基于近红外光谱的煤挥发分检测  

Detection of coal volatile matter by near-infrared spectroscopy

作　　者：肖锋[1] 翟阳阳[2] 武宝明 王雅圣[1] 魏高 孟国营 翟红 李光[1] 

机构地区：[1]浙江大学工业控制技术国家重点实验室智能系统与控制研究所,浙江杭州310027 [2]首都医科大学,北京100069 [3]汾西矿业(集团)有限责任公司,山西介休032000

出　　处：《激光与红外》2013年第8期908-912,共5页Laser & Infrared

摘　　要：近红外光谱分析是一种新型的在线分析技术,十分适合于例如煤的挥发分等有机物的含量测定。本研究选取139个煤样,采集煤样的近红外漫反射光谱,利用偏最小二乘算法进行回归建模。同时采用不同的光谱波段筛选和预处理方法对模型进行优化,取得一定效果。最后针对肥煤单独进行回归分析,模型效果进一步优化,显示了分类建模能提高模型的预测能力。Near infrared spectroscopy is a new on-line analysis technology, which is fit for the detection of organic substance, such as volatile matter of coal. In this paper, 139 coal samples were chosen to acquire diffuse reflectance near-infrared spectra. Then the spectra were correlated to volatile matter of coal with partial least squares. At the same time, different spectral band selecting and pre-treatment methods were used to optimize regression model. At last, the regression model was built for fat coal, and the model result was significantly improved. It shows that regres- sion model of homogeneous set can improve the prediction ability.

关 键 词：近红外漫反射 煤挥发分 偏最小二乘回归 预处理 分类 

分 类 号：O657.33[理学—分析化学]