A soft-sensor approach for predicting an indicator virus removal efficiency of a pilot-scale anaerobic membrane bioreactor (AnMBR)

Abstract

The anaerobic membrane bioreactor (AnMBR) is a promising technology for not only water reclamation but also virus removal; however, the virus removal efficiency of AnMBR has not been fully investigated. Additionally, the removal efficiency estimation requires datasets of virus concentration in influent and effluent, but its monitoring is not easy to perform for practical operation because the virus quantification process is generally time-consuming and requires specialized equipment and trained personnel. Therefore, in this study, we aimed to identify the key, monitorable variables in AnMBR and establish the data-driven models using the selected variables to predict virus removal efficiency. We monitored operational and environmental conditions of AnMBR in Sendai, Japan and measured virus concentration once a week for six months. Spearman's rank correlation analysis revealed that the pH values of influent and mixed liquor suspended solids (MLSS) were strongly correlated with the log reduction value of pepper mild mottle virus, indicating that electrostatic interactions played a dominant role in AnMBR virus removal. Among the candidate models, the random forest model using selected variables including influent and MLSS pH outperformed the others. This study has demonstrated the potential of AnMBR as a viable option for municipal wastewater reclamation with high microbial safety. HIGHLIGHTS Virus removal efficiency in the AnMBR was monitored.; The data-driven model to predict virus removal efficiency was built.; The random forest model had the highest prediction performance.; The pH values of influent and MLSS strongly correlated with virus removal efficiency.;

Keywords

• anaerobic membrane bioreactor
• data-driven modeling
• enteric virus
• virus removal efficiency
• wastewater reclamation