Abstract
Different organic and inorganic contaminants such as heavy metals, nutrients, and persistent organic pollutants are the major cause of water pollution. For the optimization and modelling of the water pollutants eradication, artificial intelligence (AI) has been employed as significant methodologies. The AI has achieved tremendous advancements in water treatment due to its crucial significance in the experimental design, which produces optimized operational variables. Similarly, the pollutant removal process is frequently used for the prediction of artificial neural networks (ANNs) owing to their self-adapting and self-learning capability. Genetic algorithms (GAs) and particle swarm optimization (PSO) are equally important AI tools in the quest for global goals. This chapter explains the basic advantages and limitations of AI tools, along with the deep comprehension of AI methodologies for optimization and modelling of pollutant removal techniques in wastewater treatment by employing fuzzy neural network, multilayer perceptron, self-organizing map networks, and radial basis function. Additionally, the analysis concludes that hybrid models of ANNs with GA and PSO can be effectively used in water treatment with reasonable assurances. Lastly, the limitations of current AI methods in the wastewater treatment and environmental protection are discussed.
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