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.
Bibliographical noteKAUST Repository Item: Exported on 2021-05-04
Acknowledgements: This work was funded by the Office of Sponsored Research at King Abdullah University of Science and Technology
(KAUST). The authors are thankful to Prof. Mani Sarathy and Dr. Abdul Gani Abdul Jameel for helpful discussions. The authors are also thankful to Huda Badghaish for her help with the TOC graphic.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- General Chemical Engineering
- Fuel Technology