Modeling nitrogen removal performance based on novel microbial activity indicators in WWTP by machine learning and biological interpretation

Modeling the pollutant removal performance of wastewater treatment plants (WWTPs) plays a crucial role in regulating their operation, mitigating effluent anomalies and reducing operating costs. Pollutants removal in WWTPs is closely related to microbial activity. However, there is extremely limited knowledge on the models accurately characterizing pollutants removal performance by microbial activity indicators. This study proposed a novel specific oxygen uptake rate (SOURATP) with adenosine triphosphate (ATP) as biomass. Firstly, it was found that SOURATP and total nitrogen (TN) removal rate showed similar fluctuated trends, and their correlation was stronger than that of TN removal rate and common SOURMLSS with mixed liquor suspended solids (MLSS) as biomass. Then, support vector regressor (SVR), K -nearest neighbor regressor (KNR), linear regressor (LR), and random forest (RF) models were developed to predict TN removal rate only with microbial activity as features. Models utilizing the novel SOURATP resulted in better performance than those based on SOURMLSS. A model fusion (MF) algorithm based on the above four models was proposed to enhance the accuracy with lower root mean square error (RMSE) of 2.25 mg/L/h and explained 75% of the variation in the test data with SOURATP as features as opposed to other base learners. Furthermore, the interpretation of predictive results was explored through microbial community structure and metabolic pathway. Strong correlations were found between SOURATP and the proportion of nitrifiers in aerobic pool, as well as between heterotrophic bacteria respiratory activity (SOURATP_HB) and the proportion of denitrifies in anoxic pool. SOURATP also displayed consistent positive responses with most key enzymes in Embden-Meyerhof-Parnas pathway (EMP), tricarboxylic acid cycle (TCA) and oxidative phosphorylation cycle. In this study, SOURATP provides a reliable indication of the composition and metabolic activity of nitrogen removal bacteria, revealing the potential reasons underlying the accurate predictive result of nitrogen removal rates based on novel microbial activity indicators. This study offers new insights for the prediction and further optimization operation of WWTPs from the perspective of microbial activity regulation.