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Development of a Structure-based Lumping Kinetic Model for Light Gas Oil Hydrodesulfurization Thuy Thi Hong Nguyen, Shogo Teratani, Ryuzo Tanaka, Akira Endo, and Masahiko Hirao Energy Fuels, Just Accepted Manuscript • Publication Date (Web): 20 Apr 2017 Downloaded from http://pubs.acs.org on April 24, 2017

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Energy & Fuels

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Development of a Structure-based Lumping Kinetic Model for Light Gas Oil

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Hydrodesulfurization

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Thuy T. H. Nguyen,∗†§ Shogo Teratani, ‡ Ryuzo Tanaka, ‡Akira Endo,† and Masahiko Hirao §

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5

Science and Technology, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8565, Japan

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7

Midori-ku, Chiba-city, Chiba 267-0056, Japan

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§

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Tokyo 113-8656, Japan

Research Institute for Chemical Process Technology, National Institute of Advanced Industrial

Advanced Technology and Research Institute, Japan Petroleum Energy Center, 1-4-10 Ohnodai,

Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,

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ABSTRACT

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Adopting the petroleomics concept, significant effort has been made to create extensive databases of

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molecular structures and chemical and physical properties of complex petroleum mixtures. By

13

collating the available information provided by petroleomics, this study develops a new

14

structure-based lumping kinetic model for hydrodesulfurization (HDS) of light gas oil. An advanced

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experimentation system, analytical technique, and computer software tool are employed to generate

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the necessary data. The model contains 16 structure-based lumps, each of which includes species

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having similar structure and reactivity. The model allows for the tracking of changes in molecular

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structure type of the input and output mixtures during HDS. Its prediction capability is validated over

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a wide range of HDS operation temperatures (200°C–375°C).

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Keywords: Petroleomics, hydrodesulfurization, light gas oil, structure-based lumping kinetic model 1

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1. INTRODUCTION

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In petroleum refineries, the catalytic hydrodesulfurization (HDS) process is used to remove sulfur

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from petroleum feedstock and product streams, assisting in meeting the technical constraints of the

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subsequent processes and environmental legislation. Produced by thermal catalytic cracking or

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hydrocracking processes, light gas oil (LGO) contains high amounts of sulfur- and

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nitrogen-containing compounds. The reactivity, mechanisms, and kinetics of these compounds during

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the HDS process have been widely studied1-6. Nevertheless, developing a suitable kinetic model for

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the design and optimization of an HDS reactor has not been given proper attention. Conventionally, a

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kinetic model is developed by the discrete or continuous lumping method, in which the petroleum

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components are lumped based on physical properties such as boiling points and solubility. This type

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of model can be used for testing catalyst activity and predicting product yields effectively.7-16

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However, it is unable to provide structure-based information of product mixtures. Thus, it provides

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limited opportunities for recovering high-value product compounds and tracing petroleum product

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contaminants.

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Petroleomics has emerged as a new field in petroleum technology.17,18 In petroleomics, all petroleum

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components are characterized and their properties and reactivity are correlated with the composition

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data. The application of petroleomics promises a variety of opportunities for the efficient use of both

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conventional and unconventional crude oils, the elimination of control and operation problems, and

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the reduction of production costs. The advanced analytical technique Fourier Transform Ion

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Cyclotron Resonance Mass Spectrometry (FT-ICR MS) has allowed for the characterization of 2

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thousands of molecular structures19. New computer software tool KMT (Kinetic Modeler’s Toolbox)

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has stimulated the development of molecular-level kinetic models.20-26 By combining a high

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throughput experimentation (HTE) system, FT-ICR MS and KMT, Japan Petroleum Energy Center

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has successfully developed a petro-informatics platform (PIP)27, which comprises large databases of

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the elemental compositions, physical properties, and reactivity behaviors of different petroleum

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fractions. Froment and co-workers developed single-event concept for building detail kinetic models

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capable of describing the chemistry transformation of each molecule involved in the feedstocks.28,29

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Owing to inclusion of a large number of elementary reactions and chemical species, such

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molecular-level kinetic models cannot be applied to the design, simulation, and optimization of

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refinery reactors which are frequently performed via process simulators.

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This study aims to develop a new structure-based lumping kinetic model for HDS of LGO (LGO–

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HDS). The chemical species involved in the feedstock and product mixtures are lumped in light of

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types of molecular structures. To build the model, data are obtained from different sources: HTE,

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FT-ICR MS, and KMT. The model is developed through a new approach that includes the following

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steps: (1) analyzing elementary reaction rates to reduce unimportant reactions; (2) grouping reactant

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species into lumps and defining the lump-based reaction network; (3) building a lump-based reaction

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kinetic model; and (4) estimating the kinetic parameters of the lump-based reactions. The model’s

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prediction capability is examined at different HDS temperatures ranging from 200 to 375°C.

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3

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2. ACQUISITION OF DATA

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Experimental data are obtained from an HTE system in which 16 plug-flow micro reactors loaded

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with commercial catalysts are run in series, under the following conditions:

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Temperature: 250°C, 275°C, and 300°C

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Pressure: 5.5 MPa

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Liquid hourly space velocity: 1 h−1

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Hydrogen to oil ratio: 201 NL L−1

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The basic properties of the LGO feedstock used in this study are given in Table 1. FT-ICR MS is

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used to analyze the types of molecular structures involved in the feedstock and product mixtures.

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Based on this information, the types of reaction pathways occurring during LGO–HDS can be

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tracked. This information is then used for KMT simulation.

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Utilizing the input information of the types of molecular structures and reaction pathways, KMT

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produces a detail elementary reaction network together with kinetic parameters. In addition, KMT

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calculates the distribution of the molar flowrates of the input and output species with respect to the

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constraints of the bulk physical properties of the feedstock and product mixtures. Previous studies

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have demonstrated the high accuracy of KMT simulation results.23,25,26 Data generated by KMT

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simulations such as the kinetic parameters of elementary reactions and molar flowrates of input and

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output chemical species are reliable. Thus, they can be used as reference data replacing for

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experimental data in the development of a structure-based lumping kinetic model.

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After simulation by KMT, about 800 species are generated. They are distinguished by their molecular 4

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structure attributes, i.e., type of core, and length, position, and branching degree of the alkyl chain.

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Experimental data analysis has shown that all types of alkyl chain, as well as the cores paraffin,

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cyclohexane, cyclopentane, hyrindane, and fluorine, cause negligible reactions under the considered

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reaction conditions. Species having reactive cores and their products react with hydrogen, forming a

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large network, consisting of 613 elementary reactions. Table 2 shows the core types of the input

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species and their reaction families. The reactant–product relationships between the cores are shown

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in Figure 1. The species having inert core structures are summarized in Table 3.

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Detailed information regarding to the molecular structures (represented by structural vectors) and

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molar flowrates of the species, together with the elementary reaction network and kinetic parameters

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output by KMT are shown in the Supporting Information.

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3. DEVELOPMENT OF A STRUCTURE-BASED LUMPING KINETIC MODEL

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3.1 Reducing Unimportant Reactions

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Output from KMT simulation, the elementary reaction network of LGO–HDS is markedly

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complicated, including hundreds of elementary reactions and chemical species. To facilitate the

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development of the kinetic model, the size of the elementary reaction network must be reduced by

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eliminating the unimportant elementary reactions and chemical species. Different methods have been

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proposed to reduce the complicated reaction network.30-35 In this study, analysis of the production

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rate30,31 is used. With this method, the importance of an elementary reaction is determined by its

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contribution to the total product production rate.

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ʋ  (  ,  )

 = ∑

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(1)

 ʋ  (  ,  )

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where υij is the stoichiometric coefficient of species i in reaction j, Rj is the rate of reaction j, kj is the

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kinetic parameter of reaction j, ci is the concentration of species i, and p is the total number of

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elementary reactions.

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Calculated by eq 1, sij is a function of the elementary reaction rate constant and the concentration of

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the reactant species. As the latter changes along with reaction time, sij is calculated at different

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reaction time intervals. At each time interval, the highest value of sij is defined and together with a

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selected threshold value, it is used to calculate a benchmark value, based on which the importance of

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each elementary reaction is judged. If an elementary reaction shows a negligible contribution at all

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time intervals, it is considered unimportant and should be eliminated. The types of unimportant

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reaction pathways are displayed by the dashed arrows in Figure 1, and the types of cores undergoing

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only unimportant reaction pathways are shown in Table 4.

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3.2 Developing Structure-based Lumps and the Lump-based Reaction Network

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The reaction pathways of the chemical species are mainly determined by the core types involved in

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their structures. Species that have similar core structures will undergo the same reaction pathways;

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thus, they can be grouped into the same lumps. Each species’ structure is characterized by a structural

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vector, detail of which is shown in Supporting Information. This vector is used to track the similarity

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in molecular structure, based on which the species can be grouped into lumps.

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Table 5 shows the 16 structure-based lumps of product and reactant species defined in this study.

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Each lump consists of species having similar core structures. For example, L2, L3, L4, and L5 consist 6

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of species having the cores indoline, quinoline, benzothiophene, and dibenzothiophene, respectively.

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The structure-based lumps undergo the same types of reaction pathway as their species. Thus, the

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reactant–product relationships, as displayed in Figure 1, can be generalized for building the

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lump-based reaction network.

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Figure 2 shows the reaction network of the structure-based lumps. A lump can participate in multiple

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reactions as a reactant, intermediate, or final product. For example, L7 and L8 are the intermediates of

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the hydrodenitrogenation of L2 and L3, whereas L1 and L10 are the intermediates of the saturation of

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L6. The HDS of L4 and L5 produces the intermediates L9, L11, and L12. With this type of

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structure-based lumping model, changes in the molecular structure of the considered species during

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LGO–HDS can be tracked.

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3.3 Building the Reaction Kinetics Model

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Under the reaction conditions of LGO–HDS, it is acceptable to assume the following conditions:

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the lump-based reactions are pseudo-first order reactions, and they are irreversible

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catalyst deactivation is negligible

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coke deposition is minor, so it can be excluded

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The reaction rates of the structure-based lumps are expressed by the following equations:

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136



137



  

=  ∗  −  ∗ 

(2)

= −( +  ) ∗ 

(3)

= − ∗ !

(4)

7

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138

"

139

%

140

&

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'

142

)

143

*

144

,

145



146



147



148

"

149

%

150

&

151

where km is the reaction rate constant of lump-based reaction m (m = 1–16), yn is the concentration of

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lump Ln (n = 1–16), and t is the residence time.

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3.4 Estimating Lump-based Reaction Rate Constants

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As stated above, the data obtained from KMT simulation are reliable and can be used as reference

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data for estimating the lump-based reaction rate constants and comparing with the results predicted

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by the developed model. The concentrations of species obtained by KMT simulation are used to

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calculate the concentrations of structure-based lumps ynref at different residence time intervals.













= −(# +  ) ∗ $

(5)

= −$ ∗ 

(6)

= − ∗ 

(7)

=  ∗ ! − (( + $ ) ∗ #

(8)

= ( ∗ # +  ∗  −  ∗ (

(9)

= # ∗ $ − ! ∗ +















(10)

=  ∗  − ! ∗ -

(11)

= $ ∗  − + ∗ 

(12)

= + ∗  − - ∗ 

(13)

= - ∗ 

(14)

= ! ∗ -

(15)

=  ∗  +  ∗ $ + ! ∗ + + $ ∗ # −  ∗ 

(16)

=  ∗  +  ∗ (

(17)

8

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The lump-based reaction rate constants are calculated by using MATLAB program. Firstly, initial

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values of km are estimated and attached to the differential reaction rates, eqs 2–17. These equations

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are then integrated and solved to calculate the lump concentrations yncal at different considered time

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intervals. The total error between ynref and yncal is calculated by function F:

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45 . = ∑7 8/0, − 0, 6

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where N is the total number of data points.

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New values of km are reselected by the simplex search method and the calculation process is repeated

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to minimize the function F. The values of km at which minimal F is obtained, are selected as the rate

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constants of the lump-based reactions.

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The reaction rate constants are calculated for different experimental reaction conditions: 250°C,

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275°C, and 300°C. They are then used to calculate the pre-exponential factor Am and activation

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energy Em of the lump-based reactions, following the Arrhenius equation:

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9: (; ) = 9: ( 

(19)

?

176

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4. RESULTS AND DISCUSSION

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Table 6 shows the kinetic parameters of the lump-based reactions. These values are used to predict

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the output molar flowrates of all structure-based lumps at different reaction temperatures. Figure 3

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shows the comparison results of the reference (Ref.) and predicted (Cal.) output molar flowrates. For

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a wide range of reaction temperatures, good agreement between the Ref. and Cal. results is observed

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(R2 > 0.99).

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The ability to predict the distribution of the product along the length of the reactor was also

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examined by considering different reaction time intervals. Figure 4 shows the variation in molar

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flowrates of each lump along the reactor length. At each reactor length interval, there is only a minor

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difference between the Ref. and Cal. molar flowrates of all lumps, except L9. The difference between

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the Ref. and Cal. molar flowrates of L9 increases when the reaction temperature is above 325°C,

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probably due to the inhibition of hydrogen sulfide. Hydrogen sulfide is the byproduct of HDS of the

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sulfur-containing compounds, benzothiophene and dibenzothiophene. It inhibits hydrogenolysis, but

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not hydrogenation.36 This inhibition becomes very important at high reaction temperatures and

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retards the conversion of L9 to L15. As the product of the hydrogenation of L4, the molar flow rate of

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L9 increases with an increase in temperature. The inhibition of hydrogen sulfide can be interpreted

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using a Langmuir−Hinshelwood kinetic model37, which is not considered in this study. Thus, the

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molar flowrate of L9, as predicted by the developed model, is different from the Ref. at high

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temperatures. L9 is present in the product stream in trace amounts (~10−6 mol s−1); therefore, it has

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only a negligible effect on the entire mass balance of the output stream. 10

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The main purpose of LGO–HDS is to reduce the sulfur-, and other heteroatoms such as nitrogen-,

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containing species to low levels. In light of this purpose, and based on the profiles of the

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structure-based lumps, suitable operation conditions for LGO–HDS can be proposed. As displayed in

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Figure 4, at 325°C, the profiles of the lumps containing nitrogen L2, L3, L7, and L8 demonstrate

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significant decreases in molar flowrate. These are present in trace amounts (< 10−7 mol s−1) in the

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output product. At the same temperature, smaller decreases are observed in the profiles of the

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sulfur-containing lumps. However, the molar concentration of these lumps can be significantly

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reduced if the reaction time is extended or reaction temperature is increased. For example, above

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350°C, relatively small amounts of L4 and L5 are output, i.e., approximately 8 × 10−5 and 4.4 × 10−6

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mol s−1, respectively.

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5. CONCLUSION

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Under the concept of petroleomics, a structure-based kinetic lumping model is developed for LGO–

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HDS. It demonstrates a high accuracy prediction capability for a wide range of operation

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temperatures. The model can not only predicts the composition but also provides structure-based

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information of the output mixture. By observing the yield distribution of the structure-based lumps

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along the length of the reactor, suitable operation temperatures for reducing the content of

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heteroatom-containing species (e.g., benzothiophene, dibenzothiophene, indoline, and quinoline) can

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be proposed. The model can be extrapolated to predict the structure-based composition of the product

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stream over a wide range of temperatures. As the model can help attain molecular level reaction

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chemistry information, it enables the tracking of the structure-based composition of product mixtures

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during the HDS process. Such information is indispensable for the design of optimal HDS reactors,

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opening new opportunities for a more comprehensive exploitation of petroleum feedstocks and

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further improvement of the economic benefits.

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The model is developed for LGO–HDS operated under moderate temperatures and pressures with the

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support of catalysts frequently employed in the industry. Only major reaction pathways occurring

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under these conditions are considered. If extreme operation conditions are employed, the model

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should be modified to account for the types of reaction pathways that are not considered in the

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current model. By adding more structure-based lump types, the model can be applied to more

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complicated petroleum mixtures. In future work, the model robustness should be examined, taking

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into account the impact of the ratio of hydrogen to oil, hydrogen pressure, space velocity, and

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catalyst types.

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AUTHOR INFORMATION

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Corresponding author:

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Telephone: +029-861-5171. Email: [email protected]

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Notes

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The authors declare no competing financial interest.

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Supporting Information Available: This material is available free of charge via the Internet at

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http://pubs.acs.org.

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ACKNOWLEDGEMENT

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The authors wish to express special thanks to Ministry of Economy, Trade and Industry (METI) and

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Japan Petroleum Energy Center (JPEC) for financial support.

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(28) Feng, W.; Vynckier, E.; Froment, G. F. Ind. Eng. Chem. Res. 1993, 32, 2997–3005.

281

(29) Froment, G. F. Catal. Rev.-Sci. Eng. 2005, 47, 83–124.

282

(30) Turányi, T.; Bérces, T. Int. J. Chem. Kinet. 1989, 2, 83–99.

283

(31) Turányi, T. New J. Chem. 1990, 14, 795-803.

284

(32) Edwards, K.; Edgar, T. F.; Manousiouthakis, V. I. Comput. Chem. Eng. 1998, 22, 239–246.

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(33) Briesen, H.; Marquart, W. Comput. Chem. Eng. 2000, 24, 1287–1292.

286

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9004–9016.

288

(35) Liu, A.; Jiao, Y.; Li, S.; Wang, F.; Li, X. Energy Fuels 2014, 28, 5426–5433.

289

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Energy & Fuels

Table 1. Basic Properties of Feedstocks and Products Property

Feed

Product

Volume fraction of aromatics (vol %)

0.314

0.148

Mono-aromatic Di-aromatic

0.124 0.139

0.117 0.013

Tri+-aromatic Elemental content Sulfur (wt %)

0.051

0.019

1.0

0

Nitrogen (ppmw) Carbon (wt %) Hydrogen (wt %)

112.0 86.4 12.6

0 86.2 13.8

Density (g/mL) Distillation characteristics IBP (°C) 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 95% FBP (°C)

0.860

0.833

211 236 247 260 274 287 295 306 316 329 347 356 374

211 232 239 254 269 277 289 300 312 325 344 353 373

294

295

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Table 2. Types of Reaction Family Core

Reaction family

Reactive group

N-paraffin

-

-

I-paraffin

-

-

Cyclopentane

-

-

Cyclohexane

-

-

Hyrindane

-

-

Fluorine

-

-

Acenaphthene

Saturation 4H

Multi-ring

Benzene

Saturation 6H

Aromatic

Biphenyl

Saturation 6H

Multi-ring

Dibenzothiophene

Hydrodesulfurization

Sulfur

Indane

Saturation 6H

Multi-ring

Hydrodesulfurization

Sulfur

Hydrogenation

Sulfur

Hydrodenitrogenation

Nitrogen

Saturation 6H

Nitrogen

Naphthalene

Saturation 4H

Multi-ring

Tetralin

Saturation 6H

Multi-ring

Phenanthrene

Saturation 2H

Multi-ring

Quinoline

Saturation 4H

Nitrogen

Benzothiophene Indoline

297

298

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Energy & Fuels

Table 3. Structure and Molar Flow Rate of Unreactive Cores

Number of species

Input molar flow rate (mol s−1)

1

88

4.410E-01

2

58

1.560E-07

3

18

1.800E-10

4

3

1.130E-03

ID

Representative structure

300

301

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Table 4. Structure and Molar Flow Rate of Unimportant Cores ID

Representative structure Number of species

Input Molar flowrate (mol s−1)

1

28

1.090E-10

2

20

0.000E+00

3

32

3.400E-02

4

11

0.000E+00

5

22

2.090E-07

303

304

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Energy & Fuels

Table 5. Representative Structures and Molar Flow Rate of Lumps of Important Species Lump name ID

Core structure

Number of species

Input molar flowrate (mol s−1)

9 Lump 1

L1

15

1.337E-04

8

Lump 2

L2

11

8.123E-04

Lump 3

L3

13

1.006E-03

Lump 4

L4

11

5.195E-02

Lump 5

L5

6

1.819E-02

Lump 6

L6

8

5.476E-02

Lump 7

L7

26

0.000E+00

11 Lump 8

0.000E+00

L8 13

Lump 9

L9

11

0.000E+00

17

Lump 10

L10

0.000E+00 23

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Lump 11

L11

25

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1.553E-01

26 Lump 12

8.103E-05

L12 47

26 Lump 13

L13

0.000E+00 25

9 Lump 14

L14

0.000E+00 8

52

10 Lump 15

L15

1.254E-01 11 11

52 1.143E-01 10 Lump 16

L16 11

11

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Energy & Fuels

Table 6. Kinetic Parameters of Lump-based Reactions k

A

E (kJ mol−1)

k1

6.68E+06

30.12

k2

1.54E+07

30.78

k3

8.75E+06

28.37

k4

1.28E+08

29.89

k5

1.28E+11

37.21

k6

1.88E+09

28.46

k7

5.91E+02

28.63

k8

3.54E+06

25.49

k9

3.68E+07

29.42

k10

2.93E+07

29.69

k11

3.00E+06

25.10

k12

1.30E+08

29.91

k13

5.96E+02

17.27

k14

3.85E+11

39.23

k15

2.55E+11

38.00

k16

1.96E+06

29.11

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310 311 312

Figure 1. Reactant-product relationships between the core types (Solid and dashed arrows indicate important and unimportant reaction pathways, respectively)

313

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Energy & Fuels

314 315 316

Figure 2. Lump-based reaction network (ki: Lump-based reaction rate constant)

317

318

319

320

321

322 25

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323 324

Figure 3. Comparison between reference and predicted output molar flow rates of all lumps

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327

328

329

Figure 4. Variation in molar flow rates of lumps along the reactor length

330

(Markers and dash lines indicate reference and calculated data, respectively)

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