Harnessing the Influence of Pressure and Nutrients on Biological CO2 Methanation Using Response Surface Methodology and Artificial Neural Network-Genetic Algorithm Approaches

The biological methanation process has emerged as a promising alternative to thermo-catalytic methods due to its ability to operate under milder conditions. However, challenges such as low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain methane production yield. This study investigates the combined effects of trace element concentrations and applied pressure on biological methanation, addressing their synergistic interactions. Using a face-centered composite design, batch mode experiments were conducted to optimize methane production. Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) approaches were employed to model and optimize the process. RSM identified optimal ranges for trace elements and pressure, while ANN-GA demonstrated superior predictive accuracy, capturing nonlinear relationships with a high R-2 (>0.99) and minimal prediction errors. ANN-GA optimization indicated 97.9% methane production efficiency with a reduced conversion time of 15.9 h under conditions of 1.5 bar pressure and trace metal concentrations of 25.0 mg/L Fe(II), 0.20 mg/L Ni(II), and 0.02 mg/L Co(II). Validation experiments confirmed these predictions with deviations below 5%, underscoring the robustness of the models. The results highlight the synergistic effects of pressure and trace metals in enhancing gas-liquid mass transfer and enzymatic pathways, demonstrating the potential of computational modeling and experimental validation to optimize biological methanation systems, contributing to sustainable methane production.