IoT-Enabled Real-Time Monitoring and Automated Control of pH and Turbidity in Municipal Water Treatment Plants

Abstract

Efficient monitoring and control of water quality parameters are essential for ensuring safe and reliable operation of municipal water treatment plants. This study presents an Internet of Things (IoT)-enabled real-time monitoring and automated control system for managing pH and turbidity in municipal water treatment processes. The proposed framework integrates smart sensors, wireless communication networks, cloud-based data processing, and automated control mechanisms to continuously monitor water quality and optimize treatment performance under varying operational conditions. IoT-enabled sensing units were deployed to measure critical parameters such as pH, turbidity, temperature, and flow rate in real time, enabling continuous assessment of treatment efficiency and water quality stability. The automated control system was designed to regulate chemical dosing, coagulation-flocculation processes, and filtration operations based on sensor-driven feedback and intelligent decision-making algorithms. Experimental and simulation analyses demonstrated significant improvements in monitoring accuracy, treatment responsiveness, and operational efficiency compared to conventional manual monitoring approaches. The system effectively minimized water quality fluctuations, reduced chemical overuse, and enhanced process reliability through adaptive real-time control strategies. Furthermore, cloud-based data integration and remote monitoring capabilities enabled predictive maintenance, operational transparency, and rapid detection of abnormal treatment conditions. Comparative evaluation indicated improved turbidity removal efficiency, stable pH regulation, and reduced operational costs through intelligent automation and continuous process optimization.