Potential of AI Optimization in Wastewater Treatment Processes

These days, artificial intelligence (AI) has been proving to be an efficient emerging technology in diverse fields. It is the process of mimicking human intellect for a range of uses. Artificial Intelligence is evolving at a very quick speed in comparison to normal procedures and has proved its importance in several sectors, like finance, aerospace research, agriculture, and computational creativity. AI has now found applications in the wastewater treatment industry because of its effectiveness, speed, and independence from human operations. Optimization of the process control for wastewater treatment is a challenging problem in a majorly nonlinear setting. Some excellent options for optimizing wastewater treatment process control are Adaptive Neuro-Fuzzy Inference Systems, Artificial Neural Networks, Genetic Algorithms, etc., which are famous for their capacity to outperform humans in making certain complex decisions. These AI technologies or ML techniques are employed in various procedures like disinfection, membrane filtration, adsorption, etc., which are necessary processes for the removal of impurities from wastewater. Environmental engineers have been sluggish to implement these optimisation tactics, even though these techniques can enhance effluent quality and reduce expenses. This chapter gives an overview of the most popular AI technologies used in treating wastewater and the literature's current AI applications for the same, putting current process models into practice, handling data, realistically implementing AI, fostering confidence in empirical control techniques, and filling up the gaps in professional education. It also details hybrid AI techniques, that combine different technologies for more efficient working. This chapter concludes by outlining possible limitations and obstacles one may encounter while implementing AI, such as reproducibility challenges, data handling, etc.