IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2019i1p59-d300414.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/1/59/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/1/59/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ming Dong & Sufen Li & Jun Xie & Jian Han, 2013. "Experimental Studies on the Normal Impact of Fly Ash Particles with Planar Surfaces," Energies, MDPI, vol. 6(7), pages 1-18, July.
    2. Chen, Yujiao & Tong, Zheming & Wu, Wentao & Samuelson, Holly & Malkawi, Ali & Norford, Leslie, 2019. "Achieving natural ventilation potential in practice: Control schemes and levels of automation," Applied Energy, Elsevier, vol. 235(C), pages 1141-1152.
    3. 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.
    4. Zheming Tong & Zhewu Cheng & Shuiguang Tong, 2019. "Preliminary Design of Multistage Radial Turbines Based on Rotor Loss Characteristics under Variable Operating Conditions," Energies, MDPI, vol. 12(13), pages 1-15, July.
    5. Du, Mingxi & Wang, Xiaoge & Peng, Changhui & Shan, Yuli & Chen, Huai & Wang, Meng & Zhu, Qiuan, 2018. "Quantification and scenario analysis of CO2 emissions from the central heating supply system in China from 2006 to 2025," Applied Energy, Elsevier, vol. 225(C), pages 869-875.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    4. Ming Dong & Jun Xie & Linying Bai & Sufen Li, 2014. "An Experimental Investigation on the Influence of Temperature on the Normal Impact of Fine Particles with a Plane Surface," Energies, MDPI, vol. 7(4), pages 1-16, March.
    5. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    6. Hang Yin & Yingai Jin & Liang Li & Wenbo Lv, 2022. "Numerical Investigation on the Impact of Exergy Analysis and Structural Improvement in Power Plant Boiler through Co-Simulation," Energies, MDPI, vol. 15(21), pages 1-19, October.
    7. Yu, Ying & Xiang, Tianhui & Wang, Di & Yang, Liu, 2024. "Optimization control strategy for mixed-mode buildings based on thermal comfort model: A case study of office buildings," Applied Energy, Elsevier, vol. 358(C).
    8. Wang, Zhiqi & Xie, Baoqi & Xia, Xiaoxia & Luo, Lan & Yang, Huya & Li, Xin, 2023. "Entropy production analysis of a radial inflow turbine with variable inlet guide vane for ORC application," Energy, Elsevier, vol. 265(C).
    9. Zheming Tong & Zhongqin Yang & Qing Huang & Qiang Yao, 2022. "Numerical Modeling of the Hydrodynamic Performance of Slanted Axial-Flow Urban Drainage Pumps at Shut-Off Condition," Energies, MDPI, vol. 15(5), pages 1-17, March.
    10. Tong, Zheming & Cheng, Zhewu & Tong, Shuiguang, 2021. "A review on the development of compressed air energy storage in China: Technical and economic challenges to commercialization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    12. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
    13. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "Impact of adjustment strategies on building design process in different climates oriented by multiple performance," Applied Energy, Elsevier, vol. 266(C).
    14. Chen, Han & Chen, Wenying, 2019. "Potential impacts of coal substitution policy on regional air pollutants and carbon emission reductions for China's building sector during the 13th Five-Year Plan period," Energy Policy, Elsevier, vol. 131(C), pages 281-294.
    15. 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.
    16. Jun Xie & Haodong Ma & Chenxi Li & Shaobai Li & Zhengren Zhu & Zheng Fu, 2021. "The Critical Capture Velocity of Coal Ash Particles Oblique Impact on a Stainless Steel Surface," Energies, MDPI, vol. 14(17), pages 1-17, August.
    17. Cai, Yongtie & Tay, Kunlin & Zheng, Zhimin & Yang, Wenming & Wang, Hui & Zeng, Guang & Li, Zhiwang & Keng Boon, Siah & Subbaiah, Prabakaran, 2018. "Modeling of ash formation and deposition processes in coal and biomass fired boilers: A comprehensive review," Applied Energy, Elsevier, vol. 230(C), pages 1447-1544.
    18. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    19. Francisco David Moya & José Luis Torres-Moreno & José Domingo Álvarez, 2020. "Optimal Model for Energy Management Strategy in Smart Building with Energy Storage Systems and Electric Vehicles," Energies, MDPI, vol. 13(14), pages 1-18, July.
    20. Lei Tang & Zhengtao Ai & Chunyan Song & Guoqiang Zhang & Zhengxuan Liu, 2021. "A Strategy to Maximally Utilize Outdoor Air for Indoor Thermal Environment," Energies, MDPI, vol. 14(13), pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:59-:d:300414. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.