Source code for watertap.property_models.anaerobic_digestion.adm1_properties

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# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
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# of Energy). All rights reserved.
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"""
Thermophysical property package to be used in conjunction with ADM1 reactions.
"""

# Import Pyomo libraries
import pyomo.environ as pyo

# Import IDAES cores
from idaes.core import (
    declare_process_block_class,
    MaterialFlowBasis,
    PhysicalParameterBlock,
    StateBlockData,
    StateBlock,
    MaterialBalanceType,
    EnergyBalanceType,
    LiquidPhase,
    Component,
    Solute,
    Solvent,
)
from idaes.core.util.model_statistics import degrees_of_freedom
from idaes.core.util.initialization import fix_state_vars, revert_state_vars
import idaes.logger as idaeslog
import idaes.core.util.scaling as iscale

# Some more information about this module
__author__ = "Alejandro Garciadiego, Adam Atia, Xinhong Liu"
# Using Andrew Lee's formulation of ASM1 as a template

# Set up logger
_log = idaeslog.getLogger(__name__)


[docs]@declare_process_block_class("ADM1ParameterBlock") class ADM1ParameterData(PhysicalParameterBlock): """ Property Parameter Block Class """
[docs] def build(self): """ Callable method for Block construction. """ super().build() self._state_block_class = ADM1StateBlock # Add Phase objects self.Liq = LiquidPhase() # Add Component objects self.H2O = Solvent() # All soluble components on kg COD/m^3 basis self.S_su = Solute(doc="Monosaccharides") self.S_aa = Solute(doc="Amino acids") self.S_fa = Solute(doc="Long chain fatty acids") self.S_va = Solute(doc="Total valerate") self.S_bu = Solute(doc="Total butyrate") self.S_pro = Solute(doc="Total propionate") self.S_ac = Solute(doc="Total acetate") self.S_h2 = Solute(doc="Hydrogen gas") self.S_ch4 = Solute(doc="Methane gas") self.S_IC = Solute(doc="Inorganic carbon") self.S_IN = Solute(doc="Inorganic nitrogen") self.S_I = Solute(doc="Soluble inerts") self.X_c = Solute(doc="Composites") self.X_ch = Solute(doc="Carbohydrates") self.X_pr = Solute(doc="Proteins") self.X_li = Solute(doc="Lipids") self.X_su = Solute(doc="Sugar degraders") self.X_aa = Solute(doc="Amino acid degraders") self.X_fa = Solute(doc="Long chain fatty acid (LCFA) degraders") self.X_c4 = Solute(doc="Valerate and butyrate degraders") self.X_pro = Solute(doc="Propionate degraders") self.X_ac = Solute(doc="Acetate degraders") self.X_h2 = Solute(doc="Hydrogen degraders") self.X_I = Solute(doc="Particulate inerts") # TODO: Additional components not in Table but referred to in text self.S_cat = Component(doc="Total cation equivalents concentration") self.S_an = Component(doc="Total anion equivalents concentration") # Heat capacity of water self.cp_mass = pyo.Param( mutable=False, initialize=4182, doc="Specific heat capacity of water", units=pyo.units.J / pyo.units.kg / pyo.units.K, ) # Density of water self.dens_mass = pyo.Param( mutable=False, initialize=997, doc="Density of water", units=pyo.units.kg / pyo.units.m**3, ) # Thermodynamic reference state self.pressure_ref = pyo.Param( within=pyo.PositiveReals, mutable=True, default=101325.0, doc="Reference pressure", units=pyo.units.Pa, ) self.temperature_ref = pyo.Param( within=pyo.PositiveReals, mutable=True, default=298.15, doc="Reference temperature", units=pyo.units.K, )
[docs] @classmethod def define_metadata(cls, obj): obj.add_properties( { "flow_vol": {"method": None}, "pressure": {"method": None}, "temperature": {"method": None}, "conc_mass_comp": {"method": None}, } ) obj.define_custom_properties( { "anions": {"method": None}, "cations": {"method": None}, } ) obj.add_default_units( { "time": pyo.units.s, "length": pyo.units.m, "mass": pyo.units.kg, "amount": pyo.units.kmol, "temperature": pyo.units.K, } )
[docs]class _ADM1StateBlock(StateBlock): """ This Class contains methods which should be applied to Property Blocks as a whole, rather than individual elements of indexed Property Blocks. """
[docs] def initialize( self, state_args=None, state_vars_fixed=False, hold_state=False, outlvl=idaeslog.NOTSET, solver=None, optarg=None, ): """ Initialization routine for property package. Keyword Arguments: state_args : Dictionary with initial guesses for the state vars chosen. Note that if this method is triggered through the control volume, and if initial guesses were not provided at the unit model level, the control volume passes the inlet values as initial guess.The keys for the state_args dictionary are: flow_mol_comp : value at which to initialize component flows (default=None) pressure : value at which to initialize pressure (default=None) temperature : value at which to initialize temperature (default=None) outlvl : sets output level of initialization routine state_vars_fixed: Flag to denote if state vars have already been fixed. True - states have already been fixed and initialization does not need to worry about fixing and unfixing variables. False - states have not been fixed. The state block will deal with fixing/unfixing. optarg : solver options dictionary object (default=None, use default solver options) solver : str indicating which solver to use during initialization (default = None, use default solver) hold_state : flag indicating whether the initialization routine should unfix any state variables fixed during initialization (default=False). True - states variables are not unfixed, and a dict of returned containing flags for which states were fixed during initialization. False - state variables are unfixed after initialization by calling the release_state method. Returns: If hold_states is True, returns a dict containing flags for which states were fixed during initialization. """ init_log = idaeslog.getInitLogger(self.name, outlvl, tag="properties") if state_vars_fixed is False: # Fix state variables if not already fixed flags = fix_state_vars(self, state_args) else: # Check when the state vars are fixed already result in dof 0 for k in self.keys(): if degrees_of_freedom(self[k]) != 0: raise Exception( "State vars fixed but degrees of freedom " "for state block is not zero during " "initialization." ) if state_vars_fixed is False: if hold_state is True: return flags else: self.release_state(flags) init_log.info("Initialization Complete.")
[docs] def release_state(self, flags, outlvl=idaeslog.NOTSET): """ Method to release state variables fixed during initialization. Keyword Arguments: flags : dict containing information of which state variables were fixed during initialization, and should now be unfixed. This dict is returned by initialize if hold_state=True. outlvl : sets output level of logging """ init_log = idaeslog.getInitLogger(self.name, outlvl, tag="properties") if flags is None: return # Unfix state variables revert_state_vars(self, flags) init_log.info("State Released.")
[docs]@declare_process_block_class("ADM1StateBlock", block_class=_ADM1StateBlock) class ADM1StateBlockData(StateBlockData): """ StateBlock for calculating thermophysical properties associated with the ADM1 reaction system. """
[docs] def build(self): """ Callable method for Block construction """ super().build() # Create state variables self.flow_vol = pyo.Var( initialize=1, domain=pyo.NonNegativeReals, doc="Total volumetric flowrate", units=pyo.units.m**3 / pyo.units.s, ) self.pressure = pyo.Var( domain=pyo.NonNegativeReals, initialize=101325.0, bounds=(1e1, 1e6), doc="Pressure", units=pyo.units.Pa, ) self.temperature = pyo.Var( domain=pyo.NonNegativeReals, initialize=298.15, bounds=(273.15, 323.15), doc="Temperature", units=pyo.units.K, ) Comp_dict = { "S_su": 0.012, "S_aa": 0.0053, "S_fa": 0.099, "S_va": 0.012, "S_bu": 0.013, "S_pro": 0.016, "S_ac": 0.20, "S_h2": 2.3e-7, "S_ch4": 0.055, "S_IC": 0.15 * 12, "S_IN": 0.13 * 14, "S_I": 0.33, "X_c": 0.31, "X_ch": 0.028, "X_pr": 0.10, "X_li": 0.029, "X_su": 0.42, "X_aa": 1.18, "X_fa": 0.24, "X_c4": 0.43, "X_pro": 0.14, "X_ac": 0.76, "X_h2": 0.32, "X_I": 25.6, } self.conc_mass_comp = pyo.Var( self.params.solute_set, domain=pyo.NonNegativeReals, initialize=Comp_dict, doc="Component mass concentrations", units=pyo.units.kg / pyo.units.m**3, ) self.anions = pyo.Var( domain=pyo.NonNegativeReals, initialize=0.02, doc="Anions in molar concentration", units=pyo.units.kmol / pyo.units.m**3, ) self.cations = pyo.Var( domain=pyo.NonNegativeReals, initialize=0.04, doc="Cations in molar concentration", units=pyo.units.kmol / pyo.units.m**3, ) def material_flow_expression(self, j): if j == "H2O": return self.flow_vol * self.params.dens_mass elif j == "S_an": # Convert moles of anions to mass assuming all is CL- return ( self.flow_vol * self.anions * (35 * pyo.units.kg / pyo.units.kmol) ) elif j == "S_cat": # Convert moles of cations to mass assuming all is Na+ return ( self.flow_vol * self.cations * (23 * pyo.units.kg / pyo.units.kmol) ) else: return self.flow_vol * self.conc_mass_comp[j] self.material_flow_expression = pyo.Expression( self.component_list, rule=material_flow_expression, doc="Material flow terms", ) def enthalpy_flow_expression(self): return ( self.flow_vol * self.params.dens_mass * self.params.cp_mass * (self.temperature - self.params.temperature_ref) ) self.enthalpy_flow_expression = pyo.Expression( rule=enthalpy_flow_expression, doc="Enthalpy flow term" ) def material_density_expression(self, j): if j == "H2O": return self.params.dens_mass elif j == "S_cat": # Convert moles of alkalinity to mass of catioons assuming all is Na return self.cations * (23 * pyo.units.kg / pyo.units.kmol) elif j == "S_an": # Convert moles of alkalinity to mass of aniona assuming all is Cl return self.anions * (35 * pyo.units.kg / pyo.units.kmol) else: return self.conc_mass_comp[j] self.material_density_expression = pyo.Expression( self.component_list, rule=material_density_expression, doc="Material density terms", ) def energy_density_expression(self): return ( self.params.dens_mass * self.params.cp_mass * (self.temperature - self.params.temperature_ref) ) self.energy_density_expression = pyo.Expression( rule=energy_density_expression, doc="Energy density term" ) iscale.set_scaling_factor(self.flow_vol, 1e5) iscale.set_scaling_factor(self.temperature, 1e-1) iscale.set_scaling_factor(self.pressure, 1e-6) iscale.set_scaling_factor(self.conc_mass_comp, 1e2) iscale.set_scaling_factor(self.conc_mass_comp["S_h2"], 1e5) iscale.set_scaling_factor(self.anions, 1e2) iscale.set_scaling_factor(self.cations, 1e2)
[docs] def get_material_flow_terms(self, p, j): return self.material_flow_expression[j]
[docs] def get_enthalpy_flow_terms(self, p): return self.enthalpy_flow_expression
[docs] def get_material_density_terms(self, p, j): return self.material_density_expression[j]
[docs] def get_energy_density_terms(self, p): return self.energy_density_expression
def default_material_balance_type(self): return MaterialBalanceType.componentPhase def default_energy_balance_type(self): return EnergyBalanceType.enthalpyTotal
[docs] def define_state_vars(self): return { "flow_vol": self.flow_vol, "anions": self.anions, "cations": self.cations, "conc_mass_comp": self.conc_mass_comp, "temperature": self.temperature, "pressure": self.pressure, }
[docs] def define_display_vars(self): return { "Volumetric Flowrate": self.flow_vol, "Molar anions": self.anions, "Molar cations": self.cations, "Mass Concentration": self.conc_mass_comp, "Temperature": self.temperature, "Pressure": self.pressure, }
[docs] def get_material_flow_basis(self): return MaterialFlowBasis.mass
def calculate_scaling_factors(self): # Get default scale factors and do calculations from base classes super().calculate_scaling_factors() # No constraints in this model as yet, just need to set scaling factors # for expressions sf_F = iscale.get_scaling_factor(self.flow_vol, default=1, warning=True) sf_T = iscale.get_scaling_factor(self.temperature, default=1, warning=True) # Mass flow and density terms for j in self.component_list: if j == "H2O": sf_C = pyo.value(1 / self.params.dens_mass) elif j == "S_cat": sf_C = 1e-1 * iscale.get_scaling_factor( self.cations, default=1, warning=True ) elif j == "S_an": sf_C = 1e-1 * iscale.get_scaling_factor( self.anions, default=1, warning=True ) else: sf_C = iscale.get_scaling_factor( self.conc_mass_comp[j], default=1e2, warning=True ) iscale.set_scaling_factor(self.material_flow_expression[j], sf_F * sf_C) iscale.set_scaling_factor(self.material_density_expression[j], sf_C) # Enthalpy and energy terms sf_rho_cp = pyo.value(1 / (self.params.dens_mass * self.params.cp_mass)) iscale.set_scaling_factor( self.enthalpy_flow_expression, sf_F * sf_rho_cp * sf_T ) iscale.set_scaling_factor(self.energy_density_expression, sf_rho_cp * sf_T)