Enhancing Sequencing Batch Reactor Performance Under Organic Shock Loading: A Comparative Analysis Using Machine Learning and STOAT Simulations for Improved Wastewater Management

Abstract

ABSTRACT Sequencing batch reactors (SBRs) are systems that are used in municipal wastewater treatment that maintain consistent performance, even under varying organic shock loading (OSL). This study analyzes the application of machine learning (ML) with STOAT software simulations to improve SBR performance by predicting effluent quality under flocculating OSL scenarios. Comparative analysis was carried out using ML models and STOAT software simulations to evaluate their efficiency. The first tests revealed that the 2× and 1.6× applied loads exceeded effluent environmental requirements; therefore, OSL was reduced to 1.3× of the influent organic load that complied with the environmental limits. Both ML models and STOAT software simulations results were closely aligned; this shows that these methods are effective for predicting SBR WWTP performance with minimal effort and no cost. These results highlight that ML models and STOAT software can be practical tools for reducing operational risk and improving decision‐making when varying OSL conditions.