A Bayesian Approach to Predict Sustainable Aviation Fuels Properties Using an Ensemble of Property Methods

We propose in this work a new methodology for predicting aviation fuel properties by combining the predictions of existing methods into an ensemble. The performance of current methods varies among different types of aviation fuels, including conventional jet fuels, Sustainable Aviation Fuels (SAF), and biofuels. Aviation fuels are a complex mixture of several hydrocarbons, which may also lead to high prediction errors. The predictions of the methods in the ensemble are weighted, where the weights are obtained using a probabilistic Bayesian linear regression (BLR) model. We tested the methodology used to predict the mass density of different aviation fuels. We also compared the results of BLR with those of deterministic optimization using Particle Swarm Optimization (PSO). Both BLR and PSO reduced the prediction error of mass density from 1.24% to 0.57% and 0.49%, respectively. We noticed a trade-off between uncertainty and accuracy in BLR results, where higher accuracy is accompanied by higher uncertainty. Accordingly, more accurate results provided by PSO or possibly other deterministic approaches may be misleading, and uncertainty should be considered to draw more robust conclusions. Results also showed that the individual performance of the methods does not follow their importance in the ensemble, i.e., the most accurate models do not necessarily have the highest weights; less accurate models could be more important. As a result, new predicting methods can be added even if they are, to some extent, less accurate than the methods in the ensemble.