Small-data-trained model for predicting nitrate accumulation in one-stage partial nitritation-anammox processes controlled by oxygen supply rate
Year: 2024
Type: article
Abstract: • Oxygen supply rate outperforms dissolved oxygen in controlling NO 3 − -N accumulation. • Develop deep learning models to predict NO 3 − -N accumulation in PN/A systems. • Develop a metric to optimize model predictive accuracy and avoid overfitting risk. Nitrate (NO 3 − -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 NO 3 − -N accumulation, but the performance is usually unstable. This study proposes a novel strategy for controlling NO 3 − -N accumulation based on oxygen supply rate (OSR). In comparison, limiting the OSR is more effective than limiting DO in controlling NO 3 − -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 NO 3 − -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 NO 3 − -N accumulation in the system ten days in advance, showcasing its potential contribution to system design and performance enhancement.
Source: Water Research
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,520
OpenAlex ID: W4404355802