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Coal classification and analysis based on shadowgraphy and deep learning methods.

Tong Peng, Junrong Feng, Wen Yi

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    Shadowgraphy with automated laser imaging and convolutional neural networks (CNNs) accurately classifies coal types (98.38%) and predicts key components. This advancement aids energy production and resource management.

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    Area of Science:

    • Materials Science
    • Analytical Chemistry
    • Optical Physics

    Background:

    • Accurate coal classification and component analysis are vital for energy production and resource management.
    • Traditional methods for analyzing energetic materials can be complex or limited in resolution.
    • Shadowgraphy offers a potentially simpler approach by visualizing shockwave-induced optical property changes.

    Purpose of the Study:

    • To develop an automated system for high-resolution imaging of laser-induced shock waves.
    • To classify coal types and predict their key components using shadowgram analysis.
    • To evaluate the accuracy and applicability of this novel method for material analysis.

    Main Methods:

    • An automated laser excitation and image acquisition system using optical fibers was designed.
    • High-resolution shadowgrams of laser-induced shock wave propagation were captured (nanosecond to microsecond timescales).
    • A convolutional neural network (CNN) was utilized for analyzing shadowgrams and predicting coal properties.

    Main Results:

    • The CNN achieved a coal classification accuracy of 98.38% across 29 different coal types.
    • Accurate predictions for ash content (RMSEP 1.75%), volatile matter (RMSEP 1.04%), and fixed carbon (RMSEP 2.74%) were obtained.
    • The imaging system provided high resolution without the trade-offs seen in traditional high-speed cameras.

    Conclusions:

    • Automated shadowgraphy coupled with CNN analysis is a highly effective method for coal classification and component prediction.
    • This technique offers a powerful, non-destructive approach for rapid material screening and identification in laboratory settings.
    • The developed system demonstrates significant potential for improving coal analysis efficiency and accuracy.