|
|
|
|
|
|
| Classification Method of Coal and Gangue Based on Hyperspectral
Imaging Technology |
| LI Lian-jie1, 2, FAN Shu-xiang2, WANG Xue-wen1, LI Rui1, WEN Xiao1, WANG Lu-yao1, LI Bo1* |
1. College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
|
|
|
|
|
Abstract The separation of coal and gangue is a crucial step in coal mining, but the existing methods such as manual selection and mechanical separation are ineffective and environmentally hazardous. This study aimed to explore the feasibility of the accurate classification of coal and gangue with black background based on the visible and near-infrared hyperspectral imaging technology, simplify classification models using feature selection methods, and provide a reference for constructing a multispectral system for coal and gangue separation. Hyperspectral imaging technology is a fast and non-destructive detection method without sample pretreatment and environmental contamination. Firstly, a hyperspectral imaging system was developed to collect hyperspectral data of 85 coal samples and 83 gangue samples in the range of 400～1 000 nm (Vis/NIR) and 1 000～2 500 nm (NIR) from the XiMing mine. After removing background information of hyperspectral images, the average spectra in the randomly selected regions of 100 pixel×100 pixels in 400～1 000 nm and 50 pixel×50 pixels in 1 000～2 500 nm were extracted. After repeating 10 times, 850 coal spectra and 830 gangue spectra were obtained in each of the two bands. Savitzky-Golay smoothing and standard normal variate transformation were performed successively to reduce the impact of errors and noise on the spectra. Three models, including support vector machine (SVM), k-nearest neighbor (KNN), partial least squares discrimination analysis (PLS-DA), were established based on full-band spectra. The classification accuracy rate of each model for the prediction set was greater than 0.95, which revealed that coal and gangue could be distinguished by spectral information. Subsequently, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were employed to select characteristic wavelengths to simplify models. Considering factors such as accuracy and cost, the SVM model based on the 3 characteristic wavelengths screened by SPA in the Vis/NIR range had the best performance, that not only effectively reduced the number of wavelengths, but also improved the classification capacity and the corresponding sensitivity, specificity, accuracy was: 1.000 0, 0.965 2, 0.983 3, respectively. Based on the discriminant model and the average spectra of the samples, the classification and visualization of coal and gangue can also be realized. The research results have great potential for developing a low-cost and multi-spectral separation system for coal and gangue based on the characteristic wavelengths to achieve fast and accurate non-destructive separation.
|
|
Received: 2021-03-14
Accepted: 2021-05-13
|
|
|
|
Corresponding Authors:
LI Bo
E-mail: libo@tyut.edu.cn
|
|
[1] Zhang N, Liu C. Scientific Reports, 2018, 8(1): 190.
[2] Pu Y, Apel D B, Szmigiel A, et al. Energies, 2019, 12(9): 1735.
[3] Wang S H, Zhao Y, Hu R, et al. Chinese Journal of Analytical Chemistry, 2019, 47(4): E19034.
[4] Le B T, Xiao D, Mao Y, et al. Infrared Physics & Technology, 2018, 93: 34.
[5] Begum N, Maiti A, Chakravarty D, et al. Fuel, 2020, 280: 118676.
[6] SONG Liang, LIU Shan-jun, MAO Ya-chun, et al（宋 亮，刘善军，毛亚纯，等）. Journal of Northeastern University·Natural Science（东北大学学报·自然科学版）, 2017, 38(10): 1473.
[7] Lei M, Yu X, Li M, et al. Infrared Physics & Technology, 2018, 92: 177.
[8] SONG Liang, LIU Shan-jun, YU Mo-li, et al（宋 亮，刘善军，虞茉莉，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2017, 37(2): 416.
[9] Yang E, Ge S, Wang S, et al. Journal of Spectroscopy, 2018, 2018: ID2754908.
[10] Mao Y, Le B T, Xiao D, et al. Optics and Laser Technology, 2019, 114: 10.
[11] Hu F, Zhou M, Yan P, et al. IEEE Access, 2019, 10.1109/ACCESS.2019.2955725.
[12] Lai W, Zhou M, Hu F, et al. IEEE Access, 2020, 10.1109/ACCESS.2020.2983740.
[13] Zou L, Yu X, Li M, et al. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 8043.
[14] Cloutis E A. Fuel, 2003, 82(18): 2239: 1.
|
| [1] |
LIU Yan-de, WANG Shun. Research on Non-Destructive Testing of Navel Orange Shelf Life Imaging Based on Hyperspectral Image and Spectrum Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1792-1797. |
| [2] |
MIAO Shu-guang1, SHAO Dan1*, LIU Zhong-yu2, 3, FAN Qiang1, LI Su-wen1, DING En-jie2, 3. Study on Coal-Rock Identification Method Based on Terahertz
Time-Domain Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1755-1760. |
| [3] |
FAN Qing-jie, SONG Yan, LAI Shi-quan*, YUE Li, ZHU Ya-ming, ZHAO Xue-fei. XRD Structural Analysis of Raw Material Used as Coal-Based Needle Coke in the Coking Process[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1979-1984. |
| [4] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
| [5] |
ZHANG Xiao-hong1, JIANG Xue-song1*, SHEN Fei2*, JIANG Hong-zhe1, ZHOU Hong-ping1, HE Xue-ming2, JIANG Dian-cheng1, ZHANG Yi3. Design of Portable Flour Quality Safety Detector Based on Diffuse
Transmission Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1235-1242. |
| [6] |
XIAO Shi-jie1, WANG Qiao-hua1, 2*, LI Chun-fang3, 4, DU Chao3, ZHOU Zeng-po4, LIANG Sheng-chao4, ZHANG Shu-jun3*. Nondestructive Testing and Grading of Milk Quality Based on Fourier Transform Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1243-1249. |
| [7] |
ZHANG Dian-kai1, LI Yan-hong1*, ZI Chang-yu1, ZHANG Yuan-qin1, YANG Rong1, TIAN Guo-cai2, ZHAO Wen-bo1. Molecular Structure and Molecular Simulation of Eshan Lignite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1293-1298. |
| [8] |
SHI Ji-yong, LIU Chuan-peng, LI Zhi-hua, HUANG Xiao-wei, ZHAI Xiao-dong, HU Xue-tao, ZHANG Xin-ai, ZHANG Di, ZOU Xiao-bo*. Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1299-1305. |
| [9] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, YUAN Rui-rui, SUN You-rui, LI Yue. Rapid Determination of TBARS Contents in Tan Mutton Using Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 713-718. |
| [10] |
YANG En1, WANG Shi-bo2*. Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 847-858. |
| [11] |
WANG Fang-fang1, ZHANG Xiao-dong1, 2*, PING Xiao-duo1, ZHANG Shuo1, LIU Xiao1, 2. Effect of Acidification Pretreatment on the Composition and Structure of Soluble Organic Matter in Coking Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 896-903. |
| [12] |
LI Xue-ping1, 2, 3, ZENG Qiang1, 2, 3*. Development and Progress of Spectral Analysis in Coal Structure Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 350-357. |
| [13] |
QIAO Lu, WANG Song-lei*, GUO Jian-hong, HE Xiao-guang. Combination of Spectral and Textural Informations of Hyperspectral Imaging for Predictions of Soluble Protein and GSH Contents in Mutton[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 176-183. |
| [14] |
YE Rong-ke1, KONG Qing-chen1, LI Dao-liang1, 2, CHEN Ying-yi1, 2, ZHANG Yu-quan1, LIU Chun-hong1, 2*. Shrimp Freshness Detection Method Based on Broad Learning System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 164-169. |
| [15] |
ZHANG Jing1, 2, XU Yang1, JIANG Yan-wu1, ZHENG Cheng-yu2, ZHOU Jun1,2, HAN Chang-jie1*. Recent Advances in Application of Near-Infrared Spectroscopy for Quality Detections of Grapes and Grape Products[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3653-3659. |
|
|
|
|
