Expanded Framework for the Prediction of Alternative Fuel Content and Alternative Fuel Blend Performance Properties Using Near-Infrared Spectroscopic Data

Abstract

Partial least-squares (PLS) regression models can be constructed from near-infrared (NIR) spectroscopic data to identify and predict critical specification properties of jet and diesel fuels for quality surveillance prescreening. This same approach has also been used previously to identify Fischer–Tropsch synthetic fuels and fatty acid methyl ester fuels, predict their quantities in blends with jet and diesel petrochemical fuels, and even correct fuel property predictions when alternative fuel contents in blends would affect the predictions of the properties in question. The present work expands upon these previous results by incorporating several additional alternative fuel types into a more generalized alternative fuel content and property modeling framework than was developed previously. The framework consists of a single generalized PLS modeling solution to simultaneously accommodate multiple alternative fuels considered isoparaffinic in nature, as well as smaller-scale modeling solutions to accommodate individual alternative fuels that are not similarly isoparaffinic in nature. This expanded framework provides the means to allow NIR PLS models to predict and quantify alternative fuel contents in blends, and accurately predict affected fuel properties, in a robust fashion that, because of the use of more-generalized modeling than has been seen in previous work, better accommodates a future of unknown and unknowable alternative fuel types.