TY - JOUR ID - 6621 TI - The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks JO - Iranian Journal of Oil and Gas Science and Technology JA - IJOGST LA - en SN - 2345-2412 AU - Khazaei, Ali AU - Parhizgar, Hossein AU - Dehghani, Mohammad Reza AD - Thermodynamics Research Laboratory, School of Chemical Engineering, Iran University of Science & Technology, Tehran, Iran AD - Young Researchers and Elites Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran Y1 - 2014 PY - 2014 VL - 3 IS - 3 SP - 47 EP - 61 KW - Surface tension KW - Mixtures KW - Artificial Neural Network DO - 10.22050/ijogst.2014.6621 N2 - In this work, artificial neural network (ANN) has been 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 according to conventional corresponding-state models. 80% of the data points were employed for training ANN and the remaining data were utilized for testing the generated model. The average absolute relative deviations (AARD%) of the model for the training set, the testing set, and the total data points were obtained 1.69, 1.86, and 1.72 respectively. Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory has proved the high prediction capability of the attained model. UR - https://ijogst.put.ac.ir/article_6621.html L1 - https://ijogst.put.ac.ir/article_6621_ff843a2170482610bf4fe7e4d3951240.pdf ER -