Open AccessSearching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning
Author(s) -
Ismail I.I. Alkhatib,
Carlos G. Albà,
Ahmad S. Darwish,
Fèlix Llovell,
Lourdes F. Vega
Publication year - 2022
Publication title -
industrial and engineering chemistry research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.878
H-Index - 221
eISSN - 1520-5045
pISSN - 0888-5885
DOI - 10.1021/acs.iecr.2c00719
Subject(s) - refrigerant , bridging (networking) , polar , thermodynamics , enthalpy of vaporization , acentric factor , equation of state , soft computing , cosmo rs , vapor pressure , computer science , enthalpy , machine learning , chemistry , ionic liquid , artificial neural network , computer network , physics , astronomy , gas compressor , biochemistry , catalysis
We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3-10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.