Novel Method to Estimate the Octane Ratings of Ethanol–Gasoline Mixtures Using Base Fuel Properties

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, Z, that quantifies the synergistic/antagonistic blending: ON_blend = (1 – x_e)­ON_g + x_eON_e + Zx_e(1 – x_e), in which x_e is the molar ethanol fraction and ON_g, ON_e, and ON_blend 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_g = RON_g – MON_g) or saturate and aromatic fraction (Sat_g, Arom_g). 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: Z_RON = 15.0 – 1.76OS_g + 11.3x_e and Z_RON = −47.8 + 64.3Sat_g + 24.6Arom_g + 12.0x_e. These models provide greatly improved predictions as compared to generic models that do not utilize base fuel information.