Potentially predict Tc from Tb

dev/generate_Tm_Tc_relationships.py

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+# -*- coding: utf-8 -*-
+"""Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
+Copyright (C) 2021, 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.
+"""
+
+from thermo import *
+from chemicals import *
+import numpy as np
+
+from chemicals.critical import critical_data_IUPAC, critical_data_Matthews, critical_data_CRC, critical_data_PassutDanner
+dfs = [critical_data_IUPAC, critical_data_Matthews, critical_data_CRC, critical_data_PassutDanner]
+inorganic_Tc_CASs = []
+for d in dfs:
+    index = d.index
+    for CAS in index:
+        c = None
+        try:
+            c = Chemical(CAS)
+        except:
+            continue
+        mol = c.rdkitmol_Hs
+        if mol is not None:
+            inorganic_Tc_CASs.append(CAS)
+
+inorganic_Tc_CASs = list(set(inorganic_Tc_CASs))
+len(inorganic_Tc_CASs)
+
+
+filters = {'organic': lambda c: is_organic(c.rdkitmol_Hs),
+'inorganic': lambda c: is_inorganic(c.rdkitmol_Hs),
+'element': lambda c:  is_inorganic(c.rdkitmol_Hs) and len(c.atoms) == 1,
+'binary': lambda c:  is_inorganic(c.rdkitmol_Hs) and len(c.atoms) == 2,
+'ternary': lambda c:  is_inorganic(c.rdkitmol_Hs) and len(c.atoms) == 3,
+'Cl': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'Cl' in c.atoms,
+'N': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'N' in c.atoms,
+'C': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'C' in c.atoms,
+'F': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'F' in c.atoms,
+'O': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'O' in c.atoms,
+'I': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'I' in c.atoms,
+'Br': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'Br' in c.atoms,
+'Si': lambda c: is_inorganic(c.rdkitmol_Hs) and len(c.atoms) >1 and 'Si' in c.atoms,
+
+}
+
+for method, lbd in filters.items():
+    working_Tcs, working_Tms, working_CASs, working_chemicals, working_MWs = [], [], [], [], []
+    for CAS in inorganic_Tc_CASs:
+        crit = Tc(CAS)
+        boil = Tm(CAS)
+        c = Chemical(CAS)
+        if crit is not None and boil is not None and lbd(c):
+            working_Tcs.append(crit)
+            working_Tms.append(boil)
+            working_CASs.append(CAS)
+            working_chemicals.append(c)
+            working_MWs.append(c.similarity_variable)
+
+    Tc_ratios = np.array(working_Tcs)/np.array(working_Tms)
+    print(method)
+    print(len(working_chemicals), round(np.mean(Tc_ratios),3), np.min(Tc_ratios), np.max(Tc_ratios))