Determination of Olefin Hydrocarbons in Gasoline by Partial Least-Squares

Author

Jun, Gao ; Yandong, Song ; Jibin, Ding ; Bo, Zheng ; Jiangtao, Xu

Author_Institution

Binjiang Coll., Nanjing Univ. of Inf. Sci. &Technol., Nanjing, China

Volume

2

fYear

2010

fDate

11-12 May 2010

Firstpage

79

Lastpage

81

Abstract

By using near-infrared technology, a method is presented to determine the alkenes content in gasoline. There are 30 gasoline samples to be used as trained set for building calibration model and their alkenes hydrocarbons content is determined by GC. In the region of 1000-2000nm, the near-infrared spectroscopy is optimized by Fourier transformation. According to the optimized data, the cross validated and partial least-squares regression is performed to establish the model. In training set, the average error and square correlation coefficient is 0.2% and 0.9881, average error in test set was 0.39, which shows good correlation and predictability.

Keywords

chromatography; infrared spectroscopy; least squares approximations; organic compounds; petroleum; regression analysis; GC; alkene hydrocarbons; gas chromatography; gasoline; near infrared technology; olefin hydrocarbons; partial least square regression; square correlation coefficient; wavelength 1000 nm to 2000 nm; Automation; Calibration; Computer industry; Educational institutions; Gas industry; Hydrocarbons; Mechanical engineering; Petroleum; Spectroscopy; Testing; gasoline; near-infrared spectrum; olefin hydrocarbons; partial least-squares regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.728

Filename

5522778