Routes to optimum conditions of plant based microbial fuel cells by reinforcement learning

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dc.authorid0000-0001-8599-0450
dc.authorid0000-0003-1282-718X
dc.contributor.authorTapan, N. Alper
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorGurbuz, Tugba
dc.date.accessioned2026-04-04T18:55:30Z
dc.date.available2026-04-04T18:55:30Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractPlant-based microbial fuel cells (PMFC) are fascinating technologies that have the potential to combine plants and bacteria to produce electricity from different solid and aqueous media like constructed wetlands and wastewater treatment facilities. Although PMFCs are evolving and demonstrating promising performance results for the development of sustainable energy and water treatment, they have not reached their full potential due to issues with continuous bioenergy generation and fuel cell system optimization through plant selection, operating conditions, electrodes, and light source, all of which are critical for optimum microbial activity on the roots and exudate rhizodeposition. In light of this, the Q learning algorithm was used in this study to determine the routes that lead to the best operating and material conditions for PMFCs. A database of 231 observations from 51 recent publications with 271 descriptors (input variables) and 3 output variables (under 9 categories were used to determine the routes leading to high maximum power density, medium open circuit potential and current density. It was seen that high maximum power density routes are achievable through the nodes of stainless-steel mesh cathode with metal-based chitosan smart catalysts, bicarbonate wastewater, and anaerobic wetland sediment. Data visualizations by radar charts also exhibited similar results for cathode material and wastewater type. For medium open circuit potential, Iris pseudacorus and for medium maximum current density anaerobic sludge inoculation and steel wire mesh/nickel current collectors are found to be important indicators.
dc.identifier.doi10.1016/j.ijhydene.2024.12.292
dc.identifier.doi10.1016/j.ijhydene.2024.12.292
dc.identifier.endpage824
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.scopus2-s2.0-85213223680
dc.identifier.scopusqualityQ1
dc.identifier.startpage813
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2024.12.292
dc.identifier.urihttps://hdl.handle.net/11411/10449
dc.identifier.volume142
dc.identifier.wosWOS:001511529700010
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.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectPlant
dc.subjectMicrobial Fuel Cell
dc.subjectReinforcement Learning
dc.subjectMachine Learning
dc.subjectQ Learning Algorithm
dc.titleRoutes to optimum conditions of plant based microbial fuel cells by reinforcement learning
dc.typeArticle

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