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As the world market develops for biodiesel fuels, it is likely that a wider variety of biodiesels will become available, both locally and globally, and require engines to operate on a wider variety of fuels than experienced today. At the same time, tighter emissions regulations and a drive for improved fuel economy have focused interest on advanced combustion modes such as HCCI or PCCI, which are known to be more sensitive to fuel properties. This research covers two series of biodiesel fuels. In the first, B20 blends of natural methyl esters derived from palm, coconut, rape, soy, and mustard were evaluated at light load in an HCCI research engine to determine combustion and performance characteristics. These fuels showed performance differences between the biodiesels and the base #2 ULSD fuel, but did not allow separation of chemical effects due to the small number of fuels and correlation of various properties.

In order to meet common fuel specifications such as cetane number and volatility, a refinery must blend a number of refinery stocks derived from various process units in the refinery. Fuel chemistry can be significantly altered in meeting fuel specifications. Additionally, fuel specifications are seldom changed in isolation, and the drive to meet one specification may alter other specifications. Homogeneous charge compression ignition (HCCI) engines depend on the kinetic behavior of a fuel to achieve reliable ignition and are expected to be more dependent on fuel specifications and chemistry than today's conventional engines. Regression analysis can help in determining the underlying relationships between fuel specifications, chemistry, and engine performance. Principal Component Analysis (PCA) is used as an adjunct to regression analysis in this work, because of its ability to deal with co-linear variables and potential to uncover ‘hidden’ relationships between the variables.