Prediction of the derived cetane number and carbon/hydrogen ratio from infrared spectroscopic data

Abstract

A model for the prediction of the derived cetane number (DCN) and carbon/hydrogen ratio (C/H) of hydrocarbon mixtures, diesel fuels, and diesel−gasoline blends has been developed on the basis of infrared (IR) spectroscopy data of pure components. IR spectra of 65 neat hydrocarbon species were used to generate spectra of 127 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 44 real fuels were calculated using n-paraffin, isoparaffin, olefin, naphthene, aromatic, and oxygenate (PIONA-O) class averages of pure components. It is shown that this strategy retains knowledge of C/H, an important indicator of the chemical structure. Three methods were compared to assess the prediction of DCN and C/H ratio from the assembled IR spectra, i.e., partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). It was found that ANNs gave the best performance with DCN prediction errors of ±1.1 on average and C/H prediction errors of ∼0.8%. Lasso-regularized linear models were also used to find simple combinations of wavenumbers that yield acceptable estimations.