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乙醇浓度预测的多元线性回归模型建立及验证

肖雄亮 陈长明

肖雄亮, 陈长明. 乙醇浓度预测的多元线性回归模型建立及验证[J]. 红外技术, 2021, 43(12): 1228-1233.
引用本文: 肖雄亮, 陈长明. 乙醇浓度预测的多元线性回归模型建立及验证[J]. 红外技术, 2021, 43(12): 1228-1233.
XIAO Xiongliang, CHEN Changming. Establishment and Verification of Multivariate Linear Regression Model for Prediction of Ethanol Concentration[J]. Infrared Technology , 2021, 43(12): 1228-1233.
Citation: XIAO Xiongliang, CHEN Changming. Establishment and Verification of Multivariate Linear Regression Model for Prediction of Ethanol Concentration[J]. Infrared Technology , 2021, 43(12): 1228-1233.

乙醇浓度预测的多元线性回归模型建立及验证

基金项目: 

湖南省教育厅科学研究重点项目 20A347

详细信息
    作者简介:

    肖雄亮(1978-),男,副教授,主要研究方向:机械自动化控制、智能算法等。E-mail:teacher_xiao410@126.com

  • 中图分类号: O432

Establishment and Verification of Multivariate Linear Regression Model for Prediction of Ethanol Concentration

  • 摘要: 设计了由光源、气室、探测器和控制器等组成的非分散红外吸收系统,往气室内通入不同浓度的多组分气体(含有乙醇、二氧化碳和水蒸气),采用红外光谱仪进行光谱数据采集,得到多组分气体混合光谱图。根据数据集样本求解回归系数,建立了多元线性回归模型,并进行干扰修正以降低二氧化碳和水蒸气对乙醇浓度预测的影响。对建立的多元线性回归模型进行评价,结果表明:模型真实有效且具有良好的线性回归效果,可以用于预测气体浓度,乙醇、二氧化碳和水蒸气浓度预测误差均在可接受的范围之内,其中乙醇浓度预测误差最小,不超过2.0×10-4。通过干扰修正尽可能排除二氧化碳和水蒸气的干扰,能够较准确地预测乙醇浓度。
  • 图  1  非分散红外吸收系统原理示意图

    Figure  1.  Schematic diagram of non-dispersive infrared absorption system

    图  2  MLR算法和干扰修正流程

    Figure  2.  MLR algorithm and interference correction process

    图  3  某浓度下多组分气体的吸收谱图

    Figure  3.  Absorption spectra of multi-component gas at a certain concentration

    图  4  乙醇浓度预测值与实测值对比

    Figure  4.  Comparison of the predicted value and measured value of ethanol concentration

    图  5  乙醇浓度预测误差

    Figure  5.  Prediction error of ethanol concentration

    图  6  二氧化碳浓度预测值与实测值对比

    Figure  6.  Comparison of the predicted value and measured value of carbon dioxide concentration

    图  7  二氧化碳浓度预测误差

    Figure  7.  Prediction error of carbon dioxide concentration

    图  8  水蒸气浓度预测值与实测值对比

    Figure  8.  Comparison of the predicted value and measured value of vapour concentration

    图  9  水蒸气浓度预测误差

    Figure  9.  Prediction error of vapour concentration

    表  1  各个通道的响应系数估值及置信区间

    Table  1.   The response coefficient estimation and confidence interval of each channel

    Channel Single component gas Response coefficient estimation Confidence interval
    0 Ethanol 0.9864 0.9254-1.0473
    Carbon dioxide 0.0994 0.0321-0.1668
    Water vapor 0.0325 -0.0367-0.1017
    1 Ethanol 0.0617 -0.0268-0.1502
    Carbon dioxide 0.972 0.9056-1.0383
    Water vapor 0.2102 0.1300-0.2905
    2 Ethanol 0.0695 0.0107-0.1283
    Carbon dioxide 0.2298 0.1683-0.2913
    Water vapor 0.762 0.6873-0.8366
    下载: 导出CSV

    表  2  回归模型评价参数

    Table  2.   Evaluation parameters of regression model

    Channel Evaluation parameter Value
    0 R2 0.9932
    F 503.9468
    p 0.0000
    S2 0.0010
    1 R2 0.9846
    F 235.2015
    p 0.0002
    S2 0.0012
    2 R2 0.9901
    F 388.4631
    p 0.0001
    S2 0.0005
    下载: 导出CSV

    表  3  误差评价参数

    Table  3.   Evaluation parameters of error

    Evaluation parameter Value
    MSE 2.7700
    MAE 1.1767
    MAPE 0.0336
    MSPE 0.1077
    RMSE 3.9174
    SSE 30.6926
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-07
  • 修回日期:  2021-05-01
  • 刊出日期:  2021-12-20

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