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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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"
139
%
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&
141
'
142
)
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*
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,
145
146
147
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"
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)
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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
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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] 232
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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(30) Turányi, T.; Bérces, T. Int. J. Chem. Kinet. 1989, 2, 83–99.
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(31) Turányi, T. New J. Chem. 1990, 14, 795-803.
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(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
(34) Stagni, A.; Cuoci, A.; Frassoldati, A.; Faravelli, T.; Ranzi, E. Ind. Eng. Chem. Res. 2014, 53, 15
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9004–9016.
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(36) Girgis, M. J.; Gates, B. C. Ind. Eng. Chem. Res. 1991, 30, 2021–2058.
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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
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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
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298
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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
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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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L11
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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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Figure 2. Lump-based reaction network (ki: Lump-based reaction rate constant)
317
318
319
320
321
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323 324
Figure 3. Comparison between reference and predicted output molar flow rates of all lumps
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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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