Facilitating renewable natural gas production for a circular bioeconomy: AI-driven process visualization and data augmentation on biochar-mediated anaerobic digestion

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dc.check.date2027-05-31en
dc.check.infoAccess to this article is restricted until 24 months after publication by request of the publisheren
dc.contributor.authorHe, Xiaomanen
dc.contributor.authorGuo, Jingyuanen
dc.contributor.authorKang, Xihuien
dc.contributor.authorNing, Xueen
dc.contributor.authorChen, Huichaoen
dc.contributor.authorLiang, Daolunen
dc.contributor.authorDeng, Chenen
dc.contributor.authorLi, Zutanen
dc.contributor.authorShen, Dekuien
dc.contributor.authorZhang, Huiyanen
dc.contributor.authorLin, Richenen
dc.contributor.authorMurphy, Jerry D.en
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderNatural Science Foundation of Jiangsu Provinceen
dc.contributor.funderNational Science Fund for Distinguished Young Scholarsen
dc.contributor.funderState Key Laboratory of Clean Energy Utilizationen
dc.date.accessioned2025-07-09T15:08:28Z
dc.date.available2025-07-09T15:08:28Z
dc.date.issued2025-05-31en
dc.description.abstractThe conversion of biomass residues into biochar is a promising strategy for enhancing sustainability within the circular bioeconomy, particularly through its role in improving renewable natural gas production. However, engineering biochar with optimal properties remains a complex challenge, as the relationship between preparation conditions, biochar characteristics, and anaerobic digestion (AD) performance is not fully understood. This study presents an AI-derived full-process prediction approach that integrates machine learning and generative models to guide the rational design of biochar, and optimize its use for biomethane production. Three tree-based regression models were employed to predict AD performance, with the eXtreme Gradient Boosting Regression model demonstrating superior accuracy. Feature importance analysis identified key biochar properties, including electrical conductivity, oxygen content, and specific surface area, as critical factors influencing biomethane production. These properties can be fine-tuned by adjusting pyrolysis conditions and selecting suitable biomass sources. A generative adversarial network was further used to explore a broader data space, helping to identify the optimal combination of parameters for maximizing AD efficiency. This novel AI-driven framework facilitates biochar-mediated renewable natural gas production, offering a scalable and sustainable approach for advancing circular bioeconomy.en
dc.description.sponsorshipNational Natural Science Foundation of China (No. 52276177; 52376172); Natural Science Foundation of Jiangsu Province (BK20241315); National Natural Science Fund for Distinguished Young Scholars (No. 52425607); State Key Laboratory of Clean Energy Utilization (Open Fund Project No. ZJUCEU2023008)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid164179en
dc.identifier.citationHe, X., Guo, J., Kang, X., Ning, X., Chen, H., Liang, D., Deng, C., Li, Z., Shen, D., Zhang, H., Lin, R. and Murphy, J.D. (2025) 'Facilitating renewable natural gas production for a circular bioeconomy: AI-driven process visualization and data augmentation on biochar-mediated anaerobic digestion', Chemical Engineering Journal, 516, 164179 (11pp). https://doi.org/10.1016/j.cej.2025.164179en
dc.identifier.doi10.1016/j.cej.2025.164179en
dc.identifier.endpage11en
dc.identifier.issn1385-8947en
dc.identifier.journaltitleChemical Engineering Journalen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17689
dc.identifier.volume516en
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.relation.ispartofChemical Engineering Journalen
dc.rights© 2025, Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectRenewable natural gasen
dc.subjectCircular bioeconomyen
dc.subjectMachine learningen
dc.subjectData augmentationen
dc.subjectBiochar-mediated anaerobic digestionen
dc.subject~Civil and Environmental Engineering - Journal articles~en
dc.titleFacilitating renewable natural gas production for a circular bioeconomy: AI-driven process visualization and data augmentation on biochar-mediated anaerobic digestionen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
oaire.citation.volume516en
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