Surface Tension Estimation of Binary Mixtures of Organic Compounds Using Artificial Neural Networks

Abstract

The surface tension of binary mixtures at different temperatures and compositions is required in much scientific and technological research. Therefore, having an exact correlation between surface tension and easily accessible physical properties is essential. In this work, the sensitivity of the surface tension to some physical properties was studied by using artificial neural networks (ANNs) to find the most effective ones. Furthermore, ANNs were used to estimate the surface tension of binary systems as a function of the most effective physical properties including critical pressure, reduced temperature, acentric factor, and molar density. The experimental data, collected for training and verifying the networks, include various materials such as alkanes, alkenes, aromatics, alcohols, organic acids, as well as chlorine, iodine, sulfur, nitrogen, and fluorine-containing compounds in the composition ranges from 0 to 100 mole percent, and temperatures between 116 K and 393 K. The average absolute relative deviation (AARD) of the most accurate network, obtained for all 2038 data points regarding 83 binary mixtures, is 1.75%.