Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network

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dc.contributor.authorSözen, Adnan
dc.contributor.authorÖzalp, Mehmet
dc.contributor.authorArcaklioğlu, Erol
dc.date.accessioned2020-06-25T17:43:59Z
dc.date.available2020-06-25T17:43:59Z
dc.date.issued2007
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractThis study proposes a alternative approach based on artificial neural networks (ANNs) to determine the thermodynamic properties - specific volume, enthalpy and entropy - of an alternative refrigerant (R508b) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. In the ANN, the back-propagation learning algorithm with two different variants, namely scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM), and Logistic Sigmoid transfer function were used to determine the best approach. The most suitable algorithm and with appropriate number of neurons (i.e. 7) in the hidden layer is found to be the LM algorithm which has provided the minimum error. For wet vapor region, R-2 values - which are errors known as absolute fraction of variance - are 0.983495, 0.969027, 0.999984, 0.999963, 0.999981, and 0.999975, for specific volume, enthalpy and entropy for training and testing, respectively. Similarly, for superheated vapor, they are: 0.995346, 0.996947, 0.999996, 0.999997, 0.999974, and 0.999975, for training and testing, respectively. According to the regression analysis results, R-2 values are 0.9312, 0.9708, 0.9428, 0.9343, 0.967 and 0.9546 for specific volume, enthalpy and entropy for wet vapor region and superheated vapor, respectively. The comparisons of the results suggest that, ANN provided results comfortably within the acceptable range. This study, deals with the potential application of the ANNs to represent PVTx (pressure-specific volume-temperature-vapor quality) data. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.applthermaleng.2006.06.003
dc.identifier.endpage559en_US
dc.identifier.issn1359-4311
dc.identifier.issue2-3en_US
dc.identifier.scopus2-s2.0-33749680649
dc.identifier.scopusqualityQ1
dc.identifier.startpage551en_US
dc.identifier.urihttps://doi.org10.1016/j.applthermaleng.2006.06.003
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3972
dc.identifier.volume27en_US
dc.identifier.wosWOS:000241706500031
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofApplied Thermal Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectR508ben_US
dc.subjectthermodynamic propertiesen_US
dc.subjectozone safe refrigeranten_US
dc.subjectartificial neural networksen_US
dc.titleCalculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural networken_US
dc.typeArticle

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