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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received August 23, 2010
Accepted November 25, 2010

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Predicting the vapor-liquid equilibrium of carbon dioxide+alkanol systems by using an artificial neural network

Bahman Zarenezhad^† Ali Aminian

School of Chemical, Petroleum and Gas Engineering, Semnan University, P. O. Box 35195-363, Semnan, Iran

Korean Journal of Chemical Engineering, May 2011, 28(5), 1286-1292(7), 10.1007/s11814-010-0492-0

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Abstract

A multi-layer feed-forward artificial neural network has been presented for accurate prediction of the vapor liquid equilibrium (VLE) of CO2+alkanol mixtures. Different types of alkanols namely, 1-propaol, 2-propanol, 1-butanol, 1-pentanol, 2-pentanol, 1-hexanol and 1-heptanol, are used in this study. The proposed network is trained using the Levenberg-Marquardt back propagation algorithm, and the tan-sigmoid activation function is applied to calculate the output values of the neurons of the hidden layers. According to the network's training, validation and testing results,_x000D_ a six layer neural network is selected as the best architecture. The presented model is very accurate over wide ranges of experimental pressure and temperatures. Comparison of the suggested neural network model with the most important thermodynamic correlations shows that the proposed neuromorphic model outperforms the other available alternatives. The predicted equilibrium pressure and vapor phase CO2 mole fraction are in good agreement with experimental data suggesting the accuracy of the proposed neural network model for process design.

Keywords

Artificial Neural Network Vapor-liquid Equilibrium Supercritical CO2 Alkanol

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