Emerging AI Technologies in Wastewater Treatment

The potential for transforming conventional approaches and addressing urgent challenges is great when the Internet of Things (IoT) is integrated into areas related to the treatment of wastewater treatment and prediction of water quality, especially considering the growing global need for sustainable systems and clean water. Intelligence is evolving at a very quick speed in comparison to normal procedures. The popularity of AI systems may be attributed to their accuracy, precision, and continuous error-free operating capabilities in several fields. Artificial Intelligence is efficacious in domains such as wastewater treatment monitoring. In the context of the aforementioned technology, researchers have been investing persistent efforts in investigating areas like removal and identification of contaminants; estimation of contaminant levels for initiation of the process; and selection of suitable removal technique followed by the best treatment processes based on the nature of the contaminants detected. The evaluation revealed that although many water treatment companies use instrumentation-based automation, there isn’t the required sharing of ideas between researchers and practitioners. Additionally, ecologists, engineers, and IT professionals share cutting-edge water technologies. This technology has been utilized by the wastewater industry to track various aspects of water treatment plant performance, such as efficiency parameters, sulfur and nitrogen elimination, determination of BOD and COD, prediction of hardness and turbidity, uptake of contaminants, etc. According to research, it is possible to remove organics, heavy metals, BOD, and COD with determination coefficient values as accurate as 0.99 with the use of several AI models as well as hybrid intelligent models. This chapter delves into recent research that employs all feasible models of AI adopted in wastewater treatment which interestingly is found to increase contaminant removal efficiency by almost 84–90%. An in-depth discussion of the transformative applications of several IoT technologies is provided in the chapter.