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Received April 20, 2011
Accepted September 8, 2011
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PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system
1CFD Research Center, Department of Chemical Engineering, Razi University, Kermanshah, Iran 2Faculty of Engineering, Department of Chemical Engineering, University of Kurdistan, Sanandaj, Iran 3Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran 4Faculty of Mechanical Engineering, College of Engineering and Petroleum, Kuwait University, Kuwait
Korean Journal of Chemical Engineering, May 2012, 29(5), 657-667(11), 10.1007/s11814-011-0235-x
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Abstract
Two different modeling methods have been proposed to relate the Peng-Robinson-Stryjek-Vera (PRSV) parameter, κ1, to some common thermodynamic constants, including critical temperature (Tc), critical pressure (Pc), acentric factor (ω) and molecular weight (Mw). The methods are artificial neural network (ANN) and adaptive networkbased fuzzy inference System (ANFIS). A set of 159 data points (116, 23 and 20) was used for construct training, validating and testing, respectively. The radius parameter of ANFIS was determined through genetic algorithm (GA) optimization technique. The ANN and especially ANFIS results are in a good agreement with most of the compound groups.
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References
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Takagi T, Sugeno M, IEEE Trans. Syst. Man Cybernet., 15, 116 (1985)
Sugeno M, Kang GT, Fuzzy Sets Syst., 28, 15 (1998)
Kulkarni AD, Computer vision and fuzzy neural systems, Prentice-Hall, Englewood Cliffs, NJ (2001)
Entchev E, Yang LB, J. Power Sources, 170(1), 122 (2007)
Perendeci A, Arslan S, Tanyola A, Celebi SS, Bioresour. Technol., 100, 4579 (2009)
Chen X, Wang N, Chem. Eng. J., 150(2-3), 527 (2009)
Buyukbingol E, Sisman A, Akyildiz M, Alparslan FN, Adejare A, Bioorg. Med. Chem., 15, 4265 (2007)
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