Data-Driven Airflow Prediction for Wastewater Treatment Plant Aeration System
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A wastewater treatment plant is an intricate system with a wealth of information, where the aeration system ofthe active sludge process is designed to provide oxygen to microorganisms. Owing to the time delay inbiochemical reactions, adjustments made by operational staff to the airflow often lead to delayed changes indissolved oxygen concentration, frequently causing overaeration. The paper introduces a machine learningmodel that utilizes water quality indicators and air blower indicators to predict current airflow. By leveragingthe airflow predicted by this model, the dissolved oxygen concentration for the next hour is successfullymaintained within the optimal range of 2 mg/L to 4 mg/L. In the case of airflow prediction, the Transformermodel proved more effective than the random forest and long short-term memory models, owing to its selfattentionmodel architecture. In conclusion, the study demonstrates the successful applicability of machinelearning models to predict airflow on the promise of maintaining dissolved oxygen stability. These findingspresent a data-driven approach to guarantee the steady operation of wastewater treatment plants.
Abstract
A wastewater treatment plant is an intricate system with a wealth of information, where the aeration system ofthe active sludge process is designed to provide oxygen to microorganisms. Owing to the time delay inbiochemical reactions, adjustments made by operational staff to the airflow often lead to delayed changes indissolved oxygen concentration, frequently causing overaeration. The paper introduces a machine learningmodel that utilizes water quality indicators and air blower indicators to predict current airflow. By leveragingthe airflow predicted by this model, the dissolved oxygen concentration for the next hour is successfullymaintained within the optimal range of 2 mg/L to 4 mg/L. In the case of airflow prediction, the Transformermodel proved more effective than the random forest and long short-term memory models, owing to its selfattentionmodel architecture. In conclusion, the study demonstrates the successful applicability of machinelearning models to predict airflow on the promise of maintaining dissolved oxygen stability. These findingspresent a data-driven approach to guarantee the steady operation of wastewater treatment plants.