Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification

Abstract

Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best prediction (R² = 0.8743). Based on the ICML framework, hydraulic retention time (HRT), mixture chemical oxygen demand/total nitrogen (COD/TN = C/N), mixture COD concentration, and pretreatment technology were identified as important features affecting the denitrification performance. Further, tapping point and partial dependence analyses provided the range of key factors that precisely regulate denitrification. In the application analysis, HRT (6–10.5 h), mixture C/N (6–12), and mixture COD concentration (300–600 mg L⁻¹) were the appropriate operating ranges, achieving TN removal of approximately 73 %–77 %. The effluent TN and COD concentrations met the discharge standards for wastewater in China (class 1A) and EU. These findings provide support for regulating excess sludge as internal carbon source to promote denitrification.