The Octane Number (ON) of a fuel compound is an important scalar measurement for the quantification of its ignition behavior. In this study, Density Functional Theory (DFT) based chemical reactivity descriptors are investigated for the development of predictive ON models to aid the selection and screening of new fuel compounds. Quantitative Structure–Property Relationship (QSPR) models for ON for a data set containing 105 congeners (40 alkanes, 40 alkenes, and 25 cycloalkanes) are analyzed using the conceptual DFT based descriptors. In addition, an attempt has been made to develop predictive models by correlating ON with boiling point and hydration energy data available for these compounds. The developed predictive computer-aided design method has widespread applicability to prioritize novel molecular entities as potential fuel blend stock components.

Octane number prediction using density functional theory

251st National Meeting (2016)

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