Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems

Journal Article (2021)
Author(s)

Qingzhou Zhang (Zhejiang University)

Feifei Zheng (Zhejiang University)

Yueyi Jia (Zhejiang University)

Dragan Savic (University of Exeter)

Z. Kapelan (TU Delft - Sanitary Engineering)

Research Group
Sanitary Engineering
Copyright
© 2021 Qingzhou Zhang, Feifei Zheng, Yueyi Jia, Dragan Savic, Z. Kapelan
DOI related publication
https://doi.org/10.1016/j.watres.2020.116544
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Qingzhou Zhang, Feifei Zheng, Yueyi Jia, Dragan Savic, Z. Kapelan
Research Group
Sanitary Engineering
Bibliographical Note
Accepted Author Manuscript@en
Volume number
188
Pages (from-to)
1-16
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Abstract

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors. Within the proposed method, the relationship between FSS manholes and WDS water consumption nodes are determined based on their underlying physical connections. An optimization approach is subsequently proposed to identify the transfer factor k between nodal water consumption and FSS manhole inflows based on historical observations. These identified k values combined with the acquired real-time nodal water consumption data drive the FSS real-time modelling. The proposed method is applied to two real FSSs. The results obtained show that it can produce simulated sewer flows and manhole water depths matching well with observations at the monitoring locations. The proposed method achieved high R2, NSE and KGE (Kling-Gupta efficiency) values of 0.99, 0.88 and 0.92 respectively. It is anticipated that real-time models developed by the proposed method can be used for improved FSS management and operation.

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