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dc.contributor.authorDemir, Mehmet Hakan
dc.contributor.authorEren, Berkay
dc.date.accessioned2022-11-15T10:49:46Z
dc.date.available2022-11-15T10:49:46Z
dc.date.issued2022en_US
dc.identifier.citationDemir, M.H., Eren, B. (2022). Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller. International Journal of Hydrogen Energy, 47 (45), pp. 19837-19849. https://doi.org/10.1016/j.ijhydene.2022.03.113en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2022.03.113
dc.identifier.urihttps://hdl.handle.net/20.500.12508/2251
dc.description.abstractMicrobial fuel cell (MFC) has become a very important biotechnological tool to produce clean energy in recent years. It is very important to adjust the output voltage and power density in order to obtain the desired energy quickly and smoothly at the output of the MFC. In this study, an optimization-based neuro-fuzzy inference controller is proposed for improving voltage tracking performance of the MFC. A double-chambers MFC model including biochemical reactions, Butler-Volmer expressions and mass/charge balances was studied and Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) algorithms are used to adjust the parameters of the neuro-fuzzy controller. The results show that PSO and IGWO based controllers have efficient performances to follow the reference voltage pattern quickly and robustly against external load changes, distributions and parameter uncertainties. Moreover, it was observed that IGWO was a more stable and robust controller than PSO according to rise time, overshoot and peak time. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.ijhydene.2022.03.113en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectControlen_US
dc.subjectGrey wolfen_US
dc.subjectMicrobial fuel cellen_US
dc.subjectNeuro-fuzzyen_US
dc.subjectOptimizationen_US
dc.subjectParticle swarmen_US
dc.subject.classificationBioenergy
dc.subject.classificationRegenerative Fuel Cells
dc.subject.classificationBioelectricity
dc.subject.classificationChemistry
dc.subject.classificationElectrochemistry
dc.subject.classificationEnergy & Fuels
dc.subject.classificationAgriculture, Environment & Ecology - Bioengineering - Microbial Fuel Cell
dc.subject.otherMathematical-model
dc.subject.otherPerformance
dc.subject.otherAnode
dc.subject.otherPso
dc.subject.otherControllers
dc.subject.otherFuzzy inference
dc.subject.otherMicrobial fuel cells
dc.subject.otherAdaptive neuro-fuzzy inference
dc.subject.otherBiotechnological tools
dc.subject.otherClean energy
dc.subject.otherDouble chamber microbial fuel cells
dc.subject.otherGray wolves
dc.subject.otherNeuro-Fuzzy
dc.subject.otherOptimisations
dc.subject.otherOutput power
dc.subject.otherOutput voltages
dc.subject.otherParticle swarm
dc.subject.otherParticle swarm optimization (PSO)
dc.titleOutput voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controlleren_US
dc.typearticleen_US
dc.relation.journalInternational Journal of Hydrogen Energyen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Mekatronik Mühendisliği Bölümüen_US
dc.identifier.volume47en_US
dc.identifier.issue45en_US
dc.identifier.startpage19837en_US
dc.identifier.endpage19849en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorDemir, Mehmet Hakan
dc.contributor.isteauthorEren, Berkay
dc.relation.indexWeb of Science - Scopusen_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


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