Optimization monitoring distribution method for gas pipeline leakage detection in underground spaces

Zhang, Zewei; Hou, Longfei; Yuan, Mengqi; Fu, Ming; Qian, Xinming; Duanmu, Weike; Li, Yuanzhi

doi:10.1016/j.tust.2020.103545

Cited by 18 publications

(4 citation statements)

References 26 publications

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“…The leakage of the pipelines increases the risk of contaminant entry and further increases the projected expenditure of resources and costs. Pipeline leakage poses potential risks to public health and causes financial losses [ 1 , 2 ], while also burdening the environment by wasting energy [ [3] , [4] , [5] , [6] , [7] , [8] ]. These phenomena have gained worldwide attention.…”

Section: Introductionmentioning

confidence: 99%

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Chang

Lin

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Chang

Lin

et al. 2023

Heliyon

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“…In order to visualize the air volume, an air volume reconstruction algorithm is proposed in this paper. In past studies, optimization and reconstruction technologies of sensor networks were widely used in flow monitoring [ 12 ], for the development of intelligent systems [ 13 ], and for leak location diagnosis for water [ 14 , 15 , 16 ], oil [ 17 , 18 ], and gas [ 19 , 20 , 21 ] pipeline networks. Singh et al [ 22 ] proposed a method to identify ideal monitoring sites for water quality.…”

Section: Introductionmentioning

confidence: 99%

Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network

Liu

Gao

et al. 2022

Sensors

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The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction.

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“…The correlation between MID and each parameter was established under the condition of HSG, and the MID prediction equation was obtained. The MID provides a reference for the layout of urban underground gas leakage monitoring points . The MILS and MILD under different ground conditions provide a safe reference distance for emergency evacuation.…”

Section: Introductionmentioning

confidence: 99%

“…The MID provides a reference for the layout of urban underground gas leakage monitoring points. 29 The MILS and MILD under different ground conditions provide a safe reference distance for emergency evacuation.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Methane Invasion Distance Considering Real Ground Boundary

Liu

Chen

et al. 2021

ACS Omega

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Natural gas has become a global energy consumption hotspot because of its large reserves and clean combustion. Due to soil corrosion, construction damage, and natural disasters, leakage accidents of buried natural gas pipelines often occur. In this paper, the steady simulation method was used to study the methane invasion limit state (MILS) and the methane invasion limit distance (MILD) under the conditions of hardened surface ground (HSG), unhardened surface ground (UHSG), and semihardened surface ground (SHSG), and the transient simulation of methane invasion distance (MID) under the condition of HSG with the largest MILD was carried out. The results showed that regardless of ground conditions, with the increase of leakage time, the diffusion range of methane in soil will not increase all the time, and there was a limit state (MILS). The distribution range and concentration of methane in the soil under HSG condition were the largest, followed by the SHSG condition, and the UHSG condition was the smallest. When the ground condition changed from UHSG to HSG, the MILD increased from 3.41 to 9.32 m. The HSG condition will increase the MILD and the range of dangerous areas. The buried depth of the pipeline had a serious impact on the MILD. When the buried depth of the pipeline increased from 0.3 to 1.5 m, the MILD increased from 1.75 to 3.49 m under the condition of UHSG and exceeded 10 m under the condition of HSG. The average error of the MID prediction model was 2.37% under the condition of HSG, which can accurately predict the leakage of buried pipeline. The MID provides a reference for the layout of urban underground gas leakage monitoring points. The MILD can provide guidance for the safe distance between natural gas pipeline and structures in the design code of natural gas pipeline.

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Optimization monitoring distribution method for gas pipeline leakage detection in underground spaces

Cited by 18 publications

References 26 publications

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network

Analysis and Prediction of Methane Invasion Distance Considering Real Ground Boundary

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