Adaptive control framework for intelligent water treatment plants integrating optical sensing and reinforcement learning

The intelligent water treatment plant represents a critical component of modern urban infrastructure, addressing the increasing demands for clean water and sustainable resource management. This study proposes an adaptive control framework that integrates optical sensing technologies with reinforcement learning (RL) algorithms, enabling real-time monitoring and optimization of water treatment operations. Optical sensors provide high-precision data on water quality parameters, such as turbidity, color, and chemical concentrations, which are used as critical inputs to the RL-based control system. The RL model dynamically adjusts operational parameters, such as chemical dosing and pump speeds, to achieve improved water quality, enhanced energy efficiency, and reduced operational costs. A deep reinforcement learning approach, particularly Proximal Policy Optimization (PPO), was employed to handle the complex, nonlinear dynamics without requiring explicit process models. A high-fidelity simulation environment replicating key water treatment processes was developed to train and validate the RL model. Real-world experiments conducted in a water treatment facility with a daily capacity of 50,000 m³ demonstrated a 20% improvement in turbidity reduction and a 15% reduction in energy consumption compared to traditional PID control systems. The results emphasize the significant advancements in operational efficiency and sustainability achieved through the proposed RL-based control framework. By integrating optical sensing for precise water quality monitoring, this approach bridges the gap between advanced sensing technologies, machine learning techniques, and practical applications in water treatment.