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Scaler for equilibrium reactor and saponification properties #1500
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Original file line number | Diff line number | Diff line change |
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@@ -31,11 +31,13 @@ | |
from idaes.core.util.misc import add_object_reference | ||
from idaes.core.util.constants import Constants as const | ||
import idaes.logger as idaeslog | ||
from idaes.core.scaling import CustomScalerBase | ||
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# Some more information about this module | ||
__author__ = "Andrew Lee" | ||
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from idaes.core.util.scaling import get_scaling_factor | ||
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# Set up logger | ||
_log = idaeslog.getLogger(__name__) | ||
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@@ -111,12 +113,77 @@ | |
) | ||
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class SaponificationReactionScaler(CustomScalerBase): | ||
DEFAULT_SCALING_FACTORS = {"reaction_rate": 1e2} | ||
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def variable_scaling_routine( | ||
self, model, overwrite: bool = False, submodel_scalers: dict = None | ||
): | ||
if model.is_property_constructed("k_rxn"): | ||
# First check to see if k_rxn is already scaled | ||
sf = get_scaling_factor(model.k_rxn) | ||
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if sf is not None and not overwrite: | ||
# k_rxn already has a scaling factor and we are not set to overwrite - move on | ||
pass | ||
else: | ||
# Hopefully temperature has been scaled, so we can get the nominal value of k_rxn | ||
# by walking the expression in the constraint. | ||
nominals = self.get_expression_nominal_values(model.arrhenius_eqn) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Saponification has a rather tight range of temperatures. This is probably overkill. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. For this specific case probably, but this also stands as an example of how to do it for a more general case. |
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# We should get two values, k_rxn (LHS) and the Arrhenius equation (RHS) | ||
# As of 10/3/2024, the LHS will be the 0-th element of the list, and the RHS the 1st | ||
# However, we cannot assume this will always be the case | ||
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# If LHS has been scaled, nominal will be 1/sf, otherwise it will be k_rxn.value | ||
# Find the value which does NOT match this - guess that this is the 1st element | ||
if nominals[1] != model.k_rxn.value and sf is None: | ||
# This is the most likely case, so check it first | ||
nominal = nominals[1] | ||
elif sf is not None and nominals[1] != 1 / sf: | ||
# Next, check for case where k_rxn was already scaled | ||
nominal = nominals[1] | ||
else: | ||
# Otherwise we have the case where something changed in Pyomo since 10/3/2024 | ||
nominal = nominals[0] | ||
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self.set_variable_scaling_factor( | ||
model.k_rxn, | ||
1 / nominal, | ||
overwrite=overwrite, | ||
) | ||
Comment on lines
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Order of magnitude scaling is probably not appropriate here. Based on heuristic analysis, a fixed scaling factor of 1/3 would work. |
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if model.is_property_constructed("reaction_rate"): | ||
for j in model.reaction_rate.values(): | ||
self.scale_variable_by_default(j, overwrite=overwrite) | ||
Comment on lines
+163
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+165
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. If the user provides a default, we should do that, but we can get a decent scaling factor by multiplying the maximum scaling factor among the reactants provided by some period of time (I used 3600 s). (Equivalent to dividing the minimum default value by that period of time). |
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def constraint_scaling_routine( | ||
self, model, overwrite: bool = False, submodel_scalers: dict = None | ||
): | ||
if model.is_property_constructed("arrhenius_eqn"): | ||
self.scale_constraint_by_nominal_value( | ||
model.arrhenius_eqn, | ||
scheme="inverse_maximum", | ||
overwrite=overwrite, | ||
) | ||
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if model.is_property_constructed("rate_expression"): | ||
for j in model.rate_expression.values(): | ||
self.scale_constraint_by_nominal_value( | ||
j, | ||
scheme="inverse_maximum", | ||
overwrite=overwrite, | ||
) | ||
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class _ReactionBlock(ReactionBlockBase): | ||
""" | ||
This Class contains methods which should be applied to Reaction Blocks as a | ||
whole, rather than individual elements of indexed Reaction Blocks. | ||
""" | ||
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default_scaler = SaponificationReactionScaler | ||
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def initialize(blk, outlvl=idaeslog.NOTSET, **kwargs): | ||
""" | ||
Initialization routine for reaction package. | ||
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@@ -44,6 +44,7 @@ | |
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 | ||
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# Some more information about this module | ||
__author__ = "Andrew Lee" | ||
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@@ -135,12 +136,48 @@ | |
) | ||
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class SaponificationPropertiesScaler(CustomScalerBase): | ||
UNIT_SCALING_FACTORS = { | ||
# "QuantityName: (reference units, scaling factor) | ||
"Pressure": (units.Pa, 1e-5), | ||
} | ||
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DEFAULT_SCALING_FACTORS = { | ||
"flow_vol": 1e2, | ||
"conc_mol_comp": 1e-2, | ||
} | ||
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def variable_scaling_routine( | ||
self, model, overwrite: bool = False, submodel_scalers: dict = None | ||
): | ||
self.scale_variable_by_default(model.flow_vol, overwrite=overwrite) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Is there a way for the user to provide a default value, rather than just using whatever happens to be the default default value? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Yes, although it might not be the best way to do it. The default values are in a class attribute (dict) which users can interact with. That said, that is probably not the most obvious API for it (we could have a method to update the defaults). |
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self.scale_variable_by_units(model.pressure, overwrite=overwrite) | ||
self.scale_variable_by_bounds(model.temperature, overwrite=overwrite) | ||
for k, v in model.conc_mol_comp.items(): | ||
if k == "H2O": | ||
self.set_variable_scaling_factor(v, 1e-4, overwrite=overwrite) | ||
else: | ||
self.scale_variable_by_default(v, overwrite=overwrite) | ||
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def constraint_scaling_routine( | ||
self, model, overwrite: bool = False, submodel_scalers: dict = None | ||
): | ||
if model.is_property_constructed("conc_water_eqn"): | ||
self.set_constraint_scaling_factor( | ||
model.conc_water_eqn, | ||
1e-4, | ||
overwrite=overwrite, | ||
) | ||
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class _StateBlock(StateBlock): | ||
""" | ||
This Class contains methods which should be applied to Property Blocks as a | ||
whole, rather than individual elements of indexed Property Blocks. | ||
""" | ||
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default_scaler = SaponificationPropertiesScaler | ||
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def fix_initialization_states(self): | ||
""" | ||
Fixes state variables for state blocks. | ||
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How does the scaling framework know to use this value?
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When you tell it to
scale_variable_by_default
orscale_constraint_by_default
.