Data-driven strategic sensor placement for detecting disinfection by-products in water distribution networks

TL;DR

This paper introduces DBPFinder, a simulation tool for optimal sensor placement in water networks to detect harmful disinfection byproducts efficiently, tested on a real-world case in Coimbra, Portugal.

Contribution

The paper presents a novel simulation software that optimizes sensor placement for detecting disinfection byproducts considering multiple objectives.

Findings

DBPFinder effectively identifies sensor placements in real-world water networks.

The tool demonstrates high efficiency and scalability in various experiments.

It provides tailored recommendations based on specific performance needs.

Abstract

Disinfection byproducts are contaminants that can cause long-term effects on human health, occurring in chlorinated drinking water when the disinfectant interacts with natural organic matter. Their formation is affected by many environmental parameters, making it difficult to monitor and detect disinfection byproducts before they reach households. Due to the large variety of disinfection byproduct compounds that can be formed in water distribution networks, plus the constrained number of sensors that can be deployed throughout a system to monitor these contaminants, it is of outmost importance to place sensory equipment efficiently and optimally. In this paper, we present DBPFinder, a simulation software that assists in the strategic sensor placement for detecting disinfection byproducts, tested at a real-world water distribution network in Coimbra, Portugal. This simulator addresses multiple performance objectives at once in order to provide optimal solution placement recommendations to water utility operators based on their needs. A number of different experiments performed indicate its correctness, relevance, efficiency and scalability.