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Online Ash Fouling Prediction for Boiler Heating Surfaces based on Wavelet Analysis and Support Vector Regression

Author

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  • Shuiguang Tong

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xiang Zhang

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zheming Tong

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yanling Wu

    (School of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Ning Tang

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Wei Zhong

    (School of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Depending on its operating conditions, traditional soot blowing is activated for a fixed time. However, low-frequency soot blowing can cause heat transfer efficiency to decrease. High-frequency soot blowing not only wastes high-pressure steam, but also abrades surface pipes, reducing the working life of a heat exchange device. Therefore, it is necessary to design an online ash fouling monitoring system to perform soot blowing that is dependent on the status of ash accumulation. This study presents an online monitoring model of ash-layer thermal resistance that reflects the degree of ash fouling. A wavelet threshold denoising algorithm was applied to smooth the thermal resistance of the ash layer calculated by the heat balance mechanism model. Thus, the variation in thermal resistance becomes more visible, which is more conducive to optimizing the operation of soot blowing. The designed Support Vector Regression (SVR) model could achieve the online prediction of thermal resistance denoising for low-temperature superheaters. Experimental analysis indicates that the prediction accuracy was 98.5% during the testing phase. By using the method proposed in this study, online monitoring of heating surfaces during the ash fouling process can be realized without adding complicated and expensive equipment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:59-:d:300414
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    References listed on IDEAS

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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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