10.1021/acs.energyfuels.9b04204.s001
James E. Anderson
James E.
Anderson
Timothy J. Wallington
Timothy J.
Wallington
Novel Method to Estimate the Octane Ratings of Ethanol–Gasoline
Mixtures Using Base Fuel Properties
American Chemical Society
2020
octane rating responses
BOB
research octane number
base fuel information
OS
base fuels
molar ethanol fraction
MON
24.6 Arom g
12.0 x e
11.3 x e
Z RON
modeling ethanol octane
Base Fuel Properties Ethanol
base gasoline octane sensitivity
motor octane number
x e
hydrocarbon composition data
blend
2020-03-20 22:13:24
Journal contribution
https://acs.figshare.com/articles/journal_contribution/Novel_Method_to_Estimate_the_Octane_Ratings_of_Ethanol_Gasoline_Mixtures_Using_Base_Fuel_Properties/12014970
Ethanol blending
into gasoline yields a wide range of
octane rating responses, most frequently synergistic (i.e., greater
than expected by linear blending), but also linear or antagonistic.
A new approach to modeling ethanol octane blending, applicable for
both research octane number (RON) and motor octane number (MON), is
proposed, which predicts different
ethanol blending responses in base fuels of different compositions
and properties. The new model adds an interaction term to the linear
molar blending model with a coefficient, <i>Z</i>, that
quantifies the synergistic/antagonistic blending: ON<sub>blend</sub> = (1 – <i>x</i><sub>e</sub>)ON<sub>g</sub> + <i>x</i><sub>e</sub>ON<sub>e</sub> + <i>Zx</i><sub>e</sub>(1 – <i>x</i><sub>e</sub>), in which <i>x</i><sub>e</sub> is the molar ethanol fraction and ON<sub>g</sub>, ON<sub>e</sub>, and ON<sub>blend</sub> are the octane numbers of the base
gasoline, ethanol, and their blend, respectively. Fuel property and
hydrocarbon composition data for 299 ethanol–gasoline blends
and their 90 complex base fuels were collected from the literature,
primarily for market gasolines, blendstocks for oxygenate blending
(BOBs), and research fuels. Correlations of octane blending parameters
for several model approaches were highest for base gasoline octane
sensitivity (OS<sub>g</sub> = RON<sub>g</sub> – MON<sub>g</sub>) or saturate and aromatic fraction (Sat<sub>g</sub>, Arom<sub>g</sub>). Multivariate forward-step linear regression used these same properties
to predict the octane blending response in different base fuels over
a wide range of ethanol content. For example, the two equations for
RON blending are as follows: <i>Z</i><sub>RON</sub> = 15.0
– 1.76OS<sub>g</sub> + 11.3<i>x</i><sub>e</sub> and <i>Z</i><sub>RON</sub> = −47.8 + 64.3Sat<sub>g</sub> + 24.6Arom<sub>g</sub> + 12.0<i>x</i><sub>e</sub>. These models provide
greatly improved predictions as compared to generic models that do
not utilize base fuel information.