X-ray Spectroscopy and Chemometric Methods for Real-Time

Article pubs.acs.org/EF

X‑ray Spectroscopy and Chemometric Methods for Real-Time Characterization of Petroleum for the Refining Process through True Boiling Point Curve and American Petroleum Institute Gravity Claudete Bernardo Henriques,†,‡ Julio Cesar Laurentino Alves,*,§ Ronei Jesus Poppi,§ Rubens Maciel Filho,† and Maria Izabel Maretti Silveira Bueno§ †

School of Chemical Engineering, University of Campinas (UNICAMP), Post Office Box 6066, 13081-970 Campinas, São Paulo, Brazil ‡ Planalto Refinery (REPLAN), Petrobras, 13140-000 Paulinia, São Paulo, Brazil § Institute of Chemistry, University of Campinas (UNICAMP), Post Office Box 6154, 13083-970 Campinas, São Paulo, Brazil ABSTRACT: The previous characterization of petroleum by real-time analysis is a need in many refining plants and is considered a complex challenge because of the feedstock characteristics, which can vary over a widely extended range. Typical examples consist of petroleum analysis for the determination of quality parameters, such as true boiling point (TBP) distillation curve and American Petroleum Institute (API) gravity, which are excessive time-consuming analysis when traditional standard methods are used. A multivariate calibration using spectroscopy data combined with chemometric methods can be used to overcome this drawback, but in calibration problems involving analysis of complex mixtures, such as petroleum and its derivatives, there is a difficult to reproduce composition variability of real samples by means of optimized experimental designs and a chemometric method with a good generalization performance is required. The algorithm support vector machines (SVM) is able to adequately treat nonlinear relationships and with high generalization performance, providing in many cases better results than traditional partial least-squares (PLS) regression models. This work demonstrates the development of an innovative method based on the fast energy-dispersive X-ray fluorescence and scattering spectroscopy and chemometric methods, such as SVM and PLS, aiming to determine TBP curve and API gravity of crude petroleum samples. Many advantages can be attributed to the developed method when compared to reference method determinations, such as low cost and mainly higher speed, which are of great interest for process optimization activities.

1. INTRODUCTION

X-ray spectroscopy (XRS) is a well-established analytical technique, mainly used to identify and quantify inorganic species in a fast, simple, low-cost, non-destructive way, and available as portable equipment for in loco analyses, especially using its energy-dispersive variant, energy-dispersive X-ray fluorescence (EDXRF). It is also possible to characterize complex organic matrices using conventional EDXRF equipment, specifically observing the X-ray source scattering region and using chemometric data treatment. X-ray fluorescence spectroscopy is based on the photoelectron phenomenon,4 which consists of exciting atoms with an energy source able to remove electrons from inner orbitals close to the nucleus. Besides the photoelectron phenomenon, which is a specific elemental interaction that provides absorption/emission effects, it is interesting to consider the Rayleigh and Compton effects that are caused by radiation scattering, with higher intensities for low-Z elements. The Compton effect is related to inelastic (incoherent) scattering, with some energy loss caused by momentum transfers between photons and electrons, and the Rayleigh effect to elastic (coherent) scattering, without any energy variation.5,6 Another weak process related to photon energy transfer to inner electrons causing its excitation to nonoccupied continuous levels is called Raman X-ray scattering, and

The real-time characterization of crude petroleum is a need for distillation process optimization and is a complex problem to overcome because of the fact that the widely extended variability in the feedstock characteristics received by petroleum refineries, obtained at different production fields, cannot be detected in real time through the conventional analytical standard methods. Among the methods commonly used for feedstock characterization for the distillation process, the determinations of the true boiling point (TBP) distillation curve, sulfur content, and American Petroleum Institute (API) gravity stand out. The TBP curve is determined by the standard methods ASTM D28921 (distillation at atmospheric pressure) for components that boil at temperatures below 400 °C and ASTM D52362 (distillation at reduced pressure) for components that boil at temperatures above 400 °C. The results are expressed by the accumulated distillate volume at different cut temperatures, up to approximately 550 °C, making it possible to estimate fraction yields. The API gravity can be determined by the standard method ASTM D12983 through calculation using the sample relative density (specific gravity) value. Because of the impossibility of TBP curve and API gravity determinations in real time using the reference standard methods, the development of analytical methods that can provide information about these required parameters in real time, with low cost and adequate accuracy, becomes essential for distillation process optimization. © XXXX American Chemical Society

Received: February 7, 2013 Revised: April 24, 2013

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dx.doi.org/10.1021/ef400230m | Energy Fuels XXXX, XXX, XXX−XXX

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Table 1. Experimental Values of API Gravity and TBP Curve for the Petroleum Sample Set Used sample number

API gravity

122 °C (%, v/v)

162 °C (%, v/v)

212 °C (%, v/v)

237 °C (%, v/v)

262 °C (%, v/v)

287 °C (%, v/v)

337 °C (%, v/v)

375 °C (%, v/v)

425 °C (%, v/v)

475 °C (%, v/v)

575 °C (%, v/v)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

43.9 20.0 28.3 51.3 24.9 33.2 20.5 28.3 28.1 19.6 22.8 23.1 41.5 41.9 53.0 30.6 27.0 44.7 14.7 51.0 25.1 19.1

21.69 4.00 10.91 29.90 9.06 12.18 6.35 8.85 11.33 5.39 6.64 6.77 22.79 23.61 44.39 11.93 8.95 26.39 1.19 45.10 6.70 5.72

33.45 7.66 15.83 43.46 13.21 18.65 9.14 14.07 16.08 8.24 11.52 12.08 33.25 33.97 60.35 17.96 15.15 37.20 1.65 59.30 11.65 10.14

46.19 12.71 22.95 58.03 19.72 28.12 13.75 22.49 23.63 12.57 17.75 18.62 45.69 44.77 73.58 23.05 23.45 47.63 5.64 73.74 18.62 15.05

51.75 15.64 26.78 64.93 23.2 33.66 16.37 28.55 27.48 15.34 21.16 22.13 51.50 50.58 79.30 25.57 27.74 52.60 6.29 77.80 20.8 16.70

57.3 18.86 30.70 71.15 26.80 40.04 19.23 36.29 31.42 18.76 24.78 25.80 56.96 56.52 84.23 28.28 32.14 57.33 7.05 81.80 25.81 20.15

63.47 22.38 34.67 76.50 30.54 47.32 22.35 44.52 35.39 22.92 28.58 29.63 62.00 62.42 88.27 31.18 36.43 61.83 9.82 85.45 28.49 23.60

76.25 30.10 42.44 84.49 38.40 61.22 29.34 58.71 43.38 32.91 33.63 37.66 70.78 73.50 93.81 37.71 44.73 70.17 18.89 99.00 37.67 30.00

81.54 36.61 48.30 88.41 44.70 69.18 35.21 66.81 49.36 40.67 43.11 43.18 76.16 80.46 96.01 43.64 50.72 75.55 21.40 99.90 43.98 36.22

87.18 46.68 56.60 92.35 54.05 78.45 42.64 76.88 58.05 50.26 52.7 53.2 82.32 88.01 97.78 56.01 58.85 81.81 40.10 100.00 53.10 51.68

91.95 55.42 64.58 95.06 62.26 87.79 52.32 85.91 66.13 57.64 60.90 61.12 87.29 93.26 98.67 62.61 66.36 86.85 54.40

97.67 74.57 81.13 98.19 78.70 94.50 70.40 93.64 82.14 71.96 69.71 76.57 94.19 98.35 99.48 79.19 81.15 93.96 71.10

61.12 55.45

76.57 70.17

specific lines of the X-ray Raman process are only observable under adequate conditions.4 On the common EDXRF equipment geometry of source and detector, the Raman X-ray scattering is overlapped by Compton X-ray scattering. The most common use of XRS in analysis of hydrocarbon mixtures occurs for S, Fe, Ni, and V determinations.5 Moreover, recent studies have shown important results related to the use of scattered radiation for classification or determination of quality parameters of organic compound samples, such as alcohols, sugars, vegetable oils, and paints.6−11 Rapid analyses of trace bioavailable macronutrients (i.e., C, N, Na, Mg, and P) in soils using fluorescence and scattering regions were also conducted in recent work.12 The importance of the analytical signal related to the scattering of incident radiation was essential to open new perspectives in employing XRS for analysis of organic compound samples. The use of XRS for these purposes becomes possible by applying multivariate calibration chemometric techniques, such as principal component analysis (PCA),13 hierarchical cluster analysis (HCA),14 and partial least-squares (PLS).15 However, linear calibration methods, such as PLS, may have difficulty in properly fitting possible nonlinear relationships between the analytical signal and the parameter under study. In this case, nonlinear calibration methods, such as support vector machines (SVM),16 can provide better model adjustment and better prediction results.17−21 Moreover, the SVM has a high generalization performance and provides good models, even through the use of small sample sets.22,23 Support vector machines for regression (SVR)16,24−27 seeks to estimate function f (x) = (wx) + b

Figure 1. XRS spectra of petroleum samples.

where C is a constant determining the trade-off between minimizing the training error or empirical risk and the model complexity term ∥w∥2. In ν-support vector regression (ν-SVR),26,27 the parameter ν controls the number of support vectors and the number of points that come to lie outside of the so-called ε-insensitive tube. To obtain a small risk, one needs to control both training error and model complexity, i.e., explain the data with a simple model. The minimization of eq 2 is equivalent to a constrained optimization problem, which tends to solve the following equation: minimize: τ(w , ξ(*) , ε)

(1)

2

1 = w 2

based on data (x1, y1), ..., (xi, yi), by minimizing the regularized risk functional 1 w 2

1 l

l

∑ (ξi + ξi*)) i=1

(3)

To move from linear to nonlinear functions, the input vectors xi were mapped into a high-dimensional feature space Z through some nonlinear mapping, ϕ: xi → zi chosen a priori.

2 ε + CR emp

+ C(νε +

(2) B


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The optimization problem was then solved in the feature space Z. Nevertheless, under general conditions, these expensive calculations can be reduced significantly using a suitable function k, leading to nonlinear regression functions. The nonlinear function k is called a kernel. In this work, a radial basis function (RBF) kernel was used. A suitable kernel function makes it possible to map a nonlinear input space to a high-dimensional feature space, where linear regression can be performed. The SVR parameters ν and C do not follow from the optimization problem and have to be adequately tuned by the user. Considering the intrinsic properties of the SVR algorithm, it has the potential to adequately determine unexpected samples not present in the calibration analytical range, which is a need because of the extended variability of the characteristics related to the feedstock processed at refineries. This paper presents a new methodology for crude petroleum characterization for the refining process, by developing regression models using XRS data, on the basis of simultaneous acquisition of both fluorescence and scattering spectra from samples, combined with chemometric methods, such as SVR and PLS, to predict the TBP curve and API gravity of crude petroleum samples from different production regions, such as Brazil, west Africa, Mediterranean, and Middle East. 1.1. Process Simulation. The process simulation is designed to predict the process behavior without performing tests on an experimental or industrial plant. The simulation aims at obtaining an adequate model representing the main aspects of the process. To design, optimize, and operate processing plants obtaining petroleum derivatives with the desired characteristics and with maximized profitability, it is

necessary to know some properties of crude petroleum and its fractions and derivatives. It is possible to divide into two groups the necessary input properties for the process simulator: (i) temperatureindependent or obtained in a reference condition: API gravity, TBP curve, molecular weight, refractive index, flash point, aniline point, etc.; (ii) temperature-dependent: specific gravity, vapor pressure, enthalpy, etc. Eckert and Vanek28 have described models for petroleum process simulation and demonstrate the importance of the TBP characterization curve as pure components. The process simulator program can use the TBP characterization curve as pure components, and if light compound compositions are not available, they are calculated from the mass balance, on the basis of a database of previous processed crude. In this work, the process simulator program used has a crude assay database, which contains information for more than 400 internationally trade crude oils.29 In this study, the results of the TBP curve obtained by the standard experimental method, the developed calibration models, and the process simulator program are compared.

2. EXPERIMENTAL SECTION The sample set of crude petroleum used in this study for determination of API gravity and TBP curve is shown in Table 1. The API gravity ranges between 14.7 and 53.0, including light, medium, and heavy petroleum samples. The calibration set has 13 samples, and the validation set 1 has 5 samples, both obtained using the Kennard−Stone algorithm30 applied to samples 1−18. For the validation set 1, the samples 1, 3, 5, 7, and 12 were selected. This study used the distillated cumulative volumes at 11 cut temperatures for determination of TBP curve, and a calibration model was developed for each one using the XRS data. A second set of validation samples was used to evaluate the obtained models, called validation set 2, which has the samples 19, 20, 21, and 22, for which just eight cut temperatures were considered. The validation set 2 includes samples that are in the extreme or even beyond the used calibration analytical range to check the generalization performance of developed models. Because the prediction of samples that extrapolate the calibration analytical range can increase the RMSEP value, the samples of the validation sets 1 and 2 were studied in a separate manner, to better know the applicability of the models to predict samples in these different situations. All samples were dried, using the dehydration practice described in the standard method ASTM D2892,1 because water can cause interference in fluorescence X-ray analysis. The standard methods ASTM D28921 and ASTM D52362 for crude petroleum distillation provide gravimetric and volumetric data of the distillate recovered at the distillation temperature range of 122− 575 °C to obtain the TBP curve. This process can take up to 5 days to be completed for each sample.

Figure 2. PC1 and PC2 scores plot of the PCA of light and medium petroleum samples.

Table 2. SVR Model Results and the Optimized Parameters parameters

RMSEC (%, v/v)

RMSEP validation set 1 (%, v/v)


R2

C

ν

API gravity 122 °C 162 °C 212 °C 237 °C 262 °C 287 °C 337 °C 375 °C 425 °C 475 °C 575 °C

0.15 0.98 1.44 1.07 1.25 1.11 0.92 1.25 1.19 1.36 1.98 1.56

1.99 0.87 0.89 1.97 1.85 1.59 1.71 1.85 2.60 4.79 4.40 3.52

1.22 1.67

0.991 0.992 0.991 0.994 0.994 0.996 0.996 0.994 0.994 0.979 0.968 0.952

59.69 29.69 29.69 42.04 26.82 26.82 26.82 26.82 59.69 29.69 46.61 59.69

0.3292 0.3392 0.3392 0.3092 0.8232 0.7292 0.7292 0.8200 0.3000 0.6292 0.2920 0.3392

2.90 1.59 2.20 3.70 3.12 4.40 2.51 C


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Table 3. PLS Model Results

a

parameters

RMSEC (%, v/v)




2.13 1.61 2.09 2.00 1.77 4.42 4.07 5.10 4.74 3.62 4.01 3.27

0.86 4.27 5.32 5.46 4.79 2.22 2.51 5.01 5.28 6.30 5.55 3.86

1.70 12.04 13.43 8.52 10.56 12.93 8.43 5.61 4.82

R2

number of latent variables

explained variancea (%)

0.992 0.831 0.877 0.930 0.959 0.978 0.970 0.900 0.906 0.860 0.868 0.830

3 5 5 5 5 3 3 2 2 2 2 2

99.64 98.03 99.43 98.78 99.14 95.02 95.92 93.64 93.83 95.38 93.02 90.13

Cumulative explained variance in Y block.

Figure 3. SVR model results for the distillation temperatures of the TBP curve at (a) 122 °C, (b) 212 °C, (c) 337 °C, and (d) 375 °C.

(4)

For optimization of parameters C and ν in SVR models, the genetic algorithm was used,18,33 for which the objective function was to minimize the crossvalidation error. The specific RBF kernel parameter was kept constant. The multiplicative signal correction (MSC) was applied for preprocessing data. For process simulation, the Petro-SIM simulator program was used.29

An EDXRF compact spectrometer PANalytical 4 miniPal Sulfur with an X-ray tube based on an Ag anode was used to obtain the X-ray spectra. For sample analysis, 5 mL of each sample was transferred to an appropriate Chemplex cell of 27 mm in diameter fitted with a Prolener film of 2.5 mm thick. All samples were irradiated for 300 s under a helium atmosphere and using a Ti filter. The spectra were obtained between 0.024 and 16.103 keV, with intervals of 0.0072 keV, with the application of 8 kV and 1000 μA. The LIBSVM package31 was used for development of SVR models, and the PLS Toolbox 4.032 was used for development of PLS models. All of the programs are ready for Matlab 7.7 from The Mathworks, Inc.

3. RESULTS AND DISCUSSION The X-ray spectra of the sample set used are show in Figure 1. The spectral range is 1.908−3.762 keV, with a total of 257 variables, where the intense fluorescence of S Kα at 2.3 keV and the intense scattering of incident radiation between 2.680 and 3.418 keV can be observed. The bands of radiation scattering can be assigned as Ag Lα at 2.984 keV, Ag Lβ1 at 3.150 keV, and Ag Lβ2 at 3.347 keV. These Compton and Rayleigh scattering bands of incident radiation appear as expected for this type of sample.

The API gravity was determined according to the standard method ASTM D12983 using eq 4, where ρ is the relative density (specific gravity) value of the sample.

API (deg) = (141.5/ρ) − 131.5

D


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Figure 4. Comparison between the TBP curves obtained by the SVR model, PLS model, and experimental procedure for the validation set 1 for (a) sample 1, (b) sample 3, (c) sample 5, (d) sample 7, and (e) sample 12.

In previous studies,34 a PCA of the 18 samples of the calibration and validation set 1 demonstrates that both fluorescence and scattering regions are important for classification of light (API gravity ≥ 32) and medium (19 < API gravity < 32) petroleum samples. It was observed that mainly the scattering region provides information for adequate classification of the light petroleum samples, whereas the S Kα fluorescence line provides information for adequate classification of the medium petroleum samples. Figure 2 illustrates the scores plot of the PCA analysis. The X-ray spectra scattering region is mainly associated with light elements (C, H, and O, among others) from the matrix, and the characteristic fluorescence lines for these elements are not

visualized on EDXRF spectra. The X-ray radiation interaction with the sample matrix gives different scattering signal intensities depending upon the chemical environment around each atom and the molecular chemical structures, and these sample characteristics are related to properties under study: cumulative volumetric recoveries of TBP curve cut temperatures and API gravity. The variations of these properties are mainly associated with relative content variations of normal alkanes, isoalkanes, cycloalkanes, and aromatic compounds in the samples. Thus, it is possible to use the scattering signal to obtain calibration models for the prediction of these quality parameters. Thus, it is possible to use the scattering signals to obtain calibration models for these parameters. E


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Figure 5. Comparison between the TBP curves obtained by the simulator program, SVR model, and experimental procedure for the validation set 1 for (a) sample 1, (b) sample 3, (c) sample 5, (d) sample 7, and (e) sample 12.

where ym is the measured value, yp is the predicted value, and n is the number of calibration or validation samples. Equation 5 was considered for both SVR and PLS results, because for these regression models, no general definition of degrees of freedom exists. Many approaches have been proposed in the analytical chemistry literature35−38 to define the number of degrees of freedom used for calculation of RMSEC, but for calculation of RMSEP, there is a general use of n as the denominator value. The results obtained using the SVR models are shown in Table 2. For comparison to the SVR performance, PLS models for the studied parameters were also developed. The results of the PLS models are shown in Table 3. The F test was used to statistically compare the RMSEP values obtained with SVR and PLS models using the ratio

Sulfur compounds are mainly associated with heavy petroleum fractions, where there is the occurrence of asphaltenes and resins. In this manner, the sulfur fluorescence signal also gives relevant information for the prediction of the studied parameters. All of the samples in the original set were used to test the fit ability of developed models. To verify the performance of the proposed models, the RMSE for the calibration (RMSEC) and validation (RMSEP) sample sets was used. The calculation of the RMSE was performed as the PLS Toolbox32 approach n

RMSE =

∑i = 1 (ym − yp )2 n

(5) F


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Table 4. SVR and PLS Results Comparison by the F Test

a

parameters

validation set 1 calculated F valuea

validation set 2 calculated F valuea


5.35 24.09 35.60 7.68 6.70 1.95 2.15 7.33 4.12 1.73 1.59 1.20

1.94 51.98

4. CONCLUSION This study demonstrates the possibility of use of the energydispersive X-ray fluorescence and scattering spectroscopy combined with chemometric methods for determination of crude petroleum quality parameters. A total of 11 models for distillated cumulative volumes at different cut temperatures for TBP curve determination and a model for determining the API gravity in an extended analytical range using SVR and PLS were developed. For the API gravity determination, the SVR and PLS models produce similar results but, on the other hand, SVR provided better results than PLS for TBP curve determination, with better generalization performance and accuracy considered adequate even with the use of a small calibration sample set. Further studies can be carried out using a higher number of samples on the calibration and validation sets to ensure an adequate practical use of the developed method. The obtained results allow for a fast construction of the TBP characterization curve, which can produce results closer to the experimental determinations in relation to the results of the process simulator program. Moreover, the process optimization in refineries can be improved by real-time determinations provided with the development of these effective and efficient calibration models.

21.45 28.71 23.04 12.21 7.30 1.63 3.69

For comparison of RMSEP values by SVR and PLS models.

of the squared RMSEP being compared.39 This approach was used for both validation sets predictions at 95% confidence level, and the results are shown in Table 4. Considering the critical F8,8 value of 3.44, we can see that the SVR models provide better results for the accumulated volumes on distillation temperatures of 122, 162, 212, 237, 337, and 375 °C for validation set 1 and distillation temperatures of 122, 212, 262, 337, 375, 425, and 575 °C for validation set 2. There was no statistically significant difference between the SVR and PLS models for the distillation temperatures of 287 and 475 °C. The predicted cumulative volumes for the validation set 2 demonstrate the better generalization performance of SVR models, which adequately predict samples over the entire calibration analytical range and even beyond the calibration analytical range. Figure 3 illustrates the SVR performance, showing the results of each distillation temperature for which SVR models provided improvement in relation to PLS models in both validation sets. Figure 4 illustrates a comparison of the SVR model and PLS model TBP curves using the results obtained for the samples of validation set 1. We can see the better agreement of the experimental TBP curves and the SVR model TBP curves, which have better performance than PLS, especially for the extreme values of TBP curves. For the API gravity, the PLS model provides better results for the validation set 1 but similar results for the validation set 2, when compared to the SVR model. Nevertheless, in some cases, it appears that SVR and/or PLS models present results of RMSEC and RMSEP that suggest an overfitting, and we consider that this may be caused by the low number of samples of the validation sets 1 and 2.40 The results of the SVR models for the validation set 1 were used as input data in a process simulator program. The input data were the API gravity and volumetric cumulative recoveries of TBP curve obtained in the range of 122−575 °C. The results obtained by the SVR models allow us to obtain adequate information in the simulator using predicted cumulative volumes of a few cut temperature points, for both regions of low temperatures, which can be affected by the conditioning of samples, which can cause the loss of light fractions, and regions of high temperatures. A comparison between the results obtained by the simulator program used at a refinery and the SVR models shows the better performance of the models based on XRS data. Figure 5 illustrates the TBP curves obtained by the SVR models for samples of validation set 1. They are very close and in better agreement with the experimental TBP curves than the simulator program results.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank Petróleo Brasileiro S.A. (Petrobras) for providing the petroleum samples and spectra for the development of this work.

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REFERENCES

(1) ASTM International. ASTM Standard D2892, 2011a, Standard Test Method for Distillation of Crude Petroleum (15-Theoretical Plate Column); ASTM International: West Conshohocken, PA, 2011; DOI: 10.1520/D2892-11A, www.astm.org. (2) ASTM International. ASTM Standard D5236, 2003 (2011), Standard Test Method for Distillation of Heavy Hydrocarbon Mixtures (Vacuum Potstill Method); ASTM International: West Conshohocken, PA, 2011; DOI: 10.1520/D5236-03R11, www.astm.org. (3) ASTM International. ASTM Standard D1298, 1999 (2005), Standard Test Method for Density, Relative Density (Specific Gravity), or API Gravity of Crude Petroleum and Liquid Petroleum Products by Hydrometer Method; ASTM International: West Conshohocken, PA, 2005; DOI: 10.1520/D1298-99R05, www.astm.org. (4) Jenkis, R. X-ray Fluorescence Spectrometry, 2nd ed.; WileyInterscience: New York, 1999. (5) Henriques, C. B. Espectrometria de raios-X aliada a quimiometria na determinaçaõ de parâmetros de petróleo. M.Sc. Dissertation, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil, 2008. (6) Bueno, M. I. M. S.; Castro, M. T. P. O.; Souza, A. M.; Oliveira, E. B. S.; Teixeira, A. P. X-ray scattering processes and chemometrics for differentiating complex samples using conventional EDXRF equipament. Chemom. Intell. Lab. Syst. 2005, 78, 96−102. (7) Pereira, F. M. V.; Bueno, M. I. M. S. Image evaluation with chemometric strategies for quality control of paints. Anal. Chim. Acta 2007, 588, 184−191. (8) Bortoleto, G. G.; Pataca, L. C. M.; Bueno, M. I. M. S. A new application of X-ray scattering using principal component analysis Classification of vegetable oils. Anal. Chim. Acta 2005, 539, 283−287. G


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(9) Goraieb, K.; Alexandre, T. L.; Bueno, M. I. M. S. X-ray spectrometry and chemometrics in sugar classification, correlation with degree of sweetness and specific rotation of polarized light. Anal. Chim. Acta 2007, 595, 170−175. (10) Bortoleto, G. G.; Borges, S. S. O.; Bueno, M. I. M. S. X-ray scattering and multivariate analysis for classification of organic samples: A comparative study using Rh tube and synchrotron radiation. Anal. Chim. Acta 2007, 595, 38−42. (11) Pereira, F. M. V.; Bueno, M. I. M. S. Calibration of paint and varnish properties: Potentialities using X-ray spectroscopy and partial least squares. Chemom. Intell. Lab. Syst. 2008, 92, 131−137. (12) Kaniu, M. I.; Angeyo, K. H.; Mwala, A. K.; Mangala, M. J. Direct rapid analysis of trace bioavailable soil macronutrients by chemometrics-assisted energy dispersive X-ray fluorescence and scattering spectrometry. Anal. Chim. Acta 2012, 729, 21−25. (13) Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987, 2, 37−52. (14) Martens, H.; Naes, T. Multivariate Calibration; John Wiley and Sons: Chichester, U.K., 1989. (15) Geladi, P.; Kowalski, B. R. Partial least squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1−17. (16) Cortes, C.; Vapnik, V. Support vector networks. Mach. Learn. 1995, 20, 273−297. (17) Alves, J. C. L.; Henriques, C. B.; Poppi, R. J. Near infrared spectroscopy combined with support vector machines as a process analytical chemistry tool at petroleum refineries. NIR News 2012, 23 (8), 8−10. (18) Alves, J. C. L.; Henriques, C. B.; Poppi, R. J. Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system. Fuel 2012, 97, 710−717. (19) Alves, J. C. L.; Poppi, R. J. Diesel oil quality parameter determinations using support vector regression and near infrared spectroscopy for hydrotreating feedstock monitoring. J. Near Infrared Spectrosc. 2012, 20, 419−425. (20) Li, H.; Liang, Y.; Xu, Q. Support vector machines and its application in chemistry. Chemom. Intell. Lab. Syst. 2009, 95, 188−198. (21) Thissen, U.; Pepers, M.; Ustun, B.; Melssen, W. J.; Buydens, L. M. C. Comparing support vector machines to PLS for spectral regression applications. Chemom. Intell. Lab. Syst. 2004, 73, 169−179. (22) Chen, N.; Lu, W.; Yang, J.; Li, G. Support Vector Machine in Chemistry; Word Scientific Publishing Co. Pte. Ltd.: Singapore, 2004. (23) Balabin, R. M.; Smirnov, S. V. Interpolation and extrapolation problems of multivariate regression in analytical chemistry: Benchmarking the robustness on near-infrared (NIR) spectroscopy data. Analyst 2012, 137, 1604−1610. (24) Smola, A. J.; Scholkopf, B. A tutorial on support vector regression. Stat. Comput. 2004, 14, 199−222. (25) Scholkopf, B.; Smola, A. J. Learning with Kernels; MIT Press: Cambridge, MA, 2002. (26) Scholkopf, B.; Smola, A. J.; Williamson, R. C.; Bartlett, P. L. New support vector algorithms. Neural Comput. 2000, 12, 1207−1245. (27) Chalimourda, A.; Scholkopf, B.; Smola, A. J. Experimentally optimal ν in support vector regression for different noise models and parameters settings. Neural Networks 2004, 17, 127−141. (28) Eckert, E.; Vanek, T. New approach to the characterisation of petroleum mixtures used in the modelling of separation processes. Comput. Chem. Eng. 2005, 30, 343−356. (29) KBC Advanced Technologies plc. Petro-SIM Simulator Program; KBC Advanced Technologies plc: Surrey, U.K.; www.kbcat.com. (30) Kennard, R. W.; Stone, L. A. Computer aided design of experiments. Technometrics 1969, 11, 137−148. (31) Chang, C. C.; Lin, C. J. LIBSVM: A Library for Support Vector Machines, 2001; http://www.csie.ntu.edu.tw/∼cjlin/libsvm (accessed April 20, 2009). (32) Wise, B. M.; Gallagher, N. B.; Bro, R.; Shaver, J. M.; Windig, W.; Koch, R. S. PLS Toolbox Version 4.0 for Use with Matlab; Eigenvector Research, Inc.: Wenatchee, WA; http://www.eigenvector.com.

(33) Wehrens, R.; Buydens, L. M. C. Evolutionary optimization: a tutorial. Trends Anal. Chem. 1998, 17, 193−203. (34) Henriques, C. B. Caracterizaçaõ prévia de petróleo com vistas a otimizaçaõ de processos. Ph.D. Thesis, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil, 2011. (35) van der Voet, H. Pseudo-degrees of freedom for complex predictive models: The example of partial least squares. J. Chemom. 1999, 13, 195−208. (36) Zhang, L.; Garcia-Munoz, S. A comparisom of different methods to estimate prediction uncertainty using partial least squares (PLS): A practioner’s perspective. Chemom. Intell. Lab. Syst. 2009, 97, 152−158. (37) Faber, K.; Kowalski, B. R. Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares. J. Chemom. 1997, 11, 181−238. (38) Wiklund, S.; Nilsson, D.; Eriksson, L.; Sjostrom, M.; Wold, S.; Faber, K. A randomization test for PLS component selection. J. Chemom. 2007, 21, 427−439. (39) Santos, V. O., Jr.; Oliveira, F. C. C.; Lima, D. G.; Petry, A. C.; Garcia, E.; Suarez, P. A. Z.; Rubim, J. C. A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis. Anal. Chim. Acta 2005, 547, 188− 196. (40) Faber, N. M. Estimating the uncertainty in estimates of root mean square error of prediction: Application to determining the size of an adequate test set in multivariate calibration. Chemom. Intell. Lab. Syst. 1999, 49, 79−89.

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X-ray Spectroscopy and Chemometric Methods for Real-Time

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