Ind. Eng. Chem. Res. 1999, 38, 571-574
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RESEARCH NOTES Dewaxing Process Control Using Near-Infrared Spectroscopy Philippe Iwanski† EniTecnologie, V. F. Maritano 26, 20090 San Donato Milanese (Mi), Italy
Roberto Giardino‡ and Bruno Janis*,‡ Euron SpA, V. F. Maritano 26, 20090 San Donato Milanese (Mi), Italy
Near-infrared (NIR) spectroscopy and multivariate analysis were applied to estimate the oil content in paraffin streams. On the basis of studies of prepared mixtures, the system NIR/PLS (partial least-squares method) has good predictive capability and could be used to develop an integrated process control for base oils and wax production. The accuracy, difference between predicted and measured oil content, was within 0.5% weight equivalent to that specified in the standard test method ASTM D 721. Introduction
Table 1. Base Oils Composition
Infrared spectroscopy combined with multivariate data analysis offers many interesting perspectives in the advanced control of petroleum processes due to highspeed computations and reliability.1-4 The basis of these processes involves establishing relationships (models) between absorbances in the midinfrared (MIR) or near-infrared (NIR) region and chemical-physical characteristics of the products. The most studied application in the petroleum field is gasoline and gas-oil quality prediction, mainly for efficient control of fuel blending plants, but applications to other petroleum products and processes have also been described.5-8 In this work the NIR/PLS (partial least-squares) technique is applied to determine the oil content in petroleum wax.9 This technique can be applied in the raffinate distillates solvent dewaxing section of the lubricating base oils production cycle and in the de-oiling/fractionation section of the hard wax production cycle. In the first case a rapid control of process efficiency could be obtained using NIR/PLS to monitor the slack wax oil content (10-20% w/w). In the second case, the NIR/PLS technique determines the oil content, essentially isoparaffins, in hard wax; the oil content must be kept below 0.5% w/w to comply with specification limits. This fast feedback analysis is proposed as an alternative to the ASTM D 721 laboratory test. Experimental Section Sample Preparation. The sample set consisted of 49 laboratory mixtures of base oils in paraffins in the range 1-16% w/w of oil; 41 samples were used to build the model (calibration set), while 8 samples constituted † ‡
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isoviscosity aromatics paraffins naphthenes at 40 °C sample (% wt) (% wt) (% wt) (cSt) mediumviscosity base oils heavyviscosity base oils
BO1 BO2 BO3 BO4 BO9 BO5 BO6 BO7 BO8 BO10
13.0 19.8 21.1 30.5 25.9 22.3 33.1 34.3 37.8 32.1
25.3 25.6 23.0 21.1 20.6 16.3 17.6 16.0 15.6 13.8
61.7 54.6 55.9 48.4 53.5 61.4 49.3 49.7 46.6 54.1
29-31
91-99
an external test set to confirm the prediction ability of the PLS model. To evaluate the influence of oil composition, basestocks of different nature, produced from pure or mixed Middle East and North Africa crude oils, were considered. The main characteristics of the base oils are reported in Table 1. Commercial hard waxes with an average carbon number of 28 (mp 56-58 °C) were used for the mixtures with medium-viscosity base oils, while commercial hard waxes with an average carbon number of 32 (mp 63-64 °C) were mixed with heavy-viscosity base oils. Spectroscopy. NIR transmission spectra were recorded between 1100 and 1600 nm at 2 nm resolution with a Guided Wave model 260 spectrometer interfaced to a Compaq microcomputer. The spectrometer was configured with a tungsten-halogen lamp as the light source, a 300 line‚mm-1 diffraction grating, and a PbS detector. Two Guided Wave optical transmission probes with sapphire windows were mounted on a thermostated cylindrical steel measurement cell and connected to the spectrometer through 2.5 m long optical fibers. The cell was maintained at 80 °C using a band heater and a thermocouple connected to a temperature controller. The optical path length was 2 cm. The samples were melted in an oven at 80 °C and carefully stirred before introduction in the measurement cell. The cell was
10.1021/ie9804327 CCC: $18.00 © 1999 American Chemical Society Published on Web 01/05/1999
572 Ind. Eng. Chem. Res., Vol. 38, No. 2, 1999
Figure 1. NIR spectra of samples.
cleaned with n-heptane between measurements. Four spectra were recorded for each sample and averaged. The reference spectrum was made using the same clean heated cell. The spectra were baseline-corrected, using the absorbance minimum values at 1118 and 1332 nm for offset and tilting correction. Then they were normalized between 1118 and 1600 nm according to the mean normalization method. Prediction Model. PLS methods were used to compute predictive models of base oil content in paraffin. Two programs were used to run PLS analysis: the UNSCRAMBLER 3.0 modeling program, developed by CAMO (Trondheim, Norway), and the GOLPE program, developed by the group of Prof. Clementi at the University of Perugia (Italy). The absorbance values in the NIR spectra of the n calibration samples were used as independent variables (X). The oil content of the samples was the dependent variable (Y). The relationship between the NIR spectrum of a mixture and its oil content is given by the equation
ability of models based on reduced sets of variables were calculated, and the set of variables leading to the lowest SDEP was selected. In the second step, to evaluate whether an individual variable is helpful or not for increasing the predictivity of the model, the GOLPE (Generation of Optimal Linear PLS Estimations) procedure was applied.11-12 PLS models were determined using different combinations of variables, extracted from the previously selected reduced set according to a fractional factorial design (FFD). Such a design was based on 1024 factors, where each variable can assume two levels (+1, -1), corresponding to the presence and absence of the variable in the factor. The prediction ability of each model was evaluated by its SDEP value. The effect of each original variable on the decrease of the SDEP, i.e., an increase of the model predictivity, is then determined and leads to selection of these variables which are relevant for the prediction of the Y value (oil content). That was done by building a PLS model wherein the SDEP value was the regressor and the X-block matrix is constituted by the FFD factors. Only variables with significantly negative loadings are effective in decreasing the SDEP.
n
% oil (w/w) )
[BiA(λi)] ∑ i-1
where A(λi) is the absorbance at wavelength λi and Bi is the regression coefficient calculated with the PLS algorithm. The predictive ability of the models was evaluated through the standard deviation of the error of prediction (SDEP) value, defined as follows:10
SDEP )
x
n
(Yi,ref - Yi,calc)2/n ∑ i)1
where Yi,ref and Yi,calc are respectively the weighed and calculated oil contents in the ith sample. The calculated Y values were obtained by the leave-one-out (LOO) method or by the grouped cross-validation method.10 The selection of the X variables was carried out in two steps. In the first step, by a cutoff procedure, the variables with variance smaller than fixed values were discarded, leading to reduced sets of variables corresponding to different variance levels. The predictive
Results and Discussion The group of overlapping bands at low wavelengths (1150-1230 nm) in the NIR spectra (Figure 1) is assigned to the second overtone of the fundamental stretching vibrations of the carbon-hydrogen bond.13,14 In particular, we have methyl absorption around 1190 nm and methylene absorption around 1210 nm. The complex band between 1350 and 1460 nm is the sum of combination bands due to stretching and bending of carbon-hydrogen bond vibrations. The SDEP values calculated for the different kselected X-variables sets are reported in Table 2. In the original setting of X variables, 242 wavelengths were considered. The first selection discarded wavelengths below a variance threshold at 0.0002 and led to a PLS model with 82 X variables and a decrease in SDEP of 66% with respect to the complete set of absorbance values. The second selection of the most relevant variables for the oil content prediction, through the GOLPE procedure, led to 11 variables. This set was further reduced to 6 variables by a trial-and-error
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Figure 2. Prediction of oil content in paraffin mixtures of the calibration set. Plot of the predicted values (from LOO method) versus the actual values. Table 2. SDEP Values of PLS Models Calculated with k-Selected X Variables for a 41-Sample Calibration Set
Table 3. Prediction of Oil Contents in the Test Set Samples oil (% wt)
selected x variables, k
SDEP (by LOO method), % oil
LVs
original set, k ) 242 reduced set, k ) 82 reduced set, k ) 11 optimized set, k ) 6
1.44 0.48 0.33 0.31
6 5 5 3
process in which some variables with low regression coefficients were discarded. The minimum value of the SDEP of the PLS model, i.e., 0.31% oil, was found using three latent variables (LVs). The SDEP, computed either by LOO or by grouped cross-validation (14-group distribution randomized 250 times), was nearly the same, thus indicating a fairly stable model. The C-H stretching of methyl and methylene groups and combination bands at 1358, 1410, and 1448 nm are among the final selected 6 variables. The regression equation on NIR-normalized absorbances can be written:
% oil (w/w) ) 54.25A1192 - 51.17A1210 + 32.02A1248 + 58.49A1358 - 9.52A1410 - 20.89A1448 The predicted values of the calibration samples, obtained with the LOO method, are plotted versus the actual values in Figure 2. The error is in the range (0.5% oil. The predictive capability of the model was further checked using the 8 samples of the external test set. The results are listed in Table 3. The average error is 0.57% oil for medium-viscosity samples and 0.07% oil for high-viscosity samples. A slight improvement in SDEP (0.29% oil) was obtained by including the test samples in the calibration set.
actual
predicted
A B C D
1.67 2.20 3.33 13.33
Medium Viscosity 2.04 2.62 4.06 14.09
E F G H
1.74 2.00 7.20 10.00
High Viscosity 1.81 2.15 7.23 9.97
difference 0.37 0.42 0.73 0.76 0.07 0.15 0.03 -0.03
Concerning the samples with an oil content smaller than 4% w/w, the SDEP is equal to 0.29% oil and the errors are always in the range (0.4% oil. Therefore, the model is able to give, with the required accuracy, an estimate of the hard wax maximum oil content specification. Conclusions Although the absorption and combination bands of the various types of hydrocarbon C-H bonds are broad overlapping features in the NIR region of the spectrum, a multivariate statistical method such as PLS can be applied to NIR spectra to predict the content of oil in a paraffin stream. The advantage of the NIR/PLS approach is its speed and simplicity compared to more traditional methods. Moreover, the use of fiber optics allows it be used on-line. The standard deviation of the residuals of prediction (0.31) is quite good compared to the reproducibility standard deviation of the ASTM method. The stability of the model was checked with different cross-validation
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methods and with an even more critical external test set method. The model obtained is independent of the origin and viscosity grade of the oil. Literature Cited (1) Welch, W. T.; Bain, M. L.; Maggard, S. M.; May, J. M. Experience leads to accurate design of analysis systems. Oil Gas J. 1992, 48, 51. (2) Maggard, S. Process for predicting properties of multicomponent fluid blends. WO Patent 17392-A1, 1994. (3) Michael, J. H. Method for prediction of physical property data of hydrocarbon products. WO Patent 17391-A1, 1994. (4) Espinosa, A.; Lambert, D.; Martens, A.; Ventron, G. Method for the direct determination of physical properties of hydrocarbon products. EP Patent 0304232-A2, 1988. (5) Lambert, D.; Descales, B.; Bages, S.; Bellet, S.; Llinas, J. R.; Loublier, M.; Maury, J. P.; Martens, A. Optimize steam cracking with on-line NIR analysis. Hydrocarbon Process. 1995, 74 (12), 103. (6) Espinosa, A.; Lambert, D.; Valleur, M. Use NIR technology to optimize plant operations. Hydrocarbon Process. 1995, 74 (2), 86. (7) Chimenti, R. J. L.; Halpern, G. M. Method and system for controlling and optimizing isomerization processes. U.S. Patent 5404015, 1995.
(8) Faraci, G.; Giardino, R.; Janis, B.; Iwanski, P. Procedimento per produrre basi lubrificanti raffinate. IT Patent 1269182, 1997. (9) Freund, M.; Csikos, R.; Keszthelyi, S.; Mozes, G.Y. In Paraffin products; Mozes, G. Y., Eds.; Elsevier Scientific Publishing Co.: Amsterdam, The Netherlands, 1992. (10) Cruciani, G.; Baroni, M.; Clementi, S.; Costantino, G.; Riganelli, D.; Skagerberg, B. Predictive ability of regression models. Part I. J. Chemom. 1992, 6, 335. (11) Baroni, M.; Clementi, S.; Cruciani, G.; Costantino, G.; Riganelli, D.; Oberrauch, E. Predictive ability of regression models. Part II. J. Chemom. 1992, 6, 347. (12) Baroni, M.; Costantino, G.; Cruciani, G.; Riganelli, D.; Valigi, R.; Clementi, S. Generating optimal linear PLS estimations (GOLPE): an advanced chemometric tool for handling 3D-QSAR problems. Quant. Struct.-Act. Relat. 1993, 12, 9. (13) Weyer, L. G. Near-infrared spectroscopy of organic substances. Appl. Spectrosc. Rev. 1985, 21, 1. (14) Wheeler, O. H. NIR spectra of organic compounds. Chem. Rev. 1959, 59, 629.
Received for review July 6, 1998 Revised manuscript received October 9, 1998 Accepted November 23, 1998 IE9804327
