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Characterization of Catalytic Reforming Streams by NIR Spectroscopy Alexis Bueno,† Carlos A. Baldrich,† and Daniel Molina V*,‡ ECOPETROL-Instituto Colombiano del Petro´leo, Piedecuesta, Colombia and Escuela de Quı´mica, UniVersidad Industrial de Santander, Apartado Ae´reo 678, Bucaramanga, Colombia ReceiVed January 22, 2009. ReVised Manuscript ReceiVed April 4, 2009
The ability of near-infrared (NIR) spectroscopy with multivariate methods of analysis to predict the chemical composition of different process streams obtained from a catalytic reforming unit was demonstrated. One hundred and forty samples were used to develop calibration models by partial least-squares (PLS) regression. For calibration and validation stages, 92 and 48 samples were employed, respectively. Total paraffin, total isoparaffin, total naphthene, and total aromatic content in naphtha have been successfully determined. Prediction of individual carbon chain length (C6-C8) in each hydrocarbon family was also studied with promising results. Calibration models for compounds with carbon chain length smaller than C6 and bigger than C8 were not developed because the concentration ranges of those components were too low, and there is a limitation in NIR sensitivity. The proposed methodology takes less than 5 min to perform, and it can be used for online process control. Besides, it is faster than the standard method, which takes about 4 h. The results showed a high repeatability and a good correlation with the GC data.
Introduction Catalytic reforming is one of the most important processes used for production of pure aromatic compounds (benzene, toluene, and xylene) for petrochemical use, high-octane components for gasoline and high-purity hydrogen for industrial use. This process involves chemical conversion of paraffins and naphthenes into aromatics under specific operational conditions that are highly dependent on the naphtha feed composition.1,2 Depending on the requirements, it is necessary to optimize the process in many different ways to obtain targeted products. Several analytical parameters (distillation curve, relative density, and chemical composition) are provided to ensure the control of the reforming process. However, detailed compositional information of the naphtha used as feedstock is required for monitoring, control, and selection of optimal process conditions. Usually, naphthas are characterized by a gas chromatographic method to determine their n-paraffins (P), isoparaffins (I), naphthenes (N), olefins (O), and aromatic (A) contents. This methodology is called PIANO analysis and also provides detailed information about individual hydrocarbons and their distribution as a function of the carbon chain length in each hydrocarbon family (ASTM D 67293). This information is useful for process control; however, its application for real-time monitoring is limited by the required analysis time (3-4 h). Recently, NIR spectroscopy has demonstrated its potential like an alternative physicochemical characterization method in * To whom correspondence may be addressed. E-mail: dmolina@ uis.edu.co. † ECOPETROL. ‡ Universidad Industrial de Santander. (1) Antos, G. J.; Aitani, A. M. Catalytic Naphtha Reforming; CRC Press: New York, 1994. (2) Speight, J. E. EnVironmental Analysis and Technology for the Refining Industry; John Wiley & Sons: Laramie, 2005. (3) ASTM D 6729-04. Standard Test Method for Determination of Individual Components in Spark Ignition Engine Fuels by 100 Meter Capillary High Resolution Gas Chromatography.
the petrochemical industry.4 Combined with multivariate techniques sucha s principal component analysis5 and partial leastsquares,6 NIR spectroscopy can in few minutes provide information about important parameters of petroleum-derived products. Octane numbers;7,8 reid vapor pressure (RVP) and distillation curve in gasoline;9 total sulfur in diesel;10 penetration value of bitumen11 and saturate; and aromatic, resin, and asphaltenic (SARA) components in crude oil12 are some of the principal quantitative applications reported in the literature. Identification and classification of petroleum products in real-time,13,14 gasoline classification by source,15 identification of adulterated gasoline,16 and monitoring of distillation processes17 have shown the potential of NIR spectroscopy for qualitative applications. Determination of compositional parameters in derived petroleum products, especially gasoline and naphtha, is also possible using NIR spectroscopy. Variables such as spectral (4) Burns, D. A.; Ciurczak, E. W. Handbook of Near-infrared Analysis; Taylor & Francis Group: New York, 2001. (5) Jackson, J. E. A user’s guide to principal components; John Wiley & Sons: New York, 1991. (6) Wold, S.; Sjo¨stro¨m, M.; Eriksson, L. Chemom. Intell. Lab. Syst. 2001, 58, 109–130. (7) Chung, H.; Lee, H.; Jun, C. Bull. Korean Chem. Soc. 2001, 22, 37– 42. ¨ zdemir, D. Petrol. Sci. Technol. 2005, 23, 1139–1152. (8) O (9) Boha´cs, G.; Ova´di, Z.; Salgo´, A. J. Near Infrared Spectrosc. 1998, 6, 341–348. (10) Breitkreitz, M.; Raimundo, I.; Rohwedder, J.; Pasquini, C.; Dantas, H.; Jose´b, G.; Arau´jo, M. Analyst. 2003, 128, 1204–1207. (11) Blanco, M.; Maspoch, S.; Villarroya, I.; Peralta, X.; Gonza´lez, M.; Torres, J. Analyst. 2000, 125, 1823–1828. (12) Aske, N.; Kallevik, H.; Sjo¨blom, J. Energy Fuels. 2001, 15, 1304– 1312. (13) Chung, H.; Choi, H.; Ku, M. Bull. Korean Chem. Soc. 1999, 20, 1021–1025. (14) Kim, M.; Lee, Y.; Han, C. Comput. Chem. Eng. 2000, 24, 513– 517. (15) Balabin, R. M.; Safieva, R. Z. Fuel. 2008, 87, 1096–1101. (16) Pereira, R.; Skrobot, V.; Castro, E.; Fortes, I.; Pasa, V. Energy Fuels. 2006, 20, 1097–1102. (17) Pasquini, C.; Scali, S. Anal. Chem. 2003, 75, 2270–2275.
10.1021/ef9000677 CCC: $40.75 2009 American Chemical Society Published on Web 04/29/2009
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Figure 2. Sample distribution for each stream in the calibration and validation data sets.
Figure 1. Schematic diagram for the process catalytic unit at ECOPETROL, Barrancabermeja Refinery.
resolution,18 signal-to-noise ratio,19 and selection of spectral region20 must be taken into account because they affect the performance of the models. Moreover to provide reliable results, it is necessary to cover the possible chemical and physical variation range expected for unknown samples. The complete analysis of a naphtha has been done using midinfrared spectral data and multivariable techniques of data analysis21 and there are also patents that mention the main spectral region to obtain good correlations between infrared spectra and chemical composition of naphtha, reformate, and gasoline.22,23 In spite of that, the information related to models for predicting the main hydrocarbons families by carbon number in different streams of a catalytic reforming unit is scarce. Taking this into account plus all the possible improvements that this could generate for the operational control of the catalytic reforming industrial unit located in the Ecopetrol S.A., Barrancabermeja Refinery, this research was undertaken for developing models that predict the chemical composition of naphtha by carbon number and hydrocarbon families, which are input parameters for the software that predict the behavior of the unit. Principal component analysis (PCA) was used for evaluating spectral data. Several models were developed using a partial least-squares (PLS) algorithm over different spectral regions. Performance of the models was evaluated by a full crossvalidation method, and an external validation data set was also used. Experimental Section Samples. One hundred and forty (140) samples were obtained over a period of five months from three process streams from the (18) Chung, H.; Choi, S.; Choo, J.; Lee, Y. Bull. Korean Chem. Soc. 2004, 25, 647–651. (19) Cho, S.; Chung, H. Anal. Sci. 2003, 19, 1327–1329. (20) Lee, Y.; Chung, H.; Kim, N. Appl. Spectrosc. 2006, 60, 892–897. (21) Macho, S.; Boque´, R.; Larrechi, M.; Rius, F. Analyst 1999, 124, 1827–1831. (22) Maggard, S.; Near infrared analysis of PIANO constituents and octane number of hydrocarbons; US Patent 5,349,188; 1994. (23) Welch, W.; Sumner, M.; Wilt, B.; Bledsoe, R.; Maggard, S. Process and apparatus for analysis of hydrocarbon species by near infrared spectroscopy; US Patent 5,712,481; 1998.
Figure 3. Average composition for naphtha calibration data set.
Catalytic Processing Unit of ECOPETROL Barrancabermeja Refinery located in Colombia (Figure 1). The selected streams were: debutanized naphtha (M13-0), hydrotreated naphtha (M13-9), and reformate product (M13-10). Samples were collected in special recipients and cooled below 4 °C to reduce the loss of light components and the natural degradation processes. Samples were divided into two sets: 92 samples for calibration and 48 for validation. The distribution of each stream in the calibration and validation data sets is showed in Figure 2. Samples in the calibration and validation data sets were randomly chosen. GC Analysis. Detailed compositional analysis (test procedure based on the ASTM D 6729) for all samples was determined with a Hewlett-Packard gas chromatograph (model HP 6890) equipped with a programmed temperature vaporization system, a flame ionization detector (FID), a cryogenic cooled oven compartment and a capillary column of fused silica, 100 m long and 0.5 um wide. The compositional analysis software used was Hydrocarbon Expert of Separation Systems. For the analysis, 0.2 mL of naphtha sample was injected using an automatic sampler, with Helium used as carrier gas at a flow of 2 mL/min. The GC analysis provided the chemical composition by hydrocarbon family (n-paraffins, isoparaffins, naphthenes, and aromatic compounds) plus carbon number distribution (C6-C8) for all samples. NIR Spectra. All of the NIR spectra were collected using an ABB Bomen NIR spectrometer, model FTLA2000-154, equipped with a SiC dual source and an extended-range DTGS detector. A transmission cell with CaF2 windows with a 0.5 mm path length was used to collect spectra over the 8000-4000 cm-1 spectral range. The NIR spectrum consisted of 32 scans obtained at room temperature. The resolution of the spectra was 4 cm-1. Air was used as a background for all of the samples. The software Grams LT, v. 7.0, (Galactic Industries Corporation, Salem, NH) was used for recording the NIR spectra. Reproducibility measure and wavenumber shift test were performed on the spectrometer to ensure high-quality spectra. Data Analysis. All data analysis was done using the Unscrambler software (CAMO). A first exploratory analysis using
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Table 1. Variation Ranges for Each for Each Hydrocarbon Group in the Calibration Data Set stream
component
maximum (wt %)
minimum (wt %)
average (wt %)
standard deviation
M13-0
paraffin isoparaffin aromatic naphthene olefin paraffin isoparaffin aromatic naphthene oleffin paraffin isoparaffin aromatic naphthene oleffin
21.68 35.90 17.97 31.72 0.17 19.86 33.86 16.72 38.16 0.15 9.81 23.84 66.98 2.12 1.51
19.08 30.65 12.15 26.16 0.04 17.13 27.87 9.89 29.6 0.00 8.80 21.65 63.81 1.62 0.25
20.34 33.78 15.12 29.26 0.10 18.19 31.87 14.25 35.18 0.02 9.19 22.80 65.36 1.91 0.53
0.70 1.40 1.81 1.35 0.03 0.72 1.25 2.31 1.93 0.04 0.28 0.61 0.82 0.18 0.47
M13-9
M13-10
principal components (PCA) was performed over all the spectral data to find outlier points in the calibration data set. The detected outliers were left out of the calibration set. For each of the determined properties (hydrocarbon composition and carbon number distribution), a calibration model was developed using a PLS algorithm and different spectral regions. The root-meansquare error of full cross validation (RMSECV) and the explained variance were used to estimate the optimal number of latent variables for each model without overfitting and for defining the best spectral region for modeling. Using a validation data set, performance of the models was evaluated by root-mean-
square error of prediction (RMSEP) and correlation coefficients (R2) between experimental and predicted values. Precision of the NIR prediction method was compared with the reproducibility of the GC method.
Results and Discussion GC Analysis. The average concentration (% w/w) of each component in the calibration data set is shown in Figure 3. There
Figure 5. Typical NIR spectra for process catalytic unit streams.
Figure 6. Principal component decomposition of spectral data. Table 2. Principal Component Analysis of Spectral Data
Figure 4. Carbon atom distribution for each stream in the calibration data set.
principal component
variation (%)
total variation (%)
PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7
98.09 1.16 0.43 0.07 0.05 0.04 0.03
98.09 99.25 99.68 99.75 99.80 99.84 99.87
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Figure 7. Variation of MSECV with number of PLS factors for modeling of aromatic content. Table 3. Calibration Models Developed for Prediction of Detailed Composition in Naphtha Streams group paraffin
total C6 C7 C8 isoparaffin total C6 C7 C8 aromatic total C6 C7 C8 naphthene total C6 C7 C8
factors RMSECV (% w/w) RMSEP (% w/w) 4 10 8 8 5 8 10 5 5 5 3 5 4 8 5 5
0.28 0.2 0.1 0.1 0.36 0.46 0.21 0.24 0.42 0.06 0.24 0.22 0.71 0.12 0.17 0.23
0.4 0.39 0.31 0.37 1.12 0.41 0.43 0.48 1.23 0.14 0.46 0.86 1.9 0.22 0.36 0.71
R2 0.99 0.97 0.95 0.96 0.94 0.97 0.92 0.97 0.99 0.99 0.99 0.98 0.98 0.99 0.99 0.88
were only trace olefin concentrations in all samples, so this component was excluded. As expected, the reformate product (M13-10 stream) has a high content of aromatic compounds, and it is easily distinguished from the other studied streams.
Minor differences in chemical composition can be observed in the other two streams. Usually, naphthenes content in the M13-9 stream is higher than the M13-0 stream, but this parameter is not enough for correct identification of streams. Table 1 resumes some statistical information about the calibration data set. For M13-0 and M13-9 streams, maximum and minimum levels of certain components can overlap, making it difficult to identify them without additional information. The GC method also provided information about the distribution by carbon atoms chain length in each hydrocarbon family. Figure 4 shows the average distribution in the calibration data set for all the studied streams. Observed carbon number distribution was in agreement with compositional changes expected for the studied processes. In each stream, there is a different carbon number distribution but, in general, species between C6 and C8 predominate for all of them. Although the M13-10 stream had a high aromatic content of components between C6 and C8, M13-0 and M-13-9 presented a high saturated content within the same range. NIR Spectral Data. Figure 5 shows characteristic NIR spectra for each stream. The spectra were linearly offset to enhance qualitative comparison. M13-0 and M13-9 streams do not show appreciable differences in their NIR spectra. However, the M13-10 stream is clearly different because of its high aromatic hydrocarbons content. This stream shows characteristics absorption bands for aromatic compounds in the range 4500-4600 cm-1 (-CH and -CdC combination bands) and 5400-6300 cm-1 (methyl, methylene and -CH aromatic overtones) regions. The weak band observed around 7200 cm-1 corresponds to the -CH second overtone of fundamental stretch at 3050-2700 cm-1 in the mid-infrared region. Because the transmission cell used had a pathlenght of 0.5 mm, the most useful spectral information is located in the 3600-4800 and 5300-6300 cm-1 spectral ranges. These bands present strong absorptions within the linear response according to Beer’s law and have high signal-to-noise ratio that make them suitable for quantitative and analytical purposes. The other regions present
Figure 8. Correlations between NIR predictions and GC method for the group type analysis.
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Figure 9. Correlations between NIR predictions and GC method for the individual carbon chain length. Table 4. Repeatability Test Results of PLS Calibration Models for Group Type Analysis M13-0 paraffins isoparaffins aromatics naphthenes
M13-9
M13-10
mean
deviation
r
mean
deviation
r
mean
deviation
r
reproducibility GC method
22.11 36.29 12.76 29.13
0.012 0.040 0.029 0.012
0.033 0.11 0.081 0.032
19.09 32.05 13.98 35.08
0.013 0.040 0.033 0.020
0.035 0.11 0.093 0.056
9.89 23.32 64.28 2.04
0.003 0.014 0.031 0.006
0.007 0.039 0.085 0.017
0.52 0.34 0.9 0.09
weak absorptions that can be significantly affected for changes in baseline or instrumental parameters and, therefore, they were excluded from further analysis. PCA Analysis. A principal component analysis (PCA) was performed using spectral information in the calibration data set (92 samples). The performed PCA analysis allowed identifying three samples as outliers. A visual inspection of these NIR signals suggests mistakes in spectral data acquisition stage. Therefore, outliers samples were excluded from development of calibration models. Only the first three principal components are required to explain more than 99% of the variation in the
calibration samples (Table 2). Figure 6 displays a plot of the calibration data set projected onto the first three principal components. These components showed that it is possible to classify samples according to their NIR spectra. Three clusters were clearly identified corresponding to the three studied streams. Each component has information about any particular characteristic of the samples. PC 1 classified streams by their aromatic content and PC 3 classified them by naphthenic to paraffinic ratio. It is not clear what kind of information PC 2 contains. Results of PCA analysis also showed that outlier samples are absent from the calibration data set.
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Calibration Models. From the NIR spectra of the samples (Figure 5), it was easy to identify two regions that contain the most useful information: 3600-4800 and 5300-6300 cm-1. Several PLS models were developed using the total spectral region, the two regions mentioned before and a combination of them, for prediction of detailed compositional parameters in reformate catalytic process streams. Figure 7 shows the RMSECV plotted as a function of the number of PLS factors used to determine the aromatic content with different spectral ranges. As expected, the MSECV decreases as the number of PLS factors increase in each model. This trend is typical in many types of regressions. The developed model in the 3600-4800 cm-1 spectral region presented the lower MSECV values for the initial 3 PLS factors. Five PLS factors were chosen as the optimum for modeling aromatic content. Examining the same type of plots for other components, including the carbon number distribution, the best spectral region and the optimal number of PLS factors were determined. The performances of the developed models were evaluated calculating the root-mean-square error of prediction (RMSEP) in the validation data set. In general, it was found that the 3600-4800 cm-1 spectral region showed the most reliable potential for predicting the compositional parameters in catalytic reforming streams. This region provided better calibrations than models developed with other regions. This is possibly due to a higher sensitivity and a higher signal-to-noise ratio in the 3600-4800 cm-1 spectral region. The results for each component are summarized in Table 3. In this table, the optimal number of factors, RMSECV and RMSEP values, and correlation between reference and predicted values (R2) are shown. The results for calibration and validation sets of total paraffin content are better than those of total isoparaffin, naphthene, and aromatic content. The standard errors of prediction for the carbon number distribution are lower than their corresponding total hydrocarbon content for each family. Scatter plots with the correlation between NIR predictions and GC analyses for samples in the validation data set are shown in Figures 8 and 9 for the group type analysis (paraffins,
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isoparaffins, aromatics, and naphthenes) and for the individual carbon chain length, respectively. In all of the cases, the prediction results by NIR show an excellent correlation with GC standard analysis, and many points fall very close to the unity line, as could be easily seen in these figures. Test Model Accuracy. Test model accuracy was determined using one sample randomly chosen for each stream at the validation data set. Ten spectra of each sample were acquired and preprocessing in the same way used for samples of the calibration set. Then, PLS models developed were used for prediction of compositional parameters. The repeatability has been estimated as r ) 1.96√2S with r being the repeatability and S the standard deviation of the predicted values. The results are given in Table 4. These results allow concluding that the repeatability of the predicted NIR values is comparable to that of the GC reference method. Conclusions The ability of NIR spectroscopy for the qualitative and quantitative analysis of complex samples such as catalytic reforming process streams has been demonstrated. Using PCA, it was possible to classify the three process streams from their spectral differences. All the correlations developed in this work showed that it is possible to predict the detailed chemical composition of petroleum distillates. The prediction results of NIR calibration models developed with PLS regression showed good correlation with the GC data. The proposed NIR method for chemical composition analysis is faster (less than 2 min) than the characterization GC standard method. The NIR method may be utilized for real-time process control and monitoring. Acknowledgment. This work has been supported by research grants from ECOPETROL-ICP. EF9000677
