Numerical Approaches to Determine Cetane Number of Hydrocarbons and Oxygenated Compounds, Mixtures, and their Blends

In the present work, we report the
development and use
of models
to predict the cetane number of hydrocarbons and oxygenated compounds,
mixtures, and their blends. The study is divided in three steps: (i)
the prediction of pure compounds’ CN using ML-based approaches,
(ii) the development and application of mixing rules, and (iii) the
external validation of models on a set of real fuels. Experimental
CN values for 658 pure compounds are collected from the literature
and merged to obtain a consistent and comprehensive database. ML-based
models are then trained on the database. A second database is built
from the collection of 572 experimental CN values for mixtures. Existing
and proposed mixing rules powered by either experimental CN or CN
predicted using the ML-based models are then assessed on the basis
of the second database. The new mixing rule involving the activity
coefficients of mixtures’ components shows the best performance.
Finally, the application of our predictive numerical approach to 27
real fuels demonstrates its accuracy and relevance, and that it could
be further used for testing large numbers of samples.