Application of Artificial Intelligence Optimization Algorithms Coupled with Machine Learning in Water Treatment Process Optimization and Water Quality Prediction

Abstract

The increasing scarcity of water resources and the continuous aggravation of water pollution have made the efficient optimization of water treatment processes and the accurate prediction of water quality parameters critical technical supports for ensuring urban and rural water supply safety. Traditional water treatment operation modes generally exhibit limitations such as unstable treatment performance, high energy and chemical consumption, and limited precision in automated control when addressing complex water quality variations and multi-objective synergistic optimization requirements. In recent years, the deep integration of artificial intelligence optimization algorithms with machine learning methods has provided a data-driven new pathway for the intelligent upgrading of water treatment systems. This paper systematically reviews the fundamental principles and application characteristics of typical intelligent optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Harris Hawks Optimization (HHO). It focuses on analyzing the fusion strategies and operational mechanisms of these algorithms with predictive models such as deep neural networks (DNN), Support Vector Machines (SVM), and Random Forests (RF). Furthermore, the paper elaborates on the practical application effectiveness of this technical framework in process parameter optimization, water quality variation forecasting, and multi-objective system scheduling. Finally, future development directions are proposed to address existing challenges, including data reliability, model generalizability, and computational costs, providing theoretical references for related research and engineering implementation.