Assessment of water electrolysis projects for green hydrogen production with a novel hybrid q-learning algorithm and molecular fuzzy-based modelling

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dc.contributor.authorDinçer, Hasan
dc.contributor.authorEti, Serkan
dc.contributor.authorAcar, Merve
dc.contributor.authorYüksel, Serhat
dc.date.accessioned2025-11-18T08:37:22Z
dc.date.available2025-11-18T08:37:22Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İMÜ Meslek Yüksekokulu, Bilgisayar Programcılığı Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Uluslararası Ticaret ve Finansman Bölümü
dc.description.abstractDetermining the most important criteria for increasing the efficiency of water electrolysis investments provides businesses with a competitive advantage. Although there are many studies in the literature on this importance, there are very few studies determining the most important of these performance indicators. To satisfy this gap, the purpose of this study is to make assessment of water electrolysis projects for green hydrogen production via a novel model. First, the balanced expert evaluation matrices are obtained by q-learning algorithm. Secondly, the criteria for water electrolysis investments are prioritized using molecular fuzzy Bayesian network (BANEW). Thirdly, green hydrogen strategies for water electrolysis investments are ranked with molecular fuzzy multi-objective particle swarm optimization (MOPSO). The most important contribution of this study to the literature is the determination of the criteria that should be applied primarily for the performance increase of water electrolysis investments by creating a new model. The use of molecular fuzzy numbers is a very important contribution of the model to the literature. In this process, the use of three-dimensional geometric figures allows the reduction of uncertainties in decision-making processes. The findings indicate that lifespan of electrolysers and production capacity are the most essential criteria. Additionally, proton exchange membrane electrolysers and alkaline water electrolysis are found as the most critical green hydrogen strategies. Extending the life of electrolysers is crucial to increase sustainability in hydrogen production and reduce long-term costs. In this context, research incentives should be provided for the development of materials and technologies to increase the durability of electrolysers. Similarly, establishing quality standards to extend the life of electrolysers also contributes to achieving this goal.
dc.identifier.citationDinçer, H., Eti, S., Acar, M. ve Yüksel, S. (2024). Assessment of water electrolysis projects for green hydrogen production with a novel hybrid q-learning algorithm and molecular fuzzy-based modelling. International Journal of Hydrogen Energy, 95, 721-733. http://dx.doi.org/10.1016/j.ijhydene.2024.11.262
dc.identifier.doi10.1016/j.ijhydene.2024.11.262
dc.identifier.endpage733
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.scopus2-s2.0-85209399048
dc.identifier.scopusqualityQ1
dc.identifier.startpage721
dc.identifier.urihttp://dx.doi.org/10.1016/j.ijhydene.2024.11.262
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13219
dc.identifier.volume95
dc.identifier.wosWOS:001362960400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorDinçer, Hasan
dc.institutionauthorEti, Serkan
dc.institutionauthorAcar, Merve
dc.institutionauthorYüksel, Serhat
dc.institutionauthorid0000-0002-8072-031X
dc.institutionauthorid0000-0002-4791-4091
dc.institutionauthorid0000-0001-5853-4943
dc.institutionauthorid0000-0002-9858-1266
dc.language.isoen
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectEnergy Investment
dc.subjectGreen Hydrogen
dc.subjectMolecular Fuzzy
dc.subjectQ-Learning
dc.subjectWater Electrolysis
dc.titleAssessment of water electrolysis projects for green hydrogen production with a novel hybrid q-learning algorithm and molecular fuzzy-based modelling
dc.typeArticle

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