Investigating hermetic reciprocating compressor performance by using various machine learning methods

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dc.authorid0000-0003-3157-1992en_US
dc.authorid0000-0001-9297-8134en_US
dc.contributor.authorBacak, Aykut
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorDalkılıç, Ahmet Selim
dc.date.accessioned2024-03-26T13:06:38Z
dc.date.available2024-03-26T13:06:38Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractDue to their durability and efficiency, hermetic reciprocating compressors (HRCs) are used in refrigeration and air conditioning. Compressor performance and reliability concerns reduce system efficiency and raise maintenance costs. Machine learning (ML) is being used to improve hermetic reciprocating compressor performance, reliability, and energy economy. ML is used in hermetic reciprocating compressors for issue identification, performance improvement, predictive maintenance, and energy management. This research compared HRC performance factors such as mass flow rate, cooling capacity, compression power, coefficient of performance, exhaust line losses, and volumetric efficiency. Simple regression, probabilistic neural network, gradient boosted, polynomial regression, and random forest (RF) were used to examine and evaluate these parameters as outputs. Over three cycles, the Fluid-Structure Interaction (FSI) approach assessed compressor performance parameters. For compressor speeds of 1300, 2100, and 3000 rpm, mass flow rate, compression power, cooling efficiency coefficient, and exhaust line energy losses varied by 10%, 4%, 5%, and 6%. To gather ML algorithm inputs, the research used experimental, fluid-structure interaction, and ML methodologies. Experimental and FSI approaches produced 108 data points. These data points were randomly assigned, with 70% for learning and 30% for prediction. The mean convergence criterion for mass flow rate, cooling capacity, compression power, cooling efficiency coefficient, exhaust line energy losses, and volumetric efficiency parameters was 0.9966, 0.9969, 0.9572, 0.0561, 0.9925, and 0.4640 for all ML methods. Simple regression, probabilistic neural networks, gradient boosted, polynomial regression, and RF convergence criteria were 0.8978, 0.9999, 0.6016, 0.4439, and 0.7761.en_US
dc.identifier.doi10.1177/09544062231213276en_US
dc.identifier.scopus2-s2.0-85181225228en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7201
dc.identifier.urihttps://doi.org/10.1177/09544062231213276
dc.identifier.wosWOS:001133814100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSAGE Publications Ltden_US
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectHermetic reciprocating compressor; machine learning; simple regression; PNN; gradient boosted; polynomial regression; random foresten_US
dc.titleInvestigating hermetic reciprocating compressor performance by using various machine learning methodsen_US
dc.typeArticleen_US

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