Prediction of the Cetane Number of Diesel Compounds Using the Quantitative Structure Property Relationship

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).