A DIGITAL TWIN FRAMEWORK FOR INTELLIGENT WATER TREATMENT, QUALITY MONITORING, AND AUTONOMOUS CONTROL

Abstract

Water scarcity, contamination, and rising operational demands require modern utilities to adopt intelligent and sustainable water management strategies. Conventional Supervisory Control and Data Acquisition (SCADA) systems offer real-time monitoring but lack predictive analytics and autonomous decision-making. To overcome these limitations, this study proposes a comprehensive Digital Twin (DT) framework that integrates Internet of Things (IoT)-based sensing, hybrid physics–machine learning (ML) modeling, and AI-driven control for real-time simulation, predictive optimization, and closed-loop water treatment management. The DT continuously synchronizes with the physical infrastructure, fusing multi-source sensor and laboratory data to model key parameters such as turbidity, pH, and residual chlorine. An AI-based control layer employing Model Predictive Control (MPC) and Reinforcement Learning (RL) autonomously optimizes chemical dosing and energy usage. The framework is validated on a pilot-scale water treatment setup, demonstrating a 74% reduction in RMSE for turbidity prediction, 12% decrease in chemical consumption, and 10% reduction in energy usage, while improving anomaly detection accuracy to 95%. Moreover, the system enhances compliance with SDG 6.1.1 and 6.3.2 indicators by ensuring consistent water quality and operational efficiency. The proposed DT establishesafoundationfornext-generationsmartwatersystemscapable of self-learning, adaptive control, and sustainable performance aligned with global water management goals.