Xuefei Li, Changqing Liu, Shuqi Liu, and Sheng Miao, Journal of Information Processing Systems Vol. 21, No. 2, pp. 193-203, Apr. 2025  

https://doi.org/10.3745/JIPS.04.0344
Keywords: Airflow Prediction, Decision support, Machine Learning, Wastewater treatment plant
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Abstract

A wastewater treatment plant is an intricate system with a wealth of information, where the aeration system of the active sludge process is designed to provide oxygen to microorganisms. Owing to the time delay in biochemical reactions, adjustments made by operational staff to the airflow often lead to delayed changes in dissolved oxygen concentration, frequently causing overaeration. The paper introduces a machine learning model that utilizes water quality indicators and air blower indicators to predict current airflow. By leveraging the airflow predicted by this model, the dissolved oxygen concentration for the next hour is successfully maintained within the optimal range of 2 mg/L to 4 mg/L. In the case of airflow prediction, the Transformer model proved more effective than the random forest and long short-term memory models, owing to its selfattention model architecture. In conclusion, the study demonstrates the successful applicability of machine learning models to predict airflow on the promise of maintaining dissolved oxygen stability. These findings present a data-driven approach to guarantee the steady operation of wastewater treatment plants.

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