'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2016, 2017, 2018, 2019, 2020 Caleb Bell <Caleb.Andrew.Bell@gmail.com>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
This module contains implementations of :obj:`TDependentProperty <thermo.utils.TDependentProperty>`
representing liquid, vapor, and solid heat capacity. A variety of estimation
and data methods are available as included in the `chemicals` library.
Additionally liquid, vapor, and solid mixture heat capacity predictor objects
are implemented subclassing :obj:`MixtureProperty <thermo.utils.MixtureProperty>`.
For reporting bugs, adding feature requests, or submitting pull requests,
please use the `GitHub issue tracker <https://github.com/CalebBell/thermo/>`_.
.. contents:: :local:
Pure Liquid Heat Capacity
=========================
.. autoclass:: HeatCapacityLiquid
:members: calculate, test_method_validity,
name, property_max, property_min,
units, ranked_methods
:undoc-members:
:show-inheritance:
:exclude-members:
.. autodata:: heat_capacity_liquid_methods
Pure Gas Heat Capacity
======================
.. autoclass:: HeatCapacityGas
:members: calculate, test_method_validity,
name, property_max, property_min,
units, ranked_methods
:undoc-members:
:show-inheritance:
:exclude-members:
.. autodata:: heat_capacity_gas_methods
Pure Solid Heat Capacity
========================
.. autoclass:: HeatCapacitySolid
:members: calculate, test_method_validity,
name, property_max, property_min,
units, ranked_methods
:undoc-members:
:show-inheritance:
:exclude-members:
.. autodata:: heat_capacity_solid_methods
Mixture Liquid Heat Capacity
============================
.. autoclass:: HeatCapacityLiquidMixture
:members: calculate, test_method_validity,
name, property_max, property_min,
units, Tmin, Tmax, ranked_methods
:undoc-members:
:show-inheritance:
:exclude-members:
.. autodata:: heat_capacity_liquid_mixture_methods
Mixture Gas Heat Capacity
=========================
.. autoclass:: HeatCapacityGasMixture
:members: calculate, test_method_validity,
name, property_max, property_min,
units, Tmin, Tmax, ranked_methods
:undoc-members:
:show-inheritance:
:exclude-members:
.. autodata:: heat_capacity_gas_mixture_methods
Mixture Solid Heat Capacity
===========================
.. autoclass:: HeatCapacitySolidMixture
:members: calculate, test_method_validity,
name, property_max, property_min,
units, Tmin, Tmax, ranked_methods
:undoc-members:
:show-inheritance:
:exclude-members:
.. autodata:: heat_capacity_solid_mixture_methods
'''
__all__ = ['heat_capacity_gas_methods',
'HeatCapacityGas',
'heat_capacity_liquid_methods',
'HeatCapacityLiquid',
'heat_capacity_solid_methods',
'HeatCapacitySolid', 'HeatCapacitySolidMixture',
'HeatCapacityGasMixture', 'HeatCapacityLiquidMixture']
from chemicals import heat_capacity, miscdata
from chemicals.heat_capacity import (
Dadgostar_Shaw,
Dadgostar_Shaw_integral,
Dadgostar_Shaw_integral_over_T,
Lastovka_Shaw,
Lastovka_Shaw_integral,
Lastovka_Shaw_integral_over_T,
Lastovka_Shaw_term_A,
Lastovka_solid,
Lastovka_solid_integral,
Lastovka_solid_integral_over_T,
Rowlinson_Bondi,
Rowlinson_Poling,
TRCCp,
TRCCp_integral,
TRCCp_integral_over_T,
)
from chemicals.identifiers import CAS_to_int
from chemicals.miscdata import JOBACK, lookup_VDI_tabular_data
from chemicals.utils import mixing_simple, property_mass_to_molar
from fluids.constants import R, calorie
from fluids.numerics import horner, isnan, log, quad
from thermo import electrochem
from thermo.coolprop import (
CoolProp_T_dependent_property,
Cp_ideal_gas_Helmholtz,
H_ideal_gas_Helmholtz,
Helmholtz_A0_data,
coolprop_dict,
coolprop_fluids,
has_CoolProp,
)
from thermo.electrochem import Laliberte_heat_capacity
from thermo.utils import COOLPROP, HEOS_FIT, JANAF_FIT, LINEAR, UNARY, VDI_TABULAR, MixtureProperty, TDependentProperty
TRCIG = 'TRCIG'
POLING_POLY = 'POLING_POLY'
POLING_CONST = 'POLING_CONST'
CRCSTD = 'CRCSTD'
LASTOVKA_SHAW = 'LASTOVKA_SHAW'
WEBBOOK_SHOMATE = 'WEBBOOK_SHOMATE'
heat_capacity_gas_methods = [HEOS_FIT, COOLPROP, TRCIG, WEBBOOK_SHOMATE, POLING_POLY, LASTOVKA_SHAW, CRCSTD,
POLING_CONST, JOBACK, VDI_TABULAR]
"""Holds all methods available for the :obj:`HeatCapacityGas` class, for use in
iterating over them."""
# These are also internal methods
WEBBOOK_SHOMATE_INTERVALS = [WEBBOOK_SHOMATE+f'_{i}' for i in range(6)]
[docs]class HeatCapacityGas(TDependentProperty):
r'''Class for dealing with gas heat capacity as a function of temperature.
Consists of three coefficient-based methods, two constant methods,
one tabular source, one simple estimator, one group-contribution estimator,
one component specific method, and the external library CoolProp.
Parameters
----------
CASRN : str, optional
The CAS number of the chemical
MW : float, optional
Molecular weight, [g/mol]
similarity_variable : float, optional
similarity variable, n_atoms/MW, [mol/g]
load_data : bool, optional
If False, do not load property coefficients from data sources in files
[-]
extrapolation : str or None
None to not extrapolate; see
:obj:`TDependentProperty <thermo.utils.TDependentProperty>`
for a full list of all options, [-]
method : str or None, optional
If specified, use this method by default and do not use the ranked
sorting; an exception is raised if this is not a valid method for the
provided inputs, [-]
Notes
-----
A string holding each method's name is assigned to the following variables
in this module, intended as the most convenient way to refer to a method.
To iterate over all methods, use the list stored in
:obj:`heat_capacity_gas_methods`.
**TRCIG**:
A rigorous expression derived in [1]_ for modeling gas heat capacity.
Coefficients for 1961 chemicals are available.
**POLING_POLY**:
Simple polynomials in [2]_ not suitable for extrapolation. Data is
available for 308 chemicals.
**COOLPROP**:
CoolProp external library; with select fluids from its library.
Range is limited to that of the equations of state it uses, as
described in [3]_. The heat capacity and enthalpy are implemented
analytically and fairly fast; the entropy integral has no analytical
integral and so is numerical. CoolProp's amazing coefficient collection
is used directly in Python.
**LASTOVKA_SHAW**:
A basic estimation method using the `similarity variable` concept;
requires only molecular structure, so is very convenient. See
:obj:`Lastovka_Shaw <chemicals.heat_capacity.Lastovka_Shaw>` for details.
**CRCSTD**:
Constant values tabulated in [4]_ at 298.15 K; data is available for
533 gases.
**POLING_CONST**:
Constant values in [2]_ at 298.15 K; available for 348 gases.
**VDI_TABULAR**:
Tabular data up to the critical point available in [5]_. Note that this
data is along the saturation curve.
**WEBBOOK_SHOMATE**:
Shomate form coefficients from [6]_ for ~700 compounds.
**JOBACK**:
An estimation method for organic substances in [7]_
**HEOS_FIT**:
A series of higher-order polynomial fits to the calculated results from
fundamental helmholtz equations of state as calculated with REFPROP
See Also
--------
chemicals.heat_capacity.TRCCp
chemicals.heat_capacity.Shomate
chemicals.heat_capacity.Lastovka_Shaw
chemicals.heat_capacity.Rowlinson_Poling
chemicals.heat_capacity.Rowlinson_Bondi
thermo.joback.Joback
Examples
--------
>>> CpGas = HeatCapacityGas(CASRN='142-82-5', MW=100.2, similarity_variable=0.2295)
>>> CpGas(700)
317.305
References
----------
.. [1] Kabo, G. J., and G. N. Roganov. Thermodynamics of Organic Compounds
in the Gas State, Volume II: V. 2. College Station, Tex: CRC Press, 1994.
.. [2] Poling, Bruce E. The Properties of Gases and Liquids. 5th edition.
New York: McGraw-Hill Professional, 2000.
.. [3] Bell, Ian H., Jorrit Wronski, Sylvain Quoilin, and Vincent Lemort.
"Pure and Pseudo-Pure Fluid Thermophysical Property Evaluation and the
Open-Source Thermophysical Property Library CoolProp." Industrial &
Engineering Chemistry Research 53, no. 6 (February 12, 2014):
2498-2508. doi:10.1021/ie4033999. http://www.coolprop.org/
.. [4] Haynes, W.M., Thomas J. Bruno, and David R. Lide. CRC Handbook of
Chemistry and Physics. [Boca Raton, FL]: CRC press, 2014.
.. [5] Gesellschaft, V. D. I., ed. VDI Heat Atlas. 2nd edition.
Berlin; New York:: Springer, 2010.
.. [6] Shen, V.K., Siderius, D.W., Krekelberg, W.P., and Hatch, H.W., Eds.,
NIST WebBook, NIST, http://doi.org/10.18434/T4M88Q
.. [7] Joback, K.G., and R.C. Reid. "Estimation of Pure-Component
Properties from Group-Contributions." Chemical Engineering
Communications 57, no. 1-6 (July 1, 1987): 233-43.
doi:10.1080/00986448708960487.
'''
name = 'gas heat capacity'
units = 'J/mol/K'
interpolation_T = None
"""No interpolation transformation by default."""
interpolation_property = None
"""No interpolation transformation by default."""
interpolation_property_inv = None
"""No interpolation transformation by default."""
tabular_extrapolation_permitted = True
"""Allow tabular extrapolation by default; gases are fairly linear in
heat capacity at high temperatures even if not low temperatures."""
property_min = 0
"""Heat capacities have a minimum value of 0 at 0 K."""
property_max = 1E4
"""Maximum valid of Heat capacity; arbitrarily set. For fluids very near
the critical point, this value can be obscenely high."""
ranked_methods = [HEOS_FIT, TRCIG, WEBBOOK_SHOMATE, miscdata.JANAF, POLING_POLY, COOLPROP, JOBACK,
LASTOVKA_SHAW, CRCSTD, POLING_CONST, VDI_TABULAR]
"""Default rankings of the available methods."""
extra_correlations_internal = TDependentProperty.extra_correlations_internal.copy()
extra_correlations_internal.add(JOBACK)
extra_correlations_internal.add(POLING_CONST)
extra_correlations_internal.add(POLING_POLY)
extra_correlations_internal.add(CRCSTD)
extra_correlations_internal.add(TRCIG)
extra_correlations_internal.add(WEBBOOK_SHOMATE)
extra_correlations_internal.update(WEBBOOK_SHOMATE_INTERVALS)
_fit_force_n = {}
"""Dictionary containing method: fit_n, for use in methods which should
only ever be fit to a specific `n` value"""
_fit_force_n[CRCSTD] = 1
_fit_force_n[POLING_CONST] = 1
custom_args = ('MW', 'similarity_variable')
_json_obj_by_CAS = ('CP_f',)
@classmethod
def _load_json_CAS_references(cls, d):
CASRN = d['CASRN']
if 'CP_f' in d:
d['CP_f'] = coolprop_fluids[CASRN]
def __init__(self, CASRN='', MW=None, similarity_variable=None,
extrapolation='linear', iscyclic_aliphatic=False, **kwargs):
self.CASRN, self.MW, self.similarity_variable, self.iscyclic_aliphatic = CASRN, MW, similarity_variable, iscyclic_aliphatic
super().__init__(extrapolation, **kwargs)
def load_all_methods(self, load_data=True):
r'''Method which picks out coefficients for the specified chemical
from the various dictionaries and DataFrames storing it. All data is
stored as attributes. This method also sets :obj:`Tmin`, :obj:`Tmax`,
and :obj:`all_methods` as a set of methods for which the data exists for.
Called on initialization only. See the source code for the variables at
which the coefficients are stored. The coefficients can safely be
altered once the class is initialized. This method can be called again
to reset the parameters.
'''
methods = []
self.all_methods = set()
self.T_limits = T_limits = {}
CASRN = self.CASRN
if load_data and CASRN:
CASRN_int = None if not CASRN else CAS_to_int(CASRN)
jb_df = miscdata.joback_predictions
if CASRN_int in jb_df.index:
Cpg3 = float(jb_df.at[CASRN_int, 'Cpg3'])
if not isnan(Cpg3):
Tmin_jb, Tmax_jb = float(jb_df.at[CASRN_int, 'Tm']),float(jb_df.at[CASRN_int, 'Tc'])*2.5
# if isnan(Tmin_jb): Tmin_jb = 100.0 # The same groups are defined for Tm as for Cp, should never be a nan
if isnan(Tmax_jb): Tmax_jb = 10000.0
self.add_correlation(name=JOBACK, model='DIPPR100', Tmin=Tmin_jb, Tmax=Tmax_jb, A=float(jb_df.at[CASRN_int, 'Cpg0']),
B=float(jb_df.at[CASRN_int, 'Cpg1']), C=float(jb_df.at[CASRN_int, 'Cpg2']),
D=Cpg3, select=False)
if CASRN in heat_capacity.WebBook_Shomate_coefficients:
phase_values = heat_capacity.WebBook_Shomate_coefficients[CASRN]
Cp_dat = phase_values[2]
if Cp_dat is not None:
method_names = []
T_ranges = [Cp_dat[0][0]]
for i, range_data in enumerate(Cp_dat):
name = WEBBOOK_SHOMATE if len(Cp_dat) == 1 else f'{WEBBOOK_SHOMATE}_{i+1}'
method_names.append(name)
self.add_correlation(name=name, model='Shomate', Tmin=range_data[0], Tmax=range_data[1],
A=range_data[2], B=range_data[3], C=range_data[4], D=range_data[5],
E=range_data[6], select=False
)
T_ranges.append(range_data[1])
if len(Cp_dat) > 1:
self.add_piecewise_method(name=WEBBOOK_SHOMATE, method_names=method_names, T_ranges=T_ranges, select=False)
if CASRN in heat_capacity.TRC_gas_data.index:
Tmin, Tmax, a0, a1, a2, a3, a4, a5, a6, a7, _, _, _ = heat_capacity.TRC_gas_values[
heat_capacity.TRC_gas_data.index.get_loc(CASRN)].tolist()
self.add_correlation(
name=TRCIG,
model='TRCCp',
Tmin=Tmin,
Tmax=Tmax,
a0=a0,
a1=a1,
a2=a2,
a3=a3,
a4=a4,
a5=a5,
a6=a6,
a7=a7,
select=False
)
if CASRN in heat_capacity.Cp_data_Poling.index and not isnan(heat_capacity.Cp_data_Poling.at[CASRN, 'a0']):
POLING_Tmin, POLING_Tmax, a0, a1, a2, a3, a4, Cpg, Cpl = heat_capacity.Cp_values_Poling[heat_capacity.Cp_data_Poling.index.get_loc(CASRN)].tolist()
if isnan(POLING_Tmin):
POLING_Tmin = 50.0
if isnan(POLING_Tmax):
POLING_Tmax = 1000.0
self.add_correlation(name=POLING_POLY, model='DIPPR100', Tmin=POLING_Tmin, Tmax=POLING_Tmax, A=R*a0, B=R*a1, C=R*a2, D=R*a3, E=R*a4, select=False)
if CASRN in heat_capacity.Cp_data_Poling.index and not isnan(heat_capacity.Cp_data_Poling.at[CASRN, 'Cpg']):
self.add_correlation(name=POLING_CONST, model='DIPPR100', Tmin=298.15-50.0, Tmax=298.15+50.0,
A=float(heat_capacity.Cp_data_Poling.at[CASRN, 'Cpg']), select=False)
if CASRN in heat_capacity.CRC_standard_data.index and not isnan(heat_capacity.CRC_standard_data.at[CASRN, 'Cpg']):
self.add_correlation(name=CRCSTD, model='DIPPR100', Tmin=298.15-50.0, Tmax=298.15+50.0,
A=float(heat_capacity.CRC_standard_data.at[CASRN, 'Cpg']), select=False)
if CASRN in miscdata.VDI_saturation_dict:
# NOTE: VDI data is for the saturation curve, i.e. at increasing
# pressure; it is normally substantially higher than the ideal gas
# value
Ts, props = lookup_VDI_tabular_data(CASRN, 'Cp (g)')
self.add_tabular_data(Ts, props, VDI_TABULAR, check_properties=False, select=False)
if self.CASRN in heat_capacity.Cp_dict_JANAF_gas:
methods.append(miscdata.JANAF)
Ts, props = heat_capacity.Cp_dict_JANAF_gas[self.CASRN]
self.add_tabular_data(Ts, props, miscdata.JANAF, check_properties=False, select=False)
if has_CoolProp() and CASRN in coolprop_dict:
methods.append(COOLPROP)
self.CP_f = coolprop_fluids[CASRN]
if CASRN in Helmholtz_A0_data:
# We can do the fast calculation in Python
CoolProp_dat = Helmholtz_A0_data[CASRN]
A0_dat = CoolProp_dat['alpha0']
self.CoolProp_A0_args = (CoolProp_dat['Tc'], CoolProp_dat['R'],
A0_dat.get('IdealGasHelmholtzLead_a1', 0.0),
A0_dat.get('IdealGasHelmholtzLead_a2', 0.0),
A0_dat.get('IdealGasHelmholtzLogTau_a', 0.0),
A0_dat.get('IdealGasHelmholtzPlanckEinstein_ns', None),
A0_dat.get('IdealGasHelmholtzPlanckEinstein_ts', None),
A0_dat.get('IdealGasHelmholtzPower_ns', None),
A0_dat.get('IdealGasHelmholtzPower_ts', None),
A0_dat.get('IdealGasHelmholtzPlanckEinsteinGeneralized_ns', None),
A0_dat.get('IdealGasHelmholtzPlanckEinsteinGeneralized_ts', None),
A0_dat.get('IdealGasHelmholtzPlanckEinsteinGeneralized_cs', None),
A0_dat.get('IdealGasHelmholtzPlanckEinsteinGeneralized_ds', None),
)
Tmin = min(self.CP_f.Tt, self.CP_f.Tmin)
Tmax = max(self.CP_f.Tc, self.CP_f.Tmax)
else:
# Use the more conservative limits to try to get CoolProp to solve
self.CoolProp_A0_args = None
Tmin = max(self.CP_f.Tt, self.CP_f.Tmin)
Tmax = min(self.CP_f.Tc, self.CP_f.Tmax)
T_limits[COOLPROP] = (Tmin, Tmax)
if self.MW is not None and self.similarity_variable is not None:
methods.append(LASTOVKA_SHAW)
T_limits[LASTOVKA_SHAW] = (1e-3, 1e5)
self.Lastovka_Shaw_term_A = Lastovka_Shaw_term_A(self.similarity_variable, self.iscyclic_aliphatic)
self.all_methods.update(methods)
@property
def T_limits_fitting(self):
values = self.T_limits.copy()
if LASTOVKA_SHAW in values:
values[LASTOVKA_SHAW] = (150, 3000)
return values
@staticmethod
def _method_indexes():
'''Returns a dictionary of method: index for all methods
that use data files to retrieve constants. The use of this function
ensures the data files are not loaded until they are needed.
'''
return {TRCIG: heat_capacity.TRC_gas_data.index,
POLING_POLY: [i for i in heat_capacity.Cp_data_Poling.index if not isnan(heat_capacity.Cp_data_Poling.at[i, 'a0'])],
POLING_CONST: [i for i in heat_capacity.Cp_data_Poling.index if not isnan(heat_capacity.Cp_data_Poling.at[i, 'Cpg'])],
CRCSTD: [i for i in heat_capacity.CRC_standard_data.index if not isnan(heat_capacity.CRC_standard_data.at[i, 'Cpg'])],
COOLPROP: coolprop_dict,
VDI_TABULAR: list(miscdata.VDI_saturation_dict.keys()),
}
[docs] def calculate(self, T, method):
r'''Method to calculate surface tension of a liquid at temperature `T`
with a given method.
This method has no exception handling; see :obj:`T_dependent_property <thermo.utils.TDependentProperty.T_dependent_property>`
for that.
Parameters
----------
T : float
Temperature at which to calculate heat capacity, [K]
method : str
Method name to use
Returns
-------
Cp : float
Calculated heat capacity, [J/mol/K]
'''
if method == COOLPROP:
if self.CoolProp_A0_args is not None:
Cp = Cp_ideal_gas_Helmholtz(T, *self.CoolProp_A0_args)
else:
return CoolProp_T_dependent_property(T, self.CASRN, 'CP0MOLAR', 'g')
elif method == LASTOVKA_SHAW:
Cp = Lastovka_Shaw(T, self.similarity_variable, self.iscyclic_aliphatic, self.MW)
else:
return self._base_calculate(T, method)
return Cp
def calculate_integral(self, T1, T2, method):
r'''Method to calculate the integral of a property with respect to
temperature, using a specified method. Implements the analytical
integrals of all available methods except for tabular data.
Parameters
----------
T1 : float
Lower limit of integration, [K]
T2 : float
Upper limit of integration, [K]
method : str
Method for which to find the integral
Returns
-------
integral : float
Calculated integral of the property over the given range,
[`units*K`]
'''
if method == LASTOVKA_SHAW:
similarity_variable = self.similarity_variable
iscyclic_aliphatic = self.iscyclic_aliphatic
MW = self.MW
term_A = self.Lastovka_Shaw_term_A
return (
Lastovka_Shaw_integral(T2, similarity_variable, iscyclic_aliphatic, MW, term_A)
- Lastovka_Shaw_integral(T1, similarity_variable, iscyclic_aliphatic, MW, term_A)
)
elif method == COOLPROP and self.CoolProp_A0_args is not None:
return (H_ideal_gas_Helmholtz(T2, *self.CoolProp_A0_args)
-H_ideal_gas_Helmholtz(T1, *self.CoolProp_A0_args))
else:
return super().calculate_integral(T1, T2, method)
def calculate_integral_over_T(self, T1, T2, method):
r'''Method to calculate the integral of a property over temperature
with respect to temperature, using a specified method. Implements the
analytical integrals of all available methods except for tabular data.
Parameters
----------
T1 : float
Lower limit of integration, [K]
T2 : float
Upper limit of integration, [K]
method : str
Method for which to find the integral
Returns
-------
integral : float
Calculated integral of the property over the given range,
[`units`]
'''
if method == LASTOVKA_SHAW:
similarity_variable = self.similarity_variable
iscyclic_aliphatic = self.iscyclic_aliphatic
MW = self.MW
term_A = self.Lastovka_Shaw_term_A
return (
Lastovka_Shaw_integral_over_T(T2, similarity_variable, iscyclic_aliphatic, MW, term_A)
- Lastovka_Shaw_integral_over_T(T1, similarity_variable, iscyclic_aliphatic, MW, term_A)
)
return super().calculate_integral_over_T(T1, T2, method)
ZABRANSKY_SPLINE = 'ZABRANSKY_SPLINE'
ZABRANSKY_QUASIPOLYNOMIAL = 'ZABRANSKY_QUASIPOLYNOMIAL'
ZABRANSKY_SPLINE_C = 'ZABRANSKY_SPLINE_C'
ZABRANSKY_QUASIPOLYNOMIAL_C = 'ZABRANSKY_QUASIPOLYNOMIAL_C'
ZABRANSKY_SPLINE_SAT = 'ZABRANSKY_SPLINE_SAT'
ZABRANSKY_QUASIPOLYNOMIAL_SAT = 'ZABRANSKY_QUASIPOLYNOMIAL_SAT'
ROWLINSON_POLING = 'ROWLINSON_POLING'
ROWLINSON_BONDI = 'ROWLINSON_BONDI'
DADGOSTAR_SHAW = 'DADGOSTAR_SHAW'
ZABRANSKY_SPLINE_INTERVALS = [ZABRANSKY_SPLINE+f'_{i}' for i in range(6)]
ZABRANSKY_SPLINE_C_INTERVALS = [ZABRANSKY_SPLINE_C+f'_{i}' for i in range(6)]
ZABRANSKY_SPLINE_SAT_INTERVALS = [ZABRANSKY_SPLINE_SAT+f'_{i}' for i in range(6)]
heat_capacity_liquid_methods = [HEOS_FIT, ZABRANSKY_SPLINE, ZABRANSKY_QUASIPOLYNOMIAL,
ZABRANSKY_SPLINE_C, ZABRANSKY_QUASIPOLYNOMIAL_C,
ZABRANSKY_SPLINE_SAT, ZABRANSKY_QUASIPOLYNOMIAL_SAT,
WEBBOOK_SHOMATE, VDI_TABULAR, UNARY, ROWLINSON_POLING, ROWLINSON_BONDI, COOLPROP,
DADGOSTAR_SHAW, POLING_CONST, CRCSTD]
"""Holds all methods available for the :obj:`HeatCapacityLiquid class`, for use in
iterating over them."""
[docs]class HeatCapacityLiquid(TDependentProperty):
r'''Class for dealing with liquid heat capacity as a function of temperature.
Consists of seven coefficient-based methods, two constant methods,
one tabular source, two CSP methods based on gas heat capacity, one simple
estimator, and the external library CoolProp.
Parameters
----------
CASRN : str, optional
The CAS number of the chemical
MW : float, optional
Molecular weight, [g/mol]
similarity_variable : float, optional
similarity variable, n_atoms/MW, [mol/g]
Tc : float, optional
Critical temperature, [K]
omega : float, optional
Acentric factor, [-]
Cpgm : float or callable, optional
Idea-gas molar heat capacity at T or callable for the same, [J/mol/K]
load_data : bool, optional
If False, do not load property coefficients from data sources in files
[-]
extrapolation : str or None
None to not extrapolate; see
:obj:`TDependentProperty <thermo.utils.TDependentProperty>`
for a full list of all options, [-]
method : str or None, optional
If specified, use this method by default and do not use the ranked
sorting; an exception is raised if this is not a valid method for the
provided inputs, [-]
Notes
-----
A string holding each method's name is assigned to the following variables
in this module, intended as the most convenient way to refer to a method.
To iterate over all methods, use the list stored in
:obj:`heat_capacity_liquid_methods`.
**ZABRANSKY_SPLINE, ZABRANSKY_QUASIPOLYNOMIAL, ZABRANSKY_SPLINE_C,
and ZABRANSKY_QUASIPOLYNOMIAL_C**:
Rigorous expressions developed in [1]_ following critical evaluation
of the available data. The spline methods use the form described in
:obj:`Zabransky_cubic <chemicals.heat_capacity.Zabransky_cubic>` over short ranges with varying coefficients
to obtain a wider range. The quasi-polynomial methods use the form
described in :obj:`Zabransky_quasi_polynomial <chemicals.heat_capacity.Zabransky_quasi_polynomial>`, more suitable for
extrapolation, and over then entire range. Respectively, there is data
available for 588, 146, 51, and 26 chemicals. 'C' denotes constant-
pressure data available from more precise experiments. The others
are heat capacity values averaged over a temperature changed.
**ZABRANSKY_SPLINE_SAT and ZABRANSKY_QUASIPOLYNOMIAL_SAT**:
Rigorous expressions developed in [1]_ following critical evaluation
of the available data. The spline method use the form described in
:obj:`Zabransky_cubic <chemicals.heat_capacity.Zabransky_cubic>` over short ranges with varying coefficients
to obtain a wider range. The quasi-polynomial method use the form
described in :obj:`Zabransky_quasi_polynomial <chemicals.heat_capacity.Zabransky_quasi_polynomial>`, more suitable for
extrapolation, and over their entire range. Respectively, there is data
available for 203, and 16 chemicals. Note that these methods are for
the saturation curve!
**VDI_TABULAR**:
Tabular data up to the critical point available in [5]_. Note that this
data is along the saturation curve.
**ROWLINSON_POLING**:
CSP method described in :obj:`Rowlinson_Poling <chemicals.heat_capacity.Rowlinson_Poling>`. Requires a ideal gas
heat capacity value at the same temperature as it is to be calculated.
**ROWLINSON_BONDI**:
CSP method described in :obj:`Rowlinson_Bondi <chemicals.heat_capacity.Rowlinson_Bondi>`. Requires a ideal gas
heat capacity value at the same temperature as it is to be calculated.
**COOLPROP**:
CoolProp external library; with select fluids from its library.
Range is limited to that of the equations of state it uses, as
described in [3]_. Very slow.
**DADGOSTAR_SHAW**:
A basic estimation method using the `similarity variable` concept;
requires only molecular structure, so is very convenient. See
:obj:`Dadgostar_Shaw <chemicals.heat_capacity.Dadgostar_Shaw>` for details.
**POLING_CONST**:
Constant values in [2]_ at 298.15 K; available for 245 liquids.
**CRCSTD**:
Constant values tabulated in [4]_ at 298.15 K; data is available for 433
liquids.
**WEBBOOK_SHOMATE**:
Shomate form coefficients from [6]_ for ~200 compounds.
**HEOS_FIT**:
A series of higher-order polynomial fits to the calculated results from
fundamental helmholtz equations of state as calculated with REFPROP
See Also
--------
chemicals.heat_capacity.Zabransky_quasi_polynomial
chemicals.heat_capacity.Zabransky_cubic
chemicals.heat_capacity.Rowlinson_Poling
chemicals.heat_capacity.Rowlinson_Bondi
chemicals.heat_capacity.Dadgostar_Shaw
chemicals.heat_capacity.Shomate
Examples
--------
>>> CpLiquid = HeatCapacityLiquid(CASRN='142-82-5', MW=100.2, similarity_variable=0.2295, Tc=540.2, omega=0.3457, Cpgm=165.2)
References
----------
.. [1] Zabransky, M., V. Ruzicka Jr, V. Majer, and Eugene S. Domalski.
Heat Capacity of Liquids: Critical Review and Recommended Values.
2 Volume Set. Washington, D.C.: Amer Inst of Physics, 1996.
.. [2] Poling, Bruce E. The Properties of Gases and Liquids. 5th edition.
New York: McGraw-Hill Professional, 2000.
.. [3] Bell, Ian H., Jorrit Wronski, Sylvain Quoilin, and Vincent Lemort.
"Pure and Pseudo-Pure Fluid Thermophysical Property Evaluation and the
Open-Source Thermophysical Property Library CoolProp." Industrial &
Engineering Chemistry Research 53, no. 6 (February 12, 2014):
2498-2508. doi:10.1021/ie4033999. http://www.coolprop.org/
.. [4] Haynes, W.M., Thomas J. Bruno, and David R. Lide. CRC Handbook of
Chemistry and Physics. [Boca Raton, FL]: CRC press, 2014.
.. [5] Gesellschaft, V. D. I., ed. VDI Heat Atlas. 2nd edition.
Berlin; New York:: Springer, 2010.
.. [6] Shen, V.K., Siderius, D.W., Krekelberg, W.P., and Hatch, H.W., Eds.,
NIST WebBook, NIST, http://doi.org/10.18434/T4M88Q
'''
name = 'Liquid heat capacity'
units = 'J/mol/K'
interpolation_T = None
"""No interpolation transformation by default."""
interpolation_property = None
"""No interpolation transformation by default."""
interpolation_property_inv = None
"""No interpolation transformation by default."""
tabular_extrapolation_permitted = False
"""Disallow tabular extrapolation by default; higher-temeprature behavior
is not well predicted by most extrapolation."""
property_min = 1
"""Allow very low heat capacities; arbitrarily set; liquid heat capacity
should always be somewhat substantial."""
property_max = 1E4 # Originally 1E4
"""Maximum valid of Heat capacity; arbitrarily set. For fluids very near
the critical point, this value can be obscenely high."""
ranked_methods = [HEOS_FIT, ZABRANSKY_SPLINE, ZABRANSKY_QUASIPOLYNOMIAL,
ZABRANSKY_SPLINE_C, ZABRANSKY_QUASIPOLYNOMIAL_C,
ZABRANSKY_SPLINE_SAT, ZABRANSKY_QUASIPOLYNOMIAL_SAT,
WEBBOOK_SHOMATE, miscdata.JANAF, UNARY, VDI_TABULAR, COOLPROP, DADGOSTAR_SHAW, ROWLINSON_POLING,
ROWLINSON_BONDI,
POLING_CONST, CRCSTD]
"""Default rankings of the available methods."""
_fit_force_n = {}
"""Dictionary containing method: fit_n, for use in methods which should
only ever be fit to a specific `n` value"""
_fit_force_n[CRCSTD] = 1
_fit_force_n[POLING_CONST] = 1
_json_obj_by_CAS = ('Zabransky_spline', 'Zabransky_spline_iso', 'Zabransky_spline_sat',
'Zabransky_quasipolynomial', 'Zabransky_quasipolynomial_iso',
'Zabransky_quasipolynomial_sat', 'CP_f',
)
obj_references = pure_references = ('Cpgm',)
obj_references_types = pure_reference_types = (HeatCapacityGas,)
extra_correlations_internal = TDependentProperty.extra_correlations_internal.copy()
extra_correlations_internal.add(POLING_CONST)
extra_correlations_internal.add(CRCSTD)
extra_correlations_internal.add(WEBBOOK_SHOMATE)
extra_correlations_internal.update(WEBBOOK_SHOMATE_INTERVALS)
extra_correlations_internal.add(ZABRANSKY_SPLINE)
extra_correlations_internal.add(ZABRANSKY_QUASIPOLYNOMIAL)
extra_correlations_internal.add(ZABRANSKY_SPLINE_C)
extra_correlations_internal.add(ZABRANSKY_QUASIPOLYNOMIAL_C)
extra_correlations_internal.add(ZABRANSKY_SPLINE_SAT)
extra_correlations_internal.add(ZABRANSKY_QUASIPOLYNOMIAL_SAT)
extra_correlations_internal.update(ZABRANSKY_SPLINE_INTERVALS)
extra_correlations_internal.update(ZABRANSKY_SPLINE_C_INTERVALS)
extra_correlations_internal.update(ZABRANSKY_SPLINE_SAT_INTERVALS)
custom_args = ('MW', 'similarity_variable', 'Tc', 'omega', 'Cpgm')
def __init__(self, CASRN='', MW=None, similarity_variable=None, Tc=None,
omega=None, Cpgm=None, extrapolation='linear', **kwargs):
self.CASRN = CASRN
self.MW = MW
self.Tc = Tc
self.omega = omega
self.Cpgm = Cpgm
self.similarity_variable = similarity_variable
super().__init__(extrapolation, **kwargs)
@staticmethod
def _method_indexes():
'''Returns a dictionary of method: index for all methods
that use data files to retrieve constants. The use of this function
ensures the data files are not loaded until they are needed.
'''
return {ZABRANSKY_SPLINE: list(heat_capacity.zabransky_dict_const_s),
ZABRANSKY_QUASIPOLYNOMIAL: list(heat_capacity.zabransky_dict_const_p),
ZABRANSKY_SPLINE_C: list(heat_capacity.zabransky_dict_iso_s),
ZABRANSKY_QUASIPOLYNOMIAL_C: list(heat_capacity.zabransky_dict_iso_p),
ZABRANSKY_SPLINE_SAT: list(heat_capacity.zabransky_dict_sat_s),
ZABRANSKY_QUASIPOLYNOMIAL_SAT: list(heat_capacity.zabransky_dict_sat_p),
POLING_CONST: [i for i in heat_capacity.Cp_data_Poling.index if not isnan(heat_capacity.Cp_data_Poling.at[i, 'Cpl'])],
CRCSTD: [i for i in heat_capacity.CRC_standard_data.index if not isnan(heat_capacity.CRC_standard_data.at[i, 'Cpl'])],
COOLPROP: coolprop_dict,
VDI_TABULAR: list(miscdata.VDI_saturation_dict.keys()),
}
@classmethod
def _load_json_CAS_references(cls, d):
CASRN = d['CASRN']
if 'CP_f' in d:
d['CP_f'] = coolprop_fluids[CASRN]
def load_all_methods(self, load_data=True):
r'''Method which picks out coefficients for the specified chemical
from the various dictionaries and DataFrames storing it. All data is
stored as attributes. This method also sets :obj:`Tmin`, :obj:`Tmax`,
and :obj:`all_methods` as a set of methods for which the data exists for.
Called on initialization only. See the source code for the variables at
which the coefficients are stored. The coefficients can safely be
altered once the class is initialized. This method can be called again
to reset the parameters.
'''
methods = []
self.all_methods = set()
self.T_limits = T_limits = {}
CASRN = self.CASRN
if load_data and CASRN:
if CASRN in heat_capacity.WebBook_Shomate_coefficients:
phase_values = heat_capacity.WebBook_Shomate_coefficients[CASRN]
Cp_dat = phase_values[1] # Index 1 for liquids
if Cp_dat is not None:
# First collect and sort all range data
range_data_list = []
for i, range_data in enumerate(Cp_dat):
name = WEBBOOK_SHOMATE if len(Cp_dat) == 1 else f'{WEBBOOK_SHOMATE}_{i+1}'
range_data_list.append((range_data, name))
# Sort by Tmin (first element of range_data)
range_data_list.sort(key=lambda x: x[0][0])
# Process in sorted order
method_names = []
T_ranges = [range_data_list[0][0][0]] # Start with first Tmin
for range_data, name in range_data_list:
method_names.append(name)
self.add_correlation(
name=name,
model='Shomate',
Tmin=range_data[0],
Tmax=range_data[1],
A=range_data[2],
B=range_data[3],
C=range_data[4],
D=range_data[5],
E=range_data[6],
select=False
)
T_ranges.append(range_data[1])
if len(Cp_dat) > 1:
self.add_piecewise_method(
name=WEBBOOK_SHOMATE,
method_names=method_names,
T_ranges=T_ranges,
select=False
)
if CASRN in heat_capacity.Cp_data_Poling.index and not isnan(heat_capacity.Cp_data_Poling.at[CASRN, 'Cpl']):
self.add_correlation(
name=POLING_CONST,
model='constant',
Tmin=298.15-50.0,
Tmax=298.15+50.0,
value=float(heat_capacity.Cp_data_Poling.at[CASRN, 'Cpl']),
select=False
)
if CASRN in heat_capacity.CRC_standard_data.index and not isnan(heat_capacity.CRC_standard_data.at[CASRN, 'Cpl']):
self.add_correlation(
name=CRCSTD,
model='constant',
Tmin=298.15-50.0,
Tmax=298.15+50.0,
value=float(heat_capacity.CRC_standard_data.at[CASRN, 'Cpl']),
select=False
)
quasi_dict_mapping = {
ZABRANSKY_QUASIPOLYNOMIAL: heat_capacity.zabransky_dict_const_p,
ZABRANSKY_QUASIPOLYNOMIAL_C: heat_capacity.zabransky_dict_iso_p,
ZABRANSKY_QUASIPOLYNOMIAL_SAT: heat_capacity.zabransky_dict_sat_p
}
for method_name, data_dict in quasi_dict_mapping.items():
if CASRN in data_dict:
model = data_dict[CASRN]
self.add_correlation(
name=method_name,
model='Zabransky_quasi_polynomial',
Tmin=model.Tmin,
Tmax=model.Tmax,
Tc=model.Tc,
a1=model.coeffs[0],
a2=model.coeffs[1],
a3=model.coeffs[2],
a4=model.coeffs[3],
a5=model.coeffs[4],
a6=model.coeffs[5],
select=False
)
# Handle spline cases - piecewise correlations
spline_dict_mapping = {
ZABRANSKY_SPLINE: heat_capacity.zabransky_dict_const_s,
ZABRANSKY_SPLINE_C: heat_capacity.zabransky_dict_iso_s,
ZABRANSKY_SPLINE_SAT: heat_capacity.zabransky_dict_sat_s
}
for method_name, data_dict in spline_dict_mapping.items():
if CASRN in data_dict:
spline_list = data_dict[CASRN].models # Get list of models from PiecewiseHeatCapacity
# First collect all models and their temperature ranges
models_and_ranges = []
for i, model in enumerate(spline_list):
name = method_name if len(spline_list) == 1 else f"{method_name}_{i+1}"
models_and_ranges.append((model, name, model.Tmin, model.Tmax))
# Sort by Tmin
models_and_ranges.sort(key=lambda x: x[2])
# Now process in sorted order
method_names = []
T_ranges = [models_and_ranges[0][2]] # Start with first Tmin
for model, name, Tmin, Tmax in models_and_ranges:
method_names.append(name)
T_ranges.append(Tmax)
self.add_correlation(
name=name,
model='Zabransky_cubic',
Tmin=model.Tmin,
Tmax=model.Tmax,
a1=model.coeffs[0],
a2=model.coeffs[1],
a3=model.coeffs[2],
a4=model.coeffs[3],
select=False
)
# Only add piecewise method if there are multiple ranges
if len(spline_list) > 1:
self.add_piecewise_method(
name=method_name,
method_names=method_names,
T_ranges=T_ranges,
select=False
)
if CASRN in heat_capacity.Cp_dict_JANAF_liquid:
methods.append(miscdata.JANAF)
Ts, props = heat_capacity.Cp_dict_JANAF_liquid[CASRN]
self.add_tabular_data(Ts, props, miscdata.JANAF, check_properties=False, select=False)
if CASRN in miscdata.VDI_saturation_dict:
# NOTE: VDI data is for the saturation curve, i.e. at increasing
# pressure; it is normally substantially higher than the ideal gas
# value
Ts, props = lookup_VDI_tabular_data(CASRN, 'Cp (l)')
self.add_tabular_data(Ts, props, VDI_TABULAR, check_properties=False, select=False)
if has_CoolProp() and CASRN in coolprop_dict:
methods.append(COOLPROP)
self.CP_f = coolprop_fluids[CASRN]
Tmin = max(self.CP_f.Tt, self.CP_f.Tmin)
Tmax = min(self.CP_f.Tc*.9999, self.CP_f.Tmax)
T_limits[COOLPROP] = (Tmin, Tmax)
if self.Tc and self.omega:
methods.extend([ROWLINSON_POLING, ROWLINSON_BONDI])
limits_Tc = (0.3*self.Tc, self.Tc-0.1)
T_limits[ROWLINSON_POLING] = limits_Tc
T_limits[ROWLINSON_BONDI] = limits_Tc
if self.MW and self.similarity_variable:
methods.append(DADGOSTAR_SHAW)
T_limits[DADGOSTAR_SHAW] = (1e-3, 10000. if self.Tc is None else self.Tc)
self.all_methods.update(methods)
[docs] def calculate(self, T, method):
r'''Method to calculate heat capacity of a liquid at temperature `T`
with a given method.
This method has no exception handling; see :obj:`T_dependent_property <thermo.utils.TDependentProperty.T_dependent_property>`
for that.
Parameters
----------
T : float
Temperature at which to calculate heat capacity, [K]
method : str
Name of the method to use
Returns
-------
Cp : float
Heat capacity of the liquid at T, [J/mol/K]
'''
# if method == ZABRANSKY_SPLINE:
# return self.Zabransky_spline.force_calculate(T)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL:
# return self.Zabransky_quasipolynomial.calculate(T)
# elif method == ZABRANSKY_SPLINE_C:
# return self.Zabransky_spline_iso.force_calculate(T)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL_C:
# return self.Zabransky_quasipolynomial_iso.calculate(T)
# elif method == ZABRANSKY_SPLINE_SAT:
# return self.Zabransky_spline_sat.force_calculate(T)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL_SAT:
# return self.Zabransky_quasipolynomial_sat.calculate(T)
if method == COOLPROP:
return CoolProp_T_dependent_property(T, self.CASRN , 'CPMOLAR', 'l')
elif method == ROWLINSON_POLING:
Cpgm = self.Cpgm(T) if hasattr(self.Cpgm, '__call__') else self.Cpgm
return Rowlinson_Poling(T, self.Tc, self.omega, Cpgm)
elif method == ROWLINSON_BONDI:
Cpgm = self.Cpgm(T) if hasattr(self.Cpgm, '__call__') else self.Cpgm
return Rowlinson_Bondi(T, self.Tc, self.omega, Cpgm)
elif method == DADGOSTAR_SHAW:
return Dadgostar_Shaw(T, self.similarity_variable, self.MW)
else:
return self._base_calculate(T, method)
def calculate_integral(self, T1, T2, method):
r'''Method to calculate the integral of a property with respect to
temperature, using a specified method. Implements the
analytical integrals of all available methods except for tabular data,
the case of multiple coefficient sets needed to encompass the temperature
range of any of the ZABRANSKY methods, and the CSP methods using the
vapor phase properties.
Parameters
----------
T1 : float
Lower limit of integration, [K]
T2 : float
Upper limit of integration, [K]
method : str
Method for which to find the integral
Returns
-------
integral : float
Calculated integral of the property over the given range,
[`units*K`]
'''
# if method == ZABRANSKY_SPLINE:
# return self.Zabransky_spline.calculate_integral(T1, T2)
# elif method == ZABRANSKY_SPLINE_C:
# return self.Zabransky_spline_iso.force_calculate_integral(T1, T2)
# elif method == ZABRANSKY_SPLINE_SAT:
# return self.Zabransky_spline_sat.calculate_integral(T1, T2)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL:
# return self.Zabransky_quasipolynomial.calculate_integral(T1, T2)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL_C:
# return self.Zabransky_quasipolynomial_iso.calculate_integral(T1, T2)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL_SAT:
# return self.Zabransky_quasipolynomial_sat.calculate_integral(T1, T2)
if method == DADGOSTAR_SHAW:
dH = (Dadgostar_Shaw_integral(T2, self.similarity_variable)
- Dadgostar_Shaw_integral(T1, self.similarity_variable))
return property_mass_to_molar(dH, self.MW)
# elif method in self.tabular_data or method == COOLPROP or method in [ROWLINSON_POLING, ROWLINSON_BONDI]:
# return float(quad(self.calculate, T1, T2, args=(method,))[0])
return super().calculate_integral(T1, T2, method)
def calculate_integral_over_T(self, T1, T2, method):
r'''Method to calculate the integral of a property over temperature
with respect to temperature, using a specified method. Implements the
analytical integrals of all available methods except for tabular data,
the case of multiple coefficient sets needed to encompass the temperature
range of any of the ZABRANSKY methods, and the CSP methods using the
vapor phase properties.
Parameters
----------
T1 : float
Lower limit of integration, [K]
T2 : float
Upper limit of integration, [K]
method : str
Method for which to find the integral
Returns
-------
integral : float
Calculated integral of the property over the given range,
[`units`]
'''
# if method == ZABRANSKY_SPLINE:
# return self.Zabransky_spline.calculate_integral_over_T(T1, T2)
# elif method == ZABRANSKY_SPLINE_C:
# return self.Zabransky_spline_iso.calculate_integral_over_T(T1, T2)
# elif method == ZABRANSKY_SPLINE_SAT:
# return self.Zabransky_spline_sat.calculate_integral_over_T(T1, T2)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL:
# return self.Zabransky_quasipolynomial.calculate_integral_over_T(T1, T2)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL_C:
# return self.Zabransky_quasipolynomial_iso.calculate_integral_over_T(T1, T2)
# elif method == ZABRANSKY_QUASIPOLYNOMIAL_SAT:
# return self.Zabransky_quasipolynomial_sat.calculate_integral_over_T(T1, T2)
if method == DADGOSTAR_SHAW:
dS = (Dadgostar_Shaw_integral_over_T(T2, self.similarity_variable)
- Dadgostar_Shaw_integral_over_T(T1, self.similarity_variable))
return property_mass_to_molar(dS, self.MW)
# elif method in self.tabular_data or method == COOLPROP or method in [ROWLINSON_POLING, ROWLINSON_BONDI]:
# return float(quad(lambda T: self.calculate(T, method)/T, T1, T2)[0])
return super().calculate_integral_over_T(T1, T2, method)
LASTOVKA_S = 'LASTOVKA_S'
PERRY151 = """PERRY151"""
heat_capacity_solid_methods = [JANAF_FIT, WEBBOOK_SHOMATE, PERRY151, CRCSTD, LASTOVKA_S]
"""Holds all methods available for the :obj:`HeatCapacitySolid` class, for use in
iterating over them."""
[docs]class HeatCapacitySolid(TDependentProperty):
r'''Class for dealing with solid heat capacity as a function of temperature.
Consists of two temperature-dependent expressions, one constant
value source, and one simple estimator.
Parameters
----------
similarity_variable : float, optional
similarity variable, n_atoms/MW, [mol/g]
MW : float, optional
Molecular weight, [g/mol]
CASRN : str, optional
The CAS number of the chemical
load_data : bool, optional
If False, do not load property coefficients from data sources in files
[-]
extrapolation : str or None
None to not extrapolate; see
:obj:`TDependentProperty <thermo.utils.TDependentProperty>`
for a full list of all options, [-]
method : str or None, optional
If specified, use this method by default and do not use the ranked
sorting; an exception is raised if this is not a valid method for the
provided inputs, [-]
Notes
-----
A string holding each method's name is assigned to the following variables
in this module, intended as the most convenient way to refer to a method.
To iterate over all methods, use the list stored in
:obj:`heat_capacity_solid_methods`.
**PERRY151**:
Simple polynomials with vaious exponents selected for each expression.
Coefficients are in units of calories/mol/K. The full expression is:
.. math::
Cp = a + bT + c/T^2 + dT^2
Data is available for 284 solids, from [2]_.
**CRCSTD**:
Values tabulated in [1]_ at 298.15 K; data is available for 529
solids.
**LASTOVKA_S**:
A basic estimation method using the `similarity variable` concept;
requires only molecular structure, so is very convenient. See
:obj:`Lastovka_solid <chemicals.heat_capacity.Lastovka_solid>` for details.
**WEBBOOK_SHOMATE**:
Shomate form coefficients from [3]_ for ~300 compounds.
See Also
--------
chemicals.heat_capacity.Lastovka_solid
chemicals.heat_capacity.Shomate
Examples
--------
>>> CpSolid = HeatCapacitySolid(CASRN='142-82-5', MW=100.2, similarity_variable=0.2295)
>>> CpSolid(200)
131.205824
References
----------
.. [1] Haynes, W.M., Thomas J. Bruno, and David R. Lide. CRC Handbook of
Chemistry and Physics. [Boca Raton, FL]: CRC press, 2014.
.. [2] Green, Don, and Robert Perry. Perry's Chemical Engineers' Handbook,
Eighth Edition. McGraw-Hill Professional, 2007.
.. [3] Shen, V.K., Siderius, D.W., Krekelberg, W.P., and Hatch, H.W., Eds.,
NIST WebBook, NIST, http://doi.org/10.18434/T4M88Q
'''
name = 'solid heat capacity'
units = 'J/mol/K'
interpolation_T = None
"""No interpolation transformation by default."""
interpolation_property = None
"""No interpolation transformation by default."""
interpolation_property_inv = None
"""No interpolation transformation by default."""
tabular_extrapolation_permitted = True
"""Allow tabular extrapolation by default; a theoretical solid phase exists
for all chemicals at sufficiently high pressures, although few chemicals
could stably exist in those conditions."""
property_min = 0
"""Heat capacities have a minimum value of 0 at 0 K."""
property_max = 1E4
"""Maximum value of Heat capacity; arbitrarily set."""
ranked_methods = [WEBBOOK_SHOMATE, JANAF_FIT, miscdata.JANAF, UNARY, PERRY151, CRCSTD, LASTOVKA_S]
"""Default rankings of the available methods."""
extra_correlations_internal = TDependentProperty.extra_correlations_internal.copy()
extra_correlations_internal.add(PERRY151)
extra_correlations_internal.add(CRCSTD)
extra_correlations_internal.add(WEBBOOK_SHOMATE)
extra_correlations_internal.update(WEBBOOK_SHOMATE_INTERVALS)
_fit_force_n = {}
"""Dictionary containing method: fit_n, for use in methods which should
only ever be fit to a specific `n` value"""
_fit_force_n[CRCSTD] = 1
custom_args = ('MW', 'similarity_variable')
_json_obj_by_CAS = tuple()
def __init__(self, CASRN='', similarity_variable=None, MW=None,
extrapolation='linear', **kwargs):
self.similarity_variable = similarity_variable
self.MW = MW
self.CASRN = CASRN
super().__init__(extrapolation, **kwargs)
def _method_indexes():
'''Returns a dictionary of method: index for all methods
that use data files to retrieve constants. The use of this function
ensures the data files are not loaded until they are needed.
'''
return {PERRY151: [i for i in heat_capacity.Cp_dict_PerryI.keys() if 'c' in heat_capacity.Cp_dict_PerryI[i]],
CRCSTD: [i for i in heat_capacity.CRC_standard_data.index if not isnan(heat_capacity.CRC_standard_data.at[i, 'Cps'])],
}
def load_all_methods(self, load_data=True):
r'''Method which picks out coefficients for the specified chemical
from the various dictionaries and DataFrames storing it. All data is
stored as attributes. This method also sets :obj:`Tmin`, :obj:`Tmax`,
and :obj:`all_methods` as a set of methods for which the data exists for.
Called on initialization only. See the source code for the variables at
which the coefficients are stored. The coefficients can safely be
altered once the class is initialized. This method can be called again
to reset the parameters.
'''
methods = []
self.T_limits = T_limits = {}
self.all_methods = set()
CASRN = self.CASRN
if load_data and CASRN:
if CASRN in heat_capacity.WebBook_Shomate_coefficients:
phase_values = heat_capacity.WebBook_Shomate_coefficients[CASRN]
Cp_dat = phase_values[0]
if Cp_dat is not None:
method_names = []
T_ranges = [Cp_dat[0][0]]
for i, range_data in enumerate(Cp_dat):
name = WEBBOOK_SHOMATE if len(Cp_dat) == 1 else f'{WEBBOOK_SHOMATE}_{i+1}'
method_names.append(name)
self.add_correlation(name=name, model='Shomate', Tmin=range_data[0], Tmax=range_data[1],
A=range_data[2], B=range_data[3], C=range_data[4], D=range_data[5],
E=range_data[6], select=False
)
T_ranges.append(range_data[1])
if len(Cp_dat) > 1:
self.add_piecewise_method(name=WEBBOOK_SHOMATE, method_names=method_names, T_ranges=T_ranges, select=False)
if CASRN in heat_capacity.Cp_dict_JANAF_solid:
methods.append(miscdata.JANAF)
Ts, props = heat_capacity.Cp_dict_JANAF_solid[CASRN]
self.add_tabular_data(Ts, props, miscdata.JANAF, check_properties=False, select=False)
if CASRN and CASRN in heat_capacity.Cp_dict_PerryI and 'c' in heat_capacity.Cp_dict_PerryI[CASRN]:
data = heat_capacity.Cp_dict_PerryI[CASRN]['c']
Tmin = data['Tmin'] if data['Tmin'] else 0.0
Tmax = data['Tmax'] if data['Tmax'] else 2000.0
self.add_correlation(
name=PERRY151,
model='Shomate',
Tmin=Tmin,
Tmax=Tmax,
A=data['Const']*calorie,
B=data['Lin']*calorie,
C=data['Quad']*calorie,
D=0.0,
E=data['Quadinv']*calorie,
select=False
)
if CASRN in heat_capacity.CRC_standard_data.index and not isnan(heat_capacity.CRC_standard_data.at[CASRN, 'Cps']):
self.add_correlation(
name=CRCSTD,
model='constant',
Tmin=298.15-50.0,
Tmax=298.15+50.0,
value=float(heat_capacity.CRC_standard_data.at[CASRN, 'Cps']),
select=False
)
if self.MW and self.similarity_variable:
methods.append(LASTOVKA_S)
T_limits[LASTOVKA_S] = (1.0, 1e4)
# Works above roughly 1 K up to 10K.
self.all_methods.update(methods)
[docs] def calculate(self, T, method):
r'''Method to calculate heat capacity of a solid at temperature `T`
with a given method.
This method has no exception handling; see :obj:`T_dependent_property <thermo.utils.TDependentProperty.T_dependent_property>`
for that.
Parameters
----------
T : float
Temperature at which to calculate heat capacity, [K]
method : str
Name of the method to use
Returns
-------
Cp : float
Heat capacity of the solid at T, [J/mol/K]
'''
if method == LASTOVKA_S:
return Lastovka_solid(T, self.similarity_variable, self.MW)
return self._base_calculate(T, method)
def calculate_integral(self, T1, T2, method):
r'''Method to calculate the integral of a property with respect to
temperature, using a specified method. Implements the analytical
integrals of all available methods except for tabular data.
Parameters
----------
T1 : float
Lower limit of integration, [K]
T2 : float
Upper limit of integration, [K]
method : str
Method for which to find the integral
Returns
-------
integral : float
Calculated integral of the property over the given range,
[`units*K`]
'''
if method == LASTOVKA_S:
return (Lastovka_solid_integral(T2, self.similarity_variable, self.MW)
- Lastovka_solid_integral(T1, self.similarity_variable, self.MW))
else:
return super().calculate_integral(T1, T2, method)
def calculate_integral_over_T(self, T1, T2, method):
r'''Method to calculate the integral of a property over temperature
with respect to temperature, using a specified method. Implements the
analytical integrals of all available methods except for tabular data.
Parameters
----------
T1 : float
Lower limit of integration, [K]
T2 : float
Upper limit of integration, [K]
method : str
Method for which to find the integral
Returns
-------
integral : float
Calculated integral of the property over the given range,
[`units`]
'''
if method == LASTOVKA_S:
return (Lastovka_solid_integral_over_T(T2, self.similarity_variable, self.MW)
- Lastovka_solid_integral_over_T(T1, self.similarity_variable, self.MW))
return super().calculate_integral_over_T(T1, T2, method)
### Mixture heat capacities
LALIBERTE = 'LALIBERTE'
heat_capacity_gas_mixture_methods = [LINEAR]
"""Holds all methods available for the :obj:`HeatCapacityGasMixture` class, for use in
iterating over them."""
heat_capacity_liquid_mixture_methods = [LALIBERTE, LINEAR]
"""Holds all methods available for the :obj:`HeatCapacityLiquidMixture` class, for use in
iterating over them."""
heat_capacity_solid_mixture_methods = [LINEAR]
"""Holds all methods available for the :obj:`HeatCapacitySolidMixture` class, for use in
iterating over them."""
[docs]class HeatCapacityLiquidMixture(MixtureProperty):
'''Class for dealing with liquid heat capacity of a mixture as a function
of temperature, pressure, and composition.
Consists only of mole weighted averaging, and the Laliberte method for
aqueous electrolyte solutions.
Parameters
----------
MWs : list[float], optional
Molecular weights of all species in the mixture, [g/mol]
CASs : str, optional
The CAS numbers of all species in the mixture
HeatCapacityLiquids : list[HeatCapacityLiquid], optional
HeatCapacityLiquid objects created for all species in the mixture [-]
Notes
-----
To iterate over all methods, use the list stored in
:obj:`heat_capacity_liquid_mixture_methods`.
**LALIBERTE**:
Electrolyte model equation with coefficients; see
:obj:`thermo.electrochem.Laliberte_heat_capacity` for more details.
**LINEAR**:
Mixing rule described in :obj:`mixing_simple <chemicals.utils.mixing_simple>`.
'''
name = 'Liquid heat capacity'
units = 'J/mol'
property_min = 1
"""Allow very low heat capacities; arbitrarily set; liquid heat capacity
should always be somewhat substantial."""
property_max = 1E4 # Originally 1E4
"""Maximum valid of Heat capacity; arbitrarily set. For fluids very near
the critical point, this value can be obscenely high."""
ranked_methods = [LALIBERTE, LINEAR]
pure_references = ('HeatCapacityLiquids',)
pure_reference_types = (HeatCapacityLiquid,)
obj_references = ('HeatCapacityLiquids',)
pure_constants = ('MWs', )
custom_args = pure_constants
def __init__(self, MWs=[], CASs=[], HeatCapacityLiquids=[], **kwargs):
self.MWs = MWs
self.CASs = CASs
self.HeatCapacityLiquids = HeatCapacityLiquids
super().__init__(**kwargs)
def load_all_methods(self, load_data=True):
r'''Method to initialize the object by precomputing any values which
may be used repeatedly and by retrieving mixture-specific variables.
All data are stored as attributes. This method also sets :obj:`Tmin`,
:obj:`Tmax`, and :obj:`all_methods` as a set of methods which should
work to calculate the property.
Called on initialization only. See the source code for the variables at
which the coefficients are stored. The coefficients can safely be
altered once the class is initialized. This method can be called again
to reset the parameters.
'''
methods = [LINEAR]
if len(self.CASs) > 1 and '7732-18-5' in self.CASs:
Laliberte_data = electrochem.Laliberte_data
a1s, a2s, a3s, a4s, a5s, a6s = [], [], [], [], [], []
laliberte_incomplete = False
for CAS in self.CASs:
if CAS == '7732-18-5':
continue
if CAS in Laliberte_data.index:
dat = Laliberte_data.loc[CAS].values
if isnan(dat[22]):
laliberte_incomplete = True
break
a1s.append(float(dat[22]))
a2s.append(float(dat[23]))
a3s.append(float(dat[24]))
a4s.append(float(dat[25]))
a5s.append(float(dat[26]))
a6s.append(float(dat[27]))
else:
laliberte_incomplete = True
break
if not laliberte_incomplete:
self.Laliberte_a1s = a1s
self.Laliberte_a2s = a2s
self.Laliberte_a3s = a3s
self.Laliberte_a4s = a4s
self.Laliberte_a5s = a5s
self.Laliberte_a6s = a6s
wCASs = [i for i in self.CASs if i != '7732-18-5']
methods.append(LALIBERTE)
self.wCASs = wCASs
self.index_w = self.CASs.index('7732-18-5')
self.all_methods = all_methods = set(methods)
[docs] def calculate(self, T, P, zs, ws, method):
r'''Method to calculate heat capacity of a liquid mixture at
temperature `T`, pressure `P`, mole fractions `zs` and weight fractions
`ws` with a given method.
This method has no exception handling; see :obj:`mixture_property <thermo.utils.MixtureProperty.mixture_property>`
for that.
Parameters
----------
T : float
Temperature at which to calculate the property, [K]
P : float
Pressure at which to calculate the property, [Pa]
zs : list[float]
Mole fractions of all species in the mixture, [-]
ws : list[float]
Weight fractions of all species in the mixture, [-]
method : str
Name of the method to use
Returns
-------
Cplm : float
Molar heat capacity of the liquid mixture at the given conditions,
[J/mol]
'''
if method == LALIBERTE:
ws = list(ws)
ws.pop(self.index_w)
Cpl = Laliberte_heat_capacity(T, ws, self.wCASs)
MW = mixing_simple(zs, self.MWs)
return property_mass_to_molar(Cpl, MW)
return super().calculate(T, P, zs, ws, method)
[docs] def test_method_validity(self, T, P, zs, ws, method):
if method in self.all_methods:
return True
return super().test_method_validity(T, P, zs, ws, method)
[docs]class HeatCapacitySolidMixture(MixtureProperty):
'''Class for dealing with solid heat capacity of a mixture as a function of
temperature, pressure, and composition.
Consists only of mole weighted averaging.
Parameters
----------
CASs : list[str], optional
The CAS numbers of all species in the mixture, [-]
HeatCapacitySolids : list[HeatCapacitySolid], optional
HeatCapacitySolid objects created for all species in the mixture [-]
MWs : list[float], optional
Molecular weights of all species in the mixture, [g/mol]
Notes
-----
To iterate over all methods, use the list stored in
:obj:`heat_capacity_solid_mixture_methods`.
**LINEAR**:
Mixing rule described in :obj:`mixing_simple <chemicals.utils.mixing_simple>`.
'''
name = 'Solid heat capacity'
units = 'J/mol'
property_min = 0
"""Heat capacities have a minimum value of 0 at 0 K."""
property_max = 1E4
"""Maximum value of Heat capacity; arbitrarily set."""
ranked_methods = [LINEAR]
pure_references = ('HeatCapacitySolids',)
pure_reference_types = (HeatCapacitySolid,)
obj_references = ('HeatCapacitySolids',)
pure_constants = ('MWs', )
custom_args = pure_constants
def __init__(self, CASs=[], HeatCapacitySolids=[], MWs=[], **kwargs):
self.CASs = CASs
self.HeatCapacitySolids = HeatCapacitySolids
self.MWs = MWs
super().__init__(**kwargs)
def load_all_methods(self, load_data=True):
r'''Method to initialize the object by precomputing any values which
may be used repeatedly and by retrieving mixture-specific variables.
All data are stored as attributes. This method also sets :obj:`Tmin`,
:obj:`Tmax`, and :obj:`all_methods` as a set of methods which should
work to calculate the property.
Called on initialization only. See the source code for the variables at
which the coefficients are stored. The coefficients can safely be
altered once the class is initialized. This method can be called again
to reset the parameters.
'''
methods = [LINEAR]
self.all_methods = all_methods = set(methods)
[docs] def calculate(self, T, P, zs, ws, method):
r'''Method to calculate heat capacity of a solid mixture at
temperature `T`, pressure `P`, mole fractions `zs` and weight fractions
`ws` with a given method.
This method has no exception handling; see :obj:`mixture_property <thermo.utils.MixtureProperty.mixture_property>`
for that.
Parameters
----------
T : float
Temperature at which to calculate the property, [K]
P : float
Pressure at which to calculate the property, [Pa]
zs : list[float]
Mole fractions of all species in the mixture, [-]
ws : list[float]
Weight fractions of all species in the mixture, [-]
method : str
Name of the method to use
Returns
-------
Cpsm : float
Molar heat capacity of the solid mixture at the given conditions, [J/mol]
'''
return super().calculate(T, P, zs, ws, method)
[docs] def test_method_validity(self, T, P, zs, ws, method):
if method in self.all_methods:
return True
return super().test_method_validity(T, P, zs, ws, method)
[docs]class HeatCapacityGasMixture(MixtureProperty):
'''Class for dealing with the gas heat capacity of a mixture as a function
of temperature, pressure, and composition. Consists only of mole weighted
averaging.
Parameters
----------
CASs : list[str], optional
The CAS numbers of all species in the mixture, [-]
HeatCapacityGases : list[HeatCapacityGas], optional
HeatCapacityGas objects created for all species in the mixture [-]
MWs : list[float], optional
Molecular weights of all species in the mixture, [g/mol]
Notes
-----
To iterate over all methods, use the list stored in
:obj:`heat_capacity_gas_mixture_methods`.
**LINEAR**:
Mixing rule described in :obj:`mixing_simple <chemicals.utils.mixing_simple>`.
'''
name = 'Gas heat capacity'
units = 'J/mol'
property_min = 0
"""Heat capacities have a minimum value of 0 at 0 K."""
property_max = 1E4
"""Maximum valid of Heat capacity; arbitrarily set. For fluids very near
the critical point, this value can be obscenely high."""
ranked_methods = [LINEAR]
pure_references = ('HeatCapacityGases',)
pure_reference_types = (HeatCapacityGas,)
obj_references = ('HeatCapacityGases',)
pure_constants = ('MWs', )
custom_args = pure_constants
def __init__(self, CASs=[], HeatCapacityGases=[], MWs=[], **kwargs):
self.CASs = CASs
self.HeatCapacityGases = HeatCapacityGases
self.MWs = MWs
super().__init__(**kwargs)
def load_all_methods(self, load_data=True):
r'''Method to initialize the object by precomputing any values which
may be used repeatedly and by retrieving mixture-specific variables.
All data are stored as attributes. This method also sets :obj:`Tmin`,
:obj:`Tmax`, and :obj:`all_methods` as a set of methods which should
work to calculate the property.
Called on initialization only. See the source code for the variables at
which the coefficients are stored. The coefficients can safely be
altered once the class is initialized. This method can be called again
to reset the parameters.
'''
methods = [LINEAR]
self.all_methods = set(methods)
[docs] def calculate(self, T, P, zs, ws, method):
r'''Method to calculate heat capacity of a gas mixture at
temperature `T`, pressure `P`, mole fractions `zs` and weight fractions
`ws` with a given method.
This method has no exception handling; see :obj:`mixture_property <thermo.utils.MixtureProperty.mixture_property>`
for that.
Parameters
----------
T : float
Temperature at which to calculate the property, [K]
P : float
Pressure at which to calculate the property, [Pa]
zs : list[float]
Mole fractions of all species in the mixture, [-]
ws : list[float]
Weight fractions of all species in the mixture, [-]
method : str
Name of the method to use
Returns
-------
Cpgm : float
Molar heat capacity of the gas mixture at the given conditions,
[J/mol]
'''
return super().calculate(T, P, zs, ws, method)
[docs] def test_method_validity(self, T, P, zs, ws, method):
if method in self.all_methods:
return True
return super().test_method_validity(T, P, zs, ws, method)