Study on Coal and Gangue Recognition by Vis-NIR Spectroscopy Under Different Working Conditions
LIU Tao, LI Bo, XIA Rui*, LI Rui, WANG Xue-wen
Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Abstract：In the process of realizing the efficient utilization of coal, coal and gangue separation is a very important step, but the existing coal and gangue separation technology has the problems of resource waste and low efficiency. It can be seen that Vis-NIR(visible near-infrared) spectroscopy identification technology has the advantages of being fast and reliable and has a certain research foundation in the field of coal and gangue recognition, but most of the studies have not been effectively analyzed in combination with actual conditions. Firstly, this paper set up a Vis-NIR spectrum acquisition device in the laboratory to simulate three conditions in the actual environment: different detection angles (0°, 10°, 20°, 30°), different detection distances (10, 15, 20 and 25 cm), and different illumination angles (15°, 25°, 35°, 45°). The spectral data of coal and gangue samples from Ximing Coal Mine in Shanxi are collected in the Vis-NIR spectrum band under the single-factorandmulti-factor conditions of orthogonal experimental design. Secondly, the collected spectral data were analyzed and successively underwent standard normal variable transformation and Savitzky-Golay convolution smoothing to reduce the impact of noise and error on the data. Finally, in the single factor experiment, the spectral data were trained based on five machine learning models, including decision tree (DT), k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and AdaBoost, combined with the preprocessing algorithm. The results of the single factor experiment show that the AdaBoost algorithm adopted in this paper has strong learning ability, and the recognition accuracy of coal and gangue under different working conditions is 100%, which is better than other recognition models. In orthogonal experiments, a support vector machine (SVM) is used as the recognition model for training. The results show that, in the rawand preprocessed data, the three conditions have different degrees of influence on the recognition accuracy of coal and gangue, and the order of influence from large to small is different illumination angles, different detection distances and different detection angles. In the preprocessed data, the optimal combination of working conditions is a detection angle of 0°, detection distance of 20 cm and illumination angle of 35°. At the same time, by comparing the experimental results, it can be found that the appropriate pretreatment and modeling methods can reduce the influence of different working conditions on recognition accuracy. A group of conditions were randomly selected to perform three repeated control trials with the optimal group. The results showed that the optimal group had better recognition performance than the random control group. The research results have reference significance for finding the optimal working conditions of coal and gangue identification and provide a theoretical basis for the practical application of Vis-NIR spectroscopy in coal and gangue identification.
Key words：Vis-NIR spectroscopy; Different working conditions; Coal and gangue identification; AdaBoost;Orthogonal experiment
刘 涛，李 博，夏 蕊，李 瑞，王学文. 不同工况下可见-近红外光谱的煤矸识别研究[J]. 光谱学与光谱分析, 2024, 44(03): 821-828.
LIU Tao, LI Bo, XIA Rui, LI Rui, WANG Xue-wen. Study on Coal and Gangue Recognition by Vis-NIR Spectroscopy Under Different Working Conditions. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 821-828.
[1] Li J, Wang J. Journal of Cleaner Production, 2019, 239: 117946.
[2] Li D, Wu D, Xu F, et al. Journal of Cleaner Production, 2018, 185: 959.
[3] LI Man, DUAN Yong, CAO Xian-gang, et al(李 曼，段 雍，曹现刚，等). Journal of China Coal Society(煤炭学报), 2020, 45(10): 3636.
[4] GUO Yong-cun, HE Lei, LIU Pu-zhuang, et al(郭永存，何 磊，刘普壮，等). Journal of China Coal Society(煤炭学报), 2021, 46(1): 300.
[5] YANG En, WANG Shi-bo, GE Shi-rong(杨 恩，王世博，葛世荣). Journal of Mine Automation(工矿自动化), 2019, 45(3): 45.
[6] Liu W, Peng B, Liu X, et al. Journal of Applied Spectroscopy, 2021, 88(3): 645.
[7] Wang S H, Zhao Y, Hu R, et al. Chinese Journal of Analytical Chemistry, 2019, 47(4): e19034.
[8] Lei M, Yu X, Li M, et al. Infrared Physics & Technology, 2018, 92: 177.
[9] 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 Edition)[东北大学学报（自然科学版)], 2017, 38(10): 1473.
[10] Yang E, Ge S R, Wang S B, et al. Journal of Spectroscopy, 2018, 2018: 2754908.
[11] Le B T, Xiao D, Mao Y, et al. Infrared Physics & Technology, 2018, 93: 34.
[12] ZHOU Yue, WANG Shi-bo, GE Shi-rong, et al(周 悦，王世博，葛世荣，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(9): 2737.
[13] DING Zhen, CHANG Bo-shen(丁 震，常博深). Journal of Mine Automation(工矿自动化), 2021, 47(12): 93.
[14] Zhang L L, Lu Y D, Kang J F, et al. Journal of Materials Science: Materials in Electronics, 2018, 29(7): 5746.
[15] Tang J Y, Henderson A, Gardner P. Analyst, 2021, 146(19): 5880.
