Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer

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dc.authorid0000-0001-5037-6119en_US
dc.authorid0000-0003-3752-6308en_US
dc.authorid0000-0002-7540-7935en_US
dc.authorid0000-0002-4143-9226en_US
dc.authorid0000-0001-7311-9342en_US
dc.authorid0000-0003-0799-889Xen_US
dc.contributor.authorGupta, Ankur
dc.contributor.authorDas, Biplab
dc.contributor.authorArslan, Erhan
dc.contributor.authorDas, Mehmet
dc.contributor.authorKoşan, Meltem
dc.contributor.authorCan, Ömer Faruk
dc.date.accessioned2024-04-05T13:04:43Z
dc.date.available2024-04-05T13:04:43Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractThis study proposes a mixed-mode dryer with a semi-transparent photovoltaic thermal (PVT) collector for the assessment of drying and thermal performance using computational and experimental findings. The thermal behavior and fluid flow characteristics have been analyzed to optimize the air flow rate in the PVT solar dryer by considering three different inlet velocities of 0.048 m/s (Case 1), 0.096 m/s (Case 2), and 0.144 m/s (Case 3). The temperature distribution is obtained more uniformly for the PVT collector and dryer cabin in Case 2. The results of the investigation show that Case 3 has a positive impact on the PVT solar dryer performance. In numerical and experimental methods, the enhanced thermal efficiency is attained as 30.78% and 29.78% for Case 2, and 33.20% and 31.14% for Case 3, respectively, in comparison to Case 1. Case 3 has improved Reynolds and Nussselt numbers by 3.06 and 2.45 times, respectively compared to Case 1. Experimental results varied by 2.24 to 4.90% from simulated outcomes obtained from CFD. The machine learning approach of ANN has been implemented with different hidden layers network models to choose the best drying conditions by predicting the drying performance parameters.en_US
dc.identifier.citationGupta, A., Das, B., Arslan, E., Das, M., Koşan, M. ve Can, Ö. F. (2024). Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer. Process Safety and Environmental Protection, 183, 1170-1185.en_US
dc.identifier.endpage1185en_US
dc.identifier.issn0957-5820
dc.identifier.scopusScopusIdYok
dc.identifier.scopusqualityQ1
dc.identifier.startpage1170en_US
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957582024000831?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/11468/13862
dc.identifier.volume183en_US
dc.identifier.wosWOS:001175980000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorCan, Ömer Faruk
dc.language.isoenen_US
dc.publisherInstitution of Chemical Engineersen_US
dc.relation.ispartofProcess Safety and Environmental Protection
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectComputational fluid dynamicsen_US
dc.subjectPartially covered solar dryeren_US
dc.subjectPhotovoltaic thermal systemen_US
dc.titleArtificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryeren_US
dc.titleArtificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer
dc.typeArticleen_US

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