Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1355
DC FieldValueLanguage
dc.contributor.authorSencan, Arzu-
dc.contributor.authorYakut, Kemal A.-
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2009-05-27T05:20:48Zen
dc.date.accessioned2013-05-17T05:22:55Z-
dc.date.accessioned2015-12-02T10:16:35Z-
dc.date.available2009-05-27T05:20:48Zen
dc.date.available2013-05-17T05:22:55Z-
dc.date.available2015-12-02T10:16:35Z-
dc.date.issued2006-01-
dc.identifier.citationRenewable Energy, 2006, vol. 31, no. 1, pp. 29-43en_US
dc.identifier.issn09601481-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1355-
dc.description.abstractThermodynamic analysis of absorption systems is a very complex process, mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. In order to simplify this complex process, Artificial Neural Networks (ANNs) are used. In this study, ANNs are used as a new approach for the determination of the thermodynamic properties of LiBr–water and LiCl–water solutions which have been the most widely used in the absorption heat pump systems. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. It was found that the coefficient of multiple determination (R2-value) between the actual and ANN predicted data is equal to about 0.999 for the enthalpy of both LiBr–water and LiCl–water solutions. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable limits. In addition, the coefficient of performance (COP) of absorption systems operating under different conditions with LiBr–water and LiCl–water solutions is calculated. The use of the derived equations, which can be employed with any programming language or spreadsheet program for the estimation of the enthalpy of the solutions, as described in this paper, may make the use of dedicated ANN software unnecessary.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRenewable Energyen_US
dc.rights© Elsevier 2005en_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectAbsorption heat pumpen_US
dc.subjectLithium bromide–wateren_US
dc.subjectLithium chloride–wateren_US
dc.subjectThermodynamic propertiesen_US
dc.titleThermodynamic analysis of absorption systems using artificial neural networken_US
dc.typeArticleen_US
dc.collaborationSüleyman Demirel Universityen_US
dc.collaborationHigher Technical Institute Cyprusen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsHybrid Open Accessen_US
dc.countryCyprusen_US
dc.countryTurkeyen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.renene.2005.03.011en_US
dc.dept.handle123456789/54en
dc.relation.issue1en_US
dc.relation.volume31en_US
cut.common.academicyear2020-2021en_US
dc.identifier.spage29en_US
dc.identifier.epage43en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
crisitem.journal.journalissn0960-1481-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
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