Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

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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.authorscopusid57210823610en_US
dc.authorscopusid6603168119en_US
dc.authorscopusid58814313900en_US
dc.authorwosidTran Viet, Dung/AGT-2066-2022en_US
dc.authorwosidSHARMA, PRABHAKAR/ISU-9669-2023en_US
dc.contributor.authorNguyen, Van Giao
dc.contributor.authorSharma, Prabhakar
dc.contributor.authorAgbulut, Uemit
dc.contributor.authorLe, Huu Son
dc.contributor.authorTruong, Thanh Hai
dc.contributor.authorDzida, Marek
dc.contributor.authorTran, Minh Ho
dc.date.accessioned2024-08-23T16:07:19Z
dc.date.available2024-08-23T16:07:19Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far-reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data-driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.en_US
dc.identifier.doi10.1002/bbb.2596
dc.identifier.endpage593en_US
dc.identifier.issn1932-104X
dc.identifier.issn1932-1031
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85184219783en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage567en_US
dc.identifier.urihttps://doi.org/10.1002/bbb.2596
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14587
dc.identifier.volume18en_US
dc.identifier.wosWOS:001157466500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofBiofuels Bioproducts & Biorefining-Biofpren_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectbiomassen_US
dc.subjectbiochar yielden_US
dc.subjectmachine learningen_US
dc.subjectprecise prognosticsen_US
dc.subjectdata-driven approachen_US
dc.subjectsustainable energyen_US
dc.subjectArtificial Neural-Networksen_US
dc.subjectPyrolysis Temperatureen_US
dc.subjectPredictionen_US
dc.subjectPerformanceen_US
dc.subjectRegressionen_US
dc.subjectFeedstocken_US
dc.subjectBiofuelsen_US
dc.titleMachine learning for the management of biochar yield and properties of biomass sources for sustainable energyen_US
dc.typeReviewen_US

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