Molecular Representation of Petroleum Residues Using Fourier

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Molecular Representation of Petroleum Residues using FT-ICR-MS and Conventional Analysis. Claudia Ximena Ramirez Novoa, Juan Esteban Torres, Diana Catalina Palacio Lozano, Juan Pablo Arenas-Diaz, Enrique Mejía-Ospino, Viatcheslav Kafarov, Alexander Guzman, and Jorge Ancheyta Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b02507 • Publication Date (Web): 31 Oct 2017 Downloaded from http://pubs.acs.org on November 1, 2017

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Molecular Representation of Petroleum Residues using FT-ICR-MS and Conventional Analysis Claudia X. Ramírez1*, Juan E. Torres2+, Diana Catalina Palacio Lozano1, Juan P. Arenas-Diaz1, Enrique Mejia-Ospino1, Viatcheslav Kafarov1 , Alexander Guzman2 and Jorge Ancheyta3± 1

Universidad Industrial de Santander, Carrera 27 Calle 9, Bucaramanga-Santander. A,A. 678, Colombia

2

Ecopetrol S.A., Instituto Colombiano del Petróleo, kilometro 7 via Piedecuesta, PiedecuestaSantander. A,A. 4185, Colombia. 3

Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas 152, Col. San Bartolo Atepehuacan, Mexico City 07730, Mexico.

*[email protected], [email protected], ±[email protected]. ABSTRACT A methodology for representing structurally the molecules of three Colombian vacuum residues (538+ °C) and one Mexican atmospheric residue (300+°C) is reported. Information obtained by Fourier Transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) coupled to positive electrospray ionization (ESI(+)), negative electrospray ionization (ESI(−)) and positive atmospheric pressure photoionization (APPI(+)) sources, and conventional standardized analytic methods was used for molecular representation of the samples. The generation of molecules was performed by Monte Carlo technique, obtaining a set of representative structures for the global representation through attributes for each residue. The structural attributes considered are CH, CH2, CH3 in paraffinic chains; number of naphthenic rings; CH2 and CH naphthenic; number of 1 ACS Paragon Plus Environment

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aromatic rings; aromatic carbon type; cata-condensed and peri-condensed carbons; number of sheets in asphaltenes; -SH, aromatic S (thiophene); aromatic N (pyridine and pyrrole) and -NH2. Each attribute in the residues can be represented by a probability distribution function (PDF), which are optimized for the purpose of adjusting the structures of residues and its composition to the experimental data. The PDFs for aromatics, nitrogen and sulfur components were obtained by ultra-high mass resolution data. As a result, 150 molecules per each residue were obtained, the mode of representation was single-feed (only one feed is characterized at the same time). The bulk properties for each residue were in good agreement with the experimental structural information. Keywords: Molecular Representation; Monte Carlo; FT-ICR-MS; Vacuum Residue; NMR; Process Modeling. 1. INTRODUCTION Currently, light crude oil reserves (that is conventional oil) are declining and the future production scenarios show a higher contribution of unconventional heavy crude oils to be used as feed to refineries1. These types of feeds are characterized by their high density, high content of heteroatoms, high structural complexity, and high components number. Due to this complex nature there is a lack of detailed molecular characterization of these heavy hydrocarbons. Thus, new characterization techniques are required in order to provide detailed information of complex feeds. Standard techniques are not sufficient for proper characterization of heavy oils due to the complexity of most of the components presents in heavy cuts such as vacuum residue.

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In this regard, ultra-high resolution mass spectrometry, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) emerges as a powerful technique that can provide a more detailed and vast information on chemical composition of very heavy and complex hydrocarbons that are present in vacuum residue 2. The information obtained by this technique depends on the ionization efficiency of each molecule in each ionization method employed. Therefore, a structural characterization is obtained, but the exact composition of each molecule in the residue is unknown. Thus, it is necessary to incorporate conventional and nonconventional analyses to try to obtain a real characterization of this class of feeds. For instance Neurock 3 and Trauth 4 developed two algorithms to generate model molecules from physical and chemical properties of the oil, such as density, molecular weight (MW), SARA analysis, true boiling point (TBP) distillation , hydrogen-to-carbon ratio (H/C), nuclear magnetic resonance spectroscopy (NMR) and sulfur content. One algorithm minimizes the error between measured and calculated properties by varying structures that would represent the stream, which are obtained by stochastic reconstruction. The estimation of the chemical properties of each molecule is based on group contribution so that the properties of the mixture are calculated; this reconstruction is done in an equimolar manner 5. The other algorithm also minimizes the error between measured and calculated properties, but in this case by performing an adjustment of the molecules compositions by means of simulated annealing. Hudebine et al.6 employed two algorithms by generate a set of model molecules from overall petroleum analyses. The first is based on stochastic reconstruction (SR), and the second on reconstruction by entropy maximization (REM), methodology applied to light cycle oil (LCO) gas oils, subsequently extended to heavy cuts as vacuum gas oils7 and vacuum residues8.


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L. Pereira de Oliveira et al.9 modified and applied the method proposed by Hudebine et al.6 in the molecular representation of various vacuum residues from different locations, with different characteristics. The objective in this work was evaluated the versatility of the methodology proposed.

Linzhou Zhang et al.10 proposed a methodology by sampling with PDFs and a juxtaposition of the structural attributes for representing petroleum residues, incorporating structural information additional inferred from FT-ICR MS coupled to fragmentation by collision induced dissociation (CID) and knowledge of light petroleum fractions. The aromatic ring configuration of cores was inferred from CID FT-ICR MS results. The basis for obtaining a set of representative structures present in a hydrocarbon mixture is the global representation of attributes. For instance, the number of aromatic rings, the number of side chains, the number of carbons cata-condensed and peri-condensed, etc. each attribute can be represented by a probability distribution function (PDF). The PDF of the attributes for each structure is built following chemical rules in order to obtain the set of structures that better represent the bulk properties of the residue under consideration. Therefore, each density function employed for representing a structural attribute allows for moving the probability of occurrence of events of a random experience to a numeric characteristic. Incorporating FT-ICR-MS information in models based on molecular representation can be an approach to improve their representativeness. The data obtained by FT-ICR-MS can supply new structural parameters such as the maximum number of carbons, aromatic rings and naphthenic longitude in each structure. Also, it can provide the PDF's for aromatics, resins, nitrogen and sulfur compounds.


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In this study three Colombian residues (538°C +) and one Mexican residue (300°C +) were characterized by FT-ICR-MS and molecularly represented. The mode of representation was single-feed (only one feed is characterized at the same time). 150 molecules per each residue were obtained. The bulk properties for each residue were in good agreement with the experimental structural information. The molecular representation was evaluated studying the behavior of each feed in a separation process with n-heptane in a commercial process simulator. 2. MODELING APPROACH 2.1.

Modeling of structural attributes

Any structure present in the samples can be represented by their structural attributes, such as the number of aromatic rings, the number of side chains, the number of cata-condensed and pericondensed carbons, the number atoms of sulfur and nitrogen, etc. Each structural attribute can be also represented by a PDF. A PDF is defined by Equations 1 and 2, where f(x) is the probability density function of the variable x in the interval [0, 1]. 0≤≤1

(1)

∑ = 1

(2)

According to the literature, the gamma probability density function , which is defined by Equation 3, with parameters α, γ > 0 and the mean = µ, is the best PDF for representing the structural attributes of heavy oil cuts. While a combination of gamma, exponential, and chisquare is more suitable for light cuts11.


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=

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(3)

≤ ≤ ∞

Where:

! = "− $ ="− = %&'() %*+*, ≥ 0 Trauth4 and Campbell

12

showed the characteristics that a PDF should have in order to be used

for the representation of heavy cuts of petroleum. Good flexibility in its form for the representation of any structural attribute is a main characteristic. Thus, the M parameters of the distributions are important for molecular representation (shape parameter «alpha, Mα » and scale parameter «beta, Mβ»). On this basis, the PDF parameters are required for the representation of cuts of petroleum which are unknown initially. Therefore, PDF parameters are to be assumed (initial guess) for representation and then adjusted to achieve a minimum error in the system. 2.2.

Molecular Representation of Petroleum Residues

Two algorithms, one for obtaining of molecules (Monte Carlo) and another for adjustment of their compositions (simulated annealing) are employed into the molecular representation of residues using the same objective function, Equation 4. The molecular representation algorithm is shown in Figure 1. EFGBAHIJK

∑L

./0(,*1( 2345,*&4 6 = 7*4*+*8( 9: ;

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ACS Paragon Plus Environment

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EcKHGdeHIJK ∑L ;M]^_ W `a. ?@ M]^_ W `a ABCA D

:

f

Ei@IK

∑L

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:

iIHBC

PhSY?@ hSY ABCA R \

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(4)

The initial molecular reconstruction of feeds from the information obtained by the characterization techniques uses Monte Carlo simulation, which is a quantitative technique based on statistic and computer simulations to mimic the behavior of non-dynamic random real systems 13, using mathematical models. This technique is much more accurate if large number of random potential components are generated (molecules). Defining appropriate molecular attributes and developing adequate algorithmic construction, are important aspects of this modeling technique. Once the molecules are represented by appropriate structural attributes, the PDF corresponding to these molecular attributes need to be optimized to match the experimental data. The first PDF for aromatic components was obtained by ESI(+)/ESI(-)/APPI(+)/FT-ICR-MS information and was assigned as a restriction of the objective function. With a representative set of molecules obtained by molecular representation, the next step is the estimation of properties. These calculations were performed by contribution methods. Each method employed in this part, was adjusted based on its original correlation: density was calculated by GCVOL-60 method

14

, boiling point by Retzecas

15

, fusion solid properties by

Coutinho Method 16, fusion heat by Won Correlation 17, refractive index by Satow estimation 18 and critical properties by Riazi method 19.


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In the first algorithm (Monte Carlo Method), the error between the predicted and calculated properties is minimized, changing the M parameters of the PDF, the molecules obtained have an equimolar composition. With the second algorithm, simulated annealing, the molar fraction of molecules in feed is adjusted in order to minimize the error between experimental and calculated properties, without changing the structures obtained in the first step. The structural attributes considered in this work are CH, CH2, CH3 in paraffinic chains, number of naphthenic rings, CH2 and CH naphthenic number of aromatic rings, aromatic carbon type C, CH, cata-condensed and peri-condensed, number of islands in asphaltenes, -SH, aromatic S (thiophene), aromatic N (pyridine and pyrrole) and -NH2. SARA analysis helps to classify of structural attributes vacuum residue molecules. Based on the considerations proposed by L. Pereira De Oliveira et al.20, Saturate fractions have no heteroatom is composed of n-paraffins and naphthenes(cyclohexane rings and aliphatic side chains). Aromatic fraction is composed of aromatic rings, saturate rings and aliphatic side chains. Due to their similar structures, resin fraction is just like aromatic fraction but this fraction contains heteroatoms of sulfur and/or nitrogen. Asphaltenes are constructed identically to resin molecules with the addition of a degree of polymerization and cannot have a molecular weight below 400 g/mol.

Figure 1 The calculation of molecular representation begins with an estimation of the objective function, which employs a set of initial PDF parameters estimated from the information obtained by FTICR-MS. For the case of aliphatics structures, these distributions were initialized randomly.


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3. EXPERIMENTAL 3.1.

Materials

Three Colombian vacuum residues (538°C +) from different geographical areas, named hereafter VR-T, VR-P and VR-S and, one Mexican atmospheric residue (300°C +) named hereafter ARMX were used as samples, which were characterized and represented using model molecules. 3.2.

Conventional analysis

The residue samples were characterized by the following bulk properties: density was measured by means of a digital densimeter (ASTM D4052); molecular weight by vapor pressure osmometry (VPO) (ASTM 2503); weight percent of C, H, N and by elemental analysis (ASTM D5291); saturates, aromatics, resins and asphaltenes weight fractions by SARA compositional analysis (ASTM D2007); distillation curve by high temperature simulated distillation (HTSD) method (ASTM D7169); waxes content by UOP 46 method; carbon residue by micro method (ASTM D4530); aromatic and aliphatic carbon by nuclear magnetic resonance-

13

C NMR

(standard procedure in house). 3.3.

Non-conventional analysis

The non-conventional analyses used for the characterization were the ultra-high mass resolution spectrometry, a combination of positive electrospray ionization (ESI(+)), negative electrospray ionization (ESI(−)) and positive atmospheric pressure photoionization (APPI(+)) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). FT-ICR-MS analyses were performed using a 15 T SolariX FT-ICR mass spectrometer of Bruker Daltonics (Billerica, MA). Nitrogen was used as the drying and nebulizing gas. All prepared samples were directly injected with a syringe pump.


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For APPI, samples were diluited to 0.1 mg/mL using toluene. The nebulizing temperature was 300°C at a constant pressure of 0.7 bar. The drying temperature was 200°C at a flowrate of 4.5 L/min. An ion accumulation time in the collision cell of 0.010 s was set, which was followed by a time-of-flight of 0.1 s to transfer the ions to the ICR cell. For ESI, samples were diluted in 40:60 (v/v) toluene/methanol solution to yield a 0.1 mg/mL solution. Acetic acid was added (10 µL to each 1 mL of sample solution) to facilitate protonation of basic compounds by positive ion mode, and for negative ion mode, ammonium hydroxide was added (10 µL to each 1 mL of sample solution) to promote deprotonation of acidic compounds. In all spectra, time-domain transient signals were accumulated and averaged (100 scans) to enhance the signal-to-noise ratio of each spectrum. 4. RESULTS AND DISCUSSION 4.1.

Characterization of residues

The conventional characterization of residues is given in Table 1. According to the HTSD experimental data, the initial boiling point for all residues is higher than 430 °C, which indicates that the structures present in these samples have more than 20 carbon atoms. This information was further confirmed by the results obtained by FT-ICR-MS. The vacuum residues have such high boiling points that are not possible recover the highest boiling fractions by HTSD, therefore the complete distillation curve of vacuum residue cannot be obtained. For acquiring a complete description of the sample in function to boiling point, the data know must be fitted to a continuous curve that represents the limits of samples studied to a final boiling and this way has the tendency of the distillation curve so that it values can be compared


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with the results obtained by molecular representation. The boiling points over 750 ºC were calculated through probability extrapolation method21–23.

Table 1 The data of non-conventional analysis, obtained by ultra-high mass spectrometry were processed and analyzed. The complex feeds studied were directly related to the class and number of components that make up this type of residues. Figure 2 summarizes the results of characterization with FT-ICR-MS for each residue in terms of percent of each species detected in each ionization method for class evaluated and used as input information for the molecular representation (the classes showed have a percent higher than 1, in relative abundance). In this figure is appreciated as some classes were characterized by two or three ionization methods. For example, HC is the class that contains only carbon and hydrogen atoms and was detected by APPI(+)/FT-ICR-MS. The N1 class, apart from containing carbon and hydrogen atoms, includes a nitrogen atom in each structure, class detected by APPI(+)/ESI(+)/ESI(-)/FT-ICR-MS. The aim of using multiple ionization methods for sample characterization was to provide a detailed information about of its composition and obtaining of distributions employed in molecular representation.

Figure 2 FT-ICR-MS provides qualitative information from twelve carbons onwards. The FT-ICR-MS information shows the presence of a large amount of classes defined by the interaction of functional groups. By all structures in each class the following parameters are obtained: molecular formula, intensity of ionization, distribution of number of carbons, and double bond


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equivalent -DBE (according to Eq(6), where c, h and n are the numbers of carbon, hydrogen, and nitrogen atoms, respectively). DBE is defined as the number of rings plus the number of doubles bonds in a chemical structure. The degree of aromaticity of a compound can be related directly to its DBE value. rst = 5 − ℎ/2 + 4/2 + 1

(6)

A set of parameters and restrictions has been determined for molecular representation of the four samples, with FT-ICR-MS information. Each detected class contains data of relative abundance, molecular formula, and molecular weight. Data used to obtain the DBE for each compound identified within each class by method of ionization. Based on this information, the probability density functions for aromatic, sulfur, and nitrogen compounds were obtained by relating the relative abundance and DBE in the assessed class (was obtained the PDF for sulfur by relating of all sulfur detected classes in a method, the same manner for nitrogen PDF). An example is shown in Figure 3, in this case, the probability density functions for aromatic was obtained from of HC class distributions relating the relative abundance and its DBE. Also, the maximum number of carbons per molecule and the maximum number of aromatics rings were the restrictions established with FT-ICR-MS information (Figure 4).

Figure 3 Figure 4 4.2.

Molecular Representation of Petroleum Residues

The molecular representation by structural attributes was developed by taking into account a set of features. First, the set of molecules must represent the feed and its behavior into the system. Second, the generated structures should be appropriate for its use in process simulators and


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economic evaluation tools. Additionally, the required time for CPU usage in molecular representation must be affordable. Therefore, it was necessary to analyze each of the variables mentioned above. First, the maximum number of components supported by commercial simulators was examined. In the particular case of PRO/II process simulator, the limits for the use of resources are according to each category 24, the number of streams, components, units, among others. 300 components are the maximum number of structures that can be used in this simulator. Accordingly, the number of structures that characterizes each sample is restricted to the maximum number of molecules that can be supported and employed in the simulator, due to the future applications for these as an input stream in reaction and separation compositional models. It is appropriate to clarify that for reaction systems the number of structures present can be tripled due to the present reactions and, as previously mentioned, a large number of molecules cannot be supported in a commercial simulator, with all the criteria set assessed, so that a set of 150 molecules for each sample evaluated was obtained. With the number of molecules sectioned, the next step is to obtain the PDF parameters by FTICR-MS. Each pair of parameters, in each distribution used, was generated for an ionization method; the PDF parameters for non-basic nitrogen compounds was obtained from ESI(-)/FTICR-MS; the PDF parameters for basic nitrogen compounds from ESI(+)/FT-ICR-MS; the PDF parameters for sulfur compounds, aromatic distributions and aromatic distributions in resins from APPI(+)/FT-ICR-MS. These values are shown in Table 2. The molecular representation uses up to 76 distribution parameters, the first 18 are used in the generation of saturated, aromatic structures and resins. The other 58 parameters are used for the generation of asphaltenes, with the following restrictions: maximum of seven islands present in the asphaltenes archipelago type; 13 ACS Paragon Plus Environment

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10 aromatics rings present in any aromatic structure and a maximum of 80 carbons presents in other structures. The restrictions were obtained from the FT-ICR-MS information. Each island or archipelago have its own set of distributions and may be different from the other islands and / or archipelagos. Before generating the molecules using all the information obtained, incorporating the information of conventional analytical, were generated a molecular representation having as unique input values the PDF parameters (Table 2) and, constraints obtained with FT-ICR-MS information (without conventional analytic information), with the aim to analyze system behavior. It was observed that SARA values obtained for the representation of each sample were close to the experimental values (R2>0.98), the comparisons are shown in Figure 5. This indicates that the PDF employed is suitable for the reconstruction of molecules.

Table 2 Figure 5 The molecular representation using FT-ICR-MS information and conventional analysis was applied to three vacuum residues and one atmospheric residue, using the previously-described distributions and molecular rules scheme. A set of 150 molecules for each sample was generated. The absolute error between calculated and experimental properties for each set of molecules that represents the samples are shown in Table 3, this information evidences that molecular representation depicts quite well most of the information supplied in the proposed model. In this reconstruction, the molecular weight was the property that presented the higher error. The method of Vapor Pressure Osmometry (VPO) was used to obtain the molecular weight, which


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normally reports high values of molecular weight for samples with high content of asphaltenes. The high values of molecular weight obtained with VPO are attributed to aggregation of asphaltenes in the employed solvent (toluene)

25

. To minimize the impact of using values of

molecular weight determined by VPO, it was considered as a not fixed property, which means that it was included as a comparative parameter in the objective function.

Table 3 The predictions of properties for VS-S and VR-T are worst than those for AR-MX and VR-P but in all of the cases they presented a low absolute errors, which indicates that a good set of structures was selected. The difference in predictions of properties of VR-S/VR-T and AR-MX can be due to the distribution of the number of molecules per class in the set of 150 which is directly linked to their SARA. It is possible that more structures in a class are needed than others for obtaining better prediction of bulk properties, in certain feeds; for example, an increase of the number of saturate molecules and a decrease of the number of aromatic structures in the feed. However, this condition increases the complexity of the system and will require new optimization approaches. Table 4 shows the ten first molecules and their compositions generated by molecular representation for the four residues. Some of these structures have ten or more naphthenic rings but no more than ten aromatic rings, which have been validated with FT-ICR-MS information. In all of the cases most of the structures were different.

Table 4


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With all the information obtained in the characterization of the residues with conventional and non-conventional analytical tests and with the result of their representation by structural groups, (Table 4 and Figure 6), it is possible to describe the AR-MX sample as one that presents the highest degree of aromaticity and condensation of asphaltenic structures, which have short lengths in their alkyl chains.

Figure 6 Regarding the number of aromatic rings present in the compounds of the aromatic and resin fractions, they have mostly low aromaticity and degree of condensation, but longer length in side chains associated to these species. Concerning the individual composition of each feed, the structures that possess greater composition correspond to resins and naphthenes. VR-P has the highest aromaticity and CH2 lengths in the aromatic and resin fractions compared with the other residues, and this fraction has low degree of condensation. This sample was found to have asphaltenes of continental and archipelago types, distributions shown in Figure 6 and hereafter VR-P(1), VR-P(2), VR-P(3). Most structures present in the asphaltenic fraction are archipelago of low aromaticity and degree of aromatic condensation. The molecules with the highest individual composition are aromatics and resins. VR-S presented the lowest degree of aromaticity and cycloaliphaticity in the aromatic and resin fractions. This residue also has the lowest number of naphthenic rings in saturate fraction. The molecules with the highest individual composition are of the type of resins and asphaltenes.


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VR- T has greater degree of condensation in the fraction of aromatics and resins. The molecules with the highest individual composition are aromatics and resins. 4.3.

Evaluation with commercial simulator

After obtaining the representative structures using molecular representation with conventional properties and FT-ICR-MS, an evaluation based on a liquid-liquid separation system was carried out. The liquid-liquid equilibrium depends considerably on the structure of the components present in the feed 26. Each residue is put into contact with heptane solvent which promotes the separation of the heavy fraction (Pitch) from the deasphalted oil (DAO). The degree of separation of the DAO-Pitch mixture was evaluated using different solvent/feed ratios. For the operating conditions of the proposed system (1 atmosphere of pressure and 30 °C), the MODIFIED UNIFAC thermodynamic model was used. The model was developed in SimSci PRO / II version 9.1. A low-solubility with polyaromatic hydrocarbons and heavy-resin hydrocarbons and high solubility with saturate hydrocarbons were found, this behavior is shown in Figure 7. In all cases, if solvent/feed ratio increases the percent of DAO increases and the percent of Pitch decreases. For the case of VR-P, due to its characteristics: low percent of asphaltenes and most of the structures present in the asphaltene fraction of archipelago type of low aromaticity and degree of aromatic condensation, the percent of Pitch obtained was low. Wauquier 27 and Torres & Picon 28 reported that the selectivity of a solvent may vary depending on feed/solvent ratio in deasphalting process (Figure 8). If this ratio increases, the selectivity towards asphaltene compounds also increases, the same behavior was observed in the evaluation for residues evaluated. 17 ACS Paragon Plus Environment

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Figure 7 Figure 8 CONCLUSIONS Three Colombian vacuum residues and one Mexican atmospheric residue were characterized and molecularly represented. A set of 150 molecules per each residue was obtained. The bulk properties of residues were in good agreement with the experimental structural information. The representation was evaluated the behavior of each residue in a separation process with n-heptane in a commercial process simulator. APPI(+)/ESI(+)/ESI(-)/FT-ICR-MS information was used for molecular representation, obtaining parameters of PDF for attributes such as the distribution of aromatics, sulfur, and nitrogen compounds. Also, restrictions such as the maximum number of carbons per molecule and the maximum number of aromatics rings were stablished. SARA analysis of petroleum residues can be calculated from the distribution parameters obtained by FT-ICR MS and the molecular representation methodology. It can be concluded that the maximum number of aromatic rings present in asphaltene structures is 10. The asphaltenes structures present in the vacuum residues are continental and/or archipelago type, in the case when the two types of asphaltenes are present always as a type, it is the one that predominates. ACKNOWLEDGMENTS


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The authors thank the Colombian Institute of Petroleum (ICP) for the financial support, and the Mexican Institute of Petroleum (IMP) for providing the AR-MX sample data. NOMENCLATURE APPI(+)=Positive atmospheric pressure photoionization AR= Atmospheric residue (300°C +) Arom. Dist = Aromatic Distribution DAO = Deasphalted oil DBE= Double bond equivalent ESI(−)=Negative electrospray ionization ESI(+)=Positive electrospray ionization FT-ICR-MS= Fourier Transform ion cyclotron resonance mass spectrometry H/C =Hydrogen-to-carbon ratio HTSD= High temperature simulated distillation MW= Molecular weight N= Nitrogen (Fraction) NMR= Nuclear magnetic resonance spectroscopy PDF= Probability distribution function Pitch = Heavy product after deasphalting S= Sulfur (Fraction) SARA= Saturates, Aromatics, Resins and Asphaltenes weight percentage TBP= True boiling point VPO =Vapor Pressure Osmometry VR= Vacuum Residue (538+ °C) xC= Carbon (Fraction) =gamma probability density function


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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

α= Shape parameter γ= Scale parameter x= Density μ= mean exp= Subscript that refers to experimental data calc= Subscript that refers to calculated data

REFERENCES (1)

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(2)

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(4)

Trauth, D. M. Structure of Complex Mixtures through Characterization, Reaction, and Modeling. Ph.D. Thesis, University of Delaware, 1993.

(5)

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(8)

Verstraete, J. J.; Schnongs, P.; Dulot, H.; Hudebine, D. Chem. Eng. Sci. 2010, 65 (1), 20 ACS Paragon Plus Environment

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304–312. (9)

De Oliveira, L. P.; Vazquez, A. T.; Verstraete, J. J.; Kolb, M. Energy and Fuels 2013, 27 (7), 3622–3641.

(10)

Zhang, L.; Hou, Z.; Horton, S. R.; Klein, M. T.; Shi, Q.; Zhao, S.; Xu, C. Energy and Fuels 2014, 28 (3), 1736–1749.

(11)

Klein, M. T.; Hou, G.; Bertolacini, R. J.; Broadbelt, L. J.; Kumar, A. Molecular Modeling in Heavy Hydrocarbon Conversions; CRC Press Taylor and Francis Group: Nueva York, 2006.

(12)

Campbell, D. M. Sthochastic Modeling of Structure and Reaction in Hydrocarbon Conversion. Ph.D. Thesis, University of Delawere, 1998.

(13)

Faulin, J.; Juan, A. A.; Martorell, S.; Ramírez-Márquez, J.-E. Simulation methods for reliability and availability of complex systems; Springer-Verlag: London, 2010.

(14)

Ihmels, E. C.; Gmehling, J. Ind. Eng. Chem. Res. 2003, 42 (2), 408–412.

(15)

Retzekas, E.; Voutsas, E.; Magoulas, K.; Tassios, D. Ind. Eng. Chem. Res 2002, 41 (6), 1695–1702.

(16)

Coutinho, J. a P.; Edmonds, B.; Moorwood, T.; Szczepanski, R.; Zhang, X. Energy & Fuels 2006, 20 (3), 1081–1088.

(17)

K.W.Won. Fluid Phase Equilib. 1986, 30, 265–279.

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Satou, M.; Yamaguchi, H.; Murai, T.; Yokoyama, S.; Sanada, Y. J. Japan Pet. Inst. 1992, 35 (6), 466–473. 21 ACS Paragon Plus Environment

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Riazi, M. R. Characterization and Properties of Petroleum Fractions, 1st Ed.; ASTM International: West Conshohocken, 2005.

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De Oliveira, L. P.; Vazquez, A. T.; Verstraete, J. J.; Kolb, M. Energy and Fuels 2013, 27 (7), 3622–3641.

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(22)

Meirelles, L. B.; Chrisman, E. C. A. N.; Andrade, F. B. De; Oliveira, L. C. M. De. 2017, 11 (3), 237–241.

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Ancheyta, J.; Trejo, F.; Rana, M. S. Asphaltenes: chemical transformation during hydroprocessing of heavy oils, 1st ed.; CRC Press Taylor and Francis Group: New York, 2010.

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Seader, J. .; Henley, E. . SEPARATION PROCESS PRINCIPLES, 2nd ed.; John Wiley & Sons, Inc: United States of America, 2006.

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FIGURE CAPTIONS Figure 1. Molecular reconstruction algorithm Figure 2. Percentage of detected species by ionization method for class Figure 3. Information obtained by FT-ICR-MS. (●) Gamma distribution, (∆) APPI(+)/FT-ICRMS distribution Figure 4. Plots of the number of carbons vs DBE for classes obtained by APPI/ESI(+)/ESI()/FT-ICR-MS employed in molecular representation. A. AR-MX. B. VR-T. C. VR-P. D. VR-S Figure 5. Parity graphs. A. Parameters PDF, B. SARA Figure 6.

Distributions for structural classes in feeds evaluated obtained in the molecular

representation. Figure 7. Flowsheet and behavior of products (Pitch and DAO) in separation process with heptane. 23 ACS Paragon Plus Environment

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Figure 8. Influence of feed/solvent ratio on extraction selectivity. (Adapted from Wauquer


Page 24 of 37

27

)

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Figure 1.

100% 80% 60% 40% 20% 0% HC AR-MX

N1 VR-T

N1O1 VR-P

VR-S

N1S1 AR-MX

O1 VR-T

O1S1 VR-P

VR-S


O2S1 AR-MX

S1 VR-T

S2 VR-P

VR-S

Energy & Fuels

Figure 2.

0.1

Normalized Relative Abundance

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

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0.08 0.06 0.04 0.02 0 0

2

4

6

8

10

Number of Rings (Based on DBE)

Figure 3.


12

Page 27 of 37

30

Class HC/APPI

30

Class S1/APPI

10

0

0

Class N1/ ESI+

30 Class

Class N1/ ESI-

20

Class N1/ESI-

N1/ ESI+

DBE

DBE

20

10 0

Class S1/APPI

DBE

DBE

10

30

Class HC/APPI

20

20

10

20

40 60 80 Number of Carbons

100

20


100

0

20


100

A. 30

Class HC/APPI

30 Class

Class S1/APPI


100

HC/APPI

Class S1/APPI

N1/ESI+

Class N1/ESI-

20

DBE

DBE

20 10

10

0

0

30 Class

Class N/ESI-

N1/ESI+

30 Class

20

20

DBE

10 0

20

B.

DBE

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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10

20


100

20


100

0

20


C.

100

D.


20


100

Energy & Fuels

Figure 4.

14 12 10 8 6 4 2 0

P S MX 0

5

0.4

P

0.3 S

0.2 0.1

MX

0

10

0

Experimental parameter PDF

A.

T

0.5

T

Calculated SARA

Calculated Parameter PDF

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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B.

0.2

0.4

Experimental SARA


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Figure 5.


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Figure 6.


Page 30 of 37

AR-MX % Feed

80%

56%

63%

59%

VR-P

100%

100%

66%

67%

60%

40%

40%

8%

4.73%

4.68%

4.50%

4.60%

20% 0%

0

5

10

2% 0%

0

Solvent/Feed

6% 4%

4.70%

0%

10%

80%

60%

20%

5

10

Solven/Feed

VR-T

VR-S 100%

100%

75%

80%

59%

54%

60%

58% 61% 60%

45% 31%

40%

17%

40%

47%

20%

20%

0%

0%

0

5

10

Solvent/Feed

Figure 7.


0

5

10

Solvent/Feed

Feed

68%

80%

% Feed

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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Feed

Page 31 of 37

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100 90 80

Oil Medium Cut point with low solvent ratio

70 60 %

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

50 Cut point with at high solvent ratio

40

Resins

30 20 Cut point with at infinite solvent ratio

10 Asphaltenes

0

Figure 8.


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Table 1. Conventional analysis for residue samples. Property

VR-T VR-P VP-S AR-MX

Density (g/cm3)

1.0337 1.0251 1.0687 1.0336

SARA compositional analysis: Saturates (wt %)

14

11

3

21

Aromatics (wt %)

36

46

36

30

Resins (wt %)

44

37

34

24

Asphaltenes (wt %)

6

6

27

25

Carbon (Fraction)

0.86

0.86

0.84

0.82

Nitrogen (Fraction)

0.009 0.009 0.007

0.004

Sulfur (Fraction)

0.023 0.018 0.021

0.057

Carbon Residue (wt %)

18.8

35.1

23.2

Waxes (Fraction)

0.009 0.009 0.006

0.007

Elemental analysis:

Number average molecular

784

19.5

809

871

704

439

448

461

438

5 wt %

504

519

532

538

50 wt %

674

670

730

800*

90 wt %

824*

834*

863*

1023*

99 wt %

909*

927*

947*

1141*

0.12

0.12

0.11

0.10

0.57

0.54

0.51

0.56

0.23

0.25

0.28

0.26

0.08

0.09

0.10

0.08

weight (g/mol)

High temperature simulated distillation: 0.5 wt % (°C)

Nuclear Magnetic resonance (NMR): 13

C NMR - Aliphatic CH3

(Fraction) 13

C NMR - Aliphatic CH2 and

CH (Fraction) 13

C NMR - Aromatic C

(Fraction) 13

C NMR – Substituted

Aromatic C (Fraction)

*Temperatures calculated using ASTM distillation probability curves.


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Table 2. Experimental parameters of PDFs obtained by FT-ICR MS PDF

AR-MX VR-T VR-P VR-S

α- Non Basic Nitrogen Compounds 4.950 0.120

5.2

5.0

β- Non Basic Nitrogen Compounds 0.200 0.800 0.19

0.2

α- Basic Nitrogen compounds

5.100

β- Basic Nitrogen compounds

0.6

5.1

4.7

0.205 1.970

0.2

0.28

α- Sulfur Compounds

4.700 0.120

5.2

4.7

β- Sulfur Compounds

0.250 1.000 0.310 0.29

α- Aromatic rings

3.250 5.700

β- Aromatic rings

0.850 0.700 0.83 0.800

α- Aromatic rings in Resins

7.700 8.000 10.200 10.6

β- Aromatic rings in Resins

0.550 0.500 1.46


4.3

5.0

0.53

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Table 3. Absolute error between calculated and experimental properties.

AR-MX ICR beta resin ICR alpha resin ICR beta aromatic ring ICR alpha aromatic ring ICR beta sulfur ICR alpha sulfur ICR beta Pyridine ICR alpha Pyridine ICR beta Pirrol ICR alpha Pyrrol RMN CH2, AC (% wt) RMN arom (% wt) RMN CH2,CH-alinf (% wt) T 99 (% wt) T 90 (% wt) T 50 (% wt) T 5 (% wt) T 0,5 (% wt) Molecular Weight (g/mol) x Wax (Fraction) RCC (wt %) x Total Nitrogen (Fraction) x sulfure (Fraction) x Carbon (Fraction) Asphaltenes (% w) Resins (% w) Aromatic (% w) Saturates (% w) Density (g/cm3)

VR-P

19,6

VR-T

11,5

VR-S

9,81

6,86

10,7

3,13

7,70 8,60 6,16 3,17 9,84 7,65 15,6

11,8 12,1 6,01

9,43 7,47 9,13

5,23 4,11 7,77 6,89 6,71 11,3 8,26

4,74 4,04 4,66 42,4 5,65 13,2 3,63 9,78 7,00

49,7

52,4

9,32 6,40

6,80 7,32 5,21

8,06 2,92 8,21 6,65 2,41

0

20

40

0

2,90

20

40

0

20

40

Absolute Error (%)


0

20

40

60

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Page 36 of 37

Table 4. Main molecules and compositions generated by molecular representation. ( Asphaltene molecules, x = Composition.) AR-MX

VR-T

VR-P

X=0.060

VR-S

X=0.081

X=0.046 X=0.048

X=0.031

X=0.074

X=0.043 X=0.058

X=0.053

X=0.028

X=0.044

X=0.040

X=0.036

X=0.027

X=0.045

X=0.037

X=0.037 X=0.035 X=0.031

X=0.024 X=0.023

X=0.030

X=0.035

X=0.034 X=0.031

X=0.023

X=0.030

X=0.024

X=0.022 X=0.023

X=0.030

X=0.028

X=0.019 X=0.019

X=0.030

X=0.027

X=0.018

X=0.025 X=0.019

X=0.026


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Others

X=0.689

Others

X=0.622

Others

X=0.558


Others

X=0.737

Molecular Representation of Petroleum Residues Using Fourier

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