Challenges and requirements of AI-based waste water treatment systems

Abstract

Artificial Intelligence (AI) has emerged as a promising tool for enhancing the efficiency, accuracy, and sustainability of water treatment systems. However, integrating AI into water treatment comes with its own set of challenges, and specific requirements must be met to fully utilize the potential of these techniques. This study delves into the complexities associated with implementing AI in waste water treatment (WWT) and the necessary prerequisites for developing effective AI-based solutions. The most commonly utilized AI techniques in WWT applications fall under the umbrella of supervised Machine Learning (ML). Supervised ML models serve as excellent tools (correlation coefficient >0,8) for modeling, predicting, and optimizing WWT processes. They have a wide range of applications, including data cleansing, system design, control optimization and predictive maintenance. ML models are particularly useful in optimizing process parameters with significant energy savings achievable (up to 30 % reported in literature). The main challenges for the implementation of such models in WWT are: quality data availability, efficient data management along the data chain and the choice of appropriate ML models. These challenges are highlighted with two concrete examples in the field of water reuse for microalgae cultivation and predictive maintenance of cooling towers. These examples showcase the diverse range of potential use cases for AI and machine learning, especially in wastewater applications.