Estimation of specific surface area and higher heating value of biochar and activated carbon produced by pyrolysis and physico-chemically assisted pyrolysis of biomass using an artificial neural network (ANN)

Basit Öğe Kaydı
dc.contributor.authorBalde, Mamadou Saliou
dc.contributor.authorKarakis, Rukiye
dc.contributor.authorAtes, Ayten
dc.date.accessioned2025-05-04T16:47:22Z
dc.date.available2025-05-04T16:47:22Z
dc.date.issued2025
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThe physical and chemical activation of biomass prior to pyrolysis significantly affects the properties of the activated carbon produced. In this study, raw tea waste (TW) and hazelnut shells (HS) were used to produce biochar and activated carbon samples by pyrolysis at different pyrolysis temperatures with and without chemical and physical activation. Subsequently, an artificial neural network (ANN) was developed based on the pyrolysis conditions, proximate and elemental analyses of the biomass feedstocks and the obtained biochar and activated carbon to predict the higher heating value (HHV) and specific surface area (SSA) of the biochar. For this purpose, machine learning algorithms such as ANN, Gaussian process regression (GPR), regression trees (RT), and support vector machines (SVM) were compared to find the best-performing algorithm for the prediction of HHV and SSA of biochar. Algorithms based on ANNs performed better than SVM, RT, and GPR models, with higher regressions and lower prediction errors. The resilient backpropagation (RProp) algorithm proved to be the most suitable training algorithm as it provided satisfactory results with a low percentage of mean squared error (MSE) and mean absolute error (MAE). The ANN models showed moderate to strong performance in the tests, with correlation coefficient (R) values of 0.82 and 0.95, coefficient of determination (R2) values of 0.67 and 0.90, and low MAE and MSE, indicating reasonable prediction accuracy for HHV and SSA of the biochar. The energy efficiency of biochar produced with conventional pyrolysis ranged from 9.84% to 21.13%, while the energy efficiency of activated carbon ranged from 45.26% to 67.21%, with the maximum reached at 300 degrees C. Based on the results of the thermodynamic analysis, it was found that the energy and exergy yields of the biochar and activated carbon produced depend on the activation conditions and temperature.
dc.description.sponsorshipSivas Cumhuriyet University; [M-2021-825]; [M-2023-847]
dc.description.sponsorshipThe study was financed by Sivas Cumhuriyet University Research Funding with projects numbered M-2021-825 and M-2023-847. This paper is the result of the Master of Science (MS) thesis study of the first author.
dc.identifier.doi10.1007/s13399-025-06728-w
dc.identifier.issn2190-6815
dc.identifier.issn2190-6823
dc.identifier.scopus2-s2.0-105000487840
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s13399-025-06728-w
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35574
dc.identifier.wosWOS:001448384000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofBiomass Conversion and Biorefinery
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectPyrolysis
dc.subjectChemical activation
dc.subjectPhysical activation
dc.subjectMachine learning
dc.titleEstimation of specific surface area and higher heating value of biochar and activated carbon produced by pyrolysis and physico-chemically assisted pyrolysis of biomass using an artificial neural network (ANN)
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu