AI in Wastewater Treatment Applications

The infusion of artificial intelligence (AI) in wastewater treatment (WWT) is a revolutionary tool that plays a potential role in filling this gap against ever-increasing water pollution, rising urbanization, and the needs for sustainable environmental management. Traditional WWT processes have been known in the art and may work well in practice, but typically require manual intervention, and all the variables in the process (chemicals, pH, temperature, sludge levels) are all based simply on empirical models which are not efficient for dynamic and complex treatment processes. AI is now being used in all phases of WWT including influent quality prediction, real-time monitoring, anomaly identification, process control, and energy saving. Machine learning (ML), artificial neural networks (ANNs), fuzzy logic (FL), and evolutionary computation methods are more and more used to predict biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient removals, and to manage the sludge. Furthermore, AI-based systems automate sensor networks and Internet of Things (IoTs) devices for real-time control with reduced need for human intervention. AI signifies a revolution in WWT by facilitating intelligent and sustainable systems management. With the increasing computational capacity and data being generated, it is anticipated that the applications of AI will be more advanced, such as in smart water infrastructure. AI techniques relevant to WWT and various applications have been discussed in this chapter.