Gas Pipe Network Research Articles

Operation Optimization of Natural Gas Pipe Network Considering Energy Metering

Operation Optimization of Natural Gas Pipe Network Considering Energy Metering

Simulation Analysis of Influence of Accident Conditions on Hydraulic Reliability of Gas Pipe Network

Taking reliability as the starting point, based on Pipeline Studio software and combined with the actual situation of medium-pressure gas pipe network in Weidu District, Xuchang City, the operation conditions of gas pipe network under design conditions and accident conditions were simulated, and the accident conditions were assumed to be the shutdown of important pipe segments for reasons or the failure of the pipe segment with the maximum flow rate. According to the simulated hydraulic calculation results, the overall gas supply capacity of the pipeline network is comprehensively evaluated and analyzed, and the conclusion is reached that the pipe network can meet the requirements of normal and accident conditions by properly enlarging the pipe diameter closer to the gas source point.

Study on the Physical Properties of Hydrogen-Doped Natural Gas Based on Equation of State

As a zero-emission green clean energy, hydrogen energy is an important means to achieve the strategic goal of carbon neutrality and an important part of the energy Internet. Hydrogen-doped natural gas transportation is an economical and feasible scheme to realize long-distance transportation of hydrogen by using the existing natural gas pipeline network. However, the addition of hydrogen to natural gas will cause obvious changes in the physical properties of the transport medium, which will have a certain impact on the transport characteristics and flow measurement of the pipeline. The main problem is that we need to understand the changes of physical properties after natural gas mixed with hydrogen to ensure the safe operation of natural gas pipe network mixed with hydrogen. Based on this situation, this paper takes BWRS equation as the theoretical basis, selects typical natural gas temperament, calculates and solves the physical parameters of natural gas under different hydrogen blending ratios, and compares them with the standard physical reference values. After verification, the results are used to explore the physical properties of hydrogen-doped natural gas with hydrogen blending ratio, temperature and pressure as variables. The relative average absolute value error (RAAD) of the calculated physical properties of hydrogen-doped natural gas in this paper is less than 2% compared with the reference value of standard physical properties, which can provide theoretical data basis for studying hydrogen-doped pipeline transportation, ensuring pipeline transportation safety and hydrogen-doped natural gas metering.

Identification of corrosion factors in blast furnace gas pipe network with corrosion big data online monitoring technology

To mitigate the accelerated corrosion of blast furnace gas pipelines induced by severe environmental conditions, this study has established an extensive high-throughput sensor system in the field. It leverages corrosion big data technology to investigate the corrosion factors affecting industrial gas pipelines and to precisely analyze the influence of related variables. The results indicated that temperature reduction is the driving factor that causes increased pipeline corrosion. On one hand, temperature fluctuations impact the evaporation and condensation of moisture in the tube. On the other hand, temperature fluctuations, in conjunction with corrosive gases, exhibit significant coupling effects, resulting in more severe corrosion. In addition, critical points for relative humidity and temperature are evident. When humidity exceeds 50%, corrosion in the pipe begins to increase, reaching its peak at 70%. The increase in temperature has a slowing effect on pipeline corrosion, reaching a peak value at 40°C. The response of corrosion rate to the concentration of corrosive gases follows a linear pattern. Based on these observations, we propose an effective corrosion control scheme.

Optimal operation of electric-heat-gas-hydrogen integrated energy system considering source-load uncertainty

Faced with the threat of global climate change crisis, increasing the use of hydrogen-enriched compressed natural gas (HCNG) and the utilization rate of renewable energy can both reduce the carbon emissions of the integrated energy system (IES). This paper establishes an electric-heat-gas-hydrogen integrated energy system (EHGH-IES) framework considering source-load uncertainty, and the natural gas pipe network in the traditional IES is transformed into a HCNG pipe network with a certain hydrogen blending ratio. An optimal operation model of the EHGH-IES considering source-load uncertainty is proposed in this paper. The chance constrained programming method is adopted to deal with source-load uncertainty. We pay attention to comparing different confidence coefficient and hydrogen blending ratio in terms of the changes in the cost of the IES. The results show that the total cost of the EHGH-IES will increase by considering source-load uncertainty and using HCNG pipeline, but will bring some stability and environmental benefits to the IES by them.

Leakage Diffusion Modeling of Key Nodes of Gas Pipeline Network Based on Leakage Concentration

In order to achieve the prediction and early warning of city gas pipe network leakage accidents, as well as to provide rapid and precise support for emergency response to such accidents, this study focuses on a Gaussian diffusion model applied to a large urban gas pipeline network. Specifically, it investigates the gas gate wells, which are key nodes in the pipeline network, to develop a leakage model. The objective is to analyze the variation in internal gas concentration in the gate wells and determine the range of danger posed by external gas diffusion from the gate wells. In addition, Fluent simulation is utilized to compare the accuracy of the model’s calculations. The findings of this study indicate that the gas concentration inside the gate well, as predicted by the model fitting results and Fluent simulation, exhibit a high level of agreement, with coefficient of determination (R2) values exceeding 0.99. Moreover, when predicting the hazardous distance of gas leakage outside the gate well, the model’s results show an average relative error of 0.15 compared to the Fluent simulation results. This demonstrates that the model is highly accurate and meets the practical application requirements.

Preface

The 10th International Conference on Environmental and Engineering Geophysics (ICEEG) is hosted by China University of Geosciences, Beijing, China, from June 07-12, 2023. Held biennially since 2004, the ICEEG Conference is one of the most important events for geophysical research and applications in environmental and engineering. Drawing together scientists, engineers, industrial delegates and end-users, the conference is a place to see all of the advanced developments, ranging from fundamental achievement in geophysical methods to the most interesting case studies. Traditionally, the proceedings “Engineering Environmental Geophysics and Intelligent Detection Technology” of the 10th International Conference on Environment and Engineering geophysics is published for the readers from the near-surface geophysics communityShallow surface geophysics refers to the exploration theory and application research on the surface of the earth using physical principles and methods. The shallow surface supports the needs of human survival and development. On the one hand, human beings obtain water and minerals from it, and build the necessary underground space to contain water, electricity, gas, communication, transportation and other pipe network systems in modern cities. On the other hand, various kinds of waste generated by human activities is discharged and buried in it, forming a unique geological layer of the Anthropocene. Whether from the technical level (physics) or non-technical level (politics, society, law), shallow surface geophysics is becoming more and more important. Artificial intelligence technology has been widely used in various fields in recent years, and the construction of smart cities cannot be separated from the support of shallow surface geophysical exploration and monitoring methods and technologies. Due to the integration of urbanization and the development of geophysical technology in China, geophysicists have more opportunities for scientific research. In order to promote exchanges, improve the level of environmental and engineering geophysical exploration, and provide a first-class platform for extensive exchanges among workers engaged in scientific research, technology development and production applications in this field, the 10th International Conference on Environment and Engineering Geophysics will be held in China University of Geosciences (Beijing), a famous university of geosciences. We warmly welcome experts and scholars to contribute and participate in the conferenceIn the proceedings of 10th ICEEG, 160 peer-reviewed research expended abstracts are published according to the following topics: geophysical exploration of urban underground space, high frequency surface wave theory and application, ground penetrating radar technology and application, advances in shallow surface geophysics, engineering geophysics, hydrology and environmental geophysics, disaster geophysical survey, archaeological geophysics, unmanned aerial vehicle detection, smart city geophysics, intelligent detection technology, geophysical monitoring technique, geophysical instruments and software, shallow active source and passive source seismic detection, and young scientists forum.We are deeply grateful to the members of the scientific committee and institutional scientific partners of ICEEG: Chinese Geophysical Society, China University of Geosciences (Beijing), Shallow Surface Geophysical Committee of Chinese Geophysical Society, Professional Committee of Environmental Geophysics, Chinese Geophysical Society, Department of Earth Sciences, National Natural Science Foundation of China, China University of Geosciences (Wuhan), Central South University, Chengdu University of Technology, Chang’ An University, Institute of Geology and Geophysics, Chinese Academy of Sciences, China University of Mining and Technology (Beijing), Zhejiang University and Jilin UniversityWe would like to thank all reviewers for their great efforts to achieve the high quality of published articles.We would also like to extend our gratitude to all participants of 10th ICEEG for their interests and great contributions. Our special thanks go to the invited speakers for their significant contributions to the conference and the proceedings.We would also like to thank all the sponsors. It is your years’ support that makes the ICEEG conference a success.We would also extend our thanks to all committee members and student volunteers for their great efforts in organizing this wonderful event.We hope the proceedings show the challenges and the latest developments in the application of Engineering Environmental Geophysics and Intelligent Detection Technology.

Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Deep learning has been widely applied to automated leakage detection and location of natural gas pipe networks. Prevalent deep learning approaches do not consider the spatial dependency of sensors, which limits leakage detection performance. Graph deep learning is a promising alternative to prevailing approaches as it can model spatial dependency. However, the challenge of collecting real-world anomaly data for training limits the accuracy and robustness of currently used graph deep learning approaches. This study proposes a deep probabilistic graph neural network in which attention-based graph neural network is built to model spatial sensor dependency. Variational Bayesian inference is integrated to model the posterior distribution of sensor dependency so that the leakage can be localized. An urban natural gas pipe network experiment is employed to construct the benchmark dataset, in which normal time-series data is applied to develop our proposed model while anomaly leakage data is used for performance comparison between our model and other state-of-the-art models. The results demonstrate that our model exhibits competitive detection accuracy (AUC)=0.9484, while the additional uncertainty interval provides more comprehensive leakage detection information compared to state-of-the-art deep learning models. In addition, our model’s posterior distribution enhances the leakage localization with the accuracy of positioning (PAc)= 0.8, which is higher than that of other state-of-the-art graph deep learning models. This study provides a comprehensive and robust alternative for subsequent decision-making to mitigate natural gas leakage from pipe networks.

Mobile augmented reality-based visualization framework for lifecycle O&M support of urban underground pipe networks

The structural deterioration of urban underground pipe networks is the accumulated combined effects of physical, environmental and operational factors during the whole lifecycle. Accurate on-site locating and mapping of pipes and their conditions has been a great challenge for construction, maintenance and emergency response in a dynamic manner. Existing approaches, mostly based on design-centric and static visualization, are unsuitable for implementation within the operation and maintenance (O&M) phase because they are performed in a completely virtual environment and are unable to interact with buried assets on site. An integrated hyperrealistic visualization framework based on mobile augmented reality (MAR) is developed to support various real-life project tasks for O&M support of urban underground pipe networks. The proposed approach consists of three parts: a cloud-based data management platform, a 3D visualization development platform, and a location-based registration method for MAR. A cloud–edge–mobile layer is proposed to manage, analyze, process and visualize multisource data, especially for dynamic data. Field experiments on a real-life urban gas pipe network were carried out to investigate the feasibility of the proposed framework to achieve informed O&M support. The results demonstrate that (1) a smartphone linked to a Lite RTK device allows for both accurate locating and easy operation; (2) both the 3D virtual digital model and the multisource data visually superimpose onto the realistic environment, enabling on-site human–machine interaction in a hyperrealistic manner; and (3) MAR-based immersive visualization contributes to avoiding excavation damage, improving O&M efficiency and assisting emergency responders to make timely and better–informed decisions based on multisource information fusion.

Blowout Prediction on a Salt Cavern Selected for a Hydrogen Storage Pilot

To prevent climate change, Europe and the world must shift to low-carbon and renewable energies. Hydrogen, as an energy vector, provides viable solutions for replacing polluting and carbon-emitting fossil fuels. Gaseous hydrogen can be stored underground and coupled with existing natural gas pipe networks. Salt cavern storage is the best suited technology to meet the challenges of new energy systems. Hydrogen storage caverns are currently operated in the UK and Texas. A preliminary risk analysis dedicated to underground hydrogen salt caverns highlighted the importance of containment losses (leaks) and the formation of gas clouds following blowouts, whose ignition may generate dangerous phenomena such as jet fires, unconfined vapor cloud explosions (UVCEs), or flashfires. A blowout is not a frequent accident in gas storage caverns. A safety valve is often set at a 30 m depth below ground level; it is automatically triggered following a pressure drop at the wellhead. Nevertheless, a blowout remains to be one of the significant accidental scenarios likely to occur during hydrogen underground storage in salt caverns. In this paper, we present modelling the subterraneous and aerial parts of a blowout on an EZ53 salt cavern fully filled with hydrogen.

Performance analysis of a power generation system for pressure energy recovery at natural gas city gate stations

Worldwide consensus of energy conservation and emissions reduction calls for effective and eco-friendly energy recovery infrastructures in natural gas pipe networks. However, a considerable amount of energy is abandoned by employing pressure regulators in existing city gate stations. In order to recover the exergy and get rid of fossil fuels in the natural gas pressure reduction process, a power generation system for pressure energy recovery at natural gas city gate stations is proposed. Turboexpander is employed to convert the pressure energy into mechanical work, and a carbon dioxide (CO2) cycle is combined to recycle the cold exergy in expanded natural gas. Based on the performance investigation of CO2 cycle, the operation boundary of system with variations of inlet parameters is studied. Furthermore, the system design and off-design performance analysis are also carried out. Results show that system energy efficiency and exergy efficiency reach 42.54% and 88.77% respectively, while the energy recovery rate is 88.11% under design conditions. Under off-design conditions, the system exergy efficiency has a parabolic trend with the optimum value 87.1% under 95% mass flow rate. The increased inlet pressure results in raised exergy efficiency and energy recovery rate, but the curves flatten out when the inlet pressure reaches 105% design value.

On Port-Hamiltonian Approximation of a Nonlinear Flow Problem on Networks

This paper deals with the systematic development of structure-preserving and robust approximations for a class of nonlinear partial differential equations on networks. The class includes, for example, gas pipe network systems described by barotropic Euler equations. Our approach is guided throughout by energy-based modeling concepts (port-Hamiltonian formalism, theory of Legendre transformation), which provide a convenient and general line of reasoning. Under mild assumptions on the approximation, local conservation of mass, an energy bound, and the inheritance of the port-Hamiltonian structure can be shown. Our approach is not limited to conventional space discretization but also covers complexity reduction of the nonlinearities by inexact integration. Thus, it can serve as a basis for structure-preserving model reduction. Combined with an energy stable time integration, we numerically demonstrate the applicability and good stability properties of the approach using the Euler equations as an example.

Selected Mathematical Models Describing Flow in Gas Pipelines

The main aim of simulation programs is to study the behavior of gas pipe networks in certain conditions. Solving a specified set of differential equations describing transient (unsteady) flow in a gas pipeline for the adopted parameters of load and supply will help us find out the value of pressure or flow rate at selected points or along selected sections of the network. Transient gas flow may be described by a set of simple or partial differential equations classified as hyperbolic or parabolic. Derivation of the mathematical model of transient gas flow involves certain simplifications, of which one-dimensional flow is most important. It is very important to determine the conditions of pipeline/transmission network operation in which the hyperbolic model and the parabolic model, respectively, should be used. Parabolic models can be solved numerically in a much simpler way and can be used to design simulation programs which allow us to calculate the network of any structure and any number of non-pipe elements. In some conditions, however, they describe the changes occurring in the network less accurately than hyperbolic models do. The need for analysis, control, and optimization of gas flows in high-pressure gas pipelines with complex structure increases significantly. Very often, the time allowed for analysis and making operational decisions is limited. Therefore, efficient models of unsteady gas flows and high-speed algorithms are essential.

Reactive Power Optimization Control for Multi-Energy System Based on Pressure Limit of Hot and Gas Pipe Network

Reactive Power Optimization Control for Multi-Energy System Based on Pressure Limit of Hot and Gas Pipe Network

A machine learning-based surrogate model for the rapid control of piping flow: Application to a natural gas flowmeter calibration system

With the notion of intelligent pipelines gaining ground in natural gas pipe networks, the requirements for fully automated daily operations are increasing. The flow control problem, especially in the complex station-level process piping network (SLPPN), has become the main challenge. This paper proposed a machine learning-based surrogate model that used the precise SLPPN flow control. A hybrid modeling method that uses data-driven method to surrogate numerical model was adopted to reveal the flow characteristics in the SLPPN, retaining the accuracy of the numerical simulation model while increasing its calculation speed simultaneously. Then, a two-step controller combining open-loop optimal control and closed-loop feedback control was designed to replace the classic proportional-integral-differential (PID) controller. First, genetic algorithms (GA) were used to determine the optimal control scheme of the surrogate model for fast simulation. Second, the follow-up PID controller was employed to compensate for the gap between the setpoint and the flow after the first control step. The controller was verified using the actual calibration station of a natural gas flowmeter in Guangzhou, China. The results showed that this two-step control method based on the surrogate model outperformed the conventional PID control technique regarding control duration and time, highlighting its potential in automating and intellectualizing the natural gas transportation industry.

Seismic risk assessment of lifelines in Bucharest

This study is focused on the seismic risk assessment of lifelines from Bucharest, the capital city of Romania. The damage sustained by the lifelines in Bucharest during the major Vrancea intermediate-depth event of March 1977 is presented. The seismic exposure for the gas, electricity, sewage and water networks is evaluated using publicly available information collected from various sources. The seismic fragility is evaluated using the recommendations of the SYNER-G research project. Finally, seismic risk metrics, in terms of repair rates and probabilities associated with various damage states are evaluated using a Monte Carlo simulated earthquake catalogue for the Vrancea intermediate depth seismic source. The impact of considering the spatial correlation of ground motions and of the anchoring of the elements in the stations for the gas, electricity and water systems is also evaluated in this study. The largest mean probabilities associated with the moderate, extensive and complete damage states for Mw 7 and Mw 7.5 earthquake scenarios are obtained for the components of the electricity distribution system, namely electric substations and small generation plants. In addition, it has been also observed that the mean repair rate for the gas pipe network is smaller as compared to the water and sewage systems.

A State Monitoring Method of Gas Regulator Station Based on Evidence Theory Driven by Time-Domain Information

Regulator stations are widely used in gas transmission and distribution systems. Their state monitoring is of great significance to the safe operation of gas pipe networks. Due to the complexity of the working environment and the limitation of sensors, the acquired information is uncertain, which makes the state monitoring result prone to errors. The evidence theory has the ability to solve the uncertainty problem effectively. Most of the improvement methods of the evidence theory are in the spatial domain. These methods are not applicable to the fusion of the time-domain information. In this article, an improved method of the evidence theory is proposed for the state monitoring of a gas regulator station. It can meet the requirement of the dynamic fusion of the time-domain information. First, the back-propagation neural network is used to judge whether the evidence conflicts with each other. The simulation results demonstrate that it can judge the conflicts well. On this basis, the relative conflict factor is proposed to modify the evidence, and the calculation method of the adaptive time attenuation factor is proposed to reduce the accumulated error. The dynamic fusion of the time-domain information is realized by combining the time attenuation factor and the relative conflict factor. Finally, the proposed method is applied to the state monitoring of the gas regulator station. The feasibility and effectiveness of the method are verified by experiments. It verifies that the variation of the support degree of the proposed method for the correct proposition is 0.1478 higher than that of the temporal evidence combination based on relative reliability factor when the evidence is strongly conflicting.

Feasibility study of detecting gas leakage in buried pipeline based on resistivity method

Urban gas pipe networks are mostly buried underground and it is hard to monitor, detect and locate the leakage area effectively. The primary structure of the soil could change before and after aeration, so the detection method of gas leakage based on resistivity method has some physical basis. Based on the theoretical research of the predecessors, corresponding geoelectric model is established by combining two typical gas leakage geological models to study the influences of testing device, leakage mode and leakage intensity on the distribution characteristics of gas leakage geoelectric field. Obtained by using forward and inverse numerical computation methods, the theoretical resistivity profile image is compared with the resistivity profile measured by outdoor gas leakage simulation experiment to evaluate comprehensively the feasibility and effectiveness of detecting gas leakage of buried pipelines based on resistivity differential characteristics. The research results show that resistivity method could describe the spatial distribution location effectively and the electrical resistivity has positive correlation with leakage pressure. And the pressure inside the pipeline also determines the migration path of the gas leakage, which diffuses outwards in the shape of “smoke ring” in high-pressure leakage.

Effects of hydrogen blending on hydraulic and thermal characteristics of natural gas pipeline and pipe network

Blending a fraction of hydrogen into the natural gas pipeline or urban pipe network is an efficient approach for hydrogen delivery. In this paper, the mathematical model of Hydrogen-Blended Natural Gas (HBNG) transportation is established, and the influences of hydrogen blending on hydraulic and thermal characteristics of natural gas pipeline and pipe network are numerically investigated. The impact of hydrogen blending ratio on the performance of centrifugal compressor and the operating point for joint operation of pipeline and compressor is discussed. Results illustrate that compared with natural gas without hydrogen, the hydrogen blending can reduce the pipeline friction resistance and increase the volume flow rate. However, due to the lower volumetric calorific value of HBNG, the energy flow rate actually decreases under the same transportation condition. Meanwhile, the temperature drop along the pipeline slows down due to the blended hydrogen. The performance degradation of centrifugal compressor occurs with the increasing hydrogen blending ratio, and the operating point for joint operation of pipeline and centrifugal compressor moves to the direction of higher volume flow rate and lower pressure. This study is expected to shed a light on the hydrogen delivery by natural gas pipelines and pipe networks.

The application of Internet of things technology in integrity management of urban gas pipe network

On the Internet of things technology in the gas pipeline integrity management the present situation of the study is less. This paper aims to improve the integrity management level of gas pipe network and combines the mature experience of Internet of things in the integrity management of long-distance pipelines. The integrity management system of urban gas pipe network based on Internet of things technology is proposed, and the main application scenarios are analyzed. The results show that the application of Internet of things technology improves the uniformity of data collection in pipe network operation management, promotes the sharing of data, and avoids multiple data conversion. The detection results of various sensing means, methods and instruments can be integrated to ensure the reliability of risk assessment and integrity assessment. To realize the scientific and reasonable allocation of limited human resources and equipment, and to evaluate the effectiveness of the existing integrity management system, to achieve the goal of gradually improving the integrity management. This paper provides specific guidance and ideas for urban gas pipe network management and operation units to carry out high level integrity management.