Thermodynamic analyses of refrigerant mixtures using artificial neural networks

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dc.contributor.authorArcaklioglu, E
dc.contributor.authorCavusoglu, A
dc.contributor.authorErisen, A
dc.date.accessioned2020-06-25T17:40:07Z
dc.date.available2020-06-25T17:40:07Z
dc.date.issued2004
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractThe aim of this study is to make a contribution towards the efforts of reducing the use of CFCs by finding a drop-in replacement for pure refrigerants used in domestic and industrial appliances. The suggested solution is the use of HFC and HC based refrigerant mixtures. In this study, we investigate different possible ratios of these mixtures and their corresponding performances by using Artificial Neural-Networks (ANNs). We believe this dramatically reduces the times and efforts required to achieve these targets. Coefficients of Performances (COPs) and Total Irreversibilities (TIs) of refrigerants and their mixtures have been calculated for a vapor-compression refrigeration system with a liquid/suction line heat-exchanger. The constant cooling-load method is taken as a reference. The thermodynamic properties of refrigerants have been taken from REFPROP 6.01. To train the network, based on Scaled Conjugate Gradient (SCG), Pola-Ribiere Conjugate Gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function, we have used various ratios of 7 refrigerant mixtures of HFCs and HCs along with three CFCs (R12, R22, and R502). They were used as inputs while the COP and TI values, calculated,as above, were the outputs. The network has yielded R-2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively. (C) 2003 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.apenergy.2003.08.001
dc.identifier.endpage230en_US
dc.identifier.issn0306-2619
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-1842815958
dc.identifier.scopusqualityQ1
dc.identifier.startpage219en_US
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2003.08.001
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3282
dc.identifier.volume78en_US
dc.identifier.wosWOS:000220677400007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural-networksen_US
dc.subjectrefrigerant mixtureen_US
dc.subjectcoefficient of performanceen_US
dc.subjectirreversibilityen_US
dc.subjectREFPROPen_US
dc.titleThermodynamic analyses of refrigerant mixtures using artificial neural networksen_US
dc.typeArticle

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