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Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling

Author

Listed:
  • Yuanhao Shi

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Mengwei Li

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Jie Wen

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Yanru Yang

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Fangshu Cui

    (School of Data Science and Technology, North University of China, Taiyuan 030051, China)

  • Jianchao Zeng

    (School of Data Science and Technology, North University of China, Taiyuan 030051, China)

Abstract

Ash fouling has been an important factor in reducing the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating a reasonable soot blowing strategy to improve energy efficiency. This study presented a comprehensive approach of dynamic prediction of the ash fouling of heat transfer surfaces in coal-fired power plant boilers. At first, the cleanliness factor is used to reflect the fouling level of the heat transfer surfaces. Then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). To construct a reasonable network model, the minimum information criterion and trial-and-error method are used to determine the delay orders and hidden layers. Finally, the experimental object is established on the 300 MV economizer clearness factor dataset of the power station, and the root mean square error and mean absolute percentage error of the proposed method are the smallest. In addition, the experimental results show that this multiscale prediction model is more competitive than the Elman model.

Suggested Citation

  • Yuanhao Shi & Mengwei Li & Jie Wen & Yanru Yang & Fangshu Cui & Jianchao Zeng, 2021. "Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling," Energies, MDPI, vol. 14(13), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4000-:d:587800
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    References listed on IDEAS

    as
    1. Yuanhao Shi & Qiang Li & Jie Wen & Fangshu Cui & Xiaoqiong Pang & Jianfang Jia & Jianchao Zeng & Jingcheng Wang, 2019. "Soot Blowing Optimization for Frequency in Economizers to Improve Boiler Performance in Coal-Fired Power Plant," Energies, MDPI, vol. 12(15), pages 1-19, July.
    2. Yuanhao Shi & Jie Wen & Fangshu Cui & Jingcheng Wang, 2019. "An Optimization Study on Soot-Blowing of Air Preheaters in Coal-Fired Power Plant Boilers," Energies, MDPI, vol. 12(5), pages 1-15, March.
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    Cited by:

    1. Fangshu Cui & Sheng Qin & Jing Zhang & Mengwei Li & Yuanhao Shi, 2022. "A Hybrid Method for Prediction of Ash Fouling on Heat Transfer Surfaces," Energies, MDPI, vol. 15(13), pages 1-15, June.

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