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首页> 《中国测试》期刊 >本期导读>拉曼光谱法快速测定汽油中芳烃和烯烃含量研究

拉曼光谱法快速测定汽油中芳烃和烯烃含量研究

2991    2015-09-09

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作者:徐贺明, 李建华, 崔建方, 梁逸曾

作者单位:1. 北京化工大学化学工程学院, 北京 100029;
2. 开滦煤化工研发中心, 河北 唐山 063611;
3. 中南大学化学化工学院, 湖南 长沙 410083


关键词:拉曼光谱;汽油;芳烃;烯烃;特征波段


摘要:

该研究采用拉曼光谱法对汽油中的总芳烃和总烯烃含量进行直接测定,通过随机蛙跳方法选择特征波段并采用偏最小二乘法建立模型。总芳烃模型的测试集相关系数为0.985,预测均方根误差为1.08,总烯烃模型的测试集相关系数为0.942,预测均方根误差为0.78。结果表明模型相关性较好,可满足实际应用需求,为拉曼光谱在汽油成分检测中的应用奠定基础。


Rapid determination of aromatic hydrocarbon and olefin hydrocarbon content in gasoline by raman spectroscopy

XU Heming, LI Jianhua, CUI Jianfang, LIANG Yizeng

1. College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China;
2. Coal Chemical R&D Center of Kailuan Group, Tangshan 063611, China;
3. College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China

Abstract: In this paper, the Raman spectroscopy was used to determine the contents of aromatic hydrocarbon and olefin hydrocarbon in gasoline. Random Frog method was employed to select the Raman bands and the best model was established on the basis of optimized conditions. The correlation coefficients of prediction were 0.985 and 0.942 for aromatic hydrocarbon and olefin hydrocarbon, respectively. The RMSEP (Root Mean Square Error of Prediction) was 1.08 and 0.78, respectively.The results show this model has good linear correlation and adapt to the practical application demand,lay a foundation for gasoline composition detection used raman spectroscopy.

Keywords: raman spectroscopy;gasoline;aromatic hydrocarbon;olefinic hydrocarbon;raman bands

2015, 41(8): 40-43  收稿日期: 2015-1-3;收到修改稿日期: 2015-3-17

基金项目: 国家自然科学基金项目(21275164)

作者简介: 徐贺明(1962-),男,高级工程师,主要从事能源化工技术及管理工作。

参考文献

[1] GB/T 17930—2011车用汽油[S]. 北京:中国质检出版社,2011.
[2] 徐广通,杨玉蕊,陆婉珍. 多维气相色谱快速测定汽油中的烯烃、芳烃和苯含量[J]. 石油炼制与化工,2003,134(3):61-65.
[3] GB/T 11132—2002液体石油产品烃类测定法(荧光指示剂吸附法)[S]. 北京:中国标准出版社,2002.
[4] 林艺玲. 成品汽油关键成分的拉曼光谱分析[D]. 杭州:浙江大学,2011:136-139.
[5] 陆婉珍. 现代近红外光谱分析技术[M]. 北京:中国石化出版社,2006:77.
[6] Cooper J B, Wise K L, Welch W, et al. Comparison of Near-IR, raman, and Mid-IR spectroscopies for the determination of BTEX in petroleum fuels[J]. Applied Spectroscopy,1997,51(11):1613-1620.
[7] Vianney O S, Flavia C C, Daniella G L, et al. A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis[J]. Analytica Chimica Acta,2005(547):188-196.
[8] 包鑫,戴连奎. 汽油多参数拉曼光谱分析仪的稳健支持向量机方法[J]. 仪器仪表学报,2009,30(9):1829-1835.
[9] 梁逸曾,许青松. 复杂体系仪器分析-白灰黑分析体系及其多变量解析方法[M]. 北京:化学工业出版社,2012:42-63.
[10] Li H D, Xu Q S, Liang Y Z. Random frog: An efficient reversible jump markov chain monte carlo-like approach for variable selection with applications to gene selection and disease classification[J]. Analytica Chimica Acta,2012(740):20-26.
[11] Yun Y H, Li H D, Wood L R, et al. An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration[J]. Spectrochim Acta A Mol Biomol Spectrosc,2013(111):31-36.
[12] Galvao R K H. A method for calibration and validation subset partitioning[J]. Talanta,2005,67(4):736.
[13] Hansen S B. The application of raman spectroscopy for analysis of multi-component systems[D]. Copenhagen: Technical University of Denmark,2000.
[14] 张华. 现代有机波谱分析[M]. 北京:化学工业出版社,2005:336-347.