgBgHkWN/JSmHVCUEgWgoUOHXvc2eAAAKhoureFqJQpAy5cvL6cyAADw5HSGKjMzXHa7QzbbuVIfh0truJYST4IGAKC87d7dThs39pdh+MlicSkpaZPi4vaU6lhcWsO1lPo2eAAAyoPTGeoOP5JkGH7auLG/nM5QH1eGqoQABACoUDIzw93hJ59h+Ckz0+6jilAVEYAAABWK3e6QxeL5WCWLxSW7PdNHFaEqIgABACoEq9UqSbLZzikpaZM7BOXPAcqfCJ3frrSczlAdORLDJTWTYxI0AKBCCA8PV3Jysnsy8pQpZ3T0aDXFxPyqevU6SOpww7erl+XkalRuBCAAQIVxZbiJipLaty/+Zwtb6+fs2bOSCp9cHRt76IZus0flRAACAFR6xVnrp6jJ1TbbuRu+tIbKhQAEAKj0irOGj93ukJ+fIZfrtyca+PsbevLJvoqJqcYiiCbDJGgAgCnYbOc0e7ZT/v6X3/v7S2+8YVH79hGEHxNiBAgAYBoPPPCLBg6sqUOHpKZNpQYNfF0RfIUABAAwlQYNCD7gEhgAoApirR9cDyNAAIAqhbV+UByMAAEAqgwepIriIgABACq9/DV8rvcgVdb6QT6LYRiGr4uoaLKysmSz2eR0OhUWFubrcgAAxeBwOHT06K/q2LFugbV+vvjiNGv9mEBJfn8zAgQAqBLCw8PVvn2E3nzTwlo/uC4mQQMAqpRHHpESE8VaPygSAQgAUOWw1g+uh0tgAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdHwegBYuXKiYmBgFBQWpU6dO2rlzZ6FtlyxZom7duqlWrVqqVauWEhISCrQ/f/68kpOT1aBBAwUHB6tly5ZavHhxeZ8GAACoRHwagNauXauUlBRNnTpVu3fvVps2bZSYmKjTp09fs/22bds0ePBgbd26VTt27FB0dLTuvPNO/fDDD+42KSkp2rx5s1atWqX9+/dr7NixSk5O1oYNG7x1WgAAoIKzGIZh+OrLO3XqpA4dOmjBggWSJJfLpejoaD355JOaOHHidT+fl5enWrVqacGCBRo6dKgk6dZbb9XAgQP13HPPudu1b99effv21YwZM4pVV1ZWlmw2m5xOp8LCwkpxZgAAwNtK8vvbZyNAOTk5Sk9PV0JCwm/F+PkpISFBO3bsKNYxLl68qNzcXNntdve2Ll26aMOGDfrhhx9kGIa2bt2qb7/9VnfeeWehx8nOzlZWVpbHCwAAVF0+C0Bnz55VXl6eIiIiPLZHRETo5MmTxTrGs88+q3r16nmEqPnz56tly5Zq0KCBrFar+vTpo4ULF6p79+6FHic1NVU2m839io6OLt1JAQCASsHnk6BLa+bMmVqzZo3ee+89BQUFubfPnz9fn3/+uTZs2KD09HS98sorGj16tLZs2VLosSZNmiSn0+l+ff/99944BQAA4CPVfPXFtWvXlr+/v06dOuWx/dSpU4qMjCzysy+//LJmzpypLVu2qHXr1u7tv/zyi/7zP/9T7733nvr16ydJat26tTIyMvTyyy97jBRdKTAwUIGBgTd4RgAAoLLw2QiQ1WpV+/btlZaW5t7mcrmUlpamzp07F/q52bNna/r06dq8ebPi4+M99uXm5io3N1d+fp6n5e/vL5fLVbYnAAAAKi2fjQBJl29ZHzZsmOLj49WxY0fNnTtXFy5c0IgRIyRJQ4cOVf369ZWamipJmjVrlqZMmaLVq1crJibGPVeoRo0aqlGjhsLCwtSjRw+NHz9ewcHBatSokf7xj3/oz3/+s+bMmeOz8wQAABWLTwPQwIEDdebMGU2ZMkUnT55U27ZttXnzZvfE6GPHjnmM5ixatEg5OTm67777PI4zdepUTZs2TZK0Zs0aTZo0SQ8++KAyMzPVqFEjvfDCC3rssce8dl4AAKBi8+k6QBUV6wABAFD5VIp1gAAAAHyFAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEynmq8LAAAABTkcDuXk5EiSfvzRT0eOVFPjxr+qXj2XJMlqtSo8PNyXJVZqBCAAACoYh8OhBQsWSJJ2726njRv7yzD8ZLG4lJS0SXFxeyRJycnJhKBS4hIYAAAVTP7Ij9MZ6g4/kmQYftq4sb+czlCPdig5AhAAABVUZma4O/zkMww/ZWbafVRR1UEAAgCggrLbHbJYXB7bLBaX7PZMH1VUdRCAAACooGy2c0pK2uQOQflzgGy2cz6urPJjEjQAABVYXNwexcYeUmamXXZ7JuGnjBCAAACo4Gy2cwSfMsYlMAAAYDoEIAAAKhir1Vqm7VAQl8AAAKhgwsPDlZycXOQ6P6wEfWMIQAAAVECEm/LFJTAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6Pg9ACxcuVExMjIKCgtSpUyft3Lmz0LZLlixRt27dVKtWLdWqVUsJCQnXbL9//34NGDBANptNISEh6tChg44dO1aepwEAACoRnwagtWvXKiUlRVOnTtXu3bvVpk0bJSYm6vTp09dsv23bNg0ePFhbt27Vjh07FB0drTvvvFM//PCDu83hw4fVtWtXNW/eXNu2bdPevXv13HPPKSgoyFunBQAAKjiLYRiGr768U6dO6tChgxYsWCBJcrlcio6O1pNPPqmJEyde9/N5eXmqVauWFixYoKFDh0qSBg0apICAAK1cubLUdWVlZclms8npdCosLKzUxwEAAN5Tkt/fPhsBysnJUXp6uhISEn4rxs9PCQkJ2rFjR7GOcfHiReXm5sput0u6HKA++OAD3XTTTUpMTFTdunXVqVMnvf/++0UeJzs7W1lZWR4vAABQdfksAJ09e1Z5eXmKiIjw2B4REaGTJ08W6xjPPvus6tWr5w5Rp0+f1vnz5zVz5kz16dNHH3/8se655x79/ve/1z/+8Y9Cj5OamiqbzeZ+RUdHl/7EAABAhVdpV4KeOXOm1qxZo23btrnn97hcLknSXXfdpaefflqS1LZtW3322WdavHixevTocc1jTZo0SSkpKe73WVlZhCAAAKownwWg2rVry9/fX6dOnfLYfurUKUVGRhb52ZdfflkzZ87Uli1b1Lp1a49jVqtWTS1btvRo36JFC3366aeFHi8wMFCBgYGlOAsAAFAZ+ewSmNVqVfv27ZWWlube5nK5lJaWps6dOxf6udmzZ2v69OnavHmz4uPjCxyzQ4cOOnDggMf2b7/9Vo0aNSrbEwAAAJWWTy+BpaSkaNiwYYqPj1fHjh01d+5cXbhwQSNGjJAkDR06VPXr11dqaqokadasWZoyZYpWr16tmJgY91yhGjVqqEaNGpKk8ePHa+DAgerevbtuv/12bd68WRs3btS2bdt8co4AAKDi8WkAGjhwoM6cOaMpU6bo5MmTatu2rTZv3uyeGH3s2DH5+f02SLVo0SLl5OTovvvu8zjO1KlTNW3aNEnSPffco8WLFys1NVVjxozRzTffrHXr1qlr165eOy8AAFCx+XQdoIqKdYAAAFWVw+FQTk5OofutVqvCw8O9WFHZKcnv70p7FxgAACgZh8PhXnxYkpzOUGVmhstud8hmO+fenpycXGlDUHERgAAAMIkrR352726njRv7yzD8ZLG4lJS0SXFxewq0q6p8/jBUAADgXU5nqDv8SJJh+Gnjxv5yOkN9XJn3EIAAADCZzMxwd/jJZxh+ysy0+6gi7yMAAQBgMna7QxaLy2ObxeKS3Z7po4q8jwAEAIDJ2GznlJS0yR2C8ucAXTkRuqpjEjQAACYUF7dHsbGHlJlpl92eaarwIxGAAAAwLZvtnOmCTz4ugQEAYBJWq7VM21VmjAABAGAS4eHhSk5OrrIrQZcEAQgAABMxQ7gpDi6BAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0+FhqAAA4IY5HI5K9ZR5AhAAALghDodDCxYsuG675OTkChOCuAQGAABuSFEjP6Vp5w0EIAAAUKaczlAdORIjpzPU16UUiktgAACgzOze3U4bN/aXYfjJYnEpKWmT4uL2+LqsAhgBAgAAZcLpDHWHH0kyDD9t3Ni/Qo4EEYAAAECZyMwMd4effIbhp8xMu48qKhwBCAAAlAm73SGLxeWxzWJxyW7P9FFFhSMAAQCAMmGznVNS0iZ3CMqfA2SznfNxZQUxCRoAANwQq9Xq/ntc3B7Fxh5SZqZddnumR/i5sp2vEYAAAMANCQ8PV3JyMitBAwAAc6lI4aY4mAMEAABMhxEgAABQririg1IJQAAAoNxc/aBUpzNUmZnhstsdHhOkvf2gVAIQAAAoN1eO/BT1mAxvPyiVOUAAAKDcVbTHZBCAAABAuatoj8kgAAEAgHJX0R6TQQACAADlrqI9JoNJ0AAAwCuKekyGtxGAAACA19hs5yrEw1G5BAYAAMpNcR+A6u0HpTICBAAAyk1FfVAqAQgAAJSrivigVC6BAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA02El6GswDEOSlJWV5eNKAABAceX/3s7/PV4UAtA1nDt3+Sm10dHRPq4EAACU1Llz52Sz2YpsYzGKE5NMxuVy6ccff1RoaKgsFkuB/VlZWYqOjtb333+vsLAwH1RY8dFH10cfFY3+uT766Proo+urSn1kGIbOnTunevXqyc+v6Fk+jABdg5+fnxo0aHDddmFhYZX+h6W80UfXRx8Vjf65Pvro+uij66sqfXS9kZ98TIIGAACmQwACAACmQwAqhcDAQE2dOlWBgYG+LqXCoo+ujz4qGv1zffTR9dFH12fWPmISNAAAMB1GgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAqxcOFCxcTEKCgoSJ06ddLOnTsLbfvVV1/p3nvvVUxMjCwWi+bOneu9Qn2oJH20ZMkSdevWTbVq1VKtWrWUkJBQZPuqoCT9s379esXHx6tmzZoKCQlR27ZttXLlSi9W6xsl6aMrrVmzRhaLRXfffXf5FlgBlKSPli9fLovF4vEKCgryYrW+UdKfo59//lmjR49WVFSUAgMDddNNN+nDDz/0UrW+UZI+6tmzZ4GfI4vFon79+nmxYi8wUMCaNWsMq9VqvPXWW8ZXX31lPProo0bNmjWNU6dOXbP9zp07jWeeecb4y1/+YkRGRhqvvvqqdwv2gZL20QMPPGAsXLjQ2LNnj7F//35j+PDhhs1mM44fP+7lyr2jpP2zdetWY/369cbXX39tHDp0yJg7d67h7+9vbN682cuVe09J+yjfkSNHjPr16xvdunUz7rrrLu8U6yMl7aNly5YZYWFhxokTJ9yvkydPerlq7yppH2VnZxvx8fHGf/zHfxiffvqpceTIEWPbtm1GRkaGlyv3npL2kcPh8PgZ2rdvn+Hv728sW7bMu4WXMwLQNXTs2NEYPXq0+31eXp5Rr149IzU19bqfbdSokSkC0I30kWEYxq+//mqEhoYaK1asKK8SfepG+8cwDKNdu3bGH//4x/Ior0IoTR/9+uuvRpcuXYz/+q//MoYNG1blA1BJ+2jZsmWGzWbzUnUVQ0n7aNGiRUaTJk2MnJwcb5Xoczf679Grr75qhIaGGufPny+vEn2CS2BXycnJUXp6uhISEtzb/Pz8lJCQoB07dviwsoqjLPro4sWLys3Nld1uL68yfeZG+8cwDKWlpenAgQPq3r17eZbqM6Xtoz/96U+qW7euHnnkEW+U6VOl7aPz58+rUaNGio6O1l133aWvvvrKG+X6RGn6aMOGDercubNGjx6tiIgI3XrrrXrxxReVl5fnrbK9qiz+vV66dKkGDRqkkJCQ8irTJwhAVzl79qzy8vIUERHhsT0iIkInT570UVUVS1n00bPPPqt69ep5/J+yqiht/zidTtWoUUNWq1X9+vXT/Pnz1bt37/Iu1ydK00effvqpli5dqiVLlnijRJ8rTR/dfPPNeuutt/TXv/5Vq1atksvlUpcuXXT8+HFvlOx1pemjf//733r33XeVl5enDz/8UM8995xeeeUVzZgxwxsle92N/nu9c+dO7du3TyNHjiyvEn2Gp8HD62bOnKk1a9Zo27ZtppigWVyhoaHKyMjQ+fPnlZaWppSUFDVp0kQ9e/b0dWk+d+7cOQ0ZMkRLlixR7dq1fV1OhdW5c2d17tzZ/b5Lly5q0aKF3njjDU2fPt2HlVUcLpdLdevW1Ztvvil/f3+1b99eP/zwg1566SVNnTrV1+VVOEuXLlWrVq3UsWNHX5dS5ghAV6ldu7b8/f116tQpj+2nTp1SZGSkj6qqWG6kj15++WXNnDlTW7ZsUevWrcuzTJ8pbf/4+fmpadOmkqS2bdtq//79Sk1NrZIBqKR9dPjwYR09elRJSUnubS6XS5JUrVo1HThwQLGxseVbtJeVxb9FAQEBateunQ4dOlQeJfpcafooKipKAQEB8vf3d29r0aKFTp48qZycHFmt1nKt2dtu5OfowoULWrNmjf70pz+VZ4k+wyWwq1itVrVv315paWnubS6XS2lpaR7/ZWVmpe2j2bNna/r06dq8ebPi4+O9UapPlNXPkMvlUnZ2dnmU6HMl7aPmzZvryy+/VEZGhvs1YMAA3X777crIyFB0dLQ3y/eKsvg5ysvL05dffqmoqKjyKtOnStNHt912mw4dOuQO0JL07bffKioqqsqFH+nGfo7eeecdZWdn66GHHirvMn3D17OwK6I1a9YYgYGBxvLly42vv/7aGDVqlFGzZk337aRDhgwxJk6c6G6fnZ1t7Nmzx9izZ48RFRVlPPPMM8aePXuMgwcP+uoUyl1J+2jmzJmG1Wo13n33XY/bK8+dO+erUyhXJe2fF1980fj444+Nw4cPG19//bXx8ssvG9WqVTOWLFniq1ModyXto6uZ4S6wkvbR888/b3z00UfG4cOHjfT0dGPQoEFGUFCQ8dVXX/nqFMpdSfvo2LFjRmhoqJGcnGwcOHDA2LRpk1G3bl1jxowZvjqFclfa/6917drVGDhwoLfL9RoCUCHmz59vNGzY0LBarUbHjh2Nzz//3L2vR48exrBhw9zvjxw5Ykgq8OrRo4f3C/eikvRRo0aNrtlHU6dO9X7hXlKS/pk8ebLRtGlTIygoyKhVq5bRuXNnY82aNT6o2rtK0kdXM0MAMoyS9dHYsWPdbSMiIoz/+I//MHbv3u2Dqr2rpD9Hn332mdGpUycjMDDQaNKkifHCCy8Yv/76q5er9q6S9tE333xjSDI+/vhjL1fqPRbDMAwfDT4BAAD4BHOAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAMCHhg8frrvvvtvXZQCmQwACcE3Dhw+XxWJxv8LDw9WnTx/t3bvX16WViSvPLf/VtWvXcvu+o0ePymKxKCMjw2P7a6+9puXLl5fb9wK4NgIQgEL16dNHJ06c0IkTJ5SWlqZq1aqpf//+vi6rzCxbtsx9fidOnNCGDRuu2S43N7fcarDZbKpZs2a5HR/AtRGAABQqMDBQkZGRioyMVNu2bTVx4kR9//33OnPmjHr16qXk5GSP9mfOnJHVanU/eTomJkbTp0/X4MGDFRISovr162vhwoUen5kzZ45atWqlkJAQRUdH64knntD58+fd+7/77jslJSWpVq1aCgkJ0S233KIPP/xQkvTTTz/pwQcfVJ06dRQcHKxmzZpp2bJlxT6/mjVrus8vMjJSdrvdPVKzdu1a9ejRQ0FBQXr77bflcDg0ePBg1a9fX9WrV1erVq30l7/8xeN4LpdLs2fPVtOmTRUYGKiGDRvqhRdekCQ1btxYktSuXTtZLBb17NlTUsFLYNnZ2RozZozq1q2roKAgde3aVbt27XLv37ZtmywWi9LS0hQfH6/q1aurS5cuOnDgQLHPGwABCEAxnT9/XqtWrVLTpk0VHh6ukSNHavXq1crOzna3WbVqlerXr69evXq5t7300ktq06aN9uzZo4kTJ+qpp57SJ5984t7v5+enefPm6auvvtKKFSv097//XRMmTHDvHz16tLKzs/U///M/+vLLLzVr1izVqFFDkvTcc8/p66+/1t/+9jft379fixYtUu3atcvkfPNr3b9/vxITE3Xp0iW1b99eH3zwgfbt26dRo0ZpyJAh2rlzp/szkyZN0syZM911rV69WhEREZLkbrdlyxadOHFC69evv+b3TpgwQevWrdOKFSu0e/duNW3aVImJicrMzPRoN3nyZL3yyiv617/+pWrVqunhhx8uk/MGTMPXT2MFUDENGzbM8Pf3N0JCQoyQkBBDkhEVFWWkp6cbhmEYv/zyi1GrVi1j7dq17s+0bt3amDZtmvt9o0aNjD59+ngcd+DAgUbfvn0L/d533nnHCA8Pd79v1aqVxzGvlJSUZIwYMaJU5yfJCAoKcp9fSEiI8d577xlHjhwxJBlz58697jH69etnjBs3zjAMw8jKyjICAwONJUuWXLNt/nH37Nnjsf3Kp9qfP3/eCAgIMN5++233/pycHKNevXrG7NmzDcMwjK1btxqSjC1btrjbfPDBB4Yk45dffilJFwCmxggQgELdfvvtysjIUEZGhnbu3KnExET17dtX3333nYKCgjRkyBC99dZbkqTdu3dr3759Gj58uMcxOnfuXOD9/v373e+3bNmiO+64Q/Xr11doaKiGDBkih8OhixcvSpLGjBmjGTNm6LbbbtPUqVM9JmE//vjjWrNmjdq2basJEybos88+K9H5vfrqq+7zy8jIUO/evd374uPjPdrm5eVp+vTpatWqlex2u2rUqKGPPvpIx44dkyTt379f2dnZuuOOO0pUw5UOHz6s3Nxc3Xbbbe5tAQEB6tixo0efSVLr1q3df4+KipIknT59utTfDZgNAQhAoUJCQtS0aVM1bdpUHTp00H/913/pwoULWrJkiSRp5MiR+uSTT3T8+HEtW7ZMvXr1UqNGjYp9/KNHj6p///5q3bq11q1bp/T0dPccoZycHPd3/Pvf/9aQIUP05ZdfKj4+XvPnz5ckdxh7+umn9eOPP+qOO+7QM888U+zvj4yMdJ9f06ZNFRIS4nHuV3rppZf02muv6dlnn9XWrVuVkZGhxMREd53BwcHF/t6yEBAQ4P67xWKRdHkOEoDiIQABKDaLxSI/Pz/98ssvkqRWrVopPj5eS5Ys0erVq685D+Xzzz8v8L5FixaSpPT0dLlcLr3yyiv63e9+p5tuukk//vhjgWNER0frscce0/r16zVu3Dh3AJOkOnXqaNiwYVq1apXmzp2rN998syxP2W379u2666679NBDD6lNmzZq0qSJvv32W/f+Zs2aKTg42D0B/GpWq1XS5ZGkwsTGxspqtWr79u3ubbm5udq1a5datmxZRmcCQJKq+boAABVXdna2Tp48KenyHVcLFizQ+fPnlZSU5G4zcuRIJScnKyQkRPfcc0+BY2zfvl2zZ8/W3XffrU8++UTvvPOOPvjgA0lS06ZNlZubq/nz5yspKUnbt2/X4sWLPT4/duxY9e3bVzfddJN++uknbd261R2gpkyZovbt2+uWW25Rdna2Nm3a5N5X1po1a6Z3331Xn332mWrVqqU5c+bo1KlT7mASFBSkZ599VhMmTJDVatVtt92mM2fO6KuvvtIjjzyiunXrKjg4WJs3b1aDBg0UFBQkm83m8R0hISF6/PHHNX78eNntdjVs2FCzZ8/WxYsX9cgjj5TLeQFmxQgQgEJt3rxZUVFRioqKUqdOnbRr1y6988477lu4JWnw4MGqVq2aBg8erKCgoALHGDdunP71r3+pXbt2mjFjhubMmaPExERJUps2bTRnzhzNmjVLt956q95++22lpqZ6fD4vL0+jR49WixYt1KdPH9100016/fXXJV0eVZk0aZJat26t7t27y9/fX2vWrCmXvvjjH/+ouLg4JSYmqmfPnoqMjCywgvNzzz2ncePGacqUKWrRooUGDhzonpdTrVo1zZs3T2+88Ybq1aunu+6665rfM3PmTN17770aMmSI4uLidOjQIX300UeqVatWuZwXYFYWwzAMXxcBoPI6evSoYmNjtWvXLsXFxXnsi4mJ0dixYzV27FjfFAcAheASGIBSyc3NlcPh0B//+Ef97ne/KxB+AKAi4xIYgFLZvn27oqKitGvXrgLzdnztxRdfVI0aNa756tu3r6/LA1ABcAkMQJWTmZlZYOXkfMHBwapfv76XKwJQ0RCAAACA6XAJDAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmM7/B7AiAeKgi0mjAAAAAElFTkSuQmCC", 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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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", 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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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p7Nmz6t+/v2Na+/bt1bRpU23ZssWlnw0AkFKz8x3BpliRMUrLPuWegqoQIzcukvRtuiMx+9ikhKFRGt69qUs+68CBAzLGqH379hds99hjj6lXr14aPHjwBdv16tVLPj7OubegoEAxMTFlrikzM1N+fn6OYFUsLCxMmZmZZV4PAKBiWoTUlo9NTgHH12ZT8xDr38uNcOMCVT0UaIy5aJvly5dr7dq12rFjx0XbJiUlqUOHDk7T7rjjjgrXBwCoehHBgUoYGqWpybtUZIx8bTa9MLSz5Q9JSYQbl7jQUKArdqo2bdrIZrNp7969pbZZu3atDh48eN5Iyq233qqrr75a69atc0yLjIxU69atndoVPxqhrMLDw3XmzBmdOHHC6TOzsrJcfkgMAHDO8O5N1adtqNKyT6l5SK1qEWwkzrlxieKhwN9y5VBg/fr1FR8frzlz5ig/P/+8+SdOnNDTTz+t77//XikpKY6XJM2aNUvz58+v9Jq6deummjVrOl2Gvm/fPqWnpys2NrbSPw8AULKI4EDFtmpQbYKNxMiNS7hjKHDOnDnq3bu3evTooZkzZ6pLly769ddftXr1as2dO1d79uwpccSkadOmatGiRbk/73//+5/S09N19OhRSeeCi3RuxCY8PFzBwcG65557NHnyZNWvX19BQUF6+OGHFRsby5VSAACXIty4SFUPBbZs2VLbt2/Xn//8Zz3++OPKyMhQaGiounXrprlz51b65y1fvlzjxo1zvB8xYoQkadq0aZo+fbqkc6NCPj4+uvXWW1VYWKj4+Hi98cYblV4LAAC/ZTNlORvVQnJzcxUcHKycnBwFBQU5zTt9+rRSU1PVokULBQQEuKnC6o3/BgDg3TJyCpSana8WIbUr9Q/7C/1+/x4jNwAAoFJU5W1QLoQTigEAwCUr7TYo7nimFeEGAABcMk+6IzLhBgAAXLKqvg3KhRBuSlDNzrH2KPQ9AHin4tug+NrOJRx33hGZE4p/o2bNmpLOPWCyvHfkReU4derc8GXxfwsAgPfwlDsiE25+w9fXV/Xq1dOxY8ckSbVq1ZLNZrvIUqgMxhidOnVKx44dU7169eTr6+vukgAAFRARHOj2uyETbn6n+C6+xQEHVatevXo8ewoAcEkIN79js9kUERGhhg0b6uzZs+4up1qpWbMmIzYAgEtGuCmFr68vP7QAAHghrpYCAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACW4tZwk5CQoO7du6tu3bpq2LChhgwZon379l1wmcTERNlsNqdXQEBAFVUMAAA8nVvDzfr16zVhwgR99dVXWr16tc6ePavrr79e+fn5F1wuKChIGRkZjtfhw4erqGIAAODparjzw1euXOn0PjExUQ0bNtS2bdvUp0+fUpez2WwKDw93dXkAAMALedQ5Nzk5OZKk+vXrX7BdXl6emjVrpsjISA0ePFi7d++uivIAAIAX8JhwY7fbNWnSJPXu3VudO3cutV27du30zjvvaNmyZVqwYIHsdrt69eqlH3/8scT2hYWFys3NdXoBAADrshljjLuLkKQHH3xQ//nPf7Rx40Y1adKkzMudPXtWHTp00MiRI/X888+fN3/69OmaMWPGedNzcnIUFBR0STUDAICqkZubq+Dg4DL9fnvEyM3EiRP1ySef6IsvvihXsJGkmjVrqmvXrjpw4ECJ86dMmaKcnBzH68iRI5VRMgAA8FBuPaHYGKOHH35YS5Ys0bp169SiRYtyr6OoqEg7d+7UjTfeWOJ8f39/+fv7X2qpAADAS7g13EyYMEELFy7UsmXLVLduXWVmZkqSgoODFRgYKEkaM2aMGjdurISEBEnSzJkzdeWVV6p169Y6ceKE/vrXv+rw4cMaP36827YDAAB4DreGm7lz50qS+vbt6zR9/vz5uuuuuyRJ6enp8vH5v6Nnv/zyi+69915lZmbqsssuU7du3bR582Z17NixqsoGAAAezGNOKK4q5TkhCQAAeAavO6EYAACgshBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApbg13CQkJKh79+6qW7euGjZsqCFDhmjfvn0XXe6jjz5S+/btFRAQoKioKK1YsaIKqgUAAN7AreFm/fr1mjBhgr766iutXr1aZ8+e1fXXX6/8/PxSl9m8ebNGjhype+65Rzt27NCQIUM0ZMgQ7dq1qworBwAAnspmjDHuLqLY8ePH1bBhQ61fv159+vQpsc3w4cOVn5+vTz75xDHtyiuvVExMjObNm3fRz8jNzVVwcLBycnIUFBRUabUDAADXKc/vt0edc5OTkyNJql+/fqlttmzZov79+ztNi4+P15YtW0psX1hYqNzcXKcXAACwLo8JN3a7XZMmTVLv3r3VuXPnUttlZmYqLCzMaVpYWJgyMzNLbJ+QkKDg4GDHKzIyslLrBgAAnsVjws2ECRO0a9cuffjhh5W63ilTpignJ8fxOnLkSKWuHwCqk4ycAm0+mK2MnAJ3lwKUqoa7C5CkiRMn6pNPPtGGDRvUpEmTC7YNDw9XVlaW07SsrCyFh4eX2N7f31/+/v6VVisAVFdJ36ZrSvJO2Y3kY5MShkZpePem7i4LOI9bR26MMZo4caKWLFmitWvXqkWLFhddJjY2VmvWrHGatnr1asXGxrqqTACo9jJyChzBRpLsRpqavIsRHHgkt4abCRMmaMGCBVq4cKHq1q2rzMxMZWZmqqDg//5nGTNmjKZMmeJ4/+ijj2rlypV65ZVXtHfvXk2fPl1bt27VxIkT3bEJAFAtpGbnO4JNsSJjlJZ9yj0FARfg1nAzd+5c5eTkqG/fvoqIiHC8kpKSHG3S09OVkZHheN+rVy8tXLhQ//jHPxQdHa3Fixdr6dKlFzwJGQBwaVqE1JaPzXmar82m5iG13FMQcAEedZ+bqsB9bgCgYpK+TdfU5F0qMka+NpteGNqZc25QZcrz++0RJxQDADzf8O5N1adtqNKyT6l5SC1FBAe6uySgRIQbAECZRQQHEmrg8TzmPjcAAACVgXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADWFRGToE2H8xWRk6Bu0sBgCpVw90FAKh8Sd+ma0ryTtmN5GOTEoZGaXj3pu4uCwCqBCM3gMVk5BQ4go0k2Y00NXkXIzgAqg3CDWAxqdn5jmBTrMgYpWWfck9BAFDFCDeAxbQIqS0fm/M0X5tNzUNquacgAKhihBvAYiKCA5UwNEq+tnMJx9dm0wtDOysiONDNlQFA1eCEYsCChndvqj5tQ5WWfUrNQ2oRbABUK4QbwKIiggMJNQCqJQ5LAQAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAAS6nQfW6++eYbbdmyRZmZmZKk8PBwxcbGqkePHpVaHAAAQHmVK9wcO3ZMt956qzZt2qSmTZsqLCxMkpSVlaXHHntMvXv31scff6yGDRu6pFgAAICLKddhqYceekhFRUXas2eP0tLS9PXXX+vrr79WWlqa9uzZI7vdrgkTJriqVgAAgIuyGWNMWRvXrVtXGzZsUNeuXUucv23bNvXt21cnT56stAIrW25uroKDg5WTk6OgoCB3lwMAAMqgPL/f5Rq58ff3V25ubqnzT548KX9///KsEgAAoFKVK9wMHz5cY8eO1ZIlS5xCTm5urpYsWaJx48Zp5MiRlV4kAABAWZXrhOJXX31VdrtdI0aM0K+//io/Pz9J0pkzZ1SjRg3dc889evnll11SKAAAQFmU65ybYrm5udq2bZvTpeDdunXzinNYOOcGAADvU57f7wrd5yYoKEj9+vWrUHEAAACuVKl3KM7KytLMmTMrc5UAAADlUqnhJjMzUzNmzChz+w0bNmjQoEFq1KiRbDabli5desH269atk81mO+9VfHgMAACgXIelvv/++wvO37dvX7k+PD8/X9HR0br77rs1dOjQMi+3b98+p+Nt3BEZAAAUK1e4iYmJkc1mU0nnIBdPt9lsZV7fgAEDNGDAgPKUIOlcmKlXr165lwMAANZXrnBTv359/eUvf9G1115b4vzdu3dr0KBBlVLYhcTExKiwsFCdO3fW9OnT1bt371LbFhYWqrCw0PH+QjchBAAA3q9c4aZbt246evSomjVrVuL8EydOlDiqU1kiIiI0b948XXHFFSosLNRbb72lvn376uuvv9bll19e4jIJCQnlOg8IAAB4t3Ld52bJkiXKz8/XnXfeWeL8X375RcuXL9fYsWPLX4jNpiVLlmjIkCHlWi4uLk5NmzbV+++/X+L8kkZuIiMjuc8NAABexGX3ubnlllsuOP+yyy6rULC5FD169NDGjRtLne/v78/zrgAAqEYq9VJwd0hJSVFERIS7ywAAAB6iXCM3kydPLlO7V199tUzt8vLydODAAcf71NRUpaSkqH79+mratKmmTJmin376Se+9954kafbs2WrRooU6deqk06dP66233tLatWu1atWq8mwGAACwsHKFmx07dji937hxo7p166bAwEDHtPJcCr5161anxzgUh6exY8cqMTFRGRkZSk9Pd8w/c+aMHn/8cf3000+qVauWunTpos8//5xHQQAAAIcKPTizWN26dfXdd9+pZcuWlVmTS/HgTAAAvE95fr+9/pwbb5eRU6DNB7OVkVPg7lIAALCECj0VHJUj6dt0TUneKbuRfGxSwtAoDe/e1N1lAeWWkVOg1Ox8tQiprYjgwIsvAAAuRLhxk4ycAkewkSS7kaYm71KftqH8OMCrENIBeJpLenCmMUZ79+5VXl6e0/QuXbpcemUWl5qd7wg2xYqMUVr2KcINvAYhHYAnuuQHZ950002SnB+cWVRUVLlVWlCLkNrysckp4PjabGoeUst9RQHlREgH4InKFW5SU1NdVUe1ExEcqIShUZqavEtFxsjXZtMLQzvzgwCvQkgH4InKFW5Ke2AmKmZ496bq0zZUadmn1DykFsEGXoeQDsATVcoJxddcc43mz59P+KmAiOBAfgjg1QjpADxNucLN8uXLS5y+YcMGffLJJ4qMjJQk3XzzzZdeGQCvQUgH4EnKdYdiHx+f804oPm+FHn5CMXcoBgDA+7jsDsXx8fEaMGCAMjMzZbfbHS9fX1/t2rVLdrvdo4MNAACwvnKFm//85z+69tprdcUVV+iTTz5xVU0AAAAVVu5nSz322GNavny5nnrqKd1///06deqUK+oCAACokAo9ODMmJkZbt26VzWZTTEzMBc/BAQAAqEoVvhQ8MDBQ8+bN0/Lly/XFF18oJCSkMusCAACokHKN3Kxdu1YdO3ZUbm6uY9rNN9+sWbNmyd/fX506ddKXX35Z6UUCAACUVbnCzezZs3XvvfeWeAlWcHCw7r//fr366quVVhwAAEB5lSvcfPfdd7rhhhtKnX/99ddr27Ztl1wUAABARZUr3GRlZalmzZqlzq9Ro4aOHz9+yUV5q4ycAm0+mK2MnAJ3lwIAQLVVrhOKGzdurF27dql169Ylzv/+++8VERFRKYV5m6Rv0zUleafsRvKxSQlDozS8e1N3lwUAQLVTrpGbG2+8Uc8++6xOnz593ryCggJNmzZNN910U6UV5y0ycgocwUaS7EaamryLERwAANygXCM3zzzzjJKTk9W2bVtNnDhR7dq1kyTt3btXc+bMUVFRkf74xz+6pFBPlpqd7wg2xYqMUVr2KR4mCABAFStXuAkLC9PmzZv14IMPasqUKY6b99lsNsXHx2vOnDkKCwtzSaGerEVIbfnY5BRwfG02NQ+p5b6iAACopsp9E79mzZppxYoV+uWXX3TgwAEZY9SmTRtddtllrqjPK0QEByphaJSmJu9SkTHytdn0wtDOjNoAAOAGNlPNnp1Qnkeml1dGToHSsk+peUgtgg0AAJWoPL/fFX78As4XERxIqAEAwM0q9OBMAAAAT0W4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AYBqKCOnQJsPZisjp8DdpQCVroa7CwAAVK2kb9M1JXmn7EbysUkJQ6M0vHtTd5cFVBpGbgCgGsnIKXAEG0myG2lq8i5GcGAphBsAqEZSs/MdwaZYkTFKyz7lnoIAFyDcAEA10iKktnxsztN8bTY1D6nlnoIAFyDcAEA1EhEcqIShUfK1nUs4vjabXhjaWRHBgW6uDKg8bg03GzZs0KBBg9SoUSPZbDYtXbr0osusW7dOl19+ufz9/dW6dWslJia6vE4AsJLh3Ztq49P99MG9V2rj0/04mRiW49Zwk5+fr+joaM2ZM6dM7VNTUzVw4ED169dPKSkpmjRpksaPH6/PPvvMxZUCgLVEBAcqtlUDRmxgSW69FHzAgAEaMGBAmdvPmzdPLVq00CuvvCJJ6tChgzZu3KhZs2YpPj7eVWUCAAAv4lXn3GzZskX9+/d3mhYfH68tW7aUukxhYaFyc3OdXgAAwLq8KtxkZmYqLCzMaVpYWJhyc3NVUFDyPRoSEhIUHBzseEVGRlZFqQAAwE28KtxUxJQpU5STk+N4HTlyxN0lAQAAF/Kqxy+Eh4crKyvLaVpWVpaCgoIUGFjySXH+/v7y9/evivIAAIAH8KqRm9jYWK1Zs8Zp2urVqxUbG+umigAAgKdxa7jJy8tTSkqKUlJSJJ271DslJUXp6emSzh1SGjNmjKP9Aw88oEOHDukPf/iD9u7dqzfeeEOLFi3SY4895o7yAQCAB3JruNm6dau6du2qrl27SpImT56srl276rnnnpMkZWRkOIKOJLVo0UKffvqpVq9erejoaL3yyit66623uAwcAAA42Iwx5uLNrCM3N1fBwcHKyclRUFCQu8sBAABlUJ7fb6865wYAAOBiCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDeAh8vIKdDmg9nKyClwdykA4BVquLsAAKVL+jZdU5J3ym4kH5uUMDRKw7s3dXdZAODRGLkBPFRGToEj2EiS3UhTk3cxggMAF0G4ATxUana+I9gUKzJGadmn3FMQAHgJwg3goVqE1JaPzXmar82m5iG13FMQAHgJwg3goSKCA5UwNEq+tnMJx9dm0wtDOysiONDNlQGAZ+OEYsCDDe/eVH3ahiot+5Sah9Qi2ABAGRBuAA8XERxIqAGAcuCwFAAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBSPCDdz5sxR8+bNFRAQoJ49e+qbb74ptW1iYqJsNpvTKyAgoAqrBQAAnszt4SYpKUmTJ0/WtGnTtH37dkVHRys+Pl7Hjh0rdZmgoCBlZGQ4XocPH67CigEAgCdze7h59dVXde+992rcuHHq2LGj5s2bp1q1aumdd94pdRmbzabw8HDHKywsrAorBgAAnsyt4ebMmTPatm2b+vfv75jm4+Oj/v37a8uWLaUul5eXp2bNmikyMlKDBw/W7t27S21bWFio3NxcpxfgDhk5Bdp8MFsZOQXuLgUALM2t4SY7O1tFRUXnjbyEhYUpMzOzxGXatWund955R8uWLdOCBQtkt9vVq1cv/fjjjyW2T0hIUHBwsOMVGRlZ6dsBXEzSt+nq/eJajfrn1+r94lolfZvu7pIAwLLcfliqvGJjYzVmzBjFxMQoLi5OycnJCg0N1Ztvvlli+ylTpignJ8fxOnLkSBVXjOouI6dAU5J3ym7OvbcbaWryLkZwAMBFarjzw0NCQuTr66usrCyn6VlZWQoPDy/TOmrWrKmuXbvqwIEDJc739/eXv7//JdcKVFRqdr4j2BQrMkZp2acUERzonqIAwMLcOnLj5+enbt26ac2aNY5pdrtda9asUWxsbJnWUVRUpJ07dyoiIsJVZQKXpEVIbfnYnKf52mxqHlLLPQUBgMW5/bDU5MmT9c9//lPvvvuu9uzZowcffFD5+fkaN26cJGnMmDGaMmWKo/3MmTO1atUqHTp0SNu3b9edd96pw4cPa/z48e7aBOCCIoIDlTA0Sr62cwnH12bTC0M7M2oDAC7i1sNSkjR8+HAdP35czz33nDIzMxUTE6OVK1c6TjJOT0+Xj8//ZbBffvlF9957rzIzM3XZZZepW7du2rx5szp27OiuTQAuanj3purTNlRp2afUPKQWwQYAXMhmjDEXb2Ydubm5Cg4OVk5OjoKCgtxdDgAAKIPy/H67/bAUAABAZSLcVDPcSA4AYHVuP+cGVSfp23TH/VZ8bFLC0CgN797U3WUBlpORU6DU7Hy1CKnN+VWAGxBuqonSbiTXp20oX75AJeKPCMD9OCxVTVzoRnIAKgd3owY8A+GmmuBGcoDr8UcE4BkIN9UEN5IDXI8/IgDPwDk31Qg3kgNcq/iPiKnJu1RkDH9EAG5CuKlmIoID+aIFXIg/ImAV3nzVH+EGACpZVf8R4c0/QvBM3n7VH+EGALyYt/8IwfNY4dYhnFAMAF6KS8/hCla46o9wAwBeygo/QvA8Vrjqj3ADAF7KCj9C8DxWuHUI59wAgJfi0nO4irdf9Ue4AQAv5u0/QvBc3nzrEMINAHg5b/4RAlyBc24AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAGWWkVOgzQezecQDPBqXggMAyoSHdMJbMHIDALgoHtKJsvKE0T1GbgAAF3Whh3RyA0EU85TRPUZuAAAXxUM6cTGeNLpHuAEAXJQVnhQN17rQ6F5V47AUAKBMeEgnLqR4dO+3Acddo3uM3AAAyiwiOFCxrRoQbHAeTxrdY+QGAABUCk8Z3SPcABaVkVOg1Ox8tQipzV/ZAKpMRHCg279zCDeABXnK5ZgA4A6ccwNYjCddjgkA7kC4ASzGky7HBAB3INwAFsPN1gBUd4QbwGI86XJMAHAHTigGLMhTLscEUP14wpWahBvAojzhckwA1YunXKnJYSkAAHDJPOlKTcINAAC4ZJ50pSbhBgAAXDJPulKTcAMA8EgZOQXafDCbG1B6CU+6UpMTigEP5wlXHgBVzVNOTEX5eMqVmoQbwIPxBY/qqLQTU/u0DSXgewFPuFKTw1KAh/KkKw+AquRJJ6bCOxFuAA/FFzyqK086MRXeiXADeCi+4FFdedKJqfBOnHMDeKjiL/ipybtUZAxf8KhWPOXEVHgnwg3gwfiCR3XmCSemwjt5xGGpOXPmqHnz5goICFDPnj31zTffXLD9Rx99pPbt2ysgIEBRUVFasWJFFVUKVL2I4EDFtmrAlzwAlJHbw01SUpImT56sadOmafv27YqOjlZ8fLyOHTtWYvvNmzdr5MiRuueee7Rjxw4NGTJEQ4YM0a5du6q4cgAA4Ilsxhhz8Wau07NnT3Xv3l2vv/66JMlutysyMlIPP/ywnn766fPaDx8+XPn5+frkk08c06688krFxMRo3rx5F/283NxcBQcHKycnR0FBQZW3IQAAwGXK8/vt1pGbM2fOaNu2berfv79jmo+Pj/r3768tW7aUuMyWLVuc2ktSfHx8qe0LCwuVm5vr9AIAANbl1nCTnZ2toqIihYWFOU0PCwtTZmZmictkZmaWq31CQoKCg4Mdr8jIyMopHgAAeCS3n3PjalOmTFFOTo7jdeTIEXeXBAAAXMitl4KHhITI19dXWVlZTtOzsrIUHh5e4jLh4eHlau/v7y9/f//KKRgAAHg8t47c+Pn5qVu3blqzZo1jmt1u15o1axQbG1viMrGxsU7tJWn16tWltgcAANWL22/iN3nyZI0dO1ZXXHGFevToodmzZys/P1/jxo2TJI0ZM0aNGzdWQkKCJOnRRx9VXFycXnnlFQ0cOFAffvihtm7dqn/84x/u3AwAAOAh3B5uhg8fruPHj+u5555TZmamYmJitHLlSsdJw+np6fLx+b8Bpl69emnhwoV65plnNHXqVLVp00ZLly5V586d3bUJAADAg7j9PjdVjfvcAADgfbzmPjcAAACVze2Hpapa8UAVN/MDAMB7FP9ul+WAU7ULNydPnpQkbuYHAIAXOnnypIKDgy/Yptqdc2O323X06FHVrVtXNpvtktaVm5uryMhIHTlypNqev0Mf0AcSfSDRBxJ9INEHkuv6wBijkydPqlGjRk4XGpWk2o3c+Pj4qEmTJpW6zqCgoGq7ExejD+gDiT6Q6AOJPpDoA8k1fXCxEZtinFAMAAAshXADAAAshXBzCfz9/TVt2rRq/ewq+oA+kOgDiT6Q6AOJPpA8ow+q3QnFAADA2hi5AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4+Z05c+aoefPmCggIUM+ePfXNN99csP3s2bPVrl07BQYGKjIyUo899phOnz7tmD99+nTZbDanV/v27V29GZekPH1w9uxZzZw5U61atVJAQICio6O1cuXKS1qnJ6jsPvCm/WDDhg0aNGiQGjVqJJvNpqVLl150mXXr1unyyy+Xv7+/WrdurcTExPPaeNM+4Io+8KZ9QCp/H2RkZGjUqFFq27atfHx8NGnSpBLbffTRR2rfvr0CAgIUFRWlFStWVH7xlcQVfZCYmHjefhAQEOCaDagE5e2D5ORkXXfddQoNDVVQUJBiY2P12WefndfO1d8HhJvfSEpK0uTJkzVt2jRt375d0dHRio+P17Fjx0psv3DhQj399NOaNm2a9uzZo7fffltJSUmaOnWqU7tOnTopIyPD8dq4cWNVbE6FlLcPnnnmGb355pt67bXX9MMPP+iBBx7QLbfcoh07dlR4ne7mij6QvGc/yM/PV3R0tObMmVOm9qmpqRo4cKD69eunlJQUTZo0SePHj3f6QvO2fcAVfSB5zz4glb8PCgsLFRoaqmeeeUbR0dElttm8ebNGjhype+65Rzt27NCQIUM0ZMgQ7dq1qzJLrzSu6APp3J17f7sfHD58uLJKrnTl7YMNGzbouuuu04oVK7Rt2zb169dPgwYNqvrfBAOHHj16mAkTJjjeFxUVmUaNGpmEhIQS20+YMMFcc801TtMmT55sevfu7Xg/bdo0Ex0d7ZJ6XaG8fRAREWFef/11p2lDhw41d9xxR4XX6W6u6ANv2w+KSTJLliy5YJs//OEPplOnTk7Thg8fbuLj4x3vvW0f+K3K6gNv3QeMKVsf/FZcXJx59NFHz5s+bNgwM3DgQKdpPXv2NPfff/8lVuh6ldUH8+fPN8HBwZVWV1Uqbx8U69ixo5kxY4bjfVV8HzBy8/+dOXNG27ZtU//+/R3TfHx81L9/f23ZsqXEZXr16qVt27Y5htMOHTqkFStW6MYbb3Rqt3//fjVq1EgtW7bUHXfcofT0dNdtyCWoSB8UFhaeN6QaGBjo+Iu0Iut0J1f0QTFv2Q/Ka8uWLU79JUnx8fGO/vK2faAiLtYHxay6D5RVWfvJ6vLy8tSsWTNFRkZq8ODB2r17t7tLchm73a6TJ0+qfv36kqru+4Bw8/9lZ2erqKhIYWFhTtPDwsKUmZlZ4jKjRo3SzJkzddVVV6lmzZpq1aqV+vbt63RYqmfPnkpMTNTKlSs1d+5cpaam6uqrr9bJkydduj0VUZE+iI+P16uvvqr9+/fLbrdr9erVSk5OVkZGRoXX6U6u6APJu/aD8srMzCyxv3Jzc1VQUOB1+0BFXKwPJGvvA2VVWj9ZZT8oi3bt2umdd97RsmXLtGDBAtntdvXq1Us//viju0tziZdffll5eXkaNmyYpKr7TSDcXIJ169bphRde0BtvvKHt27crOTlZn376qZ5//nlHmwEDBuj2229Xly5dFB8frxUrVujEiRNatGiRGyuvPH/729/Upk0btW/fXn5+fpo4caLGjRt30cfRW0lZ+sDq+wEujn0AkhQbG6sxY8YoJiZGcXFxSk5OVmhoqN588013l1bpFi5cqBkzZmjRokVq2LBhlX529fkFuoiQkBD5+voqKyvLaXpWVpbCw8NLXObZZ5/V6NGjNX78eEVFRemWW27RCy+8oISEBNnt9hKXqVevntq2basDBw5U+jZcqor0QWhoqJYuXar8/HwdPnxYe/fuVZ06ddSyZcsKr9OdXNEHJfHk/aC8wsPDS+yvoKAgBQYGet0+UBEX64OSWGkfKKvS+skq+0FF1KxZU127drXcfvDhhx9q/PjxWrRokdMhqKr6PiDc/H9+fn7q1q2b1qxZ45hmt9u1Zs0axcbGlrjMqVOnzhuh8PX1lSSZUh7ZlZeXp4MHDyoiIqKSKq88FemDYgEBAWrcuLF+/fVXffzxxxo8ePAlr9MdXNEHJfHk/aC8YmNjnfpLklavXu3oL2/bByriYn1QEivtA2VVkX6yuqKiIu3cudNS+8EHH3ygcePG6YMPPtDAgQOd5lXZ90GlnZpsAR9++KHx9/c3iYmJ5ocffjD33XefqVevnsnMzDTGGDN69Gjz9NNPO9pPmzbN1K1b13zwwQfm0KFDZtWqVaZVq1Zm2LBhjjaPP/64WbdunUlNTTWbNm0y/fv3NyEhIebYsWNVvn1lUd4++Oqrr8zHH39sDh48aDZs2GCuueYa06JFC/PLL7+UeZ2exhV94E37wcmTJ82OHTvMjh07jCTz6quvmh07dpjDhw8bY4x5+umnzejRox3tDx06ZGrVqmWefPJJs2fPHjNnzhzj6+trVq5c6WjjbfuAK/rAm/YBY8rfB8YYR/tu3bqZUaNGmR07dpjdu3c75m/atMnUqFHDvPzyy2bPnj1m2rRppmbNmmbnzp1Vum1l5Yo+mDFjhvnss8/MwYMHzbZt28yIESNMQECAUxtPUt4++Ne//mVq1Khh5syZYzIyMhyvEydOONpUxfcB4eZ3XnvtNdO0aVPj5+dnevToYb766ivHvLi4ODN27FjH+7Nnz5rp06ebVq1amYCAABMZGWkeeughpx+14cOHm4iICOPn52caN25shg8fbg4cOFCFW1R+5emDdevWmQ4dOhh/f3/ToEEDM3r0aPPTTz+Va52eqLL7wJv2gy+++MJIOu9VvM1jx441cXFx5y0TExNj/Pz8TMuWLc38+fPPW6837QOu6ANv2geMqVgflNS+WbNmTm0WLVpk2rZta/z8/EynTp3Mp59+WjUbVAGu6INJkyY5/j8ICwszN954o9m+fXvVbVQ5lbcP4uLiLti+mKu/D2zGlHL8BAAAwAtxzg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AALAUwg0AeIB169bJZrPpxIkT7i4F8HqEG6Caueuuu2Sz2fTiiy86TV+6dKlsNpvjvTFG//znPxUbG6ugoCDVqVNHnTp10qOPPlrmh/ydOnVKU6ZMUatWrRQQEKDQ0FDFxcVp2bJljjbNmzfX7NmzK2XbXK2472w2m2rWrKkWLVroD3/4g06fPl2u9fTt21eTJk1ymtarVy9lZGQoODi4EisGqifCDVANBQQE6KWXXtIvv/xS4nxjjEaNGqVHHnlEN954o1atWqUffvhBb7/9tgICAvSnP/2pTJ/zwAMPKDk5Wa+99pr27t2rlStX6rbbbtPPP/9cmZtTpW644QZlZGTo0KFDmjVrlt58801Nmzbtktfr5+en8PBwp4AJoIIq9WEOADze2LFjzU033WTat29vnnzyScf0JUuWmOKvhA8++MBIMsuWLStxHXa7vUyfFRwcbBITE0udX9JzaIp9+eWX5qqrrjIBAQGmSZMm5uGHHzZ5eXmO+e+9957p1q2bqVOnjgkLCzMjR440WVlZjvnFz8RZuXKliYmJMQEBAaZfv34mKyvLrFixwrRv397UrVvXjBw50uTn55dpe8aOHWsGDx7sNG3o0KGma9eujvfZ2dlmxIgRplGjRiYwMNB07tzZLFy40Gkdv9/m1NRUR72/fTbd4sWLTceOHY2fn59p1qyZefnll8tUJ1DdMXIDVEO+vr564YUX9Nprr+nHH388b/4HH3ygdu3a6eabby5x+bKOLoSHh2vFihU6efJkifOTk5PVpEkTzZw5UxkZGcrIyJAkHTx4UDfccINuvfVWff/990pKStLGjRs1ceJEx7Jnz57V888/r++++05Lly5VWlqa7rrrrvM+Y/r06Xr99de1efNmHTlyRMOGDdPs2bO1cOFCffrpp1q1apVee+21Mm3P7+3atUubN2+Wn5+fY9r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", 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", 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", 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", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\omlt\\keras_flowsheet_optimization_doc_17_70.png" - } - }, - "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": [ - "# 4. IDAES Flowsheet Integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build an IDAES flowsheet and import the surrogate model object. A single Keras neural network model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 2567\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 80\n", - "\n", - "Total number of variables............................: 231\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 192\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 231\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 1.02e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 2.71e+03 4.59e+01 -1.0 2.08e+04 - 1.55e-02 7.34e-01f 1\n", - " 2 0.0000000e+00 3.06e-02 5.09e+00 -1.0 5.74e+03 - 9.18e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 1.07e-05 1.06e-03 -1.0 1.12e+02 - 1.00e+00 1.00e+00h 1\n", - " 4 0.0000000e+00 1.82e-12 3.85e-16 -2.5 2.61e-02 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 4\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 6.0616859024126514e-15 1.8189894035458565e-12\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 6.0616859024126514e-15 1.8189894035458565e-12\n", - "\n", - "\n", - "Number of objective function evaluations = 5\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 5\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 5\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 4\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - } - ], - "source": [ - "# create the IDAES model and flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "# create flowsheet input variables\n", - "m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - ")\n", - "m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - ")\n", - "\n", - "# create flowsheet output variables\n", - "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - "# create input and output variable object lists for flowsheet\n", - "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - "outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C4H10,\n", - " m.fs.C3H8,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - "]\n", - "\n", - "# create the Pyomo/IDAES block that corresponds to the surrogate\n", - "# Keras\n", - "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", - "m.fs.surrogate = SurrogateBlock()\n", - "m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - ")\n", - "\n", - "# fix input values and solve flowsheet\n", - "m.fs.bypass_frac.fix(0.5)\n", - "m.fs.ng_steam_ratio.fix(1)\n", - "\n", - "solver = SolverFactory(\"ipopt\")\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print some model results:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Steam flowrate = 0.6077815115578293\n", - "Reformer duty = 20894.99849692314\n", - "Mole Fraction Ar = 0.003685928708625731\n", - "Mole Fraction C2H6 = 0.004174498761104526\n", - "Mole Fraction C3H8 = 0.0005304604040521441\n", - "Mole Fraction C4H10 = 0.0009065647744530309\n", - "Mole Fraction CH4 = 0.12893526499890756\n", - "Mole Fraction CO = 0.0973757815625882\n", - "Mole Fraction CO2 = 0.04594305744149133\n", - "Mole Fraction H2 = 0.2947117425194844\n", - "Mole Fraction H2O = 0.12005265455241615\n", - "Mole Fraction N2 = 0.3067577132635225\n", - "Mole Fraction O2 = 2.4775618019229293e-20\n" - ] - } - ], - "source": [ - "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", - "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", - "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", - "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", - "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", - "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", - "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", - "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", - "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", - "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", - "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", - "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", - "print(\"Mole Fraction O2 = \", value(m.fs.O2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Optimizing the Autothermal Reformer\n", - "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", - "\n", - "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 2569\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 80\n", - "\n", - "Total number of variables............................: 233\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 194\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 231\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 0\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 -2.9471174e-01 1.82e-12 7.70e-04 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -2.9581536e-01 1.20e-04 8.37e-03 -1.0 1.91e+02 - 1.00e+00 1.00e+00f 1\n", - " 2 -2.9633393e-01 3.93e-05 4.70e-03 -2.5 1.27e+02 - 1.00e+00 1.00e+00h 1\n", - " 3 -3.0889147e-01 1.93e-02 1.12e-02 -3.8 3.11e+03 - 8.54e-01 1.00e+00f 1\n", - " 4 -3.2218328e-01 2.63e-02 7.48e-03 -3.8 6.21e+03 - 1.00e+00 7.13e-01h 1\n", - " 5 -3.2632547e-01 1.41e-02 4.84e-02 -3.8 3.83e+03 - 1.00e+00 5.68e-01h 1\n", - " 6 -3.2962499e-01 1.80e-02 1.29e-01 -3.8 4.15e+03 - 7.03e-01 1.00e+00h 1\n", - " 7 -3.2891795e-01 1.64e-03 1.88e-04 -3.8 9.52e+02 - 1.00e+00 1.00e+00h 1\n", - " 8 -3.2891270e-01 2.73e-06 1.19e-06 -3.8 4.83e+01 - 1.00e+00 1.00e+00h 1\n", - " 9 -3.3134549e-01 4.77e-03 1.44e-02 -5.7 3.35e+03 - 7.85e-01 7.19e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 -3.3265276e-01 3.97e-03 1.14e-02 -5.7 2.96e+03 - 9.82e-01 5.04e-01h 1\n", - " 11 -3.3253961e-01 1.13e-05 1.91e-02 -5.7 8.04e+01 - 9.43e-01 1.00e+00h 1\n", - " 12 -3.3255403e-01 2.51e-07 2.88e-07 -5.7 1.84e+01 - 1.00e+00 1.00e+00h 1\n", - " 13 -3.3256308e-01 2.83e-08 5.83e-04 -8.6 6.96e+00 - 1.00e+00 9.64e-01h 1\n", - " 14 -3.3256321e-01 7.97e-11 9.37e-03 -8.6 1.78e-01 - 5.45e-01 1.00e+00f 1\n", - " 15 -3.3256321e-01 3.64e-12 2.50e-14 -8.6 1.77e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 15\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: -3.3256321112789822e-01 -3.3256321112789822e-01\n", - "Dual infeasibility......: 2.5035529205297280e-14 2.5035529205297280e-14\n", - "Constraint violation....: 1.2123371804825303e-14 3.6379788070917130e-12\n", - "Complementarity.........: 2.5392404073929864e-09 2.5392404073929864e-09\n", - "Overall NLP error.......: 2.5392404073929864e-09 2.5392404073929864e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 16\n", - "Number of objective gradient evaluations = 16\n", - "Number of equality constraint evaluations = 16\n", - "Number of inequality constraint evaluations = 16\n", - "Number of equality constraint Jacobian evaluations = 16\n", - "Number of inequality constraint Jacobian evaluations = 16\n", - "Number of Lagrangian Hessian evaluations = 15\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.012\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[+ 0.06] solve\n", - "Model status: \n", - "Problem: \n", - "- Lower bound: -inf\n", - " Upper bound: inf\n", - " Number of objectives: 1\n", - " Number of constraints: 232\n", - " Number of variables: 233\n", - " Sense: unknown\n", - "Solver: \n", - "- Status: ok\n", - " Message: Ipopt 3.13.2\\x3a Optimal Solution Found\n", - " Termination condition: optimal\n", - " Id: 0\n", - " Error rc: 0\n", - " Time: 0.05810403823852539\n", - "Solution: \n", - "- number of solutions: 0\n", - " number of solutions displayed: 0\n", - "\n", - "Solve time: 0.06416339997667819\n", - "fs.bypass_frac : 0.10000021929992625\n", - "fs.ng_steam_ratio : 1.1046796492824769\n", - "fs.steam_flowrate : 1.1959739002454326\n", - "fs.reformer_duty : 38028.68461541513\n", - "fs.AR : 0.004110458211653681\n", - "fs.C2H6 : 0.00044761800884280994\n", - "fs.C4H10 : 0.00011014452363028629\n", - "fs.C3H8 : 7.244928916496591e-05\n", - "fs.CH4 : 0.017681638298364968\n", - "fs.CO : 0.10597963482121954\n", - "fs.CO2 : 0.053167024686581285\n", - "fs.H2 : 0.3325632111278982\n", - "fs.H2O : 0.14710267683917747\n", - "fs.N2 : 0.3400000024525687\n", - "fs.O2 : 3.321321379533168e-20\n" - ] - } - ], - "source": [ - "# unfix input values and add the objective/constraint to the model\n", - "m.fs.bypass_frac.unfix()\n", - "m.fs.ng_steam_ratio.unfix()\n", - "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - "# solve the model\n", - "tmr = TicTocTimer()\n", - "status = solver.solve(m, tee=True)\n", - "solve_time = tmr.toc(\"solve\")\n", - "\n", - "# print and check results\n", - "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", - "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", - "print(\"Model status: \", status)\n", - "print(\"Solve time: \", solve_time)\n", - "for var in inputs:\n", - " print(var.name, \": \", value(var))\n", - "for var in outputs:\n", - " print(var.name, \": \", value(var))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb index a3b477fa..603e227a 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb @@ -250,7 +250,7 @@ "# 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_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", ")\n", "history = model.fit(\n", " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", @@ -268,7 +268,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")\n", + "keras_surrogate.save_to_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", "\n", "# revert back to normal output capture\n", "sys.stdout = oldstdout\n", @@ -411,7 +411,7 @@ "\n", "# create the Pyomo/IDAES block that corresponds to the surrogate\n", "# Keras\n", - "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", "m.fs.surrogate = SurrogateBlock()\n", "m.fs.surrogate.build_model(\n", " keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb index a3b477fa..603e227a 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb @@ -250,7 +250,7 @@ "# 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_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", ")\n", "history = model.fit(\n", " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", @@ -268,7 +268,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")\n", + "keras_surrogate.save_to_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", "\n", "# revert back to normal output capture\n", "sys.stdout = oldstdout\n", @@ -411,7 +411,7 @@ "\n", "# create the Pyomo/IDAES block that corresponds to the surrogate\n", "# Keras\n", - "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n", "m.fs.surrogate = SurrogateBlock()\n", "m.fs.surrogate.build_model(\n", " keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras b/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras new file mode 100644 index 00000000..55b0068b Binary files /dev/null and b/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5 b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5 deleted file mode 100644 index ee565e8f..00000000 Binary files a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5 and /dev/null differ diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.keras b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.keras new file mode 100644 index 00000000..c090ddac Binary files /dev/null and b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.keras differ 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 921a163b..b4ba5863 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 @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ "# 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" ] }, { @@ -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", @@ -689,9 +699,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file 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 45b7dd73..b4ba5863 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 @@ -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", @@ -689,9 +699,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file 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 45b7dd73..b4ba5863 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 @@ -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", @@ -689,9 +699,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file 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 32827f85..cc95e24b 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -748,7 +748,7 @@ "# 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", + " \".mdl_co2.keras\", 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", @@ -793,7 +793,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" + "keras_surrogate.save_to_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")" ] }, { 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 79bbaf1d..49a26fbf 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 @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ "# 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" ] }, { @@ -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", - 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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 - 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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 - 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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: 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", + "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 - 208ms/epoch - 16ms/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 - 106ms/epoch - 8ms/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 - 193ms/epoch - 15ms/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 - 117ms/epoch - 9ms/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 - 168ms/epoch - 13ms/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 - 109ms/epoch - 8ms/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 - 156ms/epoch - 12ms/step\n", "Epoch 92/250\n", - 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"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", + "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 - 129ms/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 - 219ms/epoch - 17ms/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 - 218ms/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", + "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 - 128ms/epoch - 10ms/step\n", "Epoch 98/250\n", - 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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 - 218ms/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", + "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 - 141ms/epoch - 11ms/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", + "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 - 176ms/epoch - 14ms/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", + "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 - 421ms/epoch - 32ms/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", + "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 - 159ms/epoch - 12ms/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", + "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 - 307ms/epoch - 24ms/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", + "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 - 193ms/epoch - 15ms/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", + "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 - 133ms/epoch - 10ms/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", + "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 - 107ms/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", + "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 - 165ms/epoch - 13ms/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", + "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 - 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: 3.4933e-04 - 165ms/epoch - 13ms/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", + "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 - 153ms/epoch - 12ms/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 - 187ms/epoch - 14ms/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 - 209ms/epoch - 16ms/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 - 175ms/epoch - 13ms/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 - 234ms/epoch - 18ms/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 - 130ms/epoch - 10ms/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 - 122ms/epoch - 9ms/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 - 128ms/epoch - 10ms/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 - 321ms/epoch - 25ms/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 - 167ms/epoch - 13ms/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 - 129ms/epoch - 10ms/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 - 191ms/epoch - 15ms/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 - 119ms/epoch - 9ms/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 - 122ms/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", + "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 - 150ms/epoch - 12ms/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 - 127ms/epoch - 10ms/step\n" ] }, { @@ -738,7 +748,7 @@ "# 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", + " \".mdl_co2.keras\", 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", @@ -759,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -783,7 +793,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" + "keras_surrogate.save_to_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")" ] }, { @@ -797,14 +807,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 3ms/step\n" + "13/13 [==============================] - 1s 3ms/step\n" ] }, { @@ -878,7 +888,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 3ms/step\n" + "13/13 [==============================] - 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" ] }, { @@ -1075,7 +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_doc.ipynb](./surrogate_embedding_doc.ipynb) 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](./surrogate_embedding_doc.md) file." ] } ], @@ -1101,4 +1111,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of 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 a4c68e87..132b5b88 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 @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ "# 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" ] }, { @@ -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 - 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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 - 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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: 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", + "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 - 208ms/epoch - 16ms/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 - 106ms/epoch - 8ms/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 - 193ms/epoch - 15ms/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 - 117ms/epoch - 9ms/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 - 168ms/epoch - 13ms/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 - 109ms/epoch - 8ms/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 - 156ms/epoch - 12ms/step\n", "Epoch 92/250\n", - 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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 - 187ms/epoch - 14ms/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 - 209ms/epoch - 16ms/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 - 175ms/epoch - 13ms/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 - 234ms/epoch - 18ms/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 - 130ms/epoch - 10ms/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 - 122ms/epoch - 9ms/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 - 128ms/epoch - 10ms/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 - 321ms/epoch - 25ms/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 - 167ms/epoch - 13ms/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 - 129ms/epoch - 10ms/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 - 191ms/epoch - 15ms/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 - 119ms/epoch - 9ms/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 - 122ms/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", + "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 - 150ms/epoch - 12ms/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 - 127ms/epoch - 10ms/step\n" ] }, { @@ -738,7 +748,7 @@ "# 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", + " \".mdl_co2.keras\", 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", @@ -759,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -783,7 +793,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" + "keras_surrogate.save_to_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")" ] }, { @@ -797,14 +807,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 3ms/step\n" + "13/13 [==============================] - 1s 3ms/step\n" ] }, { @@ -878,7 +888,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 3ms/step\n" + "13/13 [==============================] - 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" ] }, { @@ -1101,4 +1111,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of 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 f59ea874..3252b330 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 @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ "# 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" ] }, { @@ -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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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", + "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", "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 - 112ms/epoch - 9ms/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 - 137ms/epoch - 11ms/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 - 109ms/epoch - 8ms/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 - 174ms/epoch - 13ms/step\n", "Epoch 94/250\n", - "13/13 - 0s - loss: 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- "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", + "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 - 136ms/epoch - 10ms/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", + "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 - 254ms/epoch - 20ms/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", + "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 - 167ms/epoch - 13ms/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", + "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 - 157ms/epoch - 12ms/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", + "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 - 218ms/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", + "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 - 141ms/epoch - 11ms/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", + "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 - 176ms/epoch - 14ms/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", + "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 - 421ms/epoch - 32ms/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", + "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 - 159ms/epoch - 12ms/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", + "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 - 307ms/epoch - 24ms/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", + "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 - 193ms/epoch - 15ms/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", + "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 - 133ms/epoch - 10ms/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", + "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 - 107ms/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", + "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 - 165ms/epoch - 13ms/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", + "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 - 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: 3.4933e-04 - 165ms/epoch - 13ms/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", + "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 - 153ms/epoch - 12ms/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 - 187ms/epoch - 14ms/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 - 209ms/epoch - 16ms/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 - 175ms/epoch - 13ms/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 - 234ms/epoch - 18ms/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 - 130ms/epoch - 10ms/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 - 122ms/epoch - 9ms/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 - 128ms/epoch - 10ms/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 - 321ms/epoch - 25ms/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 - 167ms/epoch - 13ms/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 - 129ms/epoch - 10ms/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 - 191ms/epoch - 15ms/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 - 119ms/epoch - 9ms/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 - 122ms/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", + "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 - 150ms/epoch - 12ms/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 - 127ms/epoch - 10ms/step\n" ] }, { @@ -738,7 +748,7 @@ "# 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", + " \".mdl_co2.keras\", 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", @@ -759,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -783,7 +793,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" + "keras_surrogate.save_to_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")" ] }, { @@ -797,14 +807,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 3ms/step\n" + "13/13 [==============================] - 1s 3ms/step\n" ] }, { @@ -878,7 +888,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 3ms/step\n" + "13/13 [==============================] - 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" ] }, { @@ -1101,4 +1111,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py index bde029c3..94d6d95f 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("sco2_keras_surr") + self.keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name="sco2_keras_surr", keras_model_name="sco2_keras_model") self.surrogate_enth = SurrogateBlock() self.surrogate_enth.build_model( self.keras_surrogate, 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 deleted file mode 100644 index c94ddb92..00000000 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/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/omlt/sco2_keras_surr/keras_metadata.pb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/keras_metadata.pb deleted file mode 100644 index 0429ce74..00000000 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/keras_metadata.pb +++ /dev/null @@ -1,10 +0,0 @@ - -¥5root"_tf_keras_sequential*€5{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 2]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_input"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 2]}, "dtype": "float32", "units": 20, "activation": "tanh", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", 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b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/sco2_keras_model.keras new file mode 100644 index 00000000..c090ddac Binary files /dev/null and b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/sco2_keras_model.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/dummy_file b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/dummy_file deleted file mode 100644 index 6ca8147f..00000000 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/dummy_file +++ /dev/null @@ -1 +0,0 @@ -This is a dummy file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.data-00000-of-00001 b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.data-00000-of-00001 deleted file mode 100644 index 15d19da8..00000000 Binary files a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.data-00000-of-00001 and /dev/null differ diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.index b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.index deleted file mode 100644 index 0cd2376b..00000000 Binary files a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.index and /dev/null differ 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 a83738c4..ebb98023 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(\"sco2_keras_surr\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")\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 d880c31c..1dc8df95 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 @@ -31,6 +31,7 @@ "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 parameter 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", @@ -239,7 +240,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\"sco2_keras_surr\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", @@ -443,16 +444,30 @@ "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](./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). " + "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.md). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages_doc.md). " ] } ], "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 }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file 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 d3328bcd..8ef72841 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 @@ -31,6 +31,7 @@ "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 parameter 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", @@ -90,8 +91,6 @@ "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", @@ -241,7 +240,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder('sco2_keras_surr')\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", @@ -445,7 +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, [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). " + "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). " ] } ], @@ -471,4 +470,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file 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 bdae332b..eacdd9ac 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 @@ -31,6 +31,7 @@ "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 parameter 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", @@ -239,7 +240,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\"sco2_keras_surr\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\")\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", @@ -443,16 +444,30 @@ "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](./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). " + "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). " ] } ], "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 }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb index 3a4ad898..acadec3a 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb @@ -208,7 +208,9 @@ "from idaes.core.util.model_statistics import degrees_of_freedom\n", "\n", "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" ] }, { @@ -988,7 +990,13 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb index 44fe440f..9eebd4a0 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb @@ -1,2212 +1,1364 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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 Simulation and Optimization\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an 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", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", - "\n", - "- hda_ideal_VLE.py\n", - "- hda_reaction.py\n", - "\n", - "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](HDA_flowsheet.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " value,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "- **Flash**\n", - "- Separator (splitter) \n", - "- PressureChanger" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " StoichiometricReactor,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models\n", - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction package\n", - "\n", - "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", - "\n", - "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", - "\n", - "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", - "
    \n", - "
  • hda_ideal_VLE as thermo_props
    • \n", - "
  • hda_reaction as reaction_props
    • \n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", - "from idaes_examples.mod.hda import hda_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.thermo_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = StoichiometricReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.thermo_params,\n", - " ideal_separation=False,\n", - " outlet_list=[\"purge\", \"recycle\"],\n", - ")\n", - "\n", - "\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.thermo_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")\n", - "\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute purity and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", - "\n", - "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "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", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", - "
    \n", - "
  • 2.2E-4 dollars/kW for H101
    • \n", - "
  • 1.9E-4 dollars/kW for F102
    • \n", - "
\n", - "Note that the heat duty is in units of watt (J/s). " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "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", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.75)\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for Flash F102 to the following conditions:\n", - "
    \n", - "
  • T = 375 K
    • \n", - "
  • deltaP = -200000
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "\n", - "\n", - "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "![](HDA_flowsheet.png) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first create an object for the SequentialDecomposition and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "scrolled": true - }, - "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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "\n", - "![](HDA_tear_stream.png) \n", - "\n", - "\n", - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:38 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:38 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.F102.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.F102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:39 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:42 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Wegstein failed to converge in 3 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:43 [INFO] idaes.init.fs.F102.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:43 [INFO] idaes.init.fs.F102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", - " \n", - "results = solver.solve(m, tee=True)\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "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...: 1031\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 934\n", - "\n", - "Total number of variables............................: 340\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 146\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 340\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 6.60e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.42e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 3.05e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.58e+03 1.55e+05 -1.0 1.41e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.49e+02 8.87e+08 -1.0 8.43e+03 - 1.00e+00 9.57e-01h 1\n", - " 5 0.0000000e+00 4.25e+03 2.87e+10 -1.0 8.02e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 2.25e+03 1.51e+10 -1.0 8.39e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 2.27e+01 1.40e+08 -1.0 2.45e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 2.45e-03 1.23e+04 -1.0 2.38e-05 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 3.06e-01 -2.5 9.06e-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......: 2.8284422320850682e+05 2.8284422320850682e+05\n", - "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 2.8284422320850682e+05\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) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Create the solver object\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the results of the square problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $ 419122.3387677983\n" - ] - } - ], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F102 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 7352.5 : watt : False : (None, None)\n", - " Pressure Change : -2.0000e+05 : 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 0.20460 1.0000e-08 0.062620 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 0.062520 1.0000e-08 0.032257 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 2.6712e-07 1.0000e-08 9.4877e-08 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 2.6712e-07 1.0000e-08 9.4877e-08 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 1.0000e-08 0.14198 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 1.0000e-08 0.030264 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0000e-08 1.8224e-07 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 1.0000e-08 1.8224e-07 1.0000e-08 \n", - " temperature kelvin 325.00 375.00 375.00 \n", - " pressure pascal 3.5000e+05 1.5000e+05 1.5000e+05 \n", - "====================================================================================\n", - "\n", - "benzene purity = 0.8242962943918918\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 Simulation and Optimization\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an 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", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", + "\n", + "- hda_ideal_VLE.py\n", + "- hda_reaction.py\n", + "\n", + "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](HDA_flowsheet.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " value,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "- **Flash**\n", + "- Separator (splitter) \n", + "- PressureChanger" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " StoichiometricReactor,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models\n", + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction package\n", + "\n", + "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", + "\n", + "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", + "\n", + "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", + "
    \n", + "
  • hda_ideal_VLE as thermo_props
    • \n", + "
  • hda_reaction as reaction_props
    • \n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", + "from idaes_examples.mod.hda import hda_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.thermo_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = StoichiometricReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.thermo_params,\n", + " ideal_separation=False,\n", + " outlet_list=[\"purge\", \"recycle\"],\n", + ")\n", + "\n", + "\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.thermo_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")\n", + "\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute purity and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", + "\n", + "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "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", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", + "
    \n", + "
  • 2.2E-4 dollars/kW for H101
    • \n", + "
  • 1.9E-4 dollars/kW for F102
    • \n", + "
\n", + "Note that the heat duty is in units of watt (J/s). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "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", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.75)\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for Flash F102 to the following conditions:\n", + "
    \n", + "
  • T = 375 K
    • \n", + "
  • deltaP = -200000
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "\n", + "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "![](HDA_flowsheet.png) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first create an object for the SequentialDecomposition and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "![](HDA_tear_stream.png) \n", + "\n", + "\n", + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", + " \n", + "results = solver.solve(m, tee=True)\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the results of the square problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 cooling duty provided\n", + "- F101 outlet temperature\n", + "- F102 outlet temperature\n", + "- F102 deltaP in the flash tank\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.heat_duty.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.F102.vap_outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102\n", + "m.fs.F102.deltaP.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 800] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - F102 outlet temperature [298, 450] K\n", + " - F102 outlet pressure [105000, 110000] Pa\n", + "\n", + "Let us first set the variable bound for the H101 outlet temperature as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the variable bound for the R101 outlet temperature.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the bounds for the rest of the decision variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", + "m.fs.F102.vap_outlet.pressure[0].setub(110000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "\n", + "print()\n", + "print(\"Product flow rate and purity in F102\")\n", + "\n", + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))\n", + "\n", + "print()\n", + "print(\"Overhead loss in F101\")\n", + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", + "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "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" - ] - } - ], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 cooling duty provided\n", - "- F101 outlet temperature\n", - "- F102 outlet temperature\n", - "- F102 deltaP in the flash tank\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.heat_duty.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.F102.vap_outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102\n", - "m.fs.F102.deltaP.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 800] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - F102 outlet temperature [298, 450] K\n", - " - F102 outlet pressure [105000, 110000] Pa\n", - "\n", - "Let us first set the variable bound for the H101 outlet temperature as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the variable bound for the R101 outlet temperature.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the bounds for the rest of the decision variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", - "m.fs.F102.vap_outlet.pressure[0].setub(110000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 1057\n", - "Number of nonzeros in inequality constraint Jacobian.: 5\n", - "Number of nonzeros in Lagrangian Hessian.............: 937\n", - "\n", - "Total number of variables............................: 345\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 149\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 340\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 4.1912234e+05 2.99e+05 6.94e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.1628385e+05 2.99e+05 6.94e+00 -1.0 4.82e+09 - 1.80e-05 5.83e-06f 1\n", - " 2 4.1616723e+05 2.99e+05 1.59e+02 -1.0 1.46e+09 - 5.83e-04 1.47e-05f 1\n", - " 3 4.0789953e+05 2.94e+05 4.83e+02 -1.0 1.36e+09 - 2.64e-04 9.30e-04f 1\n", - " 4 2.9668590e+05 2.83e+06 6.97e+02 -1.0 4.80e+08 - 7.26e-05 1.50e-03f 1\n", - " 5 2.9555461e+05 2.83e+06 4.95e+04 -1.0 1.90e+08 - 1.88e-01 1.04e-03f 1\n", - " 6 2.9451022e+05 2.73e+06 4.60e+05 -1.0 4.43e+07 - 1.87e-01 3.43e-02f 1\n", - " 7 2.9628497e+05 2.13e+06 4.43e+05 -1.0 1.48e+07 - 7.40e-02 2.18e-01h 1\n", - " 8 2.9632658e+05 2.13e+06 4.41e+05 -1.0 5.91e+06 - 6.37e-01 3.36e-03h 1\n", - " 9 2.9642679e+05 2.11e+06 4.39e+05 -1.0 6.54e+06 - 7.26e-01 7.12e-03h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.9954735e+05 1.64e+06 4.13e+05 -1.0 6.57e+06 - 3.57e-02 2.24e-01h 1\n", - " 11 3.0435085e+05 9.50e+05 6.95e+05 -1.0 5.56e+06 - 9.46e-01 4.20e-01h 1\n", - " 12 3.0895827e+05 3.69e+05 1.22e+07 -1.0 4.03e+06 - 9.90e-01 6.11e-01h 1\n", - " 13 3.1246277e+05 1.42e+06 1.80e+10 -1.0 2.25e+06 - 9.95e-01 9.65e-01h 1\n", - " 14 3.1266092e+05 5.66e+05 7.10e+10 -1.0 2.77e+05 - 4.14e-01 6.11e-01h 1\n", - " 15 3.1266072e+05 5.65e+05 7.08e+10 -1.0 1.18e+06 - 1.09e-02 2.60e-04h 1\n", - " 16 3.1266230e+05 5.58e+05 7.01e+10 -1.0 1.08e+05 - 1.00e+00 1.26e-02h 1\n", - " 17 3.1271669e+05 3.14e+05 7.23e+10 -1.0 1.07e+05 - 4.05e-01 4.39e-01h 1\n", - " 18 3.1278583e+05 3.89e+03 1.58e+10 -1.0 6.01e+04 - 7.76e-03 9.91e-01h 1\n", - " 19 3.1278664e+05 1.57e+03 6.81e+10 -1.0 5.59e+02 - 9.87e-01 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 3.1278678e+05 2.39e+01 1.24e+09 -1.0 1.96e+02 - 1.00e+00 1.00e+00f 1\n", - " 21 3.1278674e+05 1.19e+01 6.32e+08 -1.0 1.30e+02 - 1.00e+00 5.00e-01f 2\n", - " 22 3.1278674e+05 1.21e-02 9.81e+04 -1.0 2.70e+00 - 1.00e+00 1.00e+00f 1\n", - " 23 3.1278642e+05 2.23e-05 2.00e+05 -1.7 1.62e+02 - 1.00e+00 1.00e+00f 1\n", - " 24 3.1278642e+05 1.49e-08 2.16e-03 -1.7 6.37e-01 - 1.00e+00 1.00e+00h 1\n", - " 25 3.1278634e+05 1.38e-06 1.26e+04 -7.0 4.04e+01 - 1.00e+00 1.00e+00f 1\n", - " 26 3.1278634e+05 1.49e-08 7.76e-05 -7.0 6.55e-03 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 26\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 3.1278633834102686e+05 3.1278633834102686e+05\n", - "Dual infeasibility......: 7.7559902882953563e-05 7.7559902882953563e-05\n", - "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", - "Complementarity.........: 9.0926527280252943e-08 9.0926527280252943e-08\n", - "Overall NLP error.......: 6.6903080882730816e-09 7.7559902882953563e-05\n", - "\n", - "\n", - "Number of objective function evaluations = 28\n", - "Number of objective gradient evaluations = 27\n", - "Number of equality constraint evaluations = 28\n", - "Number of inequality constraint evaluations = 28\n", - "Number of equality constraint Jacobian evaluations = 27\n", - "Number of inequality constraint Jacobian evaluations = 27\n", - "Number of Lagrangian Hessian evaluations = 26\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.016\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $ 312786.3383410268\n", - "\n", - "Product flow rate and purity in F102\n", - "\n", - "====================================================================================\n", - "Unit : fs.F102 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 8377.0 : watt : False : (None, None)\n", - " Pressure Change : -2.4500e+05 : pascal : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 0.21743 1.0000e-08 0.067425 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 0.070695 1.0000e-08 0.037507 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 2.8812e-07 1.0000e-08 1.0493e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 2.8812e-07 1.0000e-08 1.0493e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 1.0000e-08 0.15000 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 1.0000e-08 0.033189 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0000e-08 1.9319e-07 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 1.0000e-08 1.9319e-07 1.0000e-08 \n", - " temperature kelvin 301.88 362.93 362.93 \n", - " pressure pascal 3.5000e+05 1.0500e+05 1.0500e+05 \n", - "====================================================================================\n", - "\n", - "benzene purity = 0.8188276578112285\n", - "\n", - "Overhead loss in F101\n", - "\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 : -56353. : 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 4.3534e-08 1.0000e-08 0.21743 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 7.5866e-07 1.0000e-08 0.070695 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.8812e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.8812e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.27178 0.054356 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.076085 0.0053908 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2414 1.2414 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.35887 0.35887 1.0000e-08 \n", - " temperature kelvin 696.11 301.88 301.88 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] + ], + "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" } - ], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "\n", - "print()\n", - "print(\"Product flow rate and purity in F102\")\n", - "\n", - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))\n", - "\n", - "print()\n", - "print(\"Overhead loss in F101\")\n", - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 500.0 K\n", - "\n", - "R101 outlet temperature = 696.1117584980856 K\n", - "\n", - "F101 outlet temperature = 301.8784760569282 K\n", - "\n", - "F102 outlet temperature = 362.93476830548985 K\n", - "F102 outlet pressure = 105000.0 Pa\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", - "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb index cd42cc58..e563cfdb 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb @@ -56,7 +56,7 @@ "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", @@ -194,7 +194,9 @@ "from idaes.core.util.model_statistics import degrees_of_freedom\n", "\n", "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" ] }, { @@ -885,7 +887,13 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1334,4 +1342,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb index 923daf60..52573e32 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb @@ -56,7 +56,7 @@ "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", @@ -208,7 +208,9 @@ "from idaes.core.util.model_statistics import degrees_of_freedom\n", "\n", "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" ] }, { @@ -960,7 +962,13 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1473,4 +1481,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb index 7098c670..6110381d 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb @@ -56,7 +56,7 @@ "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", @@ -195,7 +195,9 @@ "from idaes.core.util.model_statistics import degrees_of_freedom\n", "\n", "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" ] }, { @@ -923,7 +925,13 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1488,4 +1496,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb index 923daf60..52573e32 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb @@ -56,7 +56,7 @@ "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", @@ -208,7 +208,9 @@ "from idaes.core.util.model_statistics import degrees_of_freedom\n", "\n", "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" ] }, { @@ -960,7 +962,13 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver=get_solver()\n", + " solver.solve(unit)\n", + " " ] }, { @@ -1473,4 +1481,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py b/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py index 0bf2a5ed..40607b83 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py +++ b/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py @@ -36,110 +36,136 @@ # --------------------------------------------------------------------- -# Configuration dictionary for an ideal ethylene oxide, water, -# sulfuric acid, and ethylene glycol system +# Configuration dictionary for an ideal ethylene glycol and water system # Data Sources: # [1] The Properties of Gases and Liquids (1987) # 4th edition, Chemical Engineering Series - Robert C. Reid # [2] Perry's Chemical Engineers' Handbook 7th Ed. # [3] NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ -# Retrieved 23rd September, 2021 +# Retrieved 18th March, 2024 config_dict = { # Specifying components "components": { - 'water': - {"type": Component, - "elemental_composition": {"H": 2, "O": 1}, - "dens_mol_liq_comp": Perrys, - "enth_mol_liq_comp": Perrys, - "enth_mol_ig_comp": RPP4, - "pressure_sat_comp": RPP4, - "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, - "parameter_data": { - "mw": (18.015E-3, pyunits.kg/pyunits.mol), # [1] - "pressure_crit": (221.2e5, pyunits.Pa), # [1] - "temperature_crit": (647.3, pyunits.K), # [1] - "dens_mol_liq_comp_coeff": { - 'eqn_type': 2, - '1': (-13.851, pyunits.kmol/pyunits.m**3), # [2]pg. 2-98 - '2': (0.64038, pyunits.kmol/pyunits.m**3/pyunits.K), - '3': (-0.00191, pyunits.kmol/pyunits.m**3/pyunits.K**2), - '4': (1.8211E-6, pyunits.kmol/pyunits.m**3/pyunits.K**3)}, - "cp_mol_ig_comp_coeff": { - 'A': (3.194E1, pyunits.J/pyunits.mol/pyunits.K), # [1] - 'B': (1.436E-3, pyunits.J/pyunits.mol/pyunits.K**2), - 'C': (2.432E-5, pyunits.J/pyunits.mol/pyunits.K**3), - 'D': (-1.176E-8, pyunits.J/pyunits.mol/pyunits.K**4)}, - "cp_mol_liq_comp_coeff": { - '1': (2.7637E2, pyunits.J/pyunits.kmol/pyunits.K), # [2] - '2': (-2.0901, pyunits.J/pyunits.kmol/pyunits.K**2), - '3': (8.125E-3, pyunits.J/pyunits.kmol/pyunits.K**3), - '4': (-1.4116E-5, pyunits.J/pyunits.kmol/pyunits.K**4), - '5': (9.3701E-9, pyunits.J/pyunits.kmol/pyunits.K**5)}, - "enth_mol_form_liq_comp_ref": ( - -285.83e3, pyunits.J/pyunits.mol), # [3] - "enth_mol_form_vap_comp_ref": ( - -241.836e3, pyunits.J/pyunits.mol), # [3] - "pressure_sat_comp_coeff": {'A': (-7.76451, None), # [1] - 'B': (1.45838, None), - 'C': (-2.77580, None), - 'D': (-1.23303, None)}}}, - 'ethylene_glycol': - {"type": Component, - "elemental_composition": {"C": 2, "H": 6, "O": 2}, - "dens_mol_liq_comp": Perrys, - "enth_mol_liq_comp": Perrys, - "enth_mol_ig_comp": RPP4, - "pressure_sat_comp": RPP4, - "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, - "parameter_data": { - "mw": (62.069E-3, pyunits.kg/pyunits.mol), # [1] - "pressure_crit": (77e5, pyunits.Pa), # [1] - "temperature_crit": (645, pyunits.K), # [1] - "dens_mol_liq_comp_coeff": { - 'eqn_type': 1, - '1': (1.315, pyunits.kmol*pyunits.m**-3), # [2] pg. 2-98 - '2': (0.25125, None), - '3': (720, pyunits.K), - '4': (0.21868, None)}, - "cp_mol_ig_comp_coeff": { - 'A': (3.570E1, pyunits.J/pyunits.mol/pyunits.K), # [1] - 'B': (2.483E-1, pyunits.J/pyunits.mol/pyunits.K**2), - 'C': (-1.497E-4, pyunits.J/pyunits.mol/pyunits.K**3), - 'D': (3.010E-8, pyunits.J/pyunits.mol/pyunits.K**4)}, - "cp_mol_liq_comp_coeff": { - '1': (3.5540E1, pyunits.J/pyunits.kmol/pyunits.K), # [2] - '2': (4.3678E-1, pyunits.J/pyunits.kmol/pyunits.K**2), - '3': (-1.8486E-4, pyunits.J/pyunits.kmol/pyunits.K**3), - '4': (0, pyunits.J/pyunits.kmol/pyunits.K**4), - '5': (0, pyunits.J/pyunits.kmol/pyunits.K**5)}, - "enth_mol_form_liq_comp_ref": ( - -455.24e3, pyunits.J/pyunits.mol), # [3] - "enth_mol_form_vap_comp_ref": ( - -389.37e3, pyunits.J/pyunits.mol), # [3] - "pressure_sat_comp_coeff": {'A': (13.6299, None), # [1] - 'B': (6022.18, None), - 'C': (-28.25, None), - 'D': (0, None)}}}}, - + "water": { + "type": Component, + "elemental_composition": {"H": 2, "O": 1}, + "dens_mol_liq_comp": Perrys, + "enth_mol_liq_comp": Perrys, + "enth_mol_ig_comp": RPP4, + "pressure_sat_comp": RPP4, + "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, + "parameter_data": { + "mw": (18.015e-3, pyunits.kg / pyunits.mol), # [1] pg. 667 + "pressure_crit": (221.2e5, pyunits.Pa), # [1] pg. 667 + "temperature_crit": (647.3, pyunits.K), # [1] pg. 667 + "dens_mol_liq_comp_coeff": { # [2] pg. 2-98 + "eqn_type": 1, + "1": (5.459, pyunits.kmol * pyunits.m**-3), + "2": (0.30542, None), + "3": (647.13, pyunits.K), + "4": (0.081, None), + }, + "cp_mol_ig_comp_coeff": { # [1] pg. 668 + "A": (3.224e1, pyunits.J / pyunits.mol / pyunits.K), + "B": (1.924e-3, pyunits.J / pyunits.mol / pyunits.K**2), + "C": (1.055e-5, pyunits.J / pyunits.mol / pyunits.K**3), + "D": (-3.596e-9, pyunits.J / pyunits.mol / pyunits.K**4), + }, + "cp_mol_liq_comp_coeff": { # [2] pg. 2-174 + "1": (2.7637e5, pyunits.J / pyunits.kmol / pyunits.K), + "2": (-2.0901e3, pyunits.J / pyunits.kmol / pyunits.K**2), + "3": (8.1250, pyunits.J / pyunits.kmol / pyunits.K**3), + "4": (-1.4116e-2, pyunits.J / pyunits.kmol / pyunits.K**4), + "5": (9.3701e-6, pyunits.J / pyunits.kmol / pyunits.K**5), + }, + "enth_mol_form_liq_comp_ref": ( + -285.830e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "enth_mol_form_vap_comp_ref": ( + -241.826e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "pressure_sat_comp_coeff": { + "A": (-7.76451, None), # [1] pg. 669 + "B": (1.45838, None), + "C": (-2.77580, None), + "D": (-1.23303, None), + }, + }, + }, + "ethylene_glycol": { + "type": Component, + "elemental_composition": {"C": 2, "H": 6, "O": 2}, + "dens_mol_liq_comp": Perrys, + "enth_mol_liq_comp": Perrys, + "enth_mol_ig_comp": RPP4, + "pressure_sat_comp": RPP4, + "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, + "parameter_data": { + "mw": (62.069e-3, pyunits.kg / pyunits.mol), # [1] pg. 676 + "pressure_crit": (77e5, pyunits.Pa), # [1] pg. 676 + "temperature_crit": (645, pyunits.K), # [1] pg. 676 + "dens_mol_liq_comp_coeff": { # [2] pg. 2-95 + "eqn_type": 1, + "1": (1.3151, pyunits.kmol * pyunits.m**-3), + "2": (0.25125, None), + "3": (719.7, pyunits.K), + "4": (0.2187, None), + }, + "cp_mol_ig_comp_coeff": { # [1] pg. 677 + "A": (3.570e1, pyunits.J / pyunits.mol / pyunits.K), + "B": (2.483e-1, pyunits.J / pyunits.mol / pyunits.K**2), + "C": (-1.497e-4, pyunits.J / pyunits.mol / pyunits.K**3), + "D": (3.010e-8, pyunits.J / pyunits.mol / pyunits.K**4), + }, + "cp_mol_liq_comp_coeff": { # [2] pg. 2-171 + "1": (3.5540e4, pyunits.J / pyunits.kmol / pyunits.K), + "2": (4.3678e2, pyunits.J / pyunits.kmol / pyunits.K**2), + "3": (-1.8486e-1, pyunits.J / pyunits.kmol / pyunits.K**3), + "4": (0, pyunits.J / pyunits.kmol / pyunits.K**4), + "5": (0, pyunits.J / pyunits.kmol / pyunits.K**5), + }, + "enth_mol_form_liq_comp_ref": ( + -460.0e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "enth_mol_form_vap_comp_ref": ( + -394.4e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + # [1] pg. 678 pressure sat coef values for alternative equation form + # ln Pvp = A - B/(T + C) with A = 13.6299, B = 6022.18, C = -28.25 + # reformulated for generic property supported form + # ln Pvp = [(1 - x)^-1 * (A*x + B*x^1.5 + C*x^3 + D*x^6)] * Pc where x = 1 - T/Tc + "pressure_sat_comp_coeff": { + "A": (-16.4022, None), + "B": (10.0100, None), + "C": (-6.5216, None), + "D": (-11.1182, None), + }, + }, + }, + }, # Specifying phases - "phases": {'Liq': {"type": LiquidPhase, - "equation_of_state": Ideal}}, - + "phases": {"Liq": {"type": LiquidPhase, "equation_of_state": Ideal}}, # Set base units of measurement - "base_units": {"time": pyunits.s, - "length": pyunits.m, - "mass": pyunits.kg, - "amount": pyunits.mol, - "temperature": pyunits.K}, - + "base_units": { + "time": pyunits.s, + "length": pyunits.m, + "mass": pyunits.kg, + "amount": pyunits.mol, + "temperature": pyunits.K, + }, # Specifying state definition "state_definition": FpcTP, - "state_bounds": {"flow_mol_phase_comp": (0, 100, 1000, - pyunits.mol/pyunits.s), - "temperature": (273.15, 298.15, 450, pyunits.K), - "pressure": (1e3, 1e5, 1e6, pyunits.Pa)}, + "state_bounds": { + "flow_mol_phase_comp": (0, 100, 1000, pyunits.mol / pyunits.s), + "temperature": (273.15, 298.15, 450, pyunits.K), + "pressure": (1e3, 1e5, 1e6, pyunits.Pa), + }, "pressure_ref": (1e5, pyunits.Pa), - "temperature_ref": (298.15, pyunits.K)} + "temperature_ref": (298.15, pyunits.K), +} diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb index 922f495f..12aac7c6 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb @@ -583,18 +583,18 @@ "\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1974, rel=1e-3)\n", + ") == pytest.approx(0.19741534, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.6597, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(1038, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" + ") == pytest.approx(0.65973425, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" ] }, { @@ -671,18 +671,18 @@ "\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.6998, rel=1e-3)\n", + ") == pytest.approx(0.69981938, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.1573, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(100.0, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" + ") == pytest.approx(0.15733020, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" ] }, { @@ -719,9 +719,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb index 0273e79d..e3cbb4d7 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb @@ -1,1208 +1,727 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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": [ - "# IDAES Skeleton Unit Model\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "This notebook demonstrates usage of the IDAES Skeleton Unit Model, which provides a generic \"bare bones\" unit for user-defined models and custom variable and constraint sets. To allow maximum versatility, this unit may be defined as a surrogate model or a custom equation-oriented model. Users must add ports and variables that match connected models, and this is facilitated through a provided method to add port-variable sets.\n", - "\n", - "For users who wish to train surrogates with IDAES tools and insert obtained models into a flowsheet, see more detailed information on [IDAES Surrogate Tools](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/index.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Motivation\n", - "\n", - "In many cases, a specific application requires a unique unit operation that does not exist in the IDAES repository. Custom user models may source from external scripts, import surrogate equations or use first-principles calculations. However, IDAES flowsheets adhere to a standardized modeling hierarchy and simple Pyomo models do not always follow these conventions. Additionally, simple flowsheet submodels often require integration with other IDAES unit models which requires consistency between corresponding port variables, stream properties and physical unit sets, as well as proper usage of `ControlVolume` blocks.\n", - "\n", - "The IDAES `SkeletonUnitModel` allows custom creation of user models blocks that do not require `ControlVolume` blocks, and enabling connection with standard IDAES unit models that do contain `ControlVolume` blocks. To motivate the usefulness and versatility of this tool, we will consider a simple pervaporation unit. The custom model does not require rigorous thermodynamic calculations contained in adjacent unit models, and using a Skeleton model allows definition of only required variables and constraints. The new block does require state variable connections for the inlet and outlet streams. We will demonstrate this scenario below to highlight the usage and benefits of the Skeleton model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Example - Pervaporation\n", - "\n", - "Pervaporation is a low-energy separation process, and is particularly advantageous over distillation for azeotropic solutions or aqueous mixtures of heavy alcohols. Ethylene glycol is more environmentally friendly than typical chloride- and bromide-based dessicants, and is a common choice for commercial recovery of water from flue gas via liquid spray columns. Due to ethylene glycol's high boiling point, diffusion-based water recovery is economically favorable compared to distillation-based processes. The following example and flux correlation are taken from the literature source below:\n", - "\n", - "Jennifer Runhong Du, Amit Chakma, X. Feng, Dehydration of ethylene glycol by pervaporation using poly(N,N-dimethylaminoethyl methacrylate)/polysulfone composite membranes, Separation and Purification Technology, Volume 64, Issue 1, 2008, Pages 63-70, ISSN 1383-5866, https://doi.org/10.1016/j.seppur.2008.08.004.\n", - "\n", - "The process is adapted from the literature, utilizing an inlet aqueous glycol feed circulated through a feed tank-membrane-feed tank recycle loop while permeate is continuously extracted by the membrane. To demonstrate the usefulness of the Skeleton model, we will model this system as a Mixer and custom Pervaporation unit per the diagram below and define the flux as an empirical custom mass balance term rather than requiring rigorous diffusion calculations. We will also circumvent the need for a vapor phase and VLE calculations by manually calculating the duty to condense and collect permeate vapor, and use correlations for steady-state fluxes to avoid a recycle requiring tear calculations.\n", - "\n", - "![](pervaporation_process.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.1 Pyomo and IDAES Imports\n", - "We will begin with relevant imports. We will need basic Pyomo and IDAES components:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import (\n", - " check_optimal_termination,\n", - " ConcreteModel,\n", - " Constraint,\n", - " Expression,\n", - " Objective,\n", - " maximize,\n", - " Var,\n", - " Set,\n", - " TransformationFactory,\n", - " value,\n", - " exp,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.unit_models import Feed, SkeletonUnitModel, Mixer, Product\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "# import thermophysical properties\n", - "import eg_h2o_ideal as thermo_props\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.core.util.constants import Constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Build Flowsheet\n", - "\n", - "We will build a simple model manually defining state variables relations entering and exiting the pervaporation unit. As shown below, we may define our pre-separation mixer as usual:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# build the flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "\n", - "m.fs.WATER = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.GLYCOL = Feed(property_package=m.fs.thermo_params)\n", - "\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"water_feed\", \"glycol_feed\"]\n", - ")\n", - "\n", - "m.fs.RETENTATE = Product(property_package=m.fs.thermo_params)\n", - "m.fs.PERMEATE = Product(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Defining Skeleton Model and Connections\n", - "\n", - "Now that our flowsheet exists, we can manually define variables, units, constraints and ports for our custom pervaporation unit model. By using a Skeleton model, we avoid rigorous mass and energy balances and phase equilibrium which impact model tractability. Instead, we define state variable relations as below - note that we include the fluxes as outlet flow terms. In this model, the variables specify an `FpcTP` system where molar flow of each component, temperature and pressure are selected as state variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# define Skeleton model for pervaporation unit\n", - "m.fs.pervap = SkeletonUnitModel(dynamic=False)\n", - "m.fs.pervap.comp_list = Set(initialize=[\"water\", \"ethylene_glycol\"])\n", - "m.fs.pervap.phase_list = Set(initialize=[\"Liq\"])\n", - "\n", - "# input vars for skeleton\n", - "# m.fs.time is a pre-initialized Set belonging to the FlowsheetBlock; for dynamic=False, time=[0]\n", - "m.fs.pervap.flow_in = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.temperature_in = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", - "m.fs.pervap.pressure_in = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", - "\n", - "# output vars for skeleton\n", - "m.fs.pervap.perm_flow = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.ret_flow = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.temperature_out = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", - "m.fs.pervap.pressure_out = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", - "m.fs.pervap.vacuum = Var(m.fs.time, initialize=1.3e3, units=pyunits.Pa)\n", - "\n", - "# dictionaries relating state properties to custom variables\n", - "inlet_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.flow_in,\n", - " \"temperature\": m.fs.pervap.temperature_in,\n", - " \"pressure\": m.fs.pervap.pressure_in,\n", - "}\n", - "retentate_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.ret_flow,\n", - " \"temperature\": m.fs.pervap.temperature_out,\n", - " \"pressure\": m.fs.pervap.pressure_out,\n", - "}\n", - "permeate_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.perm_flow,\n", - " \"temperature\": m.fs.pervap.temperature_out,\n", - " \"pressure\": m.fs.pervap.vacuum,\n", - "}\n", - "\n", - "m.fs.pervap.add_ports(name=\"inlet\", member_dict=inlet_dict)\n", - "m.fs.pervap.add_ports(name=\"retentate\", member_dict=retentate_dict)\n", - "m.fs.pervap.add_ports(name=\"permeate\", member_dict=permeate_dict)\n", - "\n", - "# internal vars for skeleton\n", - "energy_activation_dict = {\n", - " (0, \"Liq\", \"water\"): 51e3,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 53e3,\n", - "}\n", - "m.fs.pervap.energy_activation = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=energy_activation_dict,\n", - " units=pyunits.J / pyunits.mol,\n", - ")\n", - "m.fs.pervap.energy_activation.fix()\n", - "\n", - "permeance_dict = {\n", - " (0, \"Liq\", \"water\"): 5611320,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 22358.88,\n", - "} # calculated from literature data\n", - "m.fs.pervap.permeance = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=permeance_dict,\n", - " units=pyunits.mol / pyunits.s / pyunits.m**2,\n", - ")\n", - "m.fs.pervap.permeance.fix()\n", - "\n", - "m.fs.pervap.area = Var(m.fs.time, initialize=6, units=pyunits.m**2)\n", - "m.fs.pervap.area.fix()\n", - "\n", - "latent_heat_dict = {\n", - " (0, \"Liq\", \"water\"): 40.660e3,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 56.9e3,\n", - "}\n", - "m.fs.pervap.latent_heat_of_vaporization = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=latent_heat_dict,\n", - " units=pyunits.J / pyunits.mol,\n", - ")\n", - "m.fs.pervap.latent_heat_of_vaporization.fix()\n", - "m.fs.pervap.heat_duty = Var(\n", - " m.fs.time, initialize=1, units=pyunits.J / pyunits.s\n", - ") # we will calculate this later" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define our surrogate equations for flux and permeance, and link them to the port variables. Users can use this structure to write custom relations between inlet and outlet streams; for example, here we define the outlet flow of the pervaporation unit as a sum of the inlet flow and calculated recovery fluxes. By defining model constraints in lieu of rigorous mass balances, we add the flux as a custom mass balance term via an empirical correlation and calculate only the condensation duty rather than implementing full energy balance calculations:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Surrogate and first principles model equations\n", - "\n", - "# flux equation (gas constant is defined as J/mol-K)\n", - "\n", - "\n", - "def rule_permeate_flux(pervap, t, p, i):\n", - " return pervap.permeate.flow_mol_phase_comp[t, p, i] / pervap.area[t] == (\n", - " pervap.permeance[t, p, i]\n", - " * exp(\n", - " -pervap.energy_activation[t, p, i]\n", - " / (Constants.gas_constant * pervap.inlet.temperature[t])\n", - " )\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_permeate_flux = Constraint(\n", - " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_permeate_flux\n", - ")\n", - "\n", - "# permeate condensation equation\n", - "# heat duty based on condensing all of permeate product vapor\n", - "# avoids the need for a Heater or HeatExchanger unit model\n", - "\n", - "\n", - "def rule_duty(pervap, t):\n", - " return pervap.heat_duty[t] == sum(\n", - " pervap.latent_heat_of_vaporization[t, p, i]\n", - " * pervap.permeate.flow_mol_phase_comp[t, p, i]\n", - " for p in pervap.phase_list\n", - " for i in pervap.comp_list\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_duty = Constraint(m.fs.time, rule=rule_duty)\n", - "\n", - "# flow equation adding total recovery as a custom mass balance term\n", - "def rule_retentate_flow(pervap, t, p, i):\n", - " return pervap.retentate.flow_mol_phase_comp[t, p, i] == (\n", - " pervap.inlet.flow_mol_phase_comp[t, p, i]\n", - " - pervap.permeate.flow_mol_phase_comp[t, p, i]\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_retentate_flow = Constraint(\n", - " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_retentate_flow\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's define the Arc connecting our two models (IDAES Mixer and custom Pervaporation) and build the flowsheet network:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.WATER.outlet, destination=m.fs.M101.water_feed)\n", - "m.fs.s02 = Arc(source=m.fs.GLYCOL.outlet, destination=m.fs.M101.glycol_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.pervap.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.pervap.permeate, destination=m.fs.PERMEATE.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.pervap.retentate, destination=m.fs.RETENTATE.inlet)\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how many degrees of freedom the flowsheet has:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "11\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.3 Inlet Specifications\n", - "\n", - "To obtain a square problem with zero degrees of freedom, we specify the inlet water flow, ethylene glycol flow, temperature and pressure for each feed stream, as well as the permeate stream pressure:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(0.34) # mol/s\n", - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(1e-6) # mol/s\n", - "m.fs.WATER.outlet.temperature.fix(318.15) # K\n", - "m.fs.WATER.outlet.pressure.fix(101.325e3) # Pa\n", - "\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(1e-6) # mol/s\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(0.66) # mol/s\n", - "m.fs.GLYCOL.outlet.temperature.fix(318.15) # K\n", - "m.fs.GLYCOL.outlet.pressure.fix(101.325e3) # Pa" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, we need to pass rules defining the temperature and pressure outlets of the pervaporation unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a constraint to calculate the outlet temperature.\n", - "# Here, assume outlet temperature is the same as inlet temperature for illustration\n", - "# in reality, temperature change from latent heat loss through membrane is negligible\n", - "\n", - "\n", - "def rule_temp_out(pervap, t):\n", - " return pervap.inlet.temperature[t] == pervap.retentate.temperature[t]\n", - "\n", - "\n", - "m.fs.pervap.temperature_out_calculation = Constraint(m.fs.time, rule=rule_temp_out)\n", - "\n", - "# Add a constraint to calculate the retentate pressure\n", - "# Here, assume the retentate pressure is the same as the inlet pressure for illustration\n", - "# in reality, pressure change from mass loss through membrane is negligible\n", - "\n", - "\n", - "def rule_pres_out(pervap, t):\n", - " return pervap.inlet.pressure[t] == pervap.retentate.pressure[t]\n", - "\n", - "\n", - "m.fs.pervap.pressure_out_calculation = Constraint(m.fs.time, rule=rule_pres_out)\n", - "\n", - "# fix permeate vacuum pressure\n", - "m.fs.PERMEATE.inlet.pressure.fix(1.3e3)\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.4 Custom Initialization\n", - "In addition to allowing custom variable and constraint definitions, the Skeleton model enables implementation of a custom initialization scheme. Complex unit operations may present unique tractability issues, and users have precise control over piecewise unit model solving." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "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", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.WATER.properties: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Skeleton Unit Model\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "This notebook demonstrates usage of the IDAES Skeleton Unit Model, which provides a generic \"bare bones\" unit for user-defined models and custom variable and constraint sets. To allow maximum versatility, this unit may be defined as a surrogate model or a custom equation-oriented model. Users must add ports and variables that match connected models, and this is facilitated through a provided method to add port-variable sets.\n", + "\n", + "For users who wish to train surrogates with IDAES tools and insert obtained models into a flowsheet, see more detailed information on [IDAES Surrogate Tools](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/index.html)." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.WATER.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Motivation\n", + "\n", + "In many cases, a specific application requires a unique unit operation that does not exist in the IDAES repository. Custom user models may source from external scripts, import surrogate equations or use first-principles calculations. However, IDAES flowsheets adhere to a standardized modeling hierarchy and simple Pyomo models do not always follow these conventions. Additionally, simple flowsheet submodels often require integration with other IDAES unit models which requires consistency between corresponding port variables, stream properties and physical unit sets, as well as proper usage of `ControlVolume` blocks.\n", + "\n", + "The IDAES `SkeletonUnitModel` allows custom creation of user models blocks that do not require `ControlVolume` blocks, and enabling connection with standard IDAES unit models that do contain `ControlVolume` blocks. To motivate the usefulness and versatility of this tool, we will consider a simple pervaporation unit. The custom model does not require rigorous thermodynamic calculations contained in adjacent unit models, and using a Skeleton model allows definition of only required variables and constraints. The new block does require state variable connections for the inlet and outlet streams. We will demonstrate this scenario below to highlight the usage and benefits of the Skeleton model." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.WATER.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Example - Pervaporation\n", + "\n", + "Pervaporation is a low-energy separation process, and is particularly advantageous over distillation for azeotropic solutions or aqueous mixtures of heavy alcohols. Ethylene glycol is more environmentally friendly than typical chloride- and bromide-based dessicants, and is a common choice for commercial recovery of water from flue gas via liquid spray columns. Due to ethylene glycol's high boiling point, diffusion-based water recovery is economically favorable compared to distillation-based processes. The following example and flux correlation are taken from the literature source below:\n", + "\n", + "Jennifer Runhong Du, Amit Chakma, X. Feng, Dehydration of ethylene glycol by pervaporation using poly(N,N-dimethylaminoethyl methacrylate)/polysulfone composite membranes, Separation and Purification Technology, Volume 64, Issue 1, 2008, Pages 63-70, ISSN 1383-5866, https://doi.org/10.1016/j.seppur.2008.08.004.\n", + "\n", + "The process is adapted from the literature, utilizing an inlet aqueous glycol feed circulated through a feed tank-membrane-feed tank recycle loop while permeate is continuously extracted by the membrane. To demonstrate the usefulness of the Skeleton model, we will model this system as a Mixer and custom Pervaporation unit per the diagram below and define the flux as an empirical custom mass balance term rather than requiring rigorous diffusion calculations. We will also circumvent the need for a vapor phase and VLE calculations by manually calculating the duty to condense and collect permeate vapor, and use correlations for steady-state fluxes to avoid a recycle requiring tear calculations.\n", + "\n", + "![](pervaporation_process.png)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.WATER: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Pyomo and IDAES Imports\n", + "We will begin with relevant imports. We will need basic Pyomo and IDAES components:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.GLYCOL.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import (\n", + " check_optimal_termination,\n", + " ConcreteModel,\n", + " Constraint,\n", + " Expression,\n", + " Objective,\n", + " maximize,\n", + " Var,\n", + " Set,\n", + " TransformationFactory,\n", + " value,\n", + " exp,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.unit_models import Feed, SkeletonUnitModel, Mixer, Product\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "# import thermophysical properties\n", + "import eg_h2o_ideal as thermo_props\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.core.util.constants import Constants" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.GLYCOL.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Build Flowsheet\n", + "\n", + "We will build a simple model manually defining state variables relations entering and exiting the pervaporation unit. As shown below, we may define our pre-separation mixer as usual:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.GLYCOL.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# build the flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "\n", + "m.fs.WATER = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.GLYCOL = Feed(property_package=m.fs.thermo_params)\n", + "\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"water_feed\", \"glycol_feed\"]\n", + ")\n", + "\n", + "m.fs.RETENTATE = Product(property_package=m.fs.thermo_params)\n", + "m.fs.PERMEATE = Product(property_package=m.fs.thermo_params)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.GLYCOL: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Defining Skeleton Model and Connections\n", + "\n", + "Now that our flowsheet exists, we can manually define variables, units, constraints and ports for our custom pervaporation unit model. By using a Skeleton model, we avoid rigorous mass and energy balances and phase equilibrium which impact model tractability. Instead, we define state variable relations as below - note that we include the fluxes as outlet flow terms. In this model, the variables specify an `FpcTP` system where molar flow of each component, temperature and pressure are selected as state variables:" + ] }, { - "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...: 11\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 7\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 7\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 1.13e-16 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.1275702593849246e-16 1.1275702593849246e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1275702593849246e-16 1.1275702593849246e-16\n", - "\n", - "\n", - "Number of objective function evaluations = 1\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 1\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 1\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define Skeleton model for pervaporation unit\n", + "m.fs.pervap = SkeletonUnitModel(dynamic=False)\n", + "m.fs.pervap.comp_list = Set(initialize=[\"water\", \"ethylene_glycol\"])\n", + "m.fs.pervap.phase_list = Set(initialize=[\"Liq\"])\n", + "\n", + "# input vars for skeleton\n", + "# m.fs.time is a pre-initialized Set belonging to the FlowsheetBlock; for dynamic=False, time=[0]\n", + "m.fs.pervap.flow_in = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.temperature_in = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", + "m.fs.pervap.pressure_in = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", + "\n", + "# output vars for skeleton\n", + "m.fs.pervap.perm_flow = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.ret_flow = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.temperature_out = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", + "m.fs.pervap.pressure_out = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", + "m.fs.pervap.vacuum = Var(m.fs.time, initialize=1.3e3, units=pyunits.Pa)\n", + "\n", + "# dictionaries relating state properties to custom variables\n", + "inlet_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.flow_in,\n", + " \"temperature\": m.fs.pervap.temperature_in,\n", + " \"pressure\": m.fs.pervap.pressure_in,\n", + "}\n", + "retentate_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.ret_flow,\n", + " \"temperature\": m.fs.pervap.temperature_out,\n", + " \"pressure\": m.fs.pervap.pressure_out,\n", + "}\n", + "permeate_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.perm_flow,\n", + " \"temperature\": m.fs.pervap.temperature_out,\n", + " \"pressure\": m.fs.pervap.vacuum,\n", + "}\n", + "\n", + "m.fs.pervap.add_ports(name=\"inlet\", member_dict=inlet_dict)\n", + "m.fs.pervap.add_ports(name=\"retentate\", member_dict=retentate_dict)\n", + "m.fs.pervap.add_ports(name=\"permeate\", member_dict=permeate_dict)\n", + "\n", + "# internal vars for skeleton\n", + "energy_activation_dict = {\n", + " (0, \"Liq\", \"water\"): 51e3,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 53e3,\n", + "}\n", + "m.fs.pervap.energy_activation = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=energy_activation_dict,\n", + " units=pyunits.J / pyunits.mol,\n", + ")\n", + "m.fs.pervap.energy_activation.fix()\n", + "\n", + "permeance_dict = {\n", + " (0, \"Liq\", \"water\"): 5611320,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 22358.88,\n", + "} # calculated from literature data\n", + "m.fs.pervap.permeance = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=permeance_dict,\n", + " units=pyunits.mol / pyunits.s / pyunits.m**2,\n", + ")\n", + "m.fs.pervap.permeance.fix()\n", + "\n", + "m.fs.pervap.area = Var(m.fs.time, initialize=6, units=pyunits.m**2)\n", + "m.fs.pervap.area.fix()\n", + "\n", + "latent_heat_dict = {\n", + " (0, \"Liq\", \"water\"): 40.660e3,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 56.9e3,\n", + "}\n", + "m.fs.pervap.latent_heat_of_vaporization = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=latent_heat_dict,\n", + " units=pyunits.J / pyunits.mol,\n", + ")\n", + "m.fs.pervap.latent_heat_of_vaporization.fix()\n", + "m.fs.pervap.heat_duty = Var(\n", + " m.fs.time, initialize=1, units=pyunits.J / pyunits.s\n", + ") # we will calculate this later" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Custom initialization routine complete: Ipopt 3.13.2\\x3a Optimal Solution Found\n", - "2023-11-02 10:27:47 [INFO] idaes.init.fs.PERMEATE.properties: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define our surrogate equations for flux and permeance, and link them to the port variables. Users can use this structure to write custom relations between inlet and outlet streams; for example, here we define the outlet flow of the pervaporation unit as a sum of the inlet flow and calculated recovery fluxes. By defining model constraints in lieu of rigorous mass balances, we add the flux as a custom mass balance term via an empirical correlation and calculate only the condensation duty rather than implementing full energy balance calculations:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.PERMEATE.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Surrogate and first principles model equations\n", + "\n", + "# flux equation (gas constant is defined as J/mol-K)\n", + "\n", + "\n", + "def rule_permeate_flux(pervap, t, p, i):\n", + " return pervap.permeate.flow_mol_phase_comp[t, p, i] / pervap.area[t] == (\n", + " pervap.permeance[t, p, i]\n", + " * exp(\n", + " -pervap.energy_activation[t, p, i]\n", + " / (Constants.gas_constant * pervap.inlet.temperature[t])\n", + " )\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_permeate_flux = Constraint(\n", + " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_permeate_flux\n", + ")\n", + "\n", + "# permeate condensation equation\n", + "# heat duty based on condensing all of permeate product vapor\n", + "# avoids the need for a Heater or HeatExchanger unit model\n", + "\n", + "\n", + "def rule_duty(pervap, t):\n", + " return pervap.heat_duty[t] == sum(\n", + " pervap.latent_heat_of_vaporization[t, p, i]\n", + " * pervap.permeate.flow_mol_phase_comp[t, p, i]\n", + " for p in pervap.phase_list\n", + " for i in pervap.comp_list\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_duty = Constraint(m.fs.time, rule=rule_duty)\n", + "\n", + "# flow equation adding total recovery as a custom mass balance term\n", + "def rule_retentate_flow(pervap, t, p, i):\n", + " return pervap.retentate.flow_mol_phase_comp[t, p, i] == (\n", + " pervap.inlet.flow_mol_phase_comp[t, p, i]\n", + " - pervap.permeate.flow_mol_phase_comp[t, p, i]\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_retentate_flow = Constraint(\n", + " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_retentate_flow\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.PERMEATE.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's define the Arc connecting our two models (IDAES Mixer and custom Pervaporation) and build the flowsheet network:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.PERMEATE: Initialization Complete.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.WATER.outlet, destination=m.fs.M101.water_feed)\n", + "m.fs.s02 = Arc(source=m.fs.GLYCOL.outlet, destination=m.fs.M101.glycol_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.pervap.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.pervap.permeate, destination=m.fs.PERMEATE.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.pervap.retentate, destination=m.fs.RETENTATE.inlet)\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.RETENTATE.properties: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how many degrees of freedom the flowsheet has:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.RETENTATE.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.RETENTATE.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Inlet Specifications\n", + "\n", + "To obtain a square problem with zero degrees of freedom, we specify the inlet water flow, ethylene glycol flow, temperature and pressure for each feed stream, as well as the permeate stream pressure:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:27:47 [INFO] idaes.init.fs.RETENTATE: Initialization Complete.\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(0.34) # mol/s\n", + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(1e-6) # mol/s\n", + "m.fs.WATER.outlet.temperature.fix(318.15) # K\n", + "m.fs.WATER.outlet.pressure.fix(101.325e3) # Pa\n", + "\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(1e-6) # mol/s\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(0.66) # mol/s\n", + "m.fs.GLYCOL.outlet.temperature.fix(318.15) # K\n", + "m.fs.GLYCOL.outlet.pressure.fix(101.325e3) # Pa" + ] }, { - "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...: 113\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 74\n", - "\n", - "Total number of variables............................: 47\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 33\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 47\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 7.37e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 7.03e+05 2.30e+01 -1.0 1.01e+05 - 5.55e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 2.33e+05 1.02e+02 -1.0 1.00e+03 - 7.60e-01 6.58e-01h 1\n", - " 3 0.0000000e+00 6.05e+03 5.91e+01 -1.0 1.56e+03 - 9.90e-01 9.90e-01h 1\n", - " 4 0.0000000e+00 5.50e+01 7.15e+02 -1.0 1.97e+03 - 9.90e-01 1.00e+00h 1\n", - " 5 0.0000000e+00 5.29e-05 2.18e+00 -1.0 1.90e+02 - 1.00e+00 1.00e+00h 1\n", - " 6 0.0000000e+00 4.28e-10 5.17e-03 -3.8 2.59e-01 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.1421975304747320e-10 4.2843950609494641e-10\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.1421975304747320e-10 4.2843950609494641e-10\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Add this to the imports\n", - "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", - "\n", - "\n", - "def my_initialize(unit, **kwargs):\n", - " # Callback for user provided initialization sequence\n", - " # Fix the inlet state\n", - " unit.inlet.flow_mol_phase_comp.fix()\n", - " unit.inlet.pressure.fix()\n", - " unit.inlet.temperature.fix()\n", - "\n", - " # Calculate the values of the remaining variables\n", - " for t in m.fs.time:\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", - " unit.eq_permeate_flux[t, \"Liq\", \"water\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", - " unit.eq_permeate_flux[t, \"Liq\", \"ethylene_glycol\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(unit.heat_duty[t], unit.eq_duty[t])\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", - " unit.eq_retentate_flow[t, \"Liq\", \"water\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", - " unit.eq_retentate_flow[t, \"Liq\", \"ethylene_glycol\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.temperature[t], unit.temperature_out_calculation[t]\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.pressure[t], unit.pressure_out_calculation[t]\n", - " )\n", - "\n", - " assert degrees_of_freedom(unit) == 0\n", - " if degrees_of_freedom(unit) == 0:\n", - " res = solver.solve(unit, tee=True)\n", - " unit.inlet.flow_mol_phase_comp.unfix()\n", - " unit.inlet.temperature.unfix()\n", - " unit.inlet.pressure.unfix()\n", - " print(\"Custom initialization routine complete: \", res.solver.message)\n", - "\n", - "\n", - "solver = get_solver()\n", - "\n", - "m.fs.WATER.initialize()\n", - "propagate_state(m.fs.s01)\n", - "\n", - "m.fs.GLYCOL.initialize()\n", - "propagate_state(m.fs.s02)\n", - "\n", - "m.fs.pervap.config.initializer = my_initialize\n", - "my_initialize(m.fs.pervap)\n", - "propagate_state(m.fs.s03)\n", - "\n", - "m.fs.PERMEATE.initialize()\n", - "propagate_state(m.fs.s04)\n", - "\n", - "m.fs.RETENTATE.initialize()\n", - "\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the results:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we need to pass rules defining the temperature and pressure outlets of the pervaporation unit:" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.WATER Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Outlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 0.34000\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 1.0000e-06\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1.0132e+05\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.GLYCOL Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Outlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 1.0000e-06\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.66000\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1.0132e+05\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.PERMEATE Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 0.14259\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.00026675\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1300.0\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.RETENTATE Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 0.19742\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.65973\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1.0132e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "# print results\n", - "\n", - "m.fs.WATER.report()\n", - "m.fs.GLYCOL.report()\n", - "m.fs.PERMEATE.report()\n", - "m.fs.RETENTATE.report()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a constraint to calculate the outlet temperature.\n", + "# Here, assume outlet temperature is the same as inlet temperature for illustration\n", + "# in reality, temperature change from latent heat loss through membrane is negligible\n", + "\n", + "\n", + "def rule_temp_out(pervap, t):\n", + " return pervap.inlet.temperature[t] == pervap.retentate.temperature[t]\n", + "\n", + "\n", + "m.fs.pervap.temperature_out_calculation = Constraint(m.fs.time, rule=rule_temp_out)\n", + "\n", + "# Add a constraint to calculate the retentate pressure\n", + "# Here, assume the retentate pressure is the same as the inlet pressure for illustration\n", + "# in reality, pressure change from mass loss through membrane is negligible\n", + "\n", + "\n", + "def rule_pres_out(pervap, t):\n", + " return pervap.inlet.pressure[t] == pervap.retentate.pressure[t]\n", + "\n", + "\n", + "m.fs.pervap.pressure_out_calculation = Constraint(m.fs.time, rule=rule_pres_out)\n", + "\n", + "# fix permeate vacuum pressure\n", + "m.fs.PERMEATE.inlet.pressure.fix(1.3e3)\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inlet water mole fraction: 0.34000031999936\n", - "Permeate water mole fraction: 0.9981326967912869\n", - "Separation factor: 1037.61881493386\n", - "Condensation duty: 5.81271115195759 kW\n", - "Duty per mole water recovered: 0.011324013423297915 kW-h / mol\n" - ] - } - ], - "source": [ - "# separation factor for results analysis\n", - "m.fs.inlet_water_frac = Expression(\n", - " expr=(\n", - " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " / sum(\n", - " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", i]\n", - " for i in m.fs.pervap.comp_list\n", - " )\n", - " )\n", - ")\n", - "m.fs.permeate_water_frac = Expression(\n", - " expr=(\n", - " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " / sum(\n", - " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", i]\n", - " for i in m.fs.pervap.comp_list\n", - " )\n", - " )\n", - ")\n", - "m.fs.separation_factor = Expression(\n", - " expr=(m.fs.permeate_water_frac / (1 - m.fs.permeate_water_frac))\n", - " / (m.fs.inlet_water_frac / (1 - m.fs.inlet_water_frac))\n", - ")\n", - "\n", - "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", - "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", - "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", - "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", - "print(\n", - " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# check results\n", - "assert check_optimal_termination(results)\n", - "assert_units_consistent(m)\n", - "\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1974, rel=1e-3)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.6597, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(1038, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Optimization\n", - "\n", - "Suppose we wish to characterize the membrane behavior by calculating the maximum inlet water mole fraction allowing a separation factor of at least 100 (typical value for high-efficiency separation processes such as gas separation of CO2/N2). We need to fix total inlet flow to ensure physically-sound solutions. We can quickly modify and resolve the model, and check some key results:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Custom Initialization\n", + "In addition to allowing custom variable and constraint definitions, the Skeleton model enables implementation of a custom initialization scheme. Complex unit operations may present unique tractability issues, and users have precise control over piecewise unit model solving." + ] + }, { - "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...: 121\n", - "Number of nonzeros in inequality constraint Jacobian.: 4\n", - "Number of nonzeros in Lagrangian Hessian.............: 88\n", - "\n", - "Total number of variables............................: 49\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 35\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 48\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 1\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 -3.4000032e-01 7.27e+03 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -3.7368840e-01 7.00e+02 5.28e+01 -1.0 4.41e-02 - 9.90e-01 8.91e-01h 1\n", - " 2 -5.0140067e-01 2.19e+02 1.12e+02 -1.0 2.27e-01 - 9.90e-01 6.56e-01h 1\n", - " 3 -6.1937184e-01 8.96e+00 1.15e+04 -1.0 1.39e-01 - 9.91e-01 9.93e-01h 1\n", - " 4 -6.1863718e-01 1.51e-02 8.34e+01 -1.0 1.28e-03 - 1.00e+00 9.99e-01h 1\n", - " 5 -6.1150206e-01 2.30e-04 7.88e+02 -1.0 8.32e-03 - 1.00e+00 1.00e+00f 1\n", - " 6 -6.1019582e-01 1.79e-06 1.61e+01 -1.0 1.52e-03 - 1.00e+00 1.00e+00h 1\n", - " 7 -7.3642276e-01 1.05e-02 2.29e+04 -2.5 1.47e-01 - 7.70e-01 1.00e+00f 1\n", - " 8 -8.1765712e-01 2.02e-02 9.69e+02 -2.5 1.47e-01 - 1.00e+00 6.43e-01f 1\n", - " 9 -8.3576869e-01 3.85e-03 4.60e+01 -2.5 2.11e-02 - 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", - " 10 -8.3954012e-01 1.72e-04 1.05e+00 -2.5 4.40e-03 - 1.00e+00 1.00e+00h 1\n", - " 11 -8.3930724e-01 8.78e-08 2.93e-03 -2.5 2.72e-04 - 1.00e+00 1.00e+00h 1\n", - " 12 -8.4239161e-01 6.01e-05 9.69e+01 -3.8 3.73e-03 - 1.00e+00 9.63e-01f 1\n", - " 13 -8.4225198e-01 7.26e-08 1.37e-02 -3.8 1.63e-04 - 1.00e+00 1.00e+00f 1\n", - " 14 -8.4225232e-01 5.82e-11 3.86e-08 -3.8 3.92e-07 - 1.00e+00 1.00e+00h 1\n", - " 15 -8.4240230e-01 1.48e-07 2.22e-02 -5.7 1.75e-04 - 1.00e+00 1.00e+00f 1\n", - " 16 -8.4240161e-01 5.82e-11 1.66e-08 -5.7 8.12e-07 - 1.00e+00 1.00e+00h 1\n", - " 17 -8.4240336e-01 1.16e-10 3.07e-06 -7.0 2.05e-06 - 1.00e+00 1.00e+00f 1\n", - " 18 -8.4240336e-01 5.82e-11 4.20e-11 -7.0 5.41e-07 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 18\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: -8.4240336290561868e-01 -8.4240336290561868e-01\n", - "Dual infeasibility......: 4.1950443119276315e-11 4.1950443119276315e-11\n", - "Constraint violation....: 1.2786210408238111e-14 5.8207660913467407e-11\n", - "Complementarity.........: 9.0909090909094912e-08 9.0909090909094912e-08\n", - "Overall NLP error.......: 9.0909090909094912e-08 9.0909090909094912e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 19\n", - "Number of objective gradient evaluations = 19\n", - "Number of equality constraint evaluations = 19\n", - "Number of inequality constraint evaluations = 19\n", - "Number of equality constraint Jacobian evaluations = 19\n", - "Number of inequality constraint Jacobian evaluations = 19\n", - "Number of Lagrangian Hessian evaluations = 18\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# unfix inlet flows but fix total to prevent divergence during solve\n", - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].unfix()\n", - "m.fs.total_flow = Constraint(\n", - " expr=m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " + m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " == 1 * pyunits.mol / pyunits.s\n", - ")\n", - "\n", - "# set criteria for separation factor\n", - "m.fs.sep_min = Constraint(expr=m.fs.separation_factor >= 100)\n", - "\n", - "# set objective - defaults to minimization\n", - "m.fs.obj = Objective(expr=m.fs.inlet_water_frac, sense=maximize)\n", - "\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add this to the imports\n", + "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", + "\n", + "\n", + "def my_initialize(unit, **kwargs):\n", + " # Callback for user provided initialization sequence\n", + " # Fix the inlet state\n", + " unit.inlet.flow_mol_phase_comp.fix()\n", + " unit.inlet.pressure.fix()\n", + " unit.inlet.temperature.fix()\n", + "\n", + " # Calculate the values of the remaining variables\n", + " for t in m.fs.time:\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", + " unit.eq_permeate_flux[t, \"Liq\", \"water\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", + " unit.eq_permeate_flux[t, \"Liq\", \"ethylene_glycol\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(unit.heat_duty[t], unit.eq_duty[t])\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", + " unit.eq_retentate_flow[t, \"Liq\", \"water\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", + " unit.eq_retentate_flow[t, \"Liq\", \"ethylene_glycol\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.temperature[t], unit.temperature_out_calculation[t]\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.pressure[t], unit.pressure_out_calculation[t]\n", + " )\n", + "\n", + " assert degrees_of_freedom(unit) == 0\n", + " if degrees_of_freedom(unit) == 0:\n", + " res = solver.solve(unit, tee=True)\n", + " unit.inlet.flow_mol_phase_comp.unfix()\n", + " unit.inlet.temperature.unfix()\n", + " unit.inlet.pressure.unfix()\n", + " print(\"Custom initialization routine complete: \", res.solver.message)\n", + "\n", + "\n", + "solver = get_solver()\n", + "\n", + "m.fs.WATER.initialize()\n", + "propagate_state(m.fs.s01)\n", + "\n", + "m.fs.GLYCOL.initialize()\n", + "propagate_state(m.fs.s02)\n", + "\n", + "m.fs.pervap.config.initializer = my_initialize\n", + "my_initialize(m.fs.pervap)\n", + "propagate_state(m.fs.s03)\n", + "\n", + "m.fs.PERMEATE.initialize()\n", + "propagate_state(m.fs.s04)\n", + "\n", + "m.fs.RETENTATE.initialize()\n", + "\n", + "results = solver.solve(m, tee=True)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.WATER Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Outlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 0.84240\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 1.0000e-06\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1.0132e+05\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.GLYCOL Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Outlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 1.0000e-06\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.15760\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1.0132e+05\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.PERMEATE Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 0.14259\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.00026675\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1300.0\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.RETENTATE Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet \n", - " Molar Flowrate ('Liq', 'water') mole / second 0.69982\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.15733\n", - " Temperature kelvin 318.15\n", - " Pressure pascal 1.0132e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "# print results\n", - "\n", - "m.fs.WATER.report()\n", - "m.fs.GLYCOL.report()\n", - "m.fs.PERMEATE.report()\n", - "m.fs.RETENTATE.report()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print results\n", + "\n", + "m.fs.WATER.report()\n", + "m.fs.GLYCOL.report()\n", + "m.fs.PERMEATE.report()\n", + "m.fs.RETENTATE.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# separation factor for results analysis\n", + "m.fs.inlet_water_frac = Expression(\n", + " expr=(\n", + " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " / sum(\n", + " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", i]\n", + " for i in m.fs.pervap.comp_list\n", + " )\n", + " )\n", + ")\n", + "m.fs.permeate_water_frac = Expression(\n", + " expr=(\n", + " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " / sum(\n", + " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", i]\n", + " for i in m.fs.pervap.comp_list\n", + " )\n", + " )\n", + ")\n", + "m.fs.separation_factor = Expression(\n", + " expr=(m.fs.permeate_water_frac / (1 - m.fs.permeate_water_frac))\n", + " / (m.fs.inlet_water_frac / (1 - m.fs.inlet_water_frac))\n", + ")\n", + "\n", + "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", + "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", + "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", + "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", + "print(\n", + " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check results\n", + "assert check_optimal_termination(results)\n", + "assert_units_consistent(m)\n", + "\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.19741534, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.65973425, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Optimization\n", + "\n", + "Suppose we wish to characterize the membrane behavior by calculating the maximum inlet water mole fraction allowing a separation factor of at least 100 (typical value for high-efficiency separation processes such as gas separation of CO2/N2). We need to fix total inlet flow to ensure physically-sound solutions. We can quickly modify and resolve the model, and check some key results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# unfix inlet flows but fix total to prevent divergence during solve\n", + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].unfix()\n", + "m.fs.total_flow = Constraint(\n", + " expr=m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " + m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " == 1 * pyunits.mol / pyunits.s\n", + ")\n", + "\n", + "# set criteria for separation factor\n", + "m.fs.sep_min = Constraint(expr=m.fs.separation_factor >= 100)\n", + "\n", + "# set objective - defaults to minimization\n", + "m.fs.obj = Objective(expr=m.fs.inlet_water_frac, sense=maximize)\n", + "\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print results\n", + "\n", + "m.fs.WATER.report()\n", + "m.fs.GLYCOL.report()\n", + "m.fs.PERMEATE.report()\n", + "m.fs.RETENTATE.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", + "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", + "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", + "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", + "print(\n", + " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check results\n", + "assert check_optimal_termination(results)\n", + "assert_units_consistent(m)\n", + "\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.69981938, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.15733020, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Summary\n", + "\n", + "The IDAES Skeleton Unit Model is a powerful tool for implementing relatively simple first-princples, surrogate-based or empirical unit operations. More crucially, users can add their own custom models and integrate them into a larger IDAES flowsheet without adding control volumes or rigorous flow balance and equilibrium calculations when not required. The pervaporation example displays a case where all model equations are empirical correlations or simple manual calculations, with a small number of state variable and port connections, and the Skeleton model avoids complex calculations that impact model tractability. The example also demonstrates adding a custom initialization scheme to handle internally model degrees of freedom, a feature providing greater user control than with most IDAES unit models." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inlet water mole fraction: 0.8424033629056187\n", - "Permeate water mole fraction: 0.9981326967914326\n", - "Separation factor: 100.00006747653647\n", - "Condensation duty: 5.812711140380676 kW\n", - "Duty per mole water recovered: 0.011324013423295606 kW-h / mol\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.18" } - ], - "source": [ - "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", - "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", - "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", - "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", - "print(\n", - " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# check results\n", - "assert check_optimal_termination(results)\n", - "assert_units_consistent(m)\n", - "\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.6998, rel=1e-3)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.1573, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(100.0, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Summary\n", - "\n", - "The IDAES Skeleton Unit Model is a powerful tool for implementing relatively simple first-princples, surrogate-based or empirical unit operations. More crucially, users can add their own custom models and integrate them into a larger IDAES flowsheet without adding control volumes or rigorous flow balance and equilibrium calculations when not required. The pervaporation example displays a case where all model equations are empirical correlations or simple manual calculations, with a small number of state variable and port connections, and the Skeleton model avoids complex calculations that impact model tractability. The example also demonstrates adding a custom initialization scheme to handle internally model degrees of freedom, a feature providing greater user control than with most IDAES unit models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb index 9d9adfbe..e3cbb4d7 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb @@ -583,18 +583,18 @@ "\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1974, rel=1e-3)\n", + ") == pytest.approx(0.19741534, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.6597, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(1038, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" + ") == pytest.approx(0.65973425, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" ] }, { @@ -671,18 +671,18 @@ "\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.6998, rel=1e-3)\n", + ") == pytest.approx(0.69981938, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.1573, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(100.0, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" + ") == pytest.approx(0.15733020, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" ] }, { @@ -719,9 +719,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb index 9d9adfbe..e3cbb4d7 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb @@ -583,18 +583,18 @@ "\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1974, rel=1e-3)\n", + ") == pytest.approx(0.19741534, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.6597, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(1038, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" + ") == pytest.approx(0.65973425, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" ] }, { @@ -671,18 +671,18 @@ "\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.1426, rel=1e-3)\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", "assert value(\n", " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.0002667, rel=1e-3)\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.6998, rel=1e-3)\n", + ") == pytest.approx(0.69981938, rel=1e-5)\n", "assert value(\n", " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.1573, rel=1e-3)\n", - "assert value(m.fs.separation_factor) == pytest.approx(100.0, rel=1e-3)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5813, rel=1e-3)" + ") == pytest.approx(0.15733020, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" ] }, { @@ -719,9 +719,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/tests/__init__.py b/idaes_examples/notebooks/docs/unit_models/operations/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py b/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py new file mode 100644 index 00000000..99bbcd25 --- /dev/null +++ b/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py @@ -0,0 +1,693 @@ +################################################################################# +# 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. +################################################################################# +""" +Author: Brandon Paul +""" +import pytest +from pyomo.environ import ( + assert_optimal_termination, + ConcreteModel, + Set, + value, + Var, + units as pyunits, + as_quantity, +) +from pyomo.common.unittest import assertStructuredAlmostEqual + +from idaes.core import Component +from idaes.core.util.model_statistics import ( + degrees_of_freedom, + fixed_variables_set, + activated_constraints_set, +) +from idaes.core.solvers import get_solver + +from idaes.models.properties.modular_properties.base.generic_property import ( + GenericParameterBlock, +) + +from idaes.models.properties.modular_properties.state_definitions import FpcTP + +from idaes_examples.notebooks.docs.unit_models.operations.eg_h2o_ideal import ( + config_dict, +) + +from idaes.models.properties.tests.test_harness import PropertyTestHarness + +from idaes.core.util.model_diagnostics import DiagnosticsToolbox + +from idaes.core import VaporPhase + +from idaes.models.properties.modular_properties.eos.ideal import Ideal + +import copy + + +# ----------------------------------------------------------------------------- +# Get default solver for testing +solver = get_solver() + + +class TestEGProdIdeal(PropertyTestHarness): + def configure(self): + self.prop_pack = GenericParameterBlock + self.param_args = config_dict + self.prop_args = {} + self.has_density_terms = True + + +class TestParamBlock(object): + @pytest.mark.unit + def test_build(self): + model = ConcreteModel() + model.params = GenericParameterBlock(**config_dict) + + assert isinstance(model.params.phase_list, Set) + assert len(model.params.phase_list) == 1 + for i in model.params.phase_list: + assert i in [ + "Liq", + ] + assert model.params.Liq.is_liquid_phase() + + assert isinstance(model.params.component_list, Set) + assert len(model.params.component_list) == 2 + for i in model.params.component_list: + assert i in ["water", "ethylene_glycol"] + assert isinstance(model.params.get_component(i), Component) + + assert isinstance(model.params._phase_component_set, Set) + assert len(model.params._phase_component_set) == 2 + for i in model.params._phase_component_set: + assert i in [ + ("Liq", "water"), + ("Liq", "ethylene_glycol"), + ] + + assert model.params.config.state_definition == FpcTP + + assertStructuredAlmostEqual( + model.params.config.state_bounds, + { + "flow_mol_phase_comp": (0, 100, 1000, pyunits.mol / pyunits.s), + "temperature": (273.15, 298.15, 450, pyunits.K), + "pressure": (1e3, 1e5, 1e6, pyunits.Pa), + }, + item_callback=as_quantity, + ) + + assert value(model.params.pressure_ref) == 1e5 + assert value(model.params.temperature_ref) == 298.15 + + assert value(model.params.water.mw) == 18.015e-3 + assert value(model.params.water.pressure_crit) == 221.2e5 + assert value(model.params.water.temperature_crit) == 647.3 + + assert value(model.params.ethylene_glycol.mw) == 62.069e-3 + assert value(model.params.ethylene_glycol.pressure_crit) == 77e5 + assert value(model.params.ethylene_glycol.temperature_crit) == 645 + + dt = DiagnosticsToolbox(model) + dt.assert_no_structural_warnings() + + +class TestStateBlock(object): + @pytest.fixture(scope="class") + def model(self): + model = ConcreteModel() + model.params = GenericParameterBlock(**config_dict) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", "water"].fix(100) + model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"].fix(100) + model.props[1].temperature.fix(300) + model.props[1].pressure.fix(101325) + + return model + + @pytest.mark.unit + def test_build(self, model): + # Check state variable values and bounds + assert isinstance(model.props[1].flow_mol_phase_comp, Var) + assert value(model.props[1].flow_mol_phase_comp["Liq", "water"]) == 100 + assert ( + value(model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"]) == 100 + ) + assert model.props[1].flow_mol_phase_comp["Liq", "water"].ub == 1000 + assert model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"].ub == 1000 + assert model.props[1].flow_mol_phase_comp["Liq", "water"].lb == 0 + assert model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"].lb == 0 + + assert isinstance(model.props[1].pressure, Var) + assert value(model.props[1].pressure) == 101325 + assert model.props[1].pressure.ub == 1e6 + assert model.props[1].pressure.lb == 1e3 + + assert isinstance(model.props[1].temperature, Var) + assert value(model.props[1].temperature) == 300 + assert model.props[1].temperature.ub == 450 + assert model.props[1].temperature.lb == 273.15 + + @pytest.mark.unit + def test_define_state_vars(self, model): + sv = model.props[1].define_state_vars() + + assert len(sv) == 3 + for i in sv: + assert i in ["flow_mol_phase_comp", "temperature", "pressure"] + + @pytest.mark.unit + def test_define_port_members(self, model): + sv = model.props[1].define_state_vars() + + assert len(sv) == 3 + for i in sv: + assert i in ["flow_mol_phase_comp", "temperature", "pressure"] + + @pytest.mark.unit + def test_define_display_vars(self, model): + sv = model.props[1].define_display_vars() + + assert len(sv) == 3 + for i in sv: + assert i in [ + "Molar Flowrate", + "Temperature", + "Pressure", + ] + + @pytest.mark.unit + def test_structural_diagnostics(self, model): + dt = DiagnosticsToolbox(model) + dt.assert_no_structural_warnings() + + @pytest.mark.unit + def test_basic_scaling(self, model): + assert len(model.props[1].scaling_factor) == 12 + assert model.props[1].scaling_factor[model.props[1].flow_mol] == 1e-2 + assert ( + model.props[1].scaling_factor[model.props[1].flow_mol_comp["water"]] == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_comp["ethylene_glycol"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[model.props[1].flow_mol_phase["Liq"]] == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_phase_comp["Liq", "water"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[model.props[1].mole_frac_comp["water"]] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_comp["ethylene_glycol"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_phase_comp["Liq", "water"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_phase_comp["Liq", "ethylene_glycol"] + ] + == 1000 + ) + assert model.props[1].scaling_factor[model.props[1].pressure] == 1e-5 + assert model.props[1].scaling_factor[model.props[1].temperature] == 1e-2 + + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_initialize(self, model): + orig_fixed_vars = fixed_variables_set(model) + orig_act_consts = activated_constraints_set(model) + + model.props.initialize(optarg={"tol": 1e-6}) + + assert degrees_of_freedom(model) == 0 + + fin_fixed_vars = fixed_variables_set(model) + fin_act_consts = activated_constraints_set(model) + + assert len(fin_act_consts) == len(orig_act_consts) + assert len(fin_fixed_vars) == len(orig_fixed_vars) + + for c in fin_act_consts: + assert c in orig_act_consts + for v in fin_fixed_vars: + assert v in orig_fixed_vars + + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_solve(self, model): + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + @pytest.mark.unit + def test_numerical_diagnostics(self, model): + dt = DiagnosticsToolbox(model) + dt.assert_no_numerical_warnings() + + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_solution(self, model): + # Check results + assert value( + model.props[1].flow_mol_phase_comp["Liq", "water"] + ) == pytest.approx(100, abs=1e-4) + assert value( + model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"] + ) == pytest.approx(100, abs=1e-4) + + assert value(model.props[1].temperature) == pytest.approx(300, abs=1e-4) + assert value(model.props[1].pressure) == pytest.approx(101325, abs=1e-4) + + +class TestPerrysProperties(object): + @pytest.fixture(scope="class") + def density_temperatures(self): + # water, ethylene glycol reference temperatures + # from Perry's Chemical Engineers' Handbook 7th Ed. 2-94 to 2-98 + components = ["water", "ethylene_glycol"] + temperatures = dict(zip(components, [[273.16, 333.15], [260.15, 719.7]])) + + return temperatures + + @pytest.fixture(scope="class") + def densities(self): + # water, ethylene glycol densities from + # Perry's Chemical Engineers' Handbook 7th Ed. 2-94 to 2-98 + components = ["water", "ethylene_glycol"] + densities = dict(zip(components, [[55.583, 54.703], [18.31, 5.234]])) + + return densities + + @pytest.fixture(scope="class") + def heat_capacity_temperatures(self): + # water, ethylene glycol reference temperatures + # from Perry's Chemical Engineers' Handbook 7th Ed. 2-170 to 2-174 + components = ["water", "ethylene_glycol"] + temperatures = dict(zip(components, [[273.16, 533.15], [260.15, 493.15]])) + + return temperatures + + @pytest.fixture(scope="class") + def heat_capacities(self): + # water, ethylene glycol heat capacities from + # Perry's Chemical Engineers' Handbook 7th Ed. 2-170 to 2-174 + components = ["water", "ethylene_glycol"] + heat_capacities = dict( + zip(components, [[0.7615e5, 0.8939e5], [1.36661e5, 2.0598e5]]) + ) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference(self): + # water, ethylene glycol heat capacities from + # NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + heat_capacities = dict(zip(components, [0.7538e5, 0.1498e5])) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference_temperatures(self): + # water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + temperatures = dict(zip(components, [298.0, 298.0])) + + return temperatures + + @pytest.mark.parametrize("component", ["water", "ethylene_glycol"]) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_liquid_densities( + self, component, test_point, density_temperatures, densities + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", component].fix(100) + + # change lower bound for testing + model.props[1].temperature.setlb(150) + + model.props[1].temperature.fix(density_temperatures[component][test_point]) + model.props[1].pressure.fix(101325) + + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + assert value( + pyunits.convert( + model.props[1].dens_mol, to_units=pyunits.kmol / pyunits.m**3 + ) + ) == pytest.approx(densities[component][test_point], rel=1e-4) + + @pytest.mark.parametrize("component", ["water", "ethylene_glycol"]) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_liquid_heat_capacities_enthalpy( + self, + component, + test_point, + heat_capacity_temperatures, + heat_capacities, + heat_capacity_reference, + heat_capacity_reference_temperatures, + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", component].fix(100) + + model.props[1].pressure.fix(101325) + + # calculate reference point + + model.props[1].temperature.fix(heat_capacity_reference_temperatures[component]) + + results = solver.solve(model) + + enth_mol_ref = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_ref = heat_capacity_reference_temperatures[component] * pyunits.K + cp_mol_ref = ( + heat_capacity_reference[component] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # calculate test point + + model.props[1].temperature.fix( + heat_capacity_temperatures[component][test_point] + ) + + results = solver.solve(model) + + enth_mol_test = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_test = heat_capacity_temperatures[component][test_point] * pyunits.K + cp_mol_test = ( + heat_capacities[component][test_point] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + + assert value( + pyunits.convert(enth_mol_test, to_units=pyunits.J / pyunits.mol) + ) == pytest.approx( + value( + pyunits.convert( + 0.5 * (cp_mol_test + cp_mol_ref) * (temp_test - temp_ref) + + enth_mol_ref, + to_units=pyunits.J / pyunits.mol, + ) + ), + rel=1e-1, # using 1e-1 tol to check against trapezoid rule estimation of integral + ) + + +class TestRPP4Properties(object): + @pytest.fixture(scope="class") + def heat_capacity_temperatures(self): + # water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + temperatures = dict(zip(components, [[545, 632], [500, 600]])) + + return temperatures + + @pytest.fixture(scope="class") + def heat_capacities(self): + # water, ethylene glycol heat capacities from + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + heat_capacities = dict(zip(components, [[35.70, 36.69], [113.64, 125.65]])) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference(self): + # water, ethylene glycol heat capacities from + # NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + heat_capacities = dict(zip(components, [35.22, 97.99])) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference_temperatures(self): + # water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + temperatures = dict(zip(components, [500, 400])) + + return temperatures + + @pytest.fixture(scope="class") + def saturation_pressure_temperatures(self): + # water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + temperatures = dict(zip(components, [[300.25, 350.16], [387, 473]])) + + return temperatures + + @pytest.fixture(scope="class") + def saturation_pressures(self): + # ethylene glycol saturation pressures from + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["water", "ethylene_glycol"] + pressures = dict( + zip(components, [[0.03591e5, 0.4194e5], [0.04257e5, 1.0934e5]]) + ) + + return pressures + + @pytest.mark.parametrize("component", ["water", "ethylene_glycol"]) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_vapor_heat_capacities_enthalpy( + self, + component, + test_point, + heat_capacity_temperatures, + heat_capacities, + heat_capacity_reference, + heat_capacity_reference_temperatures, + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + config_dict_component_only["phases"] = { + "Vap": {"type": VaporPhase, "equation_of_state": Ideal} + } + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Vap", component].fix(100) + + model.props[1].pressure.fix(101325) + + # calculate reference point + + model.props[1].temperature.fix(heat_capacity_reference_temperatures[component]) + + results = solver.solve(model) + + enth_mol_ref = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_ref = heat_capacity_reference_temperatures[component] * pyunits.K + cp_mol_ref = ( + heat_capacity_reference[component] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # calculate test point + + model.props[1].temperature.fix( + heat_capacity_temperatures[component][test_point] + ) + + results = solver.solve(model) + + enth_mol_test = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_test = heat_capacity_temperatures[component][test_point] * pyunits.K + cp_mol_test = ( + heat_capacities[component][test_point] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + + assert value( + pyunits.convert(enth_mol_test, to_units=pyunits.J / pyunits.mol) + ) == pytest.approx( + value( + pyunits.convert( + 0.5 * (cp_mol_test + cp_mol_ref) * (temp_test - temp_ref) + + enth_mol_ref, + to_units=pyunits.J / pyunits.mol, + ) + ), + rel=1.15e-1, # using 1.15e-1 tol to check against trapezoid rule estimation of integral + # all values match within 1e-1, except ethylene glycol test point 0 + ) + + @pytest.mark.parametrize("component", ["water", "ethylene_glycol"]) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_saturation_pressures( + self, + component, + test_point, + saturation_pressure_temperatures, + saturation_pressures, + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + config_dict_component_only["phases"] = { + "Vap": {"type": VaporPhase, "equation_of_state": Ideal} + } + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Vap", component].fix(100) + + model.props[1].temperature.fix( + saturation_pressure_temperatures[component][test_point] + ) + model.props[1].pressure.fix(101325) + + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + print(value(model.props[1].pressure_sat_comp[component])) + assert value(model.props[1].pressure_sat_comp[component]) == pytest.approx( + saturation_pressures[component][test_point], rel=1.5e-2 + ) # match within 1.5% diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb index 95e99c96..0d0fc742 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb @@ -237,9 +237,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = CSTR(\n", @@ -584,7 +582,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -599,7 +597,7 @@ "source": [ "import pytest\n", "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.458138, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(8.004012, rel=1e-5)" ] }, { @@ -622,8 +620,8 @@ "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", ")" ] }, @@ -637,10 +635,10 @@ }, "outputs": [], "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, abs=1e-3)\n", - "assert value(m.fs.R101.volume[0]) == pytest.approx(5.5380, abs=1e-3)\n", - "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.6566, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2827, abs=1e-3)" + "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, rel=1e-5)\n", + "assert value(m.fs.R101.volume[0]) == pytest.approx(5.5380, rel=1e-5)\n", + "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.8675, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.29261, rel=1e-5)" ] }, { @@ -751,7 +749,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -774,8 +772,8 @@ }, "outputs": [], "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.889709, abs=1e-3)\n", - "assert value(m.fs.R101.volume[0]) == pytest.approx(18.927, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(8.318177, rel=1e-5)\n", + "assert value(m.fs.R101.volume[0]) == pytest.approx(18.927, rel=1e-5)" ] }, { @@ -794,17 +792,17 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", ")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -820,10 +818,10 @@ }, "outputs": [], "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, abs=1e-3)\n", - "assert value(m.fs.R101.volume[0] * 1.2) == pytest.approx(22.712, abs=1e-3)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415, abs=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, abs=1e-3)" + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", + "assert value(m.fs.R101.volume[0] * 1.2) == pytest.approx(22.712471, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-5)" ] }, { @@ -851,7 +849,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb index 0186ee75..891c3495 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb @@ -1,1254 +1,723 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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", - "# Flowsheet Continuous Stirred Tank Reactor (CSTR) Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES CSTR unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160.\n", - "\n", - "Ethylene glycol (EG) is a high-demand chemical, with billions of pounds produced every year for applications such as vehicle anti-freeze. EG may be readily obtained from the hydrolysis of ethylene oxide in the presence of a catalytic intermediate. In this example, an aqueous solution of ethylene oxide hydrolizes after mixing with an aqueous solution of sulfuric acid catalyst:\n", - "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", - "\n", - "This reaction often occurs by two mechanisms, as the catalyst may bind to either reactant before the final hydrolysis step; we will simplify the reaction to a single step for this example.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing ethylene oxide and catalyst solutions of fixed concentrations to produce 200 MM lb/year of EG. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a heater H101 to preheat the feed to the reaction temperature, and a CSTR unit R101 with an external cooling system to remove heat generated by the exothermic reaction. We will assume ideal solutions and thermodynamics for this flowsheet, as well as well-mixed liquid behavior (no vapor phase) in the reactor. The properties required for this module are available in the same directory:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](egprod_flowsheet.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import (\n", - " GenericParameterBlock,\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, CSTR, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", - "\n", - "The reaction package here assumes Arrhenius kinetic behavior for the CSTR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", - "\n", - "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", - "\n", - "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", - "$k$ - reaction rate constant per second \n", - "$V$ - volume of CSTR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", - "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", - "$k_0$ - pre-exponential Arrhenius factor per second \n", - "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", - "$R$ - gas constant in J/mol-K \n", - "$T$ - reactor temperature in K\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "ParamEst parameter estimation: \n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `CSTR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = CSTR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `CSTR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom (reactor conversion or reactor volume) since we have not yet defined the CSTR performance equation. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and volume." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15\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", + "# Flowsheet Continuous Stirred Tank Reactor (CSTR) Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES CSTR unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160.\n", + "\n", + "Ethylene glycol (EG) is a high-demand chemical, with billions of pounds produced every year for applications such as vehicle anti-freeze. EG may be readily obtained from the hydrolysis of ethylene oxide in the presence of a catalytic intermediate. In this example, an aqueous solution of ethylene oxide hydrolizes after mixing with an aqueous solution of sulfuric acid catalyst:\n", + "\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", + "\n", + "This reaction often occurs by two mechanisms, as the catalyst may bind to either reactant before the final hydrolysis step; we will simplify the reaction to a single step for this example.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing ethylene oxide and catalyst solutions of fixed concentrations to produce 200 MM lb/year of EG. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a heater H101 to preheat the feed to the reaction temperature, and a CSTR unit R101 with an external cooling system to remove heat generated by the exothermic reaction. We will assume ideal solutions and thermodynamics for this flowsheet, as well as well-mixed liquid behavior (no vapor phase) in the reactor. The properties required for this module are available in the same directory:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](egprod_flowsheet.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import (\n", + " GenericParameterBlock,\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, CSTR, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", + "\n", + "The reaction package here assumes Arrhenius kinetic behavior for the CSTR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", + "\n", + "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", + "\n", + "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", + "$k$ - reaction rate constant per second \n", + "$V$ - volume of CSTR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", + "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", + "$k_0$ - pre-exponential Arrhenius factor per second \n", + "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", + "$R$ - gas constant in J/mol-K \n", + "$T$ - reactor temperature in K\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "ParamEst parameter estimation: \n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `CSTR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = CSTR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `CSTR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom (reactor conversion or reactor volume) since we have not yet defined the CSTR performance equation. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and volume." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll add constraints defining the reactor volume and conversion in relation to the stream properties. Particularly, we want to use our CSTR performance relation: \n", + "\n", + "$V = \\frac{v_0 X} {k(1-X)}$, where the `CSTR` reaction volume $V$ will be specified, the inlet volumetric flow $v_0$ is determined by stream properties, $k$ is calculated by the reaction package, and $X$ will be calculated. Reactor volume is commonly selected as a specification in simulation problems, and choosing conversion is often to perform reactor design.\n", + "\n", + "For the `CSTR`, we have to define the conversion in terms of ethylene oxide as well as the `CSTR` reaction volume. This requires us to create new variables and constraints relating reactor properties to stream properties. Note that the `CSTR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `CSTR` model. Additionally, the heat duty is not fixed, since the heat of reaction depends on the reactor conversion (through the extent of reaction and heat of reaction). We'll estimate 80% conversion for our initial flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)\n", + "\n", + "m.fs.R101.volume.fix(5.538 * pyunits.m**3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.R101.volume.unfix()\n", + "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", + "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"CSTR reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", + ")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll add constraints defining the reactor volume and conversion in relation to the stream properties. Particularly, we want to use our CSTR performance relation: \n", - "\n", - "$V = \\frac{v_0 X} {k(1-X)}$, where the `CSTR` reaction volume $V$ will be specified, the inlet volumetric flow $v_0$ is determined by stream properties, $k$ is calculated by the reaction package, and $X$ will be calculated. Reactor volume is commonly selected as a specification in simulation problems, and choosing conversion is often to perform reactor design.\n", - "\n", - "For the `CSTR`, we have to define the conversion in terms of ethylene oxide as well as the `CSTR` reaction volume. This requires us to create new variables and constraints relating reactor properties to stream properties. Note that the `CSTR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `CSTR` model. Additionally, the heat duty is not fixed, since the heat of reaction depends on the reactor conversion (through the extent of reaction and heat of reaction). We'll estimate 80% conversion for our initial flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)\n", - "\n", - "m.fs.R101.volume.fix(5.538 * pyunits.m**3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] + ], + "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": "3.9.18" } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.OXIDE.properties: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.OXIDE.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.OXIDE.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.OXIDE: Initialization Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.ACID.properties: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.ACID.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.ACID.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.ACID: Initialization Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.reagent_feed_state: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.reagent_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.catalyst_feed_state: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.catalyst_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:02 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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...: 345\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 393\n", - "\n", - "Total number of variables............................: 96\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 87\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 96\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 1.30e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 2.66e+06 1.65e+01 -1.0 9.75e+06 - 6.77e-02 9.90e-01h 1\n", - " 2 0.0000000e+00 2.36e+04 2.90e+02 -1.0 9.74e+04 - 7.00e-01 9.90e-01h 1\n", - " 3 0.0000000e+00 2.43e+02 1.43e+03 -1.0 9.75e+02 - 9.75e-01 9.90e-01h 1\n", - " 4 0.0000000e+00 1.85e+00 3.22e+03 -1.0 1.07e+01 - 9.90e-01 9.92e-01h 1\n", - " 5 0.0000000e+00 7.45e-08 4.66e+03 -1.0 8.41e-02 - 9.92e-01 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 5\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.6686895357338362e+06 1.6686895357338362e+06\n", - "Constraint violation....: 1.5633344889834636e-09 7.4505805969238281e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.5633344889834636e-09 1.6686895357338362e+06\n", - "\n", - "\n", - "Number of objective function evaluations = 6\n", - "Number of objective gradient evaluations = 6\n", - "Number of equality constraint evaluations = 6\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 6\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 5\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $3.458 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -5.6566e+06 : watt : False : (None, None)\n", - " Volume : 5.5380 : meter ** 3 : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 11.600\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 193.20\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 46.400\n", - " Temperature kelvin 328.15 328.27\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n", - "\n", - "Assuming a 20% design factor for reactor volume,total CSTR volume required = 6.646 m^3 = 1755.582 gal\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.R101.volume.unfix()\n", - "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", - "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "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...: 348\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 408\n", - "\n", - "Total number of variables............................: 98\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 89\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 96\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 1\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 3.4581382e+06 1.76e+06 6.34e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.4605407e+06 1.75e+06 1.17e+01 -1.0 6.97e+05 - 7.82e-02 6.15e-03h 1\n", - " 2 3.4957712e+06 1.61e+06 5.18e+01 -1.0 6.96e+05 - 6.78e-02 8.29e-02h 1\n", - " 3 3.5296145e+06 1.47e+06 7.10e+01 -1.0 6.42e+05 - 3.96e-01 8.63e-02h 1\n", - " 4 3.6874124e+06 8.26e+05 2.09e+03 -1.0 5.90e+05 - 7.61e-01 4.38e-01h 1\n", - " 5 3.8876849e+06 1.02e+04 2.43e+03 -1.0 3.31e+05 - 9.39e-01 9.90e-01h 1\n", - " 6 3.8896921e+06 8.92e+01 2.75e+00 -1.0 3.31e+03 - 9.90e-01 9.91e-01h 1\n", - " 7 3.8897098e+06 3.14e-05 1.82e+03 -1.0 2.89e+01 - 9.91e-01 1.00e+00h 1\n", - " 8 3.8897096e+06 1.38e-06 3.37e-04 -1.7 1.42e-01 - 1.00e+00 1.00e+00f 1\n", - " 9 3.8897096e+06 8.68e-08 2.12e-05 -5.7 3.57e-02 - 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", - " 10 3.8897096e+06 2.79e-09 4.13e-07 -7.0 6.61e-06 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 3.8897095750395539e+06 3.8897095750395539e+06\n", - "Dual infeasibility......: 4.1300208155718159e-07 4.1300208155718159e-07\n", - "Constraint violation....: 8.9819498380703823e-15 2.7939677238464360e-09\n", - "Complementarity.........: 9.0909160154398524e-08 9.0909160154398524e-08\n", - "Overall NLP error.......: 9.0909160154398524e-08 4.1300208155718159e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 11\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 11\n", - "Number of Lagrangian Hessian evaluations = 10\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" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $3.890 million per year\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 699.26 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 58.000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 239.60\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 2.0000e-05\n", - " Temperature kelvin 298.15 328.15\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n", - "\n", - "CSTR reactor results\n", - "\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 : -6.3635e+06 : watt : False : (None, None)\n", - " Volume : 18.927 : meter ** 3 : False : (0.0, 18.927058919999997)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 5.8000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 187.40\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 52.200\n", - " Temperature kelvin 328.15 338.37\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"CSTR reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 328.150 K\n", - "\n", - "Assuming a 20% design factor for reactor volume,total CSTR volume required = 22.712 m^3 = 6000.000 gal\n", - "\n", - "Ethylene glycol produced = 225.415 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", - ")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb index 517309b5..a77d3809 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb @@ -54,7 +54,7 @@ "\n", "Ethylene glycol (EG) is a high-demand chemical, with billions of pounds produced every year for applications such as vehicle anti-freeze. EG may be readily obtained from the hydrolysis of ethylene oxide in the presence of a catalytic intermediate. In this example, an aqueous solution of ethylene oxide hydrolizes after mixing with an aqueous solution of sulfuric acid catalyst:\n", "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", "\n", "This reaction often occurs by two mechanisms, as the catalyst may bind to either reactant before the final hydrolysis step; we will simplify the reaction to a single step for this example.\n", "\n", @@ -237,9 +237,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = CSTR(\n", @@ -584,7 +582,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -599,7 +597,7 @@ "source": [ "import pytest\n", "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.458138, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(8.004012, rel=1e-5)" ] }, { @@ -622,8 +620,8 @@ "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", ")" ] }, @@ -637,10 +635,10 @@ }, "outputs": [], "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, abs=1e-3)\n", - "assert value(m.fs.R101.volume[0]) == pytest.approx(5.5380, abs=1e-3)\n", - "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.6566, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2827, abs=1e-3)" + "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, rel=1e-5)\n", + "assert value(m.fs.R101.volume[0]) == pytest.approx(5.5380, rel=1e-5)\n", + "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.8675, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.29261, rel=1e-5)" ] }, { @@ -751,7 +749,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -774,8 +772,8 @@ }, "outputs": [], "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.889709, abs=1e-3)\n", - "assert value(m.fs.R101.volume[0]) == pytest.approx(18.927, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(8.318177, rel=1e-5)\n", + "assert value(m.fs.R101.volume[0]) == pytest.approx(18.927, rel=1e-5)" ] }, { @@ -794,17 +792,17 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", ")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -820,10 +818,10 @@ }, "outputs": [], "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, abs=1e-3)\n", - "assert value(m.fs.R101.volume[0] * 1.2) == pytest.approx(22.712, abs=1e-3)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415, abs=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, abs=1e-3)" + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", + "assert value(m.fs.R101.volume[0] * 1.2) == pytest.approx(22.712471, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-5)" ] }, { @@ -851,9 +849,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb index 40b3f0a5..946169b9 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb @@ -54,7 +54,7 @@ "\n", "Ethylene glycol (EG) is a high-demand chemical, with billions of pounds produced every year for applications such as vehicle anti-freeze. EG may be readily obtained from the hydrolysis of ethylene oxide in the presence of a catalytic intermediate. In this example, an aqueous solution of ethylene oxide hydrolizes after mixing with an aqueous solution of sulfuric acid catalyst:\n", "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", "\n", "This reaction often occurs by two mechanisms, as the catalyst may bind to either reactant before the final hydrolysis step; we will simplify the reaction to a single step for this example.\n", "\n", @@ -237,9 +237,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = CSTR(\n", @@ -540,7 +538,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -563,8 +561,8 @@ "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", ")" ] }, @@ -647,7 +645,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -676,17 +674,17 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", ")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -717,9 +715,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py b/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py index d9aab2f4..1c18b1fc 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py +++ b/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py @@ -45,10 +45,9 @@ # 4th edition, Chemical Engineering Series - Robert C. Reid # [2] Perry's Chemical Engineers' Handbook 7th Ed. # [3] NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ -# Retrieved 23rd September, 2021 -# [4] B. Ruscic and D. H. Bross, Active Thermochemical Tables (ATcT) -# values based on ver. 1.122r of the Thermochemical Network (2021); -# available at ATcT.anl.gov +# Retrieved 18th March, 2024 +# [4] Chemeo - Chemical properties of Sulfuric Acid, +# https://www.chemeo.com/cid/24-837-6/Sulfuric-Acid # [5] CRC Handbook of Chemistry and Physics, 97th Ed., W.M. Haynes # [6] Journal of Physical and Chemical Reference Data 20, 1157 # (1991); https:// doi.org/10.1063/1.555899 @@ -56,175 +55,233 @@ config_dict = { # Specifying components "components": { - 'ethylene_oxide': - {"type": Component, - "elemental_composition": {"C": 2, "H": 4, "O": 1}, - "dens_mol_liq_comp": Perrys, - "enth_mol_liq_comp": Perrys, - "enth_mol_ig_comp": RPP4, - "pressure_sat_comp": RPP4, - "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, - "parameter_data": { - "mw": (44.054E-3, pyunits.kg/pyunits.mol), # [1] - "pressure_crit": (71.9e5, pyunits.Pa), # [1] - "temperature_crit": (469, pyunits.K), # [1] - "dens_mol_liq_comp_coeff": { - 'eqn_type': 1, - '1': (1.1836, pyunits.kmol*pyunits.m**-3), # [2] pg. 2-98 - '2': (0.26024, None), - '3': (469.15, pyunits.K), - '4': (0.2696, None)}, - "cp_mol_ig_comp_coeff": { - 'A': (-7.519E0, pyunits.J/pyunits.mol/pyunits.K), # [1] - 'B': (2.222E-1, pyunits.J/pyunits.mol/pyunits.K**2), - 'C': (-1.256E-4, pyunits.J/pyunits.mol/pyunits.K**3), - 'D': (2.592E-8, pyunits.J/pyunits.mol/pyunits.K**4)}, - "cp_mol_liq_comp_coeff": { - '1': (1.4471E2, pyunits.J/pyunits.kmol/pyunits.K), # [2] - '2': (-7.5887E-1, pyunits.J/pyunits.kmol/pyunits.K**2), - '3': (2.8261E-3, pyunits.J/pyunits.kmol/pyunits.K**3), - '4': (-3.064E-6, pyunits.J/pyunits.kmol/pyunits.K**4), - '5': (0, pyunits.J/pyunits.kmol/pyunits.K**5)}, - "enth_mol_form_liq_comp_ref": ( - -95.7e3, pyunits.J/pyunits.mol), # [4] - "enth_mol_form_vap_comp_ref": ( - -52.61e3, pyunits.J/pyunits.mol), # [3] - "pressure_sat_comp_coeff": {'A': (-6.56234, None), # [1] - 'B': (0.42696, None), - 'C': (-1.25638, None), - 'D': (-3.18133, None)}}}, - 'water': - {"type": Component, - "elemental_composition": {"H": 2, "O": 1}, - "dens_mol_liq_comp": Perrys, - "enth_mol_liq_comp": Perrys, - "enth_mol_ig_comp": RPP4, - "pressure_sat_comp": RPP4, - "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, - "parameter_data": { - "mw": (18.015E-3, pyunits.kg/pyunits.mol), # [1] - "pressure_crit": (221.2e5, pyunits.Pa), # [1] - "temperature_crit": (647.3, pyunits.K), # [1] - "dens_mol_liq_comp_coeff": { - 'eqn_type': 2, - '1': (-13.851, pyunits.kmol/pyunits.m**3), # [2]pg. 2-98 - '2': (0.64038, pyunits.kmol/pyunits.m**3/pyunits.K), - '3': (-0.00191, pyunits.kmol/pyunits.m**3/pyunits.K**2), - '4': (1.8211E-6, pyunits.kmol/pyunits.m**3/pyunits.K**3)}, - "cp_mol_ig_comp_coeff": { - 'A': (3.194E1, pyunits.J/pyunits.mol/pyunits.K), # [1] - 'B': (1.436E-3, pyunits.J/pyunits.mol/pyunits.K**2), - 'C': (2.432E-5, pyunits.J/pyunits.mol/pyunits.K**3), - 'D': (-1.176E-8, pyunits.J/pyunits.mol/pyunits.K**4)}, - "cp_mol_liq_comp_coeff": { - '1': (2.7637E2, pyunits.J/pyunits.kmol/pyunits.K), # [2] - '2': (-2.0901, pyunits.J/pyunits.kmol/pyunits.K**2), - '3': (8.125E-3, pyunits.J/pyunits.kmol/pyunits.K**3), - '4': (-1.4116E-5, pyunits.J/pyunits.kmol/pyunits.K**4), - '5': (9.3701E-9, pyunits.J/pyunits.kmol/pyunits.K**5)}, - "enth_mol_form_liq_comp_ref": ( - -285.83e3, pyunits.J/pyunits.mol), # [3] - "enth_mol_form_vap_comp_ref": ( - -241.836e3, pyunits.J/pyunits.mol), # [3] - "pressure_sat_comp_coeff": {'A': (-7.76451, None), # [1] - 'B': (1.45838, None), - 'C': (-2.77580, None), - 'D': (-1.23303, None)}}}, - 'sulfuric_acid': - {"type": Component, - "elemental_composition": {"H": 2, "S": 1, "O": 4}, - "dens_mol_liq_comp": Perrys, # fitted to this equation form - "enth_mol_liq_comp": Perrys, # fitted to this equation form - "enth_mol_ig_comp": NIST, - "pressure_sat_comp": RPP4, # fitted to this equation form - "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, - "parameter_data": { - "mw": (98.078E-3, pyunits.kg/pyunits.mol), # [4] - "pressure_crit": (129.4262e5, pyunits.Pa), # [4] - "temperature_crit": (590.76, pyunits.K), # [4] - "dens_mol_liq_comp_coeff": { - 'eqn_type': 2, - '1': (23.669, pyunits.kmol/pyunits.m**3), # [5] - '2': (-2.5307E-2, pyunits.kmol/pyunits.m**3/pyunits.K), - '3': (3.3523E-4, pyunits.kmol/pyunits.m**3/pyunits.K**2), - '4': (-1.8538E-7, pyunits.kmol/pyunits.m**3/pyunits.K**3)}, - "cp_mol_ig_comp_coeff": { - 'A': (47.28924, pyunits.J/pyunits.mol/pyunits.K), # [3] - 'B': (190.3314, pyunits.J/pyunits.mol/pyunits.K/pyunits.kK), - 'C': (-148.1299, pyunits.J/pyunits.mol/pyunits.K/pyunits.kK**2), - 'D': (43.86631, pyunits.J/pyunits.mol/pyunits.K/pyunits.kK**3), - 'E': (-0.740016, pyunits.J/pyunits.mol/pyunits.K/pyunits.kK**-2), - 'F': (-758.9525, pyunits.kJ/pyunits.mol), - 'G': (301.2961, pyunits.J/pyunits.mol/pyunits.K), - 'H': (-735.1288, pyunits.kJ/pyunits.mol)}, - "cp_mol_liq_comp_coeff": { - '1': (-202.695, pyunits.J/pyunits.kmol/pyunits.K), # [6] - '2': (2.9994, pyunits.J/pyunits.kmol/pyunits.K**2), - '3': (-9.239e-3, pyunits.J/pyunits.kmol/pyunits.K**3), - '4': (1.0113e-5, pyunits.J/pyunits.kmol/pyunits.K**4), - '5': (0, pyunits.J/pyunits.kmol/pyunits.K**5)}, - "enth_mol_form_liq_comp_ref": ( - -868.73e3, pyunits.J/pyunits.mol), # [4] - "enth_mol_form_vap_comp_ref": ( - -801.14e3, pyunits.J/pyunits.mol), # [4] - "pressure_sat_comp_coeff": {'A': (-18.122, None), # [5] - 'B': (10.596, None), - 'C': (-18.908, None), - 'D': (-32.728, None)}}}, - 'ethylene_glycol': - {"type": Component, - "elemental_composition": {"C": 2, "H": 6, "O": 2}, - "dens_mol_liq_comp": Perrys, - "enth_mol_liq_comp": Perrys, - "enth_mol_ig_comp": RPP4, - "pressure_sat_comp": RPP4, - "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, - "parameter_data": { - "mw": (62.069E-3, pyunits.kg/pyunits.mol), # [1] - "pressure_crit": (77e5, pyunits.Pa), # [1] - "temperature_crit": (645, pyunits.K), # [1] - "dens_mol_liq_comp_coeff": { - 'eqn_type': 1, - '1': (1.315, pyunits.kmol*pyunits.m**-3), # [2] pg. 2-98 - '2': (0.25125, None), - '3': (720, pyunits.K), - '4': (0.21868, None)}, - "cp_mol_ig_comp_coeff": { - 'A': (3.570E1, pyunits.J/pyunits.mol/pyunits.K), # [1] - 'B': (2.483E-1, pyunits.J/pyunits.mol/pyunits.K**2), - 'C': (-1.497E-4, pyunits.J/pyunits.mol/pyunits.K**3), - 'D': (3.010E-8, pyunits.J/pyunits.mol/pyunits.K**4)}, - "cp_mol_liq_comp_coeff": { - '1': (3.5540E1, pyunits.J/pyunits.kmol/pyunits.K), # [2] - '2': (4.3678E-1, pyunits.J/pyunits.kmol/pyunits.K**2), - '3': (-1.8486E-4, pyunits.J/pyunits.kmol/pyunits.K**3), - '4': (0, pyunits.J/pyunits.kmol/pyunits.K**4), - '5': (0, pyunits.J/pyunits.kmol/pyunits.K**5)}, - "enth_mol_form_liq_comp_ref": ( - -455.24e3, pyunits.J/pyunits.mol), # [3] - "enth_mol_form_vap_comp_ref": ( - -389.37e3, pyunits.J/pyunits.mol), # [3] - "pressure_sat_comp_coeff": {'A': (13.6299, None), # [1] - 'B': (6022.18, None), - 'C': (-28.25, None), - 'D': (0, None)}}}}, - + "ethylene_oxide": { + "type": Component, + "elemental_composition": {"C": 2, "H": 4, "O": 1}, + "dens_mol_liq_comp": Perrys, + "enth_mol_liq_comp": Perrys, + "enth_mol_ig_comp": RPP4, + "pressure_sat_comp": RPP4, + "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, + "parameter_data": { + "mw": (44.054e-3, pyunits.kg / pyunits.mol), # [1] pg. 676 + "pressure_crit": (71.9e5, pyunits.Pa), # [1] pg. 676 + "temperature_crit": (469, pyunits.K), # [1] pg. 676 + "dens_mol_liq_comp_coeff": { # [2] pg. 2-97 + "eqn_type": 1, + "1": (1.836, pyunits.kmol * pyunits.m**-3), + "2": (0.26024, None), + "3": (469.15, pyunits.K), + "4": (0.2696, None), + }, + "cp_mol_ig_comp_coeff": { # [1] pg. 677 + "A": (-7.519e0, pyunits.J / pyunits.mol / pyunits.K), + "B": (2.222e-1, pyunits.J / pyunits.mol / pyunits.K**2), + "C": (-1.256e-4, pyunits.J / pyunits.mol / pyunits.K**3), + "D": (2.592e-8, pyunits.J / pyunits.mol / pyunits.K**4), + }, + "cp_mol_liq_comp_coeff": { # [2] pg. 2-173 + "1": (1.4471e5, pyunits.J / pyunits.kmol / pyunits.K), + "2": (-7.5887e2, pyunits.J / pyunits.kmol / pyunits.K**2), + "3": (2.8261, pyunits.J / pyunits.kmol / pyunits.K**3), + "4": (-3.0640e-3, pyunits.J / pyunits.kmol / pyunits.K**4), + "5": (0, pyunits.J / pyunits.kmol / pyunits.K**5), + }, + "enth_mol_form_liq_comp_ref": ( + -95.7e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "enth_mol_form_vap_comp_ref": ( + -52.64e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "pressure_sat_comp_coeff": { + "A": (-6.56234, None), # [1] pg. 678 + "B": (0.42696, None), + "C": (-1.25638, None), + "D": (-3.18133, None), + }, + }, + }, + "water": { + "type": Component, + "elemental_composition": {"H": 2, "O": 1}, + "dens_mol_liq_comp": Perrys, + "enth_mol_liq_comp": Perrys, + "enth_mol_ig_comp": RPP4, + "pressure_sat_comp": RPP4, + "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, + "parameter_data": { + "mw": (18.015e-3, pyunits.kg / pyunits.mol), # [1] pg. 667 + "pressure_crit": (221.2e5, pyunits.Pa), # [1] pg. 667 + "temperature_crit": (647.3, pyunits.K), # [1] pg. 667 + "dens_mol_liq_comp_coeff": { # [2] pg. 2-98 + "eqn_type": 1, + "1": (5.459, pyunits.kmol * pyunits.m**-3), + "2": (0.30542, None), + "3": (647.13, pyunits.K), + "4": (0.081, None), + }, + "cp_mol_ig_comp_coeff": { # [1] pg. 668 + "A": (3.224e1, pyunits.J / pyunits.mol / pyunits.K), + "B": (1.924e-3, pyunits.J / pyunits.mol / pyunits.K**2), + "C": (1.055e-5, pyunits.J / pyunits.mol / pyunits.K**3), + "D": (-3.596e-9, pyunits.J / pyunits.mol / pyunits.K**4), + }, + "cp_mol_liq_comp_coeff": { # [2] pg. 2-174 + "1": (2.7637e5, pyunits.J / pyunits.kmol / pyunits.K), + "2": (-2.0901e3, pyunits.J / pyunits.kmol / pyunits.K**2), + "3": (8.1250, pyunits.J / pyunits.kmol / pyunits.K**3), + "4": (-1.4116e-2, pyunits.J / pyunits.kmol / pyunits.K**4), + "5": (9.3701e-6, pyunits.J / pyunits.kmol / pyunits.K**5), + }, + "enth_mol_form_liq_comp_ref": ( + -285.830e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "enth_mol_form_vap_comp_ref": ( + -241.826e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "pressure_sat_comp_coeff": { + "A": (-7.76451, None), # [1] pg. 669 + "B": (1.45838, None), + "C": (-2.77580, None), + "D": (-1.23303, None), + }, + }, + }, + "sulfuric_acid": { + "type": Component, + "elemental_composition": {"H": 2, "S": 1, "O": 4}, + "dens_mol_liq_comp": Perrys, # fitted to this equation form + "enth_mol_liq_comp": Perrys, # fitted to this equation form + "enth_mol_ig_comp": NIST, + "pressure_sat_comp": RPP4, # fitted to this equation form + "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, + "parameter_data": { + "mw": (98.08e-3, pyunits.kg / pyunits.mol), # [4] 5/20/24 + "pressure_crit": (129.4262e5, pyunits.Pa), # [4] 5/20/2024 + "temperature_crit": (590.76, pyunits.K), # [4] 5/202/24 + "dens_mol_liq_comp_coeff": { + "eqn_type": 2, # [5] pg. 15-41 regressed from 100% H2SO4 density data + "1": (23.669, pyunits.kmol / pyunits.m**3), + "2": (-2.5307e-2, pyunits.kmol / pyunits.m**3 / pyunits.K), + "3": (3.3523e-5, pyunits.kmol / pyunits.m**3 / pyunits.K**2), + "4": (-1.8538e-8, pyunits.kmol / pyunits.m**3 / pyunits.K**3), + }, + "cp_mol_ig_comp_coeff": { # [3] valid on 298-1200 K, updated 5/10/2024 + "A": (47.28924, pyunits.J / pyunits.mol / pyunits.K), + "B": (190.3314, pyunits.J / pyunits.mol / pyunits.K / pyunits.kK), + "C": ( + -148.1299, + pyunits.J / pyunits.mol / pyunits.K / pyunits.kK**2, + ), + "D": ( + 43.86631, + pyunits.J / pyunits.mol / pyunits.K / pyunits.kK**3, + ), + "E": ( + -0.740016, + pyunits.J / pyunits.mol / pyunits.K / pyunits.kK**-2, + ), + "F": (-758.9525, pyunits.kJ / pyunits.mol), + "G": (301.2961, pyunits.J / pyunits.mol / pyunits.K), + "H": (-735.1288, pyunits.kJ / pyunits.mol), + }, + "cp_mol_liq_comp_coeff": { # [6] pg. 1189 regressed from x1=1 and Cp/R for all T values + "1": (-202.695, pyunits.J / pyunits.kmol / pyunits.K), + "2": (2.9994, pyunits.J / pyunits.kmol / pyunits.K**2), + "3": (-9.239e-3, pyunits.J / pyunits.kmol / pyunits.K**3), + "4": (1.0113e-5, pyunits.J / pyunits.kmol / pyunits.K**4), + "5": (0, pyunits.J / pyunits.kmol / pyunits.K**5), + }, + "enth_mol_form_liq_comp_ref": ( + -810.4097e3, + pyunits.J / pyunits.mol, + ), # [6] Table 4 pg. 1183 + "enth_mol_form_vap_comp_ref": ( + -735.13e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "pressure_sat_comp_coeff": { + "A": (-18.122, None), # [5] data for regression on pg. 6-122 + "B": (10.596, None), + "C": (-18.908, None), + "D": (-32.728, None), + }, + }, + }, + "ethylene_glycol": { + "type": Component, + "elemental_composition": {"C": 2, "H": 6, "O": 2}, + "dens_mol_liq_comp": Perrys, + "enth_mol_liq_comp": Perrys, + "enth_mol_ig_comp": RPP4, + "pressure_sat_comp": RPP4, + "phase_equilibrium_form": {("Vap", "Liq"): fugacity}, + "parameter_data": { + "mw": (62.069e-3, pyunits.kg / pyunits.mol), # [1] pg. 676 + "pressure_crit": (77e5, pyunits.Pa), # [1] pg. 676 + "temperature_crit": (645, pyunits.K), # [1] pg. 676 + "dens_mol_liq_comp_coeff": { # [2] pg. 2-95 + "eqn_type": 1, + "1": (1.3151, pyunits.kmol * pyunits.m**-3), + "2": (0.25125, None), + "3": (719.7, pyunits.K), + "4": (0.2187, None), + }, + "cp_mol_ig_comp_coeff": { # [1] pg. 677 + "A": (3.570e1, pyunits.J / pyunits.mol / pyunits.K), + "B": (2.483e-1, pyunits.J / pyunits.mol / pyunits.K**2), + "C": (-1.497e-4, pyunits.J / pyunits.mol / pyunits.K**3), + "D": (3.010e-8, pyunits.J / pyunits.mol / pyunits.K**4), + }, + "cp_mol_liq_comp_coeff": { # [2] pg. 2-171 + "1": (3.5540e4, pyunits.J / pyunits.kmol / pyunits.K), + "2": (4.3678e2, pyunits.J / pyunits.kmol / pyunits.K**2), + "3": (-1.8486e-1, pyunits.J / pyunits.kmol / pyunits.K**3), + "4": (0, pyunits.J / pyunits.kmol / pyunits.K**4), + "5": (0, pyunits.J / pyunits.kmol / pyunits.K**5), + }, + "enth_mol_form_liq_comp_ref": ( + -460.0e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + "enth_mol_form_vap_comp_ref": ( + -394.4e3, + pyunits.J / pyunits.mol, + ), # [3] updated 5/10/24 + # [1] pg. 678 pressure sat coef values for alternative equation form + # ln Pvp = A - B/(T + C) with A = 13.6299, B = 6022.18, C = -28.25 + # reformulated for generic property supported form + # ln Pvp = [(1 - x)^-1 * (A*x + B*x^1.5 + C*x^3 + D*x^6)] * Pc where x = 1 - T/Tc + "pressure_sat_comp_coeff": { + "A": (-16.4022, None), + "B": (10.0100, None), + "C": (-6.5216, None), + "D": (-11.1182, None), + }, + }, + }, + }, # Specifying phases - "phases": {'Liq': {"type": LiquidPhase, - "equation_of_state": Ideal}}, - + "phases": {"Liq": {"type": LiquidPhase, "equation_of_state": Ideal}}, # Set base units of measurement - "base_units": {"time": pyunits.s, - "length": pyunits.m, - "mass": pyunits.kg, - "amount": pyunits.mol, - "temperature": pyunits.K}, - + "base_units": { + "time": pyunits.s, + "length": pyunits.m, + "mass": pyunits.kg, + "amount": pyunits.mol, + "temperature": pyunits.K, + }, # Specifying state definition "state_definition": FpcTP, - "state_bounds": {"flow_mol_phase_comp": (0, 100, 1000, - pyunits.mol/pyunits.s), - "temperature": (273.15, 298.15, 450, pyunits.K), - "pressure": (5e4, 1e5, 1e6, pyunits.Pa)}, + "state_bounds": { + "flow_mol_phase_comp": (0, 100, 1000, pyunits.mol / pyunits.s), + "temperature": (273.15, 298.15, 450, pyunits.K), + "pressure": (5e4, 1e5, 1e6, pyunits.Pa), + }, "pressure_ref": (1e5, pyunits.Pa), - "temperature_ref": (298.15, pyunits.K)} + "temperature_ref": (298.15, pyunits.K), +} diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb index 0a3935a7..bafa95cf 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb @@ -1,1352 +1,1098 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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", - "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES EquilibriumReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", - "\n", - "**CH4 + H2O → CO + 3H2** \n", - "**CO + H2O → CO2 + H2**\n", - "\n", - "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- EquilibriumReactor\n", - "- Product\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties.base.generic_property import (\n", - " GenericParameterBlock,\n", - ")\n", - "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " EquilibriumReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", - "\n", - "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", - "\n", - "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", - "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", - "\n", - "The correlations are taken from the following literature: \n", - "\n", - "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "### Determining $k_{eq}^{ref}$\n", - "\n", - "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", - "\n", - "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", - "- msr_reaction as reaction_props (contains configuration dictionary)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", - "import msr_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = EquilibriumReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=True,\n", - " has_rate_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", - "\n", - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", - "\n", - "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_doc.md) demonstrates adding a check before writing an additional constraint that may overspecify the system." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "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", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES EquilibriumReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", + "\n", + "**CH4 + H2O \u2192 CO + 3H2** \n", + "**CO + H2O \u2192 CO2 + H2**\n", + "\n", + "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- EquilibriumReactor\n", + "- Product\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " EquilibriumReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.CH4: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", + "\n", + "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", + "\n", + "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", + "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", + "\n", + "The correlations are taken from the following literature: \n", + "\n", + "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "### Determining $k_{eq}^{ref}$\n", + "\n", + "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", + "\n", + "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", + "- msr_reaction as reaction_props (contains configuration dictionary)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", + "import msr_reaction as reaction_props" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.H2O: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = EquilibriumReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=True,\n", + " has_rate_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", + "\n", + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", + "\n", + "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_doc.md) demonstrates adding a check before writing an additional constraint that may overspecify the system." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:20 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "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...: 562\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 477\n", + "\n", + "Total number of variables............................: 204\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 174\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-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) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $45.933 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] + "cell_type": "code", + "execution_count": 24, + "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 : 2.7605e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", + " Temperature kelvin 500.00 868.56\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:30:21 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # pressurize to at least 2 bar\n", + "m.fs.C101.outlet.pressure[0].setub(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 10 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # outlet temperature is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\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...: 562\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 477\n", - "\n", - "Total number of variables............................: 204\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 174\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.49e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 2.8421709430404007e-14 2.4912878870964050e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.8421709430404007e-14 2.4912878870964050e-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) = 0.002\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "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...: 569\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 493\n", + "\n", + "Total number of variables............................: 206\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 176\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\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 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", + " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", + " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", + " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", + " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", + " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", + " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", + " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 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", + " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", + "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", + "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", + "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", + "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $45.933 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $43.309 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", + " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 423.34\n", + " Pressure pascal 1.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 423.34 423.34\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Equilibrium reactor results\n", + "\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 : 3.2486e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", + " Temperature kelvin 423.34 910.04\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Equilibrium reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, { - "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 : 2.7605e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", - " Temperature kelvin 500.00 868.56\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # pressurize to at least 2 bar\n", - "m.fs.C101.outlet.pressure[0].setub(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 10 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # outlet temperature is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, { - "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...: 569\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 493\n", - "\n", - "Total number of variables............................: 206\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 176\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\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 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", - " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", - " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", - " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", - " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", - " 6 4.3309131e+07 5.79e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", - " 7 4.3309131e+07 7.77e-09 1.30e-06 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", - " 8 4.3309131e+07 2.20e-08 1.95e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", - " 9 4.3309131e+07 1.79e-08 1.27e-06 -5.7 3.08e-05 - 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", - " 10 4.3309131e+07 5.88e-09 1.09e-06 -7.0 3.56e-07 - 1.00e+00 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3309130854568668e+07 4.3309130854568668e+07\n", - "Dual infeasibility......: 1.0873799957492332e-06 1.0873799957492332e-06\n", - "Constraint violation....: 1.4551915228366852e-11 5.8780968648563987e-09\n", - "Complementarity.........: 9.0909090913936446e-08 9.0909090913936446e-08\n", - "Overall NLP error.......: 9.0909090913936446e-08 1.0873799957492332e-06\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 10\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.007\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 0.200 MPa\n", + "\n", + "C101 outlet temperature = 423.345 K\n", + "\n", + "H101 outlet temperature = 423.345 K\n", + "\n", + "R101 outlet temperature = 910.044 K\n", + "\n", + "Hydrogen produced = 33.648 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $43.309 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", - " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 423.34\n", - " Pressure pascal 1.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 423.34 423.34\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Equilibrium reactor results\n", - "\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 : 3.2486e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", - " Temperature kelvin 423.34 910.04\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Equilibrium reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 0.200 MPa\n", - "\n", - "C101 outlet temperature = 423.345 K\n", - "\n", - "H101 outlet temperature = 423.345 K\n", - "\n", - "R101 outlet temperature = 910.044 K\n", - "\n", - "Hydrogen produced = 33.648 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] + ], + "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": "3.9.18" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb index 9adb969e..a845483e 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -54,8 +54,8 @@ "\n", "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", "\n", - "**CH4 + H2O → CO + 3H2** \n", - "**CO + H2O → CO2 + H2**\n", + "**CH4 + H2O \u2192 CO + 3H2** \n", + "**CO + H2O \u2192 CO2 + H2**\n", "\n", "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", "\n", @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -248,10 +248,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = EquilibriumReactor(\n", @@ -276,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -342,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -373,18 +371,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "tags": [ "testing" @@ -405,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -457,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -487,16 +493,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "tags": [ "testing" @@ -517,9 +531,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], "source": [ "# Initialize and solve each unit operation\n", "m.fs.CH4.initialize()\n", @@ -548,11 +611,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 562\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 477\n", + "\n", + "Total number of variables............................: 204\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 174\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-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) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Solve the model\n", "results = solver.solve(m, tee=True)" @@ -560,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "tags": [ "testing" @@ -586,16 +722,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $45.933 million per year\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "tags": [ "testing" @@ -617,9 +761,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "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 : 2.7605e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", + " Temperature kelvin 500.00 868.56\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], "source": [ "m.fs.R101.report()\n", "\n", @@ -629,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "tags": [ "testing" @@ -660,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -676,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -699,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "tags": [ "testing" @@ -722,18 +898,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 569\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 493\n", + "\n", + "Total number of variables............................: 206\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 176\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\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 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", + " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", + " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", + " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", + " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", + " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", + " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", + " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 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", + " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", + "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", + "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", + "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", + "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "tags": [ "testing" @@ -749,9 +1006,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $43.309 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", + " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 423.34\n", + " Pressure pascal 1.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 423.34 423.34\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Equilibrium reactor results\n", + "\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 : 3.2486e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", + " Temperature kelvin 423.34 910.04\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", "\n", @@ -773,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "tags": [ "testing" @@ -793,9 +1136,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 0.200 MPa\n", + "\n", + "C101 outlet temperature = 423.345 K\n", + "\n", + "H101 outlet temperature = 423.345 K\n", + "\n", + "R101 outlet temperature = 910.044 K\n", + "\n", + "Hydrogen produced = 33.648 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -820,7 +1183,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "tags": [ "testing" @@ -861,9 +1224,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb index 20b6e5a8..6185b931 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -54,8 +54,8 @@ "\n", "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", "\n", - "**CH4 + H2O → CO + 3H2** \n", - "**CO + H2O → CO2 + H2**\n", + "**CH4 + H2O \u2192 CO + 3H2** \n", + "**CO + H2O \u2192 CO2 + H2**\n", "\n", "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", "\n", @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -248,10 +248,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = EquilibriumReactor(\n", @@ -276,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -342,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -373,11 +371,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -391,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -425,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -473,9 +479,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -489,9 +503,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], "source": [ "# Initialize and solve each unit operation\n", "m.fs.CH4.initialize()\n", @@ -520,11 +583,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 562\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 477\n", + "\n", + "Total number of variables............................: 204\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 174\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-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) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Solve the model\n", "results = solver.solve(m, tee=True)" @@ -542,9 +678,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $45.933 million per year\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" ] @@ -558,9 +702,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "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 : 2.7605e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", + " Temperature kelvin 500.00 868.56\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], "source": [ "m.fs.R101.report()\n", "\n", @@ -586,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -635,20 +811,187 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 569\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 493\n", + "\n", + "Total number of variables............................: 206\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 176\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\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 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", + " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", + " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", + " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", + " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", + " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", + " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", + " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 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", + " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", + "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", + "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", + "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", + "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $43.309 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", + " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 423.34\n", + " Pressure pascal 1.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 423.34 423.34\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Equilibrium reactor results\n", + "\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 : 3.2486e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", + " Temperature kelvin 423.34 910.04\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", "\n", @@ -677,9 +1020,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 0.200 MPa\n", + "\n", + "C101 outlet temperature = 423.345 K\n", + "\n", + "H101 outlet temperature = 423.345 K\n", + "\n", + "R101 outlet temperature = 910.044 K\n", + "\n", + "Hydrogen produced = 33.648 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -727,9 +1090,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb index 11dbd1da..069a9a84 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb @@ -1,1298 +1,1049 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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", - "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES GibbsReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_doc.md), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", - "\n", - "**CH4 + H2O → CO + 3H2** \n", - "**CO + H2O → CO2 + H2**\n", - "\n", - "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- GibbsReactor\n", - "- Product" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " GibbsReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical Package\n", - "\n", - "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", - "\n", - "Let us import the following module from the IDAES library:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = GibbsReactor(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the literature source, we will initialize our simulation with the following values:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "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", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES GibbsReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_doc.md), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", + "\n", + "**CH4 + H2O \u2192 CO + 3H2** \n", + "**CO + H2O \u2192 CO2 + H2**\n", + "\n", + "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- GibbsReactor\n", + "- Product" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " GibbsReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical Package\n", + "\n", + "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", + "\n", + "Let us import the following module from the IDAES library:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.CH4: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "Let us create a `ConcreteModel` and add the flowsheet block. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H2O: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = GibbsReactor(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the literature source, we will initialize our simulation with the following values:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "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...: 591\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 490\n", + "\n", + "Total number of variables............................: 203\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 179\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-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" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $39.958 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 24, + "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 : 1.7819e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", + " Temperature kelvin 500.00 920.80\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:35 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:36 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:36 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:36 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:36 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # equals inlet pressure\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 1 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # ensures outlet is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:36 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\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...: 591\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 490\n", - "\n", - "Total number of variables............................: 203\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 179\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 1.75e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 1.1641532182693481e-10 1.7462298274040222e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1641532182693481e-10 1.7462298274040222e-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) = 0.002\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "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...: 598\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 506\n", + "\n", + "Total number of variables............................: 205\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 181\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\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 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", + " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", + " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", + " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", + " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", + " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", + " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", + " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", + " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 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", + " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", + " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 12\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", + "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", + "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", + "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", + "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 13\n", + "Number of objective gradient evaluations = 13\n", + "Number of equality constraint evaluations = 13\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 13\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.011\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $39.958 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $107.218 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", + " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 619.25\n", + " Pressure pascal 1.0000e+05 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 619.25 619.25\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Gibbs reactor results\n", + "\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 : 2.1076e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", + " Temperature kelvin 619.25 1087.4\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Gibbs reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, { - "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 : 1.7819e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", - " Temperature kelvin 500.00 920.80\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # equals inlet pressure\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 1 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # ensures outlet is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, { - "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...: 598\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 506\n", - "\n", - "Total number of variables............................: 205\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 181\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\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 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", - " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", - " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", - " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", - " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", - " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", - " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", - " 8 1.0721794e+08 8.85e-09 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", - " 9 1.0721794e+08 1.50e-08 1.92e-06 -2.5 1.72e-02 - 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", - " 10 1.0721794e+08 1.02e-08 2.70e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", - " 11 1.0721794e+08 1.02e-08 2.31e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", - " 12 1.0721794e+08 1.49e-08 1.00e-06 -7.0 3.06e-07 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 12\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.0721793780338201e+08 1.0721793780338201e+08\n", - "Dual infeasibility......: 1.0011035875619890e-06 1.0011035875619890e-06\n", - "Constraint violation....: 1.5205920451933321e-12 1.4901161193847656e-08\n", - "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", - "Overall NLP error.......: 9.0909090914354020e-08 1.0011035875619890e-06\n", - "\n", - "\n", - "Number of objective function evaluations = 13\n", - "Number of objective gradient evaluations = 13\n", - "Number of equality constraint evaluations = 13\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 13\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.004\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 1.000 MPa\n", + "\n", + "C101 outlet temperature = 619.248 K\n", + "\n", + "H101 outlet temperature = 619.248 K\n", + "\n", + "R101 outlet temperature = 1087.385 K\n", + "\n", + "Hydrogen produced = 32.070 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $107.218 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", - " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 619.25\n", - " Pressure pascal 1.0000e+05 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 619.25 619.25\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Gibbs reactor results\n", - "\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 : 2.1076e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", - " Temperature kelvin 619.25 1087.4\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Gibbs reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 1.000 MPa\n", - "\n", - "C101 outlet temperature = 619.248 K\n", - "\n", - "H101 outlet temperature = 619.248 K\n", - "\n", - "R101 outlet temperature = 1087.385 K\n", - "\n", - "Hydrogen produced = 32.070 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] + ], + "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": "3.9.18" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb index 21f63514..7a9cba50 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -61,8 +61,7 @@ "\n", "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", "\n", - "![](msr_flowsheet.png)\n", - "" + "![](msr_flowsheet.png)\n" ] }, { @@ -85,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -187,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -209,10 +208,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = GibbsReactor(\n", @@ -233,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -324,18 +321,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "tags": [ "testing" @@ -356,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -408,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -438,16 +443,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "tags": [ "testing" @@ -468,9 +481,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], "source": [ "# Initialize and solve each unit operation\n", "m.fs.CH4.initialize()\n", @@ -499,11 +560,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 591\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 490\n", + "\n", + "Total number of variables............................: 203\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 179\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-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" + ] + } + ], "source": [ "# Solve the model\n", "results = solver.solve(m, tee=True)" @@ -511,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "tags": [ "testing" @@ -537,16 +671,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $39.958 million per year\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "tags": [ "testing" @@ -568,9 +710,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "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 : 1.7819e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", + " Temperature kelvin 500.00 920.80\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], "source": [ "m.fs.R101.report()\n", "\n", @@ -580,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "tags": [ "testing" @@ -611,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -650,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "tags": [ "testing" @@ -673,18 +847,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 598\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 506\n", + "\n", + "Total number of variables............................: 205\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 181\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\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 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", + " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", + " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", + " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", + " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", + " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", + " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", + " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", + " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 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", + " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", + " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 12\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", + "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", + "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", + "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", + "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 13\n", + "Number of objective gradient evaluations = 13\n", + "Number of equality constraint evaluations = 13\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 13\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.011\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "tags": [ "testing" @@ -700,9 +957,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $107.218 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", + " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 619.25\n", + " Pressure pascal 1.0000e+05 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 619.25 619.25\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Gibbs reactor results\n", + "\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 : 2.1076e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", + " Temperature kelvin 619.25 1087.4\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", "\n", @@ -724,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "tags": [ "testing" @@ -744,9 +1087,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 1.000 MPa\n", + "\n", + "C101 outlet temperature = 619.248 K\n", + "\n", + "H101 outlet temperature = 619.248 K\n", + "\n", + "R101 outlet temperature = 1087.385 K\n", + "\n", + "Hydrogen produced = 32.070 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -771,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "tags": [ "testing" @@ -812,7 +1175,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb index abe0717b..b7984618 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -61,8 +61,7 @@ "\n", "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", "\n", - "![](msr_flowsheet.png)\n", - "" + "![](msr_flowsheet.png)\n" ] }, { @@ -85,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -187,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -209,10 +208,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = GibbsReactor(\n", @@ -233,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -324,11 +321,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -342,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -376,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -394,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -424,9 +429,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -440,9 +453,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], "source": [ "# Initialize and solve each unit operation\n", "m.fs.CH4.initialize()\n", @@ -471,11 +532,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 591\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 490\n", + "\n", + "Total number of variables............................: 203\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 179\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\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 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 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....: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-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" + ] + } + ], "source": [ "# Solve the model\n", "results = solver.solve(m, tee=True)" @@ -493,9 +627,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $39.958 million per year\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" ] @@ -509,9 +651,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "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 : 1.7819e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", + " Temperature kelvin 500.00 920.80\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], "source": [ "m.fs.R101.report()\n", "\n", @@ -537,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -553,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -586,20 +760,189 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "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...: 598\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 506\n", + "\n", + "Total number of variables............................: 205\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 181\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\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 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", + " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", + " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", + " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", + " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", + " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", + " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", + " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", + " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 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", + " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", + " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 12\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", + "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", + "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", + "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", + "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 13\n", + "Number of objective gradient evaluations = 13\n", + "Number of equality constraint evaluations = 13\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 13\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.011\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $107.218 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", + " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 619.25\n", + " Pressure pascal 1.0000e+05 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 619.25 619.25\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Gibbs reactor results\n", + "\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 : 2.1076e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", + " Temperature kelvin 619.25 1087.4\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n" + ] + } + ], "source": [ "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", "\n", @@ -628,9 +971,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 1.000 MPa\n", + "\n", + "C101 outlet temperature = 619.248 K\n", + "\n", + "H101 outlet temperature = 619.248 K\n", + "\n", + "R101 outlet temperature = 1087.385 K\n", + "\n", + "Hydrogen produced = 32.070 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -678,7 +1041,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb index 2fe06cf1..989a3458 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb @@ -626,7 +626,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -641,7 +641,7 @@ "source": [ "import pytest\n", "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(2.082, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(6.589, rel=1e-5)" ] }, { @@ -667,15 +667,15 @@ " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", " f\" MJ\"\n", ")\n", "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", "print()\n", - "print(f\"Tube volume required = {value(m.fs.R101.volume):0.3f} m^3\")" + "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" ] }, { @@ -688,8 +688,8 @@ }, "outputs": [], "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.5000, abs=1e-3)\n", - "assert value(m.fs.R101.area) == pytest.approx(1.1490, abs=1e-3)\n", + "assert value(m.fs.R101.conversion) == pytest.approx(0.5000, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(0.987071, rel=1e-5)\n", "assert (\n", " value(m.fs.R101.length)\n", " / value(m.fs.R101.config.finite_elements)\n", @@ -705,8 +705,8 @@ " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", " )\n", " / 2\n", - ") / 1e6 == pytest.approx(-4.734, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, abs=1e-3)" + ") / 1e6 == pytest.approx(-4.881815, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, rel=1e-5)" ] }, { @@ -829,7 +829,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -852,8 +852,8 @@ }, "outputs": [], "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(0.3184, abs=1e-3)\n", - "assert value(m.fs.R101.area) == pytest.approx(2.0870, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(4.421530, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(2.9300, rel=1e-5)" ] }, { @@ -872,31 +872,31 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", "print(\n", " \"Total heat duty required = \",\n", " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", " f\" MJ\",\n", ")\n", "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", "\n", "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", "\n", "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", ")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -912,7 +912,7 @@ }, "outputs": [], "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, abs=1e-3)\n", + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", "assert (\n", " value(m.fs.R101.length)\n", " / value(m.fs.R101.config.finite_elements)\n", @@ -932,12 +932,12 @@ " )\n", " / 2\n", " )\n", - ") / 1e6 == pytest.approx(-3.440, abs=1e-3)\n", - "assert value(m.fs.R101.area) == pytest.approx(2.0870, abs=1e-3)\n", - "assert value(m.fs.R101.control_volume.length) == pytest.approx(4.9788, abs=1e-3)\n", - "assert value(m.fs.R101.volume * 1.2) == pytest.approx(12.469, abs=1e-3)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415, abs=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.000, abs=1e-3)" + ") / 1e6 == pytest.approx(-3.789565, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(2.930001, rel=1e-5)\n", + "assert value(m.fs.R101.control_volume.length) == pytest.approx(4.982470, rel=1e-5)\n", + "assert value(m.fs.R101.volume * 1.2) == pytest.approx(17.518369, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415471, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.000, rel=1e-5)" ] }, { @@ -972,9 +972,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 3 + "nbformat_minor": 4 } diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb index 3ecaff51..05620386 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb @@ -1,1340 +1,809 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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", - "# Flowsheet Plug Flow Reactor (PFR) Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES PFR unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a plug flow reactor (PFR). As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike a CSTR which assumes well-mixed liquid behavior, the concentration profiles will vary spatially in one dimension. In actuality, following start-up flow reactor exhibit dynamic behavior as they approach a steady-state equilibrium; we will assume our system has already achieved steady-state behavior. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", - "\n", - "Chemical reaction:\n", - "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", - "\n", - "Property Packages:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "Flowsheet\n", - "\n", - "![](egprod_flowsheet.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- PFR\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import (\n", - " GenericParameterBlock,\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, PFR, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", - "\n", - "The reaction package here assumes Arrhenius kinetic behavior for the PFR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", - "\n", - "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", - "\n", - "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", - "$k$ - reaction rate constant per second \n", - "$V$ - volume of PFR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", - "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", - "$k_0$ - pre-exponential Arrhenius factor per second \n", - "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", - "$R$ - gas constant in J/mol-K \n", - "$T$ - reactor temperature in K\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `PFR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = PFR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - " transformation_method=\"dae.finite_difference\",\n", - " transformation_scheme=\"BACKWARD\",\n", - " finite_elements=20,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `PFR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. To calculate cooling cost, it is important to note that the heat duty is not constant throughout the reactor's length and is expressed in terms of heat per length (J/m/s). This is why we utilize the trapezoid rule to calculate the total heat duty of the reactor:$Q=\\Delta x\\big(\\sum_{k=1}^{N-1}(Q_k)+\\frac{Q_N+Q_0}{2}\\big)$ \n", - "where k is the subinterval in the length domain, N is the number of intervals, and $\\Delta x$ is the length of the interval.\n", - "Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year, assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8\n", - " * m.fs.R101.length\n", - " / m.fs.R101.config.finite_elements\n", - " * (\n", - " -sum(\n", - " m.fs.R101.heat_duty[0, k]\n", - " for k in m.fs.R101.control_volume.length_domain\n", - " if 0.0 <= k < 1.0\n", - " )\n", - " )\n", - " - (\n", - " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " - value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", - " )\n", - " / 2\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 2 unit specifications and 1 specification for each finite element. Therefore, we have 35 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; a reactor property such as conversion or heat duty at each finite element; reactor volume and reactor length." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "35\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the `PFR`, we have to define the conversion in terms of ethylene oxide. Note that the `PFR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `PFR` model. We'll estimate 50% conversion for our initial flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " bounds=(0, 1), initialize=0.80, units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "for x in m.fs.R101.control_volume.length_domain:\n", - " if x == 0:\n", - " continue\n", - " m.fs.R101.control_volume.properties[0, x].temperature.fix(\n", - " 328.15 * pyunits.K\n", - " ) # equal inlet reactor temperature\n", - "\n", - "m.fs.R101.conversion.fix(0.5)\n", - "\n", - "m.fs.R101.length.fix(1 * pyunits.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we did not place a specification on reactor duty, the solver may try positive values to increase the reaction temperature and rate. To prevent the optimization from diverging, we need to set an upper bound restricting heat flow to cooling only:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.heat_duty.setub(\n", - " 0 * pyunits.J / pyunits.m / pyunits.s\n", - ") # heat duty is only used for cooling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Plug Flow Reactor (PFR) Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES PFR unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a plug flow reactor (PFR). As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike a CSTR which assumes well-mixed liquid behavior, the concentration profiles will vary spatially in one dimension. In actuality, following start-up flow reactor exhibit dynamic behavior as they approach a steady-state equilibrium; we will assume our system has already achieved steady-state behavior. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", + "\n", + "Chemical reaction:\n", + "\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", + "\n", + "Property Packages:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "Flowsheet\n", + "\n", + "![](egprod_flowsheet.png)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- PFR\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.OXIDE.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import (\n", + " GenericParameterBlock,\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, PFR, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.OXIDE.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", + "\n", + "The reaction package here assumes Arrhenius kinetic behavior for the PFR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", + "\n", + "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", + "\n", + "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", + "$k$ - reaction rate constant per second \n", + "$V$ - volume of PFR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", + "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", + "$k_0$ - pre-exponential Arrhenius factor per second \n", + "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", + "$R$ - gas constant in J/mol-K \n", + "$T$ - reactor temperature in K\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.OXIDE.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.OXIDE: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.ACID.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.ACID.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.ACID.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.ACID: Initialization Complete.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `PFR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.reagent_feed_state: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.reagent_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m.fs.R101 = PFR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + " transformation_method=\"dae.finite_difference\",\n", + " transformation_scheme=\"BACKWARD\",\n", + " finite_elements=20,\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.catalyst_feed_state: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `PFR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.catalyst_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. To calculate cooling cost, it is important to note that the heat duty is not constant throughout the reactor's length and is expressed in terms of heat per length (J/m/s). This is why we utilize the trapezoid rule to calculate the total heat duty of the reactor:$Q=\\Delta x\\big(\\sum_{k=1}^{N-1}(Q_k)+\\frac{Q_N+Q_0}{2}\\big)$ \n", + "where k is the subinterval in the length domain, N is the number of intervals, and $\\Delta x$ is the length of the interval.\n", + "Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year, assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8\n", + " * m.fs.R101.length\n", + " / m.fs.R101.config.finite_elements\n", + " * (\n", + " -sum(\n", + " m.fs.R101.heat_duty[0, k]\n", + " for k in m.fs.R101.control_volume.length_domain\n", + " if 0.0 <= k < 1.0\n", + " )\n", + " )\n", + " - (\n", + " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " - value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", + " )\n", + " / 2\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 2 unit specifications and 1 specification for each finite element. Therefore, we have 35 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; a reactor property such as conversion or heat duty at each finite element; reactor volume and reactor length." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.R101.control_volume.properties: Starting initialization\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.R101.control_volume.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" - ] + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the `PFR`, we have to define the conversion in terms of ethylene oxide. Note that the `PFR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `PFR` model. We'll estimate 50% conversion for our initial flowsheet:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " bounds=(0, 1), initialize=0.80, units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "for x in m.fs.R101.control_volume.length_domain:\n", + " if x == 0:\n", + " continue\n", + " m.fs.R101.control_volume.properties[0, x].temperature.fix(\n", + " 328.15 * pyunits.K\n", + " ) # equal inlet reactor temperature\n", + "\n", + "m.fs.R101.conversion.fix(0.5)\n", + "\n", + "m.fs.R101.length.fix(1 * pyunits.m)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we did not place a specification on reactor duty, the solver may try positive values to increase the reaction temperature and rate. To prevent the optimization from diverging, we need to set an upper bound restricting heat flow to cooling only:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.heat_duty.setub(\n", + " 0 * pyunits.J / pyunits.m / pyunits.s\n", + ") # heat duty is only used for cooling" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:21:57 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, { - "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...: 1923\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1323\n", - "\n", - "Total number of variables............................: 608\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 257\n", - " variables with only upper bounds: 20\n", - "Total number of equality constraints.................: 608\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 1.30e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.67e+07 5.52e+03 -1.0 1.17e+08 - 2.85e-01 9.90e-01f 1\n", - " 2 0.0000000e+00 2.61e+05 6.25e+03 -1.0 1.17e+06 - 8.25e-01 9.90e-01h 1\n", - " 3 0.0000000e+00 2.59e+03 3.50e+01 -1.0 1.17e+04 - 9.90e-01 9.90e-01h 1\n", - " 4 0.0000000e+00 1.97e+01 3.22e+03 -1.0 1.15e+02 - 9.90e-01 9.92e-01h 1\n", - " 5 0.0000000e+00 3.19e-07 4.81e+03 -1.0 8.77e-01 - 9.91e-01 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 5\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.6686898115291998e+06 1.6686898115291998e+06\n", - "Constraint violation....: 3.1874515116214752e-07 3.1874515116214752e-07\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.1874515116214752e-07 1.6686898115291998e+06\n", - "\n", - "\n", - "Number of objective function evaluations = 6\n", - "Number of objective gradient evaluations = 6\n", - "Number of equality constraint evaluations = 6\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 6\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 5\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.003\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $2.082 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, { - "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", - " Area : 1.1490 : meter ** 2 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 29.000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 210.60\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 29.000\n", - " Temperature kelvin 328.15 328.15\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 50.0%\n", - "\n", - "Total heat duty required = -3.469 MJ\n", - "\n", - "Tube area required = 1.149 m^2\n", - "\n", - "Tube length required = 1.000 m\n", - "\n", - "Tube volume required = 1.149 m^3\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", - "print()\n", - "print(\n", - " f\"Total heat duty required = \"\n", - " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", - " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", - " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", - " f\" MJ\"\n", - ")\n", - "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", - "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", - "print()\n", - "print(f\"Tube volume required = {value(m.fs.R101.volume):0.3f} m^3\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", - "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", - "\n", - "m.fs.R101.length.unfix()\n", - "m.fs.R101.length.setlb(0 * pyunits.m)\n", - "m.fs.R101.length.setub(5 * pyunits.m)\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)\n", - "\n", - "for x in m.fs.R101.control_volume.length_domain:\n", - " if x == 0:\n", - " continue\n", - " m.fs.R101.control_volume.properties[\n", - " 0, x\n", - " ].temperature.unfix() # allow for temperature change in each finite element" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] }, { - "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...: 2067\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 1886\n", - "\n", - "Total number of variables............................: 631\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 280\n", - " variables with only upper bounds: 21\n", - "Total number of equality constraints.................: 608\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 1\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 2.0817113e+06 3.66e+06 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.7272642e+06 5.24e+05 9.25e+01 -1.0 2.16e+06 - 7.82e-02 9.90e-01h 1\n", - " 2 3.7439283e+06 5.50e+03 4.53e+02 -1.0 2.14e+04 - 7.22e-01 9.91e-01h 1\n", - " 3 3.7441018e+06 2.31e-01 7.45e+03 -1.0 3.16e+02 - 9.25e-01 1.00e+00h 1\n", - " 4 3.7445196e+06 7.21e-02 1.59e+04 -1.0 3.84e+02 - 9.90e-01 1.00e+00f 1\n", - " 5 3.7434412e+06 6.03e-01 2.88e+02 -1.0 3.91e+04 - 9.82e-01 1.00e+00F 1\n", - " 6 3.4569523e+06 1.90e+05 1.52e+02 -1.0 3.75e+06 - 4.73e-01 1.00e+00F 1\n", - " 7 1.3257122e+06 6.54e+06 1.34e+02 -1.0 7.17e+07 - 1.17e-01 4.66e-01F 1\n", - " 8 5.4503925e+05 4.55e+06 4.94e+01 -1.0 4.27e+07 - 6.32e-01 3.47e-01f 1\n", - " 9 2.2925409e+05 1.16e+06 2.53e+01 -1.0 1.78e+07 - 8.45e-01 6.02e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.1904785e+05 1.55e+06 3.36e+01 -1.0 1.16e+07 - 1.00e+00 1.76e-01h 1\n", - " 11 2.4068883e+05 7.81e+05 4.18e+01 -1.0 4.64e+06 - 5.75e-01 5.16e-01h 1\n", - " 12 3.6492667e+05 3.58e+03 3.24e+01 -1.0 2.13e+05 - 5.89e-01 1.00e+00h 1\n", - " 13 3.3687811e+05 1.24e+03 5.19e+05 -1.7 8.40e+05 - 1.00e+00 6.11e-01h 1\n", - " 14 3.2827810e+05 6.28e+02 3.94e-02 -1.7 1.36e+05 - 1.00e+00 1.00e+00h 1\n", - " 15 3.2852907e+05 1.59e+00 1.32e-03 -1.7 5.68e+03 - 1.00e+00 1.00e+00h 1\n", - " 16 3.1973165e+05 1.33e+01 5.15e+04 -3.8 1.94e+05 - 9.86e-01 8.31e-01f 1\n", - " 17 3.1849743e+05 2.37e-01 1.46e-03 -3.8 2.16e+04 - 1.00e+00 1.00e+00h 1\n", - " 18 3.1850187e+05 5.49e-03 1.29e-07 -3.8 1.93e+02 - 1.00e+00 1.00e+00h 1\n", - " 19 3.1843083e+05 8.45e-04 3.52e-06 -5.7 1.29e+03 - 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 3.1843086e+05 4.64e-07 2.07e-11 -5.7 1.29e+00 - 1.00e+00 1.00e+00h 1\n", - " 21 3.1842998e+05 3.55e-07 5.40e-10 -8.6 1.59e+01 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 21\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.5329173454170544e+01 3.1842997505757213e+05\n", - "Dual infeasibility......: 5.3983226027300468e-10 1.1213831827784419e-05\n", - "Constraint violation....: 3.5460107028484344e-07 3.5460107028484344e-07\n", - "Complementarity.........: 2.5137892688647162e-09 5.2218461522255445e-05\n", - "Overall NLP error.......: 3.5460107028484344e-07 5.2218461522255445e-05\n", - "\n", - "\n", - "Number of objective function evaluations = 26\n", - "Number of objective gradient evaluations = 22\n", - "Number of equality constraint evaluations = 26\n", - "Number of inequality constraint evaluations = 26\n", - "Number of equality constraint Jacobian evaluations = 22\n", - "Number of inequality constraint Jacobian evaluations = 22\n", - "Number of Lagrangian Hessian evaluations = 21\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.028\n", - "Total CPU secs in NLP function evaluations = 0.005\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $0.318 million per year\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 699.26 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 58.000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 239.60\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 2.0000e-05\n", - " Temperature kelvin 298.15 328.15\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n", - "\n", - "PFR reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Area : 2.0871 : meter ** 2 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 5.8000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 187.40\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 52.200\n", - " Temperature kelvin 328.15 273.15\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"PFR reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", + "print()\n", + "print(\n", + " f\"Total heat duty required = \"\n", + " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", + " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", + " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", + " f\" MJ\"\n", + ")\n", + "print()\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", + "print()\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", + "print()\n", + "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 328.150 K\n", - "\n", - "Total heat duty required = -3.440 MJ\n", - "\n", - "Tube area required = 2.087 m^2\n", - "\n", - "Tube length required = 4.979 m\n", - "\n", - "Assuming a 20% design factor for reactor volume,total CSTR volume required = 12.469 m^3 = 3294.093 gal\n", - "\n", - "Ethylene glycol produced = 225.415 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", + "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", + "\n", + "m.fs.R101.length.unfix()\n", + "m.fs.R101.length.setlb(0 * pyunits.m)\n", + "m.fs.R101.length.setub(5 * pyunits.m)\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)\n", + "\n", + "for x in m.fs.R101.control_volume.length_domain:\n", + " if x == 0:\n", + " continue\n", + " m.fs.R101.control_volume.properties[\n", + " 0, x\n", + " ].temperature.unfix() # allow for temperature change in each finite element" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"PFR reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(\n", + " \"Total heat duty required = \",\n", + " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", + " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", + " f\" MJ\",\n", + ")\n", + "print()\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", + "\n", + "print()\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", + "\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", + ")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.18" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(\n", - " \"Total heat duty required = \",\n", - " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", - " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", - " f\" MJ\",\n", - ")\n", - "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", - "\n", - "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", - "\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.3f} gal\"\n", - ")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb index dcfe84f8..76d32312 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb @@ -53,7 +53,7 @@ "\n", "Chemical reaction:\n", "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", "\n", "Property Packages:\n", "\n", @@ -626,7 +626,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -641,7 +641,7 @@ "source": [ "import pytest\n", "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(2.082, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(6.589, rel=1e-5)" ] }, { @@ -667,15 +667,15 @@ " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", " f\" MJ\"\n", ")\n", "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", "print()\n", - "print(f\"Tube volume required = {value(m.fs.R101.volume):0.3f} m^3\")" + "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" ] }, { @@ -688,8 +688,8 @@ }, "outputs": [], "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.5000, abs=1e-3)\n", - "assert value(m.fs.R101.area) == pytest.approx(1.1490, abs=1e-3)\n", + "assert value(m.fs.R101.conversion) == pytest.approx(0.5000, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(0.987071, rel=1e-5)\n", "assert (\n", " value(m.fs.R101.length)\n", " / value(m.fs.R101.config.finite_elements)\n", @@ -705,8 +705,8 @@ " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", " )\n", " / 2\n", - ") / 1e6 == pytest.approx(-4.734, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, abs=1e-3)" + ") / 1e6 == pytest.approx(-4.881815, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, rel=1e-5)" ] }, { @@ -829,7 +829,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -852,8 +852,8 @@ }, "outputs": [], "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(0.3184, abs=1e-3)\n", - "assert value(m.fs.R101.area) == pytest.approx(2.0870, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(4.421530, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(2.9300, rel=1e-5)" ] }, { @@ -872,31 +872,31 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", "print(\n", " \"Total heat duty required = \",\n", " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", " f\" MJ\",\n", ")\n", "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", "\n", "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", "\n", "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", ")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -912,7 +912,7 @@ }, "outputs": [], "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, abs=1e-3)\n", + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", "assert (\n", " value(m.fs.R101.length)\n", " / value(m.fs.R101.config.finite_elements)\n", @@ -932,12 +932,12 @@ " )\n", " / 2\n", " )\n", - ") / 1e6 == pytest.approx(-3.440, abs=1e-3)\n", - "assert value(m.fs.R101.area) == pytest.approx(2.0870, abs=1e-3)\n", - "assert value(m.fs.R101.control_volume.length) == pytest.approx(4.9788, abs=1e-3)\n", - "assert value(m.fs.R101.volume * 1.2) == pytest.approx(12.469, abs=1e-3)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415, abs=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.000, abs=1e-3)" + ") / 1e6 == pytest.approx(-3.789565, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(2.930001, rel=1e-5)\n", + "assert value(m.fs.R101.control_volume.length) == pytest.approx(4.982470, rel=1e-5)\n", + "assert value(m.fs.R101.volume * 1.2) == pytest.approx(17.518369, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415471, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.000, rel=1e-5)" ] }, { @@ -972,9 +972,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb index b0473332..30984465 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb @@ -53,7 +53,7 @@ "\n", "Chemical reaction:\n", "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", "\n", "Property Packages:\n", "\n", @@ -582,7 +582,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -608,15 +608,15 @@ " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", " f\" MJ\"\n", ")\n", "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", "print()\n", - "print(f\"Tube volume required = {value(m.fs.R101.volume):0.3f} m^3\")" + "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" ] }, { @@ -710,7 +710,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -739,31 +739,31 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", "print(\n", " \"Total heat duty required = \",\n", " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.3f}\"\"\"\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", " f\" MJ\",\n", ")\n", "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.3f} m^2\")\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", "\n", "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.3f} m\")\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", "\n", "print()\n", "print(\n", " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.3f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.3f} gal\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", ")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -801,9 +801,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb index c12fd541..b0ed3611 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb @@ -219,9 +219,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = StoichiometricReactor(\n", @@ -564,7 +562,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -579,7 +577,7 @@ "source": [ "import pytest\n", "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.458140, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(8.019605, rel=1e-5)" ] }, { @@ -611,9 +609,9 @@ }, "outputs": [], "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, abs=1e-3)\n", - "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.6566, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, abs=1e-3)" + "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, rel=1e-5)\n", + "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.8931, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, rel=1e-5)" ] }, { @@ -724,7 +722,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -747,7 +745,7 @@ }, "outputs": [], "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.888050, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(6.670729, rel=1e-5)" ] }, { @@ -766,13 +764,13 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -788,10 +786,10 @@ }, "outputs": [], "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(4.5000, abs=1e-3)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415, abs=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, abs=1e-3)" + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(4.5000, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-5)" ] }, { @@ -819,7 +817,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb index d7b699c7..99443416 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb @@ -1,1210 +1,693 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "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", - "# Flowsheet Stoichiometric Reactor Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES StochiometricReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a stochiometric (or yield) reactor. As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike the previous two reactors which apply performance equations to calculate reaction extent, this simplified reactor model neglects all geometric properties and allows the user to specify a yield per reaction. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", - "\n", - "Chemical reaction:\n", - "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", - "\n", - "Property Packages:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "Flowsheet:\n", - "\n", - "![](egprod_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties.base.generic_property import (\n", - " GenericParameterBlock,\n", - ")\n", - "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, StoichiometricReactor, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only. \n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `StoichiometricReactor`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = StoichiometricReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `StoichiometricReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or overall conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom since we have not yet defined the yield of the sole rate-kinetics reaction. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and duty." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15\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", + "# Flowsheet Stoichiometric Reactor Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES StochiometricReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a stochiometric (or yield) reactor. As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike the previous two reactors which apply performance equations to calculate reaction extent, this simplified reactor model neglects all geometric properties and allows the user to specify a yield per reaction. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", + "\n", + "Chemical reaction:\n", + "\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", + "\n", + "Property Packages:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "Flowsheet:\n", + "\n", + "![](egprod_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, StoichiometricReactor, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only. \n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `StoichiometricReactor`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = StoichiometricReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `StoichiometricReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or overall conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom since we have not yet defined the yield of the sole rate-kinetics reaction. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and duty." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will need to specify both initial reactant extent (conversion or yield) and heat duty values (these are the only two free variables to choose from). The reaction extent can be specified directly, as a molar or mass yield ratio of product to a particular reactant, or fractional conversion of a particular reactant. Here, we choose fractional conversion in terms of ethylene oxide. Since heat duty and the outlet reactor temperature are interdependent, we can choose to specify this quantity instead. While the reaction kinetic parameters exist in the property package, we also do not need to add a rate constant expression since generation is explicitly defined through the conversion/yield. Note that our initial problem will solve with zero *temperature change* but will be infeasible with zero *heat duty*; this is due to the heat of reaction enforced by allowing heat transfer and mandating a non-zero conversion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)\n", + "\n", + "m.fs.R101.outlet.temperature.fix(328.15 * pyunits.K) # equal inlet reactor temperature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)\n", + "\n", + "m.fs.R101.outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Stoichiometric reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will need to specify both initial reactant extent (conversion or yield) and heat duty values (these are the only two free variables to choose from). The reaction extent can be specified directly, as a molar or mass yield ratio of product to a particular reactant, or fractional conversion of a particular reactant. Here, we choose fractional conversion in terms of ethylene oxide. Since heat duty and the outlet reactor temperature are interdependent, we can choose to specify this quantity instead. While the reaction kinetic parameters exist in the property package, we also do not need to add a rate constant expression since generation is explicitly defined through the conversion/yield. Note that our initial problem will solve with zero *temperature change* but will be infeasible with zero *heat duty*; this is due to the heat of reaction enforced by allowing heat transfer and mandating a non-zero conversion." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)\n", - "\n", - "m.fs.R101.outlet.temperature.fix(328.15 * pyunits.K) # equal inlet reactor temperature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] + ], + "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": "3.9.18" } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.OXIDE.properties: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.OXIDE.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.OXIDE.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.OXIDE: Initialization Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.ACID.properties: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.ACID.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.ACID.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.ACID: Initialization Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.reagent_feed_state: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.reagent_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.catalyst_feed_state: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.catalyst_feed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:59 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "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...: 337\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 383\n", - "\n", - "Total number of variables............................: 95\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 86\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 95\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 1.30e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 2.66e+06 1.28e+01 -1.0 9.75e+06 - 6.77e-02 9.90e-01h 1\n", - " 2 0.0000000e+00 2.36e+04 2.90e+02 -1.0 9.75e+04 - 7.00e-01 9.90e-01h 1\n", - " 3 0.0000000e+00 2.43e+02 1.44e+01 -1.0 9.74e+02 - 9.90e-01 9.90e-01h 1\n", - " 4 0.0000000e+00 1.85e+00 3.18e+03 -1.0 9.62e+00 - 9.90e-01 9.92e-01h 1\n", - " 5 0.0000000e+00 8.94e-08 3.34e+03 -1.0 7.33e-02 - 9.94e-01 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 5\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.6686898422600192e+06 1.6686898422600192e+06\n", - "Constraint violation....: 1.9895196601282805e-13 8.9406967163085938e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.9895196601282805e-13 1.6686898422600192e+06\n", - "\n", - "\n", - "Number of objective function evaluations = 6\n", - "Number of objective gradient evaluations = 6\n", - "Number of equality constraint evaluations = 6\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 6\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 5\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $3.458 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "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 : -5.6566e+06 : watt : False : (None, None)\n", - " Reaction Extent [R1] : 46.400 : mole / second : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 11.600\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 193.20\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 46.400\n", - " Temperature kelvin 328.15 328.15\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)\n", - "\n", - "m.fs.R101.outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "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...: 341\n", - "Number of nonzeros in inequality constraint Jacobian.: 1\n", - "Number of nonzeros in Lagrangian Hessian.............: 403\n", - "\n", - "Total number of variables............................: 97\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 88\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 95\n", - "Total number of inequality constraints...............: 1\n", - " inequality constraints with only lower bounds: 1\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 3.4581399e+06 1.76e+06 6.34e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.4605363e+06 1.75e+06 1.17e+01 -1.0 6.95e+05 - 7.82e-02 6.15e-03h 1\n", - " 2 3.4956495e+06 1.61e+06 6.65e+01 -1.0 6.94e+05 - 1.27e-01 8.29e-02h 1\n", - " 3 3.5669528e+06 1.31e+06 4.78e+02 -1.0 6.33e+05 - 1.61e-01 1.84e-01h 1\n", - " 4 3.6648118e+06 9.11e+05 3.39e+02 -1.0 5.26e+05 - 9.11e-01 3.05e-01h 1\n", - " 5 3.8858215e+06 1.14e+04 1.07e+01 -1.0 3.65e+05 - 9.88e-01 9.90e-01h 1\n", - " 6 3.8880314e+06 1.02e+02 2.00e+00 -1.0 3.65e+03 - 9.90e-01 9.91e-01h 1\n", - " 7 3.8880511e+06 4.13e-05 1.64e-03 -1.0 3.23e+01 - 1.00e+00 1.00e+00h 1\n", - " 8 3.8880508e+06 2.06e-06 1.09e+01 -5.7 3.42e-01 - 1.00e+00 1.00e+00f 1\n", - " 9 3.8880508e+06 2.05e-08 3.69e-07 -5.7 2.15e-05 - 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", - " 10 3.8880508e+06 4.66e-09 4.00e-07 -7.0 6.02e-06 - 1.00e+00 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 3.8880508414204163e+06 3.8880508414204163e+06\n", - "Dual infeasibility......: 4.0007411785832306e-07 4.0007411785832306e-07\n", - "Constraint violation....: 7.1054273576010019e-15 4.6566128730773926e-09\n", - "Complementarity.........: 9.0909183706024063e-08 9.0909183706024063e-08\n", - "Overall NLP error.......: 9.0909183706024063e-08 4.0007411785832306e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 11\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 11\n", - "Number of Lagrangian Hessian evaluations = 10\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" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $3.888 million per year\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 699.26 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 58.000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 239.60\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 2.0000e-05\n", - " Temperature kelvin 298.15 328.15\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n", - "\n", - "Stoichiometric reactor results\n", - "\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 : -6.3608e+06 : watt : False : (None, None)\n", - " Reaction Extent [R1] : 52.200 : mole / second : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 5.8000\n", - " Molar Flowrate ('Liq', 'water') mole / second 239.60 187.40\n", - " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", - " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 52.200\n", - " Temperature kelvin 328.15 450.00\n", - " Pressure pascal 1.0000e+05 1.0000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Stoichiometric reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 328.150 K\n", - "\n", - "R101 outlet temperature = 450.000 K\n", - "\n", - "Ethylene glycol produced = 225.415 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb index 6ae99dc8..675c7fea 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb @@ -53,7 +53,7 @@ "\n", "Chemical reaction:\n", "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", "\n", "Property Packages:\n", "\n", @@ -219,9 +219,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = StoichiometricReactor(\n", @@ -564,7 +562,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -579,7 +577,7 @@ "source": [ "import pytest\n", "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.458140, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(8.019605, rel=1e-5)" ] }, { @@ -611,9 +609,9 @@ }, "outputs": [], "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, abs=1e-3)\n", - "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.6566, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, abs=1e-3)" + "assert value(m.fs.R101.conversion) == pytest.approx(0.8000, rel=1e-5)\n", + "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(-5.8931, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, rel=1e-5)" ] }, { @@ -724,7 +722,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -747,7 +745,7 @@ }, "outputs": [], "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(3.888050, abs=1e-3)" + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(6.670729, rel=1e-5)" ] }, { @@ -766,13 +764,13 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -788,10 +786,10 @@ }, "outputs": [], "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(4.5000, abs=1e-3)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415, abs=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, abs=1e-3)" + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(4.5000, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-5)" ] }, { @@ -819,9 +817,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb index 06425641..5660e55a 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb @@ -53,7 +53,7 @@ "\n", "Chemical reaction:\n", "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", "\n", "Property Packages:\n", "\n", @@ -219,9 +219,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "m.fs.R101 = StoichiometricReactor(\n", @@ -520,7 +518,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" ] }, { @@ -621,7 +619,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", "\n", "print()\n", "print(\"Heater results\")\n", @@ -650,13 +648,13 @@ "print(\"Optimal Values\")\n", "print()\n", "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.6f} K\")\n", "\n", "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.3f} MM lb/year\")\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", "\n", "print()\n", "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" @@ -687,9 +685,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/tests/__init__.py b/idaes_examples/notebooks/docs/unit_models/reactors/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py b/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py new file mode 100644 index 00000000..7b9ca054 --- /dev/null +++ b/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py @@ -0,0 +1,1074 @@ +################################################################################# +# 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. +################################################################################# +""" +Author: Brandon Paul +""" +import pytest +from pyomo.environ import ( + assert_optimal_termination, + ConcreteModel, + Set, + value, + Var, + units as pyunits, + as_quantity, +) +from pyomo.common.unittest import assertStructuredAlmostEqual + +from idaes.core import Component +from idaes.core.util.model_statistics import ( + degrees_of_freedom, + fixed_variables_set, + activated_constraints_set, +) +from idaes.core.solvers import get_solver + +from idaes.models.properties.modular_properties.base.generic_property import ( + GenericParameterBlock, +) + +from idaes.models.properties.modular_properties.state_definitions import FpcTP + +from idaes_examples.notebooks.docs.unit_models.reactors.egprod_ideal import config_dict + +from idaes.models.properties.tests.test_harness import PropertyTestHarness + +from idaes.core.util.model_diagnostics import DiagnosticsToolbox + +from idaes.core.util.constants import Constants as const + +from idaes.core import VaporPhase + +from idaes.models.properties.modular_properties.eos.ideal import Ideal + +import copy + + +# ----------------------------------------------------------------------------- +# Get default solver for testing +solver = get_solver() + + +class TestEGProdIdeal(PropertyTestHarness): + def configure(self): + self.prop_pack = GenericParameterBlock + self.param_args = config_dict + self.prop_args = {} + self.has_density_terms = True + + +class TestParamBlock(object): + @pytest.mark.unit + def test_build(self): + model = ConcreteModel() + model.params = GenericParameterBlock(**config_dict) + + assert isinstance(model.params.phase_list, Set) + assert len(model.params.phase_list) == 1 + for i in model.params.phase_list: + assert i in [ + "Liq", + ] + assert model.params.Liq.is_liquid_phase() + + assert isinstance(model.params.component_list, Set) + assert len(model.params.component_list) == 4 + for i in model.params.component_list: + assert i in ["ethylene_oxide", "water", "sulfuric_acid", "ethylene_glycol"] + assert isinstance(model.params.get_component(i), Component) + + assert isinstance(model.params._phase_component_set, Set) + assert len(model.params._phase_component_set) == 4 + for i in model.params._phase_component_set: + assert i in [ + ("Liq", "ethylene_oxide"), + ("Liq", "water"), + ("Liq", "sulfuric_acid"), + ("Liq", "ethylene_glycol"), + ] + + assert model.params.config.state_definition == FpcTP + + assertStructuredAlmostEqual( + model.params.config.state_bounds, + { + "flow_mol_phase_comp": (0, 100, 1000, pyunits.mol / pyunits.s), + "temperature": (273.15, 298.15, 450, pyunits.K), + "pressure": (5e4, 1e5, 1e6, pyunits.Pa), + }, + item_callback=as_quantity, + ) + + assert value(model.params.pressure_ref) == 1e5 + assert value(model.params.temperature_ref) == 298.15 + + assert value(model.params.ethylene_oxide.mw) == 44.054e-3 + assert value(model.params.ethylene_oxide.pressure_crit) == 71.9e5 + assert value(model.params.ethylene_oxide.temperature_crit) == 469 + + assert value(model.params.water.mw) == 18.015e-3 + assert value(model.params.water.pressure_crit) == 221.2e5 + assert value(model.params.water.temperature_crit) == 647.3 + + assert value(model.params.sulfuric_acid.mw) == 98.08e-3 + assert value(model.params.sulfuric_acid.pressure_crit) == 129.4262e5 + assert value(model.params.sulfuric_acid.temperature_crit) == 590.76 + + assert value(model.params.ethylene_glycol.mw) == 62.069e-3 + assert value(model.params.ethylene_glycol.pressure_crit) == 77e5 + assert value(model.params.ethylene_glycol.temperature_crit) == 645 + + dt = DiagnosticsToolbox(model) + dt.assert_no_structural_warnings() + + +class TestStateBlock(object): + @pytest.fixture(scope="class") + def model(self): + model = ConcreteModel() + model.params = GenericParameterBlock(**config_dict) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", "ethylene_oxide"].fix(100) + model.props[1].flow_mol_phase_comp["Liq", "water"].fix(100) + model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"].fix(100) + model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"].fix(100) + model.props[1].temperature.fix(300) + model.props[1].pressure.fix(101325) + + return model + + @pytest.mark.unit + def test_build(self, model): + # Check state variable values and bounds + assert isinstance(model.props[1].flow_mol_phase_comp, Var) + assert value(model.props[1].flow_mol_phase_comp["Liq", "ethylene_oxide"]) == 100 + assert value(model.props[1].flow_mol_phase_comp["Liq", "water"]) == 100 + assert value(model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"]) == 100 + assert ( + value(model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"]) == 100 + ) + assert model.props[1].flow_mol_phase_comp["Liq", "ethylene_oxide"].ub == 1000 + assert model.props[1].flow_mol_phase_comp["Liq", "water"].ub == 1000 + assert model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"].ub == 1000 + assert model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"].ub == 1000 + assert model.props[1].flow_mol_phase_comp["Liq", "ethylene_oxide"].lb == 0 + assert model.props[1].flow_mol_phase_comp["Liq", "water"].lb == 0 + assert model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"].lb == 0 + assert model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"].lb == 0 + + assert isinstance(model.props[1].pressure, Var) + assert value(model.props[1].pressure) == 101325 + assert model.props[1].pressure.ub == 1e6 + assert model.props[1].pressure.lb == 5e4 + + assert isinstance(model.props[1].temperature, Var) + assert value(model.props[1].temperature) == 300 + assert model.props[1].temperature.ub == 450 + assert model.props[1].temperature.lb == 273.15 + + @pytest.mark.unit + def test_define_state_vars(self, model): + sv = model.props[1].define_state_vars() + + assert len(sv) == 3 + for i in sv: + assert i in ["flow_mol_phase_comp", "temperature", "pressure"] + + @pytest.mark.unit + def test_define_port_members(self, model): + sv = model.props[1].define_state_vars() + + assert len(sv) == 3 + for i in sv: + assert i in ["flow_mol_phase_comp", "temperature", "pressure"] + + @pytest.mark.unit + def test_define_display_vars(self, model): + sv = model.props[1].define_display_vars() + + assert len(sv) == 3 + for i in sv: + assert i in [ + "Molar Flowrate", + "Temperature", + "Pressure", + ] + + @pytest.mark.unit + def test_structural_diagnostics(self, model): + dt = DiagnosticsToolbox(model) + dt.assert_no_structural_warnings() + + @pytest.mark.unit + def test_basic_scaling(self, model): + assert len(model.props[1].scaling_factor) == 20 + assert model.props[1].scaling_factor[model.props[1].flow_mol] == 1e-2 + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_comp["ethylene_oxide"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[model.props[1].flow_mol_comp["water"]] == 1e-2 + ) + assert ( + model.props[1].scaling_factor[model.props[1].flow_mol_comp["sulfuric_acid"]] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_comp["ethylene_glycol"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[model.props[1].flow_mol_phase["Liq"]] == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_phase_comp["Liq", "ethylene_oxide"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_phase_comp["Liq", "water"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"] + ] + == 1e-2 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_comp["ethylene_oxide"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[model.props[1].mole_frac_comp["water"]] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_comp["sulfuric_acid"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_comp["ethylene_glycol"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_phase_comp["Liq", "ethylene_oxide"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_phase_comp["Liq", "water"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_phase_comp["Liq", "sulfuric_acid"] + ] + == 1000 + ) + assert ( + model.props[1].scaling_factor[ + model.props[1].mole_frac_phase_comp["Liq", "ethylene_glycol"] + ] + == 1000 + ) + assert model.props[1].scaling_factor[model.props[1].pressure] == 1e-5 + assert model.props[1].scaling_factor[model.props[1].temperature] == 1e-2 + + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_initialize(self, model): + orig_fixed_vars = fixed_variables_set(model) + orig_act_consts = activated_constraints_set(model) + + model.props.initialize(optarg={"tol": 1e-6}) + + assert degrees_of_freedom(model) == 0 + + fin_fixed_vars = fixed_variables_set(model) + fin_act_consts = activated_constraints_set(model) + + assert len(fin_act_consts) == len(orig_act_consts) + assert len(fin_fixed_vars) == len(orig_fixed_vars) + + for c in fin_act_consts: + assert c in orig_act_consts + for v in fin_fixed_vars: + assert v in orig_fixed_vars + + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_solve(self, model): + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + @pytest.mark.unit + def test_numerical_diagnostics(self, model): + dt = DiagnosticsToolbox(model) + dt.assert_no_numerical_warnings() + + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_solution(self, model): + # Check results + assert value( + model.props[1].flow_mol_phase_comp["Liq", "ethylene_oxide"] + ) == pytest.approx(100, abs=1e-4) + assert value( + model.props[1].flow_mol_phase_comp["Liq", "water"] + ) == pytest.approx(100, abs=1e-4) + assert value( + model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"] + ) == pytest.approx(100, abs=1e-4) + assert value( + model.props[1].flow_mol_phase_comp["Liq", "ethylene_glycol"] + ) == pytest.approx(100, abs=1e-4) + + assert value(model.props[1].temperature) == pytest.approx(300, abs=1e-4) + assert value(model.props[1].pressure) == pytest.approx(101325, abs=1e-4) + + +class TestPerrysProperties(object): + @pytest.fixture(scope="class") + def density_temperatures(self): + # ethylene oxide, water, ethylene glycol reference temperatures + # from Perry's Chemical Engineers' Handbook 7th Ed. 2-94 to 2-98 + components = ["ethylene_oxide", "water", "ethylene_glycol"] + temperatures = dict( + zip(components, [[160.65, 469.15], [273.16, 333.15], [260.15, 719.7]]) + ) + + return temperatures + + @pytest.fixture(scope="class") + def densities(self): + # ethylene oxide, water, ethylene glycol densities from + # Perry's Chemical Engineers' Handbook 7th Ed. 2-94 to 2-98 + components = ["ethylene_oxide", "water", "ethylene_glycol"] + densities = dict( + zip(components, [[23.477, 7.055], [55.583, 54.703], [18.31, 5.234]]) + ) + + return densities + + @pytest.fixture(scope="class") + def heat_capacity_temperatures(self): + # ethylene oxide, water, ethylene glycol reference temperatures + # from Perry's Chemical Engineers' Handbook 7th Ed. 2-170 to 2-174 + components = ["ethylene_oxide", "water", "ethylene_glycol"] + temperatures = dict( + zip(components, [[160.65, 283.85], [273.16, 533.15], [260.15, 493.15]]) + ) + + return temperatures + + @pytest.fixture(scope="class") + def heat_capacities(self): + # ethylene oxide, water, ethylene glycol heat capacities from + # Perry's Chemical Engineers' Handbook 7th Ed. 2-170 to 2-174 + components = ["ethylene_oxide", "water", "ethylene_glycol"] + heat_capacities = dict( + zip( + components, + [[0.8303e5, 0.8693e5], [0.7615e5, 0.8939e5], [1.36661e5, 2.0598e5]], + ) + ) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference(self): + # ethylene oxide, water, ethylene glycol heat capacities from + # NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + heat_capacities = dict(zip(components, [0.8690e5, 0.7538e5, 0.1498e5])) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference_temperatures(self): + # ethylene oxide, water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + temperatures = dict(zip(components, [285, 298.0, 298.0])) + + return temperatures + + @pytest.mark.parametrize( + "component", ["ethylene_oxide", "water", "ethylene_glycol"] + ) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_liquid_densities( + self, component, test_point, density_temperatures, densities + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", component].fix(100) + + # change lower bound for testing + model.props[1].temperature.setlb(150) + + model.props[1].temperature.fix(density_temperatures[component][test_point]) + model.props[1].pressure.fix(101325) + + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + assert value( + pyunits.convert( + model.props[1].dens_mol, to_units=pyunits.kmol / pyunits.m**3 + ) + ) == pytest.approx(densities[component][test_point], rel=1e-4) + + @pytest.mark.parametrize( + "component", ["ethylene_oxide", "water", "ethylene_glycol"] + ) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_liquid_heat_capacities_enthalpy( + self, + component, + test_point, + heat_capacity_temperatures, + heat_capacities, + heat_capacity_reference, + heat_capacity_reference_temperatures, + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", component].fix(100) + + model.props[1].pressure.fix(101325) + + # calculate reference point + + model.props[1].temperature.fix(heat_capacity_reference_temperatures[component]) + + results = solver.solve(model) + + enth_mol_ref = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_ref = heat_capacity_reference_temperatures[component] * pyunits.K + cp_mol_ref = ( + heat_capacity_reference[component] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # calculate test point + + model.props[1].temperature.fix( + heat_capacity_temperatures[component][test_point] + ) + + results = solver.solve(model) + + enth_mol_test = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_test = heat_capacity_temperatures[component][test_point] * pyunits.K + cp_mol_test = ( + heat_capacities[component][test_point] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + + assert value( + pyunits.convert(enth_mol_test, to_units=pyunits.J / pyunits.mol) + ) == pytest.approx( + value( + pyunits.convert( + 0.5 * (cp_mol_test + cp_mol_ref) * (temp_test - temp_ref) + + enth_mol_ref, + to_units=pyunits.J / pyunits.mol, + ) + ), + rel=1e-1, # using 1e-1 tol to check against trapezoid rule estimation of integral + ) + + +class TestRPP4Properties(object): + @pytest.fixture(scope="class") + def heat_capacity_temperatures(self): + # ethylene oxide, water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + temperatures = dict(zip(components, [[307.18, 371.23], [545, 632], [500, 600]])) + + return temperatures + + @pytest.fixture(scope="class") + def heat_capacities(self): + # ethylene oxide, water, ethylene glycol heat capacities from + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + heat_capacities = dict( + zip(components, [[49.37, 58.41], [35.70, 36.69], [113.64, 125.65]]) + ) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference(self): + # ethylene oxide, water, ethylene glycol heat capacities from + # NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + heat_capacities = dict(zip(components, [61.66, 35.22, 97.99])) + + return heat_capacities + + @pytest.fixture(scope="class") + def heat_capacity_reference_temperatures(self): + # ethylene oxide, water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + temperatures = dict(zip(components, [400, 500, 400])) + + return temperatures + + @pytest.fixture(scope="class") + def saturation_pressure_temperatures(self): + # ethylene oxide, water, ethylene glycol reference temperatures + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + temperatures = dict( + zip(components, [[250.01, 300.02], [300.25, 350.16], [387, 473]]) + ) + + return temperatures + + @pytest.fixture(scope="class") + def saturation_pressures(self): + # ethylene oxide, water, ethylene glycol saturation pressures from + # from NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + components = ["ethylene_oxide", "water", "ethylene_glycol"] + pressures = dict( + zip( + components, + [[0.2189e5, 1.8604e5], [0.03591e5, 0.4194e5], [0.04257e5, 1.0934e5]], + ) + ) + + return pressures + + @pytest.mark.parametrize( + "component", ["ethylene_oxide", "water", "ethylene_glycol"] + ) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_vapor_heat_capacities_enthalpy( + self, + component, + test_point, + heat_capacity_temperatures, + heat_capacities, + heat_capacity_reference, + heat_capacity_reference_temperatures, + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + config_dict_component_only["phases"] = { + "Vap": {"type": VaporPhase, "equation_of_state": Ideal} + } + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Vap", component].fix(100) + + model.props[1].pressure.fix(101325) + + # calculate reference point + + model.props[1].temperature.fix(heat_capacity_reference_temperatures[component]) + + results = solver.solve(model) + + enth_mol_ref = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_ref = heat_capacity_reference_temperatures[component] * pyunits.K + cp_mol_ref = ( + heat_capacity_reference[component] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # calculate test point + + model.props[1].temperature.fix( + heat_capacity_temperatures[component][test_point] + ) + + results = solver.solve(model) + + enth_mol_test = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_test = heat_capacity_temperatures[component][test_point] * pyunits.K + cp_mol_test = ( + heat_capacities[component][test_point] + * 1e-3 + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + + assert value( + pyunits.convert(enth_mol_test, to_units=pyunits.J / pyunits.mol) + ) == pytest.approx( + value( + pyunits.convert( + 0.5 * (cp_mol_test + cp_mol_ref) * (temp_test - temp_ref) + + enth_mol_ref, + to_units=pyunits.J / pyunits.mol, + ) + ), + rel=1.15e-1, # using 1.15e-1 tol to check against trapezoid rule estimation of integral + # all values match within 1e-1, except ethylene glycol test point 0 + ) + + @pytest.mark.parametrize( + "component", ["ethylene_oxide", "water", "ethylene_glycol"] + ) + @pytest.mark.parametrize("test_point", [0, 1]) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_saturation_pressures( + self, + component, + test_point, + saturation_pressure_temperatures, + saturation_pressures, + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == component: + pass + else: + config_dict_component_only["components"].pop(key) + + config_dict_component_only["phases"] = { + "Vap": {"type": VaporPhase, "equation_of_state": Ideal} + } + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Vap", component].fix(100) + + model.props[1].temperature.fix( + saturation_pressure_temperatures[component][test_point] + ) + model.props[1].pressure.fix(101325) + + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + print(value(model.props[1].pressure_sat_comp[component])) + assert value(model.props[1].pressure_sat_comp[component]) == pytest.approx( + saturation_pressures[component][test_point], rel=1.5e-2 + ) # match within 1.5% + + +class TestSulfuricAcidProperties(object): + # sulfuric acid liquid density data from + # CRC Handbook of Chemistry and Physics, 97th Ed., W.M. Haynes pg. 15-41 + @pytest.mark.parametrize( + "temperature_density_data", + [ + [273.15, 1.8517], # K, g/mL + [283.15, 1.8409], + [288.15, 1.8357], + [293.15, 1.8305], + [298.15, 1.8255], + [303.15, 1.8205], + [313.15, 1.8107], + [323.15, 1.8013], + [333.15, 1.7922], + ], + ) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_liquid_densities(self, temperature_density_data): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == "sulfuric_acid": + pass + else: + config_dict_component_only["components"].pop(key) + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"].fix(100) + + # change lower bound for testing + model.props[1].temperature.setlb(150) + + model.props[1].temperature.fix(temperature_density_data[0]) + model.props[1].pressure.fix(101325) + + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + assert value( + pyunits.convert( + model.props[1].dens_mol * model.props[1].params.sulfuric_acid.mw, + to_units=pyunits.g / pyunits.mL, + ) + ) == pytest.approx(temperature_density_data[1], rel=1e-4) + + # sulfuric acid liquid heat capacity data from + # Journal of Physical and Chemical Reference Data 20, 1157 (1991); https:// doi.org/10.1063/1.555899 + @pytest.mark.parametrize( + "temperature_liquid_heat_capacity_data", + [ + [250, 15.1606], # K, Cp/R + [287.93, 16.4195], + [293.14, 16.4653], + [298.15, 16.6818], + [300, 16.7319], + [305.35, 16.8788], + [350, 17.8491], + ], + ) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_liquid_heat_capacities_enthalpy( + self, temperature_liquid_heat_capacity_data + ): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == "sulfuric_acid": + pass + else: + config_dict_component_only["components"].pop(key) + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Liq", "sulfuric_acid"].fix(100) + + model.props[1].pressure.fix(101325) + + # calculate reference point + + model.props[1].temperature.fix(200) + + results = solver.solve(model) + + enth_mol_ref = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_ref = 200 * pyunits.K + cp_mol_ref = 13.1352 * const.gas_constant + + # calculate test point + + model.props[1].temperature.fix(temperature_liquid_heat_capacity_data[0]) + + results = solver.solve(model) + + enth_mol_test = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_test = temperature_liquid_heat_capacity_data[0] * pyunits.K + cp_mol_test = temperature_liquid_heat_capacity_data[1] * const.gas_constant + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + + assert value( + pyunits.convert(enth_mol_test, to_units=pyunits.J / pyunits.mol) + ) == pytest.approx( + value( + pyunits.convert( + 0.5 * (cp_mol_test + cp_mol_ref) * (temp_test - temp_ref) + + enth_mol_ref, + to_units=pyunits.J / pyunits.mol, + ) + ), + rel=1e-1, # using 1e-1 tol to check against trapezoid rule estimation of integral + ) + + # sulfuric acid vapor heat capacity data from + # NIST Chemistry WebBook, https://webbook.nist.gov/chemistry/ + @pytest.mark.parametrize( + "temperature_vapor_heat_capacity_data", + [ + [300, 84.02], # K, J/mol-K + [400, 97.90], + [500, 107.9], + [600, 115.6], + [700, 121.5], + [800, 126.1], + [900, 129.7], + [1000, 132.6], + [1100, 135.2], + [1200, 137.2], + ], + ) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_vapor_heat_capacities_enthalpy(self, temperature_vapor_heat_capacity_data): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == "sulfuric_acid": + pass + else: + config_dict_component_only["components"].pop(key) + + config_dict_component_only["phases"] = { + "Vap": {"type": VaporPhase, "equation_of_state": Ideal} + } + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Vap", "sulfuric_acid"].fix(100) + + model.props[1].pressure.fix(101325) + + # calculate reference point + + model.props[1].temperature.fix(298) + + results = solver.solve(model) + + enth_mol_ref = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_ref = 298 * pyunits.K + cp_mol_ref = 83.68 * pyunits.J / pyunits.mol / pyunits.K + + # calculate test point + + model.props[1].temperature.fix(temperature_vapor_heat_capacity_data[0]) + + results = solver.solve(model) + + enth_mol_test = value(model.props[1].enth_mol) * pyunits.get_units( + model.props[1].enth_mol + ) + temp_test = temperature_vapor_heat_capacity_data[0] * pyunits.K + cp_mol_test = ( + temperature_vapor_heat_capacity_data[1] + * pyunits.J + / pyunits.mol + / pyunits.K + ) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + + assert value( + pyunits.convert(enth_mol_test, to_units=pyunits.J / pyunits.mol) + ) == pytest.approx( + value( + pyunits.convert( + 0.5 * (cp_mol_test + cp_mol_ref) * (temp_test - temp_ref) + + enth_mol_ref, + to_units=pyunits.J / pyunits.mol, + ) + ), + rel=1e-1, # using 1e-1 tol to check against trapezoid rule estimation of integral + ) + + # sulfuric acid saturation pressure data from + # CRC Handbook of Chemistry and Physics, 97th Ed., W.M. Haynes pg. 6-122 + @pytest.mark.parametrize( + "temperature_saturation_pressure_data", + [ + [295.3722222, 1], # K, Pa + [312.5944444, 10], + [333.15, 100], + [359.2611111, 1000], + [393.15, 10000], + [438.7055556, 100000], + ], + ) + @pytest.mark.skipif(solver is None, reason="Solver not available") + @pytest.mark.component + def test_saturation_pressures(self, temperature_saturation_pressure_data): + + config_dict_component_only = copy.deepcopy(config_dict) + for key in config_dict["components"].keys(): + if key == "sulfuric_acid": + pass + else: + config_dict_component_only["components"].pop(key) + + config_dict_component_only["phases"] = { + "Vap": {"type": VaporPhase, "equation_of_state": Ideal} + } + + model = ConcreteModel() + + model.params = GenericParameterBlock(**config_dict_component_only) + + model.props = model.params.build_state_block([1], defined_state=True) + + model.props[1].calculate_scaling_factors() + + # Fix state + model.props[1].flow_mol_phase_comp["Vap", "sulfuric_acid"].fix(100) + + model.props[1].temperature.fix(temperature_saturation_pressure_data[0]) + model.props[1].pressure.fix(101325) + + results = solver.solve(model) + + # Check for optimal solution + assert_optimal_termination(results) + + # Check results + assert value( + model.props[1].pressure_sat_comp["sulfuric_acid"] + ) == pytest.approx( + temperature_saturation_pressure_data[1], rel=1.5e-2 + ) # match within 1.5% diff --git a/pyproject.toml b/pyproject.toml index 0ef26107..8a4cb88d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ description = "IDAES Process Systems Engineering Examples" readme = "README.md" version = "2.6.dev0" license = {text="BSD"} -requires-python = ">=3.8" +requires-python = ">=3.9" authors = [ {name="The IDAES Project"}, {name="Dan Gunter", email="dkgunter@lbl.gov"} @@ -28,10 +28,10 @@ classifiers = [ "Operating System :: Unix", "Programming Language :: Python", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", "Programming Language :: Python :: Implementation :: CPython", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Chemistry", @@ -43,7 +43,7 @@ dependencies = [ # Pyomo "pyomo>=6.5.0", "jupyter", - "importlib_resources ; python_version < '3.9'", # importlib.resources.files() + "importlib_resources", # importlib.resources.files() ] keywords = ["IDAES", "energy systems", "chemical engineering", "process modeling"] @@ -51,7 +51,7 @@ keywords = ["IDAES", "energy systems", "chemical engineering", "process modeling omlt = [ # For Keras/OMLT "omlt", - 'tensorflow<2.16.1 ; python_version < "3.12"', + "tensorflow >= 2.16.1", ] idaes = [ "idaes-pse", # installing IDAES (from release) is opt-in diff --git a/requirements-dev.txt b/requirements-dev.txt index 202127d7..f8255530 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,3 +1,6 @@ --editable .[dev,omlt] idaes-pse @ git+https://github.com/IDAES/idaes-pse@main +# if you want to install idaes-pse from a PR instead of the main branch, +# uncomment the line below replacing XYZ with the PR number +# idaes-pse @ git+https://github.com/IDAES/idaes-pse@refs/pull/XYZ/merge