PCR+ In Diesel Fuels and Emissions Research

Abstract

In past work for the U.S. Department of Energy (DOE) and Oak Ridge National Laboratory (ORNL), PCR+ was developed as an alternative methodology for building statistical models. PCR+ is an extension of Principal Components Regression (PCR), in which the eigenvectors resulting from Principal Components Analysis (PCA) are used as predictor variables in regression analysis. The work was motivated by the observation that most heavy-duty diesel (HDD) engine research was conducted with test fuels that had been ''concocted'' in the laboratory to vary selected fuel properties in isolation from each other. This approach departs markedly from the real world, where the reformulation of diesel fuels for almost any purpose leads to changes in a number of interrelated properties. In this work, we present new information regarding the problems encountered in the conventional approach to model-building and how the PCR+ method can be used to improve research on the relationship between fuel characteristics and engine emissions. We also discuss how PCR+ can be applied to a variety of other research problems related to diesel fuels.