AI-Driven Wastewater Treatment: A Review of Predictive Models, Hybrid Approaches, and Optimization Strategies

This paper explores the application of artificial intelligence (AI) models in wastewater treatment (WWT) processes, focusing on their potential to predict treatment outcomes, optimize processes, and ensure compliance with water quality standards. Various studies across different treatment process areas, including coagulation, adsorption, and biological treatment, are reviewed to highlight the diverse applications of AI-driven approaches in WWT. The reviewed papers were systematically collected from reputable academic databases, including Scopus, Web of Science, and Google Scholar. Despite facing challenges such as reproducibility concerns and data quality limitations, ongoing advancements in AI technology underscore its transformative potential in revolutionizing WWT practices. Collaborative efforts to refine and deploy AI-driven solutions hold promise for improving environmental sustainability and safeguarding public health. By harnessing the predictive capabilities of AI, WWT facilities can streamline operations, maximize resource utilization, and ultimately contribute to a healthier, cleaner environment for present and future generations. This research underscores the importance of ongoing research and innovation to leverage AI and optimize WWT processes.