Determination of gas oil cetane number and cetane index using near-infrared Fourier-transform Raman spectroscopy

Williams, K. P. J.; Aries, R. E.; Cutler, David J.; Lidiard, David P.

doi:10.1021/ac00222a008

Cited by 31 publications

(8 citation statements)

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“…Using a multivariate calibration scheme as coded in the QUANT module of the OPUS TM software program using partial least square (PLS), a predictive model of determining %PDZ in zircon samples was developed. Applications of multivariate methods with Raman spectroscopy for quantitative calibrations have been discussed . The basic aim of the method is to determine a property Y from an experimentally observable X (such as concentration) that is correlated by a calibration function b ( Y = X · b ).…”

Section: Resultsmentioning

confidence: 99%

“…Several improvements in the methods of acquisition of Raman spectra, including the use of excitation radiation in the near‐infrared region to minimize fluorescence effects and the use of charge‐coupled device detectors with Fourier transform (FT)–Raman instruments greatly improved the reproducibility of the Raman spectra, leading to increased use of multivariate analysis techniques with Raman spectroscopy …”

Section: Introductionmentioning

confidence: 99%

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The determination of percentage dissociation of zircon (ZrSiO₄) to plasma‐dissociated zircon (ZrO₂^.SiO₂) by Raman spectroscopy

Kock

Lekgoathi

Snyders

et al. 2011

J Raman Spectroscopy

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Raman spectroscopy and multivariate calibration techniques are used to determine the percentage conversion of zircon (ZrSiO4) to plasma‐dissociated zircon, ZrO2.SiO2 (PDZ). The integrated area of a consistent ZrO2 (monoclinic) Raman band at 477 cm–1 assigned to the Ag symmetry type from different conversion percentages of the PDZ spectra is used in a multivariate analysis scheme (partial least squares) to develop a predictive model for the subsequent determination of percentage dissociation of zircon to PDZ. In contrast to wet chemical methods, this approach eliminates the use of corrosive acids, e.g. hydrofluoric acid, thus leading to significant reductions in analysis time and material wastage, and it is a quick method that can be used online in a PDZ production facility. These results show best correlation with determination coefficient (R2) values in the range between 99.45 and 99.99% for zircon percentage dissociation in cross‐validation tests. This illustrates not only the versatility of the Raman technique in industrial applications but also the importance of noninvasive testing in the study of zircon and related materials. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The determination of percentage dissociation of zircon (ZrSiO₄) to plasma‐dissociated zircon (ZrO₂^.SiO₂) by Raman spectroscopy

Kock

Lekgoathi

Snyders

et al. 2011

J Raman Spectroscopy

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“…All the previous approaches using multivariate analysis to predict fuel attributes [4][5][6][7][8][9][10][11][12][13][14][15][16][17] used existing, real-world fuel samples (i.e., existing gasoline, diesel, jet fuels) as the training data set to predict performance attributes of those specific fuels. This work used hydrocarbons-neat or combined as mixtures for gasoline surrogate fuels including up to five neat components-to provide model input for predicting RON of the Fuels for Advanced Combustion Engines (FACE) gasolines designed by the Coordinating Research Council (CRC) and manufactured by ChevronPhillips Chemical Co [20].…”

Section: Introductionmentioning

confidence: 99%

Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons

Daly

Niemeyer

Cannella

et al. 2016

Fuel

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Liquid transportation fuels require costly and time-consuming tests to characterize metrics, such as Research Octane Number (RON) for gasoline. If fuel sale restrictions requiring use of standard Cooperative Fuel Research (CFR) testing procedures do not apply, these tests may be avoided by using multivariate statistical models to predict RON and other quantities. Existing techniques inform these models using information about existing, similar fuels-for example, training a model for gasoline RON with a large number of characterized gasoline samples. While this yields the most accurate predictive models for these fuels, this approach lacks the ability to predict characteristics of fuels outside the training data set. Here we show that an accurate statistical model for the RON of gasoline and gasoline-like fuels can be constructed by ensuring the representation of key functional groups in the spectroscopic data set are used to train the model. We found that a principal component regression model for RON based on IR absorbance and informed using neat and 134 mixtures of n-heptane, isooctane, toluene, ethanol, methylcyclohexane, and 1-hexene could predict RON for the 10 Coordinating Research Council (CRC) Fuels for Advanced Combustion Engine (FACE) gasolines and 12 FACE gasoline blends with ethanol within 34.8±36.1 on average and 51.2 in the worst case. We next studied the effect of adding 28 additional minor components found in the FACE gasolines to the statistical model, and determined that it was necessary to add additional representatives of the branched alkane and aromatics classes to reduce model error. For example, adding 2,3-dimethylpentane and xylene to the previous model allowed it to predict RON for the 22 target fuels within 0.3±4.4 on average and 7.9 in the worst case. However, we determined that the specific choice of fuel in those classes mattered less than ensuring the representation of the relevant functional group. This work builds upon previous efforts by creating models informed by neat and surrogate fuels-rather than complex real fuels-that could predict the performance of complex unknown fuels.

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“…The ability of vibrational spectroscopy to determine fuel performance properties was first demonstrated by correlating near-infrared spectra to octane numbers for gasoline samples and Raman spectra to cetane numbers for diesel fuel samples using multivariate statistics. 2,3 Correlations between mid-infrared (IR) spectra and octane numbers, aromatics, olefinics, saturates, methyl tert -butyl ether (MTBE), and ethanol for gasoline samples soon followed. 4 Since these first measurements, advances in analyzer designs and chemometric modeling have improved the abilities of Raman, 1,5–11 IR, 8,9,12 and NIR 8–11,13–15 to determine an expanding list of fuel properties.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Twenty-Two Performance Properties of Diesel, Gasoline, and Jet Fuels Using a Field-Portable Near-Infrared (NIR) Analyzer

et al. 2016

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The change in custody of fuel shipments at depots, pipelines, and ports could benefit from an analyzer that could rapidly verify that properties are within specifications. To meet this need, the design requirements for a fuel analyzer based on near-infrared (NIR) spectroscopy, such as spectral region and resolution, were examined. It was found that the 1000 to 1600 nm region, containing the second CH overtone and combination vibrational modes of hydrocarbons, provided the best near-infrared to fuel property correlations when path length was taken into account, whereas 4 cm(-1) resolution provided only a modest improvement compared to 16 cm(-1) resolution when four or more latent variables were used. Based on these results, a field-portable near-infrared fuel analyzer was built that employed an incandescent light source, sample compartment optics to hold 2 mL glass sample vials with ∼1 cm path length, a transmission grating, and a 256 channel InGaAs detector that measured the above stated wavelength range with 5-6 nm (∼32 cm(-1)) resolution. The analyzer produced high signal-to-noise ratio (SNR) spectra of samples in 5 s. Twenty-two property correlation models were developed for diesel, gasoline, and jet fuels with root mean squared error of correlation - cross-validated values that compared favorably to corresponding ASTM reproducibility values. The standard deviations of predicted properties for repeat measurements at 4, 24, and 38℃ were often better than ASTM documented repeatability values. The analyzer and diesel property models were tested by measuring seven diesel samples at a local ASTM certification laboratory. The standard deviations between the analyzer determined values and the ASTM measured values for these samples were generally better than the model root mean squared error of correlation-cross-validated values for each property.

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Determination of gas oil cetane number and cetane index using near-infrared Fourier-transform Raman spectroscopy

Cited by 31 publications

References 10 publications

The determination of percentage dissociation of zircon (ZrSiO₄) to plasma‐dissociated zircon (ZrO₂^.SiO₂) by Raman spectroscopy

The determination of percentage dissociation of zircon (ZrSiO₄) to plasma‐dissociated zircon (ZrO₂^.SiO₂) by Raman spectroscopy

Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons

Analysis of Twenty-Two Performance Properties of Diesel, Gasoline, and Jet Fuels Using a Field-Portable Near-Infrared (NIR) Analyzer

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Determination of gas oil cetane number and cetane index using near-infrared Fourier-transform Raman spectroscopy

Cited by 31 publications

References 10 publications

The determination of percentage dissociation of zircon (ZrSiO4) to plasma‐dissociated zircon (ZrO2.SiO2) by Raman spectroscopy

The determination of percentage dissociation of zircon (ZrSiO4) to plasma‐dissociated zircon (ZrO2.SiO2) by Raman spectroscopy

Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons

Analysis of Twenty-Two Performance Properties of Diesel, Gasoline, and Jet Fuels Using a Field-Portable Near-Infrared (NIR) Analyzer

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Product

Resources

About

The determination of percentage dissociation of zircon (ZrSiO₄) to plasma‐dissociated zircon (ZrO₂^.SiO₂) by Raman spectroscopy

The determination of percentage dissociation of zircon (ZrSiO₄) to plasma‐dissociated zircon (ZrO₂^.SiO₂) by Raman spectroscopy