Research on Prominent Early Warning Technology based on Dynamic Characteristics of Gas Emission

Jiangchun Fan

doi:10.54691/8sw0bc19

Authors

Jiangchun Fan

DOI:

https://doi.org/10.54691/8sw0bc19

Keywords:

Gas Emission Characteristics; Prominent Early Warning Technology; Xiayukou Coal Mine; Real-Time Monitoring; Intelligent Early Warning.

Abstract

This paper elaborates in detail on the construction process and application effects of the prominent early warning technology and system based on the dynamic characteristics of gas emission in Xiayukou Coal Mine. In response to the frequent occurrence of coal and gas outburst disasters in Xiayukou Coal Mine, an early warning indicator system was constructed based on the dynamic characteristics of gas emission, and an early warning system was developed. This system enables real-time monitoring and early warning of coal and gas outburst hazards at the working face. Field applications have demonstrated that the system operates stably and reliably, with a high accuracy rate in early warning, thus providing a strong guarantee for the safe production of the coal mine.

Downloads

Download data is not yet available.

References

[1] Feng Jianzhi, Kang Meng. Study on the reasonable staggered distance of mining roadways in the 23306 lower working face of Xiayukou Coal Mine [J]. Shaanxi Coal, 2024, 43(11): 134-138+161.

[2] Ji Dong, Zhang Bo, Jia Weidong, et al. Design and optimization of gas control schemes for Xiayukou Coal Mine [J]. Shaanxi Coal, 2022, 41(01): 45-48.

[3] Yuan Hongjun, Zhong Fei. Comprehensive evaluation study on geological factors of the 23210 working face in Xiayukou Coal Mine [J]. Inner Mongolia Coal Economy, 2022, (02): 166-168.

[4] Lei Yang, Cheng Yuanping. Cascade fracture development mechanism of coal and gas outbursts [J/OL]. Journal of China Coal Society, 1-21 [2025-05-22].

[5] Cao Jie. Statistical laws and dynamic effect characteristics analysis of coal and gas outburst accidents in China over the past decade [J]. Mining Safety & Environmental Protection, 2024, 51(03): 36-42+49.

[6] Liu Yi, Lu Shouqing, Zhao Kang, et al. Literature review and research progress on prediction indicators of coal and gas outbursts based on CiteSpace [J]. Coal Mine Safety, 2025, 56(05): 30-39.

[7] Xue Sheng, Zheng Xiaoliang, Yuan Liang, et al. Research progress and prospects of coal and gas outburst prediction based on machine learning [J]. Journal of China Coal Society, 2024, 49(02): 664-694.

[8] Liang Yunpei, Zheng Menghao, Li Quangui, et al. Current research status of prediction and early warning of coal and gas outbursts in China [J]. Journal of China Coal Society, 2023, 48(08): 2976-2994.