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Energy & Fuels 1993,7, 598-601
Analysis of Middle Distillate Fuels by Midband Infrared Spectroscopy G.E.Fodor* and K. B.Kohl Belvoir Fuels and Lubricants Research Facility, Southwest Research Institute, San Antonio, Texas 78228-0510 Received April 27, 1993. Revised Manuscript Received June 28, 199P
A new analytical method based on infrared spectroscopy in the 4000-650 cm-l wavenumber region has been developed that allows for rapid and reliable measurement of several pertinent fuel properties simultaneously. The method may be automated and, by using ruggedized commercially available Fourier transform infrared (FTIR) equipment, would be eminently suitable for commercial and military field applications. Preliminary studies show that middle region infrared spectroscopic data may be calibrated to determine those middle distillate fuel properties that are due to chemical structural features that give rise to active infrared resonance bands. The measured fuel property values must relate linearly to spectral intensities; Le., they must obey Beer’s law. In this study, the initially selected fuel properties were aromatic hydrocarbon content, carbon-to-hydrogen ratio, heat of combustion, cetane index, viscosity, and density. Other properties such as cloud point, pour point, octane number, cetane number, etc. are also expected to be successfully modeled. Usefulness of the method will be extended for the analysis of other fossil, as well as alternate fuels.
I. Introduction and Background
11. Fuels and Equipment
Most routine evaluations in petroleum analytical laboratories are performed using test methods established by ASTM, API, IP, military, or other agencies. While these methods may be accurate and enjoy a high degree of acceptance, they also have some disadvantages. For example, they may require relatively large sample sizes, they may be cumbersome, they may use toxic or environmentally dangerous chemicals, or they may be time consuming. We report the development of anew analytical method, based on FTIR spectroscopy in the midband region of 4000-650 cm-l, that allows for the simple, rapid, and reliable measurement of several pertinent fuel properties simultaneously. The method may also be automated and, using ruggedized equipment, would be eminently suitable for commercial and military field applications. Several articles have described the use of near-infrared (near-IR)spectroscopyto determine gasoline1t2and middle distillate fuel proper tie^.^ Near-IR is the result of second and third overtones and combination tones of the fundamental frequenciesthat produce the directly measurable midband region of the infrared spectrum (IR). Since midband IR spectroscopy is based on the measurement of characteristic fundamental resonances, it produces specific, usually sharp, well-defined peaks at substantially increased extinction coefficients. As expected, this preliminary study indicates that midband IR spectroscopy provides data in the analysis of middle distillate fuels superior to that derived by near-IR.3
Over 300fuelsampleawere colleded fromdomesticand foreign sources, including diesel fuels (DF-2) and jet fuels of grades Jet A, Jet A-1, JP-5, and JP-8. A calibration data set consisting of 111 randomly selected samples from this large matrix of fuels was built to provide the widest range of fuel property values within eachmeasurement category. This calibration set included measurements on 42 samples of DF-2, 30 samples of JP-5, 31 samples of JP-8, and 8 samples of Jet A and Jet A-1 fuels. The remaining over 200 fuels of the large set of fuel matrix provided the independent aknownnsamples, or test data set, to test the validity of the models developed from the calibration set of samples. The fuel samples were analyzed according to ASTM procedures. Carbon and hydrogen contents were measured by Test MethodB ofASTMD 5291,aninstrumentadclassicalcombustion technique. Aromatic hydrocarbon content was measured by supercritical fluid chromatography(SFC),as described in ASTM D 5186, which uses supercritical carbon dioxideas the carrier gas in a chromatographic procedure. Heat of combustiondata were determined in a bomb calorimeter according to ASTM D 240. Density was measured by the hydrometer method, as given in ASTM D 1298. Kinematic viscosity at 40 O C was determined in glasscapillaryviscometersaccordingto ASTMD 445. The cetane index was calculated from distillation temperature and density measurements, as described in ASTM D 4737. Repeatability and reproducibility of these ASTM methods are summarized in Table I. Spectroscopicdata were collected on a Nicolet Model 510FTIR spectrometer, equipped with a DTGS detector and a horizontal attenuated total reflectance (ATR) zinc selenide cell. Crystal angle of the cell is 45’, with 12 internal reflections through the sample. Depth of beam penetration at lo00 cm-l is 2 pm, resulting in an effectivepath length of 0.024 mm. Baselinecorrected FTIR spectra of the averageof 32 FTIFt scansof each fuelwere collected at a resolution of 4 cm-l. Each spectrum was truncated to the region of 4000450 cm-l wavenumbers. FTIR spectra without baseline correction resulted in essentially identical calibrations. Spectroscopicdata were correlated to fuel property values using Galactic Industries’ PLS plus program within the GRAMS/386 softwarepackage. Since all fuels examinedexhibited essentially
Abstract published in Advance ACS Abstracts, Auguat 16, 1993. (1)Swarin, S. J.; Dru”, C. A. “Prediction of Gasoline Properties with Near-Infrared Spectroscopy and Chemometrica”;SAE Paper No. 912390,1991. (2) Kelly, J. J.; Callis, J. B. Nondestructive Analytical Procedure for Simultaneous Estimation of the Major Classes of HydrocarbonConstituents of Finished Gasolines. Anal. Chem. 1990,62, 1444. (3) Westbrook, S. R. “Army Use of Near-Infrared Spectroscopy to Estimate SelectedProperties of CompressionIgnition Fuels”;SAE Tech. Paper 930734,1993. e
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Energy & Fuels, Vol. 7, No. 5, 1993 599
Analysis of Middle Distillate Fuels Table 1. Repeatability and Reproducibility of Selected ASTM Tests ASTM property aromatics, wt % carbon, wt % hydrogen, wt % heat of combustion MJ/kg Btu/lb density, g/mL kinematic viscosity at 40 "C,mm2/s calculated cetane index
D 5-75 5186 75-87 5291 9-16 5291 240
repeatability
range
0.7 0.89-0.98 0.35-0.65
reproducibility
4.0 2.22-2.44 0.69-0.93
0.13 56 1298 445
0.4 172 0.0012 O.OOO6 0.35% of av 0.7% of av
30-60 4737
only carbon-hydrogen bonds in their IR spectra, no region was excluded from building the correlation models. The use of transmission c e b is expected toproduce superiorresults, however, during this study an ATR cell was used to collect the spectra to explore the utility of the simplest sample handling procedures. Due to the relatively low volatility of the middle distillate fuel samples and the speed of the analysis, sample integrity was not believed to have been excessively compromised.
111. Discussion Our preliminary studies show that midband infrared spectroscopy may be calibrated to determine those middle distillate properties that are due to chemical structural features that give rise to active infrared resonance bands. The measured fuel property values must relate linearly to spectral intensities, i.e.; they must obey Beer's law. In this study, these initially selected properties were aromatic hydrocarbon content, carbon-to-hydrogen ratio, heat of combustion, cetane index, viscosity, and density. Other properties such as cloud point, pour point, octane number, cetane number, etc. are also expected to be successfully modeled. Establishing these correlations is the subject of our ongoing research. To illustrate some of the composition versus physical property relationships, the following generally accepted arguments may be considered. For any given fuel type, high specific gravity is associated with aromatic or naphthenic hydrocarbons and low specific gravity with paraffinic hydrocarbons. Heat of combustion of a fuel is influenced by the oxidation state, i.e., the carbon-tohydrogen ratio of the fuel. A fuel will have higher cetane number if the normal to is0 paraffin ratio is increased. Increased aromatic hydrocarbon concentrations decrease cetane number and increase octane number. Cloud point of a fuel is decreased by increasing is0 to normal paraffin ratio and increasing aromatic content in the fuel. Thus, these properties are determined by the branching of the saturated hydrocarbons,the ratio of normal to is0 paraffins, the aromatic hydrocarbon content, including the type and degree of substitution on the aromatic rings, and the ratio of these various constituents. All these chemical features are reflected to some degree in the IR spectra of compound~.~ During these feasibility studies, calibrations were performed using both the entire spectral region from 4000650 cm-' and with baseline regions of 4000-3200 and 25001800 cm-1 excluded. Except for reduced computer time required to develop a calibration file, no benefits were found in using less than the full range of the spectra. (4)Bellamy, L. J. The Infrared Spectra of Complex Molecules; Chapman and Hall Ltd.: London, 1975 and 1980.
Table 11. Estimation of Fuel Properties by FTIR. property aromatics, wt % heat of combustion Btu/lb MJ/kg carbon/hydrogen density, g/mL kinematic viscosity at 40 "C, mm2/s calculated cetane index
calibration range
9.22-40.4
R* SEP 0.967 1.016
17900-18630 41.8-43.3 5.887.18 0.78&0.878 1.06-4.30 37-58
0.921 0.921 0.972 0.992 0.957 0.950
39.80 0.093 0.048 0.002 0.166 0.943
a R2 = squared correlation coefficient. SEP = standard error of prediction.
Models relating the selected fuel properties to the FTIR spectra were developedusing a multivariate (chemometric) software package based on the partial least squares (PLS) spectral decomposition technique. The PLS method creates a simplified representation of the spectroscopic data by a process known as spectral decomposition. Good summary treatises of PLS were published by Martens: and Haaland and tho ma^.^^^ The PLS algorithm initially calculates the concentration, or property value, weighted average spectrum of all the spectra of the fuels in the calibration matrix. This calculation is followed by a computationally intensive procedure, accomplished by performing cross-validation calculations for all samples in the training set. In the cross-validation procedure, a sample is removed from the calibration data set, and a calibration model, calculated from the remaining samples in the training set, is used to predict the concentration (property) of the removed samples. The residual errors, or the difference between the predicted and known concentrationvalues, are squared and summed to determine the prediction error. Repeating this cross-validation process for the other samples in the training set results in a refined regression model useful in predicting the unknown fuel property. After a calibration model is established, it may be tested by comparing data obtained through standard tests on known fuels to those predicted by the calibration equations. It is important to note that each of the randomly selected fuels in the calibration set was kept in the experimental matrix even when statistical treatment identified data of some samples as spectral or property value outliers, or both. Spectra or property values were replaced only if enough sample was available for new analyses that indicated that the original data were in error. No fuel sample was removed from the matrix only because the data did not properly fit the predicted values. As is shown, even with such treatment, the predictive equations yield excellent overall results. The only case in which samples were excludedwas during calibration to calculated cetane index (CCI) values. Correlation between the ASTM calculated and the FTIR predicted CCI values identified four out of 111fuels as having property value outliers, yielding a corresponding (5)Martens, H. A. Multivariate Calibration: Quantitative Intapretation of Non-Selective Chemical Data. Parts I and I1 of the 1985 Ph.D. Dissertation, The Norwegian Institute of Technology, University of Trondheim,Trondheim,Norway, 1986,NR-ReportNo. 786and 787,ISBN 82-539-0273-5and 82-539-274-3. (6) Haaland, D.; Thomas, E. V. Partial Least-Squares Method for Spectral Analysis. 1. Relation to other Quantitative Calibration Methods and the Extraction of Qualitative Information. Anal. Chem. 1988, 60, 1193.
(7)Haaland, D.; Thomas, E. V. Partial Least-Squares Method for Spectral Analysis. 2. Application to Simulated and Glass Spectral Data. Anal. Chem. 1988,60, 1202.
Fodor and Kohl
600 Energy & Fuels, Vol. 7, No. 5,1993 45
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Figure 4. Correlation of FTIR predicted vs calculated cetane
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R2 = 0.728. Since the original distillation and density data could not be confirmed due to lack of sufficient quantities of samples, these four fuel samples were excluded from a second calibration run. The new calibration, shown in Figure 4 , yielded substantially improved statistics, including R2= 0.950,and a standard error of prediction, SEP = 0.934. Statistical results of the partial least squares treatment of the data are summarized in Table 11. Graphical presentations of the measured versus the predicted fuel property values are shown for aromatic hydrocarbon content, heat of combustion, carbon-tohydrogen ratio, cetane index, density, and viscosity in Figures 1-6,respectively. Numbers around the calibration lines represent individual fuel identification numbers. Estimation of aromatic hydrocarbon content of middle distillate fuels by midband FTIR gave a standard error of prediction (SEP) of 1.02wt 5% ,which compares favorably
1
15
2
25
3
35
4
45
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Figure 6. Correlation of FTIR predicted vs measured kinematic
viscosity. with the 4.0wt % reproducibility value of ASTM D 5186. Carbon-to-hydrogenratio was determined by IR to better than 0.05. As shown in Table 11, the range of available calibration data for heat of combustion values is narrow, reflecting the normal characteristics of these fuels. While the IR derived model for estimation of heat of combustion gave R2 = 0.921,a relatively low value, the SEP of 0.092 MJ/kg (39Btu/lb) is below the repeatability figure of 0.13 MJ/kg (56Btu/lb) specified in ASTM D 240. Models to estimate density and kinematic viscosity values are outstanding with R2 = 0.992 and 0.956,respectively. IV. Conclusions The results of this preliminary study show that midband FTIR spectroscopy, when combined with multivariate calibration analysis, is a versatile, efficient, and accurate technique for estimating several key fuel properties. The
Analysis of Middle Distillate Fuels
accuracy of this procedure is comparable to the reproducibility of the standard laboratory analysis designed for direct measurement of the specific fuel properties. Overall accuracy of this FTIR method is limited by the accuracy of the methods that yielded the calibration data used in the statistical analysis. However, ease and speed of analysis,portability of equipment, the low per test cost, and small sample size requirements make midband FTIR analyses highly desirable in many applications. For these reasons, further development of this method is planned.
Acknowledgment. This work was performed a t the Belvoir Fuels and Lubricants Research Facility (BFLRF)
Energy & Fuels, Vol. 7, No. 5,1993 601
at Southwest Research Institute (SwRI) located at San Antonio, TX, under Contract No. DAAK70-92-C-0059. Funding was provided by the U S . Army Belvoir Research, Development, and Engineering Center (Belvoir RDE Center), Ft. Belvoir, VA. Mr. T. C. Bowen, Belvoir RDE Center (SATBE-FL), served as contracting officer’s representative and technical monitor. The authors gratefully acknowledge the valued contribution to the statistical interpretation of the data by Dr. R. L. Mason and the invaluable technical assistance provided by Ms. M. S. Voigt. Editorial support by Mr. J. W.Pryor and Ms. L. A. Pierce is also thankfully acknowledged.
