Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

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dc.authoridTran Viet, Dung/0000-0002-3598-5829en_US
dc.authoridSHARMA, PRABHAKAR/0000-0002-7585-6693en_US
dc.authorscopusid57790254900en_US
dc.authorscopusid58961316700en_US
dc.authorscopusid57202959651en_US
dc.authorscopusid57283939900en_US
dc.authorscopusid57210821909en_US
dc.authorscopusid6603168119en_US
dc.authorscopusid55327003700en_US
dc.authorwosidTran Viet, Dung/AGT-2066-2022en_US
dc.authorwosidSHARMA, PRABHAKAR/AFS-6314-2022en_US
dc.contributor.authorNguyen, Van Giao
dc.contributor.authorSharma, Prabhakar
dc.contributor.authorAgbulut, Uemit
dc.contributor.authorLe, Huu Son
dc.contributor.authorCao, Dao Nam
dc.contributor.authorDzida, Marek
dc.contributor.authorOsman, Sameh M.
dc.date.accessioned2024-08-23T16:04:23Z
dc.date.available2024-08-23T16:04:23Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors' own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption.en_US
dc.identifier.doi10.1080/15435075.2024.2326076
dc.identifier.endpage2798en_US
dc.identifier.issn1543-5075
dc.identifier.issn1543-5083
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85188314940en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2771en_US
dc.identifier.urihttps://doi.org/10.1080/15435075.2024.2326076
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14194
dc.identifier.volume21en_US
dc.identifier.wosWOS:001185059000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofInternational Journal of Green Energyen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiochar productionen_US
dc.subjectexplainable artificial intelligenceen_US
dc.subjectinterpretable machine learningen_US
dc.subjectprecise prognosticsen_US
dc.subjectsustainable energyen_US
dc.subjectFast Pyrolysisen_US
dc.subjectHydrothermal Carbonizationen_US
dc.subjectArtificial-Intelligenceen_US
dc.subjectHydrogen-Productionen_US
dc.subjectProcess Parametersen_US
dc.subjectGasificationen_US
dc.subjectWasteen_US
dc.subjectTorrefactionen_US
dc.subjectEnergyen_US
dc.subjectFuelen_US
dc.titleImproving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approachesen_US
dc.typeReviewen_US

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