Combustion characteristics of crude oils for gas turbine applications by DCN measurements and NMR spectroscopy

Won, Sang Hee; Lim, Seung Jae; Nates, Stuart; Alwahaibi, Ayuob K.; Dryer, Frederick L.; Farid, Farinaz; Hase, Matthias

doi:10.1016/j.proci.2020.06.184

Cited by 4 publications

(8 citation statements)

References 23 publications

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“…The results in Table show an evident correlation between the UNIFAC group composition and the cetane number of the fuel. It is also worth mentioning that unlike NMR-based studies, ,,,,,, the CH2 group composition evaluated in this work does not distinguish between the parent structure contributing to the CH2 group and combines CH2 groups from all parent structures. The NMR-based studies provide a comprehensive CH2 group composition by segregating the CH2 groups based on their parent structures, namely, straight chain, cyclic, and aromatics.…”

Section: Resultsmentioning

confidence: 96%

“…The successful predictive capabilities of correlative models ,,,− , support the hypothesis that fuel properties can be expressed as a function of their molecular structures and thereby be predicted if the relevant molecular structure information is known. The molecular structure information can be efficiently and succinctly provided, in one form, as its functional group composition.…”

Section: Introductionmentioning

confidence: 81%

“…Correlation models between molecular structure information of the fuel and physical and chemical properties, like the derived cetane number (DCN), have been developed by several groups − using various data science techniques. These studies have used different analytical techniques to evaluate the molecular structures of different fuels and mixtures, thereby developing a database to train and test the correlation models.…”

Section: Introductionmentioning

confidence: 99%

“…The resulting predictions for ignition delay times were shown to simulate the experimental data very well, particularly at high temperatures. In the second representative work by many of the same authors of the Dussan et al. article, similar tools encompassing both 1 H and 13 C NMR were applied to the analysis of crude oils.…”

Section: Introductionmentioning

confidence: 99%

“…The primary means of obtaining functional groups for the prediction of properties have been nuclear magnetic resonance spectroscopy (NMR) and optical spectroscopy. Examples of the use and value of NMR are represented in the work of Dussan et al 9 and Won et al 3 In the first of these works, Dussan et al 9 took the low-dimensional approach of using a limited number of chemical functional groups, specifically CH3, CH2, and benzyl type, as determined quantitatively by NMR, to correlate the appearance of these groups in real petroleum jet fuels and alternative jet fuel with ignition delay times. The correlation method, broadly denoted as the quantitative structure−property relationship, 10 was based on a polynomial regression analysis where the regression analysis takes a nonlinear form such as quadratic or cubic.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Chemical Functional Group Composition of Jet Fuels Using Two-Dimensional Gas Chromatography

et al. 2023

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The growing interest in fuel property prediction models for simplification of fuel characterization has led to techniques for evaluating the chemical functional group compositions of fuels and mixtures as lowdimensional representations of the more detailed complete fuel composition. A new technique for quantitatively evaluating the UNIFAC group composition, chosen for its predictive versatility, from twodimensional gas chromatography (GC × GC) analysis of complex real and alternative jet fuels is discussed. GC × GC analysis provides a more detailed and comprehensive chemical composition for the extraction of chemical functional groups when compared to optical spectroscopy-based techniques like Fourier transform infrared and Raman spectroscopy. Furthermore, the operating cost is lower and GC × GC analysis is more direct than the NMR spectroscopic evaluation of functional groups. The conversion of GC × GC, time-of-flight mass spectrometry (TOF-MS), and flame ionization detection (FID) analysis data to UNIFAC group composition is done with a new method and software tool developed in this work. The tool can be decoupled from the fuel analysis method, used independently with other methods, and applied to already available fuel composition databases. Using GC × GC combined with TOF-MS and FID analysis, the UNIFAC group compositions of eleven different complex jet fuels are demonstrated as consisting of 10 groups of lower dimensional yet chemically significant features for data science prediction of fuel properties.

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