Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network

Authors:
Aboozar Khajeh;Hamid Modarress
Affiliations:
Islamic Azad University, Birjand Branch, Birjand, Southern Khorasan, Iran;Amirkabir University of Technology, Department of Chemical Engineering, Hafez Avenue, 15914 Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) and Radial Basis Function Neural Network (RBF NN) have been developed for prediction of solubility of various gases in polystyrene. Solubility of butane, isobutene, carbon dioxide, 1,1,1,2-tetrafluoroethane (HFC-134a), 1-chloro-1,1-difluoroethane (HCFC-142b), 1,1-difluoroethane (HFC-l52a) and nitrogen in polystyrene is modeled by ANFIS and RBF NN in a wide range of pressure and temperature with high accuracy. The results obtained in this work indicate that ANFIS and RBF NN are effective methods for prediction of solubility of gases in polystyrene and have better accuracy and simplicity compared with the classical methods.