Artificial Intelligence for Predicting the Performance of Adsorption Processes in Wastewater Treatment: A Critical Review

Wastewater treatment is crucial to ensure clean and safe water for various applications. Adsorption is a commonly used method for removing pollutants from wastewater. Conventional methods for optimizing adsorption processes rely on trial-and-error approaches and empirical models. With the advent of artificial intelligence (AI), recent studies have shown potential in optimizing pollutant adsorption processes. This review provides a critical analysis of AI applications in optimizing the pollutant adsorption process in wastewater treatment. Modern AI methods, including machine learning and neural networks, are discussed, along with how they can be used to model pollutant adsorption. The review additionally addresses the difficulties and potential areas for further study in this area. The findings can offer valuable insights for researchers and practitioners to improve the effectiveness of pollutant adsorption processes in wastewater treatment. The review concludes that AI-aided adsorption processes have the potential to enhance wastewater treatment by providing accurate predictions of adsorption performance, reducing costs and time, and optimizing resource utilization.