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Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms

Author

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  • Konstantin Osintsev

    (Institute of Engineering and Technology, South Ural State University, 76 Prospekt Lenina, 454080 Chelyabinsk, Russia)

  • Sergei Aliukov

    (Institute of Engineering and Technology, South Ural State University, 76 Prospekt Lenina, 454080 Chelyabinsk, Russia)

  • Yuri Prikhodko

    (Institute of Engineering and Technology, South Ural State University, 76 Prospekt Lenina, 454080 Chelyabinsk, Russia)

Abstract

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.

Suggested Citation

  • Konstantin Osintsev & Sergei Aliukov & Yuri Prikhodko, 2021. "Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms," Energies, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1909-:d:526942
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    References listed on IDEAS

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    1. Konstantin Osintsev & Sergei Aliukov & Sulpan Kuskarbekova, 2021. "Experimental Study of a Coil Type Steam Boiler Operated on an Oil Field in the Subarctic Continental Climate," Energies, MDPI, vol. 14(4), pages 1-23, February.
    2. Sunil, P.U. & Barve, Jayesh & Nataraj, P.S.V., 2017. "Mathematical modeling, simulation and validation of a boiler drum: Some investigations," Energy, Elsevier, vol. 126(C), pages 312-325.
    3. Evgeniy Toropov & Konstantin Osintsev & Sergei Aliukov, 2019. "New Theoretical and Methodological Approaches to the Study of Heat Transfer in Coal Dust Combustion," Energies, MDPI, vol. 12(1), pages 1-14, January.
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    Cited by:

    1. Konstantin Osintsev & Sergei Aliukov, 2022. "Experimental and Mathematical Investigation of the Thermophysical Properties of Coal–Water Slurries Based on Lignite," Energies, MDPI, vol. 15(10), pages 1-20, May.

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