Data driven physicochemical gasoline fuel surrogate formulation method for use in predicting properties of gasoline-biofuel blends

Shen, A. Data Driven Physicochemical Gasoline Fuel Surrogate Formulation Method for Use in Predicting Properties of Gasoline-Biofuel Blends. University of Wisconsin-Madison, 2024.

The objective of this work was to develop and validate a surrogate formulation method for surrogates that are a good representation of a reference gasoline fuel in both physical and chemical properties. The purpose of developing this method, and the subsequent gasoline surrogate, was to be able to model gasoline-biofuel blends with different oxygenates, specifically ethanol, iso-butanol (IBA), and 2-methyl-3-buten-2-ol (methyl butenol, MBO).

The surrogate formulation method produced a number of surrogates, two of which were validated against reference gasoline property data and engine performance. The model results of the first surrogate, the Best Combo surrogate, were compared against the properties of the reference gasoline it was based off of, a gasoline blendstock for oxygenated blending, or BOB. Property comparisons showed that the model was able to estimate values within the reproducibility uncertainty of the property measurements of the reference gasoline BOB.

The second surrogate studied was the Best Cost Combo surrogate, chosen for its low cost while still being an accurate representation of the reference gasoline BOB properties. The Best Cost Combo surrogate was put through property measurements, distillation tests, and several engine experiments. Not unexpectedly, the Best Cost Combo surrogate did not match the reference gasoline as well as the Best Combo surrogate (εopt of 0.07529 vs. 0.07288). The property measurement results showed that the research and motor octane numbers (RON and MON respectively) and Reid vapor pressure (RVP) of the Best Cost Combo surrogate had the largest differences. The model output a RON of 87.2 but testing measured a RON of 92.0. For MON, the model output was 83.9 vs. a measurement of 86.6. The RVP ended up measuring lower than the model estimated, 83.1 vs 87.4 kPa. Distillation tests conducted in-house were able to match the reference gasoline BOB very well; out of the 7 blends tested (E0, E10, E30, IBA16, IBA49, MBO18, and MBO56) the maximum difference seen was 7% (29 K difference). Engine tests showed that, at the same combustion phasing, the Best Cost Combo surrogate and the reference gasoline BOB performed nearly identically, differences were due to slight variations in equivalence ratio, spark timing, and day-to-day variability . However, during the knock limiting spark advance (KLSA) tests, it was observed that the pure surrogate was able to handle more advanced combustion phasing (more knock resistant), about 5 CAD. Once the oxygenates were blended in, since only the highest blend levels were tested for cost reasons (E30, IBA49, and MBO56), the blends performed very similarly. The particulate results were the most different out of all tests conducted with the Best Cost Combo surrogate, showing much higher particulate levels than the reference gasoline BOB. This surrogate had a particulate matter index (PMI) of 0.992 and was chosen for its low cost to make, not for how closely it would match particulate output of the reference gasoline BOB which had a PMI of 1.156. Preliminary tests of adjusting the surrogate by replacing the component with the highest yield sooting index (YSI), 1,2,4-trimethylbenzene (Tb = 443 K, PMI = 4.876 1/kPa, YSI = 308.3), with a component with lower YSI in the same temperature bin, propylbenzene (Tb = 432 K, PMI = 3.933 1/kPa, YSI = 235.7), showed that the blend YSI can be reduced and could potentially lower the particulate levels seen during engine operation. While the overall blend YSI did not change much from the Best Cost Combo surrogate to the modified surrogate, 78.6 vs. 77.3 respectively, the peak YSI was lowered from 210 to 166. Additional engine testing and analysis needs to be conducted to understand whether this component swap successfully decreases the particulate levels of the blend.