基于反射光谱的煤岩感知实验研究  

Experimental study on coal-rock perception based on reflectance spectroscopy

作　　者：杨恩 王世博[1] 葛世荣[1] 向阳 YANG En;WANG Shibo;GE Shirong;XIANG Yang(School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]中国矿业大学机电工程学院

出　　处：《煤炭学报》2019年第12期3912-3920,共9页Journal of China Coal Society

基　　金：国家自然科学基金联合基金资助项目(U1610251);国家重点研发计划资助项目(2018YFC0604503);江苏省高校优势学科建设工程资助项目(PAPD)

摘　　要：为研究利用反射光谱进行煤岩感知识别,收集了同一综采工作面煤层与顶板交界处外观较为相似的炭质页岩和烟煤块状试样75个,在实验室搭建了由近红外分光光谱仪、卤钨光源、光纤准直镜、Y型光纤等部件组成的煤岩反射光谱采集实验装置。根据常见顶板高度,设置光纤准直镜与试样距离为3 m,采集了试样表面近红外波段(1000~2500 nm)反射光谱,然后测定了试样表面光谱采集区域的灰分产率。使用一阶微分(FD)、二阶微分(SD)、连续统去除(CR)、标准正态变量变换(SNV)4种方法对13点Savitzky-Golay(SG)卷积去噪后的试样光谱反射率曲线进行了预处理,对其中50个煤岩试样的灰分产率与其预处理后光谱进行了相关性分析,得到最大相关系数为0.777,由连续统去除预处理方法获得,其所在波长点为1698 nm,位于与煤岩主要有机成分有关的1700 nm光谱带附近。取此50个试样最大相关系数波长点1698 nm左右区间[1693 nm,1703 nm]共11个波长点处的连续统去除预处理光谱值,及其灰分产率、煤岩类型,建立了支持向量煤岩灰分回归(SVR)、支持向量煤岩分类(SVC)模型,2种原位煤岩感知识别模型对其余25个测试煤岩试样的预测精度分别为92%,96%,同时对单个样本识别总耗时均小于0.1 s,其中,支持向量煤岩灰分回归模型对此25个测试煤岩试样表面灰分产率的预测均方根误差(RMSE)达到了5%,决定系数达到0.88。In order to study the perception and recognition of coal and rock using reflectance spectroscopy,75 carbonaceous shale and bituminous coal samples with similar appearance were collected from the boundary of coal seam and roof in the same fully mechanized coal face.An experimental device for collecting the reflectance spectra of these coal and rock samples was built in the laboratory,which consists of one near-infrared spectrometer,four tungsten halogen lights,one fiber collimator,one Y-type fiber and so on.According to the heights of common seam roofs,near-infrared(1000-2500 nm)reflectance spectra of the surfaces of these samples were obtained with the distance of 3 m between sample and fiber collimator.Ash yield of spectral acquisition region in the surface of each sample was then measured.Four methods including first derivative(FD),second derivative(SD),continuum removal(CR)and standardized normal variate(SNV)were employed to preprocess the spectral reflectance curves of these samples after Savitzky-Golay(SG)convolution denoising with 13 points.The correlations between ash yields and preprocessed spectra of 50 out of the 75 samples were analyzed.It was found that the maximum correlation coefficient is 0.777 obtained by CR,and its wavelength point falls at 1698 nm very approaching to the spectral band of 1700 nm which is related to the main organic components of coal and rock.Based on the continuum removal spectra at 11 wavelength points in the left and right interval-[1693 nm,1703 nm]of 1698 nm with the maximum correlation coefficient,ash yields and coal-rock categories of the 50 samples,the models of support vector regression(SVR)of coal-rock ash yields and support vector classification(SVC)of coal-rock categories were established.With the two models of in-situ coal-rock perception and recognition used,the prediction accuracies of the remaining 25 test coal and rock samples were 92%and 96%,respectively,and the total time taken for single sample recognition was both less than 0.1 s.Meanwhile,the root mean squar

关 键 词：反射光谱 近红外 煤岩感知识别 灰分产率 支持向量机 

分 类 号：TD67[矿业工程—矿山机电]