Machine learning for algal biofuels: a critical review and perspective for the future

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dc.authoridGunay, M. Erdem/0000-0003-1282-718X|YILDIRIM, RAMAZAN/0000-0001-5077-5689
dc.contributor.authorCosgun, Ahmet
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2024-07-18T20:56:58Z
dc.date.available2024-07-18T20:56:58Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, machine learning (ML) applications in microalgal biofuel production are reviewed. First, the basic steps of algal biofuel production are summarized followed by a bibliometric analysis to demonstrate the major research trends in the field. Also, the major challenges related to the commercialization of technology are identified. Then, ML applications for various steps in the value chain are reviewed and analyzed systematically. Finally, a future perspective on the contribution of ML in the field is provided. Our analysis indicates that ML applications should focus on screening and selecting suitable strains, preferably together with some other value-added products, requiring close collaborations among the researchers in the field to construct an extensive microalgal strain database. Optimization of cultivation conditions appears to be another area where ML can be helpful. Although most published ML works on cultivation are not usually suitable to extract generalizable knowledge (due to the nonstandard nature of strains, wastewater, and irradiation), standard testing and methodologies related to reporting protocols should also be built through collaboration to build comparable and generalizable ML models.en_US
dc.identifier.doi10.1039/d3gc00389b
dc.identifier.endpage3373en_US
dc.identifier.issn1463-9262
dc.identifier.issn1463-9270
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85153799359en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3354en_US
dc.identifier.urihttps://doi.org/10.1039/d3gc00389b
dc.identifier.urihttps://hdl.handle.net/11411/8932
dc.identifier.volume25en_US
dc.identifier.wosWOS:000972910800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherRoyal Soc Chemistryen_US
dc.relation.ispartofGreen Chemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLipid Productionen_US
dc.subjectTechnoeconomic Assessmenten_US
dc.subjectEthyl-Acetateen_US
dc.subjectChlorella Spen_US
dc.subjectMicroalgaeen_US
dc.subjectExtractionen_US
dc.subjectBiodieselen_US
dc.subjectBiomassen_US
dc.subjectOptimizationen_US
dc.subjectGrowthen_US
dc.titleMachine learning for algal biofuels: a critical review and perspective for the futureen_US
dc.typeReview Articleen_US

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