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Demand and leakage management
Published in Bogumil Ulanicki, Kalanithy Vairavamoorthy, David Butler, Peter L.M. Bounds, Fayyaz Ali Memon, Water Management Challenges in Global Change, 2020
Bogumil Ulanicki, Kalanithy Vairavamoorthy, David Butler, Peter L.M. Bounds, Fayyaz Ali Memon
The numerical solution of the detection of leaks in pipes has been presented in this paper. The study of the leak detection is necessary to avoid economic losses, ensure safety, and control environmental and health problems. This problem is based on two coupled linear partial differential equations describing transient flows in pipes.
Leak and Break Detection
Published in Mavis Sika Okyere, Mitigation of Gas Pipeline Integrity Problems, 2020
Leak detection determines where a leak has occurred in liquid and gas pipeline systems. Methods of detection include hydrostatic testing (hydrotest), infrared thermography, and laser technology after pipeline erection and leak detection during service.
POF Applications
Published in Marcelo Martins Werneck, Regina Célia da Silva Barros Allil, Plastic Optical Fiber Sensors, 2019
In any application, either floating or submarine, leakage from the hose results in undesirable consequences. In order to minimize the damage resulting from an undetected leak, various leak detection systems have been proposed and adopted. Leak detection devices present various configurations, operable under varying principles, mounted at the nipple region of an underwater hose connection as shown in Figure 15.18.
Pipeline leak diagnosis based on leak-augmented scalograms and deep learning
Published in Engineering Applications of Computational Fluid Mechanics, 2023
Muhammad Farooq Siddique, Zahoor Ahmad, Jong-Myon Kim
Pipelines provide the cheapest medium for the transportation of gas, oil, and water. However, prolonged exposure to harsh conditions can lead to corrosion, leaks, and cracks, causing significant financial losses and environmental damage. To mitigate these issues, advanced techniques that can quickly detect and locate leaks in pipelines are of primary interest (Ahmad et al., 2022). In recent years, advanced techniques based on artificial intelligence (AI) and machine learning (ML) have been developed to improve the accuracy and efficiency of pipeline leak detection (Banjara et al., 2020). Different monitoring and protection strategies have been devised to maintain the safe and effective functioning of pipelines. These consist of time-domain reflectometry, techniques based on vibration, methods involving pressure waves, and the utilization of AE technology (Che et al., 2021). Among these strategies, AE technology has been particularly attractive due to its sensitivity to leaks and ability to detect leaks in real-time (Bergmann et al., 2018). Therefore, in this study, AE technology is used for the identification of pipeline health conditions. In order to prevent serious consequences, it is essential to detect pipeline leaks early. Leak detection involves evaluating the working status of the pipeline to determine whether a leak is present or not. Nowadays, the pipeline industry prioritizes cost-effective remediation methods, such as using repair clamps and encapsulation collars if the size of the leak allows it, rather than automatically replacing the damaged pipeline (Ahmad et al., 2023; Pipeline Repair Guide, 2020).
Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms
Published in Urban Water Journal, 2018
Ali M. Sadeghioon, Nicole Metje, David Chapman, Carl Anthony
Table 1 shows that overall all methods had an accuracy of greater than 90% in the detection of leaks. This is very promising as it further validates the feasibility of using non-absolute readings for leak detection in pipes. Comparing the accuracy of the three different methods given in Table 1, shows that the proposed anomaly detection algorithm performed better, giving higher accuracy, sensitivity and specificity values, when compared to the other two methods. This was expected as this algorithm is less susceptible to errors caused by changes in the baseline values or sequential leaks. It can also be shown from Table 1 that method C has a significantly better sensitivity compared to the other methods. Sensitivity of the leak detection methods is particularly important, as missing leak events can have costly or even catastrophic consequences. In addition, the results presented in Table 1 show that method C also has no false positives. This is important for users of the algorithm as the number of false alarms can significantly affect their trust in the algorithm. Table 1 also shows that by using the temperature difference measurements the rate of false positives in all methods is significantly reduced (or even eliminated). This shows that the temperature difference measurements can be used to differentiate abnormal changes in relative pressure from daily/systematic changes.
Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network
Published in Engineering Applications of Computational Fluid Mechanics, 2023
Zahoor Ahmad, Tuan-Khai Nguyen, Jong-Myon Kim
Pipelines are among the top five modes of transport in the modern world. Pipelines are cheap, safe, and provide economic transportation for gas and fluids. However, material corrosion, fatigue cracks, earthquakes, material defects, and discontinuities in the pipelines due to the external environment can all lead to pipeline leaks (B. Zhang, Kang et al., 2022; Z. Zhang, Zhang, et al., 2022). The repercussions that arise from leaks are very severe and can include economic losses, impacts to public safety, pollution, and waste of resources (Che et al., 2021; Duan et al., 2020). Fatalities due to leakage in the pipeline are about 46% around the world. A case study presented in 2020 reveals that in Guizhou (China) leakage in diesel pipelines resulted in the loss of 1.5 million RMB and cross-provincial environmental pollution. The same case study also reported that over 120 deaths and dozens of injuries were reported in Hidalgo (Mexico) due to the explosion that occurred there because of a leak in the petroleum pipeline (Xing et al., 2020). To avoid such severe consequences, early pipeline leak detection is of primary importance. Leak detection is the assessment of the working condition of the pipeline in terms of the existence of the leak or no leak. The modern pipeline industry focuses on the most economical methods of pipeline remediation such as using repair clamps and encapsulation collars when the leak size permits rather than automatically replacing the damaged pipeline (Pipeline Repair Guide, 2020). Therefore, to minimize the industry’s financial expenditure on maintenance, an intelligent technique is needed to detect the leak in the pipeline and accurately identify its size.