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Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest

Hongkun Du1, Shaoying Ke2, Wei Zhang1

  • 1Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.

Analytical Sciences : the International Journal of the Japan Society for Analytical Chemistry
|June 5, 2024
PubMed
Summary

This study introduces a rapid coal analysis method using laser-induced breakdown spectroscopy and the random forest algorithm. The technique accurately determines coal ash content and calorific value, improving resource utilization.

Keywords:
Coal analysisLaser-induced breakdown spectroscopyRandom forest algorithm

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Spectroscopy

Background:

  • Coal is China's primary energy source, necessitating advanced analysis techniques.
  • Variations in coal quality require rapid and accurate detection methods.
  • Current methods face challenges in speed and precision for coal analysis.

Purpose of the Study:

  • To develop a rapid quantitative analysis method for coal quality.
  • To combine laser-induced breakdown spectroscopy (LIBS) with machine learning for coal analysis.
  • To accurately predict coal ash content and calorific value.

Main Methods:

  • Utilized Q-switched Nd:YAG laser for plasma generation in coal samples.
  • Employed wavelet transform for spectral data preprocessing.
  • Applied random forest algorithm for quantitative analysis and compared with SVM, ANN, and PLS.

Main Results:

  • Optimized wavelet transform parameters (Db4, 3 levels) for enhanced model performance.
  • Achieved high predictive accuracy for coal ash content (R²=0.9470) and calorific value (R²=0.9485).
  • Demonstrated superior performance of the Wavelet Transform-Random Forest model over other methods.

Conclusions:

  • Laser-induced breakdown spectroscopy combined with the random forest algorithm offers an effective approach for rapid and accurate coal analysis.
  • The developed model provides precise coal composition monitoring and analysis.
  • This method enhances coal resource utilization and supports emission reduction efforts.