Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids

Abstract

Ionic liquids (ILs) are widely under research and being more and more used in many industrial fields due to their unique properties. The knowledge of physicochemical properties of ILs is vital for their applications. Viscosity is one of the most important physical properties of ILs. Since the experimental measurement of viscosity is expensive, time consuming and cumbersome, seeking for a reliable and accurate model for predicting the viscosity of IL mixtures is essential. To this end, a large data bank, covering a wide range of ternary mixtures containing ILs, was collected from open literature sources. Afterwards, a rigorous modeling approach, namely least square support vector machine (LSSVM) was employed to predict the viscosity of ternary mixtures. The parameters of the model were optimized using coupled simulated annealing (CSA) optimization tool. The inputs of the model are boiling point temperature, molecular weight and composition of mixture compounds. Statistical and graphical error analyses indicated that the developed model can satisfactorily predict ternary mixture viscosity. Moreover, it was demonstrated that the proposed model is able to predict the actual physical trend of viscosity with variation of molecular weight and boiling point of ILs, ethanol content and water content. Furthermore, the relevancy factor demonstrated that the average boiling point of the ternary mixture has the greatest impact on the viscosity. Finally, the Leverage approach was performed, in which the statistical Hat matrix, Williams Plot, and the residuals of the model results led to recognition of the probable outliers. All the experimental data seem to be reliable except five data points. Thus, the developed model could be reliable for the prediction of ternary mixture viscosity in its applicability domain.