典型块状煤的可见-近红外光谱特征研究
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摘要
因可见-近红外波段反射光谱测试方便,仪器成本较低,适用于在线分析,为此针对煤在可见-近红外波段的反射光谱曲线特征规律及其产生机理进行了研究分析。从晋、鲁、宁、吉地区煤矿收集了无烟煤、烟煤、褐煤三大类型中的12种典型煤样,按煤阶从高到低具体包括无烟煤一号、无烟煤二号、贫煤、贫瘦煤、瘦煤、焦煤、肥煤、 1/3焦煤、气肥煤、气煤、褐煤一号、褐煤二号,在实验室利用地物光谱仪采集了块状煤样在可见-近红外波段的反射光谱曲线。通过对光谱曲线特征分析,发现无烟煤的反射光谱曲线整体上趋于水平方向,吸收谷特征不明显,随煤阶的降低,光谱反射率、近红外波段光谱斜率整体上呈增加趋势,较明显的吸收谷特征增多且吸收强度增加,有13个较明显的吸收谷特征波段。通过X射线衍射分析(XRD)测定了煤样的碳材料结构和矿物成分,煤非晶质性分子结构的芳构化趋势对煤阶升高时光谱反射率降低、反射曲线趋于平缓起到主要作用。当煤阶降低时,以脂肪侧链为主的有机吸收基团的中红外波段基频在近红外波段的倍频和合频产生众多吸收叠加,绝大多数吸收谷特征不明显,相对较为明显的吸收谷产生在1 700和2 300 nm附近。同时含Fe等过渡金属的矿物、 H_2O、粘土矿物等无机物成分也是煤反射光谱曲线吸收谷特征增多的因素。通过对实验煤样X射线荧光分析(XRF)和工业分析测定了煤样中Fe和Al等矿物元素成分含量和空气干燥基水分、灰分、挥发分、固定碳含量,得出煤反射光谱曲线的近红外波段光谱斜率与挥发分产率、固定碳含量分别呈正、负相关性。H_2O谱带吸收深度之和与内在水分含量线性相关性较好, Fe和Al含量与相关波段吸收谷深度之和基本呈线性关系,而主要由有机基团倍频和合频所产生的1 700和2 300 nm附近两处吸收谷深度之和与挥发分产率线性相关性较差。获得典型块状煤种的可见-近红外波段反射光谱特征,为煤矿区高光谱遥感以及煤光谱数据库的建立提供依据,也为直接利用可见-近红外波段的反射光谱曲线波形特征快速、低成本、定性地识别煤种类提供参考;同时对煤矿用煤炭探测光谱传感器的研制具有重要意义。
Because the visible and near-infrared reflectance spectrum is easily to be acquired using cheap instrument and suitable for online analysis, the features and generation principle of spectral reflectance curve of coal in the visible and near-infrared range were studied and analyzed in this paper. 12 typical types in the three major coal types of anthracite, bituminous and lignite were collected from coal mines in Shanxi, Shandong, Ningxia and Jilin. With decreasing coal rank, these coals included No. 1 anthracite, No.2 anthracite, meager coal, meager lean coal, lean coal, coking coal, fat coal, 1/3 coking coal, gas fat coal, gas coal, No.1 lignite and No.2 lignite. Spectral reflectance curves of these lump coals were acquired in the visible and near-infrared range by a field spectrometer in the laboratory. By analyzing features of these spectral curves, it was found that reflectance curves of anthracites tend to be horizontal in the whole wavelength range and the absorption valleys are not obvious. With decreasing coal rank, spectral reflectance, spectral slope in the near-infrared range, the numbers of obvious absorption valleys and the absorption intensities all increased. Bands of 13 obvious absorption valleys were selected. Carbon structures and mineral compositions of these coal samples were measured through X-ray diffraction(XRD) analyses. With increasing coal rank, the aromatization tendency of amorphous molecular structures of coal plays a major role in reducing the spectral reflectance and flatting the reflectance curve. With decreasing coal rank, overtones and combinations in the near-infrared range generated by the fundamental frequencies of absorption groups mainly including aliphatic side chains in the mid-infrared range generate a lot of absorption superposition. And most of the absorption valleys are not obvious due to the absorption superposition and the relatively more pronounced absorption valleys appear near 1 700 and 2 300 nm. At the same time, transition metals mainly Fe-contained minerals, H_2O, clay minerals and other inorganic components are also the factors that increase the number of absorption valleys of reflectance curve of coal. Through X-ray fluorescence(XRF) and proximate analyses of these coal samples, the contents of major mineral elements such as Fe and Al and the contents of moisture, ash, volatile, and fixed carbon based on air-dried basis were acquired. It was shown that the spectral slope of reflectance curve of coal in the near-infrared range is positively and negatively correlated with volatile yield and fixed carbon content respectively. The sum of absorption depths of H_2O bands is well linearly correlated with intrinsic moisture content. There is a basically linear relationship between Fe or Al content and the sum of absorption depths of the relevant bands. However, there is a poorly linear relationship between volatile yield and the sum of depths of 1 700 and 2 300 nm absorption valleys which are mainly caused by overtones and combinations of the organic fundamental absorption bands. The acquisition of reflectance spectrum features of typical lump coals in the visible and near-infrared range provides the basis for hyperspectral remote sensing of coal mine areas and establishment of spectral library of coals, and also the reference for the rapid, low cost and qualitative identification of coal categories by shapes of spectral reflectance curves directly. At the same time, it is of great significance to the development of spectral sensor for coal detection in coal mine.
Because the visible and near-infrared reflectance spectrum is easily to be acquired using cheap instrument and suitable for online analysis, the features and generation principle of spectral reflectance curve of coal in the visible and near-infrared range were studied and analyzed in this paper. 12 typical types in the three major coal types of anthracite, bituminous and lignite were collected from coal mines in Shanxi, Shandong, Ningxia and Jilin. With decreasing coal rank, these coals included No. 1 anthracite, No.2 anthracite, meager coal, meager lean coal, lean coal, coking coal, fat coal, 1/3 coking coal, gas fat coal, gas coal, No.1 lignite and No.2 lignite. Spectral reflectance curves of these lump coals were acquired in the visible and near-infrared range by a field spectrometer in the laboratory. By analyzing features of these spectral curves, it was found that reflectance curves of anthracites tend to be horizontal in the whole wavelength range and the absorption valleys are not obvious. With decreasing coal rank, spectral reflectance, spectral slope in the near-infrared range, the numbers of obvious absorption valleys and the absorption intensities all increased. Bands of 13 obvious absorption valleys were selected. Carbon structures and mineral compositions of these coal samples were measured through X-ray diffraction(XRD) analyses. With increasing coal rank, the aromatization tendency of amorphous molecular structures of coal plays a major role in reducing the spectral reflectance and flatting the reflectance curve. With decreasing coal rank, overtones and combinations in the near-infrared range generated by the fundamental frequencies of absorption groups mainly including aliphatic side chains in the mid-infrared range generate a lot of absorption superposition. And most of the absorption valleys are not obvious due to the absorption superposition and the relatively more pronounced absorption valleys appear near 1 700 and 2 300 nm. At the same time, transition metals mainly Fe-contained minerals, H_2O, clay minerals and other inorganic components are also the factors that increase the number of absorption valleys of reflectance curve of coal. Through X-ray fluorescence(XRF) and proximate analyses of these coal samples, the contents of major mineral elements such as Fe and Al and the contents of moisture, ash, volatile, and fixed carbon based on air-dried basis were acquired. It was shown that the spectral slope of reflectance curve of coal in the near-infrared range is positively and negatively correlated with volatile yield and fixed carbon content respectively. The sum of absorption depths of H_2O bands is well linearly correlated with intrinsic moisture content. There is a basically linear relationship between Fe or Al content and the sum of absorption depths of the relevant bands. However, there is a poorly linear relationship between volatile yield and the sum of depths of 1 700 and 2 300 nm absorption valleys which are mainly caused by overtones and combinations of the organic fundamental absorption bands. The acquisition of reflectance spectrum features of typical lump coals in the visible and near-infrared range provides the basis for hyperspectral remote sensing of coal mine areas and establishment of spectral library of coals, and also the reference for the rapid, low cost and qualitative identification of coal categories by shapes of spectral reflectance curves directly. At the same time, it is of great significance to the development of spectral sensor for coal detection in coal mine.
引文
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[3] 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.
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[5] Xie Xing,Zhao Yan,Qiu Penghua,et al.Fuel,2018,216:521.
[6] Cloutis E A.Fuel,2003,82(18):2239.
[7] Andrés J M,Bona M T.Analytica Chimica Acta,2005,535(1):123.
[8] YAN Shou-xun,ZHANG Bing,ZHAO Yong-chao,et al(燕守勋,张兵,赵永超,等).Remote Sensing Technology and Application(遥感技术与应用),2003,18(4):191.
[9] Li Wu,Zhu Yanming,Wang Geoff,et al.Fuel,2016,185:298.
[10] Kim D W,Lee J M,Kim J S.Korean Journal of Chemical Engineering,2009,26(2):489.
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