Source code for thermo.datasheet

'''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
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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'''


__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

global pd
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 = min(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 = 'Mole flow, mol/s %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 = 'Mass flow, kg/s %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 = 'Mass fraction %s' %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 = 'Mole fraction %s' %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')