Small-data-trained model for predicting nitrate accumulation in one-stage partial nitritation-anammox processes controlled by oxygen supply rate

Nitrate (NO₃^--N) accumulation is the biggest obstacle for wastewater treatment via partial nitritation-anammox process. Dissolved oxygen (DO) control is the most used strategy to prevent NO₃^--N accumulation, but the performance is usually unstable. This study proposes a novel strategy for controlling NO₃^--N accumulation based on oxygen supply rate (OSR). In comparison, limiting the OSR is more effective than limiting DO in controlling NO₃^--N accumulation through mathematical simulation. A laboratory-scale one-stage partial nitritation-anammox system was continuously operated for 135 days, which was divided into five stages with different OSRs. A novel deep learning model integrating Gated Recurrent Unit and Multilayer Perceptron was developed to predict NO₃^--N accumulation load. To tackle with the general obstacle of limited environmental samples, a generic evaluation was proposed to optimise the model structure by leveraging predictive performance and overfitting risk. The developed model successfully predicted the NO₃^--N accumulation in the system ten days in advance, showcasing its potential contribution to system design and performance enhancement.