The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

Abstract

In this work, artificial neural network (ANN) has b een employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocarbon components. The ANN model has been developed as a function of temperature, critical properties, and acentric factor of the mixture acco rding to conventional corresponding-state models. 80% of the data points were employed for training A NN and the remaining data were utilized for testing the generated model. The average absolute r elative deviations (AARD%) of the model for the training set, the testing set, and the total data p oints were obtained 1.69, 1.86, and 1.72 respective ly. Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory has proved the high prediction capability of the attain ed model.