Predictive Modeling of Aerospace Fuel Properties Using Comprehensive Two-Dimensional Gas Chromatography with Time-Of-Flight Mass Spectrometry and Partial Least Squares Analysis

Abstract

Increasingly stringent requirements for aerospace propulsion system performance, reliability, and operability motivate quantitative connections between fuel composition, physical characteristics, and system performance. Chemically accurate assessment of aviation turbine fuels (Jet-A, JP-8, etc.) and kerosene-based rocket propellants (RP-1 and RP-2) is requisite to mature these models. Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC–TOFMS) is an excellent analytical tool for measuring detailed chemical information contained in complex fuels. Additionally, multivariate data analysis methods, referred to as chemometrics, are ideally suited to relate detailed chemical information contained within the GC × GC–TOFMS data to fuel properties and performance in a predictive manner. Herein, we apply these techniques to a chemically diverse set of 74 distillate and multicomponent aerospace fuels, resulting in an improved understanding of the chemical compositional basis for physical and thermochemical behavior. Informed by GC × GC–TOFMS data, highly reliable partial least squares (PLS) models are developed and employed in the prediction of physical properties (measured separately using conventional test methods). Root-mean-square errors of cross-validation (RMSECV) were relatively low: values of 0.0450 cSt, 41.3 Btu/lbm, 0.130 mass %, and 0.0064 g/mL were obtained for viscosity, heat of combustion, hydrogen content, and density, respectively. The corresponding normalized root-mean-square errors of cross-validation (NRMSECV) were 6.01, 10.3, 8.71, and 7.12%, respectively. Investigation of the linear regression vectors (LRVs) provides valuable insight into the relationship between the chemical composition and physical properties, enabling, in principle, the model-informed selection of fuel chemical composition to achieve desired performance criteria.

Lower Heating Value of Jet Fuel from Hydrocarbon Class Concentration Data and Thermo-Chemical Reference Data: An Uncertainty Quantification

“…Related research by other teams is introduced in this paragraph. Two previous works will be revisited later to help underscore the differences between the fundamental approach described here and the correlation methods [3,4].…”

“…Once the class concentration data is available, it can be used to calculate or estimate other properties of interest via physical models, such as the fundamental approach taken in this work, or regression models such as the approach taken by Shi et al[3] and by Berrier et al[4]. Properties that are physically determined by molecular bonds and atomic characteristics can be accurately determined by physical models, while properties that are heavily influenced by inter-molecular interactions in the condensed phase, typically are harder to determine accurately from first principles.…”

Lower Heating Value of Jet Fuel from Hydrocarbon Class Concentration Data and Thermo-Chemical Reference Data: An Uncertainty Quantification

“…However, a fundamental approach for LHV determination is still possible. The use of multidimensional gas chromatography with flame ionization detection (GCxGC-FID) has been used in the past [3,4]. However, all previous methods with GCxGC-FID have used correlative methods, instead of the fundamental approach described in this work, which is based on Hess' Law.…”

“…A physically based modeling approach utilizing GCxGC-FID data is attractive because such a method would require minimal sample and labor, while yielding a diverse array of additional property determinations or predictions [3][4][5].…”

Control-Normalized Fisher Ratio Analysis of Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Data for Enhanced Biomarker Discovery in a Metabolomic Study of Orthopedic Knee-Ligament Injury

“…Since the pioneering introduction in 1991 by Liu and Phillips, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC–TOFMS) has become an indispensable instrumental platform capable of separating highly complex mixtures. − Indeed, GC × GC provides a 10-fold or more increase in peak capacity as compared to one-dimensional gas chromatography, 1D-GC, and simultaneously improves identification and quantification of trace level analytes. ,, While GC × GC–TOFMS first gained popularity in the petroleum industry, − it has since been extended to other fields such as, but not limited to, food science, , forensics, , wastewater, , and metabolomics. − …”