Optimal sensor placement for pipe burst detection in water distribution systems using cost–benefit analysis

Zhao, Mengke; Zhang, Chi; Liu, Haixing; Fu, Guangtao; Wang, Yuntao

doi:10.2166/hydro.2020.158

Cited by 21 publications

(15 citation statements)

References 32 publications

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“…The pressure residual ∆P i,j is calculated by Equation ( 14), which can be obtained from the simulation results. The detection threshold ∆P threshold i,j is related to the accuracy of S i , and in Equation ( 15), the normal pressure difference ∆P normal i,j is generally assumed to be zero [26]. Therefore,…”

Section: Sensor Deployment Methodsmentioning

confidence: 99%

Optimal Pressure Sensor Deployment for Leak Identification in Water Distribution Networks

Yang¹,

Wang²

2023

Preprint

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Pipe leakage is an inevitable phenomenon in water distribution networks (WDNs), leading to energy waste and economic damage. Leakage events can be reflected quickly by pressure values, and the deployment of pressure sensors is significant for minimizing the leakage ratio of WDNs. Concerning the restriction of realistic factors, including project budgets, available sensor installation locations, and sensor fault uncertainties, a practical methodology is proposed in this paper to optimize pressure sensor deployment for leak identification in terms of these realistic issues. Two indexes are identified to evaluate the leak identification ability, that is, detection coverage rate (DCR) and total detection sensitivity (TDS), and the principle is to determine priority to ensure an optimal DCR and retain the largest TDS with an identical DCR. Leakage events are generated by a model simulation and the essential sensors for maintaining the DCR are obtained by subtraction. In the event of a surplus budget, and if we suppose the partial sensors have failed, then we can determine the supplementary sensors that can best complement the lost leak identification ability. Moreover, a typical WDN Net3 is employed to show the specific progress, and the result shows that the methodology is largely appropriate for real projects.

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Section: Sensor Deployment Methodsmentioning

confidence: 99%

Optimal Pressure Sensor Deployment for Leak Identification in Water Distribution Networks

Yang¹,

Wang²

2023

Preprint

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“…Additionally, a single sensor arrangement has been demonstrated for lab‐scale leak detection experiments (Cody, Dey, et al, 2020). Moreover, the optimal sensor placing on the pipe is one important issue for leak detection and localization (Zhao et al, 2020). Wang (2021) explores optimal sensor placement for leak localization by changing the sensor location for different leak conditions and pipe geometry.…”

Section: Design Parameters Of Leak Experimentsmentioning

confidence: 99%

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

Bakhtawar

Zayed

2023

WIREs Water

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Effective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water > Methods Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

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“…It electrochemically forms an iron oxide matrix that weakens the pipe strength of the gray cast iron pipe and reduces its resistance to risk. Zhao et al defined several pressure-related influences; they experimentally confirmed that the maximum pressure in the line and the magnitude of pressure variation between day and night could significantly impact the occurrence of leakage in the pipe [15]. Jeihouni et al compared the rainfall pattern in the study area with the time of leakage events in the water supply network.…”

Section: Related Workmentioning

confidence: 99%

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

Chen¹,

Yang

2022

Mathematical Problems in Engineering

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In this paper, model construction of random matrix theory is carried out to analyze water leakage of urban water supply pipeline network, and a multivariate statistical model of water leakage of urban water supply pipeline network based on random matrix theory is proposed. This paper presents a three-level DMA, short-term water demand prediction method based on water use patterns, aiming to solve the problem of inaccurate water demand prediction caused by climate and social factors. Correlation analysis and factor analysis analyze the main factors affecting daily water demand changes. The extracted information of the main influencing factors and historical monitoring data are used to establish the Elman water demand prediction model. At the same time, the daily water consumption pattern was extracted by correlation analysis, matched with the water consumption pattern of the predicted day, and combined with the expected value of water demand for momentary water demand allocation, which improved the prediction accuracy of short-term water demand. This paper is based on the precise leakage location based on the real-time hydraulic model. The real-time hydraulic model under leakage conditions is established, GA and NSGA2 are designed, and the nondominated ranking algorithm with elite strategy is used to achieve accurate leakage location using hydraulic model calibration search. This paper uses a random matrix algorithm to establish the leakage estimation model. A laboratory case pipe network is built to simulate the water supply conditions in residential areas, and the operating data of the laboratory case pipe network are used to confirm that the random matrix leakage estimation model can estimate the leakage. The applicability of the random matrix leakage estimation model and the wavelet transform algorithm for leakage calculation are compared in an engineering example. The example confirms that the leakage amount calculated by the random matrix model has a less relative error and more similar trend than the wavelet transform algorithm.

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Optimal sensor placement for pipe burst detection in water distribution systems using cost–benefit analysis

Cited by 21 publications

References 32 publications

Optimal Pressure Sensor Deployment for Leak Identification in Water Distribution Networks

Optimal Pressure Sensor Deployment for Leak Identification in Water Distribution Networks

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

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