Machine learning application in municipal wastewater treatment to enhance the performance of a sequencing batch reactor wastewater treatment plant

Abstract

Municipal wastewater treatment plants (WWTPs) with sequencing batch reactors (SBRs) face many challenges due to organic shock load (OSL) flocculation caused by population growth and industrialization. Guaranteeing that effluent quality remains within regulatory limits is vital for environmental protection and public health. Using conventional methods for managing variations in OSL faces a lot of difficulties, specifically when it comes to accurately predicting the effluent quality that complies with regulatory standards. This study addressed this by integrating a machine learning (ML) model, to anticipate how varying OSL can affect the effluent quality of an operational SBR WWTP located in Egypt. The novelty of this research lies in using ML to predict the system's performance when applied to different OSL scenarios, showing a dynamic method for SBR optimization operations. Initial trials with OSL values of 2× and 1.6× the actual influent levels resulted in non-compliance with regulatory standards, whereas the optimal OSL was determined to be 1.3×. The study illustrates that the incorporation of ML into the process results in superior plant performance and greater decision-making amid variable settings, presenting an innovative approach for employing data-driven models in municipal wastewater treatment, and yielding fresh perspectives on the improvement of WWTP operations.