Experimental exergy analysis of low-GWP R290 refrigerant and derivation of exergetic performance equations with regression algorithms

dc.contributor.authorPektezel, Oguzhan
dc.contributor.authorDas, Mehmet
dc.contributor.authorAcar, Halil Ibrahim
dc.date.accessioned2026-08-12T17:07:18Z
dc.date.issued2023
dc.departmentFırat Üniversitesi
dc.description.abstractThis study analyses the derivation of performance equations for a refrigeration system operating with R290 and R404A refrigerants using different regression models. Results of pace regression for coefficient of performance (COP), second law efficiency, and total exergy destruction showed mean absolute error (MAE) are 0.0993, 0.0159, and 0.0066, respectively. In all cases, the pace regression model made better predictions than elastic net regression. It was concluded that predictions made with regression models showed a good agreement with the actual experimental results. The derived equations can be utilised for refrigerants working in similar operational ranges.
dc.identifier.doi10.1504/IJEX.2023.130371
dc.identifier.endpage482
dc.identifier.issn1742-8297
dc.identifier.issn1742-8300
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85162052908
dc.identifier.scopusqualityQ3
dc.identifier.startpage467
dc.identifier.urihttps://doi.org/10.1504/IJEX.2023.130371
dc.identifier.urihttps://hdl.handle.net/11508/49600
dc.identifier.volume40
dc.identifier.wosWOS:000975574000006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInderscience Enterprises Ltd
dc.relation.ispartofInternational Journal of Exergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260511
dc.subjectexergy analysis
dc.subjectmachine learning
dc.subjectequation derivation
dc.subjectGWP
dc.subjectR290
dc.subjectR404A
dc.titleExperimental exergy analysis of low-GWP R290 refrigerant and derivation of exergetic performance equations with regression algorithms
dc.typeArticle

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