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A Hybrid Method for Prediction of Ash Fouling on Heat Transfer Surfaces

Author

Listed:
  • Fangshu Cui

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

  • Sheng Qin

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

  • Jing Zhang

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

  • Mengwei Li

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

  • Yuanhao Shi

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
    Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Soot blowing optimization is a key, but challenging question in the health management of coal-fired power plant boiler. The monitoring and prediction of ash fouling for heat transfer surfaces is an important way to solve this problem. This study provides a hybrid data-driven model based on advanced machine-learning techniques for ash fouling prediction. First, the cleanliness factor is utilized to represent the level of ash fouling, which is the original data from the distributed control system. The wavelet threshold denoising algorithm is employed as the data preprocessing approach. Based on the empirical mode decomposition (EMD), the denoised cleanliness factor data is decoupled into a series of intrinsic mode functions (IMFs) and a residual component. Second, the support vector regression (SVR) model is used to fit the residual, and the Gaussian process regression (GPR) model is applied to estimate the IMFs. The cleanliness factor data of ash accumulation on the heat transfer surface of diverse devices are deployed to appraise the performance of the proposed SVR + GPR model in comparison with the sole SVR, sole GPR, SVR + EDM and GPR + EDM models. The illustrative results prove that the hybrid SVR + GPR model is superior to other models and can obtain satisfactory effects both in one-step- and the multistep-ahead cleanliness factor predictions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4658-:d:847693
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. 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.
    5. Shuiguang Tong & Xiang Zhang & Zheming Tong & Yanling Wu & Ning Tang & Wei Zhong, 2019. "Online Ash Fouling Prediction for Boiler Heating Surfaces based on Wavelet Analysis and Support Vector Regression," Energies, MDPI, vol. 13(1), pages 1-20, December.
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