Application of gene expression programming for predicting density of binary and ternary mixtures of ionic liquids and molecular solvents

Abstract

Ionic Liquids (ILs) have received increased attention across a number of disciplines in recent years. This noticeable importance of ILs is attributed to their attractive proprieties. Precise evaluation of the thermophysical properties of ionic liquids and their mixtures with molecular solvents is essential for distinct multidisciplinary applications. In this study, a rigorous white-box intelligent technique, viz. gene expression programming (GEP) was implemented for establishing new correlations for accurate prediction of density of binary and ternary mixtures of ILs and molecular solvents. The newly suggested correlations were developed using a comprehensive experimental database with 1985 real measurements under a variety of operational conditions. The obtained results revealed that the newly established GEP-based correlations can predict the density of binary and ternary mixtures of ILs and molecular solvents with a high degree of integrity. The GEP-based correlations exhibited overall average absolute relative error (AARE) values of 0.5621% and 0.2128% for binary and ternary cases, respectively. Besides, it was found that our proposed explicit correlations followed the expected tendency with respect to the considered variables. Furthermore, the superiority and the reliability of the GEP-based correlations was testified against the best-existing approaches in the literature. Finally, the leverage approach was performed and the statistical validity of the correlations and the experimental data was testified.