By Topic

Prediction of Gas Chromatographic Retention Index for Hydrocarbons in FCC Gasoline

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Ling Ding ; Liaoning Key Lab. of Petrochem. Eng., Liaoning Shihua Univ., Fushun, China ; Xiaotong Zhang ; Zhaolin Sun ; Lijuan Song
more authors

A series of hydrocarbons in FCC gasoline have been used to develop quantitative structure-retention relationships (QSRR) for their gas chromatographic retention index (RI) by using molecular descriptors which were calculated by Dragon software. QSRR models were built by adopting multiple linear regression (MLR) and artificial neural network (ANN). However, the results showed more or less the same quality with the predictive correlation coefficient R of 0.9952 and 0.9953 for MLR and ANN respectively. The obtained results told us that linear method is good enough to model the gas chromatographic retention index at least to the current dataset.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:1 )

Date of Conference:

March 31 2009-April 2 2009