Open Access
E3S Web Conf.
Volume 233, 2021
2020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
Article Number 01002
Number of page(s) 6
Section NESEE2020-New Energy Science and Environmental Engineering
Published online 27 January 2021
  1. ZENG Qiang. Study on the thermal dynamic characteristics of combustion system for coal fires in xinjiang region[D]. Xuzhou: China University of Mining and Technology, 2012. [Google Scholar]
  2. BP (2018) (2018) BP Statistical Review of World Energy [Google Scholar]
  3. HOU Shen-jian, WANG DONG, ZHANG BO, et al. Development of China’s coal rsources exploration in the new era[J]. Journal of Xi’an University of Science and Technology, 2019, 39 (2): 341-346. [Google Scholar]
  4. ZHANG Jianmin, GUAN Haiyan, CAO Daiyong, etc. Research and treatment of underground coal fire in China [M]. Beijing: Coal Industry Press, 2008. [Google Scholar]
  5. WU Jian-ming,ZHANG Jin-hua,WU Yu-guo. Detection on Spontaneous Combustion Area in Surface Mine and Study on Comprehensive Control Technology of Spontaneous Combustion Area[J]. Coal Engineering, 2012(02):53-56. [Google Scholar]
  6. Slavecki R.J. Detection and location of subsurface coal fires; proceedings of the Proceedings of the third Symposium on Remote Sensing of Environment, F, 1964 [C]. [Google Scholar]
  7. Li J., Voight S., Kunzer C., et al. The Progress in Detecting of Coal Fire on Remote Sensing*The first Result of the Joint Sino-German Research Project on Innovative Technologies for Exploration, Extinction and Monitoring of Coal Fires in North China; proceedings of the 2005 Dragon Symposium, F, 2006 [C]. [Google Scholar]
  8. Elick Jennifer-M.. Mapping the coal fire at Centralia, Pa using thermal infrared imagery[J]. International Journal of Coal Geology, 2011, 87(3): 197-203. [CrossRef] [Google Scholar]
  9. Mine Fire Disaster Control Based on Infrared Imaging Technology[J].Coal Science and Technology, 2010, 38(1): 28-30. [Google Scholar]
  10. ZHENG Xuezhao; JIA Yongxiao; GUO Jun; et al, Research status and prospect of coalfield fire monitoring technologies[J]. Industry and Automation, 2019,45(05):6-10+61. [Google Scholar]
  11. ZHANG Xin-hai, NIU Heng, FEI Jin-biao. Using Measuring Radon Method to Detect Spontaneous Fire Location in Bayili Coal Mine[J]. Safety in Coal Mines, 2009, 40(7):29–30. [Google Scholar]
  12. FEI Jin-biao, WEN HU, JIN Yong-fei. Application of radon method in detection of fire area in shallow coal seam[J]. Journal of Xi’an University of Science and Technology, 2018, 38(01): 26-30. [Google Scholar]
  13. Song Yanchao, Wang Junlin, Shang Bing, Cui Hongxing, Wu Yunyun. Study on a new charcoal closed chamber method for measuring radon exhalation rate of building materials[J]. Radiation Measurements, 2020. [Google Scholar]
  14. Determining the Spontaneous Combustion Boundary of Northern Shaanxi Coal Seam Using High Precision Magnetic Method[J]. Advances in Geosciences, 2019, 9(5): 360-367. [CrossRef] [Google Scholar]
  15. ZHANG Xinhai; MIAO Yuhui; ZHANG Duo; et al, Experimental Study on Change Laws of Characteristic Gases and Magnetic in the Process of Coal Spontaneous Combustion[J]. Safety in Coal Mines, 2017,48(03):36-39. [Google Scholar]
  16. Ide Taku-S, Crook N, Orr jr. et al, Magnetomete rmeasurements to characterize a subsurface coal fire[J]. International Journal of Coal Geology, 2011, 87(3): 190-196. [Google Scholar]
  17. WANG Wen-zheng, MA Li-yang, LU Shi-yuan, et al . Forward Modeling for Magnetic Anomaly of Multilayer Burnt Rocks in Coal Fire Area[J]. Science Technology and Engineering, 2013, 13(20): 5924-5926. [Google Scholar]
  18. Shao Z, Wang D, Wang Y, et al. Theory and application of magnetic and self-potential methods in the detection of the Heshituoluogai coal fire, China[J]. Journal of Applied Geophysics, 2014, 104:64-74. [Google Scholar]

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