… To address these issues, scientists have explored various solutions, among which artificial
intelligence models have emerged as a promising approach [20]. These models facilitate …

… This investigation tries to generate the concept of the artificial intelligence on the basis of
the predictive model for eradication of AY23 in water by utilizing a collection of chosen …

… the number of measured data, artificial intelligence techniques have been intensively used in
… This review paper investigated artificial intelligence techniques integrated with optimization …

… Abstract— Pollutant removal percentage is a key parameter for every WWTPs, and it is
crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can …

… Water pollution is the foremost cause of water squander as it wastes the chance for water to
… the color removal and training artificial results to ensure the tested results. Artificial neural …

… of water pollution monitoring in the Yangtze River Basin. Consequently, artificial intelligence
… value to improve the efficiency of sewage pollutant removal, reduce the cost of sewage …

… in the removal of organic and inorganic pollutants [6, 23]. Organic compounds include
chlorinated ethane, chlorinated hydrocarbons, poly- and monoaromatic hydrocarbons, and …

… to propose a sustainable air pollutant removal configuration that integrates economic, …
implementing an interpretable data-guided artificial intelligence design approach. A generic …

… their ability to remove persistent organic pollutants. In the case of pharmaceutical pollutants,
for … In this work, applications of artificial intelligence techniques in modelling electrochemical …

… Pollutant phase transfer, not degradation, is what drives the absorption process. … removal
of soluble organic chemicals, including synthetic azo dyes. Notably, the removal of pollutants …