A synergistic framework integrating statistical design and machine learning for enhancing biogas yield

• ML and RSM were integrated to optimize biogas from microwave-treated sludge • XGBoost captured complex nonlinear effects on anaerobic digestion performance • Moderate microwave power enhanced yield; excessive power caused inhibition • Non-thermal microwave effects were isolated using equal-temperature controls • ML control strategies improve energy efficiency and AD process scalability Anaerobic digestion (AD) of waste-activated sludge is hindered by slow hydrolysis and complex sludge structure, which limit biogas recovery and threaten process stability. Although pretreatment methods such as microwave pretreatment (MP) have shown promise, their energy efficiency and underlying nonthermal effects remain poorly understood. In parallel, existing predictive models often neglect pretreatment conditions, restricting their applicability for process optimization. To bridge these gaps, this study proposes a synergistic framework that integrates statistical optimization with advanced machine learning (ML) to enhance biogas production from MP-assisted AD. Response Surface Methodology (RSM) identified optimal pretreatment conditions—holding time, pH, total solids (TS%), and microwave power—predicting a cumulative biogas yield (CBY) of 1020.5 mL/gVS, closely validated by experiments (1013.8 mL/gVS). These optimized datasets were further used to train ensemble ML models (AdaBoost, LightGBM, XGBoost), achieving high predictive accuracy (XGBoost R 2 = 0.992) with strong external validation. SHapley Additive exPlanations (SHAP) revealed TS% as the dominant factor, contrasting with RSM’s linear emphasis on microwave power, thereby highlighting nonlinear interactions and hidden effects. Complementary fluorescence microscopy and ImageJ analysis confirmed microwave-induced structural disruption of extracellular polymeric substances (EPS) and microbial accessibility, particularly at moderate power (600 W), evidencing distinct nonthermal contributions. By combining RSM’s statistical optimization with ML’s predictive and interpretive capacity, this framework not only refines operational conditions for maximum yield but also elucidates the mechanisms of MP, balancing energy input and performance. These insights support the design of more energy-efficient, scalable, and sustainable AD systems.