A computational intelligence scheme for prediction of interfacial tension between pure hydrocarbons and water

Abstract

Interfacial tension plays a major role in many disciplines of science and engineering. Complex nature of this property has restricted most of the previous theoretical studies on thermophysical properties to bulk properties measured far from the interface. Considering the drawbacks and deficiencies of preexisting models, there is yet a huge interest in accurate determination of this property using a rather simple and more comprehensive modeling approach. In recent years, inductive machine learning algorithms have widely been applied in solving a variety of engineering problems. This study introduces least-square support vector machines (LS-SVM) approach as a viable and powerful tool for predicting the interfacial tension between pure hydrocarbon and water. Comparing the model to experimental data, an excellent agreement was observed yielding the overall squared correlation coefficient (R2) of 0.993. Proposed model was also found to outperform when compared to some previously presented multiple regression models. An outlier detection method was also introduced to determine the model applicability domain and diagnose the outliers in the gathered dataset. Results of this study indicate that the model can be applied in systems over temperature ranges of 454.40–890°R and pressure ranges of 0.1–300MPa.