Deriving the Molecular Composition of Middle Distillates by Integrating

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Deriving the Molecular Composition of Middle Distillates by Integrating Statistical Modeling with Advanced Hydrocarbon Characterization Anton Alvarez Majmutov, Jinwen Chen, Rafal Michal Gieleciak, Darcy Hager, Nicole Heshka, and Sara Salmon Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef5018169 • Publication Date (Web): 10 Nov 2014 Downloaded from http://pubs.acs.org on November 16, 2014

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Deriving the Molecular Composition of Middle Distillates by Integrating Statistical Modeling with Advanced Hydrocarbon Characterization Anton Alvarez-Majmutov, Jinwen Chen∗, Rafal Gieleciak†, Darcy Hager, Nicole Heshka, and Sara Salmon CanmetENERGY, Natural Resources Canada One Oil Patch Drive, Devon, AB, T9G 1A8, Canada Abstract

To fully advance our understanding of hydrocarbon conversion chemistry requires powerful analytical methods to qualitatively and quantitatively characterize complex petroleum fractions at the molecular level. In the absence of such tools, an alternative solution is to model the molecular composition of hydrocarbon mixtures with limited analytical data. The objective of this study is to integrate modeling techniques with conventional and advanced petroleum characterization methods to derive the composition of middle distillate fractions at the molecular level. In the present approach, analytical petroleum characterization data are used as input to computationally generate a mixture of representative molecules that mimics the properties of the real sample. The representing molecules are constructed according to coherent chemical/thermodynamic criteria by Monte Carlo sampling of a set of statistical functions assigned to each possible molecular feature. The assembled mixture is built on a large set of chemical species and is further optimized with the principle of Maximum Entropy. The approach is applied to simulating two middle distillates differing significantly in hydrocarbon type composition and origin. The samples are experimentally characterized by standard and advanced analytical methods: density, simulated distillation, elemental analysis, hydrocarbon types/distributions

and

sulfur

compound

speciation

∗

by

two-dimensional

Corresponding author, tel.: 780-987-8763; fax: 780-987-5349; email: [email protected] On leave from the Institute of Chemistry, University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland

†

1 ACS Paragon Plus Environment

gas

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chromatography

with

flame

ionization

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detector

chemiluminescence detector (GC×GC–SCD), and

13

(GC×GC–FID)

and

sulfur

C nuclear magnetic resonance

(NMR), to obtain sufficient information for parameter fitting and model validation. Simulation results showed that the model is capable of generating representative mixtures that reasonably match the actual physical samples in analytical properties and carbon number distributions.

Keywords: Molecular composition; middle distillates; stochastic simulation; entropy maximization.


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1. Introduction

Understanding hydrocarbon chemistry at the molecular level is crucial to improving process efficiency and performance of current petroleum conversion technologies as well as to developing new break-through processes. In this sense, petroleum fractions are challenging hydrocarbon mixtures characterized by a vast molecular spectrum.1 Above the naphtha boiling range (>200°C), oil fractions may contain from a few thousand to hundreds of thousands of chemical species. Although advanced characterization methods, such as Fourier transform–ion cyclotron resonance– mass spectrometry (FT–ICR–MS), can identify individual species in heavy petroleum fractions, simultaneous detection and quantification of all molecular species in such fractions is not possible with current analytical methods. This greatly limits the comprehension and prediction of hydrocarbon behaviors during the processing and refining steps. Therefore, accurate and detailed characterization of hydrocarbon systems is still one of the important subjects in petroleum R&D. Modeling the molecular composition of hydrocarbon mixtures with limited analytical data represents a key component in tackling this problem.2 Integration of advanced molecular modeling and simulation with modern experimental methodologies for petroleum characterization is a viable approach to deriving the molecular make up of petroleum fractions. Once the composition is known, the information can be readily used to describe the detailed hydrocarbon conversion chemistry using fundamental theory.3,4 The main advantage of this integrated approach is the possibility of tracking the evolution of hydrocarbon molecules throughout processing and upgrading steps. This will enable better predicting of the molecular composition of petroleum feedstocks and their upgraded/refined products. Building modeling tools with such capabilities is particularly important for developing break-through technologies, optimizing the performance of existing processes, and ultimately reducing greenhouse gas emissions and other environmental impacts during hydrocarbon processing. There have been a number of modeling approaches for representing the composition of complex petroleum fractions reported in the literature.5–17 In these 3 ACS Paragon Plus Environment

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approaches, hydrocarbon mixtures are typically described by sets of representative molecular species that are chemically consistent with the available analytical information. Molecular structures are judiciously proposed or selected by following chemical rules that are established upon expert knowledge. In most cases, the problem of dealing with a large number of molecular species (103–104), typically found in the petroleum fractions, is resolved by breaking down the full hydrocarbon spectrum into a limited number of fundamental structural blocks or features (101), such as alkyl chains, naphthenic rings, aromatic rings, etc. Molecules are then computationally assembled by combining and replicating these structural blocks within the hydrocarbon mixtures. The representation of hydrocarbon mixtures varies from one method to another. For instance, Liguras and Allen5,6 proposed a method to derive a reduced set of hypothetical components (102) for gasoil feeds from their mass spectrometry and

13

C

NMR analyses. Quann and Jaffe developed the structure-oriented lumping (SOL) approach, in which any type of hydrocarbon molecule can be represented as a vector of 22–24 structural increments.7,8 The most relevant characteristic of this approach is that all structural isomers are assumed to have the same chemical properties in order to reduce modeling complexity. Neurock et al.9,10 introduced a stochastic algorithm for generating mixtures of larger number of representative molecules (104) using routine petroleum characterization methods as inputs (density, elemental composition, hydrocarbon types, boiling point distribution, etc.). Molecules from any type of petroleum feed are assembled by Monte Carlo sampling of statistical distributions related to its molecular structure. Khorasheh et al.11 presented another stochastic method for building heavy hydrocarbon molecules using the elemental composition, 1H NMR and 12,13

13

C NMR analyses. Ha et al.

developed a deterministic method for deriving structural isomers in middle distillate

fractions starting from the hydrocarbon type-carbon number matrix obtained by gas chromatography–field-ionization 14

Verstraete

mass

spectrometry

(GC–FIMS).

Hudebine

and

extended the Monte Carlo molecule generation algorithm proposed by

Neurock et al. by coupling it with the Maximum Entropy criterion as a finishing step to fine-tune the molecular concentrations. This two-step methodology has been applied to a variety of complex petroleum fractions such as gas oils and vacuum residues.15–17


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One aspect of all the methods reviewed above that has not been properly addressed is how to properly verify and refine the consistency of the simulated composition. Generally, it is considered acceptable enough if the representing mixture closely matches the bulk properties of the actual oil sample, when theoretically there could be multiple molecular combinations that lead to the same result. In the case of middle distillate fractions (∼200–400°C), advanced hydrocarbon characterization methods, such as comprehensive two-dimensional gas chromatography (GC×GC) and GC–FIMS, can offer valuable insights for this purpose. The focus of this study is on the molecular representation and simulation of the composition of middle distillates and assessment of the results using information obtained by advanced hydrocarbon characterization methods. Future work will focus on the application of the developed approach to heavy distillate samples (∼400–524°C).

2. Modeling approach

In this paper, we use a modeling approach whereby hydrocarbon molecules are represented in terms of their fundamental structural blocks: alkyl chains, aromatic rings, naphthenic rings, and thiophenic rings.10 The occurrence of each of these blocks in the mixture is assumed to follow known statistical distributions (e.g. gamma and exponential distributions). By combining and replicating these fundamental blocks according to specific chemical rules12,13, we are able to construct a wide spectrum of hydrocarbon molecules. The molecule assembly process is carried out computationally by Monte Carlo sampling of the statistical distributions assigned to each possible structural feature. The “virtual” oil mixture is built on a large set of chemical species (typically 5,000– 10,000 molecules18,19) and its bulk properties are matched to measured

20

by stochastic optimization.

those experimentally

The abundance of each molecule is further

optimized by Entropy Maximization.14,21 The key components of this procedure are discussed below.


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2.1. Structural representation of middle distillate molecules

Before starting the assembly process, it is necessary to identify all the required structural features to represent the full range of molecules present in the sample and determine the steps to specify the entire molecular structure. These elements are compiled into a hierarchal building sequence that controls the molecular assembly process. Figure 1 shows the proposed building sequence for middle distillates molecules. The process starts with determining the molecule class to be constructed: paraffins, naphthenes or aromatics. Each class follows a specific path to define the molecule’s structure. For paraffins, the first step is to specify the chain length and then determine whether or not alkyl branches are accepted. If alkyl branches are accepted, the resulting structure is an isoparaffin. Otherwise the molecule becomes a normal paraffin. The next steps comprise establishing the total number of alkyl branch carbons, the number of branches and their collocation along the main chain. Functional groups can also be inserted along the alkyl chain to generate heteroatom components. For naphthenes, first, the number of naphthenic rings is selected, whereas for aromatics the number of benzene, naphthenic and thiophenic rings is sequentially determined. Other types of rings, such as pyridine, pyrrole, and furan, can be considered as well.17 Subsequently, the number of carbons that will be attached to the ring core as alkyl branches is specified. The remaining steps include selecting the number of alkyl branches and their location on the periphery of the ring core. Structural limitations were imposed upon the building scheme to avoid creating illogical structures and to establish coherent structural isomer distributions. The rules inferred by Ha et al.12 were considered for the representation of middle distillates: 1. There are only single-core molecules and each core has a maximum of 4 rings. 2. Only six-membered naphthenic rings are considered. 3. There are a maximum of 3 alkyl branches in isoparaffin molecules. 4. The maximum length of an alkyl branch in isoparaffins is 4 carbons. 5. Quaternary carbons are not allowed in saturated molecules. 6. There are a maximum of 4 alkyl branches on either aromatic or naphthenic ring cores. 6 ACS Paragon Plus Environment

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7. In the event of multiple substitutions, only one non-methyl branch is allowed. 8. Multiple alkyl branches are located as far as possible from each other on the parent structure. The structural representation of the middle distillate samples was limited to hydrocarbon molecules that fell within both the C8–C30 carbon number range and experimental boiling point distribution (∼120–450°C). It also includes aliphatic and aromatic sulfur components, but it does not consider nitrogen and oxygen since these compounds are found in very low concentrations in the samples studied. Each of the structural features presented in Figure 1 is modeled with a probability distribution function (PDF) in its cumulative form. Details of the PDFs used for representing the hydrocarbon mixture are summarized in Table 1. Structural features with a reduced number of possible values (up to 4) are described by histograms. This refers to molecule class, acceptance of alkyl branches on a paraffin skeleton, the number of rings (naphthenic, benzene, and thiophenic), and the number of chains on both paraffins and ring cores. The values of these features are defined based on the chemical rules previously established and the respective number of statistical parameters is related to the type of PDF used. The number of alkyl branch carbons on paraffins was represented by an exponential distribution, in accordance with the observations reported in previous studies on alkane isomers in crude oils:13,22

݂௜ ሺ‫ݔ‬௜ , ߙሻ = ߙ݁ ିఈ௫೔

(1)

where fi is the probability of selecting structural feature i, xi is the value of structural feature i, and α is the statistical parameter. In this particular case, the maximum allowed number of alkyl branch carbons was set to 4. Other structural features with a wider span of possibilities, such as paraffin chain length and the number of alkyl branch carbons on ring cores, were assumed to follow gamma distributions:23

௫ ି ೔

݁ ఊ ݂௜ ሺ‫ݔ‬௜ , ߚ, ߛሻ = ‫ݔ‬௜ ఉିଵ ఉ ߛ Γሺߚሻ

(2)


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where Γ is the gamma function, β is the shape parameter, and γ is the scale parameter. For the number of alkyl branch carbons on ring cores, it was allowed to have zero alkyl branch carbons attached in order to include unsubstituted cores in the same statistical distribution. The values “xi” of each structural feature “i” are specified by Monte Carlo sampling of the corresponding PDFs. The algorithm must go through the whole building sequence presented in Figure 1 to assemble the hydrocarbon molecule.

2.2. Generation of the virtual oil mixture

A flow chart, for which the algorithm is based on for generating virtual oil mixtures, is presented in Figure 2. The Monte Carlo assembly process described above is repeated as many times as the number of molecules required in order to construct a hydrocarbon mixture of a sufficiently large number of molecules. Previous studies demonstrate that the optimum sample size to balance computational burden and model accuracy is of 5,000 to 10,000 molecules.18,19 For each individual molecule, physical and chemical properties are determined from its chemical structure. Properties such as molecule class, elemental composition, carbon types, and molecular weight are established directly by inspection of the molecular structure. Physical properties such as density and boiling point are estimated using group contributions. The global properties of the virtual oil mixture are calculated by applying linear mixing rules.24 These global properties are then compared and matched with the experimental data through a leastsquares objective function to achieve minimum difference between the virtual oil sample and the physical sample. If the match between them does not meet the pre-set accuracy, the statistical parameters of the PDFs are adjusted to rebuild the mixture. This parameter optimization process is executed with the Simulated Annealing algorithm. Once the final set of representative molecules has been obtained, the last step is to fine-tune the molecular concentrations by using the following constrained entropy criterion:21 ே

ே

௡ୀଵ

௡ୀଵ

‫ = ܧ‬− ෍ ‫ݕ‬௡ lnሺ‫ݕ‬௡ ሻ + ߤ ൭1 − ෍ ‫ݕ‬௡ ൱ +

௃

௘௫௣ ෍ ߣ௝ ൭‫݌‬௝ ௝ୀଵ

ே

− ෍ ‫ݕ‬௡ ‫݌‬௝,௡ ൱ ௡ୀଵ


(3)

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where E is the entropy criterion to be maximized, yn is the molar fraction of molecule n, N is the total number of molecules, µ and λj are Lagrange multipliers, pjexp is the analytical property j, and pn,j is the calculated property j of molecule n. The first, second and third terms in Equation 3 represent the information entropy, the mass balance constraint, and the constraints associated to the analytical information, respectively.

3. Sample selection and characterization

Two middle distillate samples, differing significantly in chemical properties and composition, geographic origin, and processing history, were selected for analysis and model validation. One sample was light gas oil (LGO) from crude distillation; the other was light cycle oil (LCO) from fluid catalytic cracking. These two samples represent two extremes of middle distillates fractions in terms of chemical properties and composition, with LGO being the most paraffinic and LCO being the most aromatic. The two middle distillates samples were characterized with standard and advanced petroleum analytical methods. The full characterization included the following tests: liquid density (ASTM D4052), elemental analysis C,H,N,S,O (ASTM D5373/D4269/D4294), simulated distillation (ASTM D6352/D7169),

13

C spectrum

(Inova 600 Mhz NMR), and hydrocarbon distributions and sulfur compound speciation by two-dimensional gas chromatography with flame ionization detector (GC×GC–FID) and sulfur chemiluminescence detector (GC×GC–SCD), respectively. This analytical information, excluding carbon number distributions, was used as model input for optimizing the molecular representation of the hydrocarbon mixture. Detailed carbon number distributions by hydrocarbon type were used to validate the simulation results of the virtual oil mixture.

4. Results and discussion 4.1. Simulation of the middle distillate samples Simulations based on 5,000 molecules were conducted with the selected samples. The analytical characterization used as model input and the corresponding predictions are


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shown in Table 2. Experimental and simulated boiling point distributions are illustrated in Figure 3. From the experimental data presented in Table 2 and Figure 3, the two samples are easily differentiated in terms of physical and chemical properties. The LGO is a highly saturated hydrocarbon mixture (72.0 wt% saturates), with a relatively low density (0.8370 g/cm3) and high H/C ratio (1.87). The saturates fraction is largely composed of paraffins (55.9 wt%) and monocycloparaffins (14.2 wt%), whereas the aromatic fraction mainly consists of alkylbenzenes (10.3 wt%), tetralins (5.6 wt%), and naphthalenes (7.6 wt%). The LCO sample, on the other hand, is predominantly aromatic (56.3 wt% aromatics), which is directly reflected on its higher density (0.9080 g/cm3) and lower H/C ratio (1.53). In this case, all aromatic hydrocarbon families are present in significant amounts, among which naphthalenes are the standout component (23.7 wt%). The amount of paraffins in LCO (14.8 wt%) is substantially lower than that in LGO whereas the amount of monocycloparaffins in LCO (23.2 wt%) is moderately higher. Both samples exhibit noticeable amounts of aromatic sulfur compounds, such as thiophenes, benzothiophenes, and dibenzothiophenes. Model predictions also shown in Table 2 and Figure 3 for the two samples are in good agreement with the analytical characterization. The algorithm can reproduce liquid density, elemental composition, main hydrocarbon types, boiling point distributions, and even sulfur speciation at the ppm level. There were some deviations for which indicates a possible conflict between

13

13

C groups,

C group calculations and the other

properties. Conflicts of this nature can be expected due to the inconsistencies between the various analytical methods used for generating the model inputs.12 In addition, the model has the capability to generate detailed information on chemical composition that cannot be obtained from experiment, such as molecular weights, chemical structures, and concentrations of the full molecular spectrum. Figure 4 presents the complete molecular weight distributions of the two samples. It is noted that these distributions follow a multimodal behavior due to the effect of combining multiple sub-distributions corresponding to each hydrocarbon family. In both cases, the calculated molecular weights are in the range of 100–400 g/mol. Table 3 provides a list of selected LGO molecular structures with their respective concentrations in the mixture.


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4.2. Model assessment with GC×GC data To support the assumption that the virtual oil mixture is composed of consistent molecular structures, the model was tested against comprehensive hydrocarbon type distributions obtained with GC×GC. Figure 5 provides an example of the detailed information

obtained

with

this

analytical

technique.

Peak

information

was

computationally processed to enhance visual aspects of GC×GC chromatograms. The first retention time dimension on normal GC×GC chromatograms was converted into a temperature scale by using the boiling point of n-paraffins as reference to match both scales. This transformation allows the chromatograms to be presented in the simulated distillation temperature domain. The peaks on the chromatogram are presented as bubbles, of which the size and color are associated with the compound concentration and hydrocarbon type, respectively. Boiling point distributions obtained by simulated distillation (dashed magenta line) and GC×GC (dashed blue line) are found consistently superimposed on the chromatogram. The three vertical dashed lines divide the chromatogram into 4 boiling ranges: 10 wt% cut off (T10), 50 wt% cut off (T50), and 90 wt% cut off (T90) temperatures. Finally, just above the horizontal axis there is another scale corresponding to n-paraffins carbon numbers (C6–C30) that parallels the temperature domain. Carbon number distributions by hydrocarbon type were extracted from the chromatograms and compared with that of the simulated oils presented in the previous section. The results for the two middle distillate samples are shown in Figures 6 and 7. The following hydrocarbon families are included in the comparison: n-paraffins, isoparaffins,

cycloparaffins

(mono-,

di-,

and

tricycloparaffins),

monoaromatics

(alkylbenzenes and tetralins), diaromatics (naphthalenes and naphthocycloalkanes), triaromatics (anthracenes), benzothiophenes (BTs), and dibenzothiophenes (DBTs). It was observed that generally the model predicts the carbon number distributions for the various hydrocarbon families, considering that this information was not used as input for the simulations. The measured carbon distributions clearly follow gamma-type behavior, which justifies the use of gamma PDFs for modeling paraffins chain length and alkyl branch carbons on ring cores. It should be noted that the distributions of all hydrocarbon families with ring core structures were modeled with one common gamma function. This 11 ACS Paragon Plus Environment

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explains why in some cases there are certain mismatches between the model and the analytical data (e.g. DBTs in Figure 6 and diaromatics in Figure 7). Assigning individual PDFs to each core structure might improve the match; however, this would imply more model parameters to be estimated. The peaks for some hydrocarbon families (marked as “monocycloparaffins C25+”, “tetralins C18+”, etc. in Figure 7) represent hydrocarbon lumps for which further separation was not possible due to the crossing of different hydrocarbon families in specific regions of the 2D-GC plane. Processing GC×GC chromatograms can be subjected to errors as this is done by using manually created templates to assign regions of the 2D-GC plane to specific hydrocarbon families. Thorough inspection of these chromatograms and verification with available standards is therefore necessary to improve accuracy. Results presented in Figures 6 and 7 confirm that the virtual oils are chemically consistent at the carbon number level, but not necessarily unique. For n-paraffins, the carbon number distributions refer exactly to the pure components, whereas in the case of other hydrocarbon families there is an unknown sub-distribution of structural isomers within each carbon number group. This means that it is not possible to verify the simulations beyond this level of molecular detail. Nevertheless, reasonable isomer distributions are expected as a result of the specific chemical rules and experimental data used to build the virtual oil mixture. For instance, the imposed

13

C spectrum allows a

certain degree of differentiation between structural isomers in saturated structures by specifying the abundance of primary, secondary and tertiary aliphatic carbons.

6,25

The

representation of structural isomers can be further refined by thermodynamic equilibrium calculations to discard unstable molecules.13 Despite the possible deviations at the structural isomer level, the molecular representation in its present form can already be used as basis for modeling refining chemistry in terms of molecular reactions and elementary steps.2,3,25 For instance, the information on alkyl-substituted aromatic sulfur compounds and polynuclear aromatic molecules presented in Table 3, allows establishing detailed pathways and reactivities of hydrodesulfurization and hydrogenation reactions during the hydrotreating process.


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5. Conclusions

This study is conducted to integrate modeling and simulation tools with standard and advanced petroleum characterization methods to derive the molecular composition of complex petroleum middle distillates. In the present approach, analytical petroleum characterization was computationally transformed into a virtual mixture of 5000 representative hydrocarbon molecules that mimics the actual oil sample. Molecular structures were built by Monte Carlo sampling and the abundance of each molecule was optimized with the principle of Maximum Entropy. Simulations of two representative middle distillate samples revealed that the virtual oil mixtures closely reproduce the analytical properties and hydrocarbon type compositions of the real samples. In addition, the model was capable of predicting detailed carbon number distributions and generating comprehensive information on the molecular structure and composition of the middle distillate samples. This will enable understanding and better predicting the processing behavior of middle distillates, which is essential for achieving meaningful improvements in process efficiency and minimizing the associated environmental impact.

Acknowledgments Partial funding for this study was provided by Natural Resources Canada and government of Canada’s interdepartmental Program of Energy Research and Development (PERD). Comments and suggestions from Dr. Edward Little and Dr. Antonio De Crisci on revising the manuscript are greatly appreciated. The authors are grateful to the analytical lab staff at CanmetENERGY for performing the analyses.

Nomenclature E = Entropy criterion fi = Probability of structural feature i J = Number of analytical constrains defined in Equation 3 N = Number of molecules in the mixture pjexp = Analytical property j 13 ACS Paragon Plus Environment

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pn,j = Calculated property j of molecule n xi = Value of structural feature i yn = Molar fraction of molecule n

Greek Symbols

α = Statistical parameter defined in Equation 1 β = Statistical parameter defined in Equation 2 γ = Statistical parameter defined in Equation 2 Γ = Gamma function defined in Equation 2 λj = Lagrange multiplier j defined in Equation 3 µ = Lagrange multiplier defined in Equation 3

Abbreviations DBT = Dibenzothiophene BT = Benzothiophene LGO = Light gasoil LCO = Light cycle oil PDF = Probability distribution function GC = Gas chromatography FID = Flame ionization detector SCD = Sulfur chemiluminescence detector NMR = Nuclear magnetic resonance FIMS = Field ionization mass spectrometry ASTM = American society for testing and materials


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López-García, C.; Hudebine, D.; Schweitzer, J-M.; Verstraete, J.J.; Ferré, D. Indepth modeling of gasoil hydrotreating: From feedstock reconstruction to reactor stability analysis. Catal. Today 2010, 150, 279-299.

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Verstraete, J.J.; Schnongs, Ph.; Dulot, H.; Hudebine, D. Molecular reconstruction of heavy petroleum residue fractions. Chem. Eng. Sci. 2010, 65, 304-312.

17.

Pereira de Oliveira, L.; Trujillo Vazquez, A.; Verstraete, J.J. ; Kolb, M. Molecular reconstruction of petroleum fractions: Application to vacuum residues from different origins. Energy Fuels 2013, 27, 3622-3641.

18.

Petti, T.F.; Trauth, D.M.; Stark, S.M.; Neurock, M.N.; Yasar, M.; Klein, M.T. CPU issues in the representation of the molecular structure of petroleum resid through characterization, reaction, and Monte Carlo Modeling. Energy Fuels 1994, 8, 570575.

19.

Pereira de Oliveira, L.; Verstraete, J.J.; Kolb, M. Molecule-based kinetic modeling by Monte Carlo methods for heavy petroleum conversion. Sci. China Chem. 2013, 56, 1608-1622.

20.

Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P. Optimization by simulated annealing. Science 1983, 220, 671-680.

21.

Hudebine, D.; Verstraete, J.J. Reconstruction of petroleum feedstocks by entropy maximization. Application to FCC gasolines. Oil & Gas Sci. Technol. 2011, 66, 437-460.

22.

Martin, R.L.; Winters, J.C.; Williams, J. Composition of crude oils by gas chromatography: geological significance of hydrocarbon distribution. 6th World Pet. Congr., Sec. 5 1963, 231-260.

23.

Trauth, D.M.; Stark, S.M.; Petti, T.F.; Neurock, M.N.; Klein, M.T. Representation of the molecular structure of petroleum resid through characterization and Monte Carlo modeling. Energy Fuels 1994, 8, 576-580.

24.

Hudebine, D.; Verstraete, J.J.; Chapus, T. Statistical reconstruction of gas oil cuts. Oil & Gas Sci. Technol. 2011, 66, 461-477.


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25.

Kumar, H.; Froment, G.F. Mechanistic modeling of the hydrocracking of complex feedstocks, such as vacuum gas oils. Ind. Eng. Chem. Res. 2007, 46, 5881-5897.


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Page 18 of 28

Table 1. Statistical description of structural features Structural feature

Values

PDF

Number of parameters

Molecule class

1,2,3

histogram

2

Paraffin chain length

8..30

gamma

2

Acceptance of alkyl branches

yes or no

histogram

1

Alkyl branch carbons

1..4

exponential

1

Number of alkyl branches

1..3

histogram

2

1..4

histogram

3

Number of benzene rings

1..4

histogram

3

Number of naphthenic rings

0..2

histogram

2

Number of thiophenic rings

0,1

histogram

1


≥0

gamma

2


1..4

histogram

3

Paraffins

Naphthenes Number of naphthenic rings Aromatics

Ring cores

Molecule class: 1 - paraffins; 2 - naphthenes; 3 - aromatics.


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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

Energy & Fuels

Table 2. Experimental and simulated characterization of the middle distillate samples Property

LGO

LCO

Experimental

Predicted

Experimental

Predicted

Density at 15.6ºC, g/cm

0.8370

0.8382

0.9080

0.9064

C, wt%

85.36

85.22

87.71

87.98

H, wt%

13.42

13.61

11.25

11.26

Atomic H/C ratio

1.87

1.90

1.53

1.53

S, wt%

1.21

1.17

0.77

0.76

Paraffins, wt%

55.9

55.2

14.8

15.0

Monocycloparaffins, wt%

14.2

13.9

23.2

23.4

Dicycloparaffins, wt%

1.7

2.0

4.5

4.6

Tricycloparaffins, wt%

0.2

0.2

0.4

0.5

Alkylbenzenes, wt%

10.3

10.8

7.3

6.8

Tetralins, wt%

5.6

5.3

12.6

13.8

Naphthalenes, wt%

7.6

8.2

23.7

22.1

Naphthocycloalkanes, wt%

2.2

2.2

8.9

9.6

Anthracenes, wt%

2.3

2.2

3.9

4.2

Thiophenes, wppm S

1834

1570

195

159

Benzothiophenes, wppm S

7586

7521

5827

5910

Dibenzothiophenes, wppm S

2721

2650

1718

1498

Aliphatic CH3, wt%

20.0

21.2

18.9

20.3

Aliphatic CH2, wt%

50.0

55.8

29.8

30.6

Aliphatic CH, wt%

12.5

9.1

8.6

9.7

Aromatic CH, wt%

9.1

5.9

22.4

20.7

Substituted aromatic C, wt%

6.0

5.3

12.0

9.5

Bridge aromatic C, wt%

2.4

2.7

8.4

9.2

3

Hydrocarbon types

Sulfur speciation

13

C Spectrum


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Page 20 of 28

Table 3. Representative molecules for LGO Structure

Mole fraction 1.241×10-2 2.722×10-4 2.451×10-3 8.875×10-4

2.498×10-4

1.759×10-4

2.053×10-4

3.612×10-4

2.998×10-4

1.669×10-4


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Energy & Fuels

Figure captions Figure 1. Molecule building sequence for middle distillates. Figure 2. Middle distillate simulation algorithm. Figure 3. Boiling point distribution curves for (a) LGO and (b) LCO. Figure 4. Simulated molecular weight distributions for (a) LGO and (b) LCO. Figure 5. Bubble plot representation of a GC×GC chromatogram as function of polarity vs. boiling point for LCO sample. Figure 6. Carbon number distributions for LGO. Experimental (-●-), simulated (bars). Figure 7. Carbon number distributions for LCO. Experimental (-●-), simulated (bars).


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Molecule class

Paraffins

Naphthenes

Aromatics

Chain length


Number of benzene rings

Acceptance of alkyl branches






Position of alkyl branches

Position of alkyl branches

Figure 1


Number of thiophenic rings

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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

Energy & Fuels

Structural blocks Molecule

PDFs

Mole fraction 0.025 0.010

S

0.015

“N” molecule assembly by Monte Carlo sampling of PDFs

0.132

Optimization of PDF parameters by Simulated Annealing

0.007 S

Poor match

Property calculation

Objective function

Good match

Experimental data PDF = Probability Distribution Function

Figure 2 23 ACS Paragon Plus Environment

Adjust composition by Entropy Maximization

Experimental data

Energy & Fuels

500 Experimental Simulated

Temperature, °C

450 400 350 300 250 200 150

(a) 100 0

10

20

30

40

50

60

70

80

90 100

Weight % off 500 Experimental Simulated

450

Temperature, °C

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

400 350 300 250 200 150

(b) 100 0

10

20

30

40

50

60

70

80

90 100

Weight % off


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0.12

(a)

Molar fractions

0.10 0.08 0.06 0.04 0.02 0.00 100

150

200

250

300

350

400

Molecular weight, g/mol 0.12

(b)

0.10

Molar fractions

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

Energy & Fuels

0.08 0.06 0.04 0.02 0.00 100

150

200

250

300

350

400

Molecular weight, g/mol


Energy & Fuels

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a) n-paraffins

3.0

b) isoparaffins

4.0

Weight %

Weight %

2.5 2.0 1.5 1.0 0.5 0.0

3.0 2.0 1.0 0.0

5

10

15

20

25

30

5

10

Carbon number

Weight %

Weight %

Monocycloparaffins C 25+

1.0

25

30

0.5

Alkylbenzenes C21+

Tetralins C18+

2.5

1.5

20

d) monoaromatics

3.0

2.0

2.0 1.5 1.0 0.5

0.0

0.0 5

10

15

20

25

30

5

10

Carbon number

4.0

20

25

f) triaromatics

2.0

Naphthocycloalkanes C 16+

3.0 2.0

Weight %

Naphthalenes C15+

5.0

15

Carbon number

e) diaromatics

6.0

Weight %

15

Carbon number

c) cycloparaffins

2.5

1.0 0.0

Anthracenes C 18+

1.5 1.0 0.5 0.0

6

8

10

12

14

16

18

20

10

12

g) benzothiophenes

2000

16

18

20

h) dibenzothiophenes

1000 800

wppm

BTs C 18+

1500

14

Carbon number

Carbon number

wppm

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

Energy & Fuels

1000 500

DBTs C19+

600 400 200 0

0 6

8

10

12

14

16

18

20

10

12

14

16

Carbon number

Carbon number


18

20

Energy & Fuels

a) n-paraffins

1.2

b) isoparaffins

2.0

Weight %

Weight %

1.0 0.8 0.6 0.4 0.2 0.0

1.5 1.0 0.5 0.0

5

10

15

20

25

30

5

10

Carbon number c) cycloparaffins

5.0

4.0

Weight %

Weight %

20

Monocycloparaffins C 25+

3.0 2.0

25

30

d) monoaromatics

5.0

4.0

1.0

Tetralins C 18+

3.0 2.0 1.0

0.0

0.0 5

10

15

20

25

30

6

8

10

Carbon number

8.0

Naphthocycloalkanes C 16+

14

16

18

20

6.0 4.0

f) triaromatics

2.0

Weight %

Naphthalenes C15+

12

Carbon number

e) diaromatics

10.0

Weight %

15

Carbon number

2.0 0.0

Anthracenes C 18+

1.5 1.0 0.5 0.0

6

8

10

12

14

16

18

20

10

12

g) benzothiophenes

2500

14

16

18

20

Carbon number

Carbon number

h) dibenzothiophenes

500 400

wppm

2000

wppm

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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1500 1000 500

300 200 100

0

0 6

8

10

12

14

16

18

20

10

12

14

16

Carbon number

Carbon number


18

20

Deriving the Molecular Composition of Middle Distillates by Integrating

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