Modeling Interfacial Tension of n-Alkane/Water-Salt System Using Artificial Neural Networks

Abstract

In this paper, a feed forward neural network is built and trained using experimental data reported in the literature to model interfacial tension of n-alkane/water-salt systems. Temperature, pressure, molecular weight of n-alkane, and ionic strength of electrolyte solution are used as input to the neural network. The model succeeded to predict interfacial tension of liquid n-alkane/water system with or without the presence of electrolyte and yielded average absolute deviation of 0.58% over all data points. The performance of the model is analyzed and compared against the performance of the other alternative models. It was found out that the proposed model outperforms the other alternatives.