Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics

TL;DR

This paper introduces a novel computational method for optimal water quality sensor placement in water distribution networks, focusing on maximizing network observability and minimizing state estimation error using Kalman filtering.

Contribution

It presents an observability-driven sensor placement algorithm that accounts for the dynamic nature of water quality and hydraulic states, filling a gap in existing metric-based approaches.

Findings

The proposed method improves state estimation accuracy in water networks.

Sensor placement adapts to time-varying hydraulic conditions.

Case studies demonstrate practical benefits for water network monitoring.

Abstract

Real-time water quality (WQ) sensors in water distribution networks (WDN) have the potential to enable network-wide observability of water quality indicators, contamination event detection, and closed-loop feedback control of WQ dynamics. To that end, prior research has investigated a wide range of methods that guide the geographic placement of WQ sensors. These methods assign a metric for fixed sensor placement (SP) followed by \textit{metric-optimization} to obtain optimal SP. These metrics include minimizing intrusion detection time, minimizing the expected population and amount of contaminated water affected by an intrusion event. In contrast to the literature, the objective of this paper is to provide a computational method that considers the overlooked metric of state estimation and network-wide observability of the WQ dynamics. This metric finds the optimal WQ sensor placement that minimizes the state estimation error via the Kalman filter for noisy WQ dynamics -- a metric that quantifies WDN observability. To that end, the state-space dynamics of WQ states for an entire WDN are given and the observability-driven sensor placement algorithm is presented. The algorithm takes into account the time-varying nature of WQ dynamics due to changes in the hydraulic profile -- a collection of hydraulic states including heads (pressures) at nodes and flow rates in links which are caused by a demand profile over a certain period of time. Thorough case studies are given, highlighting key findings, observations, and recommendations for WDN operators. Github codes are included for reproducibility.