Main Applications of Machine Learning in Sludge Anaerobic Digestion: From Process Optimization to Intelligent Decision-Making

• • Hybrid ML models and deep learning achieve high-precision methane yield prediction, with reported R² values exceeding 0.95 in recent studies (e.g., stacking ensemble approaches), enabling reliable biogas forecasting for energy recovery optimization. • • Soft-sensing models using easy-to-measure parameters (e.g., pH, temperature) enable real-time estimation of volatile fatty acids (VFA) and total ammonia nitrogen (TAN) with errors below 10%, facilitating early warning of digester instability. • • Coupling surrogate models with optimization algorithms (e.g., PSO, genetic algorithms) allows dynamic adjustment of co-digestion ratios and pretreatment conditions, improving methane yield by up to 20% compared to static operation. • • Interpretable ML methods (e.g., SHAP, LIME) identify key process variables, enhancing model transparency and operator trust, which is critical for industrial adoption and regulatory compliance.