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

XIAO Xiongliang; CHEN Changming

Volume 43 Issue 12

Dec. 2021

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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.

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.

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

School of Electronic Science and Engineering, Hunan Institute of Information Technology, Changsha 410151, China

Received Date: 2021-02-07
Rev Recd Date: 2021-05-01
Publish Date: 2021-12-20

Abstract

Abstract

A non-dispersive infrared absorption system comprising a light source, gas chamber, detector, and controller is designed, and multi-component gases of different concentrations (including ethanol, carbon dioxide, and water vapor) are injected into the chamber. An infrared spectrometer is used to collect the spectral data, and amulti-component gas mixture spectrum isobtained. A multiple linear regression model is established based on the regression coefficients of the dataset samples, and interference correction is performed to reduce the effect of carbon dioxide and water vapor on the concentration of ethanol. The established multiple linear regression model is evaluated, and the results indicate that the model is reliable and effective with a good linear regression effect. The model can be used to predict the gas concentration, and the prediction errors of ethanol, carbon dioxide, and water vapor concentration are within an acceptable range. The prediction error of ethanol concentration is the minimum, which is less than 2.0×10^-4. The interference of carbon dioxide and water vapor can be mostly eliminated through interference correction. Importantly, the ethanol concentration can be predicted more accurately.
- multiple linear regression model,
- prediction of ethanol concentration,
- non-dispersive infrared,
- interference correction

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References(12)

References

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