Partial least squares analysis of rocket propulsion fuel data using diaphragm valve-based comprehensive two-dimensional gas chromatography coupled with flame ionization detection

Freye, Chris E.; Fitz, Brian D.; Billingsley, Matthew C.; Synovec, Robert E.

doi:10.1016/j.talanta.2016.03.016

Cited by 15 publications

(12 citation statements)

References 42 publications

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“…Cramer et al [21] used parallel factor analysis (PARAFAC) and GC-MS to develop an improved peak selection strategy to automatically detect minute compositional changes in fuels. Kehimkar et al [22] and Freye, et al [23] applied PLS to rocket kerosene data obtained using GCxGC-TOFMS and GCxGC coupled with flame ionization detection, respectively, to develop multivariate predictive models of fuel composition and engine-relevant fuel properties.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Rocha

Schantz

Sheen

et al. 2017

Fuel

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As feedstocks transition from conventional oil to unconventional petroleum sources and biomass, it will be necessary to determine whether a particular fuel or fuel blend is suitable for use in engines. Certifying a fuel as safe for use is time-consuming and expensive and must be performed for each new fuel. In principle, suitability of a fuel should be completely determined by its chemical composition. This composition can be probed through use of detailed analytical techniques such as gas chromatography-mass spectroscopy (GC-MS). In traditional analysis, chromatograms would be used to determine the details of the composition. In the approach taken in this paper, the chromatogram is assumed to be entirely representative of the composition of a fuel, and is used directly as the input to an algorithm in order to develop a model that is predictive of a fuel's suitability. When a new fuel is proposed for service, its suitability for any application could then be ascertained by using this model to compare its chromatogram with those of the fuels already known to be suitable for that application. In this paper, we lay the mathematical and informatics groundwork for a predictive model of hydrocarbon properties. The objective of this work was to develop a reliable model for unsupervised classification of the hydrocarbons as a prelude to developing a predictive model of their engine-relevant physical and chemical properties. A set of hydrocarbons including biodiesel fuels, gasoline, highway and marine diesel fuels, and crude oils was collected and GC-MS profiles obtained. These profiles were then analyzed using multi-way principal components analysis (MPCA), principal factors analysis (PARAFAC), and a self-organizing map (SOM), which is a kind of artificial neural network. It was found that, while MPCA and PARAFAC were able to recover descriptive models of the fuels, their linear nature obscured some of the finer physical details due to the widely varying composition of the fuels. The SOM was able to find a descriptive classification model which has the potential for practical recognition and perhaps prediction of fuel properties.

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Section: Methodsmentioning

confidence: 99%

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Rocha

Schantz

Sheen

et al. 2017

Fuel

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“…17−19,23−26 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 [17][18][19][20]25,27 and is implemented herein. As with any technology that produces inordinate volumes of data, a critical issue in the practical application of GC × GC−TOFMS to the analysis of complex fuels is the conversion of that data into useful information.…”

Section: Introductionmentioning

confidence: 99%

“…In the absence of this compositional diversity, collinearity between compounds (or classes of compounds) can result in misleading information and errant conclusions, even when PLS calibrations are accurate. 19,20 To address this issue, this study encompasses 74 compositionally diverse, multicomponent fuels comprising both middle distillate commodity products and blended formulations of hydrocarbon solvents.…”

Section: Introductionmentioning

confidence: 99%

“…The current study constitutes a significant expansion of our previous studies to gain further insight and create robust PLS models with a large, diverse set of fuels. 19,20 2. EXPERIMENTAL SECTION 2.1.…”

Section: Introductionmentioning

confidence: 99%

“…49 Thus, leave-one-out cross-validation (LOOCV) is often implemented with PLS. 19,20,30,37 However, with a large data set, LOOCV would result in excessively long computation time and potential overfitting of the data. Therefore, the PLS model construction implemented an alternative cross-validation method known as Venetian blinds crossvalidation to test the overall predictive ability of the models and to determine the correct number of latent variables.…”

Section: Introductionmentioning

confidence: 99%

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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

Self Cite

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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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Advanced data handling in comprehensive two-dimensional gas chromatography

Berrier

Prebihalo

Synovec

2020

Separation Science and Technology

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Partial least squares analysis of rocket propulsion fuel data using diaphragm valve-based comprehensive two-dimensional gas chromatography coupled with flame ionization detection

Cited by 15 publications

References 42 publications

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

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

Advanced data handling in comprehensive two-dimensional gas chromatography

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