Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning.

ABSTRACT: 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 thermophys

SUBMITTER: Alkhatib III 

PROVIDER: S-EPMC9165071 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature