The presentation and webinar recording from the first 2019 web conference for Future Fuel Outlook members is now posted. The webinar focused on the study, “Effects of Ethanol Blends on Light-Duty Vehicle Emissions: A Critical Review.”
This is a study I have worked on for the past year with colleagues Nigel Clark, professor emeritus of West Virginia University, Terry Higgins and David McKain. Nigel is an expert energy systems R&D, emphasizing engines, fuels, emissions, and transportation. Terry is an expert in fuel composition and refining. And, David is an expert in vehicles, fuels, emissions and aftertreatment systems. Our bios are linked:
A few of our conclusions, which we discussed during the webinar, include the following:
The blending of fuels used in the studies reviewed represents a major cause of differences in conclusions and draws into question applicability to real-world predictions. This is a major study finding here, and that is something that needs to be highlighted to the affected industries and policymakers alike.
Many of the blends used in emissions studies do not reflect typical makeup of in-use fuels. They are not reflective of real-world fuel blending that happens at the refinery or terminal. This is certainly the case in studies where “match blended” fuels were used to test a few select parameters, such as ethanol.
In match blending, it is just not possible to add ethanol to a blendstock for oxygenate blending (BOB) and hold all other properties constant. Matching select properties such as aromatics or T50 requires addition of higher emission aromatic streams which significantly contribute to emissions. The match blending essentially alters the emission characteristics (higher emissions) of the E0 fuel, increasing the emissions of the match blended ethanol blend and misrepresenting study conclusions on the impact of ethanol. Further, the blending of different hydrocarbon groups and the blending of ethanol produces nonlinear property responses, causing added uncertainty.
Splash-blended fuels provide a study fuel closer than match blending to real world fuel blending but ignores any AKI effects on emissions and fails to account for the reduction in fuel aromatics results in real world blending, and its impact on emissions.
Variability between studies in itself suggests that many studies should not be used to predict real-world emissions effects, and the causes of the variability between studies are also likely to cause differences in emissions effects between most study conclusions and the real-world application.
Study conclusions are suited to real-world predictions only if the study addresses the fuels of interest holistically. This means all appropriate parameters in a multivariate model derived from the study must be employed. For example, real-world ethanol blends typically have reduced T50 and lower aromatics than an E0, and all three property changes must be considered in using a model to predict PM or NOx.
Studies adopt different parameters for planning, blending and modeling, stressing a lack of agreement on root cause of emissions. Variation in emissions is assigned to parameters, but those assignments vary between studies. Reselection of one parameter may change the attribution of emissions to another parameter, such as ethanol level. This is important, yet not accounted for, explained or acknowledged in many studies reviewed.
Future studies seeking to clarify real world ethanol level effects on emissions, whether multivariate analyses or direct comparisons, should seek to use fuels at different blend levels that are derived from real refinery streams or thoroughly represent real world fuels, without imposing limiting parameters in the blending. They should also consider refinery economics and not blend just high-value streams. Suggestions for potential study designs are incorporated in the report.
Care must be taken in addressing emissions from mid blends, insofar as the step in properties from E0 to E10 is very different than the step from E10 to E15 or to E20. Linear extrapolation is not possible, though some studies have not defined their model limits.