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Estimation of vapour–liquid equilibrium data for binary refrigerant systems containing 1,1,1,2,3,3,3‐heptafluoropropane (R227ea) by using artificial neural networks
Author(s) -
Nikkholgh M. R.,
Moghadassi A. R.,
Parvizian F.,
Hosseini S.M.
Publication year - 2010
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20272
Subject(s) - refrigerant , perceptron , artificial neural network , binary number , thermodynamics , vapor–liquid equilibrium , multilayer perceptron , range (aeronautics) , chemistry , computer science , materials science , mathematics , artificial intelligence , physics , arithmetic , gas compressor , composite material
In this research, the ability of multilayer perceptron neural networks to estimate vapour–liquid equilibrium data have been studied. Four typical binary refrigerant systems containing R227ea have been investigated in a large range of temperatures and pressures. The systems are categorised into four groups, based on their different deviations from the Raoult's law. The networks with one hidden layer consisted of five neurons are developed as the optimal structure. For these binary systems, uncertainties in the artificial neural networks (ANNs) estimations were not more than 1.03%. In addition, the abilities of ANNs are shown by comparisons with Margules, van Laar, and some other correlations.