'''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.
'''
__all__ = ['tabulate_solid', 'tabulate_liq', 'tabulate_gas',
'tabulate_constants', 'tabulate_streams']
from collections import OrderedDict
from fluids.numerics import numpy as np
from thermo.chemical import Chemical
pd = None
def pandas():
global pd
if pd is None:
import pandas as pd
return pd
[docs]def tabulate_solid(chemical, Tmin=None, Tmax=None, pts=10):
pd = pandas()
chem = Chemical(chemical)
(rhos, Cps) = ([] for i in range(2))
if not Tmin: # pragma: no cover
if chem.Tm:
Tmin = max(chem.Tm-100, 1e-2)
else:
Tmin = 150.
if not Tmax: # pragma: no cover
if chem.Tm:
Tmax = chem.Tm
else:
Tmax = 350
Ts = np.linspace(Tmin, Tmax, pts)
for T in Ts:
chem = Chemical(chemical, T=T)
rhos.append(chem.rhos)
Cps.append(chem.Cps)
data = OrderedDict()
data['Density, kg/m^3'] = rhos
data['Constant-pressure heat capacity, J/kg/K'] = Cps
df = pd.DataFrame(data, index=Ts)
df.index.name = 'T, K'
return df
[docs]def tabulate_liq(chemical, Tmin=None, Tmax=None, pts=10):
pd = pandas()
chem = Chemical(chemical)
(rhos, Cps, mugs, kgs, Prs, alphas, isobarics, JTs, Psats, sigmas, Hvaps,
permittivities) = ([] for i in range(12))
if not Tmin: # pragma: no cover
if chem.Tm:
Tmin = chem.Tm
else:
Tmin = 273.15
if not Tmax: # pragma: no cover
if chem.Tc:
Tmax = chem.Tc
else:
Tmax = 450
Ts = np.linspace(Tmin, Tmax, pts)
for T in Ts:
chem = Chemical(chemical, T=T)
rhos.append(chem.rhol)
Cps.append(chem.Cpl)
mugs.append(chem.mul)
kgs.append(chem.kl)
Prs.append(chem.Prl)
alphas.append(chem.alphal)
isobarics.append(chem.isobaric_expansion_l)
JTs.append(chem.JTg)
Psats.append(chem.Psat)
Hvaps.append(chem.Hvap)
sigmas.append(chem.sigma)
permittivities.append(chem.permittivity)
data = OrderedDict()
data['Saturation pressure, Pa'] = Psats
data['Density, kg/m^3'] = rhos
data['Constant-pressure heat capacity, J/kg/K'] = Cps
data['Heat of vaporization, J/kg'] = Hvaps
data['Viscosity, Pa*s'] = mugs
data['Thermal conductivity, W/m/K'] = kgs
data['Surface tension, N/m'] = sigmas
data['Prandtl number'] = Prs
data['Thermal diffusivity, m^2/s'] = alphas
data['Isobaric expansion, 1/K'] = isobarics
data['Joule-Thompson expansion coefficient, K/Pa'] = JTs
data['PermittivityLiquid'] = permittivities
df = pd.DataFrame(data, index=Ts)
df.index.name = 'T, K'
return df
[docs]def tabulate_gas(chemical, Tmin=None, Tmax=None, pts=10):
chem = Chemical(chemical)
pd = pandas()
(rhos, Cps, Cvs, mugs, kgs, Prs, alphas, isobarics, isentropics, JTs) = ([] for i in range(10))
if not Tmin: # pragma: no cover
if chem.Tm:
Tmin = chem.Tm
else:
Tmin = 273.15
if not Tmax: # pragma: no cover
if chem.Tc:
Tmax = chem.Tc
else:
Tmax = 450
Ts = np.linspace(Tmin, Tmax, pts)
for T in Ts:
chem = Chemical(chemical, T=T)
rhos.append(chem.rhog)
Cps.append(chem.Cpg)
Cvs.append(chem.Cvg)
mugs.append(chem.mug)
kgs.append(chem.kg)
Prs.append(chem.Prg)
alphas.append(chem.alphag)
isobarics.append(chem.isobaric_expansion_g)
isentropics.append(chem.isentropic_exponent)
JTs.append(chem.JTg)
data = OrderedDict()
data['Density, kg/m^3'] = rhos
data['Constant-pressure heat capacity, J/kg/K'] = Cps
data['Constant-volume heat capacity, J/kg/K'] = Cvs
data['Viscosity, Pa*s'] = mugs
data['Thermal conductivity, W/m/K'] = kgs
data['Prandtl number'] = Prs
data['Thermal diffusivity, m^2/s'] = alphas
data['Isobaric expansion, 1/K'] = isobarics
data['Isentropic exponent'] = isentropics
data['Joule-Thompson expansion coefficient, K/Pa'] = JTs
df = pd.DataFrame(data, index=Ts) # add orient='index'
df.index.name = 'T, K'
return df
[docs]def tabulate_constants(chemical, full=False, vertical=False):
pd = pandas()
pd.set_option('display.max_rows', 100000)
pd.set_option('display.max_columns', 100000)
all_chemicals = OrderedDict()
if isinstance(chemical, str):
cs = [chemical]
else:
cs = chemical
for chemical in cs:
chem = Chemical(chemical)
data = OrderedDict()
data['CAS'] = chem.CAS
data['Formula'] = chem.formula
data['MW, g/mol'] = chem.MW
data['Tm, K'] = chem.Tm
data['Tb, K'] = chem.Tb
data['Tc, K'] = chem.Tc
data['Pc, Pa'] = chem.Pc
data['Vc, m^3/mol'] = chem.Vc
data['Zc'] = chem.Zc
data['rhoc, kg/m^3'] = chem.rhoc
data['Acentric factor'] = chem.omega
data['Triple temperature, K'] = chem.Tt
data['Triple pressure, Pa'] = chem.Pt
data['Heat of vaporization at Tb, J/mol'] = chem.Hvap_Tbm
data['Heat of fusion, J/mol'] = chem.Hfusm
data['Heat of sublimation, J/mol'] = chem.Hsubm
data['Heat of formation, J/mol'] = chem.Hf
data['Dipole moment, debye'] = chem.dipole
data['Molecular Diameter, Angstrom'] = chem.molecular_diameter
data['Stockmayer parameter, K'] = chem.Stockmayer
data['Refractive index'] = chem.RI
data['Lower flammability limit, fraction'] = chem.LFL
data['Upper flammability limit, fraction'] = chem.UFL
data['Flash temperature, K'] = chem.Tflash
data['Autoignition temperature, K'] = chem.Tautoignition
data['Time-weighted average exposure limit'] = str(chem.TWA)
data['Short-term exposure limit'] = str(chem.STEL)
data['logP'] = chem.logP
if full:
data['smiles'] = chem.smiles
data['InChI'] = chem.InChI
data['InChI key'] = chem.InChI_Key
data['IUPAC name'] = chem.IUPAC_name
data['solubility parameter, Pa^0.5'] = chem.solubility_parameter
data['Parachor'] = chem.Parachor
data['Global warming potential'] = chem.GWP
data['Ozone depletion potential'] = chem.ODP
data['Electrical conductivity, S/m'] = chem.conductivity
all_chemicals[chem.name] = data
if vertical:
df = pd.DataFrame.from_dict(all_chemicals)
else:
df = pd.DataFrame.from_dict(all_chemicals, orient='index')
return df
[docs]def tabulate_streams(names=None, *args, **kwargs):
# Names are the names of the streams to be displayed; input
# strings for each of them or bad things happen!
pd = pandas()
Ts = [i.T for i in args]
Ps = [i.P for i in args]
VFs = [i.V_over_F for i in args]
phases = [i.phase for i in args]
Hs = [i.H for i in args]
ms = [i.m for i in args]
ns = [i.n for i in args]
CASs = set()
IDs = {}
for stream in args:
CASs.update(stream.CASs)
for CAS, i in zip(stream.CASs, stream.names):
IDs[CAS] = i
CASs = list(CASs) # So it can be indexed
mole_fractions = []
mole_flows = []
mass_fractions = []
mass_flows = []
for stream in args:
mole_fractions_i = []
mass_fractions_i = []
mole_flows_i = []
mass_flows_i = []
for CAS in CASs:
if CAS in stream.CASs:
ind = stream.CASs.index(CAS)
zi = stream.zs[ind]
wi = stream.ws[ind]
n = stream.ns[ind]
m = stream.ms[ind]
else:
zi, wi, n, m = 0, 0, 0, 0
mole_fractions_i.append(zi)
mass_fractions_i.append(wi)
mole_flows_i.append(n)
mass_flows_i.append(m)
mole_fractions.append(mole_fractions_i)
mass_fractions.append(mass_fractions_i)
mass_flows.append(mass_flows_i)
mole_flows.append(mole_flows_i)
dat = OrderedDict([['Temperature, K', Ts],
['Pressure, Pa', Ps],
['Phase', phases],
['Vapor fraction', VFs],
['Enthalpy, J', Hs],
['Mass flows, kg/s', ms],
['Mole flows, mol/s', ns]])
if kwargs.get('Mole flows', True):
for i, CAS in enumerate(CASs):
s = f'Mole flow, mol/s {IDs[CAS]}'
vals = [j[i] for j in mole_flows]
dat[s] = vals
if kwargs.get('Mass flows', True):
for i, CAS in enumerate(CASs):
s = f'Mass flow, kg/s {IDs[CAS]}'
vals = [j[i] for j in mass_flows]
dat[s] = vals
if kwargs.get('Mass fractions', True):
for i, CAS in enumerate(CASs):
s = f'Mass fraction {IDs[CAS]}'
vals = [j[i] for j in mass_fractions]
dat[s] = vals
if kwargs.get('Mole fractions', True):
for i, CAS in enumerate(CASs):
s = f'Mole fraction {IDs[CAS]}'
vals = [j[i] for j in mole_fractions]
dat[s] = vals
# print(dat, names)
if names is None:
df = pd.DataFrame(dat)
else:
df = pd.DataFrame(dat, index=names)
return df.transpose()
#chemicals = ['Sodium Hydroxide', 'sodium chloride', 'methanol',
#'hydrogen sulfide', 'methyl mercaptan', 'Dimethyl disulfide', 'dimethyl sulfide',
# 'alpha-pinene', 'chlorine dioxide', 'sulfuric acid', 'SODIUM CHLORATE', 'carbon dioxide', 'Cl2', 'formic acid',
# 'sodium sulfate']
#for i in chemicals:
# print tabulate_solid(i)
# print tabulate_liq(i)
# print tabulate_gas(i)
# tabulate_constants(i)
#tabulate_constants('Methylene blue')