Forecasting the emergency explosive environment with the use of fuzzy data

Authors

  • Oleh Zemlianskiy Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034, Ukraine https://orcid.org/0000-0002-2728-6972
  • Ihor Maladyka Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034, Ukraine https://orcid.org/0000-0001-8784-2814
  • Oleg Miroshnik Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034, Ukraine https://orcid.org/0000-0001-8951-9498
  • Ihor Shkarabura Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034, Ukraine https://orcid.org/0000-0002-3882-7623
  • Galina Kaplenko Dnipropetrovsk State Agrarian and Economic University S. Yefremova str., Dnipro, Ukraine, 49600, Ukraine https://orcid.org/0000-0002-9545-8414

DOI:

https://doi.org/10.15587/1729-4061.2017.116839

Keywords:

gas-steam-air explosive environment, models and methods of forecasting, fuzzy data, emergency

Abstract

We resolved the scientific and technical problem to improve the efficiency of a decision-making processes carried out by a head of emergencies elimination of accidents at potentially dangerous objects by forecasting an emergency explosive environment under conditions of uncertainty.

We completed a formalized statement of the problem of identification of the concentration of an explosive gas-air mixture, which makes possible to use fuzzy input and output data. We determined the aspects of solution of the problem of forecasting with a use of expert conclusions in the case of absence or unreliability of input data.

We developed the technology of forecasting of parameters of an emergency explosive environment based on the obtained results. The proposed technology can be used in the post-emergency period to clarify fields of an explosive environment. A neuro-fuzzy network can be re-trained in the shortest possible time and used to solve a forecasting problem at all possible points in the zone of explosive environment on the base of the results of measurements of explosive concentration of devices. In addition, this technology can be used to clarify initial values of parameters of an accident, which will improve and objectify a decision making carried out by the head of emergencies elimination

Author Biographies

Oleh Zemlianskiy, Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034

PhD, Associate Professor

Department of automatic safety systems and electrical installations

Ihor Maladyka, Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034

PhD, Associate Professor

Department of fire tactics and emergency rescue works

Oleg Miroshnik, Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034

PhD, Associate Professor

Department of fire tactics and emergency rescue works

Ihor Shkarabura, Cherkasy Institute of Fire Safety named after Chornobyl Heroes of National University of Civil Defense of Ukraine Onoprienka str., 8, Cherkasy, Ukraine, 18034

Postgraduate student

Department of fire tactics and emergency rescue works

Galina Kaplenko, Dnipropetrovsk State Agrarian and Economic University S. Yefremova str., Dnipro, Ukraine, 49600

PhD, Associate Professor

Department of Life Safety

References

  1. Brushlinsky, N., Ahrens, M., Sokolov, S., Wagner, P. (2016). World Fire Statistics. CTIF, No. 21. Available at: http://www.ctif.org/sites/default/files/ctif_report21_world_fire_statistics_2016.pdf
  2. Biloshytskyi, M. V. (2009). Rozbizhnist u vyznachenni pozhezhnoi nebezpeky vyrobnychykh protsesiv ta prymishchen za pokaznykamy nyzhnoi kontsentratsiynoi mezhi zaimannia i rozrakhunkovym znachenniam nadlyshkovoho tysku vybukhu. Naukovyi visnyk UkrNDIPB, 2 (20), 91–98.
  3. Basmanov, A. E., Govalenkov, S. S. (2010). Opredelenie zon vzryvoopasnyh kontsentratsiy opasnogo himicheskogo veshchestva v vozduhe. Materialy III Mizhn. nauk.-prakt. konf. «Aktualni problemy tekhnichnykh ta pryrodnychykh nauk u zabezpechenni tsyvilnoho zazystu». Cherkasy, APB im. Heroiv Chornobylia, 66–69.
  4. Levchenko, A. D., Levchenko, D. Ye., Kryshtal, V. M., Zemlianskyi, O. M. (2010). Okremi aspekty kompleksnykh system rannoho vyiavlennia nadzvychainykh sytuatsiy. Pozhezhna bezpeka: teoriya i praktyka, 5, 76–80.
  5. Tlyasheva, R. R., Solodovnikov, A. V. (2006). Prognozirovanie veroyatnyh zon zastoya na naruzhnoy ustanovke neftepererabatyvayushchego predpriyatiya. Neftegazovoe delo. Available at: http://www.ogbus.ru/authors/Tlyasheva/Tlyasheva_2.pdf
  6. Johnson, D. M., Puttock, J. S., Richardson, S. A., Betteridge, S. (2011). Investigation of Deflagration and Detonation as an Explanation for the Buncefield Vapour Cloud Explosion. Proceedings of the Sixth International Seminar on Fire and Explosion Hazards. doi: 10.3850/978-981-08-7724-8_01-04
  7. Raj, P. K. (2007). LNG fires: A review of experimental results, models and hazard prediction challenges. Journal of Hazardous Materials, 140 (3), 444–464. doi: 10.1016/j.jhazmat.2006.10.029
  8. Atkinson, G., Cowpe, E., Halliday, J., Painter, D. (2017). A review of very large vapour cloud explosions: Cloud formation and explosion severity. Journal of Loss Prevention in the Process Industries, 48, 367–375. doi: 10.1016/j.jlp.2017.03.021
  9. Vapour Cloud Development in Over-filling Incidents. Technical Note 12 (2013). FABIG. Available at: http://fabig.com/video-publications/TechnicalGuidance#
  10. Snytyuk, V. E., Zemlianskyi, O. N. (2013). The method of parametric optimization of the model reflecting the level of concentrated hazardous chemicals in post-accident period. Nauka i Studia, 42 (110), 26–33.
  11. Zemlianskyi, O., Snytyuk, V. (2013). Parametric identification for model of a chemical hazardous substance concentration using soft computing. International Journal Information Technologies & Knowledge, 7 (4), 337–346.
  12. Zgurovskiy, M. Z., Pankratova, N. D. (2005). Sistemnyy analiz. Problemy, metodologiya, prilozheniya. Kyiv: Naukova dumka, 743.
  13. Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), 665–685. doi: 10.1109/21.256541
  14. Zaichenko, Yu. P. (2004). Osnovy proektuvannia intelektualnykh system. Kyiv: Vydavnychyi Dim "Slovo", 352.
  15. Holland J. H. (1994). Adaptation in natural and artificial systems. An introductory analysis with application to biology, control and artificial intelligence. London: Bradford book edition, 211.
  16. Snytiuk, V. Ye. (2012). Spriamovana optymizatsiya i osoblyvosti evoliutsiynoi heneratsiy potentsiynykh rozviazkiv. Materialy V Mizhn. shkoly-seminaru «Teoriya pryiniattia rishen». Uzhhorod, 182–183.
  17. Rechenberg, I. (1994). Evolutionsstrategie '94. Stuttgart: Frommann-Holzboog, 434.
  18. Borisov, A. N., Krumberg, O. A., Fedorov, I. P. (1990). Prinyatie resheniy na osnove nechetkih modeley: primery ispol'zovaniya. Riga: Zinatne, 184.
  19. Saati, T. (1993). Prinyatie resheniy. Metod analiza ierarhiy. Moscow: Radio i svyaz', 278.

Downloads

Published

2017-12-01

How to Cite

Zemlianskiy, O., Maladyka, I., Miroshnik, O., Shkarabura, I., & Kaplenko, G. (2017). Forecasting the emergency explosive environment with the use of fuzzy data. Eastern-European Journal of Enterprise Technologies, 6(4 (90), 19–27. https://doi.org/10.15587/1729-4061.2017.116839

Issue

Vol. 6 No. 4 (90) (2017): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2017 Oleh Zemlianskiy, Ihor Maladyka, Ihor Maladyka, Oleg Miroshnik, Oleg Miroshnik, Ihor Shkarabura, Ihor Shkarabura, Galina Kaplenko, Galina Kaplenko

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.