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A New Predictive Vehicle Particulate Emissions Index Based on Gasoline Simulated Distillation
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 29, 2022 by SAE International in United States
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Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample. A review of particulate indices and lab methods to support them will be discussed, along with a less time-consuming simulated distillation based index.
Simulated Distillation (SimDis) is used for the volatility characterization of gasoline by wide-bore capillary gas chromatography (GC) (ASTM D7096). This GC method provides a rapid (15 minute) separation of gasoline hydrocarbons that then can be used to predict a fuel’s boiling point properties. Extracted from this boiling point distribution are boiling point ranges that can be associated with the clusters of hydrocarbon chemicals. The aromatic class hydrocarbons can then be used to calculate PEI. An updated PEI-SimDis equation that strongly correlates to the current PMI equation will be reviewed.
CitationGeng, P., Collin, W., and Akers, V., "A New Predictive Vehicle Particulate Emissions Index Based on Gasoline Simulated Distillation," SAE Technical Paper 2022-01-0489, 2022, https://doi.org/10.4271/2022-01-0489.
- Chapman , E. , Winston-Galant , M. , Geng , P. , and Pryor , S. Development of an Alternative Predictive Model for Gasoline Vehicle Particulate Matter and Particulate Number SAE Technical Paper 2019-01-1184 2019 https://doi.org/10.4271/2019-01-1184.
- Aikawa , K. , Sakurai , T. , and Jetter , J. Development of a Predictive Model for Gasoline Vehicle Particulate Matter Emissions SAE Int. J. Fuels Lubr 3 2 2010 610 622 10.4271/2010-01-2115
- Chapman , E. , Geng , P. , Zhao , Y. , Gong , J. et al. China Market Gasoline Range Fuel Review using Fuel Particulate Emission Correlation Indices SAE Technical Paper 2017-01-2401 2017 https://doi.org/10.4271/2017-01-2401
- Chapman , E. , Geng , P. , and Konzack , A. Five Years Trend of Global Market Gasoline Quality Review using Fuel Particulate Emission Correlation Indices SAE World Congress 2021 Detroit MI
- CRC Project AVFL-29-2 Effect of DHA Development on PMI Variability
- Lu , H. , Tian , H. , Zhang , S. , Geng , P. et al. Effects of Oxygenates and Aromatics in Gasoline on Vehicle Particulate Emissions SAE Technical Paper 2021-01-0542 2021 https://doi.org/10.4271/2021-01-0542.
- Tian , H. , Lu , H. , Guo , X. , Geng , P. et al. Influence of Ethanol and MTBE Proportion in China VIB Gasoline on Vehicle Particulate Emissions SAE 2021-01-0540 2021 https://doi.org/10.4271/2021-01-0540.
- Montemayor , R. Introduction and a Brief Historical Background Montemayor , R. Distillation and Vapor Pressure Measurement in Petroleum Products West Conshohocken, PA ASTM International 2008 1 5 https://doi.org/10.1520/MNL51-EB
- Boczkaj , G. , Kaminski , M. Research on the Separation Properties of Empty-Column Gas Chromatography (EC-GC) and Conditions for Simulated Distillation (SIMDIS) Anal Bioanal Chem 2013 405 25 8377 82 10.1007/s00216-013-7236- z
- Chapman , E. , Winston-Galant , M. , and Geng , P. Global Market Gasoline Range Fuel Review using Fuel Particulate Emission Correlation Indices SAE Technical Paper 2016-01-2251 2016 https://doi.org/10.4271/2016-01-2251.