Machine Learning-Based Analysis of Sustainable Biochar Production Processes

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dc.authorid0000-0003-1282-718X
dc.authorid0000-0003-0576-8724
dc.contributor.authorCosgun, Ahmet
dc.contributor.authorOral, Burcu
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2026-04-04T18:55:24Z
dc.date.available2026-04-04T18:55:24Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractBiochar production from biomass sources is a highly complex, multistep process that depends on several factors, including feedstock composition (e.g., type of biomass, particle size) and operating conditions (e.g., reaction temperature, pressure, residence time). However, the optimal set of variables for producing the maximum amount of biochar with the required characteristics can be determined by using machine learning (ML). In light of this, the purpose of this paper is to examine ML applications in biochar processes for the production of sustainable fuels. First, recent developments in the field are summarized, and then, a detailed review of ML applications in biochar production is presented. Following that, a bibliometric analysis is done to illustrate the major trends and construct a comprehensive perspective for future studies. It is found that biochar yield is the most common target variable for ML applications in biochar production. It is then concluded that ML can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties of biochar which can be later used for decision-making, resource allocation, and fuel production.
dc.identifier.doi10.1007/s12155-024-10796-7
dc.identifier.doi10.1007/s12155-024-10796-7
dc.identifier.endpage2327
dc.identifier.issn1939-1234
dc.identifier.issn1939-1242
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85200963240
dc.identifier.scopusqualityQ1
dc.identifier.startpage2311
dc.identifier.urihttps://doi.org/10.1007/s12155-024-10796-7
dc.identifier.urihttps://hdl.handle.net/11411/10411
dc.identifier.volume17
dc.identifier.wosWOS:001287970900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofBioenergy Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectPyrolysis
dc.subjectTorrefaction
dc.subjectBiogas
dc.subjectBio-Oil
dc.subjectBiomass
dc.subjectBibliometric Analysis
dc.titleMachine Learning-Based Analysis of Sustainable Biochar Production Processes
dc.typeArticle

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