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A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography

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  • Yuhui Wu
  • Xinzhi Zhou
  • Li Zhao
  • Chenlong Dong
  • Hailin Wang

Abstract

Acoustic tomography (AT), as a noninvasive temperature measurement method, can achieve temperature field measurement in harsh environments. In order to achieve the measurement of the temperature distribution in the furnace and improve the accuracy of AT reconstruction, a temperature field reconstruction algorithm based on the radial basis function (RBF) interpolation method optimized by the evaluation function (EF-RBFI for short) is proposed. Based on a small amount of temperature data obtained by the least square method (LSM), the RBF is used for interpolation. And, the functional relationship between the parameter of RBF and the root-mean-square (RMS) error of the reconstruction results is established in this paper, which serves as the objective function for the effect evaluation, so as to determine the optimal parameter of RBF. The detailed temperature description of the entire measured temperature field is finally established. Through the reconstruction of three different types of temperature fields provided by Dongfang Boiler Works, the results and error analysis show that the EF-RBFI algorithm can describe the temperature distribution information of the measured combustion area globally and is able to reconstruct the temperature field with high precision.

Suggested Citation

  • Yuhui Wu & Xinzhi Zhou & Li Zhao & Chenlong Dong & Hailin Wang, 2021. "A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, September.
  • Handle: RePEc:hin:jnlmpe:9922698
    DOI: 10.1155/2021/9922698
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    Cited by:

    1. Qirong Qiu & Wanting Zhou & Qing Zhao & Shi Liu, 2022. "An Explicable Neighboring-Pixel Reconstruction Algorithm for Temperature Distribution by Acoustic Tomography," Energies, MDPI, vol. 15(9), pages 1-16, April.
    2. Yin, Linfei & Zhou, Hang, 2024. "Modal decomposition integrated model for ultra-supercritical coal-fired power plant reheater tube temperature multi-step prediction," Energy, Elsevier, vol. 292(C).

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