Anal. Chem. 1990, 62, 2553-2556
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Determination of Gas Oil Cetane Number and Cetane Index Using Near-Infrared Fourier Transform Raman Spectroscopy Kenneth P. J. Williams*
B P Research, Sunbury Research Centre, Chertsey Road, Sunbury-on- Thames, Middlesex T W 1 6 7 L N , U.K. Rupert E. Aries, David J. Cutler, and David P. Lidiard
Perkin-Elmer Limited, Post Office Lane, Beaconsfield, Bucks HP9 1QA, U.K.
Fourier-transform (FT) Raman data were obtained, from a series of gas oil samples, by using a 1.064-pm laser source. These were analyzed with a proprletry mukivarlate software package (CIRCOM) to obtain a correlation between the spectral changes and fuel quality (cetane number and cetane index). The correlation coefficient (r2), between the FT Raman data and cetane index, was 0.93, with a standard error of estimate (SEE) and standard error of prediction (SEP) of 1.13 and 1.22 cetane units, respectively. I n addition, the chemical information relating to fuel quality was elucidated which agreed well with that previously understood.
INTRODUCTION The ignition quality of gas oils (diesel fuels) is normally characterized by using two alternative parameters, the cetane number and cetane index. The cetane number is a measure of the ignition delay properties of the gas oil; a high cetane number translates to a shorter ignition delay. The measurement of cetane number to ASTM standard D613 is time-consuming, difficult, and expensive, since the measurement requires the provision of a “standard” test engine. Few locations exist where cetane number determinations are performed; however, this still remains the primary measure of gas oil fuel quality. The cetane number is defined by the relative proportions of n-hexadecane (cetane) and cy-methylnaphthalene, which have the same ignition delay properties as the test fuel, when tested in a “standard” engine under controlled conditions. In terms of performance cetane possesses very good ignition delay characteristics, while cy-methylnaphthalene is very poor. The quality of data that can be achieved by these relatively empirical methods is rather poor. The ASTM standard quotes a repeatability of between 0.6 and 0.9 and a reproducibility of between 2.5 and 3.3 cetane units; both depend on the cetane number measured. The cetane index is a calculated parameter that depends on the density of the fuel and its boiling range. The cetane index clearly has advantages over the cetane number in that a test engine is not required and the sample size required for analysis is comparatively small. However, the cetane index is not a primary standard, being derived from cetane number data on wide range of diesel fuels and components. The measure of cetane index can only be applied to additive free samples and is also not applicable to synthetic fuels or to pure hydrocarbons. The precision of the cetane index is better than cetane number since it is a laboratory-derived value. The Institute of Petroleum (IP) has recently proposed an equation, IP method 380, which uses density and 10,50, and 90% recovery temperature ( I ) . The correlation between the cetane index obtained from this equation and cetane number is better than previous methods with a correlation coefficient of 0.94 and standard error of 2.09 cetane units. Clearly, there is scope
within the petroleum industry to improve and supply the methods for gas oil analysis. Raman spectroscopy is a nonintrusive method which offers potential advantages in this area and has been applied to analytical problems for many years ( 2 , 3 ) . However, the universal application of the method has been hindered by problems of laser-induced sample fluorescence which is often orders of magnitude more intense than the desired Raman scatter. The development of FT Raman spectroscopy, using a near-infrared (near-IR) laser source, overcomes many of these fluorescence problems (4). The advent of this method has marked a major advance in the applicability of Raman spectroscopy in the petrochemical industry. A significant number of publications concerning the development and applications of near-IR FT Raman spectroscopy have appeared in the literature over the past 3 years (5-7). To date, only one paper has discussed the possibilities of using near-IR FT Raman spectroscopy for the quantitative analysis of liquid fuel mixtures (8). This study employed multivariate analysis techniques (multiple linear regression (MLR), principal components regression (PCR), and partial least squares (PLS)) to model and predict the composition of a set of synthetic liquid fuel mixtures generated from the individual pure components, unleaded, superunleaded, and diesel fuel, respectively. While the work indicated adequate prediction of component concentrations using PCR and PLS, no information was presented on the underlying chemistry of the correlations obtained. Furthermore, the study employed synthetic mixtures of the three components minimizing the sources of variation in both the measurement (chemical and instrumental) and independent (property) data. In “real” samples the number of sources of variation is likely to be considerably higher. The purpose of this paper is to illustrate the potential of near-IR FT Raman spectroscopy for the analysis of gas oils by the use of a proprietary multivariate software package called CIRCOM. The study demonstrates that a correlation with cetane number and cetane index values can be obtained. We also demonstrate the value of multivariate calibration techniques for elucidating the underlying chemistry of complex systems. EXPERIMENTAL SECTION Near-IR FT Raman Spectra. Spectra were recorded with an experimental system comprising a modified Perkin-Elmer 1700X near-infrared Fourier transform spectrometer fitted with a liquid nitrogen cooled germanium detector as described elsewhere (9). The system utilized a NdYAG laser (Spectron Lasers, Rugby, U.K.) capable of producing 5 W of continuous wave radiation at 1.064 bm. Gas oil samples (18 in total) were pipetted into a 10-mm path length cuvette silvered on three sides. Raman radiation was collected by using a 180’ backscattering geometry. The cuvette was flushed between samples with petroleum ether and dried. Near-IR FT Raman data were generated under the following conditions, wavenumber range 4000-400 cm-’, resolution
0003-2700/90/0362-2553$02.50/00 1990 American Chemical Society
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PC 3
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Figure 1. Near-IR FT Raman spectra of gas oils in arbitrary intensity units: (a) sample with low cetane character, cetane number 42, cetane index 39.1; (b) sample with high laser induced fluorescence, cetane number 45.4, cetane W e x 47.2; (c) sample with high cetane character, cetane number 50.6, cetane in index 5 1.1,
4 cm-l, scans averaged 32. Spectra of each gas oil were obtained on a daily basis for 3 days in an attempt to model variations in sampling and laser power. In total 54 near-IR FT Raman spectra were collected for multivariate calibration, although it should be emphasized that these spectra only represent 18 independent gas
oil samples. CIRCOM Analysis. Multivariate calibration was performed with a proprietary software package, CIRCOM (Perkin-Elmer Limited, Beaconsfield, Bucks). Data were analyzed over the spectral range 3200-600 cm-I using a data interval of 4 cm-'. All data were mean centered. CIRCOM is a modified form of principal components regression (PCR) and has been described in detail elsewhere (IO, 11). However, for completeness, a brief description will be given here. CIRCOM comprises three steps. The first step involves subjecting the spectra to principal components analysis (PCA),sometimes called abstract factor analysis. PCA attempts to find a series of abstract spectra called principal components which may be weighted in different proportions and summed to reproduce the original spectral data. Each spectrum can thus be represented by a set of principal component weightings called scores resulting in considerable data reduction. PCA also allows the detection of atypical spectra or outliers. The second step in the analysis involves multiple linear regression of the principal component scores against values for the property of properties of interest determined by independent analysis. Unlike PCR, CIRCOM incorporates a third step, which involves retaining only those principal components found to be statistically significant to the regression. This avoids overfitting and results in a simpler and more robust regression equation. This regression equation may be represented as a spectrum which we call the property coefficient weightings spectrum. Unknown samples may be predicted simply by finding the scores for the unknown spectrum and inserting these in the regression equation or by multiplying the unknown spectrum with the coefficient weightings spectrum and summing the resulting area. Cetane Values. Values of both cetane number and cetane index for the 18 samples analyzed were obtained by using conventional ASTM and IP methods. These values varied between a low cetane character of cetane number 42.0 and cetane index 39.1 and a high cetane character of cetane number 50.6 and cetane index 51.1.
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Wavenumber cm-1 Figure 2. Significant principal components (PCs) identified by CIRCOM.
RESULTS AND DISCUSSION Near-IR FT Raman Spectra of Gas Oils. The aim of this research was to assess the potential of near-IR FT Raman data coupled with multivariate calibration for the determination of the cetane number and cetane index of gas oils. An understanding of the chemistry involved in any calibration was considered to be an important aspect of the study. Near-IR F T Raman spectra recorded from three gas oil samples that represent extremes of the spectral data generated in this study are shown in Figure 1. The differences in the spectra are largely attributable to variation in the background fluorescence (Figure 1 parts a vs b) and variations in the chemical composition of the samples relating to fuel performance (Figure 1 parts a vs c), that is the relative proportions of aliphatic and aromatic (naphthalenic) species present in the gas oils. The near-IR FT Raman spectra clearly contain information relating to cetane number and cetane index which, if extracted from the irrelevant sources of variation within the data, would provide the means for quantitation. This extraction of relevant information is an application ideally suited to multivariate calibration techniques such as partial least squares (PLS), principal components regression (PCR), or modified forms of PCR such as CIRCOM. CIRCOM Analysis of Cetane Number a n d Cetane Index. Preliminary analysis of the spectral data (54 spectra) using the CIRCOM software identified six outliers based on their Mahalanobis distance. Visual examination of these spectra revealed that the abnormal features were due to either laser artifacts or detector rf pickup associated with the experimental instrumentation used. These abnormalities translate as "spikes" in the spectra. All subsequent analyses were carried after elimination of these spectral outliers from the data set. CIRCOM analysis of the reduced data set (48 spectra) revealed five significant principal components (PCs,
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Wavenumber cm-1 Flgure 3. Comparison between (a) the near-IR FT Raman spectrum of a-methylnaphthalene and (b) inverted PC 2.
Figure 2) expressing 99.4% of the total variance within the spectral data. Visual examination of these PCs might be expected to reveal the underlying chemistry of the data set since each PC represents an independent source of variation. Unfortunately, the PCs represent abstract spectra and, as such, may have no easily discernible physical or chemical significance. Chemical significance is normally expressed by a combination of PCs and may be elucidated by the application of factor analysis techniques (12). In this case, however, some chemical significance may be attributed to the individual principal component spectra. The first principal component (PC 1) represents 94% of the variance within the data and may be attributed predominantly to laser-induced sample fluorescence. Inspection of extremes of the Raman data shown in Figure 1 illustrates that the variation in background fluorescence is very marked. Principal component 2 (PC 2) represents the second largest source of variation (4.3%) within the near-IR FT Raman data. The negative portion has the appearance of a Raman spectrum with a very intense feature at 1378 cm-', together with other features of medium intensity at 1662,1606,1578,and 1432 cm-'. The similarity between this inverted PC and the near-IR FT-Raman spectrum of a-methylnaphthalene is striking (Figure 3). Clearly, a significant spectral variation in the gas oil data relates to the proportion of naphthalenic material present in the fuel. Principal components 3 and 4 both contain features of aliphatic origin. Indeed, application of factor analysis to these two principal components (50' rotation) resulted in a factor spectrum which strongly resembled that of the near-IR FT Raman spectrum of hexadecane (Figure 4). Intense features are evident at 2852,1436,and 1304 cm-' in both spectra. The somewhat noisier factor spectrum (Figure 4b) reflects the diminished variance contributions of PC 3 and PC 4,0.7 and 0.3% of the total variance, respectively. It is
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Wavenumber cm-1 Figure 4. Comparison between (a) the near-IR FT Raman spectrum of hexadecaneand (b) the factor spectrum generated by a 50' rotation of PC 3 and PC 4.
Table I. Summary of Circom Analysis of Fuel Quality Parameters cetane no.
cetane index
maximum value
50.6
52.7
minimum value
39.1
39.7
average value correlation coefficient (9) std error of estimate (SEE) std error of prediction (SEP)
45.1
46.1
0.77 2.03 2.19
0.93 1.13 1.22
difficult to attach any chemical significance to PC 5 (0.1% variance), but it is evident that there is structure within the PC and that it cannot be regarded as noise. The individual principal components contain information relating to cetane number and cetane index. Application of multiple linear regression to the principal component scores (five PCs) for each fuel against the data generated for cetane number and cetane index by traditional methods resulted in significant correlations for both cetane index and cetane number (Table I). A correlation coefficient (9) of 0.93with a standard error of estimate (SEE) and standard error of prediction (SEP) of 1.13 and 1.22 cetane units, respectively, was obtained for cetane index while an 9 of 0.77,SEE of 2.03,and SEP of 2.19 cetane units was obtained for cetane number. There is substantially better correlation between the Raman data and the cetane index than the cetane number values. However, this is not entirely unexpected since the measurement of cetane number is based on the ignition delay characteristics of the fuel in a "standard" diesel engine. The error associated with such measurements is likely to be greater than that associated with density or recovery temperature required for calculation of the cetane index. It should be noted that the SEP values quoted in this work are cross-validated SEP estimates based
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phthalenic species, respectively (see Figures 3a and 4a). This is in excellent agreement with the previously known chemistry of fuel performance, that is, as stated previously, that cetane number is by definition the proportion of hexadecane and a-methylnaphthalene, which has the same ignition delay properties as the test fuel. In terms of performance hexadecane possesses very good ignition delay characteristics while a-methylnaphthalene is very poor.
CONCLUSIONS Near-IR FT Raman data have been shown to correlate well with cetane index (1.2 = 0.93) and to a lesser degree with cetane number (r2= 0.77) using multivariate calibration. Although the number of samples used in this study was somewhat limited, from a statistical viewpoint the method shows great promise as a rapid and inexpensive method of assessing fuel quality. Additional studies, with an increased number of fuels covering additional sources of variation and a greater range of cetane values, would be beneficial. The chemistry extracted from the analysis also correlated well with the previous knowledge of gas oil quality and the chemistry that influences fuel performance. While we can use our prior knowledge to assess the quality of the chemical information produced from this analysis, this method clearly offers potential to be used in a predictive manner in systems where the chemistry is less well defined.
d (a x c)
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ACKNOWLEDGMENT We thank Dr. S. M. Mason for providing some of the initial data and Miss J. A. Lander for providing samples and cetane values. 2200
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Wavenumber cm- 1 Figure 5. Property coefficient weighting spectra generated by CIRCOM: (a) cetane index and (b) cetane number; (c) average near-IR FT Raman spectrum calculated by CIRCOM; (d) property coefficient weighting spectrum for cetane index multiplied by the average spectrum to facilitate interpretation.
on dropping samples solely from the regression analysis (13) and as such are not true SEP values which would require an independent set of prediction samples. Examination of the property coefficient weightings spectrum for both cetane index and cetane number also suggests a greater error associated with the cetane number values (Figure 5). The property coefficient weightings spectra generated by CIRCOM are indistinguishable (Figure 5 part a vs b), which indicates that the underlying chemistry of the two properties is identical. Interpretation of the coefficient weightings spectra is aided by multiplication by the average near-IR FT Raman spectrum (Figure 5c) calculated by CIRCOM. The resulting “spectrum” (Figure 5d) shows positive and negative features associated with aliphatic and na-
LITERATURE CITED Bartlett, C. J. S. Pet. Rev. 1087, (June) 48. Gerrard, D. L.: Bowley, H. J. Anal. Chem. 1088, 60, 365R. Gerrard, D. L. Anal. Chem. 1088, 58, 6R. Hirshfeld. T.; Chase, B. Appl. Spectrosc. 1086, 40, 133. Williams, K. P. J.; Parker, S. F.; Hendra, P. J.; Turner, A. J. Microchim. Acta 1088, I I , 231. Hendra, P. J.; Barenzer, P.-Ce.; Crookell, A. J Raman Spectrosc. 1080, 3 0 , 35. Williams, K. P. J.; Mason, S. M. TrAC, Trends Anal. Chem. 1000, 9 , 119. Seaholtz, M. B.; Archibakl, D. D.; Lorber, A.; Kowalski, 9. R. Appl. Spectrosc. 1080, 4 3 , 1067. Crookell, A.; Hendra, P. J.; Mould, H. M.; Turner, A. J. J . Ramen Spectrosc. 1000, 21, 85. Fredricks, P. M.; Lee, J. B.; Osborn, P. R.; Swlnkels, D. A. J. Appl. Spectrosc. 1085, 3 9 , 303. Fredricks, P. M.; Lee, J. 9.; Osborn, P. R.; Swinkels, D. A. J. Anal. Chem. 1085, 57, 1947. Mailnowski. E. R.; Howery, D. 0. Factor Analysis in Chemistty; John Wiley and Sons: New York, 1960. Sorber, A.: Kowalski, 9. R. Centre for Process Analytical Chemistty, University of Washington. Unpubllshed results.
RECEIVED for review July 3,1990. Accepted August 28,1990. Permission to publish this paper has been granted by British Petroleum plc.
