A New Predictive Vehicle Particulate Emissions Index Based on Gasoline Simulated Distillation 2022-01-0489

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.