Efficiency development of surface tension for different ionic liquids through novel model of Machine learning Technique: Application of in-thermal engineering.

• High-performance hybrid thermodynamic model for simulation of molecular separation. • Understanding role of solvent on separation of solutions containing ionic liquid. • Investigation of thermodynamic analysis at the interface using the developed model. Nowadays, industrial-based application of ionic liquids (ILs) has gained numerous attentions due to their brilliant advantages like very low volatility and excellent chemical stability. Various efforts have been recently made to theoretically/experimentally obtain their physicochemical properties and structure–property relationships. The purpose of the current paper is to calculate the surface tension of two prevalently employed ILs including 1-octyl-3-methylimidazolium bis[(trifluoromethyl) sulfonyl] imide and 1-butyl-2,3-dimethylimidazolium bis[(trifluoromethyl)sulfonyl] imide by developing machine learning-based predictive models. To do this, we are dealing with a dataset with many input features. As a result, feature selection plays an important role in our data modeling. Three distinct optimization algorithms were selected to be used for feature selection with the KNN algorithm, including GA, PSO, and FOA optimization. GENETIC-KNN, FOA-KNN, and PSO-KNN had R2 scores of 0.962, 0.941, and 0.827 respectively. With MAPE metric their error rates are 1.67E-02, 2.13E-02, and 4.26E-02 in same order. Finally GENETIC-KNN is selected as the best and novel model in this study. [ABSTRACT FROM AUTHOR]