Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using s-profile descriptors

Abstract

Ionic liquids (IL) are a new class of liquids considered as green solvents; less toxic, less flammable, and less polluting which retain their liquid state over wide temperature ranges and are considered alternatives to volatile organic solvents. The surface tension of IL-organic solvent mixtures plays an important role in the design and development of many industrial processes. This work investigated the capability and feasibility of four ANN model topologies (“trainbr, logsig”; “trainbr, tansig”; “trainlm, logsig”; “trainlm, tansig”), a PSO-SVM model, and an LSSVM model to predict the surface tension of binary systems containing IL. For this purpose, 1623 data points corresponding to the experimental surface tension values of binary mixtures containing IL were collected from the literature. The surface tension values were between 18.9 and 72.7 mN m 1 . The temperature, the composition in mole fraction of IL (XIL), descriptors based on the sigma profiles, relating to the H-bond donor and to the H-bond acceptor character, the anion, the cation and the solvent were used as input variables of the model in order to differentiate the different compounds involved in the binary systems. A comparison of the experimental and the predicted values in terms of several statistical metrics showed good agreement, however, the prediction (trainbr, logsig) was better than the other approaches with an overall average absolute relative deviation of .8466% and a mean square error of .4952. These results are very encouraging for future projects modeling other physical and chemical properties of ILs