International Journal of Energy and Environmental Science

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Research Article |

Assessment Method of Coal and Gas Outburst: From the Perspective of TFN-MCS Theory

The prediction of coal and gas outburst risk can effectively prevent underground coal mine accidents. Due to the overlapping, coupling and complexity of coal and gas outburst in the development process, coal and gas outburst is generally Gaussian distribution or nearly Gaussian distribution, especially when the sample data in the research area is not accurate or the information is insufficient, the traditional evaluation model and method have certain limitations. To further improve the scientific and accurate prediction of coal and gas outburst risk level, a coupling model of coal and gas outburst risk assessment is established based on Monte Carlo stochastic simulation (MCS) and triangular fuzzy number (TFN) theory. Firstly, the index weight measurement value is determined by using expert opinions and AHP method. Then, the risk level and risk importance of coal and gas outburst risk assessment index are quantitatively described by using fuzzy semantics with five-level classification standards. Finally, the confidence interval of the comprehensive risk value of the coal mines to be evaluated in the research area is established. The research results show that after 20,000 simulation experiments with the coupling model, the calculation results have converged, and the confidence interval value of the system comprehensive risk simulation value of each coal mine is 95%, which can provide relevant decision support for the prevention and control planning of coal and gas outburst.

Coal, Coal and Gas Outburst, Risk Assessment, Triangular Fuzzy Number Theory, Monte Carlo Stochastic Simulation

APA Style

Zhie Wang, Jingde Xu, Jun Ma. (2023). Assessment Method of Coal and Gas Outburst: From the Perspective of TFN-MCS Theory . International Journal of Energy and Environmental Science, 8(5), 107-117. https://doi.org/10.11648/j.ijees.20230805.13

ACS Style

Zhie Wang; Jingde Xu; Jun Ma. Assessment Method of Coal and Gas Outburst: From the Perspective of TFN-MCS Theory . Int. J. Energy Environ. Sci. 2023, 8(5), 107-117. doi: 10.11648/j.ijees.20230805.13

AMA Style

Zhie Wang, Jingde Xu, Jun Ma. Assessment Method of Coal and Gas Outburst: From the Perspective of TFN-MCS Theory . Int J Energy Environ Sci. 2023;8(5):107-117. doi: 10.11648/j.ijees.20230805.13

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Xue X, Han W, Xin Y, et al. Proposal and energetic and exergetic evaluation of a hydrogen production system with synergistic conversion of coal and solar energy, Energy, 2023, 283, 128489. https://doi.org/10.1016/j.energy.2023.128489.
2. He Y, Guo S, Dong P, et al. Feasibility analysis of decarbonizing coal-fired power plants with 100% renewable energy and flexible green hydrogen production, Energy Conversion and Management, 2023, 290, 117232. https://doi.org/10.1016/j.enconman.2023.117232.
3. Li B, Wang E, Shang Z, et al. Optimize the early warning time of coal and gas outburst by multi-source information fusion method during the tunneling process. Process Safety and Environmental Protection, 2021, 149, 839–849. https://doi.org/10.1016/j.psep.2021.03.029
4. Zhang M, Cao X, Li B, et al. Quantitative study on the role of desorption gas on coal-gas outbursts: Energy contribution and dynamic characteristics, Process Safety and Environmental Protection, 2023, 171: 437-446. https://doi.org/10.1016/j.psep.2023.01.019.
5. He X Q, Chen W X, Nie B S, et al. Classification technique for danger classes of coal and gas outburst in deep coal mines, Safety Science, 2010, 48 (2): 173-178. https://doi.org/10.1016/j.ssci.2009.07.007.
6. Lu S, Wang C, Liu Q, Zhang Y, et al. Numerical assessment of the energy instability of gas outburst of deformed and normal coal combinations during mining. Process Safety and Environmental Protection, 2019, 132: 351–366. https://doi.org/10.1016/j.psep.2019.10.017
7. Mahdevari, S., Shahriar, K., Esfahanipour, A. Human health and safety risks management in underground coal mines using fuzzy TOPSIS, Science of The Total Environment, 2014, 488-489, 85-99, https://doi.org/10.1016/j.scitotenv.2014.04.076
8. Miao D, Lv Y, Yu K, et al. Research on coal mine hidden danger analysis and risk early warning technology based on data mining in China, 2023, Process Safety and Environmental Protection, 171: 1-17. https://doi.org/10.1016/j.psep.2022.12.077.
9. Deng H, Yeh C H, Robert J W. Inter-company comparison using modified TOPSIS with objective weights, Computers & Operations Research, 2000, 27 (10): 963-973. https://doi.org/10.1016/S0305-0548(99)00069-6
10. S. Opricovic, G.-H. Tzeng. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research, 156 (2004) 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1
11. Xu K, Li S, Lu C, et al. Risk assessment of coal mine gas outburst based on cloud integrated similarity and fuzzy DEMATEL, Process Safety and Environmental Protection, 2023, 117: 1211-1224. https://doi.org/10.1016/j.psep.2023.07.043
12. Li M, Wang H, Wang D, et al. Risk assessment of gas outburst in coal mines based on fuzzy AHP and bayesian network. Process Safety and Environmental Protection. 2020, 135, 207-218. https://doi.org/10.1016/j.psep.2020.01.003
13. Brodny J, Felka D, Tutak M. The use of the neuro-fuzzy model to predict the methane hazard during the underground coal mining production process, Journal of Cleaner Production, 2022, 368, 133258. https://doi.org/10.1016/j.jclepro.2022.133258.
14. Ruilin Z, Lowndes I. S. The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts. International Journal of Coal Geology. 2010, 84, 141–152. https://doi.org/10.1016/j.coal.2010.09.004
15. Harish G, Dimple R, Novel distance measures for intuitionistic fuzzy sets based on various triangle centers of isosceles triangular fuzzy numbers and their applications, Expert Systems with Applications, 2022, 191, 116228. https://doi.org/10.1016/j.eswa.2021.116228.
16. Franco M. A new criterion of choice between generalized triangular fuzzy numbers, Fuzzy Sets and Systems, 2016, 296: 51-69. https://doi.org/10.1016/j.fss.2015.11.022.
17. Nathaniel H, Smith C, Eric H, A Monte Carlo approach to integrating uncertainty into the levelized cost of electricity, The Electricity Journal, 2016, 29 (3): 21-30. https://doi.org/10.1016/j.tej.2016.04.001.
18. Raphael S, Flávio V, Sávio S. V. Combining the bow-tie method and fuzzy logic using Mamdani inference model, Process Safety and Environmental Protection, 2023, 169: 159-168. https://doi.org/10.1016/j.psep.2022.11.005.
19. Marius V, Rasa R P. Quantitative risk prognostics framework based on Petri Net and Bow-Tie models, Reliability Engineering & System Safety, 2017, 165: 62-73. https://doi.org/10.1016/j.ress.2017.03.026.
20. Sobczyk J. The influence of sorption processes on gas stresses leading to the coal and gas outburst in the laboratory conditions, 2011, Fuel, 90 (3): 1018-1023. https://doi.org/10.1016/j.fuel.2010.11.004.
21. Li Q, Li Z, Wang E, et al. Characteristics and precursor information of electromagnetic signals of mining-induced coal and gas outburst, 2018, Journal of Loss Prevention in the Process Industries, 54, 206-215. https://doi.org/10.1016/j.jlp.2018.04.004
22. Zhou A, Zhang M, Wang K. Rapid gas desorption and its impact on gas-coal outbursts as two-phase flows. Process Safety and Environmental Protection, 2021, 150, 478-488. https://doi.org/10.1016/j.psep.2021.04.042
23. Zhou B, Yang S, Wang C, et al. Experimental study on the influence of coal oxidation on coal and gas outburst during invasion of magmatic rocks into coal seams. Process Safety and Environmental Protection, 2019, 124, 213-222. https://doi.org/10.1016/j.psep.2019.02.017
24. Xie X, Shen S, Fu G, et al. Accident case data–accident cause model hybrid-driven coal and gas outburst accident analysis: Evidence from 84 accidents in China during 2008–2018, Process Safety and Environmental Protection, 2022, 164: 67-90. https://doi.org/10.1016/j.psep.2022.05.048.
25. Adrian I. B, Lucian C. Existence, uniqueness, calculus and properties of triangular approximations of fuzzy numbers under a general condition, International Journal of Approximate Reasoning, 2015, 62: 1-26, https://doi.org/10.1016/j.ijar.2015.05.004.
26. L. A. Zadeh. Fuzzy sets, Information and Control, 1965, 8 (3): 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
27. Van Laarhoven P. J. M, Pedrycz W. A fuzzy extension of Satty’s priority theory, fuzzy Sets and Systems, 1983, 11: 229-241. https://doi.org/10.1016/S0165-0114(83)80082-7
28. Wang Z; Lin J. Acceptability measurement and priority weight elicitation of triangular fuzzy multiplicative preference relations based on geometric consistency and uncertainty indices. Information Sciences, 2017, 402 (1), 105–123. https://doi.org/10.1016/j.ins.2017.03.028
29. Ronald E G, Robert E Y. Analysis of the error in the standard approximation used for multiplication of triangular and trapezoidal fuzzy numbers and the development of a new approximation [J]. Fuzzy Sets and Systems, 1997, 91 (1): 1-13. https://doi.org/10.1016/S0165-0114(96)00118-2
30. Lakshmana V, Murugan J. Triangular approximation of intuitionistic fuzzy numbers on multi-criteria decision making problem. Soft Computing, 2021, 25: 9887–9914. https://doi.org/10.1007/s00500-020-05346-0
31. Chen S J, Hwang C L. Fuzzy Multiple Attribute Decision Making: Methods and Applications [M]. Berlin: Springer-Verlag, 1992. https://doi.org/10.1007/978-3-642-46768-4
32. Burkhard S, Tobias W. Biofuels: A model based assessment under uncertainty applying the Monte Carlo method, Journal of Policy Modeling, 2011, 33 (1): 92-126. https://doi.org/10.1016/j.jpolmod.2010.10.008.
33. Lobzang C, Sunil K. Evaluation of groundwater heavy metal pollution index through analytical hierarchy process and its health risk assessment via Monte Carlo simulation, Process Safety and Environmental Protection, 2023, 170, 855-864, https://doi.org/10.1016/j.psep.2022.12.063.
34. Akram S, Majid D, Vijay P. Singh. Uncertainty analysis of water quality index (WQI) for groundwater quality evaluation: Application of Monte-Carlo method for weight allocation, Ecological Indicators, 2020, 117, 106653, https://doi.org/10.1016/j.ecolind.2020.106653.