DocumentCode :
3284103
Title :
Determination of Vapour Pressure of gasoline by double ANN algorithm combined with multidimensional gas chromatography
Author :
Ming-Yang Liu ; Zhao, Jing-hong ; Chen, Xin-Yue ; Wang, Qiu-yan ; Zhou, Zhong-xin
Author_Institution :
Centre of Tech., Liaoning Entry-Exit Inspection & Quarantine Bur., Dalian, China
fYear :
2011
fDate :
15-17 April 2011
Firstpage :
1337
Lastpage :
1339
Abstract :
In this paper, a double artificial neural network (ANN) algorithm has been established for calculating the Vapour Pressure of gasoline from the results of multidimensional gas chromatography analysis. Multidimensional resolution column was applied to obtain the results of the detailed hydrocarbon analysis of gasoline. The double ANN regression model has been established between the results of the detailed hydrocarbon analysis and the actually determined Vapour Pressure. When the method was applied to determine Vapour Pressure of export gasoline samples, the deviation of results was about 0.05 Psi (1 Psi=6.89 KPa) 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 modeling process was fast and easy to achieve. It was suitable for measuring the Vapour Pressure of the gasoline samples from the refinery and the export inspection.
Keywords :
chromatography; neural nets; petroleum; production engineering computing; regression analysis; vapour pressure; artificial neural network; double ANN regression model; gasoline; hydrocarbon analysis; multidimensional gas chromatography; partial least square regression; vapour pressure; Algorithm design and analysis; Artificial neural networks; Gas chromatography; Hydrocarbons; Inspection; Training; Double ANN; Vapour Pressure; gasoline; hydrocarbon analysis; multidimensional GC;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
Type :
conf
DOI :
10.1109/ICEICE.2011.5777798
Filename :
5777798
Link To Document :
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