Search Results (105)

Green hydrogen is a clean, versatile, and future-oriented fuel that can play a key role in the energy transition, decarbonization of the economy, and climate protection. It offers an alternative to fossil fuels and can be used in various applications, including power generation, industry, and transportation. However, due to its wide flammability range, small molecular size, and high diffusivity, special attention must be paid to ensuring safety during its use, particularly in leakage control. This paper provides a review and analysis of equipment leakage testing methods used for natural gas, with a view to applying these methods to the leakage testing of gas meters intended for hydrogen-containing gas mixtures and pure hydrogen. Tests of simulated leaks were carried out using two common methods: the bubble method and the pressure decay method, for three different gases: nitrogen (most commonly used for leak testing), helium, and hydrogen. The results obtained from the tests and analyses made it possible to verify and select optimum leak-testing methods for gas meters designed for measuring fuels containing hydrogen. Full article

An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

Carbon Capture and Storage (CCS) is a key technology for reducing anthropogenic greenhouse gas emissions, in which pipelines play a vital role in transporting CO₂ captured from industrial emitters to geological storage sites. To aid the efficient and safe operation of the CO₂ transport infrastructure, robust, accurate, and reliable solutions for monitoring pipelines transporting industrial CO₂ streams are urgently needed. This literature review study summarizes the monitoring objectives and identifies the problems and relevant mathematical algorithms developed for optimization of monitoring systems for pipeline transportation of water, oil, and natural gas, which can be relevant to the future CO₂ pipelines and pipeline networks for CCS. The impacts of the physical properties of CO₂ and complex designs and operation scenarios of CO₂ transport on the pipeline monitoring systems design are discussed. It is shown that the most relevant to liquid- and dense-phase CO₂ transport are the sensor placement optimization methods developed in the context of detecting leaks and flow anomalies for water distribution systems and pipelines transporting oil and petroleum liquids. The monitoring solutions relevant to flow assurance and monitoring impurities in CO₂ pipelines are also identified. Optimizing the CO₂ pipeline monitoring systems against several objectives, including the accuracy of measurements, the number and type of sensors, and the safety and environmental risks, is discussed. Full article

With the introduction of the “dual carbon” strategy, public attention to green energy has surged, leading to a notable increase in the demand for natural gas. Consequently, the storage and transportation of liquefied natural gas (LNG) have emerged as critical aspects to ensure its safe and cost-effective utilization. For onshore LNG storage, LNG storage tanks play a pivotal role. However, in extreme scenarios such as fires, these tanks may be exposed to radiant heat, which not only jeopardizes their structural integrity but could also result in LNG leaks, triggering severe safety incidents and environmental disasters. Against this backdrop, this study delves into the evaporation characteristics of large-scale LNG storage tanks under fire radiation conditions. Given the unique properties of LNG and the similarity between the bubble-point lines and heat exchange curves of nitrogen and LNG, liquid nitrogen is employed as a substitute for LNG in experimental investigations to observe evaporation behaviors. Furthermore, the FLUENT 2022R1 software is utilized to conduct numerical simulations on a 160,000-cubic-meter LNG storage tank, aiming to model the intricate process of internal evaporation and the impact of environmental factors. The findings of this research aim to furnish a scientific basis for enhancing the storage safety of large-scale LNG storage tanks. Full article

With the extensive use of liquefied petroleum gas (LPG) in the catering industry, leakage explosions have become frequent. This study employs numerical simulations to investigate the diffusion patterns of LPG leakage under various ventilation conditions. The results show that there is a logarithmic relationship between the wind speed and the volume of a propane gas cloud under natural ventilation. In the wind speed ranges of 1.5 to 3.3 m/s and 7.9 to 10.7 m/s, a small increase in wind speed leads to a significant reduction in gas cloud volume (97.2% and 95.05%, respectively). Under forced ventilation, the volume of the gas cloud decreases by 90.6%, from 6.67 m³ at higher air exchange rates (22.1 and 24.3 times/h), reducing explosion risks. When leakage occurs at the stove, the optimal combination for dispersing the propane combustible gas cloud is window opening at position 1 and fan at position a. The volume of the gas cloud at window position 1 increases exponentially with the distance between the fan and the leak source. The fan is installed within 2.786 m from the leak source to ensure that the gas cloud volume remains below 0.5 m³. These findings provide valuable insights for the design and the optimization of ventilation systems and layouts in commercial kitchens. Full article

Numerical simulations reveal the combustion dynamics of hydrogen-blended natural gas (H-BNG) in semi-open spaces. In the typical semi-open space scenario, increasing the hydrogen blending ratio from 0% to 60% elevates peak internal pressure by 107% (259.3 kPa → 526.0 kPa) while reducing pressure rise time by 56.5% (95.8 ms → 41.7 ms). A vent size paradox emerges: 0.5 m openings generate 574.6 kPa internal overpressure, whereas 2 m openings produce 36.7 kPa external overpressure. Flame propagation exhibits stabilized velocity decay (836 m/s → 154 m/s, 81.6% reduction) at hydrogen concentrations ≥30% within 2–8 m distances. In street-front restaurant scenarios, 80% H-BNG leaks reach alarm concentration (0.8 m height) within 120 s, with sensor response times ranging from 21.6 s (proximal) to 40.2 s (distal). Forced ventilation reduces hazard duration by 8.6% (151 s → 138 s), while door status shows negligible impact on deflagration consequences (412 kPa closed vs. 409 kPa open), maintaining consistent 20.5 m hazard radius at 20 kPa overpressure threshold. These findings provide crucial theoretical insights and practical guidance for the prevention and management of H-BNG leakage and explosion incidents. Full article

The leakage of hazardous chemical gases in chemical plants can lead to severe consequences. Source term estimation (STE) algorithms are effective in locating the leak source. The layout of the sensor network significantly affects the performance of the STE algorithm, yet the underlying mechanism remains unclear. In this study, we first applied computational fluid dynamics (CFD) to simulate 160 hazardous chemical gas leakage scenarios under multi-directional wind conditions in two hypothetic scenes with a natural convection environment, creating an accident dataset. Subsequently, a mathematical model for sensor placement optimization was developed and applied to the dataset to generate a series of sensor layout solutions. Based on these layouts, 12,216 STE cases were calculated. By analyzing the error distribution of these cases, the relationship between sensor placement and STE performance was systematically investigated, and the most effective sensor layout optimization strategies were discussed. This study found that in scenarios with complex obstacles, increasing the average measured concentration of the sensor network can significantly reduce the errors in the STE algorithm. Full article

To address the challenges of insufficient model generalization, high false alarm rates due to the scarcity of leakage data, and frequent minor leakage alarms in traditional weak leakage (the leakage amount is less than 1%) detection methods for gas transmission pipelines, this paper proposes a real-time weak leakage detection framework for natural gas pipelines based on the combination of the generalized likelihood ratio (GLR) and ensemble learning. Compared to traditional methods, the core innovations of this study include the following: (1) For the first time, GLR statistics are integrated with an ensemble learning strategy to construct a dynamic detection model for pipeline operating states through multi-sensor collaboration, significantly enhancing the model’s robustness in noisy environments by fusing pressure data from the pipeline inlet and outlet, as well as outlet flow data. (2) An adaptive threshold selection mechanism that dynamically optimizes alarm thresholds using the distribution characteristics of GLR statistics is designed, overcoming the sensitivity limitations of traditional fixed thresholds in complex operating conditions. (3) An ensemble decision module is developed based on a voting strategy, effectively reducing the high false alarm rates associated with single models. The model’s leakage detection capability under normal and noisy pipeline conditions was validated using a self-built gas pipeline leakage test platform. The results show that the proposed method can achieve the precise detection of pipeline leakage rates as small as 0.5% under normal and low-noise conditions while reducing the false alarm rate to zero. It can also detect leakage rates of 1.5% under strong noise interference. These findings validate its practical value in complex industrial scenarios. This study provides a high-sensitivity, low-false-alarm, intelligent solution for pipeline safety monitoring, which is particularly suitable for early warning of weak leaks in long-distance pipelines. Full article

Gas pipeline networks are vital urban infrastructure, susceptible to leaks caused by natural disasters and adverse weather, posing significant safety risks. Detecting and localizing these leaks is crucial for mitigating hazards. However, existing methods often fail to effectively model the time-varying structural data of pipelines, limiting their detection capabilities. This study introduces a novel approach for leak detection using a spatial–temporal attention network (STAN) tailored for small-hole leakage conditions. A graph attention network (GAT) is first used to model the spatial dependencies between sensors, capturing the dynamic patterns of adjacent nodes. An LSTM model is then employed for encoding and decoding time series data, incorporating a temporal attention mechanism to capture evolving changes over time, thus improving detection accuracy. The proposed model is evaluated using Pipeline Studio software and compared with state-of-the-art models on a gas pipeline simulation dataset. Results demonstrate competitive precision (91.7%), recall (96.5%), and F1-score (0.94). Furthermore, the method effectively identifies sensor statuses and temporal dynamics, reducing leakage risks and enhancing model performance. This study highlights the potential of deep learning techniques in addressing the challenges of leak detection and emphasizes the effectiveness of spatial–temporal modeling for improved detection accuracy. Full article

This study investigates the impact of natural gas pipeline leakage on the soil temperature field through numerical simulations. Physical and mathematical models were developed to analyze the temperature and flow field changes resulting from pipeline leaks. The study explores the influence of various leakage factors on the temperature distribution in the surrounding soil. Key findings include the identification of the buried pipeline temperature as a critical factor influencing the soil temperature gradient when surface temperatures are similar to the subsurface constant temperature. Upon leakage, the pressure distribution around the leak is symmetrical, with a higher pressure at the leak point, and the Joule–Thomson effect causes a rapid decrease in gas temperature, forming a permafrost zone. The study also reveals that increased transport pressure expands the permafrost area, with pressure playing a significant role in the temperature field distribution. Additionally, an increase in the leak orifice diameter accelerates the expansion of the permafrost area and reduces the time for temperature stabilization at monitoring points. Conversely, changes in the leak direction mainly affect the spatial distribution of the permafrost zone without significantly altering its size. The findings provide valuable insights for monitoring natural gas pipeline leaks through temperature field variations. Full article

The thermal insulation integrity of liquefied natural gas storage tanks is essential for their life-cycle safety. However, perlite settlement (insulation material) can result in thermal leaks and lead to engineering risks. The direct measurement of perlite settlement is difficult due to the enclosed structure of these tanks. To address this challenge, this study presents a data-driven approach based on machine learning and real-time monitoring data. This study proposes a multi-fidelity machine learning framework to enhance generalizability and leverage multi-fidelity data effectively. Low-fidelity data are readily available but contain systematic errors, while high-fidelity data are accurate but limited in accessibility. By combining both types of data, this framework enhances the generalisability and prediction accuracy of trained models. The results of the data experiments demonstrate that the multi-fidelity framework outperforms models trained solely on low- or high-fidelity data, achieving a coefficient of determination of 0.980 and a root mean square error of 0.078 m. Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. This approach provides a reliable method for the real-time monitoring of thermal insulation integrity in liquefied natural gas storage tanks, contributing to improved industrial safety and operational efficiency. Full article

Methane is a significant contributor to anthropogenic greenhouse gas emissions. Blending hydrogen with natural gas in existing networks presents a promising strategy to reduce these emissions and support the transition to a carbon-neutral energy system. However, hydrogen’s potential for atmospheric release raises safety and environmental concerns, necessitating an assessment of its impact on methane emissions and leakage behavior. This study introduces a methodology for estimating how fugitive emissions change when a natural gas network is shifted to a 10% hydrogen blend by combining analytical flowrate models with data from sampled leaks across a natural gas network. The methodology involves developing conversion factors based on existing methane emission rates to predict corresponding hydrogen emissions across different sections of the network, including mainlines, service lines, and facilities. Our findings reveal that while the overall volumetric emission rates increase by 5.67% on the mainlines and 3.04% on the service lines, primarily due to hydrogen’s lower density, methane emissions decrease by 5.95% on the mainlines and 8.28% on the service lines. However, when considering the impact of a 10% hydrogen blend on the Global Warming Potential, the net reduction in greenhouse gas emissions is 5.37% for the mainlines and 7.72% for the service lines. This work bridges the gap between research on hydrogen leakage and network readiness, which traditionally focuses on safety, and environmental sustainability studies on methane emission. Full article

With the robust growth of the national economy, the demand for oil and natural gas continues to rise, heightening the significance of pipeline transportation in the energy sector. However, long-term pipeline operations are often subjected to factors such as corrosion, aging, and damage, which can result in leaks and safety incidents, posing significant threats to life, property, and environmental integrity. Consequently, timely and precise detection of pipeline defects and estimation of their sizes hold paramount practical importance. This research endeavors to employ advanced information technology and artificial intelligence to explore methods for pipeline defect detection and size estimation grounded in prior knowledge. The aim is to enhance the accuracy and reliability of pipeline defect diagnosis and ensure the safe operation of pipelines. The primary innovative work includes the development of a preprocessing method based on prior knowledge, the design of an adaptive algorithm for estimation of defect size, and the creation of an algorithm for estimation of deep cascade pipeline defect size. These methods effectively combine traditional mechanisms and data-driven approaches, leveraging the strengths of both to improve performance, accuracy, and robustness. The proposed methodology demonstrates superior accuracy and stability in defect inversion, providing strong technical support for the quantitative assessment of pipeline defects, which is significant for fault diagnosis and the precise maintenance of long-distance pipelines. Full article

With the breakthrough in mixing hydrogen into natural gas pipelines for urban use, the widespread application of hydrogen-blended natural gas (HBNG) in energy delivery is imminent. However, this development also introduces significant safety concerns due to notable disparities in the physical and chemical properties between methane and hydrogen, heightening the risks associated with gas leaks. Current models that simulate the diffusion of leaked HBNG from buried pipelines into the atmosphere often employ fixed average leakage rates, which do not accurately represent the dynamic nature of gas leakage and diffusion. This study uses computational fluid dynamics (CFD) 2024R1 software to build a three-dimensional simulation model under a soil–atmosphere coupling model for HBNG leakage and diffusion. The findings reveal that, in the soil–atmosphere coupling model, the gas diffusion range under a fixed leakage rate is smaller than that under a dynamic leakage rate. Under the same influencing factors in calm wind conditions, the gas primarily diffuses in the vertical direction, whereas under the same influencing factors in windy conditions, the gas mainly diffuses in the horizontal direction. Full article