Comprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flow

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dc.authoridOLMUS, UMUTCAN/0000-0002-5799-1840
dc.authoridCERCI, KAMIL NEYFEL/0000-0002-3126-707X
dc.authoridGuzelel, Yunus Emre/0000-0002-7122-0241
dc.contributor.authorGuzelel, Yunus Emre
dc.contributor.authorOlmus, Umutcan
dc.contributor.authorCerci, Kamil Neyfel
dc.contributor.authorBuyukalaca, Orhan
dc.date.accessioned2025-03-17T12:27:27Z
dc.date.available2025-03-17T12:27:27Z
dc.date.issued2021
dc.departmentTarsus Üniversitesi
dc.description.abstractIn this paper, several alternative models were developed with multiple linear regression and machine learning algorithms to determine the output states of silica gel desiccant wheels for balanced flow. The decision tree method was used for this purpose for the first time in open literature. All the models developed include six input parameters and a wider range than those available in the literature. Predictions from the models were compared with the master dataset used to derive the models, each of the five sub-datasets that make up the master dataset and with data available in the literature. It was determined that the most suitable models are those coded as multiple linear regression-IV (MLR-IV), multilayer perceptron regressor-III (MLPR-III) and decision tree-VII (DTVII), and DT-VII is the best among them. The determination coefficient and root mean square error for temperature were found to be 0.9894 and 0.8743 degrees C for MLR-IV, 0.9817 and 1.1526 degrees C for MLPR-III, 0.9986 and 0.3295 degrees C for DT-VII, respectively. The corresponding values for humidity ratio were 0.9912 and 0.3701 g kg(-1) for MLR-IV, 0.9885 and 0.4227 g kg(-1) for MLPR-III, 0.9994 and 0.0995 g kg(-1) for DT-VII, respectively. The results obtained revealed that the proposed models can be used safely in preliminary design, simulation and dynamic energy analysis of systems with desiccant wheels.
dc.identifier.doi10.1016/j.applthermaleng.2021.117544
dc.identifier.issn1359-4311
dc.identifier.issn1873-5606
dc.identifier.scopus2-s2.0-85114948590
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.applthermaleng.2021.117544
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2264
dc.identifier.volume199
dc.identifier.wosWOS:000707387100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofApplied Thermal Engineering
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.subjectMultilayer Perceptron
dc.subjectDecision Tree
dc.subjectModelling
dc.titleComprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flow
dc.typeArticle

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