Rethinking Activated Sludge Modeling: A Critical Review of Modeling Strategies and the Role of Hybrid Integration

Efficient control of wastewater treatment is vital for achieving the United Nations' Sustainable Development Goals (SDGs). Increasing industrialization and population growth threatens aquatic ecosystems, necessitating effective clean water solutions and enhanced efficiency through advanced technologies. These technologies need to focus on innovative methods to reduce energy use and reliance on external chemicals. Conventional mechanistic (henceforth referred to as white-box) modeling techniques, such as Activated Sludge Models, have significantly advanced our understanding of activated sludge processes by providing a structured and mechanistic framework. However, challenges remain in capturing dynamic microbial interactions and responding to influent variability, particularly in real-world applications that are outside the realm of the mechanistic framework. This paper explores the use of artificial intelligence (AI) (henceforth referred to as black-box) techniques in modeling practices, particularly machine learning, a subset of AI, to address the limitations faced by white-box models. AI models provide a powerful data-driven approach to wastewater treatment modeling, enabling them to identify complex patterns from large datasets. However, their effectiveness depends on high-quality training data, and their reliance on statistical correlations limits interpretability. This review advocates for the integration of hybrid modeling in the current modeling practice, which combines empirical patterns with mechanistic understanding, to enhance the predictive capabilities and robustness of existing models. Hybrid models can provide more accurate and adaptable solutions for wastewater treatment challenges by employing advanced AI techniques alongside mechanistic frameworks. The review also introduces a perspective framework with the incorporation of multi-omics data in a hybrid digital twin framework, which would enhance wastewater treatment efficiency through proactive monitoring, anomaly detection, and improved decision-making.