Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network

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dc.authoridSEYHAN, Mehmet/0000-0002-5927-9128
dc.authoridAkansu, Selahaddin Orhan/0000-0002-0085-7915
dc.authoridmurat, mirac/0000-0001-9980-9608
dc.contributor.authorSeyhan, Mehmet
dc.contributor.authorAkansu, Yahya Erkan
dc.contributor.authorMurat, Mirac
dc.contributor.authorKorkmaz, Yusuf
dc.contributor.authorAkansu, Selahaddin Orhan
dc.date.accessioned2024-11-07T13:24:03Z
dc.date.available2024-11-07T13:24:03Z
dc.date.issued2017
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description9th International Conference on Sustainable Energy and Environmental Protection (SEEP) -- SEP 22-25, 2016 -- Kayseri, TURKEY
dc.description.abstractEffects of serpentine flow channel having sinusoidal wave at the rib surface on performance of PEMFC having 25 cm(2) active area are investigated at different flow rates, three different amplitudes changing from 0.25 mm to 0.75 mm and three different cell operation temperatures. A proton exchange membrane fuel cell (PEMFC) is modeled for the prediction of the output current by using artificial neural network (ANN) that is utilized the aforementioned experimental parameters. Effect of hydrogen and air flow rate, the fuel cell temperature, amplitude of channel is tested. The results indicated that model C1 having lowest amplitude is enhanced maximum power output up to 20.15% as compared to indicated conventional serpentine channel (model C4) for 0.7 SLPM H-2 and 1.5 SLPM air and also model C1 has better performance than C2, C3 and C4 models. The maximum power output is augmented with increasing the cell temperature due to raising the fuel and oxidant diffusion ratio. Cell temperature, amplitude, H2 and air flow rate and input voltage is used as input variables in train and test of the developing ANN model. MAPE of training and testing is determined as 239 and 2.059, respectively. Prediction results of developed ANN model including two hidden layer shows similar trend with experimental results. Developed ANN model can be used to both decrease the number of required experiments and find the optimum operation condition within the range of input parameters. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [2241-A-139B411402808]
dc.description.sponsorshipThe authors would like to acknowledge the financial support of this work by the Scientific and Technological Research Council of Turkey (TUBITAK) under the Contract Number of 2241-A-139B411402808. The authors would like to thank Mr. O. Demirci and Mr. Y. Kargin for their assistance in the implementation of the experimental part of the study.
dc.identifier.doi10.1016/j.ijhydene.2017.04.001
dc.identifier.endpage25629
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue40
dc.identifier.scopus2-s2.0-85018755828
dc.identifier.scopusqualityQ1
dc.identifier.startpage25619
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2017.04.001
dc.identifier.urihttps://hdl.handle.net/11480/13869
dc.identifier.volume42
dc.identifier.wosWOS:000413284500045
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectPEM fuel cell
dc.subjectWavy shaped serpentine
dc.subjectArtificial neural network
dc.titlePerformance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network
dc.typeConference Object

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