Correlation of Detailed Hydrocarbon Analysis with Simulated Distillation of US Market Gasoline Samples and its Effect on the PEI-SimDis Equation of Calculated Vehicle Particulate Emissions 2023-01-0298

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEI_SimDis equation are detailed herein. The PEI_SimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time. A faster (< 15 min.), but lower resolution chromatography technique known as SimDis, which uses wide bore capillary GC and a flame ionization detector (FID) can be used to analyze a gasoline’s boiling point (volatility) characteristics. The PEI_SimDis equation was developed using multiple Boiling Point range windows from the SimDis GC analysis relating to the aromatic species identified by the DHA. An enhanced PEI_SimDis equation is shown [Eq. 5] and has been shown to correlate strongly to other predictive particulate emissions equations such as Particulate Matter Index (PMI) and PEI. The SimDis GC method and subsequent enhanced PEI_SimDis predictive equation provides a rapid estimate of a gasoline’s tendency to produce vehicle particulate emissions [7].

As a continuation from previous work (P.Geng et al., 2022), this paper presents a study of the correlation of DHA and SimDis, but this time using a wide range of U.S. market gasoline samples.