Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system

Fuel, Volume 97, July 2012, Pages 710–717

Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system

Julio Cesar L. Alves a, Claudete B. Henriques b, Ronei J. Poppi a

a Institute of Chemistry, University of Campinas – UNICAMP, P.O. Box 6154, 13083-970 Campinas, SP, Brazil

b Planalto Refinery-REPLAN – Petrobras, Paulinia, SP, Brazil

Abstract

Demonstrates the application of support vector regression (SVR) applied to near infrared spectroscopy (NIR) data to solve regression problems associated to determination of quality parameters of diesel oil for an in-line blending optimizer system in a petroleum refinery.

Investigators determined the flash point and cetane number using SVR.  Results were compared with those obtained using the PLS algorithm.  They performed parametric optimization using a genetic algorithm to choose the parameters in the SVR regression models. The best models using SVR presented a RBF kernel and spectra preprocessed with baseline correction and mean centered data.  The SVR provided significantly better results when compared with PLS.

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