Predicting Diesel Engine Cetane Number and Similarity for Newer Commercial Sustainable Aviation Fuels

Abstract

Abstract A database of physical and chemical properties from one-hundred different jet fuels was used to create supervised Machine Learning (ML) models that predict Derived Cetane Number (DCN) as well as an unsupervised Self Organizing Map (SOM) of these conventional fuels. The best supervised ML models predict jet fuel DCN with just over one CN average error. The two dominant physical properties in the determination of DCN are fuel density and T50. The various models agree with increases in n-alkanes leading to higher DCN values, while increases in cyclo-aromatic content leading to decreases in DCN. The prediction of eleven newer Sustainable Aviation Fuel (SAF) DCNs showed at best errors of 3 to 5 DCN which corresponds to a 5 to 10 percent prediction error. There were two SAF anomalies with much greater DCN prediction errors (over-prediction), as these two fuels are heavily branched iso-alkanes with very low DCNs. An unsupervised ML approach was also used to characterize fuel similarity. The unsupervised SOM created and trained with the conventional jet fuels was then used to evaluate the SAF fuels. Two of the SAFs which had DCNs similar to the baseline database fuel mean had Quantization Errors (QEs) double the average QE of the database fuels. The two low DCN iso-alkane branched SAF fuels had very high QEs and thus were correctly determined by the SOM ML based approach as most dissimilar to the conventional jet fuels. This unsupervised ML approach shows promise for detecting new fuel differences early in the fuel evaluation process.