diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0ba5606 --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +*.pyc +*.txt +__pycache__/ +Rotary packed bed/RPB_working_notebook.ipynb +Rotary packed bed/finitevolume.py +Rotary packed bed/RPB sensitivity runs.ipynb +temp/ +Rotary packed bed/RPB_notebook copy.ipynb diff --git a/Rotary packed bed/RPB flowsheet 081924, limited disc.json.gz b/Rotary packed bed/RPB flowsheet 081924, limited disc.json.gz new file mode 100644 index 0000000..6a5d312 Binary files /dev/null and b/Rotary packed bed/RPB flowsheet 081924, limited disc.json.gz differ diff --git a/Rotary packed bed/RPB flowsheet 081924.json.gz b/Rotary packed bed/RPB flowsheet 081924.json.gz new file mode 100644 index 0000000..20bc1e2 Binary files /dev/null and b/Rotary packed bed/RPB flowsheet 081924.json.gz differ diff --git a/Rotary packed bed/RPB_model.py b/Rotary packed bed/RPB_model.py new file mode 100644 index 0000000..a943c6d --- /dev/null +++ b/Rotary packed bed/RPB_model.py @@ -0,0 +1,3996 @@ +################################################################################# +# 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. +################################################################################# + +""" +Rotary Packed Bed Model +""" + +# importing libraries +from idaes.logger import NOTSET +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from pyomo.environ import ( + ConcreteModel, + Var, + Param, + Constraint, + units, + exp, + log, + TransformationFactory, + SolverFactory, + value, + Set, + Objective, + Block, + PositiveReals, + NonNegativeReals, + Reals, + check_optimal_termination, + Reference, +) +from pyomo.network import Port +from pyomo.dae import ContinuousSet, DerivativeVar +from idaes import * +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util import to_json, from_json, StoreSpec +import idaes.core.util.scaling as iscale +from idaes.core.util.model_diagnostics import DegeneracyHunter +from idaes.models.unit_models import SkeletonUnitModel +from idaes.core.util.exceptions import ConfigurationError, InitializationError + +from idaes.core.util.tables import ( + create_stream_table_dataframe, + stream_table_dataframe_to_string, +) + +from idaes.core import declare_process_block_class, UnitModelBlockData, useDefault +from idaes.core.util.config import is_transformation_method, is_physical_parameter_block + +import finitevolume +from idaes.core.solvers.homotopy import homotopy +from idaes.core.initialization.block_triangularization import ( + BlockTriangularizationInitializer, +) + +from pyomo.common.config import ConfigBlock, ConfigValue, In + +from idaes.core.util.math import smooth_max +from idaes.core.util.constants import Constants as const +from idaes.core.initialization import ModularInitializerBase + +from idaes.models_extra.power_generation.properties import FlueGasParameterBlock + +import idaes.logger as idaeslog + +__author__ = "Ryan Hughes" + +# Set up logger +_log = idaeslog.getLogger(__name__) + + +@declare_process_block_class("RotaryPackedBed") +class RotaryPackedBedData(UnitModelBlockData): + """ + Standard Rotary Packed Bed Unit Model Class + """ + + CONFIG = UnitModelBlockData.CONFIG() + + CONFIG.declare( + "z_init_points", + ConfigValue( + default=None, + domain=tuple, + description="initial axial nodes", + ), + ) + + CONFIG.declare( + "z_transformation_method", + ConfigValue( + default="dae.finite_difference", + domain=is_transformation_method, + description="Axial discretization method", + ), + ) + + CONFIG.declare( + "z_nfe", + ConfigValue( + default=10, + domain=int, + description="Number of finite elements for axial direction", + ), + ) + + CONFIG.declare( + "z_collocation_points", + ConfigValue( + default=2, + domain=int, + description="Number of collocation points for axial direction. Only used with z_transformation_method = dae.collocation", + ), + ) + + CONFIG.declare( + "o_init_points", + ConfigValue( + default=None, + domain=tuple, + description="initial o nodes", + ), + ) + + CONFIG.declare( + "o_nfe", + ConfigValue(default=10, domain=int, description="Number of o finite elements"), + ) + + CONFIG.declare( + "o_collocation_points", + ConfigValue( + default=2, domain=int, description="Number of o collocation points" + ), + ) + + CONFIG.declare( + "o_transformation_method", + ConfigValue( + default="dae.finite_difference", + domain=is_transformation_method, + description="o discretization method", + ), + ) + + CONFIG.declare( + "flow_configuration", + ConfigValue( + default="counter-current", + domain=In(["counter-current", "co-current"]), + description="Flow configuration for flue gas and regeneration gas", + ), + ) + + CONFIG.declare( + "property_package", + ConfigValue( + default=useDefault, + domain=is_physical_parameter_block, + description="Property package to use for RPB model", + doc="""Property parameter object used to define property calculations, +**default** - useDefault. +**Valid values:** { +**useDefault** - use default package from parent model or flowsheet, +**PhysicalParameterObject** - a PhysicalParameterBlock object.}""", + ), + ) + + CONFIG.declare( + "property_package_args", + ConfigBlock( + implicit=True, + description="Arguments to use for constructing property packages", + doc="""A ConfigBlock with arguments to be passed to a property block(s) +and used when constructing these, +**default** - None. +**Valid values:** { +see property package for documentation.}""", + ), + ) + + def build(self): + """ + General build method for RPB + + Inheriting models should call `super().build`. + + Args: + None + + Returns: + None + + """ + + # call UnitModel.build to build default attributes + super().build() + + # Add general parameters + self._add_general_parameters() + + # Add sorbent parameters + self._add_sorbent_parameters() + + # Add expressions for constants + @self.Expression(doc="gas constant [kJ/mol/K]") + def R(b): + return units.convert( + const.gas_constant, to_units=units.kJ / units.mol / units.K + ) + + @self.Expression(doc="gas constant [m^3*bar/K/mol]") + def Rg(b): + return units.convert( + const.gas_constant, + to_units=units.m**3 * units.bar / units.K / units.mol, + ) + + # add design and operating variables + self.D = Var( + initialize=(10), domain=PositiveReals, units=units.m, doc="Bed diameter [m]" + ) + self.D.fix() + self.L = Var( + initialize=(3), + domain=PositiveReals, + bounds=(0.1, 10.001), + units=units.m, + doc="Bed Length [m]", + ) + self.L.fix() + + self.w_rpm = Var( + self.flowsheet().time, + initialize=(1), + domain=PositiveReals, + bounds=(0.00001, 2), + units=units.revolutions / units.min, + doc="bed rotational speed [revolutions/min]", + ) + self.w_rpm.fix() + + @self.Expression(self.flowsheet().time, doc="bed rotational speed [radians/s]") + def w(b, t): + return ( + b.w_rpm[t] + * (2 * const.pi * units.radians / units.revolutions) + / (60 * units.sec / units.min) + ) + + self._add_section( + name="ads", gas_flow_direction="forward", initial_guesses="adsorption" + ) + + if self.CONFIG.flow_configuration == "counter-current": + self._add_section( + name="des", gas_flow_direction="reverse", initial_guesses="desorption" + ) + elif self.CONFIG.flow_configuration == "co-current": + self._add_section( + name="des", gas_flow_direction="forward", initial_guesses="desorption" + ) + + self._connect_sections() + + def _add_general_parameters(self): + """ + Method to add general parameters to the RPB model. This includes ... + """ + + self.component_list = Set( + initialize=self.config.property_package.component_list, + doc="List of components", + ) + + self.ads_components = Set( + initialize=["CO2"], + within=self.component_list, + doc="list of adsorbing components", + ) + + self.MW = Param( + self.component_list, + initialize=({"CO2": 44.01e-3, "N2": 28.0134e-3, "H2O": 18.01528e-3}), + units=units.kg / units.mol, + doc="component molecular weight [kg/mol]", + ) + + self.mu = Param( + self.component_list, + initialize=({"CO2": 1.87e-5, "N2": 2.3e-5, "H2O": 1.26e-5}), + units=units.Pa * units.s, + doc="pure component gas phase viscosity [Pa*s]", + ) + + self.k = Param( + self.component_list, + initialize=({"CO2": 0.020e-3, "N2": 0.030e-3, "H2O": 0.025e-3}), + units=units.kJ / units.s / units.m / units.K, + doc="pure component gas phase thermal conductivity [kW/m/K, kJ/s/m/K]", + ) + + self.DmCO2 = Param( + initialize=(5.3e-5), + units=units.m**2 / units.s, + doc="gas phase CO2 diffusivity [m^2/s]", + ) + + def _add_sorbent_parameters(self): + """ + Method to add sorbent parameters (and accompanying expressions) to the RPB model. This includes ... + """ + + # general parameters + self.eb = Param(initialize=(0.68), doc="bed voidage") + self.ep = Param(initialize=(0.68), doc="particle porosity") + self.dp = Param( + initialize=(0.000525), units=units.m, doc="particle diameter [m]" + ) + self.Cp_sol = Param( + initialize=(1.457), + units=units.kJ / units.kg / units.K, + doc="solid heat capacity [kJ/kg/K]", + ) + self.rho_sol = Param( + initialize=(1144), + units=units.kg / units.m**3, + mutable=True, + doc="solid particle densitry [kg/m^3]", + ) + + @self.Expression(doc="particle radius [m]") + def rp(b): + return b.dp / 2 + + @self.Expression( + doc="specific particle area for mass transfer, bed voidage" + "included [m^2/m^3 bed]" + ) + def a_s(b): + return 6 / b.dp * (1 - b.eb) + + # Isotherm Parameters + self.q_inf_1 = Param( + initialize=2.87e-02, units=units.mol / units.kg, doc="isotherm parameter" + ) + self.q_inf_2 = Param( + initialize=1.95, units=units.mol / units.kg, doc="isotherm parameter" + ) + self.q_inf_3 = Param( + initialize=3.45, units=units.mol / units.kg, doc="isotherm parameter" + ) + + self.d_inf_1 = Param( + initialize=1670.31, units=units.bar**-1, doc="isotherm parameter" + ) + self.d_inf_2 = Param( + initialize=789.01, units=units.bar**-1, doc="isotherm parameter" + ) + self.d_inf_3 = Param( + initialize=10990.67, units=units.bar**-1, doc="isotherm parameter" + ) + self.d_inf_4 = Param( + initialize=0.28, + units=units.mol / units.kg / units.bar, + doc="isotherm parameter", + ) + + self.E_1 = Param( + initialize=-76.15, units=units.kJ / units.mol, doc="isotherm parameter" + ) + self.E_2 = Param( + initialize=-77.44, units=units.kJ / units.mol, doc="isotherm parameter" + ) + self.E_3 = Param( + initialize=-194.48, units=units.kJ / units.mol, doc="isotherm parameter" + ) + self.E_4 = Param( + initialize=-6.76, units=units.kJ / units.mol, doc="isotherm parameter" + ) + + self.X_11 = Param(initialize=4.20e-2, doc="isotherm parameter") + self.X_21 = Param(initialize=2.97, units=units.K, doc="isotherm parameter") + self.X_12 = Param(initialize=7.74e-2, doc="isotherm parameter") + self.X_22 = Param(initialize=1.66, units=units.K, doc="isotherm parameter") + + self.P_step_01 = Param( + initialize=1.85e-03, units=units.bar, doc="isotherm parameter" + ) + self.P_step_02 = Param( + initialize=1.78e-02, units=units.bar, doc="isotherm parameter" + ) + + @self.Expression() + def ln_P0_1(b): + return log(b.P_step_01 / units.bar) + + @self.Expression() + def ln_P0_2(b): + return log(b.P_step_02 / units.bar) + + self.H_step_1 = Param( + initialize=-99.64, units=units.kJ / units.mol, doc="isotherm parameter" + ) + self.H_step_2 = Param( + initialize=-78.19, units=units.kJ / units.mol, doc="isotherm parameter" + ) + + self.gamma_1 = Param(initialize=894.67, doc="isotherm parameter") + self.gamma_2 = Param(initialize=95.22, doc="isotherm parameter") + + self.T0 = Param(initialize=363.15, units=units.K, doc="isotherm parameter") + + # Mass transfer parameters + self.C1 = Param( + initialize=(2.562434e-12), + units=units.m**2 / units.K**0.5 / units.s, + doc="lumped MT parameter [m^2/K^0.5/s]", + ) + + # heat of adsorption parameters + self.delH_a1 = Param( + initialize=21.68, + units=units.kg / units.mol, + doc="heat of adsorption parameter", + ) + self.delH_a2 = Param( + initialize=29.10, + units=units.kg / units.mol, + doc="heat of adsorption parameter", + ) + self.delH_b1 = Param( + initialize=1.59, + units=units.mol / units.kg, + doc="heat of adsorption parameter", + ) + self.delH_b2 = Param( + initialize=3.39, + units=units.mol / units.kg, + doc="heat of adsorption parameter", + ) + self.delH_1 = Param( + initialize=98.76, + units=units.kJ / units.mol, + doc="heat of adsorption parameter", + ) + self.delH_2 = Param( + initialize=77.11, + units=units.kJ / units.mol, + doc="heat of adsorption parameter", + ) + self.delH_3 = Param( + initialize=21.25, + units=units.kJ / units.mol, + doc="heat of adsorption parameter", + ) + + def _add_section( + self, name, gas_flow_direction="forward", initial_guesses="Adsorption" + ): + """ + Method to add a single section to the RPB model. This includes ... + + Args: + name : str : name of the section + gas_flow_direction : str : direction of gas flow in the section + initial_guesses : str : initial guesses keyword for variables in the section + + Returns: + None + + """ + + setattr(self, name, SkeletonUnitModel()) + blk = getattr(self, name) + + blk.CONFIG = ConfigBlock() + + blk.CONFIG.declare( + "gas_flow_direction", + ConfigValue( + default=gas_flow_direction, + domain=In(["forward", "reverse"]), + description="gas flow direction, used for simulation of counter-current configuration. Forward flows from 0 to 1, reverse flows from 1 to 0", + ), + ) + + blk.z = ContinuousSet( + doc="axial dimension [dimensionless]", + bounds=(0, 1), + initialize=self.config.z_init_points, + ) + + blk.o = ContinuousSet( + doc="adsorption theta nodes [dimensionless]", + bounds=(0, 1), + initialize=self.config.o_init_points, + ) + + # add section parameters + if initial_guesses == "adsorption": + theta_0 = 0.75 + elif initial_guesses == "desorption": + theta_0 = 0.25 + else: + theta_0 = 0.5 + + blk.theta = Var( + initialize=(theta_0), + domain=PositiveReals, + bounds=(0.01, 0.99), + doc="Fraction of bed occupied by the section[-]", + ) + blk.theta.fix() + + # embedded heat exchanger and area calculations + blk.Hx_frac = Param( + initialize=(1 / 3), # current assumption, HE takes up 1/3 of bed + mutable=True, + doc="fraction of total reactor volume occupied by the embedded" + "heat exchanger", + ) + + blk.a_sp = Param( + initialize=(50), + units=units.m**2 / units.m**3, + doc="specific surface area for heat transfer [m^2/m^3]", + ) + + @blk.Expression(doc="cross sectional area, total area*theta [m^2]") + def A_c(b): + return const.pi * self.D**2 / 4 * b.theta + + @blk.Expression(doc="cross sectional area for flow [m^2]") + def A_b(b): + return (1 - b.Hx_frac) * b.A_c # current assumption, HE takes up 1/3 of bed + + @blk.Expression(doc="cross sectional area of the heat exchanger [m^2]") + def Ahx(b): + return b.Hx_frac * b.A_c + + @blk.Expression(doc="specific heat transfer area [m^2/m^3]") + def a_ht(b): # current assumption, can update this later + return b.a_sp + + # ============================ Gas Inlet ======================================= + # build state block + blk.inlet_properties = self.config.property_package.build_state_block( + self.flowsheet().time, + doc="Material properties in gas inlet", + defined_state=True, + has_phase_equilibrium=False, + **self.config.property_package_args, + ) + + # Add references to all state vars + s_vars = blk.inlet_properties[self.flowsheet().time.first()].define_state_vars() + for s in s_vars: + l_name = s_vars[s].local_name + if s_vars[s].is_indexed(): + slicer = blk.inlet_properties[:].component(l_name)[...] + else: + slicer = blk.inlet_properties[:].component(l_name) + + r = Reference(slicer) + setattr(blk, s + "_inlet", r) + + # Add inlet port + self.add_port( + name=name + "_gas_inlet", block=blk.inlet_properties, doc="Inlet Port" + ) + + # initialize inlet values + if initial_guesses == "adsorption": + Tg_in = 90 + 273 + y_in = {(0, "CO2"): 0.04, (0, "N2"): 0.87, (0, "H2O"): 0.09} + elif initial_guesses == "desorption": + Tg_in = 120 + 273 + y_in = {(0, "CO2"): 1e-5, (0, "N2"): 1e-3, (0, "H2O"): (1 - 1e-5 - 1e-3)} + else: + Tg_in = 90 + 273 + y_in = {(0, "CO2"): 0.04, (0, "N2"): 0.87, (0, "H2O"): 0.09} + + for t in self.flowsheet().time: + blk.flow_mol_inlet[t] = 400 + blk.pressure_inlet[t] = 1.1 * 101325 + blk.temperature_inlet[t] = Tg_in + for k in self.component_list: + blk.mole_frac_comp_inlet[t, k] = y_in[t, k] + + # adjusting bounds on inlet state variables + # blk.pressure_inlet.setlb(0.99 * 1e5) + # blk.pressure_inlet.setub(1.2 * 1e5) + # blk.temperature_inlet.setlb(25 + 273.15) + # blk.temperature_inlet.setub(180 + 273.15) + + @blk.Expression(self.flowsheet().time, doc="Inlet adsorber gas flow [mol/s]") + def F_in(b, t): + return units.convert(b.flow_mol_inlet[t], to_units=units.mol / units.s) + + @blk.Expression(self.flowsheet().time, doc="Inlet flue gas pressure [bar]") + def P_in(b, t): + return units.convert(b.pressure_inlet[t], to_units=units.bar) + + @blk.Expression(self.flowsheet().time, doc="Inlet flue gas temperature [K]") + def Tg_in(b, t): + return units.convert(b.temperature_inlet[t], to_units=units.K) + + blk.y_in = Reference(blk.mole_frac_comp_inlet[...]) + + # Inlet values, mainly used for initialization of state variables within the bed + blk.dens_mol_inlet = Reference(blk.inlet_properties[:].dens_mol) + blk.conc_mol_comp_inlet = Reference(blk.inlet_properties[:].conc_mol_comp[...]) + + @blk.Expression( + self.flowsheet().time, doc="inlet gas velocity, adsorption [m/s]" + ) + def vel0(b, t): + return units.convert( + b.F_in[t] / b.dens_mol_inlet[t] / b.A_b, to_units=units.m / units.s + ) + + # =========================== Gas Outlet ======================================= + # build state block + blk.outlet_properties = self.config.property_package.build_state_block( + self.flowsheet().time, + doc="Material properties in gas outlet", + defined_state=True, + has_phase_equilibrium=False, + **self.config.property_package_args, + ) + + # Add references to all state vars + s_vars = blk.outlet_properties[ + self.flowsheet().time.first() + ].define_state_vars() + for s in s_vars: + l_name = s_vars[s].local_name + if s_vars[s].is_indexed(): + slicer = blk.outlet_properties[:].component(l_name)[...] + else: + slicer = blk.outlet_properties[:].component(l_name) + + r = Reference(slicer) + setattr(blk, s + "_outlet", r) + + # Add port to RPB model + self.add_port( + name=name + "_gas_outlet", block=blk.outlet_properties, doc="Outlet Port" + ) + + # adjusting bounds on inlet state variables + # blk.pressure_outlet.setlb(0.99 * 1e5) + # blk.pressure_outlet.setub(1.2 * 1e5) + # blk.temperature_outlet.setlb(25 + 273.15) + # blk.temperature_outlet.setub(180 + 273.15) + + @blk.Expression(self.flowsheet().time, doc="Outlet flue gas pressure [bar]") + def P_out(b, t): + return units.convert(b.pressure_outlet[t], to_units=units.bar) + + @blk.Expression(self.flowsheet().time, doc="Outlet adsorber gas flow [mol/s]") + def F_out(b, t): + return units.convert(b.flow_mol_outlet[t], to_units=units.mol / units.s) + + blk.y_out = Reference(blk.mole_frac_comp_outlet[...]) + + @blk.Expression(self.flowsheet().time, doc="Outlet flue gas temperature [K]") + def Tg_out(b, t): + return units.convert(b.temperature_outlet[t], to_units=units.K) + + # ======================== Heat exchanger ====================================== + blk.hgx = Param( + initialize=(25 * 1e-3), # assumed value + units=units.kW / units.m**2 / units.K, + doc="heat exchanger heat transfer coeff. kW/m^2/K", + ) + + if initial_guesses == "adsorption": + Tx = 90 + 273 + elif initial_guesses == "desorption": + Tx = 120 + 273 + else: + Tx = 90 + 273 + + blk.Tx = Var( + self.flowsheet().time, + initialize=Tx, + domain=PositiveReals, + units=units.K, + doc="heat exchange fluid temperature, constant [K]", + ) + # ============================================================================== + + # Variable declaration ========================================================= + # ============================== Gas Phase ===================================== + blk.gas_properties = self.config.property_package.build_state_block( + self.flowsheet().time, + blk.z, + blk.o, + doc="Material properties in gas outlet", + defined_state=True, + has_phase_equilibrium=False, + **self.config.property_package_args, + ) + + # Add references to all state vars + s_vars = blk.gas_properties[ + self.flowsheet().time.first(), blk.z.first(), blk.o.first() + ].define_state_vars() + for s in s_vars: + l_name = s_vars[s].local_name + if s_vars[s].is_indexed(): + slicer = blk.gas_properties[...].component(l_name)[...] + else: + slicer = blk.gas_properties[...].component(l_name) + + r = Reference(slicer) + setattr(blk, s, r) + + # adjusting bounds + # blk.pressure.setlb(0.99 * 1e5) + # blk.pressure.setub(1.2 * 1e5) + # blk.temperature.setlb(25 + 273.15) + # blk.temperature.setub(180 + 273.15) + + # blk.y = Var( + # self.flowsheet().time, + # blk.z, + # blk.o, + # self.component_list, + # bounds=(1e-20, 1), + # initialize=0.1, + # domain=NonNegativeReals, + # units=units.dimensionless, + # doc="gas phase mole fraction", + # ) + + blk.y = Reference(blk.mole_frac_comp[...]) + + # blk.C_tot = Var( + # self.flowsheet().time, + # blk.z, + # blk.o, + # initialize=blk.dens_mol_inlet[self.flowsheet().time.first()](), + # bounds=(1e-20, 100), + # domain=NonNegativeReals, + # doc="Total conc., [mol/m^3] (ideal gas law)", + # units=units.mol / units.m**3, + # ) + + # blk.C_tot = Reference(blk.gas_properties[...].dens_mol) + + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="Total conc., [mol/m^3]" + ) + def C_tot(b, t, z, o): + return units.convert( + blk.gas_properties[t, z, o].dens_mol, to_units=units.mol / units.m**3 + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + self.component_list, + doc="gas species concentration [mol/m^3]", + ) + def C(b, t, z, o, k): + return units.convert( + blk.gas_properties[t, z, o].conc_mol_comp[k], + to_units=units.mol / units.m**3, + ) + + # blk.Tg = Var( + # self.flowsheet().time, + # blk.z, + # blk.o, + # initialize=blk.Tg_in[0](), + # domain=PositiveReals, + # bounds=(25 + 273.15, 180 + 273.15), + # doc="Gas phase temperature [K]", + # units=units.K, + # ) + + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="Gas phase temperature [K]" + ) + def Tg(b, t, z, o): + return units.convert(b.temperature[t, z, o], to_units=units.K) + + blk.heat_flux = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=0, + units=units.kJ / units.m**2 / units.s, + doc="heat flux [kW/m^2 or kJ/s/m^2]", + ) + + blk.dheat_fluxdz = DerivativeVar( + blk.heat_flux, + wrt=blk.z, + units=units.kW / units.m**2, + doc="axial derivative of heat flux [kW/m^2/dimensionless bed length]", + ) + + blk.vel = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=blk.vel0[self.flowsheet().time.first()](), + bounds=(1e-20, 5), + domain=NonNegativeReals, + units=units.m / units.s, + doc="superficial gas velocity [m/s], adsorption", + ) + + # blk.P = Var( + # self.flowsheet().time, + # blk.z, + # blk.o, + # initialize=blk.P_in[self.flowsheet().time.first()](), + # bounds=(0.99, 1.2), + # domain=PositiveReals, + # units=units.bar, + # doc="Gas Pressure [bar]", + # ) + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="Gas Pressure [bar]") + def P(b, t, z, o): + return units.convert(b.pressure[t, z, o], to_units=units.bar) + + # blk.P = Reference(blk.pressure[...]) + + # get pressure units + pressure_units = blk.pressure[ + self.flowsheet().time.first(), blk.z.first(), blk.o.first() + ].get_units() + + blk.dPdz = DerivativeVar( + blk.pressure, + wrt=blk.z, + bounds=(None, None), + units=pressure_units, + doc="axial derivative of pressure [bar/dimensionless bed length]", + ) + + blk.Flux_kzo = Var( + self.flowsheet().time, + blk.z, + blk.o, + self.component_list, + bounds=(-1000, 1000), + units=units.mol / units.m**2 / units.s, + doc="Gas phse component flux [mol/m^2/s]", + ) + + blk.dFluxdz = DerivativeVar( + blk.Flux_kzo, + wrt=blk.z, + units=units.mol / units.m**2 / units.s, + doc="axial derivative of component flux [mol/m^2 bed/s/dimensionless bed length]", + ) + + blk.Cs_r = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=blk.conc_mol_comp_inlet[self.flowsheet().time.first(), "CO2"](), + domain=NonNegativeReals, + bounds=(1e-20, 100), + units=units.mol / units.m**3, + doc="particle surface concentration of CO2 [mol/m^3]", + ) + + # ========================= Solids ============================================= + if initial_guesses == "adsorption": + qCO2_in_init = 1 + Ts_in_init = 100 + 273 + elif initial_guesses == "desorption": + qCO2_in_init = 2.5 + Ts_in_init = 110 + 273 + else: + qCO2_in_init = 1 + Ts_in_init = 100 + 273 + + blk.qCO2 = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=qCO2_in_init, + domain=NonNegativeReals, + bounds=(0, 5), + doc="CO2 loading [mol/kg]", + units=units.mol / units.kg, + ) + + blk.dqCO2do = DerivativeVar( + blk.qCO2, + wrt=blk.o, + units=units.mol / units.kg, + doc="theta derivative of loading [mol/kg/dimensionless bed fraction]", + ) + + blk.Ts = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=Ts_in_init, + domain=PositiveReals, + bounds=(25 + 273.15, 180 + 273.15), + doc="solid phase temperature [K], adsorption", + units=units.K, + ) + + blk.dTsdo = DerivativeVar( + blk.Ts, + wrt=blk.o, + units=units.K, + doc="theta derivative of solid phase temp. [K/dimensionless bed fraction]", + ) + # ============================================================================== + + # Initialization factors ======================================================= + blk.R_MT_gas = Var( + initialize=1, + doc="init. factor for mass transfer in gas phase MB (0=off, 1=on)", + ) + blk.R_MT_gas.fix() + + blk.R_MT_solid = Var( + initialize=1, + doc="init. factor for mass transfer in solid phase MB (0=off, 1=on)", + ) + blk.R_MT_solid.fix() + + blk.R_HT_gs = Var( + initialize=1, + doc="init. factor for gas-to-solid heat transfer (0=off, 1=on)", + ) + blk.R_HT_gs.fix() + + blk.R_HT_ghx = Var( + initialize=1, doc="init. factor for gas-to-HE heat transfer (0=off, 1=on)" + ) + blk.R_HT_ghx.fix() + + blk.R_delH = Var( + initialize=1, doc="init. factor for heat of adsorption (0=off, 1=on)" + ) + blk.R_delH.fix() + + blk.R_dP = Var(initialize=1, doc="init. factor for pressure drop (0=off, 1=on)") + blk.R_dP.fix() + + blk.R_MT_coeff = Var( + initialize=1, + doc="init. factor for the mass transfer coefficient (0=constant value, 1=model prediction)", + ) + blk.R_MT_coeff.fix() + # ============================================================================== + + # Gas phase equations ========================================================== + # @blk.Constraint( + # self.flowsheet().time, + # blk.z, + # blk.o, + # doc="total concentration equation (ideal gas law) [mol/m^3]", + # ) + # def C_tot_eq(b, t, z, o): + # return b.C_tot[t, z, o] * self.Rg * b.Tg[t, z, o] == b.P[t, z, o] + + # @blk.Integral( + # self.flowsheet().time, + # blk.z, + # blk.o, + # self.component_list, + # wrt=blk.o, + # doc="Component flux integrated over theta, function of z [mol/s/m^2]", + # ) + # def Flux_kz(b, t, z, o, k): + # return b.Flux_kzo[t, z, o, k] + + # @blk.Expression( + # self.flowsheet().time, + # blk.z, + # self.component_list, + # doc="Component flow integrated over theta, function of z [mol/s]", + # ) + # def Flow_kz(b, t, z, k): + # return b.Flux_kz[t, z, k] * b.A_b + + @blk.Integral( + self.flowsheet().time, + blk.z, + blk.o, + self.component_list, + wrt=blk.o, + doc="Component flow integrated over theta, function of z [mol/s]", + ) + def Flow_kz(b, t, z, o, k): + return b.Flux_kzo[t, z, o, k] * b.A_b + + # @blk.Expression( + # self.flowsheet().time, + # blk.z, + # blk.o, + # self.component_list, + # doc="Component flow integrated over theta, function of z [mol/s]", + # ) + # def Flow_kzo(b, t, z, o, k): + # return b.Flux_kzo[t, z, o, k] / b.Flux_kz[t, z, k] + + blk.Flow_z = Var( + self.flowsheet().time, + blk.z, + initialize=blk.F_in[blk.flowsheet().time.first()](), + bounds=(0, None), + units=units.mol / units.s, + doc="Total flow integrated over theta, function of z [mol/s]", + ) + + @blk.Constraint( + self.flowsheet().time, + blk.z, + doc="Total flow integrated over theta, function of z [mol/s]", + ) + def Flow_z_eq(b, t, z): + return b.Flow_z[t, z] == sum( + b.Flow_kz[t, z, k] for k in self.component_list + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + doc="Total flow integrated over theta, function of z [mol/s]", + ) + def Flow_z_test(b, t, z): + return sum(b.Flow_kz[t, z, k] for k in self.component_list) + + blk.y_kz = Var( + self.flowsheet().time, + blk.z, + self.component_list, + initialize=0.1, + bounds=(1e-20, 1.001), + doc="Component mole fraction integrated over theta, function of z [-]", + ) + + @blk.Constraint( + self.flowsheet().time, + blk.z, + self.component_list, + doc="Component mole fraction integrated over theta, function of z [-]", + ) + def y_kz_eq(b, t, z, k): + return b.y_kz[t, z, k] * b.Flow_z[t, z] == b.Flow_kz[t, z, k] + + # @blk.Expression( + # self.flowsheet().time, + # blk.z, + # self.component_list, + # doc="Component mole fraction integrated over theta, function of z [-]", + # ) + # def y_kz(b, t, z, k): + # return b.Flow_kz[t, z, k] / b.Flow_z[t, z] + + def Cp_g_(k, Tg): + if k == "H2O": + return ( + 30.09200 * units.kJ / units.mol / units.K + + 6.832514 * (Tg / units.K / 1000) * units.kJ / units.mol / units.K + + 6.793435 + * (Tg / units.K / 1000) ** 2 + * units.kJ + / units.mol + / units.K + + -2.534480 + * (Tg / units.K / 1000) ** 3 + * units.kJ + / units.mol + / units.K + + 0.082139 + / (Tg / units.K / 1000) ** 2 + * units.kJ + / units.mol + / units.K + ) / 1000 + elif k == "N2": + return ( + 28.98641 * units.kJ / units.mol / units.K + + 1.853978 * (Tg / units.K / 1000) * units.kJ / units.mol / units.K + + -9.647459 + * (Tg / units.K / 1000) ** 2 + * units.kJ + / units.mol + / units.K + + 16.63537 + * (Tg / units.K / 1000) ** 3 + * units.kJ + / units.mol + / units.K + + 0.000117 + / (Tg / units.K / 1000) ** 2 + * units.kJ + / units.mol + / units.K + ) / 1000 + elif k == "CO2": + return ( + 24.99735 * units.kJ / units.mol / units.K + + 55.18696 * (Tg / units.K / 1000) * units.kJ / units.mol / units.K + + -33.69137 + * (Tg / units.K / 1000) ** 2 + * units.kJ + / units.mol + / units.K + + 7.948387 + * (Tg / units.K / 1000) ** 3 + * units.kJ + / units.mol + / units.K + + -0.136638 + / (Tg / units.K / 1000) ** 2 + * units.kJ + / units.mol + / units.K + ) / 1000 + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + self.component_list, + doc="pure component heat capacities, function of T [kJ/mol/K]", + ) + def Cp_g(b, t, z, o, k): + return units.convert( + blk.gas_properties[t, z, o].cp_mol_phase_comp["Vap", k], + to_units=units.kJ / units.mol / units.K, + ) + + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="average molecular weight [kg/mol]" + ) + def AMW(b, t, z, o): + # return sum([b.y[t, z, o, k] * self.MW[k] for k in self.component_list]) + return units.convert( + blk.gas_properties[t, z, o].mw, to_units=units.kg / units.mol + ) + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="gas density [kg/m^3]") + def rhog(b, t, z, o): + # return b.AMW[t, z, o] * b.C_tot[t, z, o] + return units.convert( + blk.gas_properties[t, z, o].dens_mass, to_units=units.kg / units.m**3 + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas phase mixture heat capacity [kJ/mol/K]", + ) + def Cp_g_mix(b, t, z, o): + # return sum( + # [b.y[t, z, o, k] * b.Cp_g[t, z, o, k] for k in self.component_list] + # ) + return units.convert( + blk.gas_properties[t, z, o].cp_mol, + to_units=units.kJ / units.mol / units.K, + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas phase mixture heat capacity [kJ/kg/K]", + ) + def Cp_g_mix_kg(b, t, z, o): + # return b.Cp_g_mix[t, z, o] / b.AMW[t, z, o] + return units.convert( + blk.gas_properties[t, z, o].cp_mass_phase["Vap"], + to_units=units.kJ / units.kg / units.K, + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas phase mixture viscosity [Pa*s]", + ) + def mu_mix(b, t, z, o): + # return sum( + # [ + # b.y[t, z, o, k] * self.mu[k] * self.MW[k] ** 0.5 + # for k in self.component_list + # ] + # ) / sum([b.y[t, z, o, k] * self.MW[k] ** 0.5 for k in self.component_list]) + return units.convert( + blk.gas_properties[t, z, o].visc_d_phase["Vap"], + to_units=units.Pa * units.s, + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas mixture thermal conductivity [kW/m/K]", + ) + def k_mix(b, t, z, o): + # return sum([b.y[t, z, o, k] * self.k[k] for k in self.component_list]) + return units.convert( + blk.gas_properties[t, z, o].therm_cond_phase["Vap"], + to_units=units.kW / units.m / units.K, + ) + + @blk.Constraint( + self.flowsheet().time, blk.z, blk.o, doc="heat flux equation [kJ/s/m^2]" + ) + def heat_flux_eq(b, t, z, o): + return ( + b.heat_flux[t, z, o] + == b.C_tot[t, z, o] + * b.Cp_g_mix[t, z, o] + * b.vel[t, z, o] + * b.Tg[t, z, o] + ) + + @blk.Constraint( + self.flowsheet().time, blk.z, blk.o, doc="mole fraction summation" + ) + def mole_frac_sum(b, t, z, o): + if blk.CONFIG.gas_flow_direction == "forward": + if z > 0: + return sum([b.y[t, z, o, k] for k in self.component_list]) == 1 + else: + return Constraint.Skip + elif blk.CONFIG.gas_flow_direction == "reverse": + if z < 1: + return sum([b.y[t, z, o, k] for k in self.component_list]) == 1 + else: + return Constraint.Skip + + # Dimensionless groups === + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="Prandtl number") + def Pr(b, t, z, o): + return b.mu_mix[t, z, o] * b.Cp_g_mix_kg[t, z, o] / b.k_mix[t, z, o] + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="Reynolds number") + def Re(b, t, z, o): + return b.rhog[t, z, o] * b.vel[t, z, o] * self.dp / b.mu_mix[t, z, o] + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="Schmidt number") + def Sc(b, t, z, o): + return b.mu_mix[t, z, o] / (b.rhog[t, z, o] * self.DmCO2) + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="Sherwood number") + def Sh(b, t, z, o): + return ( + 2.0 + + 0.6 + * smooth_max(0, b.Re[t, z, o]) ** 0.33 + * smooth_max(0, b.Sc[t, z, o]) ** 0.5 + ) + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="Nusselt number") + def Nu(b, t, z, o): + return ( + 2.0 + + 1.1 + * smooth_max(0, b.Re[t, z, o]) ** 0.6 + * smooth_max(0, b.Pr[t, z, o]) ** 0.33 + ) + + # ======================= + + # Mass/Heat Transfer coefficients === + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="Gas-solid heat transfer coefficient equation [kW/m^2/K]", + ) + def h_gs(b, t, z, o): + return b.Nu[t, z, o] * b.k_mix[t, z, o] / self.dp + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="Gas phase film mass transfer coefficient [m/s]", + ) + def k_f(b, t, z, o): + return b.Sh[t, z, o] * self.DmCO2 / self.dp + + # =================================== + + # Isotherm Model Equations === + def d_1(T): + return self.d_inf_1 * exp( + -self.E_1 / (self.R * self.T0) * (self.T0 / T - 1) + ) + + def d_2(T): + return self.d_inf_2 * exp( + -self.E_2 / (self.R * self.T0) * (self.T0 / T - 1) + ) + + def d_3(T): + return self.d_inf_3 * exp( + -self.E_3 / (self.R * self.T0) * (self.T0 / T - 1) + ) + + def d_4(T): + return self.d_inf_4 * exp( + -self.E_4 / (self.R * self.T0) * (self.T0 / T - 1) + ) + + def sigma_1(T): + return self.X_11 * exp(self.X_21 * (1 / self.T0 - 1 / T)) + + def sigma_2(T): + return self.X_12 * exp(self.X_22 * (1 / self.T0 - 1 / T)) + + def ln_pstep1(T): + return self.ln_P0_1 + (-self.H_step_1 / self.R * (1 / self.T0 - 1 / T)) + + def ln_pstep2(T): + return self.ln_P0_2 + (-self.H_step_2 / self.R * (1 / self.T0 - 1 / T)) + + def q_star_1(P, T): + return self.q_inf_1 * d_1(T) * P / (1 + d_1(T) * P) + + def q_star_2(P, T): + return self.q_inf_2 * d_2(T) * P / (1 + d_2(T) * P) + + def q_star_3(P, T): + return self.q_inf_3 * d_3(T) * P / (1 + d_3(T) * P) + d_4(T) * P + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="Partial pressure of CO2 at particle surface [bar] (ideal gas law)", + ) + def P_surf(b, t, z, o): + # smooth max operator: max(0, x) = 0.5*(x + (x^2 + eps)^0.5) + eps = 1e-8 + Cs_r_smooth_max = 0.5 * ( + b.Cs_r[t, z, o] + + (b.Cs_r[t, z, o] ** 2 + eps * (units.mol / units.m**3) ** 2) ** 0.5 + ) + return Cs_r_smooth_max * self.Rg * b.Ts[t, z, o] + # return smooth_max(0,m.Cs_r[z, o]) * self.Rg * m.Ts[z, o] #idaes smooth_max doesn't carry units through + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="log(Psurf)") + def ln_Psurf(b, t, z, o): + return log(b.P_surf[t, z, o] / units.bar) # must make dimensionless + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="weighting function term1: (ln_Psurf-ln_Pstep)/sigma", + ) + def iso_w_term1(b, t, z, o): + return (b.ln_Psurf[t, z, o] - ln_pstep1(b.Ts[t, z, o])) / sigma_1( + b.Ts[t, z, o] + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="weighting function term2: (ln_Psurf-ln_Pstep)/sigma", + ) + def iso_w_term2(b, t, z, o): + return (b.ln_Psurf[t, z, o] - ln_pstep2(b.Ts[t, z, o])) / sigma_2( + b.Ts[t, z, o] + ) + + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="log of weighting function 1" + ) + def ln_w1(b, t, z, o): + # return gamma_1*log(exp(m.iso_w_term1[z,o])/(1+exp(m.iso_w_term1[z,o]))) + # return gamma_1*(log(exp(m.iso_w_term1[z,o])) - log(1+exp(m.iso_w_term1[z,o]))) + return self.gamma_1 * ( + b.iso_w_term1[t, z, o] - log(1 + exp(b.iso_w_term1[t, z, o])) + ) + + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="log of weighting function 2" + ) + def ln_w2(b, t, z, o): + # return gamma_2*log(exp(m.iso_w_term2[z,o])/(1+exp(m.iso_w_term2[z,o]))) + # return gamma_2*(log(exp(m.iso_w_term2[z,o])) - log(1+exp(m.iso_w_term2[z,o]))) + return self.gamma_2 * ( + b.iso_w_term2[t, z, o] - log(1 + exp(b.iso_w_term2[t, z, o])) + ) + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="weighting function 1") + def iso_w1(b, t, z, o): + return exp(b.ln_w1[t, z, o]) + + @blk.Expression(self.flowsheet().time, blk.z, blk.o, doc="weighting function 2") + def iso_w2(b, t, z, o): + return exp(b.ln_w2[t, z, o]) + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="isotherm loading expression [mol/kg]", + ) + def qCO2_eq(b, t, z, o): + return ( + (1 - b.iso_w1[t, z, o]) * q_star_1(b.P_surf[t, z, o], b.Ts[t, z, o]) + + (b.iso_w1[t, z, o] - b.iso_w2[t, z, o]) + * q_star_2(b.P_surf[t, z, o], b.Ts[t, z, o]) + + b.iso_w2[t, z, o] * q_star_3(b.P_surf[t, z, o], b.Ts[t, z, o]) + ) + + # ============================ + + # Mass transfer coefficient ======================================================= + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="effective diffusion in solids [m^2/s]", + ) + def Deff(b, t, z, o): + return self.C1 * b.Ts[t, z, o] ** 0.5 + + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="internal MT coeff. [1/s]" + ) + def k_I(b, t, z, o): + return ( + b.R_MT_coeff * (15 * self.ep * b.Deff[t, z, o] / self.rp**2) + + (1 - b.R_MT_coeff) * 0.001 / units.s + ) + + # Heat of adsorption ============================================================== + @blk.Expression( + self.flowsheet().time, blk.z, blk.o, doc="heat of adsorption [kJ/mol]" + ) + def delH_CO2(b, t, z, o): + return -( + self.delH_1 + - (self.delH_1 - self.delH_2) + * exp(self.delH_a1 * (b.qCO2_eq[t, z, o] - self.delH_b1)) + / (1 + exp(self.delH_a1 * (b.qCO2_eq[t, z, o] - self.delH_b1))) + - (self.delH_2 - self.delH_3) + * exp(self.delH_a2 * (b.qCO2_eq[t, z, o] - self.delH_b2)) + / (1 + exp(self.delH_a2 * (b.qCO2_eq[t, z, o] - self.delH_b2))) + ) + + # Mass/heat transfer rates ========================================================= + # flux limiter equation === + a1_FL = 0.02 + a2_FL = 0.98 + sig_FL = 0.01 + + def FL(z): + def FL_1(z): + return exp((z - a1_FL) / sig_FL) / (1 + exp((z - a1_FL) / sig_FL)) + + def FL_2(z): + return exp((z - a2_FL) / sig_FL) / (1 + exp((z - a2_FL) / sig_FL)) + + return FL_1(z) - FL_2(z) + + # ======================== + + # Mass Transfer Rates === + blk.Rs_CO2 = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=0, + domain=Reals, + units=units.mol / units.s / units.m**3, + doc="solids mass transfer rate [mol/s/m^3 bed]", + ) + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="solids mass transfer rate [mol/s/m^3 bed]", + ) + def Rs_CO2_eq(b, t, z, o): + flux_lim = FL(z) + + if 0 < z < 1 and 0 < o < 1: + return ( + b.Rs_CO2[t, z, o] + == flux_lim + * b.k_I[t, z, o] + * (b.qCO2_eq[t, z, o] - b.qCO2[t, z, o]) + * (1 - self.eb) + * self.rho_sol + ) + else: + return b.Rs_CO2[t, z, o] == 0 * units.mol / units.s / units.m**3 + + @blk.Expression( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas mass transfer rate [mol/s/m^3 bed]", + ) + def Rg_CO2(b, t, z, o): + if 0 < z < 1 and 0 < o < 1: # no mass transfer at boundaries + return b.Rs_CO2[t, z, o] # option 2 + else: + return 0 * units.mol / units.s / units.m**3 + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas and solid phase mass transfer continuity", + ) + def constr_MTcont(b, t, z, o): + """ + Mass transfer continuity between the gas and solid phase. Used to calculate + Csurf which sets driving force for gas phase mass transfer. A couple options + for how to write this. + + If m.Rg_CO2 = m.kf*m.a_s*(m.C['CO2']-m.Cs_r) set as expression, then: + m.Rg_CO2[z,o] == m.Rs_CO2[z,o] + + If m.Rg_CO2 = m.Rs_CO2 set as expression, then: + m.Rg_CO2[z,o] == m.k_f[z,o]*m.a_s*(m.C['CO2',z,o]-m.Cs_r[z,o]) + + """ + return b.Cs_r[t, z, o] == b.C[t, z, o, "CO2"] - b.Rg_CO2[t, z, o] / ( + b.k_f[t, z, o] * self.a_s + ) # option 2b + + # ======================== + + # Heat transfer rates ==== + blk.Q_gs = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=0, + domain=Reals, + units=units.kJ / units.s / units.m**3, + doc="Gas-to-solid heat transfer rate [kW/m^3 bed or kJ/s/m^3 bed]", + ) + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="Gas-to-solid heat transfer rate [kW/m^3 bed or kJ/s/m^3 bed]", + ) + def Q_gs_eq(b, t, z, o): + flux_lim = FL(z) + + if 0 < z < 1 and 0 < o < 1: # no heat transfer at boundaries + return b.Q_gs[t, z, o] == flux_lim * b.R_HT_gs * b.h_gs[ + t, z, o + ] * self.a_s * (b.Ts[t, z, o] - b.Tg[t, z, o]) + else: + return b.Q_gs[t, z, o] == 0 + + # @blk.Expression( + # self.flowsheet().time, + # blk.z, + # blk.o, + # doc="Gas-to-solid heat transfer rate [kW/m^3 bed or kJ/s/m^3 bed]", + # ) + # def Q_gs(b, t, z, o): + # flux_lim = FL(z) + + # if 0 < z < 1 and 0 < o < 1: # no heat transfer at boundaries + # return ( + # flux_lim + # * b.R_HT_gs + # * b.h_gs[t, z, o] + # * self.a_s + # * (b.Ts[t, z, o] - b.Tg[t, z, o]) + # ) + # else: + # return 0 * units.kW / units.m**3 + + blk.Q_ghx = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=0, + domain=Reals, + units=units.kJ / units.s / units.m**3, + doc="Gas-to-HX heat transfer rate [kW/m^3 bed or kJ/s/m^3 bed]", + ) + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="Gas-to-HX heat transfer rate [kW/m^3 bed or kJ/s/m^3 bed]", + ) + def Q_ghx_eq(b, t, z, o): + flux_lim = FL(z) + + if 0 < z < 1 and 0 < o < 1: # no heat transfer at boundaries + return b.Q_ghx[t, z, o] == flux_lim * b.R_HT_ghx * b.hgx * b.a_ht * ( + b.Tg[t, z, o] - b.Tx[t] + ) + else: + return b.Q_ghx[t, z, o] == 0 + + # @blk.Expression( + # self.flowsheet().time, + # blk.z, + # blk.o, + # doc="Gas-to-HX heat transfer rate [kW/m^3 bed or kJ/s/m^3 bed]", + # ) + # def Q_ghx(b, t, z, o): + # flux_lim = FL(z) + + # if 0 < z < 1 and 0 < o < 1: # no heat transfer at boundaries + # return ( + # flux_lim * b.R_HT_ghx * b.hgx * b.a_ht * (b.Tg[t, z, o] - b.Tx[t]) + # ) + # else: + # return 0 * units.kW / units.m**3 + + blk.Q_delH = Var( + self.flowsheet().time, + blk.z, + blk.o, + initialize=0, + domain=Reals, + units=units.kJ / units.s / units.m**3, + doc="adsorption/desorption heat rate [kJ/s/m^3 bed]", + ) + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="adsorption/desorption heat rate [kJ/s/m^3 bed]", + ) + def Q_delH_eq(b, t, z, o): + return ( + b.Q_delH[t, z, o] == b.R_delH * b.delH_CO2[t, z, o] * b.Rs_CO2[t, z, o] + ) + + # @blk.Expression( + # self.flowsheet().time, + # blk.z, + # blk.o, + # doc="adsorption/desorption heat rate [kJ/s/m^3 bed]", + # ) + # def Q_delH(b, t, z, o): + # return b.R_delH * b.delH_CO2[t, z, o] * b.Rs_CO2[t, z, o] + + # ===================================================================================== + + # PDE equations ======================================================================= + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + self.component_list, + doc="gas phase species balance PDE [mol/m^3 bed/s]", + ) + def pde_gasMB(b, t, z, o, k): + if blk.CONFIG.gas_flow_direction == "forward": + if 0 < z < 1: + if k == "CO2": + return ( + b.dFluxdz[t, z, o, k] + == (-b.Rg_CO2[t, z, o] * b.R_MT_gas) * self.L + ) + else: + return b.dFluxdz[t, z, o, k] == 0 + if z == 1: # at exit of column, dFluxdz=0 + return b.dFluxdz[t, z, o, k] == 0 + else: # no balance at z=0, inlets are specified + return Constraint.Skip + elif blk.CONFIG.gas_flow_direction == "reverse": + if 0 < z < 1: + if k == "CO2": + return ( + -b.dFluxdz[t, z, o, k] + == (-b.Rg_CO2[t, z, o] * b.R_MT_gas) * self.L + ) + else: + return -b.dFluxdz[t, z, o, k] == 0 + if z == 0: # at exit of column, dFluxdz=0 + return -b.dFluxdz[t, z, o, k] == 0 + else: # no balance at z=0, inlets are specified + return Constraint.Skip + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + self.component_list, + doc="flux equation [mol/m^2 bed/s]", + ) + def flux_eq(b, t, z, o, k): + return b.Flux_kzo[t, z, o, k] == b.C[t, z, o, k] * b.vel[t, z, o] + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="solid phase mass balance PDE [mol/m^3 bed/s]", + ) + def pde_solidMB(b, t, z, o): + if 0 < o < 1: + return (1 - self.eb) * self.rho_sol * b.dqCO2do[t, z, o] * self.w[ + t + ] == (b.Rs_CO2[t, z, o] * b.R_MT_solid) * ( + (2 * const.pi * units.radians) * b.theta + ) + elif o == 1: # at solids exit, flux is zero + return b.dqCO2do[t, z, o] == 0 + else: # no balance at o=0, inlets are specified + return Constraint.Skip + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="gas phase energy balance PDE [kJ/m^3 bed/s]", + ) + def pde_gasEB(b, t, z, o): + if blk.CONFIG.gas_flow_direction == "forward": + if 0 < z < 1: + return ( + b.dheat_fluxdz[t, z, o] + == (b.Q_gs[t, z, o] - b.Q_ghx[t, z, o]) * self.L + ) + elif z == 1: + return b.dheat_fluxdz[t, z, o] == 0 + else: + return Constraint.Skip + elif blk.CONFIG.gas_flow_direction == "reverse": + if 0 < z < 1: + return ( + -b.dheat_fluxdz[t, z, o] + == (b.Q_gs[t, z, o] - b.Q_ghx[t, z, o]) * self.L + ) + elif z == 0: + return -b.dheat_fluxdz[t, z, o] == 0 + else: + return Constraint.Skip + + @blk.Constraint( + self.flowsheet().time, + blk.z, + blk.o, + doc="solid phase energy balance PDE [kJ/s/m^3 bed]", + ) + def pde_solidEB(b, t, z, o): + if 0 < o < 1: + return (1 - self.eb) * self.rho_sol * self.Cp_sol * self.w[t] * b.dTsdo[ + t, z, o + ] == (-b.Q_gs[t, z, o] - b.Q_delH[t, z, o]) * ( + (2 * const.pi * units.radians) * b.theta + ) + elif o == 1: + return b.dTsdo[t, z, o] == 0 + else: + return Constraint.Skip + + @blk.Constraint( + self.flowsheet().time, blk.z, blk.o, doc="Ergun Equation [bar/m]" + ) + def pde_Ergun(b, t, z, o): + # Pa_to_bar = 1e-5 * units.bar / units.Pa + RHS_ = b.R_dP * -( + (150 * b.mu_mix[t, z, o] * ((1 - self.eb) ** 2) / (self.eb**3)) + / self.dp**2 + * b.vel[t, z, o] + + 1.75 + * (1 - self.eb) + / self.eb**3 + * b.rhog[t, z, o] + / self.dp + * b.vel[t, z, o] ** 2 + ) + RHS = units.convert(RHS_, to_units=units.bar / units.meter) + LHS_ = b.dPdz[t, z, o] / self.L + LHS = units.convert(LHS_, to_units=units.bar / units.meter) + if blk.CONFIG.gas_flow_direction == "forward": + if z > 0: + return LHS == RHS + else: + return Constraint.Skip + elif blk.CONFIG.gas_flow_direction == "reverse": + if z < 1: + return -1 * LHS == RHS + else: + return Constraint.Skip + + # ===================================================================================== + + # Boundary Conditions === + if blk.CONFIG.gas_flow_direction == "forward": + inlet_node = 0 + outlet_node = 1 + elif blk.CONFIG.gas_flow_direction == "reverse": + inlet_node = 1 + outlet_node = 0 + + @blk.Constraint(self.flowsheet().time, blk.o, doc="inlet gas temp. B.C. [K]") + def bc_gastemp_in(b, t, o): + return b.Tg[t, inlet_node, o] == b.Tg_in[t] + + @blk.Constraint(self.flowsheet().time, blk.o, doc="inlet pressure [bar]") + def bc_P_in(b, t, o): + return b.P[t, inlet_node, o] == b.P_in[t] + + @blk.Constraint(self.flowsheet().time, doc="inlet flow B.C. [mol/s]") + def bc_flow_in(b, t): + return b.Flow_z[t, inlet_node] == b.F_in[t] + + @blk.Constraint( + self.flowsheet().time, + blk.o, + self.component_list, + doc="inlet mole fraction B.C. [-]", + ) + def bc_y_in(b, t, o, k): + return b.y[t, inlet_node, o, k] == b.y_in[t, k] + + @blk.Integral( + self.flowsheet().time, blk.o, wrt=blk.o, doc="outlet gas enthalpy [kJ/mol]" + ) + def Hg_out(b, t, o): + return b.heat_flux[t, outlet_node, o] * b.A_b + + @blk.Constraint(self.flowsheet().time, doc="Outlet flow B.C.") + def bc_flow_out(b, t): + return b.F_out[t] == b.Flow_z[t, outlet_node] + + @blk.Constraint( + self.flowsheet().time, self.component_list, doc="Outlet mole fraction B.C." + ) + def bc_y_out(b, t, k): + return b.y_out[t, k] == b.y_kz[t, outlet_node, k] + + @blk.Constraint(self.flowsheet().time, blk.o, doc="outlet pressure B.C. [bar]") + def bc_P_out(b, t, o): + return b.P[t, outlet_node, o] == b.P_out[t] + + @blk.Expression( + self.flowsheet().time, doc="outlet gas heat capacity [kJ/mol/K]" + ) + def Cp_g_out(b, t): + return sum( + [b.y_out[t, k] * Cp_g_(k, b.Tg_out[t]) for k in self.component_list] + ) + + @blk.Constraint( + self.flowsheet().time, doc="eq. for calculating outlet gas temperature" + ) + def Tg_out_eq(b, t): + return b.Hg_out[t] / b.F_out[t] == b.Cp_g_out[t] * b.Tg_out[t] + + # ====================================================================== + + # Metrics ============================================================== + @blk.Expression( + self.flowsheet().time, blk.z, doc="inlet solids loading B.C. [mol/kg]" + ) + def qCO2_in(b, t, z): + return b.qCO2[t, z, 0] + + @blk.Expression(self.flowsheet().time, blk.z, doc="inlet solids temp. [K]") + def Ts_in(b, t, z): + return b.Ts[t, z, 0] + + @blk.Expression(self.flowsheet().time, doc="CO2 captured [mol/s]") + def delta_CO2(b, t): + return b.F_in[t] * b.y_in[t, "CO2"] - b.F_out[t] * b.y_out[t, "CO2"] + + blk.CO2_capture = Var( + self.flowsheet().time, + initialize=0.5, + domain=Reals, + doc="CO2 capture fraction", + ) + + @blk.Constraint(self.flowsheet().time, doc="CO2 capture fraction") + def CO2_capture_eq(b, t): + return ( + # m.CO2_capture * m.F_in == m.F_in - m.F_out * m.y_out["CO2"] / m.y_in["CO2"] + b.CO2_capture[t] * b.F_in[t] * b.y_in[t, "CO2"] + == b.F_in[t] * b.y_in[t, "CO2"] - b.F_out[t] * b.y_out[t, "CO2"] + ) + + @blk.Integral( + self.flowsheet().time, + blk.z, + blk.o, + wrt=blk.o, + doc="Gas to HX heat transfer integrated over theta, function of z [kW/m^3 bed]", + ) + def Q_ghx_z(b, t, z, o): + return b.Q_ghx[t, z, o] + + @blk.Integral( + self.flowsheet().time, + blk.z, + wrt=blk.z, + doc="Gas to HX heat transfer integrated over z, [kW/m^3 bed]", + ) + def Q_ghx_tot(b, t, z): + return b.Q_ghx_z[t, z] + + @blk.Expression(doc="section bed volume [m^3 bed]") + def bed_vol_section(b): + return const.pi * (self.D / 2) ** 2 * self.L * (1 - b.Hx_frac) * b.theta + + @blk.Expression(self.flowsheet().time, doc="Total heat transfer to HX [kW]") + def Q_ghx_tot_kW(b, t): + return b.Q_ghx_tot[t] * b.bed_vol_section + + # ================================================================================== + + # Mass and energy balance checks =================================================== + @blk.Expression( + self.flowsheet().time, + self.component_list, + doc="component gas flow in for MB check [mol/s]", + ) + def g_k_in_MB(b, t, k): + return b.F_in[t] * b.y_in[t, k] + + @blk.Expression( + self.flowsheet().time, + self.component_list, + doc="gas flow out for MB check [mol/s]", + ) + def g_k_out_MB(b, t, k): + return b.F_out[t] * b.y_out[t, k] + + @blk.Expression(doc="total bed volume [m^3]") + def vol_tot(b): + return const.pi * (self.D / 2) ** 2 * self.L * (1 - b.Hx_frac) + + @blk.Expression(doc="total solids volume [m^3]") + def vol_solids_tot(b): + return b.vol_tot * (1 - self.eb) + + @blk.Expression(doc="total solids mass [kg]") + def mass_solids_tot(b): + return b.vol_solids_tot * self.rho_sol + + @blk.Expression(self.flowsheet().time, doc="total solids flow [kg/s]") + def flow_solids_tot(b, t): + return ( + (b.mass_solids_tot / units.revolution) + * self.w_rpm[t] + / (60 * units.sec / units.min) + ) + + @blk.Expression( + self.flowsheet().time, + blk.z, + doc="change in solids loading at each z index [mol/kg]", + ) + def delta_q(b, t, z): + return b.qCO2[t, z, 1] - b.qCO2[t, z, 0] + + @blk.Integral( + self.flowsheet().time, + blk.z, + wrt=blk.z, + doc="total flow of adsorbed CO2 at inlet [mol/s]", + ) + def flow_CO2solids_in(b, t, z): + return b.qCO2[t, z, 0] * b.flow_solids_tot[t] + + @blk.Integral( + self.flowsheet().time, + blk.z, + wrt=blk.z, + doc="total flow of adsorbed CO2 at outlet [mol/s]", + ) + def flow_CO2solids_out(b, t, z): + return b.qCO2[t, z, 1] * b.flow_solids_tot[t] + + @blk.Expression( + self.flowsheet().time, + self.component_list, + doc="% mass balance error for each component", + ) + def MB_error(b, t, k): + if k == "CO2": + return ( + (b.flow_CO2solids_out[t] + b.g_k_out_MB[t, "CO2"]) + / (b.flow_CO2solids_in[t] + b.g_k_in_MB[t, "CO2"]) + - 1 + ) * 100 + else: + return ( + b.g_k_out_MB[t, k] / b.g_k_in_MB[t, k] - 1 + ) * 100 # (out-in)/in*100 or (out/in-1)*100 + + # ================================================================================== + + # Miscellaneous ==================================================================== + @blk.Integral( + self.flowsheet().time, + blk.z, + blk.o, + wrt=blk.z, + doc="solids CO2 loading integrated over z, function of theta [mol/kg]", + ) + def qCO2_o(b, t, z, o): + return b.qCO2[t, z, o] + + @blk.Integral( + self.flowsheet().time, + blk.z, + blk.o, + wrt=blk.z, + doc="solids temperature integrated over z, function of theta [K]", + ) + def Ts_o(b, t, z, o): + return b.Ts[t, z, o] + + @blk.Integral( + self.flowsheet().time, + blk.z, + blk.o, + wrt=blk.o, + doc="gas temperature integrated over theta, function of z [K]", + ) + def Tg_z(b, t, z, o): + return b.Tg[t, z, o] + + # ============================================================================== + + # DAE Transformations ========================================================== + if self.config.z_transformation_method == "dae.collocation": + z_discretizer = TransformationFactory("dae.collocation") + z_discretizer.apply_to( + blk, + wrt=blk.z, + nfe=self.config.z_nfe, + ncp=self.config.z_Collpoints, + scheme="LAGRANGE-RADAU", + ) + elif self.config.z_transformation_method == "dae.finite_difference": + z_discretizer = TransformationFactory("dae.finite_difference") + if blk.CONFIG.gas_flow_direction == "forward": + z_discretizer.apply_to( + blk, wrt=blk.z, nfe=self.config.z_nfe, scheme="BACKWARD" + ) + elif blk.CONFIG.gas_flow_direction == "reverse": + z_discretizer.apply_to( + blk, wrt=blk.z, nfe=self.config.z_nfe, scheme="FORWARD" + ) + elif self.config.z_transformation_method == "Finite Volume": + z_discretizer = TransformationFactory("dae.finite_volume") + if blk.CONFIG.gas_flow_direction == "forward": + z_discretizer.apply_to( + blk, + wrt=blk.z, + nfv=self.config.z_nfe, + scheme="WENO3", + flow_direction=1, + ) + elif blk.CONFIG.gas_flow_direction == "reverse": + z_discretizer.apply_to( + blk, + wrt=blk.z, + nfv=self.config.z_nfe, + scheme="WENO3", + flow_direction=-1, + ) + + if self.config.o_transformation_method == "dae.collocation": + o_discretizer = TransformationFactory("dae.collocation") + o_discretizer.apply_to( + blk, + wrt=blk.o, + nfe=self.config.o_nfe, + ncp=self.config.o_Collpoints, + ) + elif self.config.o_transformation_method == "dae.finite_difference": + o_discretizer = TransformationFactory("dae.finite_difference") + o_discretizer.apply_to(blk, wrt=blk.o, nfe=self.config.o_nfe) + elif self.config.o_transformation_method == "Finite Volume": + o_discretizer = TransformationFactory("dae.finite_volume") + o_discretizer.apply_to( + blk, + wrt=blk.o, + nfv=self.config.o_nfe, + scheme="WENO3", + flow_direction=1, + ) + # ============================================================================== + + # initializing variables ======================================================= + for t in self.flowsheet().time: + for z in blk.z: + for o in blk.o: + blk.temperature[t, z, o] = blk.temperature_inlet[t]() + blk.pressure[t, z, o] = blk.pressure_inlet[t]() + for k in self.component_list: + blk.y[t, z, o, k] = ( + blk.conc_mol_comp_inlet[t, k]() / blk.C_tot[t, z, o]() + ) + blk.Flux_kzo[t, z, o, k] = ( + blk.conc_mol_comp_inlet[t, k]() * blk.vel[t, z, o]() + ) + + # scaling factors ================================ + iscale.set_scaling_factor(blk.theta, 1e5) + + for t in self.flowsheet().time: + iscale.set_scaling_factor(blk.bc_y_out[t, "CO2"], 25) + iscale.set_scaling_factor( + blk.bc_y_out[t, "H2O"], 1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor( + blk.bc_y_out[t, "N2"], 1 / value(blk.y_in[t, "N2"]) + ) + iscale.set_scaling_factor( + blk.y_out[t, "H2O"], 1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor(blk.y_out[t, "N2"], 1 / value(blk.y_in[t, "N2"])) + iscale.set_scaling_factor(blk.y_out[t, "CO2"], 25) + + for z in blk.z: + iscale.set_scaling_factor( + blk.y_kz[t, z, "N2"], 1 / value(blk.y_in[t, "N2"]) + ) + iscale.set_scaling_factor(blk.y_kz[t, z, "CO2"], 25) + iscale.set_scaling_factor( + blk.y_kz[t, z, "H2O"], 1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor( + blk.y_kz_eq[t, z, "N2"], 0.1 / value(blk.y_in[t, "N2"]) + ) + iscale.set_scaling_factor(blk.y_kz_eq[t, z, "CO2"], 2.5) + iscale.set_scaling_factor( + blk.y_kz_eq[t, z, "H2O"], 0.1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor(blk.Flow_z[t, z], 0.001) + iscale.set_scaling_factor(blk.Flow_z_eq[t, z], 0.001) + for o in blk.o: + iscale.set_scaling_factor(blk.vel[t, z, o], 10) + iscale.set_scaling_factor(blk.qCO2[t, z, o], 10) + iscale.set_scaling_factor(blk.temperature[t, z, o], 1e-2) + iscale.set_scaling_factor(blk.Ts[t, z, o], 1e4) + iscale.set_scaling_factor(blk.pressure[t, z, o], 1e-4) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "CO2"], 25) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "CO2"], 25) + iscale.set_scaling_factor( + blk.flux_eq[t, z, o, "H2O"], 1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor( + blk.Flux_kzo[t, z, o, "H2O"], 1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor(blk.heat_flux_eq[t, z, o], 0.1) + iscale.set_scaling_factor(blk.heat_flux[t, z, o], 0.05) + iscale.set_scaling_factor( + blk.y[t, z, o, "H2O"], 1 / value(blk.y_in[t, "H2O"]) + ) + iscale.set_scaling_factor( + blk.y[t, z, o, "N2"], 1 / value(blk.y_in[t, "N2"]) + ) + iscale.set_scaling_factor(blk.y[t, z, o, "CO2"], 25) + iscale.set_scaling_factor(blk.Cs_r[t, z, o], 2.5) + iscale.set_scaling_factor(blk.constr_MTcont[t, z, o], 2.5) + + if o == 0 or o == 1: + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "CO2"], 1e1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "CO2"], 1e1) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "H2O"], 1e1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "H2O"], 1e1) + + if 0 < z < 1 and 0 < o < 1: + iscale.set_scaling_factor(blk.dqCO2do[t, z, o], 1e-2) + iscale.set_scaling_factor(blk.dqCO2do_disc_eq[t, z, o], 1e-2) + iscale.set_scaling_factor(blk.pde_gasEB[t, z, o], 1e0) + iscale.set_scaling_factor(blk.pde_solidEB[t, z, o], 1e-4) + iscale.set_scaling_factor(blk.pde_solidMB[t, z, o], 1e-3) + iscale.set_scaling_factor(blk.dheat_fluxdz[t, z, o], 1e-2) + iscale.set_scaling_factor(blk.dTsdo[t, z, o], 1e-1) + iscale.set_scaling_factor(blk.dTsdo_disc_eq[t, z, o], 1e-1) + iscale.set_scaling_factor(blk.pde_gasMB[t, z, o, "CO2"], 100) + # iscale.set_scaling_factor(blk.Q_gs_eq[t, z, o], 1) + iscale.set_scaling_factor(blk.Q_gs[t, z, o], 0.01) + iscale.set_scaling_factor(blk.Q_delH[t, z, o], 0.01) + # iscale.set_scaling_factor(blk.Q_delH_eq[t, z, o], 0.01) + iscale.set_scaling_factor(blk.Rs_CO2[t, z, o], 0.5) + iscale.set_scaling_factor(blk.Rs_CO2_eq[t, z, o], 1) + iscale.set_scaling_factor(blk.temperature[t, z, o], 1e2) + + if blk.CONFIG.gas_flow_direction == "forward": + if z > 0: + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "CO2"], 0.4 + ) + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "H2O"], + 10 * value(blk.y_in[t, "H2O"]), + ) + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "N2"], 0.1 + ) + iscale.set_scaling_factor(blk.dPdz[t, z, o], 1e-4) + iscale.set_scaling_factor(blk.dPdz_disc_eq[t, z, o], 1e-4) + iscale.set_scaling_factor(blk.pde_Ergun[t, z, o], 100) + iscale.set_scaling_factor( + blk.dheat_fluxdz_disc_eq[t, z, o], 1e-2 + ) + iscale.set_scaling_factor(blk.dFluxdz[t, z, o, "CO2"], 0.4) + iscale.set_scaling_factor( + blk.dFluxdz[t, z, o, "H2O"], + 10 * value(blk.y_in[t, "H2O"]), + ) + iscale.set_scaling_factor(blk.mole_frac_sum[t, z, o], 100) + + if z == 1: + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "CO2"], 0.1 + ) + iscale.set_scaling_factor(blk.dFluxdz[t, z, o, "CO2"], 0.1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "CO2"], 1) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "CO2"], 1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "H2O"], 1) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "H2O"], 1) + + if z == 0: + iscale.set_scaling_factor( + blk.y[t, z, o, "CO2"], 1 / value(blk.y_in[t, "CO2"]) + ) + elif blk.CONFIG.gas_flow_direction == "reverse": + if z < 1: + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "CO2"], 0.5 + ) + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "H2O"], 0.5 + ) + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "N2"], 0.1 + ) + iscale.set_scaling_factor(blk.dPdz[t, z, o], 1e-4) + iscale.set_scaling_factor(blk.dPdz_disc_eq[t, z, o], 1e-4) + iscale.set_scaling_factor(blk.pde_Ergun[t, z, o], 100) + iscale.set_scaling_factor( + blk.dheat_fluxdz_disc_eq[t, z, o], 1e-2 + ) + iscale.set_scaling_factor(blk.dFluxdz[t, z, o, "CO2"], 0.5) + iscale.set_scaling_factor(blk.dFluxdz[t, z, o, "H2O"], 0.5) + iscale.set_scaling_factor(blk.mole_frac_sum[t, z, o], 100) + + if z == 0: + iscale.set_scaling_factor( + blk.dFluxdz_disc_eq[t, z, o, "CO2"], 0.1 + ) + iscale.set_scaling_factor(blk.dFluxdz[t, z, o, "CO2"], 0.1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "CO2"], 1) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "CO2"], 1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "H2O"], 1) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "H2O"], 1) + + if z == 1: + iscale.set_scaling_factor( + blk.y[t, z, o, "CO2"], 1 / value(blk.y_in[t, "CO2"]) + ) + + for o in blk.o: + iscale.set_scaling_factor(blk.bc_gastemp_in[t, o], 1e-2) + iscale.set_scaling_factor(blk.bc_P_in[t, o], 10) + iscale.set_scaling_factor(blk.bc_P_out[t, o], 10) + # iscale.set_scaling_factor( + # blk.bc_y_in[t, o, "CO2"], 1 / value(y_in[t, "CO2"]) + # ) + iscale.set_scaling_factor( + blk.bc_y_in[t, o, "H2O"], 1 / value(y_in[t, "H2O"]) + ) + iscale.set_scaling_factor( + blk.bc_y_in[t, o, "N2"], 1 / value(y_in[t, "N2"]) + ) + + if initial_guesses == "desorption": + iscale.set_scaling_factor(blk.CO2_capture[t], 1e-4) + iscale.set_scaling_factor(blk.CO2_capture_eq[t], 1e-4) + iscale.set_scaling_factor(blk.F_in[t], 1e-2) + iscale.set_scaling_factor(blk.F_out[t], 1e-2) + iscale.set_scaling_factor(blk.bc_flow_in[t], 1e-2) + iscale.set_scaling_factor(blk.bc_flow_out[t], 1e-2) + for z in blk.z: + iscale.set_scaling_factor(blk.Flow_z[t, z], 1e-2) + # iscale.set_scaling_factor(blk.Flow_z_eq[t, z], 1e-2) + iscale.set_scaling_factor( + blk.y_kz[t, z, "N2"], 0.1 / value(blk.y_in[t, "N2"]) + ) + iscale.set_scaling_factor( + blk.y_kz_eq[t, z, "N2"], 0.01 / value(blk.y_in[t, "N2"]) + ) + for o in blk.o: + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "N2"], 1e1) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "N2"], 1e1) + iscale.set_scaling_factor( + blk.y[t, z, o, "N2"], 0.1 / value(blk.y_in[t, "N2"]) + ) + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "CO2"], 2.5) + iscale.set_scaling_factor(blk.Flux_kzo[t, z, o, "CO2"], 2.5) + + if o == 0 or o == 1: + iscale.set_scaling_factor(blk.flux_eq[t, z, o, "H2O"], 1e-1) + iscale.set_scaling_factor( + blk.Flux_kzo[t, z, o, "H2O"], 1e-1 + ) + + # fixing flow state variable, it is not needed + blk.flow_mol.fix(1) + # ============================================================================== + + def _connect_sections(self): + """ + Method for connecting the sorbent streams between adsorption and desorption sections. + """ + + # connect rich stream + # add equality constraint equating inlet desorption loading to outlet adsorption loading. Same for temperature. + for t in self.flowsheet().time: + for z in [0, 1]: + self.des.qCO2[t, z, 0].fix() + self.des.Ts[t, z, 0].fix() + + @self.Constraint(self.flowsheet().time, self.des.z) + def rich_loading_constraint(b, t, z): + if 0 < z < 1: + return b.des.qCO2[t, z, 0] == b.ads.qCO2[t, z, 1] + else: + return Constraint.Skip + + @self.Constraint(self.flowsheet().time, self.des.z) + def rich_temp_constraint(b, t, z): + if 0 < z < 1: + return b.des.Ts[t, z, 0] == b.ads.Ts[t, z, 1] + else: + return Constraint.Skip + + # connect lean stream + # add equality constraint equating inlet adsorption loading to outlet desorption loading + for t in self.flowsheet().time: + for z in [0, 1]: + self.ads.qCO2[t, z, 0].fix() + self.ads.Ts[t, z, 0].fix() + + @self.Constraint(self.flowsheet().time, self.ads.z) + def lean_loading_constraint(b, t, z): + if 0 < z < 1: + return b.ads.qCO2[t, z, 0] == b.des.qCO2[t, z, 1] + else: + return Constraint.Skip + + @self.Constraint(self.flowsheet().time, self.ads.z) + def lean_temp_constraint(b, t, z): + if 0 < z < 1: + return b.ads.Ts[t, z, 0] == b.des.Ts[t, z, 1] + else: + return Constraint.Skip + + # these variables are inactive, just fixing them to same value for plotting purposes + for t in self.flowsheet().time: + self.ads.qCO2[t, 0, 0].fix(1) + self.ads.qCO2[t, 1, 0].fix(1) + self.des.qCO2[t, 0, 0].fix(1) + self.des.qCO2[t, 1, 0].fix(1) + + self.ads.Ts[t, 0, 0].fix(100 + 273) + self.ads.Ts[t, 1, 0].fix(100 + 273) + self.des.Ts[t, 0, 0].fix(100 + 273) + self.des.Ts[t, 1, 0].fix(100 + 273) + + # add constraint so that the fraction of each section adds to 1 + self.des.theta.unfix() # unfix des side var + + @self.Constraint(doc="Theta summation constraint") + def theta_constraint(b): + return b.ads.theta + b.des.theta == 1 + + # metrics =============================================== + self.steam_enthalpy = Param( + initialize=2257.92, + mutable=True, + units=units.kJ / units.kg, + doc="saturated steam enthalpy at 1 bar[kJ/kg]", + ) + + @self.Expression(self.flowsheet().time, doc="Steam energy [kW]") + def steam_energy(b, t): + return b.des.F_in[t] * b.des.y_in[t, "H2O"] * b.MW["H2O"] * b.steam_enthalpy + + @self.Expression( + self.flowsheet().time, doc="total thermal energy (steam + HX) [kW]" + ) + def total_thermal_energy(b, t): + return b.steam_energy[t] - b.des.Q_ghx_tot_kW[t] + + @self.Expression(self.flowsheet().time, doc="Energy requirement [MJ/kg CO2]") + def energy_requirement(b, t): + return units.convert( + b.total_thermal_energy[t] / b.ads.delta_CO2[t] / b.MW["CO2"], + to_units=units.MJ / units.kg, + ) + + @self.Expression(self.flowsheet().time, doc="Productivity [kg CO2/h/m^3]") + def productivity(b, t): + return units.convert( + b.ads.delta_CO2[t] * b.MW["CO2"] / b.ads.vol_tot, + to_units=units.kg / units.h / units.m**3, + ) + + # add scaling factors + iscale.set_scaling_factor(self.theta_constraint, 1e2) + for t in self.flowsheet().time: + for z in self.ads.z: + if 0 < z < 1: + iscale.set_scaling_factor(self.lean_loading_constraint[t, z], 10) + iscale.set_scaling_factor(self.rich_loading_constraint[t, z], 10) + + def initialize_build( + blk, + outlvl=idaeslog.NOTSET, + optarg=None, + initialization_points: list = [1e-5, 0.1, 0.5, 1], + ): + """ + Initialization routine for the RPB unit model. + """ + + # Set up logger for initialization and solve + init_log = idaeslog.getInitLogger(blk.name, outlvl, tag="unit") + solve_log = idaeslog.getSolveLogger(blk.name, outlvl, tag="unit") + + # create Block init object + init_obj = BlockTriangularizationInitializer() + init_obj.config.block_solver_options = optarg + + # create ipopt solver object + Solver = SolverFactory("ipopt") + Solver.options = optarg + + initialization_points.sort() + if min(initialization_points) < 0 or max(initialization_points) > 1: + raise InitializationError("Initialization points must be between 0 and 1") + + if initialization_points[-1] != 1: + init_log.info_high( + "Final initialization point must be = 1. Adding 1 to initialization_points." + ) + initialization_points.append(1) + + init_log.info("Beginning Initialization") + + # getting port states ========================= + ads_gas_inlet_flags = {} + for n, v in blk.ads_gas_inlet.vars.items(): + for i in v: + ads_gas_inlet_flags[n, i] = v[i].fixed + + ads_gas_outlet_flags = {} + for n, v in blk.ads_gas_outlet.vars.items(): + for i in v: + ads_gas_outlet_flags[n, i] = v[i].fixed + + des_gas_inlet_flags = {} + for n, v in blk.des_gas_inlet.vars.items(): + for i in v: + des_gas_inlet_flags[n, i] = v[i].fixed + + des_gas_outlet_flags = {} + for n, v in blk.des_gas_outlet.vars.items(): + for i in v: + des_gas_outlet_flags[n, i] = v[i].fixed + # ============================================= + + # setting port states for initialization. + # 1) Fixing deltaP and calculating flow rates. 2) Fixing inlet temperatures and mole fractions. + init_log.info_high("Fixing Port States") + # ads flue gas in + blk.ads_gas_inlet.temperature.fix() + blk.ads_gas_inlet.pressure.fix() + blk.ads_gas_inlet.mole_frac_comp.fix() + blk.ads_gas_inlet.flow_mol.unfix() + # ads gas exit + blk.ads_gas_outlet.temperature.unfix() + blk.ads_gas_outlet.pressure.fix() + blk.ads_gas_outlet.mole_frac_comp.unfix() + blk.ads_gas_outlet.flow_mol.unfix() + # des flue gas in + blk.des_gas_inlet.temperature.fix() + blk.des_gas_inlet.pressure.fix() + blk.des_gas_inlet.mole_frac_comp.fix() + blk.des_gas_inlet.flow_mol.unfix() + # des gas exit + blk.des_gas_outlet.temperature.unfix() + blk.des_gas_outlet.pressure.fix() + blk.des_gas_outlet.mole_frac_comp.unfix() + blk.des_gas_outlet.flow_mol.unfix() + # ================================= + + # initializing variables based on inlet values ============================================ + init_log.info_high("Initializing Variables") + # state variables + for k in blk.component_list: + blk.ads.mole_frac_comp[:, :, :, k] = blk.ads_gas_inlet.mole_frac_comp[ + blk.flowsheet().time.first(), k + ]() + blk.ads_gas_outlet.mole_frac_comp[:, k] = blk.ads_gas_inlet.mole_frac_comp[ + blk.flowsheet().time.first(), k + ]() + blk.des.mole_frac_comp[:, :, :, k] = blk.des_gas_inlet.mole_frac_comp[ + blk.flowsheet().time.first(), k + ]() + blk.des_gas_outlet.mole_frac_comp[:, k] = blk.des_gas_inlet.mole_frac_comp[ + blk.flowsheet().time.first(), k + ]() + + blk.ads.pressure[:, :, :] = blk.ads_gas_inlet.pressure[ + blk.flowsheet().time.first() + ]() + blk.des.pressure[:, :, :] = blk.des_gas_inlet.pressure[ + blk.flowsheet().time.first() + ]() + + blk.ads.temperature[:, :, :] = blk.ads_gas_inlet.temperature[ + blk.flowsheet().time.first() + ]() + blk.ads_gas_outlet.temperature[:] = blk.ads_gas_inlet.temperature[ + blk.flowsheet().time.first() + ]() + blk.des.temperature[:, :, :] = blk.des_gas_inlet.temperature[ + blk.flowsheet().time.first() + ]() + blk.des_gas_outlet.temperature[:] = blk.des_gas_inlet.temperature[ + blk.flowsheet().time.first() + ]() + + # model variables + for k in blk.component_list: + blk.ads.y_kz[:, :, k] = blk.ads_gas_inlet.mole_frac_comp[ + blk.flowsheet().time.first(), k + ]() + blk.des.y_kz[:, :, k] = blk.des_gas_inlet.mole_frac_comp[ + blk.flowsheet().time.first(), k + ]() + # ========================================================================================= + + init_log.info_high("Checking degrees of freedom") + if degrees_of_freedom(blk) != 0: + raise InitializationError( + "Degrees of freedom is not zero during initialization. Fix/unfix appropriate number of variables to " + "result in zero degrees of freedom for " + "initialization." + ) + + # initializing property packages ====================== + init_log.info_high("Initializing property packages") + blk.ads.inlet_properties.initialize(outlvl=outlvl) + blk.ads.outlet_properties.initialize(outlvl=outlvl) + blk.ads.gas_properties.initialize(outlvl=outlvl) + + blk.des.inlet_properties.initialize(outlvl=outlvl) + blk.des.outlet_properties.initialize(outlvl=outlvl) + blk.des.gas_properties.initialize(outlvl=outlvl) + # ==================================================== + + init_log.info( + "Initialization using BlockTriangularizationInitializer() Starting" + ) + + for i in initialization_points: + init_log.info_high(f"Initialization point: {i}") + blk.ads.R_MT_gas = i + blk.des.R_MT_gas = i + blk.ads.R_MT_coeff = i + blk.des.R_MT_coeff = i + blk.ads.R_HT_ghx = i + blk.des.R_HT_ghx = i + blk.ads.R_HT_gs = i + blk.des.R_HT_gs = i + blk.ads.R_delH = i + blk.des.R_delH = i + with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc: + init_obj.config.block_solver_call_options = {"tee": slc.tee} + init_obj.initialization_routine(blk) + + init_log.info_high("Final solve using IPOPT") + with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc: + results = Solver.solve(blk, tee=slc.tee) + + if check_optimal_termination(results): + init_log.info_high("Final solve {}.".format(idaeslog.condition(results))) + else: + init_log.error("{} Initialization Failed.".format(blk.name)) + + # revert port states ========================= + init_log.info_high("Reverting Port States") + for n, v in blk.ads_gas_inlet.vars.items(): + for i in v: + if ads_gas_inlet_flags[n, i]: + v[i].fix() + else: + v[i].unfix() + + for n, v in blk.ads_gas_outlet.vars.items(): + for i in v: + if ads_gas_outlet_flags[n, i]: + v[i].fix() + else: + v[i].unfix() + + for n, v in blk.des_gas_inlet.vars.items(): + for i in v: + if des_gas_inlet_flags[n, i]: + v[i].fix() + else: + v[i].unfix() + + for n, v in blk.des_gas_outlet.vars.items(): + for i in v: + if des_gas_outlet_flags[n, i]: + v[i].fix() + else: + v[i].unfix() + # ============================================= + + init_log.info("Initialization Routine Finished") + + def plotting(self, save_option: bool = False): + full_contactor_plotting(self, save_option=save_option) + + def _get_stream_table_contents(self, time_point=0): + """ + Assume unit has standard configuration of 1 inlet and 1 outlet. + + Developers should overload this as appropriate. + """ + try: + return create_stream_table_dataframe( + { + "Flue Gas Feed": self.ads_gas_inlet, + "Cleaned Flue Gas": self.ads_gas_outlet, + "Steam Sweep": self.des_gas_inlet, + "Regeneration Product": self.des_gas_outlet, + }, + time_point=time_point, + ) + except AttributeError: + raise ConfigurationError( + f"Unit model {self.name} does not have the standard Port " + f"names (inlet and outlet). Please contact the unit model " + f"developer to develop a unit specific stream table." + ) + + def _get_performance_contents(self, time_point=0): + var_dict = {} + var_dict["Length"] = self.L + var_dict["Diameter"] = self.D + var_dict["Adsorption Volume Fraction"] = self.ads.theta + var_dict["Desorption Volume Fraction"] = self.des.theta + var_dict["Rotational Velocity"] = self.w_rpm[time_point] + var_dict["Adsorption Tx"] = self.ads.Tx[time_point] + var_dict["Desorption Tx"] = self.des.Tx[time_point] + if hasattr(self.ads, "CO2_capture"): + var_dict["CO2 Capture"] = self.ads.CO2_capture[time_point] + + exprs_dict = {} + if hasattr(self, "energy_requirement"): + exprs_dict["Energy Requirement"] = self.energy_requirement[time_point] + if hasattr(self, "productivity"): + exprs_dict["Productivity"] = self.productivity[time_point] + + params_dict = {} + # placeholder to add params if needed + + return {"vars": var_dict, "exprs": exprs_dict, "params": params_dict} + + def report_new(self): + st = create_stream_table_dataframe( + { + "Flue Gas Feed": self.ads_gas_inlet, + "Cleaned Flue Gas": self.ads_gas_outlet, + "Steam Sweep": self.des_gas_inlet, + "Regeneration Product": self.des_gas_outlet, + }, + ) + print(stream_table_dataframe_to_string(st)) + + def report_custom(self): + return report_old(self) + + +# Creating upper level RPB block +def RotaryPackedBed_old(): + + # blk.time = Set(initialize=[0], doc="time domain [s]") + + # Initial/Inlet/Outlet Values + + # =========================== Dimensions ======================================= + + # blk.D.fix() + + # blk.L.fix() + + # blk.w_rpm.fix() + + # =========================== Solids Properties ================================ + + # Isotherm Parameter values + + # Mass transfer parameters + + # heat of adsorption === + + # ============================================================================== + + # ========================== gas properties ==================================== + + # blk.gas_props = FlueGasParameterBlock(components=blk.component_list) + + # ============================================================================== + + return None + + +def add_single_section_equations( + RPB, section_name, gas_flow_direction="forward", initial_guesses="Adsorption" +): + + # blk.theta.fix() + + # embedded heat exchanger and area calculations + + # ============================ Gas Inlet ======================================= + + # =========================== Gas Outlet ======================================= + + # ======================== Heat exchanger ====================================== + + # Variable declaration + # ============================== Gas Phase ===================================== + + # ========================= Solids ============================================= + + # Initialization factors === + + # Section 2: Gas Equations, Gas Properties, Dimensionless Groups, and Variables related + + # Dimensionless groups ==== + + # === + + # Mass/Heat Transfer variables + + # Model equations + + # Mass transfer coefficient + + # heat of adsorption + + # mass transfer rates === + + # flux limiter equation + + # heat transfer rates === + + # PDE equations, boundary conditions, and model constraints + + # Boundary Conditions === + + # Metrics ============== + # ============== + + # Mass and energy balance checks + # ============== + + # miscellaneous + # ============== + + # DAE Transformations + + # initializing some variables and setting scaling factors + + # need to do this after discretizer is applied or else the new indices won't get the scaling factor + + # initializing variables + # ================================================= + + # fixing solid inlet variables + + return None + + +def plotting(blk): + def find_closest_ind(ind_list, query_values): + closest_ind = [] + for j in query_values: + closest_j = min(ind_list, key=lambda x: abs(x - j)) + closest_ind.append(ind_list.index(closest_j)) + + return closest_ind + + theta_query = [0.05, 0.5, 0.95] + z_query = [0.05, 0.5, 0.95] + + def model_plot_CO2g_RKH(m): + z = list(m.z) + theta = list(m.o) + y_CO2 = [[], [], []] + # theta_query=[0.05,0.5,0.95] + theta_test = find_closest_ind(theta, theta_query) + k = 0 + for j in theta_test: + for i in z: + y_CO2[k].append(m.y[0, i, theta[j], "CO2"]()) + k += 1 + + # fig = plt.figure(figsize=(10,6)) + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas phase CO$_{2}$ mole fraction", fontsize=16) + ax.set_ylim([0, 0.05]) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_test)): + ax.plot(z, y_CO2[i], "-o", label="theta=" + str(theta[theta_test[i]])) + ax.legend() + + model_plot_CO2g_RKH(blk) + + def model_plot_CO2g_conc_RKH(m): + z = list(m.z) + theta = list(m.o) + C_CO2 = [[], [], []] + # theta_query=[0.05,0.5,0.95] + theta_test = find_closest_ind(theta, theta_query) + k = 0 + for j in theta_test: + for i in z: + C_CO2[k].append(m.C[0, i, theta[j], "CO2"]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas phase CO$_{2}$ conc.", fontsize=16) + # ax.set_ylim([0,0.05]) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_test)): + ax.plot(z, C_CO2[i], "-o", label="theta=" + str(theta[theta_test[i]])) + ax.legend() + + model_plot_CO2g_conc_RKH(blk) + + def model_plot_N2g_RKH(m): + z = list(m.z) + theta = list(m.o) + C_N2 = [[], [], []] + # theta_query=[0.05,0.5,0.95] + theta_test = find_closest_ind(theta, theta_query) + k = 0 + for j in theta_test: + for i in z: + C_N2[k].append(m.C[0, i, theta[j], "N2"]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas phase N$_{2}$ conc.", fontsize=16) + # ax.set_ylim([0,0.05]) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_test)): + ax.plot(z, C_N2[i], "-o", label="theta=" + str(theta[theta_test[i]])) + ax.legend() + + model_plot_N2g_RKH(blk) + + def model_plot_Tg_RKH(m): + z = list(m.z) + theta = list(m.o) + Tg = [[], [], []] + # theta_query=[0.05,0.5,0.95] + theta_test = find_closest_ind(theta, theta_query) + k = 0 + for j in theta_test: + for i in z: + Tg[k].append(m.Tg[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas Temperature [K]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_test)): + ax.plot(z, Tg[i], "-o", label="theta=" + str(theta[theta_test[i]])) + ax.legend() + + model_plot_Tg_RKH(blk) + + def model_plot_Pg_RKH(m): + z = list(m.z) + theta = list(m.o) + Pg = [[], [], []] + # theta_query=[0.05,0.5,0.95] + theta_test = find_closest_ind(theta, theta_query) + k = 0 + for j in theta_test: + for i in z: + Pg[k].append(m.P[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas Pressure [bar]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_test)): + ax.plot(z, Pg[i], "-o", label="theta=" + str(theta[theta_test[i]])) + ax.legend() + + model_plot_Pg_RKH(blk) + + def model_plot_vg_RKH(m): + z = list(m.z) + theta = list(m.o) + vg = [[], [], []] + # theta_query=[0.05,0.5,0.95] + theta_test = find_closest_ind(theta, theta_query) + k = 0 + for j in theta_test: + for i in z: + vg[k].append(m.vel[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas velocity [m/s]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_test)): + ax.plot(z, vg[i], "-o", label="theta=" + str(theta[theta_test[i]])) + ax.legend() + + model_plot_vg_RKH(blk) + + def model_plot_CO2s_RKH(m): + z = list(m.z) + theta = list(m.o) + qCO2 = [[], [], []] + # z_query=[0.05,0.5,0.95] + z_nodes = find_closest_ind(z, z_query) + k = 0 + for j in z_nodes: + for i in theta: + qCO2[k].append(m.qCO2[0, z[j], i]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Theta distance (radians)", fontsize=16) + ax.set_ylabel("CO$_{2}$ Loading [mol/kg]", fontsize=16) + # ax.set_title('Adsorption CO$_{2}$ Loading') + for i in range(len(z_nodes)): + ax.plot(theta, qCO2[i], "-o", label="z=" + str(z[z_nodes[i]])) + ax.legend() + + model_plot_CO2s_RKH(blk) + + def model_plot_Ts_RKH(m): + z = list(m.z) + theta = list(m.o) + Ts = [[], [], []] + # z_query=[0.05,0.5,0.95] + z_nodes = find_closest_ind(z, z_query) + k = 0 + for j in z_nodes: + for i in theta: + Ts[k].append(m.Ts[0, z[j], i]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Theta distance (radians)", fontsize=16) + ax.set_ylabel("Solids Temperature [K]", fontsize=16) + # ax.set_title('Adsorption CO$_{2}$ Loading') + for i in range(len(z_nodes)): + ax.plot(theta, Ts[i], "-o", label="z=" + str(z[z_nodes[i]])) + ax.legend() + + model_plot_Ts_RKH(blk) + + def model_plot_yCO2theta_RKH(m): + z = list(m.z) + theta = list(m.o) + y = [[], [], []] + # z_query=[0.05,0.5,0.95] + z_nodes = find_closest_ind(z, z_query) + k = 0 + for j in z_nodes: + for i in theta: + y[k].append(m.y[0, z[j], i, "CO2"]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Theta distance (radians)", fontsize=16) + ax.set_ylabel("CO$_{2}$ mole fraction", fontsize=16) + ax.set_ylim([0, 0.05]) + # ax.set_title('Adsorption CO$_{2}$ Loading') + for i in range(len(z_nodes)): + ax.plot(theta, y[i], "-o", label="z=" + str(z[z_nodes[i]])) + ax.legend() + + model_plot_yCO2theta_RKH(blk) + + plt.show() + + +def get_init_factors(blk): + d1 = { + "HT_gs": value(blk.R_HT_gs), + "HT_ghx": value(blk.R_HT_ghx), + "delH": value(blk.R_delH), + "dP": value(blk.R_dP), + "MT_gas": value(blk.R_MT_gas), + "MT_solid": value(blk.R_MT_solid), + "MT_coeff": value(blk.R_MT_coeff), + } + + for key, v in d1.items(): + print(f"{key}: {v}") + + +def evaluate_MB_error(blk): + for k in blk.parent_block().component_list: + # print error for each component formatted in scientific notation + print(f"{k} error = {blk.MB_error[0,k]():.3} %") + + +def homotopy_solve1(blk): + blk.R_HT_gs = 1e-10 + blk.R_HT_ghx = 1e-10 + blk.R_delH = 1e-10 + blk.R_MT_coeff = 1e-10 + blk.R_dP = 1 + blk.R_MT_gas = 1e-10 + blk.R_MT_solid = 1e-10 + + solver = SolverFactory("ipopt") + solver.options = { + "warm_start_init_point": "yes", + "bound_push": 1e-22, + "nlp_scaling_method": "user-scaling", + "max_iter": 1000, + # 'halt_on_ampl_error': 'yes', + } + + hom_points = np.logspace(-3, -1, 10) + j = 0 + for i in hom_points: + j += 1 + print("point ", j) + print("init. point =", i) + blk.R_HT_gs = i + blk.R_HT_ghx = i + blk.R_delH = i + blk.R_MT_coeff = i + solver.solve(blk, tee=True).write() + + +def homotopy_init_routine(blk): + print("\nFixing boundaries to make square problem") + + blk.P_in.fix(1.1) + blk.Tg_in.fix() + blk.y_in.fix() + blk.P_out.fix(1.01325) + + print(f"DOF = {degrees_of_freedom(blk)}") + + variables_list = [ + blk.R_HT_gs, + blk.R_HT_ghx, + blk.R_delH, + blk.R_MT_coeff, + blk.R_MT_gas, + blk.R_MT_solid, + ] + + targets_list = [ + 1, + 1, + 1, + 1, + 1, + 1, + ] + + blk.R_HT_gs = 1e-10 + blk.R_HT_ghx = 1e-10 + blk.R_delH = 1e-10 + blk.R_MT_coeff = 1e-10 + blk.R_dP = 1 + blk.R_MT_gas = 1e-10 + blk.R_MT_solid = 1e-10 + + # homotopy solver + homotopy( + blk, + variables_list, + targets_list, + max_solver_iterations=100, + max_solver_time=60, + min_step=0.01, + iter_target=8, + ) + + +# Degeneracy Hunter +def degen_hunter(blk): + dh = DegeneracyHunter(blk, solver=SolverFactory("cbc")) + + # various functions + dh.check_residuals(tol=1e-6) + dh.check_variable_bounds(tol=1e-6) + # n_deficient = dh.check_rank_equality_constraints() + + +# check scaling +def check_scaling(blk): + jac, nlp = iscale.get_jacobian(blk) + + # print("Extreme Jacobian entries:") + with open("extreme_jacobian_entries.txt", "w") as f: + for i in iscale.extreme_jacobian_entries( + jac=jac, nlp=nlp, small=5e-3, large=1e3 + ): + print(f" {i[0]:.2e}, [{i[1]}, {i[2]}]", file=f) + + # print("Extreme Jacobian Columns:") + with open("extreme_jacobian_columns.txt", "w") as f: + for i in iscale.extreme_jacobian_columns( + jac=jac, nlp=nlp, small=0.1, large=1e3 + ): + print(f" {i[0]:.2e}, [{i[1]}]", file=f) + + # print("Extreme Jacobian Rows:") + with open("extreme_jacobian_rows.txt", "w") as f: + for i in iscale.extreme_jacobian_rows(jac=jac, nlp=nlp, small=0.1, large=1e3): + print(f" {i[0]:.2e}, [{i[1]}]", file=f) + + with open("badly_scaled_vars.txt", "w") as f: + for v, sv in iscale.badly_scaled_var_generator( + blk, large=1e2, small=1e-1, zero=1e-12 + ): + print(f" {v} -- {sv} -- {iscale.get_scaling_factor(v)}", file=f) + + print(f"Jacobian Condition Number: {iscale.jacobian_cond(jac=jac):.2e}") + + +# Doug script +def scaling_script(blk): + # import numpy as np + from scipy.linalg import svd + + # import pyomo.environ as pyo + # import idaes.core.util.scaling as iscale + + jac, nlp = iscale.get_jacobian(blk) + + variables = nlp.get_pyomo_variables() + constraints = nlp.get_pyomo_equality_constraints() + print("Badly scaled variables:") + for i in iscale.extreme_jacobian_columns(jac=jac, nlp=nlp, large=1e3, small=5e-3): + print(f" {i[0]:.2e}, [{i[1]}]") + print("\n\n" + "Badly scaled constraints:") + for i in iscale.extreme_jacobian_rows(jac=jac, nlp=nlp, large=1e3, small=5e-3): + print(f" {i[0]:.2e}, [{i[1]}]") + # print(f"Jacobian Condition Number: {iscale.jacobian_cond(jac=jac):.2e}") + # if not hasattr(m.fs, "obj"): + # m.fs.obj = pyo.Objective(expr=0) + n_sv = 10 + u, s, vT = svd(jac.todense(), full_matrices=False) + + print("\n" + f"Spectral condition number: {s[0]/s[-1]:.3e}") + # Reorder singular values and vectors so that the singular + # values are from least to greatest + u = np.flip(u[:, -n_sv:], axis=1) + s = np.flip(s[-n_sv:], axis=0) + vT = np.flip(vT[-n_sv:, :], axis=0) + v = vT.transpose() + print("\n" + f"Smallest singular value: {s[0]}") + print("\n" + "Variables in smallest singular vector:") + for i in np.where(abs(v[:, 0]) > 0.1)[0]: + print(str(i) + ": " + variables[i].name) + print("\n" + "Constraints in smallest singular vector:") + for i in np.where(abs(u[:, 0]) > 0.1)[0]: + print(str(i) + ": " + constraints[i].name) + + return jac, variables, constraints + + +def single_section_init(blk): + init_obj = BlockTriangularizationInitializer() + init_obj.config.block_solver_call_options = {"tee": True} + + blk.P_in.fix(1.1) + blk.Tg_in.fix() + blk.y_in.fix() + blk.P_out.fix(1.01325) + + blk.R_HT_gs = 1e-10 + blk.R_HT_ghx = 1e-10 + blk.R_delH = 1e-10 + blk.R_MT_coeff = 1e-10 + blk.R_dP = 1 + blk.R_MT_gas = 1e-10 + blk.R_MT_solid = 1e-10 + + blk.R_MT_solid = 1 + blk.R_MT_gas = 1 + + print(f"DOF = {degrees_of_freedom(blk)}") + + init_obj.initialization_routine(blk) + + blk.R_MT_coeff = 1 + blk.R_HT_ghx = 1 + blk.R_HT_gs = 1 + + init_obj.initialization_routine(blk) + + blk.R_delH = 1 + + init_obj.initialization_routine(blk) + + solver = SolverFactory("ipopt") + solver.options = { + "max_iter": 1000, + "bound_push": 1e-22, + "halt_on_ampl_error": "yes", + } + solver.solve(blk, tee=True).write() + + +def single_section_init2(blk): + blk.P_in.fix(1.1) + blk.Tg_in.fix() + blk.y_in.fix() + blk.P_out.fix(1.01325) + + blk.R_HT_gs = 1e-10 + blk.R_HT_ghx = 1e-10 + blk.R_delH = 1e-10 + blk.R_MT_coeff = 1e-10 + blk.R_dP = 1 + blk.R_MT_gas = 1e-10 + blk.R_MT_solid = 1e-10 + + # add dummy objective + blk.obj = Objective(expr=0) + + results = SolverFactory("gams").solve( + blk, + tee=True, + keepfiles=True, + solver="conopt4", + tmpdir="temp", + add_options=["gams_model.optfile=1;"], + ) + + blk.R_MT_solid = 1 + blk.R_MT_gas = 1 + blk.R_MT_coeff = 1 + + print(f"DOF = {degrees_of_freedom(blk)}") + + results = SolverFactory("gams").solve( + blk, + tee=True, + keepfiles=True, + solver="conopt4", + tmpdir="temp", + add_options=["gams_model.optfile=1;"], + ) + + blk.R_HT_ghx = 1 + blk.R_HT_gs = 1 + blk.R_delH = 1 + + results = SolverFactory("gams").solve( + blk, + tee=True, + keepfiles=True, + solver="conopt4", + tmpdir="temp", + add_options=["gams_model.optfile=1;"], + ) + + +def full_model_creation(lean_temp_connection=True, configuration="co-current"): + RPB = RotaryPackedBed_old() + + if configuration == "co-current": + add_single_section_equations( + RPB, + section_name="ads", + gas_flow_direction="forward", + initial_guesses="adsorption", + ) + add_single_section_equations( + RPB, + section_name="des", + gas_flow_direction="forward", + initial_guesses="desorption", + ) + elif configuration == "counter-current": + add_single_section_equations( + RPB, + section_name="ads", + gas_flow_direction="forward", + initial_guesses="adsorption", + ) + add_single_section_equations( + RPB, + section_name="des", + gas_flow_direction="reverse", + initial_guesses="desorption", + ) + + # fix BCs + # RPB.ads.P_in.fix(1.1) + RPB.ads.P_in.fix(1.025649) + RPB.ads.Tg_in.fix() + RPB.ads.y_in.fix() + RPB.ads.P_out.fix(1.01325) + + RPB.des.P_in.fix(1.1) + RPB.des.Tg_in.fix() + RPB.des.y_in.fix() + RPB.des.P_out.fix(1.01325) + + # connect rich stream + # unfix inlet loading and temperature to the desorption section. (No mass transfer at boundaries so z=0 and z=1 need to remain fixed.) + for z in RPB.des.z: + if 0 < z < 1: + RPB.des.qCO2[0, z, 0].unfix() + RPB.des.Ts[0, z, 0].unfix() + + # add equality constraint equating inlet desorption loading to outlet adsorption loading. Same for temperature. + @RPB.Constraint(RPB.time, RPB.des.z) + def rich_loading_constraint(b, t, z): + if 0 < z < 1: + return b.des.qCO2[t, z, 0] == b.ads.qCO2[t, z, 1] + else: + return Constraint.Skip + + @RPB.Constraint(RPB.time, RPB.des.z) + def rich_temp_constraint(b, t, z): + if 0 < z < 1: + return b.des.Ts[t, z, 0] == b.ads.Ts[t, z, 1] + else: + return Constraint.Skip + + # connect lean stream + # unfix inlet loading to the adsorption section + for z in RPB.ads.z: + if 0 < z < 1: + RPB.ads.qCO2[0, z, 0].unfix() + if lean_temp_connection: + RPB.ads.Ts[0, z, 0].unfix() + + # add equality constraint equating inlet adsorption loading to outlet desorption loading + @RPB.Constraint(RPB.time, RPB.ads.z) + def lean_loading_constraint(b, t, z): + if 0 < z < 1: + return b.ads.qCO2[t, z, 0] == b.des.qCO2[t, z, 1] + else: + return Constraint.Skip + + if lean_temp_connection: + + @RPB.Constraint(RPB.time, RPB.ads.z) + def lean_temp_constraint(b, t, z): + if 0 < z < 1: + return b.ads.Ts[t, z, 0] == b.des.Ts[t, z, 1] + else: + return Constraint.Skip + + # these variables are inactive, just fixing them to same value for plotting purposes + for t in RPB.time: + RPB.ads.qCO2[t, 0, 0].fix(1) + RPB.ads.qCO2[t, 1, 0].fix(1) + RPB.des.qCO2[t, 0, 0].fix(1) + RPB.des.qCO2[t, 1, 0].fix(1) + + RPB.ads.Ts[t, 0, 0].fix(100 + 273) + RPB.ads.Ts[t, 1, 0].fix(100 + 273) + RPB.des.Ts[t, 0, 0].fix(100 + 273) + RPB.des.Ts[t, 1, 0].fix(100 + 273) + + # add constraint so that the fraction of each section adds to 1 + RPB.des.theta.unfix() # unfix des side var + + @RPB.Constraint(doc="Theta summation constraint") + def theta_constraint(b): + return b.ads.theta + b.des.theta == 1 + + # metrics =============================================== + RPB.steam_enthalpy = Param( + initialize=2257.92, + mutable=True, + units=units.kJ / units.kg, + doc="saturated steam enthalpy at 1 bar[kJ/kg]", + ) + + @RPB.Expression(RPB.time, doc="Steam energy [kW]") + def steam_energy(b, t): + return b.des.F_in[t] * b.des.y_in[t, "H2O"] * b.MW["H2O"] * b.steam_enthalpy + + @RPB.Expression(RPB.time, doc="total thermal energy (steam + HX) [kW]") + def total_thermal_energy(b, t): + return b.steam_energy[t] - b.des.Q_ghx_tot_kW[t] + + @RPB.Expression(RPB.time, doc="Energy requirement [MJ/kg CO2]") + def energy_requirement(b, t): + return units.convert( + b.total_thermal_energy[t] / b.ads.delta_CO2[t] / b.MW["CO2"], + to_units=units.MJ / units.kg, + ) + + @RPB.Expression(RPB.time, doc="Productivity [kg CO2/h/m^3]") + def productivity(b, t): + return units.convert( + b.ads.delta_CO2[t] * b.MW["CO2"] / b.ads.vol_tot, + to_units=units.kg / units.h / units.m**3, + ) + + # add scaling factors + iscale.set_scaling_factor(RPB.theta_constraint, 1e2) + for t in RPB.time: + for z in RPB.ads.z: + if 0 < z < 1: + iscale.set_scaling_factor(RPB.lean_loading_constraint[t, z], 10) + iscale.set_scaling_factor(RPB.rich_loading_constraint[t, z], 10) + + return RPB + + +def init_routine_1(blk, homotopy_points=[1]): + # create Block init object + init_obj = BlockTriangularizationInitializer() + + init_obj.config.block_solver_call_options = {"tee": True} + init_obj.config.block_solver_options = { + # "halt_on_ampl_error": "yes", + "max_iter": 1000, + # "bound_push": 1e-22, + # "mu_init": 1e-3, + } + + blk.ads.R_MT_gas = 1e-10 + blk.des.R_MT_gas = 1e-10 + blk.ads.R_MT_coeff = 1e-10 + blk.des.R_MT_coeff = 1e-10 + blk.ads.R_HT_ghx = 1e-10 + blk.des.R_HT_ghx = 1e-10 + blk.ads.R_HT_gs = 1e-10 + blk.des.R_HT_gs = 1e-10 + blk.ads.R_delH = 1e-10 + blk.des.R_delH = 1e-10 + + # run initialization routine + print("DOF =", degrees_of_freedom(blk)) + + init_obj.initialization_routine(blk) + + for i in homotopy_points: + print(f"homotopy point {i}") + blk.ads.R_MT_gas = i + blk.des.R_MT_gas = i + blk.ads.R_MT_coeff = i + blk.des.R_MT_coeff = i + blk.ads.R_HT_ghx = i + blk.des.R_HT_ghx = i + blk.ads.R_HT_gs = i + blk.des.R_HT_gs = i + blk.ads.R_delH = i + blk.des.R_delH = i + init_obj.initialization_routine(blk) + + print("full solve") + + solver = SolverFactory("ipopt") + solver.options = { + "max_iter": 500, + "bound_push": 1e-22, + "halt_on_ampl_error": "yes", + } + solver.solve(blk, tee=True).write() + + +def init_routine_2(blk): + init_obj = BlockTriangularizationInitializer() + + init_obj.config.block_solver_call_options = {"tee": True} + init_obj.config.block_solver_options = { + # "halt_on_ampl_error": "yes", + "max_iter": 500, + } + + blk.ads.R_MT_gas = 1e-10 + blk.des.R_MT_gas = 1e-10 + blk.ads.R_MT_coeff = 1e-10 + blk.des.R_MT_coeff = 1e-10 + blk.ads.R_HT_ghx = 1e-10 + blk.des.R_HT_ghx = 1e-10 + blk.ads.R_HT_gs = 1e-10 + blk.des.R_HT_gs = 1e-10 + blk.ads.R_delH = 1e-10 + blk.des.R_delH = 1e-10 + + # turn on solids mass transfer (with the loadings connected at the rich and lean ends, solids mass transfer has to be turned on or no solution exists) + blk.ads.R_MT_solid = 1 + blk.des.R_MT_solid = 1 + + # run initialization routine + + init_obj.initialization_routine(blk) + + solver = SolverFactory("ipopt") + solver.options = { + "max_iter": 1000, + "bound_push": 1e-22, + "halt_on_ampl_error": "yes", + } + solver.solve(blk, tee=True).write() + + variables_list = [ + blk.ads.R_HT_gs, + blk.des.R_HT_gs, + blk.ads.R_HT_ghx, + blk.des.R_HT_ghx, + blk.ads.R_delH, + blk.des.R_delH, + blk.ads.R_MT_coeff, + blk.des.R_MT_coeff, + blk.ads.R_MT_gas, + blk.des.R_MT_gas, + ] + + targets_list = [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + ] + + # homotopy solver + homotopy( + blk, + variables_list, + targets_list, + max_solver_iterations=100, + max_solver_time=60, + min_step=0.01, + iter_target=8, + ) + + +def report_old(blk): + items = [ + blk.L, + blk.D, + blk.w_rpm[0], + blk.ads.theta, + blk.des.theta, + blk.ads.P_in[0], + blk.ads.P_out[0], + blk.ads.F_in[0], + blk.ads.Tg_in[0], + blk.ads.Tx[0], + blk.des.P_in[0], + blk.des.P_out[0], + blk.des.F_in[0], + blk.des.Tg_in[0], + blk.des.Tx[0], + blk.ads.CO2_capture[0], + blk.energy_requirement[0], + blk.productivity[0], + ] + + names = [] + values = [] + fixed = [] + lb = [] + ub = [] + docs = [] + for item in items: + names.append(item.to_string()) + values.append(item()) + if item.ctype != Var: + fixed.append("N/A") + lb.append("N/A") + ub.append("N/A") + else: + fixed.append(item.fixed) + lb.append(item.lb) + ub.append(item.ub) + docs.append(item.parent_component().doc) + + report_df = pd.DataFrame( + data={ + "Value": values, + "Doc": docs, + "Fixed": fixed, + "Lower Bound": lb, + "Upper Bound": ub, + }, + index=names, + ) + + indexed_items = [ + blk.ads.gas_inlet.y_in, + blk.ads.gas_outlet.y_out, + ] + + names = [] + values = [] + docs = [] + fixed = [] + lb = [] + ub = [] + for item in indexed_items: + names += [item[k].to_string() for k in item.keys()] + values += [item[k]() for k in item.keys()] + docs += [item.doc for k in item.keys()] + fixed += [item[k].fixed for k in item.keys()] + lb += [item[k].lb for k in item.keys()] + ub += [item[k].ub for k in item.keys()] + + report_indexed_df = pd.DataFrame( + data={ + "Value": values, + "Doc": docs, + "Fixed": fixed, + "Lower Bound": lb, + "Upper Bound": ub, + }, + index=names, + ) + + report_df = pd.concat([report_df, report_indexed_df]) + + return report_df + + +def full_contactor_plotting(blk, save_option=False): + z = list(blk.ads.z) + theta = list(blk.ads.o) + + theta_total_norm = [j * blk.ads.theta() for j in blk.ads.o] + [ + j * blk.des.theta() + blk.ads.theta() for j in blk.des.o + ][1:] + + z_query = [0.05, 0.25, 0.5, 0.75, 0.95] + z_nodes = [blk.ads.z.find_nearest_index(z) for z in z_query] + + theta_query = [0.01, 0.05, 0.3, 0.5, 0.8] + theta_nodes = [blk.ads.o.find_nearest_index(o) for o in theta_query] + + # Solids Loading + qCO2_ads = [[], [], [], [], []] + qCO2_des = [[], [], [], [], []] + qCO2_total = [[], [], [], [], []] + k = 0 + for j in z_nodes: + for i in theta: + qCO2_ads[k].append(blk.ads.qCO2[0, z[j], i]()) + qCO2_des[k].append(blk.des.qCO2[0, z[j], i]()) + k += 1 + + for k in range(len(z_nodes)): + qCO2_total[k] = qCO2_ads[k] + qCO2_des[k][1:] + + qCO2_avg = [blk.ads.qCO2_o[0, o]() for o in blk.ads.o] + [ + blk.des.qCO2_o[0, o]() for o in blk.des.o + ][1:] + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Rotational Distance [-]", fontsize=16) + ax.set_ylabel("CO$_{2}$ Loading [mol/kg]", fontsize=16) + # ax.set_title('Adsorption CO$_{2}$ Loading') + for i in range(len(z_nodes)): + ax.plot( + theta_total_norm, + qCO2_total[i], + "-o", + label="z=" + str(round(z[z_nodes[i]], 3)), + ) + ax.plot(theta_total_norm, qCO2_avg, "--", label="Averaged") + ax.axvline(x=blk.ads.theta(), color="k", linestyle="--") + # ymin, ymax = ax.get_ylim() + # ax.text( + # 0.1, + # 0.5 * (ymax - ymin) + ymin, + # "Adsorption Section", + # bbox=dict(facecolor="white", alpha=0.5), + # ) + # ax.text( + # 0.6, + # 0.5 * (ymax - ymin) + ymin, + # "Desorption Section", + # bbox=dict(facecolor="white", alpha=0.5), + # ) + ax.legend() + + if save_option: + fig.savefig("CO2_loading.png", dpi=300) + + # Solids temperature + Ts_ads = [[], [], [], [], []] + Ts_des = [[], [], [], [], []] + Ts_total = [[], [], [], [], []] + k = 0 + for j in z_nodes: + for i in theta: + Ts_ads[k].append(blk.ads.Ts[0, z[j], i]()) + Ts_des[k].append(blk.des.Ts[0, z[j], i]()) + k += 1 + + for k in range(len(z_nodes)): + Ts_total[k] = Ts_ads[k] + Ts_des[k][1:] + + Ts_avg = [blk.ads.Ts_o[0, o]() for o in blk.ads.o] + [ + blk.des.Ts_o[0, o]() for o in blk.des.o + ][1:] + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Rotational Distance [-]", fontsize=16) + ax.set_ylabel("Solids Temperature [K]", fontsize=16) + # ax.set_title('Adsorption CO$_{2}$ Loading') + for i in range(len(z_nodes)): + ax.plot( + theta_total_norm, + Ts_total[i], + "-o", + label="z=" + str(round(z[z_nodes[i]], 3)), + ) + ax.plot(theta_total_norm, Ts_avg, "--", label="Averaged") + ax.axvline(x=blk.ads.theta(), color="k", linestyle="--") + # ymin, ymax = ax.get_ylim() + # ax.text( + # 0.1, + # 0.5 * (ymax - ymin) + ymin, + # "Adsorption Section", + # bbox=dict(facecolor="white", alpha=0.5), + # ) + # ax.text( + # 0.6, + # 0.5 * (ymax - ymin) + ymin, + # "Desorption Section", + # bbox=dict(facecolor="white", alpha=0.5), + # ) + ax.legend() + + if save_option: + fig.savefig("solid temp.png", dpi=300) + + # Adsorber Gas phase CO2 mole fraction + y_CO2 = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + y_CO2[k].append(blk.ads.y[0, i, theta[j], "CO2"]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas phase CO$_{2}$ mole fraction, Adsorber", fontsize=12) + # ax.set_ylim([0, 0.05]) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot( + z, y_CO2[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3)) + ) + ax.plot(z, [blk.ads.y_kz[0, j, "CO2"]() for j in z], "--", label="Averaged") + ax.legend() + + if save_option: + fig.savefig("CO2_molefraction_ads.png", dpi=300) + + # Desorber Gas phase CO2 mole fraction + y_CO2 = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + y_CO2[k].append(blk.des.y[0, i, theta[j], "CO2"]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas phase CO$_{2}$ mole fraction, Desorber", fontsize=12) + # ax.set_ylim([0, 0.05]) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot( + z, y_CO2[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3)) + ) + ax.plot(z, [blk.des.y_kz[0, j, "CO2"]() for j in z], "--", label="Averaged") + ax.legend() + + if save_option: + fig.savefig("CO2_molefraction_des.png", dpi=300) + + # Adsorber Gas Temperature + Tg = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + Tg[k].append(blk.ads.Tg[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas Temperature, Adsorber [K]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot(z, Tg[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3))) + ax.plot(z, [blk.ads.Tg_z[0, j]() for j in z], "--", label="Averaged") + ax.axhline( + y=blk.ads.Tx[0](), + xmin=0, + xmax=1, + color="black", + label="Embedded Heat Exchanger Temp [K]", + ) + ax.legend() + + if save_option: + fig.savefig("GasTemp_ads.png", dpi=300) + + # Desorber Gas Temperature + Tg = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + Tg[k].append(blk.des.Tg[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas Temperature, Desorber [K]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot(z, Tg[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3))) + ax.plot(z, [blk.des.Tg_z[0, j]() for j in z], "--", label="Averaged") + ax.axhline( + y=blk.des.Tx[0](), + xmin=0, + xmax=1, + color="black", + label="Embedded Heat Exchanger Temp [K]", + ) + ax.legend() + + if save_option: + fig.savefig("GasTemp_des.png", dpi=300) + + # Adsorber Gas Pressure + Pg = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + Pg[k].append(blk.ads.P[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas Pressure, Adsorber [bar]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot(z, Pg[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3))) + ax.legend() + + if save_option: + fig.savefig("GasPress_ads.png", dpi=300) + + # Desorber Gas Pressure + Pg = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + Pg[k].append(blk.des.P[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas Pressure, Desorber [bar]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot(z, Pg[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3))) + ax.legend() + + if save_option: + fig.savefig("GasPress_des.png", dpi=300) + + # Adsorber Gas Velocity + vel = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + vel[k].append(blk.ads.vel[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas velocity, Adsorber [m/s]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot(z, vel[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3))) + ax.legend() + + if save_option: + fig.savefig("GasVel_ads.png", dpi=300) + + # Desorber Gas Velocity + vel = [[], [], [], [], []] + k = 0 + for j in theta_nodes: + for i in z: + vel[k].append(blk.des.vel[0, i, theta[j]]()) + k += 1 + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.set_xlabel("Normalized Axial distance", fontsize=16) + ax.set_ylabel("Gas velocity, Desorber [m/s]", fontsize=16) + # ax.set_title('Adsorption gas phase CO$_{2}$') + for i in range(len(theta_nodes)): + ax.plot(z, vel[i], "-o", label="theta=" + str(round(theta[theta_nodes[i]], 3))) + ax.legend() + + if save_option: + fig.savefig("GasVel_des.png", dpi=300) + + plt.show() diff --git a/Rotary packed bed/RPB_notebook.ipynb b/Rotary packed bed/RPB_notebook.ipynb new file mode 100644 index 0000000..f4aa5a4 --- /dev/null +++ b/Rotary packed bed/RPB_notebook.ipynb @@ -0,0 +1,1950 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create model from scratch" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import RPB model along with other utility functions\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.unit_models import Feed, Product\n", + "from RPB_model import RotaryPackedBed\n", + "\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " Reference,\n", + " units as pyunits,\n", + " Param,\n", + ")\n", + "\n", + "import idaes.core.util as iutil\n", + "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.util.initialization import propagate_state\n", + "\n", + "from idaes.models_extra.power_generation.properties import FlueGasParameterBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models_extra.power_generation.properties.natural_gas_PR import (\n", + " get_prop,\n", + " EosType,\n", + ")\n", + "\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core.util.model_diagnostics import DiagnosticsToolbox\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# create Flowsheet block\n", + "\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create gas phase properties block\n", + "flue_species={\"H2O\", \"CO2\", \"N2\"}\n", + "prop_config = get_prop(flue_species, [\"Vap\"], eos=EosType.IDEAL)\n", + "prop_config[\"state_bounds\"][\"pressure\"] = (0.99*1e5,1.02*1e5,1.2*1e5, pyunits.Pa)\n", + "prop_config[\"state_bounds\"][\"temperature\"] = (25+273.15,90+273.15,180+273.15, pyunits.K)\n", + "\n", + "m.fs.gas_props = GenericParameterBlock(\n", + " **prop_config,\n", + " doc = \"Flue gas properties\",\n", + ")\n", + "\n", + "m.fs.gas_props.set_default_scaling(\"temperature\", 1e-2)\n", + "m.fs.gas_props.set_default_scaling(\"pressure\", 1e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# create feed and product blocks\n", + "\n", + "m.fs.flue_gas_in = Feed(property_package = m.fs.gas_props)\n", + "m.fs.flue_gas_out = Product(property_package = m.fs.gas_props)\n", + "m.fs.steam_sweep_feed = Feed(property_package = m.fs.gas_props)\n", + "m.fs.regeneration_prod = Product(property_package = m.fs.gas_props)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# increased number of discretization points, lower mass balance error\n", + "\n", + "# z_init_points=tuple(np.geomspace(0.01, 0.5, 9)[:-1]) + tuple((1 - np.geomspace(0.01, 0.5, 9))[::-1])\n", + "# o_init_points=tuple(np.geomspace(0.005, 0.1, 8)) + tuple(np.linspace(0.1, 0.995, 10)[1:])\n", + "\n", + "# z_nfe=20\n", + "# o_nfe=20\n", + "\n", + "# m.fs.RPB = RotaryPackedBed(\n", + "# property_package = m.fs.gas_props,\n", + "# z_init_points=z_init_points,\n", + "# o_init_points=o_init_points,\n", + "# z_nfe=z_nfe,\n", + "# o_nfe=o_nfe,\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# limited discretization, much faster\n", + "\n", + "m.fs.RPB = RotaryPackedBed(\n", + " property_package = m.fs.gas_props,\n", + " z_init_points = (0.01,0.99),\n", + " o_init_points = (0.01,0.99),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# add stream connections\n", + "\n", + "m.fs.s_flue_gas = Arc(source=m.fs.flue_gas_in.outlet, destination=m.fs.RPB.ads_gas_inlet)\n", + "m.fs.s_cleaned_flue_gas = Arc(source=m.fs.RPB.ads_gas_outlet, destination=m.fs.flue_gas_out.inlet)\n", + "m.fs.s_steam_feed = Arc(source=m.fs.steam_sweep_feed.outlet, destination=m.fs.RPB.des_gas_inlet)\n", + "m.fs.s_regeneration_prod = Arc(source=m.fs.RPB.des_gas_outlet, destination=m.fs.regeneration_prod.inlet)\n", + "\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# fix state variables in feed and product blocks\n", + "# ads side\n", + "m.fs.flue_gas_in.pressure.fix(1.02*1e5)\n", + "m.fs.flue_gas_in.temperature.fix(90+273.15)\n", + "m.fs.flue_gas_out.pressure.fix(1.01325*1e5)\n", + "m.fs.flue_gas_in.mole_frac_comp[0,\"CO2\"].fix(0.04)\n", + "m.fs.flue_gas_in.mole_frac_comp[0,\"H2O\"].fix(0.09)\n", + "m.fs.flue_gas_in.mole_frac_comp[0,\"N2\"].fix(1-0.04-0.09)\n", + "\n", + "#des side\n", + "m.fs.steam_sweep_feed.pressure.fix(1.05*1e5)\n", + "m.fs.steam_sweep_feed.temperature.fix(120+273.15)\n", + "m.fs.regeneration_prod.pressure.fix(1.01325*1e5)\n", + "m.fs.steam_sweep_feed.mole_frac_comp[0,\"CO2\"].fix(1e-5)\n", + "m.fs.steam_sweep_feed.mole_frac_comp[0,\"N2\"].fix(1e-3)\n", + "m.fs.steam_sweep_feed.mole_frac_comp[0,\"H2O\"].fix(1-1e-5-1e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# fix design variables of the RPB\n", + "\n", + "m.fs.RPB.ads.Tx.fix()\n", + "m.fs.RPB.des.Tx.fix()\n", + "\n", + "m.fs.RPB.w_rpm.fix(0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DOF = 0\n" + ] + } + ], + "source": [ + "# check degrees of freedom\n", + "\n", + "print(\"DOF =\",degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_in.properties: Starting initialization\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_in.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_in.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_in: Initialization Complete.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_out.properties: Starting initialization\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_out.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_out.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.flue_gas_out: Initialization Complete.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.steam_sweep_feed.properties: Starting initialization\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.steam_sweep_feed.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.steam_sweep_feed.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.steam_sweep_feed: Initialization Complete.\n", + "2024-08-28 11:39:50 [INFO] idaes.init.fs.regeneration_prod.properties: Starting initialization\n", + "2024-08-28 11:39:51 [INFO] idaes.init.fs.regeneration_prod.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:51 [INFO] idaes.init.fs.regeneration_prod.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:39:51 [INFO] idaes.init.fs.regeneration_prod: Initialization Complete.\n" + ] + } + ], + "source": [ + "# initialize feed and product blocks\n", + "\n", + "m.fs.flue_gas_in.initialize()\n", + "m.fs.flue_gas_out.initialize()\n", + "m.fs.steam_sweep_feed.initialize()\n", + "m.fs.regeneration_prod.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# propagate feed and product blocks (for initial RPB guesses)\n", + "propagate_state(arc = m.fs.s_flue_gas, direction=\"forward\")\n", + "propagate_state(arc = m.fs.s_steam_feed, direction=\"forward\")\n", + "propagate_state(arc = m.fs.s_cleaned_flue_gas, direction=\"backward\")\n", + "propagate_state(arc = m.fs.s_regeneration_prod, direction=\"backward\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 11:40:02 [INFO] idaes.init.fs.RPB: Beginning Initialization\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.inlet_properties: Starting initialization\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.inlet_properties: State variable initialization completed.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.inlet_properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.inlet_properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.outlet_properties: Starting initialization\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.outlet_properties: State variable initialization completed.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.outlet_properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.outlet_properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.gas_properties: Starting initialization\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.gas_properties: State variable initialization completed.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.gas_properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.ads.gas_properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.des.inlet_properties: Starting initialization\n", + "2024-08-28 11:40:04 [INFO] idaes.init.fs.RPB.des.inlet_properties: State variable initialization completed.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.inlet_properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.inlet_properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.outlet_properties: Starting initialization\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.outlet_properties: State variable initialization completed.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.outlet_properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.outlet_properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.gas_properties: Starting initialization\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.gas_properties: State variable initialization completed.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.gas_properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB.des.gas_properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-08-28 11:40:05 [INFO] idaes.init.fs.RPB: Initialization using BlockTriangularizationInitializer() Starting\n", + "2024-08-28 11:44:08 [INFO] idaes.init.fs.RPB: Initialization Routine Finished\n" + ] + } + ], + "source": [ + "# Initialize RPB (about 4 mins for smaller discretization size and close to 20 mins for the larger size)\n", + "\n", + "optarg = {\n", + " # \"halt_on_ampl_error\": \"yes\",\n", + " \"max_iter\": 1000,\n", + " \"bound_push\": 1e-22,\n", + " # \"mu_init\": 1e-3,\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + "}\n", + "\n", + "init_points = [1e-5,1e-3,1e-1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n", + "\n", + "m.fs.RPB.initialize(outlvl=idaeslog.INFO, optarg=optarg, initialization_points=init_points)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 9\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 2 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 24798\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 14693\n", + "\n", + "Total number of variables............................: 7023\n", + " variables with only lower bounds: 272\n", + " variables with lower and upper bounds: 3999\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 7023\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 7.92e+03 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.14e+01 1.11e+02 -1.0 7.92e+03 - 3.78e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 2.06e-06 2.35e+01 -1.0 7.14e+01 - 9.89e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 1.78e-09 8.75e+01 -1.0 9.67e-05 - 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....: 1.7830643628258258e-09 1.7830643628258258e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.7830643628258258e-09 1.7830643628258258e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.318\n", + "Total CPU secs in NLP function evaluations = 0.051\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "# ==========================================================\n", + "# = Solver Results =\n", + "# ==========================================================\n", + "# ----------------------------------------------------------\n", + "# Problem Information\n", + "# ----------------------------------------------------------\n", + "Problem: \n", + "- Lower bound: -inf\n", + " Upper bound: inf\n", + " Number of objectives: 1\n", + " Number of constraints: 7023\n", + " Number of variables: 7023\n", + " Sense: unknown\n", + "# ----------------------------------------------------------\n", + "# Solver Information\n", + "# ----------------------------------------------------------\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: 0.687504768371582\n", + "# ----------------------------------------------------------\n", + "# Solution Information\n", + "# ----------------------------------------------------------\n", + "Solution: \n", + "- number of solutions: 0\n", + " number of solutions displayed: 0\n" + ] + } + ], + "source": [ + "# full solve with IPOPT\n", + "\n", + "Solver = SolverFactory(\"ipopt\")\n", + "Solver.solve(m, tee=True).write()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# can skip the initialization steps and load an already saved model then solve\n", + "\n", + "# if using the smaller discretization\n", + "iutil.from_json(m, fname=\"RPB flowsheet 081924, limited disc.json.gz\", gz=True)\n", + "# if using the larger discretization\n", + "# iutil.from_json(m, fname=\"RPB flowsheet 081924.json.gz\", gz=True)\n", + "\n", + "Solver = SolverFactory(\"ipopt\")\n", + "Solver.solve(m, tee=True).write()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Flowsheet : fs Time: 0.0\n", + "====================================================================================\n", + " Local Degrees of Freedom: 0\n", + " Total Variables: 7504 Activated Constraints: 7023 Activated Blocks: 267\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units s_flue_gas s_cleaned_flue_gas s_steam_feed s_regeneration_prod\n", + " Total Molar Flowrate mole / second 80.176 77.114 168.43 173.00 \n", + " Total Mole Fraction N2 dimensionless 0.87000 0.90455 0.0010000 0.00097358 \n", + " Total Mole Fraction CO2 dimensionless 0.040000 0.0018765 1.0000e-05 0.026429 \n", + " Total Mole Fraction H2O dimensionless 0.090000 0.093574 0.99899 0.97260 \n", + " Temperature kelvin 363.15 391.38 393.15 363.66 \n", + " Pressure pascal 1.0200e+05 1.0132e+05 1.0500e+05 1.0132e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.report(dof=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.RPB Time: 0.0\n", + "====================================================================================\n", + " Local Degrees of Freedom: 12\n", + " Total Variables: 7415 Activated Constraints: 6979 Activated Blocks: 249\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Adsorption Tx : 363.00 : kelvin : True : (0, None)\n", + " Adsorption Volume Fraction : 0.75000 : dimensionless : True : (0.01, 0.99)\n", + " CO2 Capture : 0.95488 : dimensionless : False : (None, None)\n", + " Desorption Tx : 393.00 : kelvin : True : (0, None)\n", + " Desorption Volume Fraction : 0.25000 : dimensionless : False : (0.01, 0.99)\n", + " Diameter : 10.000 : meter : True : (0, None)\n", + " Length : 3.0000 : meter : True : (0.1, 10.001)\n", + " Rotational Velocity : 0.010472 : radian / second : True : (1e-05, 2)\n", + "\n", + " Expressions: \n", + "\n", + " Key : Value : Units\n", + " Energy Requirement : 5.2403e+07 : joule / kilogram\n", + " Productivity : 0.00085800 : kilogram / meter ** 3 / second\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Flue Gas Feed Cleaned Flue Gas Steam Sweep Regeneration Product\n", + " Total Molar Flowrate mole / second 80.176 77.114 168.43 173.00 \n", + " Total Mole Fraction N2 dimensionless 0.87000 0.90455 0.0010000 0.00097358 \n", + " Total Mole Fraction CO2 dimensionless 0.040000 0.0018765 1.0000e-05 0.026429 \n", + " Total Mole Fraction H2O dimensionless 0.090000 0.093574 0.99899 0.97260 \n", + " Temperature kelvin 363.15 391.38 393.15 363.66 \n", + " Pressure pascal 1.0200e+05 1.0132e+05 1.0500e+05 1.0132e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.RPB.report(dof=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Diagnostics testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool = DiagnosticsToolbox(m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.display_constraints_with_extreme_jacobians()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.display_variables_with_extreme_jacobians()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.report_numerical_issues()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.display_variables_with_extreme_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.report_structural_issues()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def check_scaling(blk):\n", + " jac, nlp = iscale.get_jacobian(blk, scaled=True)\n", + "\n", + " # print(\"Extreme Jacobian entries:\")\n", + " with open(\"extreme_jacobian_entries.txt\", \"w\") as f:\n", + " for i in iscale.extreme_jacobian_entries(\n", + " jac=jac, nlp=nlp, small=1e-4, large=1e4\n", + " ):\n", + " print(f\" {i[0]:.2e}, [{i[1]}, {i[2]}]\", file=f)\n", + "\n", + " # print(\"Extreme Jacobian Columns:\")\n", + " with open(\"extreme_jacobian_columns.txt\", \"w\") as f:\n", + " for i in iscale.extreme_jacobian_columns(\n", + " jac=jac, nlp=nlp, small=1e-3, large=1e3\n", + " ):\n", + " print(f\" {i[0]:.2e}, [{i[1]}]\", file=f)\n", + "\n", + " # print(\"Extreme Jacobian Rows:\")\n", + " with open(\"extreme_jacobian_rows.txt\", \"w\") as f:\n", + " for i in iscale.extreme_jacobian_rows(jac=jac, nlp=nlp, small=1e-3, large=1e3):\n", + " print(f\" {i[0]:.2e}, [{i[1]}]\", file=f)\n", + "\n", + " with open(\"badly_scaled_vars.txt\", \"w\") as f:\n", + " for v, sv in iscale.badly_scaled_var_generator(\n", + " blk, large=1e4, small=1e-4, zero=1e-12\n", + " ):\n", + " print(f\" {v} -- {sv} -- {iscale.get_scaling_factor(v)}\", file=f)\n", + "\n", + " print(f\"Jacobian Condition Number: {iscale.jacobian_cond(jac=jac):.2e}\")\n", + "\n", + "check_scaling(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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Ze+DGjRsrfZ+VlUVWVlaN42vbW1Sbu+++m6ysLGbMmEFKSgodO3Zk5cqVBAYGApCSkkJ8fHy9ztmnTx++//57XnrpJV5++WVCQkL44YcfKlas6jKvEEIIIS6dxIJEYgIUcu1VnAqU6m/vKQo6Ly9su3e7ZHGZXUdq06ZN9T6mf//+5kx12apPHQohhBBCVM9gNDB371y+jP6SkYdKuXu5tmoidXbBxm/unAaXQLgkdaSutqRICCGEEJfeurh1zNo5i7SiNAD+am3JHRoDZ3sXV9B5eTVJHSmzEykhhBBCiItpXdw6pm6cisq/N8+6nlDRGSHZBSymPUGYNrDRK5vXhyRSQgghhGh2DEYDs3bOqpREAfSJMX2/PVTDprLfWHX7KrSaS59AnVOn8gdjx45l1qxZjTLhzJkzGTt2bKOcSwghhBBXpr3peytu551jU6oSdupcIqWQWpTK3vS9TRFehTolUl999RUrV65slAlXrlzJ4sWLG+VcQgghhLgyZRT9WwtKMaq0jzMyaqMRSwMkuUC8R9VxTUFu7QkhhBCi2fGwNWVKPY8aeWCtEfcz/77nUgg9j6nsbKtUjGsqdU6ktm7dirYJNnEJIYQQ4urT1bMrg087Mu7X7Crv2ZTBU78aWTTKha6eXZsgun/VuUWMqqqN9iWEEEIIURuNCg+sM9U4+G/NKAVQgQfWG9A0cVpRpxUpo9F44UFCCCGEEI2kaPcedJm5Nb6vATQZuRTt3oNdRM9LFld1cQghhBBCNCt1bTx8KRsUV0cSKSGEEEI0O3VtPHwpGxRXRxIpIYQQQjQ7tt27ofP2psYtUIqCztv7kjYoro4kUkIIIYRodhStFq/nnweomkydbVDs9cLzTdIW5nySSAkhhBCiWcrq1Zo5t1ZNVXReXvjNnXPJGxRXRwpyCiGEEKJZ2pe+jzN2Z8sd2Nng1ymZZAd/Ws1Z1+QrUedIIiWEEEKIZmlfxj46xJlu7OlbB+AUdJJYa2cUrRajUSXleC6F+aXYOVrh09oZjea/FacuPkmkhBBCCNEsRaVH8eDZRKq8hSsAep0dJ6PS2fzDcQpzSyvG2jlb0e/u1oSEe17SGM3eIxUcHExYWBhlZWWNGY8QQgghBLkluSRnnKJViul7va89ABnFHVi14FClJAqgMLeUVQsOcTIq/ZLGaXYilZ6ejpWVFZaWlo0ZjxBCCCEE+zP20y5RRWcEC19fjDZGjKqG+PQ+tR635cfjGI2Xrm+M2YlUSEgIubm5jRiKEEIIIYTJ+fujbHpGUJBjx86Cuygvt631uIKcUlKO516CCE3MTqTuu+8+Tp48yc6dOxszHiGEEEIIotKj6BCnku4expriGzgWO5Q9hXfX6djC/NILD2okZidSTz31FMOGDWP48OEsW7YMVW3i9stCCCGEuCKUG8s5kXQQB30YhzpMoKi0fumKnaPVRYqsKrOf2hs0aBCqqpKZmcnIkSNxcnKidevW2NnZVTteURTWr19vdqBCCCGEuDocyTpCy9OlnAy5s97H2ruYSiFcKmYnUhs3bqz0fW5uLrt27apxvKJc+toOQgghhLj87MvYR+fEEEqtXep97DV3tb6k9aTMTqQ2bNjQmHEIIYQQQgCm/VERWQ5k+tX9GHsXK66569LXkTI7kerfv39jxiHqwaDXcyRyNcU5Sdi4+NEuYghandRWFUIIcflTVZVjcVGMzKhbItWiYzldB/WUyuaibqJWL8Z3+3Q6kFXxWtpaN5J7v0r4kDFNGJkQQgjRcMmFyXgcS8c1Nw2rsmxKLZ2p/tk4I/aaLLwD8/BrO+QSR/mvRkmktm7dyqZNm0hKSqKkpIRFixZVvBcbG0tZWRlt2rRpjKmualGrFxO27UnTN+cl3R5qFh7bnmTHya04hA2XFSohhBCXrX3ppvpRCipd8r8h0v1xQKXSBx9GQOEaxy8ocX2yaQI9q0GftidOnOB///sfu3fvBkzLcYqiVEqk3nnnHRYsWMDGjRvp169fw6K9ihn0eny3TwfgvyuX577vlf4DrP1BVqiEEEJctqLSo+gWbyqp5Ol4kJ5237Kz8H+Vxthrsujr8AV21scJimi61ShoQB2ptLQ0+vfvz65du+jevTuvvfYarVq1qjLugQceQFVVfvnllwYFerU7ErkaL7KqJFHV8VCzCNv2JFGrF1/8wIQQQohGdCx2D0Fn2+Vl3TiVctUGAD+LfQxyep/bXF7if+6P0NJ6Bym9X23yOzBmJ1JvvfUWKSkpPPbYY+zYsYOXX34ZLy+vKuN69uyJg4MD27Zta1CgV7vinKQ6jz2XbPlsn45Br79IEQkhhBCNq7C8EMsDJwDQtgwi/M5HOaK9GYD2tn/TxmYLflbRZGpc2N/nw2Zx58XsNG7FihXY2dnx3nvvXbBGVMuWLUlISDB3KgHYuNTjGVBMyZQ3WUT+NpfW/Ufh4u6DojE7bxZCCCEuugMZB2gfZwDAsVcf8rOKKT5jiYIBb8sD7OryFrYeQbSLGIJ3M9kLbHYUSUlJtG/fHiurC5dht7KyIicnx9ypBNAuYghpa93wUOt2e++ciOgZED2DMlVHpsaNXJ0HRdZe6O28wdEXS1d/7Nxb4OITjKunHzoLy4t3EUIIIUQtzm00B7CNiCDuoOkJdW+LoyRZ+9HjtseaMrxqmZ1I2dvbk5GRUaex8fHxuLm5mTuVALQ6Hcm9X8Vj25MY1aobzmuShx1OFGKp6PFV0/AtT4PyQ3AGSK081qAqpCsu5Og8KLTyoszOGxx80Ln4Y+veAmevINx8WmBlXXvn7cYkNbOEEOLqcfTUTq7LNP1/2549iPsmHoBAqz3kuYU3YWQ1M/sTKTw8nL///puDBw/SqVOnGsdt2rSJ1NRURowYYe5U4qzwIWOIAny3T8frvDpS1TGqkK644fHSMcoMerJS48hLjaMwM57ynEQ4k4JlYQq2Jek46zNwV7PRKUY8ycZTnw36o1AIpFc9dzaOZGs9KLDypNTGC6ODDzpnf2zcAnD0CsTNJwg7B+cGX6/UzBJCiKuHwWjAsGe/6ZtWQah2jiQeNd3NCrLaTXzL55owupqZnUiNGzeO9evXM3bsWP744w98fHyqjDl58iRjx45FURQmTJjQoECFSfiQMRgG/I/oyNXk7/+dXmk/olJ5hcpoWhUlpfereOt0aHU6fALb4hPYtsbzGvR6MtOTyE6NpSAjnrLsBNT8FCwKk7EpScOpPAN3YxbWSjmu5ONqyIeik1AE1eV0+diSrXEn39KTEhsvDPY+aJ38sHILwNGzBa4+LXF0dqtx39aFamZFnf2zEEIIcWU4kXuCVqdKAHDufQ1JR3IwlBux16TjqouHsOuaNsAamJ1IjRo1il9//ZWff/6Z9u3bM2TIEOLjTUtwr7zyCocOHWLlypWUlZUxevRohg4d2mhBX+20Oh0d+t4EfW8iavU1VVao0hU3Uuq5aqPV6XD3DcTdN7DGMarRSG52OlkpsRRkxFKSlYial4ymIAWb4lQcyzNwNWTioBTjSBGOxngoiYcSIAf4z/MGxaolmRp38i08KLIx7dvSOPlh4eRL4I6XgeprZhnVs08kDvif3OYTQogrxPn7o+x79eLoIdPnWpDVHpI0Pvh7+TdleDVSVFVVzT1Yr9fz8ssvM2fOHEpLS/89qaKgqiqWlpZMnjyZN998E61W2ygBX07y8/NxcnIiLy8PR0fHizZPc9tHdCYvm+yUWPLT4yjOSsSQm4imIAXrolTsy9JxNWThQn6D54ke9K0poRRCCHHZm7F8Mnc+sxpVgTbbt/PN29EU5JRyk8vrZHh60mPKD5cslvp8fjfo01an0zFz5kyeeuopVq5cycGDB8nLy8Pe3p727dtz0003VXvLTzSuihWqZsLByRUHJ1do17XGMSVFBWSlxpGbGkdxVgL6nESUM8lYFqXiUXQCfzW1xmPPqU9tLSGEEM1bya49ABhaBZJboKMgpxSNUo6/5SHS/Kc1cXQ1a5RlC3d3d+6///7GOJW4Sljb2uPXsgN+LTtUeS9665/4r733gueob20tIYQQzVNGUQa+R0238px7X0PcIdOje74WB9EpZXh2uLYpw6uVVGgUzU67iCGk4Vaxab46hao1rbsNuHRBCSGEuGj2Zfy7P8q5zzUV9aNCrHeSjy2BbWu+w9HUGpxIlZaWsnjxYu699166d+9O27Zt6d69O/feey9fffUVJSUljRGnuIqcq5kFVEmmzu3os1NKiP5wBEUFeZc4OiGEEI3tSMwWvHPBqFHQhIaResr0sz3Qag+x1h3QNON91g1KpLZt20ZoaChjx47l+++/Z+/evRw/fpy9e/fy/fffM27cOEJDQ9m6dWtjxSuuEuFDxrC/z4dkKJULuaYpbmz3updS1YLwom0kzRlAZmp8E0UphBCiMRRGRgJQ1sqfxNhSVBVsLTNx0GZS6NWtiaOrndl7pKKjoxk0aBDFxcV4e3szfvx4QkND8fLyIj09nZiYGBYtWkRcXByDBw8mMjKSjh07Nmbs4gp3fs2s859I9NbpOLJzJF4rH6S1/jjJCwYQd/cPBNayuV0IIUTzVKIvwTk6EQCHXn04dbbsQYDFTtNrrfs2WWx1YXYi9dJLL1FcXMx9993HokWLsLCwqHbM+PHjWbp0Ka+88gq//vprg4IVVyGNQlGIJxlFCh62HhWFpdr1HESC618UfnMn/moK+d/fTPTgz+nQZ1gTByyEEKI+orOiCT3bqNij7w1sXGZKpDpYbcagKgSHNd+N5tCAOlJubm4YDAZSU1OxtraucVxJSQne3t5oNBqys7PNDvRydKnqSF2p1sWtY9bOWaQVpVW85mXrxbSe0xgYOBCAnIwU0haMoJ0+hjJVy4Fub9H91keaKmQhhBD19PW69+n2+OcYNQpOP6xn2bwYdBYGJrjexSldMK1e3nvJY6rP57fZe6TKyspo27ZtrUkUgLW1NW3btqW8vNzcqcRVaF3cOqZunFopiQJIL0pn6saprItbB4CLhw9BU9ex1/5aLBUD3fc+x/avpqEajU0RthBCiHrK227aR13Y2peEE4UAONoloFGMZLl0acLI6sbsRCo0NJTExMQ6jU1ISKBDh6r1goSojsFoYNbOWahUXSw999rbO9/GYDQtBVvb2tNlyu/s8P4fAL1j57Pro9GUl5VWOV4IIUTzoaoqtgdPAWAb0ZPYs2UPfDXbANAG9Wqy2OrK7ERq8uTJpKSkMHfu3FrHffjhh6SmpjJ58mRzpxJXmb3pe6usRJ1PRSW1KJW96f8u92q0Wno98gmRoS9gUBV65qwgZvYwzuRdXbeThRDicnI67zStT5t+6XXpNpTs5EIUBbpq1wDg16l/U4ZXJ2YnUvfeey9vv/02zz33HCNHjmTdunUkJSVRXl5OcnIy69ev5/bbb+fZZ5/lnXfeYdSoUY0Zt7iCZRRlmD0u4u7nONhvPkWqFZ1LdpMx93rSEk82dohCCCEaweH963E7A3qtQpY2AABHDyMO2jzSccU7oHUTR3hhdXpq70INh5ctW8ayZctqfP+5555j2rRp6PX6+kUnrk5K3YZ52HpU+3qXgfdw3M0Pl2WjaWmMJf3zgZy8/TtCOjX/JWIhhLiaZG/dRAiQ39qb7BhTM3s765NQBon2nfDUNP8GLHWKUFXVBn8ZZfOvuIByYzlfHPqCV7e+esGxVlorOrrVXJesdfi1lD2whjhNAJ5k4/XzbRzY+EtjhiuEEKKBLPYfA0DbtRtJx3IA8DJsAqDMt0eTxVUfdUqkjEZjo3wJUZN96fu4e8XdfLDnA0oMJYQ4hQCg1LA8VWoo5fG/H+dM2Zkaz+kb1BbnJzYSbRmGvVJM+w3j2fnLnIsRvhBCiHrKLckl6ITpZ7hV66EYyo04uFoRqjclUq7tmnf9qHOa/5qZuKLlleYxY/sM7v/rfo7nHMfZypk3+r7Bb8N/44PrPsDT1rPSeG9bbyaGTcTOwo6dqTsZu3osmcWZNZ7fycWd1k+tYZfTYHSKkZ4HX2X7Z5OlPIIQQjSx6L1rcC6Ecp1Cbrk3AK4B4KIUUKxaEtzx8tiOYXZl8+DgYBwdHdm1axeWlpaNGZO4Cqiqyl+n/+LtXW+TXWJ6sm5EqxFM7TYVZ2tnAAYGDuT6gOvZm76XjKIMPGw96OrZFa1Gy/UB1zNx3USOZB/hvpX3sWDQAgIdA6udy9LKmu6TfmDHF0/TK3ERvZO+ZPecBDo9uhQra9tLdclCCCHOk7p5Ha5AZit3EmJyAbBUjgBw2qot7S2tmi64ejB7RSo9PR0rKytJokS9JeQn8PDah3lu83Nkl2QT7BTMl0O+ZEbfGRVJ1DkaFdrHqfQ9rNI+TkVztrRUqFsoS4ctJcAhgKSCJO7/636iM6NrnFPRaOg1fja7Os+gXNXSPX8dJ2YPJi+7bk8ICiGEaFyaKNPP7PJO11CQU4rOQoNj0QYA8twvn96pZidSISEh5ObmNmIo4kpXbihn4YGFjPhjBNtTtmOpseTxLo/z8y0/0927e5Xx+WvWcGLAQOLHjCH56aeJHzOGEwMGkr/GVF8kwCGApTcupb1be7JLsnlw9YNsS9pWaww9Rk7iyIBFFKg2dCg7SO7H15F8+shFuV4hhBDVKzOU4Xt2c7nibdoL5d/OBZ+CKABsQvo0WWz1ZXYidd9993Hy5El27tzZmPGIK9Tu1N3csfwOPor6iFJDKb19evPb8N94OOxhLLVVVzXz16whadJk9KmplV7Xp6WRNGlyRTLlZuPGF0O+oLdPb4r1xTy2/jFWnFpRayydrh1Bxl3LSMONQGMiVosHc2zvpsa7WCGEELU6tmcdjkUqpRaQX+AGgEewBYFGU8eUoLDrmjC6+jE7kXrqqacYNmwYw4cPZ9myZZjZ+1hc4XJLcnll6ys8uPpBTuWdwtXalbf7vc2CQQto4dii2mNUg4G0t2ZCdX+nzr6W9tZMVIOpRYydhR3zBszjxuAb0at6nt/8PIujF9caV3CHCJQJ6zmpDcaNPPyX3cm+td827GKFEELUSdKmVQAkt/IlPdb05J5qOARAnMYfZ3fvJoutvszebD5o0CBUVSUzM5ORI0fi5ORE69atsbOzq3a8oiisX7/e7EDF5UVVVf44+Qfv736fnFLT8u2dbe5kUtdJOFk51Xps0e49VVai/nNy9KmpFO3eg11ETwAstBbM6jcLdxt3lh5eynu73yOjKIOp3aeiUar/fcHTLxjbSRs48MmddC7ZRactjxKZFUfEqOfNu2ghhBB1YtxzAICCttej5oKbnz3GlN8BSHPsTPWPDjVPZidSGzdurPR9bm4uu3btqnG8otSxXLW47J3OO83rO15nV6rp70Mr51a82vtVunh2qdPx+oy6bQD/7ziNouGZ7s/gYePB7D2zWXx4MVklWczoOwMLjUW157B3dCF06p/s/HQcPbOXE3FkFjvmx9HzoXloLlDRXwghRP0ZDQY8jqab/r9DV8iFwE5uOB0+2z81IKLpgjOD2YnUhg0bGjMOcQUoNZSy6OAiPj/4OeXGcqy11kzsMpHR7UfXmMhUR+dRfeuXuoxTFIUHOz6Iu407r2x9hRWnVpBTksPs62Zja1F9qQMLSyt6PL6E7UtfovfpefRK+469s5No/9h3WNva1zluIYQQF5a0byt2xSpFlhrOZNoBBvzbOeIRdQQU8Op4XVOHWC9mJ1L9+zf/jszi0olMieT1Ha8Tlx8HQD+/frwQ8QL+Dv71Ppdt925oHB0x5ufXPEijQevmWuPbt4Tcgou1C1M3TmVr8lbGrR7HvIHzcLWu/hhFo6H3mLfYvTyQzrtfoGvhPxyZPRDPh37B1dOv3tcghBCienEb/8QNONW2A2XFBqztLCgqiMZaKScXewJadWrqEOtFKpuLBskqzuL5zc8zfs144vLj8LDx4P3+7zNvwDyzkiiAgs2bMZ6pufULAEYj8fePoTi65tpR1/hdw6LBi3CxcuFQ1iHu/+t+Es8k1nra7rc8zImhX5OHHe30MRTNv4GEEwfNuQwhhBDVKNu1B4CCgL4AtOjgSs6xzQDE2XS47LZVNEoiVVhYyJo1a/j444+ZOXMmH3/8MWvWrKGwsLAxTi+aIaNq5Jdjv3Dr77ey4tQKFBTuaXcPy25bxuCgwWbviSs+FE3S1KdAVbHtFYHW25sc59akenYjx7k1Wm8ffN58E+v27TFkZxM/5gGKatmb18mjE0tuXIKvnS9x+XGM/ms0R7JrrxvVvveN5N6zkmTFE381Ffuvb+RI5BqzrkcIIcS/VIMB5yMpABh0bQEI6uSOZbLp53hRNTUFmzuzb+0BlJWV8eqrrzJv3rxqkyY7OzueeOIJXn31VamAfgU5kXOCGTtmEJVuKpzWzrUdr/R6hU4eDVuOLU9KImHiI6hFRdj16UPZxDfY9stJCnPLKsbYOVvSr2UbgpYMJnHioxTt2kX8+An4zZ2Dw3XXVXveIKcglg5bysR1EzmWc4wHVz3I3Ovn0tOnZ42xBLbtQubDf3Ps85G00R/DduW97Ml6l27DHmzQNQohxNUs5+BebEqMZDu6UppviaJR8A915syfppV/x9bXNHGE9Wf2ipTBYODWW2/lnXfeoaCgAD8/PwYMGMC9997LgAED8PPzo6CggFmzZnHrrbdiOFvzR1y+ivXFzN07lzuX30lUehQ2Ohue6f4M3930XYOTKEN+PvEPP4whIxOrNm0onfAqqxfFVEqiAApzy1i14BCxx4sI+Gwh9tdfj1paSuLjT5C3fHmN5/e09eSroV/R3as7BeUFPLLuEVbHrq41JnfvAPwnryPKtg9WSjnddk5mx9JXpOGxEEKYKfZv08/pE63CAfBu6UhuViyeZFOuamkZ1q8pwzOL2YnUggULWLNmDZ6envz444/ExcWxZs0ali5dypo1a4iLi+PHH3/E29ubtWvXsnDhwsaMW1xiW5K2MGLZCD4/+Dl6Vc8NATfwx21/cH+H+9FpGrSwiVpWRuITT1J24iQ6T0/8Pv2UrcsSao/nx+NgaYX/h3NxvPUW0OtJfuZZsr/+psZjHCwd+HTQpwwKHES5sZxnNj3Dd0e+q3UeW3snOk9dzg6POwHodXIuO+c9iL68rNbjhBBCVFV0thtKsXs3wHRbL+mgqbPEaYsQbOwcmiw2c5mdSC1ZsgRFUfjzzz+54447quyJURSFO+64g+XLl6OqKosX115pWjRPGUUZPLPpGSaum0hSQRJetl7MvX4uc2+Yi7ddwyvPqqpKyssvUxQZicbWloAFn5J5xprC3NJajyvIKSXleC6KhQW+s2bhct99AKS98QYZ8+bVWGnfSmvFu9e+y91t70ZF5a3It/hw74e1VubX6nT0euxzdrR5GqOqEJH1O9Gzb6bwTK7Z1y2EEFcbtbwch5hEDBpLDPoAwFQ/yhC7A4Bs1y5NGJ35zE6kYmJiCA0NpWvX2js0d+3alfbt23P48GFzp+KTTz4hODgYa2trunXrxubNm2scu2XLFvr27Yubmxs2Nja0a9eODz74oNKY8vJyZsyYQUhICNbW1oSFhbFq1apKY1577TUURan05e19+ZSsbyijauSHIz9w6++3sip2FRpFw+j2o1l22zJuaHFDo82T+dHH5C37A7Ra/ObOxTo0lML82pOoc86NUzQavF58AffHH684Z9pbM2u8BafVaHkx4kUe72Ia/9nBz3ht+2vojfpa5+t178vs7zOXEtWCsOJIUubcQGZyXF0vVQghrmqFhw5iWWog2bMNGDU4uFrj6mOHW84+ACyCejVtgGYy+56MwWDAwqJuRRYtLCwwmrmv5IcffmDy5Ml88skn9O3blwULFnDjjTdy+PBhWrSo2qvNzs6Oxx9/nM6dO2NnZ8eWLVt4+OGHsbOz46GHHgLgpZde4uuvv+azzz6jXbt2rF69mhEjRrBt2zbCw8MrztWhQwfWrVtX8b32Mnsk01xHs48yY/sMDmSaSvh3dOvIK71fIdQttFHnyf3lVzI/+QQA79dexb6faZOhnaNVnY4/f5yiKHg8/hhaJyfS3nyTnKVLMebn4fPGGyjV/D1VFIWHwx7G3cadGTtm8OvxX8kuzuad/u9go7Opcc7wIWM44uaP14oHaGU4SerCAcTe/T1BoZffkyZCCHEpJW76C4DYgM4ABHVyo6ggj2D9KVDAv/N1TRid+cxekQoJCeHQoUPExsbWOu706dMcOnSIkJAQs+aZPXs248aNY/z48YSGhjJnzhwCAgKYP39+tePDw8O555576NChA0FBQdx3330MGTKk0irW0qVLeeGFFxg2bBgtW7Zk4sSJDBkyhPfff7/SuXQ6Hd7e3hVfHnWsuH25Kiov4r1d73H3irs5kHkAOws7nu/5PF8P+7rRk6jCbdtIefVVANwefhiXO++seM+ntTN2zrUnU9Z2Fvi0dq7yuuvo+/B9523Qaslb9geJkyZjLK15hev2NrfzwXUfYKW1YmPiRh5a8xB5pXm1zt2u+wCK7l9FguKLNxm4/nALh7b8UesxQghxtcvfsR0VKHcwPZwU2Mmd0/v/QauopOKBl795eUJTMzuRuvPOOzEYDAwfPpwDBw5UO2b//v3cdtttGI1G7rrrrnrPUVZWxp49exg8eHCl1wcPHsy2bdvqdI6oqCi2bdtWqRJ7aWkp1tbWlcbZ2NiwZcuWSq8dP34cX19fgoODGTVqFKdOnap1rtLSUvLz8yt9XS42JmzktmW3sfjwYgyqgcGBg/njtj+4N/RetJrGXYkrOXqMxCcngV6P48034zF5UqX3NRqF3iNq/wdVUljO8V1p1b7ndOut+H/0EYqVFQV//03ChIcwFBTUeK4bWtzAZ4M/w8HSgX0Z+7j/r/tJLaylaTLg17IDDo9tIMaiPY4U0WbtA+z6fV6txwghxNVKLSvD+nAsBXZ+YHREZ6HBr40zZ45vBSDJ4fKqZn4+sxOpqVOn0rFjRw4ePEh4eDj9+/dn4sSJvPrqq0ycOJH+/fvTtWtXDh48SKdOnZg6dWq958jMzMRgMODl5VXpdS8vL1JTa/+g8/f3x8rKiu7du/PYY48xfvz4iveGDBnC7NmzOX78OEajkbVr17Js2TJSUlIqxkRERLBkyRJWr17NZ599RmpqKn369CErK6vGOWfOnImTk1PFV0BAQL2v+VJLLUxl8obJPPH3E6QUpuBn78e8AfN4/7r38bT1bPT5ytPSSHj4YYwFBdj26IHPW29WW7wz7bQpCVX+8zfU3sUKv7bOAKz76jDRm5OqncfhhusJ+GwhGjs7inbuJH7MA+izs2uMK9wznCVDl+Bl68WpvFPct/I+TuScqPVanN29CZ66jj3212GpGOix7wW2f/GslEcQQoj/KD54EIsyA8neHQHwD3VFZ6nFLs1U5VzvV3Ndv+ZOUWt7XOkCMjMzeeSRR/jtt98qnnpSFKXS/x85ciTz58/H3d293udPTk7Gz8+Pbdu20bt374rX33zzTZYuXcqRIzVXqD59+jQFBQXs2LGDadOm8fHHH3PPPfcAkJGRwYQJE1i+fDmKohASEsLAgQP58ssvKSoqqvZ8hYWFhISE8Oyzz9aYFJaWllJ63m2k/Px8AgICyMvLw9HRsd7XfzEZjAa+O/IdH0V9RJG+CJ2i4/4O9/NI2CO17hFq0JwFhcTddx+lR45g2bIlQd99i9bJqcq45BO5/PaeqQv4LU+EodVpKMwvxc7RCp/WzijA5h+Pc3Cjqd3LNXe2JmxA9UlrcXS0aUUqOxvL4GBafLEICx+fGmNMLUzl4bUPcyrvFA6WDswbMI9wz/Aax4Opk3nk55PpnbIEgF3ONxI28SssraxrPU4IIa4WcXPfo2j+Ijb2mYrRMoT+97alfV9vCl73x5EiToz4k1ZhzacYZ35+Pk5OTnX6/G5QASB3d3d+/vlnTpw4wdq1azl27BgFBQXY29vTpk0bBg8ebPbeqHPn12q1VVaf0tPTq6xS/VdwcDAAnTp1Ii0tjddee60ikfLw8OD333+npKSErKwsfH19mTZtWsUx1bGzs6NTp04cP368xjFWVlZYWdVto3RTis6KZvq26cRkxwAQ5hHGK71foY1Lm4s2p1peTtKUKZQeOYLWzY2AhQuqTaL05QY2LDUlyKF9fWjRwa3a8/W7uzU6Sw1Ra+LZ8tNxyssMdL8xqMo4mw4dCPz6a+LHjaPs9Gli7/0fLRYtwqpl9f+tve28WXLjEh5b/xj7M/YzYc0E3r32Xa5vcX2N16bRaun98EdE/hRI90Nv0CP3Lw7OHkrgxF9wdK4+fiGEuJrkbPsHxcIOg2VLFCCwoxtxR/cSTBFFqhVB7S/fFamGVVI8q1WrVrRq1aoxTlWJpaUl3bp1Y+3atYwYMaLi9bVr1zJ8+PA6n0dV1UorRedYW1vj5+dHeXk5v/zyS637uEpLS4mJiaFfv6avumowGtibvpeMogw8bD3o6tm1TvuYCsoK+Hjfx3x35DuMqhEHSwemdJvC7a1vR/Pfe2iNSFVVUme8TuHmzSjW1gR8Oh9L/+obGu9aEUtuWhG2Tpb0vb3mv1OKYtpHZWGlZefy00QuO4W+zEDErS2r3Cq0ahlM0LffED9uPGWnThH3v/8R8Pln2HToUO25nayc+GzwZzyz6Rk2JW5i8sbJvNLrFW5vc3ut1xlx59Psd2tB602P06k0itMfXk/Rg7/iHdD4/zaEEOJyYSwtRXf4FBmuXVFQcPO3x8HVmsPr/yEYOGUdSkeLy7eNXKMkUhfT1KlTGT16NN27d6d3794sXLiQ+Ph4HnnkEQCef/55kpKSWLLEdFtl3rx5tGjRgnbt2gGmulLvvfceTzzxRMU5IyMjSUpKokuXLiQlJfHaa69hNBp59tlnK8Y8/fTT3HLLLbRo0YL09HTeeOMN8vPzGTNmzCW8+qrWxa1j1s5ZpBX9u9Hay9aLaT2nMTBwYLXHqKrK+vj1zIycSXpxOgDDgofxTI9ncLep/y3X+spa+Bm5P/0EioLf++9h06n6TYUZ8WeIWhsPQP972mJlW3t5DUVR6HFTMDoLLdt+PcGev+LQlxrpe2erKsmUhY8PgV8vJWHCQ5RERxN//xj853+CXc/qfwuy0dkw5/o5zNg+g99O/MZr218jsziThzo/VGtD5rAb7uKEmy9Ov91HsDGOjEUDOTHiG1qF9a31WoQQ4kpVvH8/2nIDyZ6m/VFBHU0r9ZpEU5XzMx6116Ns7sxehkhOTuaPP/7g0KFDlV5XVZXZs2cTGhqKk5MTN9xwA/v37zc7wLvvvps5c+YwY8YMunTpwj///MPKlSsJDAwEICUlhfj4+IrxRqOR559/ni5dutC9e3c++ugjZs2axYwZMyrGlJSU8NJLL9G+fXtGjBiBn58fW7ZswdnZuWJMYmIi99xzD23btmXkyJFYWlqyY8eOinmbwrq4dUzdOLVSEgWQVpTGlI1TeGfnO+xK3YXB+G9fw+SCZJ74+wmmbJxCenE6AQ4BLBi0gLevffuSJFF5y1eQcbYgqtcLL+AwYEC14wwGI38vjUE1qrTq7knLLnUvNRE+uAXXjjLdltz/dwKbvj2Kaqy69U/n6kqLxV9h27MnxsJCEsZP4MzfG2o8r06jY3qf6UzoNAGAj/d9zJuRb1b6861Oq7BrMIxdS6ymBR7k4PPrSPZv+KnO1yOEEFeS/O3bMCoacl3aA6ayBwA++abcwDakT5PF1hjM3mz+3HPP8d577/Hdd99VuiX2/vvv8+yzz1ZqueHi4kJMTAyeno3/FFhzVp/NahdiMBoY8suQKklUdbxsvXi6x9OkFKQwf/98ivXF6DQ6xnYcy4ROE7DWXZpN0EW7dhE/dhxqeTmuY8bg9fy0GsfuXhlL5B+nsLaz4J5XI7B1rP8yb8y2ZDYsPYKqQtsIb264vx0abdXfFYylpSRNmUrB33+DVovvzLdwuvXWWs/9bcy3zNo5CxWVQYGDmNlvJlba2vfD5edmET9/JB1L96FXNezp+BIRdz5V7+sSQojL2aG7R5AfqycqfArWdhY8+O415GQk4TbftL0ib9JJnFwu/i/29VGfz2+zV6TWr1+PpaUlt912W8VrBoOBd955B41Gw6effsq+ffu49957ycnJYc6cOeZOJYC96XvrlESBaYXqmU3PMHvPbIr1xXTz6sYvt/zCE+FPXLIkqvTUKRIef8LUW2nQIDyfe7bGsdkphexaeRqAa+5qbVYSBRDax5dBYzugaBSORqayZlE0Bn3VUgQaK1OzY6fhw8FgIPnZ58hesrTWc98bei/v9n8XC40Fa+PW8sjaRzhTdqbWYxyd3WgzdTW7nIaiU4xERM9g+8InMBpqX9ESQogrhbGkBCX6OFlupqSpRUdXNBqF+P0bAYjVtGh2SVR9mZ1IJSUl4efnh6Xlvx96O3bsICMjg5tuuomHHnqIzp07s2DBAmxtbfnrr78aJeCrVUZRRr2PUVB4rfdrfDnkS1o6t7wIUVVPn5lJwoSHMOblYRMWhu+776Boqv+rZjSqbFgag1GvEtjJjTY9a38a80Ja9/Bi6EMd0egUTu7N4K8FB9GXV01cFJ0On5lv4XL/aADS3nqLjI8+rrV58ZCgIXw68FPsLOzYnbabB1Y9QHpReq3xWFpZ033Sd2xvYWpP1Dt5CVFz7qC0xFRmw6DXE731T3avWEj01j8x6Gvv9yeEEJeT4qgoNHoDaR6mvbFBHU1JU+kpU1HtdOewJoutsZidSGVnZ1epDbV582YUReHmm2+ueM3Ozo7WrVsTFyfNXRvCw7b+7WlUVFo4tqh1c3RjMxYVkTDxUcqTkrBo0QL/+Z+gsa55FezghkRST+VjYa2l/z1tGyXWll08uGliZ7QWGuIOZvHnvAOUl1aTTGk0eD3/PO5Pmh5EyJw3j7Q336q1oGZPn558NfQr3G3cOZZzjNErRxObF1trPIpGQ++x77Kry5uUq1q6nfmbk+8PYtfvH5P5Rhs6rL2X7rufocPae8l8ow1Rqxc36PqFEKK5KNwRSbG1K6U2PqCBgPauADhlRQGgtLg8GxWfz+xEytbWlrS0yreaNm7cCMC1115b6XULCwvKy8vNnUoAXT274mXrhUL9Eg1zVrLMpRoMJD3zLCUHD6J1ciJgwafoXF1rHJ+fWcyOZScB6DOyFQ6ujXfbsUUHN255IgwLKy2JR3JY/tE+yoqrrvYoioLHo4/i9fJLAOR8/TXJ06ah1vL3tZ1rO5beuJRAx0CSC5O5/6/7OZhx8IIx9bjtcY4O/JIzqg3tyw/RPepFPNTKlfI91CzCtj0pyZQQ4oqQs30zWa6mp/W8WzpibWdBaUkRLctMNRm9O/av7fDLgtmJVKdOnYiPj2fHjh0AJCQksGHDBvz8/GjTpnJhx7i4uAsW0BS102q0TOtp2qxdn2TKnJUsc6XNepuC9etRLC3xn/8JVrUUOFVVlQ1fH0FfZsSvjTMdrvFt9Hj82rhw66QuWNroSDmRx7I5UZQUVp8guf7vf/i++y7odOT/sZzEJ57EWFJS47n9HfxZcuMSOrh1IKc0h3FrxrElaUuN48/p2G846Xf8hkHVoCig+c9/ynPf+2yfLrf5hBCXNWNhIYboI2S6mRKp4E6mz6PTB7dhpZSTjSP+Lauv53c5MTuRGj9+PKqqMmzYMO644w769OmDXq+v1NMOICYmhoyMDDp27NjgYK92AwMHMvu62XXqgaeg4G3rTVfPS1OfI3vxYnKWmjZs+77zNrZda583ZlsKiUdy0FlouO6+dij/zSgaiXdLJ26bEo61nQXpcWf4fXYURfll1Y51uuVm/D8+2+x440YSxk/AcKbmDeWu1q58MeQL+vr2pVhfzBPrn2D5yeUXjKkkPxutUvPtQ40C3mRxJHL1hS9QCCGaqaK9URhVHTlnu2YEnS17kHvU9EtnnG3HGvfPXk7MvoL777+fqVOnkp+fz6+//kpSUhJ33HEH06ZVfsT9yy+/BGDQoEENi1QApmRq9e2r+WLIF4wONW2U/u8K1bnvn+v5XJ0qnjdU/po1pM16GwDPZ57GcejQWscX5pay9WdTQ+Cet7bE2dP2osbn0cKB254Kx9bRkqykAn6fvZeCnKqV7gEcrruOFos+R2NvT9Hu3cSNGYO+lkbVtha2fHTDR9zc8mb0qp4XtrzAV4e+qjWe4pzqGy2bO04IIZqjop2R5Li0QdVYYOEELj6mn/WWKbsBKPXu3pThNZoGpYLvvfceqampbN++ncTERH744YdKT/EBDB06lN9++62iz51oOK1GSw/vHjzb81k+uO6DKitUXrZezL5udo2VzhtT8b59JD/zLKgqzveMwnXs2FrHq6rKpu+OUlasxzPIscZmw43NzdeeEU91xd7FipzUIn57fw/5mcXVjrXt3p3AJYvRurpSejiGuP/dR3lyco3nttBa8OY1bzKmvanq/ft73ufdXe9iVKtfdbJx8atTzHUdJ4QQzVH+9m1kup19Wq+TO4qioBqNtCg07Sl1att8mhQ3hNkFOcWFNWZBztqY23uvocri44m9exSGnBzs+/fHf97HKLrauw4d353Gms+j0WgV7nqhB25+9hc9zvPlZxWz7IMo8jNLsHexYvjkcJy9ql8RKz19mvhx49Anp6Dz9qbFF4uwall7GYnF0Yt5b/d7gKkNzxt938BCW7nVjUGvJ/ONNnioWVX2SAEYVUhX3PB46RjaC/x5CiFEc2QoKOBIzwi295xBqbULNz8RRmAHN5JOxeC3pBdlqhbjtASsbeyaOtRqXZKCnOcrLS1l27Zt/PTTTxU978Slc26FaljLYfTw7nFJkih9Tg4JEx7CkJODdfv2+M1+/4JJVHFBGZt/OAZAtxuDLnkSBeDoZsOIp7rh4m1LQU4pv72/l6zkgmrHWgUHE/Ttt1i2bIk+NZW4/91H8cFD1Y49Z0yHMbx1zVvoFB0rT6/k8b8fp7C8sNIYrU5Hcu9XAVPS9F8KENfuIUmihBCXraLduym08aHU2gWj1oBfG2cAUg5tBOC0Retmm0TVV4MSqdLSUp577jk8PT3p168fo0aN4sEHH6w0Zty4cfj6+nL06NEGBSqaD2NpKYmPPU5ZXBw6Xx/8P52Pxu7C/yC2/Hic4jPluPra0W1o0/UstHex4rapXXHzt6cov4zf348iI776TeUW3t4EfvM11h07YsjJIX7MGAp3RNZ6/ltCbuHjAR9jo7NhW/I2xq4eS1Zx5X1W4UPGsL/Ph2QobpVeL1e1KAq0OPI5aYknG3ahQgjRRIoid5J19mk9m0ADOgvTL/iGONOT/jlu4U0WW2MzO5EqKytj8ODBvPfee6iqynXXXVelQCfAyJEjSU1N5eeff25QoKJ5UI1GkqdNo3jvXjQODrRYsACLOvRQjD2YybGdaSgK3HB/KFpd0z6pYetoyW1TwvEMdKCksJzfP4gi9VRetWN1Li60+OorbHv1MhUcfeghzqxfX+v5+/r15YshX+Bi5cLhrMPc/9f9JJxJqDQmfMgY3F86RvSgb9nd/V2iB31LzoQ9JCi++JBByRe3kp0uG86FEJefwsgdFWUPWoV5V7zukbMPAKuWvZsirIvC7E+zDz/8kM2bN3PNNddw7Ngx1q9fX6V+FJie1rO0tGTNmjUNClQ0DxmzZ3Pmr1VgYYH/Rx9h1br1BY8pK9az8RvTimTYwBZ4BV28/WL1YW1nwfDJ4fi0cqKsWM+yuftIOppT7VitvR0BCz7FfuAA1LIyEp+cRO7vv9d6/o7uHVly4xL87P2IPxPP6JWjicmKqXxenY4OfW+i+80P0aHvTXj6B2Px4B+k4k6gMZHsBbeQn1vzU4NCCNHcGPLyyD+eQL5jEABde5hygzN52QQZTF1OAjpd10TRNT6zE6lvvvkGCwsLvvvuO7y9vWscZ2lpSatWraRFzBUg5/vvyfp8EQC+b7yOXa+IOh237dcTFOaW4uRhQ89bai7S2RQsbXTc8kQXAkJd0JcaWP7xfuKiq09cNFZW+M+Zg9OIEWAwkDLtebIvsCcwyCmIpTcupa1LW7JKsnhw9YNEptR+a9C7RWvK7v2FbBxpZThJ4rxbKS6svUGyEEI0F0W7d5Pt2h4UDcVOuTi6mh7oid23EY2ikqx44e7bdNs7GpvZidSxY8do3bo1vr4Xrkjt4OBQpZ2MuLyc2biR1BmvA+D+5BM4DR9ep+OSjuYQvdlUOuD60e2wsLz4G+Hry8JKy7BHOxPU2R1DuZGVnxzg1L7qW+soOh0+b76B6xhTqYO0t2aS8eGHtTY79rD14MuhX9LDuweF5YU8su4RVp1eBZieuNyVuouVp1ayK3UXBqOpJ2CLNl3IHvF9RTuZYx+NoKy05krrQgjRXBRGRpLlZqpYbtfq35+NBSdMjYqTHTo3SVwXi9mJlE6nq3P/vKysLOzqsBlZNE/Fh6JJmvoUGI043T4S94kT63RceZmBv78+AkCHa/3wa+NyMcNsEJ2FlqEPdySkqydGg8qqhYc4tiu12rGKRoPntOfwmDwJgMxP5pP2+hu1Njt2sHTg04GfMjhwMHqjnmf/eZZXtr7CkF+GMHb1WJ7b/BxjV49lyC9DWBe3DoBWYX1JHLaYYtWSsJJdHPzobmkbI4Ro9goid5Pt0h6Atl3+rYdnn74HAIN/zyaJ62IxO5Fq06YNsbGxZGTU3hT35MmTnDhxgk6dOpk7lWhC5UlJJEx8BLWoCLs+ffB57TUUpW7tXHb+cYr8jGLsXazoMyLkIkfacFqthsHj2tO2lzeqUWXtF4c5vLX6QpyKouD+yCN4v/oKKAo5335L8rPP1drs2FJryTvXvsOotqNQUfntxG+kFVVeqU0vSmfqxqkVyVRoxBCOXzefMlVLt4KN7Jk3ptaETQghmpI+J4f01HL0FraUaAuICDOtPhn0eoJLTHtE3UOvbcoQG53ZidQdd9xBeXk5U6ZMwVjDD/aysjImTpyIoiiMGjXK7CBF0zDk5xP/8MMYMjKxatMGvw/nolhYXPhAIO10PvvXm55S639vWyxtLo+aSBqthgH3h9LhWj9QYcPSIxzYkFjjeJd77vm32fGKFSQ+/gTG4uorpoOp5tdzPZ7D3qL6GloqpmXwt3e+XXGbr/P1d3Aw4n0MqkLPnBVELnxckikhRLNUtHNXRdmDPO9k7CzP7o+K2Y29UkyBakNQ6JXRGuYcsxOpJ598ktDQUL777jv69u3Lp59+Sl6e6fHxDRs28OGHHxIeHs66desIDw9n7AVah4jmRS0rI/GJJyk7cRKdpycBCxegta9bAU2D3sjfS2NQVWgT4VXRqPJyoWgU+t/ThrCBpvY1m384xt7VNT8s4XTzTQTM+xjF2pqCTZuIHz8BQ35+jeOjMqIoKK++CCiYkqnUolT2pu+teK3bsAfZGzYdgF6p3xC55KX6XpYQQlx0RZGRFYmUU+t/98RmHt4EwGnr0Cuu2LDZiZSNjQ1r166lV69eREZG8thjjxEdHQ3AwIEDmTJlCjExMfTq1YsVK1ZgUceVDNH0VFUl5eWXKYqMRGNrS8CCT7Go5cnM/9rzVyzZyYXYOFhwzZ0XLo/QHCmKQt/bW9F9WBAA2387yc7lp2rcVG7fv7+p2bGDA8V79hA35gH0mZnVjs0oqv12eE3jeoycxI7WTwHQK3YekT/MquPVCCHEpZGxJ4ZCOx9UDLTr8m8vVW3STgAKPLs1VWgXTYOqIvr6+rJlyxb++OMPxowZQ9euXQkJCSEsLIx77rmHn3/+ma1bt9ZaHkE0P5kffUzesj9Aq8Vv7lysQ0PrfGxWUgF7/jKt3vS7uw029pYXOKL5UhSFiFtb0us2U3+9XX/Gsu3XkzUmU7bduhG4dAlaNzdKY842O06qWlDTw9ajTvNXN67X/15he8B4ACJiZrL7j/l1vRwhhLio9JmZpBaY6gSm252iW0CXivd8z5gaFdu37tsUoV1UDV5fUxSFm2++mZtvvrkx4hFNLPeXX8n85BMAvF97Fft+de/ObTQY+XtJDEajSnCYO626Xbji+eWg29AgdJZatvx4nH1r49GXGbj27jYo1XQctm7XjqBvviZ+7DjK4uKIvfd/tFj0OVatWlWM6erZFS9bL9KL0iv2RP2Xl60XXT27VvterwffZcf8PHpl/ESXPS8QZe1A+OD7GudihRDCTEW7dpHpaip7kOmTgLedaRElMzkOXzUNo6oQFNa/KUO8KJq2T4doVgq3bSPlVVMzXbeHH8blzjvrdfz+9Ymkx53B0kZH/3va1vnpvstB2A0BXH9fO1Dg0KYk/l5qShirYxkUROB332LZKgR9Whpx942m+ODBive1Gi3Tek4DQKH6P6OWTi3RKNX/81Q0Gno+soBdTkPRKUY6bJ3Eoc3LGniFQgjRMHnbd5HrYqpi7tL23+088Qc2ABCrDcTBybVJYruYGpRInT59mh9//JGPP/6YmTNnMm/ePH766SepYn4ZKjl6jMQnJ4Fej+PNN1fUSKqr3LQiIpefAqDvHa2wc7a6GGE2qfbX+DLwgfYoGoUj21NZ+0U0BkP1T89ZeHkRuHQp1p07Y8jNJX7MAxTu2FHx/sDAgcy+bjaetpVX7ZysnFBQ2J6ynVk7Z9V4G1Gj1RL++FKibPtiqehpuW4CR3f/3XgXK4QQ9ZR4KAOjxoJSTSbtQ/7dH1t22vSzL8OlSxNFdnGZdWtv6dKlzJw5k6NHj9Y4pn379rzwwgvcc889ZgcnLo3ytDQSHn4YY0EBtj164PPWm/VaTVKNKhu+PoKh3Ih/OxdC+/hcxGibVtsIb3SWGtZ8Hs2J3ekYyo0MGd8RrUXV30l0Li60+OILEp94nKLtO0iY8BB+H8zGYeBAwJRMXR9wPXvT95JRlIGHrQddPbuy4tQKXt76Mt8e+RadRsfT3Z+u9r+HzsKS9k/+zMEPhtGpNArvFfdx2nYZwe17XPQ/ByGEOF95WjqpetO+ztOu0dzo9W/JI5cs0xPI2sBeTRLbxVavFSmj0ch9993HAw88wJEjR1BVFVdXV8LCwujTpw+dO3fGxcUFVVWJjo6uGFtb+wzRtAwFhSQ8/Aj61FQsW7bE/+OP0FjWb4N49JZkko/norPUcP197a6oW3rVCQn35MZHOqG10HB6fyZ/zj9AeZmh2rGmZscLcBg0CLW83NTs+Jdf/31fo6WHdw+GtRxGD+8eaDVahrcaziu9XwFgyeElfBhVcwsaK2tbWj7+O0d17XCiEIcf7yDpVHTjX7QQQtSiMHJnRVuYBK+TtHYxrUiVFBUQXH4CAJ+O1zVVeBdVvRKpGTNm8O2336LRaJg0aRIxMTFkZGSwd+9eNm/eTFRUFJmZmURHR/P444+j0WhYunQpb7755sWKXzSAWl5O0pQplB45gtbNzVQrysmpXuc4k13Ctl9N/0h63RaCo7vNxQi12Qnq5M7Nj3VGZ6Ul4XA2Kz7aT1lJ9e1bNJaW+H0wG6fbR4LRSMqLL5L11VcAqAYDhZE7yVvxJ4WRO1ENpoTsjjZ38ELECwB8fvBzPt3/aY2x2Dk44/3oCk5rgnAnF2XpbaQnnW7cCxZCiFqkbI+m1MoFVS3Fo5UtOo3phtfpg1uxVAxk4oxvUNsmjvLiUNQ6LhdlZmbi7++P0WhkxYoVDB48+ILHrFq1iltuuQWdTkdiYiJubm4NDvhykp+fj5OTE3l5eTg6OjZ1OJWoqkrqK6+S+9NPKNbWBC5dgk092/ioqsqKjw8QH52Fd0snRjzdFU01T7JdyVJO5rHio32UlRjwCnbk5sfDsLarvmaaqqqkv/Mu2V9+CYD94EGUHDiAPvXfNjE6b2+8Xngex7P/vpZEL+Hd3e8CMKnrJMZ3Gl9jLJmp8ZQsGIy/mkKcJgDHiWtx8bhyb7MKIZqP1Xe9xgnXa8mxPIDzgzoeD38cgO1LXqb3qQ/Za9ePrs+saOIo664+n991XpFaunQpZWVlTJo0qU5JFMDQoUN58sknKSsr45tvvqnrVOISyFr4Gbk//QSKgt/779U7iQI4tjON+OgsNDqF60e3u+qSKACfECeGTwnHyk5H2ul8ls2JovhMWbVjFUXB89ln8JgyBYCCNWsrJVEA+rQ0kiZNJn/NGgDu73A/k7tOBmDu3rksjl5cYyzu3i3QPrCMNNwINCaQ+enNnMnLboSrFEKImpWnpJCu9Qcgxvsw4Z7hFe9Zp+4GoMznymoLc746J1JbtmxBURQmTpxYrwkeffRRVFXln3/+qXdw4uLIW76CjA8+AMDrhRdwGDCg3ucoyi9j84/HAOhxUzCuPnaNGuPlxDPQkRFTu2LjaElmQgG/zY6iMK+02rGKouA2fhyamn7DObtAnPbWzIrbfOM6jeOxLo8B8N7u9/gmpuZfSnwC21Jyz6/k4EhrwwkS5t1KSVHN7WiEEKKhsjbvJN8xCICT7jF09jA1KlaNRoKKDgHg3K5fU4V30dU5kTpw4ACenp60bNmyXhOEhITg6enJgQMH6h2caHxFu3aR8oJp743rmDG4jjavkOPmH45RWqjHPcCe8MEtGjPEy5Kbnz0jpoZj52xFTkohv723lzPZJdWOLdq9B2MtvfhQVfSpqRTt3lPx0iNhjzCh0wQAZu2cxY9Hf6zx8MC2Xcga8R1nVBvalx3k6EcjKS+rPrETQoiGit0RB4oGgzEBX28PHCwdAEg8eRAX8ilVLQju2LuJo7x46pxIZWdn4+Nj3n4LX19fsrKyzDpWNJ7SU6dIePwJ1PJyHAYNwvO5Z806z6l9GZzYk46iUbhhdChardR1BXDxtmPk011xcLMmL6OYX9/bQ256UZVx+oy69dr777gnwp/gwQ4PAvD6jtf57fhvNR7bKuwaEm78ihLVgrDiSA58NAqDvvrN8EII0RBJaabPgBSHaLp4dKl4PeWQ6U7UKcvWWFnbNkVol0SdPwHz8/Oxt7c3axI7Ozvya/sNXFx0+sxMEiY8hDEvD5uwMHzffQdFU/8EqKSwnE3fmuqHhQ9ugUcLh8YO9bLm6G7DyKe74uxlS0F2Kb+9v5fs5MJKY3Qedeu1999xiqIwpdsU7gs1rSK+uu1Vlp9cXuPx7XsN5eh18ylXtXQ78ze7549FNVZfQFQIIcxREpdApk0QAPv8K++PIiESgFz36ttdXSnq/ElqMFRfJ6eujPIDvMkYi4pImPgo5UlJWLRogf/8T9BYW5t1rm2/nKAovwxnL1t63BTUuIFeIexdrBnxVFdcfe0oyivjt9l7yUg4U/G+bfdu6Ly9oZZ6WzpvL2y7V+2SrigKz/Z4lrvb3o2KyktbX2JV7KoazxN2/Z0ciHgXo6oQkbWMHZ/Vr2K9EELUJm7NXvQ6WzSGMyS5xFdakfLM3QeAdfCVe1sP6lnZvLS0lPj4+HpPUloq+zOaimowkPT0M5QcPIjWyYmABZ+iczWv11HC4WxitqWAAjeMbofOQtvI0V45bB0tGTG1K398uI+M+DMs+yCKm58IwzvYCUWrxeuF50maNNmUTFVTgUTn7gEGA2ir/hkrisILES9Qbizn1+O/Mu2faVgoFgwIrP6hgW7DxrGzOJ+eB1+jd8oSti92pPcYqe0mhGi42IOZgAPFHMbVxhV/B9PTe3nZGQQZEwBoEXZd0wV4CdTr3s7u3bsJDg6u99eePXsufHLR6FRVJW3mLAr+/hvF0hL/+Z9gFRxs1rnKSvRs+PoIAJ2u88enlXMjRnplsra3YPiUcLxbOlFapOePOftIPp4DgOPgwfjNnYPOy6vSMVpXF9DpKDl0iMTJU1DLqi+loFE0vNr7VW5peQsG1cDT/zzNpoRNNcbS8/Yp7GhlKrvQ+/THRP74TiNdpRDiaqWqKilnTFt+Trsdpotnl4rOFrH7NwKQoPji5uXfVCFeEvVKpFRVNftLNJ6aqmH/V/bixeR8/TUAvu+8jW1X8+9TRy47xZnsEhxcrek1vH5Pbl7NrGx03PJkGH5tXSgvNbD8w/0kHDbVdnIcPJhW69fRYvFifN97jxaLF9N682YCPv0UxcqKgr//JnHK1FqTqdf7vs6NQTeiN+qZsnEKW5O21hhLr/teY7v/WAB6RL/F7j9qrpYuhBAXkrX/BIVWHqAa2BV0pNL+qKITpp9FqY6dmyq8S6bOt/ZOn5aWE81B/po1pL01E31qasVr/62GDZC/eg3pb5tWHTyfeRrHoUPNnjPlRC4HNiYCcN19bbG0NqvX9VXL0lrHzY91ZtXCQ8QdymLFJ/sZ+lAngju7oyoacp1bU6hpgZ2jFTaKBvtr+uI/bx6Jjz5Kwfr1JE6Ziv8Hs1Gq6YGo1Wh5s9+blBvLWRe/jkkbJjFvwDwifCKqjaXX2PeJ/CSPiMxf6LLnefbZOtFloDQWF0LU34n1MYA9liWnKbQpIcwjrOI9xwxTo2Kjf88miu7SqXOLGFF/jd0iJn/NGtO+mhr+k7mMuR+HGwaATkvC2HGopaU43zMK71deMbuRsL7cwA9v7CI3rYh2fXwYcH9oA67g6mbQG1mzKJpTURloNAqdrvfnxJ50CnP/3UNo52xFv7tbExLuScGWrSQ++ihqWRn2AwfgP7v6ZAqg3FDO1I1T2Zi4ERudDfMHzqebV9XN6gBGg4E9H46iR94aSlULjg/+ko59b7ko1yyEuHL99Oj3pBs9KTT8xg/XbmP7vdux1FqiLy+j7A1/bJVSYu9eT1Do5VfV/KK0iBFNSzUYSHtrZo1JFEDO4iXEjxlD/H2jUUtLse/fH+8XXzQ7iQLY/WcsuWlF2Dpa0vf2VmafR4BWp2HI+A606emF0aiyf31CpSQKoDC3lFULDnEyKt20MvXJJyiWlhSsW0/i1Jpv81loLXj/uvfp69eXYn0xj657lH3p+6odq9FqCX/8G6Js+2CllBO8ZjzH9ta8v0oIIf6rrLicDL3pwaXDPofp4N4BS63pF73T0ZHYKqXkY0uLNuG1neaKIInUZaJo955Kt/NqdTbZcrz5JhSd+bfhMuLPsHeN6SnN/ve0rbEZr6g7jVbD9feHorOs/Z/elh+PYzSqFbf5ziVTSU89VWMyZam1ZM51c4jwiaBIX8TEdRM5lHmo2rE6C0tCn/iZQ1ZdsFNK8PzjXmJjdjf4+oQQV4fYTUdQNTqsSzI50CKNLp5dKt7LitlsGmPdAU01Tx5faSSRukzUtRr2+dLfn13jRvQLMRiM/L00BtWoEtLVk5bhdSsiKS4s7WQe+rLa66oV5JSScjwXAPt+11QkU2fWrjMlU+Xl1R5nrbPmoxs+optXNwrKC3ho7UPEZMVUP9bGjqDHfueori3OFGD3wx0knap+rBBCnO/UjjgALMqPUG6hVKofZZG8C4DCGrYXXGkkkbpM1LUa9vn+26+tPvatjSczoQArOx3Xjmpj1jlE9Qrz61ZX7fxxpmTq43+TqalTa0ymbHQ2zBswjy4eXThTdoaH1j7EsZxj1Y61d3TBe+JyTmsC8SAHZelwMpJj631NQoirh6qqJKWatowk2O4HqLQi5XfG1FvXoXXfSx5bU5BE6jJRl2rY1TFnJSsntZBdK2IB6Hdna2wdq9/gLMxj52hl1jj7fv3qnEzZWdjxycBP6OTeidzSXCasmcCp3FPVjnVy88Jh/HISFW981TQKP7+Z3Mw63kYWQlx1MuLzKcEGjaGU/S1OEugYiKu1ab9UasIJvMlEr2oIDru2iSO9NCSRukycq4Zt+qbuyVR9V7KMRpW/lxzBoDfSooMbbSK863W8uDCf1s7YOdeeTNm7WOHT2rnq61WSqZpv8zlYOjB/4HxCXUPJLslm/JrxxOXHVTvW3TcQzZg/SMeVIGMCGZ/ezJm87HpfmxDiyndy03EAXPKOccxPX+m2XtJB04Mrsbpg7BycmyC6S08SqctITdWwq6Uo6Ly9q+3XVptDmxJJPZWHhZWW6/7XtkFP/InqaTQK/e5uXeuY4DB3NJrq/+wrkikLC86sXUvSU0/XmEw5WTmxcNBCWru0JqM4g3Grx5FwJqHasb5BbSke9Qs5ONBaf5z4ecMpKSqo38UJIa54cQczAVA0xzFolUq39cpPbwcgy6VLNUdemSSRusycXw3bZcwY04v/TXbOfu/1wvMo9XhiIj+zmO2/m27/9BkZgoOreY2NxYWFhHsy9OGOVVamLKxM/70Obkri2M6ab69VSqbWrKk1mXK2duazQZ8R4hRCWlEa41ePJ7kgudqxge26knnbdxSoNnQoO8CRj++gvEx6ZQohTIrPlJF1xvQE9zFn016o8yuau+XsA0Ab1OuSx9ZUJJG6DClaLXYRPfF+fhp+H86tskKl8/LCb+6cSpXOL0RVVTZ8fQR9qQHf1s506OfX2GGL/wgJ9+T+t/pw25RwBo1rz21Twhk3ux8d+vmCCuu+PMzx3Wk1Hm9/7bX4f/zRv8nU08/UmEy52bjx2eDPCHIMIrkwmXGrx5FWWP25W3fpR9yQLyhRLehStJ39H9+L0cynP4UQVxZTk2IF+zMJRLXIxsHSgWAnUw/XooI8gstPAuDf+fomjPLSuuiJ1IwZM5gxYwZ//PHHxZ7qqlRdv7ZW69fVK4kCOLI9hcQjOWgtNFx/XzuUGm4rical0Sj4tXWhTQ9v/Nq6oNVq6H9PW0L7+qCqsPaLw5yMSq/xePv+/f9NplavrjWZ8rD14PPBn+Nv709iQSLj14wno6j6hxE69BnG0f4fU65q6Z6/jl2fjEU11l6yQQhx5Tu9PRYA17wYTnlDF48uaBRTKnF6/xZ0ipF0XPHyD2nCKC+ti55Ivfbaa0yfPp0RI0bQtm1bPv30U4qLiy/2tFeVcytUTjffhF1Ez3rdzgMozCtl688nAOh5SzDOXrYXI0xRR4pG4fr/taNtL29Uo8qaz6I5vb/mpy/t+/fH76MP/02mnnm2xmTKy86LRUMW4WvnS2x+LOPXjCerOKvasWE3jGJ/z7cxqgoRWb+z4/PJjXF5QojLlMFgJPFUEQBG+/gq+6POHDc1Kk6074SiuXpueF30K7322mu59tpradOmDSdOnODRRx+lRYsWF3taUUeqqrLp26OUFunxDHSgy4CApg5JYEqmbrg/lNY9TO1kVi08dHZJvXoO1133bzK1alWtyZSvvS+fD/kcL1svTuWdYsLaCeSW5FY7tvtNE9jV8WUAeicvZvuSlxt8bUKIy1PqiTzKDRosys4Q7WnaT3v+/iibNFN3hDLfK79R8fkueiK1ceNGNmzYQExMDBkZGSxbtowJEyZc7GlFHZ3cm8Hp/ZloNArXjw5Fo716foto7jQahYEPhBLS1ROjQWXVgkPEH65+9QjOJlMfzq2cTOn11Y4NcAhg0ZBFeNh4cDznOA+tfYi80rxqx0bc+RQ7QiYB0PvUh0T+9F7DL04IcdmJPWBaGXfLjibStwCtoqWDWwfA1Aw9qNjUksq1Xb8mi7EpXNJPTVdXV2655RbeeuutSzmtqEFJQTn/fH8UgK43BuLub9/EEYn/0mg1DBrXnpZdPDDojaycf5CEIzXXd3K4/nr8PpwL55Kpp5+pMZkKdAzk88Gf42rtSkx2DI+sfYQzZWeqHdtr9Ay2+z0AQI9Db7B7xcIGX5sQ4vJyem8KAC4FxzjtBe1c22FrYdoKknB8P04UUqxaEtzx6nliD+Spvava5p+OUXymHFdfO7oPDWrqcEQNtFoNg8d3IKizO4ZyIyvnHSDpWE6N4x2uvx7/85OpZ2pOplo6t+SzwZ/hbOXMoaxDPLruUQrLC6sd22vcB0S6j0SjqITtmsb+v79vlOsTQjR/eRnF5OUYUFQDBo9MVE3l/VFp0f8AcMqqLRaWdevecKUwO5GKiIjg008/JTc3txHDEZdK7MFMjkWmoShw/eh2aC0kp27OtDoNQyd0pEUHN/TlRlbMO0Dyidwax1dKpv6qPZlq49KGhYMW4mDpwL6MfTy2/jGKyouqjFM0GnpM/JzdjgOxUAy03fQ40dtWNtYlCiGasbhDpj2aTrknOeRv+vlwfiJFQiQA+e5dL3VoTc7sT89du3bx2GOP4ePjw6hRo1i1ahWqqjZmbOIiKSvWs+lb0y29zgMC8A52auKIRF1oLTTc+EhHAkJd0JcaWPHxflJPVb+vCc4mU3P/TaaSn615z1SoWygLBy3E3sKePWl7eHLDk5ToS6qM02i1hD3+Lftse2OtlBO4eizHo/5ptGsUQjRPsWefHHbPPsQGT9P/P781jHe+qXmxTUifSx5bUzM7kfrxxx8ZNmwYBoOBH3/8kZtuugl/f3+mTZtGTExMY8YoGtm2305SkFOKo7s1Ebe2bOpwRD3oLLTcOLEzfm2dKS8xsPzDfaTH5dc43uGGf5Op/JV/1ZpMdXTvyPyB87HV2RKZEsnkjZMpM5RVGWdhaUW7J34h2jIMe6UY92X3Ehezp9GuUQjRvJSV6Ek6lguAS+kpYj1UfOx88LYz9WLNyUihhTEJgKCw65ooyqZjdiJ1xx13sHz5cpKSknj//ffp1KkTKSkpvPPOO3Ts2JFevXrJrb9mKOlYDtH/mP7CXz86FAvL+tWcEk3PwlLLTY+G4dvambISA3/M3UdGfPWbxOFcMjXnvGTquRqTqS6eXfhk4CfY6GzYmrSVpzY+RbmhahkFaxs7Ah9fxjFdG1w4g80Pd5B8+khjXaIQohlJPJKD0QjWxRkUB2pQFaXSalTc/o2m/9X44+x+9TW6b/DGGA8PD6ZMmcK+ffvYt28fkydPxtPTk507d1a69ffXX3/Jrb8mVl5mYMNS04dd+36++Ld1aeKIhLksrLTc9FhnvFs6UVqkZ9ncKDITa24w7HDDDeclUytJfm5ajclUN69ufHTDR1hprdiYuJFn/3mWcmPVZMre0QXPR5YTq2mBJ9moS24lMzmusS5RCNFMnGtS7J4VzeFAU9eL8/dHFZ/aBkCaY+dLHltz0Kg7jDt37szs2bNJTEzkjz/+YOTIkRgMBn766Sduvvlm/P39ef755zl9+nRjTivqaOfy0+RlFGPnbEWfka2aOhzRQJbWOm55IgzPIEdKC/UsmxNFVvIFkqk5H5iSqT//rDWZivCJYO71c7HQWLAufh0vbn4Rg7Fqvz1nd2/sxi8nSfHCT02j4PObyc2sudmyEOLyoqoqcYdM9evcsg6x3t3Uo/P8RMopMwoApcXVVfbgnIvyqFZ+fj7x8fHEx8ej1+tRVRWNRkNKSgpvv/02bdu25dFHH6W0VLrKXyppsfnsXxcPwHX3tsXKRtfEEYnGYGmj49Ynw/Bo4UBJQTnL5uwjJ7X68gUADgMGVE6mpj1fYzLV168vH1z3ATqNjr9i/+LlrS9Xm0x5+Aah3P8H6bgSZIwn/dNbKMjPwaDXE731T3avWEj01j8x1DCPEKL5ykwooDCvDK2hFGclnaPORdjobGjj0gaAstISWpaa7nR4dujflKE2mUZLpAwGAytWrODOO+/E19eXJ554gl27dtGxY0fef/99UlJSiImJ4amnnsLGxoYFCxbw/PPPN9b0ohYGvZENS2NQVWjdw4ugzu5NHZJoRFa2Ftw6qQvuAfYU55fx+wdR5KZVLV9wTkUypdORv2JFrclU/4D+vHfte2gVLctPLWfGjhkY1arNi32D21F898/k4EAb/THS5/Qn643WdFh7L913P0OHtfeS+UYbolYvbrTrFkJcfOfKHrjkHOFMqDcoCp3dO6PTmH4Zj43egbVSTi72BLTq1JShNpkGJ1L79+9n6tSp+Pn5MXz4cH755Resra156KGHiIyM5MCBA0yZMgV3d3fatm3Lu+++y759+7CxseH776Wg36WwZ1UcWUmFWNtb0O+u1k0djrgIrO1MyZSbnx1FeaZkKi/jAsnU3DmVkylD1dUmgAGBA5h17Sw0ioZfj//KW5FvVbvfMTC0Gxm3fkOJakFLYxweauUK7B5qFmHbnpRkSojLSOzBf2/rHQ0yJU/n39bLPrLZNM6mIxrt1fnwktmJ1AcffECXLl3o2rUrc+bMISMjg+uuu46lS5eSkpLC/Pnz6dGjR7XHBgcHExYWRlpamtmBi7rJSipgz1+xAFx7dxtsHCybNiBx0djYW3LrpHBcfOwozC3l9w+iyM8srnF8lZWp56bVmEwNDRrKm9e8iYLCD0d/4J1d71SbTIV07kuRYoOqgqJUfk9z9nuf7dPlNp8Ql4HiM2WkxZrKq7hnRbPRw7Q6dX4iZZm80zTWu9slj6+5MDuReuqppzhw4AABAQG8/PLLnDx5kvXr1/O///0Pa2vrCx7fo0cPrr32WnOnF3VgNKr8vfQIRoNKUGd3WnX3bOqQxEVm62jJ8MldcPaypSDblEydya5aWPMch4EDq97mqyGZurnlzUzvMx2Ar2O+5oM9H1RJpo5ErsaV/CpJ1DkaBbzJ4kjkavMuUAhxycRFZ4EK9mfisXHUssc6FQWFzh6mp/NUoxH/AlOjYsfW1zRlqE3K7ETqrrvuYvXq1Zw+fZrp06cTFBRUr+PnzJnDhg0bzJ1e1MGBvxNIj83H0lpL/3vaotT06SauKHZOVtw2JRwnDxvOZJXw+wdRFOTU/GCHw8CB+H0w25RMLV9eazI1ovUIXu71MgBfRn/Jx/s+rvR+cU5SnWKs6zghRNOJPWC6reeeFU1Bx2BQFEKcQ3C0dAQgNeE4nmRTrmppGdavKUNtUmYnUt9//z2DBg2SD+dmKje9iMhlpwDoe0dr7F2uriaSVzs7ZyuGTwnH0d2a/Ixils2JojCv5mTKcdCgysnU8zUnU3e1vYtpPacBsPDAQj7d/2nFezYufnWKr67jhBBNw2AwknD43/1RJ4JNd5rCPcMrxiQd2AjAaYsQbOwcLnmMzYXZiZRWq6V//7o96nj99dej08nj9o3FaFRJOprDsV2pJB3NwWisfHtFNaps/PoI+nIjfm1dCO3r00SRiqbk4GrN8Cnh2LtakZtWxLIPoijKr9ry5RzHQYPwm/2+KZn6o/Zk6n+h/+Pp7k8DMG/fPBYdXARAu4ghpOGGsYbau6oKqbjRLmJIwy5OCHFRpZ7Io6zEgEXZGRzPxLHFOxeovD/KELcDgGzXLlVPcBUxO5FSVbVelcqlqnnjOBmVzpIXtvH7B1GsXXSY3z+IYskL2zgZlV4x5vDWZJKO5aKz1HD9fe1k1fAq5uhmw21TumLvYkVOahHL5kRRXFBLMjV4MH7v/5tMpbzwQo3J1JgOY5jUdRIAc/bOYUn0ErQ6Hcm9XwWoNplSFEi1DkGjuSgl7IQQjST2bDVzt+xodF4ebFVOAhDu8e+KlFuOqVGxRdDV16j4fJfkp1lhYSEWFhaXYqor2smodFYtOERhbuVbNIW5paxacIjNPx3jxJ40tvx8HIBew0Nw8rBpilBFM+LkYcPwyeHYOlmSnVzIsjn7KCms2vLlHMchZ5MprZa8ZX+Q8sKLNSZT4zuN59GwRwF4d/e7fHfkO8KHjGF/nw/JUNwqjc3DDoAuJTuJ/PQRVGPVelRCiObhXDVz96xDlIW1oVzV42rtir+DPwAF+TkE603bR/w7X90Pjl30ROro0aMcOnQIPz/z90R88sknBAcHY21tTbdu3di8eXONY7ds2ULfvn1xc3PDxsaGdu3a8cEHH1QaU15ezowZMwgJCcHa2pqwsDBWrVrVoHkvNqNRZfMPx2sdc2B9Iqs/i0ZfasTJ04ZO1/tfouhEc+fsZcttU8KxcbQkK7GAP+bWIZmaPftsMrWs1mTqkbBHGN9pPABvRb7Fz8d+JnzIGNxfOkb0oG/Z3f1dogd9i/1L8ezs9BoAvdJ/kGRKiGYqL6OInNQiFNWIa3YMp0PsAdP+qHN3OGL3/4NWUUnFAy//kKYMt8nVOZGaO3cuLVu2rPgC2L17d6XX/vvl4+ND+/btKS0tZfjw4WYF+MMPPzB58mRefPFFoqKi6NevHzfeeCPx8fHVjrezs+Pxxx/nn3/+ISYmhpdeeomXXnqJhQsXVox56aWXWLBgAR999BGHDx/mkUceYcSIEURFRZk978WWcjy3ykpUbfLSizm9P+MiRiQuNy7edtw2ORwbBwsy4s+w/MN9lBbXXM+pSjL14kvVJlOKovBk+JOMaT8GgBnbZ7DsxDLQKBSFeJLe3p+iEE/QKPS8fQo7O5pu/fVK/4HIBRMlmRKimTlXhNMp7yQ6Qwk7fEw9PLt4dKkYc+aEqVFxosPV2aj4fIpax81L06dPZ/r06f8eqCh12vfk4ODAnXfeyYcffoitrW29A4yIiKBr167Mnz+/4rXQ0FBuu+02Zs6cWadzjBw5Ejs7O5YuXQqAr68vL774Io899ljFmNtuuw17e3u+/vrrRps3Pz8fJycn8vLycHR0rNMxNTm2K5W1iw7X6xh7FytGv9kHjUb2SIl/ZSUV8PvsKEoKy/Fu6cgtT3bB0rrmh0HyV60m6amnwGDA6bbb8HnzDZRqKhirqsqsnbP49si3ADhZOpFXllfxvpetF9N6TmNg4EAif3qfiOgZAOzwuoeIhz9BkX1TQjQLf3y4j4TD2bQ68SstjTGMf1hPdkk2S29cWrHZ/MCsAXQu2U1ku2lEjLry2r3V5/O7zj+5Jk+ezOnTpzl9+jSnTp1CVVV69OhR8dp/v2JjY0lLSyMvL4/PP//crCSqrKyMPXv2MHjw4EqvDx48mG3bttXpHFFRUWzbtq3SE4alpaVVioba2NiwZcuWBs1bWlpKfn5+pa/GYudY//IFBTmlpBzPbbQYxJXBzc+eWyd3wcpWR+qpfFZ8vJ+yklpWpoYO+XfP1O+/k/LSyzWuTE3rOY3ePr0BKiVRAOlF6UzdOJV1ceuIuPMpIjuY6lH1SvuOyAWPysqUEM1AWYmepGM5ALhlH0Lt2oHskmwsNZa0d2sPgNFgILg42jQm9OqtH3VOnRMpJycnAgMDCQwMJCgoiDFjxjBixIiK1/771aJFCzw8PBoUXGZmJgaDAS8vr0qve3l5kZqaWuux/v7+WFlZ0b17dx577DHGjx9f8d6QIUOYPXs2x48fx2g0snbtWpYtW0ZKSkqD5p05cyZOTk4VXwEBAfW95Br5tHbGzrn+yVRhft1vB4qrh0eAA7dO6oKljY6UE3ms/OQA5WXV74GCc8nUe6Zk6rffakymjKqRU3mnqj2HimkF++2db2MwGoi482lJpoRoZhKP5GDUq9ga8rAtSiOxtTMAHdw7YKk1tRiLO7oXB6WYItWKoPY9mzDa5sHstfQvv/ySadOmNWYsNfrv4/uqql7wkf7Nmzeze/duPv30U+bMmcN3331X8d7cuXNp3bo17dq1w9LSkscff5wHH3wQ7X9uV9R33ueff568vLyKr4SEhLpe4gVpNAr97q5/w2FzVrLE1cEz0JFbngzDwlpL0rFcVn5yAH2tydTQysnUy69USXz2pu8lrajmHpoqKqlFqexN3wtgSqbavwScTaYWPi7JlBBNKO5s2QPX1CgUYJefqcXU+fuj0qM3AXDKOhSdhfRvbdabEtzd3dFqtVVWgdLT06usFv1XcHAwnTp1YsKECUyZMoXXXnut4j0PDw9+//13CgsLiYuL48iRI9jb2xMcHNygea2srHB0dKz01ZhCwj0Z+nDHOq9M2btY4XP2twkhquMd7MQtT3RBZ6Ul8UgOf316EH35BZKp9941JVO//mpamTov8ckoqtsDDuePi7jrGSLbvwhAr9RvJJkSoomoqkrs2bIHbpkHsWjRgu36Y0DlQpyaRFOj4jMeXS95jM1RncqNjx07FgAfHx/efPPNSq/VlaIoLFq0qF7HWFpa0q1bN9auXcuIESMqXl+7dm29ngJUVZXS0qq3uKytrfHz86O8vJxffvmFu+66q1HnvRhCwj0JDvMg5Xgupw5kcGB9Yo1jr7mrtWw0FxfkE+LELY93ZvlH+4k/nM2qhYe48eFOaHXV/57leOONACQ9/Qx5v/5qOscbr6NoNHjY1u12/n/HRdz1LJE/qETEvEWv1G/YsRAiHvpYNqALcQllJhRQlFeGVjHgknsCi+uHcTJvJQBhHmEV43zyDwBgF9K3SeJsbuqUSH311VcAtGvXriKROvdaXZmTSAFMnTqV0aNH0717d3r37s3ChQuJj4/nkUceAUy305KSkliyZAkA8+bNo0WLFrRr1w4w1ZV67733eOKJJyrOGRkZSVJSEl26dCEpKYnXXnsNo9HIs88+W+d5m5JGo+DX1gW/ti74tnJm8w/HK5VGsHex4pq7WhMS7tmEUYrLiW9rF256tDMr5h0g7mAWqz87xJCHOqLV1pJMqSpJzzxbKZnq6tkVL1sv0ovSK/ZE/ZeNzoYw97Aqr0fc/RyRP1CRTG3/TKHXhI8kmRLiEjlXzdy9JA6Nqie1rekXnkDHQNxsTAV2M1MT8FdN+4kDu1zXJHE2N3VKpL788kvAtOH8v69dbHfffTdZWVnMmDGDlJQUOnbsyMqVKwkMDAQgJSWlUm0no9HI888/z+nTp9HpdISEhDBr1iwefvjhijElJSW89NJLnDp1Cnt7e4YNG8bSpUtxdnau87zNxfkrVIX5pdg5mm7nyUqUqC//dq7cNLEzf35ygNP7M1m7KJrB4zqgqSmZGjYM4N9kSgGf119nWs9pTN04FQWl2mSqWF/Mc5ufY9a1s7DSVr5NbUqmVCJiZtI75Wu2f6ah14S5kkwJcQmcq2buErsdgCj/ckisvBqVcGAT7kCspgVBLu5NEWazU+c6UqL+GrOOlBCXStyhLFZ+egCjXqV1d08GPti+xmQKIH/lSpKefgaMRpzuuB2fGTNYn/A3s3bOqrTx3NvWmxuDb+TrmK8pN5bT07snc6+fi72lfZVzRv4wi4gYU7227b7302u8JFNCXExF+WV8+dwWUKHvthdw8HPlzcne7Erdxau9X+WONncAsOPTR017GV1vJeLJpU0c9cVzUepICSGuDoEd3Rj6UCc0WoXju9NZvyQGY3UdiM9yHDbMtAFdoyHv519IeeUVBgTcwKrbVrLY8xk+NtzNYs9n+Ou2P5nafSrzB87HVmfLztSdjF09lqzirCrnjLh7GpHtTE8F905ewo7PJ8kGdCEuovjoLFDB2aIAq7I8rHt251DmIaDyE3vOWaYOIJoWEU0RZrNkdiJVXl5OfHw8WVlVfwieLysri/j4ePT6mgv+CSGal+DO7gwZ3xFFo3AsMo0NS2NQL5BM+b77TkUyFT9uPKcHDsZmykw83/kGmykzOT1oCPlr1hDhE8EXQ7/A1dqVmOwY7v/rfpIKkqqcM2LU8/9JpiZLMiXERXKuLYxbtqnQZm77AIr1xThYOtDS2dQWrrSkiJZlpqf4vDv2r/5EVyGzE6nPPvuM4ODgirYrNVm6dCnBwcF88cUX5k4lhGgCLcM9GDyuA4pG4cj2VDZ+e7TWZMrppptMyZSiULR9O/q0yvWk9GlpJE2aTP6aNXRw68DioYvxtfMl/kw8o1eO5nhO1abcEaOeZ0fb5wDonbyYHYumSDIlRCMzGIwkHDYlUs7H/gHgUAvTv/UwjzA0iilVOH1wG5aKnmwc8W/ZoWmCbYbMTqR+/vlnNBoNDzzwQK3jHnjgATQaDT/99JO5Uwkhmkirbp4MfDAURYHDW5L55/tjtfbYdBw6FE1N+wnOHpf21kxUg4EgpyCWDltKK+dWZBRnMGbVGKLSo6oc1uueF9jR1vREbe+kr9ixaKokU0I0opQTeZSVGLCyVHE8E4dV61bsOrvyFO4ZXjEu9+hmAOJsO8qexfOY/Sdx9OhRAgICKj3pVh1nZ2cCAgI4evSouVMJIZpQmx7e3DAmFBQ49E8SW348XmMyVbR7D8a8vGrfA0BV0aemUrR7DwCetp58NfQrunh04UzZGR5a8xD/JP5T5bBe97zIjjbPANA76UtJpoRoROeqmXtr01FQse0Zwb70fUDl/VFWKbsBKPXufqlDbNbMTqSysrLq3EvPw8ODjIy6VTwWQjQ/7Xr5cP19ptpsBzYksu2XE9UmU/o6/js/f5yTlRMLBy+kn18/SgwlPPn3kyw/ubzKMb3ufalyMvXFU5JMCdEIzu2Pcjlbsbw0rDVpRWloFS0d3TsCoBqNBBSaNp87t5VGxeczO5Fyc3Pj9OnTdRp7+vTpSjWohBCXn/Z9fel/b1sA9q1LYMeyU1WSKV0df7n67zgbnQ1zb5jLzS1vxqAaeGHLCyyJXlLlOFMy9TQAvRO/IPKLpyWZEqIB8jKKyE0rQqMBx8MbATjSwpQatHVti62FLQDJsTG4k0uZqiWos1Q0P5/ZiVRERARZWVmVmgFX5/vvvyczM5OICHlUUojLXcdr/bh2VBsA9q6KY+eKyr9M2Xbvhs7bG2pp7q11d8e2e7cqr1toLHjzmjcZ3X40AO/ufpe5e+dWSdZ63fsyO1o/Zfr/iYskmRKiAc6tRnm4GtAZSrBq25Y9paYHP87fH5V80NSo+LRFa6xt7C59oM2Y2YnUxIkTUVWVhx56iG+//bbaMd999x0TJkxAURQmTpxodpBCiOaj03X+XHNnawB2/xnL7pX/JlOKVovXC8+f/ab6ZMqYn0/h1q3VvqdRNDzT/RkmdZ0EwOcHP2f69unojZXLp/T63yuVkqkdXz7ToGsS4mp1bn+UZ0ksALYRPavdH2WMjwQgxy0cUZnZidSgQYN49NFHKSwsZPTo0QQEBHD77bczbtw4br/9dlq0aMF9991HYWEhEydOZOjQoY0ZtxCiCYUNCKD3yBAAIv84zd7VcRXvOQ4ejN/cOei8vCodo/PywrJNG9SyMhImPkrO9z9Ue25FURjfaTyv9X4NjaLhl+O/8PSmpyk1VG48bkqmpgLQO+Fzti96ujEvUYgrXlmJnqTjuQA4HdkAgK5bF47lmJ7Y6+LZpWKsZ84+AKxa9r6UIV4WGtwiZu7cubzxxhvVFuZ0d3fn5ZdfrtQw+GoiLWLElW73X7FELjsFQN87WtFlYIuK91SDgaLde9BnZKDz8DDdzjMYSHn1NfJ++w0At/Hj8Jg6tcZHqdfFrePZf56l3FhOD+8efHj9h1Vayuz4+jV6nfgAgO0BE+g97r2LcalCXHFO7cvgr08P4uhqSbdfTXeP8pd9zPjtk/C282btHWsByM/Nwv6DEDSKSuYjB3H3bnGBM1/+6vP5XaemxbWZNGkSDz/8MFu3biUmJob8/HwcHBzo0KEDffv2xcrK6sInEUJclrrfGITRoLJrxWm2/nwCRaMQdkMAYLrNZxfRs/IBWi0+b72JZYsAMuZ+SNbniyhLTMJ31kw01tZVzj8wcCCfDvyUJzc8ya7UXYxdPZZPBn6Cu82/zVJ73fcaO75W6XViDr0TPmP7Fwq9x757Ua9biCtB7Nnbej6OhSiAdWgom4rP1o/y+PcWXtz+TXRSVJIUL/yugiSqvhqcSAFYW1szYMAABgwY0BinE0JcRnrcFITRYGTPX3Fs+fE4Wq1Cx/7+NY5XFAX3iROx8Pcn5YUXObNqFfGpqfh/Mg+dq2uV8T19evLFkC+YuG4iMdkxjPlrDAsGLcDf4d85et03nR1fY0qm4heeTabeuSjXK8SVQFVV4g6Z7iS555jawthGRLAvYx8AYZ5hFWMLTpj2NKY4dMbv0oZ5WZDSpEKIBlEUhYhbWxI+2PSb6qbvjhG9OQmjUSXpaA7HdqWSdDSnSuNjp1tuIWDR52icnCjet4/Yu0dReqr6kirt3dqz5MYl+Nn7EX8mnvv/ur9iH8c5ve6bzo4Q0yb13vEL2P7FsxfhaoW4MmQmFFCUV4bOSott1DoAbHr24ED6AaDyE3v26aYCugb/nlVPJBqeSKWlpfHaa6/Rp08f3N3dsbKywt3dnT59+jBjxgzS09MbI04hRDOmKAq9R4RU3Nbb+M1Rvnh6M79/EMXaRYf5/YMolrywjZNRlX8e2PXsSdB332EREEB5QgKx99xD0a5d1c4R6BjIkhuXVLSUeWDVA+xN21tpTK/RM9jR8kngbDL15XMX4WqFuPydu63nH2yDIf40aLWktnblTPkZbHQ2tHExlTkx6PW0LIkBwKO9NCquToMSqb/++ovQ0FBef/11duzYQXZ2NuXl5WRnZ7Njxw6mT59OaGgoq1ataqx4hRDNlKIo9L2zFS06uAFQWlS5ZEFhbimrFhyqkkxZtQwm6IfvsQkLw5iXR/zYceQtX1HtHOdayoR7hptayqx9iE0JmyqN6XX/62w/l0zFfSrJlBDVOFc/yktn6jJg3aED+wpNrdw6u3dGpzHt/Ik9vBM7pYQC1YbAdlXrv4kG7JE6cuQIt99+OyUlJXTo0IEnn3ySDh064OXlRVpaGtHR0Xz44YdER0czcuRI9u7dS7t27Roz9iuGwWCgvLy8qcMQzZCFhQVarbapw6gzVYWspIJax2z58TjBYR5oNP/WmdK5utJi8VckP/scZ9asIfmZZyhPTMDtkUdQ/lOPysnKiQWDFvD0pqf5J/EfJm2YxIy+M7g15NaKMb3vf53tS6D3qQ9NydRXCr0fmNW4FyvEZaoov4z0uHwAXBN2UQrYRfRkf8Z+oPL+qMyYzYQAp61D6aRrlG3VVxyz/1RmzpxJSUkJjz32GB999FGl90JCQujTpw8TJkzgySef5OOPP2bWrFl89dVXDY33iqKqKqmpqeTm5jZ1KKIZc3Z2xtvbu0pC0RylHM+lMLe01jEFOaWkHM/Fr61Lpdc11tb4zfmA9PffJ3vRF2TM/ZCyhER8XnsVxdKy0lgbnQ1zrp/Da9te44+Tf/DilhfJKclhTIcxFWN63/862xcb6X36Y3rHzmf7V0gyJQSYNpmr4NHCAeO6zQBnGxXPBCrvj9ImmfrvFXhJo+KamJ1I/f3337i4uDB79uxax73//vt8/fXXrF+/3typrljnkihPT09sbW0viw9KcemoqkpRUVHFPkMfH58mjujCCvNrT6IuNE7RaPB65hksAwJInfE6eb/+SnlyMv4fzkX7n1ouFhoLXu/7Os5Wziw5vIT3dr9Hdkk2k7tOrvi31HvMm2xfrNL79Dx6x85nx1caej3wVsMuUojLXNwh0/6ogEALypOTQaejuH0L4lfEo6DQ2aNzxVjfM6bN5/at+jRJrJcDsxOp9PR0unTpgoWFRa3jLCwsaN26Nfv37zd3qiuSwWCoSKLc3NyaOhzRTNnY2ACmf2+enp7N/jafnWPd6sZdaJzLqFFY+PqSNHkKRTt2EHvvvbRYsAALv8oPX2sUDU93fxpXa1fm7J3DF4e+ILc0l5d7vVyxx6P3mLfY8RX0ip1Hr9h5bF9sek2Iq5FBbyThcDYAHqWxlAM2nTpxoNDUXy/EOQRHS9MvLRnJsfiq6RhUhaAw2WheE7M3m7u4uBAfH3/BcaqqEh8fj7Ozs7lTXZHO7YmytbVt4khEc3fu78jlsI/Op7Uzds4XTqZyUgurNCP+L/trryXwm6/ReXlRduIkp+8eRfHBQ1XGKYrCuE7jmN5nOhpFw6/Hf+WpjU9VainT64G32BH0GAC9T89j++IX63llQlwZUk7kUlZiwMbBAuuYbcB/+uud1xYm4cBGAGJ1QTg4Va3xJkzMTqT69OlDenr6BW/tffDBB6SlpdG3b19zp7qiye08cSGX098RjUah392tLzhu03fH2LD0/+ydd3gU1feH39lNb5tAekiDQAq9ptCRJk1BpClFFEG//EBQBBtNEAsoIKIIGmkCooCAGIr0klBDCwQICTU9Ib3v/P5YsrCkh4R63+fZB3bmljN3Jztn7z33cy6Sn1tQajkjb2/c1q3F0MuLgoQErg0bRloJYQL96vbj2w7fYqAwYPeN3YzZOYa03DTteb8RX3DETZM83T9ykXCmBM8lUXdFOF0b1CTrqCb+ydTXV+tI3R8flRt5BIAEy8YISqbSjtQHH2gShE6aNIlXXnmFPXv2EBsbiyzLxMbGsmfPHvr168ekSZNQKBTa8gKB4NmmTlNbuo9uUGRmyszKkG6jGuD3cm0kCS4cjuavb06QmpBVanv69va4rlqFadu2yFlZ3Bz7fyStWFFs2RdcXuCnLj9hqm/K8djjjNw+koSsBO15/xFfcsR1jOb/kYs4suKzh7xageDp4tpd2QMnOzX5sbFI+vooGvlwPlGjbt7Epom2rFXiKQCUrn6P3M6niYeakVq0aBFKpZJNmzbRuXNnHB0d0dPTw9HRkc6dO7Np0yaUSiWLFi3C319kjH5W2bt3L5Ikid2HAi11mtoy7IsAXp7QlC5v+vDyhKYMnR2AR3Nbmnd3o/f4JhiZ6ZNwI50/vjimTVVREkozU5x/XIzloIEgy8R+MYeY2V8gFxSd0Wpp35LAboHUMKrBxaSLDPt3GDfSbmjP+7/x1T1n6upCgoUzJXhOuBOXyZ3YTBQKCatEjcimcePGhGdEkqfOo4ZRDZzNNaK62ZnpuOddAcCxYcfHZvPTwEMJcr7zzjscO3aMwYMHY21tjSzL2pe1tTWvv/46x44dY8yYMVVlr6AYCtQyRyIS+Tv0FkciEilQlx578rB06NCB9957r8rblSSJTZs2VXm7D5KcnMzQoUNRqVSoVCqGDh1aphO4YcMGunXrhrW1NZIkERoaWqTM6NGjqVOnDsbGxtjY2PDSSy9x8eJFnTJ9+vTBxcUFIyMjHBwcGDp0KLdv367Cq3tyUCgknDytqNfSHidPKx3dKGevGgz4uCW2bhbkZOaz9YfTHN0aiVzKvSvp6WE/bRq2kzSz28krV3Lz/8ahzswsUta7pjcrX1yJk5kTN9JuMOzfYYQnhWvP3+9M+QlnSvCcUDgb5VDXkvwTwYAmv96pOM3MUxObJtpQgqtnDmIgFZCAJQ6u9R6PwU8JD50ipnHjxqxatYrY2FiSk5O5ceMGycnJxMbGsmLFCho3Fmur1UnQuWjafLWbwUuDGb82lMFLg2nz1W6CzkU/btOeWIYMGUJoaChBQUEEBQURGhrK0KFDS62TkZFB69at+fLLknWImjdvTmBgIBcuXGD79u3IskzXrl0puG/WpGPHjvzxxx+Eh4fz119/ERERQf/+/avs2p4mzGsY0e/9ZtRv5wQyHNsayT+Lz5CdUXJQvSRJ1HzzTZzmz0cyNCR9926uDR1Gfnx8kbIuFi6sfHElda3qkpCVwBtBb3Ai9oT2vP8bX3HEZTRw15laObXqL1IgeIIolD1wbVCDjLvxUfcHmt8fH5VySZOo+LppQySFSMtbGlU6OiqVCicnJ1QqVVU2KyiBoHPRvLPqJNEp2TrHY1KyeWfVyWpxpkaMGMG+fftYsGABkiQhSRJRUVEAnDhxghYtWmBiYkJAQADh4eE6dbds2ULz5s0xMjKidu3azJgxg/x8TRoRNzc3APr27YskSdr3ERERvPTSS9jZ2WFmZkbLli3ZtWtXpe2/cOECQUFBLFu2DH9/f/z9/Vm6dClbt24tYu/9DB06lKlTp9K5c+cSy7z99tu0a9cONzc3mjVrxqxZs7hx44Z2fAAmTJiAn58frq6uBAQEMGXKFIKDg5+KHXnVgVJfQYchnrww3BulvoJr5xJZP+cY8dfTSq1n0b0bLr8ForSyIvv8eSIHDiTn8uUi5WxMbPit+280s21GWl4ao3eOZu+Nvdrz/iO/5ojL2wD4RSwgeNW0qrw8geCJITc7n1uX7gDgaJlFQUICkqEhRo0bExofCuju2DOKOa6p59jyEVv69CHczCcIWZbJzM0v1ystO49pm89T3EJI4bHpm8NIy84rs62ytqHfz4IFC/D392fUqFFER0cTHR2Ns7NmTf2TTz5h3rx5HD9+HD09PUaOHKmtt337dl5//XXGjRtHWFgYS5Ys4bfffmP27NkAHLubqDYwMJDo6Gjt+/T0dHr06MGuXbs4deoU3bp1o3fv3jrSG2PGjMHMzKzUV2H5I0eOoFKp8PX11db38/NDpVJx+PDhco9DWWRkZBAYGIi7u7t2fB4kKSmJ1atXExAQUKYe27OOl78Dr3zYHAtrI1ITsvnrmxNcOFz6kqdJ06a4rVuLgZsb+bejiRo8hIxiPkMLAwt+6vIT7Wu1J6cgh/f2vMffV/7Wnvcf+Q1HnEcB4HdlPsGrplfptQkETwI3LySjLpBR2Rijd0mT7Nu4aVNu5cSSlJ2EvkIfn5o+AMhqNW6ZGqkRS882j83mp4VyCXLOnDnzoTuSJInPPhNxCKWRlVeAz9TtVdKWDMSkZtNw+o4yy4bN7IaJQfm0WVUqFQYGBpiYmGBvbw+gjQOaPXs27dtrRNumTJlCz549yc7OxsjIiNmzZzNlyhSGD9ek8Khduzaff/45H374IdOmTcPGxga4lw6lkMaNG+ssD8+aNYuNGzeyefNmxo4dC2juz7J2hTo6OgIaNXlbW9si521tbYmJiSnXGJTG4sWL+fDDD8nIyMDLy4udO3di8EB6k8mTJ7No0SIyMzPx8/Nj69biE/Q+b9g4m/PqRy3Z9VsY184msnvFRWIiU2k3oB5K/eJ/8xm4uOC2dg03xo4l6/gJrr89GocZM7B8pZ9OOWM9Y77r+J02pcynhz4lOTuZEQ1GAOD/5lyO/AL+N5bid+U7gleB3+vTq/mKBYJHR1Thsl7DmmQdWgdo8usduhsfVb9mfQyUmu+qmxFncSaVHFkf9wZio1hZlOvpOX36dCRJqtDMRSGF9YQj9ezTqNG9tAKF6Uzi4uJwcXHhxIkTHDt2TDsDBRp19+zsbDIzM0sUJs3IyGDGjBls3bqV27dvk5+fT1ZWls6MlK2tbbHOUUkUp8tUeI8+LK+99hpdunQhOjqauXPnMmDAAA4dOoSRkZG2zKRJk3jzzTe5du0aM2bMYNiwYWzduvWp0ouqLoxM9en5TiOO/xvF0a2RhB24TcL1NLqPboh5DaNi6ygtLXH59VeiP/6E1K1bif7kE3Jv3sBm3DidMS1MKWNlaMXysOXMOzGPpJwkJjSbgCRJd50pGf8byzTO1GoJv9fEUp/g6UdWy9pAc9f6Ncj87m58VKtWhMZrfsjdHx8VfW4/zsBVg3p4GwnR6LIolyM1bZr4MnkUGOsrCZvZrVxlj0YmMSLwWJnlfnujJa3cS1ekNdavmrQj9y9PFT7A1Gq19t8ZM2bQr1+/IvXudzIeZNKkSWzfvp25c+fi4eGBsbEx/fv3Jzc3V1tmzJgxrFq1qlTbwsLCcHFxwd7entjY2CLn4+PjsbOzK/0Cy0HhTsC6devi5+eHlZUVGzduZPDgwdoy1tbWWFtbU69ePby9vXF2diY4OFhIhNxFUki07OmOnZsFO349T9y1NP6YfYyub9XH2bv4e1lhYIDjN19j4OJMwuIfSfzxJ/Ju3MThi9ko7psRVEgKPmj5ATWMa/Ddie8IPBdIcnYy0/ynoafQw++Nbwj+Vcbv5i/4Xf72rjMlgtAFTzfxN9LITM1F31BJTRK5npyMZGyMccOGhP47C4DGtvdtDLuu2dF3x7ppcc0JHkA4Uk8QkiSVe4mtbV0bHFRGxKRkFxsnJQH2KiPa1rVBqajamQ4DAwOdnWjloVmzZoSHh+Ph4VFiGX19/SLtHjhwgBEjRtC3b19AEzN1f/A2VGxpz9/fn5SUFI4ePUqrVq0ACAkJISUlhYCAqk/KKcsyOTklJ/ItnOUtrczzikv9mgz4qCVBP58j/noaWxaG0qpPbZp3c0Uq5p6WJAmbcePQd6pF9LRppG7dSl5MNLW+/x49KyudsiMbjMTK0IrpR6az6comUnJS+Lrd1xjpGeE7ci7Bv3LXmZpH8GqEMyV4qinUaXP2rkHOibuzUU2bkkY2V+5otKLuF+K0TdHkxjVyFz/uyoMINn9KUSokpvXWBAY++EgpfD+tt0+VO1Gg2WEXEhJCVFQUCQkJ2lmn0pg6dSorVqxg+vTpnD9/ngsXLrBu3To+/fRTnXb/++8/YmJiSE5OBsDDw4MNGzYQGhrK6dOnGTJkSJH+bG1t8fDwKPWlp6dxUL29venevTujRo0iODiY4OBgRo0aRa9evfD09NS26eXlxcaNG7Xvk5KSCA0NJSwsDIDw8HBCQ0O1cVVXr15lzpw5nDhxguvXr3PkyBEGDBiAsbExPXr0AODo0aMsWrSI0NBQrl27xp49exgyZAh16tQRs1ElYGFtTL9JzfBu7YAsQ8jfV9n201lyMkve5Wj5Sj9clv6MwsyMrOMnuDZ4CLnF5AXtW7cv33X4DgOFAXtu7GHMLk1KGUmhwHfkXI7U0myW0DhTDx8nKhA8LqLO3IuPyjgaAmj0o07HaRwmF3MXahrXBCAlKR43tUbA1rWJEOIsD1XmSN2+fZtjx46xf//+qmpSUAbdGzjw4+vNsFfpLo3Zq4z48fVmdG/gUC39fvDBByiVSnx8fLCxsSlX8upu3bqxdetWdu7cScuWLfHz8+Pbb7/F1dVVW2bevHns3LkTZ2dnmjbVTCl/9913WFlZERAQQO/evenWrRvNmjV7KPtXr15Nw4YN6dq1K127dqVRo0asXLlSp0x4eDgpKSna95s3b6Zp06b07NkTgEGDBtG0aVN++uknQLM8eeDAAXr06IGHhwcDBgzA1NSUw4cPa+O3jI2N2bBhAy+88AKenp6MHDmSBg0asG/fPgwNy070+7yip6+k01BvOr7uhVJPQdSZBP6Yc5yEm+kl1jH198dtze/oOTqQGxVF1MBBZJ48VaRcJ5dO/NTlJ8z0zTgRe4I3gt4gISsBSaHAb+Q8jji9Adx1pn7/vNquUSCoLjJTc4m7ppETcfGxIvOYRtbA1LdVsbIHUaF7ALghOVLD1umR2vq0IsmViSC/jx9//JFvv/2Wq1evahqUJK02EMD777/PkSNHWLt2LS4uLg9n7VNGamoqKpWKlJQULCwsdM5lZ2cTGRmJu7t7qTFC5aFALXM0Mom4tGxszY1o5V6jWmaiBI+HqrxXnnbirqUStOQcaUnZ6Okr6PC6F56+9iWWz4+P58Y775J97hySgQGOX3+FRffuRcpdTLrImJ1jSMxOpJZZLX7u8jPOFs7IajXBv0zE/1YgAMH1JuE35NMi9QWCJ5ULh6PZveICNi7m9HnZlMh+r6AwMaFeSDBv7R7D0ZijTPWfyqv1XgXgyNL38L8VyDFVd1pOWPeYrX98lPb8fpBKz0jJsszAgQMZO3YsV69exc3NDTMzsyI7+3x9fQkODmbDhg2V7UpQBkqFhH+dmrzUxAn/OjWFEyV4ZrF1tWDAxy1x8alBfp6aXYFh7F97iYL84peX9WxscF2xHLNOnZBzc7n13gQSly0r8j3lVcNLm1LmZvpNhv47lPCkcM3M1JvfcsRpBAB+l74heM1sCvLzOX/oH45v/Znzh/6h4L4fjwLBk8S1s/ct64Vo4qOMWzSnQClxNuEsAE1t7gWVW8RrNKZkZ18E5aPSjtQvv/zC+vXr8fHxITQ0lIiICJ3t74X07NkTpVLJP//881CGCgQCAYCRmT49xzamRQ83AM7uvcmmb0+Snlx8wL7CxIRa3y/EapgmDVDc3HnETJuO/IDz42zhzMoXV1LPqh6J2YmMCBrB8Zjjd52p7zjiqNFA8wv/moxZztTfOYQWxydRf+cQEmbV49T25dV30QJBJSjIV3P9QhIAbg2tybybFsbU15fw5HCy8rMwNzCntmVtAPJyc3DP0egC2tZv93iMfgp5KEdKoVCwfv16GjZsWGI5U1NT6tSpo136EwgEgodFoZDw7VObnu82wsBYj5irqfzxxVFuhScXW15SKrH/+GPsPv4YJIk7f/zBjTHvUJCuG2dlY2JDYPdAmtk2Iz0vnTG7xrDn+h6NM/XWfE6aaFSeLdBNlGwjJ9L48DjhTAmeKKKv3CEvuwBjCwNsnEzIPK6JjzJp5avNr9fYpjEKSeMKRIUdxUTKIRVTXOoJ6YPyUmlH6vz589SuXRsvL68yy1pZWREdLZLoCgSCqsWtkTUDPm5BzVpmZKXl8feCUE7uuFaieHCNYUOp9cMiJGNjMg4e5Nprr5P3gKK9hYEFS7osoUOtDuQU5DBh7wQ2XdmEWq3GKfMCxTVduJrucGSGWOYTPDFE3SfCmXPxIuq0NBTm5hj5eGsdqftlDxIvHAAg0sgHhbJq9AWfByrtSKnV6nLvNEpNTRW7kgQCQbWgsjHhlQ+b4+lnj6yWObIhgqCfz5GbVbxDY96pE64rVqC0sSYnPJyoAQPJvnBBp4yRnhHfdfyOl+q8RIFcwGeHPuOboI+xI5GSBOgVEtiTyMWQqknzJBA8LIX6UZplvbuyBy1aICmV2h179yua69/WLP1l2jV/tIY+5VTakXJ3d+fKlSukp5e8BRk0uc3Cw8Px9vaubFcCgUBQKvoGSl4Y7k37IZ4olBJXT8Wz/svjJN3OKLa8ccMGuK9di2FdD/Lj4rj22uuk79unU0ZPocfnrT9nRP0RAKxO/Jd5VpbIQAFwzMiQbaYmHDMy5H4Z2azkW9VyjQJBRbgTm8md2EwUSgln7xpkhBTqR7UiJiOGmIwYlJKSBtYNtHWc0jTB5+Z1Wz8Wm59WKu1I9enTh5ycHKZOLV3x9/3330eWZa0ytUAgEFQHkiTRoJ0TfT9ohpmVIXdiM1n/1XEuHy+aEghA38kJ199/xzTAH3VmJjfeeZfkNWuKtPl+i/eZ2HwiAL9ZWvCmvQ3dnB0Z6WDHZFtrRjrY0c3ZkV0mxgAYWwntHcHjp3A2ysHDEn09mazjJwBNoHnhsp5nDU9M9DW59GJuXMGeBPJlBe6NRaB5Rai0I/XBBx/g6OjIggULePXVVwkKCiI7OxuAyMhINm/eTOfOnVmzZg3u7u68++67VWa0QCAQlIS9u4pXP2qJk6cV+TkF7Fh2noPrL1NQUFQiQWlujvOSJahe6QdqNTEzZhL79TfID6jnv9HgDab7TUeSZY4ZGxP7QPxInFLJRFtrNptY4uVbvnyZAkF1EnVX9sCtYU2yz59HnZmJUqXC0NOTU3Eacdr746Nundmrqafnjqm55SO29umm0o6UlZUV27dvx93dnb/++ouePXty8qRGf8LDw4O+ffuye/duateuzT///IOpqWmVGS0QCASlYWJhQJ9xjWnWTaOcf/q/G/z93SkyUopKJEj6+jjMmoXNe+MBSPr1V269NwH13R+Ghbxc92VMFcYgyzwYKCXfff99TRNC1s6ujksSCMpNbnY+ty/fATTxUYX6USatWiIpFMXGR+VFaRIVJ1o1eZSmPhM8VIqY+vXrc+bMGRYsWED79u2pUaMGSqUSlUqFv78/c+fO5fTp0zo5zATPHnv37kWSJO7cufO4TREItCiUCvz71uHFMQ0xMFISfSWFP2Yf4/aVO0XKSpKE9ZgxOM6di6SvT9qOHVwfPoL8xERtmZNxJ0mXs4s4UYXIkkSMnh76N38gePWM6rosgaBMblxIQl0go7IxxtLOhMzC+KhWvmTmZRKeFA7opoapmRwKgFIkKq4wD51rz8TEhP/7v/9j9+7dxMfHk5ubS1JSEgcPHmTixIliJupRoC6AyANw9k/Nv+qCsus8BB06dOC9996r8nYlSWLTpk1V3u6DJCcnM3ToUFQqFSqViqFDh5bpBG7YsIFu3bphbW2NJEmEhoYWKTN69Gjq1KmDsbExNjY2vPTSS1y8eFGnTJ8+fXBxccHIyAgHBweGDh3K7du3q/DqBA9Su4kNr37UkhqOpmSm5vL3t6c4/d+NYiUSVL164hL4K0qViqzTp4kaOIicuxp48Znx5eovXqnE7/K3BK+aXpWXIRCUm2tn7+3Wk3NzyTylWcoz8W3FuYRzFMgF2JvaY2+qSa+UmZ6Ce14EALUadngsNj/NPJT8geAJIGwzzG8Ay3vBX29q/p3fQHNcUCxDhgwhNDSUoKAggoKCCA0NZejQoaXWycjIoHXr1nz55ZcllmnevDmBgYFcuHCB7du3I8syXbt2paDgnmPbsWNH/vjjD8LDw/nrr7+IiIigf//+VXZtguKxtDOh/+QW1G1ph1otc3D9ZXb8cp7c7KISCSYtWuC6dg36Li7k3bxJ1OAhZBw9io2JTbn6iq+hiZHyu/IdwaumVel1CASloVbL3LyYRMSpOABc6tcg6+xZ5KwslDVqYFi3brHxUZGnD6InqYmjBna16jwO059qKu1IOTo6Mm7cOI4cOVKV9ggqQthm+GMYpD4wo5EarTleDc7UiBEj2LdvHwsWLECSJCRJIioqCoATJ07QokULTExMCAgIIDw8XKfuli1baN68OUZGRtSuXZsZM2ZoE1y7ubkB0LdvXyRJ0r6PiIjgpZdews7ODjMzM1q2bMmuXbsqbf+FCxcICgpi2bJl+Pv74+/vz9KlS9m6dWsRe+9n6NChTJ06lc6dO5dY5u2336Zdu3a4ubnRrFkzZs2axY0bN7TjAzBhwgT8/PxwdXUlICCAKVOmEBwcTF5eXqWvSVA+9A2VdBnpQ5sBdVEoJK4cj+PPr06QHFNUIsHQ3R23tWswbtIEdUoK1998C4/gW9iZ2CFRci5LI6UR/V9fxBHntwDwuzKf4JWl72wWCKqCiFNxrPj4MH/PDyU3S/PjbffKi4TvOA+ASatWmtn0u/FR9y/rpV0+BMANs0ZIiodeqHruqPSIxcXF8cMPP9CmTRtq167NJ598wtmzZ6vStucPWYbcjPK9slPh3w+B4hSc7x4LmqwpV1ZbJahAF8eCBQvw9/dn1KhRREdHEx0djbOzMwCffPIJ8+bN4/jx4+jp6TFy5Ehtve3bt/P6668zbtw4wsLCWLJkCb/99huzZ2sCc48dOwZAYGAg0dHR2vfp6en06NGDXbt2cerUKbp160bv3r25fv26tu0xY8ZgZmZW6quw/JEjR1CpVPj63kvI6efnh0ql4vDhw+Ueh7LIyMggMDAQd3d37fg8SFJSEqtXryYgIAB9ff0q61tQMpIk0biTMy9PbIqJyoDk6AzWf3lc+wv+fvRq1MDlt0DMu3eHvDxipnzE7MtNQJZRqMHnmprW59X4XFMjqTV/Q9kF2by1cxTugyZxxHkUAH4RCwhe8dmjvEzBc0bEqTiClpwj447uZoqMOzkcjnIizroxpr6tUMtqTsefBnQdKeNYzfdtnmPLR2bzs4ReZSuePXuW1atXs27dOiIjI/nyyy/58ssv8fHxYciQIQwaNAh3d/eqtPXZJy8TvnCsosZkzUzVl8U/xHX4+DYYlC+WTaVSYWBggImJCfb2mvX1wjig2bNn0759ewCmTJlCz549yc7OxsjIiNmzZzNlyhSGD9ckfq1duzaff/45H374IdOmTcPGRrNsYmlpqW0XoHHjxjRu3Fj7ftasWWzcuJHNmzczduxYAGbOnMkHH3xQqt2OjppxjYmJwdbWtsh5W1tbYh5IFVIZFi9ezIcffkhGRgZeXl7s3LkTAwMDnTKTJ09m0aJFZGZm4ufnx9atWx+6X0HFcPCwZMDHLdmx7Dy3L98haMk5mnZ1we+l2iiU935fKoyMcPp2HvHOtUhcugyLFf+wvJEHWVFXsUq9F96QbKHk5ptdWKg6yrnEcwzcOpBve33Lka3gf2MpflcXcmQF+A/7/HFcruAZRq2WObDucqllLnv0p1XLply9c5W03DSM9YzxtNJsAlMXFOCWpZm1quHVttrtfRap9IxU/fr1+eKLL4iIiODIkSOMHTsWOzs7zp8/z6effoqHhwcBAQEsWrSI2NjiBfEEzxaNGjXS/t/BwQHQzFyCZtlv5syZOrNEhbNamZmZxbYHmpmdDz/8EB8fHywtLTEzM+PixYs6M1K2trZ4eHiU+tLTu/ebQSpm15Usy8UeryivvfYap06dYt++fdStW5cBAwZo9dUKmTRpEqdOnWLHjh0olUqGDRtWYm44QfVhqjKkz3tNaNJZ82Pj1I7rbF4YSmZqrk45SaHA9v33sZ8xAxQKjM5c0XGiAKzS1DScH8RK4/9Rz6oeSdlJvLX9La63bslhF83MlP/VhRxZ/smjuTjBc0P05TtFZqJ0kCRyjGqQWGDFqXhNfFRD64boKTTfiTcun0ZFBlmyAe4N/B6Fyc8clZ6Ruh9fX198fX2ZP38+e/bs4ffff2fjxo0EBwcTEhLChAkT6NSpE9u3ixxUpaJvopkdKg/XDsPqcgQpv/YnuAaU3W8VcP/yVKFTUrgpQa1WM2PGDPr161eknpGRUYltTpo0ie3btzN37lw8PDwwNjamf//+5Obee9iNGTOGVatWlWpbWFgYLi4u2NvbF+vYx8fHY2dnV/oFloPCnYB169bFz88PKysrNm7cyODBg7VlrK2tsba2pl69enh7e+Ps7ExwcDD+/mLb8aNGqVTQun9d7NxV/LfiArfC7/DHF8fo/nYD7GurdMpa9n+F+PnzKUhOLtrQXW2p/O9+ZkXQZqaHzCQoKohZIbPo59WPfN6i3fVl+Ecu4shyGf/hXzyiKxQ862SkluJE3Udmai6hOaGA7rJe7Pl9uAJXDT2pbyBy4laGKnGkCpEkiU6dOtGpUyd+/PFH/v33XxYtWsSuXbseKkD4uUGSyr3ERp1OYOGoCSwvNk5K0pyv0wkUVZvF28DAQGcnWnlo1qwZ4eHheHh4lFhGX1+/SLsHDhxgxIgR2hRD6enpOsHbULGlPX9/f1JSUjh69CitWrUCICQkhJSUFAICynA4K4Esy+TklPxFVzgTVVoZQfXj0dyWGo6mBC05S3JMJhvnnaTNq3Vp0N5J+6Mg8/iJ4p2oQmSZ/JgYOH2Br9t9jXdNbxacXMCGyxu4Yt+IDGkEL177Df/IHwj+DfxGCGdK8PDo6ZdvYcnUwpDQ86GA7o49bmjEOlOtm1WxZc8PVepIFZKRkcGmTZtYs2YN+x5IBCqoIhRK6P6VZnceErrO1N0lqu5fVrkTBZoddiEhIURFRWFmZlYuKYypU6fSq1cvnJ2defXVV1EoFJw5c4azZ88ya9Ysbbv//fcfrVu3xtDQECsrKzw8PNiwYQO9e/dGkiQ+++yzIv3Z2toWG/dUHN7e3nTv3p1Ro0axZMkSQLPbrlevXjrCsV5eXsyZM0frwCUlJXH9+nWt5lPhDj97e3vs7e25evUq69ato2vXrtjY2HDr1i2++uorjI2N6dGjBwBHjx7l6NGjtGnTBisrK65evcrUqVOpU6eOmI16AqjhYEr/KS3YveICESfj2b/2EjGRKXR4zQt9AyX58eXTkcqPj0eSJEY2GImnlSeT9k/iTPwZvja1JtF1IK9fW4df1A8c+U2N/4iS5TQEgrKIjkhh35pLpReSZUwt9DCoVcD1YE1IRGPbe3Gn9qma4HMTj6r/Ifm8UGX7HPPy8vj7778ZNGgQdnZ2DBs2jG3btqFQKHj55ZdZt25dVXUlKMSnDwxYARYOusctHDXHffpUS7cffPABSqUSHx8fbGxsdOKVSqJbt25s3bqVnTt30rJlS/z8/Pj2229xdXXVlpk3bx47d+7E2dmZpk01qQu+++47rKysCAgIoHfv3nTr1o1mzR7ul9Pq1atp2LAhXbt2pWvXrjRq1IiVK1fqlAkPDyclJUX7fvPmzTRt2pSePXsCMGjQIJo2bcpPP/0EaJYnDxw4QI8ePfDw8GDAgAGYmppy+PBhrZNnbGzMhg0beOGFF/D09GTkyJE0aNCAffv2YWgoptSfBAyM9Og2qgGt+3sgKSQuhcTy11cnuBOXiZ5N+XSkJKN7n2Vrp9as7bkWD0sPErISmKd3jK/dNPeQf9SPHAmcXC3XIXi2kWWZ0//dYNO8k2Sm5GJqaVBSQQDaDPbmTKLGYfKw9MDCwAKA5PhoXNS3AHBt1KHa7X5WkeSHiHKVZZn//vuPNWvWsHHjRlJSUpBlGYVCQYcOHRgyZAivvPIKKpWq7MaeQVJTU1GpVKSkpGBhYaFzLjs7m8jISNzd3UuNESoX6gJNzFR6LJjZaWKiqmEmSvB4qNJ7RVBubl1KZvvSc2Sl5WFgrEfn4Z7kvzeI/NjYUiVDJDMzbMeNw2rIYKS7mxwy8zL59NCn7Ly2E4B2sivzow6gDxxxHYP/G189iksSPAPkZuWze+VFIk5qNvJ4tLCl4+te3LiQxIF1l3UCzw2zk2hsdY2W373Pt8e/JfB8IP3r9Weav0YoNnTXGpocHMM1RS1cp55/LNfzpFLa8/tBKr20N27cONavX09cXJw2zqNly5YMGTKEgQMH6mxhF1QzCiW4i22rAkFV4lTPigEft2L70nPEXE1h20/nafDSJ9j8PB4kiTuqOuQYWGCYm4plSgSSrEavlhP5N28R+8UX3Fm/HrtPPsHUzxcTfRPmtZ/HsrPL+P7U9+yXrvGqe2OWXT+L/7WfOPKrjP/Irx/3JQuecBJvpRP08znuxGaiUEq07l+Xhh00cXx1mtri3thGs4svNYe0BV9jfDwIx9kayY3iFM2zrmq082JVjXEt0pugvFTakVq0aBGgiSUZPHgwQ4YMoU4dIS0vEAieHcysDHl5YlMO/XWFs3tuci5cgWXvBWQlZpCjb64tZ5iXgl8bM7yGdeLO+j+Jnz+fnMuXuT5iBObdu2P34ST0HR0Z1WgUnjU8mbJ/ChF5yfR18WDxrSj8ry+560x98xivVvAkEx4Sw97VF8nPVWNmZUi3UUV3lioUEk6eVqgzMggP3QnImPj6kluQy/lEzYxTU9um2vKq+JMASM6+CCpPpWOk3n//fU6cOEFYWBifffaZcKIEAsEziVJPQbuB9egy0geFnsSdVIWOEwWQo69iX4iSq2cSsRo0kDpB/2I1ZAgoFKQFBRHRoycJP/6IOieHdrXa8XvP36mtqs0dshnm5MhGM1P8r//MkV9K330qeP4oyFOz9/dwdgWGkZ+rxtnbigEftyziRN1P5smTkJ+PvqMjBrVqEZYYRp46jxpGNXA21+im5eZkUztXs2nGtn77R3ItzyqVdqS++eYbbUCwQCAQPOt4tLDD0Lj0VD4H/7iMWi2jtLTEfupnuG/4C5MWLZCzs4lfsJCrPXuR9t9/uFq4srrHajo6dyQfNVNtavJFDSta3FgqnCmBltSELDbMPcH5/bdAgpY93ej1f00wNi8huPwumUc1kgYmd1NhhcaFApplvUI5j8izhzGS8riDGS51GxXbjqB8iOyEAoFAUA6iL98hKy231DLpyTlEX76jfW/k5YXLyhU4zpuLnp0deTdvcvN/Y7kx6m30b8Yxv+N83m38LgBrVOaMsrel3q1fOPLL+8jlkBURPLtEnU3gjy+OEXctDUNTPXqNbUyr3rVRKMrOwJARUuhIabTytPFR9wlxJocf1PRj3EAkKn5IxOgJBAJBOSivgvTZfTdJTczSvpckCVXPntTZ9g81R49G0tcn4+BBrvZ5ifhv5vG2x1AWdlyIqb4pJ4yNGORkj0XsckJ+/UA4U88harVMyOar/PPDGXIy87F1s2DgJ61wrV+zzLpyQQFpe/eRffYsgGY2VJYJjQ8FdOOjDKI1iYqz7JtX/UU8ZwhHSiAQCMqBqUX5tL4iTsaz8pMjbPr2JBcO3yY3Ox8AhakpthPeo/bWLZh16AD5+ST9+isRL75I05MprH5xFW4WbsTo6THcwY74O2sI/lXMTD1PZKXlsmVhKMe3RQHQsL0T/d5vhnmNsmVPUnfs4MoLnbk5ZoxWnuPa60OJ3LyGpOwk9BX6eNf0BkBWq3FO1zhbqnpix/fDIhwpgUAgKAcOdS0xtSzdmTI00cOxniVIcOvSHXavuEjgpIPs/PU8N8KSUKtlDFxdcf7pR5yX/ISBqysF8QlET/kI5Tuf8Zv7VNrVakeOQsHHNtbsz9zAoV/eE87Uc0B0RArrZh/j5sVk9AwUdBnpQ7vBnijLkQImdccObo1/T5Oi6D7yY2PJnvw5rcLV1K9ZH0Ol5v6Nvn4ZG5LJk5W4N2pTLdfzPFEtKWIEAoHgWUOhkGg7sC5BS86VWKbjUC/qNLUlLSmbS0djuHgkhjuxmVw6Gsulo7GYWhri6WuHp68DNdq3x8Tfn6Tly0n48SeyQkPJGjycT17tT8MXhvJD1EpWqSy4nPUvmb/k0eXNH0QsyzOILMuc2X2Tw39dQa2WsbI3ofvbDanhWL68q3JBAbFfzCleJPbusRE71Rx9+V5A+e2ze3EEIvXrUM/UvGg9QYUQf5XPAAXqAo7FHGPb1W0cizlGgbpiCYUflr179yJJEnfu3Hmk/QoEj5o6TW3pPrpBkZkpMytDuo9uQJ2mmnRA5jWMaN7djSHTfek/uQUN2jthaKJHxp0cTm6/zpqZIfzxxTHOHYrFdNBw6vy7DYtevUCWSfljPR0n/cXPKS9jrFYSYmzEPGkv6395U8xMPWPkZuezY9l5Dq7X7Pb0aGFL/yktyu1EgSaZ9oMzUfcjAdZp0CrWTHus4FowAEk1xM77qkA4Uk85u67tottf3Ri5fSSTD0xm5PaRdPurG7uu7aq2Pjt06MB7771X5e1KksSmTZuqvN0HSU5OZujQoahUKlQqFUOHDi3TCZw+fTpeXl6YmppiZWVF586dCQkJ0Z6PiopCkqRiX+vXr9eWu3TpEi+99BLW1tZYWFjQunVr9uzZU12XKqgG6jS1ZdgXAbw8oSld3vTh5QlNGTo7QOtE3Y8kSdi5W9B+sCdvfNWG7qMb4N7YGoVCIv56GgfWXea3yYfYuSGW7NcmUWvFSgy9vVGnpmK5+E+W/+lAwHUjbuvr8Y3eURb+Okg4U88IibfTWT/nOFdOxKFQamY7u75ZHwOjii0UlTeZtnuelfb/1smhAOi7iWTpVUG1OFIFBQWEhISwceNGoqKiqqMLARonauLeicRmxuocj8uMY+LeidXqTD3NDBkyhNDQUIKCgggKCiI0NJShQ4eWWqdevXosWrSIs2fPcvDgQdzc3OjatSvxd7/EnJ2diY6O1nnNmDEDU1NTXnzxRW07PXv2JD8/n927d3PixAmaNGlCr169iCnlF6XgyaNQQbpeS3ucPK3KtSVdqa+gTlNberzTiBFftabtwLrYupqjVstEnk4gaMk51q3L5MarX6A3YSYKlQoionhvdTpTN+ljmi6xTP8CHwT2Ij8/7xFcpaC6CA+J4c8vj3MnNhMzK0P6vt+MRh2dtRpP5UWWZXIjI8tV1sqpNgDpqcm45WvquDTpWDHDBcVS6aTF27dvZ8mSJQwYMIBBgwZpj9++fZs+ffpw6pRGt0KSJD777DOmTZtWNRY/RVQ0abEsy2TlZxXXVBEK1AW8vPll4jLjSixjZ2LHxj4bUZaRwNhYz7jcf8AjRoxg+fLlOscCAwN544032LVrF5MnTyYsLIwmTZoQGBiIp6enttyWLVuYPn0658+fx9HRkeHDh/PJJ5+gp6eHm5sb165d05Z1dXUlKiqKiIgIJk6cSHBwMBkZGXh7ezNnzhw6d+5cLnsf5MKFC/j4+BAcHIzvXbG64OBg/P39uXjxoo69pVH42e7atYsXXnih2DJNmzalWbNm/PLLLwAkJCRgY2PD/v37adtWs1MmLS0NCwuLUtsRSYufbRJvp3MpJIbw4BgyUu7pVFnaGOKYHY7lrl8xykokT1/iT3+Jrb4SHpIVPw/ejKWxVSktC540CvLUHFx/mXP7bwHg7G1Fl5H1yxTYLI686GhiPp9F+u7dpZZTA1lWxjQ/eAxJqeTs/r9puHsY0djgMP1KZS7jueCRJC1esWIFf//9N59++qnO8QkTJnDy5ElUKhXu7u6cPXuWmTNn0rlzZ1q3bl3Z7p4LsvKz8P296nIexWbGErA2oMxyIUNCMNE3KVebCxYs4NKlSzRo0ICZM2cCcP68JofTJ598wrx587CxsWHMmDGMHDmSQ4cOARrH+/XXX2fhwoW0bduWiIgI3n77bQCmTZvGsWPHsLW1JTAwkO7du6NUapy/9PR0evTowaxZszAyMmL58uX07t2b8PBwXFxcABgzZgyrVq0q1e6wsDBcXFw4cuQIKpVK60QB+Pn5oVKpOHz4cLkcqdzcXH7++WdUKhWNGzcutsyJEycIDQ3lhx9+0B6rWbMm3t7erFixgmbNmmFoaMiSJUuws7OjeXOh5fK8UtPRDP++Hvi+VIdbF5O5GBLN1ZPx3InP4Q5u4DuTmvm3sb28i1cPhfLCmRwCOyfRb00XfnxpNZ41y+f8Cx4vqYlZbP/5HHHX0kCCFj3caNnTvVyzmfcjFxSQvGYt8d9+izozE/T1Me/UibQdO+4WuDc3IqOJkUoc/RJS4XfqFc138i3zRjhUxYUJKr+0d+zYMVQqFc2aNdMeS0pKYuPGjdjY2HDp0iVOnjzJmjVrkGWZ+fPnV9rIxYsXa3+NN2/enAMHDpRY9uDBg7Ru3ZqaNWtibGyMl5cX3333XZFy8+fPx9PTE2NjY5ydnZkwYQLZ2dna89OnTy8S62Jvb1/pa3hWUKlUGBgYYGJigr29Pfb29lqnZ/bs2bRv3x4fHx+mTJnC4cOHtWM6e/ZspkyZwvDhw6lduzZdunTh888/Z8mSJQDY2NgAYGlpib29vfZ948aNGT16NA0bNqRu3brMmjWL2rVrs3nzZq1NM2fOJDQ0tNSXo6MjADExMdjaFo1lsbW1LXN5bevWrZiZmWFkZMR3333Hzp07sba2LrbsL7/8gre3NwEB9xxZSZLYuXMnp06dwtzcXNtOUFAQlpaW5Rl+wTOMQiHh7FODLm/U541v2tBpmDdO9SwBSNRz5IL3MA62/pIE+2GM2lWXN//MYuLyV9keuf3xGi4ok6izCfwxW1el3LecKuX3kx1+iWtDXiN21izUmZkYN2lC7Q1/UWvBfJwWzEfPzk6nfJKFxLx+Cmq/NER7zDTuBAAFtVo9/IUJgIeYkYqPj9fOCBSyZ88e8vPzGTx4sPZB2L9/fxwcHDh9+nSl+lm3bh3vvfceixcvpnXr1ixZsoQXX3xRO8PwIKampowdO5ZGjRphamrKwYMHGT16NKamptoZkNWrVzNlyhR+/fVXAgICuHTpEiNGjADQcbrq16/Prl334owKHYbqwljPmJAhIWUXBE7EnuDd/94ts9ziFxbT3K702Q5jPeNy9VkWjRrd217r4KD5rRMXF4eLiwsnTpzg2LFjzJ49W1umoKCA7OxsMjMzMTEpfkYsIyODGTNmsHXrVm7fvk1+fj5ZWVlcv35dW8bW1rZY56gkilvGlGW5zOXNjh07EhoaSkJCAkuXLmXAgAGEhIQU6TsrK4vff/+dzz77rEgf7777Lra2thw4cABjY2OWLVtGr169OHbsmHbMBAIDIz28AxzwDnAgNTGLSyGxXAyOJiUOYux9ibH3xTA7mdG7jnL67JdcevM07wa8X+YyvuDRolbLHNsaqRXYtHU1p9vbDbCoWbHvXHVODgk//kjisl8gPx+FqSk270/EatAgrSSGRdeumL/wgmYXX3w8twwzePfmTMwMLahjWQeAgvx83LPOgwQ1vYQQZ1VRaUcqMzOziGNx8OBBJEkqEutRq1Ytzt6VrK8o3377LW+++SZvvfUWoJlJ2r59Oz/++CNz5swpUr5p06Y6yZTd3NzYsGEDBw4c0DpSR44coXXr1gwZMkRbZvDgwRy9m+ixED09vQrNQuXk5JCTcy+NRGpqavkvFM0DvrxLbAGOAdiZ2BGXGYdM0TA3CQk7EzsCHAMe2Zervv69hK6FTon67g4jtVrNjBkz6NevX5F6pcX9TJo0ie3btzN37lw8PDwwNjamf//+5ObeiyWpyNKevb09sbGxRc7Hx8dj98CvuQcxNTXFw8MDDw8P/Pz8qFu3Lr/88gsfffSRTrk///yTzMxMhg0bpnN89+7dbN26leTkZO2a++LFi9m5cyfLly9nypQppfYveD6xqGlMix5uNH/RldjIVC4Gx3A5JJocrLjh0g1TumGyJIof/pnBwA/GYVez+FlSwaMlKy2XHb+c5+bFZAAatHeiTf+65RLYvJ+MkKPETJ1K7t0YUrMXXsD+s0/RL+bZJCmVmN7Nr7f1wmrk2xKNbBuhkDR9Xg8/gbuURaZsiJtPy4e5PMF9VNqRsrOzIyoqivz8fPT0NM1s374dhUKhDaQtJCsrC1PT8utiFJKbm8uJEyeKPGC6du3K4cOHy9XGqVOnOHz4MLNmzdIea9OmDatWreLo0aO0atWKq1evsm3bNoYPH65T9/Llyzg6OmJoaIivry9ffPEFtWvXLrGvOXPmMGPGjApcYeVRKpRMaTWFiXsnIiHpOFMSGidmcqvJ1eJEGRgYUFBQMa2qZs2aER4ejoeHR4ll9PX1i7R74MABRowYQd++fQFNzNSDO0FnzpzJBx98UGr/hUt7/v7+pKSkaD97gJCQEFJSUnSW4cqDLMs6jnMhv/zyC3369NHOyhaSmZkJgOIBUUWFQqF1OAWCkpAkCfvaKuxrq2j7al2iziYQsmInd7JsyTR1Q5nsxp8fn8TWVUHzHo1xbVATpZ5QuHkcxFxNIejnc2TcyUHPQEHH172o16pioSEFKSnEfvMNKX/+BYCejQ12n32KRdeu5ap/Ok6zCtTU5t7EQlzYAdyBq0beNNCveIC7oHgq7Ui1bduWtWvXMnPmTD788EPWrVvHxYsXad26tU68R15eHpcvX6ZevXoV7iMhIYGCgoIiMwV2dnZlxrPUqlWL+Ph48vPzmT59unZGC2DQoEHEx8fTpk0bZFkmPz+fd955R8dh8/X1ZcWKFdSrV4/Y2FhmzZpFQEAA58+fp2bN4pNHfvTRR0ycOFH7PjU1FWdn5wpfd3np7NqZbzt8y5dHv9SRQLAzsWNyq8l0dq3czraycHNzIyQkhKioKMzMzMrlBEydOpVevXrh7OzMq6++ikKh4MyZM5w9e1br5Lq5ufHff//RunVrDA0NsbKywsPDgw0bNtC7d2/tDtAH+6vI0p63tzfdu3dn1KhR2vist99+m169eukEmnt5eTFnzhz69u1LRkYGs2fPpk+fPjg4OJCYmMjixYu5efMmr776qk77V65cYf/+/Wzbtq1I3/7+/lhZWTF8+HCmTp2KsbExS5cuJTIykp49e5bLfoEA7kopNLOlTrPXOLh8Hpm7z3O7oB0ZZi7EXYd/fzqLkYmSuq0c8PSzx9bVvMJb6wUV50GVcks7E7qPbkBNR7OyK9/XRtq//xLzxRwKEhIAsBw4ENv3J6IsY/fY/ZyK1+ycb2LbRHtMcVMTOpJuIza3VClyJTl37pxsYmIiKxQK7UuSJPnff//VKffPP//IkiTJY8aMqXAft27dkgH58OHDOsdnzZole3p6llr36tWr8pkzZ+Sff/5ZrlGjhvz7779rz+3Zs0e2s7OTly5dKp85c0besGGD7OzsLM+cObPE9tLT02U7Ozt53rx55bY/JSVFBuSUlJQi57KysuSwsDA5Kyur3O2VRH5Bvnw0+qj8T8Q/8tHoo3J+Qf5Dt1ka4eHhsp+fn2xsbCwDcmBgoAzIycnJ2jKnTp2SATkyMlJ7LCgoSA4ICJCNjY1lCwsLuVWrVvLPP/+sPb9582bZw8ND1tPTk11dXWVZluXIyEi5Y8eOsrGxsezs7CwvWrRIbt++vTx+/PhK25+YmCi/9tprsrm5uWxubi6/9tprOrbLsqy9LlnWfFZ9+/aVHR0dZQMDA9nBwUHu06ePfPTo0SJtf/TRR3KtWrXkgoKCYvs+duyY3LVrV7lGjRqyubm57OfnJ2/btq1Ue6vyXhE8mwT/MVeOn1xDXtuvg7z5xSnyzyP+kheN/k/7Wj3tiHz830g5LUncQ9VFTlaeHPTzWe2YBy09K+dk5VWojdxbt+Trb4+Wwzy95DBPL/nKiz3kjOPHK2xLdHq03OC3BnLj5Y3ljNwM7fEb0+vJ8jQL+fTu9RVu83mjtOf3g1RaRwrg6NGjfP7551y5cgVnZ2fee+89evTooVNm8ODBBAUFsWLFCnr37l2h9nNzczExMWH9+vXapR2A8ePHExoayr59+8rVzqxZs1i5ciXh4eGAZjbNz8+Pb775Rltm1apVvP3226SnpxdZeimkS5cueHh48OOPP5ar34rqSAkExSHuFUF5CFk/j+bnZxKYXQOnw8ZY5HkRY+9LvG0T1NLd+EUJanla4eVnT+2mtugbFl36V6tloi/fISM1B1MLQxzqWlZ4d9nzRuLtdIKWnONObCYKhUTrVz1o2KFWuWcB5YICklevJm7+AuS7kgbWo0dT8+1RKAwqvgQXFBnEpP2T8K7hzR+9/wAgIeYG1j81QC1LpL13BZWViKUrjUeiIwXQqlUrtmzZUmqZNWvWVLp9AwMDmjdvzs6dO3UcqZ07d/LSSy+Vux35gViWzMzMIs6SUqlElmVK8itzcnK4cOFCkfgvgUAgeBLwffV9jkoSo87NYEsvE/6+Gc6A/RfwvGREnE1T4n1eJFFdk5sXk7l5MRm9NZfwaGqDp78DTnUtkRQSEafiOLDuMhl37n1fmloa0nZg3WJT4Ag0KuV7V18kP1eNmZUh3UY1wL62qtz1s8PDif5sKtlnzgBg3KwZDp/PxLBOnUrbFBofCkBT23vxUddP78UauKZ0wV04UVXKQzlSj4KJEycydOhQWrRogb+/Pz///DPXr19nzJgxgCYu6datW6xYsQKAH374ARcXF7y8vADNTsK5c+fyf//3f9o2e/fuzbfffkvTpk3x9fXlypUrfPbZZ/Tp00e7E/GDDz6gd+/euLi4EBcXx6xZs0hNTS0SkC4QCARPCq36T+SoJNH77HRq2+fzwShnXjyYzQunjuAYc4RsSydSu4/mer4TqQnZXAyO4WJwDGY1DLF1teDqqaJ52zLu5BC05JxOUmbBw6uUq7OzSVj8I4m//qqRNDAzw/aD97EcMEAraVBZTsUVjY/KjTwCQJxlY9wfqnXBgzzxjtTAgQNJTExk5syZREdH06BBA7Zt24arqysA0dHROppCarWajz76iMjISPT09KhTpw5ffvklo0eP1pb59NNPkSSJTz/9lFu3bmFjY0Pv3r11NI5u3rzJ4MGDtWk9/Pz8CA4O1vYrEAgETyKtXpnAUSRanJnOysRI3u7sxc4maYzcWYDXzVsYrZ2Ko4sLilGTuZ7ryJXjcaQn5ZCeVHry24N/XMa9sY1Y5uPhVcozgoOJnjaNvGuaZ5d5ly7YffoJ+mVIsJSHzLxMwpM0YSz3z0hZJmqcK4WL30P3IdDloWKkQLM9ffXq1Zw+fZqkpCTy8opPpilJEhEREQ/T1VOHiJESVAXiXhFUhmMbFtD89DQKJJkPnFqwWz+WtudlRu7XxzRFs3Rn2r4d1pMmc+4CHPsnqsw2X57QFCfP5zu/X9TZBHYFhpGTmY+hqR5dRtbHtX7xO7kfJD85mbhv5pKyYQMAera22E/9DPNK5g59kAJ1AasvrOab499gZWjFngF7UCqUZGdloPjSBQMpn5uvH6KWR4Mq6e9Z5pHFSP3vf//jp59+KjGu6H7E1luBQCB4dLTsN56jkkSL0KksuHWcefbtWN3oFsfq5vLmcSvaH04jY99+Mg8fQfHKe4BbmW3+u+Qstbxq4FBHo2dl7Wz23GhVPYxKuSzLpP6zjdgvvqAgKQkkCavBg7CZMAGluXmV2Lfr2i4dKZzknGS6/dWNKa2mUCtWxkvKJwkLnGr7VEl/gntU2pFatWoVP/74Iz4+PixcuJApU6Zw4sQJLl26xI0bNzh9+jTz588nLi6O7777jm7dulWl3QKBQCAog1Z9x2mW+UI/4/2Y/TjadmGpdRI/tI5newMTph9zxuDYeXKDNkGT98psLyczn4iTcUScjAM0ela2ruZaoVD72ipMLJ49ocestFx2/nqeGxcqrlKed+sW0TNmkLFfkyPWwKMODjM/x6RZ0zJqlp9d13Yxce/EIlku4jLjmLh3ImMUrfACrpk0pMZDxl8JilJpR2rZsmVIksTatWtp0KABhoaGANSpU4c6derQoUMH/ve//zFkyBDGjRvHwYMHRXyRQCAQPGJa9f0/jkkSzU99yuC4ndRU92RlbUdCOc3QF8KZ3vElGq45hWF2MjmGllDc6oEsY2IMXcY0JTYqlZirqcREpJCdkUf0lRSir6Roi1rYGONQW4V9bQvs66io4Wj2VMdVVValXC4oIGnlSuIXLETOykLS16fmO2OwfustpEpIGpREgbqAL49+WWyqMBkZCYl1eccZDeQ4tKiyfgX3qLQjdebMGVxcXGjQQLPWWrh0J9+X/FVPT4+lS5fyzz//MHv2bDZu3FgFJgsEAoGgIrR8eSzHgOanPqVrwj+Yyr35r/mrrL+8nmnKf3jxg470+mYLZ+sMBVnWdabuhm7UvfInTnU7UMurxt3DMilxWURHpBATmUJMRApJ0RmkxmeRGp9FeIgm+4S+oRI7dwvNjFUdFfbuFhia6D9o4hOH/BAq5dkXLmgkDc6dA8C4RXMcZs7EsJQUY5XlZNxJncwWDyIjk6Qs4KSRIZb12lR5/4KHcKSysrKoW7eu9r2xsWad+M6dO1hZ3QtGVKlU+Pj4lDs3nkAgEAiqHo0zJdH81Ce0TtyC3vE+eHX9lDnHvuRayH/Y3FTTIDubyx6vkmN07zvcMCeZulf+pGbCadL27MHibmC0JElY2plgaWeCd4ADADmZecRGphJzNUXzikwlL7tAq11ViJWDKQ53Z6zsa6uwtDN5ouJoc7Pz2bPyIldOaJYwPZrb0nGoFwZGpT8y1VlZJPzwA4mBv0FBAQpzc2wnfYBl//4PLWlQHBF3Ivj5zM/lKhutMKBho9ZVboPgIRwpe3t7kpPv/WE4OGj+kMLCwmjdWvfDio+PJzU1tbJdCcpALigg8/gJ8uPj0bOxwaRFcyRl1ScrLom9e/fSsWNHkpOTdfIsCgSCJ4uWL/9Ps8x38mN8kzYjbVfzy8BlrFn0PyAF24TT2CSc4Y6lBzkGFhjmpmJ55wrS3WWjW2P/j8SGDTFr2wbTtm0xbtRI57vG0EQfl/o1cbm7i02tlkm6nXHPsYpIISU+i+ToDJKjMwg7FK2pZ6qnjbFyqK3C1s2iWNX1R8GDKuUB/T1o1LFslfKMw4eJnjadvBs3ADDv1g27Tz5Gv5x5QMtLgbqAA7cOsPrCaoKjg8tdL09ywMjYtEptEWiotCPl6enJwYMHtUt5bdq0YcWKFXz11Vf89ddf6Otrpm5XrlzJ9evX8fb2rjKjBfdI3bGD2C/mkH9fEmc9e3vsPv6o3FnCK0qHDh1o0qQJ8+fPr9J2JUli48aNvPzyy1Xa7oMkJyczbtw4Nm/eDECfPn34/vvvy3QCL1y4wOTJk9m3bx9qtZr69evzxx9/4OLiQlJSEtOmTWPHjh3cuHEDa2trXn75ZT7//HNUqnsqx3369CE0NJS4uDisrKzo3LkzX331FY6OjtV5yQKBlpYvvctxSaLpiY9olbyVo+tgXOdppP+pSbguIWN153KJ9bPPniX77FkSFv+IQqXCrHUApm3bYdamNXo2NjplFQoJ61pmWNcyo0E7JwAyU3OJjdQ4VtERKcRdSyMnI59rZxO5djZRY8PdevaFsVa1VZjXNKr2Wav7VcpNLQ3p/nbZKuX5ycnEffkVKX//DYCenR3206Zi3qlTldqWmpvKxssbWXNxDbfSNSKgCklBe6f2hMaHcifnTrFxUshgX5CPg1mzKrVHcI9KO1I9e/Zkx44d7N+/n/bt2zNo0CCmTZvGP//8g6enJ82bNyc2NpZDhw4hSZJWiVxQdaTu2MGt8e9pYxgKyY+N1RxfML/anKmnmSFDhnDz5k2CgoIAePvttxk6dGip6Y4iIiJo06YNb775JjNmzEClUnHhwgWtrtPt27e5ffs2c+fOxcfHh2vXrjFmzBhu377Nn3/+qW2nY8eOfPzxxzg4OHDr1i0++OAD+vfvL5a+BY+UFn3e4TgSTU9MoVXyVoJPFaC2UKBKVVPcApQaSFEpabZxO9lHQkg/eICMQ4dRp6SQuu1fUrf9C4Chjzdmbdth1q4txo0bI+kVfcSYWBjg3tgG98Yap6sgX03CzXRiIlK0M1fpyTnEX08j/noaZ/feracywKG2CrvaKhzqqLBxNi/Xrrki11JMLkG5QNZRKa/lZUXXN0tXKZdlmdStW4n9Yg4FyckaSYMhQ7CZ8B5Ks7LjqMpLxJ0Ifr/wO1uubiErPwsACwMLXqn7CgO9BuJk5qTdtSch6ThThe8nJyZj1FjER1UXlRbkjI2NZdmyZXTs2JGAgAAATp06xYABA3SEN/X09Hjvvff4+uuvq8bip4iKCnLKsoyclVWutuWCAq727EV+XFzxBSTQs7Wj9tYtZS7zScbG5f6lN2LECJYvX65zLDAwkDfeeINdu3YxefJkwsLCaNKkCYGBgXh6emrLbdmyhenTp3P+/HkcHR0ZPnw4n3zyCXp6eri5uXHt2jVtWVdXV6KiooiIiGDixIkEBweTkZGBt7c3c+bMoXMlBewuXLiAj48PwcHB+Pr6AhAcHIy/vz8XL17Usfd+Bg0ahL6+PitXrix3X+vXr+f1118nIyMDvWIeKACbN2/m5ZdfJicnRzuL+yBCkFNQXRzfsoSmxydz0tiAn1JteH+DGhl0nCk1IAHz+ikYM+43Wtq3BEDOzyfrzBnS9+8n48BBss+f12lbYW6OaUAAZu3aYtqmTYVUu9OSsu8tB15NJeF6Gmq17qNKoSdh62J+L4i9tgpTlWGp7RaXS9DEwgA9QwWp8dnAXZXyXqWrlOfevEnM9BlkHDwIgGFdD+xnzsSkadVIGhSoC9h/cz+/X/xdZ/nOw9KD17xfo2ftnhjr6epXPagjBWBrZMPkqAt0zcokYcxZrO1dqsS+54GKCHI+tLL5g6jVao4ePUpUVBTGxsb4+flhVwWy908jFXWk1JmZhDdr/sjt9Dx5AoWJSbnKpqSk8OKLL9KgQQNmzpwJwPnz5+ncuTO+vr589dVX2NjYMGbMGAoKCjh06BAA27dvZ8CAASxcuJC2bdsSERHB22+/zYgRI5g2bRrx8fHY2toSGBhI9+7dUSqV2NjYcPr0aYKDgwkICMDIyIjly5czb948wsPDcXHRfCmMGTOGVatWlWp3WFgYLi4u/Prrr0ycOJE7d+7onLe0tOS7777jjTfeKFJXrVajUqn48MMPOXjwIKdOncLd3Z2PPvqo1GXIZcuW8dFHHxEfX3zqjaSkJN555x1u3brFwbtfyMUhHClBdXJ8yxJiLszgI7uatApXM2KnGuu0e+cTzOG3LgqOeipwMXdhsNdgurp1xdZEN/YnPyGB9IMHyThwkIyDBylISdE5b+jpedepaotJs6ZIJfxwKI783ALirqXd51ylkJVWNIuGeU0jTZzVXceqppMpCqXGLYw4FUfQknMl9qFnqKD7qIa4NihZpVzOzydpxUriv/9eI2lgYID1u+9Qc+TIKpE0SMlJYdOVTUWW7zo6d2SI1xBa2rcs9UdvgbqAk3Enic+Mx8bEBv0LUTTZ9xa3JDucpl16aPueJx6rIyW4x7PoSEHRGKnCYPNdu3bxwgsvALBt2zZ69uxJVlYWRkZGtGvXjhdffJGPPvpI286qVav48MMPuX37NlD+GKn69evzzjvvMHbsWADi4uLK3Mzg5uaGnp4eX3zxBb/99huXLul+qdSrV4833nhDx75CYmJicHBwwMTEhFmzZtGxY0eCgoL4+OOP2bNnD+3bty9SJzExkWbNmjF06FBmzZqlc27y5MksWrSIzMxM/Pz82Lp1KzVrlvzlLRwpQXWzauNUvkrVyNNIahnvGzJW6ZBsBhecJeQHZmckJJrZNaObWze6uHbB2tha57xcUED22bOkHzhI+oEDZJ89qxOCoDA1xTTAH9O2bTFr2xb9u5uVyossy6TEZxF7NYXou5pWibfTeTBESM9QiZ2bOXbuFoQdiCY7o/gUZqBZOhw+p3WJM1FZ588T89lUssPCNOVbtsR+5gwM3R8+BXB5lu8qw5Ff3sf/xjKOqbrScsL6h7bzeeKRpYgRVC2SsTGeJ0+Uq2zm8ePceHt0meWcf16CSYvSRdgk47JTHJSHRo0aaf9fuIszLi4OFxcXTpw4wbFjx3QSQxcUFJCdnU1mZiYmJThyGRkZzJgxg61bt3L79m3y8/PJysrSSVRta2uLbQV2xhT3i+5+/bMHUavVALz00ktMmDABgCZNmnD48GF++umnIo5UamoqPXv2xMfHh2nTphVpb9KkSbz55ptcu3aNGTNmMGzYMLZu3fpEbf8WPF8MfmkaPy//hztSDrJCIsz1AcdJllEpzXi7+f/YEbWD0PhQTsSe4ETsCb48+iUt7VrSzb0bnV06Y2VkhaRUYtykCcZNmmDzf2PJT0oi49Ah0g8cIOPgIQqSkkjbuYu0nbsAzdKYadt2mLVtg3Hz5ijKmN2RJAlLWxMsbU3w9NN81+Rm5d8VC03RxlvlZhdwK/wOt8LvlDkGmSm5RF++UySXoDori/jvF5G0fLlG0sDCQiNp8MorDyVpULh8t/riakKiQ7TH61rVZYjXkGKX7yqKedxxzTU4tXqodgSlU2WO1IoVK0o817FjR5ydnauqq2cWSZKQyjkzZNq6NXr29uTHxhYJNr/bGHp2dpi2bv3IpBDuj/EpdAoKnRC1Ws2MGTPo169fkXqlzbJMmjSJ7du3M3fuXDw8PDA2NqZ///7k5uZqy1Rkac/e3p7Y2KLidfHx8SUuQVtbW6Onp4ePj26OKm9v7yJLcmlpaXTv3h0zMzM2btxYbNyTtbU11tbW1KtXD29vb5ydnbVxWgLBY0EtMy4+lZm2hkiyjHyfUy/d/X4ZF51Iv3qDGeozlOj0aHZc20FQZBDnEs8REhNCSEwIs4Nn4+fgRze3bnRy6YTKULPjTa9GDVS9e6Pq3RtZrSb7/HmNU7X/AFlnzpBz+Qo5l6+Q9OuvSCYmmPr53ZVYaIdBrfLNxhgY6+HsXQNn77uCoWqZpJgMYiJSuHQ0htuXU8poATJSc3Tepx88RMz06eTdvAmA+Yvdsf/44yK7EytCact3r3m/Rgu7FlXyoyo/Lxf37IsggY1Pu4duT1AyFXKkevbsyZ49e/jss8+KLIGMGDGixA+/W7dubNu2rfJWCoogKZXYffyRZneeJOk6U3c/B7uPP6oWJ8rAwICCgoIK1WnWrBnh4eF4eHiUWEZfX79IuwcOHGDEiBH07dsXgPT0dKKionTKzJw5kw8++KDU/gvlBfz9/UlJSeHo0aO0aqX5lRYSEkJKSop208SDGBgY0LJlS8LDw3WOX7p0SSftUWpqKt26dcPQ0JDNmzeXaxmucGU9JyenjJICQfVxMWQ7/TPjsIwz5suaVsTetznCrqCAyYnJdM7M4nzIduq37omDmQPD6w9neP3h3Ey7yfao7WyP2s6FpAscun2IQ7cPMTN4JgGOAXR3605H546YGWh2skkKBcYNG2LcsCE2775LwZ07ZBw+TPr+A6QfPEhBQgLpu3eTvns3AAa1a2udKpOWLVAYlh5QXoikkKjpaEZNRzMsbU3Y9N2pMuuYWmjazk9KIvbLL0ndrNnJq+fggP3UzzDv2LFC43o/V5Kv8PvF39l6davu8l29VxjkOQhHs6qVQLl24Th1pGzSZGNcvR59yMjzRLkdqZMnT/Lvv/9Sv359Jk+eXGwZlUpF8+a6H1h4eDjbt28nNDSUJk2aPJSxAl0sunaFBfOL6kjZ2VWrjpSbmxshISFERUVhZmamnXUqjalTp9KrVy+cnZ159dVXUSgUnDlzhrNnz2pjiNzc3Pjvv/9o3bo1hoaGWFlZ4eHhwYYNG+jduzeSJPHZZ58V6a8iS3ve3t50796dUaNGsWTJEkAjf9CrVy+dHXteXl7MmTNH68BNmjSJgQMH0q5dO22M1JYtW9i7dy+gmYnq2rUrmZmZrFq1itTUVG3clo2NDUqlkqNHj3L06FHatGmDlZUVV69eZerUqdSpU0fMRgkeK1nJmpmRzplZdMzM4qSRIfFKJTYFBTTLzqHw51hmyHLueDbH0vperrla5rV4s+GbvNnwTaJSotgetZ2gqCCu3LnC/pv72X9zPwYKA9o4taG7e3fa12qPif69mXelpSUWPXpg0aMHslpNzsWLGqfqwAGyQkPJvXqVpKtXSVq+AsnYGNNWrTSxVe3aYuBSvl1oDnUtMbU01Nmt9yBmVobYe6i4s2kTcV9+RcGdOxpJg9dfx2b8eJRmFRezLFAXsO/mPn6/8DshMbrLd695vUaP2j0eevmuJBIu7KcOEGnsQ6MSdg0LqoZyj+66deuQJIkPP/wQRQnrwg0aNGDnzp06x/78808GDBjAmjVrhCNVDVh07Yr5Cy88UmXzDz74gOHDh+Pj40NWVhaBgYFl1unWrRtbt25l5syZfP311+jr6+Pl5cVbb72lLTNv3jwmTpzI0qVLcXJyIioqiu+++46RI0cSEBCAtbU1kydPfmiV/NWrVzNu3Di63nU0+/Tpw6JFi3TKhIeHk3LfrqO+ffvy008/MWfOHMaNG4enpyd//fUXbdpotFlOnDhBSIjmi/LBWbfIyEjc3NwwNjZmw4YNTJs2jYyMDBwcHOjevTtr167VJv0WCB4Hxlb3ls+UQMvs4h2Olqnbyf2+PifNW6PXfBj1276M8r6HtJvKjdGNRzO68Wgi7kRonarIlEh239jN7hu7MVIa0a5WO7q5daNtrbY6joSkUGDk44ORjw/WY0ZTkJpKxuEjpB/QSCzkx8WRvm8f6fv2EQvou7podatMWrVCUcIssEIh0XZg3VJ37fl1qsGtUaPIuKvpZlivHg6fz8S4ceMKjKSGlJwUNl7eyNrwtTrLd52cOzHEe0iVLd+VhvLWMQAybMVsVHVT7l17rVu35tixYyQkJBQbwa5QKGjTpg379+/XOZ6fn4+lpSWNGjV67kQHK7prTyAoDnGvCKqbgvx8EmbVw0ZOpLhNa2oZ0iRT4hV2eKivao/HUpOrtV7CpdPbONUuPnuFLMtcSr6kdapupN3QnjPWM6aDcwe6u3WnjVMbDJSlC2DmXLqk0a3af4DMU6cgP197XjI0xKRlS43EQtu2GLi5FXFWzgXuJPhgOjn699TKDfNSaKy6ivnuVcjZ2RpJg//9j5oj36iQRAMUv3ynMlRpdt95Dqzy5bvSuD2jLo5yHGc7raBhu5ceWb/PCtUif2BjY0ONGjWKxIkUUpIjBVC3bl1SUlKIK0k88hlFOFKCqkDcK4JHwanty2l8eByAjjNVqIN5OmAhTbsNJ+LMYRIO/IJX/L+oyNCWO2/QiKwGQ6j/wusYm5oX24csy4QlhbE9UhNTdTvjtvacmb4ZnVw60c2tG/4O/ugrS3diCtLTyThyhIy7y4D3hzcA6NeqpXWqTH19ST94kFvj30OWKTGXoImvLw4zpmPg5lbeYStx+a6eVT2GeA2p1uW7koi/HYXNz40pkCUyJ17FXFXjkfb/LFAtjpSBgQHNmzfnyJEjxZ5ft24dNjY2dComv5Cfnx+nTp167gJqhSMlqArEvSJ4VJzavhzHIzOwI1F7LIaaRPtPo2m34Tpls7MyOL9nLfpnVtMg6yQKSfMoSZONCbPuilXrkdRt0q5EiQBZljmTcEYbqB6Xee+HtoWBBS+4vEB3t+60cmiFnqL0KBRZlsm9ckUbW5V54gTk3acZpaeHpFAg37fb90EUKhV1Dx1EUc54oidh+a4kTv4bSLOQ94hQulPns9DHYsPTTrU4UjVq1MDBwYHzD6QBKA8+Pj7ExMSQlJRU4bpPM8KRElQF4l4RPEoK8vO5GLKdrORbGFs54eXbTScOqjhirl8m8r9luF7fgKN8zyGKVLgSW6c/9bq8SQ3bkmUM1LKa0LhQgqKC2BG1g8Tse46claEVnV07092tO83tmqNUlB3/qc7IICMkRLsMmHf7dpl1AFyWL8fUt3TNpcvJlzXLdxFbyS7QpJV5XMt3JRH842j8YtcSYt0P37Flx7AKilItjlTDhg25fPkyycnJGFdAwDEjI4MaNWpQr149zp49W+56zwLCkRJUBeJeETwtqAsKCDvyD9lHl9MgZR9GkmZWKFdWcs4sAOXdAHU9/ZJjoQrUBZyIPcH2qO3svLaT5Jxk7bmaRjXp4tqF7u7daWrbFIVUtiCmLMskBQYS9/U3ZZZ1nDsXVa+exdq09+Zefr/wO0djjmqP17Oqx2ver9HDvQdGek/O3+alWS2pl3+J482+okWfMY/bnKeSalE2b9euHWFhYaxfv55hw4aV25g//viDvLw82rUTgmACgUDwLKNQKmnQpg+06UNKcgKndwZS49I66uZfplnGAdh/gLj9k4lw7E2tTqNw9mhYpA2lQkkrh1a0cmjFR74fcTTmKNujtrPr2i4SsxNZG76WteFrsTWxpatrV7q7d6eRdaMSl9EkScKofoNy2f+g0GZKTgobLm9gXfg6neW7F1xeYIjXEJrbNX/iMhJkZ6bjnhcBEjg27PC4zXkuKPeM1LFjx/D19cXJyYmQkBCtwGFp3Lx5k1atWhEbG0twcDAtW7Z8aIOfJsSMlKAqEPeK4Gnn6rkQ4vb/gmfcNqy4lxE5zKAhGT6DqN95KCZmqlJagLyCPI5EH2F71HZ2X99Nel669pyjqSPd3LrRza0bPjV9ijg3ckEBV17oXGYmCI//diEplVxKvsTvF37nn6v/6Czf9a/bn4GeA3Ewq1huwEdFQX4+x/74Cr9LX5OMGeYfR6JXBcmUn0eqLWnxkCFDWLt2LbVq1WLhwoW89NJLJeYt27hxI+PHj+f27dtaHannDeFICaoCca8InhVyc7I5t2ctytBVNMg6jvJugHq6bExYjRewCHgDz+adysxhl1uQy6FbhwiKCmLvjb1k5mdqzzmbO9PNrRvd3bpTz6qe9hmVumMHN8eNB+D+p1bhA9BxwXec8NLn94u6y3eeVp4M8R7yxC3fPUhxGwViqcntYjYKCMqm2hyprKwsunbtyqFDh5AkCXt7e/z9/XFzc8PU1JSMjAyioqI4fPgwsbGxyLKMv78/u3btqlBc1bOCcKQEVYG4VwTPIrE3I4jc9Qu1rv1FLfmedME1hTPRtV/Bo/NbWNuXnaM1Oz+bA7cOEBQZxP6b+7UzSABuFm50d+9Od7fuRKZEsnbJewzfWYD1vUkxEszhty4KLjeqqY3HUkpKOrl0emKX7x6kvNIVgvJTbY4UaAQ2p02bxvfff096umZq9f6brLA5MzMzxo4dy4wZM4pN3Po8IBwpQVUg7hXBs4ysVhMWHERmyG80uLMHY0kjUZAnKzln6ovUbBgN2r9SaoB6IZl5mey7uY/tUds5cPMAuep7cgdKSUmBXICklvG+IWOVDslmcMFZQr7rfagMVPSv92Qv3z1IecRU46Sa2Hx6qczdl4J7VKsjVUhKSgrbtm3j8OHD3Lp1i7S0NMzNzXFycsLf358ePXpgaWlZmaafGR6VI6VWy0RfvkNGag6mFoY41LVEUdxfVDWxd+9eOnbsSHJy8nP/mVcHwpESPC+kpSRxYedvqC6uxTP/nvhzApZcduiFY4dRuHo2KVdb6bnp7LmxR+NU3TqAWi47J+hPnX+itVPrypr/SEhJTiA28hypNy+SF38Z89hjNMg9XWa9811+p37rojsSBcVTLbv2HkSlUjF48GAGDx5c2SYEVUDEqTgOrLusk4zT1NKQtgPrUqdp+RL5VpQOHTrQpEkT5s+fX6XtSpLExo0befnll6u03QdJTk5m3LhxbN68GdDk2vv+++9LdQLT09OZMmUKmzZtIjExETc3N8aNG8c777yjU+7IkSN88sknhISEoK+vT5MmTfj333+1S9uXLl1i0qRJHDp0iNzcXBo2bMisWbPo+BBZ5QWCZwVzVQ1a9Z8ITCTqwnFi9i6jXuw/WHMH6+hVsGYVF/R9SPcehHfnYZhZWJXYlpmBGb3r9KZ3nd78eelPZhyZUWb/KTkpZZZ5FGRnphMdeZ7kGxfIib2MMvkq5hlR2OXdpAaplB6WXzyFiakFVY+Y53uKiTgVV2wSzow7OQQtOUf30Q2qzZl6mhkyZAg3b94kKCgIgLfffpuhQ4eyZcuWEutMmDCBPXv2sGrVKtzc3NixYwfvvvsujo6OvPSSJo/VkSNH6N69Ox999BHff/89BgYGnD59WifJd8+ePalXrx67d+/G2NiY+fPn06tXLyIiIrC3t6/eCxcIniLcvFvg5t2C3JxsTu37E+nUShpmhuCdFwZnppJ5ejZHrV7Awv8NPFt2LjVA3dXCtVx92pjYlF2oisjPyyXm2iUSr4eRFROOlHQVk7RIbHJuYk887oB7CXXjsSLOoBYZZm7IKPBN+rvM/u5PTC2oWiq9tCcom4ou7cmyTH5u2dPPoFnOWzMjmIw7Jac8MLU0ZPA03zKX+fQMFOUOphwxYgTLly/XORYYGMgbb7zBrl27mDx5MmFhYTRp0oTAwEA8PT215bZs2cL06dM5f/48jo6ODB8+nE8++QQ9PT3c3Ny4du2atqyrqytRUVFEREQwceJEgoODycjIwNvbmzlz5tC5c+dy2fsgFy5cwMfHh+DgYHx9fQEIDg7G39+fixcv6th7Pw0aNGDgwIF89tln2mPNmzenR48efP7554AmFVKXLl207x8kISEBGxsb9u/fT9u2bQFIS0vDwsKCXbt28cILLxRbTyztCQQa4m9HcWXXL9SK/BNn+Z5a+Q3JkZvu/anb+S2sHYs6TQXqArr91Y24zDhkij7yJCTsTOwIeiWoXMrp5UVWq4mPvkZc1DkybocjJ1zBKC2KGtnXcSiIQV8qKLFuKqbE6DmRauJKnlUdDGzroqrlhb17fZ2ZOBEjVT08kqU9QdWTn6vm5/H7qqy9jDs5LJtQNIn0g7y9oD36huX78liwYAGXLl2iQYMGzJw5E0CbNuiTTz5h3rx52NjYMGbMGEaOHMmhQ4cA2L59O6+//joLFy6kbdu2RERE8PbbbwMwbdo0jh07hq2tLYGBgXTv3h2lUmNPeno6PXr0YNasWRgZGbF8+XJ69+5NeHg4Li4uAIwZM4ZVq1aVandYWBguLi4cOXIElUqldaJA4wCpVCoOHz5coiPVpk0bNm/ezMiRI3F0dGTv3r1cunSJBQsWABAXF0dISAivvfYaAQEBRERE4OXlxezZs2nTpg0ANWvWxNvbmxUrVtCsWTMMDQ1ZsmQJdnZ2NG/evFzjLxA8z9g4umEz7HNk9QwuHNtJ2pHfaJD8H87cxvnqQvKXLCLU1Be5yes06PAq+gaGgEbkc0qrKUzcOxEJSceZku6KIUxuNbnSTlRKYizRkedIu3mR/PjLGKZcRZV1A4f8W9hKORS7LiBBtqxPtNKJZGMXclTuKG3qYuHkha2bD1bWDliUIQMBoNTT47b/NGwOj0MtF79rL9p/GvbCiao2xMgKKoRKpcLAwAATExPtUtTFixcBmD17Nu3btwdgypQp9OzZk+zsbIyMjJg9ezZTpkxh+HDNFtzatWvz+eef8+GHHzJt2jRs7ioKW1pa6ixxNW7cmMaNG2vfz5o1i40bN7J582bGjh0LwMyZM/nggw9KtbtQQDYmJgZb26Jfa7a2tsQ8kD3+fhYuXMioUaOoVasWenp6KBQKli1bpnWSrl69CsD06dOZO3cuTZo0YcWKFbzwwgucO3eOunXrIkkSO3fu5KWXXsLc3ByFQoGdnR1BQUEiSF8gqACSQoG3bzfw7UZ6ajLHdq3A/MJavPLCaJJ5BA4fIfHwx1y264FDh1G4ejens2tnxlr2Zk3CJhL07jko1vkFDLJ+mc6upc9yZ6anEB15gTs3LpAbF45e8lXMM65jl38TK9KKj1uSIF9WEKOwI9HImSxzdyRrD0wdPLF2q4+tozvuSmWJS3jlpWm34ZyCIjpScVLxCacFVYtwpJ4g9AwUvL2gfbnK3r58h62Lyt6p0WtsYxzrWpbZb1XQqFEj7f8dHDRbh+Pi4nBxceHEiRMcO3aM2bNna8sUFBSQnZ1NZmYmJiYmxbaZkZHBjBkz2Lp1K7dv3yY/P5+srCyuX7+uLWNra1usc1QSJYnIlra8uXDhQoKDg9m8eTOurq7s37+fd999FwcHBzp37oxarVmSHT16NG+88QYATZs25b///uPXX39lzpw5yLLMu+++i62tLQcOHMDY2Jhly5bRq1cvjh07ph0zgUBQfswsrGjZbzwwnmvhodzeu5S60Vux5g41Y9fAujWE63lxR+XFWwmbeAMINTYkXqnEpqCAJlk5KK8v4pRRPRp0HETMtYskXr9Adkw4UlIEZmlRWOfexI5E6pRiRyw1STCoRbq5G3KNOhjZe1LTxRt7Vy9qGRhSq5rHoWm34RS88BrnH0g4LWaiqh8xwk8QkiSVe4nN2acGppaGOrv1HsTMyhBnnxqPTArhfr2wQqek0MFQq9XMmDGDfv36FalXWtzPpEmT2L59O3PnzsXDwwNjY2P69+9Pbu692LCKLO3Z29sTGxtb5Hx8fDx2dnbF1s3KyuLjjz9m48aN9Oyp2T7cqFEjQkNDmTt3Lp07d9Y6QT4+Pjp1vb29tU7f7t272bp1K8nJydo198WLF7Nz506WL1/OlClTSr0GgUBQOq6eTXD1/IG83G8J3b8B+eRKGmQE45l/ERIvIgP6ErTMvu97U9JkjWl4+D04PB5nSaYkGdBkzInTcyLV1JV8q9oY2Hpi6eyNg7s3dmYqiv8GeXQo9fSExMFjQDhSTykKhUTbgXWL3bVXSJsBdavFiTIwMKCgoOQgyeJo1qwZ4eHheHh4lFhGX1+/SLsHDhxgxIgR9O3bF9DETEVFRemUqcjSnr+/PykpKRw9epRWrVoBEBISQkpKCgEBAcXWzcvLIy8vT2f3HYBSqdQ6im5ubjg6OhIeHq5T5tKlS7z44osAZGZq0lg82I5CodC2IxAIHh59A0OadB4MnQeTEHODiD8+wTfpb0qadJYk0EPzN5gpGxKt58QdYxdyVe7o2dTFvJYX9m71sbK2p2TBBcHzinCknmLqNLWl++gGRXSkzKwMaTOg+nSk3NzcCAkJISoqCjMzs3I5AVOnTqVXr144Ozvz6quvolAoOHPmDGfPnmXWrFnadv/77z9at26NoaEhVlZWeHh4sGHDBnr37o0kSXz22WdF+qvI0p63tzfdu3dn1KhRLFmyBNDIH/Tq1Usn0NzLy4s5c+bQt29fLCwsaN++PZMmTcLY2BhXV1f27dvHihUr+PbbbwHNDNykSZOYNm0ajRs3pkmTJixfvpyLFy/y559/AhonzsrKiuHDhzN16lSMjY1ZunQpkZGR2pkugUBQtVjbOxNVuw2UQyIg2OsjfAd8SJ1yBHkLBIUIR+opp05TW9wb2zxSZfMPPviA4cOH4+PjQ1ZWFoGBgWXW6datG1u3bmXmzJl8/fXX6Ovr4+XlxVtvvaUtM2/ePCZOnMjSpUtxcnIiKiqK7777jpEjRxIQEIC1tTWTJ08mNTX1oexfvXo148aNo2vXroBGkHPRokU6ZcLDw0lJuSfOt3btWj766CNee+01kpKScHV1Zfbs2YwZM0Zb5r333iM7O5sJEyaQlJRE48aN2blzJ3XqaCIrrK2tCQoK4pNPPqFTp07k5eVRv359/v77b52AeoFAULWUV0PJ3LlhmQmTBYIHETpS1YjItSeoCsS9IhA8HEJrSVBRKqIjJVxvgUAgEDzTFGotwT1tpULu11oSTpSgMghHSiAQCATPPE27Ded0wELipZo6x+OkmpwOWCi0lgSVRrjfAoFAIHguEFpLgupA3D0CgUAgeG4QWkuCqkYs7T1mRKy/oCzEPSIQCARPLsKRekwUqoAXijQKBCVReI/crxwvEAgEgicDsbT3mFAqlVhaWhIXFweAiYlJqbneBM8fsiyTmZlJXFwclpaWKJWVy0wvEAgEgupDOFKPEXt7ewCtMyUQFIelpaX2XhEIBALBk4VwpB4jkiTh4OCAra0teXl5j9scwROIvr6+mIkSCASCJxjhSD0BKJVK8bAUCAQCgeApRASbCwQCgUAgEFQS4UgJBAKBQCAQVBLhSAkEAoFAIBBUEhEjVY0UCimmpqY+ZksEAoFAIBCUl8LndnkEkYUjVY2kpaUB4Ozs/JgtEQgEAoFAUFHS0tJQqVSllpFkkX+i2lCr1dy+fRtzc/MqF9tMTU3F2dmZGzduYGFhUaVtC+4hxvnRIMb50SDG+dEgxvnRUV1jLcsyaWlpODo6olCUHgUlZqSqEYVCQa1ataq1DwsLC/GH+ggQ4/xoEOP8aBDj/GgQ4/zoqI6xLmsmqhARbC4QCAQCgUBQSYQjJRAIBAKBQFBJhCP1lGJoaMi0adMwNDR83KY804hxfjSIcX40iHF+NIhxfnQ8CWMtgs0FAoFAIBAIKomYkRIIBAKBQCCoJMKREggEAoFAIKgkwpESCAQCgUAgqCTCkRIIBAKBQCCoJMKReoJZvHgx7u7uGBkZ0bx5cw4cOFBq+X379tG8eXOMjIyoXbs2P/300yOy9OmmIuO8YcMGunTpgo2NDRYWFvj7+7N9+/ZHaO3TS0Xv50IOHTqEnp4eTZo0qV4DnxEqOs45OTl88sknuLq6YmhoSJ06dfj1118fkbVPLxUd59WrV9O4cWNMTExwcHDgjTfeIDEx8RFZ+3Syf/9+evfujaOjI5IksWnTpjLrPJbnoCx4Ilm7dq2sr68vL126VA4LC5PHjx8vm5qayteuXSu2/NWrV2UTExN5/PjxclhYmLx06VJZX19f/vPPPx+x5U8XFR3n8ePHy1999ZV89OhR+dKlS/JHH30k6+vryydPnnzElj9dVHScC7lz545cu3ZtuWvXrnLjxo0fjbFPMZUZ5z59+si+vr7yzp075cjISDkkJEQ+dOjQI7T66aOi43zgwAFZoVDICxYskK9evSofOHBArl+/vvzyyy8/YsufLrZt2yZ/8skn8l9//SUD8saNG0st/7ieg8KRekJp1aqVPGbMGJ1jXl5e8pQpU4ot/+GHH8peXl46x0aPHi37+flVm43PAhUd5+Lw8fGRZ8yYUdWmPVNUdpwHDhwof/rpp/K0adOEI1UOKjrO//77r6xSqeTExMRHYd4zQ0XH+ZtvvpFr166tc2zhwoVyrVq1qs3GZ43yOFKP6zkolvaeQHJzczlx4gRdu3bVOd61a1cOHz5cbJ0jR44UKd+tWzeOHz9OXl5etdn6NFOZcX4QtVpNWloaNWrUqA4TnwkqO86BgYFEREQwbdq06jbxmaAy47x582ZatGjB119/jZOTE/Xq1eODDz4gKyvrUZj8VFKZcQ4ICODmzZts27YNWZaJjY3lzz//pGfPno/C5OeGx/UcFEmLn0ASEhIoKCjAzs5O57idnR0xMTHF1omJiSm2fH5+PgkJCTg4OFSbvU8rlRnnB5k3bx4ZGRkMGDCgOkx8JqjMOF++fJkpU6Zw4MAB9PTE11R5qMw4X716lYMHD2JkZMTGjRtJSEjg3XffJSkpScRJlUBlxjkgIIDVq1czcOBAsrOzyc/Pp0+fPnz//fePwuTnhsf1HBQzUk8wkiTpvJdlucixssoXd1ygS0XHuZA1a9Ywffp01q1bh62tbXWZ98xQ3nEuKChgyJAhzJgxg3r16j0q854ZKnI/q9VqJEli9erVtGrVih49evDtt9/y22+/iVmpMqjIOIeFhTFu3DimTp3KiRMnCAoKIjIykjFjxjwKU58rHsdzUPzUewKxtrZGqVQW+XUTFxdXxNsuxN7evtjyenp61KxZs9psfZqpzDgXsm7dOt58803Wr19P586dq9PMp56KjnNaWhrHjx/n1KlTjB07FtA88GVZRk9Pjx07dtCpU6dHYvvTRGXuZwcHB5ycnFCpVNpj3t7eyLLMzZs3qVu3brXa/DRSmXGeM2cOrVu3ZtKkSQA0atQIU1NT2rZty6xZs8SKQRXxuJ6DYkbqCcTAwIDmzZuzc+dOneM7d+4kICCg2Dr+/v5Fyu/YsYMWLVqgr69fbbY+zVRmnEEzEzVixAh+//13EeNQDio6zhYWFpw9e5bQ0FDta8yYMXh6ehIaGoqvr++jMv2pojL3c+vWrbl9+zbp6enaY5cuXUKhUFCrVq1qtfdppTLjnJmZiUKh+7hVKpXAvRkTwcPz2J6D1RrKLqg0hdtrf/nlFzksLEx+7733ZFNTUzkqKkqWZVmeMmWKPHToUG35wm2fEyZMkMPCwuRffvlFyB+Ug4qO8++//y7r6enJP/zwgxwdHa193blz53FdwlNBRcf5QcSuvfJR0XFOS0uTa9WqJffv318+f/68vG/fPrlu3bryW2+99bgu4amgouMcGBgo6+npyYsXL5YjIiLkgwcPyi1atJBbtWr1uC7hqSAtLU0+deqUfOrUKRmQv/32W/nUqVNamYkn5TkoHKknmB9++EF2dXWVDQwM5GbNmsn79u3Tnhs+fLjcvn17nfJ79+6VmzZtKhsYGMhubm7yjz/++IgtfjqpyDi3b99eBoq8hg8f/ugNf8qo6P18P8KRKj8VHecLFy7InTt3lo2NjeVatWrJEydOlDMzMx+x1U8fFR3nhQsXyj4+PrKxsbHs4OAgv/baa/LNmzcfsdVPF3v27Cn1+/ZJeQ5KsizmFQUCgUAgEAgqg4iREggEAoFAIKgkwpESCAQCgUAgqCTCkRIIBAKBQCCoJMKREggEAoFAIKgkwpESCAQCgUAgqCTCkRIIBAKBQCCoJMKREggEAoFAIKgkwpESCAQCgUAgqCTCkRIIBOVmxIgRSJLEb7/9pnP8t99+Q5IkRowY8VjsKosOHTogSRJ79+593KaUSFRUFJIk4ebmViXt7d27F0mS6NChQ5W0V4ibmxuSJBEVFaVz/GkYY4GgOhCOlEDAvYeDJEls2rSpxHKdO3cu1pEQPPvMmzdPe48sXbr0cZvzzDJ//nymT5/OnTt3HrcpAkG5EI6UQPAA06dPFxnZK4hKpcLT0xMHB4fHbUq1sXLlymL/X1Xo6+vj6elJnTp1qrztR4GLiwuenp6YmJg8VDvz589nxowZwpESPDXoPW4DBIInCaVSyenTp/nrr7/o37//4zbnqaFv37707dv3cZtRbZw9e5bTp09jampKTk4OBw8eJDIyEnd39yrrw8nJiYsXL1ZZe4+aFStWPG4TBILHgpiREgjuY/DgwQDMmDFDzEoJtBTOQL388st07doVWZZZvXr1Y7ZKIBA8CQhHSiC4j5EjR+Lm5sa5c+f4448/Klz/n3/+oXv37lhbW2NoaIi7uzvvvvsuN27cKLb8/YG7e/bs4cUXX8Ta2lonaLcwLgdg48aNBAQEYGZmhp2dHcOHDycmJkbbXmBgIM2bN8fU1BRbW1vGjBlDSkpKkX4LCgr4+++/GTlyJPXr10elUmFiYoK3tzcffvghCQkJFbrukoLNC20v7TV9+vQi7V28eFH7WRgaGlKzZk169uzJ7t27S7QhISGBd999FycnJ4yMjPD09OTzzz8nLy+vQtfyIGq1mt9//x2AIUOG8NprrwElL+8lJCTg4OCAJEnFlsnKysLb2xtJkvjiiy+0x0sLNj937hzTpk3D398fBwcHDAwMcHBwoF+/fhw+fPihrq84rl27xuuvv46trS0mJiY0atSIH374odQfFyUFm+fn57NgwQJatWqFubk5hoaGODo6EhAQwLRp07RLeIX30LVr1wBwd3fXuU/ub3fnzp2MHTuWxo0bU6NGDYyMjKhTpw7vvPMO169fL9a++zdK3L59m5EjR+Lg4ICRkRH169fnhx9+KHVMjh07xuuvv46LiwuGhobY2dkREBDA119/Xezf2M2bNxk3bhz16tXD2NgYS0tLOnbsyJ9//llqP4KnEFkgEMiurq4yIB84cEBeunSpDMje3t5yQUGBTrkXXnhBBuTAwMAibUyZMkUGZECuVauW3Lx5c9nExEQGZCsrK/nYsWMl9vvFF1/ICoVCtrKyklu2bCnXqlVL3rNnjyzLsrbNhQsXattu3LixbGhoKAOyj4+PnJWVJY8bN04G5Nq1a8v169eX9fT0ZEBu3769rFardfq9ceOGDMgKhUJ2cHCQmzVrJnt5eclGRkYyILu5uckxMTFF7B0+fHix1x8YGCgD8vDhw3WOt27dusSXsbGxDMjTpk3TqbNu3TrZwMBABmRzc3O5SZMmsr29vQzIkiTJCxcuLGJXdHS0XLt2bRmQ9fT05CZNmsh169aVAblXr15yu3btZEA7phVhx44dMiBbW1vLeXl5cnp6umxqaioDckhISLF1/vnnHxmQLSws5KioKJ1z//vf/2RADggIkPPz87XHIyMjZUB2dXUt0l7hfWdpaSl7e3vLzZo1k62trWVAViqV8urVq4vU2bNnj/bzrwhhYWFyzZo1ZUA2MjKSmzdvLru4uMiA/O6772rv2cjISJ167du3L3aMX3nlFe09XKdOHblly5ays7OzrFQqZUA+deqULMuyvG3bNrl169ba+7pFixY698vJkye1bSqVSlmSJNnW1lZu0qSJ3KBBA+1nUrNmTfn8+fNFrqvw3p0+fbpsb28vGxkZyc2aNZMdHR219s2aNavYMfnqq69kSZK0n2nz5s3lOnXqyPr6+sVe8969e2WVSiUDsrGxsdywYUPZ2dlZ28/7779foc9E8GQjHCmBQNZ1pPLy8rQP5QcfUCU5Ulu2bNE+xFetWqU9npKSIvft21frnGRmZhbbr1KplGfMmCHn5eXJsizLarVazs7OlmX5niNlamoq//7779q6N27ckD08PGRAfvnll2WVSiXv2rVLe/7MmTNyjRo1ZEDetm2bTr937tyRf/vtNzkxMVHneHJysjx27FgZkEeMGFFknCrqSJXE77//LgOySqWSL168qD1++vRp2dDQUDYyMpJ//vlnHUd28+bNsoWFhaxUKuXQ0FCd9grHuFmzZvL169e1x//77z/Z3Ny8xAdeeRg6dKjWiSjktddekwF57NixJdYbPXq0DMjt2rXTXsf27dtlSZJkMzMzOSIiQqd8aY7U+vXr5TNnzugcU6vV8qZNm2QzMzPZwsJCTk1N1TlfGUdKrVbLzZo1kwG5W7duOvfHmjVrZH19fa2DXh5H6vjx4zIgOzs7y2FhYTrlU1JS5KVLl+p8XrIsl+io3c+SJUvkW7du6RzLzMyUZ8+eLQNyhw4ditQpvHf19fXl/v37y8nJydpzixcv1jqO9x+XZVnetGmT9m903rx5cm5urvZcRkaG/PPPP+tc261bt+QaNWrIkiTJX3zxhfbvWJZl+dChQ7KTk5MMyFu2bCnx+gRPF8KREghkXUdKlu85Bp6enjqzBiU5Uq1bt5YBefz48UXazsjI0M4e/PLLL8X227t37xJtK3Skimt7yZIl2vPfffddkfOFs2Tjxo0r+eKLwdnZWTYxMdE6doVUhSN14sQJ2djYWFYoFEUcvH79+smAvGDBgmLrfv/99zIgjxw5Unvs8uXL2tmCc+fOFanz7bffaseooo5Uenq6bGZmJgPywYMHtccLZ5ysra11HqwP1i2cFfvqq6/kxMRE7ezHsmXLipQvzZEqjU8//bRYp78yjtSuXbu0syjx8fFFzhfOepbXkVqzZo0MyBMmTCi3DeVxpEqjTZs2MiDfvHlT53jhvWtvby+np6cXqVfoQG7YsEHnuI+PjwzIM2fOLFf/EydOLPWaC390derUqZxXJHjSETFSAkExDB06lLp16xIeHl5mUHF6ejpHjhwB4P/+7/+KnDcxMWHUqFEA7Nixo9g2hg0bVqZNb775ZpFjTZo00f5/5MiRRc43bdoUgKtXrxbb5u7du5kwYQI9e/akXbt2tGnThjZt2pCSkkJmZiaXL18u066KEBcXx8svv0xWVhZz5szhxRdf1J7Lzc1l27ZtKJXKEoU9+/TpA8C+ffu0x3bs2IEsy7Rr14769esXqfPWW29hYGBQKXs3btxIeno6rq6uBAQEaI937doVGxsbEhISCAoKKrauqakpK1euRKlU8tlnn9GvXz9u375Nnz59iv0sy+L69et8+eWXDBgwgE6dOmk/q3Xr1gFw+vTpSl3j/Wzfvh2AV199FWtr6yLn33333Qq15+zsDMB///1HUlLSQ9t3P8ePH2fKlCn06dOH9u3ba8fj0qVLAJw5c6bYeoMHD8bU1LTI8ZYtWwK6fytXrlwhLCwMAwMD3nvvvXLZtWHDBkBz3xVH9+7dMTAw4PDhw+Tn55erTcGTjZA/EAiKofDhN2zYMD7//HOGDBmCnl7xfy5XrlxBrVZjaGhI7dq1iy1T+IAv/JJ/EG9v7zJtKk5fyMbGRvuvhYVFiefT09N1jufm5jJw4MBSxUeBKn345eXl0b9/f27cuMHgwYP58MMPdc5funSJ7OxsDAwM6NGjR7FtyHeDnW/duqVTD0oeQ3Nzc5ycnIiMjKywzYXB4oMHD9YG/APo6ekxYMAAfvjhB1auXEnv3r2Lre/r68vHH3/M559/zr59+7C1ta2UmOfy5csZM2YM2dnZJZapis+qrLGsW7cuenp65XYA/P398fX1JSQkBGdnZ7p06UK7du1o3749zZo10xnT8iLLMmPHjmXx4sWllitpPErS6bK1tQV0/1YuXLgAgI+PD+bm5mXalp6erlV8f/vtt0stm52dTWJiInZ2dmW2K3iyETNSAkEJDBkyBE9PT65cuVKqAGPhF6+NjU2JD4bCL8u0tLRizxf3C/lBihM6LOyvJBHEwvOFDkghX375JZs2bcLe3p4VK1YQFRVFdnY2sma5QqIN3QAACPZJREFUn9atWwM89I63+/m///s/Dhw4QPPmzfnll1+KnC/c+ZSbm8uhQ4eKfRXuULvfobh//EuiMg+r6Oho/vvvP0BzLzxI4e69LVu2FLtrq5BOnTpp/9+rVy/tA7u8REREMGrUKLKzs3n//fc5deoUqampqNVqZFnWOmZV8VmVNZYKhaLYmaqSUCgU/Pvvv4wfPx5jY2P+/vtv3n//fVq0aIG7u3ulMgSsXLmSxYsXY2pqyuLFi7l8+TKZmZnae7fwcylpPEr6W1MoNI/D+/9WUlNTAbC0tCyXbfffByXdw4cOHSI3NxfQ7OAUPP0IR0ogKAGlUsnUqVMB+Pzzz0v8FW5mZgZAfHx8idvDY2NjAcr1q/ZRULhc+dtvvzF06FBcXV0xNDTUni9JrqGy/PjjjyxZsgQ7Ozs2btyIsbFxkTKF4+jk5KR9KJb2erBefHx8if3HxcVV2ObVq1dTUFAAQKNGjYrINhQu9WVnZ7N+/fpi20hPT9cu4ykUCn777TcOHDhQITv++OMP8vLyGDRoEHPnzqVJkyaYm5trneSq/KzKGku1Wk1iYmKF2rSysmL+/PnEx8dz6tQpFixYQMeOHbl27RpvvPFGheUACu/defPm8c477+Dh4aFzP1XleBT+vZZXZb1w/EDzg6Cse7iq8ioKHi/CkRIISmHQoEH4+PgQGRlZ4q9nDw8PFAoFOTk5JcYinT9/HoB69epVl6kVonD54f64n0ISExN1ls4elgMHDjB+/HgMDAz466+/tHEzD1K3bl309fWJjo6u0DJV4ZiWpAqenp7OzZs3K2x34SykpaUldnZ2xb5UKpVO2QcZP348V69epUuXLsyfPx+1Ws2wYcNKnJksjtI+K6ia2KhCyhrLK1euVHrmS5IkmjRpwrhx49i9ezdTpkwBKLLUWdZyX2njkZeXp12OqwoKl+TDwsLK9ZmpVCocHR2Be3/zgmcf4UgJBKWgUCiYNm0aALNmzSr2IWJmZqb9Uv/++++LnM/KymLZsmUAdOvWrRqtLT+Fv+ALZ8ruZ968edqZmIfl+vXr9O/fn7y8PBYtWqRdMiwOExMTunXrhlqtZuHCheXuo2vXrgDs37+fsLCwIueXLVumXUopL2fOnOHMmTNIksTJkyeJiYkp9nX06FFA4ywWCkkWsnnzZn799VcsLS359ddfGTt2LF27diUqKorx48eX25bSPquLFy+yZcuWCl1baRSO5fr164udeSorLqki+Pn5AXD79m2d44XXW9KyV2njERgYWOrMZEWpU6cODRo0IDc3t9z3ZL9+/QBNzkDB84FwpASCMnj11Vdp2LAh165d49ChQ8WWmTx5MqB50BSqYIMmJmrYsGHEx8fj5ubGoEGDHonNZdGmTRsA3n//fW1cjCzLrFixgrlz52JkZPTQfWRlZfHyyy8TFxfH//73P+3OxdL4/PPPMTQ0ZNasWXz55ZdFHqbR0dEsWLCAn376SXvMw8ODl156CVmWGT58uM7s0969e5k+fTr6+voVsr1whql169al5tOrV68eLVu2RJZlVq1apT0eFxenvd7FixdTq1YtJEkiMDCQGjVqEBgYWGagfyGFn9XixYsJDQ3VHr906RKvvvpqpXckFscLL7xA06ZNyczMZOjQoSQnJ2vP/fHHH/z4448lbroojtWrV/P5559rZ5EKSUxM1DomzZo10zlXuGHj/p2Z91M4Hp9++qmO0xQUFMSkSZOq5N69n1mzZgGaZOYLFy7U+TGVmZnJsmXLdGbBJk+eTI0aNVi+fDkTJ04ssiyYlJTEr7/+qm1X8AzwCCQWBIInngd1pB7kzz//1OrnUA5lc2dnZ7lFixZatWUrKyv56NGjJfZbmmZOYZvFUZb2UElaQsePH9cqSBcqNRdqHA0dOrREleqK6EgV9g3Ivr6+JSqcP6ittWHDBq0ivJGRkdykSRO5VatWOsrQkydP1qlz69Yt2c3NTSu42LRpU7levXoyIPfs2bNCyuYFBQXasViyZEmZ5RcsWKDVHCukT58+MiAPGDCgSPl169bJgGxjYyPHxsZqj5f0Webl5cl+fn5aUUhvb2+5QYMGsiRJsoODgzxr1qxiNbwqq2x+7tw5rZCrsbGx3KJFC+19WlFl8++++077mTk5OcktW7aUGzRooFWud3Jykq9du6bTzooVK7R1GjRoILdv315u3769VgH92rVrOvY1adJE+9l37NhRK5b64D1a0r1byLRp02QoqrQvy7I8Z84crVaZSqWSW7RoIdetW7dEodeDBw9qteP09fXlhg0byr6+vnLt2rW17QwcOLAcn4bgaUDMSAkE5aBfv346mk3FMWfOHLZs2UKXLl1IT0/nzJkzWFtbM2bMGE6fPq3VqXkSaN68Ofv376dLly6o1WouXryIra0tCxcuZPny5VXeX0hISIk7mB7Mjda3b1/CwsIYP348bm5uhIeHExYWhomJCX379mX58uXa+JpCHB0dOXr0KGPGjMHa2pqwsDBkWWbmzJls3LixQtvs//vvP27fvo2BgQGv/n97d6iiQBSGYfhbHUQmWEWEuQHFZjILZmUHGTCZ7YJJsIhpUDR5EyaNUzSoQR1EQbPBIt6A25ZdlmXdw8KW98lnhjPt5ecw5/X1x/XValWWZel4PGq5XGo8HmsymSiVSmk0Gn1Z77quPM/T9Xr99l9DH1mWpdlspkajoWQyqdPppNvtpnq9rvV6rXQ6/fS3PSOTyWi1WsnzPNm2rTAMlUgk1O/3NRgMfvWuSqWibrerYrGoaDSq3W6ny+WibDarTqejMAzlOM6nZ2q1mnzfVy6X0/l8VhAECoLgfbLjOI4Wi4XK5bJisZgOh4Pi8bja7bam0+mvJmbPajabms/ncl1Xtm1rs9nofr8rn8+r1+t9maoVCgXt93u1Wq33M5bb7VaRSESlUknD4VC+7//5PvE/Xh4PrrgHAAAwwUQKAADAECEFAABgiJACAAAwREgBAAAYIqQAAAAMEVIAAACGCCkAAABDhBQAAIAhQgoAAMAQIQUAAGCIkAIAADBESAEAABgipAAAAAy9ATw2543UElM9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m.fs.RPB.plotting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obj : Size=1, Index=None, Active=True\n", + " Key : Active : Value\n", + " None : True : 24.656884879985036\n" + ] + } + ], + "source": [ + "# add objective function\n", + "# create regularization parameter for the objective function\n", + "m.fs.RPB.alpha_obj = Param(initialize=0.5, mutable=True)\n", + "\n", + "# add objective\n", + "@m.fs.RPB.Objective()\n", + "def obj(RPB):\n", + " return RPB.alpha_obj * RPB.energy_requirement[0] - (1 - RPB.alpha_obj) * RPB.productivity[0]\n", + "\n", + "m.fs.RPB.obj.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# fix capture and free up inlet ads pressure\n", + "m.fs.RPB.ads.CO2_capture.fix(0.95)\n", + "\n", + "m.fs.flue_gas_in.pressure.unfix()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 9\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 2 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: max_iter=1000\n", + "bound_push=1e-22\n", + "halt_on_ampl_error=yes\n", + "nlp_scaling_method=user-scaling\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...: 24798\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 14962\n", + "\n", + "Total number of variables............................: 7023\n", + " variables with only lower bounds: 272\n", + " variables with lower and upper bounds: 4000\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 7023\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 2.4656885e+01 1.57e-02 7.26e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.2027660e+01 4.20e+00 7.26e+06 -1.0 1.63e+04 - 4.71e-01 1.00e+00f 1\n", + " 2 2.2516925e+01 1.04e+01 1.24e+07 -1.0 1.76e+04 - 4.42e-01 1.00e+00F 1\n", + " 3 2.4547942e+01 7.57e+00 3.83e+06 -1.0 1.47e+04 - 3.82e-01 1.00e+00h 1\n", + " 4 2.2046445e+01 1.08e+01 2.56e+06 -1.0 6.37e+04 - 6.39e-02 3.78e-01f 1\n", + " 5 2.2054651e+01 1.07e+01 2.55e+06 -1.0 4.64e+04 - 8.46e-01 3.38e-03H 1\n", + " 6 2.2054719e+01 1.07e+01 2.90e+06 -1.0 5.31e+04 - 9.33e-01 2.60e-05H 1\n", + " 7 2.2115730e+01 1.07e+01 3.07e+06 -1.0 1.06e+05 - 2.99e-03 3.36e-03h 1\n", + " 8 2.2670266e+01 1.03e+01 7.60e+07 -1.0 1.04e+05 - 1.35e-03 3.07e-02h 4\n", + " 9 2.2845530e+01 1.52e+01 7.18e+11 -1.0 5.08e+04 0.0 7.71e-06 3.39e-01f 2\n", + "MA27BD returned iflag=-4 and requires more memory.\n", + " Increase liw from 428795 to 857590 and la from 998490 to 2102720 and factorize again.\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 2.2884321e+01 1.52e+01 7.18e+11 -1.0 9.83e+05 - 1.37e-03 8.03e-04h 3\n", + " 11 2.4204364e+01 8.69e+00 1.24e+12 -1.0 1.84e+04 - 4.48e-01 7.22e-01h 1\n", + " 12 2.2749473e+01 9.97e+00 2.05e+12 -1.0 9.62e+03 - 8.75e-03 1.00e+00f 1\n", + " 13 2.3895393e+01 7.40e+00 5.34e+12 -1.0 1.90e+04 - 9.66e-01 6.10e-01h 1\n", + " 14 2.3900523e+01 7.23e+00 5.27e+12 -1.0 1.88e+04 - 9.85e-01 1.18e-02H 1\n", + " 15 2.3900572e+01 7.23e+00 5.27e+12 -1.0 1.97e+04 - 1.00e+00 1.17e-04H 1\n", + " 16 2.3832020e+01 5.17e+00 2.63e+15 -1.0 2.03e+04 - 8.20e-07 2.84e-01f 2\n", + " 17 2.1477963e+01 1.30e+01 1.24e+16 -1.0 2.47e+04 - 4.48e-01 1.00e+00f 1\n", + " 18 2.1617371e+01 1.21e+01 1.17e+16 -1.0 7.78e+04 - 9.92e-01 6.23e-02H 1\n", + " 19 2.1617786e+01 1.20e+01 1.17e+16 -1.0 5.29e+04 - 1.00e+00 1.56e-03H 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 2.2545000e+01 7.12e+00 2.68e+18 -1.0 6.16e+04 - 1.17e-05 2.29e-01f 2\n", + " 21 2.3726301e+01 7.68e+00 4.57e+18 -1.0 1.23e+04 - 5.72e-01 8.11e-01H 1\n", + " 22 2.3654883e+01 5.86e+00 4.95e+19 -1.0 2.60e+04 - 3.70e-03 2.39e-01f 2\n", + " 23 2.3268027e+01 2.27e+00 1.02e+20 -1.0 4.36e+03 - 1.00e+00 1.00e+00h 1\n", + " 24r 2.3268027e+01 2.27e+00 1.00e+03 0.4 0.00e+00 1.3 0.00e+00 3.07e-07R 17\n", + " 25r 2.3235257e+01 3.51e+00 5.09e+03 0.4 3.26e+04 - 2.55e-02 2.00e-03f 1\n", + " 26r 2.2912832e+01 1.57e+01 4.16e+03 0.4 6.05e+03 - 3.18e-03 1.90e-02f 1\n", + " 27r 2.2870552e+01 1.59e+01 1.11e+04 0.4 1.98e+00 2.0 1.53e-01 3.43e-02f 1\n", + " 28r 2.2578224e+01 1.59e+01 1.44e+04 0.4 1.25e+00 1.5 6.23e-01 1.86e-01f 1\n", + " 29r 2.2311567e+01 1.59e+01 1.07e+04 0.4 1.77e+00 1.9 2.72e-01 2.62e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30r 2.2137289e+01 1.97e+01 7.00e+03 0.4 2.13e+00 2.4 4.07e-01 2.98e-01f 1\n", + " 31r 2.1858506e+01 3.28e+01 1.17e+03 0.4 1.66e-01 2.8 9.10e-01 1.00e+00f 1\n", + " 32r 2.1817064e+01 3.48e+01 6.77e+03 0.4 1.92e-01 3.2 8.01e-01 6.13e-01f 1\n", + " 33r 2.1688753e+01 3.41e+01 2.18e+03 0.4 9.94e-02 2.7 1.00e+00 8.53e-01f 1\n", + " 34r 2.1635297e+01 3.53e+01 6.26e+02 0.4 1.52e-02 3.2 1.00e+00 1.00e+00f 1\n", + " 35r 2.1510319e+01 1.92e+01 2.18e+02 0.4 8.25e-02 2.7 1.00e+00 1.00e+00f 1\n", + " 36r 2.1506305e+01 1.92e+01 1.37e+04 0.4 4.89e-01 3.1 7.00e-01 9.31e-02H 1\n", + " 37r 2.1392888e+01 2.09e+01 2.28e+02 0.4 5.01e-02 2.6 1.00e+00 1.00e+00f 1\n", + " 38r 2.1353347e+01 5.68e+01 4.54e+03 0.4 6.36e-02 3.1 1.00e+00 1.00e+00f 1\n", + " 39r 2.1347337e+01 7.29e+01 1.21e+03 0.4 1.31e-02 3.5 1.00e+00 4.14e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 40r 2.1306253e+01 2.23e+01 1.35e+03 0.4 2.20e-02 3.0 1.00e+00 1.00e+00f 1\n", + " 41r 2.1291299e+01 2.25e+01 5.56e+02 0.4 9.25e-03 3.4 1.00e+00 1.00e+00f 1\n", + " 42r 1.9207034e+01 3.84e+01 6.06e+03 0.4 1.69e+03 - 4.65e-02 2.92e-01f 1\n", + " 43r 1.9196254e+01 4.00e+01 5.00e+04 0.4 4.83e+00 3.0 1.10e-01 1.21e-01h 1\n", + " 44r 1.9195984e+01 4.01e+01 4.99e+04 0.4 4.77e+00 2.5 2.25e-01 1.83e-03h 1\n", + " 45r 1.9139449e+01 4.25e+01 8.03e+03 0.4 1.01e+01 2.0 1.42e-01 2.07e-01f 1\n", + " 46r 1.9000495e+01 5.32e+01 4.74e+02 0.4 5.92e-01 2.4 5.10e-01 1.00e+00h 1\n", + " 47r 1.8995380e+01 5.37e+01 6.61e+02 0.4 1.23e-01 3.8 7.60e-01 7.03e-01f 1\n", + " 48r 1.8986031e+01 5.37e+01 4.60e+02 0.4 2.28e-01 3.3 1.00e+00 6.74e-01f 1\n", + " 49r 1.8951527e+01 5.41e+01 4.64e+01 0.4 2.63e-02 2.8 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", + " 50r 1.9232842e+01 5.56e+01 5.07e+01 -0.3 7.48e-02 2.3 1.00e+00 8.89e-01f 1\n", + " 51r 1.9658047e+01 5.67e+01 1.33e+02 -0.3 2.21e-01 1.9 1.00e+00 7.56e-01f 1\n", + " 52r 1.9693871e+01 5.67e+01 2.13e+01 -0.3 1.54e-02 3.2 1.00e+00 1.00e+00f 1\n", + " 53r 1.9818816e+01 5.69e+01 6.97e+01 -1.0 3.34e-02 2.7 1.00e+00 9.00e-01f 1\n", + " 54r 1.9980519e+01 5.70e+01 3.12e+02 -1.0 1.05e-01 2.2 1.00e+00 5.24e-01f 1\n", + " 55r 2.0030149e+01 5.70e+01 4.29e+02 -1.0 2.99e-01 1.8 1.00e+00 3.48e-01f 1\n", + " 56r 2.0070692e+01 5.70e+01 3.37e+01 -1.0 1.12e-01 2.2 1.00e+00 1.00e+00f 1\n", + " 57r 2.0071871e+01 5.70e+01 1.69e+01 -1.0 4.13e-02 2.6 1.00e+00 1.00e+00f 1\n", + " 58r 2.0074641e+01 5.69e+01 4.18e+01 -1.0 1.19e-01 2.1 1.00e+00 1.00e+00f 1\n", + " 59r 2.0077080e+01 5.68e+01 6.59e+02 -1.0 3.22e-01 1.7 1.00e+00 1.89e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 60r 2.0112756e+01 5.52e+01 5.97e+02 -1.0 7.41e-01 1.2 1.00e+00 5.25e-01f 1\n", + " 61r 2.0113794e+01 5.51e+01 5.93e+02 -1.0 1.26e+00 1.6 5.20e-02 3.47e-02f 1\n", + " 62r 2.0121779e+01 5.42e+01 1.49e+02 -1.0 6.05e-02 2.0 7.52e-01 7.48e-01f 1\n", + " 63r 2.0125646e+01 5.37e+01 3.53e+02 -1.0 2.27e-02 2.5 7.89e-01 1.00e+00f 1\n", + " 64r 2.0138149e+01 5.27e+01 6.53e+00 -1.0 6.80e-02 2.0 1.00e+00 1.00e+00f 1\n", + " 65r 2.0179026e+01 4.97e+01 6.52e+00 -1.0 2.04e-01 1.5 1.00e+00 1.00e+00f 1\n", + " 66r 2.0194658e+01 4.86e+01 6.51e+00 -1.0 7.63e-02 1.9 1.00e+00 1.00e+00f 1\n", + " 67r 2.0200517e+01 4.82e+01 6.51e+00 -1.0 2.86e-02 2.4 1.00e+00 1.00e+00f 1\n", + " 68r 2.0217977e+01 4.70e+01 6.50e+00 -1.0 8.57e-02 1.9 1.00e+00 1.00e+00f 1\n", + " 69r 2.0269071e+01 4.36e+01 7.16e+00 -1.0 2.57e-01 1.4 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", + " 70r 2.0271470e+01 4.35e+01 9.93e+00 -1.0 2.94e-02 2.7 1.00e+00 1.00e+00f 1\n", + " 71r 2.0278713e+01 4.30e+01 6.48e+00 -1.0 3.61e-02 2.3 1.00e+00 1.00e+00f 1\n", + " 72r 2.0281432e+01 4.28e+01 6.48e+00 -1.0 1.35e-02 2.7 1.00e+00 1.00e+00f 1\n", + " 73r 2.0357058e+01 4.18e+01 7.24e+02 -1.0 3.80e+04 - 2.83e-02 1.17e-02f 1\n", + " 74r 2.0365004e+01 4.13e+01 6.40e+00 -1.0 4.01e-02 2.2 1.00e+00 1.00e+00f 1\n", + " 75r 2.0367278e+01 4.12e+01 6.73e+01 -1.0 8.85e-01 1.7 1.08e-01 9.44e-02f 1\n", + " 76r 2.0368413e+01 4.11e+01 4.20e+01 -1.0 3.70e-02 3.1 1.00e+00 1.00e+00f 1\n", + " 77r 2.0562971e+01 3.88e+01 1.18e+02 -1.0 1.73e+04 - 4.24e-02 2.75e-02f 1\n", + " 78r 2.0565850e+01 3.87e+01 4.01e+00 -1.0 1.06e-02 2.6 1.00e+00 1.00e+00f 1\n", + " 79r 2.0580975e+01 3.84e+01 1.55e+02 -1.7 8.23e-02 2.1 6.95e-01 4.48e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 80r 2.0596306e+01 3.72e+01 3.76e+02 -1.7 8.65e-02 1.6 9.65e-01 6.02e-01f 1\n", + " 81r 2.0638107e+01 3.50e+01 2.49e+02 -1.7 2.35e-01 1.1 5.34e-01 4.22e-01f 1\n", + " 82r 2.0638995e+01 3.49e+01 2.35e+02 -1.7 2.98e+00 1.6 1.89e-02 2.36e-02f 1\n", + " 83r 2.0652057e+01 3.42e+01 2.41e+02 -1.7 5.14e-02 2.0 8.22e-01 1.00e+00f 1\n", + " 84r 2.0674642e+01 3.31e+01 2.02e+02 -1.7 1.58e-01 1.5 6.86e-01 4.88e-01f 1\n", + " 85r 2.0689896e+01 3.26e+01 9.68e+02 -1.7 2.58e-01 1.0 3.71e-01 1.03e-01f 1\n", + " 86r 2.0719933e+01 3.14e+01 4.93e+02 -1.7 1.39e-01 1.5 7.49e-01 5.57e-01f 1\n", + " 87r 2.0732936e+01 3.10e+01 3.92e+02 -1.7 1.16e-01 1.9 4.87e-01 6.31e-01f 1\n", + " 88r 2.0778388e+01 2.98e+01 2.56e+02 -1.7 1.08e-01 1.4 5.24e-01 7.17e-01f 1\n", + " 89r 2.0813524e+01 2.89e+01 1.09e+03 -1.7 2.76e-01 0.9 7.51e-01 1.78e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 90r 2.0950864e+01 2.58e+01 8.87e+02 -1.7 7.09e-01 0.5 1.83e-01 2.41e-01f 1\n", + " 91r 2.1128297e+01 2.41e+01 7.99e+02 -1.7 1.92e+00 -0.0 3.19e-02 1.10e-01f 1\n", + " 92r 2.1397564e+01 2.34e+01 7.32e+02 -1.7 4.31e+00 -0.5 1.23e-01 7.47e-02f 1\n", + " 93r 2.1413330e+01 2.31e+01 3.28e+02 -1.7 6.44e-02 1.7 5.06e-01 5.65e-01f 1\n", + " 94r 2.1423861e+01 2.29e+01 2.43e+00 -1.7 1.64e-02 2.2 1.00e+00 1.00e+00f 1\n", + " 95r 2.1457302e+01 2.22e+01 3.30e+00 -1.7 4.51e-02 1.7 1.00e+00 1.00e+00f 1\n", + " 96r 2.1560062e+01 2.05e+01 1.14e+01 -1.7 1.25e-01 1.2 1.00e+00 1.00e+00f 1\n", + " 97r 2.1571558e+01 2.03e+01 7.82e+02 -1.7 5.17e-02 1.6 1.00e+00 2.89e-01f 1\n", + " 98r 2.1605909e+01 1.98e+01 6.21e+02 -1.7 1.38e-01 1.2 4.07e-01 2.72e-01f 1\n", + " 99r 2.1629458e+01 1.95e+01 8.17e+02 -1.7 7.42e-02 1.6 8.01e-02 5.05e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 100r 2.1648516e+01 1.93e+01 7.63e+02 -1.7 3.09e-01 1.1 7.89e-01 1.29e-01f 1\n", + " 101r 2.1846232e+01 1.79e+01 3.70e+02 -1.7 3.69e-01 0.6 5.33e-01 4.43e-01f 1\n", + " 102r 2.1893945e+01 1.75e+01 8.18e+02 -1.7 1.42e-01 1.1 7.70e-01 3.23e-01f 1\n", + " 103r 2.2026058e+01 1.68e+01 5.74e+02 -1.7 3.69e-01 0.6 2.97e-01 2.89e-01f 1\n", + " 104r 2.2209260e+01 1.97e+01 6.48e+02 -1.7 8.61e-01 0.1 3.79e-01 1.66e-01f 1\n", + " 105r 2.2493984e+01 2.90e+01 3.33e+02 -1.7 1.81e+00 -0.4 3.01e-02 1.74e-01f 1\n", + " 106r 2.2537771e+01 3.06e+01 3.24e+02 -1.7 1.36e+00 0.1 2.11e-01 5.96e-02f 1\n", + " 107r 2.2613312e+01 3.40e+01 9.26e+02 -1.7 2.92e+00 -0.4 1.12e-01 6.07e-02f 1\n", + " 108r 2.2902905e+01 3.64e+01 8.07e+02 -1.7 2.10e+00 -0.9 5.48e-02 1.16e-01f 1\n", + " 109r 2.3043434e+01 3.70e+01 7.60e+02 -1.7 4.90e+00 -1.4 1.28e-01 5.06e-02f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 110r 2.3230954e+01 3.93e+01 7.23e+02 -1.7 9.32e+00 -1.9 4.52e-02 4.91e-02f 1\n", + " 111r 2.3416370e+01 3.96e+01 1.51e+03 -1.7 1.04e+01 -2.3 5.64e-01 6.56e-02f 1\n", + " 112r 2.3508709e+01 4.01e+01 7.67e+02 -1.7 1.32e+01 -2.8 1.19e-01 4.65e-01f 1\n", + " 113r 2.3400558e+01 3.93e+01 5.98e+02 -1.7 1.68e+01 -3.3 3.73e-01 6.31e-01f 1\n", + " 114r 2.3355738e+01 3.93e+01 4.66e+02 -1.7 1.07e-01 0.8 5.98e-01 3.38e-01f 1\n", + " 115r 2.3358676e+01 3.92e+01 3.17e+02 -1.7 3.16e-02 2.1 7.02e-01 9.26e-01f 1\n", + " 116r 2.3361801e+01 3.92e+01 9.00e+02 -1.7 2.55e-02 1.6 1.00e+00 4.45e-01f 1\n", + " 117r 2.3373797e+01 3.92e+01 1.33e+02 -1.7 2.27e-02 1.1 9.07e-01 1.00e+00f 1\n", + " 118r 2.3375340e+01 3.92e+01 3.34e+02 -1.7 1.73e-01 0.7 4.55e-01 2.26e-01f 1\n", + " 119r 2.3376128e+01 3.92e+01 4.58e+02 -1.7 2.32e-02 2.0 6.37e-01 8.69e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 120r 2.3377579e+01 3.92e+01 1.67e+02 -1.7 2.21e-02 1.5 1.00e+00 8.47e-01f 1\n", + " 121r 2.3379764e+01 3.93e+01 5.75e-01 -1.7 3.24e-02 1.0 1.00e+00 1.00e+00f 1\n", + " 122r 2.3382814e+01 3.93e+01 7.00e+02 -2.6 1.14e-01 0.6 7.32e-01 2.90e-01f 1\n", + " 123r 2.3388820e+01 3.93e+01 1.51e+02 -2.6 3.74e-02 1.0 9.83e-01 8.34e-01f 1\n", + " 124r 2.3390682e+01 3.93e+01 3.21e+02 -2.6 1.60e-02 1.4 1.00e+00 4.82e-01f 1\n", + " 125r 2.3394681e+01 3.93e+01 1.15e+02 -2.6 5.39e-02 0.9 7.31e-01 6.44e-01f 1\n", + " 126r 2.3394887e+01 3.93e+01 5.95e+02 -2.6 4.01e-01 1.4 2.17e-01 2.94e-02f 1\n", + " 127r 2.3396151e+01 3.93e+01 4.22e+02 -2.6 3.29e-02 0.9 2.90e-01 3.00e-01f 1\n", + " 128r 2.3396425e+01 3.93e+01 4.58e+02 -2.6 1.04e-01 0.4 1.44e-01 1.11e-01f 1\n", + " 129r 2.3397732e+01 3.93e+01 5.44e+02 -2.6 2.87e-01 -0.1 7.72e-01 3.21e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 130r 2.3400205e+01 3.93e+01 3.50e+02 -2.6 7.20e-01 -0.6 3.68e-01 3.40e-01f 1\n", + " 131r 2.3408692e+01 3.93e+01 7.94e+02 -2.6 1.39e+00 -1.0 3.85e-01 7.51e-01f 1\n", + " 132r 2.3407461e+01 3.93e+01 3.92e+02 -2.6 1.04e+00 -1.5 1.00e+00 5.33e-01f 1\n", + " 133r 2.3370385e+01 3.92e+01 5.23e+00 -2.6 1.04e+00 -2.0 1.00e+00 1.00e+00f 1\n", + " 134r 2.3317757e+01 3.91e+01 7.74e-02 -2.6 1.79e+00 -2.5 1.00e+00 1.00e+00f 1\n", + " 135r 2.3309990e+01 3.89e+01 3.23e+02 -3.9 5.40e+00 -2.9 7.75e-01 3.75e-01f 1\n", + " 136r 2.3303930e+01 3.85e+01 1.86e+02 -3.9 1.62e+01 -3.4 2.47e-01 3.33e-01f 1\n", + " 137r 2.3305226e+01 3.83e+01 1.76e+02 -3.9 4.84e+01 -3.9 6.98e-02 6.39e-02f 1\n", + " 138r 2.3306211e+01 3.77e+01 1.66e+02 -3.9 1.78e+02 -4.4 4.15e-02 5.09e-02f 1\n", + " 139r 2.3305465e+01 3.62e+01 2.44e+02 -3.9 4.32e+02 -4.9 1.18e-01 5.12e-02f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 140r 2.3303924e+01 3.51e+01 2.60e+02 -3.9 1.27e+03 -5.3 2.21e-02 1.43e-02f 1\n", + " 141r 2.3303674e+01 3.49e+01 2.49e+02 -3.9 3.49e+03 -5.8 6.20e-03 6.99e-04f 1\n", + " 142r 2.3299378e+01 2.99e+01 2.47e+02 -3.9 9.32e+03 -6.3 1.41e-03 6.75e-03f 1\n", + " 143r 2.3296980e+01 2.88e+01 4.21e+02 -3.9 3.55e+03 -5.9 6.75e-02 3.92e-03f 1\n", + " 144r 2.3280379e+01 2.28e+01 3.46e+02 -3.9 1.11e+04 -6.3 5.65e-03 9.19e-03f 1\n", + " 145r 2.3277402e+01 2.25e+01 5.59e+02 -3.9 4.66e+02 -5.0 1.05e-01 8.20e-03f 1\n", + " 146r 2.3241884e+01 1.73e+01 4.84e+02 -3.9 1.15e+03 -5.5 2.89e-02 5.18e-02f 1\n", + " 147r 2.3224473e+01 1.24e+01 5.30e+02 -3.9 2.73e+03 -6.0 4.60e-02 1.87e-02f 1\n", + " 148r 2.3217483e+01 8.60e+00 6.18e+02 -3.9 7.92e+03 -6.4 2.66e-02 5.13e-03f 1\n", + " 149r 2.3193382e+01 7.56e+00 5.99e+02 -3.9 3.03e+03 -6.0 3.02e-02 3.02e-02f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 150r 2.3207731e+01 7.34e+00 5.88e+02 -3.9 2.41e+03 -6.5 1.60e-02 2.12e-02f 1\n", + " 151r 2.3209832e+01 7.30e+00 5.65e+02 -3.9 8.63e+02 -6.1 1.94e-01 1.28e-02f 1\n", + " 152r 2.3210343e+01 7.29e+00 6.65e+02 -3.9 3.14e+02 -5.6 1.15e-01 1.06e-02f 1\n", + " 153r 2.3222483e+01 7.09e+00 5.64e+02 -3.9 7.97e+02 -6.1 1.24e-02 6.83e-02f 1\n", + " 154r 2.3221089e+01 7.01e+00 7.55e+02 -3.9 3.05e+02 -5.7 2.92e-01 6.17e-02f 1\n", + " 155r 2.3222095e+01 6.91e+00 7.36e+02 -3.9 5.78e+02 -6.2 2.97e-02 2.72e-02f 1\n", + " 156r 2.3223710e+01 6.62e+00 7.26e+02 -3.9 1.95e+03 -6.6 5.33e-02 2.91e-02f 1\n", + " 157r 2.3222807e+01 6.51e+00 8.26e+02 -3.9 6.48e+02 -6.2 2.79e-01 3.26e-02f 1\n", + " 158r 2.3220619e+01 6.19e+00 7.97e+02 -3.9 2.27e+03 -6.7 2.28e-02 3.10e-02f 1\n", + " 159r 2.3220965e+01 6.13e+00 8.19e+02 -3.9 1.83e+04 -7.2 2.49e-02 1.59e-03f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 160r 2.3221116e+01 4.96e+00 8.45e+02 -3.9 1.10e+04 -7.6 5.19e-02 1.90e-02f 1\n", + " 161r 2.3245849e+01 3.52e+00 8.74e+02 -3.9 8.08e+04 - 1.93e-02 3.57e-03f 1\n", + " 162r 2.3261888e+01 2.60e+00 1.00e+03 -3.9 1.79e+04 -8.1 2.04e-01 1.00e-02f 1\n", + " 163r 2.3262780e+01 2.23e+00 9.76e+02 -3.9 1.17e+04 -8.6 4.26e-02 5.77e-03f 1\n", + " 164r 2.3261577e+01 2.21e+00 9.57e+02 -3.9 2.27e+01 -4.6 5.82e-01 1.39e-01f 1\n", + " 165r 2.3267832e+01 1.65e+00 9.54e+02 -3.9 1.64e+04 - 1.08e-02 6.69e-03f 1\n", + " 166r 2.3272120e+01 1.50e+00 9.12e+02 -3.9 1.38e+04 - 1.23e-02 2.45e-03f 1\n", + " 167r 2.3278331e+01 9.18e-01 8.80e+02 -3.9 2.60e+04 - 6.12e-03 1.27e-02f 1\n", + " 168r 2.3278244e+01 8.86e-01 8.93e+02 -3.9 5.32e+03 - 2.84e-02 1.18e-03f 1\n", + " 169r 2.3278042e+01 8.82e-01 8.78e+02 -3.9 1.41e+02 -5.0 2.40e-01 8.93e-03f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 170r 2.3277882e+01 8.78e-01 9.62e+02 -3.9 1.22e+04 - 5.30e-02 2.05e-04f 1\n", + " 171r 2.3307761e+01 3.11e-01 1.76e+03 -3.9 1.73e+03 - 9.96e-01 9.69e-02f 1\n", + " 172r 2.3305171e+01 1.67e-01 5.34e+02 -3.9 3.33e+01 -5.5 1.00e+00 6.10e-01f 1\n", + " 173r 2.3333008e+01 1.34e-01 5.64e+02 -3.9 8.62e+02 - 5.13e-01 1.46e-01f 1\n", + " 174r 2.3344605e+01 1.17e-01 5.00e+02 -3.9 7.15e+02 - 1.00e+00 1.08e-01f 1\n", + " 175r 2.3386761e+01 5.16e-02 2.18e+02 -3.9 2.37e+02 - 1.00e+00 5.61e-01f 1\n", + " 176r 2.3406269e+01 1.23e-03 3.96e-02 -3.9 6.61e+01 - 1.00e+00 1.00e+00f 1\n", + " 177r 2.3405494e+01 1.14e-03 1.21e-04 -3.9 2.27e+00 - 1.00e+00 1.00e+00h 1\n", + " 178r 2.3409439e+01 4.54e-03 2.17e+01 -5.9 1.85e+01 - 4.22e-01 4.79e-01f 1\n", + " 179r 2.3520584e+01 3.44e-02 1.40e+02 -5.9 3.14e+02 - 9.81e-01 7.84e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 180r 2.3539091e+01 1.38e-03 9.38e-04 -5.9 4.96e+01 - 1.00e+00 1.00e+00h 1\n", + " 181r 2.3539043e+01 1.39e-03 7.36e-06 -5.9 3.19e-01 - 1.00e+00 1.00e+00h 1\n", + " 182r 2.3539267e+01 1.53e-03 1.16e+00 -8.8 1.20e+00 - 9.11e-01 9.89e-01f 1\n", + " 183r 2.3562448e+01 1.15e-03 4.26e+01 -8.8 1.90e+03 - 8.12e-01 2.79e-02f 1\n", + " 184r 2.3559918e+01 1.06e-03 8.61e+02 -8.8 6.50e+01 - 9.59e-01 8.93e-02h 1\n", + " 185r 2.3562522e+01 2.26e-05 8.14e+00 -8.8 6.18e+00 - 1.00e+00 9.91e-01h 1\n", + " 186r 2.3562522e+01 3.18e-09 3.17e-07 -8.8 3.96e-03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 186\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.3562522244178467e+01 2.3562522244178467e+01\n", + "Dual infeasibility......: 3.0650830611898394e-04 3.0650830611898394e+00\n", + "Constraint violation....: 1.6165202509910159e-10 3.1780018616700549e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.6165202509910159e-10 3.0650830611898394e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 238\n", + "Number of objective gradient evaluations = 26\n", + "Number of equality constraint evaluations = 238\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 188\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 186\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 25.702\n", + "Total CPU secs in NLP function evaluations = 2.866\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "# ==========================================================\n", + "# = Solver Results =\n", + "# ==========================================================\n", + "# ----------------------------------------------------------\n", + "# Problem Information\n", + "# ----------------------------------------------------------\n", + "Problem: \n", + "- Lower bound: -inf\n", + " Upper bound: inf\n", + " Number of objectives: 1\n", + " Number of constraints: 7023\n", + " Number of variables: 7023\n", + " Sense: unknown\n", + "# ----------------------------------------------------------\n", + "# Solver Information\n", + "# ----------------------------------------------------------\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: 28.973029136657715\n", + "# ----------------------------------------------------------\n", + "# Solution Information\n", + "# ----------------------------------------------------------\n", + "Solution: \n", + "- number of solutions: 0\n", + " number of solutions displayed: 0\n" + ] + } + ], + "source": [ + "# solve for fixed capture\n", + "solver = SolverFactory(\"ipopt\")\n", + "solver.options = {\n", + " \"max_iter\": 1000,\n", + " \"bound_push\": 1e-22,\n", + " \"halt_on_ampl_error\": \"yes\",\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + "}\n", + "solver.solve(m, tee=True).write()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.RPB Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Adsorption Tx : 363.00 : kelvin : True : (0, None)\n", + " Adsorption Volume Fraction : 0.75000 : dimensionless : True : (0.01, 0.99)\n", + " CO2 Capture : 0.95000 : dimensionless : True : (None, None)\n", + " Desorption Tx : 393.00 : kelvin : True : (0, None)\n", + " Desorption Volume Fraction : 0.25000 : dimensionless : False : (0.01, 0.99)\n", + " Diameter : 10.000 : meter : True : (0, None)\n", + " Length : 3.0000 : meter : True : (0.1, 10.001)\n", + " Rotational Velocity : 0.010472 : radian / second : True : (1e-05, 2)\n", + "\n", + " Expressions: \n", + "\n", + " Key : Value : Units\n", + " Energy Requirement : 5.0337e+07 : joule / kilogram\n", + " Productivity : 0.00089230 : kilogram / meter ** 3 / second\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Flue Gas Feed Cleaned Flue Gas Steam Sweep Regeneration Product\n", + " Total Molar Flowrate mole / second 83.810 80.625 168.27 172.88 \n", + " Total Mole Fraction N2 dimensionless 0.87000 0.90437 0.0010000 0.00097329 \n", + " Total Mole Fraction CO2 dimensionless 0.040000 0.0020790 1.0000e-05 0.026717 \n", + " Total Mole Fraction H2O dimensionless 0.090000 0.093555 0.99899 0.97231 \n", + " Temperature kelvin 363.15 391.35 393.15 363.75 \n", + " Pressure pascal 1.0203e+05 1.0132e+05 1.0500e+05 1.0132e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.RPB.report()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L : Bed Length [m]\n", + " Size=1, Index=None, Units=m\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " None : 0.1 : 3 : 20 : True : True : PositiveReals\n" + ] + } + ], + "source": [ + "# set bounds for decision variables\n", + "m.fs.RPB.L.setlb(0.1)\n", + "m.fs.RPB.L.setub(20)\n", + "m.fs.RPB.L.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tx : heat exchange fluid temperature, constant [K]\n", + " Size=1, Index=fs._time, Units=K\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " 0.0 : 298 : 363 : 368 : True : True : PositiveReals\n" + ] + } + ], + "source": [ + "m.fs.RPB.ads.Tx.setlb(25+273)\n", + "m.fs.RPB.ads.Tx.setub(95+273)\n", + "m.fs.RPB.ads.Tx.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tx : heat exchange fluid temperature, constant [K]\n", + " Size=1, Index=fs._time, Units=K\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " 0.0 : 373 : 393 : 433 : True : True : PositiveReals\n" + ] + } + ], + "source": [ + "m.fs.RPB.des.Tx.setlb(100+273)\n", + "m.fs.RPB.des.Tx.setub(160+273)\n", + "m.fs.RPB.des.Tx.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pressure : Size=1, Index=fs._time, ReferenceTo=fs.flue_gas_in.properties[:].component('pressure')\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " 0.0 : 100010.0 : 102032.48625654519 : 120000.0 : False : False : NonNegativeReals\n" + ] + } + ], + "source": [ + "m.fs.flue_gas_in.pressure.setub(1.2*1e5)\n", + "m.fs.flue_gas_in.pressure.setlb(1.0001*1e5)\n", + "m.fs.flue_gas_in.pressure.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pressure : Size=1, Index=fs._time, ReferenceTo=fs.steam_sweep_feed.properties[:].component('pressure')\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " 0.0 : 100010.0 : 105000.0 : 120000.0 : True : True : NonNegativeReals\n" + ] + } + ], + "source": [ + "m.fs.steam_sweep_feed.pressure.setub(1.2*1e5)\n", + "m.fs.steam_sweep_feed.pressure.setlb(1.0001*1e5)\n", + "m.fs.steam_sweep_feed.pressure.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w_rpm : bed rotational speed [revolutions/min]\n", + " Size=1, Index=fs._time, Units=turn/min\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " 0.0 : 1e-05 : 0.1 : 0.01 : True : True : PositiveReals\n" + ] + } + ], + "source": [ + "m.fs.RPB.w_rpm.setlb(0.00001)\n", + "m.fs.RPB.w_rpm.setub(0.01)\n", + "m.fs.RPB.w_rpm.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "theta : Fraction of bed occupied by the section[-]\n", + " Size=1, Index=None\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " None : 0.01 : 0.75 : 0.95 : True : True : PositiveReals\n" + ] + } + ], + "source": [ + "m.fs.RPB.ads.theta.setub(0.95)\n", + "m.fs.RPB.ads.theta.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# free up optimization variables\n", + "m.fs.RPB.L.unfix()\n", + "m.fs.RPB.ads.theta.unfix()\n", + "m.fs.steam_sweep_feed.pressure.unfix()\n", + "m.fs.RPB.ads.Tx.unfix()\n", + "m.fs.RPB.des.Tx.unfix()\n", + "m.fs.RPB.w_rpm.unfix()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "solver = SolverFactory(\"ipopt\")\n", + "solver.options = {\n", + " \"max_iter\": 1000,\n", + " \"bound_push\": 1e-22,\n", + " \"halt_on_ampl_error\": \"yes\",\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + "}\n", + "solver.solve(m, tee=True).write()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Job model.gms Start 08/28/24 11:46:53 40.4.0 d540b52e WEX-WEI x86 64bit/MS Windows\n", + "--- Applying:\n", + " C:\\GAMS\\40\\gmsprmNT.txt\n", + "--- GAMS Parameters defined\n", + " Input \"c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\model.gms\"\n", + " Output \"c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\output.lst\"\n", + " ScrDir \"c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\225m\\\"\n", + " SysDir C:\\GAMS\\40\\\n", + " CurDir \"c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\\"\n", + " LogOption 3\n", + "Licensee: Medium MUD - 10 User License G211229|0002CN-GEN\n", + " U.S. Department of Energy, National Energy Technology LaborDC9138\n", + " C:\\Users\\hughesr\\Documents\\GAMS\\gamslice.txt\n", + " License Admin: Anthony P. Burgard, anthony.burgard@netl.doe.gov \n", + "Processor information: 1 socket(s), 8 core(s), and 16 thread(s) available\n", + "GAMS 40.4.0 Copyright (C) 1987-2022 GAMS Development. All rights reserved\n", + "--- Starting compilation\n", + "--- model.gms(14725) 17 Mb\n", + "--- model.gms(14977) 21 Mb\n", + "--- model.gms(18169) 35 Mb\n", + "--- model.gms(18363) 39 Mb\n", + "--- model.gms(49756) 47 Mb\n", + "--- Starting execution: elapsed 0:00:00.665\n", + "--- model.gms(35645) 49 Mb\n", + "--- Generating NLP model GAMS_MODEL\n", + "--- model.gms(14561) 51 Mb\n", + "--- model.gms(14702) 52 Mb\n", + "--- model.gms(18423) 59 Mb\n", + "--- model.gms(35649) 60 Mb\n", + "--- 7,024 rows 7,030 columns 26,206 non-zeroes\n", + "--- 1,529,426 nl-code 15,087 nl-non-zeroes\n", + "--- Range statistics (absolute non-zero finite values)\n", + "--- RHS [min, max] : [ 1.000E-05, 1.013E+05] - Zero values observed as well\n", + "--- Bound [min, max] : [ 1.000E-20, 1.200E+05] - Zero values observed as well\n", + "--- Matrix [min, max] : [ 1.016E-20, 4.619E+05] - Zero values observed as well\n", + "--- model.gms(35649) 51 Mb\n", + "--- Executing CONOPT4 (Solvelink=5): elapsed 0:00:01.024\n", + "\n", + "CONOPT 4 40.4.0 d540b52e Oct 3, 2022 WEI x86 64bit/MS Window\n", + "\n", + "\n", + "Reading parameter(s) from \"c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\conopt4.opt\"\n", + ">> iterlim = 500\n", + ">> rtredg = 1e-6\n", + ">> reslim = 600\n", + "Finished reading from \"c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\conopt4.opt\"\n", + "\n", + " \n", + " \n", + " C O N O P T version 4.28\n", + " Copyright (C) ARKI Consulting and Development A/S\n", + " Bagsvaerdvej 246 A\n", + " DK-2880 Bagsvaerd, Denmark\n", + " \n", + " \n", + " The user model has 7024 constraints and 7030 variables\n", + " with 26206 Jacobian elements, 15087 of which are nonlinear.\n", + " \n", + " Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK\n", + " 0 0 7.3323226253E+03 (Input point)\n", + " \n", + " The pre-triangular part of the model has 1566 constraints and variables.\n", + " The post-triangular part of the model has 239 constraints and variables.\n", + " There are 2506 definitional constraints and defined variables.\n", + " \n", + " Preprocessed model has 2713 constraints and 2719 variables\n", + " with 17176 Jacobian elements, 15099 of which are nonlinear.\n", + " \n", + " Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK\n", + " 7.3085565373E+03 (Full preprocessed model)\n", + " 8.8141202841E+01 (After scaling)\n", + " 8.5767891650E+00 (After adjusting individual variables)\n", + " 1 0 0 8.3575209708E+00 2.5E-01 2 F F\n", + " 2 S0 0 7.6664577733E+00 2.5E-01 4 F F\n", + " 3 S0 0 7.1347003342E+00 2.5E-01 2 F F\n", + " 4 S0 0 6.9235931850E+00 5.0E-01 3 F F\n", + " 5 S0 0 6.7659908920E+00 5.0E-01 2 F F\n", + " 6 0 0 5.6063007364E+00 5.0E-01 1 F F\n", + " 7 0 0 3.6602996128E+00 5.0E-01 2 F F\n", + " \n", + " Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK\n", + " 8 0 0 2.4975581625E+00 5.0E-01 2 F F\n", + " 9 0 0 1.9154931491E+00 1.0E+00 5 T T\n", + " 10 0 0 1.1663254430E+00 1.0E+00 3 T T\n", + " 11 0 0 3.9582858358E-01 1.0E+00 1 T T\n", + " 12 0 0 2.9657081203E-01 1.0E+00 1 T T\n", + " 13 S0 0 3.0182382042E-01 5.0E-01 1 F F\n", + " 14 S0 0 3.0114812039E-01 1.2E-01 1 F F\n", + " 15 S0 0 3.0737679252E-01 2.5E-01 1 F F\n", + " 16 S0 0 2.9450337461E-01 5.0E-01 1 F F\n", + " 17 S0 0 2.7076199499E-01 1.0E+00 1 T T\n", + " \n", + " Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK\n", + " 18 S0 0 2.5792016227E-01 1.0E+00 1 T T\n", + " 19 S0 0 2.5771221665E-01 1.0E+00 1 T T\n", + " 20 0 0 2.5771117443E-01 1.0E+00 1 T T\n", + " 21 1 109 1.2650317005E-01 6.2E+01 55 8.3E-01 25 F F\n", + " 22 1 67 1.2633257524E-01 9.8E+01 40 1.0E+00 25 T T\n", + " 23 1 49 1.1798127630E-01 3.7E+03 19 1.0E+00 35 T T\n", + " 24 1 49 6.9111844643E-02 7.8E+01 39 9.6E-01 48 F F\n", + " 25 1 33 6.8977758413E-02 1.4E+02 17 1.0E+00 25 T T\n", + " 26 1 33 4.6101074251E-02 5.1E+03 19 6.3E-01 35 F F\n", + " 27 1 33 3.5028414213E-02 4.4E+01 19 7.8E-01 25 F F\n", + " \n", + " Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK\n", + " 28 1 15 3.3318637840E-02 1.0E+01 19 1.0E+00 25 T T\n", + " 29 1 15 1.1869471086E-02 1.6E+00 21 7.9E-01 22 F F\n", + " 30 1 15 8.8005965610E-03 6.6E+01 21 4.0E-01 20 F F\n", + " 31 1 8 7.1833628962E-03 3.9E+00 21 1.0E+00 21 T T\n", + " 32 1 3 5.4693700864E-03 1.3E+00 14 1.0E+00 10 T T\n", + " 33 1 3 4.4487871236E-03 1.5E-02 9 1.5E+00 2 T F\n", + " 34 1 3 2.7284828407E-03 2.2E-02 9 3.5E+00 6 T T\n", + " 35 1 3 2.2469747410E-03 2.1E-02 9 4.0E+00 6 T T\n", + " 36 1 3 1.4593712043E-03 4.0E-02 9 4.0E+00 3 T T\n", + " 37 1 2 8.3939807619E-04 1.0E+00 9 1.0E+00 3 T T\n", + " \n", + " Iter Phase Ninf Infeasibility RGmax NSB Step InItr MX OK\n", + " 38 1 1 7.5679213636E-04 1.0E+00 8 1.0E+00 3 T T\n", + " 39 1 1 6.5620332315E-04 2.5E-02 7 1.0E+00 4 T T\n", + " 40 1 1 3.1224029837E-06 7.9E-02 7 6.2E+00 5 T T\n", + " \n", + " ** Feasible solution. Value of objective = 22.8823081710\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 41 3 2.0355652481E+01 7.9E+03 6 2.8E-02 22 F F\n", + " 42 3 2.0079515026E+01 1.5E+02 6 3.7E-03 10 F F\n", + " 43 3 1.4759562081E+01 8.8E+03 6 1.6E-01 10 F F\n", + " 44 3 1.4388248902E+01 3.1E+02 6 7.5E-03 11 F F\n", + " 45 3 1.4368925172E+01 2.7E+02 6 8.6E-04 5 F F\n", + " 46 3 1.2824866050E+01 3.0E+02 6 4.2E-01 3 F F\n", + " 47 3 1.2318564869E+01 9.7E+00 6 2.8E-01 7 F F\n", + " 48 3 1.1910238624E+01 8.4E+00 6 6.9E-01 8 F F\n", + " 49 3 1.0342037989E+01 5.0E+00 6 7.8E+00 3 F F\n", + " 50 3 9.2357801327E+00 1.9E+01 6 2.9E+00 4 F F\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 51 3 8.1636760291E+00 1.6E+00 6 4.8E+00 3 F F\n", + " 52 3 7.5198330598E+00 2.1E+00 6 3.0E+00 6 F F\n", + " 53 3 6.7385872286E+00 2.5E+00 6 5.6E+00 7 F F\n", + " 54 3 6.0792799434E+00 9.0E-01 6 6.9E+00 6 F F\n", + " 55 3 5.8090452517E+00 2.8E+00 6 4.8E+00 6 F F\n", + " 56 3 5.5157473019E+00 3.7E+00 6 3.6E+00 6 F F\n", + " 57 3 5.4212348057E+00 6.5E+00 6 2.0E+00 5 F F\n", + " 58 3 5.3634825641E+00 7.8E+00 6 1.3E+00 5 F F\n", + " 59 3 5.3198102806E+00 7.5E+00 6 1.6E+00 4 F F\n", + " 60 3 5.0848888944E+00 7.7E+00 6 1.1E+01 1 F F\n", + " Elapsed time 33.8 seconds.\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 61 3 4.7743847998E+00 2.6E+00 6 2.1E+01 3 F F\n", + " 62 3 4.5799235699E+00 9.2E+00 6 1.4E+01 3 F F\n", + " 63 3 4.3604828332E+00 5.2E+00 6 1.7E+01 4 F F\n", + " 64 3 4.1223584670E+00 8.3E+00 6 2.8E+01 3 F F\n", + " 65 3 4.0186468542E+00 5.8E+00 6 1.0E+01 3 F F\n", + " 66 3 3.8000676729E+00 7.7E+00 6 2.7E+01 4 F F\n", + " 67 3 3.6884276168E+00 6.5E+00 6 1.8E+01 3 F F\n", + " 68 3 3.5193440563E+00 6.7E+00 6 2.9E+01 6 F F\n", + " 69 3 3.4057139299E+00 7.9E+00 6 8.7E+00 5 F F\n", + " 70 3 3.3962680566E+00 4.6E-01 6 8.9E-01 3 F F\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 71 3 3.3900616263E+00 5.9E+00 6 3.0E+00 4 F T\n", + " 72 3 3.3713494928E+00 5.3E+00 6 2.0E+01 3 F F\n", + " 73 3 3.3379095610E+00 5.3E+00 6 5.3E+01 1 F F\n", + " 74 3 3.1735756906E+00 5.6E+00 6 2.0E+02 10 F F\n", + " 75 3 3.1154810898E+00 5.5E+00 6 7.7E+01 5 F F\n", + " 76 3 3.0888760423E+00 4.9E+00 6 3.5E+01 6 F F\n", + " 77 3 3.0487300263E+00 6.2E+00 6 6.9E+01 6 F T\n", + " 78 3 3.0116282196E+00 3.4E+00 6 3.1E+01 8 F F\n", + " 79 3 2.9785095266E+00 5.6E+00 6 4.4E+01 5 F F\n", + " 80 3 2.9456701678E+00 5.8E+00 6 4.4E+01 6 F F\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 81 3 2.9437336232E+00 4.6E+00 6 4.6E+00 2 F F\n", + " 82 3 2.9414718657E+00 4.7E+00 6 5.7E+00 6 F T\n", + " 83 3 2.9199885128E+00 4.6E+00 6 1.4E+02 11 F F\n", + " 84 4 2.8525054414E+00 5.8E+02 6 2.7E-01 2 F F\n", + " 85 4 2.7343697778E+00 2.2E-01 6 1.7E+00 2 F T\n", + " 86 4 2.6611345807E+00 3.2E+00 6 2.1E-01 2 F F\n", + " 87 4 2.5683543202E+00 2.7E-01 6 8.6E-01 1 F F\n", + " 88 4 2.5372477186E+00 3.6E+00 6 8.5E-02 5 F F\n", + " 89 4 2.4703585277E+00 1.7E-01 6 1.3E+00 2 F F\n", + " 90 4 2.4208178578E+00 2.0E-01 6 9.4E-01 1 F F\n", + " Elapsed time 67.8 seconds.\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 91 4 2.2703781876E+00 3.3E+00 6 9.2E-01 3 F F\n", + " 92 4 2.2456819660E+00 3.9E+01 6 1.2E-01 2 F F\n", + " 93 4 2.2300892827E+00 6.2E+00 6 1.0E+00 2 T T\n", + " 94 4 2.2202087720E+00 2.5E-02 5 6.4E-01 1 F F\n", + " 95 4 2.2020451957E+00 9.0E-02 5 1.0E+00 1 F F\n", + " 96 4 2.1582350907E+00 1.8E-01 5 3.3E+00 1 F F\n", + " 97 4 2.1509975187E+00 4.1E-01 5 1.8E-01 6 F F\n", + " 98 4 2.1403268355E+00 1.4E-02 5 5.6E-01 5 F F\n", + " 99 4 2.1135483152E+00 1.3E-02 5 2.9E-01 6 F F\n", + " 100 4 2.1074638633E+00 1.3E+02 5 2.0E-01 2 F F\n", + " \n", + " Iter Phase Ninf Objective RGmax NSB Step InItr MX OK\n", + " 101 4 2.1031654909E+00 1.2E-02 5 7.0E-01 5 F F\n", + " 102 4 2.0996569222E+00 7.4E-03 5 1.0E+00 5 F T\n", + " 103 4 2.0990533471E+00 8.8E-03 5 1.0E+00 2 T T\n", + " 104 4 2.0990529780E+00 3.7E-04 4 1.0E+00 2 F T\n", + " 105 4 2.0990529780E+00 2.6E-06 4 1.0E+00 1 F T\n", + " 106 4 2.0990529780E+00 1.9E-07 4\n", + " \n", + " ** Optimal solution. Reduced gradient less than tolerance.\n", + " \n", + "--- Reading solution for model GAMS_MODEL\n", + "--- Executing after solve: elapsed 0:01:22.167\n", + "--- model.gms(35652) 51 Mb\n", + "--- model.gms(49756) 51 Mb\n", + "--- Putfile results c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\results.dat\n", + "--- Putfile statresults c:\\Users\\hughesr\\NETL\\CCSI\\fixed bed adsorption\\fixed_bed_adsorption\\Rotary packed bed\\temp\\resultsstat.dat\n", + "*** Status: Normal completion\n", + "--- Job model.gms Stop 08/28/24 11:48:16 elapsed 0:01:22.207\n", + "\n", + "GAMS WORKING DIRECTORY: temp\n", + "\n" + ] + } + ], + "source": [ + "# solve using conopt thorugh gams (usually works better than IPOPT)\n", + "results = SolverFactory(\"gams\").solve(\n", + " m,\n", + " tee=True,\n", + " keepfiles=True,\n", + " solver=\"conopt4\",\n", + " tmpdir=\"temp\",\n", + " add_options=[\"gams_model.optfile=1;\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.RPB Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Adsorption Tx : 342.38 : kelvin : False : (298, 368)\n", + " Adsorption Volume Fraction : 0.89702 : dimensionless : False : (0.01, 0.95)\n", + " CO2 Capture : 0.95000 : dimensionless : True : (None, None)\n", + " Desorption Tx : 433.00 : kelvin : False : (373, 433)\n", + " Desorption Volume Fraction : 0.10298 : dimensionless : False : (0.01, 0.99)\n", + " Diameter : 10.000 : meter : True : (0, None)\n", + " Length : 20.000 : meter : False : (0.1, 20)\n", + " Rotational Velocity : 0.00023863 : radian / second : False : (1e-05, 0.01)\n", + "\n", + " Expressions: \n", + "\n", + " Key : Value : Units\n", + " Energy Requirement : 6.0647e+06 : joule / kilogram\n", + " Productivity : 0.00051850 : kilogram / meter ** 3 / second\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Flue Gas Feed Cleaned Flue Gas Steam Sweep Regeneration Product\n", + " Total Molar Flowrate mole / second 324.67 312.33 33.018 39.357 \n", + " Total Mole Fraction N2 dimensionless 0.87000 0.90437 0.0010000 0.00083895 \n", + " Total Mole Fraction CO2 dimensionless 0.040000 0.0020790 1.0000e-05 0.16106 \n", + " Total Mole Fraction H2O dimensionless 0.090000 0.093555 0.99899 0.83810 \n", + " Temperature kelvin 363.15 372.04 393.15 380.28 \n", + " Pressure pascal 1.1708e+05 1.0132e+05 1.1287e+05 1.0132e+05 \n", + "====================================================================================\n", + "energy_requirement : Size=1\n", + " Key : Value\n", + " 0.0 : 6.064715623558013\n", + "productivity : Size=1\n", + " Key : Value\n", + " 0.0 : 1.8666096676325175\n" + ] + } + ], + "source": [ + "m.fs.RPB.report()\n", + "m.fs.RPB.energy_requirement.display()\n", + "m.fs.RPB.productivity.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool = DiagnosticsToolbox(m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.config.variable_bounds_absolute_tolerance = 1e-6\n", + "diagtool.config.variable_bounds_relative_tolerance = 1e-6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diagtool.display_variables_near_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "pareto front generation (hasn't been updated for the IDAES model version)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # list of alpha values to use in the objective function\n", + "# alpha_list=[\n", + "# 0.0001,\n", + "# 0.001,\n", + "# 0.005,\n", + "# 0.01,\n", + "# 0.02,\n", + "# 0.05,\n", + "# 0.1,\n", + "# 0.2,\n", + "# 0.3,\n", + "# 0.4,\n", + "# 0.5,\n", + "# 0.6,\n", + "# 0.7,\n", + "# 0.8,\n", + "# 0.9,\n", + "# 0.925,\n", + "# 0.95,\n", + "# 0.975,\n", + "# 0.99,\n", + "# 0.999,\n", + "# ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # optimize for every value and store the results\n", + "# E = []\n", + "# P = []\n", + "\n", + "# for j in alpha_list:\n", + "# RPB.alpha_obj = j\n", + "\n", + "# results = SolverFactory(\"gams\").solve(\n", + "# RPB,\n", + "# tee=True,\n", + "# keepfiles=True,\n", + "# solver=\"conopt4\",\n", + "# tmpdir=\"temp\",\n", + "# add_options=[\"gams_model.optfile=1;\"],\n", + "# )\n", + "\n", + "# print(f'alpha = {j}, E={RPB.energy_requirement()}, P={RPB.productivity()}')\n", + "\n", + "# E.append(RPB.energy_requirement())\n", + "# P.append(RPB.productivity())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pd.DataFrame({'alpha':alpha_list,'E':E,'P':P})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plt.scatter(E,P)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polishing step simulation and optimization (hasn't been updated for the IDAES model version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "start from previous optimized case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create pyomo model\n", + "# RPB = full_model_creation(lean_temp_connection=True, configuration = \"counter-current\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # create regularization parameter for the objective function\n", + "# RPB.alpha_obj = Param(initialize=0.5, mutable=True)\n", + "\n", + "# # add objective\n", + "# @RPB.Expression()\n", + "# def obj(RPB):\n", + "# return RPB.alpha_obj * RPB.energy_requirement - (1 - RPB.alpha_obj) * RPB.productivity\n", + "\n", + "# RPB.objective = Objective(expr=RPB.obj)\n", + "\n", + "# RPB.objective.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # set bounds for decision variables\n", + "# RPB.ads.L.setlb(0.01)\n", + "# RPB.ads.L.setub(40)\n", + "# RPB.des.L.setlb(0.01)\n", + "# RPB.des.L.setub(40)\n", + "# RPB.ads.L.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RPB.ads.Tx.setlb(25+273)\n", + "# RPB.ads.Tx.setub(95+273)\n", + "# RPB.ads.Tx.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RPB.des.Tx.setlb(100+273)\n", + "# RPB.des.Tx.setub(160+273)\n", + "# RPB.des.Tx.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RPB.ads.P_in.setub(1.5)\n", + "# RPB.ads.P_in.pprint()\n", + "# RPB.ads.P.setub(1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RPB.des.P_in.setub(1.5)\n", + "# RPB.des.P_in.setlb(1.01325)\n", + "# RPB.des.P.setub(1.5)\n", + "# RPB.des.P_in.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# RPB.ads.w_rpm.setlb(0.00001)\n", + "# RPB.ads.w_rpm.setub(0.1)\n", + "# RPB.ads.w_rpm.pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # load a previous solution and solve (will also load the inlet gas feed conc.)\n", + "# from_json(RPB, fname=\"polishing step optimized solution 031824.json.gz\", gz=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # solve using conopt thorugh gams\n", + "# results = SolverFactory(\"gams\").solve(\n", + "# RPB,\n", + "# tee=True,\n", + "# keepfiles=True,\n", + "# solver=\"conopt4\",\n", + "# tmpdir=\"temp\",\n", + "# add_options=[\"gams_model.optfile=1;\"],\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save model\n", + "iutil.to_json(m, fname=\"base case solution 012424.json.gz\", gz=True, human_read=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "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" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Rotary packed bed/__init__.py b/Rotary packed bed/__init__.py new file mode 100644 index 0000000..cbe3f4b --- /dev/null +++ b/Rotary packed bed/__init__.py @@ -0,0 +1 @@ +from .RPB_model import RotaryPackedBed diff --git a/Rotary packed bed/util.py b/Rotary packed bed/util.py new file mode 100644 index 0000000..813d1bd --- /dev/null +++ b/Rotary packed bed/util.py @@ -0,0 +1,105 @@ +################################################################################# +# 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. +################################################################################# +""" +Utility functions for RPB models +""" +import pandas as pd + +from pyomo.environ import Var + + +def report(blk): + items = [ + blk.L, + blk.D, + blk.w_rpm[0], + blk.ads.theta, + blk.des.theta, + blk.ads.P_in[0], + blk.ads.P_out[0], + blk.ads.F_in[0], + blk.ads.Tg_in[0], + blk.ads.Tx[0], + blk.des.P_in[0], + blk.des.P_out[0], + blk.des.F_in[0], + blk.des.Tg_in[0], + blk.des.Tx[0], + blk.ads.CO2_capture[0], + blk.energy_requirement[0], + blk.productivity[0], + ] + + names = [] + values = [] + fixed = [] + lb = [] + ub = [] + docs = [] + for item in items: + names.append(item.to_string()) + values.append(item()) + if item.ctype != Var: + fixed.append("N/A") + lb.append("N/A") + ub.append("N/A") + else: + fixed.append(item.fixed) + lb.append(item.lb) + ub.append(item.ub) + docs.append(item.parent_component().doc) + + report_df = pd.DataFrame( + data={ + "Value": values, + "Doc": docs, + "Fixed": fixed, + "Lower Bound": lb, + "Upper Bound": ub, + }, + index=names, + ) + + indexed_items = [ + blk.ads.inlet.y_in, + blk.ads.outlet.y_out, + ] + + names = [] + values = [] + docs = [] + fixed = [] + lb = [] + ub = [] + for item in indexed_items: + names += [item[k].to_string() for k in item.keys()] + values += [item[k]() for k in item.keys()] + docs += [item.doc for k in item.keys()] + fixed += [item[k].fixed for k in item.keys()] + lb += [item[k].lb for k in item.keys()] + ub += [item[k].ub for k in item.keys()] + + report_indexed_df = pd.DataFrame( + data={ + "Value": values, + "Doc": docs, + "Fixed": fixed, + "Lower Bound": lb, + "Upper Bound": ub, + }, + index=names, + ) + + report_df = pd.concat([report_df, report_indexed_df]) + + return report_df