Modeling and experimental evaluation of biochar-mediated biofiltration for hydrogen sulfide capture from biogas.

Efficient hydrogen sulfide (H₂S) removal is critical for enhancing biogas quality and enabling its utilization. This study proposes a novel, advanced modeling method for predicting H₂S removal efficiency (RE) in biofilters. This method requires fewer assumptions and less input data compared to traditional approaches. To investigate the influence of key parameters on RE, laboratory experiments were conducted using a biochar packed-bed biofilter. The experiments varied moisture content (MC), empty-bed residence time (EBRT), and influent H₂S concentration. All three variables significantly impacted RE, with optimal removal achieved using biochar with 30% MC, 60 seconds EBRT, and 180 ppmv H₂S. Multiple linear regression (MLR) and support vector machine (SVM) techniques were employed to model RE, achieving high accuracy with R² values of 0.97 and 0.99, respectively. These models effectively predicted H₂S removal in the biofilter, demonstrating the reliability and effectiveness of the proposed advanced modeling approach. Additionally, genetic algorithms were used for optimization, suggesting the feasibility of attaining 90-95% RE across a range of H₂S concentrations. Overall, this study introduces a groundbreaking modeling method for H₂S RE in biofilters, offering a practical solution for efficient biogas desulfurization.