A safety management system for natural gas pipeline in subsidence area of coal mine

Shi, Yonghui; Zheng, Jianqin; Wang, Chang; Wang, Bohong; Guo, Wenpeng; Liang, Yongtu

doi:10.1080/15567036.2022.2087806

Cited by 3 publications

(1 citation statement)

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“…Jabbari et al 28 used a down-covered logical hierarchy procedure to assess the risk of poisonous gas discharge and outburst in gas pipelines 29 . An analysis of danger was carried out using MATLAB software, and the mass of each item in basic risk (BRI) was outlined in a reference frame.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

Vanitha,

Easwaramoorthy,

Krishna

et al. 2023

Sci Rep

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Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions. The danger of leaks and accidents increases as a result of these damages. Pipelines must be evaluated on a regular basis to make sure they are fit for transmission. By evaluating the effects of damages and the possibility of catastrophic failures using a variety of techniques, pipeline integrity is controlled. Applying the relative risk scoring (RRS) technique, pipeline failures are predicted. One of the probabilistic techniques used to forecast risk based on an impartial assessment is machine learning. With different parameters like corrosion, leakage, materials, atmosphere, surface, earth-movements, above-ground and underground facilities, etc., the RRS method provides an accuracy of 97.5% in identifying the risk and gives a precise classification of risk, whether the pipeline has a high, medium, or low risk without any delay on the prediction compared with Naive Bayes, decision tree, support vector machine, and graph convolutional network.

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Section: Literature Reviewmentioning

confidence: 99%