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An SVR-based Machine Learning Model Depicting the Propagation of Gas Explosion Disaster Hazards

  • Research Article-Systems Engineering
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Abstract

Shock wave pressure, high temperature flames, and toxic gases are among the factors that cause mine ventilation system failure following a gas explosion. Herein, we propose a Support Vector Regression (SVR)-based machine learning model to quickly determine the propagation of these disaster-causing hazards throughout the whole ventilation network. FLACS was used to simulate the explosions in a straight roadway with different spatial geometric parameters and gas explosion equivalents. Four SVR machine learning models were constructed by incorporating the roadway’s cross-sectional area, length of gas filling, gas concentration, and the distance between the observation point and the explosion source as inputs, while the shock wave pressure, flame temperature, time to the maximum temperature, and pressure served as outputs. The proposed model can quickly and accurately predict the propagation of disaster-causing hazards for a given explosion position and equivalent. As such, it plays a significant role in determining the ventilation system failure mode and aids decision makers and rescuers in developing a rescue and refuge plan.

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Data Availability

The data that support the findings of this study are available from the Corresponding author upon the reasonable request.

Code Availability

The code generated or used during the study is available from the corresponding author by request.

References

  1. He, Z.; Wu, Q.; Wen, L.J.; Fu, G.: A process mining approach to improve emergency rescue processes of fatal gas explosion accidents in Chinese coal mines. Saf. Sci. 111, 154–166 (2019). https://doi.org/10.1016/j.ssci.2018.07.006

    Article  Google Scholar 

  2. Azatyan, V.V.: Features of the physicochemical mechanisms and kinetic laws of combustion, explosion, and detonation of gases. Kinet. Catal. 61(3), 319–338 (2020). https://doi.org/10.1134/s0023158420030039

    Article  Google Scholar 

  3. Xu, J.D.; Yang, G.Y.: Numerical simulation of the barricade encouraging effect in the process of gas explosion propagation. J. China Coal Soc. 29(1), 53–56 (2004)

    Google Scholar 

  4. He, X.Q.; Yang, Y.; Wang, E.Y.; Liu, Z.T.: Study on the influence of obstacles on the flame structure and flame propagation of gas explosion. J. China Coal Soc. 29(2), 186–189 (2004)

    Google Scholar 

  5. Jia, Z.W.; Liu, Y.W.; Jing, G.X.: Propagation characteristics of gas explosion shock wave under the condition of pipe turning. J. China Coal Soc. 36(1), 97–100 (2011)

    Google Scholar 

  6. Luo, Z.M.; Zhang, Q.; Wang, H.; Cheng, F.M.; Wang, T.; Deng, J.: Numerical simulation of gas explosion in confined space with FLACS. J. China Coal Soc. 38(8), 1381–1387 (2013)

    Google Scholar 

  7. Wang, Z.; Jiang, J.; Zheng, Y.: CFD simulation on gas explosion field in linked vessels. J. Ind. Eng. Chem. 58(4), 854–861 (2007)

    Google Scholar 

  8. Qi, Z.; Bin, Q.; Da-Chao, L.: Estimation of pressure distribution for shock wave through the bend of bend laneway. Saf. Sci. 48(10), 1263–1268 (2010). https://doi.org/10.1016/j.ssci.2010.04.003

    Article  Google Scholar 

  9. Gieras, M.; Klemens, R.; Rarata, G.; Wolanski, P.: Determination of explosion parameters of methane-air mixtures in the chamber of 40 dm3 at normal and elevated temperature. J. Loss Prev. Process Ind. 19(2–3), 263–270 (2006). https://doi.org/10.1016/j.jlp.2005.05.004

    Article  Google Scholar 

  10. Hisken, H.; Enstad, G.A.; Middha, P.; van Wingerden, K.: Investigation of concentration effects on the flame acceleration in vented channels. J. Loss Prev. Process Ind. 36, 449–461 (2015). https://doi.org/10.1016/j.jlp.2015.04.005

    Article  Google Scholar 

  11. Lin, B.Q.; Zhou, S.N.; Zhang, R.G.: Influence of barriers on flame transmission and explosion wave in gas explosion. J. China Univ. Min. Technol. 28(2), 104–106 (1999)

    Google Scholar 

  12. Mittal, M.: Explosion pressure measurement of methane-air mixtures in different sizes of confinement. J. Loss Prev. Process Ind. 46, 200–208 (2017). https://doi.org/10.1016/j.jlp.2017.02.022

    Article  Google Scholar 

  13. Salzano, E.; Cammarota, F.; Di Benedetto, A.; Di Sarli, V.: Explosion behavior of hydrogen-methane/air mixtures. J. Loss Prev. Process Ind. 25(3), 443–447 (2012). https://doi.org/10.1016/j.jlp.2011.11.010

    Article  Google Scholar 

  14. Su, B.; Luo, Z.M.; Wang, T.; Liu, L.: Experimental and numerical evaluations on characteristics of vented methane explosion. J. Cent. South Univ. 27(8), 2382–2393 (2020). https://doi.org/10.1007/s11771-020-4456-1

    Article  Google Scholar 

  15. Qu, Z.M.; Zhou, X.Q.; Wang, H.Y.; Ma, H.L.: Attenuation law of pressure of gas explosion shock wave. J. China Coal Soc. 33(4), 410–414 (2008)

    Google Scholar 

  16. Zhou, X.Q.; Wu, B.; Xu, J.D.: Basic characteristics of gas explosion in coal mine. China Coal 28, 8–11 (2002)

    Google Scholar 

  17. Tauseef, S.M.; Rashtchian, D.; Abbasi, T.; Abbasi, S.A.: A method for simulation of vapour cloud explosions based on computational fluid dynamics (CFD). J. Loss Prev. Process Ind. 24(5), 638–647 (2011). https://doi.org/10.1016/j.jlp.2011.05.007

    Article  Google Scholar 

  18. Pang, L.; Zhang, Q.; Wang, T.; Lin, D.C.; Cheng, L.: Influence of laneway support spacing on methane/air explosion shock wave. Saf. Sci. 50, 83–89 (2012). https://doi.org/10.1016/j.ssci.2011.07.005

    Article  Google Scholar 

  19. Jiang, B.Y.; Lin, B.Q.; Shi, S.L.; Zhu, C.J.; Ning, J.: Numerical simulation on the influences of initial temperature and initial pressure on attenuation characteristics and safety distance of gas explosion. Combust. Sci. Technol. 184(2), 135–150 (2012). https://doi.org/10.1080/00102202.2011.622321

    Article  Google Scholar 

  20. Ke, G.; Li, S.N.; Han, R.; Li, R.Z.; Liu, Z.M.; Qi, Z.P.; Liu, Z.Y.: Study on the propagation law of gas explosion in the space based on the goaf characteristic of coal mine. Saf. Sci. 127, 5 (2020). https://doi.org/10.1016/j.ssci.2020.104693

    Article  Google Scholar 

  21. Li, L.L.; Wang, Y.: Mechanical characteristics of partially premixed methane/air explosion in a small closed tube based on simulation technology. Energy Sources Part A- Recovery Util. Environ. Eff. 42(10), 1255–1267 (2020). https://doi.org/10.1080/15567036.2019.1604850

    Article  Google Scholar 

  22. Xie, B.J.; Du, Y.J.; Wang, L.: Experiment and numerical simulation of gas explosion flame propagation law in bifurcated pipeline. J. Chongqing Univ. 42(6), 69–77 (2019)

    Google Scholar 

  23. Zhu, Y.F.; Wang, D.M.; Shao, Z.L.; Zhu, X.L.; Xu, C.H.; Zhang, Y.T.: Investigation on the overpressure of methane-air mixture gas explosions in straight large-scale tunnels. Process Saf. Environ. Protect. 135, 101–112 (2020). https://doi.org/10.1016/j.psep.2019.12.022

    Article  Google Scholar 

  24. Cui, G.; Yang, C.; Li, Z.L.; Zhou, Z.; Li, J.L.: Experimental study and theoretical calculation of flammability limits of methane/air mixture at elevated temperatures and pressures. J. Loss Prev. Process Ind. 41, 252–258 (2016). https://doi.org/10.1016/j.jlp.2016.02.016

    Article  Google Scholar 

  25. Duan, X.W.; Wu, Y.L.; Jing, G.X.; Guo, S.S.; Shao, H.Y.: Study on the flame propagation law of gas and coal dust explosion in a semi-closed pipeline. J. Saf. Environ. 20(2), 524–529 (2020)

    Google Scholar 

  26. Li, P.; Liu, J.; Gao, K.: Experimental study on gas explosion temperature and pressure peak in pipeline. J. Saf. Environ. 15(2), 59–63 (2015)

    Google Scholar 

  27. Luo, Z.M.; Li, R.K.; Wang, T.; Cheng, F.M.; Liu, Y.; Yu, Z.J.; Fan, S.X.; Zhu, X.C.: Explosion pressure and flame characteristics of CO/CH4/air mixtures at elevated initial temperatures. Fuel (2020). https://doi.org/10.1016/j.fuel.2020.117377

    Article  Google Scholar 

  28. Ma, Q.J.; Zhang, Q.; Li, D.; Chen, J.C.; Ren, S.Y.; Shen, S.L.: Effects of premixed methane concentration on distribution of flame region and hazard effects in a tube and a tunnel gas explosion. J. Loss Prev. Process Ind. 34, 30–38 (2015). https://doi.org/10.1016/j.jlp.2015.01.018

    Article  Google Scholar 

  29. Wang, K.; Jiang, S.G.; Ma, X.P.; Wu, Z.Y.; Zhang, W.Q.; Shao, H.: Study of the destruction of ventilation systems in coal mines due to gas explosions. Powder Technol. 286, 401–411 (2015). https://doi.org/10.1016/j.powtec.2015.08.020

    Article  Google Scholar 

  30. Zipf, R.K.; Gamezo, V.N.; Sapko, M.J.; Marchewka, W.P.; Mohamed, K.M.; Oran, E.S.; Kessler, D.A.; Weiss, E.S.; Addis, J.D.; Karnack, F.A.; Sellers, D.D.: Methane-air detonation experiments at NIOSH Lake Lynn laboratory. J. Loss Prev. Process Ind. 26(2), 295–301 (2013). https://doi.org/10.1016/j.jlp.2011.05.003

    Article  Google Scholar 

  31. Zhang, L.L.; Yang, Q.Y.; Shi, B.M.; Niu, Y.H.; Zhong, Z.: Influences of a pipeline’s bending angle on the propagation law of coal dust explosion induced by gas explosion. Combust. Sci. Technol. (2019). https://doi.org/10.1080/00102202.2019.1673381

    Article  Google Scholar 

  32. He, Y.; Zhu, C.; He, Z.; Gu, C.; Cui, J.: Big data oriented root cause identification approach based on axiomatic domain mapping and weighted association rule mining for product infant failure. Comput. Ind. Eng. (2017). https://doi.org/10.1016/j.cie.2017.05.012

    Article  Google Scholar 

  33. Duan, P.; He, Z.; He, Y.; Liu, F.; Zhang, A.; Zhou, D.: Root cause analysis approach based on reverse cascading decomposition in QFD and fuzzy weight ARM for quality accidents. Comput. Ind. Eng. (2020). https://doi.org/10.1016/j.cie.2020.106643

    Article  Google Scholar 

  34. Prayogo, D.; Cheng, M.Y.; Wu, Y.W.; Tran, D.H.: Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Eng. Comput. 36(3), 1135–1153 (2020). https://doi.org/10.1007/s00366-019-00753-w

    Article  Google Scholar 

  35. Cortes, C.; Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/bf00994018

    Article  MATH  Google Scholar 

  36. Valente, J.M.; Maldonado, S.: SVR-FFS: a novel forward feature selection approach for high-frequency time series forecasting using support vector regression. Expert Syst. Appl. 160, 13 (2020). https://doi.org/10.1016/j.eswa.2020.113729

    Article  Google Scholar 

  37. Sun, Z.M.; Li, D.: Coal mine gas safety evaluation based on adaptive weighted least squares support vector machine and improved Dempster-Shafer evidence theory. Discret. Dyn. Nat. Soc. (2020). https://doi.org/10.1155/2020/8782450

    Article  MathSciNet  Google Scholar 

  38. Deng, J.; Lei, C.K.; Xiao, Y.; Cao, K.; Ma, L.; Wang, W.F.; Bin, L.W.: Determination and prediction on “three zones” of coal spontaneous combustion in a gob of fully mechanized caving face. Fuel 211, 458–470 (2018). https://doi.org/10.1016/j.fuel.2017.09.027

    Article  Google Scholar 

  39. Yu, J.Y.; Hou, B.X.; Lelyakin, A.; Xu, Z.J.; Jordan, T.: Gas detonation cell width prediction model based on support vector regression. Nucl. Eng. Technol. 49(7), 1423–1430 (2017). https://doi.org/10.1016/j.net.2017.06.014

    Article  Google Scholar 

  40. Wang, Z.F.; Wang, S.T.; Kong, D.M.; Liu, S.Y.: Methane detection based on improved chicken algorithm optimization support vector machine. Appl. Sci.-Basel (2019). https://doi.org/10.3390/app9091761

    Article  Google Scholar 

  41. Li, D.; Peng, S.P.; Du, W.F.; Guo, Y.L.: New method for predicting coal seam gas content. Energy Sources Part A-Recovery Util. Environ. Eff. 41(10), 1272–1284 (2019). https://doi.org/10.1080/15567036.2018.1545003

    Article  Google Scholar 

  42. Zheng, L.G., Yu, M.G., Yu, S.J., Jia, H.L., Pan, R.K.: comparative study of nonlinear approaches for predicting explosion limits of multi-component gas mixture. In: International Symposium on Safety Science and Technology, 7, 1096-1101 (2008)

  43. Xue, Y., Zhang, T.T.: The disaster risk assessment of coal mine gas explosion based on the model of SVM classification - regression. In: 6th Annual Meeting of Risk-Analysis-Council-of-China-Association-for-Disaster-Prevention. 102, 322–327 (2014)

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Acknowledgements

The National Key Research and Development Program of China (No. 2017YFC0804401). The Natural Science Foundation of China (No. 51574142 and No.51774169).

Funding

This paper is financially supported by the National Key Research and Development Program of China (No. 2017YFC0804401) and the Natural Science Foundation of China (No. 51574142 and No.51774169).

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Authors and Affiliations

  1. College of Safety Science & Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China

    Li Liu, Jian Liu, Qichao Zhou & Min Qu

  2. Key Laboratory of Mine Thermo-Motive Disaster & Prevention, Ministry of Education, Huludao, 125105, Liaoning, China

    Li Liu, Jian Liu, Qichao Zhou & Min Qu

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  1. Li Liu
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  2. Jian Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by LL, JL, QZ, and MQ. The first draft of the manuscript was written by LL, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jian Liu.

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The authors declare no conflict of interest.

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Liu, L., Liu, J., Zhou, Q. et al. An SVR-based Machine Learning Model Depicting the Propagation of Gas Explosion Disaster Hazards. Arab J Sci Eng 46, 10205–10216 (2021). https://doi.org/10.1007/s13369-021-05616-5

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