Molar-Mass Distributions of Asphaltenes in the Presence of Inhibitors

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Molar-Mass Distributions of Asphaltenes in the Presence of Inhibitors: Experimental and Computer Calculations Mariana Barcenas† and Pedro Orea*,‡ †

Divisi
on de Química y Bioquímica, Tecnol
ogico de Estudios Superiores de Ecatepec, Ecatepec de Morelos, 55210 Estado de M
exico, M
exico ‡ Programa de Ingeniería Molecular, Instituto Mexicano del Petr
oleo, Eje Celtral L
azaro C
ardenas 152, 07730 M
exico Distrito Federal, M
exico ABSTRACT: Molar-mass distributions (MMDs) of asphaltenes with two inhibitors have been characterized by experimental and computer calculations; experimental data of the asphaltene molecular weight were obtained from gel permeation chromatography (GPC) and compared to experimental data from vapor pressure osmometry (VPO). The MMD experimental behaviors were obtained using GPC to three systems: asphaltene þ toluene, asphaltene þ toluene þ 4-nonylphenol inhibitor, and asphaltene þ toluene þ M2 inhibitor. Besides, a molecular model of particle agglomeration control (PAC) was used to obtain tendencies of MMD in the presence of a dispersant at different conditions. Theoretical and experimental analyses presented here provide an understanding of the behavior of the asphaltenes from crude oil with two inhibitors. An anomalous effect of the asphaltene agglomeration with respect to the asphaltene concentration was found and validated using the PAC model. It has been found that the inhibitor efficiency depends upon concentration levels of the asphaltenes and inhibitors.

’ INTRODUCTION The oil industry currently faces the necessity to improve the efficiency of processes used in the extraction, refinement, and transformation of crude oil. However, asphaltenes are a barrier that must be overcome during the operation of such processes, because the asphaltenes contain groups of components that associate in solution to form complex colloidal structures.13 The asphaltene aggregation (AA) and subsequent deposition is one of the most serious problems in the production, transportation, storage, and refinement of crude oil. Many studies on this field have been carried out,49 and the aggregation mechanism has not yet been understood completely, because asphaltenes are complex chemical species and their composition depends upon the petroleum from which they are obtained.10,11 One of the most common techniques to prevent asphaltenes from agglomeration is the use of inhibitors (dispersants), which are molecules that contain a hydrophilic part and a hydrophobic part.12 The dispersants have the function of maintaining asphaltenes in the bulk.1214 Nevertheless, it has been shown that the efficiency of the inhibitors depends upon the kind of asphaltene and the environment in which they are immersed,11 a fact that represents a limitation in the use of these inhibitor molecules. Therefore, to make efficient use of inhibitors to control the harmful effect produced by AA, it is necessary to know the structure and associative properties of the inhibitor. For that reason, the molecular structure of asphaltenes has been widely studied, but a generalized theory about it has not been established. On the contrary, it is a topic of debate in the scientific community. For example, Sheremata et al.15 and Boek et al.16 found different quantitative molecular representations (QMRs) of asphaltenes using a combination of nuclear magnetic resonance (NMR) and elemental and molecular-weight analysis obtained from Athabasca bitumen. Sheremata et al.15 reported archipelago representations, r 2011 American Chemical Society

while Boek et al.16 found pericondensed molecular representations. According to these results and other studies, it is wellknown that currently there is not a generalized theory about the molecular weight and molecular-weight distribution, which is essential to determine the molecular structure of asphaltenes, and consequently, the development of efficient inhibitor molecules is limited. Therefore, in this work, we determine the molecular-weight distribution of asphaltenes in the presence of inhibitors. Different experimental techniques have been used to characterize the asphaltenes,6,8,1730 such as electron impact mass spectrometry (EIMS),17,18,23 laser desorptionmass spectrometry (LDMS),19 vapor pressure osmometry (VPO),4,17,23,25 and gel permeation chromatography (GPC),6,7,21,22,24,2630 among others. The EIMS technique may be inexact because of incomplete ionization. LDMS is considered one of the best methods to obtain the comprehensive molecular-weight distribution of an asphaltene. The GPC and VPO techniques have shown that the association of the asphaltenes depends upon the polarity of the medium, the concentration of asphaltenes, and the temperature;4,11,23,25 normally, in organic solvents, the asphaltene molar mass increases when the solute concentration increases. Particularly, Moschopedis et al.31 found that, when toluene or benzene is used, the asphaltenes tend to self-associate and show high molar-mass values. Also, Yarranton et al.4 showed that the self-association of asphaltenes decreases when the temperature increases, using toluene and o-dichlorobenzene as the solvent. Although the GPC technique has been intensively used for studying the AA, specifically to determine asphaltene Received: January 19, 2011 Revised: March 25, 2011 Published: March 30, 2011 2100

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Table 1. Saturates, Aromatics, Resins, and Asphaltenes (SARA) Fractionation Results

Table 2. Inhibitor Properties

crude oil American Petroleum Institute (API) (deg)

36

saturates (wt %) aromatics (wt %)

41.7 34.2

resins (wt %)

21.8

asphaltenes (wt %)

2.3

molecular weight,6,7,21,22,24,2630 it is important to remark that the GPC technique has a tendency to underestimate the molecular weight because of the lack of specific standards for the calibration; besides, the adsorption of some compounds in the column and molecular association of some units may overestimate the molecular weight.7,17,25 For this reason, some authors believe that the results obtained with the GPC technique are not quantitatively correct and that the technique allows us to obtain only qualitative trends;26 this issue is still a matter of debate. Nevertheless, despite its limitations, the GPC technique provides a lot of information on the aggregation mechanism through molar-mass distribution (MMD), which gives us a general idea about how many clusters there are and the way that the asphaltene aggregates are formed in different conditions. In the present work, the GPC technique is used to study the asphaltene molar-mass distribution (AMMD) in the presence of agglomeration inhibitors to reveal what are the main factors affecting the inhibitor efficiency. In a previous work,11 using VPO, we reported results of asphaltene molecular weights and established the medium effect on AA inhibitor efficiency. This paper presents an extension of the inhibition AA process study through the analysis of AMMD and molecular weights using the GPC technique. Consecutively, to support the experimental results and verify the tendencies to establish generalized behaviors of the inhibition AA process, computer calculations have been performed by applying a particle agglomeration control model10 with Monte Carlo simulation (PACMC). This model has previously been used to study the mechanism of AA,10,13,32,33 and it could obtain reliable theoretical trends with respect to the temperature and asphaltene concentrations, which provided unexpected behaviors that permitted us to establish some variables affecting the inhibitor efficiency.

’ MATERIALS AND METHODS Materials. The asphaltene sample used in this study was extracted from a crude oil from the southern production region of M
exico in Tabasco state. This crude oil is a highly unstable one, with relatively low asphaltene content (see Table 1), which presents strong flocculationdeposition problems during the production process. The asphaltenes were isolated from the crude oil by the addition of n-heptane in a ratio of 40:1 (cm3/g of oil). The suspension was left under strong mixing for 8 h, then filtered using a 1 μm porous size membrane, and dried under vacuum at 60
C overnight. To remove n-heptanesoluble material trapped in the solids, they were redissolved in methylene chloride and reprecipitated with n-heptane using the same precipitant/solution ratio of 40:1. The insoluble material was separated by filtration. Fresh n-heptane was added to the solids to prepare a suspension. The suspension was put under 20 min of ultrasonic shaking, followed by a centrifugation process, which separates the supernatant (i.e., n-heptane þ n-heptane solubles) from the n-heptane insolubles.

Figure 1. GPC calibration curve using a polystyrene standard. The supernatant was separated by decantation, and fresh n-heptane was added again to prepare a new suspension. This shakingcentrifugation cycle was repeated until the supernatant became transparent with a pale yellow color that did not change after various washing cycles. Table 1 lists SARA analysis results for crude oil.34 It is important to note that we used n-heptane to separate particles from crude oil, and it is well-known that n-heptane asphaltenes are not the true asphaltene content of the crude oil; it is a rather small asphaltene fraction compared to that found in crude oil. Amphiphile Molecules. The commercial inhibitors were 4-nonylphenol (NP), from Aldrich, analytical grade (purity >98%), used without further purification, and a new inhibitor molecule DAIM-2000 (hereafter denoted as M2), which is an oxazoline derivative from polyalkyl or polyalkenyl N-hydroxyalkyl. The molecular-weight (MW) data from VPO using toluene as a solvent at 50
C and the molecular formula of both inhibitors are reported in Table 2.34 The M2 molecular formula is a generalized structure for this kind of inhibitor, where R is the functional group and n is the chain length.35 GPC. GPC relies on separating analytes in solution on the basis of their molecular sizes. The sample is carried through the column by the mobile phase. Dependent upon the size and molar mass of molecules, they migrate through the column at different speeds because they are retained in the stationary phase and eluted at various times. The technique allows one to measure the molar-mass average number (Mn), defined as Mn ¼

∑Ni Mi ∑Ni

ð1Þ

where Ni is the number of moles with molar mass Mi. The AMMD were measured with Agilent Hewlett-Packard 1100 equipment at a constant temperature of 25
C, using toluene as the mobile phase at a flow rate of 1 mL/min; the injection volume of the sample was 5 mL. The detection with an array of diodes was carried out 2101

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Energy & Fuels at a wavelength of 360 nm. The calibration curve was drawn using polystyrene standards with a molar mass between 800 and 11 000 g/mol (see Figure 1). To determine asphaltene distributions, toluene was used as the mobile phase with a chromatographic column with a mixture of polydivynilbencene/ polystyrene Waters Styragel HR 4E packed in toluene, with a diameter equal to 7.8 mm and a length equal to 300 mm. The detection range of the molecular weights of polystyrene standards is between 50 and 100 000 Da. All samples were diluted in toluene to obtain different asphaltene CA and inhibitor Ci concentrations. All of the data presented below are the average of at least two repetitions, and the error bars do not exceed the size of the symbols on the plots. Theoretical Modeling. It is well-known that the AA is a complex phenomenon itself that depends upon many parameters; when the inhibitor molecules are added, the study of the AA mechanism becomes even more extensive and complex. If one wishes to consider all of the features of the phenomenon, one finds oneself in a hopelessly complex situation very soon. Therefore, it is necessary to ignore certain aspects of the physical situation and retain others. The AA in organic solvents has been studied for many years using a wide variety of techniques.36 This has allowed for the development of different models to predict and represent the aggregation and precipitation of asphaltenes. Models can be classified into two types: thermodynamic and colloidal models. The colloidal one is supported by experimental measurements from different techniques,4,37 whose results showed spherical or possibly disk-shaped particles dispersed in asphaltenetoluene mixtures. On the other hand, some studies have established that the asphaltenes form micellar aggregates when they are immersed in an aromatic solvent and the concentration is close to the critical micellar concentration (cmc).38 However, some studies have not found a cmc, suggesting that aggregation occurs because of other mechanisms. Merino-Garcia et al.39 showed that asphaltenes start selfassociating in toluene at low concentrations (through calorimetric experiments) and that the existence of a cmc is almost improbable. Results from single-molecule force spectroscopy36 and VPO25 suggested that AA can occur stepwise as a polymerization, and in the experiments from Yarranton et al.,4 the aggregates do not appear to be micelles. According the previous discussion, the nature of AA is a debated subject, because the AA depends upon the type of asphaltene and the solvent. In this work, toluene is used in the experimental measurements, and according to Merino et al.,39 even the toluene is a good solvent for asphaltenes, which have the tendency to associate. Fenistein et al.,40 Pedersen,41 and Rajagopal et al.42 showed that the asphaltenes exist in toluene solutions as aggregates of nanometric size. In the same way but using molecular dynamics simulation techniques, Headen et al.43 observed the formation of asphaltene dimers and trimers in toluene; those aggregates can separate and reform other aggregates with other asphaltene molecules. Their results showed the asphaltene nanoaggregation in toluene. In accordance with the previous discussion, a colloidal model is appropriate to represent the AA. In this work, we use a colloidal model named the particle agglomeration control (PAC) model10 to study the inhibition of AA, which takes into account the main features of the phenomenon. Previous results of the asphaltene agglomeration using the PAC model have shown correct trends in comparison to experimental data from VPO;11 on this basis, we apply the PAC model to validate the experimental behaviors of the MMDs and establish the effect of inhibitors on the agglomeration of asphaltenes. The model consists of two mixed components: colloidal particles (denoted as C) and inhibitor particles (denoted as D). As mentioned in previous paragraphs, when using toluene as a solvent, the asphaltenes tend to form small aggregates; therefore, the model considers small aggregates of asphaltenes as a colloidal particle (a colloidal particle is not identical to an asphaltene molecule but is the same as a small cluster of asphaltenes), as shown in Figure 2.

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Figure 2. (a) Two-dimensional sketch of the bonding topology of the PAC model. The current functionalities of colloidal particles are f(C3) = 4, f(C2) = 3, f(C1) = 2, f(C5) = 1, and f(C4) = 0. (b) Terminology used by the model, where AM is an asphaltene molecule and C is a colloidal particle (which consists of a little cluster of asphaltenes). Unlike other models, the PAC model considers the asphaltene as a colloid with limited valence (we have applied the tetrafunctional valence-limited model to this study). Aguilera et al.,44 to study the AA, proposed a model that consists of molecules formed by a central sphere surrounded by a flat ring of spheres. The size and energetic properties of the center and external spheres are different, to describe the center with an aromatic nature and the external sites with an aliphatic nature. The PAC model is simpler and considers the same effects described by the model by Aguilera et al., through different variables, e.g., medium effect (implicit way), asphaltene and inhibitor concentration, and temperature. Boek et al.45 proposed a model that assumes that the colloidal aggregates (asphaltene aggregates) consist of nanoaggregates, as the PAC model; however, they studied the effect of the solvent in an independent way. According to their results of the solvent entrainment effects, they deducted the presence of lubrication layers between the nanoaggregates, which may lead to a significant screening of the direct asphaltene asphaltene interactions. Unlike the model mentioned, the PAC model describes the solvent effect in an implicit form. The colloidal particles and inhibitors are considered as hard spheres with diameters σC and σD, respectively. Without any loss of generality, the C particle is taken as the length unit, σC = 1. Neglecting the role of solvent interactions, a pair of C particles is considered to interact through a spherical short-range square-well potential with a constraint on the maximum number of bonds each C particle can form with neighboring ones, C or D 8 > :0

if rkj < σ C if σ C e rkj e λσC and ðfC ðhÞ þ fD ðhÞÞ < F; h ¼ k or j if rkj > λσ C

ð2Þ where rkj is the distance between k and j particle centers, εC and λ are the well depth and width, respectively, fC(h) and fD(h) are the current number of connections of colloidal particle h with other C and D particles, respectively, and F is the functionality of a C particle, i.e., the maximum number of bonds per particle (see Figure 2). In other words, we have considered a valence-limited colloidal system. In the PAC model, the geometrical parameter λ (in eq 2) can serve as adjustable parameter to take into account the effective influence of a solvent on the probability of CC bonding. That is, a larger value of λ corresponds to easier colloidcolloid agglomeration (higher value of the mean cluster size). It is well-known that the interaction between asphaltenes can be represented by a short-range potential of the kind of LennardJones,4648 to particularize the kind of asphaltene, and the environment where they are immersed (as we mentioned previously) is sufficient to adjust the 2102

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parameters included in the model potential. For this reason, the PAC model is suitable to represent the AA inhibition process, because the model includes potential models of this kind. The present study is restricted to the case of F = 4. The influence of this parameter has been previously reported.10,33 In the present calculations, the value λ = 1.1 has been chosen. All of the parameters included in the model are dimensionless. Each D particle is represented by a hard sphere of diameter σD = 0.3 having one attractive site located on the surface. The inhibitor molecule has a smaller diameter than an asphaltene colloid because, according to Table 2, the molecular weight of an inhibitor is smaller in comparison to the molecular weight of an asphaltene colloid (small cluster of asphaltenes). There is associative attraction acting between the site of one D sphere and the center of one C sphere, which is explicitly given by the squarewell-like potential10 ( εD if 0:5 σC e z e w uCD ðz, ΩÞ ¼ ð3Þ 0 if z > w where Ω denotes the orientation of the D particle and z is the distance between the center of the C particle and the site of the D particle at a given centercenter separation and orientations. The appropriate choice of w, w = 0.565, guarantees that the saturation condition is satisfied; i.e., the inhibitor can react only with one colloid, while each colloid can attach to more than (up to F) one inhibitor. The value of the energies are εC = 1 and εD = 2; the energy of the inhibitor was chosen to be twice the energy of the colloid, because according to some studies,49,50 the commercial inhibitor dipolar moment with respect to asphaltene is approximately twice as big in magnitude. Monte Carlo simulation of the PAC model was performed with a canonical ensemble (NVT) that starts with a collection of NC and ND particles randomly disposed in a cubic simulation box with periodic boundary conditions. We considered the starting configuration as a collection of aggregates containing one colloidal particle for each one, and if the process generates Ni particles, the dimensionless number density, Fi, is defined as Fi = Ni/V, where V is the volume of the simulation cell. If a C particle is connected to one or more inhibitors, it moves together with them. If a particle overlaps a previous one, it is discarded and a new trial is made. The attempted MC moves are translations of both species (C and D) and rotations of D particles; they are accepted or rejected according to the traditional Metropolis algorithm. In the PAC model, the agglomeration of C particles and their peptization by D particles can occur. Competition between the potentials (eqs 2 and 3) as well as the relative species concentration describe the phenomenon of inhibition of colloidal aggregation. All of the results are averaged over at least two independent simulation runs of the same system to achieve a good statistical description. The error bar of the results presented in the next section does not exceed the symbol size. distributionWe have carried out computer experiments for different PAC systems, which have been characterized by the different colloid and inhibitor concentrations. The dimensionless inhibitor concentration is defined as K = ND/NC. Systems with NC = 600 particles and a density of colloidal particle F = 0.05 have been studied. Once a simulation is finished, we have calculated the normalized frequency distribution (NFD) of C particle clusters of size M, i.e., cluster size distribution10 NFDðMÞ ¼

1 NMC ÆmM æM NMC

∑

ð4Þ

and the mean cluster size, Z, is defined as10 Mx

Z¼

∑ Æmj æj

j¼1 Mx

∑ Æmj æ j¼1

ð5Þ

Figure 3. (a) Asphaltene molar-mass average number, Mn, at different asphaltene concentrations, using the GPC technique. (b) Mn at different asphaltene concentrations, using the VPO technique,9 with toluene as the solvent at 50
C. where NMC is the total number of MC runs, Mx denotes a maximum cluster size, and ÆmMæ is the mean number of clusters of size M.

’ RESULTS AND DISCUSSION In this study, the three following systems have been considered: (i) dispersion of asphaltene in toluene (AT), (ii) dispersion of asphaltene and NP in toluene (ATNP), and (iii) dispersion of asphaltene and M2 in toluene (ATM2). In all of these cases, toluene was used to prepare the dispersions at different asphaltene and inhibitor concentrations and, at the same time, is used as the mobile phase of the GPC technique. Merdrignac et al.7 showed that the MMDs are strongly dependent upon the operation conditions, the kind of column, and the nature of the solvent used, with the last because the interaction force increases with an increasing polarity of the solvent. Likewise, it must be recognized that the lack of realistic standards to calibration with a similar nature and molecular weight of the asphaltenes has implications on the results.24 However, the GPC technique has been used successfully to obtain MMDs of asphaltenes.2730 The mobile effluent normally used with the GPC technique is tetrahydrofuran (THF)24,29 and sometimes 1-methyl-2-pyrrolidinone (NMP);28 unlike these works, in this study, we use toluene as the mobile phase. Applying the GPC technique, one can obtain correct behavior that permits an appropriate interpretation of AA. In the same way, here, we use GPC to determine the influence of the inhibitors on the AA and asphaltene size distribution. Additionally, the experimental tendencies are validated with results obtained by using the PACMC model to provide realistic behaviors. In our measurements, we use only low asphaltene concentrations, CA, from 1 to 6 g/L, because at higher concentrations, the chromatograms show notably high polydispersity and the results cannot be reproduced. Figure 3a shows the change of the molar-mass average number (which, in this work, we call the mean molar mass), Mn, with respect to the asphaltene concentration (AT system). As seen, the value of Mn increases with the concentration, reaching its maximum at CA = 2 g/L; a further increase of the concentration leads to the lowering of Mn. This behavior may be due to a redistribution of aggregates, that is, a change in the distribution of clusters; this hypothesis will be discussed later when addressing the AMMD analysis. Similar tendencies have been recently 2103

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Figure 4. Asphaltene molar-mass average number, Mn, at different asphaltene concentrations for (circles) AT, (squares) ATM2, and (triangles) ATNP systems. Results of inhibitor applications at concentrations Ci = 0.1 and 0.2 g/L are shown in panels a and b, respectively. The black symbols correspond to experimental data from VPO11 with toluene as the solvent at 50
C: (squares) ATM2 and (triangles) ATNP.

reported for different kind of asphaltenes,7 in which, using different techniques, it has been demonstrated that the mean molar mass of the asphaltene increases with the concentration;7,4 unlike this trend, our results show a decrease and subsequent growth of the molar mass. On the other hand, Aguilera-Mercado et al.44 studied the effects of the temperature and inhibitor concentration on the asphaltene agglomeration using a molecular model, and they have concluded that the linear aggregation model is only appropriate for very low aggregation; therefore, it is possible that the origin of the asphaltenes and the kind of environment in which they are immersed are some of the reasons for the distinct aggregation behavior. In this work, a peculiar behavior was found, where Mn decreases to certain concentrations and then increases again (between 3 and 6 g/L). This effect was also found for the same asphaltenes using VPO, as seen in Figure 3b, where toluene was used as the solvent and experimental measurements were determined at 50
C (a description of these experimental data is reported in ref 11). Such an effect was found at higher asphaltene concentrations (between 15 and 22 g/L) using the VPO technique. This disagreement can be tentatively attributed to the temperature difference with which the experimental measurements were carried out. Despite the difference, the results from the two techniques discussed apparently show a redistribution of the asphaltene aggregates to certain conditions of concentration and temperature. In a previous work,33 using PACMC, we also found a similar behavior of the AA process with respect to the asphaltene concentration, as shown by the GPC experimental data. As seen in Figure 4 of ref 33 (which corresponds to a system without inhibitor), the molar mass of asphaltenes increases linearly up to an asphaltene concentration of FA = 0.075(CA ≈ 11 g/L) and then presents a decrease of the molar mass, around an asphaltene concentration of FA = 0.12(CA ≈ 19 g/L), and a subsequent growth of such a mass. In that work, we revealed the qualitative and quantitative consistency of model results with respect to the experimental data obtained by VPO, showing the reliability of the model to represent and predict the AA. Therefore, using PACMC, it is possible to validate the experimental data obtained by GPC, to establish a behavior of the AA process, where the increasing

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concentration of asphaltenes does not cause a linear increase of the molar mass and the asphaltene mean molar mass clearly depends upon the asphaltene concentration. The effect of the inhibitors on the AA process is shown in Figure 4, where the mean molar mass of asphaltene, Mn, for two systems, ATNP and ATM2, is presented and they are compared to the AT system. In general, it can be observed that the application of inhibitors at low concentrations significantly reduces the value of Mn at different asphaltene concentrations. This behavior also occurs in the VPO experimental measurements and PACMC simulation results, as shown in Figure 4 of ref 33, to an inhibitor concentration of 0.1 g/L and K = 1.6, which demonstrates that the presence of the inhibitor causes a decrease in the mean molar mass. Therefore, it was an expected result in the GPC measurements that the addition of the inhibitor to the AT system causes a decrease in the value of the molar mass. It is noteworthy that the molar mass measured by this technique may be overestimated because of the effects of absorption of the packed column; however, it is important to point out that the tendency of the experimental data is correct with respect to the results obtained by both the additional techniques described in the preceding paragraphs. In Figure 4, some experimental data from VPO11 were included; it is possible to observe a match on the value of Mn to an asphaltene concentration equal to 4 g/L in Figure 4a. This agreement allows us to have certainty of data obtained from GPC at least for that concentration. In the VPO measurements,11 the M2 inhibitor decreases the mean molarmass system by an average of one-third of the value of the molar mass of the AT system. The asphaltene concentrations studied by GPC showed a minimal reduction of half of the value of the molar mass of the AT system (see Figure 4). Applying the NP inhibitor at a low concentration, Ci = 0.1 g/L, shows its slightly higher efficacy if compared to the M2 inhibitor at the same concentration. However, increasing the inhibitor concentration up to Ci = 0.2 g/L, leads to lower values of Mn in the case of the ATM2 system and to its highest values in the ATNP system. Moreover, comparing the ATNP system at two inhibitor concentrations, 0.1 and 0.2 g/L, one can conclude that NP loses its inhibition efficiency when its concentration is increased. Some tentative explanations of such unexpected behavior have been recently presented by us.11 Summarizing, the inhibitor molecules tend to form self-assembled structures in the solvent to certain inhibitor concentrations, which decreases their adsorption on the asphaltene surfaces and diminishes the inhibitor efficiency. On the other hand, increasing the inhibitor concentration reduced the mean value of the asphaltene molar mass Mn up to ≈765 g/mol at three values of CA considered. It is important to note that, at CA = 2 and 3 g/L, the conditions in which there is a maximum asphaltene agglomeration, inhibitor M2 managed to reduce Mn over 8 times and showed the inhibitor efficiency to reduce the AA when it is used at low concentrations (≈0.2 g/L). The quantity of the M2 inhibitor necessary to reduce the mean cluster size is bigger than the quantity of the NP inhibitor used. The above results show that, according to the chemical structure of inhibitor molecules, there is a concentration that the system requires to efficiently manipulate the AA process. The MMDs of the three systems were studied as well. In Figure 5, we present the AMMD in toluene at different concentrations. Analysis of the AT system shows that the MMD for all asphaltene concentrations possesses one peak, except for CA = 1 g/L, which presents a bimodal behavior with two small peaks at 2104

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Figure 6. AMMDs at different asphaltene concentrations for the (a) ATNP system and (b) ATM2 system, using the inhibitor concentration Ci = 0.1 g/L. Figure 5. AMMDs at different asphaltene concentrations for the AT system, using the GPC technique.

5  102 and 5  103 g/mol; that behavior suggests the existence of at least two different types of species in solution.6 In this case, the first peak could correspond to asphaltenes as molecules, while the second peak corresponds to the asphaltene clusters. The highest asphaltene concentrations studied here demonstrate the distributions with a peak around 8  103 g/mol. It is interesting that AT solutions with CA = 1.5 and 2 g/L provoke the formation of asphaltene aggregates up to 4  104 g/mol, while in the case of CA = 3 and 4 g/L, the MMD extends up to 3  104 g/mol only. It is worth noting that, for CA = 3 g/L, the weight percentage of the aggregates with Mn in the range between 3.5  103 and 1.7  104 g/mol is the highest if compared to other concentrations considered here. Figure 5 shows how the clusters are redistributed. In the cases of AT solutions with CA = 1.5 and 2 g/L, the presence of big clusters can be observed; however, for the asphaltene concentration CA = 5 g/L, the quantity of big clusters is less than for CA = 2 g/L. The previous fact causes an increase in the mean cluster size for CA = 2 g/L. According to the previous discussion, it is possible to establish that an increase of the asphaltene concentration causes a redistribution of asphaltene aggregates. Therefore, the behavior that shows the mean molar mass with respect to the CA (Figure 4) is due to the redistribution of clusters previously described. The AMMDs presented in Figure 5 are qualitatively consistent with GPC data obtained by other authors for different kinds of asphaltenes.6,7,21,22,24 Namely, V
azquez et al.6 and Merdrignac et al.7 obtained bimodal curves in the AMMD, while Peramanu et al.24 reported unimodal mass distributions. We believe that the difference may be explained by the fact that the polydispersity index in the MMD depends upon the nature of the asphaltenes. In the same way, the AMMD for the ATNP and ATM2 systems were measured too, at two different inhibitor concentrations: Ci = 0.1 and 0.2 g/L. The experimental results for Ci = 0.1 g/L are presented in both panels a and b of Figure 6. The influence of the inhibitor on the MMD is almost identical for both systems considered. Even so, the M2 inhibitor permits the formation of more extensive clusters, up to 6  104 g/mol, in comparison to the NP inhibitor. On the other hand, the comparison of the effect of these two inhibitors at CA = 2 g/L shows the lower peak at Mn ≈ 1  104 g/L and notably less extensive agglomerates, up to

Figure 7. AMMDs at different asphaltene concentrations for the (a) ATNP system and (b) ATM2 system, using the inhibitor concentration Ci = 0.2 g/L.

4  104 g/mol, in the case of NP inhibitor application. Comparing ATNP and ATM2 systems to the AT system, we found that the presence of the inhibitor leads to the formation of bigger agglomerates. According to a recent study of the medium effect on the efficiency inhibitors,11 we found that, when the concentration of the inhibitor was increased using solvents such as toluene and o-dichlorobenzene, the same inhibitors promote the formation of asphaltene agglomerates. The experimental and theoretical measurements showed that the inhibitor molecules self-associate, hiding their polar head part in the toluene, and the inhibitor adsorption on the asphaltene surfaces is reduced, which allows for the formation of big aggregates. Besides, the inhibitor molecules may prefer getting together in the bulk to being adsorpted on the asphaltene surface. Therefore, it is possible to establish that the distribution tendency is due to the asphaltene concentration, the solvent, and the inhibitor concentrations used, because these variables can cause the formation of big aggregates. Figure 7 shows the MMD for the ATNP and ATM2 systems for Ci = 0.2 g/L. We can observe that all distributions are unimodal, but in the NP inhibitor case, a great amount of 2105

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Figure 8. Comparison of GPC asphaltene experimental data and PACMC simulation results. The black and gray symbols correspond to reduced MMD, NFD, for GPC measurements and the simulation results, respectively.

aggregates is formed with respect the ATNP system with Ci = 0.1 g/L. An opposite effect occurs for the ATM2 system with Ci = 0.2 g/L, where there is less formation of aggregates, and they are smaller with respect to the same system with Ci = 0.1 g/L. That is, the M2 inhibitor is more efficient at a concentration of 0.2 g/L, while the NP inhibitor is more efficient at a concentration of 0.1 g/L. According to Figures 6 and 7, the inhibitors have a better efficiency at different inhibitor concentrations (decreased Mn and smaller clusters are formed); we consider that this behavior is due to the difference between the inhibitor structures. That is, the molecule M2 has a slightly longer aliphatic tail if compared to NP; according to previous theoretical studies,11 we found that the inhibitor adsorption decreases when the length of the inhibitor tail increases. In a similar way, the polarity of the inhibitor head can cause the formation of micelles. Considering that the efficiency of an inhibitor depends upon the adsorption of the inhibitors on the asphaltene surface and taking into account the theoretical results described, we can establish that the concentration at which an inhibitor is used has an influence on its efficiency. To validate the MMD from the GPC technique, the model PAC was used to obtain theoretical results and compare them to the experimental measurements (see Figure 8). Values of the cluster size m have been obtained by reducing the molar-mass experimental data by the colloidal monomer value equal to M0 = 975 g/mol, which was estimated by the linear extrapolation of the three lowest concentration data from VPO measurements.11 Therefore, the molar mass of asphaltene aggregates has been reduced by M0 to make possible the comparison to the theoretical dimensionless results of the mean cluster size. To adjust the asphaltene concentration to number densities, the diameter of the asphaltene colloidal particles has been considered to be 2 nm. An inhibitor concentration, K = 5, was used to carry out the PACMC simulation. As seen in Figure 8, the simulation results agree surprisingly well with the experimental data. Note that, in the present study, the parameter K is just one of the model fitting parameters. The experimental measurements from the GPC technique show unimodal curves to MMD, and in Figure 8, we can observe that the model has proven the same tendency for MMD. In addition, numerically speaking, the PACMC has a

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Figure 9. Reduced MMD, NFD, for two quantities of the inhibitor for PACMC simulation: (circles) K = 0, (triangles) K = 5, and (squares) K = 8.3.

maximum at the same magnitude of NFD compared to the experimental data; the only numerical difference observed in both curves is that the PACMC model predicts smaller aggregates than the clusters obtained by the experimental data. These results show that trends in MMD obtained by GPC in the presence of the inhibitor are reliable, because the experimental data and simulation results agree qualitatively and quantitatively. Finally, Figure 9 shows MMD obtained by the use of the PAC model for three different amounts of inhibitor (K = 0, 5, and 8.3). According to the figure, unimodal distributions are very similar for all concentrations of inhibitor. Making an analysis of the MMD curves, the concentrations of K = 0 and 5 show big aggregates involving up to 11 asphaltene colloids, whereas at the highest concentration of inhibitor (K = 8.3), it is observed that the bigger aggregate includes a maximum of 7 asphaltene colloids. It can be seen from Figure 9 that, for K = 5, fewer aggregates in the size between 2 and 4 are formed, as compared to the system with K = 0, and the frequency of clusters with the size between 7 and 9 asphaltene molecules is higher. That is, the concentration of K = 5 favors the formation of big aggregates; this effect was also observed experimentally. When the morphology of the distributions obtained theoretically is compared to those measured by GPC (see Figures 68), it appears that they show the same real trend.

’ CONCLUSION In this work, using the GPC technique and PACMC simulation, we continue our investigation on the physical phenomena responsible for inhibition efficacy of asphaltene agglomeration by chemical additives.10,11,32 The effects of two different inhibitors, NP and M2, have been experimentally studied. It has been found that the asphaltene molar mass in a solvent, such as toluene, depends upon the concentration of both asphaltenes and inhibitors. Namely, applying AA inhibitors at low concentrations, 0.1 or 0.2 g/L, leads to the notable decrease of the asphaltene molar mass, 50% or more; however, in some cases, the formation of big clusters was presented. The magnitude of the decline in the value of the asphaltene molar mass depends upon the concentration of the asphaltene as well as the amount and kind of applied inhibitors. It is important to mention that the asphaltene agglomeration process depends upon the origin of the asphaltene; in this work, the results reported were obtained for one kind of asphaltene 2106

dx.doi.org/10.1021/ef200108t |Energy Fuels 2011, 25, 2100–2108

Energy & Fuels from the southern region of M
exico. The results can be very different if the experiments are performed for asphaltenes of a different origin. A general behavior of the asphaltene molar mass with respect to the asphaltene concentration was found, where Mn decreases to certain concentrations and then slightly increases again. The AMMDs in the AT system for different asphaltene concentrations show that low concentrations (2 and 3 g/L) present the greatest formation of aggregates and, therefore, a higher value of Mn. When an inhibitor is added to the system, the formation of asphaltene aggregates decreases, as expected. Nevertheless, for certain asphaltene concentrations, the formation of aggregates reduces and some extended aggregates (larger than those in the system without an inhibitor) are formed. A theoretical study was carried out using PACMC simulation. The results of the simulation were validated with experimental data from VPO;11 a qualitative and quantitative agreement of the experimental and theoretical behavior was found. The trend of the asphaltene molar mass resulting from the simulation results agrees with the behavior described in the preceding paragraph. Therefore, in this work, a general behavior of the PC asphaltene molar mass with respect to the asphaltene concentration was found. Some theoretical MMDs were obtained and compared to experimental data from the GPC technique; surprisingly, both present a similar quantitative and qualitative behavior. Comparing the morphology of the distributions obtained theoretically to those measured by GPC shows the same trend.

’ AUTHOR INFORMATION Corresponding Author

*Telephone: þþ52-55-9175-8269. Fax: þþ52-55-9175-6380. E-mail: [email protected].

’ ACKNOWLEDGMENT The authors acknowledge J. García-Martínez and A. RomeroMartínez for their helpful comments. The authors also acknowledge the late Yurko Duda because the first part of the paper was performed in collaboration with him. Pedro Orea gratefully acknowledges the financial support of the Instituto Mexicano del Petr
oleo under Project D.00406. All of the authors acknowledge the support of CONACyT. ’ NOMENCLATURE AA = asphaltene aggregation AMMD = asphaltene molar-mass distribution AT = dispersion of asphaltene in toluene ATNP = dispersion of asphaltene and 4-nonylphenol in toluene ATM2 = dispersion of asphaltene and inhibitor molecule DAIM2000 in toluene C = colloidal particle CA = asphaltene concentration Ci = inhibitor concentration cmc = critical micellar concentration D = inhibitor EIMS = electron impact mass spectrometry GPC = gel permeation chromatography K = dimensionless inhibitor concentration LDMS = laser desorptionmass spectrometry m = cluster size MC = Monte Carlo

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MMD = molar-mass distribution ÆmMæ = mean number of clusters of size M Mn = molar-mass average number MW = molecular weight Mx = maximum cluster size M2 = inhibitor molecule DAIM-2000 NMR = nuclear magnetic resonance NC = number of C particles ND = number of D particles NFD = normalized frequency distribution in the particle agglomeration control model NMC = total number of Monte Carlo runs NMP = 1-methyl-2-pyrrolidinone NP = 4-nonylphenol NVT = canonical ensemble PAC = particle agglomeration control PACMC = particle agglomeration control model with Monte Carlo simulation QMR = quantitative molecular representation THF = tetrahydrofuran VPO = vapor pressure osmometry Z = mean cluster size in the particle agglomeration control model uCC(rkj) = spherical short-range square-well potential σC = colloidal particle diameter in uCC(rkj) potential σD = inhibitor particle diameter in uCC(rkj) potential εC = well depth in the uCC(rkj) potential λ = well width in the uCC(rkj) potential fC(h) = current numbers of connections of colloidal particle h with other C particles in the uCC(rkj) potential fD(h) = current numbers of connections of colloidal particle h with other D particles in the uCC(rkj) potential F = functionality of aC particle in the uCC(rkj) potential rkj = distance between k and j particle centers in the uCC(rkj) potential uCD(z,Ω) = associative attraction potential acting between the site of one D particle and the center of one C particle Ω = orientation of the D particle in the uCD(z,Ω) potential z = distance between the center of the C particle and the site of the D particle Fi = dimensionless number density

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