The efficient removal of hydrogen sulfide (H2S) from biogas is crucial for enhancing its quality and usability. This study aims to develop a new, advanced modeling method for the removal efficiency (RE) of H2S in biofilters that requires fewer assumptions and less information than traditional methods. To investigate the effects of various parameters on RE, laboratory experiments were conducted using a biochar packed-bed biofilter, with varying levels of moisture content (MC), empty-bed residence time (EBRT), and H2S concentration. The results revealed that all variables had a significant impact on RE, with the optimal result achieved using biochar at 30% MC, 60 s EBRT, and 180 ppm H2S concentration. Multiple linear regression (MLR) and support vector machine (SVM) methods were employed to model RE, with R2 values of 0.97 and 0.99, respectively. The models accurately predicted the RE of H2S in the biofilter, showcasing the reliability and effectiveness of these advanced modeling techniques. Furthermore, genetic algorithms were utilized for optimization, revealing that an RE of 90-95% could be achieved at various levels of H2S. Overall, this study provides a groundbreaking modelying method for the RE of H2S in biofilters, offering a practical solution for efficient H2S removal from biogas.

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