Machine learning and adaptive control for optimizing MSW-algal biomass anaerobic co-digestion

Municipal solid waste (MSW)- algal biomass anaerobic co-digestion (AcoD) generates renewable energy and sustainable waste management. However, conventional AcoD systems achieve suboptimal methane yields (typically < 250 L CH₄/kg VS) due to substrate incompatibility, process instability, and lack of real-time control. We developed an integrated framework combining machine learning prediction, stoichiometric-metabolic modeling, and sensor-based adaptive control to optimize lipid-rich algal biomass co-digestion with MSW. A Substrate Synergy Estimation Model (SSEM) employs random forest regression trained on 76 co-digestion scenarios to predict optimal MSW: algae ratios for maximum methane yield. The predicted optimal ratio serves as input to a Dynamic Stoichiometric-Metabolic Coupled Reactor Model (DS-MCRM) that simulates real-time substrate degradation and methane production pathways. A sensor network with adaptive PID control achieved 64% CH₄ purity (vs. 40% in uncontrolled reactors) and reduced VFA accumulation by 53% through zone-specific process adjustments. Isotopic tracing with ¹³C-labeled palmitic acid quantified lipid-to-methane conversion efficiency at 88.6 ± 2.3%, confirming the contribution of algal lipids to overall methane production. Lifecycle assessment using the Waste Stream Circularity Index (WSCI) yielded a score of 0.83, indicating 72% nitrogen recovery, 68% phosphorus recovery, and 76% carbon loop closure. Compared to conventional co-digestion approaches, our integrated framework achieved 2.1× higher methane yield prediction accuracy (R² = 0.91), faster VFA stabilization (day 6 vs. day 12), and superior resource recovery, demonstrating potential for data-driven optimization of waste-to-energy systems.