Pipeline Leakage Research Articles

A Buried Gas Pipeline Leakage Model

A Buried Gas Pipeline Leakage Model

Development and Characterization of Thermal Protection Gels for Steel Pipelines Transporting Combustible Materials

Due to low costs, pipelines are commonly used for transporting hazardous substances such as combustible liquids and gasses. Currently, chemical industrial parks and gas production stations have dense pipeline networks. In the case of a pipeline leakage and subsequent fire accident, the adjacent pipelines could be directly impinged by the flame or engulfed in hot smoke, with the potential to result in a chain of accidents and catastrophic consequences. It is thus of practical importance to develop an efficient thermal protection material for pipelines. In this study, a new type of bio-based gel material was prepared for pipeline thermal protection, using guar gum (GG) as the gelling agent, sodium tetraborate (B) as the crosslinking agent and magnesium chloride (MgCl2) as the fire retardant. Firstly, orthogonal experiments were conducted to examine the gelling time of the gel and determine the optimal formulations that meet the protection requirements. Subsequently, water retention, thermal stability and the microstructure of these formulations were analyzed. Finally, the thermal protection performance of the gel formulations was evaluated under the direct impingement of flames or high-temperature smoke. The results indicated that the best performance was achieved by the formulation with GG, B and MgCl2 mass fractions of 2.5, 0.6 and 0.5 wt%, respectively. This formulation also exhibited the best water retention capacity and thermal stability. In the pipeline thermal protection experiments, this formulation achieved effective protection times of 216 s (for a 90 mm diameter) and 312 s (for a 120 mm diameter) for the lower part of the pipeline under direct flame impingement. Under high-temperature smoke impingement, this formulation also showed excellent performance. These research and findings can provide an important foundation for the further development of thermal protection materials for pipelines under fire conditions.

Research on gas pipeline leakage identification based on SE-2DCNN with ultra-weak fiber Bragg grating distributed acoustic sensing system

Abstract A new method for identifying gas pipeline leaks using an ultra-weak fiber Bragg grating (UWFBG) distributed acoustic sensing (DAS) system is proposed. To enhance the accuracy of gas leakage detection, an improved convolutional neural network (CNN) incorporating a two-dimensional structure based on the squeeze-and-excitation (SE) attention mechanism was introduced. The principle of acoustic leakage sensing using this technology is explained in detail, and an experimental setup simulating gas pipeline leaks is constructed. During this process, 9,340 DAS data points across varying leakage volumes and pipeline pressures were collected to create ten distinct datasets. The Mel-frequency cepstral coefficients (MFCC) are employed as the feature input for the optimized SE-2DCNN model, which performs identification tasks. The results show that this optimized model achieves an average leakage identification accuracy of 95.33%, demonstrating superior performance over other methods. This approach offers a robust reference for accurately detecting gas pipeline leakages.

Divisional intuitionistic fuzzy least squares twin SVM for pipeline leakage detection

Improving the accuracy of pipeline leak detection in noisy environments is significant for maintaining pipeline safety. Currently, the weighted support vector machine (SVM) is extensively employed. Among them, Intuitionistic fuzzy twin SVM (IFTSVM) has good robustness due to the integration of intuitionistic fuzzy set theory. However, IFTSVM does not fully consider the position information of samples in the feature space, cannot reduce the impact of heterogeneous clustering noise. Moreover, it requires solving two quadratic programming problems (QPPs), which is inefficient. To address the aforementioned issues, this paper proposes a divisional intuitionistic fuzzy least squares twin SVM (DIFL-TSVM) for pipeline leakage detection. This approach proposes a novel divisional intuitionistic fuzzy membership calculation strategy to reduce the impact of heterogeneous clustering noise and simplify the calculation of sample membership. In addition, DIFL-TSVM transforms the original QPPs into two linear equations using the least squares method to improve the efficiency of model. This paper verifies that the DIFL-TSVM model not only has the ability to eliminate the effects of discrete noise and heterogeneous clustering noise but also more efficiency on water supply pipeline leakage diagnosis experiments and UCI datasets. The experimental results show that DIFL-TSVM has higher leak identification with a detection accuracy of 95.6 %.

A Pipeline Leakage Aperture Identification Method Based on CNN-SVM Considering Sample Granularity

A Pipeline Leakage Aperture Identification Method Based on CNN-SVM Considering Sample Granularity

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

Simulation and experimental research on the propagation mechanisms of acoustic energy inside the pipeline to detect the leakage

Leakage from pipelines has negative impacts on property and environment. The diameters of internal acoustic detectors in pipelines are small and not easily blocked, making it suitable for pipelines under various working conditions. Studying the characteristics of pipeline leakage sound signal and the propagation mechanisms within pipelines is crucial for timely detection of small leaks. In this study, a 3D simulation model of pipeline leakage was established, and a CFD/CAA hybrid calculation method was used to obtain effective leakage flow field information via LES. The equivalent sound sources in the flow field were extracted based on the Lighthill acoustic analogy theory. Determine the characteristic frequency range of the leakage sound signal was 1–2 kHz. This study founded that an increase in the leakage pressure and aperture size both lead to an enhancement in the leakage sound signal, and the propagation of leakage sound signal in pipelines is affected by background flow. An experimental pipeline leakage system was designed, and the accuracy of the simulation results was verified experimentally.

A Risk Assessment Model of Gas Pipeline Leakage Based on a Fuzzy Hybrid Analytic Hierarchy Process

Given the rising urban demand for gas, it has emerged as a primary energy source for urban activities and daily life. However, China’s urban gas pipeline network has witnessed a surge in accidents, leading to significant losses and disasters. Therefore, it is particularly necessary to study the disaster risk assessment model caused by urban gas pipeline leakage. There are some problems in the previous evaluation methods, such as less consideration of the influence relationships between disaster factors. To redress this issue, a novel fuzzy hybrid analytic hierarchy process evaluation methodology is proposed. First, a hybrid hierarchical risk assessment model is developed by combining the analytic hierarchy process and the network analytic hierarchy process. Membership matrices and impact matrices are utilized to calculate comprehensive factor weights. This approach enhances the understanding of relationships between risk factors within the hierarchical structure model. Subsequently, employing a fuzzy evaluation method, the risk level matrix is derived by using multiplication and bounded operators to ascertain the risk level state. This solves the problem of the fuzzy boundaries when measuring the index factors of the gas pipeline network. Finally, experimental analysis is carried out on the gas pipeline network in the central area of a city and validates the model’s accuracy in practical applications.

Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data

China is advancing urban gas pipeline safety through the strategic deployment of methane detectors. This study introduces a systematic approach for identifying underground gas leaks, and overcoming challenges such as biogas interference and environmental factors. Abnormal methane sequences, defined by expert experience, undergo data preprocessing, including cleansing, compressing, and extracting rising segments. Feature engineering is performed on these rising segments across three dimensions: temporal, volatility, and temperature features. Different classification algorithms, including K-nearest neighbor, decision tree, and support vector machine, are employed for gas leakage recognition. The decision tree proves most effective, achieving a 92.86% recall rate for methane leakage segments. Additionally, feature importance ranking from the decision tree model highlights the maximum value of preprocessed methane concentration time series and the fitting slope of the rising segment as crucial features for methane leakage detection.

Quantitative analysis of hydrogen leakage flow measurement and calculation in the on-board hydrogen system pipelines

The mass flow rate of hydrogen leakage directly affects hydrogen diffusion and concentration distribution. In this paper, a test platform was constructed to measure mass flow rates during hydrogen leakage in the supply pipelines of the on-board hydrogen system. Hydrogen leakage tests were conducted under two different conditions. Mass flow rates were measured for tube fittings with various loosening angles and hydrogen supply pressures. The data were then fitted to extrapolate mass flow rates of hydrogen leakage beyond the tested conditions. Additionally, mass flow rates were measured for hydrogen supply pipelines with small holes of different shapes and sizes. The effects of pipeline size, hydrogen supply pressure, hole shape, and size on the hydrogen leakage mass flow rate were analyzed. Hydrogen leakage flow coefficients were corrected, and the accuracy of three calculation methods for hydrogen leakage flow was compared and analyzed based on the test results. The results provide a foundation for future numerical simulation models of hydrogen leakage diffusion.

An early warning method of pipeline leakage monitoring with limited leakage samples

Aiming at the problems of low computational efficiency of the real-time transient model (RTTM), and training difficulty of data-driven models with small leakage samples in existing pipeline leakage monitoring methods, this paper proposes a novel pipeline leakage detection model (Multiple Regression Modeling of Real-time Transients, MR-RTTM) based on the fusion of real-time transient mechanism model and data-driven inference. Firstly, the real-time transient mechanism model of pipelines is derived based on the Bernoulli equation, and the required eigenvalues and label values of the data-driven model are determined to achieve the integration of the mechanism and the data-driven model. Secondly, MR-RTTM is constructed by applying the data of pipelines in normal working conditions and combining with the multiple regression algorithm to fit the real-time transient mechanism model of pipelines. An optimal threshold interval selection method of the model is also proposed based on the probability of detection (POD) and the probability of false call (POFC). Experiments show that MR-RTTM can detect pipeline leakage by arranging common pressure and flow rate sensors in the pipeline inlets and outlets, and the minimum detectable leakage is 1 % with an acceptable interval of 80 %∼100 % for POD and 0 ∼ 10 % for POFC, and the leakage discrimination rate is up to 100 %.

Leak aperture recognition of natural gas pipeline based on variational mode decomposition and mutual information

The acoustic emission (AE) signals generated by natural gas pipeline leaks are affected by various noises, making the accurate extraction of feature signals a challenging task. Therefore, this paper introduces a novel adaptive signal denoising approach utilizing Variational Mode Decomposition (VMD).and mutual information (MI). Firstly, the parameters K and α of VMD are optimized using the energy difference. Simultaneously, the correlation coefficient between adjacent IMF components is used as the evaluation criterion to determine the optimal values of K and α. Then, based on MI, a screening criterion is proposed to select effective IMF components for reconstruction. Finally, the ISSA-SVM model was established by introducing Tent chaotic mapping and inertia weight adjustment strategy on the basis of the traditional sparrow search algorithm (SSA). Experimental results demonstrate that the proposed method effectively eliminates noise from pipeline leak AE signals, with a recognition accuracy reaching up to 96.09%.

Leak detection in water supply pipeline with small-size leakage using deep learning networks

Pipeline transportation technology has witnessed remarkable development in recent years, while the issue of leakage poses a serious limitation to this technology’s progress, especially for the small-scale leakage in water pipelines that exhibits weaker leakage signals. To address those challenges, this study investigates leak detection in water supply pipeline with an inner pipe diameter of 100 mm in the operating range of volume flow Q = 40–80 m3/h, leakage flow Ql = 0.65–1.33 m3/h and pressure p = 130–220 kPa. The characteristics and propagation of leakage pressure signals are analyzed. An adaptive improved variational mode decomposition (VMD) based on the white shark optimizer (WSO) algorithm is proposed, utilizing sample entropy as the fitness function. Additionally, tent chaotic mapping is employed for parameter initialization, mean normalization is used to minimize the influence of the DC component, and signal relative energy serves as a boundary condition. The processed data is utilized to establish leakage detection models using long short-term memory network (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and SqueezeNet framework. The performance of leakage detection models is evaluated and compared using 962 sets’ data using evaluation indicators and confusion matrix. The results indicate that SqueezeNet-GRU demonstrates superior performance in pipeline leak detection and localization applications, achieving 97.40 % accuracy for the total dataset.

Research on the generation and modulation of active pressure wave for pipelines leak diagnosis

In this paper, a novel device that uses compressed air to generate pressure waves in the liquid pipeline is proposed. Experimental investigations are carried out to explore the modulation method of the pressure wave generated and the application potential of the device in leak diagnosis. The consequences show that the device can generate safe, stable and controllable pressure waves. The characteristics of produced pressure wave under different excitation frequencies and inflation pressure are investigated with the combination of experiments and theory analysis. The parameters that affect the excitation behaviors of the device are pointed out theoretically. Finally, the performance of the device under continuous excitation and single excitation is tested in the leaking pipe. Through the extraction and analysis of the signal features in the frequency domain, the leakage can be diagnosed and distinguished under both excitation methods.

An Improved Convolutional Neural Network for Pipe Leakage Identification Based on Acoustic Emission

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy.

Dual channel visible graph convolutional neural network for microleakage monitoring of pipeline weld homalographic cracks

When using a single sensor to monitor early microleakage of nuclear power pressure pipeline leakage, there are problems such as low monitoring accuracy and poor early warning reliability due to the limitations of the monitoring range and weak difference between the leakage signals. To address these challenges, this paper proposes a dual channel visible graph convolutional neural network (DCV-GCN). Firstly, the acoustic emission time-series data of each channel are truncated and divided, and the significant frequency bands are selected based on the envelope spectrum. On this basis, the sequence group is averaged to obtain the graph structure sequence. Then, the limited penetrable visibility (LPV) graph construction algorithm is used to calculate the adjacency matrix, and the important nodes is reserved according to the eigenvector centrality. Furthermore, the inverse ratio of the distance from the sensor in each single channel to the center of the crack is used as the fusion weight, and the adjacency matrices are merged after normalization to transform the construction of the graph structure dataset. Finally, the dataset is input into the graph convolutional neural network, and the effectiveness of the method is verified by carefully designing three homalographic cracks. The results show that the proposed method can effectively extract the distinguishing features with similar frequency components and similar leakage rates, and the recognition accuracy of different leakage states can reach 98.56 %. In addition, through ablation experiments and different parameter strategy settings, the operating mechanism is explained, which can provide a reference for monitoring and analysis by industrial technicians.

Development of Model and Graphical User Interface for Leak Localization in Crude Oil Pipeline

Pipelines provide the most efficient and cost-effective means for fluid transport but the challenge posed by leaks has substantially increased the risks and hazards in pipeline fluid transportation. The development of efficient leak detection system pays off in quicker leak detection and localization, leading to quicker responses and remediation works by the pipeline emergency response team. This would ultimately bring about less severity of the pipeline leak in terms of financial losses, human and environmental consequences. Steady state modeling of crude oil pipeline flow with attempt to determine and localize leak has been achieved in this study. Mathematical models have been developed to detect and localize leaks in crude oil pipeline during leak occurrence. Leak detection was modeled using the conservation of mass equation while leak localization was modeled by modifying the Darcy-Weisbach pipeline liquid flow equations and utilizing the Swamee-Jain friction factor correlation. Equations for flowrate and pressure drop along the pipeline were developed for two cases: a case where there is no leak in the pipeline and a case where there is leak in the pipeline. Leak localization equation was determined by equating the fluid flow equation when there is no leak and when there is leak. The model was structured and simulated in Matlab software with the model tested for four field cases. The results from the simulation revealed swift leak detection, accurate localization of the leak, and determination of the pressure at the point of leak. Experimentally determined results from actual field measurements of leak incidences were used to validate the results. The result determined from the model developed proved accurate with only an average error of 0.216 miles.

Novel Adaptive Hidden Markov Model Utilizing Expectation–Maximization Algorithm for Advanced Pipeline Leak Detection

Novel Adaptive Hidden Markov Model Utilizing Expectation–Maximization Algorithm for Advanced Pipeline Leak Detection

Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization

This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe insulated with an aluminum jacket and featuring a 12 mm leak gap. A single-mirror-off-axis Schlieren system, employing a 150 mm diameter parabolic mirror, was used to visualize the leaks. The temperature of the leaking air varied between 20 and 100 °C, while the ambient temperature was maintained at 14 °C. To quantify the leaks, the coefficient of variation for pixel intensity within the leak region was calculated. Results showed that for the PTFE tube, leaks became detectable when the temperature difference exceeded 34 °C, with the coefficient of variation surpassing 0.1. However, in the insulated pipe, detecting clear leak patterns was challenging. This research demonstrates the potential of Schlieren visualization as a valuable tool in enhancing pipeline leak detection.

A Simplified Failure Assessment to Identify Crack Growth Behavior in the Gas Pipeline by Post-Hydrostatic Pressure

Hydrostatic pressure tests on pipelines have been found to be an inadequate means of critical integrity management due to the frequency of false negatives, which result from the inability of these tests to detect crack growth. It can be argued that focusing on pipe leakage does not guarantee future operability. A study presents a failure assessment methodology based on the failure assessment diagram (FAD), which aims to predict crack growth during hydrotesting. The calibrated pipe spool is validated by the application of data from hydrostatic tests and analytical techniques to ascertain the potential growth in circumferential surface cracks. A variety of factors, including pressure, material grades, flaw dimensions, and elliptical flaw angles, were examined in an effort to assess cracks. The results demonstrate that there is no pipeline leakage and minimal trapped air. Despite its location within the plastic zone, with a normalized pressure index of ≤1, the pipeline is deemed to be within acceptable limits according to the criteria established by the FAD. The assessment point was found to be predominantly influenced by the toughness ratio of the material grade. The crack propagated in the opposite direction with a maximum length a/c= 0.125 and a crack depth a/t= 0.2, which limited the toughness ratio. The load ratio indicates uniformity in elliptical angle flaw results. In this simplified failure assessment, the parameter describing the flaw size, which exhibits a strong correlation with the toughness ratio, plays a pivotal role. Further research and recommendations are also proposed.