Automated Leak Detection System for the Improvement of Water Network Management

Perfido, Domenico; Messervey, Thomas; Zanotti, Chiara; Raciti, Massimiliano; Costa, Andrea

doi:10.3390/ecsa-3-s5002

Cited by 9 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, several project initiatives [ 6 , 7 ] and prototypes of sensor systems [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] for wastewater monitoring have been proposed and studied. These include the design of sensors (electrochemical sensors, optical sensors, mass spectrometry, ion spectrometry, etc.)…”

Section: Introductionmentioning

confidence: 99%

Pollution Source Localization in Wastewater Networks

Chachuła

Nowak

Solano

2021

Sensors

View full text Add to dashboard Cite

In December 2016, the wastewater treatment plant of Baarle-Nassau, Netherlands, failed. The failure was caused by the illegal disposal of high volumes of acidic waste into the sewer network. Repairs cost between 80,000 and 100,000 EUR. A continuous monitoring system of a utility network such as this one would help to determine the causes of such pollution and could mitigate or reduce the impact of these kinds of events in the future. We have designed and tested a data fusion system that transforms the time-series of sensor measurements into an array of source-localized discharge events. The data fusion system performs this transformation as follows. First, the time-series of sensor measurements are resampled and converted to sensor observations in a unified discrete time domain. Second, sensor observations are mapped to pollutant detections that indicate the amount of specific pollutants according to a priori knowledge. Third, pollutant detections are used for inferring the propagation of the discharged pollutant downstream of the sewage network to account for missing sensor observations. Fourth, pollutant detections and inferred sensor observations are clustered to form tracks. Finally, tracks are processed and propagated upstream to form the final list of probable events. A set of experiments was performed using a modified variant of the EPANET Example Network 2. Results of our experiments show that the proposed system can narrow down the source of pollution to seven or fewer nodes, depending on the number of sensors, while processing approximately 100 sensor observations per second. Having considered the results, such a system could provide meaningful information about pollution events in utility networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Pollution Source Localization in Wastewater Networks

Chachuła

Nowak

Solano

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Several researchers have been focused on the comparison between actual field measurements and the results obtained by a calibrated numerical model [15][16][17]. In such cases, the key-point is the level of accuracy of the simulation model, which can hardly be optimal, since it should also contain information on the leakages to be discovered.…”

Section: Literature Reviewmentioning

confidence: 99%

“…in which A is the adjacency matrix [17]. Thus, the junction in which the leak is emerging is the one characterized by the lowest correlation with respect to all the others, and the related node in the complex network has the lowest degree centrality.…”

Section: Figure 1 (Bottom Right)mentioning

confidence: 99%

Localization of Emerging Leakages in Water Distribution Systems: A Complex Networks Approach

Nicolini¹

2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Water distribution networks are infrastructural systems designed for providing potable water to consumers. In these last decades, the importance of assessing and identifying emerging leakages has become a primary issue, because of the high level of water loss characterizing such systems worldwide. In this paper, a new approach aimed at the prompt localization of leakages occurring in water distribution systems is introduced. The methodology relies on the analysis of real-time pressure measurements and on Complex Networks Theory. Starting from a collection of nodes representing the locations of pressure sensors, links of a virtual, complex network are created on the basis of the values assumed by correlation coefficients between pressure measurements: if such values are above a given threshold, relevant nodes are considered to be connected to each other. In this way, information about the structure and topology of the complex network is easily derived. In particular, the degree centrality of the nodes is a key parameter allowing to identify the position of a leakage. The paper first analyzes a well-known literature example, and then proves the high reliability of the methodology for a real water distribution system.

show abstract

“…Researchers’ interest in exploring methods for the detection, localization, and quantification of pollutants in wastewater networks (WWNs) and water distribution systems (WDSs) has grown considerably in recent years, especially regarding sensor technology [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Questionable policies regarding waste disposal are prevalent in today’s industry [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Chachuła

Słojewski

Nowak

2022

Sensors

View full text Add to dashboard Cite

Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.

show abstract

Automated Leak Detection System for the Improvement of Water Network Management

Cited by 9 publications

References 1 publication

Pollution Source Localization in Wastewater Networks

Pollution Source Localization in Wastewater Networks

Localization of Emerging Leakages in Water Distribution Systems: A Complex Networks Approach

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Contact Info

Product

Resources

About