AI-Based Modeling for Predicting the Disinfection By-Products in Water

Disinfection by-products (DBPs) are chemical compounds arising from the reaction between disinfectants, like chlorine, and organic and inorganic substances in water. Consuming these by-products in high concentrations over extended periods can potentially pose health hazards. Water treatment plants must diligently monitor and regulate the creation of these by-products to guarantee the safety of drinking water. More than 250 DBPs have been identified, although the behavioral characteristics of only around 20 DBPs are sufficiently understood. Over the past 20 years, numerous modeling efforts have been undertaken to forecast the presence of DBPs in water. Artificial intelligence (AI) modeling has become an up-and-coming method for accurately predicting the development of DBPs in water treatment systems. Through the utilization of sophisticated algorithms and data analysis, these models can assist water treatment plants in optimizing their disinfection procedures and reducing the likelihood of the development of dangerous by-products. This chapter aims to evaluate prediction models for DBPs, assess their strengths and weaknesses, and explore their potential use as tools for analyzing water treatment, conducting epidemiological studies, and addressing regulatory issues. The future research requirements in this field are also discussed.