Precise prognostics of biochar yield from various biomass sources by Bayesian approach with supervised machine learning and ensemble methods

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dc.authoridSHARMA, PRABHAKAR/0000-0002-7585-6693en_US
dc.authoridTran Viet, Dung/0000-0002-3598-5829en_US
dc.authorscopusid57790254900en_US
dc.authorscopusid58961316700en_US
dc.authorscopusid57202959651en_US
dc.authorscopusid57283939900en_US
dc.authorscopusid57207245944en_US
dc.authorscopusid57210821909en_US
dc.authorwosidSHARMA, PRABHAKAR/ISU-9669-2023en_US
dc.authorwosidTran Viet, Dung/AGT-2066-2022en_US
dc.contributor.authorNguyen, Van Giao
dc.contributor.authorSharma, Prabhakar
dc.contributor.authorAgbulut, Uemit
dc.contributor.authorLe, Huu Son
dc.contributor.authorTran, Viet Dung
dc.contributor.authorCao, Dao Nam
dc.date.accessioned2024-08-23T16:04:27Z
dc.date.available2024-08-23T16:04:27Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractBiomass pyrolysis is a sustainable process for generating biochar from agricultural waste, though it is generally energy-intensive and time-consuming. To address this issue, the researchers gathered data from published literature on various biomass types and employed ensemble methods (LSBoost) and supervised machine learning (Gaussian process regression) to construct predictive models. The results reveal that both models can predict well, with excellent correlations between expected and actual values. In comparison to the LSBoost model (0.9783 for training and 0.9879 for testing), the Gaussian process regression (GPR) model had higher R values for training (0.9883) and testing (0.9969). Likewise, the R2 values during training (0.9767) and testing (0.9938) were greater in the case of the GPR model than for the LSBoost model (0.9571 for training). Nash-Sutcliffe efficiency (NSE) revealed that both models captured the data precisely. However, the GPR model outperformed the LSBoost model in both during training as well as model test stages, providing higher (0.9766 for training and 0.9933 for testing) values. The GPR model outperforms the others due to superior correlation, improved variability capture, and lower errors. These findings offer useful insights for sustainable biomass utilization and provide valuable insights for optimizing pyrolysis operations.en_US
dc.identifier.doi10.1080/15435075.2023.2297776
dc.identifier.endpage2204en_US
dc.identifier.issn1543-5075
dc.identifier.issn1543-5083
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85181212914en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2180en_US
dc.identifier.urihttps://doi.org/10.1080/15435075.2023.2297776
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14195
dc.identifier.volume21en_US
dc.identifier.wosWOS:001133806700001en_US
dc.identifier.wosqualityQ2en_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.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEnsemble methodsen_US
dc.subjectsupervised learningen_US
dc.subjectbiomassen_US
dc.subjectbiocharen_US
dc.subjectoptimizationen_US
dc.subjecthyperparametersen_US
dc.subjectgreen energyen_US
dc.subjectGaussian Process Regressionen_US
dc.subjectPyrolysis Temperatureen_US
dc.subjectPredictionen_US
dc.subjectCarbonen_US
dc.subjectProductsen_US
dc.subjectBambooen_US
dc.subjectManureen_US
dc.subjectSoilsen_US
dc.subjectModelen_US
dc.subjectStrawen_US
dc.titlePrecise prognostics of biochar yield from various biomass sources by Bayesian approach with supervised machine learning and ensemble methodsen_US
dc.typeArticleen_US

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