Biochar-Augmented Anaerobic Digestion System: Insights from an Interpretable Stacking Ensemble Deep Learning

This study presents a comprehensive approach for optimizing biochar-augmented anaerobic digestion (AD) system through an interpretable stacking ensemble deep learning model. Extensive experimental data were compiled, incorporating feedstock characteristics, operational conditions, and biochar properties, alongside stability indicators such as pH, volatile fatty acid concentrations, alkalinity, and total ammonia nitrogen levels. The proposed model integrates different configurations of convolutional neural networks and long short-term memory networks within a stacking ensemble framework, effectively capturing complex interdependencies within the AD process and improving methane yield predictions. Optimized through advanced hyperparameter tuning, the model achieved high internal predictive accuracy (mean R² of 0.91-0.94 and root-mean-square error of 60.85 mL CH₄/g volatile solids) and demonstrated strong generalization with an R² of 0.68 on external independent lab-scale datasets, outperforming all individual models. Post-hoc interpretability analysis using permutation importance and Shapley Additive Explanations (SHAP) identified critical factors influencing methane production and stability indicators. A model-based global optimization framework was implemented to tailor optimal operational conditions for real-world scenarios, ensuring high methane yield while maintaining process stability. Additionally, a user-friendly graphical interface was developed to facilitate the practical implementation of the predictive model. This work provides a robust framework for optimizing the AD process with biochar augmentation, enhancing resource recovery and waste management.