New multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions

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dc.authoridGuzelel, Yunus Emre/0000-0002-7122-0241
dc.authoridCERCI, KAMIL NEYFEL/0000-0002-3126-707X
dc.authoridOLMUS, UMUTCAN/0000-0002-5799-1840
dc.contributor.authorGuzelel, Yunus Emre
dc.contributor.authorOlmus, Umutcan
dc.contributor.authorcerci, Kamil Neyfel
dc.contributor.authorBuyukalaca, Orhan
dc.date.accessioned2025-03-17T12:27:13Z
dc.date.available2025-03-17T12:27:13Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.description.abstractIn this study, five Multiple Linear Regression, three Multilayer Perceptron Regressor, seven Decision Tree and four Support Vector Machine models were constructed to predict outlet temperature and humidity ratio of silica gel desiccant wheels using eight input parameters for unbalanced flow condition. The effect of different kernel functions of Support Vector Machine algorithms, on the modeling of desiccant wheel was investigated for the first time in the open literature. Detailed validation of the developed models showed that the Response Surface model outperformed other Multiple Linear Regression models, and the Support Vector Machine model with Pearson VII Universal kernel was the best among all models. The determination coefficient and root mean square error for temperature were found to be 0.9791 and 1.2832 degrees C for the Response Surface model and, 0.9984 and 0.3511 degrees C for the Support Vector Machine model with Pearson VII Universal kernel, respectively. In the case of humidity ratio, the corresponding statistical parameters were 0.9763 and 0.5672 g/kg for the former and, 0.9976 and 0.1810 g/kg for the latter. The proposed models can be used reliably in the analysis of solid desiccant-based air conditioning systems for design and energy analysis.
dc.identifier.doi10.1016/j.icheatmasstransfer.2022.106006
dc.identifier.issn0735-1933
dc.identifier.issn1879-0178
dc.identifier.scopus2-s2.0-85128547887
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.icheatmasstransfer.2022.106006
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2123
dc.identifier.volume134
dc.identifier.wosWOS:000804797600003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Communications in Heat and Mass Transfer
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectDesiccant wheel
dc.subjectMultiple linear regression
dc.subjectSupport vector machine
dc.subjectMultilayer perceptron
dc.subjectDecision tree
dc.subjectPredicting model
dc.titleNew multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions
dc.typeArticle

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