Design and Application of Triangular Composition Descriptors for Predicting the Physicochemical Properties of Hydrocarbon Mixtures

Sustainable aviation fuels (SAFs) are being considered as alternative, clean, sustainable, and renewable energy resources to meet the global carbon-neutral target. Currently, most SAFs are blended with conventional fuels, which alters the composition and properties of aviation fuels, but a limited understanding of the hydrocarbon mixture composition and properties hinders their development. Quantitative structure–property relationship (QSPR) models predict macroscopic properties based on molecular structures, but existing models mainly address pure substances, leaving descriptor generation for mixtures challenging. This study introduces characteristic triangles, derived from four key oil features (average carbon number, aromatics, cycloalkanes, and isomeric alkanes), to predict hydrocarbon mixture properties (density, kinematic viscosity, refractive index, surface tension, average volume boiling point, flash point, and freezing point). Results show that integrating triangular descriptors significantly enhances predictive accuracy, enabling rapid property evaluation and offering valuable design insights for new oil products.