Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography

The objective of the present study is to develop robust statistical models for the prediction of critical diesel properties such as cloud point, pour point, and cetane index with composition inputs such as n-Paraffins, Iso-paraffins, Naphthenes, and Aromatics (PINA) obtained by flow modulated two-dimensional gas chromatography with flame ionization detection (GC×GC-FID). A single gas chromatographic measurement coupled with models to predict the key physical properties is attractive for refiners to make quick decisions in optimizing diesel blending. We present a partial least-squares (PLS) linear regression statistical model that has been developed using 41 data sets of diesel samples with different compositions, out of which 33 samples were used for the calibration and eight samples for validation of the model. The R2 values obtained for cloud point, pour point, and cetane index were 0.92, 0.93, and 0.92 with standard deviations of 1.20, 1.50, and 0.40, respectively. The average relative errors for predi...