Modeling RP-1 Fuel Advanced Distillation Data using Comprehensive Two-Dimensional Gas Chromatography Coupled with Time-of-Flight Mass Spectrometry and Partial Least Squares Analysis.

摘要

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as to elucidate the relationships between fuel composition and their properties. Herein, we study multidimensional chromatographic data obtained utilizing the instrumental platform that included comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC x GC -TOFMS) to analyze RP-1 fuels. For GC x GC separations, RTX-wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc), providing a significant level of chemical compositional information. The GC x GC -TOFMS data were analyzed using partial least-squares regression (PLS) chemometric analysis, specifically to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the observed changes in the previously collected ADC data. The PLS modeling correlates compositional information found in the GC x GC - TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) with low values, typically below 2.0 deg C, at each % distilled measurement point during the ADC analysis.