Molecular Weight and Aggregation of Heavy Petroleum Fractions

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Molecular Weight and Aggregation of Heavy Petroleum Fractions Measured by Vapor Pressure Osmometry and Hindered Stepwise Aggregation Model Linzhou Zhang, Suoqi Zhao, Zhiming Xu, Keng H. Chung, Changsen Zhao, Na Zhang, Chunming Xu, and Quan Shi Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef500749d • Publication Date (Web): 02 Sep 2014 Downloaded from http://pubs.acs.org on September 4, 2014

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Molecular Weight and Aggregation of Heavy Petroleum Fractions Measured by Vapor Pressure Osmometry and Hindered Stepwise Aggregation Model Linzhou Zhang, Suoqi Zhao*, Zhiming Xu, Keng H. Chung, Changsen Zhao, Na Zhang, Chunming Xu, Quan Shi* State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China

ABSTRACT The hindered stepwise aggregation (HSA) model was used to elucidate the molecular aggregation in heavy petroleum fractions which were derived from supercritical fluid extraction fractionation (SFEF) of Venezuela Orinoco vacuum residue (VR). The SFEF fractions consisted of multiple extractable narrow fractions and a non-extractable end-cut. The SFEF fractions were diluted with toluene and their number average molecular weights (MWs) were determined using vapor pressure osmometry (VPO). The initial molecular association constants (K1) and aggregation hindrance factors (H) of HSA model for each SFEF fraction were calculated from the VPO MWs at various SFEF solution concentrations. The results showed that the HSA model fit well with VPO MW data and the parameters of the HSA model are physically significant. The values of MW and K1 increased as the SFEF fraction became heavier. The SFEF end-cut had the highest K1 and lowest H value, in which the aggregates were 2 to 8 monomers. Except for the initial fraction, all the SFEF fractions formed aggregates at solution concentrations higher than 30 g/L. The value of K1 was dependent on the number of aromatic rings, whereas H is dependent on the size of aromatic ring and side-chain length. The VPO MWs of light SFEF fractions were in agreement with those determined from electrospray ionization (ESI) mass spectrometry (MS) or gel permission chromatography (GPC). The VPO MWs of the highly aggregated SFEF fractions were higher than those of ESI MS due to low ionization efficiency, but were much lower than those of GPC.

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INTRODUCTION Molecular aggregation is a common phenomenon encountered in many chemical[1] and biochemical[2] systems due to strong association interaction between molecules. A molecular aggregation system may exhibit peculiar properties and/or functionality different than expected based on the molecules present in the system. Molecular aggregation can cause challenges with material handling and affects the reaction mechanisms of mixtures of solutions. Heavy petroleum fractions are examples of highly complex molecular aggregation systems. Heavy petroleum fractions have structural characteristics that include a high number of aromatic rings and abundance of heteroatom functional groups. This causes strong association between molecules, leading to formation of aggregates. The aggregation of molecules in heavy petroleum fractions is responsible for operational problems related to processing, handling and transportation of heavy crudes, such as low molecular diffusivity and intrinsic reactivity, high viscosity, precipitation and fouling.[3, 4] Molecular aggregation of heavy petroleum fractions has been studied using various analytical techniques, such as mass spectrometry (MS)[5-10] and small angle X-ray/neutron scattering.[11-21] However, the mechanisms of molecular aggregation are not well understood, due to the complexity composition of heavy petroleum. Since asphaltenes are the heaviest and most polar fraction of petroleum, most of the research on molecular aggregation of petroleum has been devoted to asphaltenes. Recent progress in laser-based mass spectrometry has revealed that the molecular weight of petroleum is small (~750 Da) and the aggregate size ranges from 6 to 8.[9, 22, 23] Result from solution-state 1H NMR relaxation and 2D HSQC spectroscopy also suggest asphaltene molecular weight at same range (~ 710 Da).[24] There are two competing models of asphaltenes molecular aggregation. The Yen-Mullins model[25, 26] assumes that asphaltenes exhibit an island domain aromatic structure and aromatic π-π stacking is the main interaction within asphaltenes nano-aggregates. Yen-Mullins model has been successfully applied to explain or predict heavy petroleum molecule behavior, such as oil-water interfacial 2


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rheology.[27] On the other hand, Gray et al. proposed a supermolecular assembly model,[28] in which the asphaltenes exhibit a complex bridged structure and branched linkages. Aggregation is the result of cooperative binding and hydrogen bonding, in addition to aromatic π-π stacking. Molecular dynamics and quantum mechanics studies were performed on model compounds, in which the aggregation mechanisms and contributions of various interactive forces were inferred.[29-32] However, due to the limitation of analytical techniques to quantify the complex heavy petroleum system, the true nature of aggregation is still not well defined. Attempts have been made to model asphaltenes aggregation and flocculation as a function of phase equilibrium and/or using other thermodynamic properties. In an unsteady state system with the asphaltenes dissolved in a poor solvent (such as light alkenes), asphaltenes aggregation and flocculation were found to vary with time. As a result, time-dependent kinetic models, such as diffusion limited aggregation or population balance equation were applied.[33-41] For a steady state system with the asphaltenes or heavy petroleum dissolved in polar solvent (such as hot toluene), the asphaltenes aggregation process was found to be independent of time. With the same temperature and solvent, asphaltenes aggregation in polar solvent is only dependent on concentration. Rogel[42] proposed a molecular thermodynamic model for the description of the aggregation behavior of asphaltenes in different solvents. In practice, it is more challenging to model steady state aggregation, since it is difficult to measure aggregates in strong solvent system. For an unsteady state asphaltenes aggregation processes, aggregates could be easily quantified by weight the asphaltene flocculant. Vapor pressure osmometry (VPO) is very popular MW determination method for light petroleum fractions, which generally use linear regression method to extrapolate data to zero concentration. This method could not be applied to heavy petroleum system due to the presence of molecular aggregates which leads to a non-linear trend between test values and solvent concentration. However, the deviation provides a way to characterize the steady state aggregation behavior. Yarranton et al. used VPO to determine the apparent molecular weight (MW) of asphaltenes solution and fitted the 3


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MW data to the polymerization aggregation model.[43-46] Recently, the hindered stepwise association (HSA) model was developed to describe steady state molecular aggregation, in which the model parameters (molecular association constant, K1 and aggregation hindrance factor, H) were obtained by a self-iterative computing routine using the VPO MWs.[47] Asphaltenes flocculation in the presence of poor solvents is a result of cluster-cluster interactions[25] which is distinctly different from aggregation. Hence, it is essential to understand the steady state molecular aggregation which describes the initial aggregation step from monomers to nano-aggregates. Therefore, defining the process for formation of nano-aggregates will allow the determination of fundamental properties of heavy petroleum fractions. In addition to asphaltenes, molecular aggregation of heavy petroleum fractions affects refining operations. Advances in supercritical fluid extraction fractionation (SFEF) revealed the properties dispersity of heavy petroleum fractions.[48-50] The recent commercial demonstration of the selective asphaltenes extraction (SELEX-Asp) process indicated that the most effective way to process heavy petroleum is to selectively remove asphaltenes from the feedstock prior to the catalytic reaction steps.[51] The optimization of separation-reaction refining process for heavy petroleum requires information on reactivity, properties and composition of heavy petroleum fractions. A recent study on hindered diffusion of SFEF fractions indicated that the reactant diffusivity was strongly correlated with molecular aggregation of heavy fractions.[52, 53] Molecular aggregation is an important characteristic of petroleum heavy fractions, but it is poorly understood. The present study is part of systematic investigation on chemical and physical properties of heavy petroleum SFEF fractions. Venezuela Orinoco vacuum residue (VR) was subjected to SFEF with n-pentane to prepare multiple extractable narrow fractions and a non-extractable end-cut. The SFEF fractions were diluted with toluene and their MWs were determined using VPO. The MW data were fitted to the HSA model to determine the potential formation of aggregates and the aggregate size distribution for each SFEF fraction. The VPO MW of each SFEF fraction was compared to that obtained from electrospray ionization (ESI) Fourier transform ion 4


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cyclotron resonance mass spectrometry (FT-ICR MS) and gel permission chromatography (GPC). The effects of molecular structure on the association constant and aggregation hindrance factor were evaluated and the different morphologies of aggregates found in light and heavy SFEF fractions were determined.

HINDERED STEPWISE ASSOCIATION MODEL The hindered stepwise aggregation model (HSA) was proposed by Zhang et. al.[47] In HSA model, all the aggregation is formed by the stepwise “association reaction” between smaller aggregates and monomers. K n-1 An -1 +A1 ← → An

(1)

For each step i, there is an association constant (or equilibrium constant) Ki, and the molar concentration of large aggregates can be calculated from the concentration of smaller aggregates and monomers.

C ( An ) = K n −1 × C ( An−1 ) × C ( A1 )

(2)

As the aggregate grows in size, the value of association constant diminishes due to decreased active sites number and increased steric hindrance of the aggregate.[54] In HSA model, the aggregation hindrance factor (H) represents the attenuation speed of the association constant.

K n =K n -1 × H

(3)

The association constant of each step can be calculated from the initial association constant (K1), which represent the constant for the formation of dimer.

K n =K1 × H n -1

(4)

From Equations 2 and 4, the molar concentration of the aggregate can be calculated: C (An )=C n (A1 ) × K1n -1 × H

(n -1)(n -2) 2

(5)

The introduction of H gives HSA model the variability to simulate both high and low aggregation systems. A higher H value means the aggregation hindrance is low and the association constants are reduced less during the growth of the aggregate. A lower H value means there is high aggregation hindrance with a strong reduction in 5


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association constants. For molecules with only one active aggregation site, the H value will be near zero and only the formation of dimers will occur. From the Equation 5, the mass balance equation and the apparent MW of the system were calculated from Equations 6 and 7, respectively: n ( n-2 )( n-1)  n  2 NMWmono ∑  nC ( A1 ) K1n-1 H   1  NMWapp = n n-2 )( n-1) ( n  2  ∑1 C ( A1 ) K1i -1H  

n ( n-2 )( n-1)  n  2 c =NMWmono ∑  nC ( A1 ) K1n-1 H   1 

(6)

(7)

Equations 6 and 7 are core functions in HSA model, relating the bulk properties, apparent molecular weight (MWapp) and concentration (c), to the aggregation model parameters, monomer molecular weight (MWmono), K1 and H. The MWapp is the experimental VPO MW. By solving Equations 6 and 7 using a global optimization algorithm, the values of K1 and H can be obtained. Several aggregation model have been established in previous reports. The most recent progress is the multi-component aggregation model from Yarranton group in which the heavy petroleum molecules are divided into different class (propagator, terminator and neutral).[55] Their model made simplification that all the species have same interaction parameters and the aggregation behavior is changed in terms of the ratio of the components. On the contrary, HSA is single component model and the aggregation parameter (K1 and H) are identical for different fractions. Single component model have the shortcome that it fails to simulate mix process of the fractions. But it is capable of exploring the relationship between aggregation behavior and the averaged chemical structure, which is one of the purposes of this study. Since the signal response in ultra-low concentration is hard to detect, VPO measurement is generally limited to concentrations higher than 1 g/L. The extrapolation of HSA model to lower concentration is validated if the aggregations in ultra-low and measurable concentration ranges share same mechanism. According to the review of Mullins and various supporting reports, we believe that 6


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nano-aggregation by molecule-molecule interactions is the main mechanism from zero concentration to middle concentration for end-cut.[25, 26] With this assumption, we can make extrapolation from measurable concentrations to concentrations lower than 1 g/L. Previous studies have shown that the actual MWs and aggregation strengths for various petroleum fractions can be adequately obtained from the HSA model.[47]

EXPERIMENTAL Material and Separation Venezuela Orinoco petroleum vacuum residue (VR, 500°C+ material) was subjected to supercritical fluid extraction fractionation (SFEF) using n-pentane as solvent. The extraction and fractionation sections of SFEF unit were kept at 200°C and 220°C, respectively. Solvent solubility was adjusted by increasing the extraction pressure (4-12 MPa). Thirteen extractable narrow fractions and a non-extractable end-cut were obtained. The details of the operating procedure and properties of VR and its fractions have been described elsewhere.[56] The SFEF fractions #1, 4, 7, 10, 13 and end-cut were chosen for analysis. The saturates, aromatics, resins and asphaltenes (SARA) composition for SFEF fractions is listed in Table 1.

Apparent MW by VPO The SFEF fractions were analyzed using a K-7000 vapor pressure osmometer (Knuaer, Germany) set at 60°C. The instrument was calibrated using Sucrose octaacetate (679 g/mol). The extractable VR fractions were diluted with analytical grade (AR) toluene to yield 1-30 g/L concentration solutions. The non-extractable SFEF end-cut was diluted with toluene up to 23 g/L. At concentrations higher than 23 g/L, flocculation was observed after the VPO experiment (4 h). Six diluted samples with increased solution concentrations were prepared from each SFEF fraction. Six replicate voltage response measurements were collected for each diluted sample and the mean voltage response value was obtained by averaging the measurements. The apparent MW was calculated from the mean voltage response using Equation 8, 7


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which was the similar to that used by Yarranton et al.[43] NMWapp =

K instrument × c ∆V

（8 ）

where Kinstrument is the instrumental constant of VPO, c is the solution concentration of the sample (g/L), and ∆V is the voltage response of VPO.

Proton Nuclear Magnetic Resonance (1H-NMR) The 1H-NMR data were obtained from previous publications, in which a Unity Inova 500 MHz spectrometer (Varian, USA) was used.[57] The 1H-NMR data and MW obtained from HSA model and elemental composition were used to calculate the average molecular structure of the SFEF fraction using the modified Brown-Lander method.[58]

ESI FT-ICR MS and GPC The ESI FT-ICR MS and GPC MW data of the SFEF fractions were obtained from Zhang, et al.[56] These data were used to compare the MWs from ESI FT-ICR MS and GPC with those obtained from of VPO. The MWs of SFEF fractions obtained from negative- and positive-ion ESI FT-ICR MS were calculated using the following equation.

MW =

∑ ( Mass × Intensity ) ∑ Intensity

(9)

where Mass and Intensity are the mass and absolute signal intensity for each peak and MW is the average molecular weight obtained from the ESI FT-ICR mass spectrum. For each spectrum, the peaks with signal-to-noise ratio lower than 6 were ignored. The molecular weights were calibrated by retention time of GPC using polystrenes as a reference standard. The retention time distribution of the samples was then converted to molecular weight distribution. The MW was calculated by integrating the molecular weight distribution curves.

8


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RESULTS AND DISCUSSION Molecular Aggregation of SFEF fractions Figure 1 shows the VPO MWs of SFEF fractions as function of solution concentration. Except for the SFEF fraction #1, the apparent VPO MWs were non-linearly dependent on the solution concentration. The apparent VPO MWs of SFEF fraction #1 varied slightly from 500 to 600 g/mol as the solution concentration increased from 3 to 45 g/L. On the contrary, the apparent VPO MWs of SFEF end-cut varied over a wider range from 1500 g/L to 3500 g/mol. The VPO MW data in Figure 1 shows that the extent of molecular aggregation of SFEF fractions varied significantly. The VPO MW data of each SFEF fraction as a function of solution concentration was used as input to fit the HSA model. The MWs predicted by HSA model were in good agreement with those determined by VPO. Table 2 summarizes the HSA model parameters (NMWmono, K1 and H) for SFEF fractions. The MW of the monomer in the extractable SFEF fractions increased from 500 g/mol in SFEF fraction #1 to 700 g/mol in SFEF fraction #13. For the asphaltenes enriched SFEF end-cut, the MW of the monomers was 1028 g/mol, which was larger than those of the extractable fractions. The MW of SFEF end-cut was significantly lower than those in leterature.[59] The value of initial associate constant, K1 increased steadily as the SFEF became heavier. The low K1 value of SFEF fraction #1 suggests that it is a non-aggregation system. The distinct difference between the extractable fractions and end-cut is the value of aggregation hindrance factor, H. Light SFEF fractions had very low H values (< 0.01) so it does not tend to form large aggregates. The H value of SFEF fraction #13 was 0.1, indicating the presence of large aggregates. The H value of the SFEF end-cut was 0.79, significantly higher than those of the extractable SFEF fractions. The results indicated that a system with low aggregation hindrance (large H value) had a high aggregation tendency. Molecules with largest molecular interaction and lowest aggregation hindrance were enriched in non-extractable end-cut.

NMW from VPO, ESI MS and GPC 9


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The appropriate measurement technique to be used to determine the MW has been widely debated, especially for the heavy petroleum fractions. In this study, the VPO MWs of the SFEF fractions determined by the HSA model were compared with those obtained from ESI MS and GPC (Figure 2). Positive- and negative-ion ESI ionize the basic and acid compounds, respectively.[60] ESI has been widely applied for molecular level characterization of the heavy petroleum samples.[61-68] For SFEF fractions #1 to 10, the ESI MS measured MW increased steadily and was in agreement with those determined by VPO. But from SFEF fraction #13 to the end-cut, the ESI MW decreased. The MWs determined by ESI FT-ICR MS were significantly lower than those calculated from the HSA model for the heavy fractions. These results are likely due to low ionization efficiency of the molecules due to high molecular aggregation.[56] The result is in agreement with the previous report by McKenna et al.[69] In their investigation using various ionization methods and mass spectrometers, asphaltene molecules could only be partially ionized even at ultra-low concentration as some molecules only presented as nano-aggregates. The GPC MW of SFEF fraction #1 was lower than that determined by VPO in a concentration of 100 g/mol. The GPC MW of SFEF fraction increased steadily when it became heavier, which was similar to those from VPO. However, the GPC MW of the SFEF end-cut was much higher than that of VPO. In the GPC operation, the GPC MW was calculated based on calibration by polystyrenes. The heavy petroleum molecule has a larger fused aromatic ring than polystyrenes, which lead to a higher mobile size in solution. Due to the structural diversity and complex nature of the heavy petroleum fractions, it was difficult to find an appropriate model compound for each molecule during GPC calibration. The GPC MWs of high aromatic ring system calibrated by polystyrenes will be higher than the actual values. Wu et al. have measure three petroleum asphaltene samples using laser-based mass spectrometry.[22] Their result shows that the molecular weight of petroleum asphaltene is lower than 2000 Da and averaged at ~ 700 Da. End-cut is the heaviest SFEF fraction and contains ~ 70 wt % of asphaltene. Its average molecular weight by 10


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VPO-HSA method is ~ 1000, which is larger than previous report. There are two reasons for the data inconsistence: first, VPO measurement is less concise than MS based measurement; second, the result is for asphaltene, which is the most polar species in petroleum. There are ~30 wt% of maltane in end-cut. A previous study showed that MW of maltane is higher than asphaltene.[70] The result also indicates that for heavy petroleum fraction with high polarity and aromaticity, GPC and ESI MS is not appropriate for MW measurement. The deviation arouse from aggregation and aromaticity will become larger as the fraction goes heavier.

Molecular Aggregation and Structure Relationship The structural diversity of SFEF fractions also provides an opportunity to investigate the aggregation-structure relation of heavy petroleum fractions. The detailed qualitative molecular information has been identified in previous studies.[56, 71] In this study, quantitative average structural information calculated from the modified Brown-Lander method[58] using VPO MWs, element composition, and hydrogen information from 1H-NMR analysis was used. Table 3 lists the four structural parameters of the SFEF fractions: aromatic ring number (Raromatic), naphthetic ring number (Rnaphthenic), side chain carbon number (Lsidechain) and side chain number (Nsidechain). For the extractable SFEF fractions, Lsidechain was relatively constant. The Rnaphthenic and Nsidechain increased slightly as the fraction became heavier, while Raromatic showed steady incremental increase. This is in agreement with the previous assumption that the product yield of SFEF is dependent on the aromaticity of the molecules.[56] The molecular structure of the SFEF end-cut was significantly different from that of the extractable fractions. This heaviest fraction, which showed highest molecular interactions and lowest aggregation hindrance, has high value of Raromatic and Nsidechain, but low value of Lsidechain. The Raromatic is consistent with molecular orbital calculation and solution-state 1H NMR relaxation and 2D HSQC spectroscopy.[24, 72] The high value of Nsidechain of the SFEF end-cut is likely due to the abundant available adjacent sites as the aromatic ring system become larger. Compared to the average structural parameters in Table 3 and the HSA model 11


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parameters in Table 2, Raromatic was found to be correlated with the initial association constant, K1. Previous studies identified that the aromatic rings provide the active sites for aggregation by π-π stacking.[73] For Venezuela VR SFEF fractions, the aggregate constant increases logarithmically with aromatic ring number (Figure 3). On the other hand, the long side chain (large value of Lsidechain) will have negative impact on aggregation due to steric hindrance. The hindrance factor, H is affected by the aromatic ring size and side-chain length of the molecule. The equivalent aromatic ring number can be used to illustrate the structure, which can be calculated by:

Raromatic ( eq ) = Raromatic / Lsidechain

(10)

The dependence of the hindrance factor on the equivalent aromatic ring number is shown in Figure 4. The hindrance factor is near zero for those fractions have an equivalent aromatic ring number less than 1. The SFEF end-cut had the lowest value of Lsidechain, resulting in high aggregation and low solubility. This is consistent with the previous findings that long side chains increases the solubility of hexabenzocoronene derivatives which are used as model compounds for asphaltenes.[54, 74]

Aggregates Size Distribution The free energy of aggregate formation can be calculate from aggregation equilibrium constant. The relationship of free energy and equilibrium constant is shown in Equation 11.

=

⁄

(11)

The free energy change of aggregate formation (size from 2 to 10) is shown in Figure 5. Equilibrium constant K is dependent on temperature and aggregate size. Hence, the free energy of aggregation is not constant. The value of free energy can be used to judge the aggregation is favored or not. For all the SFEF fractions, the free energy increased as the aggregate size goes larger but with different initial value and slope. The HSA model parameters in Table 2 were used to calculate the size distribution of aggregates in each SFEF fraction at various solution concentrations (1, 5, 10 and 30 g/L). The aggregate size is defined as the number of monomers contained in each 12


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aggregate. The molar fractions of various aggregates can be expressed as:

X (An )=

C (An ) ∑ C (An )

(12)

The molar fractions of various aggregates in SFEF fractions are shown in Figure 6. The SFEF fraction #1 was a non-aggregate system. Regardless of solution concentration, the molar fraction of monomers was more than 90 %. For SFEF fractions #4, 7, 10, and 13, monomers were dominant at low solution concentrations. As the concentration increased, the number of aggregates exceeded that of monomers. For SFEF fractions #4, 7, and 10, dimer aggregates were dominant due to aggregation hindrance. For SFEF fraction #13, the H value was 0.1 and trimer aggregates formed. The SFEF end-cut was enriched with highly aggregate compounds. Even at a low concentration (1 g/L) aggregates consisting of 2 to 4 monomers formed, which occupy more than 40 mol %. As the concentration increased, the size of aggregates increased. The result from high-Q ultrasonics, and DC conductivity suggested that the CNAC for petroleum asphaltene in toluene is around 100 mg/L.[75-77] Our result is inconsistent with this value. The result may due to the difference between end-cut and asphaltene compositions as there are about 30 wt % maltene in end-cut. For the SFEF end-cut, Raromatic was 10 and the dominant aggregates consisted of 2 to 8 monomers, which is in agreement with the Yen-Mullins model for asphaltenes.[25, 26] The aggregate size value is also close to laser-based mass spectrometry result on asphaltene.[22] In Figure 7, the representative aggregate morphologies of extractable SFEF fractions and the end-cut based on the aggregation parameter and Yen-Mullins model are summarized. The molecular configurations of extractable SFEF fractions were similar, consisting of small to medium aromatic ring plates with long side chains. The molar fraction of aggregates increased as the SFEF fraction became heavier. However, due to high steric hindrance, the aggregates were either dimers or trimers. Even at high solution concentrations, the effective molecular size was not very large. On the other hand, molecules in the end-cut consisted of much larger aromatic ring plates, leading to strong self-association interactions. The short side chains had low steric 13


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hindrance and the number of large aggregates with 2-8 monomers increased with the increasing of concentration. At a higher concentration, flocculation of aggregates occurred and the toluene solvent could not disperse high amount of large aggregates. The larger aggregate size was the cause of significantly low hindered diffusivity of the end-cut.[52, 53]

CONCLUSIONS The aggregation of Venezuela VR SFEF fractions were characterized using HSA model based on VPO results. MWs from VPO, GPC, and ESI were similar in the light extractable SFEF fractions, however, a significant difference in MW was observed in the SFEF end-cut. The HSA model fit well with VPO MW data. The parameters of the HSA model are physical significant which relate to the molecular structures of heavy petroleum system. The initial association constant K1 steadily increases as the SFEF fraction becomes heavier. The SFEF fraction #1 was a non-aggregate system, in which the monomer comprised more than 90% of the total molecules even in high concentrations. Other extractable SFEF fractions, as well as end-cut, were dominant with monomers at very low concentration (~1 g/L) and with aggregates at high concentrations (30 g/L). The largest aggregates in extractable SFEF fractions and the SFEF end-cut were 2-3 and 2-8 monomers, respectively.

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AUTHOR INFORMATION Corresponding Authors Suoqi Zhao, Telephone: +86 10-8973-9015. E-mail: [email protected] Quan Shi, Telephone: +86 10-8973-3738. E-mail: [email protected].

Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS The authors thank Dr. Zhentao Chen for useful discussion on the molecular aggregation of heavy petroleum, and Ms. Xuxia Liu for assisting in VPO experiments. This work was supported by the National Basic Research Program of China (2010CB226901) and the National Natural Science Foundation of China (NSFCU1162204, 21176254, 21376262).

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Page 16 of 33

NOMENCLATURES A1 = monomer An = the aggregator formed by n monomers c = mass concentration of the sample, g/L

= standard Gibbs free energy of aggregation, KJ/mol H = hindrance factor Intensity = absolute intensity of each peak K1 = initial associate constant, L/mol Kinstrument = instrumental constant of VPO Kn = associate constant of step n, L/mol Lsidechain = Side-chain carbon number Mass = mass of each peak in mass spectrum, m/z MW = number average molecular weight of the system, g/mol MWapp = apparent number average molecular weight measured by VPO, g/mol MWmono = number average molecular weight of the monomer, g/mol Nsidechain = Number of side-chains R = Gas constant Raromatic = aromatic ring number Raromatic(eq) = equivalent aromatic ring number Rnaphthenic = naphthetic ring number ∆V = voltage response of VPO X(An) = molar fraction of aggregator An

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of Fractal Aggregates. AIChE J., 1996. 42(12): 3318-3332. 36. Mozaffarian, M., Dabir, B., Sohrabi, M., Rassamdana, H., and Sahimi, M., Asphalt Flocculation and Deposition Iv. Dynamic Evolution of the Heavy Organic Compounds. Fuel, 1997. 76(14–15): 1479-1490. 37. Yudin, I. K., Nikolaenko, G. L., Gorodetskii, E. E., Kosov, V. I., Melikyan, V. R., Markhashov, E. L., Frot, D., and Briolant, Y., Mechanisms of Asphaltene Aggregation in Toluene–Heptane Mixtures. J. Petro. Sci. Eng., 1998. 20(3–4): 297-301. 38. Yudin, I. K., Nikolaenko, G. L., Gorodetskii, E. E., Markhashov, E. L., Agayan, V. A., Anisimov, M. A., and Sengers, J. V., Crossover Kinetics of Asphaltene Aggregation in Hydrocarbon Solutions. Phys. A: Stat. Mech. Appl., 1998. 251(1–2): 235-244. 39. Rastegari, K., Svrcek, W. Y., and Yarranton, H. W., Kinetics of Asphaltene Flocculation. Ind. Eng. Chem. Res., 2004. 43(21): 6861-6870. 40. Boek, E. S., Wilson, A. D., Padding, J. T., Headen, T. F., and Crawshaw, J. P., Multi-Scale Simulation and Experimental Studies of Asphaltene Aggregation and Deposition in Capillary Flow†. Energy Fuels, 2009. 24(4): 2361-2368. 41. Rahimi, H. and Solaimany Nazar, A. R., Asphaltene Aggregates Fractal Restructuring Model, a Population Balance Approach. Energy Fuels, 2009. 24(2): 1088-1093. 42. Rogel, E., Thermodynamic Modeling of Asphaltene Aggregation. Langmuir, 2004. 20(3): 1003-1012. 43. Yarranton, H. W., Alboudwarej, H., and Jakher, R., Investigation of Asphaltene Association with Vapor Pressure Osmometry and Interfacial Tension Measurements. Ind. Eng. Chem. Res., 2000. 39(8): 2916-2924. 44. Agrawala, M. and Yarranton, H. W., An Asphaltene Association Model Analogous to Linear Polymerization. Ind. Eng. Chem. Res., 2001. 40(21): 4664-4672. 45. Bressler, D. C., Wang, J., Gawrys, K. L., Gray, M. R., Kilpatrick, P. K., and Yarranton, H. W., Association Behavior of Pyrene Compounds as Models for Asphaltenes. Energy Fuels, 2005. 19(4): 1268-1271. 46. Yarranton, H. W., Fox, W. A., and Svrcek, W. Y., Effect of Resins on Asphaltene Self-Association and Solubility. Can. J. Chem. Eng., 2007. 85(5): 635-642. 47. Zhang, L., Shi, Q., Zhao, C., Zhang, N., Chung, K. H., Xu, C., and Zhao, S., Hindered Stepwise Aggregation Model for Molecular Weight Determination of Heavy Petroleum Fractions by Vapor Pressure Osmometry (Vpo). Energy Fuels, 2013. 27(3): 1331-1336. 48. Shi, T.-P., Hu, Y.-X., Xu, Z.-M., Su, T., and Wang, R.-A., Characterizing Petroleum Vacuum Residue by Supercritical Fluid Extraction and Fractionation. Ind. Eng. Chem. Res., 1997. 36(9): 3988-3992. 49. Yang, G. and Wang, R. A., The Supercritical Fluid Extractive Fractionation and the Characterization of Heavy Oils and Petroleum Residua. J. Petro. Sci. Eng., 1999. 22(1-3): 47-52. 50. Zhao, S., Sparks, B. D., Kotlyar, L. S., and Chung, K. H., Reactivity of Sulphur Species in Bitumen Pitch and Residua During Fluid Coking and Hydrocracking. Petro. Sci. Tech., 2002. 20(9): 1071 - 1085. 51. Zhao, S., Xu, C., Sun, X., Chung, K. H., and Xiang, Y., China Refinery Tests Asphaltenes Extraction Process. Oil Gas J., 2010. 108(12): 51-59. 52. Chen, Z., Xu, C., Gao, J., Zhao, S., and Xu, Z., Hindered Diffusion of Residue Narrow Cuts through Polycarbonate Membranes. AIChE J., 2010. 56(8): 2030-2038.

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53. Chen, Z., Zhao, S., Xu, Z., Gao, J., and Xu, C., Molecular Size and Size Distribution of Petroleum Residue. Energy Fuels, 2011. 25(5): 2109-2114. 54. Kastler, M., Pisula, W., Wasserfallen, D., Pakula, T., and Müllen, K., Influence of Alkyl Substituents on the Solution- and Surface-Organization of Hexa-Peri-Hexabenzocoronenes. J. Am. Chem. Soc., 2005. 127(12): 4286-4296. 55. Barrera, D. M., Ortiz, D. P., and Yarranton, H. W., Molecular Weight and Density Distributions of Asphaltenes from Crude Oils. Energy Fuels, 2013. 27(5): 2474-2487. 56. Zhang, L., Xu, Z., Shi, Q., Sun, X., Zhang, N., Zhang, Y., Chung, K. H., Xu, C., and Zhao, S., Molecular Characterization of Polar Heteroatom Species in Venezuela Orinoco Petroleum Vacuum Residue and Its Supercritical Fluid Extraction Subfractions. Energy Fuels, 2012. 26(9): 5795-5803. 57. Zhang, L., Li, S., Han, L., Sun, X., Xu, Z., Shi, Q., Xu, C., and Zhao, S., Coking Reactivity of Laboratory-Scale Unit for Two Heavy Petroleum and Their Supercritical Fluid Extraction Subfractions. Ind. Eng. Chem. Res., 2013. 52 (16): 5593–5600. 58. Zhao, S., Kotlyar, L., Woods, J., Sparks, B., and Chung, K., Molecular Nature of Athabasca Bitumen. Petro. Sci. Tech., 2000. 18(5-6): 587-606. 59. Zhao, S., Xu, Z., Xu, C., Chung, K. H., and Wang, R., Systematic Characterization of Petroleum Residua Based on SFEF. Fuel, 2005. 84(6): 635-645. 60. Qian, K., Edwards, K. E., Diehl, J. H., and Green, L. A., Fundamentals and Applications of Electrospray Ionization Mass Spectrometry for Petroleum Characterization. Energy Fuels, 2004. 18(6): 1784-1791. 61. Qian, K., Robbins, W. K., Hughey, C. A., Cooper, H. J., Rodgers, R. P., and Marshall, A. G., Resolution and Identification of Elemental Compositions for More Than 3000 Crude Acids in Heavy Petroleum by Negative-Ion Microelectrospray High-Field Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy Fuels, 2001. 15(6): 1505-1511. 62. Qian, K., Rodgers, R. P., Hendrickson, C. L., Emmett, M. R., and Marshall, A. G., Reading Chemical Fine Print: Resolution and Identification of 3000 Nitrogen-Containing Aromatic Compounds from a Single Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrum of Heavy Petroleum Crude Oil. Energy Fuels, 2001. 15(2): 492-498. 63. Rodgers, R. P., Hendrickson, C. L., Emmett, M. R., Marshall, A. G., Greaney, M., and Qian, K., Molecular Characterization of Petroporphyrins in Crude Oil by Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Can. J. Chem., 2001. 79(5-6): 546-551. 64. Smith, D. F., Rahimi, P., Teclemariam, A., Rodgers, R. P., and Marshall, A. G., Characterization of Athabasca Bitumen Heavy Vacuum Gas Oil Distillation Cuts by Negative/Positive Electrospray Ionization and Automated Liquid Injection Field Desorption Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy Fuels, 2008. 22(5): 3118-3125. 65. Smith, D. F., Schaub, T. M., Kim, S., Rodgers, R. P., Rahimi, P., Teclemariam, A., and Marshall, A. G., Characterization of Acidic Species in Athabasca Bitumen and Bitumen Heavy Vacuum Gas Oil by Negative-Ion Esi Ft−Icr Ms with and without Acid−Ion Exchange Resin Prefractionation. Energy Fuels, 2008. 22(4): 2372-2378. 66. Shi, Q., Pan, N., Liu, P., Chung, K. H., Zhao, S., Zhang, Y., and Xu, C., Characterization of Sulfur Compounds in Oilsands Bitumen by Methylation Followed by Positive-Ion Electrospray Ionization and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy Fuels, 2010. 24(5): 3014-3019.

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67. Shi, Q., Yan, Y., Wu, X., Li, S., Chung, K. H., Zhao, S., and Xu, C., Identification of Dihydroxy Aromatic Compounds in a Low-Temperature Pyrolysis Coal Tar by Gas Chromatography−Mass Spectrometry (Gc−Ms) and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICT MS). Energy Fuels, 2010. 24(10): 5533-5538. 68. Shi, Q., Zhao, S., Xu, Z., Chung, K. H., Zhang, Y., and Xu, C., Distribution of Acids and Neutral Nitrogen Compounds in a Chinese Crude Oil and Its Fractions: Characterized by Negative-Ion Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy Fuels, 2010. 24(7): 4005-4011. 69. Mckenna, A. M., Donald, L. J., Fitzsimmons, J. E., Juyal, P., Spicer, V., Standing, K. G., Marshall, A. G., and Rodgers, R. P., Heavy Petroleum Composition. 3. Asphaltene Aggregation. Energy Fuels, 2013. 27(3): 1246-1256. 70. Mckenna, A. M., Marshall, A. G., and Rodgers, R. P., Heavy Petroleum Composition 4. Asphaltene Compositional Space. Energy Fuels, 2013. 27(3): 1257-1267. 71. Zhang, L. Z., Chen, S., Zhang, N., Xu, Z. M., Shi, Q., and Zhao, S. Q., Characterization of Venezuela Vacuum Residue and Its Supercritical Fluidization Extraction Fractionation Cuts. Abs. Am. Chem. Soc., 2011. 242. 72. Ruiz-Morales, Y., Wu, X., and Mullins, O. C., Electronic Absorption Edge of Crude Oils and Asphaltenes Analyzed by Molecular Orbital Calculations with Optical Spectroscopy. Energy Fuels, 2007. 21(2): 944-952. 73. Hoeben, F. J. M., Jonkheijm, P., Meijer, E. W., and Schenning, A. P. H. J., About Supramolecular Assemblies of Π-Conjugated Systems. Chem. Rev., 2005. 105(4): 1491-1546. 74. Pisula, W., Tomović, Ž., Simpson, C., Kastler, M., Pakula, T., and Müllen, K., Relationship between Core Size, Side Chain Length, and the Supramolecular Organization of Polycyclic Aromatic Hydrocarbons. Chem. Mat., 2005. 17(17): 4296-4303. 75. Andreatta, G., Goncalves, C. C., Buffin, G., Bostrom, N., Quintella, C. M., Arteaga-Larios, F., Pérez, E., and Mullins, O. C., Nanoaggregates and Structure−Function Relations in Asphaltenes†. Energy Fuels, 2005. 19(4): 1282-1289. 76. Andreatta, G., Bostrom, N., and Mullins, O. C., High-Q Ultrasonic Determination of the Critical Nanoaggregate Concentration of Asphaltenes and the Critical Micelle Concentration of Standard Surfactants. Langmuir, 2005. 21(7): 2728-2736. 77. Zeng, H., Song, Y.-Q., Johnson, D. L., and Mullins, O. C., Critical Nanoaggregate Concentration of Asphaltenes by Direct-Current (Dc) Electrical Conductivity†. Energy Fuels, 2009. 23(3): 1201-1208.

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TABLES Table 1. Saturates, aromatics, resins and asphaltene composition for SFEF fractions. Fraction #

Saturates, wt% Aromatics, wt%

Resins, wt%

Asphaltenes, wt%

SFEF 1

39.28

26.14

11.6

0.74

SFEF 4

20.22

57.67

16.37

0.23

SFEF 7

6.84

60.97

24.78

0.18

SFEF 10

0.48

54.62

36.29

0.25

SFEF 13

0.00

27.99

59.77

1.09

end-cut

1.56

7.82

21.85

68.77

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Table 2. Yields, molecular weights of monomer and HSA model parameters for SFEF fractions. Yield

MWmono

K1

SFEF

wt%

g/mol

L/mol

H

1

5.24

526

2.54

0.009

4

5.08

543

99.02

0.007

7

5.06

615

288.39

0.000

10

4.62

671

497.89

0.010

13

3.48

765

963.79

0.104

End-cut

34.38

1028

1303.80

0.790

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Table 3. Modified Brown-Landler molecular structure parameters for SFEF fractions. SFEF

Raromatic

Rnaphthenic

Lsidechain

Nsidechain

1

1.76

2.17

5.04

3.28

4

2.02

2.18

4.66

3.74

7

2.91

2.19

4.97

3.80

10

3.84

2.15

4.83

4.04

13

5.19

2.27

4.76

4.31

End-cut

9.33

3.21

3.33

8.53

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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

25


Energy & Fuels

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 26 of 33

FIGURE LEGEND Figure 1. VPO MWs of SFEF fractions as function of solution concentration. Figure 2. MWs of SFEF fractions determined by VPO/HSA method, positive ion ESI MS, negative ion ESI MS, and GPC. Figure 3. Aggregate association constant of dimer as a function of number of aromatic rings for SFEF subfractions. Figure 4. Steric hindrance factor as a function of equivalent aromatic ring number (number of aromatic rings/length of side chain) for SFEF subfractions. Figure 5. Free energy change of aggregate formation (aggregate size from 2 to 10). Figure 6. Aggregate size distribution of SFEF fractions in various-concentration solutions (1, 5, 10, 30 g/L). Figure 7. Aggregate morphologies of SFEF fractions.

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Number Average Molecular Weight, g/mol

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

Energy & Fuels

700

900

600

800

Experimental Data Model Prediction

700 500

600

400

500

300

400 300

200

0

200

SFEF1

100

SFEF4

100

1200

0 45 0 1400

1000

1200

0

5

10

15

20

25

30

35

40

5

10

15

20

25

30

35

40

45

20

25

30

35

40

45

25

30

35

40

45

1000

800

800 600 600

400

400

200 0

SFEF7 0

5

10

15

SFEF10

200 20

25

30

35

40

0 45 0 4000

5

10

15

1600 3500

1400

3000

1200

2500

1000 800

2000

600

1500

400

1000

SFEF13

200 0

0

5

10

15

End-Cut

500 20

ACS Paragon Plus Environment 0

25

30

35

40

45

0

5

10

15

20

Concentration, g/L

1800

Energy & Fuels

Page 28 of 33

NMW, g/mol

11200 2 3 4 600 5 6 7 0 8

VPO HSA (-)ESI (+)ESI GPC

ACS Paragon Plus Environment SFEF1

SFEF4

SFEF7

SFEF10

SFEF13

End-Cut

1400

Associate Constant K, L/mol

Page 29 of 33

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Energy & Fuels

1200 1000

y = 812.5Ln(x) - 498.21 2 R = 0.97

800 600 400 200

Experimental Correlation

0 1


3

4

5

6

7

8

Average Aromatic Ring Number

9

10

1.0

0.8

Page 30 of 33 2

y = 0.12x - 0.06x 2

R = 0.99

Hindrance Factor

1 2 3 0.6 4 5 0.4 6 7 8 0.2 9 100.0 11 12 0.0 13 14

Energy & Fuels Experimental Data Correlation

ACS Paragon Plus Environment 0.5

1.0

1.5

2.0

2.5

Equivalent Aromatic Ring Number

3.0

120 31 of 33 Page

SFEF1 SFEF4 SFEF7 SFEF10 SFEF13 End-Cut

100

Free Energy, KJ/mol

1 80 2 3 60 4 5 40 6 7 20 8 9 0 10 11-20 12 13 14

2

Energy & Fuels


4

5

6

7

Aggregate Size

8

9

10

SFEF1

SFEF4

1

SFEF13

End-Cut Page 32 of 33

1

1

1

1

0 1

0 1

0 1

0 1

0 0.4

1g/L

Molar Fraction

10 21 3 40 51 6 70 81 9 100 11 12

Energy SFEF10 & Fuels

SFEF7

1

5g/L 0 1

0 1

0 1

0 0.4

0 1

10g/L

2

4

6

8

10

0 1

0 1

0

0

0 1

2

4

6

8

10

2

4

6

8

10

2

Aggregate Size

4

6

8

0 0.4

0

0

30g/L


0 1

10

2

4

6

8

10

2

4

6

8

10

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

Energy & Fuels

Pressu ure

Page 33 of 33

Light Extractable Fraction

Heavy Extractable Fraction

SFEF

Non-extractable End-cut


Molecular Weight and Aggregation of Heavy Petroleum Fractions

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