Explainable Artificial Intelligence Analysis of Simultaneous Anaerobic Nitrogen and Sulfur Removal in Anaerobic Sulfide Oxidation Bioreactor

Biological wastewater treatment systems exhibit a wide range of operating conditions and influent substrate types. Artificial intelligence methods, particularly artificial neural networks (ANNs), are increasingly being employed to manage this complexity. This study uses ANN modeling to identify the key operational parameters that influence the efficacy of anaerobic sulfide oxidation (ASO) biotechnology, which simultaneously treats nitrite and sulfide. The ANN model was further analyzed through SHAP analysis to determine the key operational parameters. The dataset used in this study was derived from previously published operational data of ASO reactors. The results revealed that the sulfide-to-nitrite (S:N) ratio and hydraulic retention time (HRT) had the greatest impact on sulfide removal. In contrast, influent sulfide and nitrite concentrations had no effect on the prediction of effluent pH. While other parameters had a positive effect, HRT had a slight negative impact on effluent pH, with the S:N ratio having the most effect. Furthermore, while other factors contributed to sulfate generation, sulfide influent and HRT had a significant impact. Predicting nitrite-nitrogen (NO2−-N) removal is mostly dependent on the S:N ratio and influent pH. To enhance ASO reactor performance for sulfide and nitrite removal, it is recommended to prioritize the optimization of the S:N ratio and HRT, as these parameters have the greatest impact on key treatment outcomes, including sulfide and NO2−-N removal and sulfate formation.