Prediction of Equilibrium Conditions for Hydrate Formation in Binary Gaseous Systems Using Artificial Neural Networks

Authors

  • M. R. Moradi,

    1. Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran (Iran)
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  • Prof. K. Nazari ,

    Corresponding author
    1. Chemical Science and Technology Division, Research Institute of Petroleum Industry, P.O. Box: 14665-137, Tehran (Iran)
    2. Chemical & Biological Engineering Department, University of British Columbia, V6T1Z3, Vancouver, BC (Canada), Fax: (+1) 6048226003
    • Chemical Science and Technology Division, Research Institute of Petroleum Industry, P.O. Box: 14665-137, Tehran (Iran)
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  • Dr. S. Alavi,

    1. Steacie Institute for Molecular Sciences, National Research Council of Canada, Ottawa, Ontario, K1A 0R6 (Canada)
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  • M. Mohaddesi

    1. Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran (Iran)
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Abstract

The present work attempts to indicate the potential of artificial neural networks (ANN) for the fast and reliable estimation of the equilibrium conditions of single, binary, and multiple hydrocarbon gas hydrates. The ANN used in this study was a network with the tangent-sigmoid (tansig) propagation transfer function in the hidden layer and a final layer with the linear (purelin) transfer function. The number of hidden neurons has been determined by minimizing the error of the calculation. The obtained results and the ANN model reliability were compared with other predictive methods. Results showed that the ANN method is able to reliably predict the hydrate equilibrium conditions of hydrocarbons, particularly for binary gas systems, using simple input parameters such as the weight fractions and normal boiling points of the components.

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