Prediction of the Octane Number: A Bayesian Pseudo-Component Method

Energy
transition leads to the development of unconventional liquid
fuels. Unconventional liquid fuels are produced at a small scale;
so, they are produced with a limited budget, and they must be characterized
at a cheap price. When liquid fuels are burned in piston engines,
they are characterized by the research octane number (RON) and the
motor octane number (MON). As the measurement of the RON and the MON
is expensive, a cheaper alternative, like the pseudo-component method,
is sought. Nevertheless, this method was only developed for the RON,
it is not applicable for complex fuels with olefins and oxygenates,
and its uncertainty has not been characterized. Moreover, it does
not differentiate the isomers. For instance, the iso-paraffins are
considered as a blend of 2-methyl-alkane, 3-methyl-alkane, 2,2-dimethyl-alkane,
and 2,3-dimethyl-alkane in equal proportions. The authors address
the limitations of the pseudo-component method using a Bayesian approach.
The validity of the method is demonstrated for three gasoline blendstocks
mixed with five oxygenated molecules: 1-propanol, 2-propanol, 1-butanol,
2-butanol, and 2-methyl-1-propanol. As a result, the octane numbers
are predicted within the theoretical uncertainty bounds and with less
than 2% error.