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

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.