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

[Display omitted] •AI-driven full-process prediction methodology was developed to guide biochar design for enhancing natural gas production.•The XGBR model achieved superior predictive accuracy for biochar-mediated AD.•Electrical conductivity, oxygen content, and specific surface area were identifie...

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Vydané v:Chemical engineering journal (Lausanne, Switzerland : 1996) Ročník 516; s. 164179
Hlavní autori: He, Xiaoman, Guo, Jingyuan, Kang, Xihui, Ning, Xue, Chen, Huichao, Liang, Daolun, Deng, Chen, Li, Zutan, Shen, Dekui, Zhang, Huiyan, Lin, Richen, Murphy, Jerry D.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 15.07.2025
Predmet:
Biochar-mediated anaerobic digestion
Circular bioeconomy
Data augmentation
Machine learning
Renewable natural gas
Renewable natural gas
Data augmentation
Biochar-mediated anaerobic digestion
Circular bioeconomy
Machine learning
ISSN:1385-8947
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Shrnutí:[Display omitted] •AI-driven full-process prediction methodology was developed to guide biochar design for enhancing natural gas production.•The XGBR model achieved superior predictive accuracy for biochar-mediated AD.•Electrical conductivity, oxygen content, and specific surface area were identified as key factors influencing AD efficiency.•Generative adversarial network expanded data space to identify the optimal feature combinations. The 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.
ISSN:1385-8947
DOI:10.1016/j.cej.2025.164179