Development of a Structure-Based Lumping Kinetic Model for Light

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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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†

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

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‡

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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

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

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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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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

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= ∗ − ∗

(2)

= −( + ) ∗

(3)

= − ∗ !

(4)

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138

"

139

%

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&

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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)

?

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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

11


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

285

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

286

(34) Stagni, A.; Cuoci, A.; Frassoldati, A.; Faravelli, T.; Ranzi, E. Ind. Eng. Chem. Res. 2014, 53, 15


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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

(36) Girgis, M. J.; Gates, B. C. Ind. Eng. Chem. Res. 1991, 30, 2021–2058.

290

(37) Kilanowski, D. R.; Gates, B. C. J. Catal. 1980, 62, 70–78.

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293

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

17


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296

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


Sulfur

Indane

Saturation 6H

Multi-ring


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

18


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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

19


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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

306

21


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Lump 11

L11

25

Page 22 of 29

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

307

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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

23


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309

310 311 312

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

313

24


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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

26


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Energy & Fuels

27


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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)

29


Development of a Structure-Based Lumping Kinetic Model for Light

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