###############################################################################
# WaterTAP Copyright (c) 2021, 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/"
#
###############################################################################
"""
Initial property package for H2O-NDMA system
"""
# Import Python libraries
import idaes.logger as idaeslog
# Import Pyomo libraries
from pyomo.environ import (
Constraint,
Expression,
Reals,
NonNegativeReals,
Var,
Param,
Suffix,
value,
check_optimal_termination,
)
from pyomo.environ import units as pyunits
# Import IDAES cores
from idaes.core import (
declare_process_block_class,
MaterialFlowBasis,
PhysicalParameterBlock,
StateBlockData,
StateBlock,
MaterialBalanceType,
EnergyBalanceType,
)
from idaes.core.components import Component, Solute, Solvent
from idaes.core.phases import LiquidPhase
from idaes.core.util.constants import Constants
from idaes.core.util.initialization import (
fix_state_vars,
revert_state_vars,
solve_indexed_blocks,
)
from idaes.core.util.misc import add_object_reference, extract_data
from idaes.core.util import get_solver
from idaes.core.util.model_statistics import (
degrees_of_freedom,
number_unfixed_variables,
)
from idaes.core.util.exceptions import (
ConfigurationError,
InitializationError,
PropertyPackageError,
)
import idaes.core.util.scaling as iscale
# Set up logger
_log = idaeslog.getLogger(__name__)
[docs]@declare_process_block_class("NDMAParameterBlock")
class NDMAParameterData(PhysicalParameterBlock):
CONFIG = PhysicalParameterBlock.CONFIG()
[docs] def build(self):
"""
Callable method for Block construction.
"""
super(NDMAParameterData, self).build()
self._state_block_class = NDMAStateBlock
# components
self.H2O = Solvent()
self.NDMA = Solute()
# phases
self.Liq = LiquidPhase()
# heat capacity
self.cp = Param(
mutable=False, initialize=4.2e3, units=pyunits.J / (pyunits.kg * pyunits.K)
)
# molecular weight
mw_comp_data = {"H2O": 18.01528e-3, "NDMA": 74.0819e-3}
self.mw_comp = Param(
self.component_list,
mutable=False,
initialize=extract_data(mw_comp_data),
units=pyunits.kg / pyunits.mol,
doc="Molecular weight kg/mol",
)
# mass density of water
self.dens_mass = Var(
domain=Reals,
initialize=997,
units=pyunits.kg / pyunits.m**3,
doc="Mass density kg/m^3",
)
# traditional parameters are the only Vars currently on the block and should be fixed
for v in self.component_objects(Var):
v.fix()
# ---default scaling---
self.set_default_scaling("temperature", 1e-2)
self.set_default_scaling("pressure", 1e-6)
self.set_default_scaling("dens_mass_phase", 1e-3, index="Liq")
class _NDMAStateBlock(StateBlock):
"""
This Class contains methods which should be applied to Property Blocks as a
whole, rather than individual elements of indexed Property Blocks.
"""
def initialize(
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_mass_phase_comp : value at which to initialize
phase component flows
pressure : value at which to initialize pressure
temperature : value at which to initialize temperature
outlvl : sets output level of initialization routine (default=idaeslog.NOTSET)
optarg : solver options dictionary object (default=None)
state_vars_fixed: Flag to denote if state vars have already been
fixed.
- True - states have already been fixed by the
control volume 1D. Control volume 0D
does not fix the state vars, so will
be False if this state block is used
with 0D blocks.
- False - states have not been fixed. The state
block will deal with fixing/unfixing.
solver : Solver object to use during initialization if None is provided
it will use the default solver for IDAES (default = None)
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.
"""
# Get loggers
init_log = idaeslog.getInitLogger(self.name, outlvl, tag="properties")
solve_log = idaeslog.getSolveLogger(self.name, outlvl, tag="properties")
# Set solver and options
opt = get_solver(solver, optarg)
# Fix state variables
flags = fix_state_vars(self, state_args)
# Check when the state vars are fixed already result in dof 0
for k in self.keys():
dof = degrees_of_freedom(self[k])
if dof != 0:
raise PropertyPackageError(
"\nWhile initializing {sb_name}, the degrees of freedom "
"are {dof}, when zero is required. \nInitialization assumes "
"that the state variables should be fixed and that no other "
"variables are fixed. \nIf other properties have a "
"predetermined value, use the calculate_state method "
"before using initialize to determine the values for "
"the state variables and avoid fixing the property variables."
"".format(sb_name=self.name, dof=dof)
)
# ---------------------------------------------------------------------
skip_solve = True # skip solve if only state variables are present
for k in self.keys():
if number_unfixed_variables(self[k]) != 0:
skip_solve = False
if not skip_solve:
# Initialize properties
with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc:
results = solve_indexed_blocks(opt, [self], tee=slc.tee)
init_log.info_high(
"Property initialization: {}.".format(idaeslog.condition(results))
)
if not check_optimal_termination(results):
raise InitializationError(
f"{self.name} failed to initialize successfully. Please "
f"check the output logs for more information."
)
# ---------------------------------------------------------------------
# If input block, return flags, else release state
if state_vars_fixed is False:
if hold_state is True:
return flags
else:
self.release_state(flags)
def release_state(self, flags, outlvl=idaeslog.NOTSET):
"""
Method to release state variables fixed during initialisation.
Keyword Arguments:
flags : dict containing information of which state variables
were fixed during initialization, and should now be
unfixed. This dict is returned by initialize if
hold_state=True.
outlvl : sets output level of of logging
"""
# Unfix state variables
init_log = idaeslog.getInitLogger(self.name, outlvl, tag="properties")
revert_state_vars(self, flags)
init_log.info_high("{} State Released.".format(self.name))
def calculate_state(
self,
var_args=None,
hold_state=False,
outlvl=idaeslog.NOTSET,
solver=None,
optarg=None,
):
"""
Solves state blocks given a set of variables and their values. These variables can
be state variables or properties. This method is typically used before
initialization to solve for state variables because non-state variables (i.e. properties)
cannot be fixed in initialization routines.
Keyword Arguments:
var_args : dictionary with variables and their values, they can be state variables or properties
{(VAR_NAME, INDEX): VALUE}
hold_state : flag indicating whether all of the state variables should be fixed after calculate state.
True - State variables will be fixed.
False - State variables will remain unfixed, unless already fixed.
outlvl : idaes logger object that sets output level of solve call (default=idaeslog.NOTSET)
solver : solver name string if None is provided the default solver
for IDAES will be used (default = None)
optarg : solver options dictionary object (default={})
Returns:
results object from state block solve
"""
# Get logger
solve_log = idaeslog.getSolveLogger(self.name, level=outlvl, tag="properties")
# Initialize at current state values (not user provided)
self.initialize(solver=solver, optarg=optarg, outlvl=outlvl)
# Set solver and options
opt = get_solver(solver, optarg)
# Fix variables and check degrees of freedom
# dictionary noting which variables were fixed and their previous state
flags = {}
for k in self.keys():
sb = self[k]
for (v_name, ind), val in var_args.items():
var = getattr(sb, v_name)
if iscale.get_scaling_factor(var[ind]) is None:
_log.warning(
"While using the calculate_state method on {sb_name}, variable {v_name} "
"was provided as an argument in var_args, but it does not have a scaling "
"factor. This suggests that the calculate_scaling_factor method has not been "
"used or the variable was created on demand after the scaling factors were "
"calculated. It is recommended to touch all relevant variables (i.e. call "
"them or set an initial value) before using the calculate_scaling_factor "
"method.".format(v_name=v_name, sb_name=sb.name)
)
if var[ind].is_fixed():
flags[(k, v_name, ind)] = True
if value(var[ind]) != val:
raise ConfigurationError(
"While using the calculate_state method on {sb_name}, {v_name} was "
"fixed to a value {val}, but it was already fixed to value {val_2}. "
"Unfix the variable before calling the calculate_state "
"method or update var_args."
"".format(
sb_name=sb.name,
v_name=var.name,
val=val,
val_2=value(var[ind]),
)
)
else:
flags[(k, v_name, ind)] = False
var[ind].fix(val)
if degrees_of_freedom(sb) != 0:
raise RuntimeError(
"While using the calculate_state method on {sb_name}, the degrees "
"of freedom were {dof}, but 0 is required. Check var_args and ensure "
"the correct fixed variables are provided."
"".format(sb_name=sb.name, dof=degrees_of_freedom(sb))
)
# Solve
with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc:
results = solve_indexed_blocks(opt, [self], tee=slc.tee)
solve_log.info_high(
"Calculate state: {}.".format(idaeslog.condition(results))
)
if not check_optimal_termination(results):
_log.warning(
"While using the calculate_state method on {sb_name}, the solver failed "
"to converge to an optimal solution. This suggests that the user provided "
"infeasible inputs, or that the model is poorly scaled, poorly initialized, "
"or degenerate."
)
# unfix all variables fixed with var_args
for (k, v_name, ind), previously_fixed in flags.items():
if not previously_fixed:
var = getattr(self[k], v_name)
var[ind].unfix()
# fix state variables if hold_state
if hold_state:
fix_state_vars(self)
return results
[docs]@declare_process_block_class("NDMAStateBlock", block_class=_NDMAStateBlock)
class NDMAStateBlockData(StateBlockData):
[docs] def build(self):
"""Callable method for Block construction."""
super(NDMAStateBlockData, self).build()
self.scaling_factor = Suffix(direction=Suffix.EXPORT)
# Add state variables
self.flow_mass_phase_comp = Var(
self.params.phase_list,
self.params.component_list,
initialize={("Liq", "H2O"): 0.999999926, ("Liq", "NDMA"): 74e-9},
bounds=(1e-18, None),
domain=NonNegativeReals,
units=pyunits.kg / pyunits.s,
doc="Mass flow rate",
)
self.temperature = Var(
initialize=298.15,
bounds=(273.15, 373.15),
domain=NonNegativeReals,
units=pyunits.degK,
doc="State temperature",
)
self.pressure = Var(
initialize=101325,
bounds=(1e4, 5e7),
domain=NonNegativeReals,
units=pyunits.Pa,
doc="State pressure",
)
# -----------------------------------------------------------------------------
# Property Methods
def _mass_frac_phase_comp(self):
self.mass_frac_phase_comp = Var(
self.params.phase_list,
self.params.component_list,
initialize={("Liq", "H2O"): 0.999999926, ("Liq", "NDMA"): 74e-9},
bounds=(
1e-18,
None,
), # upper bound set to None because of stability benefits
units=pyunits.dimensionless,
doc="Mass fraction",
)
def rule_mass_frac_phase_comp(b, j):
return b.mass_frac_phase_comp["Liq", j] == b.flow_mass_phase_comp[
"Liq", j
] / sum(
b.flow_mass_phase_comp["Liq", j] for j in self.params.component_list
)
self.eq_mass_frac_phase_comp = Constraint(
self.params.component_list, rule=rule_mass_frac_phase_comp
)
def _dens_mass_phase(self):
self.dens_mass_phase = Var(
self.params.phase_list,
initialize=997,
bounds=(5e2, 2e3),
units=pyunits.kg * pyunits.m**-3,
doc="Mass density",
)
def rule_dens_mass_phase(b):
# constant density, NDMA concentration is always minimal
return b.dens_mass_phase["Liq"] == b.params.dens_mass
self.eq_dens_mass_phase = Constraint(rule=rule_dens_mass_phase)
def _flow_vol_phase(self):
self.flow_vol_phase = Var(
self.params.phase_list,
initialize=1,
bounds=(1e-18, None),
units=pyunits.m**3 / pyunits.s,
doc="Volumetric flow rate",
)
def rule_flow_vol_phase(b):
return (
b.flow_vol_phase["Liq"]
== sum(
b.flow_mass_phase_comp["Liq", j] for j in self.params.component_list
)
/ b.dens_mass_phase["Liq"]
)
self.eq_flow_vol_phase = Constraint(rule=rule_flow_vol_phase)
def _flow_vol(self):
def rule_flow_vol(b):
return sum(b.flow_vol_phase[p] for p in self.params.phase_list)
self.flow_vol = Expression(rule=rule_flow_vol)
def _conc_mass_phase_comp(self):
self.conc_mass_phase_comp = Var(
self.params.phase_list,
self.params.component_list,
initialize=10,
bounds=(1e-18, 2e3),
units=pyunits.kg * pyunits.m**-3,
doc="Mass concentration",
)
def rule_conc_mass_phase_comp(b, j):
return (
b.conc_mass_phase_comp["Liq", j]
== b.dens_mass_phase["Liq"] * b.mass_frac_phase_comp["Liq", j]
)
self.eq_conc_mass_phase_comp = Constraint(
self.params.component_list, rule=rule_conc_mass_phase_comp
)
def _flow_mol_phase_comp(self):
self.flow_mol_phase_comp = Var(
self.params.phase_list,
self.params.component_list,
initialize=100,
bounds=(1e-18, None),
units=pyunits.mol / pyunits.s,
doc="Molar flowrate",
)
def rule_flow_mol_phase_comp(b, j):
return (
b.flow_mol_phase_comp["Liq", j]
== b.flow_mass_phase_comp["Liq", j] / b.params.mw_comp[j]
)
self.eq_flow_mol_phase_comp = Constraint(
self.params.component_list, rule=rule_flow_mol_phase_comp
)
def _mole_frac_phase_comp(self):
self.mole_frac_phase_comp = Var(
self.params.phase_list,
self.params.component_list,
initialize=0.1,
bounds=(1e-18, None),
units=pyunits.dimensionless,
doc="Mole fraction",
)
def rule_mole_frac_phase_comp(b, j):
return b.mole_frac_phase_comp["Liq", j] == b.flow_mol_phase_comp[
"Liq", j
] / sum(b.flow_mol_phase_comp["Liq", j] for j in b.params.component_list)
self.eq_mole_frac_phase_comp = Constraint(
self.params.component_list, rule=rule_mole_frac_phase_comp
)
def _molality_comp(self):
self.molality_comp = Var(
["NDMA"],
initialize=1,
bounds=(1e-18, 10),
units=pyunits.mole / pyunits.kg,
doc="Molality",
)
def rule_molality_comp(b, j):
return (
self.molality_comp[j]
== b.mass_frac_phase_comp["Liq", j]
/ (1 - b.mass_frac_phase_comp["Liq", j])
/ b.params.mw_comp[j]
)
self.eq_molality_comp = Constraint(["NDMA"], rule=rule_molality_comp)
# TODO: check enthalpy
def _enth_flow(self):
# enthalpy flow expression for get_enthalpy_flow_terms method
temperature_ref = 273.15 * pyunits.K
def rule_enth_flow(b): # enthalpy flow [J/s]
return (
b.params.cp
* sum(b.flow_mass_phase_comp["Liq", j] for j in b.params.component_list)
* (b.temperature - temperature_ref)
)
self.enth_flow = Expression(rule=rule_enth_flow)
# -----------------------------------------------------------------------------
# General Methods
# NOTE: For scaling in the control volume to work properly, these methods must
# return a pyomo Var or Expression
[docs] def get_material_flow_terms(self, p, j):
"""Create material flow terms for control volume."""
return self.flow_mass_phase_comp[p, j]
[docs] def get_enthalpy_flow_terms(self, p):
"""Create enthalpy flow terms."""
return self.enth_flow
# TODO: make property package compatible with dynamics
# def get_material_density_terms(self, p, j):
# """Create material density terms."""
# def get_enthalpy_density_terms(self, p):
# """Create enthalpy density terms."""
def default_material_balance_type(self):
return MaterialBalanceType.componentTotal
def default_energy_balance_type(self):
return EnergyBalanceType.enthalpyTotal
[docs] def get_material_flow_basis(self):
return MaterialFlowBasis.mass
[docs] def define_state_vars(self):
"""Define state vars."""
return {
"flow_mass_phase_comp": self.flow_mass_phase_comp,
"temperature": self.temperature,
"pressure": self.pressure,
}
# -----------------------------------------------------------------------------
# Scaling methods
def calculate_scaling_factors(self):
super().calculate_scaling_factors()
# setting scaling factors for variables
# default scaling factors have already been set with
# idaes.core.property_base.calculate_scaling_factors()
# for the following variables: flow_mass_phase_comp, pressure,
# temperature, dens_mass, and enth_mass
if iscale.get_scaling_factor(self.params.cp) is None:
iscale.set_scaling_factor(self.params.cp, 1e-3)
# these variables should have user input
if iscale.get_scaling_factor(self.flow_mass_phase_comp["Liq", "H2O"]) is None:
sf = iscale.get_scaling_factor(
self.flow_mass_phase_comp["Liq", "H2O"], default=1e0, warning=True
)
iscale.set_scaling_factor(self.flow_mass_phase_comp["Liq", "H2O"], sf)
if iscale.get_scaling_factor(self.flow_mass_phase_comp["Liq", "NDMA"]) is None:
sf = iscale.get_scaling_factor(
self.flow_mass_phase_comp["Liq", "NDMA"], default=1e8, warning=True
)
iscale.set_scaling_factor(self.flow_mass_phase_comp["Liq", "NDMA"], sf)
# scaling factors for parameters
for j, v in self.params.mw_comp.items():
if iscale.get_scaling_factor(v) is None:
iscale.set_scaling_factor(self.params.mw_comp, 1e2)
# these variables do not typically require user input,
# will not override if the user does provide the scaling factor
if self.is_property_constructed("mass_frac_phase_comp"):
for j in self.params.component_list:
if (
iscale.get_scaling_factor(self.mass_frac_phase_comp["Liq", j])
is None
):
if j == "NDMA":
sf = iscale.get_scaling_factor(
self.flow_mass_phase_comp["Liq", j]
) / iscale.get_scaling_factor(
self.flow_mass_phase_comp["Liq", "H2O"]
)
iscale.set_scaling_factor(
self.mass_frac_phase_comp["Liq", j], sf
)
elif j == "H2O":
iscale.set_scaling_factor(
self.mass_frac_phase_comp["Liq", j], 100
)
if self.is_property_constructed("flow_vol_phase"):
sf = iscale.get_scaling_factor(
self.flow_mass_phase_comp["Liq", "H2O"]
) / iscale.get_scaling_factor(self.dens_mass_phase["Liq"])
iscale.set_scaling_factor(self.flow_vol_phase, sf)
if self.is_property_constructed("flow_vol"):
sf = iscale.get_scaling_factor(self.flow_vol_phase)
iscale.set_scaling_factor(self.flow_vol, sf)
if self.is_property_constructed("conc_mass_phase_comp"):
for j in self.params.component_list:
sf_dens = iscale.get_scaling_factor(self.dens_mass_phase["Liq"])
if (
iscale.get_scaling_factor(self.conc_mass_phase_comp["Liq", j])
is None
):
if j == "H2O":
# solvents typically have a mass fraction between 0.5-1
iscale.set_scaling_factor(
self.conc_mass_phase_comp["Liq", j], sf_dens
)
elif j == "NDMA":
iscale.set_scaling_factor(
self.conc_mass_phase_comp["Liq", j],
sf_dens
* iscale.get_scaling_factor(
self.mass_frac_phase_comp["Liq", j]
),
)
if self.is_property_constructed("flow_mol_phase_comp"):
for j in self.params.component_list:
if (
iscale.get_scaling_factor(self.flow_mol_phase_comp["Liq", j])
is None
):
sf = iscale.get_scaling_factor(self.flow_mass_phase_comp["Liq", j])
sf /= iscale.get_scaling_factor(self.params.mw_comp[j])
iscale.set_scaling_factor(self.flow_mol_phase_comp["Liq", j], sf)
if self.is_property_constructed("mole_frac_phase_comp"):
for j in self.params.component_list:
if (
iscale.get_scaling_factor(self.mole_frac_phase_comp["Liq", j])
is None
):
if j == "NDMA":
sf = iscale.get_scaling_factor(
self.flow_mol_phase_comp["Liq", j]
) / iscale.get_scaling_factor(
self.flow_mol_phase_comp["Liq", "H2O"]
)
iscale.set_scaling_factor(
self.mole_frac_phase_comp["Liq", j], sf
)
elif j == "H2O":
iscale.set_scaling_factor(
self.mole_frac_phase_comp["Liq", j], 1
)
if self.is_property_constructed("molality_comp"):
for j in self.params.component_list:
if isinstance(getattr(self.params, j), Solute):
if iscale.get_scaling_factor(self.molality_comp[j]) is None:
sf = iscale.get_scaling_factor(
self.mass_frac_phase_comp["Liq", j]
)
sf /= iscale.get_scaling_factor(self.params.mw_comp[j])
iscale.set_scaling_factor(self.molality_comp[j], sf)
if self.is_property_constructed("enth_flow"):
sf = (
iscale.get_scaling_factor(self.params.cp)
* iscale.get_scaling_factor(self.flow_mass_phase_comp["Liq", "H2O"])
* 1e-1
) # temperature change on the order of 1e1
iscale.set_scaling_factor(self.enth_flow, sf)
# property relationships with phase index, but simple constraint
v_str_lst_simple = ["dens_mass_phase", "flow_vol_phase"]
for v_str in v_str_lst_simple:
if self.is_property_constructed(v_str):
v = getattr(self, v_str)
sf = iscale.get_scaling_factor(v, default=1, warning=True)
c = getattr(self, "eq_" + v_str)
iscale.constraint_scaling_transform(c, sf)
# property relationship indexed by component
if self.is_property_constructed("molality_comp"):
for j, c in self.eq_molality_comp.items():
sf = iscale.get_scaling_factor(
self.molality_comp[j], default=1, warning=True
)
iscale.constraint_scaling_transform(c, sf)
# property relationships indexed by component and phase
for v_str in (
"mass_frac_phase_comp",
"conc_mass_phase_comp",
"flow_mol_phase_comp",
"mole_frac_phase_comp",
):
if self.is_property_constructed(v_str):
v_comp = self.component(v_str)
for j, c in self.component("eq_" + v_str).items():
sf = iscale.get_scaling_factor(
v_comp["Liq", j], default=1, warning=True
)
iscale.constraint_scaling_transform(c, sf)