A critical review of machine learning for lignocellulosic ethanol production via fermentation route

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dc.WoS.categoriesEnergy & Fuelsen_US
dc.authorid0000-0003-0576-8724en_US
dc.contributor.authorCoşgun, Ahmet
dc.contributor.authorGünay, Mustafa Erdem
dc.contributor.authorYıldırım, Ramazan
dc.date.accessioned2024-04-04T06:47:25Z
dc.date.available2024-04-04T06:47:25Z
dc.date.issued2023-11-08
dc.description.abstractIn this work, machine learning (ML) applications in lignocellulosic bioethanol production were reviewed. First, the pretreatment-hydrolysis-fermentation route, the most commonly studied alternative, was summarized. Next, a bibliometric analysis was performed to identify the current trends in the field; it was found that ML applications in the field are not only increasing but also expanding their relative share in publications, with bioethanol seeming to be the most frequently researched topic while biochar and biogas are also receiving increased attention in recent years. Then, the implementation of ML for lignocellulosic bioethanol production via this route was reviewed in depth. It was observed that artificial neural network (ANN) is the most commonly used algorithm (appeared in almost 90% of articles), followed by response surface methodology (RSM) (in about 25% of articles) and random forest (RF) (in about 10% of articles). Bioethanol concentration is the most common output variable in the fermentation step, while fermentable sugar and glucose concentration are studied most in hydrolysis. The datasets are usually small, while the fitnesses of the models (R2) are usually high in the papers reviewed. Finally, a perspective for future studies, mostly considering improving data availability, was provided.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.18331/BRJ2023.10.2.5en_US
dc.identifier.issn2292-8782
dc.identifier.scopus2-s2.0-85163029073en_US
dc.identifier.urihttps://hdl.handle.net/11411/5243
dc.identifier.urihttps://doi.org/10.18331/BRJ2023.10.2.5
dc.identifier.wosWOS:001084610000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue2en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors3en_US
dc.pages1859-1875en_US
dc.publisherGREEN WAVE PUBL CANADAen_US
dc.relation.ispartofBIOFUEL RESEARCH JOURNAL-BRJen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiofuel productionen_US
dc.subjectBioethanolen_US
dc.subject2nd generation feedstocken_US
dc.subjectLignocellulosic ethanolen_US
dc.subjectCellulosic ethanolen_US
dc.subjectMachine learningen_US
dc.titleA critical review of machine learning for lignocellulosic ethanol production via fermentation routeen_US
dc.typeArticleen_US
dc.volume10en_US

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