Mathematical Model for Prediction of the Main Characteristics of Emissions of Chemically Hazardous Substances into the Atmosphere

  • Ekaterina KushelevaEmail author
  • Alexander Rezchikov
  • Vadim Kushnikov
  • Vladimir Ivaschenko
  • Elena Kushnikova
  • Andrey Samartsev
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)


Based on the formal apparatus of system dynamics, there was developed a mathematical model to predict the main characteristics of emissions of chemically hazardous substances into the atmosphere. When building the model on the basis of GOST R 22.1.10, the main characteristics of emissions at chemically hazardous facilities were selected. There were selected external factors which should be taken into account while building. A graph of cause-effect relationships existing between the simulated characteristics is constructed. The proposed model is described by a system of nonlinear differential equations of the first order. A model example of emission of a chemically dangerous substance into the atmosphere is presented. The calculation of the simulated characteristics is made, the corresponding graphs are presented. The numerical solution of the system of equations is obtained due to the Runge-Kutta method. The comparison of the results calculated by the model with the actual data of emergency confirms the adequacy of the proposed model. The results got by the model can be used in the development of information systems for predicting the effects of emissions of chemically hazardous substances for operational dispatching staff of the MES.


Mathematical model System dynamics Emissions of chemically hazardous substances 


  1. 1.
    Klyuev, V.V., Sosnin, F.R.: Nondestructive testing in oil refining and the chemical industry. Chem. Pet. Eng. 40(3–4), 241–247 (2004)CrossRefGoogle Scholar
  2. 2.
    Ivanov, A.S., et al.: The cause-and-effect approach to investigation of emergency situations in human-machine systems. Mechatron. Autom. Control, (2), 38–43 (2012). (in Russian)Google Scholar
  3. 3.
    Forrester, J.W.: World Dynamics, 2nd edn. Productivity Press, Portland (1973)Google Scholar
  4. 4.
    Rutkovsky, V.Y., et al.: New adaptive algorithm of flexible spacecraft control. In: Studies in Systems, Decision and Control, vol. 55, pp. 313–326 (2016)CrossRefGoogle Scholar
  5. 5.
    Glumov, V.M., et al.: Constructing the general scheme of a departmental management system. Autom. Remote Control 57(12), 1794–1806 (1996)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Bogomolov, A.S.: Analysis of the ways of occurrence and prevention of critical combinations of events in man-machine systems. Izvestiya Saratovskogo Universiteta. Novaya Seriya-Matematika Mekhanika Informatika, vol. 17, pp. 219–230 (2017)Google Scholar
  7. 7.
    Filimonyuk, L.Y.: The problem of critical events’ combinations in air transportation systems. In: Advances in Intelligent Systems and Computing, vol. 573, pp. 384–392 (2017)Google Scholar
  8. 8.
    Syrov, A.S., et al.: Motion control problems for multimode unmanned aerial vehicles. Autom. Remote Control 78(6), 1128–1137 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Glumov, V.M., et al.: Design and analysis of lateral motion control algorithms for an unmanned aerial vehicle with two control surfaces. Autom. Remote Control 78(5), 924–935 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    State Standard 22.1.10-2002. Safety in emergency situations. Monitoring of chemically dangerous objects. General requirements, p. 9. Standartinform Publications, Moscow (2002). (in Russian)Google Scholar
  11. 11.
    Brodsky, Y.: Lectures on Mathematical and Simulation Modeling. Direct Media, Moscow, Berlin (2015). (in Russian)Google Scholar
  12. 12.
    Marukhlenko, S.L., Degtyarev, S.V., Marukhlenko, A.L.: Technogenic accident hazard software module. Izvestiya Yugo-Zapadnogo Gosudarstvennogo Universiteta, no. 6–2 (39), pp. 41–45 (2011). (in Russian)Google Scholar
  13. 13.
    Kusheleva, E.V., et al.: A Model to predict the distribution of atmospheric pollutants in road congestion. Control systems and information technology, no. 2, pp. 55–60 (2018). (in Russian)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Precision Mechanics and ControlRussian Academy of SciencesSaratovRussia
  2. 2.Yuri Gagarin State Technical UniversitySaratovRussia
  3. 3.Saratov State UniversitySaratovRussia

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