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

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 shown to be successful at predicting the performance properties not only of the training data but also of uncalibrated alternative fuels, the underlying data abstraction strategy was determined to be inherently unsuitable for use with the disparate data from multiple GC-MS instruments due to instrument-based overfitting. The following report details a novel modeling strategy that makes use of normalized total ion chromatography (TIC) peak areas to both streamline the procedural complexity of the previous modeling strategy and more ably quantify chemical constituencies for the purposes of multi-instrument FFP fuel modeling.