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Rapid Quantitation of Coal Proximate Analysis by Using Laser-Induced Breakdown Spectroscopy

Author

Listed:
  • Yulin Liu

    (Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China)

  • Dongming Wang

    (Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China)

  • Xiaohan Ren

    (Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China)

Abstract

Proximate analysis of coal is of great significance to ensure the safe and economic operation of coal-fired and biomass-fired power generation units. Laser-induced breakdown spectroscopy (LIBS) assisted by chemometric methods could realize the prediction of coal proximate analysis rapidly, which makes up for the shortcomings of the traditional method. In this paper, three quantitative models were proposed to predict the proximate analysis of coal, including principal component regression (PCR), artificial neural networks (ANNs), and principal component analysis coupled with ANN (PCA-ANN). Three model evaluation indicators, such as the coefficient of determination (R 2 ), root-mean-square error of cross-validation (RMSECV), and mean square error (MSE), were applied to measure the accuracy and stability of the models. The most accurate and stable prediction of coal proximate analysis was achieved by PCR, of which the average R 2 , RMSECV, and MSE values were 0.9944, 0.39%, and 0.21, respectively. Although the R 2 values of ANN and PCA-ANN were greater than 0.9, the higher RMSECV and MSE values indicated that ANN and PCA-ANN were inferior to PCR. Compared with the other two models, PCR could not only achieve accurate prediction, but also shorten the modeling time.

Suggested Citation

  • Yulin Liu & Dongming Wang & Xiaohan Ren, 2022. "Rapid Quantitation of Coal Proximate Analysis by Using Laser-Induced Breakdown Spectroscopy," Energies, MDPI, vol. 15(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2728-:d:789325
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    References listed on IDEAS

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    1. Charbucinski, J. & Nichols, W., 2003. "Application of spectrometric nuclear borehole logging for reserves estimation and mine planning at Callide coalfields open-cut mine," Applied Energy, Elsevier, vol. 74(3-4), pages 313-322, March.
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    Keywords

    LIBS; coal; proximate analysis; PCR; ANN;
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