Table of Contents
ISRN Chemical Engineering
Volume 2013 (2013), Article ID 930484, 11 pages
http://dx.doi.org/10.1155/2013/930484
Research Article

Prediction the Vapor-Liquid Equilibria of CO2-Containing Binary Refrigerant Mixtures Using Artificial Neural Networks

1Chemical Engineering Department, Gas and Petrochemical Engineering Faculty, Persian Gulf University, Bushehr 7516913817, Iran
2Department of Petrochemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, P.O. Box 415, Mahshahr, Iran

Received 9 May 2013; Accepted 18 July 2013

Academic Editors: J. A. A. González, K. Okumura, and J. E. Ten Elshof

Copyright © 2013 Ahmad Azari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial neural network (ANN) technique has been applied for estimation of vapor-liquid equilibria (VLE) for eight binary refrigerant systems. The refrigerants include difluoromethane (R32), propane (R290), 1,1-difluoroethane (R152a), hexafluoroethane (R116), decafluorobutane (R610), 2,2-dichloro-1,1,1-trifluoroethane (R123), 1-chloro-1,2,2,2-tetrafluoroethane (R124), and 1,1,1,2-tetrafluoroethane (R134a). The related experimental data of open literature have been used to construct the model. Furthermore, some new experimental data (not applied in ANN training) have been used to examine the reliability of the model. The results confirm that there is a reasonable conformity between the predicted values and the experimental data. Additionally, the ability of the ANN model is examined by comparison with the conventional thermodynamic models. Moreover, the presented model is capable of predicting the azeotropic condition.