Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks

TL;DR

This paper explores the use of system identification algorithms to model water quality in drinking water networks solely from input-output data, addressing complex dynamics without prior network parameter knowledge.

Contribution

It is the first to apply classical SysID methods for water quality model identification in WDNs without relying on network parameters.

Findings

SysID algorithms can accurately model water quality dynamics

Performance depends on data quality and network complexity

Method reduces reliance on detailed network models

Abstract

Traditional control and monitoring of water quality in drinking water distribution networks (WDN) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (i) complex water quality and reaction dynamics and (ii) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification.