Novel Functional Group Contribution Method for Surrogate Formulation with Accurate Fuel Compositions

Current surrogate
formulation methods usually adopt distillation
profiles, density, viscosity, surface tension, molecular weight, research/motor
octane number (RON/MON), cetane number (CN), heating value, H/C ratio,
and threshold sooting index (TSI) as target properties, but these
parameters are most likely unavailable for new fuel molecules and
mixtures at the early stage of fuel development. A novel functional
group contribution method (GCM) based on accurate fuel compositions
is proposed to formulate surrogates effectively and quickly. This
method can successfully replicate the density, sound speed, kinematic
viscosity, ignition delay times, and speciations of POSF 4658, rapeseed
methyl ester, diesel, and fuels for advanced combustion engines (FACE)
C gasoline under a broad range of conditions (φ = 0.37–2.0, Tinit = 500–1600 K, Pinit = 1–20 atm), and its predictive capacity is
superior to that of traditional methods in most cases. Fuel properties
would match automatically between a surrogate and target fuel if the
discrepancies of functional groups are minimized. Three important
factors contribute to its high reproducibility: first, GCM captures
the complicated dependence of fuel physical/chemical properties on
the fuel molecular structure and functional groups; second, it correctly
assumes that fuel physical and chemical properties are a sum result
of the fuel molecular structure and functional groups; third, the
functional group interactions and their effect on fuel reactivity
are considered in the functional group classification system. The
GCM can not only formulate in the normal direction from a complex
target fuel to a simple surrogate fuel but also enable starting from
a simple target fuel toward a complex surrogate fuel during fuel design
in the refinery industry.