Iranian Journal of Oil and Gas Science and Technology
Quarterly Publication

Prediction of Kinematic Viscosity of Petroleum Fractions Using Artificial Neural Networks

Document Type : Research Paper

Authors

Bizhan Khosronezhad Gheshlaghi 1
Mohammad Reza Dehghani 2
Hossein Parhizgar 3

1 Department of Mathematics, Mahshahr Branch, Islamic Azad University, Mahshahr,

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

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

https://doi.org/10.22050/ijogst.2014.6036
Abstract
In this work, artificial neural network (ANN) was utilized to develop a new model for the prediction of the kinematic viscosity of petroleum fractions. This model was generated as a function of temperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to develop the new model, different architectures of feed-forward type were examined. Finally, the optimum structure with three hidden layers was selected. The optimum structure had five, four, and two neurons in the first, second, and third layers respectively. To prevent over-fitting problem, 70% of the experimental data were used to train and validate the new model and the remaining data which did not participate in learning process was utilized to test the ability of the new model for the prediction of the kinematic viscosity of petroleum fractions. The results showed that the predicted/calculated and experimental data are in good agreement. The average absolute relative deviation (AARD) of the new model was 1.3%. Finally, the results were compared with an Eyring-based model (Soltani et al.’s work); it was shown that, based on the reported results by the authors, the accuracy of both model were in the same order.

Keywords

ANN
Kinematic viscosity
Petroleum Fraction

  • Receive Date 04 April 2014
  • Revise Date 09 May 2014
  • Accept Date 12 June 2014

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