Oxidation Stability of Hydrocarbons: A Machine-Learning-Based Study

Having fluids that are stable over time is important
for many applications,
particularly sustainable aviation fuels (SAFs) derived from various
renewable sources. Being able to understand this characteristic as
early as possible during the development of SAFs would facilitate
the blending of renewable sources with or without fossil fuels. Oxidation
stability, defined as a hydrocarbon’s resistance to reacting
with oxygen at near-ambient temperatures, is one of the most important
hydrocarbon-stability-related properties. Indeed, the accumulation
of byproducts of oxidation reactions may result in system failures.
Assessing this property experimentally remains time-consuming; thus
developing fast and accurate predictive models becomes relevant and
approaches based on machine learning appear as valuable alternatives.
The development of quantitative structure–property relationships
(QSPRs) is subject to the availability of reference data, and unfortunately,
these are currently lacking in the literature. In this study, we built
a database containing consistent experimental results from accelerated
oxidation tests conducted on diverse pure hydrocarbonswithin
the carbon atom number range of SAFsusing the PetroOxy/RapidOxy
test method, and second, we applied two machine-learning-based techniques
(SVM and XGBoost) on the generated data set to derive QSPR-based models.
The contribution of techniques such as data augmentation applied to
our data set was also investigated and compared to more classical
approaches. The best model (RMSEP = 2.7 h) was obtained after log-transforming
the reference Induction Period, performing Smart Data Augmentation
to enrich the database content, and using XGBoost with linear learners.
While the model’s accuracy is not adequate for quantitative
predictions, it allows fast and semiquantitative predictions.