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Partial Least Squares (PLS) and Multiple Linear Correlations between Heithaus Stability Parameters (Po) and the Colloidal Instability Indices (CII) with the 1H Nuclear Magnetic Resonance (NMR) Spectra of Colombian Crude Oils. Daniel Molina-V., Roika Angulo, Fay Zuly Dueñez, and Alexander Guzman Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef4022224 • Publication Date (Web): 11 Feb 2014 Downloaded from http://pubs.acs.org on February 12, 2014

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Partial Least Squares (PLS) and Multiple Linear Correlations between Heithaus Stability Parameters (Po) and the Colloidal Instability Indices (CII) with the 1H Nuclear Magnetic Resonance (NMR) Spectra of Colombian Crude Oils Daniel Molina V.,*,†, Roika Angulo,† Fay Zuly Dueñez,† Alexander Guzmán‡ †

Laboratorio de Resonancia Magnética Nuclear, Universidad Industrial de Santander,

Apartado Aéreo 678, Bucaramanga, Colombia, ‡

Ecopetrol-Instituto Colombiano del Petróleo, Piedecuesta, Colombia

ABSTRACT Various methods were developed to predict the stability of Colombian crude oils, in which the integral areas of the resonance signals from 12 regions of 1H nuclear magnetic resonance (NMR) spectra of 29 widely different crudes oils were correlated with the stability parameter of Heithaus (Po) and the colloidal instability index (CII). Correlations between the NMR spectra and properties were obtained using partial least squares (PLS) regression and multiple linear regression (MLR). The prediction models for Po and CII by PLS had coefficients of determination (R-squared) of R2>98% and R2>99%, respectively, while the cross-validation values CV-q2 ranged from 89% to 90%, respectively. The models obtained from MLR showed a high (adjusted R-Squared) R2ad for Po and a lower R2ad for CII. The R2 values of the prediction models for Po ranged from 97% to 98%. The use of these predictive methods is faster, more environmentally friendly and less expensive than

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conventional methods. Of the set of crudes used in this study, it was observed that the crudes with low tendency to precipitate asphaltenes are those with high aromatic content and low paraffin content because they exhibited a very low CII and a very high peptizing power for asphaltenes, Po. Considering the relationship between the asphaltene content and the peptizing power Po and the colloidal stability index CII, asphaltenes cannot be considered negative factors for the stability of some crude oils.

1. INTRODUCTION Crude oils are complex colloidal systems that possess a dispersed phase composed of asphaltenes and resins whose stability is one of the most important factors in the petroleum industry.The term stability can be understood as a measure of the tendency of the asphaltenes to precipitate in relation to the oil.1 Asphaltenes, the most heavy fraction of the oils, have a tendency for precipitation, flocculation and sedimentation under specific temperature, pressure and composition (SARA: saturates, aromatics, resins and asphaltenes) conditions.2, 3, 4 Asphaltene precipitation is an indication of stability loss, and their precipitation in the petroleum reservoir, in the wells, during crude transportation and during refining causes a significant number of severe technological problems. Crude oils are classified as stable under conditions in which the oil does not have a tendency to precipitate the asphaltenes.5 An evaluation of the colloidal stability of crude oils involves determining the solubility profile or solubility distribution of the asphaltenes in these materials, and this evaluation can be described by different models and indices. Some of these indices involve the 2 ACS Paragon Plus Environment

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experimental determination of the precipitation of the asphaltenes by the addition of a nonsolvent to the oils or solutions of oils in aromatic solvents, e.g., the Heithaus titration (Pindex or Po). Other indices use the SARA composition, e.g., the colloidal instability index (CII). 6 Some include the toluene equivalence (TE) and the Bureau of Mines correlation index–toluene equivalence (BMCITE), which represents some of the parameters initially developed for the prediction of asphalts stability, and also the oil compatibility model that was developed for the prediction of the compatibilities of mixtures of oils and is also applicable to the evaluation of stability.1 The filter drop spreading method allows the accurate and rapid determination of the flocculation point of asphaltenes, which is measured in terms of the proportion of n-alkane added to the crude oil.7 With the exception of the CII, these indices require the experimental determination of the onset of precipitation of the asphaltenes. Heithaus results have been modified to generate a coke index, as the presence of coke appears to coincide with the depletion of resins, which are components that play a crucial role in the dispersion of asphaltenes. The Heithaus titration is a one-dimensional titration, and it has been replaced by a three-dimensional turbidimetric titration to determinate the structural stability of mixtures of old and new bituminous binders. The three dimensions are a visualization of the Hansen solubility parameters, displaying the dispersive, polar, and hydrogen-bonding components. The results from the three-dimensional titration demonstrated that it provides more detailed information regarding structural stability compared to the one-dimensional Heithaus titration.8 A separate model to evaluate the colloidal stability of crude oils and related products uses an on-column precipitation coupled with an evaporative light scattering detector (ELSD). 3 ACS Paragon Plus Environment

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On-column precipitated asphaltenes typically have bimodal distributions, where the separation between the peaks is related to the colloidal stability of the asphaltenes. In general, asphaltenes from unstable materials are characterized by wider solubility profile distributions than asphaltenes from stable materials.9 Various methods have been reported to determine the stability of crude oils. Some of these have used light transmission, such as the microscopic examinations of solutions, 10 refractive index,11 light scattering by particles,9 a solvent titration method with near-infrared (NIR) solid detection,12 and fluorescence spectroscopy. However, when the transmission is low, methods such as viscosimetry, conductivity, heat transfer analysis,16 critical voltage,13 electric field, 14 zeta potential, 15 filtration and other similar procedures are the only options.16 Another way to determine the stability of crude oils is with prediction models using spectroscopy data in conjunction with chemometric techniques, e.g., NIR spectroscopy in the range of 1100–2250 nm, together with a latent-variable regression technique,13 NIR and PLS.17 Multiple linear regression (MLR), partial least-squares (PLS), and principal component analysis (PCA) and artificial neural networks (ANN) are multivariable data analysis methods that allow the construction of models to predict properties of crude oils and/or their fractions from NMR spectra. PLS regression combines characteristics from principal component analysis and multiple regression, and these methods were used to predict sulfur (% wt), N (ppm), waxes (% wt), MCR (% wt) insolubles in nC-7(% wt), vanadium (ppm) and nickel (ppm) in Colombian crude oils.18 Additionally, the combination of standard 1H


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NMR spectroscopy and PLS analysis was employed for the rapid and accurate extraction of parameters pertaining to the physical and chemical properties of heavy fuel, and good prediction models were obtained for the calculated aromaticity index, the density, gross and net calorific values, and water and sulfur contents, as well as micro-carbon residue (MCR). The coefficients calculated by the PLS procedure revealed which parts of the NMR spectrum are important for the prediction of the various chemical and physical parameters. The MCR parameter is related to the oil stability, and the important regions of the NMR spectrum to predict this parameter is the region from 4.7 to 6.2 ppm, as well as small contributions from the 3.2-3.7 region and small negative effects from the aliphatic region near 1 ppm.19 The correlations between physicochemical properties and SARA components with the 1H NMR spectra of vacuum residues, represented by chemical shift regions, were determined using the MLR method. The MLR model for predicting SARA components is excellent for saturates, aromatics and resins; while the model for the asphaltene content is quite reasonable and is dependent on the chemical shift region from 6.0 to 8.9 ppm.20 In summary, chemometrics is the statistical processing of analytical chemistry data with various numerical techniques to extract information. A series of mathematical manipulations of the data are used with a previously developed calibration model to predict the property of interest: RAW DATA, X ⇒ Chemometrics: f(X) ⇒ fuel property. The following calibration model was used in this work: NMR data, chemical shifts ⇒ MLR (or PLS): f(1H-NMR chemical shifts) ⇒ CII, or Po of crude oils. Based on the calibration model the fuel property of a new unknown sample can be determined. The goal of this study was to develop models to predict the stabilities of Colombian crude oils using partial least squares (PLS) and multiple linear regressions (MLR), in which the 5 ACS Paragon Plus Environment

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integral areas of the resonance signals of 1H-NMR spectra of 29 crudes oils were correlated with the stability parameter of Heithaus (Po) and the colloidal instability index (CII). The chemometric models accurately predicted the stabilities of asphaltenes, making possible a molecular approach for the description of the stability of crude in terms of the proportion of the different protons provided by the high-resolution 1H NMR spectrum. Coefficients, calculated by the multivariate algorithms MLR and PLS, indicated which protons influence positively and negatively the stability of crude oils and therefore providing a tool to blend crudes based on their contributions to the type of protons that are determinant of compatibility.

This approach not only permits a quick and easy estimation of crude

stability but also makes possible to develop or formulate additives with specific chemical structure to control crude oils stability. 2. EXPERIMENTAL SECTION To investigate a very wide range of chemical compositions, 29 Colombian crude oils were used in this study. The SARA fractionation and the stability parameter of Heithaus (Po) were determined at the Instituto Colombiano del Petróleo (ECOPETROL-ICP). The parameter Po described in the ASTM D7112 standard method was used to determine the peptizer power of the maltenes. This method covers an automated procedure involving titration and optical detection of precipitated asphaltenes for determining the stability and compatibility parameters of crude oils. Using this method, the solubility power of the medium increases as the value of Po increases. The SARA procedure described in the ASTM-2007 standard method was used to determine the compositions of the oils. The colloidal instability index (CII) was calculated using eq. 1, 6 ACS Paragon Plus Environment

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which is defined as the mass ratio between the sum of the asphaltenes and saturates and the sum of the peptizer agents (aromatics and resins).12

% % %%

Eq. 1

Table 1 shows the maximum and minimum values of the SARA components and the Po and CII data for the set of crude oils used in this study. The SARA components of 29 Colombian crude oils and theirs CII and Po values are shown in Fig. 1 and 2, respectively. The 1H NMR spectra of the crudes were obtained using a Bruker Avance III 400 MHz spectrometer in 5% (wt) solutions in CDCl3 containing tetramethylsilane (TMS) as internal reference. This concentration was found to be optimal for avoiding a concentration dependence of the chemical shifts, and it also provided an excellent signal-to-noise ratio. The 30° pulses (Bruker zg30 pulse sequence) had a pre-scan delay of 10.0 µs and the delay time between the scans was 10 s. The sweep width was 6000.0 Hz, the acquisition time was 5.45 s and 16 scans were averaged for each spectrum used in the correlations. The phase and baseline of the resulting spectra were manually adjusted and corrected after using a very large expansion of the y-axis. The integration was performed within each of these spectral segments, minimizing the small shifts observed between samples. This was achieved by carefully setting the TMS signal to 0 ppm, and the resulting integrals of the different segments of the spectrum were normalized. Figure 3 shows the 1H NMR spectra of three crude oils with different asphaltene content. The 1H NMR spectrum of crudes is very complex and contains a large number of signals because of the large distribution of chemical complexity. These are highly overlapped and dense; therefore, it is not possible to make an assignment of each of the lines with respect to 7 ACS Paragon Plus Environment

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specific molecular structural types, but a reasonable demarcation exists between methyl (0.5-1.10 ppm), methylene (1.10 – 1.65 ppm), methine (1.65 – 2.3 ppm) protons, and for the aromatic fractions (6.7 – 8.5 ppm). Protons of cycloalkanes produce signals inconsistent with the CH3, CH2 and CH 1H spectral regions characteristic of normal and branched alkanes.21 Additionally, various authors have reported different chemical shifts for the same type of proton, e.g.,proton of paraffins CH3 type in β positions to the aromatic ring: (1.0– 2.0),22 (1.0–1.85),23 (1.0 –1.4),24 (1.0 –1.5),25 (1.1– 2.1),26 (0.3– 2.0),27 (1.0–1.85),30and (1.85 –2.1).28 Thus, for this study, the spectrum was divided in twelve chemical shift regions, and these regions were selected from different authors,29, 30, 31, 32, 33, 34, 35, 36, 37as shown in Table 2. The signal induced in the radio frequency receiver of a NMR spectrometer is detected as a time-dependent oscillating voltage, the free induction decay (FID). In high resolution NMR, we need to transfer the time domain data into the frequency domain and this way we convert the FID into the spectrum. Frequency analysis of the FID by Fourier Transformation (FT) produces the NMR spectrum (intensity vs. frequency, ppm). The full processing of the data, in summary, is: FT calculation, phase adjusting, baseline adjusting, scale setting and the integration. The most important of them to assure the quantitative results is the right adjustment of the phase. After the FT transformation, the phase of the spectrum needs to be corrected if the baseline around the peaks is not flat. In this point, the autophase routine does a pretty good job, but it is likely that the spectrum will still need a little manual phasing to get a really good phase. Our experience has shown that this automatic mode generally works well when pure


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compounds are analyzed, but in the case of complex mixtures such as crude oils, the phase obtained from autophase mode is not good and this must be done manually. Manual phasing requires two phasing adjustments: the so-called zero and first order phase parameters. The zero order phase adjustment affects the phasing of all of the peaks in the spectrum equally. The first order correction varies linearly across the spectrum, so that the correction affects the peaks differently. The zero order is used by adjusting the phase of one signal in the spectrum as judged “by eye”, and the first-order is then used by adjusting the phase of a signal far away from the first in a similar manner. Therefore, to achieve high accuracy, to the phase, baseline, ppm scale and integration was conducted six times for each spectrum, thus, the area of each chemical shift (A1, A2, ...A12; see Table 2) is the averaged result of six integrations. Fig. 4 shows the relative percentage of the chemically different types of hydrogen atoms obtained from the 1H NMR spectra of three different crude oils; at each chemical shift region, the corresponding area of the three crude oils is plotted in the order from lowest to highest asphaltene content: 0.6, 3.7 and 14.2% wt. Finally, the averaged integrals were correlated with the stability parameter of Heithaus (Po) and the colloidal instability index (CII). The average total time required, including preparation, was 30 min per sample. Multiple linear regressions The correlation between the Po and CII data with the 1H NMR spectra of the crude oils represented by chemical shift regions was determined using the MLR method. With the MLR method, a linear relationship is assumed to exist between the properties Yi (i.e., Po or 9 ACS Paragon Plus Environment

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CII) and the 12 chemical shift regions (i.e., Xi1, Xi2,…,Xi12). The proposed relationship is described by Eq. 2: ∑ β ε

Eq. 2

where the βi is the coefficient that minimizes the square of the difference between the predicted and measured values (Eq. 3), ∑ ε Eq. 3 where i represents the number of measurements of the property of interest, and j is the number of chemical shift regions of the NMR spectrum. The statistical significance of the MLR models was evaluated by analysis of the R2 and adjusted R2adj values. The values of R2 are known to increase with the number of descriptors (chemical shift regions), thus R2adj is a much better indicator of the quality of the fitting. Its value takes into account the relevance of the number of descriptors used in the model, providing a better estimation of the quality of the MLR model.38 Partial Least Squares Regression The correlation between the Po and CII data with the 1H NMR spectra of the crude oils was also determined using partial least squares (PLS) regression.38,39 To select the optimal model in each case, the predicted residual sum of squares (PRESS) for the cross-validated models was computed with various numbers of factor h. A leave-one-out cross validation (CV) regression method was employed to estimate the prediction statistics.39 This is a strict validation tool, and it easily selects the best models. According to this procedure, the best prediction model is the one that has the largest CV-q2value.39 Further information regarding


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the application of the MLR and PLS methods can be found in Molina et al., 2007,18 Montgomery and Runger, 2003,38 Otto, 1999,39 and Molina et al., 2010.40

3. RESULTS AND DISCUSSION The data exhibited in Table 1 indicate that the crudes in this study possessed a wide range of SARA values; therefore, they were considered a reasonable representation of Colombian crude oils. Fig. 3 shows the 1H NMR spectra of only 3 of the 29 crude oils used in this study, but with different asphaltene contents: 0.6; 3.7 and 14.2% wt. These have the same peaks in the same chemical shift, which indicates that the crude oils have the same proton types, but the difference in the values of the physicochemical properties of the crudes is due to the difference in the relative amount of these proton types. Fig. 4 shows the relative percentage of the chemically different types of Hydrogen atoms that are obtained from the 1H NMR spectra and are present in the same three crude oils shown in Fig 3. In each chemical shift frequency, the corresponding area (relative percentage) of the three crude oils is plotted in the order of low (left) to high (right) asphaltene content. It is readily observable from Fig. 4 that there are significant differences in the relative amount of Hydrogen atoms of the crude oil with different asphaltene content in most of the chemical shift regions. This is clearly observed in areas A2, A3, A4 and A6, where a clear trend is observed from Crudes 1 to 3. For the other areas, no definite trend was found because the size of the fluctuations within each set of the chemical shift regions prevented the determination of a clear trend. These results showed that differences in the 11 ACS Paragon Plus Environment

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chemical composition of the crude oils may be easily detected by 1H NMR spectroscopy in most chemical shift regions, and the differences can be used to distinguish their chemical differences with high accuracy, which is a valuable tool in their characterization. As shown in Fig. 1 and Fig. 2, among the set of crudes used in this study, the crudes with low tendency to precipitate asphaltenes are those with high of aromatic and low paraffin content, and they have been shown to exhibit a very low CII and a very high peptizing power for asphaltenes, Po. The tendency curves and their R2 values indicate a clear relationship between the saturates (R2 = 0.92) and the CII; an acceptable correlation with the aromatic (R2 = 0.81) and a low correlation with the resins (R2 = 0.65) was observed, but this relationship was not clear with the asphaltene content because the R2 is very low (0.20). According to the CII, when CII≥0.9, the asphaltenes are unstable in the medium; when 0.7≤CII99%) and that the values of CV-q2>89.9% were good for Po and CII, while the models from the MLR method, with R2 values >97% and CV-q2>93%, were also very good. The values of CV-q2 for CII (for MLR) were very close to one, showing that this model had the highest the quality. Figure 5 shows the predicted and measured values of CII (PLS). The PLS model exhibited a good fit (R-squared adjusted for d.f. = 98.2 ).The correlations of the other models were slightly less accurate than the former models, although they were still of high quality. All of these models showed that high-quality correlations between the Po, CII and the 1H NMR spectra can be obtained for Colombian crude oils using the PLS and MLR methods. The relationship of fuel composition to fuel property is very complex and often requires a complex solution, and the stability (CII or Po) is not an exception. The Po and CII values 16 ACS Paragon Plus Environment

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depend on the chemical composition of that fuel (in this work, the SARA components and the relative composition of different types of protons were determined by 1H NMR). Chemometric techniques such as MLR provide a means for extracting information about the property (Po or CII) from subtle variations in the NMR fuel spectrum that arise from variations in the relative compositions of the chemical constituents present in the fuel sample.

Because crude oils with high values of Po are more stable, further investigation of the coefficients calculated by the MLR procedure (Fig. 7) revealed which part of the spectrum (or which protons) influence (positively and negatively) the stability of crude oils. Protons of CH3 of paraffins (n- and iso), paraffinic hydrogen γ and more to aromatic systems (A1), α-CH3 to aromatic carbons (A5), and CH, CH2 of olefins (A8) tend to decrease the stability. It is known that paraffins and isoparaffins help to precipitate asphaltenes. The interaction A2*A2 is similar to the multiplier effect of the CH2 protons that are present in this organic compound. Protons in di-ring aromatics and some tri- and tetra- ring aromatics (A10) tend to decrease the stability. These protons are present in molecules of asphaltenes (with aromatic carbons peri- and cata-condensed);therefore, if the asphaltene content increases, the instability also increases. This is also confirmed with the A11*A11 coefficient (aromatic protons, CH of some tri- and tetra-ring aromatics) with the highest negative value. Protons in mono-ring aromatics (A9) tend to increase the stability. It is known that toluene and benzene dissolve the asphaltenes;i.e., crude oils with high benzene and toluene contents


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are more stable. Furthermore, the positive coefficient A3*A10 can be understood to represent protons present in polyaromatic compounds with naphthene rings.

Aromatic protons present in di-rings and some aromatic tri- and tetra-rings (A10) tend to decrease the stability. These protons are likely present in asphaltene molecules that contain peri-aromatic carbons and that are cata-condensed. If the asphaltene content also increases the instability, this occurrence is also confirmed with the coefficient A11 * A11 (aromatic protons CH containing some tri and tetra-aromatic) rings.

Crude oils with low values of CII are more stable, and further investigation of the coefficients calculated by the MLR procedure (Fig. 8) indicated that protons with positive coefficients are associated with molecules that cause instability in the asphaltenes as follows:α-CH3 to aromatic carbons (A5), CH of di-ring aromatics and some tri- and tetraring aromatics (A10),and CH of some tetra- ring aromatics (the highest, A12). This is also confirmed with the interaction A11*A11, protons in some tri- and tetra-ring aromatics as well as with protons in paraffinic compounds (A3*A4).

Protons associated with compounds that give stability to the oil are those with negative coefficients: A2, A7, A11, A3*A3, A4*A4, A8*A8, i.e., protons that are present are in aliphatic compounds, but these results are contradictory because it is well known that asphaltenes are unstable in their presence.

Comparing the common protons obtained in the models for predicting CII and Po (Figs. 7 and 8) using A5, A10, A2*A2 and A11*A11, the results are consistent because the sign of 18 ACS Paragon Plus Environment

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the coefficients of Po (negatives) are opposite to the sign of the coefficients of CII (positives); that is, if a crude oil is more stable, then the Po will be greater and the CII will be smaller. After the basic correlations using these well-known crudes is established, the prediction of Po and CII will require the determination of the 1H NMR spectrum of the new crudes under investigation. The total time required for this analysis is approximately 30 min.

CONCLUSION

The use of the 1H NMR spectra provided a very rapid method to predict the stability parameters of Heithaus (Po) and colloidal instability indices (CII) for a wide variety of Colombian crude oils using different regression methods. The Po and CII were more accurately predicted using the PLS method. Considering the relationship between the asphaltene content and the peptizing power Po and the colloidal stability index CII, asphaltenes cannot be considered as a negative factor for the stability of crude oils. Among the set of crudes used in this study, it was observed that the crudes with low tendency to precipitate asphaltenes are those with high of aromatic content and low paraffin content because they have shown a very low CII and a very high peptizing power for asphaltenes, Po. AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]


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ACKNOWLEDGEMENTS

The authors would like to thank ECOPETROL S.A.-InstitutoColombiano del Petróleo for allowing the publication of this paper and for generous financial support (Convenio No. 003, 2007) and Universidad Industrial de Santander.

REFERENCES

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(13) Kallevik, H.; Kvalheim, O. M.; Sjöblom, J. Journal of Colloid and Interface Science 2000, 225, 494–504. (14) Aske,N.; Kallevik, H.; Sjöblom, J. Journal of Petroleum Science and Engineering 2002, 36, 1 – 17. (15) Parra, H.; Hernández, D.; Lizard, J.; Hernández, J.; Herrera, R.; Urbina, M.; Valdez, A. Fuel 2003, 82, 869–874. (16) Andersen, S. I. Energy & Fuels 1999, 13, 315 -322. (17) Macho, S.; Larrechi, M.S. Trends in Analytical Chemistry 2002, 21, 799-806. (18) Molina, D.; Navarro, U.; Murgich, J. Energy & Fuels 2007, 21, 1674-1680 (19) Nielsen, K. E.; Dittmer, J.; Malmendal, A.; Nielsen, N. C. Energy & Fuels 2008, 22, 4070–4076. (20) Molina, D.; Navarro, U.; Murgich, J. Fuel 2010, 89, 185-192. (21) Haw, J. F.; Glass, T. E.; Dorn, H. C. Anal.Chem. 1983, 55, 22-29. (22) Gupta, P. L.; Dogra, P. V.; Kuchnal, R. K.; Kumar, P. Fuel 1986, 65, 515–519. (23) Delpuech , J .- J.; Nicole, D .; Daubenfel d, J. -M.; Boudel, J .-C. Fuel 1985, 64, 325 – 334. (24) Winschel, R. A.; Robbins, G. A.; Burke, F. P. Fuel 1986, 65, 526 – 532. (25) Rousseau, B.; Funchs, A. H. Fuel 1989, 68, 1158– 1165. (26) O'Donnell, D. J., Sigle, S. O., Berlin, K. D., Sturm, G. P., Vogh, J. W. Fuel 1980, 59, 166 – 174. (27) Cantor, D. M. Anal. Chem. 1978, 50, 1185–1187. (28) Meussinger, R.; Moros, R. Fuel 2001, 80, 613 –621. (29) Gillet, S.; Rubini, P.; Delpuech, J. J.; Escalier, J-C.; Valentin, P. Anal. Chem. 1980, 52, 813-817. (30) Gillet, S.; Rubini, P.; Delpuech, J. J.; Escalier, J-C.; Valentin, P. Fuel 1981, 60, 221225. (31) Brown, J. K.; Ladner, W. R. Fuel 1960, 39, 87-96.


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(32) Rongbao, L.; Zengmin, S.; Bailing, L. Fuel 1988, 67, 565-569. (33) Kapur, G. S.; Singh, A. P.; Sarpal, A. S. Fuel 2000, 79, 1023-1029. (34) Sarpal, A. S.; Kapur, G. S.; Mukherjee, S.; Tiwari, A. K. Fuel 2001, 80, 521-528. (35) Hue, Z.; Ring, Z.; Liu, S. Energy & Fuels 2005, 19, 152-163. (36) Kapur, G. S.; Ecker, A.; Meusinger, R. Energy & Fuels 2001, 15, 943-948. (37) Meusinger, R. Fuel 1996, 75, 1235-1243. (38) Montgomery, D. C.; Runger, G. C. Applied statistics and probability for engineers; John Wiley and Sons, 3rd ed. New York, 2003. (39) Otto, M. Chemometrics, Wiley-VCH, New York, 1999. (40) Molina, D.; Parra, M.; Murgich, J. Petroleum Science and Technology, 2011, 29, 640648. (41) Nellensteyn, F. J. The colloidal structure of bitumen. In The Science of Petroleum,; Oxford University Press: London, U.K., 1938; Vol. 4, p 2760. ( 42 ) Speight J. The Chemistry and Technology of Petroleum; second edition. Marcel Dekker, Inc. New York, 1991. (43) Sedghi, M.; Goual, L. Energy & Fuels 2010,24,2275 – 2280. (44) Pereira, J.; López, I.; Salas, R.; Silva, F.; Fernández, C.; Urbina, C.; López, J. Energy & Fuels 2007, 21, 1317-1321. (45) Murgich, J.; Rodríguez, J.; Aray, Y. Energy & Fuels 1996, 10, 68-76. (46) Molina, D.; Navarro, U.; Murgich, J., Fuel 2010, 89, 185-192.


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Table 1. Maximal and minimal composition (%wt) of the SARA fractions, stability parameter of Heithaus (Po) and colloidal instability index (CII) of the set of Colombian crude oil samples.

Property range Minimum Maximum Average

Saturates

Aromatics

Resins

Asphaltenes

Po

CII

37.9

21.3

3.7

0.6

0.31

0,86

70.8

40.2

14,7

14,2

0,93

2.62

57.3

30.5

9.2

3.1

0.59

1.62


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80 wt % R² = 0.92 70

crude 1

60

crude 2 crude 3 50 Saturates Aromatics

40

Resins Asphaltenes

30 R² = 0.81 20

10

R² = 0.65 R² = 0.20

0 0.7

1.2

1.7

2.2

2.7

CII

Figure 1. Tendency of CII from SARA components of Colombian crude oils. Crude 1 (% wt asphaltenes = 0.6); Crude 2 (% wt asphaltenes = 3.7); Crude 3 (% wt asphaltenes =14.2).


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wt % 80

70

crude 1 60 R² = 0.69 50

crude 2 Saturates

crude 3 40

Aromatics

R² = 0.49

Resins Asphaltenes

30

20

R² = 0.65

10 R² = 0.27 0 0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P0

Figure 2. Tendency of Po from SARA components of Colombian crude oils. Crude 1 (% wt asphaltenes = 0.6); Crude 2 (% wt asphaltenes = 3.7); Crude 3 (% wt asphaltenes =14.2).


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Table 2. Chemical shift regions in the 1H NMR spectra of crude oils and structural assignments. Chemical shift

Nomenclature

Hydrogen type

A1

CH3 of paraffins (n- and iso). Paraffinic hydrogen γ

(ppm) 0.5-1.0

and more to aromatic systems. 1.0-1.7

A2

CH2 of paraffins (n- and iso), CH of iso-paraffins, CH and CH2 of naphthenes. Paraffinic hydrogen β to aromatic systems.

1.7-1.9

A3

CH and CH2 of naphthenes. Mostly β-CH and βCH2 to aromatic systems

1.9-2.1

A4

α-CH2 to olefins

2.1-2.4

A5

α-CH3 to aromatic carbons

2.4-3.5

A6

α-CH and α-CH2 to aromatic carbons

3.5-4.5

A7

Bridged CH2 or CH

4.5-6.0

A8

CH and CH2 of olefins

6.0-7.2

A9

CH of mono-ring aromatics,

7.2-8.3

A10

CH of di-ring aromatics and some tri- and tetra- ring aromatics

8.3-8.9

A11

CH of some tri- and tetra-ring aromatics

8.9-9.3

A12

CH of some tetra- ring aromatics


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Figure 3. Typical 1H NMR spectra of Colombian crude oil. Crude 1 (red, % wt asphaltenes = 0.6); Crude 2 (green, % wt asphaltenes = 3.7); Crude 3 (blue; % wt asphaltenes = 14.2).


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60

H% 50

40 Crude 1 30

Crude 2 Crude 3

20

10

0 A12

A11

A10

A9

A8

A7

A6

A5

A4

A3

A2

A1

Figure 4. Relative percentage of chemically different hydrogen atoms obtained from 1H NMR spectra of three crude oils. Crude 1 (red, % wt asphaltenes = 0.6); Crude 2 (green, % wt asphaltenes = 3.7); Crude 3 (blue; % wt asphaltenes = 14.2).


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1,1

2,8 2,4

observed

observed

0,9 0,7 0,5

2 1,6 1,2

0,3 0,3

0,5

0,7

0,9

0,8 0,8

1,1

predicted Po (PLS)

1,2

1,6

2

2,4

2,8

predicted CII (PLS)

Figure 5. Experimental and predicted Po (eq. 4) and CII (eq. 5) for Colombian crude oils. PLS correlation.

1

2,8

0,8

2,4

observed

observed

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0,6

0,4

2 1,6 1,2

0,2

0,8

0,2

0,4

0,6

0,8

1

0,8

predicted Po (MLR)

1,2

1,6

2

2,4

2,8

predicted CII (MLR)

Figure 6. Experimental and predicted Po and CII for Colombian crude oils. MLR correlation.


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4 2

-4 -6 -8 -10 -12 -14 -16

Figure 7. Coefficients of model Po (MLR model).


A5*A11

A5*A9

A4*A10

A4*A9

A3*A10

A3*A9

A11*A11

A10

A9

A8

A2*A2

-2

A5

0 A1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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150

100

50

-100

-150

-200

Figure 8. Coefficients of model CII (MLR model).


A8*A9

A3*A4

A2*A3

A1*A2

A11*A11

A10*A10

-50

A9*A9

A8*A8

A7*A7

A6*A6

A5*A5

A4*A4

A3*A3

A2*A2

A12

A11

A10

A7

A5

0 A2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Table 3. R2 and CV-q2 values for prediction models of Po and CII using PLS and MLR. PLS

MLR

Po

CII

Po

CII

R2 (%)

98.6

99.6

97.6

98.8

(%) -adjusted for d.f.

97.2

98.2

95.6

95.8

Cv – q2(%)

90.5

89.9

78.1

96.0

Standard error of the estimate

0.03

0.07

0.03

0.11

Absolute error

0,02

0.02

0.02

0.04


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Appendix 1.

The procedure for a particular property (CII) will be outlined here to describe the different steps involved in the correlation between the areas of the NMR signals and properties. The notation employed is such that the NMR1 (H1) area of Crude 1 is 1H1, while area 2 for the same Crude is 1H2, while the area for Crude 2 and H1will be 2H1. These values formed matrix x,while the different properties generated matrix q as shown in Eq. 1A. 1H1 1H2 1H3 1H4 1H5 1H6 1H7 1H8 1H9 1H10 1H11 1H12  2,048  2H1 2H2 ....  2,436 2H12     3H1 ; 0,934     x = .....  q = .....  ......  ........      ......  .......   29H1 .. .... 1,898  29H12   

(1A)

The application of PLS produces the weights (W) for the different areas. In the present case, the results are shown in Table A1. Table A1. Weight of variables in Matrix X Weight

H1

H2

H3

H4 H5 H6 H7 H8 H9 H10 H11

H12

W1

-0.0755 -0.4210 0.4423 …. …. …. …. …. …. ….

….

0.0060

W2

0.2374 -0.1815 0.4404 …. …. …. …. …. …. ….

…. -0.1576

W3

0.1747 -0.0070 0.1278 …. …. …. …. …. …. ….

….

0.4916

….

….

….

….

…. …. …. …. …. …. ….

….

….

….

….

….

….

…. …. …. …. …. …. ….

….

….

0.0460 -0.1458 0.3824 …. …. …. …. …. …. ….

….

0.0301

W10

For Table A1 and matrix X, the factors (Ci) were calculated as follows: C1 of crude 1= (-0.0755 x 1H1)+(-0.4210 x 1H2)+ …..(0.0060 x 1H12)= -22.0437 C1 of crude 2= (-0.0755 x 2H1)+(-0.4210 x 2H2)+ …..(0.0060 x 2H12)=-20.6640 C10 of crude 29 = (0.0460 x 29H1)+(-0.1458 x 29H2)+ ….(0.0301 x 29H12)= -5.2625 33 ACS Paragon Plus Environment

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The values for the Ci for each crude are shown in Table 1B

Table 1B.Cis factors Cis Factors

Crude C1

C2

C3

C4 C5 C6 C7 C8 C9

C10

CII

1

-22.0437 -1.9368 3.0551 …. …. …. …. …. …. -5.6494 2.0488

2

-20.6641 -1.4004 3.4342 …. …. …. …. …. …. -5.0495 2.4364

3

-14.4837 1.6622

2.9321 …. …. …. …. …. ….

….

….

…

….

….

….

…. …. …. …. …. ….

….

….

…

….

….

….

…. …. …. …. …. ….

….

….

…

….

….

….

…. …. …. …. …. ….

….

….

29

-21.4436 -0.7297 3.5990 …. …. …. …. …. …. -5.2625 1.8986


and Multiple Linear Correlations between Heithaus ... - ACS Publications

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