Review evaluates predictive mixture modeling frameworks for aviation fuel blends, highlighting key barriers in data scarcity and molecular interactions for safe fuel design.
Jet fuels (JFs) and sustainable aviation fuels (SAFs) are complex hydrocarbon mixtures whose properties must meet strict international specifications to ensure consistent quality. Their increasing variability, driven by environmental and process constraints, raises multiple questions regarding the relationship between fuel composition, safety, and performance. In this context, predictive models can help support fuel formulation while reducing experimental effort. Quantitative structure-property relationship (QSPR) methods have been applied to fuel components such as hydrocarbons to predict various properties, showing promising results but often with limited accuracy and applicability. Several studies have also investigated mixture-QSPR (M-QSPR) approaches for hydrocarbon systems involving constituents also present in fuels; however, very few studies directly address real fuel mixtures. This is because M-QSPR remains particularly challenging due to mixture interactions, data scarcity, representation difficulties, and validation issues. This review evaluates past M-QSPR studies on fuels, fuel-component mixtures, and general M-QSPR methodologies to identify current advances, limitations, and gaps and to outline strategies for developing M-QSPR models for JFs.
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Yeghyan et al. (2026) studied this question.
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