Integration of Experimental Analysis and Predictive Modeling with Crayfish Optimization for Enhanced Biogas and Methane Production in Anaerobic Digestion

This study presents an integrated optimization framework for enhancing biogas and methane production through anaerobic digestion, addressing the challenge of identifying optimal operating conditions across multiple interacting parameters. Biochemical methane potential tests were conducted to evaluate the individual effects of four critical operational parameters: temperature, mixing regime, inoculum-to-substrate (I-S) ratio, and chemical oxygen demand load (COD-L). Experimental findings confirmed that thermophilic conditions, mixing once a day, I-S ratio of 1:2, and moderate COD loading consistently delivered the most favorable biogas and methane yields. Kinetic modeling, including the Modified Gompertz and Logistic models, showed strong predictive agreement with experimental data (R² > 0.90), reliably capturing production dynamics across all tested conditions. Polynomial response surface methodology further identified COD-L as the dominant driver of methane yield, with optimal operating conditions falling within moderate temperature and COD-L ranges. This revealed significant nonlinear interactions between parameters. Building on these findings, the Crayfish Optimization algorithm successfully determined global optimal conditions, achieving a maximum biogas production of 0.371 Nm³/kg.VS. These results highlight how combining experimental investigation with predictive modeling and metaheuristic optimization creates a powerful decision-support framework for improving the efficiency and stability of anaerobic digestion systems.