Advances in Artificial Intelligence Applications in Sustainable Water Remediation

A rapidly emerging field of study is that of artificial intelligence (AI). Various applications of human intelligence are simulated by this method. In comparison to the way things have always been done, artificial intelligence is progressing at a breakneck pace. AI has demonstrated its value in a variety of industries, including agriculture, vehicle manufacturing, banking and finance, space exploration, and artificial creativity. Wastewater treatment will soon be able to make use of AI’s increased speed, efficiency, and independence from human intervention. In the wastewater industry, this technique has been used to track the efficiency of wastewater treatment facilities; determine biochemical oxygen demand (BOD) and chemical oxygen demand (COD); eliminate nitrogen, phosphorus, and sulfur and turbidity and hardness; and capture pollutants. There are three AI models that are commonly utilized in the wastewater industry: artificial neural networks (ANN), fuzzy logic algorithms (FL), and genetic algorithms (GA). For COD, BOD, heavy metals, and organics cleanup, studies show that ANN and hybrid intelligent systems may achieve 0.99 determination coefficient values. This book chapter describes research that has improved the accuracy of pollutant removal percentages by 84% to 90% using various models of artificial intelligence (AI) in wastewater treatment, as well as perspectives on the future directions of novel research in the field, with a focus on pollution remediation, cost-effectiveness, energy economy, and water management.