样品测试条件对近红外煤炭品质分析结果的影响  

Effects of sample test conditions on analysis results of coal qualities based on near infra-red method

作　　者：徐志彬[1,2] 马敬环[1] 李翠[3] 陈凡[3] 

机构地区：[1]天津工业大学材料科学与工程学院,天津300387 [2]河北出入境检验检疫局检验检疫技术中心曹妃甸分中心,河北唐山063611 [3]中国矿业大学信息与电气工程学院,江苏徐州221008

出　　处：《工矿自动化》2015年第6期70-73,共4页Journal Of Mine Automation

基　　金：国家质检总局科研计划资助项目(2014IK270);中国博士后科学基金资助项目(2014M551695);江苏省自然科学基金资助项目(BK20140215)

摘　　要：针对样品堆积造成的近红外光谱散射、吸光度与噪声干扰差异,使得光谱的信噪比发生改变而产生分析误差的问题,研究了样品状态和测试条件对近红外分析结果的影响。采集了样品在不同装样厚度、装样次数和不同装样松紧程度条件下的近红外光谱,采用主成分分析压缩数据,建立遗传算法BP神经网络模型。通过比较不同样品状态模型的预测性能,确立了最佳的样品测试条件。实验表明重复装样和样品松紧程度不会明显改善模型预测性;在装样厚度为0.5mm时,水分、灰分、挥发分和发热量预测模型的决定系数分别为0.936 6,0.791 6,0.894 9,0.857 5,模型预测性能较好。To solve the problem of signal-noise ratio change caused by spectroscopy absorbance, scattering and noise interference resulting from sample accumulation, which caused analysis error, effects of sample state and test condition on near infra-red analysis results were studied, near infra-red spectrograms were collected under different thickness, loading times and different loading tightness, and the data were compressed using principal component analysis. BP neural network models were established based on genetic algorithm, and the prediction performance of different sample state models were compared by determination coefficient, and the sample test conditions were optimized. The experimental results show that repeated loading times and sample tightness will not significantly improve predictive capability of the model. While the sample loading thickness is 0.5 mm, the determination coefficient of testing set R2 of moisture, ash, volatile matter and heat prediction model respectively are 0. 936 6, 0. 791 6, 0. 894 9 and 0. 857 5, which show good performace of the model.

关 键 词：煤炭品质 近红外光谱 样品测试条件 BP神经网络 遗传算法 

分 类 号：TD67[矿业工程—矿山机电]