May 2017

Article Contents

Chinese Optics> 2017> 10(3): 363-369.

OUYANG Ai-guo, TANG Tian-yi, WANG Hai-yang, LIU Yan-de. Detection of key performance indicators of ethanol diesel by the infrared spectroscopy method[J]. Chinese Optics, 2017, 10(3): 363-369. doi: 10.3788/CO.20171003.0363

Citation:

OUYANG Ai-guo, TANG Tian-yi, WANG Hai-yang, LIU Yan-de. Detection of key performance indicators of ethanol diesel by the infrared spectroscopy method[J].Chinese Optics, 2017, 10(3): 363-369.doi:10.3788/CO.20171003.0363

Citation:

OUYANG Ai-guo, TANG Tian-yi, WANG Hai-yang, LIU Yan-de. Detection of key performance indicators of ethanol diesel by the infrared spectroscopy method[J].Chinese Optics, 2017, 10(3): 363-369.doi:10.3788/CO.20171003.0363

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Detection of key performance indicators of ethanol diesel by the infrared spectroscopy method

doi:10.3788/CO.20171003.0363

Institute of Optics-Mechanics-Electronics Technology and Application (OMETA), East China Jiaotong University, Nanchang 330013, China

Funds:

Foundation Project of Intelligent Management Technology and Equipment Collaborative Innovation Center of the Southern Mountain OrchardGan Jiao high [2014]60

Received Date:25 Jan 2017
Rev Recd Date:22 Mar 2017
Publish Date:01 Jun 2017

Abstract

Abstract

Ethanol diesel is one of the alternatives for the diesel, which is used more and more widely. Many indicators reflect the quality of diesel ethanol fuel. It is not only expensive but also time consuming to detect these indicators with traditional method. Near infrared spectroscopy method is an inexpensive, fast and real time online test for the quality of ethanol diesel oil. In this paper, the density, viscosity and the quality of the ethanol content of diesel are detected, and the analysis effect on the quality of ethanol diesel are compared by linear and non-linear calibration technology, including principal component regression, partial least squares regression and least squares support vector machines (LSSVM), and the effects of different pretreatment methods on the prediction model capabilities are also compared. Experimental results show that LSSVM is better than the principal component regression and partial least squares regression model, with the optimal modeling effect on the density of ethanol-diesel, viscosity, alcohol content. The correlation coefficients are 0.995 8, 0.995 7 and 0.995 3, and the root mean square error of prediction are 0.000 68, 0.011 3 and 0.571 4, respectively.
- NIRS,
- ethanol diesel,
- density,
- viscosity,
- ethanol content

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References (19)

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