A neural network (CSA-LSSVM) model for the estimation of surface tension of branched alkanes

Abstract

ABSTRACTThe current study highlights the application of a model based on least square support vector machine (LSSVM) for prediction of surface tension of branched alkanes. An optimization algorithm, namely, coupled simulated annealing (CSA) was applied to the model. Surface tensions of alkanes show a specific interaction between adjacent molecules of the branched alkanes which affects the anisotropic dispersion force component of the surface energy. In this paper, surface tension of branched alkanes was studied in temperature range of 283.15 and 448.15 K. To evaluate the performance and accuracy of this model, statistical and graphical error analyses have been used simultaneously. By applying CSA-LSSVM on 600 data points and finding optimum parameters, the estimated values of surface tension of branched alkanes were compared with experimental data which showed a reasonable agreement with the experimental results. Results demonstrate that the model is precise and viable for prediction of solubility data. The model shows an overall R2 and AARD% estimations of 0.9921 and 0.89%, respectively.