Machine learning solutions for enhanced performance in plant-based microbial fuel cells

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dc.authorid0000-0001-8599-0450
dc.authorid0000-0003-1282-718X
dc.contributor.authorGurbuz, Tugba
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorTapan, N. Alper
dc.date.accessioned2026-04-04T18:55:30Z
dc.date.available2026-04-04T18:55:30Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractIt is well known that numerous operational, material and design variables act upon the performance of a plantbased microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The PCA indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes.
dc.description.sponsorshipinterests or personal relationships that could have appeared to influence the work reported in this paper.
dc.identifier.doi10.1016/j.ijhydene.2024.06.417
dc.identifier.doi10.1016/j.ijhydene.2024.06.417
dc.identifier.endpage1069
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.scopus2-s2.0-85197067775
dc.identifier.scopusqualityQ1
dc.identifier.startpage1060
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2024.06.417
dc.identifier.urihttps://hdl.handle.net/11411/10448
dc.identifier.volume78
dc.identifier.wosWOS:001264438200001
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.subjectFuel Cell
dc.subjectClassification Tree
dc.subjectMachine Learning
dc.subjectShapley
dc.subjectPrincipal Component
dc.titleMachine learning solutions for enhanced performance in plant-based microbial fuel cells
dc.typeArticle

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