Detection and Localization of Leaks in Water Networks PDF Download
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Author: Samer El-Zahab
Publisher:
ISBN:
Category :
Languages : en
Pages : 238
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Book Description
Today, 844 million humans around the world have no access to safe drinking water. Furthermore, every 90 seconds, one child dies from water-related illnesses. Major cities lose 15% - 50% of their water and, in some cases, losses may reach up to 70%, mostly due to leaks. Therefore, it is paramount to preserve water as an invaluable resource through water networks, particularly in large cities in which leak repair may cause disruption. Municipalities usually tackle leak problems using various detection systems and technologies, often long after leaks occur; however, such efforts are not enough to detect leaks at early stages. Therefore, the main objectives of the present research are to develop and validate a leak detection system and to optimize leak repair prioritization. The development of the leak detection models goes through several phases: (1) technology and device selection, (2) experimental work, (3) signal analysis, (4) selection of parameters, (5) machine learning model development and (6) validation of developed models. To detect leaks, vibration signals are collected through a variety of controlled experiments on PVC and ductile iron pipelines using wireless accelerometers, i.e., micro-electronic mechanical sensors (MEMS). The signals are analyzed to pinpoint leaks in water pipelines. Similarly, acoustic signals are collected from a pilot project in the city of Montreal, using noise loggers as another detection technology. The collected signals are also analyzed to detect and pinpoint the leaks. The leak detection system has presented promising results using both technologies. The developed MEMS model is capable of accurately pinpointing leaks within 12 centimeters from the exact location. Comparatively, for noise loggers, the developed model can detect the exact leak location within a 25-cm radius for an actual leak. The leak repair prioritization model uses two optimization techniques: (1) a well-known genetic algorithm and (2) a newly innovative Lazy Serpent Algorithm that is developed in the present research. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal schedule using much less computation time. The developed research proves that automated real-time leak detection is possible and can help governments save water resource and funds. The developed research proves the viability of accelerometers as a standalone leak detection technology and opens the door for further research and experimentations. The leak detection system model helps municipalities and water resource agencies rapidly detect leaks when they occur in real-time. The developed pinpointing models facilitate the leak repair process by precisely determine the leak location where the repair works should be conducted. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.
Author: Stuart Hamilton
Publisher: IWA Publishing
ISBN: 1780404719
Category : Technology & Engineering
Languages : en
Pages : 106
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Book Description
Ageing infrastructure and declining water resources are major concerns with a growing global population. Controlling water loss has therefore become a priority for water utilities around the world. In order to improve efficiencies, water utilities need to apply good practices in leak detection. Leak Detection: Technology and Implementation assists water utilities with the development and implementation of leak detection programs. Leak detection and repair is one of the components of controlling water loss. In addition, techniques are discussed within this book and relevant case studies are presented. The book provides useful and practical information on leakage issues.
Author: Samer El-Zahab
Publisher:
ISBN:
Category :
Languages : en
Pages : 238
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Book Description
Today, 844 million humans around the world have no access to safe drinking water. Furthermore, every 90 seconds, one child dies from water-related illnesses. Major cities lose 15% - 50% of their water and, in some cases, losses may reach up to 70%, mostly due to leaks. Therefore, it is paramount to preserve water as an invaluable resource through water networks, particularly in large cities in which leak repair may cause disruption. Municipalities usually tackle leak problems using various detection systems and technologies, often long after leaks occur; however, such efforts are not enough to detect leaks at early stages. Therefore, the main objectives of the present research are to develop and validate a leak detection system and to optimize leak repair prioritization. The development of the leak detection models goes through several phases: (1) technology and device selection, (2) experimental work, (3) signal analysis, (4) selection of parameters, (5) machine learning model development and (6) validation of developed models. To detect leaks, vibration signals are collected through a variety of controlled experiments on PVC and ductile iron pipelines using wireless accelerometers, i.e., micro-electronic mechanical sensors (MEMS). The signals are analyzed to pinpoint leaks in water pipelines. Similarly, acoustic signals are collected from a pilot project in the city of Montreal, using noise loggers as another detection technology. The collected signals are also analyzed to detect and pinpoint the leaks. The leak detection system has presented promising results using both technologies. The developed MEMS model is capable of accurately pinpointing leaks within 12 centimeters from the exact location. Comparatively, for noise loggers, the developed model can detect the exact leak location within a 25-cm radius for an actual leak. The leak repair prioritization model uses two optimization techniques: (1) a well-known genetic algorithm and (2) a newly innovative Lazy Serpent Algorithm that is developed in the present research. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal schedule using much less computation time. The developed research proves that automated real-time leak detection is possible and can help governments save water resource and funds. The developed research proves the viability of accelerometers as a standalone leak detection technology and opens the door for further research and experimentations. The leak detection system model helps municipalities and water resource agencies rapidly detect leaks when they occur in real-time. The developed pinpointing models facilitate the leak repair process by precisely determine the leak location where the repair works should be conducted. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.
Author: Neda Mashhadi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This manuscript presents the results of research about the use of the Artificial Intelligence moths to detect and localize leaks in the water distribution networks. The manuscript is organized in three chapters:The first chapter includes a literature review about the leak in the water distribution networks. First, it presents first the origin of the water leak and its dramatic economic, social and environmental impact. Then, it presents the conventional methods used for the detection of the water leak including hardware-based and software-based methods. This chapter highlights the opportunities offered by the smart monitoring and the Artificial Intelligence methods for the detection of leaks in the water networks. It also shows a need to explore on the same example the capacity of the main AI methods to detect and localize leaks in complex water networks.The second chapter presents the water network of the scientific campus of Lille University, which is used as a support for this research. It argues the selection of this campus by its representativity of a small town, the complexity of the water network and the availability of data about the water network asset and consumption. The chapter also presents the construction of a Lab pilot to investigate of the possibility to localize water leaks from the ratios of the water supply flow rates.The third chapter presents a synthesis of the use of Machine Learning methods in leak localization. It also presents the use of the software EPANET for the generation of data including the impact of 215 individual and double leaks on the variation of the water supply flow rates and the pressure in five zones of the campus. These data are then used to investigate the capacity of five Machine Learning methods to localize leaks in the water distribution system. The chapter suggests some recommendations for the use of ML methods in water leak localization.
Author: Fei Yang
Publisher:
ISBN:
Category : Leak detectors
Languages : en
Pages : 62
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Book Description
Modern cities can no longer tolerate insufficiencies in water supply systems. Of the many options available for conserving water, detecting a leakage from a sealed pipe is an effective low cost method. The quality of the leak detection system is determined by accuracy. The aims of this study were developing a system to minimize the probability of false alarm and misdetection, and to detect the leak in time by using a multi-sensor network. Cross correlation and the Artificial Neural Network (ANN) algorithm were utilized to pinpoint the leak location. The results showed that the probabilities of false alarm and miss detection have been decreased. In addition, an analysis of the results in leak localization implies that the error of estimated location and physical location decreases if more data is fed to the ANN algorithm and it finally stabilizes within ±27.22 mm. The results of this study are presented such that they can be used as an aid to a water pipeline monitoring system.
Author: Joby Boxall
Publisher: CRC Press
ISBN: 1482266571
Category : Science
Languages : en
Pages : 834
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Book Description
A collection of articles by leading international experts on modeling and control of potable water distribution and sewerage collection systems, focusing on advances in sensors, instrumentation and communications technologies; assessment of sensor reliability, accuracy and fitness; data management including SCADA and GIS; system
Author: Vicenç Puig
Publisher: Springer
ISBN: 3319507516
Category : Technology & Engineering
Languages : en
Pages : 438
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Book Description
This book presents a set of approaches for the real-time monitoring and control of drinking-water networks based on advanced information and communication technologies. It shows the reader how to achieve significant improvements in efficiency in terms of water use, energy consumption, water loss minimization, and water quality guarantees. The methods and approaches presented are illustrated and have been applied using real-life pilot demonstrations based on the drinking-water network in Barcelona, Spain. The proposed approaches and tools cover: • decision-making support for real-time optimal control of water transport networks, explaining how stochastic model predictive control algorithms that take explicit account of uncertainties associated with energy prices and real demand allow the main flow and pressure actuators—pumping stations and pressure regulation valves— and intermediate storage tanks to be operated to meet demand using the most sustainable types of source and with minimum electricity costs;• decision-making support for monitoring water balance and distribution network quality in real time, implementing fault detection and diagnosis techniques and using information from hundreds of flow, pressure, and water-quality sensors together with hydraulic and quality-parameter-evolution models to detect and locate leaks in the network, possible breaches in water quality, and failures in sensors and/or actuators;• consumer-demand prediction, based on smart metering techniques, producing detailed analyses and forecasts of consumption patterns, providing a customer communications service, and suggesting economic measures intended to promote more efficient use of water at the household level. Researchers and engineers working with drinking-water networks will find this a vital support in overcoming the problems associated with increased population, environmental sensitivities and regulation, aging infrastructures, energy requirements, and limited water sources.
Author: Diofantos Hadjimitsis
Publisher: BoD – Books on Demand
ISBN: 9535117297
Category : Technology & Engineering
Languages : en
Pages : 86
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Book Description
Remote sensing has been used for water management purposes over the years. This book describes the combination of satellite imagery, in-situ spectroradiometric data and radar techniques for the identification of water leakages in the water supply network in both rural and urban areas in Cyprus. This book presents a holistic approach combining new technologies for a complete system of water distribution network leakage detection management, by combining Global Navigation Satellite Systems (GNSS), Geographical Information Systems (GIS), Satellite Remote Sensing techniques as well Geophysical surveys such as ground penetrating radar (GPR), Unmanned Aerial Vehicles (UAV) and spectro-radiometric measurements, which can be used to effectively identify and monitor water leakages.
Author: Md Toufikul Islam
Publisher:
ISBN:
Category : Intelligent sensors
Languages : en
Pages : 218
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Book Description
Author: Adrià Soldevila Coma
Publisher:
ISBN:
Category :
Languages : en
Pages : 211
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Book Description
This PhD thesis presents a methodology to detect, estimate and localize water leaks (with the main focus in the localization problem) in water distribution networks using hydraulic models and machine learning techniques. The actual state of the art is introduced, the theoretical basis of the machine learning techniques applied are explained and the hydraulic model is also detailed. The whole methodology is presented and tested into different water distribution networks and district metered areas based on simulated and real case studies and compared with published methods.The focus of the contributions is to bring more robust methods against the uncertainties that effects the problem of leak detection, by dealing with them using the self-similarity to create features monitored by the change detection technique intersection-of-confidence-interval, and the leak localization where the problem is tackled using machine learning techniques. By using those techniques, it is expected to learn the leak behavior considering their uncertainty to be used in the diagnosis stage after the training phase.One method for the leak detection problem is presented that is able to estimate the leak size and the time that the leak has been produced. This method captures the normal, leak-free, behavior and contrast it with the new measurements in order to evaluate the state of the network. If the behavior is not normal check if it is due to a leak. To have a more robust leak detection method, a specific validation is designed to operate specifically with leaks and in the temporal region where the leak is most apparent. A methodology to extent the current model-based approach to localize water leaks by means of classifiers is proposed where the non-parametric k-nearest neighbors classifier and the parametric multi-class Bayesian classifier are proposed. A new data-driven approach to localize leaks using a multivariate regression technique without the use of hydraulic models is also introduced. This method presents a clear benefit over the model-based technique by removing the need of the hydraulic model despite of the topological information is still required. Also, the information of the expected leaks is not required since information of the expected hydraulic behavior with leak is exploited to find the place where the leak is more suitable. This method has a good performance in practice, but is very sensitive to the number of sensor in the network and their sensor placement.The proposed sensor placement techniques reduce the computational load required to take into account the amount of data needed to model the uncertainty compared with other optimization approaches while are designed to work with the leak localization problem. More precisely, the proposed hybrid feature selection technique for sensor placement is able to work with any method that can be evaluated with confusion matrix and still being specialized for the leak localization task. This last method is good for a few sensors, but lacks of precision when the number of sensors to place is large. To overcome this problem an incremental sensor placement is proposed which is better for a larger number of sensors to place but worse when the number is small.
Author: Aburawe Salah M
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659804250
Category :
Languages : en
Pages : 236
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Book Description
Most water utilities worldwide suffer from the problem of water loss which effects the financial, social and environmental concerns. Leakage in water distribution networks is a significant issue that costs a lot, which is further complicated by the problem that the location of the leakage is usually hidden underground. In this book The author presents an advanced analytical technique for real-time leakage detection and location in water distribution networks with acceptable accuracy and reasonable cost.
