5,827 research outputs found

    Enhanced Leak Detection

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    A key requirement for Veeder-Root’s Enhanced Leak Detection System is that it be able to test in situ for the presence of leaks at gasoline dispensing facilities. Aside from the obvious issues of safety and lost product, this functionality is obligatory for compliance with environmental standards mandated by federal and state oversight bodies, such as the California State Water Resources Control Board (SWRCB). The SWRCB demands a testing procedure that includes conditions as close to operational as possible, while still using environmentally safe gases as a test fluid. Although the test parameters (e.g., pressure) are allowed to deviate from operating conditions in order to facilitate the test procedure, a prescribed rescaling of the test thresholds must then be applied to account for the deviation. Whether the test is run at operation conditions or in a slightly different parameter regime, the fact that the testing must be done on the product and return lines after installation at a service station presents significant challenges in devising an effective test strategy

    Multiple Surface Pipeline Leak Detection Using Real-Time Sensor Data Analysis

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    Pipelines enable the largest volume of both intra and international transportation of oil and gas and play critical roles in the energy sufficiency of countries. The biggest drawback with the use of pipelines for oil and gas transportation is the problem of oil spills whenever the pipelines lose containment. The severity of the oil spill on the environment is a function of the volume of the spill and this is a function of the time taken to detect the leak and contain the spill from the pipeline. A single leak on the Enbridge pipeline spilled 3.3 million liters into the Kalamazoo river while a pipeline rupture in North Dakota which went undetected for 143 days spilled 29 million gallons into the environment.Several leak detection systems (LDS) have been developed with the capacity for rapid detection and localization of pipeline leaks, but the characteristics of these LDS limit their leak detection capability. Machine learning provides an opportunity to develop faster LDS, but it requires access to pipeline leak datasets that are proprietary in nature and not readily available. Current LDS have difficulty in detecting low-volume/low-pressure spills located far away from the inlet and outlet pressure sensors. Some reasons for this include the following, leak induced pressure variation generated by these leaks is dissipated before it gets to the inlet and outlet pressure sensors, another reason is that the LDS are designed for specific minimum detection levels which is a percentage of the flow volume of the pipeline, so when the leak falls below the LDS minimum detection value, the leak will not be detected. Perturbations generated by small volume leaks are often within the threshold values of the pipeline\u27s normal operational envelop as such the LDS disregards these perturbations. These challenges have been responsible for pipeline leaks going on for weeks only to be detected by third-party persons in the vicinity of the leaks. This research has been able to develop a framework for the generation of pipeline datasets using the PIPESIM software and the RAND function in Python. The topological data of the pipeline right of way, the pipeline network design specification, and the fluid flow properties are the required information for this framework. With this information, leaks can be simulated at any point on the pipeline and the datasets generated. This framework will facilitate the generation of the One-class dataset for the pipeline which can be used for the development of LDS using machine learning. The research also developed a leak detection topology for detecting low-volume leaks. This topology comprises of the installation of a pressure sensor with remote data transmission capacity at the midpoint of the line. The sensor utilizes the exception-based transmission scheme where it only transmits when the new data differs from the existing data value. This will extend the battery life of the sensor. The installation of the sensor at the midpoint of the line was found to increase the sensitivity of the LDS to leak-induced pressure variations which were traditionally dissipated before getting to the Inlet/outlet sensors. The research also proposed the development of a Leak Detection as a Service (LDaaS) platform where the pressure data from the inlet and the midpoint sensors are collated and subjected to a specially developed leak detection algorithm for the detection of pipeline leaks. This leak detection topology will enable operators to detect low-volume/low-pressure leaks that would have been missed by the existing leak detection system and deploy the oil spill response plans quicker thus reducing the volume of oil spilled into the environment. It will also provide a platform for regulators to monitor the leak alerts as they are generated and enable them to evaluate the oil spill response plans of the operators

    Pipeline leak detection

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    In the present research two techniques are applied for leak detection in pipelines. The first method is a hardware-based technique which uses ultrasonic wave\u27s emission for pipeline inspection. Ultrasonic waves are propagated in the pipe walls and reflected signal from leakage will be used for pipe analysis. Several Pipes with various dimensions and characteristics are modeled by finite element method using ANSYS. Second order longitudinal modes of ultrasonic waves are emitted in their walls. For this purpose, excited frequency is calculated such that it excites the second order longitude mode. In order to investigate the behavior of emitted wave in contact with leakage, four sensors are used in outer surface of pipe. Waves are reflected when encountering leakage and the leak location is recognized knowing the wave emission speed and flight time of backscattered signals. Wavelet transform is used for processing these signals and recognizing leak location. This method is tested on several pipe models and it presents satisfactory results for short pipes. The second approach is a software-based method which works based on the transient model of the pipeline. In this method the outputs from simulated pipeline are compared to those measured from flow meters and if their difference goes beyond a threshold value, leak is detected. For leak localization a gradient pressure technique is applied which needs pressure slope measurements at inlet and outlet of the pipeline. Several cases with leak at various positions are studied. This method works well with high accuracy for long pipelines. --Abstract, page iii

    Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation.

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    Pipelines are often subject to leakage due to ageing, corrosion and weld defects. It is difficult to avoid pipeline leakage as the sources of leaks are diverse. Various pipeline leakage detection methods, including fibre optic, pressure point analysis and numerical modelling, have been proposed during the last decades. One major issue of these methods is distinguishing the leak signal without giving false alarms. Considering that the data obtained by these traditional methods are digital in nature, the machine learning model has been adopted to improve the accuracy of pipeline leakage detection. However, most of these methods rely on a large training dataset for accurate training models. It is difficult to obtain experimental data for accurate model training. Some of the reasons include the huge cost of an experimental setup for data collection to cover all possible scenarios, poor accessibility to the remote pipeline, and labour-intensive experiments. Moreover, datasets constructed from data acquired in laboratory or field tests are usually imbalanced, as leakage data samples are generated from artificial leaks. Computational fluid dynamics (CFD) offers the benefits of providing detailed and accurate pipeline leakage modelling, which may be difficult to obtain experimentally or with the aid of analytical approach. However, CFD simulation is typically time-consuming and computationally expensive, limiting its pertinence in real-time applications. In order to alleviate the high computational cost of CFD modelling, this study proposed a novel data sampling optimisation algorithm, called Adaptive Particle Swarm Optimisation Assisted Surrogate Model (PSOASM), to systematically select simulation scenarios for simulation in an adaptive and optimised manner. The algorithm was designed to place a new sample in a poorly sampled region or regions in parameter space of parametrised leakage scenarios, which the uniform sampling methods may easily miss. This was achieved using two criteria: population density of the training dataset and model prediction fitness value. The model prediction fitness value was used to enhance the global exploration capability of the surrogate model, while the population density of training data samples is beneficial to the local accuracy of the surrogate model. The proposed PSOASM was compared with four conventional sequential sampling approaches and tested on six commonly used benchmark functions in the literature. Different machine learning algorithms are explored with the developed model. The effect of the initial sample size on surrogate model performance was evaluated. Next, pipeline leakage detection analysis - with much emphasis on a multiphase flow system - was investigated in order to find the flow field parameters that provide pertinent indicators in pipeline leakage detection and characterisation. Plausible leak scenarios which may occur in the field were performed for the gas-liquid pipeline using a three-dimensional RANS CFD model. The perturbation of the pertinent flow field indicators for different leak scenarios is reported, which is expected to help in improving the understanding of multiphase flow behaviour induced by leaks. The results of the simulations were validated against the latest experimental and numerical data reported in the literature. The proposed surrogate model was later applied to pipeline leak detection and characterisation. The CFD modelling results showed that fluid flow parameters are pertinent indicators in pipeline leak detection. It was observed that upstream pipeline pressure could serve as a critical indicator for detecting leakage, even if the leak size is small. In contrast, the downstream flow rate is a dominant leakage indicator if the flow rate monitoring is chosen for leak detection. The results also reveal that when two leaks of different sizes co-occur in a single pipe, detecting the small leak becomes difficult if its size is below 25% of the large leak size. However, in the event of a double leak with equal dimensions, the leak closer to the pipe upstream is easier to detect. The results from all the analyses demonstrate the PSOASM algorithm's superiority over the well-known sequential sampling schemes employed for evaluation. The test results show that the PSOASM algorithm can be applied for pipeline leak detection with limited training datasets and provides a general framework for improving computational efficiency using adaptive surrogate modelling in various real-life applications

    Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing

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    Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to the neural model in order to determine the occurrence and the leak magnitude. The results indicated the great potential of the technique and of the developed neural network models. For all on-line tests, the models showed 100% accuracy in leak detection, except for a small orifice (1 mm) under 4 kgf/cm² of nominal pressure. Similarly, the neural network models could adequately predict the magnitude of the leakages.14515

    High-viscosity biphasic flow characterization in a pipeline: application to flow pattern classification and leak detection

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    Pipeline systems play an essential role in the oil industry. These systems connect ports, oil fields, refineries, and consumer markets[104]. Pipelines covering long distances require pumping stations, where products are propelled to the next pumping station, refinery, or deposit terminal, thus traveling through most of the country. The product considered in this research work is crude oil. It is usually transported with a combination of crude oil with viscosity reducers (DRA, drag reducer agent) and oil with gas in onshore/offshore pipelines. This mode of transport is efficient for large quantities and large product shipment distances. Problems may arrive when a leak occurs. In major incidents, large scale damage to humans and the environment is possible. Then, this research addresses the problem of how to detect the leak earlier to reduce the impact in the surrounding areas and economic losses, considering five research topics taking into account that the products inside the pipeline are water-glycerol and gas-glycerol mixtures (simulating oil-DRA and oil-gas in the laboratory test apparatus). The first research topic presents a mathematical model to describe the flow of a mixture of water and glycerol in pressurized horizontal pipelines, which emulates the mixture of heavy oil and a viscosity reducer. The model is based on the mass and momentum conservation principles and empirical correlations for the mixture’s density and viscosity. The set of partial differential equations is solved using finite differences. These equations were implemented in a computer platform to be able to simulate a system. This simulation platform is a tool to simulate leak cases for different fractions of water and glycerol to evaluate algorithms for leak detection and localization before their implementation in a laboratory setting.DoctoradoDoctor en Ingeniería Mecánic

    Detection and Localization of Leaks in Water Networks

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    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

    The Gas Transportation in a Pipeline Network

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