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Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning

Author

Listed:
  • Lei Han

    (School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China)

  • Lingmei Wang

    (School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China)

  • Hairui Yang

    (Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Chengzhen Jia

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Enlong Meng

    (School of Automation and Software, Shanxi University, Taiyuan 030006, China)

  • Yushan Liu

    (School of Automation and Software, Shanxi University, Taiyuan 030006, China)

  • Shaoping Yin

    (School of Automation and Software, Shanxi University, Taiyuan 030006, China)

Abstract

During the coal-fired circulating fluidized bed unit participation in the peak regulation process of the power grid, the thermal automatic control system assists the operator to adjust the mode focusing on pollutant control and ignoring the economy so that the unit’s operating performance maintains a huge potential for deep mining. The high-dimensional and coupling-related data characteristics of circulating fluidized bed boilers put forward more refined and demanding requirements for combustion optimization analysis and open-loop guidance operation. Therefore, this paper proposes a combustion optimization method that incorporates neighborhood rough set machine learning. This method first reduces the control parameters affecting multi-objective combustion optimization with the neighborhood rough set algorithm that fully considers the correlation of each variable combination and then establishes a multi-objective combustion optimization prediction model by combining the online calculation of boiler thermal efficiency. Finally, the NSGAII algorithm realizes the optimization of the control parameter setting value of the boiler combustion system. The results show that this method reduces the number of control commands involved in combustion optimization adjustment from 26 to 11. At the same time, based on the optimization results obtained by using traditional combustion optimization methods under high, medium, and medium-low load conditions, the boiler thermal efficiency increased by 0.07%, decreased by 0.02%, and increased by 0.55%, respectively, and the nitrogen oxide emission concentration decreased by 5.02 mg/Nm 3 , 7.77 mg/Nm 3 , and 7.03 mg/Nm 3 , respectively. The implementation of this method can help better account for the economy and pollutant discharge of the boiler combustion system during the variable working conditions, guide the operators to adjust the combustion more accurately, and effectively reduce the ineffective energy consumption in the adjustment process. The proposal and application of this method laid the foundation for the construction of smart power plants.

Suggested Citation

  • Lei Han & Lingmei Wang & Hairui Yang & Chengzhen Jia & Enlong Meng & Yushan Liu & Shaoping Yin, 2023. "Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning," Energies, MDPI, vol. 16(15), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5674-:d:1205007
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    References listed on IDEAS

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    1. Qimei Chen & Yurong Gou & Tangrong Wang & Pengbo Liu & Jianguo Zhu, 2024. "The Evolutionary Path and Emerging Trends of Circulating Fluidized Bed Technology: An Integrated Analysis through Bibliometric Assessment and Data Visualization," Energies, MDPI, vol. 17(14), pages 1-19, July.

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