Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks

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dc.contributor.authorSözen, A.
dc.contributor.authorArcaklioglu, E.
dc.contributor.authorÖzalp, M.
dc.date.accessioned2020-06-25T17:40:05Z
dc.date.available2020-06-25T17:40:05Z
dc.date.issued2004
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207;
dc.description.abstractThermodynamic analysis of absorption thermal systems is too complex because the analytic functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (1) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption beat pump (EAHP). The ejector has two functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic functions and the values predicted by the ANN for the properties of the couple. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R-2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COPr), exergetic coefficient of performance of the system for cooling (ECOPr) and circulation ratio (F) for all working temperatures were found to be less than 1.7%, 5.1%, and 1.9%, respectively. Deviations for COPr, ECOPr and F at a generator temperature of similar to90 degreesC (cut off temperature) at which the coefficient of performance of the system is maximum are 0.9%, 1.8%, and 0.1%, respectively, for other working temperatures. When this system was used for heating, similar deviations were obtained. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties. The results showed that the use of ANNs for determination of thermodynamic properties is acceptable in design of the EAHP. (C) 2003 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.enconman.2003.11.002
dc.identifier.endpage2253en_US
dc.identifier.issn0196-8904
dc.identifier.issue13-14en_US
dc.identifier.scopus2-s2.0-17144452705
dc.identifier.scopusqualityQ1
dc.identifier.startpage2233en_US
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2003.11.002
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3255
dc.identifier.volume45en_US
dc.identifier.wosWOS:000221590600020
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergy Conversion And Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural networken_US
dc.subjectabsorptionen_US
dc.subjectthermodynamic propertiesen_US
dc.subjectCOPen_US
dc.subjectozone safeen_US
dc.titlePerformance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networksen_US
dc.typeArticle

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