Smart Chemical Dosing System Using Reinforcement Learning for Energy-Efficient Coagulation Process Control

Abstract

Efficient chemical dosing in coagulation processes is critical for maintaining drinking water treatment performance while minimizing chemical consumption, sludge generation, and operational energy demand. Conventional dosing strategies often struggle to adapt to dynamic variations in raw water quality, leading to inefficient process control and increased treatment costs. This study presents a smart chemical dosing system using reinforcement learning for energy-efficient coagulation process control in water treatment applications. The proposed framework integrates real-time water quality monitoring, intelligent control algorithms, and adaptive reinforcement learning techniques to optimize coagulant dosage and operational parameters under varying treatment conditions. Key process variables, including turbidity, pH, temperature, flow rate, organic matter concentration, and coagulant characteristics, were continuously monitored and analyzed to support autonomous decision-making and dynamic process optimization. The reinforcement learning model was trained to identify optimal dosing strategies by maximizing treatment efficiency while minimizing chemical usage and energy consumption. Experimental and simulation analyses demonstrated significant improvements in turbidity removal efficiency, coagulant utilization, and process stability compared to conventional rule-based and manual dosing approaches. The intelligent control system also enabled rapid adaptation to fluctuations in influent water quality and reduced the occurrence of overdosing and underdosing conditions. Comparative evaluation indicated substantial reductions in operational costs, sludge production, and energy demand through optimized coagulation control strategies. Furthermore, the integration of predictive analytics and automated process adjustment enhanced treatment reliability and operational sustainability in continuous water treatment operations.