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Diesel Oil Quality Parameter Determinations Using Support Vector Regression and near Infrared Spectroscopy for Hydrotreating Feedstock Monitoring

Production monitoring and final quality control of diesel can be performed in refineries using near infrared (NIR) spectroscopy combined with regression algorithms. Partial least squares (PLS) is the multivariate regression approach commonly used for such purposes, but it is deficient for modelling complex data sets, such as found in diesel production at refineries. On the other hand, support vector regression (SVR) has demonstrated greater efficiency with high generalisation performance. The aim of this work was to develop regression models using SVR to improve the effectiveness of determining feedstock quality parameters monitored for hydrotreating process control refinery diesel production. SVR and PLS models were developed for the parameters aniline point, cetane index, density and temperature of distillation (initial boiling point and 50%, 85% and 90% recovered). The results indicate the superior modelling capability of SVR. SVR models predicted test set samples with root mean squares errors which were 21% to 54% lower than those predicted using PLS. The NIR determinations presented root mean square error lower than the reproducibility values specified by the established reference methods.