Effect of n-Alkane Precipitants on Aggregation Kinetics of Asphaltenes

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Effect of n-Alkane Precipitants on Aggregation Kinetics of Asphaltenes Nasim Haji-Akbari, Phitsanu Teeraphapkul, Arjames T. Balgoa, and H. Scott Fogler Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502743g • Publication Date (Web): 26 Feb 2015 Downloaded from http://pubs.acs.org on February 28, 2015

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Effect of n-Alkane Precipitants on Aggregation Kinetics of Asphaltenes Nasim Haji-Akbaria, Phitsanu Teeraphapkulb, Arjames T. Balgoab and H. Scott Foglera a

Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA, bPetroleum and Petrochemical College, Chulalongkorn University, Bangkok, 10330, Thailand.

Abstract The effect of different n-alkane precipitants on the kinetics of asphaltene aggregation is investigated in this study. By increasing the chain length of the n-alkane precipitant- i.e. the carbon number-, both the viscosity and the solubility parameter of the solution increase and as a result, the aggregation rate is expected to decrease. However, the actual behavior of the system is more subtle and the aggregation rate can remain constant, increase, or pass through a maximum as the carbon number, n, increases. This behavior can be explained by the polydispersity of asphaltenes and the weaker precipitating power of longer chain n-alkanes. The polidispersity of asphaltenes is successfully characterized using their solubility parameter and the model developed in our previous study (Haji-Akbari et al., Energy & Fuels, 2013) where a universal relationship between the detection time and the differences in solubility parameters was established. The solubility parameter of the precipitated asphaltenes is shown to increase with increasing the chain length of the n-alkane precipitants and is successfully used to predict the aggregation rate of asphaltenes in blends of up to five different n-alkane precipitants.

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Introduction Asphaltenes are the heaviest fraction of the petroleum crude oil and their deposition can cause severe problems at all stages of the crude oil production, transportation and refining. Asphaltenes are generally distinguished from other crude oil constituents based on their insolubility in nalkanes such as heptane or pentane and their solubility in aromatic solvents such as toluene. This definition captures a wide range of molecules with a variety of chemical and physical properties. Asphaltene molecules tend to form colloidal nano particles in the crude oil1,2. These nanoparticles can further aggregate and grow to micron-size aggregates as a result of changes in the thermodynamic driving force (i.e., pressure, temperature and composition)3,4. The aggregation rate of asphaltene nano-particles depends on the concentration of aggregating asphaltenes (i.e. collision frequency) and also the strength and range of the attraction/repulsion forces between them (i.e. the coagulation efficiency)5,6. At high precipitant concentrations, strong attractions lead to a rapid aggregation process and therefore micron-sized asphaltene particles can be immediately detected after destabilization. However, at low precipitant concentrations, detecting the instability of asphaltenes and obtaining accurate estimates of their solubility could be hampered by slow kinetics7. Therefore, identifying the factors controlling the kinetics is of crucial importance for preventing potential asphaltene-related problems. It has been shown that temperature8, asphaltene concentration9 and the solvency power of the crude oil6 play significant roles in controlling this kinetic behavior. Moreover, the properties of the precipitated asphaltenes depend on the type of the precipitant (e.g., heptane, nonane) used for their destabilization10. Consequently, the type of precipitant is also expected to be important on precipitation kinetics. Investigating the effect of different n-alkane precipitants on the kinetics of asphaltene aggregation can lead to a better understanding of the behavior of asphaltenes in


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blends of incompatible crude oils. Crude oil blending is a common process in the oil industry and is typically performed to improve certain properties of the heavy crudes (e.g., viscosity or distillation yield) by mixing them with lighter crude oils 11,12. Light crude oils are usually rich in paraffins and could act as a precipitant for aspahletenes 13. Asphaltenes are typically named after the n-alkane precipitant used for their destabilization and the most commonly investigated precipitant in the literature is n-heptane (i.e., C7 asphaltenes). Two trends are reported for the role of n-alkane precipitants on the precipitation behavior of asphaltenes. At high precipitant concentrations (e.g.,~ 90 vol% precipitant blended with ~10 % crude oil), the yield of precipitated asphaltenes was shown to increase with decreasing chain length of the n-alkane precipitant10. This behavior suggests that shorter chain n-alkanes are stronger precipitants for asphaltenes compared to their longer counterparts. This observation is consistent with smaller solubility parameters of short-chain alkanes. On the other hand, experimental measurements show that instantaneous onset volume (i.e. the amount of precipitant needed for immediate destabilization of asphaltenes, moderate precipitant concentration) passes through a maximum as the precipitant carbon number increases14. The chain length of n-alkane at the maximum varies among different crudes oils and carbon numbers ranging from 7-10 are reported in the literature14. The existence of a maximum in the instantaneous onset volume as one increases the carbon number suggests that the precipitating power of precipitants do not change monotonically with changes in chain length. In other words, the precipitating power of n-alkanes decreases with increasing carbon number up to a maximum carbon number and then increases beyond this maximum. This phenomenon is attributed to the entropy of mixing of molecules with different sizes. Flory-Huggins theory is extensively used to describe the phase


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behavior of asphaltenes15-18. Using different approximations to the regular Flory-Huggins theory, some researchers have modeled the existence of maximum at the instantaneous onset point14,19. Despite the current understanding of the phase behavior of asphaltenes at instantaneous onset point, the effect of n-alkane precipitants on the precipitation kinetics has not been investigated. The kinetic behavior is influenced by various factors. The first factor is the increase in viscosity as a result of increasing the chain length of the n-alkane precipitants. A higher viscosity is expected to decrease aggregation rates and thus increase the time needed for detecting asphaltene instability. The second factor is the increase in the solubility parameter of the mixture that can result in a smaller coagulation efficiency between aggregating asphaltenes, leading to slower aggregation rates. Finally, as discussed earlier, the yield of the precipitated asphaltenes decreases with increasing the chain length, suggesting that the properties and thus the aggregation tendencies of asphaltene that are involved in the aggregation process might vary among different n-alkane precipitants 20,21. It is generally difficult to obtain a thorough understanding of the role of different n-alkane precipitants on aggregation kinetics because of multiple factors involved in this process. In this study, first the kinetics of asphaltene precipitation in different n-alkane precipitants and blends of n-alkanes is investigated for crude oils and model oils. Then, the aggregation model developed in our earlier study6,13 is utilized to describe the kinetic behavior of asphaltenes in each sample and predict the precipitation rates in blend of precipitants.


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Experimental Section Materials One crude oil (GM2 from Gulf of Mexico) and one model oil were used in this study. For the model oil preparation, asphaltenes were extracted from K1 crude oil (Alaskan crude oil). Both crude oils (GM2 and K1) were centrifuged at 10,000 rpm for 3 hours. High performance liquid chromatography (HPLC)-grade toluene was used as the solvent. HPLC-grade n-hexane (C6), nheptane (C7), n-octane (C8), n-nonane (C9), n-decane (C10), n-dodecane (C12) and n-pentadecane (C15) from Fisher were used as the precipitant. To extract asphaltenes for model oil preparation, K1 crude oil was mixed with heptane in 1:25 volume ratio and was kept well mixed for 24 h. The solution was then centrifuged for 1 h with a Sorvall Legend X1R at 3500 rpm. Collected asphaltenic cake was Soxhlet-washed with heptane for 24 h to remove any non-asphaltenic material. The asphaltenes were then dried in a vacuum oven at 75

C. To prepare model

mixture, 1 wt% of dried asphaltenes was dissolved in toluene and was then sonicated. Methods Microscopy Experiments. A known volume of oil (crude oil/model oil) was placed in a vial/flask and the desired concentrations of precipitants/blend of precipitants (below the instantaneous onset point) were slowly added to the sample using a syringe pump. All solutions were kept well mixed during precipitant addition. As discussed elsewhere6, after sample preparation, each sample was monitored over time under an optical microscope to obtain the detection time, the time at which asphaltene particles become detectable for the first time. A Nikon (model: Eclipse E600) microscope with a 40x objective lens and a 10x eyepiece was used for monitoring the samples.


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The smallest particles detectable under the microscope were approximately 0.5 µm in diameter. A Nikon camera (DS-Fi2) was used for shooting the images off the microscope.

Results and Discussion Single Precipitants. The detection time is the time that is needed for asphaltenes to grow from the nano-scale to micron-size aggregates where they can be first detected with an optical microscope. For a polydispersed mixture such as asphaltenes, the detection time likely represents the aggregation of the most unstable asphaltenes. Figures 1 and 2 show the detection time measurements for six different n-alkane precipitants (C6, C7, C8, C9, C10, C15) mixed with GM2 crude oil and seven nalkane precipitants (C6, C7, C8, C9, C10, C12, C15) mixed with K1 model oil (1 wt% K1 asphaltenes in toluene), respectively. Despite the differences in the solubility parameter and viscosity of the n-alkanes C7-C10, no significant change in the aggregation rate of asphaltenes was detected in Figure 1. The results also reveal that the aggregation rates of asphaltenes destabilized by hexane and pentadecane are faster than those destabilized by the C7-C10 nalkanes. A similar trend is observed for the asphaltenes precipitated from the K1 model oil (Figure 2). For the precipitants ranging from C6-C10, the observed differences in the rates are within the statistical uncertainty of the experiments, while for C12 and C15, aggregation rates increase with increasing the carbon numbers. The only difference between the results for the GM2 crude oil and the K1-based model oil is the aggregation rate of asphaltenes in C6. In GM2, C6 behaves identical to C15, while in the model oil, the behavior of C6 is similar to C7, C8, C9 and C10. This is not surprising because the asphaltenes that were used for the model oil preparation were


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extracted from K1 which is different from the GM2 crude oil. These distinct trends are therefore likely a result of differences in the properties of constituent asphaltenes. The monotonic increase in viscosities and solubility parameters upon an increase in precipitant’s carbon number is expected to result in a monotonic decrease in the aggregation rate of asphaltenes. However, the observed trends (as depicted in Figures 1 and 2) are contrary to this expectation and indicate the presence of a competing effect that diminishes or cancels out the effect of the increase in viscosity and solubility parameter. This competing effect can arise from the polydispersity of asphaltenes. Note that the existing definition of asphaltenes captures a broad distribution of molecules with different physical and chemical properties. Mass spectrometry experiments have shown that the asphaltene solubility class is composed of more than 7000 distinct molecules22. In addition, sub-fractions of asphaltenes have been shown to possess different properties in terms of their molecular weight, solubility, and chemical structure20-24. Owing to this broad polydispersity, different n-alkane precipitants are expected to destabilize different fractions of this solubility class. As discussed in the introduction, it has been shown that the total amount of asphaltenes precipitated by n-alkanes decreases with increasing chain length, suggesting that the precipitating power of n-alkanes decreases with an increase in their carbon number. In addition, asphaltenes precipitated using different n-alkanes have been shown to possess different properties in terms of their aromaticity and molecular weight. For example Fuhr et al.20 investigated the properties of asphaltenes precipitated using several n-alkanes (n-pentane, n-hexane, n-octane, n-nonane) and demonstrated that the aromaticity and the molecular weight of precipitated asphaltenes increase with an increase in carbon number. It is well known that increasing the size and the aromaticity leads to an increase in the magnitude of dispersion forces and thus to the increase in the


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solubility parameter25. Therefore, it can be concluded that the solubility parameter of precipitated asphaltenes increases with increasing the chain length of the n-alkane precipitant. This behavior might appear counter-initiative due to the weaker destabilizing power of precipitants with higher carbon number. However, it should be noted that weaker precipitants (e.g. pentadecane), are only capable of precipitating the most unstable fraction of asphaltenes i.e. asphaltenes with the highest solubility parameter. On the other hand, stronger precipitants such as hexane, precipitate a wider spectrum of asphaltenes. The solubility parameter of precipitated asphaltenes will be the average of the solubility parameters of different fractions and not that of the most unstable fraction. In summary at this point, the solubility parameter of both the precipitant and destabilized asphaltenes increase with increasing carbon number. The aggregation tendency (i.e. coagulation efficiency) of asphaltenes is controlled by the difference between the solubility parameter of the asphaltenes and solubility parameter of the environment surrounding them6,9,13. Therefore for different n-alkane precipitants, the coagulation efficiency can either increase or decrease depending on the magnitude of changes in the solubility parameters of asphaltenes and the precipitant. Although this hypothesis gives a plausible explanation to the observed trends, it does not provide any quantitative measure to account for the polydispersity or to predict the aggregation rate of asphaltenes destabilized with different precipitants. One simple approach to quantitatively characterize the polydispersity of asphaltenes is to calculate the solubility parameter of different fractions. It has been shown in our earlier publication6 that the solubility parameter of asphaltenes can be obtained from the master curve relationship between the detection time and the difference in the solubility parameters of asphaltenes and the solution. This master curve has been validated for ten different crude oils and model oils6,13. The same master curve is thus used in this work to


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obtain the solubility parameters of asphaltenes that are destabilized using different n-alkane precipitants. The unified aggregation model has been developed using Smoluchowski’s aggregation equation correlating the coagulation efficiency to the difference between the solubility parameter of asphaltenes and the solution surrounding them. The following equation can be used for correlating the experimentally measured detection times to the differences in the solubility parameters13: ln

0 ∝ − 1

where and are the solubility parameters of the asphaltenes and the solution

respectively, is the local viscosity and 0 is the initial number concentration of aggregating

asphaltenes (i.e. unstable asphaltenes)13. The solubility parameter of solution , , and

viscosity, can be easily computed from the volumetric averaging and the logarithmic averaging of the solubility parameters and viscosities of the solvent and precipitant, respectively. 0 can

be obtained from the fraction of unstable asphaltenes 13. It has been shown in our earlier study9,13 that this unified aggregation model is valid for different values of n (e.g. n=2, n= -2) and n= -2 is used for the analysis presented in this paper. In addition it was shown that the fraction of "

precipitated asphaltenes is related to 1⁄ − 9.

Therefore, by knowing the

detection time, the solubility parameter of asphaltenes (i.e. ) precipitated using different nalkanes can be obtained. Figure 3 shows the agreement between the model and the experiments for obtaining the solubility parameter of asphaltenes and confirms that the utilized model can successfully explain the precipitation kinetics of asphaltenes in different n-alkanes. The experiments follow the model


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reasonably well for all samples. For K1-toluene mixtures, model predictions deviate slightly from experimental observations for the samples destabilized with pentadecane. Precipitation of asphaltenes from GM2 crude oil shows no such deviations. The deviation of K1 samples could be due to the simplifying assumptions utilized in developing the master curve. For instance, it was assumed that changes in the molar volumes do not affect the aggregation rate of asphaltenes. However, because the molar volume of pentadecane is greater than that of heptane, this assumption could come into question. To account for changes in the molar volume, the molar volumes of asphaltenes and crude oil need to be known. Measuring the molecular weights (and molar volumes) of asphaltenes and crude oils is usually very challenging. In fact, the molecular weight of asphaltenes has been the subject of extensive debate for decades due to their strong tendency for self-association and it has not been until very recently that some reasonable estimates have been reported1. Molecular weights (and molar volumes) of crude oils might also depend on the particular experimental procedure used to measure them19. Despite not accounting for the changes in molar volumes, our model can still successfully describe the experimental observations both for K1 asphaltenes and the GM2 crude oil. Tables 2 and 3 and Figure 4 show the solubility parameters obtained from our model as a function of the precipitant’s carbon number. The solubility parameters increase with increasing precipitant carbon number. This trend is in agreement with the reduction in the precipitating power of n-alkanes as their carbon number increases, leading to the destabilization of only the most unstable asphaltenes, i.e., the asphaltenes with the largest solubility parameter. Figure 4 also reveals that the solubility parameter of K1 asphaltenes has a wider distribution compared to GM2 asphaltenes, indicating that K1 asphaltenes have greater polydispersity.

Blends of Precipitants.


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The aggregation model can be used for predicting the precipitation rate of asphaltenes in blends of different n-alkanes. Precipitation rates of asphaltenes in a blend of two, three, four and five nalkane precipitants are investigated in this study. In order to predict the precipitation rates of asphaltenes in the blend, it is necessary to know the solubility parameter of asphaltenes. Properties of the precipitated asphaltenes in a blend of n-alkanes are expected to be the average of properties of asphaltenes precipitated in each of the constituent individual precipitants. Therefore, instead of fitting the detection time measurements to the master curve, solubility parameter of asphaltenes in the blend can be calculated from the volumetric average of the asphaltenes solubility parameter in each n-alkane: $% = '() + '(+ , + '(- . + ⋯ 2 $ % stands for the solubility parameter of asphaltenes in the blend of precipitants with

carbon numbers i, j and k. The '1 , represent the volume fractions of each precipitant in the

blend and 1 stand for the solubility parameters of precipitated asphaltenes obtained from

experiments of individual precipitants with crude oil/model oil(Tables 2 and 3). Figure 5 shows model predictions as well as actual experimental measurements in the blends of up to five precipitants mixed with K1 model oil. All predicted detection times are calculated from the master curve (i.e. Equation 1) with known values for the viscosity and solubility parameters. The solubility parameters of asphaltenes are calculated using Equation 2. The agreement between the model predictions and the experiments is remarkable for all the blends. It should be emphasized that no fitting parameters are used for making the predictions shown in Figure 5. Our findings reveal that Equation 2 provides accurate estimates of the solubility parameters of asphaltenes precipitated in different ratios of the same blend (i.e.C7-C15) and also in different blend compositions.


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Table 4 shows the calculated values of the viscosity and the solubility parameter of the blends shown in Figure 5 and the solubility parameter of precipitated asphaltenes calculated from Equation 2. The aggregation rate of asphaltenes from GM2 crude oil mixed with different ratios of C6-C8 blends was also investigated. An identical method of analysis was performed using Equations 1 and 2 and the results are shown in Figure 6. Similar to the model oil, the experimental results in the blends of C6-C8 with GM2 crude oil perfectly follow model predictions. Figure 7 shows the comparison between the experiments and the master curve for all the blends mixed with model oil and crude oil altogether. Overall ten different blends have been investigated in this study and all of them perfectly follow the predicted trend from the master curve after calculating the solubility parameter of asphaltenes from Equation 2. In previous investigations, the Flory-Huggins theory has been used to describe the phase behavior of asphaltenes in different n-alkanes

14,19

. However, without accounting for the

polydispersity of asphaltenes, Flory-Huggins theory could not provide a quantitative description of the behavior of asphaltenes 18,19. For example, in Wang’s19 model, two fitting parameters i.e., the solubility parameter and the molar volume of asphaltenes were used as input to FloryHuggins theory. Mathematical fits for different precipitants in his model were obtained by changing the solubility parameter of precipitated asphaltenes and no monotonic trend between the solubility parameter of precipitated asphaltenes and carbon number of n-alkane precipitants was observed. In our study, the solubility parameter of precipitated asphaltenes in individual precipitants is calculated using the measured detection times and the unified aggregation curve introduced in our earlier paper 6. However, contrary to Wang’s findings, a monotonic trend in the solubility


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parameter of precipitated asphaltenes was observed as a function of n-alkanes chain length, both in the model oils (K1 asphaltenes in toluene) and in the crude oil (GM2 crude oil). The increase in solubility parameter as an increase in the n-alkane chain length is expected due to the monotonic change in the precipitating power of n-alkanes and the polydispersity of asphaltenes.

Conclusions The aggregation kinetics of destabilized asphaltenes was investigated as a function of the carbon number of n-alkane precipitants. Despite a monotonic change in the viscosity and the solubility parameter, the aggregation rate of asphaltenes does not vary monotonically with the precipitant carbon number. This behavior can be well explained by the polydispersity of asphaltenes and the differences in the precipitating power of n-alkanes. Our model successfully predicts aggregation rates in different n-alkanes and shows that larger n-alkanes tend to precipitate the most unstable fraction of asphaltenes with the highest solubility parameter. In addition, the model predicts the aggregation rate of asphaltenes in blends of different precipitants after characterizing asphaltenes polydispersity from individual precipitants. Our investigation is the first to study and predict the aggregation rate of asphaltenes in blends of more than two precipitants. Blends of up to five precipitants were investigated and the results were all successfully predicted with the unified model.

References (1) Mullins, O. C. Energy Fuels 2010, 24, 2179–2207. (2) Mullins, O. C.; Sabbah, H.; Eyssautier, J.; Pomerantz, A. E.; Barré, L.; Andrews, A. B.; Ruiz-Morales, Y.; Mostowfi, F.; McFarlane, R.; Goual, L.; Lepkowicz, R.; Cooper, T.; Orbulescu, J.; Leblanc, R. M.; Edwards, J.; Zare, R. N. Energy Fuels 2012, 26, 3986– 4003.


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(3) Hoepfner, M. P.; Vilas Bôas Fávero, C.; Haji-Akbari, N.; Fogler, H. S. Langmuir 2013, 29, 8799–8808. (4) Hashmi, S. M.; Firoozabadi, A. J. Phys. Chem. B 2010, 114, 15780–15788. (5) Maqbool, T.; Raha, S.; Hoepfner, M. P.; Fogler, H. S. Energy Fuels 2011, 25, 1585–1596. (6) Haji-Akbari, N.; Masirisuk, P.; Hoepfner, M. P.; Fogler, H. S. Energy Fuels 2013, 27, 2497–2505. (7) Maqbool, T.; Balgoa, A. T.; Fogler, H. S. Energy Fuels 2009, 23, 3681–3686. (8) Maqbool, T.; Srikiratiwong, P.; Fogler, H. S. Energy Fuels 2011, 25, 694–700. (9) Haji-Akbari, N.; Teeraphapkul, P.; Fogler, H. S. Energy Fuels 2014. (10) Speight, J. G. The chemistry and technology of petroleum; M. Dekker: New York, 1991. (11) Li, S.; Liu, J.; Shen, B.; Xu, X.; Fan, Q.; Chen, J.; Zhao, G. Pet. Sci. Technol. 2006, 24, 737–747. (12) Shigemoto, N.; Al-Maamari, R. S.; Jibril, B. Y.; Hirayama, A. Energy Fuels 2006, 20, 2504–2508. (13) Haji Akbari Balou, N. Destabilization and Aggregation Kinetics of Asphaltenes., University of Michigan, 2014. (14) Wiehe, I. A.; Yarranton, H. W.; Akbarzadeh, K.; Rahimi, P. M.; Teclemariam, A. Energy Fuels 2005, 19, 1261–1267. (15) Wang, J. X.; Buckley, J. S. Energy Fuels 2001, 15, 1004–1012. (16) Zuo, J. Y.; Mullins, O. C.; Freed, D.; Elshahawi, H.; Dong, C.; Seifert, D. J. Energy Fuels 2013, 27, 1722–1735. (17) Freed, D. E.; Mullins, O. C.; Zuo, J. Y. Energy Fuels 2010, 24, 3942–3949. (18) Alboudwarej, H.; Akbarzadeh, K.; Beck, J.; Svrcek, W. Y.; Yarranton, H. W. AIChE J. 2003, 49, 2948–2956. (19) Wang, J. Predicting Asphaltene Flocculation in Crude Oils, New Mexico Institute of Mining and Technology: New Mexico, 2000. (20) Fuhr, B. J.; Cathrea, C.; Coates, L.; Kalra, H.; Majeed, A. I. Fuel 1991, 70, 1293–1297. (21) Calles, J. A.; Dufour, J.; Marugán, J.; Peña, J. L.; Giménez-Aguirre, R.; Merino-García, D. Energy Fuels 2008, 22, 763–769. (22) Klein, G. C.; Kim, S.; Rodgers, R. P.; Marshall, A. G.; Yen, A. Energy Fuels 2006, 20, 1973–1979. (23) Spiecker, P. M.; Gawrys, K. L.; Kilpatrick, P. K. J. Colloid Interface Sci. 2003, 267, 178– 193. (24) Wattana, P.; Fogler, H. S.; Yen, A.; Carmen Garcìa, M. D.; Carbognani, L. Energy Fuels 2005, 19, 101–110. (25) Hansen, C. M. Hansen solubility parameters: a user’s handbook; CRC Press: Boca Raton [u.a., 2007. (26) Chao, K. C.; Seader, J. D. AIChE J. 1961, 7, 598–605. (27) Handbook of Chemistry and Physics.; 94. (28) Barton, A. F. M. CRC handbook of solubility parameters and other cohesion parameters; CRC Press: Boca Raton, 1991.


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Tables: Table 1: Physical properties of compounds at room temperature.

Compound

Density (g/ml)27

Viscosity (cP)27

Solubility Parameter (MPa0.5)28

n-Hexane n-Heptane n-Octane n-Nonane n-Decane n-Dodecane n-Pentadecane Toluene GM2 crude oil

0.660 0.679 0.703 0.718 0.726 0.749 0.768 0.866 0.868

0.32 0.41 0.54 0.70 0.93 1.56 2.81 0.60 15.69

14.90 15.20 15.40 15.60 15.80 16.001 16.302 18.30 17.593

1

From references [19] and [26]. From reference [26]. 3 Calculated from refractive index of GM2 crude oil using the correlation proposed by Wang and Buckley [15]. 2

Table 2: Solubility parameter of asphaltenes precipitated from GM2 crude oil using different precipitants.

Name Precipitant n-Hexane n-Heptane n-Octane n-Nonane n-Decane n-Pentadecane

Solubility Parameter of Asphaltenes (MPa0.5) 24.09 24.15 24.19 24.22 24.32 24.53


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Page 16 of 24

Table 3: Solubility parameter of 1 wt% K1 asphaltenes in toluene precipitated using different precipitants.

Name Precipitant n-Hexane n-Heptane n-Octane n-Nonane n-Decane n-Dodecane n-Pentadecane

Solubility Parameter of Asphaltenes (MPa0.5) 24.09 24.22 24.32 24.42 24.51 24.66 24.82

Table 4: Viscosities and solubility parameters used to predict the detection times in Figure 5.

Solubility Parameter of Blend (Mpa0.5)

Solubility Parameter of Asphaltenes (Mpa0.5) *

Viscosity of Blend (cp)

C7 & C15 0.75:0.25 0.50:0.50 0.25:0.75

15.48 15.75 16.03

24.37 24.52 24.67

0.67 1.07 1.74

C7 & C12 0.50:0.50

15.60

24.44

0.80

C7 & C10 & C12 0.30:0.30:0.40

15.70

24.48

0.89

C7 & C10 & C12 & C15 0.20:0.20:0.20:0.40

15.92

24.61

1.36

C6 & C7 & C10 & C12 & C15 0.20:0.20:0.20:0.20:0.20

15.64

24.46

0.88

Blend Composition

*

Calculated from Equation 2 and values given in Table 3.


Page 17 of 24

10000

1000 Detection Time (hrs)

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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100 C6 C7

10

C8 C9 1 C10 C15 0.1 18

20

22

24

26 28 30 32 34 36 Precipitant Vol% in Crude Oil

38

40

42

44

Figure 1: Detection time vs. precipitant concentration for GM2 crude oil mixed with six different precipitants (C6, C7, C8, C9, C10, C15).


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1000 C6 C7 C8 C9 C10 C12 C15

100 Detection Time (hrs)

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

Page 18 of 24

10

1

0.1 25

30

35

40

45

50

Precipitant Vol% in Toluene Figure 2: Detection time vs. precipitant concentration for 1 wt% of K1 asphaltenes dissolved in toluene and then mixed with seven different precipitants (C6, C7, C8, C9, C10, C12, C15).


Page 19 of 24

54 52

ln 4 0@ A

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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50 48 46 44 42

C6-Crude Oil C7-Crude Oil C8-Crude Oil C9-Crude Oil C10-Crude Oil C15-Crude Oil C6-Model Oil C7-Model Oil C8-Model Oil C9-Model Oil C10-Model Oil C12-Model Oil C15-Model Oil

40 38 36 34 32 1.65E-08

1.75E-08

1.85E-08

1.95E-08

1⁄ −

2.05E-08

2.15E-08

" I

Figure 3: Plot of 23456757859:; @?A vs. =⁄BCDEF − BD:GH59:; for six different precipitants (C6, C7, C8, C9, C10, C15) mixed with GM2 crude oil and seven different precipitants (C6, C7, C8, C9, C10, C12, C15) mixed with 1wt% K1 asphaltenes in toluene.


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

Solubility Parameter of Asphaltenes (Mpa^0.5)

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Page 20 of 24

24.9 K1 asphaltenes 24.8 GM2 asphaltenes 24.7 24.6 24.5 24.4 24.3 24.2 24.1 24 5

6

7

8

9

10

11

12

13

14

Carbon # of Precipitant Figure 4: Plot of asphaltenes solubility parameter vs. the precipitant carbon number (GM2 and K1).


15

16

Page 21 of 24

(a) Blend of two precipitants (C7-C15 at different ratios), (C7-C12) 1000

(b) Blend of three precipitants (C7-C10-C12)

1000

30:30:40 C7-C10-C12 100

10

Detection Time (hrs)


Model Prediction (30:30:40 C7-C10-C12)

75:25 C7-C15 50:50 C7-C15 25:50 C7-C15 50:50 C7-C12 Model Prediction (75:25 C7-C15) Model Prediction (50:50 C7-C15) Model Prediction (25:75 C7-C15) Model Prediction (50:50 C7-C12)

1

0.1 32

34

36 38 40 42 44 Precipitant Vol% in Toluene

100

10

1

0.1 46

(c) Blend of four precipitants (C7-C10-C12-C15)

1000

32

34

1000


Model Prediction (20:20:20:40 C7-C10-C12-C15)

100

10

1

0.1


46

(d) Blend of five precipitants (C6-C7-C10-C12-C15) 20:20:20:20:20 C6-C7-C10-C12-C15

20:20:20:40 C7-C10-C12-C15


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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Model Prediction (20:20:20:20:20 C6C7-C10-C12-C15)

100

10

1

0.1 32

34


46

32

34


46

Figure 5: Comparison of model predictions with the experimental measurements of detection time for blends of precipitants mixed with 1wt% K1 asphaltenes dissolved in toluene: (a) C7-C15 at different ratios (0.75:0.25, 0.5:0.5, 0.25:0.75), C7-C12 (0.5:0.5) (b) C7-C10-C15 (0.3:0.3:0.4) (c) C7-C10-C12-C15 (0.2:0.2:0.2:0.4) and (d) C6-C7-C10-C12-C15 (0.2:0.2:0.2:0.2:0.2).


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100


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

Page 22 of 24

10

75:25 C6-C8 50:50 C6-C8 75-25 C6-C8

1 24

26

28

30

32

34

Precipitant Vol% in Toluene Figure 6: Model predictions compared to experimental measurements of detection time for blends of C6-C8 precipitants at different ratios: (0.25:0.75), (0.5:0.5) and (0.75:0.25) mixed with GM2 crude oil.


Page 23 of 24

54

25:75 C7-C15-Model Oil 50:50 C7-C15-Model Oil

52

75:25 C7-C15-Model Oil 50 ln 4 0@ A

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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50:50 C7-C12_Model Oil 30:30:40 C7-C10-C15-Model Oil

48

20:20:20:40 C7-C10-C12-C15-Model Oil 20:20:20:20:20:20 C6-C7-C10-C12-C15-Model Oil

46

25:75 C6-C8-Crude Oil 44

50:50 C6-C8-Crude Oil 75:25 C6-C8-Crude Oil

42

Linear (Model) 40 38 36 34 1.7E-08

1.8E-08

1.9E-08 1⁄ −

2E-08

2.1E-08

"

Figure 7: Model prediction (i.e master curve) compared to the experimental I measurements for plot of 23456757859:; @?A vs. =⁄BCDEF − BD:GH59:; , for all the blends of precipitants (K1 model oil and GM2 crude oil), 10 different blends.


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Effect of n-Alkane Precipitants on Aggregation Kinetics of Asphaltenes

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