Total Acid Number Determination in Residues of Crude Oil Distillation

Sep 14, 2010 - †Departamento de Quımica, Universidade Federal de Santa Maria, Campus, 97105-900 Santa Maria, Rio Grande do Sul,. Brazil, ‡Institu...
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Energy Fuels 2010, 24, 5474–5478 Published on Web 09/14/2010

: DOI:10.1021/ef1002974

Total Acid Number Determination in Residues of Crude Oil Distillation Using ATR-FTIR and Variable Selection by Chemometric Methods Graciele Parisotto,†,‡ Marco F. Ferr~ ao,§ Aline L. H. M€ uller,†,‡ Edson I. M€ uller,†,‡ Maria F. P. Santos,^ ^ ^  Regina C. L. Guimar~ aes, Julio C. M. Dias, and Erico M. M. Flores*,†,‡ †

Departamento de Quı´mica, Universidade Federal de Santa Maria, Campus, 97105-900 Santa Maria, Rio Grande do Sul, encia e Tecnologia de Bioanalı´tica (INCT;Bioanalı´tica), Campinas, S~ ao Paulo, Brazil, Brazil, ‡Instituto Nacional de Ci^ § Departamento de Quı´mica e Fı´sica, Universidade de Santa Cruz do Sul, Avenida Independ^ encia 2293, erico Miguez 96815-000 Santa Cruz do Sul, Rio Grande do Sul, Brazil, and ^Centro de Pesquisas e Desenvolvimento Leopoldo Am de Mello, TPAP-CENPES/PETROBRAS, 21941-945 Rio de Janeiro, Rio de Janeiro, Brazil Received March 13, 2010. Revised Manuscript Received August 28, 2010

The total acid number (TAN) was determined in the atmospheric residue (AR) and vacuum residue (VR) of the petroleum distillation process using mid-infrared spectroscopy with attenuated total reflection in association with chemometric methods. Calibration and prediction sets consisted of 44 and 13 samples, respectively (16 AR samples and 41 VR samples). Calibration models were developed using three variableselection models: interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and backward interval partial least squares (biPLS). Different treatments and preprocessing steps were also evaluated for development of the models. The treatment based on the first derivative with a SavitzkyGolay filter and the mean centered was selected for model construction. A root-mean-square error of prediction of 0.164 mg g-1 of KOH was achieved using the biPLS algorithm with spectra divided into 20 intervals and combinations of 5 intervals (2992-2826, 1823-1657, 1656-1490, 1489-1323, and 821-655 cm-1). This model (biPLS20) showed a correlation coefficient of 0.991 between reference and predicted values. The proposed method for TAN determination allowed a fast analysis, and it is possible to apply it for quality control in industrial processes with advantages when compared with the method recommended by American Society for Testing and Materials (ASTM D 664-09).

with a range of temperatures between 220 and 400 C.6-8 Some studies have shown that crude oil with acidity higher than 0.5 mg g-1 of KOH can already be considered potentially corrosive to the refinery pipelines.9,10 Currently, the total acid number (TAN) determination has been performed in AR and VR in order to evaluate the risk of corrosion during crude oil processing. Determination of TAN in petroleum products has been carried out by the ASTM D 664-09 method using potentiometric titration in a nonaqueous medium. This parameter is expressed in milligrams of potassium hydroxide needed to neutralize 1 g of an oil sample. However, this method requires the use of solvents that generate harmful effluents and also requires excessive time for analysis.11 On the other hand, Fourier transform mid-infrared (FTIR) spectroscopy with attenuated total reflection (ATR) provides some advantages in relation to the ASTM D 664-09 method, such as lower pretreatment of samples and faster analysis.12-14

Introduction The petrochemical industry has a great interest in light crude oil because it is easier to process and has lower operational cost for refining.1 However, light-oil world reserves are decreasing, and the production of heavier crude oils is increasing.2 Particularly in Brazil, crude oil comes from a variety of very different oil fields that predominantly produce heavy oil (API lower than 19).3 It should be noticed that petroleum refining of heavy crude oil leads to the production of large quantities of atmospheric residue (AR) and vacuum residue (VR) of the crude oil distillation step. The high temperatures currently used for AR and VR processing and the presence of acid compounds (naphthenic acids, sulfur compounds, etc.) in these residues may cause corrosion of the pipelines.1,4,5 Among the acid compounds, naphthenic acids are the main cause of pipeline corrosion in the process of crude oil refining *To whom correspondence should be addressed. Tel: þ 55 (55) 3220 9445 E-mail: [email protected]. (1) Speight, J. G. Handbook of Petroleum Analysis; John Wiley & Sons, Inc.: New York, 2001; pp 72-75. (2) Bathia, S.; Sharma, D. K. Pet. Sci. Technol. 2006, 24, 1125–1159. (3) Falla, F. S.; Larini, C.; Le Roux, G. A. C.; Quina, F. H.; Moro, L. F. L.; Nascimento, C. A. O. Pet. Sci. Eng. 2006, 51, 127–137. (4) Chung, H.; Ku, M. Appl. Spectrosc. 2000, 54, 239–245. (5) Kraft, W. W. Ind. Eng. Chem. 1948, 40, 807–819. (6) Matar, S. Chemistry of Petrochemical Process, 2nd ed.; Gulf Publishing Co.: Houston, TX, 2000; pp 49-111. (7) Jayaraman, A.; Singh, H.; Lefebvre, Y. Rev. Inst. Fr. Pet. 1986, 41, 265–274. (8) Zhang, A.; Ma, Q.; Wang, K.; Liu, X.; Shuler, P.; Tang, Y. Appl. Catal., A 2006, 303, 103–109. r 2010 American Chemical Society

(9) Babaian-Kibala, E.; Craig, H. L.; Rush, G. L.; Blanchard, K. V.; Rose, T. J.; Uehlein, B. L.; Quinter, R. C.; Summers, M. L. Mater. Perform. 1993, 32, 50–55. (10) Slavcheva, E.; Shone, B.; Turnbull, A. Br. Corros. J. 1999, 34, 125–131. (11) ASTM D 664-09 Standard Test Method for Acid Number of Petroleum Products by Potenciometric Titration. (12) De Luca, M. A.; Martinelli, M.; Jacobi, M. M.; Becker, P. L.; Ferr~ao, M. F. J. Am. Oil Chem. Soc. 2006, 83, 147–151. (13) Godoy, S. C.; Ferr~ao, M. F.; Gerbase, A. E. J. Am. Oil Chem. Soc. 2007, 84, 503–508. (14) Ferr~ao, M. F.; Godoy, S. C.; Gerbase, A. E.; Mello, C.; Furtado, J. C.; Petzhold, C. L.; Poppi, R. J. Anal. Chim. Acta 2007, 595, 114–119.

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method is the possibility of obtaining a regression model for each interval in a graphical display, making possible the choice of better intervals by allowing a comparison among interval models and the full-spectrum model.30 The synergy interval PLS (siPLS) model also splits the spectrum into a number of intervals (variable-wise) and allows application of the PLS regression models for all possible combinations of two, three, four, or more intervals.36 On the other hand, the backward interval PLS (biPLS) algorithm, as well as iPLS, allows the data set to be split into a given number of intervals.37 Finally, PLS models are calculated with each interval left out. The first excluded interval is the one that when left out, gives the poorest performance model with respect to RMSECV. The procedure either is continued until the last interval or can be stopped when the number of retained variables is lower than a predefined threshold. In the present work, determination of the TAN in atmospheric distillation and vacuum distillation residues from crude oil distillation processes is proposed using mid-IR spectroscopy with Fourier transform and attenuated total reflection (ATR-FTIR) combined with multivariate calibration techniques as an alternative to the recommended method by ASTM D 664-09. Three algorithms were used and compared in the selection of variables, i.e., iPLS, siPLS, and biPLS, and the respective performances were compared.

With the development of computational techniques, the data generated by IR spectroscopy can be treated with chemometric tools, enabling analysis of complex mixtures.15,16 The use of IR spectroscopy and chemometric tools has been described in the literature to determine parameters of crude oil and their products, such as determination of the composition, density, and emulsion stability from oil.17-26 IR and chemometrics were used for the TAN determination in used gas engine oils, and according to the authors, the proposed procedure is a promising tool for routine checks of the oil quality.27 IR spectroscopy combined with multivariate analysis allows improvement of the quality of the results obtained for complex mixtures by overcoming problems related to overlapping signals. Partial least-squares (PLS) regression is the most popular multivariate calibration technique for quantitative analysis and minimizes problems such as the loss of resolution of the analytical signal.28,29 Generally, PLS performs the calibration using information from the full spectrum to build a regression model to determine the property of interest, called the full-spectrum method. However, recent applications have been proposed using methods for spectral region selection with suitable algorithms in order to improve the performance of PLS regression.30-34 In practice, these methods are based on identification of a subset of complete data that will produce the lowest prediction error and an optimized region can be selected by reducing or increasing it by the removal or addition of new variables.30 The interval PLS (iPLS) model is based on the division of the spectrum into smaller intervals followed by the construction of a PLS regression model for each interval. Root-meansquare error of cross-validation (RMSECV) is calculated for each model and compared to the value obtained for the fullspectrum model. Then, regions showing the smallest value of RMSECV are chosen.35 One of the main advantages of this

Experimental Section Materials and Sample Preparation. A set of 57 samples of crude oil distillation residue, representing a large variety of different TAN values, was used (0.08-3.50 mg g-1 of KOH). Calibration and prediction sets were constructed using 44 and 13 samples, respectively, involving 16 AR samples and 41 VR samples. The TAN was determined in AR and VR by potentiometric titration according to the ASTM D 664-09 method using an automatic titrator (Metrohm, model Titrando 836, Zofingen, Switzerland).9 Propan-2-ol, toluene, and sodium hydroxide of analytical grade were purchased from Merck (Haar, Germany). AR and VR samples were previously heated in an oven at 180 C, homogenized with a metallic spatula, and immediately transferred to the surface of the ATR crystal to perform FTIR determination. Samples were stored in glass bottles with a maximum capacity of about 30 g that allowed the sample homogenization with a metallic spatula and minimization of a sample stratification process. Sample residues were removed from ATR crystal using about 10 mL of toluene. After dissolution of the sample, a clean paper was used for cleaning the ATR device. This procedure was carried out in a fume-hood in order to minimize the exposition risks to the operator. Using the ASTM method, the repeatability was 21.5% for samples with a TAN value near 0.5 mg g-1 of KOH using automatic titration mode. For TAN values between 0.7 and 3 mg g-1 of KOH, this value was 13.5%. Apparatus and Software. All spectra were recorded from 4000 to 650 cm-1 using a Perkin-Elmer model Spectrum One FTIR spectrometer with eight scans and a resolution of 4 cm-1, which resulted in 3351 variables. This instrument was equipped with a universal horizontal ATR-FTIR accessory (HATR PIKE Technologies, EUA) with a horizontal ZnSe crystal. Spectra of the AR and VR samples were collected in triplicate and normalized with an ordinate limit of up to 1.0 of absorbance using the tool available in the software of the spectrometer (Spectrum, 5.01 version, Perkin-Elmer, 2003), and finally the medium spectrum

(15) Brereton, R. G. Analyst 2000, 125, 2125–2154. (16) Chau, F.; Liang, Y.; Gao, J.; Shao, X. Chemometrics: From basics to wavelet transform; Chemical Analysis Series; John Wiley & Sons, Inc.: New York, 2004; pp 11-21. (17) Akhlaq, M. S. J. Pet. Sci. Eng. 1999, 22, 229–235. (18) Aske, N.; Kallevik, H.; Sj€ oblom, J. J. Pet. Sci. Eng. 2002, 36, 1–17. (19) Nascimento, C. A. O. J. Pet. Sci. Eng. 2006, 51, 127–137. (20) Hannisdal, A.; Hemmingsen, P. V.; Sj€ oblom, J. Ind. Eng. Chem. Res. 2005, 44, 1349–1357. (21) Kallevik, H.; Kvalheim, O. M.; Sj€ oblom, J. J. Colloid Interface Sci. 2000, 225, 494–504. (22) Pasquini, C.; Bueno, A. F. Fuel 2007, 86, 1927–1934. (23) Aske, N.; Kallevik, H.; Sj€ oblom, J. Energy Fuels 2001, 15, 1304– 1312. (24) Peinder, P.; Visser, T.; Petrauskas, D.; Salvatori, F.; Soulimani, F.; Weckhuysen, B. M. Energy Fuels 2009, 23, 2164–2168. (25) Satya, S.; Roehner, R. M.; Deo, M. D.; Hanson Francis, V. Energy Fuels 2007, 21, 998–1005. (26) Soares, I. P.; Rezende, T. F.; Fortes, I. C. P. Energy Fuels 2009, 23, 4143–4148. (27) Felkel, Y.; D€ orr, N.; Glatz, F.; Varmuza, K. Chemom. Intell. Lab. Syst. 2010, 101, 14–22. (28) Geladi, P.; Kowalski, B. R. Anal. Chim. Acta 1986, 185, 1–17. (29) Brereton, R. G. Chemometrics: Data analysis for the laboratory and chemical plant; Chemical Analysis Series; John Wiley & Sons, Ltd.: Chichester, U.K., 2003; pp 2154-2155. (30) Borin, A.; Poppi, R. J. Vib. Spectrosc. 2005, 37, 27–32. (31) Chen, Q.; Zhao, J.; Liu, M.; Cai, J.; Liu, J. J. Pharm. Biomed. Anal. 2008, 46, 568–573. (32) Pataca, L. M.; Neto, W. B.; Marcucci, M. C.; Poppi, R. J. Talanta 2007, 71, 1926–1931. (33) Silva, F. E. B.; Ferr~ao, M. F.; Parisotto, G.; M€ uller, E. I.; Flores, E. M. M. J. Pharm. Biomed. Anal. 2009, 49, 800–805. (34) Xiaobo, Z.; Jiewen, Z.; Yanxiao, L. Vib. Spectrosc. 2007, 44, 220– 227. (35) Norgaard, L.; Saudland, A.; Wagner, J.; Nielsen, J. P.; Munck, L.; Engelsen, S. B. Appl. Spectrosc. 2000, 54, 413–419.

(36) Munck, L.; Nielsen, J. P.; Moller, B.; Jacobsen, S.; Sondergaard, I.; Engelsen, S. B.; Norgaard, L.; Bro, R. Anal. Chim. Acta 2001, 446, 171–186. (37) Leardi, R.; Norgaard, L. J. Chemom. 2004, 18, 486–497.

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Table 1. Statistical Results for TAN Full-Spectrum PLS Calibration Models model

VN

intervals

LVsc

RMSECV (mg g-1 of KOH)

Rcal

RMSEP (mg g-1 of KOH)

PLS (MSC, A) PLS (MSC, MC) PLS (D, A) PLS (D, MC)

3351 3351 3351 3351

all all all all

10 10 5 10

0.234 0.229 0.234 0.155

0.981 0.984 0.988 0.993

0.183 0.205 0.155 0.189

a

a

b

In bold: selected model. b VN: total number of variables. c LVs: latent variables.

Figure 1. Typical spectrum of a residue of crude oil distillation (TAN value=3.5 mg g-1 of KOH).

was obtained. A typical spectrum of a residue of crude oil distillation (TAN = 3.5 mg g -1 of KOH) is shown in Figure 1. MATLAB, version 6.5 (The MathWorks, Natick, MA), using iToolbox, version 2.0, was used for variable selection and development of the multivariate models iPLS, siPLS, and biPLS.35 Chemometric Models. Initially, models were built using ATRFTIR full-spectrum information, and multiplicative scatter correction (MSC)38 and a first derivative with a SavitzkyGolay filter (D) were evaluated during the treatment.39 Autoscaling (A) and mean-centered (MC) data were used as preprocessing tools for the multivariate calibration models. Root-mean-square error (RMSE) was calculated according to eq 1.29 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP u n u ðyi - y^i Þ2 t RMSE ¼ i - 1 ð1Þ n

Figure 2. RMSECV for the prediction of the TAN content from the ATR-FTIR spectrum (in first-derivative form) of the 60 wavelength intervals used in iPLS models. The horizontal black line represents the RMSECV value for the full-spectrum PLS model. The numbers inside the rectangles are the optimal numbers of latent variables.

based on the smallest RMSECV. For these models (iPLS, siPLS, and biPLS), the subinterval or combined subintervals that presented the minor RMSECV values were selected using iToolbox software.

Results and Discussion Calibration and Prediction Sample Selection. A total of 44 samples were selected for the calibration set, with TAN values ranging from 0.08 to 3.50 mg g-1 of KOH. For the prediction set, 13 samples were obtained, with TAN values ranging from 0.13 to 2.46 mg g-1 of KOH. In Table 1, it is possible to observe that the RMSEP values of the models obtained using full spectrum, different treatments, and preprocessing were not significantly different (F test, 95% confidence level). However, the first-derivative treatment and mean-centered data (PLS (D, MC) in bold, Table 1) as preprocessing were selected for iPLS, siPLS, and biPLS model construction because they presented the best correlation (Rcal) between predicted and reference values. Moreover, RMSECV and RMSEP values were on the same magnitude, indicating that calibration and prediction sets had equivalent errors (Table 1). iPLS Models. First-derivative spectra (total of 3351 variables) were split into equidistant regions in iPLS models ranging from 57 to 335 variables (corresponding to spectra divided into from 60 to 10 intervals, respectively). The obtained results were presented in Figure 2 in order to facilitate the comparison among the RMSECV values obtained for each subinterval. Finally, the RMSECV values obtained for each subinterval were also compared to the RMSECV value of the full-spectrum PLS. The RMSECV values and numbers of latent variables (numbers inside the rectangles) obtained for each subinterval using the iPLS algorithm, first derivative, and mean,centered data are also shown in Figure 2. It was observed that all models obtained using the iPLS algorithm with spectra

where y^i is the predicted value for the test-set sample i, yi is the measured value for the test-set sample i, and n is the number of observations in the test set. RMSE of prediction (RMSEP) was used to evaluate the prediction ability between different PLS models. On the other hand, RMSECV was used in order to evaluate the error of the proposed calibration models and to select the number of latent variables. The iPLS, siPLS, and biPLS models were built with the spectra set divided into 10, 20, 40, and 60 intervals. The iPLS algorithm generates graphical information indicating the optimum number of latent variables used and RMSECV values for each interval model. For the siPLS algorithm, the PLS regression models were performed for all possible combinations of two and three intervals. For biPLS models, the data set was first divided using the same previously established intervals of spectra. In the sequence, the PLS model was calculated with each interval left out. The left-out interval was the one that when it was removed, it resulted in the poorest performing model with respect to RMSECV. This procedure was continued until one interval remained. The optimal combination was determined (38) Geladi, P.; MacDougall, D.; Martens, H. Appl. Spectrosc. 1985, 39, 491–500. (39) Savitzky, A.; Golay, M. J. E. Anal. Chem. 1964, 36, 1627–1632.

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Table 2. Statistical Results for the Best TAN iPLS Calibration Models a

models

VN

intervals

LVsc

RMSECV (mg g-1 of KOH)

Rcal

RMSEP (mg g-1 of KOH)

iPLS10 iPLS20 iPLS40 iPLS60

335 167 84 57

7 13 28 41

5 8 5 4

0.259 0.256 0.283 0.262

0.958 0.987 0.975 0.956

0.387 0.235 0.487 0.242

a

b

In bold: selected model. b VN: total number of variables. c LVs: latent variables.

Table 3. Statistical Results for the Best TAN siPLS Calibration Models modelsa

VNb

intervals

LVsc

RMSECV (mg g-1 KOH)

Rcal

RMSEP (mg g-1 KOH)

siPLS10 siPLS10 siPLS20 siPLS20 siPLS40 siPLS40 siPLS60 siPLS60

670 1004 333 500 168 253 113 170

2 and 7 2, 7 and 10 14 and 17 7, 14 and 20 28 and 39 11, 29 and 33 41 and 59 21, 41 and 58

6 8 5 9 5 10 5 10

0.225 0.183 0.217 0.152 0.199 0.180 0.198 0.151

0.977 0.985 0.967 0.991 0.977 0.991 0.975 0.990

0.227 0.263 0.246 0.211 0.306 0.281 0.227 0.323

a

In bold: selected model. b VN: total number of variables. c LVs: latent variables.

divided into 60 intervals did not produce RMSECV values better than the value of the full-spectrum PLS model (horizontal black line in Figure 2). Table 2 shows the results obtained for TAN iPLS calibration models with 10, 20, 40, and 60 intervals. For the obtained models, it was not possible to observe improvements of the RMSEP values when they are compared with the full-spectrum PLS model (F test, 95% confidence level). However, the model iPLS60 that uses the spectrum divided into 60 intervals was the simpler because it used only four latent variables (Table 2, in bold). This model was obtained using interval number 41 (corresponding to the CdO stretching vibration) and allowed the reduction of the total number of variables when compared to the full-spectrum PLS model (from 3351 to 57) to describe the data variability. However, when compared to the full-spectrum PLS model (Table 1, in bold), the selected iPLS model (Table 2, in bold) presented a higher RMSEP value (0.242 mg g-1 of KOH) than the fullspectrum PLS model (0.189 mg g-1 of KOH). This indicates that the iPLS method of variable selection for this data set did not decrease the prediction error using the maximum spectrum division of 60 intervals. It is possible that relevant information has been spread on the whole spectral range and a variable selection per interval could automatically reduce the information, causing an increase of the RMSEP value when compared with the full-spectrum PLS.32 iPLS models were considered unsuitable for TAN determination in residue samples because some prediction samples showed individual errors up to 80%. In this case, the siPLS algorithm was evaluated to decrease RMSEP values. siPLS Models. For the developed PLS models using the full-spectrum information, the inclusion of uninformative variables could negatively affect the calibration. In this case, a careful selection of spectral regions would improve the predictive ability of the PLS model. Therefore, the selection of variables by siPLS was carried out in order to verify the decrease in the RMSEP values using a combination of more than one interval. Table 3 shows the statistical results for the best siPLS calibration models. When the RMSEP values obtained for these models and the RMSEP value of the full-spectrum PLS model are compared, it was not possible to observe improvements in the predictive performance (F test, 95% confidence level). However, the siPLS model with a lower RMSEP value was obtained

when the spectral set was divided into 20 intervals; 3 intervals were combined and interval number 7 (corresponding to the C-H stretching vibration), 14 (corresponding to the CdO stretching vibration), and 20 (corresponding to the out-of-plane C-H bending vibration) were used (Table 3, in bold). This model reduced the number of used variables when compared to the full-spectrum PLS model (from 3351 to 500). However, it presented a RMSEP value higher (0.211 mg g-1 of KOH) than that using the full-spectrum PLS model (0.189 mg g-1 of KOH). Thus, it is possible that relevant information could be distributed in more spectral bands. biPLS Models. Models using the biPLS algorithm were obtained trying to decrease the RMSEP values and also split the spectra into smaller equidistant regions. For this reason, for biPLS, first the iPLS algorithm is applied to the data set, followed by backward elimination of the intervals with higher RMSECV values. The RMSECV value obtained with the biPLS algorithm is registered with N - 1 intervals (the iPLS algorithm is performed with N intervals). This elimination procedure can be adopted until the last interval, or it can be stopped when the number of retained wavelengths is lower than a predefined limit. After the biPLS models are obtained, their statistical parameters are compared to those of the fullspectrum PLS model. Table 4 shows the statistical results for the best TAN biPLS calibration models. RMSEP values of these models were not significantly different when compared to the RMSEP value of the full-spectrum PLS model (F test, 95% confidence level). However, the biPLS20 model allowed a reduction of the total number of variables when compared to the full-spectrum PLS model (from 3351 to 836). Figure 3 shows RMSECV bar plots as a function of number of latent variables used by biPLS20. As shown in Figure 3, RMSECV decreases with the initial number of latent variables and the model with 9 latent variables showed the lowest RMSECV value. The biPLS20 model was obtained when the spectral set was split into 20 intervals and the interval numbers 7, 14, 15, 16, and 20 were used (Table 4, in bold). The selected intervals included the regions from 2826 to 2992 cm-1 (interval 7), with stretching vibrations C-H corresponding to methyl and methylene groups. The interval 14 (16571823 cm -1) probably corresponds to CdO stretching vibrations from esters, lactones, carboxylic acids, and oxalates groups. For interval 15 (1490-1656 cm -1), the absorption bands can 5477

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Table 4. Statistical Results for the Best TAN biPLS Calibration Models models

VN

intervals

LVsc

RMSECV (mg g-1 of KOH)

Rcal

RMSEP (mg g-1 of KOH)

biPLS10 biPLS20 biPLS40 biPLS60

1340 836 672 225

4, 7, 8, and 10 7, 14, 15, 16, and 20 8, 14, 28, 29, 31,35, 37, and 40 41, 46, 55, and 59

9 9 10 7

0.166 0.165 0.124 0.190

0.991 0.991 0.997 0.981

0.169 0.164 0.218 0.198

a

a

b

In bold: selected model. b VN: total number of variables. c LVs: latent variables.

Figure 5. TAN values obtained using ASTM D 664-09 versus predicted TAN values for the biPLS model using 7, 14, 15, 16, and 20 intervals and 9 latent variables.

Figure 3. RMSECV bar plot as a function on the number of latent variables for biPLS20.

error provided by the best biPLS model was smaller than 13%. Besides, the individual errors were randomly distributed, indicating that the model did not show bias (Figure 4). Figure 5 shows the values of TAN obtained for the AR and VR samples obtained by the ASTM D 664-09 reference method and the predicted values using ATR-FTIR and the selected biPLS20 model. A good correlation between the reference method and ATR-FTIR values was obtained for calibration and prediction samples. Conclusions The use of FTIR and ATR-FTIR, combined with chemometric techniques, allowed the determination of TAN in AR and VR samples from heavy crude oil distillation. The determination of TAN from ATR-FTIR data, instead of the more laborious potentiometric titration, is a promising tool for routine analysis of residues of crude oil distillation. The use of methods for variable (iPLS, siPLS, and biPLS) selection did not provide models with RMSEP values significantly better than that obtained by the full-spectrum PLS model. The proposed method has some advantages for routine control: it is easily applied and needs a short time for analysis, the instruments are easy to handle, and it presents low operational cost.

Figure 4. Individual errors for the best TAN biPLS model.

be attributed to C-C stretching vibrations in the aromatic ring. Absorptions due to bending vibrations of methyl and methylene groups can be found in interval 16 (1323-1489 cm -1). Finally, the interval from 655 to 821 cm -1 (interval 20) corresponds to out-of-plane bending from H atoms attached to an aromatic ring.40 The individual error provided by the biPLS20 model was smaller than 30% for samples with a TAN value smaller than 0.7 mg g-1 of KOH. On the other hand, for samples with TAN values higher than 0.7 mg g-1 of KOH, the individual

Acknowledgment. The authors are grateful to INCT; Bioanalı´ tica, CNPq, CAPES, and FAPERGS for supporting this study and also to CENPES/PETROBRAS SA for financial support and for supplying the AR and VR samples.

(40) Silverstein, R. M.; Bassler, G. C.; Morrill, T. C. Spectrometric Identification of Organic Compounds, 5th ed.; John Wiley & Sons, Ltd.: New York, 1991; pp 70-122.

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