DocumentCode :
3584157
Title :
Determination of octane number of gasoline by double ANN algorithm combined with multidimensional gas chromatography
Author :
Liu, Ming-yang ; Zhou, Peng ; Kong, Ping ; Yang, Chun-guang ; Li, Gang ; Mu, Ming-ren
Author_Institution :
Centre of Tech., Liaoning Entry-Exit Inspection & Quarantine Bur., Dalian, China
Volume :
3
fYear :
2010
Firstpage :
1640
Lastpage :
1642
Abstract :
In this paper, a double artificial neural network (ANN) algorithm has been established for calculating the octane number (ON) of gasoline from the results of multidimensional gas chromatography analysis. Multidimensional resolution column was applied to obtain the results of the detailed hydrocarbon analysis. The double ANN regression model has been established between the results of the detailed hydrocarbon analysis and the actually determined research octane number (RON). When the method was applied to determine RON of export gasoline samples, the deviation of results was about 0.5 RON compared with the standard method. The result of double ANN regression model was better than the result of partial least square (PLS) regression model. This method was easy to manipulate, and the modelling process was fast and easy to achieve. It was suitable for measuring the ON of the gasoline samples from the refinery and the export inspection.
Keywords :
chromatography; least squares approximations; neural nets; petroleum; regression analysis; ANN regression model; PLS regression model; RON; artificial neural network; double ANN algorithm; gasoline octane number; hydrocarbon analysis; multidimensional gas chromatography; multidimensional resolution column; partial least square regression model; refinery; research octane number; Analytical models; Artificial neural networks; Gas chromatography; Hydrocarbons; Inspection; Training; Double ANN; gasoline; multidimensional GC; octane number;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5583775
Filename :
5583775
Link To Document :
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