Monitoring and Control of Quantity-Quality Dynamics for Resilient Drinking Water Networks

Abstract

The operation of drinking water distribution networks (WDNs) involves complex processes aimed at delivering sufficient water quantity while preserving its safety. These processes are governed by hydraulics and water quality (WQ), with hydraulics focusing on maintaining adequate pressure for efficient delivery and supply, and WQ ensuring compliance with safety standards. The objective of this dissertation is to address the challenges inherent in managing hydraulics and WQ within WDNs through a holistic monitoring and feedback control framework. This framework is based on: (i) advanced WQ modeling of the system's disinfectant (i.e., chlorine), incorporating nonlinear multi-species dynamics for a more realistic representation of real-world scenarios, including contamination events; (ii) acknowledging the dependency between the hydraulic and WQ dynamics and analyzing its influence on system performance, and using these insights to guide; (iii) the development of a joint control approach with the objective of obtaining optimal hydraulic settings while attaining an adequate level of chlorine control coverage across the system; and (iv) control-oriented strategic placement of actuators and sensors to maximize the performance of the monitoring and control computational algorithms while ensuring responsiveness and adaptability to dynamic conditions. This framework bridges significant research gaps in the literature, which often rely on oversimplified single-species WQ models, treat hydraulic optimization and WQ regulation as independent problems, or couple these problems without performance guarantees, leading to trade-offs between objectives. Additionally, traditional methods for determining actuator and sensor placements often prioritize specific objectives, such as cost minimization or simplified design, without considering their influence on the system's controllability, observability, and overall operational efficiency. The presented computational algorithms are validated through different case studies, demonstrating their operational effectiveness, practicality, scalability, and contribution to enhancing the resilience of WDNs in the face of dynamic and uncertain challenges.