Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning

Basit Öğe Kaydı
dc.authoridGünay, M. Erdem/0000-0003-1282-718X
dc.authorwosidGünay, M. Erdem/I-1564-2019
dc.contributor.authorCosgun, Ahmet
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2024-07-18T20:42:39Z
dc.date.available2024-07-18T20:42:39Z
dc.date.issued2022
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, biomass and lipid productivities of Yarrowia lipolytica were analyzed using machine learning techniques. A dataset containing 356 instances was constructed from the experimental results reported in 22 publications. The dataset was analyzed using decision trees to identify the features (descriptors) that lead to high biomass production, lipid content and lipid production. C/N ratio and fermentation time were found to be the most influential features for biomass production while the use of glucose and medium pH seemed to be more important for high lipid content. For the lipid production case, five generalizable paths leading to high values of this output were identified. One of those paths required pH to be < 6.3, high glucose and (NH4)(2)SO4 concentrations, lower concentration for yeast extract and the yeast strain not be H-222. Another one needed a pH greater than 6.3, a C/N ratio smaller than 75, a time greater than 14 h, and a strain other than W29. The same dataset was also explored deeper using association rule mining to determine the effects of individual features on output variables. It was then concluded that machine learning methods are very useful in determining the optimal conditions of biomass growth and lipid yield for Yarrowia lipolytica to produce renewable biofuels.en_US
dc.identifier.doi10.1016/j.fuel.2021.122817
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85121105608en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2021.122817
dc.identifier.urihttps://hdl.handle.net/11411/7373
dc.identifier.volume315en_US
dc.identifier.wosWOS:000783215000005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofFuelen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOleaginous Yeastsen_US
dc.subjectBiodieselen_US
dc.subjectBioenergyen_US
dc.subjectData Miningen_US
dc.subjectDecision Treesen_US
dc.subjectAssociation Rule Miningen_US
dc.subjectSingle-Cell Oilen_US
dc.subjectCitric-Acid Productionen_US
dc.subjectBiodiesel Productionen_US
dc.subjectMicrobial Lipidsen_US
dc.subjectCrude Glycerolen_US
dc.subjectMetabolic Shiftsen_US
dc.subjectAccumulationen_US
dc.subjectBatchen_US
dc.subjectConversionen_US
dc.subjectBiomassen_US
dc.titleAnalysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning
dc.typeArticle

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications