Artificial Intelligence-Driven Optimization of Coagulation-Flocculation Process for Drinking Water Purification

Abstract

The coagulation-flocculation process is a fundamental stage in drinking water treatment, significantly influencing the removal efficiency of suspended solids, turbidity, natural organic matter, and microbial contaminants. This study presents an artificial intelligence-driven optimization framework for enhancing coagulation-flocculation performance in drinking water purification systems. The proposed approach integrates machine learning algorithms with real-time water quality monitoring and process control techniques to optimize coagulant dosage, flocculation conditions, and operational parameters under varying influent water characteristics. Experimental datasets including turbidity, pH, temperature, alkalinity, dissolved organic content, and coagulant concentration were utilized to train and validate artificial intelligence models capable of predicting treatment efficiency and process behavior. Advanced predictive algorithms were employed to identify nonlinear relationships between treatment variables and water quality outcomes, enabling adaptive optimization and intelligent decision-making. Results demonstrated significant improvements in turbidity removal efficiency, chemical consumption reduction, and process stability compared to conventional empirical optimization methods. The AI-driven framework also enhanced operational responsiveness by enabling rapid adjustment of treatment conditions under fluctuating raw water quality scenarios. Comparative analysis indicated improved floc formation characteristics, reduced sludge generation, and enhanced treated water quality through optimized process control strategies.