Surface tension prediction of pure organic components: An artificial neural network approach

Abstract

Surface tension is an important thermodynamic property of fluids that impact several processes in the engineering. Accurate estimations of this parameter are required to correctly apply and develop operations such as flow processes and oil recovery. The present study proposes the applying of artificial neural network (ANN)-based approach to accurately predict the surface tension for 75 groups of pure organic compounds, encompassing 1,666 different compounds and 21,444 data points. The performance of the model was evaluated using the mean squared error (MSE) and the coefficient of determination (R2), with K-Fold cross-validation ensuring robustness. The optimal model (ArcD) achieved an average MSE of 8.27x10−5 during cross-validation and 1.55x10−5 during training, with an R2 value of 0.9955. Although training time for this model was 8.89 times longer than the second-best model (ArcA), which had an R2 of 0.9928. Comparative analyses with existing literature models were also realized and revealed an absolute relative deviation (ARD) value of 2.49% to ArcD, 18.39% to Brock and Bird, 23.97% to Curl and Pitzer, and 18.62% to Zuo and Stenby equations, demonstrating the applicability and precision of the developed model. The results highlighted the reliability, accuracy and robustness of the developed ANN-based model, providing a significant advancement in the prediction of surface tension for a wide range of pure organic compounds.