In the present work, a quantitative structure property relationship (QSPR) methodology has been applied to predict the cetane number (CN) of hydrocarbons that are likely to be found in diesel fuels. A database containing 147 molecules has been set up with experimental CNs available in the literature. The prediction of the CN was improved by dividing the database into four chemical families: (i) linear (n-) and branched (iso-) paraffins, (ii) naphthenes, (iii) aromatics, and (iv) n- and iso-olefins. A genetic algorithm working on molecular descriptors was used to build specific CN models for each of these classes. The predictive models return CN values roughly in the range from 0 to 100, which is in line with the definition of CN, with average absolute deviations similar to the experimental reproducibility (3−5 points).

The automobile industry currently faces the challenge of developing a new generation of diesel motor engines that satisfy both increasingly stringent emission regulations and reduces specific fuel consumption. The performance of diesel engines, seen in terms of emissions and specific fuel consumption, generally improves with increasing fuel-injection pressure. The design of the next generation of diesel fuel injection systems requires the knowledge of the thermophysical properties, in particular viscosity, of a wide-type of diesel fuels at pressures up to 300 MPa or more. The objective of the present work is to demonstrate that it is possible to predict the viscosity of any petroleum-based diesel fuel, using, exclusively, its molar fraction distribution as provided by multidimensional gas chromatography techniques. The precise knowledge of the fuel chemical constituents allows the understanding of the influence of the different hydrocarbon families on the fluid viscosity by means of molecular dynamics simulations. The accuracy of the Anisotropic United Atom force-field was tested and was found to be in agreement with experimental viscosities obtained with a new vibrating wire device at different temperatures and pressures up to 300 MPa. Finally, the experimental and simulated viscosities have been compared with improved group contribution method that has been coupled with gas chromatography experimental measurements for a viscosity prediction that was estimated to be of less than 18% of mean absolute deviation.

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