Development of coal and rock identification device based on near-infrared spectroscopy
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摘要: 目前近红外光谱煤岩识别都是在静态下采集光谱数据进行离线识别，无法适应放顶煤作业时需要实时识别输送机上高速移动煤岩的需求。针对该问题，基于近红外光谱技术研制了一种煤岩识别装置。该装置由数据采集与处理装置和光源探头一体化装置组成，通过光源探头一体化装置搜集煤岩反射光，利用数据采集与处理装置中改进的煤岩识别算法（余弦角算法和相关系数法）分析光谱数据，可在获取到煤岩光谱曲线后立即分析光谱信息并判断当前煤岩类别。为得到改进煤岩识别算法最佳特征波段与标准光谱库大小，通过实验得到了不同特征波段和标准光谱库大小对识别准确度的影响：1 300～1 500，1 800～2 000，2 100～2 300 nm特征宽度适用于大多数煤岩样本，标准光谱库大小与正确率正相关，识别时标准光谱库有必要增加曲线数量。为提高煤岩识别装置采集的光谱质量，在实验室模拟了煤岩与光源探头一体化装置的相对运动，探究了不同光谱采集参数对光谱质量的影响规律：积分时间主要参考光源的光照强度，当采集条件较好时积分时间设置为比下限略高5~10 ms最佳；考虑综放工作面对煤岩识别实时性要求高且放煤过程中刮板输送机上煤岩变化较快，积分次数设置为1最佳；平滑次数主要参考环境波动快慢，只需设置为可消除环境光变化即可。为提高煤岩识别装置在工作面煤流运动状态下识别的准确性，探究了改进余弦角算法与相关系数法在煤岩与光源探头一体化装置相对运动中识别的准确性，得到改进相关系数法是更适合在工作面使用的识别算法，正确率达到91.3%。煤矿现场煤岩识别试验结果表明，该装置在采集到1个放煤周期内放落煤岩的光谱曲线后，可通过改进识别算法立即分析光谱信息并准确判断当前煤岩类别，实现了放煤过程中煤岩实时识别。Abstract: The current near-infrared spectroscopy identification of coal and rock is to collect spectral data in a static state for offline identification. The technology cannot meet the need for real-time identification of high-speed moving coal and rock on conveyor during caving operation. In order to solve this problem, a coal and rock identification device is developed based on near-infrared spectroscopy technology. The device consists of a data acquisition and processing device, and a light source and probe integrated device. The light source and probe integrated device is used to collect the reflected light of coal and rock. The improved coal and rock identification algorithms (cosine angle algorithm and correlation coefficient method) in the data acquisition and processing device is used to analyze the spectrum data. The spectrum information can be analyzed immediately after obtaining a coal and rock spectrum curve. Then the current coal and rock type can be determined. In order to obtain the best characteristic band and standard spectral library size of the improved coal and rock identification algorithms, the effects of different characteristic bands and standard spectral library sizes on the identification accuracy are obtained through experiments. The characteristic widths of 1 300 -1 500 nm, 1 800-2 000 nm and 2 100-2 300 nm are suitable for most coal and rock samples. The size of the standard spectral library is positively correlated with the accuracy. It is necessary to increase the number of curves in the standard spectral library during identification. In order to improve the spectral quality collected by the coal and rock identification device, the relative motion of coal and rock and the light source and probe integrated device is simulated in the laboratory. The influence law of different spectral acquisition parameters on spectral quality is explored. The integration time mainly refers to the light intensity of the light source. When the acquisition conditions are good, the integration time should be set to be slightly higher than the lower limit by 5-10 ms. For the fully mechanized top coal caving face, the real-time requirement of coal and rock identification is high, and the coal and rock on the scraper conveyor change rapidly during the coal caving process. The integration number is set to one for the best. The smoothing times mainly refer to the speed of environmental fluctuation, which can be set to eliminate the change of ambient light. In order to improve the identification accuracy of coal and rock identification device in the coal flow movement state of working face, the identification accuracy of improved cosine algorithm and correlation coefficient method in the relative movement of coal and rock and light source and probe integrated device is explored. The improved correlation coefficient method is more suitable for the identification algorithm used in working face, and the accuracy rate is 91.3%. The results of the coal and rock identification test in coal mine show that after collecting the spectral curves of coal and rock in a coal drawing cycle, the device immediately analyzes the spectral information and determines the current coal and rock category by the improved identification algorithm. The device realizes the real-time identification of coal and rock in the coal drawing process.
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图 8 模拟试验台与煤岩摆放实物
Figure 8. Simulation test bench and physical map of coal and rock placement
图 9 不同积分时间下获取的光谱曲线
Figure 9. Acquired spectral curves under different integration times
图 10 不同积分次数下获取的光谱曲线
Figure 10. Acquired spectral curves under different integration numbers
图 11 不同平滑次数下获取的光谱曲线
Figure 11. Acquired spectral curves under different smoothing times
图 14 煤岩识别装置现场安装
Figure 14. Installation of coal and rock identification device on-site
表 1 煤岩类型、外观、分布
Table 1. Type, appearance and distribution of coal and rock
序号 样本类型 外观特征 分布位置 1 烟煤 灰黑色，密度较大，不易破碎 煤层 2 烟煤 亮黑色，分层结构明显，质地较坚硬 煤层 3 烟煤 暗黑色，条带状结构，局部有反光性 煤层 4 烟煤 暗黑色，层状结构，断口参差状 煤层 5 烟煤 暗黑色，质地较坚硬，易破碎 煤层 6 灰黑色炭
质泥岩深灰泛黑色，层理结构不明显，
粒径较小，易破碎煤层夹矸 7 灰白色高岭
质泥岩浅灰泛白色，断口光滑，硬度较高 煤层夹矸 8 深灰色砂
质泥岩深灰色，层理结构明显，易破碎，
透水性差煤层夹矸 9 深黑色炭
质泥岩整体呈深黑色，层理结构不明显，
致密块状，较坚硬直接顶 10 白色粉砂岩 断面呈白色，粗糙且有砂质感，
性脆，层理结构不明显，砂砾黏结性差直接顶 
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表 2 未改进算法与改进算法识别正确率比较
Table 2. Identification accuracy comparison between unimproved algorithm and improved algorithm
算法 余弦角算法 相关系数法 改进余弦角算法 改进相关系数法 正确率/% 72.55 77.45 95.1 95.1
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表 3 不同特征提取宽度下识别结果比较
Table 3. Identification results comparison under different feature extraction width
识别波段 标准光谱库大小/条 正确率/% 煤 岩 余弦角
算法相关系
数法波段1 11 15 93.12 87.25 波段2 11 15 96.08 94.12 波段3 11 15 93.14 93.14
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表 4 不同大小标准光谱库识别结果比较
Table 4. Identification results comparison of different sizes standard spectral library
标准光谱库
大小/条不同种类条数 识别时间/s 正确率/% 煤 岩 余弦角算法 相关系数法 4 2 2 0.33 53.92 44.12 13 5 8 0.35 95.10 81.37 20 8 12 0.38 96.08 83.33 26 11 15 0.42 96.08 94.12
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表 5 动态采集下煤岩识别正确率
Table 5. Accuracy of coal and rock identification under dynamic acquisition
算法 改进余弦角算法 改进相关系数法 正确率/% 56.52 91.3
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[1] 于斌,徐刚,黄志增,等. 特厚煤层智能化综放开采理论与关键技术架构[J]. 煤炭学报,2019,44(1):42-53. doi: 10.13225/j.cnki.jccs.2018.5050YU Bin,XU Gang,HUANG Zhizeng,et al. Theory and its key technology framework of intelligentized fully-mechanized caving mining in extremely thick coal seam[J]. Journal of China Coal Society,2019,44(1):42-53. doi: 10.13225/j.cnki.jccs.2018.5050 [2] 马英. 基于尾梁振动信号采集的煤矸识别智能放煤方法研究[J]. 煤矿开采,2016,21(4):40-42.MA Ying. Intelligent coal caving with gangue identification based on tail beam vibration signal collection[J]. Coal Mining Technology,2016,21(4):40-42. [3] 魏文艳. 综采工作面放顶煤自动控制系统[J]. 工矿自动化,2015,41(7):10-13.WEI Wenyan. Automatic control system of top coal caving on fully-mechanized coal mining face[J]. Industry and Mine Automation,2015,41(7):10-13. [4] XUE Guanghui,LIU Ermeng,ZHAO Xinying,et al. Coal-rock character recognition in fully mechanized caving faces based on acoustic pressure data time domain analysis[J]. Applied Mechanics and Materials,2015,789/790:566-570. doi: 10.4028/www.scientific.net/AMM.789/790.566 [5] SONG Qingjun,JIANG Haiyan,ZHAO Xieguang,et al. An automatic decision approach to coal-rock recognition in top coal caving based on MF-Score[J]. Pattern Analysis and Applications,2017,20(4):1307-1315. doi: 10.1007/s10044-017-0618-7 [6] 张宁波,鲁岩,刘长友,等. 综放开采煤矸自动识别基础研究[J]. 采矿与安全工程学报,2014,34(4):532-536.ZHANG Ningbo,LU Yan,LIU Changyou,et al. Basic study on automatic detection of coal and gangue in the fully mechanized top coal caving mining[J]. Journal of Mining & Safety Engineering,2014,34(4):532-536. [7] 张宁波,刘长友,陈现辉,等. 综放煤矸低水平自然射线的涨落规律及测量识别分析[J]. 煤炭学报,2015,40(5):988-993.ZHANG Ningbo,LIU Changyou,CHEN Xianhui,et al. Measurement analysis on the fluctuation characteristics of low level natural radiation from gangue[J]. Journal of China Coal Society,2015,40(5):988-993. [8] 朱世刚. 综放工作面煤岩性状识别方法研究[D]. 北京: 中国矿业大学（北京） , 2014.ZHU Shigang. Study on coal and rock character recognition method in fully mechanized caving faces[D]. Beijing: China University of Mining and Technology-Beijing, 2014. [9] 褚小立,史云颖,陈瀑,等. 近五年我国近红外光谱分析技术研究与应用进展[J]. 分析测试学报,2019,38(5):603-611. doi: 10.3969/j.issn.1004-4957.2019.05.016CHU Xiaoli,SHI Yunying,CHEN Pu,et al. Research and application progresses of near infrared spectroscopy analytical technique in China in past five years[J]. Journal of Instrumental Analysis,2019,38(5):603-611. doi: 10.3969/j.issn.1004-4957.2019.05.016 [10] 张玲,邱芳萍,于健. 现代近红外光谱技术[J]. 长春工业大学学报,2003, 24(4): 23-25.ZHANG Ling,QIU Fangping,YU Jian. Modern near-infrared spectroscopic techniques[J]. Journal of Changchun University of Technology,2003, 24(4): 23-25. [11] PASQUINI C. Near infrared spectroscopy:fundamentals,practical aspects and analytical applications[J]. Journal of the Brazilian Chemical Society,2003,14(2):198-219. doi: 10.1590/S0103-50532003000200006 [12] XIU Liancun, ZHENG Zhizhong, CHEN Chunxia, et al. Mineral identification and geological mapping using near-infrared spectroscopy analysis[C]// IEEE International Conference on Progress in Informatics and Computing （PIC）, 2018: 119-123. [13] 宋亮,刘善军,毛亚纯,等. 基于可见光−近红外光谱的煤种分类方法[J]. 东北大学学报(自然科学版),2017,38(10):1473-1476.SONG Liang,LIU Shanjun,MAO Yachun,et al. Coal classification based on visible and near-infrared spectrum[J]. Journal of Northeastern University(Natural Science),2017,38(10):1473-1476. [14] 杨恩,王世博,葛世荣. 典型块状煤的可见−近红外光谱特征研究[J]. 光谱学与光谱分析,2019,39(6):1717-1723.YANG En,WANG Shibo,GE Shirong. Study on the visible and near-infrared spectra of typical of lump coal[J]. Spectroscopy and Spectral Analysis,2019,39(6):1717-1723. [15] 杨恩,王世博,葛世荣. 典型煤系岩石的可见−近红外光谱特征研究[J]. 工矿自动化,2019,45(3):45-51.YANG En,WANG Shibo,GE Shirong. Research on visible-near infrared spectrum features of typical coal measures rocks[J]. Industry and Mine Automation,2019,45(3):45-51. [16] 宋亮,刘善军,虞茉莉,等. 基于可见−近红外和热红外光谱联合分析的煤和矸石分类方法研究[J]. 光谱学与光谱分析,2017,37(2):416-422.SONG Liang,LIU Shanjun,YU Moli,et al. A classification method based on the combination of visible near-infrared and thermal infrared spectrum for coal and gangue distinguishment[J]. Spectroscopy and Spectral Analysis,2017,37(2):416-422. [17] 王赛亚,王世博,葛世荣,等. 综放工作面煤岩近红外光谱特征与机理[J]. 煤炭学报,2020,45(8):3024-3032.WANG Saiya,WANG Shibo,GE Shirong,et al. Study on near-infrared spectrum characteristics and mechanism of and rock in mechanized caving face[J]. Journal of China Coal Society,2020,45(8):3024-3032. [18] 向阳,王世博,葛世荣,等. 粉尘环境下典型煤岩近红外光谱特征及识别方法[J]. 光谱学与光谱分析,2020,40(11):3430-3437.XIANG Yang,WANG Shibo,GE Shirong,et al. Study on near-infrared spectrum features and identification methods of typical coal-rock in dust environment[J]. Spectroscopy and Spectral Analysis,2020,40(11):3430-3437. [19] 韦任,徐良骥,孟雪莹,等. 基于高光谱特征吸收峰的煤岩识别方法[J]. 光谱学与光谱分析,2021,41(6):1942-1948.WEI Ren,XU Liangji,MENG Xueying,et al. Coal and rock identification method based on hyper spectral feature absorption peak[J]. Spectroscopy and Spectral Analysis,2021,41(6):1942-1948. [20] 周悦,王世博,葛世荣,等. 不同探测距离与角度下典型煤岩近红外光谱特征与定性分析[J]. 光谱学与光谱分析,2020,40(9):2737-2742.ZHOU Yue,WANG Shibo,GE Shirong,et al. Near infrared spectral characteristics and qualitative analysis of typical coal-rock under different detection distances and angle[J]. Spectroscopy and Spectral Analysis,2020,40(9):2737-2742. [21] 杨恩,王世博,葛世荣,等. 基于反射光谱的煤岩感知实验研究[J]. 煤炭学报,2019,44(12):3912-3920. doi: 10.13225/j.cnki.jccs.2019.0051YANG En,WANG Shibo,GE Shirong,et al. Experimental study on coal-rock perception based on reflectance spectroscopy[J]. Journal of China Coal Society,2019,44(12):3912-3920. doi: 10.13225/j.cnki.jccs.2019.0051 [22] 徐良骥,孟雪莹,韦任,等. 基于可见光−近红外光谱的煤岩识别方法实验研究[J]. 光谱学与光谱分析,2022,42(7): 2135-2142.XU Liangji,MENG Xueying,WEI Ren,et al. Experimental research on coal-rock identification method based on visible-near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis,2022,42(7): 2135-2142. [23] ZOU Liang,YU Xinhui,LI Ming,et al. Nondestructive identification of coal and gangue via near-infrared spectroscopy based on improved broad learning[J]. IEEE Transactions on Instrumentation and Measurement,2020,69(10):8043-8052. [24] 张昊. 基于高光谱的煤岩识别技术研究[D]. 徐州: 中国矿业大学, 2017: 35-36.ZHANG Hao. Study on identification technology of coal and rock based on hyper-spectrum[D]. Xuzhou: China University of Mining and Technology, 2017: 35-36. -
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