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Energy & Fuels 2008, 22, 860–866
Effects of Nitrogen and Aromatics on Hydrodesulfurization of Light Cycle Oil Predicted by a System Dynamics Model Zhengliang Liu,† Qikai Zhang,† Ying Zheng,*,† and Jinwen Chen‡ Department of Chemical Engineering, UniVersity of New Brunswick, 15 Dineen DriVe, P.O. Box 4400, Fredericton, New Brunswick, Canada E3B 5A3, and National Centre for Upgrading Technology (NCUT), 1 Oil Patch DriVe, DeVon, Alberta, Canada T9G 1A8 ReceiVed October 19, 2007. ReVised Manuscript ReceiVed December 19, 2007
The production of ultralow-sulfur diesel from light cycle oil (LCO) by hydrotreatment is promising. Unfortunately, hydrodesulfurization (HDS) of LCO is seriously inhibited by the coexistence of organic nitrogen and aromatic compounds during the hydrotreating process. It is of great interest to understand this inhibition confidently by simulations. In this study, the inhibition effects from both organonitrogen and aromatic compounds are simulated by a proposed system dynamics (SD) model for the first time. The axial profiles of the concentrations of organic sulfur, nitrogen, and aromatics in the hydrotreating reactor are predicted by the SD model. Impact factors for nitrogen and aromatic compounds respectively are used to characterize their inhibition effects. Studies were also conducted on the variation of the impact factors along the axial position of the hydrotreator. The simulation results of HDS, with consideration of the nitrogen and aromatic inhibition effects, are compared with published experimental data. The influence of the operating conditions such as inlet system temperature and liquid hourly space velocity on the impact factors is also investigated.
1. Introduction In recent years, light cycle oil (LCO) produced by fluid catalytic cracking units is increasingly added to the diesel pool because of an increasing demand for diesel fuel worldwide.1 Unfortunately, LCO containing high sulfur (>1.0 wt %), nitrogen, and aromatics content has a low cetane number and a poor blending quality. However, more stringent environmental legislation demands that the sulfur content in diesel fuel be less than 15 ppm.2 The production of this ultralow-sulfur diesel (ULSD) from LCO has been a focus of the petroleum refining industry and research community. In spite of some emerging new technologies,3,4 it is expected that hydrodesulfurization (HDS) will continue to play a dominant role in the ULSD production.2 Extensive research has been carried out on HDS by both experimental and simulation approaches.5–8 By assuming the reactions to be first-order with respect to sulfur, the HDS kinetics of 14 dibenzothiophenic compounds in LCO were obtained from the experimental data.9 Previous research showed that the removal of sulfur compounds was tremendously hindered by the existence of nitrogen * To whom correspondence should be addressed. Telephone: 1-5064473329. Fax: 1-506-4533591. E-mail:
[email protected]. † University of New Brunswick. ‡ National Centre for Upgrading Technology. (1) Laredo, G. C.; Saint-Martin, R.; Martinez, M. C.; Castillo, J.; Cano, J. L. Fuel 2004, 83, 1381–1389. (2) Song, C.-S. Catal. Today 2003, 86, 211–263. (3) Levy, R. E.; Rappas, A. S.; Lee, F. M.; Nero, V. P.; DeCanio, S. J. Hydrocarbon Eng. 2002, 7, 25–28. (4) Ma, X.-L.; Sun, L.; Song, C.-S. Catal. Today 2003, 77, 107–116. (5) Ho, T. C.; Sobel, J. E. J. Catal. 1991, 128, 581–584. (6) Chen, J.-W.; Te, M.; Yang, H.; Ring, Z. Pet. Sci. Technol. 2003, 21, 911–935. (7) Froment, G. F.; Depauw, G. A.; Vanrysselberghe, V. Ind. Eng. Chem. Res. 1994, 33, 2975–2988. (8) Vanrysselberghe, V.; Froment, G. F. Ind. Eng. Chem. Res. 1998, 37, 4231–4240. (9) Chen, J.-W.; Yang, H.; Ring, Z. Catal. Today 2004, 98, 227–233.
and aromatics.10 The inhibition of nitrogen compounds on the HDS of alkyl-substituted dibenzothiophenes (DBTs) in LCO has been studied extensively.11–13 It is demonstrated that both basic and nonbasic nitrogen compounds can dramatically inhibit the HDS reactivity of refractory sulfur compounds, such as 4-methyldibenzothiophene (4-MDBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT). Chen et al. and Song et al. investigated the inhibition effect of aromatics on deep HDS of DBTs and reported that the adverse effect was more pronounced for 4,6-DMDBT than for DBT.14,15 In the laboratory, it is not possible to obtain the HDS kinetic data for all the feeds that the industrial unit treats, and experiments give different results for each feed and catalyst. Therefore, process simulation and modeling gradually play an important role in studying the HDS of LCO. Bellos et al. presented the simulation of the performance of industrial HDS reactors, with the kinetics parameters predicted using a hybrid neural network.16 However, enormous experimental kinetics data were required for the neural network training to reach satisfactory simulation results. An adiabatic multiphase reactor for diesel HDS was simulated with a one-dimensional heterogeneous model.7 It was reported that the total number of rate and adsorption parameters for the HDS of a set of substitute DBTs (10) Ho, T. C. J. Catal. 2003, 219, 442–451. (11) Laredo, G. C.; Reyes, A. D.; Cano, J. L.; Castillo, J. Appl. Catal., A 2001, 207, 103–112. (12) Yang, H.; Chen, J.-W.; Fairbridge, C.; Briker, Y.; Zhu, Y.-J.; Ring, Z. Fuel Process. Technol. 2004, 85, 1415–1429. (13) Yang, H.; Chen, J.-W.; Briker, Y.; Szynkarczuk, R.; Ring, Z. Catal. Today 2005, 109, 16–23. (14) Chen, J.-W.; Nakajima, N.; Yang, H.; Ring, Z.; Song, T.; Zhang, J. Conference Proceedings of 2005 AIChE Spring National Meeting 2005, 1165–1174. (15) Song, T.; Zhang, Z.-S.; Chen, J.-W.; Ring, Z.; Yang, H.; Zheng, Y. Energy Fuels 2006, 20, 2344–2349. (16) Bellos, G. D.; Kallinikos, L. E.; Gounaris, C. E.; Papayannakos, N. G. Chem. Eng. Process. 2005, 44, 505–515.
10.1021/ef700622q CCC: $40.75 2008 American Chemical Society Published on Web 02/06/2008
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simulation modeling technique for understanding complex issues and problems.17 Bala presented a dynamic model of biogas production systems on the basis of the SD methodology.18 It was demonstrated that SD was the simplest and quickest route to the understanding of dynamic systems through models. However, the application of SD for the simulation of industrial processes has not yet been well documented. This paper is an attempt to simulate the complicated HDS process of LCO and the inhibition effects of nitrogen compounds and aromatics on HDS by a proposed SD model. One advantage of this SD model is that the relationships between the variables, which are difficult to express with explicit mathematical functions, can be easily addressed in the model. 2. SD Model of HDS Figure 1. Causal loop diagram of HDS affected by HDN and HDA.
was reduced from 1133 to 93.7 It can be seen that the simulation of HDS of industrial LCO is complicated, and many parameters have to be determined for different feeds and catalysts. This complexity is a great challenge for the previously reported simulations. In addition, the aforementioned inhibition effects from the nitrogen and aromatic compounds on the HDS activity, which should be taken into account, have not been considered by the reported simulations. System dynamics (SD), first introduced by Forrester in the early 1960s for long-term decision making in dynamic industrial management problems, is a powerful methodology and computer
Figure 2. Stock-flow diagram of HDS.
2.1. Model Variables and Causal Loop Diagrams. SD has become a valuable tool to represent, analyze, and understand the dynamic behavior of complex systems for strategic and policy related decision making in the public service and industries areas.19–21 Instead of breaking a system into smaller pieces, SD treats them as a whole and tries to understand how individual pieces interact with each other. The interplays among them determine the different statuses of the system which vary during the course of time, so it is known as the dynamic behavior of the system. To capture the relationships among system variables, appropriate causal loop diagrams are required to be constructed. Causal loop diagrams are maps of cause and effect relationships
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Table 1. Feed Composition of the LCO Feed concentration (wt %)
feed component total:
easy sulfur hard sulfur other hard sulfur
basic nonbasic
a
100
Sulfur Componentsa subtotal: BT DBT 4- or 6-MDBT 4,6-DMDBT BNT
8843 wppm S 4228 wppm S 811 wppm S 867 wppm S 314 wppm S 2623 wppm S
Nitrogen Componentsa,b subtotal: QL IND CBZ
774 wppm N 55 wppm N 114 wppm N 605 wppm N
Mono Di Tri Tetra
Aromaticsa,b subtotal: tetralin naphthalene phenanthrene pyrene
71.3 19 38 5.8 8.5
paraffins naphthenes
Saturatesa subtotal: n-hexadecane cis-decalin
28.7 10.0 18.7
Yang et al.13 b Laredo et al.11 and Laredo et al.27
Table 2. Summary of Operating Conditions variable
rangea
inlet temperature (K) total pressure (atm) hydrogen partial pressure (atm) gas-to-oil ratio (NL/L)b gas rate (NL/h)b LHSV (mL3/(h mcat3)) H2S concentration (vol %) NH3 concentration (vol %)
514–674 69.1–98.7 48.4–69.1
a
135–334.5 1.03–3.24 0–3.0 0–0.5
base condition 623 70 70 1000 1.6 0 0
Chen et al.6 b NL: normal liter.
Table 3. Activation Energy and Pre-Exponential Factors of Reactions reaction
pre-exponential factor, k0 (h-1)
activation energy, Ea (J/mol)
HDS of easy sulfura HDS of DBTa HDS of 4- or 6-MDBTa HDS of 4,6-DMDBTa HDS of other hard sulfura HDN of QLb HDN of INDb HDN of CBZb HDA of Monoc HDA of Dic HDA of Tric HDA of Tetrac
25.2 2.48 × 106 4.60 × 106 1.16 × 1013 6.05 × 106 1.60 × 1010 1.92 × 1010 2.40 × 1010 4.4 × 108 2.9 × 107 1.7 × 108 2.3 × 108
1.46 × 103 6.12 × 104 6.85 × 104 1.49 × 105 7.08 × 104 1.15 × 105 1.17 × 105 1.23 × 105 1.04 × 105 8.96 × 104 9.92 × 104 9.96 × 104
a Calculated from the experimental results from Yang et al.13 Activation energy from Girgis and Gates.24 c Activation energy from Song et al.;15 nonindicated values are arbitrarily determined.
the variables change in the same direction, and a “-” sign dictates the opposite. An example of a negative feedback loop is formed by the following: an increase of “S concentration” increases the “HDS rate”, which, in reverse, decreases the “S concentration”. For legibility, the diagram of Figure 1 is simplified by displaying the sulfur, nitrogen, and aromatic compounds as lumped ones. During model setup and simulation, all the compounds are considered individually. An example of the partially detailed structure is shown in Figure 2 for HDS of sulfur compounds. 2.2. Kinetics Modeling. Following the SD methodology, a mathematical model is presented as a stock-flow diagram that captures the model structure and the interrelationships among the variables. The SD model consists of stock (level) and flow (rate) variables. Stock variables are the accumulations during the hydrotreating process, for instance, the concentrations of sulfur, nitrogen, and aromatics compounds. The flow variables stand for the change rates of these stock variables. The stock-flow diagram is translated into a system of differential equations and then solved via simulation using the commercial software Vensim 5.6b. The stock-flow diagram of the whole system is complicated. As an example, a portion of the diagram for HDS of sulfur compounds is shown in Figure 2. The diagram is constructed with stocks, flows, auxiliaries, and constants. Generally, SD models show the variables changing with the time scale. In this study, however, it is more straightforward to study the parameters changing along the axial position of the reactor during the hydrotreating process. Consequently, the dimensionless axial position of the reactor is adopted as the scale by introducing a constant to remove the time scale. To simulate the impacts of nitrogen and aromatic compounds on the HDS performance of LCO, kinetics modeling plays a key role. With an excess of hydrogen in the hydrotreating processes, the reaction kinetics of sulfur and nitrogen compounds are usually represented by a type of Langmuir–Hinshelwood (L-H) equation.5 Also, it is well accepted that deep HDS is inhibited by the nitrogen compounds and aromatics.10–15 Nitrogen and aromatic compounds compete with sulfur compounds on the hydrogenation sites and inhibit the hydrogenation pathway, whereas the inhibition effect from H2S and sulfur compounds themselves is negligible.15 Assuming the catalysts are at the stage of stabilization with no deactivation, the rate equation of HDS is -rHDS,i ) -
)
(1)
where i ) easy sulfur (or benzothiophene (BT)), DBT, 4- or 6-MDBT, 4,6-DMDBT, and other hard sulfur (or BNT) for different sulfur compounds; εN and εA are impact factors from nitrogen and aromatic compounds, respectively.
b
between individual system variables, which form closed loops when linked. Through feedback loops, SD shows how a change of one variable affects other variables dynamically, which in turn affects this variable, and so on. Figure 1 illustrates the causal loop diagram of the HDS process influenced by hydrodenitrogenation (HDN) and hydrodearomatization (HDA). The model was developed using the commercial software Vensim 5.6b provided by Ventana Systems, Inc. The arrows and their direction represent the cause and effect relationships among variables. The sign “+” or “-” at the upper end of the arrows shows the sign of the effect. A “+” sign dictates that
(
kS,iCS,i 1 dNS,i ) Vcat dt 1 + εN + εA
(∑ C ) ) K (∑ C )
nN
εN ) KN
N,j
εA
A,k
nA
A
(2) (3)
where j ) quinoline (QL), indole (IND), and carbazole (CBZ) for different nitrogen compounds; k ) monoaromatics (Mono), diaromatics (Di), triaromatics (Tri), and tetra-aromatics (Tetra) for various aromatic compounds. It is known that different nitrogen and aromatic compounds have dissimilar inhibition effects on HDS.11 To simplify the model, all nitrogen compounds and aromatics are separately lumped together for calculation.
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For the L-H rate equation of HDN, the inhibition effect of nitrogen compounds themselves should be included:13
nitrogen compounda
1 dNN,j kN,jCN,j ) -rHDN,j ) Vcat dt 1 + εN
(4)
To simplify the modeling work, a type of power law kinetics is applied to the hydrogenation of each aromatic group. In addition, it is assumed that sulfur and nitrogen compounds do not affect the hydrogenation reactions of aromatics, which can be represented by the following irreversible reactions in series:14 ka4
ka3
ka2
ka1
Tetra 98 Tri 98 Di 98 Mono 98 Saturates The reaction rates for the four aromatic groups are written as -rMono ) -
1 dNMono na1 na2 ) ka1CMono - ka2CDi Vcat dt
-rTri ) -
(5)
1 dNDi na2 na3 ) ka2CDi - ka3CTri Vcat dt
(6)
1 dNTri na3 na4 ) ka3CTri - ka4CTetra Vcat dt
(7)
1 dNTetra na4 ) ka4CTetra Vcat dt
(8)
-rDi ) -
-rTetra ) -
According to the Arrhenius law, the reaction rate constants in the above equations of HDS, HDN, and HDA are
( )
Ea (9) RT where Ea and k0 (mL3/(mcat3 · h)) are the activation energy and the pre-exponential factor, respectively. In a commercial hydroprocessing of LCO, the reactors are most likely operated in a trickle-flow regime, which can be reasonably described by a plug-flow model. The above rate equations of HDS, HDN, and HDA can be expressed in terms of the liquid hourly space velocity (LHSV), the component concentrations, and the dimensionless axial coordinate: k ) k0 exp -
(
kS,iCS,i dCS,i 1 )dZ LHSV 1 + εN + εA
(
dCN,i kN,jCN,j 1 )dZ LHSV 1 + εN
)
)
(10) (11)
f
dCa f ) MaCap dZ
where
[ ]
CMono f CDi Ca ) , CTri CTetra
[]
(12)
na1 CMono
f
Cap )
na2 CDi
na3 CTri
na4 CTetra
,
[
ka1 0 1 Ma ) LHSV 0 0
-ka2 ka2 0 0
0 -ka3 ka3 0
Table 4. Adsorption and Fractional Constants for Nitrogen and Aromatic Compounds
]
0 0 (13) -ka4 ka4
3. Results and Discussion The LCO feed is represented by “lumped” components of sulfur, nitrogen, aromatics, and saturates.22 The details of these
nN KN (m3/mol)nN a
0.042 0.73
aromatics nA KA (m3/mol)nA
0.08 0.2
Yang et al.13
components and an example of their concentrations are listed in Table 1. To make the simulation matchable to the corresponding industrial applications, a wide range of operating conditions, as listed in Table 2, in the range of commercial interest have been covered by the simulation analysis. The base condition, similar to that adopted by Chen et al., is used as an example of the operating conditions for the simulation.6 The values of the activation energy and pre-exponential factor for the reactions of HDS, HDN, and HDA were obtained from the literature, as listed in Table 3. Some of them are arbitrarily determined from the experimental results reported in the literature. The adsorption constants for the nitrogen and aromatic compounds were also obtained from the reported documents, as listed in Table 4. For different types of catalyst, not only the competitive adsorption of reaction components but also the activation energy and pre-exponential factors of HDS, HDN, and HDA are different. To be able to adopt values of these parameters from the literature and to compare simulation results with available experimental data, the type of catalyst studied by Yang et al.13 is assumed in this study. One main criterion for the validation of SD models is the direct structure test, which validates the relationships among variables in the model as compared to the real processes, to detect the structure flaws. This test involves a qualitative and comparative evaluation of each model equation against its counterpart in the real system.23 To perform the direct structure validity of the SD model, the hydrotreating process under a base operating condition, as listed in Table 2, is examined at first and discussed in the following subsection. As shown in the following discussion, the behaviors of the SD model are consistent with the empirical and theoretical evidence in the hydrotreating process of LCO. Furthermore, the individual and total sulfur compounds obtained by simulation are compared with the reported experimental data, as listed in Table 5. It can be seen that the simulation results are in a reasonable agreement with the experimental data under both operating conditions of different inlet temperatures. 3.1. Simulation of HDS, HDN, and HDA. The concentrations of organic sulfur, nitrogen, and aromatics changing along the dimensionless axial position under the base operating condition are shown in Figures 3-5. From Figure 3, it can be seen that the reactivity of the sulfur compounds is in the order of BT > DBT > 4- or 6-MDBT > 4,6-DMDBT. The alkylsubstituted DBTs are much less reactive than DBT because of the steric hindrance effect of the one or two methyl groups at (17) Forrester, J. W. Industrial dynamics; MIT Press: Cambridge, MA, 1961. (18) Bala, B. K. Renewable Energy 1991, 1, 723–728. (19) Sterman, J. D. Business dynamics: systems thinking and modeling for a complex world; McGraw-Hill: New York, 2000. (20) Berends, P. A. J.; Romme, A. G. L. Omega 2001, 29, 543–552. (21) Barlas, Y. System dynamics: systemic feedback modeling for policy analysis in knowledge for sustainable deVelopment - an insight into the encyclopedia of life support systems; UNESCO Publishing-Eolss Publishers: Oxford, U.K., 2002. (22) Toulhoat, H.; Hudebine, D.; Raybaud, P.; Guillaume, D.; Kressmann, S. Catal. Today 2005, 109, 135–153. (23) Barlas, Y. Syst. Dyn. ReV. 1996, 12, 183–210. (24) Girgis, M. J.; Gates, B. C. Ind. Eng. Chem. Res. 1991, 30, 2021– 2058.
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Table 5. Comparison between the Experimental Data (Yang et al.13) and Simulation Resultsa F1 (T ) 623 K) sulfur compound
feed (ppm)
total sulfur easy sulfur DBTs 4- or 6-MDBTs 4,6-DMDBTs other hard sulfur
8843 4228 811 867 314 2623
a
F1 (T ) 648 K)
F2 (T ) 648 K)
experiment
simulation
experiment
simulation
experiment
simulation
497.7 11.1 2.7 68.9 100.6 309.2
494.4 12.7 3.0 68.7 100.7 309.3
102.7 10.4 0.0 11.8 9.9 69.7
75.3 7.3 0.1 8.9 7.4 51.6
17.6 4.6 0.0 0.4 2.9 9.7
21.9 1.1 0.0 2.3 2.4 16.1
Note: 70 atm, LHSV ) 1.6 h-1; F1, nitrogen ) 774 wppm; F2, nitrogen ) 16.5 wppm.
Figure 3. Axial profiles of sulfur compounds under the base operating condition (T ) 623 K, LHSV ) 1.6 h-1).
Figure 4. Axial profiles of nitrogen compounds under the base operating condition (T ) 623 K, LHSV ) 1.6 h-1).
4 or/and 6 positions. BT and DBT can be almost completely removed at the outlet of the reactor. For nitrogen compounds, the basic nitrogen (QL) is more reactive than the nonbasic nitrogen (IND and CBZ), and CBZ is less reactive than IND. Generally, the nitrogen compounds are more difficult to hydrogenate than the sulfur compounds. This prediction is consistent with the reported experimental results.24 In Figure 5, the Tetra reduce rapidly because of their high reactivity. The concentration of the Tri first increases and then decreases along the downward axial direction. From the inlet to the outlet of the reactor, the concentration of Mono increases significantly. The reactivity of the aromatics is in the order of Tetra > Tri > Di > Mono from the simulation results.
Figure 5. Axial profiles of aromatics under the base operating condition (T ) 623 K, LHSV ) 1.6 h-1).
Figure 6 shows the influence of inlet feed temperature and LHSV on the efficiency of HDS. The hydrogenation of sulfur compounds is greatly improved by increasing the system temperature or/and decreasing the LHSV. Similar tendencies also happened to HDN and HDA. On the other hand, a higher inlet temperature requires a higher consumption of energy, and a lower LHSV decreases the daily output of oil production. Hence, an optimal operating condition is desired for the hydrotreating process of LCO. 3.2. Nitrogen and Aromatics Inhibition Effects On HDS. As previously reported, deep HDS is seriously inhibited by the nitrogen and aromatic compounds because of their competition with sulfur compounds on the active sites of catalysts.10–12,14,15 It is possible that individual nitrogen and aromatic compounds have dissimilar inhibition effects on HDS. However, the previously reported work did not provide sufficient information to differentiate the effects of nitrogen and aromatic compounds. Consequently, their inhibition effects are lumped and represented by the nitrogen and aromatics impact factors, as defined in eqs 2 and 3, respectively. The axial profiles of both impact factors under different operating conditions are illustrated in Figure 7. From the inlet to the outlet of the reactor, the nitrogen impact factor decreases steadily while the aromatic impact factor has little variation. Nitrogen compounds are hydrogenated gradually, and the total nitrogen concentration decreases along the axial position of the hydrotreator. On the other hand, saturation of Mono is scarcely observed, in spite of the large amounts of polyaromatics being hydrogenated to Mono during this process. This makes the total aromatics remain almost unchanged. The diminishing concentration of total nitrogen makes the inhibition effect on HDS from the nitrogen compounds become weaker step by step while the nearly unchanged content of total aromatics makes the inhibition from the aromatics remain almost similar in the whole reactor.
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Figure 6. Axial profiles of total S in the liquid stream under different (a) inlet temperatures and (b) LHSVs.
Figure 7. Axial profiles of (a) nitrogen impact factor and (b) aromatic impact factor under different inlet temperatures and LHSVs.
When the inlet feed temperature increases or/and the LHSV decreases, the nitrogen impact factor declines more quickly along the reactor. However, both the system temperature and the LHSV have insignificant influence on the aromatic impact factor, as shown in Figure 7 b. From the above discussion, it can be predicted that the nitrogen and aromatic inhibition effects on HDS would be greatly diminished if the concentrations of nitrogen and aromatics in the feed are reduced. For LCO feed, it is hard to reduce the aromatics content by any pretreatment while the organic nitrogen can be removed to a lower concentration by a pretreatment of oxidation or adsorption.25,26 When the organic nitrogen content in the feed is reduced from 774 wppm to 16.5 wppm, the simulation results indicate that the outlet organic sulfur (from the feed of 8843 wppm) after the hydrotreating process can be reduced from 75.3 wppm to 21.9 wppm for similar hydrotreating conditions. This prediction reasonably agrees with what was obtained by the experiments, as listed in detail in Table 5.13 However, some of the simulation results do not fit very well with the experimental data. The main reason for this inconsistency is that the pre-exponential factors and activation energies of HDS, HDN, and HDA used by the simulation were also obtained from other literature besides the one providing the experimental data, as shown in Table 3. It is
well-known that the pre-exponential factor and the activation energy of a reaction are highly dependent on catalyst types, catalyst properties, and others. If the activation energy and preexponential factors of HDS, HDN, and HDA for a specific catalyst can be obtained by experiments, the inconsistency between simulation and experimental results could be diminished.
(25) Ishihara, A.; Wang, D.-H.; Dumeignil, F.; Amano, H.; Qian, E.; Kabe, T. Appl. Catal., A 2005, 279, 279–287. (26) Kim, J. H.; Ma, X.-L.; Zhou, A.; Song, C.-S. Catal. Today 2006, 111, 74–83. (27) Laredo, G. C.; Leyva, S.; Alvarez, R.; Mares, M. T.; Castillo, J.; Cano, J. L. Fuel 2002, 81, 1341–1350.
4. Conclusions The hydrodesulfurization of LCO and the inhibition effects of organic nitrogen and aromatic compounds on it have been successfully simulated by an SD model. The simulation shows that the contents of organic sulfur and nitrogen are reduced along the axial position of the hydrotreating reactor. Polyaromatics are efficiently hydrogenated into aromatics with fewer rings, whereas the total content of aromatics decreases only a small amount during the hydrotreating process because of the much lower reactivity of Mono. The inhibition effects are quantitatively characterized by the impact factors. The nitrogen impact factor decreases gradually while the aromatics impact factor keeps nearly unchanged along the axial position of the reactor. The nitrogen impact factor diminishes more quickly with higher system temperature or/and lower LHSV. Nevertheless, both the system temperature and the LHSV have insignificant influence on the aromatics impact factor. This study mainly shows how the SD model works for the HDS of LCO, with all the parameters of reaction kinetics and adsorption (Tables 3 and 4) from the literature. In real HDS processes, however, none of the parameters are known or similar to what have been reported because of the different feedstocks
866 Energy & Fuels, Vol. 22, No. 2, 2008
and operating conditions. Fortunately, once the concentrations of the products along the axis of the reactor can be obtained, then all the parameters in Tables 3 and 4 can be optimized in this SD model by the optimization process of Vensim (a modified Powell search method). This optimization is very difficult to achieve with other previously reported simulation methods. Conversely, on the basis of the optimized parameters, the products can also be predicted for different feedstocks and operating conditions. Acknowledgment. The authors gratefully acknowledge the financial assistance from the Atlantic Innovation Fund, Canada Foundation for Innovation, and NSERC.
Nomenclature CMono, CDi, CTri, CTetra ) mole concentrations of Mono, Di, Tri, and Tetra aromatics, mol · mL-3 CN,j ) molar concentration of nitrogen component j in liquid phase, mol · mL-3 CS,i ) molar concentration of sulfur component i in liquid phase, mol · mL-3 k ) reaction rate constant, mL3 · mcat-3 · h-1 k0 ) pre-exponential factor mL3 · mcat-3 · hour-1 Ea ) activation energy, J · mol-1 ka1, ka2, ka3, ka4 ) reaction rate constants for Mono, Di, Tri, and Tetra aromatics, mL3 · mcat-3 · h-1 K ) adsorption constant na1, na2, na3, na4 ) reaction order for Mono, Di, Tri, and Tetra aromatics N ) reacted molar number of the reactant, mol r ) reaction rate per unit catalyst volume, mol · mcat-3 · h-1 R ) gas rate constant, J · mol-1 · K-1 T ) absolute temperature, K
Liu et al. Tin ) gas and liquid inlet temperature, K Vcat ) catalyst volume, mcat3 z ) axial coordinate in the reactor, mr Z ) dimensionless axial coordinate in the reactor Greek symbols εA ) impact factor of aromatics εN ) impact factor of nitrogen compounds Subscripts A ) aromatic compounds N ) nitrogen compounds S ) sulfur compounds AbbreViations BT ) benzothiophene or easy sulfur BNT ) benzonaphthothiophene CBZ ) carbazole DBT ) dibenzothiophene Di ) diaromatics 4,6-DMDBT ) 4,6-dimethyldibenzothiophene HDA ) hydrodearomatization HDN ) hydrodenitrogenation HDS ) hydrodesulfurization IND ) indole LHSV ) liquid hourly space velocity 4-MDBT ) 4-methyldibenzothiophene 6-MDBT ) 6-methyldibenzothiophene Mono ) monoaromatics QL ) quinoline Tetra ) tetra-aromatics Tri ) triaromatics EF700622Q
