#################################################################################
# WaterTAP Copyright (c) 2020-2024, The Regents of the University of California,
# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
# National Renewable Energy Laboratory, and National Energy Technology
# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
# of Energy). All rights reserved.
#
# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
# information, respectively. These files are also available online at the URL
# "https://github.com/watertap-org/watertap/"
#################################################################################
"""
Thermophysical property package to be used in conjunction with modified ASM2d reactions.
Reference:
[1] X. Flores-Alsina, K. Solon, C.K. Mbamba, S. Tait, K.V. Gernaey, U. Jeppsson, D.J. Batstone,
Modelling phosphorus (P), sulfur (S) and iron (Fe) interactions for dynamic simulations of anaerobic digestion processes,
Water Research. 95 (2016) 370-382. https://www.sciencedirect.com/science/article/pii/S0043135416301397
[2] K. Solon, X. Flores-Alsina, C. Kazadi Mbamba, D. Ikumi, E.I.P. Volcke, C. Vaneeckhaute, G. Ekama,
P.A. Vanrolleghem, D.J. Batstone, K.V. Gernaey, U. Jeppsson, Plant-wide modelling of phosphorus transformations in
wastewater treatment systems: Impacts of control and operational strategies, Water Research. 113 (2017) 97-110
https://www.sciencedirect.com/science/article/pii/S0043135417300829
"""
# Import Pyomo libraries
import pyomo.environ as pyo
# Import IDAES cores
from idaes.core import (
declare_process_block_class,
MaterialFlowBasis,
StateBlockData,
StateBlock,
MaterialBalanceType,
EnergyBalanceType,
LiquidPhase,
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
from idaes.core.scaling import CustomScalerBase
from idaes.core.base.property_base import PhysicalParameterBlock
# Some more information about this module
__author__ = "Marcus Holly, Adam Atia, Xinhong Liu"
# Set up logger
_log = idaeslog.getLogger(__name__)
[docs]@declare_process_block_class("ModifiedASM2dParameterBlock")
class ModifiedASM2dParameterData(PhysicalParameterBlock):
"""
Property Parameter Block Class
"""
[docs] def build(self):
"""
Callable method for Block construction.
"""
super().build()
self._state_block_class = ModifiedASM2dStateBlock
# Add Phase objects
self.Liq = LiquidPhase()
# Add Component objects
self.H2O = Solvent()
# Soluble species
self.S_A = Solute(
doc="Fermentation products, considered to be acetate. [kg COD/m^3]"
)
self.S_F = Solute(
doc="Fermentable, readily bio-degradable organic substrates. [kg COD/m^3]"
)
self.S_I = Solute(doc="Inert soluble organic material. [kg COD/m^3]")
self.S_N2 = Solute(
doc="Dinitrogen, N2. SN2 is assumed to be the only nitrogenous product of denitrification. [kg N/m^3]"
)
self.S_NH4 = Solute(doc="Ammonium plus ammonia nitrogen. [kg N/m^3]")
self.S_NO3 = Solute(
doc="Nitrate plus nitrite nitrogen (N03' + N02' -N). SN03 is assumed to include nitrate as well as nitrite nitrogen. [kg N/m^3]"
)
self.S_O2 = Solute(doc="Dissolved oxygen. [kg O2/m^3]")
self.S_PO4 = Solute(
doc="Inorganic soluble phosphorus, primarily ortho-phosphates. [kg P/m^3]"
)
self.S_K = Solute(doc="Potassium, [kg K/m^3]")
self.S_Mg = Solute(doc="Magnesium, [kg Mg/m^3]")
self.S_IC = Solute(doc="Inorganic carbon, [kg C/m^3]")
# Particulate species
self.X_AUT = Solute(doc="Autotrophic nitrifying organisms. [kg COD/m^3]")
self.X_H = Solute(doc="Heterotrophic organisms. [kg COD/m^3]")
self.X_I = Solute(doc="Inert particulate organic material. [kg COD/m^3]")
self.X_PAO = Solute(doc="Phosphate-accumulating organisms. [kg COD/m^3]")
self.X_PHA = Solute(
doc="A cell internal storage product of phosphorus-accumulating organisms, primarily comprising poly-hydroxy-alkanoates (PHA). [kg COD/m^3]"
)
self.X_PP = Solute(doc="Poly-phosphate. [kg P/m^3]")
self.X_S = Solute(doc="Slowly biodegradable substrates. [kg COD/m^3]")
# Create sets for use across ASM models and associated unit models
self.non_particulate_component_set = pyo.Set(
initialize=[
"S_A",
"S_F",
"S_I",
"S_N2",
"S_NH4",
"S_NO3",
"S_O2",
"S_PO4",
"S_K",
"S_Mg",
"S_IC",
"H2O",
]
)
self.particulate_component_set = pyo.Set(
initialize=[
"X_AUT",
"X_H",
"X_I",
"X_PAO",
"X_PHA",
"X_PP",
"X_S",
]
)
self.tss_component_set = pyo.Set(
initialize=[
"X_AUT",
"X_H",
"X_I",
"X_PAO",
"X_PHA",
"X_PP",
"X_S",
]
)
# Heat capacity of water
self.cp_mass = pyo.Param(
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(
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,
)
# COD to VSS coefficients
self.CODtoVSS_XI = pyo.Var(
initialize=1.5686,
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="mass COD per mass VSS of XI",
)
self.CODtoVSS_XS = pyo.Var(
initialize=1.5686,
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="mass COD per mass VSS of XS",
)
self.CODtoVSS_XBM = pyo.Var(
initialize=1.3072,
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="mass COD per mass VSS of biomass",
)
self.CODtoVSS_XPHA = pyo.Var(
initialize=1.9608,
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="mass COD per mass VSS of XPHA",
)
# Inorganic solids parameters
self.ISS_P = pyo.Var(
initialize=3.23,
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="mass ISS per mass P",
)
self.f_ISS_BM = pyo.Var(
initialize=0.15,
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="ISS fractional content of biomass",
)
# Effluent Quality Index (EQI) parameters [2]
self.i_NSF = pyo.Var(
initialize=0.03352,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="N content of fermentable substrate, S_F, [kg N/kg COD]",
)
self.i_NSI = pyo.Var(
initialize=0.06003,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="N content of inert soluble COD S_I, [kg N/kg COD]",
)
self.i_NXI = pyo.Var(
initialize=0.06003,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="N content of inert particulate COD X_I, [kg N/kg COD]",
)
self.i_NXS = pyo.Var(
initialize=0.03352,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="N content of slowly biodegradable substrate X_S, [kg N/kg COD]",
)
self.i_NBM = pyo.Var(
initialize=0.08615,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="N content of biomass, X_H, X_PAO, X_AUT, [kg N/kg COD]",
)
self.f_SI = pyo.Var(
initialize=0.00,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="Production of S_I in hydrolysis, [kg COD/kg COD]",
)
self.f_XIH = pyo.Var(
initialize=0.1,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="Fraction of inert COD generated in lysis of X_H, [kg COD/kg COD]",
)
self.f_XIP = pyo.Var(
initialize=0.1,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="Fraction of inert COD generated in lysis of X_PAO and X_PHA, [kg COD/kg COD]",
)
self.f_XIA = pyo.Var(
initialize=0.1,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="Fraction of inert COD generated in lysis of X_AUT, [kg COD/kg COD]",
)
self.i_PSF = pyo.Var(
initialize=0.00559,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="P content of fermentable substrate, S_F, [kg P/kg COD]",
)
self.i_PSI = pyo.Var(
initialize=0.00649,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="P content of inert soluble COD S_I, [kg P/kg COD]",
)
self.i_PXI = pyo.Var(
initialize=0.00649,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="P content of inert particulate COD X_I, [kg P/kg COD]",
)
self.i_PXS = pyo.Var(
initialize=0.00559,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="P content of slowly biodegradable substrate X_S, [kg P/kg COD]",
)
self.i_PBM = pyo.Var(
initialize=0.02154,
units=pyo.units.dimensionless,
domain=pyo.NonNegativeReals,
doc="P content of biomass, X_H, X_PAO, X_AUT, [kg P/kg COD]",
)
self.BOD5_factor = pyo.Param(
["raw", "effluent"],
initialize={"raw": 0.65, "effluent": 0.25},
units=pyo.units.dimensionless,
domain=pyo.PositiveReals,
doc="Conversion factor for BOD5",
)
# Fix Vars that are treated as Params
for v in self.component_objects(pyo.Var):
v.fix()
[docs]class ModifiedASM2dPropertiesScaler(CustomScalerBase):
"""
Scaler for the Activated Sludge Model No.2d property package.
Flow and temperature are scaled by the default value (if no user input provided), and
pressure is scaled assuming an order of magnitude of 1e5 Pa.
"""
UNIT_SCALING_FACTORS = {
# "QuantityName: (reference units, scaling factor)
"Pressure": (pyo.units.Pa, 1e-5),
}
DEFAULT_SCALING_FACTORS = {
"flow_vol": 1e1,
"temperature": 1e-2,
}
[docs] def variable_scaling_routine(
self, model, overwrite: bool = False, submodel_scalers: dict = None
):
self.scale_variable_by_default(model.temperature, overwrite=overwrite)
self.scale_variable_by_default(model.flow_vol, overwrite=overwrite)
self.scale_variable_by_units(model.pressure, overwrite=overwrite)
# There are currently no constraints in this model
[docs] def constraint_scaling_routine(
self, model, overwrite: bool = False, submodel_scalers: dict = None
):
pass
[docs]class _ModifiedASM2dStateBlock(StateBlock):
"""
This Class contains methods which should be applied to Property Blocks as a
whole, rather than individual elements of indexed Property Blocks.
"""
default_scaler = ModifiedASM2dPropertiesScaler
[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_vol : value at which to initialize total volumetric flow (default=None)
conc_mass_comp : value at which to initialize component concentrations (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 relase state variables fixed during initialization.
Keyword Arguments:
flags : dict containing information of which state variables
were fixed during initialization, and should now be
unfixed. This dict is returned by initialize if
hold_state=True.
outlvl : sets output level of of logging
"""
init_log = idaeslog.getInitLogger(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(
"ModifiedASM2dStateBlock", block_class=_ModifiedASM2dStateBlock
)
class ModifiedASM2dStateBlockData(StateBlockData):
"""
StateBlock for calculating thermophysical proeprties associated with the ASM2d
reaction system.
"""
[docs] def build(self):
"""
Callable method for Block construction
"""
super().build()
# Create state variables
self.flow_vol = pyo.Var(
initialize=1.0,
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=(1e3, 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,
)
self.conc_mass_comp = pyo.Var(
self.params.solute_set,
domain=pyo.NonNegativeReals,
initialize=0.1,
doc="Component mass concentrations",
units=pyo.units.kg / pyo.units.m**3,
)
# TODO: X_SRB not included yet in biomass term summation
def _VSS(self):
vss = (
self.conc_mass_comp["X_I"] / self.params.CODtoVSS_XI
+ self.conc_mass_comp["X_S"] / self.params.CODtoVSS_XS
+ (
self.conc_mass_comp["X_H"]
+ self.conc_mass_comp["X_PAO"]
+ self.conc_mass_comp["X_AUT"]
)
/ self.params.CODtoVSS_XBM
+ self.conc_mass_comp["X_PHA"] / self.params.CODtoVSS_XPHA
)
return vss
self.VSS = pyo.Expression(rule=_VSS, doc="Volatile suspended solids")
def _ISS(self):
iss = (
self.params.f_ISS_BM
* (
self.conc_mass_comp["X_H"]
+ self.conc_mass_comp["X_PAO"]
+ self.conc_mass_comp["X_AUT"]
)
/ self.params.CODtoVSS_XBM
+ self.params.ISS_P * self.conc_mass_comp["X_PP"]
)
return iss
self.ISS = pyo.Expression(rule=_ISS, doc="Inorganic suspended solids")
def _TSS(self):
tss = self.VSS + self.ISS
return tss
self.TSS = pyo.Expression(rule=_TSS, doc="Total suspended solids")
def _COD(self):
cod = (
self.conc_mass_comp["S_F"]
+ self.conc_mass_comp["S_A"]
+ self.conc_mass_comp["S_I"]
+ self.conc_mass_comp["X_I"]
+ self.conc_mass_comp["X_S"]
+ self.conc_mass_comp["X_H"]
+ self.conc_mass_comp["X_PAO"]
+ self.conc_mass_comp["X_PHA"]
+ self.conc_mass_comp["X_AUT"]
)
return cod
self.COD = pyo.Expression(rule=_COD, doc="Chemical oxygen demand")
def _TKN(self):
tkn = (
self.conc_mass_comp["S_NH4"]
+ self.params.i_NSF * self.conc_mass_comp["S_F"]
+ self.params.i_NSI * self.conc_mass_comp["S_I"]
+ self.params.i_NXI * self.conc_mass_comp["X_I"]
+ self.params.i_NXS * self.conc_mass_comp["X_S"]
+ self.params.i_NBM
* (
self.conc_mass_comp["X_H"]
+ self.conc_mass_comp["X_PAO"]
+ self.conc_mass_comp["X_AUT"]
)
)
return tkn
self.TKN = pyo.Expression(rule=_TKN, doc="Kjeldahl nitrogen")
def _SNOX(self):
snox = self.conc_mass_comp["S_NO3"]
return snox
self.SNOX = pyo.Expression(rule=_SNOX, doc="Nitrogen oxide")
def _BOD5(self, i):
bod5 = (
self.conc_mass_comp["S_F"]
+ self.conc_mass_comp["S_A"]
+ (1 - self.params.f_SI) * self.conc_mass_comp["X_S"]
+ (1 - self.params.f_XIH) * self.conc_mass_comp["X_H"]
+ (1 - self.params.f_XIP)
* (self.conc_mass_comp["X_PAO"] + self.conc_mass_comp["X_PHA"])
+ (1 - self.params.f_XIA) * self.conc_mass_comp["X_AUT"]
)
return self.params.BOD5_factor[i] * bod5
self.BOD5 = pyo.Expression(
["raw", "effluent"], rule=_BOD5, doc="Five-day biological oxygen demand"
)
def _SP_organic(self):
sp_organic = (
self.conc_mass_comp["X_PP"]
+ self.params.i_PSF * self.conc_mass_comp["S_F"]
+ self.params.i_PSI * self.conc_mass_comp["S_I"]
+ self.params.i_PXI * self.conc_mass_comp["X_I"]
+ self.params.i_PXS * self.conc_mass_comp["X_S"]
+ self.params.i_PBM
* (
self.conc_mass_comp["X_H"]
+ self.conc_mass_comp["X_PAO"]
+ self.conc_mass_comp["X_AUT"]
)
)
return sp_organic
self.SP_organic = pyo.Expression(rule=_SP_organic, doc="Organic phosphorus")
def _SP_inorganic(self):
sp_inorganic = self.conc_mass_comp["S_PO4"]
return sp_inorganic
self.SP_inorganic = pyo.Expression(
rule=_SP_inorganic, doc="Inorganic phosphorus"
)
[docs] def get_material_flow_terms(self, p, j):
if j == "H2O":
return self.flow_vol * self.params.dens_mass
else:
return self.flow_vol * self.conc_mass_comp[j]
[docs] def get_enthalpy_flow_terms(self, p):
return (
self.flow_vol
* self.params.dens_mass
* self.params.cp_mass
* (self.temperature - self.params.temperature_ref)
)
[docs] def get_material_density_terms(self, p, j):
if j == "H2O":
return self.params.dens_mass
else:
return self.conc_mass_comp[j]
[docs] def get_energy_density_terms(self, p):
return (
self.params.dens_mass
* self.params.cp_mass
* (self.temperature - self.params.temperature_ref)
)
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,
"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,
"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):
super().calculate_scaling_factors()