Advertisement

Forecasting of Methane Hazard State in the Exploitation Wall Using Neural-Fuzzy System

  • Dariusz FelkaEmail author
  • Jarosław Brodny
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 934)

Abstract

In hard coal mines the series of natural hazards take place, one of the most dangerous are gaseous hazards. However, the most commonly occurring gaseous hazard is methane hazard. Methane is odorless and colorless, lighter than air organic gas, of which large quantities are emitted during an exploitation of hard coal. It is inflammable gas which with appropriate air concentration becomes a very dangerous explosive gas. In order to ensure safety of the staff, the critical concentration of this gas cannot be allowed to occur in mining atmosphere. Therefore, it is reasonable to apply mechatronic systems which task is to register current methane concentrations in mining atmosphere, also to develop analytical systems in order to forecast these concentrations. Application of advanced artificial intelligence systems provides opportunities for effective forecasting of methane concentrations in underground headings. This forecast is based on the registered measurement data and determined ventilated parameters. In the paper, an example of application of artificial intelligence methods to build an analyzing methane hazard model in the region of exploited wall was presented. The way of formation of neural-fuzzy system based on numerical data was described. Hybrid system was built to determine current level of methane hazard and forecast its future concentration in the region of longwall during normal mining operations. For determination of system parameters automatically, methods of knowledge recovery (based on fuzzy clustering of measured data) and adaptive learning algorithm of neural-fuzzy system were used. Developed tool gives a lot of opportunities for practical application.

Keywords

Mechatronic system Methane hazard Neural-fuzzy system Forecasting 

References

  1. 1.
    Brodny, J., Tutak, M.: Analysis of methane emission into the atmosphere as a result of mining activity. In: 16th International Multidisciplinary Scientific GeoConference SGEM 2016, SGEM Vienna GREEN Extended Scientific Sessions, SGEM2016 Conference Proceedings, Book 4, vol. 3, pp. 83–90 (2016).  https://doi.org/10.5593/sgem2016/hb43/s06
  2. 2.
    Brodny, J., Tutak, M.: Determination of the zone endangered by methane explosion in goaf with caving of operating longwalls. In: 16th International Multidisciplinary Scientific GeoConference SGEM 2016, SGEM 2016 Conference Proceedings, Book 1, vol. 2, pp. 299–306 (2016).  https://doi.org/10.5593/sgem2016/b12/s03.039
  3. 3.
    Dokumentacja projektu europejskiego AVENTO (Zaawansowane narzędzia do kontroli wentylacji i emisji metanu), Instytut Technik Innowacyjnych EMAG, Katowice 2014–2015 (niepublikowana)Google Scholar
  4. 4.
    Dokumentacja ściany N-2 w pokładzie 404/2 w KWK Pniówek, Pawłowice (2013)Google Scholar
  5. 5.
    Felka, D.: Metody budowy inteligentnych modeli na bazie danych numerycznych. Monografia: Młodzi Innowacyjni. Innowacyjne rozwiązania z dziedzinie automatyki, robotyki i pomiarów, Wydawnictwo PIAP, Warszawa (2012)Google Scholar
  6. 6.
    Felka, D., Brodny, J.: Application of neural-fuzzy system in prediction of methane hazard. In: Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. Advances in Intelligent Systems and Computing, vol. 637, pp. 151–160. Springer, Cham (2018)Google Scholar
  7. 7.
    Fuzzy Logic Toolbox User’s Guide. Revised for Version 2.2.24 (Release 2016b), The MathWorks, Inc. (2016)Google Scholar
  8. 8.
    Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Upper Saddle River (1997)CrossRefGoogle Scholar
  9. 9.
    Krause, E., Łukowicz, K.: Zasady prowadzenia ścian w warunkach zagrożenia metanowego. Instrukcja nr 17 GIG, Katowice – Mikołów (2004)Google Scholar
  10. 10.
    Łęski, J.: Systemy neuronowo – rozmyte, Wydawnictwo Naukowo – Techniczne, Warszawa (2008)Google Scholar
  11. 11.
    Miśkiewicz, K., Wojaczek, A., Wojtas, P.: Systemy Dyspozytorskie kopalń podziemnych i ich integracja – Wybrane problemy. Monografia, Wydawnictwo Politechniki Śląskiej, Gliwice (2011)Google Scholar
  12. 12.
    Mrozek, B., Felka, D.: Inteligentny model wskaźnika zagrożenia pożarowego w kopalni węgla. PAR Pomiary Automatyka Robotyka 02, 540–545 (2012)Google Scholar
  13. 13.
    Mróz, J., Felka, D., Broja, A., Małachowski, M.: Devices for measuring ventilation parameters and methane concentration as well as concept of complex monitoring of methane hazard in longwall area. Min. – Inform. Autom. Electr. Eng. 01(529), 7–18 (2017)Google Scholar
  14. 14.
    Osowski, S.: Sieci neuronowe w ujęciu algorytmicznym. Wydawnictwo Naukowo-Techniczne, Warszawa (1996)Google Scholar
  15. 15.
    Pawiński, J., Roszkowski, J., Strzemiński, J.: Przewietrzanie kopalń. Śląskie Wydawnictwo Techniczne, Katowice (1995)Google Scholar
  16. 16.
    Roszczynialski, W., Trutwin, W., Wacławik, J.: Kopalniane pomiary wentylacyjne. Wydawnictwo Śląsk, Katowice (1999)Google Scholar
  17. 17.
    Tutak, M., Brodny, J.: Assessment of hydrodynamics of gas flow through homogeneous and heterogeneous porous rock media. In: 17th International Multidisciplinary Scientific GeoConference SGEM 2017, vol. 17, no. 14, pp. 539–546 (2017).  https://doi.org/10.5593/sgem2017/14
  18. 18.
    Tutak, M., Brodny, J.: Influence of auxiliary ventilation devices on a distribution of methane concentration at the crossing of longwall and ventilation roadway. In: 17th International Multidisciplinary Scientific Geoconference SGEM (2017), vol. 17, no. 13, pp. 437–444 (2017).  https://doi.org/10.5593/sgem2017/13
  19. 19.
    Tutak, M., Brodny, J.: Analysis of influence of goaf sealing from tailgate on the methane concentration at the outlet from the longwall. In: IOP Conference Series: Earth and Environmental Science, vol. 95, p. 042025 (2017).  https://doi.org/10.1088/1755-1315/95/4/042025
  20. 20.
    Tutak, M., Brodny, J.: Determination of particular endogenous fires hazard zones in goaf with caving of longwall. In: IOP Conference Series: Earth and Environmental Science, vol. 95, p. 042026 (2017).  https://doi.org/10.1088/1755-1315/95/4/042026
  21. 21.
    Tutak, M.: Analysis of varying levels of methane emissions from coal mines in Poland. In: SGEM 2017 Vienna GREEN Conference Proceedings, vol. 17, no. 43, pp. 301–308 (2017).  https://doi.org/10.5593/sgem2017h/43/s19.038
  22. 22.
    Tutak, M.: Assessment of hydrodynamics of gas flow through the porous rock structures. In: 17th International Multidisciplinary Scientific GeoConference SGEM 2017, SGEM2017 Vienna GREEN Conference Proceedings, vol. 17, no. 15, pp. 53–60 (2017).  https://doi.org/10.5593/sgem2017h/15/s06.007

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Innovative Technologies EMAGKatowicePoland
  2. 2.Silesian University of TechnologyGliwicePoland

Personalised recommendations