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A novel particle size distribution correction method based on image processing and deep learning for coal quality

Rui Gao1, Jiaxin Yin1, Ruonan Liu1

  • 1State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan, 030006, China; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, 030006, China.

Talanta
|December 22, 2024
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Summary

This study introduces a novel particle size correction method using image processing and deep learning to improve coal quality analysis with near-infrared (NIRS) and X-ray fluorescence (XRF) spectroscopy. The method significantly enhances accuracy and repeatability by mitigating particle size effects on spectral measurements.

Keywords:
CNNNIRSParticle size distributionSAMSTNXRF

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Data Science

Background:

  • Combined near-infrared (NIRS) and X-ray fluorescence (XRF) spectroscopy offers robust coal quality analysis.
  • Particle size variations negatively impact NIRS and XRF accuracy and repeatability.
  • Existing methods lack effective mitigation strategies for particle size effects in spectral coal analysis.

Purpose of the Study:

  • To develop and validate an innovative particle size correction method for NIRS-XRF coal analysis.
  • To enhance the accuracy and repeatability of coal quality predictions by addressing particle size variations.
  • To improve the adaptability of spectral analysis models for diverse coal particle sizes.

Main Methods:

  • Integration of image processing and deep learning techniques for particle size distribution analysis.
  • Microscopic image capture and Segment Anything Model (SAM) for binarization and particle representation.
  • Spatial Transformer Network (STN) for geometric correction and Convolutional Neural Network (CNN) for feature extraction and error correlation modeling.

Main Results:

  • Significant reduction in prediction errors: Standard Deviation (SD) decreased from 0.321% to 0.229%, Mean Absolute Error (MAE) from 0.317% to 0.225%, and Root Mean Square Error of Prediction (RMSEP) from 0.335% to 0.257%.
  • Error reduction percentages of 64.06% for SD, 50% for MAE, and 60.80% for RMSEP compared to pre-correction values.
  • Demonstrated improvement in model repeatability and accuracy, effectively mitigating sub-millimeter particle size effects.

Conclusions:

  • The proposed deep learning and image analysis method effectively corrects for particle size variations in NIRS-XRF coal analysis.
  • This approach significantly enhances the accuracy, repeatability, and adaptability of spectral quality detection models.
  • The automated analysis and real-time correction hold promise for online quality detection technologies for bulk materials.