The surface tension of pure ionic liquids (ILs) and their mixtures with other compounds play a key role in the
design and development of many industrial processes. Therefore, its modeling is extremely important from an
industrial point of view. This study examined the capability and feasibility of three intelligence algorithms
for predicting the surface tension of binary systems containing ILs. To construct and test the models, 748 data
points corresponding to the experimental surface tension values of binary mixtures containing ILswere extracted
fromthe literature. The surface tensionwas between 0.0157 and 0.07185 N·m−1. The absolute temperature (T),
mole fraction and molecular weight of the IL components (xIL and MwIL) and the density of the IL components
(ρIL) together with the boiling point (Tbnon-IL) and molecular weight (Mwnon-IL) of the non-IL component were
considered as model input variables to differentiate between the various compounds involved in binary systems.
A comparison of the experimental data and predicted values using all three methods (in terms of statistical
parameters) showed good agreement; however, the CSA-LSSVM prediction was better than the other two
approaches.
کلید واژگان :
Ionic liquids Surface tension Binary mixtures Prediction Support Vector Machine
