Use, Potential, Needs, and Limits of AI in Wastewater Treatment Applications

Artificial intelligence (AI) uses highly powerful computers to mimic human intelligent behavior; it is a major research hotspot in science and technology, with an increasing number of applications to a wider range of fields, including complex process supervision and control. Wastewater treatment is an example of a complex process involving many uncertainties and external factors to achieve a final product with specific requisites (effluents with prescribed quality). Reducing process energy consumption, greenhouse gas emissions, and resources recovery are additional requirements of these facilities' operation. AI could extend the purpose and the expected results of previously adopted tools and present operational approaches by leveraging superior simulation, prediction, control, and adaptation capabilities. This paper reviews current AI research in the wastewater field and discusses present achievements and potentials. So far, almost all applications in the sector involve predictive studies, often at a small scale or with limited data use. Frontline research aimed at the creation of AI-supported digital twins of real systems is being conducted, with few encouraging but still limited applications. This paper aims at identifying and discussing key barriers to wider AI adoption in the field, which include laborious instrumentation maintenance, lack of process expertise in the design of current software, instability of control loops, and insufficient incentives for resource efficiency achievement.