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Energy & Fuels 1998, 12, 304-311
Determination of Physicochemical Properties and Carbon-Type Analysis of Base Oils Using Mid-IR Spectroscopy and Partial Least-Squares Regression Analysis M. I. S. Sastry, Anju Chopra, A. S. Sarpal,* S. K. Jain, S. P. Srivastava, and A. K. Bhatnagar Research & Development Centre, Indian Oil Corporation Limited, Sector-13, Faridabad 121 007, Haryana, India Received July 21, 1997. Revised Manuscript Received December 3, 1997
It has been well recognized that the chemical composition of the base oil is primarily responsible for its physicochemical properties such as viscosity, viscosity index (VI) and pour point etc., which in turn influence the performance of the finished formulation. Hence, it is very essential to determine the chemical composition of the base oil to understand the physicochemical properties. The paper highlights an experimental and computational protocol for the simultaneous determination of physicochemical properties (viscosity index and pour point) and carbon type analysis (CA, CP, CN, and isoparaffin carbon content, IP) of base oils from their IR spectral features. IR spectra of 60 base oils of different origin and processing schemes are recorded using the horizontal attenuated total reflectance accessory with ZnSe windows. Partial least-squares (PLS) technique has been applied to construct the mathematical models which correlate the IR spectral features with the experimentally determined values (using NMR and standard ASTM tests). The regression coefficients are found to be in the range 0.80-0.95.
Introduction Mineral lube base oils contain literally tens of thousands of individual hydrocarbons in the carbon number range of 20 to 40+, classified in terms of “hydrocarbon types”, viz., paraffins, isoparaffins, naphthenes, aromatics, and heteroaromatics. The naphthenes and aromatics are further classified according to their ring numbers by mass spectrometry techniques. The distribution of various hydrocarbon classes in lube base oils is determined by the crude source, the method, and degree of refinement. It has been recognized that the chemical composition of the base oil is primarily responsible for its physicochemical properties, such as viscosity, viscosity index (VI), pour point, and oxidation stability, which in turn influence the performance of the finished lubricant.1-3 Base oils can be produced through different processes, viz., hydrofinishing/conventional (high viscosity index (HVI) oils), severely hydrofinishing techniques (very high viscosity index (VHVI) oils), and hydrocracking and wax isomerization (extra high viscosity index (XHVI) oils). The high-quality base oils produced by hydroprocessing techniques are necessary for modern day equipment which operate under increasingly severe conditions, and the oils will have good oxidation stability and high viscosity index/low pour (1) Murray, D. W.; Mac Donald, J. M.; White, A. M.; Wright, P. G. Pet. Rev. 1982, 36-40. (2) Korcek, S.; Jensen, R. K. ASLE Trans. 1976, 19, 83-94. (3) Murray, D. W.; MacDonald, J. M.; White, A. M.; Wright, P. G. Proc. 11th World Pet. Congr. 1983, 4, 447-461.
point. The chemical classes, isoparaffins, and mononaphthenes are favorable for high viscosity/low pour point base oils. The low viscosity index compounds such as polynaphthenes and polyaromatics are converted to mono naphthenes and paraffins/wax into isoparaffins in the hydrocracking and wax isomerization processes (high viscosity index and low pour point) to base oils.4 As the chemical composition of the base oil is very important to understand the physicochemical properties, it is essential to find a suitable method for their determination. A large number of analytical techniques including HPLC, NMR, IR, and MS have been extensively applied for the hydrocarbon type analysis of base oils. Sarpal and co-workers5 have reported a method for the estimation of isoparaffin content and various isoparaffin structures in severely hydrocracked/hydrocracked base oils by 13C NMR spectral analysis. They reported the presence of isopropyl, ethyl, butyl, pentyl and other types of branches in these base stocks. In a recent communication6 the same authors presented the analysis of base oils produced through different processes. It was pointed that specific isoparaffin structures are responsible for very high VI and low pour point of XHVI and other types of oils. (4) Ushio, M.; Kamiya, K.; Yoshida, T.; Honjou, I. Presented at the American Chemical Society Symposium, Washington, DC, August 1992. (5) Sarpal, A. S.; Kapur, G. S.; Anju Chopra; Jain, S. K.; Srivastava, S. P.; Bhatnagar, A. K. Fuel 1996, 75 (4), 483. (6) Sarpal, A. S.; Kapur, G. S.; Jain, S. K.; Srivastava, S. P.; Bhatnagar, A. K. Fuel 1997, 76 (10), 931-937.
S0887-0624(97)00125-4 CCC: $15.00 © 1998 American Chemical Society Published on Web 01/24/1998
Analysis of Base Oils
IR spectroscopy has been widely used for the hydrocarbon type analysis of the base oils varying in nature and complexity.7-11 In the past five years, several articles appeared on the application of near-IR/mid-IR spectroscopy in the determination of gasoline12-17 and middle distillate fuel properties18,19 using partial leastsquares (PLS) technique. Fodor et al.16,17 have shown that the correlation obtained by using FT-IR derived data of middle distillate fuels for various properties like viscosity, density, cetane index, cloud point, etc., and RON of gasoline were superior to those derived from other methods. They pointed out that for good correlation the IR spectral features should show differences with the change in physical properties, which are dependent on the chemical composition. They have also mentioned that several fuel/base oil properties are determined by the presence of special atomic groups, the branching of saturated hydrocarbons, the ratio of normal to isoparaffins, the aromatic hydrocarbon content, including their types and degree of substitution on the aromatic rings, and the ratio of these constituents. All these chemical features are reflected to some degree in IR spectra of the compounds.20 In this paper, a FT-IR (mid-IR) spectroscopic method developed for the prediction of physicochemical properties such as viscosity index (VI), pour point, and hydrocarbon type analysis in terms of CA, CP, CN, and isoparaffin content (IP) of base oils is reported. The use of chemometric techniques such as partial least-squares (PLS) regression analysis is demonstrated. The instrument manufacturer’s software for PLS regression was utilized. The property VI was chosen because it is strongly influenced by small changes in base oil composition and it is a complex function reflecting a logarithmic relationship between base oil viscosities at 40 and 100 °C, which is not easily “modeled”. VI is dependent on the branching of the paraffins and paraffinic chains attached to aromatic and naphthenic rings and the content and type of aromatics and naphthenes.4,6,21 Pour point of base oils is also dependent upon similar compositional parameters; however, viscosity index can be determined much more precisely than the pour point. Two pertinent infrared absorption regions were selected for correlation model development. (7) Brandes, G. Brennstoff-Chem. 1956, 37, 263; Erdol Kohle 1958, 11, 700, 781. (8) Mohammed, A. A. K.; Hankish, K. Analyst 1985, 110, 1477. (9) Van de Ven, M. C.; Jansen, L.; den Heijer, J. Presented at the Symposium on Processing, Characterization and Application of Lubricant Base Oils, Part II, Division of Petroleum Chemistry, American Chemical Society, 1994. (10) Sastry, M. I. S.; Chopra, A.; Sarpal, A. S.; Jain, S. K.; Srivastava, S. P.; Bhatnagar, A. K. Fuel 1996, 75 (12), 1471. (11) Sastry, M. I. S.; Mukherjee, S.; Kapur, G. S.; Sarpal, A. S.; Jain, S. K.; Srivastava, S. P. Fuel 1995, 74 (9), 1343. (12) Lasaght, M. J.; Van Zee, J. A.; Callis, J. B. Rev. Sci. Instrum. 1991, 62, 507-545. (13) Kelly, J. J.; Callis, J. B. Anal. Chem. 1990, 62, 1444. (14) Swarin, S. J.; Drumm, C. A. SAE Paper No. 912390, 1991. (15) Fodor, G. E. SAE Technical Paper No. 941019, 1994. (16) Fodor, G. E.; Kohl, K. B.; Mason, R. L. Anal. Chem. 1996, 68, 23-30. (17) Mohammed, A. A. A.; Tawabini, B. S.; Abbas, N. M. Fuel 1996, 75 (9), 1060. (18) Fodor, G. E.; Kohl. K. B. Energy Fuels 1993, 7, 598. (19) Westbrook, S. R. SAE Paper No. 930734, 1993. (20) Bellamy, L. J. The Infrared Spectra of Complex Molecules; Chapman and Hall Ltd.: London, 1980. (21) Han Zhou; Ke Li; Xiequing Wang; Yifang Xu; Fu Shen. Fuel. Sci. Technol. Int. 1992, 10, 1085.
Energy & Fuels, Vol. 12, No. 2, 1998 305 Table 1. Hydrocarbon Compositional Data and Physical Properties of Base Oilsa sample
CA
CP
CN
IP
HF1 HF2 HF3 HF4 HF5 HF6 HF7 HF8 HF9 HF10 HF11 HF12 HF13 HF14 HF15 HF16 HF17 HF18 HF19 HF20 HF21 HF22 SHF1 SHF2 SHF3 SHF4 SHF5 SHF6 SHF7 N1 N2 N3 N4 HC1 HC2 HC3 HC4 HC5 HC6 HC7 HC8
6.2 7.4 8.4 7.3 7.0 9.0 5.1 4.8 6.0 4.9 4.7 4.8 5.3 6.3 19.0 7.8 8.1 10.2 19.0 19.0 5.9 5.6 2.8 4.0 5.7 0.0 2.3 0.0 6.0 6.1 5.2 7.0 9.0 0.0 0.0 0.0 2.2 1.8 1.8 0.0 0.0
72.8 72.6 71.0 72.7 73.0 66.0 71.9 75.2 72.0 72.2 73.3 71.6 73.1 68.7 59.0 67.2 74.9 64.8 60.0 57.0 72.4 76.0 73.0 74.0 70.6 77.0 75.6 75.0 71.0 53.9 46.6 53.4 49.5 93.0 89.0 90.0 90.8 84.2 88.2 85.0 85.0
21.0 20.0 20.6 20.0 20.0 25.0 24.0 20.0 22.0 22.9 22.0 23.6 21.6 25.0 22.0 25.0 17 25.0 21.0 24.0 21.7 18.4 24.2 22.0 23.7 23.0 22.1 25.0 23.0 40.1 48.2 40.0 41.5 7.0 11.0 10.0 7.0 14.0 10.0 15.0 15.0
40.2 40.4 40.6 40.7 38.5 32.4 40.5 45. 2 42.5 40.5 44.1 40.6 45.1 36.3 31.5 33.2 37.7 33.0 30.0 33.0 41.1 44.4 46.0 44.2 35.0 44.0 48.0 41.2 37.2 38.9 34.2 50.8 31.8 61.9 54.8 58.0 56.4 48.0 49.0 48.3 50.0
viscosity viscosity pour point (40 °C) index (°C, K) 12.9 12.1 58.8 32.8 13.1 27.8 92.9 101.5 111.6 102.6 76.4 78.1 30.7 188.3 14.5 183.6 505.2 205.2 26.7 117.5 74.5 31.6 30.5 30.9 77.1 92.8 76.2 32.0 12.8 99.3 100.4 99.3 99.0 16.7 23.8 44.0 17.2 24.1 31.6 16.2 33.4
100 98 95 100 101 98 96 97 95 94 97 92 95 92 67 97 94 91 66 66 101 106 103 102 106 111 108 110 103 29 47 29 39 141 151 146 123 131 126 120 123
-9, 264 -12, 261 -3, 270 -6, 267 -6, 267 -6, 267 -9, 264 -12, 261 -6, 267 -12, 261 -9, 264 -9, 264 -12, 261 - 6, 267 -12, 261 -9, 264 -6, 267 -9, 264 -6, 267 -9, 264 -3, 270 -6, 267 -12, 261 -15, 258 -6, 267 -6, 267 -15, 258 -18, 255 -9, 264 -36, 237 -27, 246 -36, 237 -33, 240 -18, 255 -18, 255 -15, 258 -27, 246 -18, 255 -12, 261 -12, 261 -12, 261
a HF, hydrofinished; SHF, severely hydrofinished; N, naphthenic; HC, hydrocracked.
Experimental Section Selection of Base Oils. Sixty base oils produced through different processes, viz., hydrofinished/conventional (HF) of high viscosity index (HVI), severely hydrofinished (SHF) of very high viscosity index (VHVI), and special base oils (hydrocracked and wax isomerized) (HC) of extra high viscosity index (XHVI) were considered for the present study. The physicochemical parameters such as viscosity at 40 °C, pour point, viscosity index, and hydrocarbon type analysis data (CA, CP, CN, and IP) of base oils (derived from 1H and 13C NMR studies4,6) used for this study are given in Table 1. IR Spectroscopy. The IR spectra of the base oils in the 4000-650 cm-1 region were collected using FT-IR instrument equipped with a DTGS detector. The spectra were recorded using Spectra Tech horizontal attenuated total reflectance (HATR) accessory. Liquid trough cell with ZnSe window was employed to collect the spectra. FT-IR spectra of the average 40 scans of each base oil were collected at a resolution of 2 cm-1. The spectral region from 3200 to 2700 cm-1 and 1800 to 650 cm-1 have been used for building the correlation models. NMR Spectroscopy. The 1H and 13C NMR spectra were recorded on a 300 MHz NMR spectrometer operating at 300 and 75.4 MHz, respectively. Solutions of base stocks (30-40 vol %) were prepared in CDCl3 containing 0.15 M chromium acetylacetonate as the relaxation agent and TMS as the internal reference. Quantitative 13C NMR spectra were recorded under inverse gated conditions.4,6
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Figure 1. IR spectra of base oils produced by different processes. VI/Pour Point Estimation. Viscosity index and pour point of the base oils are estimated as per ASTM D-2270 and ASTM D-97 procedures, respectively.
Results and Discussion The HATR IR spectra of the three base oils produced by different processes are shown in Figure 1. Subtle changes can be observed in the IR spectra indicating the changes in the chemical composition of the base oils. Generally, for paraffins, CH3 groups have characteristic C-H stretching bands at 2962 and 2872 cm-1, while CH2 groups have bands at 2926 and 2853 cm-1. The bending vibrations of methyl and methylene groups appear at 1375, 1450, and 1475 cm-1, respectively. Aromatic C-H stretching bands occur in 3100-3000 cm-1 region, while the in-plane and out-of-plane bending bands appear in the 1300-1000 and 900-675 cm-1 regions, respectively. These latter bands are the most prominent and most informative for aromatics. Naphthenes (cycloparaffins) are the components that give the least characteristic bands, in most cases overlapping with bands of other functional groups. The isoparaffinic structures provide some additional features in the IR spectra of the base oils. The isopropyl and isobutyl branches show splitting of the methyl bending band at 1370 cm-1 into two bands appearing at 1380 and 1360 cm-1. The methyl branches generally show a weak but characteristic band at 935 cm-1, which is assigned to the methyl rocking mode. The propyl and longer branches give a peak at 890 cm-1, whereas the CH2 rocking mode of ethyl branches gives a weak band at 770 cm-1. Isoparaffin structures show medium to strong skeletal vibrational bands in the 1200-1000 cm-1 region, along with additional bands in the 1000700 cm-1 region.20 The IR spectrum of solvent-refined base oil (VI ) 100, pour ) 264 K, CA ) 6.2, CP ) 72.8, CN ) 21, IP ) 40.2) shows bands at 1605, 815 cm-1 due to aromatics, 723 cm-1 due to paraffinic chains, and bands in the 1200900 cm-1 region which can be assigned to naphthenic
and isoparaffinic structures. The bands due to methyl, methylene group stretching and bending bands appear in the 3100-2700 and 1470-1350 cm-1 regions in all base oil samples. The relative intensities of these bands will differ depending on the structures and alkyl chain length. A shoulder at 790 cm-1 can also be observed. The hydrofinished naphthenic base oil (VI ) 47, pour ) 246 K, CA ) 5.2, CP ) 46.6, CN ) 48.2, IP ) 34.2) shows characteristic bands in the 1800-600 cm-1 region. The spectrum shows multiple bands with medium intensity in the 1200-900 and 800-600 cm-1 region due to the presence of more isoparaffinic and naphthenic structures as compared to solvent refined oils. The band at 720 cm-1 is medium in intensity due to lower concentrations of paraffinic structures. As indicated by the pour value of the base oil, the presence of high naphthenic structures lowers the pour point considerably. The hydrocracked base oil (VI ) 151, pour ) 255 K, CA ) 0.0, CP ) 89, CN ) 11, IP ) 54.8) shows strong intense bands at 720 cm-1 and weak to medium intense bands in the 1200-750 cm-1 region. The bands due to aromatic structures at 1600 and 810 cm-1 are absent, indicating the low concentration of aromatics in the sample. The IR spectrum also shows a clear band at 790 cm-1, which is assigned to the presence of isoparaffinic structures. From Figure 1 it is clear that the variations in physical properties of base oils along with chemical composition are clearly represented in their IR spectral features. Hence, the changes in the spectral features of base oils can be correlated to their physicochemical properties. Although these distinctive bands for various structures have been used for qualitative and limited quantitative use, their overlap prevents the use of specific bands for accurate estimation of hydrocarbon types and the physicochemical properties. To overcome this problem, a chemometric approach of partial least-squares (PLS) factorial analysis of IR spectra with proper choice of spectral regions was utilized. The IR spectral features of various base oils are correlated with the
Analysis of Base Oils
hydrocarbon type data obtained from 13C NMR spectral analysis, VI, and pour point data obtained from standard ASTM procedures. Chemometric Analysis. To carryout the partial least-squares (PLS) analysis, the IR spectral data was divided into calibration set or training set and validation set. The training set was selected in such a way that it covers the complete range of all the property data that are to be correlated. The subdivision into two groups was random and was performed prior to any PLS analysis. To develop the PLS models for all the properties of interest, two spectral regions were selected: (a) the region from 3200 to 2700 cm-1, where -CH3, -CH2, and -CH, and aromatic C-H stretching bands absorb and strong in intensity, and (b) the region from 1800 to 650 cm-1, where the C-H bending modes, skeletal vibrations, aromatic substitution bands, etc. are predominant. All the spectra are background corrected before subjecting to PLS analysis. The partial least squares (PLS), a powerful multivariate statistical spectral decomposition technique, is described in detail by Haaland and Thomas.22,23 In the PLS model, an unknown spectrum is expressed as a linear combination of the principal components, or factors, of the standard spectra. The factors are selected for maximum spectral variance subject to the fact that the factors are correlated to the property value information. The number of factors that are significant for predicting the component concentration/property value are generally determined by cross validation. The TURBOQUANT software provided with the instrument was employed for the development of PLS models. As suggested by Haaland and Thomas,22 mean centering and variance scaling of the spectral data techniques have been applied during the PLS calculations. Mean centering makes the data more stable by removing any offset effects from the data. The software first calculates the concentration, or property value, weighted average spectrum of all the spectra of base oils in the training set. In the second step, the software calculates one set of principal component scores and one set of factors for each property of interest. This will be followed by the “leave one out” validation calculations for all samples in the training set. In the software used, in the leave one out validation procedure, one sample is removed from the training data set at a time, and a new calibration model calculated from the remaining samples in the training set is used to estimate the property value of the omitted sample. The leave one out cross validation provides information on standard error of prediction. The predicted base oil property values calculated from the model depend on how many factors are used in the model. PLS software calculates as many factors as there are standards or data points, whichever is smaller. The first few factors generally contain the relevant information on the property value. The later factors are associated with experimental errors such as noise or spectral variations not related to the required property, leading to less than optimum prediction for samples in prediction set. The software available with the IR instrument provides data for selecting the optimum (22) Haaland, D.; Thomas, E. V. Anal. Chem. 1988, 60, 1193-1202. (23) Haaland, D.; Thomas, E. V. Anal. Chem. 1988, 60, 1202-1208.
Energy & Fuels, Vol. 12, No. 2, 1998 307
Figure 2. Predicted vs actual value plot for viscosity index calibration.
Figure 3. Predicted vs actual value plot for pour point (K) calibration. Table 2. Parameters Used for Calibration (Spectral Region Used: 3100-2700 and 1800-650 cm-1) property
range
VI 29-151 pour point (K) 237-270 CP (%) 42-93 IP (%) 32-62 0-19 CA (%) CN (%) 7-51
no. of standards no. of factors correln in model used coeff 36 36 36 36 40 40
13 13 13 9 8 10
0.95 0.90 0.95 0.90 0.90 0.90
number of factors to use for each property by plotting the predicted residual sum of squares (PRESS) versus number of factors employed in the calibration. The PRESS values are based on cross-validation results. As factors which represent useful information are added to the analysis, the PRESS value decreases, indicating improvement in the PLS calibration error. At some
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Figure 4. Predicted vs actual value plot for CA calibration.
Figure 6. Predicted vs actual value plot for CN calibration.
Figure 5. Predicted vs actual value plot for IP calibration.
Figure 7. Predicted vs actual value plot for CP calibration.
point the factors add noise or other information unrelated to the property value/concentration, at which the PRESS value either levels off or increases. The factor may be selected as (a) the point at which the PRESS value is at minimum, and (b) where there is no distinct minimum, the point at which the curve indicates that further increase in factors should have negligible effects, i.e., where the curve levels off. In the present work, the optimum number of factors were selected from the PRESS plots, and the number was selected as the first factor, at which the PRESS plot has a distinct minimum or leveled off. The parameters used for the development of calibration models for all the properties are given in Table 2. The predicted versus actual value plots for VI, pour point, CA, IP, CN and CP are given in Figures 2-7. The correlation coefficients obtained for the training set for all properties are also given in Table 2. The correlation
Table 3. Parameters Used for Validation property
no. of samples used for prediction
correln coeff
VI pour point CP IP CA CN
25 25 25 12 17 17
0.95 0.80 0.95 0.80 0.85 0.86
coefficients of >0.90 indicate that the IR spectral data can be effectively used for the property estimation. The standard prediction errors estimated are found to be 0.4%, 0.55%, 1.1%, 1.6%, 1.05%, and 1.04% for CA, CP, CN, IP, VI, and pour point, respectively. After the calibration model is established, the model is tested by validation experiments, in which the calibration model is applied to similar base oils present in a validation set. The predicted property values were
Analysis of Base Oils
Energy & Fuels, Vol. 12, No. 2, 1998 309
Figure 8. Q-Q plot and residual vs experimental value plot for pour point.
Figure 9. Q-Q plot and residual vs experimental value plot for viscosity index.
Figure 10. Q-Q plot and residual vs experimental value plot for isoparaffin content (IP).
then compared with those derived from standard tests, i.e., ASTM and NMR methods. The various parameters used for the validation of the models developed for different properties are given in Table 3. The correlation coefficients are in the range of 0.80-0.96. Good correlation’s have been obtained for VI and CP values. The lower correlation coefficient for pour point and IP may be due to the use of generalized model for all types of base oils and due to the absence of specific bands for
the structures responsible for these properties. The use of more rigorous models based on nonlinear PLS and neural networks will help in solving these problems. Statistical Analysis To check the validity of the models developed, statistical tests on the residuals were carried out. The assumption that the errors were normally distributed
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Figure 11. Q-Q plot and residual vs experimental value plot aromatic carbon content (CA).
Figure 12. Q-Q plot and residual vs experimental value plot for paraffinic carbon content (CP).
Figure 13. Q-Q plot and residual vs experimental value plot naphthenic carbon content (CN).
was checked by quantile-quantile plots on residuals. The quantile-quantile plot (Q-Q plot) compares the ordered observed quantiles to the theoretical quantiles (for the same probability). The Q-Q plot will be a straight line through the origin with slope 1 if the data are normally distributed. If the normal density is incorrect by a scale factor and/or a location factor, the Q-Q plot will still remain as a straight line. However, in the case of a location difference, the intercept in the
Q-Q plot becomes nonzero, while for a scale difference, the slope will no longer be 1. Linearity of the Q-Q plot also indicates the approximate normality of sample distribution.24,25 The Q-Q plots for all properties considered are shown in Figures 8-13. The Q-Q plots in all cases are almost (24) Applied Multivariate Statistical Analysis; Johnson, Richard A., Wichern, Dean W., Eds.; Prentice Hall of India Private Ltd.: New Delhi, 1996.
Analysis of Base Oils
linear and this indicates that the distribution of errors is normal in nature. The straightness of the Q-Q plot can be measured from the correlation coefficient of the points in the plot.24 The correlation coefficients (RQ) for the Q-Q plots of the properties are found to be 0.99, 0.988, 0.984, 0.982, 0.98, and 0.984 for VI, pour point, CP, IP, CA and CN, respectively. Formally, the hypothesis of normality at a significance level of R is rejected if RQ falls below the appropriate value given in the standard statistical tables at a given sample population. Therefore, a test of normality at 90% (R ) 0.10) level of significance was carried out on the correlation coefficients. For all the properties under consideration, at R )0.10 and sample population n ) 36, 40, the values from standard tables are found to be 0.974 and 0.977, respectively. The RQ values of the above Q-Q plots for all properties are found to be greater than the values obtained from the standard tables and this indicates that the residuals are normal in nature and the developed models are valid for the predictions. The residual errors versus actual value plots for all properties are also given in Figures 8-13. It can be (25) Applied Multivariate Data Analysis, Vol. 1. Regression and Experimental Design; Jobson, J. D. Ed.; Springer-Verlag: New York, 1991.
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observed that the residuals form more or less a horizontal band in all cases. This indicates equal variances of residuals and no dependence on the actual and predicted values, thereby ensuring the adequacy of the model. Conclusions A new analytical method was developed, based on mid-IR FT-IR spectroscopy in the 4000-650 cm-1 region. The HATR accessory is easy to handle and gives reproducible IR spectra to carry out highly accurate quantitative analysis. The PLS correlation models developed for the prediction of various properties of base oils are useful as this reduces the evaluation time considerably. Very good correlation for paraffin content and viscosity index were obtained and indicate that the paraffin content directly influences the physical property, VI. The statistical analysis proves the validity of the models developed. The spectral regions selected for correlation analysis 3200-2700 and 1800-650 cm-1 are quite good to give results. When both the regions were used to develop the models, there is not much change in the accuracy as compared to the single region 32002700 cm-1. EF970125Y
