Locating Leaks with TrustRank Algorithm Support

Ribeiro, Luísa; Sousa, Joaquim; Marques, Alfeu Sá; Simões, Nuno

doi:10.3390/w7041378

Cited by 17 publications

(12 citation statements)

References 64 publications

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“…The number of sensor locations may also be reduced by clustering water demand nodes into groups [7], grouping nodes according to water quality characteristics [8], and using reduced network models [9]. A ranking technique is used to select nodes for locating pressure sensors in [10]. Loop flows are estimated in [11] using known nodal demands in order to address the problem of a low measurement redundancy.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization

Letting

Hamam

Abu‐Mahfouz

2017

Water

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Demand estimation in a water distribution network provides crucial data for monitoring and controlling systems. Because of budgetary and physical constraints, there is a need to estimate water demand from a limited number of sensor measurements. The demand estimation problem is underdetermined because of the limited sensor data and the implicit relationships between nodal demands and pressure heads. A simulation optimization technique using the water distribution network hydraulic model and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows are estimated when the number of estimated parameters is more than the number of measured values. The water demand at each node is determined by using the PSO algorithm to identify a corresponding demand multiplier. The demand multipliers are encoded with varying step sizes and the optimization algorithm particles are also discretized in order to improve the computation time. The sensitivity of the estimated water demand to uncertainty in demand multiplier discrete values and uncertainty in measured parameters is investigated. The sensor placement locations are selected using an analysis of the sensitivity of measured nodal heads and pipe flows to the change in the water demand. The results show that nodal demands and pipe flows can be accurately determined from a limited number of sensors.

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Section: Introductionmentioning

confidence: 99%

Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization

Letting

Hamam

Abu‐Mahfouz

2017

Water

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“…The minimum night flow analysis involves flow measurements in the period of least consumption typically between the hours of 02:00 h to 04:00 h [10]. During the MNF period, the water demand is usually very low, the head losses in each pipe get reduced, the pressure head and leakages reach their maximum values [10,12]. The water loss in the network is then obtained by subtracting the measured legitimate night use from the minimum night flow measurements.…”

Section: Background and Related Workmentioning

confidence: 99%

“…A network element. (12) where P i and P g are the pressure at both ends of the pipe, Q j is the flow in the pipe j and k represents the pipe hydraulic resistance.…”

Section: Wdn Topology and Model Formulationmentioning

confidence: 99%

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Adedeji

Hamam

Abe

et al. 2017

Water

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Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact leaking pipelines connected to this segment(s) where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.

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“…The use of modelling and optimization tools, supported by flow and pressure measurements, is showing to be a challenging alternative to the traditional procedure. Following previous works (Ribeiro L. S., 2012;Ribeiro L. S., 2015;Sousa, 2015), this paper presents the application of the proposed methodology to a real WDN. The methodology requires monitoring the water distribution tank (level and flow) and some specific locations in the network (pressure) during the night period.…”

Section: Introductionmentioning

confidence: 98%

“…In the literature, numerous papers have contributed to the location of unreported leaks (Pudar, 1992): considering pressure-driven analysis (Giustolisi, 2008;Wu Z. S., 2010) or demand-driven analysis (Ribeiro L. S., 2015;Wu Z. S., 2006).…”

Section: Introductionmentioning

confidence: 99%

Locating Unreported Leaks with Modelling Tools and Pressure Monitoring: a Case Study

Ribeiro¹,

Sousa²,

Muranho³

et al.

EPiC Series in Engineering

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Water losses are a major concern for water companies, mostly due to their economical, technical, social and environmental negative impacts.Unreported leaks are a major cause of water losses in water distribution networks (WDNs) and they are difficult to locate, particularly in plastic pipes, large diameters and low pressure conditions. The location of these leaks is very time consuming and requires specialized human resources, using sophisticated and costly acoustic equipment.The use of modelling and optimization tools, supported by flow and pressure measurements, is showing to be a challenging alternative to the traditional procedure. This paper presents the application of the methodology proposed in Ribeiro L. S., 2012; Ribeiro L. S., 2015; Sousa, 2015 to a real WDN, highlighting the major difficulties faced when dealing with real world conditions, namely gathering and checking data, and building and calibrating the water distribution model.The results obtained in this case study show that this approach is very promising, encouraging future applications and developments.Engineering

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Locating Leaks with TrustRank Algorithm Support

Cited by 17 publications

References 64 publications

Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization

Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Locating Unreported Leaks with Modelling Tools and Pressure Monitoring: a Case Study

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