Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning

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dc.authoridGunay, M. Erdem/0000-0003-1282-718X
dc.contributor.authorCosgun, Ahmet
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2024-07-18T20:56:04Z
dc.date.available2024-07-18T20:56:04Z
dc.date.issued2021
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, the algal biomass productivity and its lipid content were explored using a database containing 4670 instances extracted from the experimental results reported in 102 published articles. First, the influences of critical factors such as microalgae species, cultivation conditions, light intensity, CO2 amount, nutrient concentrations, reactor type, stress conditions, cell disruption methods, and lipid extraction solvents on the biomass and lipid production were reviewed. Then, the database was analyzed using machine learning techniques; decision trees were utilized to determine the combination of variables leading to high biomass and lipid content while association rule mining was used to find the specific conditions leading to very high biomass and lipid levels. Decision tree analysis discovered 11 different combinations of variables leading to high biomass productivity and 13 combinations for high lipid content; whereas, association rule mining analysis helped to identify the levels of specific factors for very high biomass and lipid production. It was then concluded that machine learning methods can help to determine the best conditions for optimum biomass growth and lipid yield for microalgae to manufacture renewable biofuels, and this can guide the planning of new experimental works. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.renene.2020.09.034
dc.identifier.endpage1317en_US
dc.identifier.issn0960-1481
dc.identifier.issn1879-0682
dc.identifier.scopus2-s2.0-85091236595en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1299en_US
dc.identifier.urihttps://doi.org/10.1016/j.renene.2020.09.034
dc.identifier.urihttps://hdl.handle.net/11411/8848
dc.identifier.volume163en_US
dc.identifier.wosWOS:000591527500010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofRenewable Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMicroalgaeen_US
dc.subjectBiodieselen_US
dc.subjectBioenergyen_US
dc.subjectData Miningen_US
dc.subjectDecision Treesen_US
dc.subjectAssociation Rule Miningen_US
dc.subjectFresh-Water Microalgaeen_US
dc.subjectFatty-Acid-Compositionen_US
dc.subjectSelective Co Oxidationen_US
dc.subjectDecision Tree Analysisen_US
dc.subjectNoble-Metal Catalystsen_US
dc.subjectBiodiesel Productionen_US
dc.subjectChlorella-Vulgarisen_US
dc.subjectBiofuel Productionen_US
dc.subjectLight-Intensityen_US
dc.subjectScenedesmus Spen_US
dc.titleExploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learningen_US
dc.typeArticleen_US

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