A data-driven approach for the comprehensive analysis of process parameter effects on industrial methane production

The transition to renewable energy is critical in addressing global energy demands and environmental challenges. Methane production through anaerobic digestion is a promising bioenergy source. However, maximizing methane yields requires precise control and optimization of process parameters. This study presents an integrated approach using Artificial Neural Networks (ANN) for accurate prediction and Particle Swarm Optimization (PSO) for optimizing process variables to enhance methane production. A comprehensive dataset comprising key input variables such as Total Solids (TS), Volatile Solids (VS), Volatile Fatty Acids (VFA), Alkalinity (Alk), and pH was used. The developed ANN model achieved a high predictive accuracy (R² > 0.98). The PSO algorithm, coupled with the ANN, identified the optimal combination of input parameters, leading to a significant increase in methane yield by 5.16 %. The results demonstrate the efficacy of the ANN-PSO framework in improving bioenergy production processes. The effect of various process parameter on methane production was studied by performing the sensitivity analysis of the optimized scenario.