Performance comparison of CFCs with their substitutes using artificial neural network

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dc.contributor.authorArcaklioglu, E
dc.date.accessioned2020-06-25T17:40:03Z
dc.date.available2020-06-25T17:40:03Z
dc.date.issued2004
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractIn order to decrease global pollution due to chlorofluorocarbons (CFCs), the usage of HFC- and HC-based refrigerants and their mixtures are considered instead of CFCs (R12, R22, and R502). This was confirmed by an international consensus (i.e. Montreal Protocol signed in 1987). This paper offers to determine coefficient of performance (COP) and total irreversibility (TI) values of vapour-compression refrigeration system with different refrigerants and their mixtures mentioned above using artificial neural networks (ANN). In order to train the network, COPs and TIs of refrigerants and their some binary, ternary and quartet mixtures of different ratios have been calculated in a vapour-compression refrigeration system with liquid/suction line heat exchanger. In the calculations thermodynamic properties of refrigerants have been taken from REFPROP 6.01 which was prepared based on Helmholtz energy equation of state. To achieve this, a new software has been written in FORTRAN programming language using sub-programs of REFPROP, and all related calculations have been performed using this software using constant temperature method as reference. Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt learning algorithms and logistic sigmoid transfer function were used in the network. Mixing ratios of refrigerants, and evaporator temperature were used as input layer; COP and TI values were used as output layer. It is shown that R-2 values are about 0.9999, maximum errors for training and test data are smaller than 2 and 3%, respectively. It is concluded that, ANNs can be used for prediction of COP and TI as an accurate method in the systems. Copyright (C) 2004 John Wiley Sons, Ltd.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1002/er.1020
dc.identifier.endpage1125en_US
dc.identifier.issn0363-907X
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-4544286829
dc.identifier.scopusqualityQ1
dc.identifier.startpage1113en_US
dc.identifier.urihttps://doi.org/10.1002/er.1020
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3226
dc.identifier.volume28en_US
dc.identifier.wosWOS:000224064200008
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherJohn Wiley & Sons Ltden_US
dc.relation.ispartofInternational Journal Of Energy Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural networksen_US
dc.subjectrefrigerant mixturesen_US
dc.subjectcoefficient of performanceen_US
dc.subjectirreversibilityen_US
dc.subjectREFPROPen_US
dc.titlePerformance comparison of CFCs with their substitutes using artificial neural networken_US
dc.typeArticle

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