Iranian Journal of Oil and Gas Science and Technology
Quarterly Publication

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

Authors

Ali Khazaei 1
Hossein Parhizgar 2
Mohammad Reza Dehghani 1

1 Thermodynamics Research Laboratory, School of Chemical Engineering, Iran University of Science & Technology, Tehran, Iran

2 Young Researchers and Elites Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

https://doi.org/10.22050/ijogst.2014.6621
Abstract
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.

Keywords

Surface tension
Mixtures
Artificial Neural Network

  • Receive Date 04 May 2014
  • Revise Date 05 June 2014
  • Accept Date 10 June 2014

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