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Optimizing the Controlling Parameters of a Biomass Boiler Based on Big Data

Author

Listed:
  • Jiaxin He

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Junjiao Zhang

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Lezhong Wang

    (School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiaoying Hu

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Junjie Xue

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Ying Zhao

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Xiaoqiang Wang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Changqing Dong

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

Abstract

This paper presents a comprehensive method for optimizing the controlling parameters of a biomass boiler. The historical data are preprocessed and classified into different conditions with the k-means clustering algorithm. The first-order derivative (FOD) method is used to compensate for the lag of controlling parameters, the backpropagation (BP) neural network is used to map the controlling parameters with the boiler efficiency and unit load, and the ant colony optimization (ACO) algorithm is used to search the opening of air dampers. The results of the FOD-BP-ACO model show an improvement in the boiler efficiency compared to the predicted values of FOD-BP and the data compared to the historical true values were observed. The results suggest that this FOD-BP-ACO method can also be used to search and optimize other controlling parameters.

Suggested Citation

  • Jiaxin He & Junjiao Zhang & Lezhong Wang & Xiaoying Hu & Junjie Xue & Ying Zhao & Xiaoqiang Wang & Changqing Dong, 2023. "Optimizing the Controlling Parameters of a Biomass Boiler Based on Big Data," Energies, MDPI, vol. 16(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7783-:d:1288243
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    References listed on IDEAS

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    1. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
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