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中红外光谱技术对乙醇汽油乙醇含量的检测

欧阳爱国 张宇 程梦杰 王海阳 刘燕德

欧阳爱国, 张宇, 程梦杰, 王海阳, 刘燕德. 中红外光谱技术对乙醇汽油乙醇含量的检测[J]. 中国光学(中英文), 2017, 10(6): 752-759. doi: 10.3788/CO.20171006.0752
引用本文: 欧阳爱国, 张宇, 程梦杰, 王海阳, 刘燕德. 中红外光谱技术对乙醇汽油乙醇含量的检测[J]. 中国光学(中英文), 2017, 10(6): 752-759. doi: 10.3788/CO.20171006.0752
OUYANG Ai-guo, ZHANG Yu, CHENG Meng-jie, WANG Hai-yang, LIU Yan-de. Determination of the content of ethanol in ethanol gasoline using mid-infrared spectroscopy[J]. Chinese Optics, 2017, 10(6): 752-759. doi: 10.3788/CO.20171006.0752
Citation: OUYANG Ai-guo, ZHANG Yu, CHENG Meng-jie, WANG Hai-yang, LIU Yan-de. Determination of the content of ethanol in ethanol gasoline using mid-infrared spectroscopy[J]. Chinese Optics, 2017, 10(6): 752-759. doi: 10.3788/CO.20171006.0752

中红外光谱技术对乙醇汽油乙醇含量的检测

doi: 10.3788/CO.20171006.0752
基金项目: 

国家自然科学基金资助项目 61640417

江西省协同创新中心计划项目 gjgz[2014]60

详细信息
    作者简介:

    欧阳爱国(1968-), 男, 江西南昌人, 学士, 教授, 主要从事车辆性能检测方面的研究。E-mail:ouyangaiguo1968711@163.com

    刘燕德(1967—)女,江西泰和人,博士,教授,博士生导师,主要从事农产品监测方面的研究

    通讯作者:

    刘燕德, E-mail:jxliuyd@163.com

  • 中图分类号: O657.33;TE626.9

Determination of the content of ethanol in ethanol gasoline using mid-infrared spectroscopy

Funds: 

National Natural Science Foundation of China 61640417

Collaborative Innovation Center of Jiangxi Province gjgz[2014]60

More Information
  • 摘要: 乙醇汽油是一种新型清洁燃料,燃料乙醇在乙醇汽油中的含量会影响发动机的性能。为了确保发动机的工作可靠性,需要对乙醇汽油中的乙醇含量进行快速精准检测。本文使用中红外光谱技术对采集到的乙醇汽油的光谱数据进行定量分析。首先对原始光谱数据使用多元散射校正、基线校正、一阶导数、二阶导数等预处理方法进行预处理。然后利用ELM、LSSVM、PLS对乙醇汽油中的乙醇含量建立预测模型,通过比较3种建模方法对乙醇含量的预测能力发现,PLS方法的精度比其余两种方法更高。模型决定因子R2为0.958,预测均方误差RMSEP为1.479%(V/V,体积比)。中红外光谱技术对乙醇汽油乙醇含量的快速准确检测提供了新的思路。

     

  • 图 1  乙醇汽油原始光谱图

    Figure 1.  Mid-infrared spectra of ethanol gasoline

    图 2  ELM对乙醇含量预测的散点图

    Figure 2.  Scatter plots of prediction results of ethanol content by ELM

    图 3  LSSVM预测乙醇含量的散点图

    Figure 3.  Scatter plots of prediction results of ethanol content by LSSVM

    图 4  均方根误差随着主成分因子变化

    Figure 4.  RMSE varies with principal component factor

    图 5  PLS预测乙醇含量的散点图

    Figure 5.  Scatter plots of prediction results of ethanol content by PLS

    表  1  30组乙醇汽油样品浓度

    Table  1.   Concentrations of 30 groups of ethanol gasoline samples

    (%, V/V)
    样品序号 浓度 样品序号 浓度 样品序号 浓度
    1 0.8 11 9.0 21 17.4
    2 1.6 12 10.0 22 18.2
    3 2.4 13 10.8 23 19.0
    4 3.2 14 11.6 24 20.0
    5 4.0 15 12.4 25 20.8
    6 5.0 16 13.2 26 21.6
    7 5.8 17 14.0 27 22.4
    8 6.6 18 15.0 28 23.2
    9 7.4 19 15.8 29 24.0
    10 8.2 20 16.6 30 25.0
    下载: 导出CSV

    表  2  ELM建立的乙醇汽油中乙醇含量的模型结果

    Table  2.   Prediction results of ethanol content in ethanol gasoline by ELM method

    预处理方法 sin sig hardlim
    ib RMSEP/%
    (V/V)
    R2 ib RMSEP/%
    (V/V)
    R2 ib RMSEP/%
    (V/V)
    R2
    Original spectral 23 1.657 0.817 78 1.621 0.812 69 1.573 0.919
    MSC 30 1.681 0.801 71 1.658 0.791 67 1.623 0.903
    Baseline 24 1.630 0.862 64 1.608 0.869 47 1.631 0.896
    1st derivatives 20 1.696 0.769 18 1.725 0.766 20 1.698 0.850
    2nd derivatives 15 1.758 0.690 28 1.779 0.682 24 1.725 0.798
    下载: 导出CSV

    表  3  LSSVM建立的乙醇汽油中乙醇含量模型的预测结果

    Table  3.   Prediction results of ethanol content in ethanol gasoline by LSSVM model

    预处理方法 Lin-kernel RBF-kernel
    γ RMSEP/%(V/V) R2 γσ2 RMSEP/%(V/V) R2
    Original spectra 0.408 3.388 0.931 568 910, 148.13 2.332 0.945
    MSC 0.011 3.725 0.891 42.358, 731.80 3.010 0.929
    Baseline 0.124 3.620 0.928 16.416, 492.81 3.112 0.902
    1st derivatives 1.875×109 3.271 0.893 1 267 600, 182 430 3.271 0.894
    2nd derivatives 3.63×109 3.386 0.890 952 810, 560 680 3.386 0.891
    下载: 导出CSV

    表  4  PLS建立的乙醇汽油乙醇含量的模型结果

    Table  4.   Prediction results of ethanol content in ethanol gasoline by PLS model

    预处理方法 因子数 校正集 预测集
    相关系数 均方根误差/%(V/V) 相关系数 均方根误差/%(V/V)
    Original spectra 7 0.940 1.731 0.900 2.290
    MSC 11 0.956 1.433 0.913 1.941
    Baseline 7 0.963 1.417 0.958 1.479
    1st derivatives 4 0.959 1.457 0.938 1.719
    2nd derivatives 3 0.961 1.301 0.897 2.245
    下载: 导出CSV

    表  5  3种模型对比结果

    Table  5.   Comparison results of three kinds of models

    预处理方法 建模方法 参数 R2 RMSEP/%(V/V)
    Original spectral ELM hardlim, ib=69 0.919 1.573
    Original spectral LSSVM RBF, γ=5.69×105, σ2=148.13 0.945 2.332
    Baseline PLS Pc=7 0.958 1.479
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-11
  • 修回日期:  2017-08-13
  • 刊出日期:  2017-12-01

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