Automated Method for Determining Hydrocarbon Distributions in Mobility Fuels

Begue, Nathan J.; Cramer, Jeffery A.; Myers, Kristina M.; Johnson, Kevin; Morris, Robert E.

doi:10.1021/ef101635a

Cited by 11 publications

(10 citation statements)

References 42 publications

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“…This flash point model conforms to ASTM D3828 (gas ignition option), ASTM D1655 (gas ignition option), ASTM D3278, ASTM D7236, and ASTM E502 as described in the manufacturer's literature. The flashpoint of n-decane (Aldrich, > 99 % pure) was measured to be 322 ± 1 K, which matches the closed-cup literature value of 321 K. 26 The DSH-76 fuel composition was characterized by gas chromatography−mass spectrometry (GC-MS) using methods described in Begue et al 27 GC-MS data were acquired with an Agilent 7890 GC equipped with an Agilent 5975C mass selective detector configured for electron impact ionization. Samples were diluted to 1:100 in dichloromethane, and injections of 1.0 μL were made with an autoinjector.…”

Section: Methodsmentioning

confidence: 53%

“…The DSH-76 fuel composition was characterized by gas chromatography–mass spectrometry (GC-MS) using methods described in Begue et al GC-MS data were acquired with an Agilent 7890 GC equipped with an Agilent 5975C mass selective detector configured for electron impact ionization. Samples were diluted to 1:100 in dichloromethane, and injections of 1.0 μL were made with an autoinjector.…”

Section: Methodsmentioning

confidence: 99%

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Density, Viscosity, Speed of Sound, Bulk Modulus, Surface Tension, and Flash Point of Direct Sugar to Hydrocarbon Diesel (DSH-76) and Binary Mixtures of N-Hexadecane and 2,2,4,6,6-Pentamethylheptane

et al. 2013

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The physical properties of direct sugar to hydrocarbon diesel (DSH-76) and several binary mixtures of n-hexadecane and 2,2,4,6,6-pentamethylheptane were measured in this work. The density and viscosity were measured at temperatures ranging from (293.15 to 393.15) K, and the pure component values fell within the range of previously reported values. Speed of sound data at temperatures ranging from (293.15 to 323.15) K increased from (1089 to 1357) m•s −1 . The bulk modulus was calculated from the density and speed of sound data, and its values ranged from (858 to 1425) MPa. Flash point values ranged from (318 to 408) K, and the surface tension values ranged from (21.8 to 27.3) mN•m −1 . The values of density, viscosity, speed of sound, bulk modulus, flash point (378 K), and surface tension (25.0 mN•m −1 ) for the DSH-76 fell within the range of values measured for the binary mixtures of nhexadecane and 2,2,4,6,6-pentamethylheptane. These data suggest that a binary mixture of n-hexadecane and 2,2,4,6,6pentamethylheptane may be a suitable surrogate for renewable fuels such as DSH-76.

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Section: Methodsmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 99%

Density, Viscosity, Speed of Sound, Bulk Modulus, Surface Tension, and Flash Point of Direct Sugar to Hydrocarbon Diesel (DSH-76) and Binary Mixtures of N-Hexadecane and 2,2,4,6,6-Pentamethylheptane

et al. 2013

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“…Chemical composition has a demonstrable effect on the properties of multicomponent hydrocarbon fuels and ultimately the performance and reliability of the systems that use them. − For this reason, specifications for aerospace kerosene fuels intentionally limit impurities and hydrocarbon classes (e.g., sulfur, olefins, oxygenates, and aromatic compounds) with detrimental system level impacts . Given the diversity in chemical sources and production methods for commodity fuels and the increasingly stringent operational requirements in aerospace energy conversion devices, determining quantitative relationships between fuel chemical composition, specification properties, and performance is vitally important. − With regard to advancing a foundational connection between fuel composition and physical properties, evaluation of compositionally controlled laboratory blends and “field” fuel samples is instructive. − ,− However, as the number of fuels analyzed increases, a reliable and straightforward analytical protocol is needed to glean significant information while keeping analysis time reasonably short.…”

Section: Introductionmentioning

confidence: 99%

“…Gas chromatography (GC) is a conventional analytical technique that is ideal for the separation and analysis of volatile and semivolatile mixtures. , When GC is coupled with mass spectrometry (MS), spectral information is gathered that allows further selectivity and increases confidence in chemical compound identification. GC–MS has been shown to be convenient and effective in the analysis of kerosene-based fuel. − Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC–TOFMS) significantly improves upon the separation power of one-dimensional (1D) GC and provides additional insight into complex mixtures of volatile compounds including kerosene-based fuels. − ,− In typical GC × GC column configurations, the first separation dimension ( 1 D) uses a nonpolar stationary phase, while the second separation dimension ( 2 D) uses a polar stationary phase. However, a reversed-column configuration with a polar 1 D column and a nonpolar 2 D column has been shown to provide better selectivity for petroleum-based samples − ,, and is implemented herein.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Thus was a multistep data abstraction and modeling strategy initially developed and effectively implemented, the results of which having been reported upon previously. , In this initial strategy, the GC-MS chromatograms obtained from fuels were first reduced in dimensionality by using a GC-MS based compositional profiler, designed in-house, to determine compound identities from mass spectra and collate these identities into 2D chemical component metaspectra for individual samples, with the number of appearances of each chemical component reported along the y -axis for each component listed, arbitrarily, along the x -axis.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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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, moreextensive 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.

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Automated Method for Determining Hydrocarbon Distributions in Mobility Fuels

Cited by 11 publications

References 42 publications

Density, Viscosity, Speed of Sound, Bulk Modulus, Surface Tension, and Flash Point of Direct Sugar to Hydrocarbon Diesel (DSH-76) and Binary Mixtures of N-Hexadecane and 2,2,4,6,6-Pentamethylheptane

Density, Viscosity, Speed of Sound, Bulk Modulus, Surface Tension, and Flash Point of Direct Sugar to Hydrocarbon Diesel (DSH-76) and Binary Mixtures of N-Hexadecane and 2,2,4,6,6-Pentamethylheptane

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

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

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