Earthquakes and Structures

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Prediction of coal and gas outburst risk at driving working face based on Bayes discriminant analysis model

  • Chen, Liang (School of Energy & Environment Engineering, Zhongyuan University of Technology) ;
  • Yu, Liang (School of Energy & Environment Engineering, Zhongyuan University of Technology) ;
  • Ou, Jianchun (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology) ;
  • Zhou, Yinbo (School of Safety Engineering, Henan University of Engineering) ;
  • Fu, Jiangwei (School of Energy & Environment Engineering, Zhongyuan University of Technology) ;
  • Wang, Fei (School of Mechanics and Safety Engineering, Zhengzhou University)
  • Received : 2018.11.01
  • Accepted : 2019.10.18
  • Published : 2020.01.25
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Abstract

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh2, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

Keywords

Acknowledgement

Supported by : Henan Universities, National Science Foundation of China, National Natural Science Foundation of Jiangsu

References

  1. Aguado, M.B.D. and Nicieza, C.G. (2007), "Control and prevention of gas outbursts in coal mines, Riosa-Olloniego coalfield, Spain", Int. J. Coal Geol., 69(4), 253-266. https://doi.org/10.1016/j.coal.2006.05.004.
  2. Chao, J.W., Lv, W.W., Ma, Z.H., Liu, Y. and Zhang, J. (2015), "Analysis on main controlling factors of coal and gas outburst in Liangbei Coal Mine", Saf. Coal Min., 46(7), 187-190.
  3. Chen, L., Wang, E., Feng, J., Wang, X. and Li, X. (2017), "Hazard prediction of coal and gas outburst based on Fisher discriminant analysis", Geomech. Eng., 13(5), 861-879. https://doi.org/10.12989/gae.2017.13.5.861.
  4. Chen, L., Wang, E., Ou, J. and Fu, J. (2018), "Coal and gas outburst hazards and factors of the No. B-1 Coalbed, Henan, China", Geosci. J., 22(1), 171-182. https://doi.org/10.1007/s12303-017-0024-6.
  5. Chen, S.L., Liu, J. and Chen, L. (2014), "The coal and gas outburst prediction model research based on SVM", Int. J. Earth Sci. Eng., 7(2), 616-623.
  6. Cheng, L., Liu, Y.J., Zhang, H.L. (2015), "An improved coal and gas outburst prediction algorithm based on BP neural network", Int. J. Control Autom., 8(6), 169-176. http://dx.doi.org/10.14257/ijca.2015.8.6.17.
  7. Fernandez-diaz, J.J., Gonzalez-nicieza, C., Alvarez-fernandez, M.I. and Lopez-gayarre, F. (2013), "Analysis of gas-dynamic phenomenon in underground coal mines in the central basin of Asturias (Spain)", Eng. Fail. Anal., 34, 464-477. https://doi.org/10.1016/j.engfailanal.2013.07.027.
  8. He, X., Nie, B., Chen, W., Wang, E., Dou, L., Wang, Y., Liu, M. and Hani, M. (2012), "Research progress on electromagnetic radiation in gas-containing coal and rock fracture and its applications", Saf. Sci., 50(4), 728-735. https://doi.org/10.1016/j.ssci.2011.08.044.
  9. Hu, Q., Peng, S., Xu, J., Zhang, L. and Liu, D. (2015), "Application of gray target models in the prediction of coal and gas outburst: The case of jinzhushan coal mine in China", Int. J. Saf. Secur. Eng., 5(2), 142-149. https://doi.org/10.2495/SAFE-V5-N2-142-149.
  10. Hu, Y.X. and Li, X.B. (2012), "Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application", Tran. Nonfer. Metal. Soc. China, 22(2), 189-195. https://doi.org/10.1016/S1003-6326(11)61194-1.
  11. Jiang, C., Xu, L., Li, X., Tang, J., Chen, Y., Tian, S. and Liu, H. (2014), "Identification model and indicator of outburst-prone coal seams", Rock Mech. Rock Eng., 48(1), 409-415. https://doi.org/10.1007/s00603-014-0558-0
  12. Kong, B., Wang, E.Y., Li, Z.H. and Lu, W. (2019), "Study on the feature of electromagnetic radiation under coal oxidation and temperature rise based on multi-fractal theory", Fract., 27(3), 1-14. https://ui.adsabs.harvard.edu/link_gateway/2019Fract.. 2750038K/doi:10.1142/S0218348X19500385.
  13. Li, X.J. and Zhou, W.G. (2012), "The risk forecast of coal and gas outburst on blasting working face by the method of gas peak-to-valley ratio", J. Chin. Coal Soc., 37(S1), 108-112.
  14. Li, Z., Wang, E., Ou, J. and Liu, Z. (2015), "Hazard evaluation of coal and gas outbursts in a coal-mine roadway based on logistic regression model", Int. J. Rock Mech. Min. Sci., 80, 185-195. http://dx.doi.org/10.1016%2Fj.ijrmms.2015.07.006. https://doi.org/10.1016/j.ijrmms.2015.07.006
  15. Liu, J., Wang, E., Song, D., Wang, S. and Niu, Y. (2015), "Effect of rock strength on failure mode and mechanical behavior of composite samples", Arab. J. Geosci., 8(7), 4527-4539. https://doi.org/10.1007/s12517-014-1574-9.
  16. Liu, J., Zhang, R., Song, D. and Wang, Z. (2019), "Experimental investigation on occurrence of gassy coal extrusion in coalmine", Saf. Sci., 113, 362-371. https://doi.org/10.1016/j.ssci.2018.12.012.
  17. Lu, C.P., Dou, L.M., Liu, H., Liu, H. S., Liu, B. and Du, B.B. (2012), "Case study on microseismic effect of coal and gas outburst process", Int. J. Rock Mech. Min. Sci., 53, 101-110. http://dx.doi.org/10.1016%2Fj.ijrmms.2012.05.009. https://doi.org/10.1016/j.ijrmms.2012.05.009
  18. Lu, C.P., Dou, L.M., Zhang, N., Xue, J.H. and Liu, G.J. (2014), "Microseismic and acoustic emission effect on gas outburst hazard triggered by shock wave: a case study", Nat. Hazard., 73(3), 1715-1731. https://doi.org/10.1007/s11069-014-1167-7.
  19. Luo, K., Wu, C., Yang, F.Q. and Li, Z.J. (2014), "Bayes discriminant analysis of spontaneous combustion tendency classification of sulfide minerals in metal mines", J. Central South Univ., 45(7), 106-111.
  20. Sa, Z., Liu, J., Li, J. and Zhang, Y. (2019), "Research on effect of gas pressure in the development process of gassy coal extrusion", Saf. Sci., 115, 28-35. https://doi.org/10.1016/j.ssci.2019.01.023.
  21. Skoczylas, N. (2012), "Laboratory study of the phenomenon of methane and coal outburst", Int. J. Rock Mech. Min. Sci., 55, 102-107. http://dx.doi.org/10.1016%2Fj.ijrmms.2012.07.005. https://doi.org/10.1016/j.ijrmms.2012.07.005
  22. Torano, J., Torno, S., Alvarez, E. and Riesgo, P. (2012), "Application of outburst risk indices in the underground coal mines by sublevel caving", Int. J. Rock Mech. Min. Sci., 50, 94-101. https://doi.org/10.1016/j.ijrmms.2012.01.005.
  23. Wang, E., Chen, P., Liu, Z., Liu, Y., Li, Z. and Li, X. (2019), "Fine detection technology of gas outburst area based on direct current method in Zhuxianzhuang Coal Mine, China", Saf. Sci., 115, 12-18. https://doi.org/10.1016/j.ssci.2019.01.018.
  24. Wang, E., He, X., Wei, J., Nie, B. and Song, D. (2011), "Electromagnetic emission graded warning model and its applications against coal rock dynamic collapses", Int. J. Rock Mech. Min. Sci., 48(4), 556-564. https://doi.org/10.1016/j.ijrmms.2011.02.006.
  25. Wold, M.B., Connell, L.D. and Choi, S.K. (2008), "The role of spatial variability in coal seam parameters on gas outburst behaviour during coal mining", Int. J. Coal Geol., 75(1), 1-14. https://doi.org/10.1016/j.coal.2008.01.006.
  26. Xu, T., Tang, C.A., Yang, T.H., Zhu, W.C. and Liu, J. (2006), "Numerical investigation of coal and gas outbursts in underground collieries", Int. J. Rock Mech. Min. Sci., 43(6), 905-919. https://doi.org/10.1016/j.ijrmms.2006.01.001.
  27. Xu, W., Jing, S., Yu, W., Wang, Z., Zhang, G. and Huang, J. (2013), "A comparison between bayes discriminant analysis and logistic regression for prediction of debris flow in southwest Sichuan, China", Geomorphol., 201, 45-51. https://doi.org/10.1016/j.geomorph.2013.06.003.
  28. Zhang, R.L. and Lowndes, I.S. (2010), "The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts", Int. J. Coal Geol., 84(2), 141-152. https://doi.org/10.1016/j.coal.2010.09.004.
  29. Zhang, T.J., Ren, S.X., Li, S.G., Zhang, T.C. and Xu, H.J. (2009), "Application of the catastrophe progression method in predicting coal and gas outburst", Min. Sci. Technol., 19(4), 26-30. https://doi.org/10.1016/S1674-5264(09)60080-6.
  30. Zheng, G.Q., Zhang, H.J., Liu, T., Wu, J.D., Hou, X.F. and Ye, Z.H. (2009), "Prediction model of flush flood and debris flow in Miyun County based on bayes discriminatory analysis", Bull. Soil Water Conserv., 29(1), 85-89.