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

AbstractTraditional control and monitoring of water quality in drinking water distribution networks (WDNs) 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...

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Bibliographic Details
Published in:Journal of water resources planning and management 2023-04, Vol.149 (4)
Main Authors: Wang, Shen, Chakrabarty, Ankush, Taha, Ahmad F.
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Computer applications
Computing time
Drinking water
Dynamical systems
Dynamics
Identification
Mathematical models
Network topologies
Parameter identification
Parameters
Pollution monitoring
System identification
Technical Papers
Topology
Water distribution
Water engineering
Water quality
Water quality control
Water resources management
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Summary:AbstractTraditional control and monitoring of water quality in drinking water distribution networks (WDNs) 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 (1) complex water quality and reaction dynamics; and (2) 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.
ISSN:0733-9496
1943-5452
DOI:10.1061/JWRMD5.WRENG-5431