AI-Guided Mechanistic Framework for Biochar-Augmented Anaerobic Digestion: Interpretable Insights for Carbon-Efficient Methane Recovery

The sustainable valorization of biomass waste via biochar (BC)-augmented anaerobic digestion (AD) offers a carbon-efficient route for the production of renewable methane production. However, the mechanistic interplay among BC properties, operational parameters, and methanogenic pathways remains poorly resolved, particularly in data-limited systems. Here, we proposed a data-driven and interpretable machine learning (ML) framework that integrated classical Gompertz kinetics with advanced predictive models to holistically optimize biochar-mediated AD at both process and microbial levels. The AI-assisted approach identified electron transfer-related biochar properties and operational temperature as dominant levers controlling methane yield and startup efficiency and pinpointed practical design windows (e.g., 20–30 g/L volatile solids, 3–30g/L biochar dosage) as preliminary references to guide future experimental design. A novel cross-validated feature attribution scheme further revealed that targeted biochar tailoring (particularly in iron content) preferentially strengthened acetoclastic methanogenesis, offering mechanistic insights for process optimization. This work demonstrates how AI-driven, mechanistically transparent modeling can accelerate sustainable biochemical process design, offering actionable guidance for circular bioeconomy waste-to-energy systems.