Persian Gulf University Journal of Oil, Gas and Petrochemical Technology 2383-2770 2 Number 1 2015 03 01 Surface Tension Prediction of Hydrocarbon Mixtures Using Artificial Neural Network 14 26 9713 10.22034/jogpt.2015.9713 EN Gholamreza Bakeri Advanced Membrane and Biotechnology Research Center, Babol Noshirvani University of Technology, Babol, Iran Maedeh Delavar Advanced Membrane and Biotechnology Research Center, Babol Noshirvani University of Technology, Babol, Iran Mohammad Soleimani Lashkenari Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran Journal Article 2014 01 27 In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The artificial neural network was more accurate as the result showed that while standard deviation of ARD for artificial neural network was 3.63001, the standard deviation of ARD for Brock and Bird, Pitzer, Zuo-Stenby and Sastri-Rao models were 23.77569, 18.44848, 13.00388 and 9.63137 respectively. https://jogpt.pgu.ac.ir/article_9713_d5776aeecb3c45ab15adce6f5cb355f3.pdf