Performance analysis and modeling of a closed-loop heat pump dryer for bay leaves using artificial neural network

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dc.authoridAKTAS, MUSTAFA/0000-0003-1187-5120
dc.authoridSevik, Seyfi/0000-0003-4063-0456
dc.contributor.authorAktas, Mustafa
dc.contributor.authorSevik, Seyfi
dc.contributor.authorOzdemir, M. Bahadir
dc.contributor.authorGonen, Emrah
dc.date.accessioned2024-09-29T15:54:58Z
dc.date.available2024-09-29T15:54:58Z
dc.date.issued2015
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study discusses the performance analysis and modeling of bay leaves drying in a closed-loop heat pump dryer. To control relative humidity and temperatures, a controller has been designed, developed and tested to perform at low temperature drying applications (40 degrees C, 45 degrees C and 50 degrees C). New techniques are applied simultaneously such as control of relative humidity, drying air temperature and air velocity for the closed-loop heat pump dryer. A closed-loop heat pump dryer using water as a secondary fluid has been used to determine drying characteristics of bay leaves. Moreover, a Programmable Logic Control (PLC) has been used to control drying air temperature, air velocity, relative humidity and the mass obtained for the dried product. The performance of the facility has been carried out but several experimental tests under different psychrometric conditions from the test results. COPhp and COPws values were 2.8-3.7 and 2.4-3.2, respectively, while Energy Utilization Ratio (EUR) values were found to vary 0.22-0.75. From experimental data the system was analyzed and modeled by using Artificial Neural Network (ANN) and drying kinetics of bay leaf. The ANN model was used to predict the moisture content (MC, g water/g dry matter) and the total energy consumption (TEC, kWh) of the system. The back-propagation learning algorithm with Levenberg-Marquardt (LM) and Fermi transfer function were used in the network. The coefficient of determination (R-2), the root means square error (RMSE) and the mean absolute percentage error (MAPE) were calculated as 0.996, 0.0002053, 0.4161673 and 0.997, 0.0005013, 0.4280322, respectively. Accordingly, it can be concluded that predicted MC and TEC results are in good agreement with experimental results. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.applthermaleng.2015.05.049
dc.identifier.endpage723en_US
dc.identifier.issn1359-4311
dc.identifier.scopus2-s2.0-84931275627en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage714en_US
dc.identifier.urihttps://doi.org/10.1016/j.applthermaleng.2015.05.049
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4392
dc.identifier.volume87en_US
dc.identifier.wosWOS:000359504500072en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofApplied Thermal Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBay leafen_US
dc.subjectDryingen_US
dc.subjectHeat pump dryeren_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectMoisture outputen_US
dc.subjectEnergy consumptionen_US
dc.titlePerformance analysis and modeling of a closed-loop heat pump dryer for bay leaves using artificial neural networken_US
dc.typeArticleen_US

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