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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Coal is a crucial energy source, necessitating precise component analysis for efficient utilization.
  • Traditional methods for detecting coal's sulfur content are destructive, slow, and expensive.
  • Existing methods do not meet the demands of modern industry for rapid and accurate testing.

Purpose of the Study:

  • To develop an innovative, non-destructive method for detecting sulfur content in coal.
  • To apply machine learning algorithms to laser-induced breakdown spectroscopy (LIBS) data for improved coal analysis.
  • To enhance the accuracy and efficiency of coal component detection.

Main Methods:

  • Laser-induced breakdown spectroscopy (LIBS) was used to acquire spectral data from coal samples.
  • Wavelet transform was employed for denoising spectral data.
  • Principal component analysis (PCA) was utilized for dimensionality reduction.
  • A detection model combining a two hidden layer extreme learning machine (TELM) with the hippopotamus optimization (HO) algorithm was constructed.

Main Results:

  • The HO-TELM algorithm demonstrated significantly improved accuracy in detecting coal component content compared to standard ELM algorithms.
  • Sensitivity analysis confirmed the robustness of the developed model.
  • The proposed method provides an efficient and reliable approach for intelligent coal resource detection.

Conclusions:

  • Machine learning, particularly the HO-TELM algorithm, offers a superior alternative for sulfur content detection in coal using LIBS.
  • This approach addresses the limitations of traditional destructive testing methods.
  • The study presents a viable solution for intelligent and precise coal analysis in industrial settings.