Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples

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dc.contributor.authorSözen, Adnan
dc.contributor.authorArcaklıoğlu, Erol
dc.contributor.authorÖzalp, Mehmet
dc.date.accessioned2020-06-25T17:40:28Z
dc.date.available2020-06-25T17:40:28Z
dc.date.issued2005
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractThis paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANN's are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R-2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations. (c) 2004 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.applthermaleng.2004.11.003
dc.identifier.endpage1820en_US
dc.identifier.issn1359-4311
dc.identifier.issue11-12en_US
dc.identifier.scopus2-s2.0-17644406395
dc.identifier.scopusqualityQ1
dc.identifier.startpage1808en_US
dc.identifier.urihttps://doi.org/10.1016/j.applthermaleng.2004.11.003
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3450
dc.identifier.volume25en_US
dc.identifier.wosWOS:000229277400018
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.subjectartificial neural networken_US
dc.subjectthermodynamic propertiesen_US
dc.subjectozone safe refrigerantsen_US
dc.subjectmethanol/LiBren_US
dc.subjectmethanol/LiClen_US
dc.titleFormulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couplesen_US
dc.typeArticle

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