Python-Based Algorithm for Estimating the Parameters of Physical Property Models for Substances Not Available in Database.

An in-house Python-based algorithm was developed using simplified molecular-input line-entry specification (SMILES) strings and a dipole moment for estimating the normal boiling point, critical properties, standard enthalpy, vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, viscosity, thermal conductivity, and surface tension of molecules. Normal boiling point, critical properties, and standard enthalpy were estimated by using the Joback group contribution method. Vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, and surface tension were estimated by using the Riedel model, Gunn-Yamada model, Clausius-Clapeyron equation, Joback group contribution method, and Brock-Bird model, respectively. Viscosities of liquid and gas were estimated by using the Letsou-Stiel model and the Chapman-Enskog-Brokaw model, respectively. Thermal conductivities of liquid and gas were estimated by using the Sato-Riedel model and Stiel-Thodos model, respectively. Dipole moment was calculated through molecular dynamics simulation using the MMFF94 force field, performed with Avogadro software. A case study was conducted with dihydro-2-methyl-3-furanone (DHMF), 2-furaldehyde diethyl acetal (FDA), 1,1-diethoxy-3-methyl butane (DEMB), glutathione (GSH), vitamin B5 (VITB5), homocysteine (HCYS), and O-acetyl-l-homoserine (AH), which are not present in the existing property database. Cross-validation indicated that the developed Python-based algorithm provided pure component model parameters nearly identical with those obtained with the Aspen Property Constant Estimation System (PCES) method, except for the enthalpy of vaporization. The parameters for estimating the enthalpy of vaporization using the current Python-based algorithm accurately represented the behavior of the actual substances, as determined using the Clausius-Claperyon equation. This Python-based algorithm provides a detailed and clear reference for estimating pure property parameters.