• 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