Experimental comparison of R290 and R600a and prediction of performance with machine learning algorithms

Basit Öğe Kaydı
dc.authorid0000-0002-8356-181Xtr
dc.authorid0000-0003-3951-6138tr
dc.contributor.authorPektezel, Oğuzhan
dc.contributor.authorAcar, Halil İbrahim
dc.date.accessioned2024-03-08T05:58:52Z
dc.date.available2024-03-08T05:58:52Z
dc.date.issued2023tr
dc.departmentMühendislik Fakültesitr
dc.description.abstractThe use of alternative refrigerants is among the popular topics of the refrigeration industry. In the first part of this study, thermodynamic performances of R290 and R600a gases were compared in a vapor compression refrigeration experiment setup. Although R600a caused an average of 33.44% less compressor power consumption compared to R290 refrigerant, R290 provided an average of 23.77% increase in COP (coefficient of performance), 82.55% in cooling capacity, and 20.99% increase in second law efficiency compared to R600a. In the second part of the study, the performance parameters of the refrigeration system were predicted with MLP (multi-layer perceptron), SVM (support vector machine), and DT (decision tree) machine learning algorithms. It was detected that the SVM method predicted all parameters with the least error. MAE (mean absolute error) values detected in the COP prediction with test set were 0.0317, 0.0324, and 0.0989 for SVM, MLP, and DT, respectively. Results revealed that performance of the refrigeration system increased when utilizing R290, and SVM was superior in prediction of performance indicators compared to other machine learning methods.tr
dc.description.sponsorshipTokat Gaziosmanpaşa Üniversitesi Bilimsel Araştırma Projeleri Ofisitr
dc.identifier.citationPektezel, O., Acar, H. İ. (2023). Experimental Comparison of R290 and R600a and Prediction of Performance with Machine Learning Algorithms. Science and Technology for the Built Environment, 29(5), 508–522.tr
dc.identifier.doi10.1080/23744731.2023.2197815en_US
dc.identifier.endpage522tr
dc.identifier.issue5tr
dc.identifier.scopus2-s2.0-85153527929en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage508tr
dc.identifier.urihttps://hdl.handle.net/20.500.12418/15001
dc.identifier.volume29tr
dc.identifier.wosWOS:000975334700001en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofScience and Technology for the Built Environmenten_US
dc.relation.publicationcategoryUluslararası Hakemli Dergide Makale - Kurum Öğretim Elemanıtr
dc.rightsinfo:eu-repo/semantics/closedAccesstr
dc.titleExperimental comparison of R290 and R600a and prediction of performance with machine learning algorithmsen_US
dc.typeArticleen_US

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