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

Fermentation, Год журнала: 2025, Номер 11(1), С. 43 - 43

Опубликована: Янв. 18, 2025

The biological methanation process has emerged as a promising alternative to thermo-catalytic methods due its ability operate under milder conditions. However, challenges such low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain methane production yield. This study investigates combined effects of concentrations applied pressure on methanation, addressing their synergistic interactions. Using face-centered composite design, batch mode experiments were conducted optimize production. Response Surface Methodology (RSM) Artificial Neural Network (ANN)—Genetic Algorithm (GA) approaches employed model process. RSM identified optimal ranges elements pressure, while ANN-GA demonstrated superior predictive accuracy, capturing nonlinear relationships with high R² (>0.99) minimal prediction errors. optimization indicated 97.9% efficiency reduced conversion time 15.9 h conditions 1.5 bar metal 25.0 mg/L Fe(II), 0.20 0.02 Co(II). Validation confirmed these predictions deviations below 5%, underscoring robustness models. results highlight metals in enhancing gas–liquid mass transfer enzymatic pathways, demonstrating potential computational modeling experimental validation systems, contributing sustainable

Язык: Английский

Nano-Selenium Modified Green Eggshell Biochar Reduces Cadmium Accumulation in Shallots (Allium schoenoprasum L.)

Environmental Research, Год журнала: 2025, Номер 277, С. 121635 - 121635

Опубликована: Апрель 17, 2025

Язык: Английский