ABSTRACT
Water is one of the important resources on earth, essential for all life forms and over the years there has been a decline in its availability and accessibility. Wastewater treatment is one of the approaches to resolve its availability-based issues involving collection, treatments and reuse of water for less potential utilities. In recent years there has been use of artificial intelligence (AI) and machine learning (ML) applications to address wastewater collection and treatment. These AI/ML methods help in the analysis of different parameters (physical, biological, chemical) using AI algorithms. There has also been use of applications like Internet of Things (IoT), sensors, and systems based on these technologies. This chapter discusses applications of AI by using different models, such as artificial neural network (ANN), random forest (RF), radial basis function (RBF) kernel, and adaptive neuro-fuzzy inference systems (ANFIS), that help to analyse the data and predict the best adsorbent that can be used for the treatment of wastewater. As per the studies, ANN and ANFIS are known to produce the best results with R 2 values of 0.9991 and 0.9997. The chapter also highlights the adsorption process associated with the removal of pollutants such as heavy metals, dyes, chemicals, etc., while focusing on the demerits of traditional techniques used for wastewater treatment. Furthermore, limitations to be addressed, such as poor data availability, modelling and analysis in certain methodologies associated with the adsorption process, are also discussed.
