A data-driven approach for predicting the cetane number of renewable and unconventional fuels
Recent research on engine combustion has focused on new less carbon-intensive alternatives that meet current standards and can serve as drop-in fuels. However, developing surrogate fuels is challenging due to the difficulty of predicting key properties such as derived cetane number, which quantifies the ignition quality in compression ignition engines. Higher values indicate highly reactive fuels whereas lower values indicates longer ignition times that will lead to misfiring and increased emissions in diesel engines. The measurement of this property entails the test of the candidate fuels under specific conditions, making it costly and time-consuming. This work explores the use of data-driven models to predict the derived cetane number of both single-component fuels and blends. NREL’s compendium is used as reference, while the ignition quality tester is replaced by a 1D spray model coupled with detailed chemical kinetics. The models are trained on fuel composition, basic thermophysical properties, and simulated ignition delay. Additionally, symbolic regression is employed to derive an explicit equation linking derived cetane number to key fuel descriptors. The resulting expression achieves comparable accuracy to state-of-the-art neural networks (MAE 3.5, R^{2} 0.96–0.97), but reveals a physically interpretable structure where ignition delay dominates predictions, with double bond equivalent and heat of vaporization acting as corrective terms for specific chemical families, enhancing generalizability across diverse fuels. The accuracy of the resulting models is then compared to the available literature. This research provides a robust framework for predicting derived cetane number in unconventional fuels, enabling the exploration of new candidates for on-road, off-road, and aviation applications.
Ana Larrañaga, James R. MacDonald, Steven L. Brunton, Jacobo Porteiro, and Dario Lopez-Pintor. A data-driven approach for predicting the cetane number of renewable and unconventional fuels. Energy Conversion and Management, 350:120940, 2026.
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