Understanding the blending octane behaviour of unsaturated hydrocarbons: A case study of C4 molecules and comparison with toluene

Yang Li, Vijai Shankar, Kiran K. Yalamanchi, Jihad Badra, André Nicolle, Mani Sarathy

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Octane number (ON) is an important empirical parameter for developing and optimizing internal combustion engine (ICE) for knock resistance. Primary reference fuels (PRF) comprising iso-octane and n-heptane are the simplest gasoline surrogates. C4 hydrocarbons: butane isomers (n-butane and isobutane), butene isomers (1-butene, 2-butene and isobutene) and 1,3-butadiene are the smallest hydrocarbons with isomeric, saturated, unsaturated and conjugated bond structures, which makes them good candidates for understanding the blending octane behavior of saturated and unsaturated hydrocarbons. In this study, the blending octane behaviors of six PRF60 & C4 hydrocarbon mixtures were systematically investigated. A state-of-the-art kinetic models were used by merging the latest KAUST gasoline surrogate model with the AramcoMech 3.0 model. IDTs of stoichiometric fuel/air mixtures were simulated at wide range of pressure (20–50 atm) and temperature (600–1400 K). Three correlation equations were employed from the literature to predict the research octane number (RON) and motor octane number (MON) of all blends based on these calculated IDTs. Compared with the experimentally measured ON, the best correlation conditions and errors were identified. With the highest degree of unsaturation, 1,3-butadiene was found to be the strongest ON enhancer. Moreover, based on a polynomial correlation, a TPRF (PRF + toluene) blend was formulated by matching the RON and MON of PRF plus 1,3-butadiene blend, for a comparative analysis. Finally, the reactants’ consumption profile, flux and sensitivity analysis were simultaneously performed for explaining the chemistry behind the blending octane behavior of the PRF blends with 1,3-butadiene and toluene.
Original languageEnglish (US)
Pages (from-to)117971
StatePublished - May 11 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors acknowledge the KAUST Supercomputing Laboratory (KSL) for providing the computing resources and technical supports.


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