Novel Data Abstraction Strategy Utilizing Gas Chromatography–Mass Spectrometry Data for Fuel Property Modeling

Abstract

The high cost and limited availability of emerging alternative fuels and/or fuel blending stocks with unknown compositions are often major impediments to the certification of these materials as Fit-For-Purpose (FFP) for the U.S. Navy. A method was desired whereby a candidate fuel could be rapidly prescreened to determine if it would be suitable for further, more-extensive FFP testing. The goal of this research was to employ statistical analysis strategies to establish linkages between the chemical constituency of any given fuel or fuel stock, regardless of type or source, and the resultant performance, and/or fuel properties. A chemical profiler developed during the course of this work has previously been used to quantify the constituencies of fuels using gas chromatography–mass spectrometry (GC-MS) data. These constituencies were then correlated to specification properties using partial least-squares regression modeling reconfigured into a multistep, iterative strategy. While this modeling strategy was s...