Fast and Nondestructive Proximate Analysis of Coal from Hyperspectral Images with Machine Learning and Combined Spectra-Texture Features
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Hyperspectral Image Collection
2.2.1. Image Acquisition
2.2.2. Data Preprocessing
2.3. Data Processing and Modeling Methods
2.3.1. Spectra Data Processing
2.3.2. Feature Wavelengths Selection
2.3.3. Extraction of Image Texture Features
2.4. Model Development
2.5. Model Performance Evaluation
2.6. Shapley Additive Explanations (SHAP)
2.7. Software
3. Results
3.1. Statistics Analysis of Coal Indices Reference
3.2. Spectra Analysis and Preprocessing Results
3.3. Results of Feature Wavelengths Extracted in Coal Spectra
3.4. Results of Combined Features by Wavelengths and Texture Information
3.5. Influence of Variables on Coal Quality Indices
3.6. Visualization of Coal Quality Indices Distribution
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Models | Hyperparameter List | Range |
---|---|---|
PLSR | n_compoments | [2, min (5, n 1)] |
RF | n_estimators | [55,300] step = 50 |
max_depth | [2,23] step = 1 | |
BPNN | Input_dim | n |
hidden_dim | m1 | |
output_dim | 1 | |
learning_rate | [0.001, 0.5] | |
LSSVR | Kernel | ‘linear’, ‘rbf’ |
C | [0.1, 30.0] | |
Gamma | [Sqrt(n)/2, Sqrt(n), Sqrt(n)×2] |
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Featured Wavelength (nm) | Groups or Ions | References |
---|---|---|
1150 | Electron leaps in -dominated metals | [34] |
1400 | Combined bands of vibrations | [34] |
1700 | First overtones of symmetric/asymmetric stretch. | [34,35] |
1900 | Combined bands of vibrations | [35] |
2150 | Combination of aromatic C-H stretch and C-C stretch | [35] |
2200 | Combination Al-OH vibration | [35,36] |
2300 | Combination of asymmetric stretch and bend | [34,35] |
Coal Quality Indices | Calibration Set (46) | Prediction Set (15) | ||||||
---|---|---|---|---|---|---|---|---|
Min | Max | Mean | Min | Max | Mean | |||
Ash (%) | 17.79 | 60.24 | 36.48 | 11.51 | 22.56 | 57.70 | 34.16 | 8.06 |
MC (%) | 0.38 | 3.60 | 1.71 | 0.79 | 0.38 | 2.82 | 1.25 | 0.80 |
VM (%) | 9.54 | 30.49 | 20.00 | 6.52 | 10.5 | 30.18 | 21.97 | 5.77 |
FC (%) | 29.12 | 54.34 | 41.60 | 5.25 | 31.44 | 49.63 | 43.23 | 3.81 |
CV () | 11.61 | 26.55 | 19.96 | 3.87 | 12.75 | 25.05 | 21.41 | 3.00 |
Coal Quality Indices | Spectra | PCs | Calibration Set | Prediction Set | ||
---|---|---|---|---|---|---|
Ash (%) | OR | 7 | 0.957 | 2.297 | 0.930 | 2.242 |
SG | 7 | 0.955 | 2.333 | 0.929 | 2.250 | |
FD | 7 | 0.995 | 0.741 | 0.971 | 1.723 | |
MSC | 7 | 0.985 | 1.387 | 0.972 | 1.342 | |
VM (%) | OR | 7 | 0.964 | 1.255 | 0.930 | 1.378 |
SG | 7 | 0.963 | 1.272 | 0.928 | 1.399 | |
FD | 7 | 0.996 | 0.436 | 0.948 | 1.030 | |
MSC | 7 | 0.982 | 0.919 | 0.969 | 0.801 | |
MC (%) | OR | 7 | 0.755 | 0.418 | 0.803 | 0.300 |
SG | 7 | 0.747 | 0.424 | 0.803 | 0.300 | |
FD | 4 | 0.961 | 0.167 | 0.473 | 0.438 | |
MSC | 6 | 0.806 | 0.384 | 0.581 | 0.366 | |
FC (%) | OR | 7 | 0.895 | 1.678 | 0.812 | 1.618 |
SG | 7 | 0.890 | 1.724 | 0.828 | 1.550 | |
FD | 7 | 0.982 | 0.699 | 0.850 | 1.534 | |
MSC | 7 | 0.942 | 1.256 | 0.859 | 1.426 | |
CV (MJ/kg) | OR | 16 | 0.999 | 0.116 | 0.908 | 0.869 |
SG | 10 | 0.981 | 0.537 | 0.964 | 0.545 | |
FD | 7 | 0.992 | 0.343 | 0.967 | 0.516 | |
MSC | 13 | 0.995 | 0.283 | 0.973 | 0.440 |
Coal Quality Indices | Methods | Number of Wavelengths | Modeling | Calibration Set | Prediction Set | |||||
---|---|---|---|---|---|---|---|---|---|---|
Ash (%) | SPA | 6 | PLSR | 0.838 | 4.502 | 0.799 | 5.024 | 0.740 | 4.176 | 2.032 |
RF | 0.981 | 1.534 | 0.904 | 3.462 | 0.940 | 2.008 | 4.225 | |||
BPNN | 0.992 | 0.995 | 0.873 | 3.988 | 0.885 | 2.785 | 3.047 | |||
LSSVR | 0.978 | 1.670 | 0.967 | 2.043 | 0.942 | 1.975 | 4.296 | |||
CARS | 65 | PLSR | 0.945 | 2.627 | 0.924 | 3.094 | 0.932 | 2.184 | 3.971 | |
RF | 0.985 | 1.362 | 0.872 | 4.020 | 0.921 | 2.359 | 3.676 | |||
BPNN | 0.992 | 0.924 | 0.819 | 4.789 | 0.754 | 4.159 | 2.085 | |||
LSSVR | 0.997 | 0.636 | 0.987 | 1.278 | 0.984 | 1.057 | 8.202 | |||
Boruta | 12 | PLSR | 0.976 | 1.809 | 0.971 | 1.980 | 0.951 | 1.455 | 4.656 | |
RF | 0.988 | 1.275 | 0.919 | 3.319 | 0.963 | 1.256 | 5.392 | |||
BPNN | 0.974 | 1.883 | 0.904 | 3.614 | 0.786 | 3.027 | 2.237 | |||
LSSVR | 0.977 | 1.790 | 0.971 | 2.004 | 0.949 | 1.481 | 4.572 | |||
iVISSA | 84 | PLSR | 0.980 | 1.592 | 0.968 | 1.998 | 0.974 | 1.364 | 6.400 | |
RF | 0.986 | 1.306 | 0.906 | 3.435 | 0.930 | 2.228 | 3.919 | |||
BPNN | 0.986 | 1.291 | 0.826 | 4.666 | 0.699 | 4.626 | 1.887 | |||
LSSVR | 0.992 | 1.008 | 0.980 | 1.571 | 0.971 | 1.425 | 6.128 | |||
VM (%) | SPA | 10 | PLSR | 0.936 | 1.723 | 0.905 | 2.096 | 0.888 | 1.388 | 3.094 |
RF | 0.975 | 1.068 | 0.812 | 2.958 | 0.847 | 1.623 | 2.647 | |||
BPNN | 0.951 | 1.502 | 0.888 | 2.276 | 0.909 | 1.249 | 3.438 | |||
LSSVR | 0.965 | 1.274 | 0.935 | 1.744 | 0.945 | 0.977 | 4.398 | |||
CARS | 26 | PLSR | 0.905 | 2.056 | 0.864 | 2.462 | 0.829 | 1.896 | 2.504 | |
RF | 0.954 | 1.435 | 0.682 | 3.766 | 0.824 | 1.922 | 2.470 | |||
BPNN | 0.983 | 0.855 | 0.868 | 2.430 | 0.782 | 2.140 | 2.218 | |||
LSSVR | 0.991 | 0.647 | 0.977 | 1.014 | 0.971 | 0.787 | 6.030 | |||
Boruta | 11 | PLSR | 0.801 | 3.012 | 0.772 | 3.227 | 0.923 | 1.288 | 3.725 | |
RF | 0.955 | 1.434 | 0.807 | 2.970 | 0.982 | 0.621 | 7.729 | |||
BPNN | 0.967 | 1.224 | 0.900 | 2.142 | 0.856 | 1.760 | 2.725 | |||
LSSVR | 0.893 | 2.213 | 0.867 | 2.469 | 0.967 | 0.846 | 5.669 | |||
iVISSA | 136 | PLSR | 0.912 | 1.956 | 0.872 | 2.366 | 0.887 | 1.738 | 3.076 | |
RF | 0.938 | 1.643 | 0.573 | 4.323 | 0.790 | 2.368 | 2.257 | |||
BPNN | 0.985 | 0.740 | 0.812 | 2.870 | 0.707 | 2.797 | 1.911 | |||
LSSVR | 0.989 | 0.701 | 0.941 | 1.607 | 0.929 | 1.373 | 3.894 | |||
MC (%) | SPA | 4 | PLSR | 0.656 | 0.491 | 0.608 | 0.525 | 0.704 | 0.359 | 1.902 |
RF | 0.832 | 0.343 | 0.398 | 0.651 | 0.584 | 0.425 | 1.605 | |||
BPNN | 0.878 | 0.293 | 0.731 | 0.435 | 0.770 | 0.313 | 2.178 | |||
LSSVR | 0.695 | 0.463 | 0.635 | 0.507 | 0.771 | 0.314 | 2.174 | |||
CARS | 25 | PLSR | 0.672 | 0.496 | 0.560 | 0.574 | 0.601 | 0.373 | 1.640 | |
RF | 0.781 | 0.405 | 0.276 | 0.737 | −0.255 | 0.662 | 0.924 | |||
BPNN | 0.844 | 0.341 | 0.586 | 0.557 | 0.539 | 0.401 | 1.524 | |||
LSSVR | 0.965 | 0.163 | 0.904 | 0.268 | 0.772 | 0.282 | 2.165 | |||
Boruta | 8 | PLSR | 0.566 | 0.546 | 0.510 | 0.580 | 0.661 | 0.430 | 1.778 | |
RF | 0.754 | 0.411 | 0.516 | 0.577 | 0.784 | 0.343 | 2.225 | |||
BPNN | 0.816 | 0.354 | 0.413 | 0.635 | 0.473 | 0.535 | 1.426 | |||
LSSVR | 0.643 | 0.495 | 0.546 | 0.558 | 0.714 | 0.395 | 1.934 | |||
iVISSA | 91 | PLSR | 0.717 | 0.460 | 0.641 | 0.518 | 0.363 | 0.451 | 1.297 | |
RF | 0.778 | 0.407 | 0.438 | 0.648 | 0.394 | 0.440 | 1.329 | |||
BPNN | 0.821 | 0.366 | 0.648 | 0.513 | 0.083 | 0.541 | 1.081 | |||
LSSVR | 0.771 | 0.414 | 0.657 | 0.506 | 0.449 | 0.419 | 1.395 | |||
FC (%) | SPA | 6 | PLSR | 0.802 | 2.308 | 0.731 | 2.694 | 0.845 | 1.539 | 2.631 |
RF | 0.971 | 0.877 | 0.789 | 2.385 | 0.830 | 1.614 | 2.507 | |||
BPNN | 0.864 | 1.913 | 0.727 | 2.715 | 0.236 | 3.418 | 1.184 | |||
LSSVR | 0.877 | 1.818 | 0.810 | 2.262 | 0.794 | 1.774 | 2.283 | |||
CARS | 35 | PLSR | 0.898 | 1.752 | 0.829 | 2.269 | 0.121 | 1.857 | 1.104 | |
RF | 0.959 | 1.108 | 0.715 | 2.929 | 0.208 | 1.763 | 1.163 | |||
BPNN | 0.896 | 1.764 | 0.774 | 2.608 | −3.790 | 4.333 | 0.473 | |||
LSSVR | 0.981 | 0.755 | 0.925 | 1.503 | 0.649 | 1.172 | 1.748 | |||
Boruta | 14 | PLSR | 0.888 | 1.789 | 0.846 | 2.099 | 0.865 | 1.179 | 2.813 | |
RF | 0.966 | 0.979 | 0.767 | 2.582 | 0.909 | 0.964 | 3.439 | |||
BPNN | 0.801 | 2.383 | 0.603 | 3.375 | 0.717 | 1.703 | 1.947 | |||
LSSVR | 0.894 | 1.743 | 0.845 | 2.105 | 0.884 | 1.093 | 3.033 | |||
iVISSA | 104 | PLSR | 0.819 | 2.231 | 0.720 | 2.776 | 0.817 | 1.539 | 2.422 | |
RF | 0.937 | 1.312 | 0.533 | 3.584 | 0.816 | 1.543 | 2.414 | |||
BPNN | 0.863 | 1.938 | 0.622 | 3.224 | 0.042 | 3.523 | 1.058 | |||
LSSVR | 0.899 | 1.665 | 0.799 | 2.353 | 0.878 | 1.256 | 2.965 | |||
CV (MJ/kg) | SPA | 8 | PLSR | 0.886 | 1.304 | 0.855 | 1.471 | 0.897 | 0.938 | 3.232 |
RF | 0.973 | 0.637 | 0.935 | 0.983 | 0.935 | 0.745 | 4.066 | |||
BPNN | 0.959 | 0.786 | 0.919 | 1.096 | 0.933 | 0.756 | 4.007 | |||
LSSVR | 0.983 | 0.510 | 0.973 | 0.630 | 0.973 | 0.480 | 6.315 | |||
CARS | 48 | PLSR | 0.953 | 0.842 | 0.923 | 1.074 | 0.947 | 0.646 | 4.490 | |
RF | 0.981 | 0.535 | 0.867 | 1.413 | 0.911 | 0.835 | 3.474 | |||
BPNN | 0.988 | 0.428 | 0.801 | 1.732 | 0.653 | 1.651 | 1.757 | |||
LSSVR | 0.995 | 0.267 | 0.981 | 0.535 | 0.991 | 0.260 | 11.156 | |||
Boruta | 14 | PLSR | 0.974 | 0.636 | 0.964 | 0.750 | 0.956 | 0.550 | 4.958 | |
RF | 0.975 | 0.619 | 0.886 | 1.334 | 0.937 | 0.663 | 4.113 | |||
BPNN | 0.962 | 0.774 | 0.919 | 1.127 | 0.909 | 0.793 | 3.439 | |||
LSSVR | 0.975 | 0.626 | 0.964 | 0.755 | 0.962 | 0.512 | 5.321 | |||
iVISSA | 122 | PLSR | 0.874 | 1.357 | 0.826 | 1.596 | 0.768 | 1.425 | 2.148 | |
RF | 0.916 | 1.109 | 0.423 | 2.904 | 0.447 | 2.200 | 1.392 | |||
BPNN | 0.988 | 0.397 | 0.683 | 2.152 | 0.367 | 2.354 | 1.301 | |||
LSSVR | 0.987 | 0.441 | 0.949 | 0.860 | 0.945 | 0.696 | 4.401 |
Coal Quality Indices | Feature Data | Number of Features | Calibration Set | Prediction Set | |||||
---|---|---|---|---|---|---|---|---|---|
Ash (%) | HS | 50 | 0.986 | 1.331 | 0.852 | 4.326 | 0.783 | 3.901 | 2.223 |
GLEM | 44 | 0.980 | 1.661 | 0.580 | 7.591 | 0.644 | 3.569 | 1.736 | |
Gabor | 37 | 0.992 | 1.027 | 0.947 | 2.601 | 0.811 | 3.452 | 2.379 | |
Combined | 71 | 0.998 | 0.476 | 0.986 | 1.341 | 0.993 | 0.659 | 12.217 | |
VM (%) | HS | 41 | 0.980 | 0.880 | 0.822 | 2.623 | 0.846 | 2.371 | 2.634 |
GLEM | 44 | 0.991 | 0.616 | 0.856 | 2.414 | 0.892 | 1.999 | 3.149 | |
Gabor | 49 | 0.994 | 0.509 | 0.923 | 1.878 | 0.927 | 1.135 | 3.842 | |
Combined | 43 | 0.999 | 0.230 | 0.994 | 0.497 | 0.989 | 0.583 | 9.904 | |
MC (%) | HS | 37 | 0.963 | 0.152 | 0.730 | 0.412 | 0.583 | 0.486 | 1.603 |
GLEM | 18 | 0.856 | 0.299 | 0.638 | 0.476 | 0.502 | 0.563 | 1.467 | |
Gabor | 33 | 0.991 | 0.075 | 0.923 | 0.218 | 0.957 | 0.174 | 4.991 | |
Combined | 56 | 0.995 | 0.055 | 0.961 | 0.154 | 0.979 | 0.112 | 7.159 | |
FC (%) | HS | 41 | 0.865 | 1.960 | 0.472 | 3.875 | 0.514 | 2.259 | 1.485 |
GLEM | 62 | 0.967 | 0.975 | 0.357 | 4.315 | −0.932 | 2.847 | 0.745 | |
Gabor | 22 | 0.942 | 1.311 | 0.736 | 2.794 | −0.904 | 2.261 | 0.750 | |
Combined | 78 | 0.989 | 0.553 | 0.923 | 1.438 | 0.948 | 0.838 | 4.544 | |
CV (MJ/kg) | HS | 37 | 0.979 | 0.567 | 0.874 | 1.391 | 0.565 | 1.481 | 1.570 |
GLEM | 49 | 0.970 | 0.665 | 0.595 | 2.452 | 0.646 | 1.771 | 1.741 | |
Gabor | 56 | 0.992 | 0.365 | 0.864 | 1.487 | 0.619 | 0.994 | 1.676 | |
Combined | 45 | 0.998 | 0.157 | 0.989 | 0.409 | 0.994 | 0.217 | 13.849 |
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Mao, J.; Zhao, H.; Xie, Y.; Wang, M.; Wang, P.; Shi, Y.; Zhao, Y. Fast and Nondestructive Proximate Analysis of Coal from Hyperspectral Images with Machine Learning and Combined Spectra-Texture Features. Appl. Sci. 2024, 14, 7920. https://doi.org/10.3390/app14177920
Mao J, Zhao H, Xie Y, Wang M, Wang P, Shi Y, Zhao Y. Fast and Nondestructive Proximate Analysis of Coal from Hyperspectral Images with Machine Learning and Combined Spectra-Texture Features. Applied Sciences. 2024; 14(17):7920. https://doi.org/10.3390/app14177920
Chicago/Turabian StyleMao, Jihua, Hengqian Zhao, Yu Xie, Mengmeng Wang, Pan Wang, Yaning Shi, and Yusen Zhao. 2024. "Fast and Nondestructive Proximate Analysis of Coal from Hyperspectral Images with Machine Learning and Combined Spectra-Texture Features" Applied Sciences 14, no. 17: 7920. https://doi.org/10.3390/app14177920