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Applying the Random Forest Method to Improve Burner Efficiency

Author

Listed:
  • Vladislav Kovalnogov

    (Laboratory of Inter-Disciplinary Problems in Clean Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Ruslan Fedorov

    (Laboratory of Inter-Disciplinary Problems in Clean Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Vladimir Klyachkin

    (Laboratory of Inter-Disciplinary Problems in Clean Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Dmitry Generalov

    (Laboratory of Inter-Disciplinary Problems in Clean Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Yulia Kuvayskova

    (Laboratory of Inter-Disciplinary Problems in Clean Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Sergey Busygin

    (Laboratory of Inter-Disciplinary Problems in Clean Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

Abstract

Fuel power plants are one of the main sources of pollutant emissions, so special attention should be paid to improving the efficiency of the fuel combustion process. The mathematical modeling of processes in the combustion chamber makes it possible to reliably predict and find the best dynamic characteristics of the operation of a power plant, in order to quantify the emission of harmful substances, as well as the environmental and technical and economic efficiency of various regime control actions and measures, and the use of new types of composite fuels. The main purpose of this article is to illustrate how machine learning methods can play an important role in modeling and predicting the performance and control of the combustion process. The paper proposes a mathematical model of an unsteady turbulent combustion process, presents a model of a combustion chamber with a combined burner, and performs a numerical study using the STAR-CCM+ multidisciplinary platform. The influence of various input indicators on the efficiency of burner devices, which is evaluated by several parameters at the output, is investigated. In this case, three possible states of the burners are assumed: optimal, satisfactory and unsatisfactory.

Suggested Citation

  • Vladislav Kovalnogov & Ruslan Fedorov & Vladimir Klyachkin & Dmitry Generalov & Yulia Kuvayskova & Sergey Busygin, 2022. "Applying the Random Forest Method to Improve Burner Efficiency," Mathematics, MDPI, vol. 10(12), pages 1-24, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2143-:d:843067
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    References listed on IDEAS

    as
    1. Sartor, K. & Quoilin, S. & Dewallef, P., 2014. "Simulation and optimization of a CHP biomass plant and district heating network," Applied Energy, Elsevier, vol. 130(C), pages 474-483.
    2. Yue Xin & Ke Wang & Yindi Zhang & Fanjin Zeng & Xiang He & Shadrack Adjei Takyi & Paitoon Tontiwachwuthikul, 2021. "Numerical Simulation of Combustion of Natural Gas Mixed with Hydrogen in Gas Boilers," Energies, MDPI, vol. 14(21), pages 1-15, October.
    3. Pavel Skryja & Igor Hudak & Jiří Bojanovsky & Zdeněk Jegla & Lubomír Korček, 2022. "Effects of Oxygen-Enhanced Combustion Methods on Combustion Characteristics of Non-Premixed Swirling Flames," Energies, MDPI, vol. 15(6), pages 1-21, March.
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    Cited by:

    1. Lefa Zhao & Yafei Zhu & Tianyu Zhao, 2022. "Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    2. Ruslan V. Fedorov & Dmitry A. Generalov & Vyacheslav V. Sherkunov & Valeriy V. Sapunov & Sergey V. Busygin, 2023. "Improving the Efficiency of Fuel Combustion with the Use of Various Designs of Embrasures," Energies, MDPI, vol. 16(11), pages 1-15, May.
    3. Sungur, Bilal & Basar, Cem & Kaleli, Alirıza, 2023. "Multi-objective optimisation of the emission parameters and efficiency of pellet stove at different supply airflow positions based on machine learning approach," Energy, Elsevier, vol. 278(PA).

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