近红外光谱煤岩识别装置研制  

Development of coal and rock identification device based on near-infrared spectroscopy

作　　者：吕渊博 王世博[1] 葛世荣 周悦 王赛亚 柏永泰 LYU Yuanbo;WANG Shibo;GE Shirong;ZHOU Yue;WANG Saiya;BAI Yongtai(School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou 221116,China;School of Mechanical Electronic&Infbrmation Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China;Department of Electrical and Electronic Engineering,Suzhou College of Infbnnation Technology,Suzhou 215200,China;Research and Development Center,CAS Nanjing Astronomical Instruments Co.,Ltd.,Nanjing 210042,China;Xuzhou Huada Electromechanical Technology Co.,Ltd.,Xuzhou 221116,China)

机构地区：[1]中国矿业大学机电工程学院,江苏徐州221116 [2]中国矿业大学(北京)机电与信息工程学院,北京100083 [3]苏州信息职业技术学院电气与电子工程系,江苏苏州215200 [4]中科院南京天文仪器有限公司研发中心,江苏南京210042 [5]徐州华达机电科技有限公司,江苏徐州221100

出　　处：《工矿自动化》2022年第7期32-42,共11页Journal Of Mine Automation

基　　金：国家重点研发计划项目(2018YFC0604503);国家自然科学基金联合基金项目(U1610251,51874279);江苏省高校优势学科建设工程项目(PAPD)

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:1300—1500,1800—2000,2100—2300 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。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 highspeed 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 2100-2300 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 t

关 键 词：放顶煤开采 煤岩识别 近红外光谱 标准光谱库 光谱曲线 余弦角算法 相关系数 

分 类 号：TD67[矿业工程—矿山机电]