A Hydroprocessing Multicatalyst Deactivation and ... - ACS Publications

Energy & Fuels 2005, 19, 753-764

753

A Hydroprocessing Multicatalyst Deactivation and Reactor Performance Model-Pilot-Plant Life Test Applications E. K. T. Kam,* M. Al-Shamali, M. Juraidan, and H. Qabazard Petroleum Research and Studies Center, Kuwait Institute for Scientific Research (KISR), P.O. Box 24885, Safat 13109, Kuwait Received July 7, 2004. Revised Manuscript Received February 1, 2005

A simple plug-flow model was developed to study the deactivation mechanisms of hydroprocessing catalysts in atmospheric residue desulfurization (ARDS) units. The three stages of catalyst deactivationsat the start of the run, middle of the run, and end of the runsare considered. The catalyst deactivation is mainly due to metal and coke deposition. The model parameters considered are the unit temperature, fluid velocity, reaction rate constant, catalyst combination, catalyst bed length, and feed concentration of sulfur, metals, and asphaltenes. The simulated results compared very well with our laboratory data for a long catalyst life test operated under constanttemperature mode. The model is further applied to a parametric study that examines the effects of space velocity, unit temperature, and maximum metal capacity on the performance of catalyst systems. A constant-sulfur-mode simulation is also given.

1. Introduction Because of the continuing depletion in the quality of crude oils, the stringent environmental regulations, and the increasing world demand on middle distillates, the importance of hydroprocessing units in petroleum refineries increases daily. Atmospheric residue desulfurization (ARDS) processes are widely used worldwide to convert heavy oil into lighter and low-sulfur fuel oils. Although the ARDS process is primarily used to reduce the product sulfur level, other impurities such as metals and nitrogen are also removed and mild hydrocracking to lighter products also occurs. ARDS processes are multireactor processes, which normally consists of 1 or 2 guard reactors to hydrodemetallize the metal contaminants in the high-sulfurcontent residue feed and 3-5 other reactors to hydrotreat and hydrocrack the partially demetallized residue. The catalysts used in the guard reactor are usually loaded with hydrotreating base catalysts, which have wider pore sizes than those used in the three downstream catalytic reactors. A wide range of hydrotreating reactions occurs in ARDS reactors. Hydrodemetallization (HDM) and heteroatoms removal, such as hydrodesulfurization (HDS) and hydrodenitrogenation (HDN), are the principal ones. Ramsbottom carbon removal, which is thought to occur via saturation of multi-aromatic rings and followed by cracking of the rings, is another important reaction. Another important reaction in ARDS processes is residue hydrocracking, which converts residue molecules to gases, naphtha, distillates, and gas oils. The reaction kinetics and catalyst deactivation are strongly * Author to whom correspondence should be addressed. E-mail: [email protected].

influenced by ARDS unit operation conditions, such as temperature, pressure, or space velocity. Residuum hydroprocessing catalysts deactivate in a very characteristic fashion in three distinct stages, as will be described later. To compensate for the loss of catalyst activity from the gradual deactivation of catalyst particles and then the catalytic bed, the normal practice is to increase the reactor temperature. Subsequently, all the rates of the different reactions change, according to the activation energies. The changes in local temperatures due to the difference in thermicity factors also modify the coke and metal deposition rates, as well as the deposited coke characteristics. The optimization of the ARDS process efficiency is a main concern for refineries. Extensive research efforts are dedicated to the development of catalysts and catalyst systems that are more tolerant to deactivation. To minimize the costs, effort, and time required in experimental work, mathematical modeling is a powerful tool to assist in achieving such research targets.1 Several models on hydrotreating and hydrocracking processes have been published; however, most of these investigations involve reactions of lighter petroleum or mildly mixed fluids.2-16 Only a few were developed for (1) Kam, E. K. T.; Fukase, S.; Togawa, S.; Koide, R.; Al-Shamali, M.; Al-Bazzazz, H.; Mutsushita, K. A Strategic Plan for ARDS Process Model Development. Kuwait Institute for Scientific Research, Report No. KISR5892, Kuwait, 2000. (2) Collins, G. M.; Hess, R. K.; Hook, B. D.; Ackgerman, A. Modeling of Trickled Bed Reactors at High Temperatures and Pressures with Volatile Feeds. In Proceedings of the 1984 AIChE Annual Meeting, San Francisco, CA, pp E13-G13. (3) Kheshgi, H. S.; Reys, S. C.; Hu, R.; Ho, T. C.; Chen, T. C. Phase Transition and Steady-State Multiplicity in a Tricked-Bed Reactor. Chem. Eng. Sci. 1992, 47, 1771-1777. (4) El-Hisnawi, A. A.; Dudukovic, M. P.; Mills, P. L. Trickle-Bed Reactors: Dynamics Tracer Tests, Reaction Studies, and Modeling of Reactor Performance. ACS Symp. Ser. 1982, 196, 421-440.

10.1021/ef049843s CCC: $30.25 © 2005 American Chemical Society Published on Web 03/17/2005

754

Energy & Fuels, Vol. 19, No. 3, 2005

hydrotreating heavy residues, such as the Shell,17 Chiyoda,18 Chevron,19 Halder-Topsoe,20 Amoco,21 modified Amoco,22 and extended Chiyoda23,24 models. Most these models address a single catalyst system. In the current development, a simple plug-flow model for a multicatalyst system is derived. The model parameters use the actual experimental kinetic values of various reactions with the corresponding feedstocks. The three distinct stages of catalyst deactivation are also considered: an initial rapid deactivation at the start of the run (SOR), intermediate slow deactivation at the middle of the run (MOR), and final rapid deactivation at the end of the run (EOR). Moreover, both the removal of vanadium and nickel are considered separately in the HDM reactions. This approach leads to more-reliable simulated results, and this will be demonstrated during the model verification with pilot-plant experimental data obtained from our laboratories. (5) Mills, P. L.; Dudukovic M. P. A Comparison of Current Models for Isothermal Trickle-Bed ReactorssApplication to a Model Reaction System. ACS Symp. Ser. 1984, 234, 37-59. (6) Hekmat, D.; Vortmeyer, D. Modelling of Biodegrading Process in Trickle-Bed Reactors. Chem. Eng. Sci. 1994, 49, 4327-4345. (7) Rajashekharam, M. V.; Jag, N. R.; Chuddar, R. V. A TrickleBed Reactor Model for Hydrogenation of 2,4-Trinitrotoluene: Experimental Verification. Chem. Eng. Sci. 1998, 52, 787-805. (8) Chu, C. F.; Ng, K. M. Liquid Dispersion in Trickle-Bed Reactors with Gas-Liquid Downflow. In Proceedings of the 1986 AIChE Annual Meeting, Miami Beach, FL, pp A1-A10. (9) Harold, M. P.; Watson, P. C. Bimolecular Exothermic Reaction with Vaporization in the Half-Wetted Slab Catalyst. Chem. Eng. Sci. 1993, 48, 981-1004. (10) Mears, D. E. The Role of Axial Dispersion in Trickle-Flow Laboratory Reactors. Chem. Eng. Sci. 1971, 26, 1361-1366. (11) Henry, H. C.; Gilbert, J. B. Scale-up of Pilot Plant Data for Catalyst Hydroprocessing. Ind. Eng. Chem., Proc. Des. Dev. 1973, 12, 328-334. (12) Dudukovic, M. P. Catalyst Effectiveness Factor and Contacting Efficiency in Trickle-Bed Reactors. AIChE J. 1977, 23 (6), 940-944. (13) Callejas, M. A.; Martinez, M. T. Evaluation of Kinetic and Hydrodynamic Models in the Hydroprocessing of a Trickle-Bed Reactor. Energy Fuels 2002, 16, 647-652. (14) Gualda, G.; Kasztelan, S. Initial Deactivation of Residue Hydrodemetallization Catalysts. J. Catal. 1996, 161, 319-337. (15) Chen, J. W.; Ring, Z. N.; Dabros, T. Modeling and Simulation of a Fixed Bed Pilot Plant Hydrotreater. Ind. Eng. Chem. Res. 2001, 40, 3294-3300. (16) Chiang, C. L.; Tiou, H. H. Optimal Design for the Residual Oil Hydrodemetalation in a Fixed Bed Reactor. Chem. Eng. Commun. 1992, 117, 383-399. (17) Dautzenberg, F. M.; Van Klinken, J.; Pronk, K. M. A.; Sie, S. T.; Wijffels, J. B. Catalyst Deactivation through Pore Mouth Plugging during Residue Desulphurisation. ACS Symp. Ser. 1978, 65, 254-267. (18) Kodama, S.; Nitta, H.; Takatsuka, T.; Yokoyama, T. Simulation of Residue Hydrodesulphurization Reaction Based on Catalyst Deactivation Model. J. Jpn. Pet. Inst. 1980, 23, 310-320. (19) Takatsuka, T.; Wada, Y.; Inoue, S. A Catalyst Deactivation Model for Residual Oil Hydrodesulphurisation and Application to Deep Hydrodesulphurisation of Diesel Fuel. ACS Symp. Ser. 1995, 634, 414427. (20) Hannerup, P. N.; Jacobsen, A. C. A Model for Deactivation of Residue Hydrodesulphurization Catalysts. ACS Symp. Ser. 1983, 28, 576-599. (21) Khang, S. J.; Mosby, J. F. Catalyst Deactivation due to Deposition of Reaction Product in Macropores during Hydroprocessing of Petroleum Residuals. Ind. Eng. Chem., Proc. Des. Dev. 1986, 25, 437-442. (22) Qabazard, H.; Kam, E. K. T.; Al-Shamali, M. Simulation of Atmospheric Residue Desulphurisation Units in Kuwait National Petroleum Company via a Modified Amoco Catalyst Deactivation Model. In Proceedings of the 17th International Symposium Chemical Reaction Engineering, Hong Kong, China, August 25-28, 2002. (23) Lababidi, H. M. S.; Shaban, H. I.; Al-Radwan, S.; Alper, E. Simulation of an Atmospheric Residue Desulphurization Unit by Quasi-Steady-State Modeling. Chem. Eng. Technol. 1998, 21, 193200. (24) Al-Adwani, H. A. H.; Lababidi, H. M. S.; Al-Dafferi, F. S. Optimization Study of Atmospheric Residue Hydrodesulfurization Process. In Proceedings of the 11th Annual Saudi-Japanese Symposium on Catalysts in Petroleum Refining and Petrochemicals, Dhahran, Saudi Arabia, November 11-12, 2001.

Kam et al.

Figure 1. Typical activity characteristics.

2. Model Development The primary purpose of the present development is to address the chemical reaction kinetics of various hydrocracking, hydrotreating, catalyst deactivation reactions, and catalyst system performance in ARDS reactors. Therefore, the stages of catalyst deactivation mechanisms and model formulation are discussed here. 2.1. Deactivation Characteristics. A typical deactivation for a multiple-catalyst system in a residue hydrotreating process is depicted in Figure 1, which shows three distinct stages of catalyst deactivation. The initial rapid decline of the catalyst system activity is mostly due to the coke deposition accumulating quickly on the catalyst surfaces, usually within the first few hundred hours on stream.14,25 However, some studies26,27 also have suggested that metal sulfide deposition may also contribute to the initial rapid deactivation. After reaching a quasi-steady state, there is a relatively long period of slower deactivation, which is attributed to the gradual buildup of metal depositions on the catalyst pore surface. Furthermore, because HDM reactions are relatively fast, most metals have a tendency to deposit at the front end of the catalyst system. It is assumed that the accumulation of coke deposits is minima during this stage; however, this assumption may not be valid during later stages as the nature of coke changes with the time on stream.28,29 At the EOR, a rapid deactivation occurs. At this stage, a significant portion of the catalyst system is thought to be already deactivated. Hence, to maintain the unit performance, the temperature is increased. However, the high temperature also accelerates coke formation and metal deposition, which lead to the final rapid loss of catalyst activity. 2.2. Deactivation Models Due to “Young” Coke Deposition. After catalyst presulfiding, the catalytic (25) Higashi, H.; Takashi, T.; Kai, T. The Effect of Start-Up Conditions on Deactivation of Hydrotreating Catalyst for Heavy Residue with High Asphaltenes Content. Catal. Surv. Jpn. 2002, 5 (2), 111-119. (26) Tamm, P. W.; Harnsberger, H. F.; Bridge, A. G. Effects of Feed Metals on Catalyst Aging in Hydroprocess Residuum. Ind. Eng. Chem., Process Des. Dev. 1981, 20, 262-273. (27) Baumgart, J.; Yang, Y. T.; Ernst, W. R.; Carruthers, J. D. Characteristics of Laboratory-Coked Resid HDS Catalysts. J. Catal. 1990, 126, 477-488. (28) Bartholomew, C. H. Catalyst Deactivation in Hydrotreating of Residues. In Catalyst Hydroprocessing of Petroleum and Distillates. Obella, M. C., Shih, S. S., Eds.; Marcel Dekker: New York, 1994; pp 1-32. (29) Furimsky, E.; Massoth, F. E. Deactivation of Hydroprocessing Catalysts. Catal. Today 1999, 52, 381-495.

A Hydroprocessing Multicatalyst Performance Model

Energy & Fuels, Vol. 19, No. 3, 2005 755

activity is very high, because of the presence of strong acid sites, which favor coking.30 Hence, the start-up conditions for the hydrotreating catalyst are very important to maximize its performance and life. Carbon is known to deposit on hydrotreating catalyst surfaces very quickly in the initial stage of the run. Gualda and Kasztelan14 reported that carbon deposits accumulated on the catalyst surface at a level of 11 wt % after 6 h and reached 14 wt % after 140 h on stream. Furimsky and Massoth29 observed an initial fast coking period of 50 h. Callejas et al.31 found a coke level of 16 wt % after 100 h of operations. This initial deposition of coke was termed “young coke” and had a relative high hydrogen to carbon (H/C) ratio (ranging from 0.8 to 1.2). As the coke deposit reached an equilibrium value, its accumulation leveled off. The most widely used coking model was proposed by Voorhies32 to describe coke deposition on a cracking catalyst as

C0 ) Atn

(for 0.5 e n e 1)

(1)

where C0 is the amount of coke on the catalyst at time t and the correlation coefficients A and n are obtained experimentally. However, this equation is more suited for cracking catalysts, where the effect of coke on the activity loss is simpler than that of hydroprocessing catalysts. Attempts were made to advance the Voorhies model for hydrotreating catalysts. Richardson et al.33 adopted the following model to describe the initial coke buildup and to predict coke formation during the initial stages of processing heavy residues:

C ) Cmax[1 - exp(-Kω)]

(2)

where C is the actual amount of carbon on the catalyst, Cmax the maximum carbon deposition of a complete monolayer, ω the cumulative feed-to-catalyst ratio, and K an adsorption constant. Its application is limited, because of the continuing changes in feed and catalyst properties. No theoretical study relating the way catalyst surfaces are covered by coke to catalyst deactivation is available in the open literature. Based on experimental observations, Froment and Bischoff34 proposed the following empirical expressions between coke contents and a deactivation function, φ:

φ ) exp(- RCF) φ)

1 1 + RCF

(3) (4)

(30) Gray, M. R.; Zhao, Y. X.; McKnight, C. M.; Komar, D. A.; Carruthers, J. D. Coking of Hydroprocessing Catalyst by Residue Fractions of Bitumen. Energy Fuels 1999, 13, 1037-1045. (31) Callejas, M. A.; Matinez, M. T.; Blasco, T.; Sastre, E. Coke Characterization in Aged Hydrotreating Catalysts by Solid-State 13C NMR Spectroscopy and Temperature-Programmed Oxidation. Appl. Catal., A 2001, 218, 181-188. (32) Voorhies, A. Carbon Formation in Catalytic Cracking. Ind. Eng. Chem. 1945, 37, 318-322. (33) Richardson, S. M.; Nagaishi, H.; Gray, M. R. Initial Coke Deposition in NiMo/γ-Al2O3 Bitumen Hydroprocessing Catalyst. Ind. Eng. Chem. Res. 1996, 35, 3940-3950. (34) Froment, G. F.; Bischoff, K. B. Kinetic Data and Product Distributions from Fixed Bed Catalytic Reactors due to Catalyst Fouling. Chem. Eng. Sci. 1962, 17, 105-114.

where R is the deactivation constant and CF is the concentration of the foulant. The corresponding changes in the rate of activity from eqs 3 and 4, respectively, can be expressed as

dφ ) Rφ dt

(5)

dφ ) Rφ2 dt

(6)

From our pilot-plant experimental results,35 the rapid initial catalyst deactivation for HDS, HDV, HDNi, and HDAsph reactions are obtained, and hence, an exponential type of decay function is considered to be more appropriate. Moreover, the HDS catalyst, which is wellprotected by the HDM catalyst in the front end of the reactors, is deactivated mainly by coke. Accordingly, an empirical decay function for the initial rapid deactivation for the HDS catalyst is proposed. The changes in the reaction rate constant of the main reactions can be represented by the following equations: / ) k0,i + 0.5∆k0,iΨi k0,i

[(

Ψi ) -exp

cp,i(Target)

(for i ) 1-4)

)]

(7)

n

cf,i(Product from Previous Reactor) (for i ) 1-4) (8)

∆k0,i ) k0,i|t)12 h - k0,i k0,i|t)12 h )

∫(dc/cni ) ∫dt

(for i ) 1-4) (for i ) 1-4)

(9) (10)

/ where k0,i and k0,i are apparent rate constants in the initial rapid deactivation period for species i estimated by eq 7 and determined experimentally, respectively; i is the species index (1 for sulfur, 2 for vanadium, 3 for nickel, and 4 for asphaltenes); Ψi is a decay function for species i; cp and cf are concentrations of the product and the feed, respectively; and n is the reaction order. The initial rapid deactivation period is completed when the decay function for sulfur becomes ΨS e 0.01. 2.3. Deactivation Models Due to Metal Deposition. Many reaction/deactivation models related to metal deposition on hydrotreating catalysts17-21 have been studied thoroughly. Reviews of the model development, assumptions made, and mathematical formulations for each of these models were discussed by Kam and co-workers.1,36 Subsequently, the Amoco model developed by Khang and Mosby,21 which is based on the pore-filling model by only metal deposits, was selected for our model development for catalyst deactivation due to metal deposits. The relative HDS activity and the relative metal loading are the two parameters to consider in determining the behavior of catalyst deactivation, which is influenced by two adjustable parameters of the Thiele modulii for fresh and deactivated

(35) Matsushita, K.; Hauser, A.; Marafi, A.; Koide, R; Stanislaus, A. Initial Coke Deposition on Hydrotreating Catalyst. Part I. Changes in Coke Properties as a Function of Time on Stream. Fuel 2004, 83, 1031-1038. (36) Kam, E. K. T.; Fukase, S.; Koide, R. A Review of Published Industrial Kinetic/Deactivation Models in Hydroprocessing of Petroleum Residues. Kuwait Institute for Scientific Research, Report No. KISR 5444, Kuwait, 1998.

756


Kam et al.

catalysts. One Thiele modulus is based on the bulk conditions, reaction kinetics, and diffusivity in the deactivated pores (Φ0). The other Thiele modulus is based on the reaction kinetics and the diffusivity of the deactivated pores (Φz). The activity equation is as follows:

(Z/Φ){(1/Φ) tanh[2Φz(1 - Z)] + 1}

a)

(1/Φ) + tanh[2Φz(1 - Z)]

(11)

where a is the normalized activity, Z is the parameter for the unconverted metals, and Φ and Φz are the two Thiele modulii to facilitate the catalyst deactivation model simulation. Moreover,

Φ0 ) Φz )

( )() ( )() k0 Dz

1/2

kz Dz

1/2

Φ)

(

Z) 1-

rp 2

(12)

rp 2

(13)

Φ20 Φz Mi Mmax

(14)

)

1/2

(15)

( ) 100 W

t

FL

{[FV(CV,f - CV,p) + ∑ i)1 FNi(CNi,f - CNi,p)] ∆ti} × 106 (16)

Mmax )

( ) 100 W

tmax

FL

FNi(CNi,f - CNi,p)i]∆ti} × 106 (17)

}

Z (1/Φ) tanh[2Φz(1 - Z) + 1] kT | t ) Φ (1/Φ) + tanh[2Φz(1 - Z)] kT0|t )

kT|t exp{[- E/(RT0)][1 - (T0/T)]}

ds ) [kf1f1(Cf1,T) + kf2f2(Cf2,T)]s dt

(for 0 e z e L)

where kf1 and kf2 are the apparent rate constants for parallel and series coking, respectively; Cf1 and Cf2 are the concentration of asphaltenes and products from the cracked asphaltenes species; and T is the reaction temperature. The term fi(Cfi,T) is defined as

[

(

(18) (19)

2.4. Deactivation Models Due to Hard Coke Deposition. Froment and Bischoff37 gave the first extended analysis of fixed-bed reactor behavior resulting from catalyst deactivation due to coking. Their study concentrated on the reactor transients and activity profiles, with respect to the fouling mechanisms, rather (37) Froment, G. F.; Bischoff, K. B. Non-steady-state Behaviour of Fixed Bed Catalytic Reactors due to Catalyst Fouling. Chem. Eng. Sci. 1961, 16, 189-201.

)]

Efi T0 fi(Cfi,T) ) Cfi exp 1RT0 T

(for i ) 1, 2) (21)

where Efi is the activation energy for parallel or series coking reaction, R the gas constant, and T0 the initial reaction temperature. The initial conditions for a parallel fouling mechanism are

s)1 Cf1 ) CAsph

{[FV(CV,f - CV,p)i + ∑ i)1

( ){

-

(20)

where k0 is the initial reaction rate constant of the active sites of fresh catalysts, Dz the diffusivity in the deposited layer, Mi the metal deposited on the catalyst (MOC), Mmax the maximum MOC that can be held in the catalyst, rp the initial pore radius, and kz the reaction rate constant of the deposited layer. Additional equations required to determine the MOC (Mi) and the maximum MOC (Mmax, expressed in grams per 100 g of catalyst), and to normalize the reaction rate constants to a specific temperature T0, are

Mi )

than a time-dependent correlation, as suggested by Voorhies.32 Three fouling mechanisms can be broadly identified: parallel, series, and side-by-side, as suggested by Levenspiel.38 The key difference in the fouling mechanisms of decay reactions is that the coke deposition is dependent, respectively, on the concentration of the reactant (parallel), product (series), and the combination of both feed and products (side-by-side) in the processing stream. Because the distribution of these substances varies with reactor length, the extent of deactivation is dependent largely on the decay mechanism. The coking precursors in the hydroprocessing catalyst deactivation are mainly from asphaltenes.30,31,39 Seki and Kumata40 confirmed that the aromaticity of asphaltenes in the residue oil is a clear index of the coking of HDS catalysts. Because there are no data or information available on the coking reactions from our laboratories data, separable kinetics developed by Kam et al.41 and Kam and Hughes42 for catalyst deactivation due to coke deposition are used. The reduction of catalyst activity (s) is dependent on the deactivation rate constant, the first-order kinetics, and the concentration of the coking precursors, i.e.,

Cf2 ) 0

(at t ) 0) (at z ) 0) (at z ) 0)

(22) (23) (24)

where t is the processing time and z is the reactor axial coordinate. Equation 20 is applicable to catalyst deactivation for the three fouling mechanisms depending on (38) Levenspiel, O. Chemical Reaction Engineering; Wiley: New York, 1962. (39) McKnight, C. A.; Nowlan, V. Metals Accumulation and Particle Mixing in a Commercial Residue Hydroprocessor with Continuous Catalyst Addition. In Proceedings of the 205th National Meeting of the American Chemical Society, Denver, CO, March 28-April 2, 1993, pp 1-3. (40) Seki, H.; Kumata; F. Deactivation of Hydrodesulphurisation Catalysts for Resides: Effect of Hydrodemetallization Operation Conditions. In Proceedings of the 8th International Symposium on Catalyst Deactivation, October 10-13, 1999, Brugge, Belgium, pp 357364. (41) Kam, E. K. T.; Ramachandran, P. A.; Hughes, R. Isothermal Fouling of Catalyst Pellets. J. Catal. 1975, 38, 283-293. (42) Kam, E. K. T.; Hughes, R. The Effect of Catalyst Fouling on the Performance of Adiabatic Packed Bed ReactorssA Theoretical Study. Chem. Eng. J. 1979, 18, 93-102.



kf1 and kf2: kf2 ) 0 for parallel fouling, and kf1 ) 0 for series fouling. When both kf1 and kf2 have finite values, side-by-side fouling occurs. 2.5. Reactor Deactivation Model. The reactor model is a simple one-dimensional homogeneous plugflow reactor model.42,43 A schematic diagram of the reactor model is shown in Figure 2. The assumptions used to develop the model are as follows: (1) A four-reactor system is applied; however, the number of reactors in the ARDS processes can be added or reduced. (2) The physical properties of fluid and catalysts are constant. (3) The model describes a plug-flow reactor, and axial dispersion is not important. (4) The reactor is isothermal only in the radial direction in a commercial plant reactor. (5) There is no interphase heat- and mass-transfer resistance between the fluid and the catalyst particles, but the intraparticle mass transfer resistance is considered. (6) The heat released by the HDV, HDNi, or HDAsph reaction is negligible compared with that from the HDS reaction. (7) The HDS, HDV, HDNi, HDAsph, and coking reactions can be represented by separable kinetics. The external field equations for the mass and heat balance in any one of the four reactors, under pseudosteady-state and given assumptions, are as follows:

dCS,j LuL + rS,j ) 0 dzj

(for j ) 1-N)

(25)

LuL

dCV,j + rV,j ) 0 dzj

(for j ) 1-N)

(26)

LuL

dCNi,j + rNi,j ) 0 dzj

(for j ) 1-N)

(27)

LuL

dCAsph,j + rAsph,j ) 0 dzj

(for j ) 1-N) (28)

dTj - (-∆H)rS,j ) 0 (LuLFLCpL + GuGFGCpG) dzj (for j ) 1-N) (29) Because the unit consists of four reactors cascaded in series, the boundary conditions for each reactor are considered as follows:

Ci ) Ci,j-1 (at z ) 0, for i ) 1 (S), 2 (V), 3 (Ni), and 4 (Asph) and for j ) 1-N) (30) T ) Tj-1

(for j ) 1-N)

(31)

where CS,j-1, CV,j-1, CNi,j-1, and CAsph,j-1 are the inlet reactant concentrations of each reactor and Tj-1 is the temperature of each reactor; z is the reactor axial (43) Chao, Y. C.; Liaw, H. J.; Huang, H. P. A Mathematical Model for the Catalytic Deactivation in a Commercial Residue Hydrodesulphurization Reactor System. Chem. Eng. Commun. 1991, 104, 267290.

Figure 2. Schematic diagram of a multiple-reactor system.

coordinate; i is the species index, as defined in eq 30; and j is the reactor index, between 1 and N. It is customary to reduce the model equations in dimensionless form before developing a computer program. To do this, several dimensionless groups are introduced. In terms of concentration and axial coordinate, the initial concentration (CL0) and catalyst bed length (L) are used:

Ci Ci,j-1 (for i ) S, V, Ni, or Asph and for j ) 1-N) (32)

ci,j-1 )

and

ZRj )

zj Lj

(for j ) 1-N)

(33)

Thus, the rate equations of the HDS, HDV, HDNi, and HDAsph reactions become

Ri,j ) ηi,jki,j exp[- Ei/(RGTj)]ci,jCi0,jsj (for i ) S, V, Ni, or Asph and for j ) 1-N) (34) and the dimensionless mass balance equations of these reactions (eqs 25-28) become

dci,j ) - η*i,jΩi,jf(ci,j,θj,sj) dZRj (for i ) S, V, Ni, or Asph and for j ) 1-N) (35) where Ωi,j is the reaction modulus, which is defined as

ki,jLj(Ci,j-1)n-1 Ωi,j ) uLj (for i ) S, V, Ni or Asph and j ) 1-N) (36) / ηi,j is the contacting efficiency factor, which is defined as

/ ) ηi,jLj ηi,j

(for i ) S, V, Ni, or Asph; and j ) 1-N) (37) f(‚‚‚) is the function due to reaction, and sj is the catalyst activity. Moreover, by introducing the reduced temperature (θ), the Arrhenius parameter (γ), and a thermicity factor (β), which are defined, respectively, as

758


θj )

Tj T0

(-Ei) γi ) RT0

(for j ) 1-N)

(for i ) S, V, Ni, or Asph) βs )

(-∆Hs)CL0 FLcpLT0

Kam et al. Table 1. Feedstock Characteristicsa

(38) (39) (40)

the heat balance equation (eq 29) can then be simplified to / Ωsβsf(csj,θj,sj) ηs,j

dθj ) dZRj (uLFLcpLL + uGFGcpGG)|j

(for j ) 1-N) (41)

and the reaction functions become

[ ( )]

fi,j(ci,j,θj,sj) ) exp γi

1 - θj ci,j(n-1)sj θ0 (for i ) S, V, Ni, or Asph) (42)

To summarize, the model equations that are used to describe the reactors are eqs 35 and 41. Values of the axial concentrations and temperatures in the first-order ordinary differential equations (ODEs) can be solved numerically and simultaneously as an initial value ODE problem. The initial values are taken as follows:

ci,0 ) 1.0 θ0 ) 1.0

(inlet at ZR ) 0; i ) S, V, Ni, or Asph) (inlet at ZR ) 0), sj ) 1.0 (t ) 0, j ) 1-N) (43)

2.6. Model Integration. The differential equations (eqs 35 and 41) were solved simultaneously to determine the axial concentration profiles of the key elements of sulfur, vanadium, nickel, and asphaltenes in the product oil. Equations 7-24 are used to simulate the catalyst deactivation due to coking and metal deposition. After coupling with the initial conditions, simulation can commence. The following considerations in developing the algorithms are made: (A) The reaction rate constants, which vary with the zone location and processing time, become three-dimensional matrix variables in the form of k[Reactor, Reaction, Zone]. (B) The Mi variable also changes with reactors, species, and zones and become another three-dimensional matrix variable as Mi[Reactor, Species, Zone]. (C) In each time interval, the deactivation subprogram performs the following main steps: (1) Based on the Mi value in each zone, the k values are updated accordingly. (2) The increment in the metal deposits accumulated on the catalyst is calculated. (3) The total Mi value is updated. The aforementioned procedure is repeated for each reactant species, each zone in a reactor, and each reactor. The algorithms are quite straightforward. All the differential equations can be written directly in the MATLAB syntax. 2.7. Input Data from Pilot-Plant Experiments. All model input data were obtained from our laborato-

a

feed property

value

API (°) total sulfur concentration (wt %) asphaltenes concentration (wt %) total nitrogen concentration (wt-ppm) kinematic viscosity @ 50°C (cSt) Conradson carbon residue, CCR (wt %) nickel concentration (wt-ppm) vanadium concentration (wt-ppm)

12.27 4.30 3.75 2670.00 871.20 12.20 21.00 69.00

Data taken from Marafi and co-workers.44,45

ries. The kinetic determination and deactivation of guard-bed experiments were undertaken in a single fixed-bed reactor. The catalyst long-life test was conducted in a multireactor ARDS pilot-plant unit operating in an up-flow mode. Two reactors were used: a HDM catalyst was packed in the first reactor, whereas HDS and HDS/HDN catalysts were packed in the second reactor. Kuwaiti atmospheric residues from a Kuwait National Petroleum Company (KNPC) refinery were used as feedstocks, which are typical feedstocks processed in the local refineries. The feed properties are presented in Table 1, and the operating conditions are presented in Table 2. Samples of the inter-reactor and final product were collected every 12 h to analyze the sulfur, vanadium, nickel, asphaltenes, nitrogen, and Conradson carbon residue (CCR) contents, viscosity, density, and distillate yield. Further experimental details can be found elsewhere.44,45 3. Results and Discussions A short test run on the guard-bed reactor and longlife pilot-plant test run were conducted in our laboratories. The same catalyst system was used in both runs, and it was similar to a system used in an industrial refinery ARDS unit. The rate constant is an important parameter to determine the reactor performance and catalyst deactivation. The kinetics data for different catalyst types and for the original and treated feedstocks have been reported by Marafi and co-workers.44,45 Two reactors were used in our long-life pilot-plant run; therefore, N ) 2. The kinetic data of each catalyst in the three-catalyst system hydrotreating the original and treated feeds are given in Table 3. The maximum MOC (Mmax) dictates the characteristics of catalyst deactivation due to metal deposition. It is necessary to determine the changes of catalyst activity with processing time and the overall catalyst system performance. A Mmax value of 57 g/100 g fresh catalyst was reported after 3100 h on stream.46 In this study, a value of 60 g/100 g of catalyst is assumed. An HDM catalyst was used in the short test run, using a single-reactor pilot plant. The experiment was designed to estimate some model parameters that cannot be determined directly. For instance, the con(44) Marafi, A.; Fukase, S.; Al-Marri, M.; Stanislaus, A. A Comparative Study of the Effect of Catalyst Type on Hydrotreating Kinetics of Kuwait Atmospheric Residue. Energy Fuels 2003, 17, 661-668. (45) Marafi, A.; Al-Bazzaz, H.; Al-Marri, M.; Maruyama, F.; AbsiHalabi, M.; Stanislaus, A. Residual-Oil Hydrotreating Kinetics for Graded Catalyst System. Effect of Original and Treated Feedstocks. Energy Fuels 2003, 17, 1191-1197. (46) Callejas, M. A.; Matinez, M. T.; Fierro, J. L. G.; Rial, C.; Jimenez-Mateos, J. M.; Gomez-Garcia, F. J. Structural and Morphological Study of Metal Deposition on an Aged Hydrotreating Catalyst. Appl. Catal., A 2001, 220, 93-104.



Table 2. Operating Conditionsa

a

process parameter

kinetics study

guard-bed study

life test

temperature (K) pressure (bar) liquid hourly space velocity, LHSV (h-1) H2/oil (mL/mL) time on stream (h)

633.0, 653.0, 673.0, 693.0 120.0 0.5, 1.0, 2.0, 4.0 570.0 120.0

648.00 120.00 1.00 570.00 240.00

648.00 120.00 0.28 570.00 9500.00

Data taken from Marafi and co-workers44,45 and Kam et al.50

Table 3. Pilot-Plant Kinetics Data of HDS, HDV, HDNi, and HDAsph Reactions for HDM, HDS, and HDS/HDN Catalysts on Atmospheric Residue (AR), Partially Demetallized-AR, and Partially Demetallized/Desulfurized-AR Feedsa parameter feed concentration CSO (wt %) CVO (wt-ppm) CNiO (wt-ppm) CAsphO (wt %) rate constant @ 633 K kS ((wt %)0.5 h)-1 or ((wt %) h)-1 kV (wt-ppm0.5 h)-1 or (wtppm h)-1 kNi (wt-ppm0.5 h)-1 or (wtppm h)-1 kAsph (wt % h)-1 activation energy (kcal/mol) ES EV @633-673 K @673-693 K ENi @633-673 K @673-693 K EAsph reaction order, n HDS HDV HDNi HDAsph a

HDM HDS catalyst, HDS/HDN HDS catalyst, HDS/HDN catalyst, catalyst AR feed catalyst partially HDM AR feed partially HDM and HDS AR feed 4.30 69.00 21.00 3.75 0.0764 0.0946 0.1630 0.1820

0.492 0.021 0.052 0.064

0.431 0.015 0.024 0.158

0.794 0.040 0.052 0.468

0.67 15.00 8.00 0.90 0.744 0.034 0.026 1.197

26.1

25.5

28.6

31.9

24.7

27.7 62.2

29.6

23.4

25.5

15.7 46.4

19.5

25.3

23.9

31.5

15.1

23.0

22.7 46.4 3.3

2.0 2.0 2.0 2.0

2.0 2.0 2.0 2.0

2.0 2.0 2.0 2.0

1.5 2.0 2.0 2.0

12.5 23.6 23.6 2.0 1.5 1.5 2.0

Data taken from Marafi and co-workers.44,45

tacting efficiency (η*) between the packed catalyst particles and the reactant liquid was unknown. This was estimated by conducting a numerical experiment to find an optimized value for the pilot-plant reactor. The kinetic data for the HDS and HDNi and HDV reactions over a HDM catalyst were also obtained. Simulations for four processing temperaturessat 633, 653, 673, and 693 Kswere made, and the results were expressed by plotting the η* factor against Err %, which is defined as

Err % ) predicted value - measured value 2 0.5 × 100 (44) measured value

[(

2.30 21.00 12.00 2.52

)]

3.1. Parameter Estimation. A typical plot of the simulated results at 633 K is illustrated in Figure 3. When comparing HDS and HDM (the total of HDV and HDNi), the optimal η* values are different. However, when the HDV and HDNi are considered separately, a very good comparison is obtained. This observation applies to all four temperatures. The Err % values are presented in Table 4. The η* for all reactions is located at 0.7 at all processing temperatures. This value is well within the range of pilot-plant-scale reactors that have been suggested by Kimbara et al.47 The simulated HDV results were better than those of HDNi at the lower reaction temperatures (i.e., at 633 or 653 K). However, when the temperature was increased to 673 or 693 K, the HDNi simulated results fitted the pilot-plant data better than those of the HDV reaction.

The effective diffusivity values of the residual oil in the catalyst substrate were not available; therefore, to determine the Thiele modulus (Φ), a parameter estimation procedure was applied. An Err % analysis that was similar to that used in the previous parameter estimation was applied to the pilot-plant study of the HDM catalyst in a guard-bed reactor using AR feed; 10 days of operational data were obtained,48 covering product concentrations of the HDS, HDV, HDNi and HDAsph reactions. The initial rates for the HDS, HDV, HDNi, and HDAsph reactions were assumed from the values of the first data point at the 12th hour, and the Mmax is 60 g/100 g catalyst.49 From this analysis, an optimal Thiele modulus, Φ ) 0.85, at the minimum error of 0.96% was found (Figure 4). Using the estimated Φ, model simulation of the product concentrations was commenced. The concentrations of sulfur, vanadium, and nickel, relative to the processing time, were plotted in Figure 5, and the (47) Kimbara, N.; Hashiguchi; T.; Fujita, K.; Miyauchi, Y. Correlation of Catalyst Performance between Laboratory Tests and Commercial Units for Resid Hydrotreating. In Proceedings of the 210th National Meeting of the American Chemical Society, Chicago, IL, August 20-25, 1995, pp 527-532. (48) Stanislaus, A.; Matsushita, K.; Jasem, F.; Al-Barood, A.; Koide, R.; Fukase, S.; Marafi, A.; Absi-Halabi, M. Studies on the Effect of Time-on-Stream on the Deactivation of an Industrial HDM Catalyst during Hydrotreating Kuwait Atmospheric Residue. Kuwait Institute for Scientific Research, Report No. KISR 6059, Kuwait, 2001. (49) Kam, E. K. T.; Al-Bazzaz, H.; Juraidan, M. Study of HDS and HDM Reactions in a Guard-Bed Reactor of an Atmospheric Residue Desulphurisation Pilot Plant Unit at Start-of-Run. In Proceedings of the 17th International Symposium Chemical Reaction Engineering, Hong Kong, China, August 25-28, 2002.

760


Kam et al.

Figure 3. Effect of contacting efficiency (η*) of HDS, HDV, and HDNi at 633 K.

Figure 4. Parameter estimation of the Thiele modulus (Φ). Table 4. Minimum Error of the Contacting Efficiency for HDS, HDV, and HDNi at the Four Reaction Temperatures

Figure 5. Comparison of measured and simulated product concentrations.

Minimum Error (%) reaction

633 K

653 K

673 K

693 K

HDS HDV HDNi

1.21 2.59 4.69

1.59 3.89 5.25

2.66 6.55 5.64

2.68 7.42 6.28

simulated results matched the measured values well. From the measured metal product concentrations, the MOC values were calculated. A comparison between the calculated Mi and simulated Mi values was made, and the results also matched very well (Figure 6). 3.2. Comparison of Simulation with Pilot-Plant Test under Catalyst Deactivation. The following comparison was made between the simulated results with the experimental data from a long-life test run in a multireactor pilot plant at a constant temperature mode,25 in which the process temperature was not increased to compensate the gradual loss in catalyst activity. The first reactor was packed with HDM catalyst only, whereas the second was packed with two types of HDS catalysts. The catalyst types and their respective ratios, initial process temperature, liquid hourly space velocity (LHSV), and ARDS feedstocks were same as those used in a local refinery. In the comparison, the main performance indicatorssnamely, HDS, HDV, HDNi, and HDAsph variation with processing times were determined for the two fixed-bed reactors of the pilot-plant unit. Figure 7 presents the changes in the product concentration of sulfur and asphaltenes with processing time

Figure 6. Comparison of measured and simulated Mi values.

at the exit of the second reactor after passing through the entire catalyst system. The left y-axis shows the sulfur concentration and the right y-axis shows that of the asphaltenes. In the simulation, the pilot-plant data regarding the changes of sulfur concentration with processing time show an initial rapid decline in HDS, followed by a long period of gradual deactivation. The model captures these features. Generally, both the measured and simulated values are very similar. In the case of changes in asphaltenes concentration with processing time, the simulated results are generally lower than the measured values. This may be due to higher rate constants. Figure 8 presents the changes in vanadium and nickel product concentration with processing time from the


Figure 7. Comparison of measured and simulated sulfur and asphaltenes product concentrations with processing time from the second reactor.

Figure 8. Comparison of measured and simulated vanadium and nickel product concentrations with processing time from the second reactor.

second reactor. Both the HDV and HDNi reactions exhibit an initial rapid deactivation period for the first 60 hours. The simulation matches well with the HDV reaction for 4100 hours of operation. However, the HDNi simulation can match well with pilot-plant data, up to 2000 hours of operation. Generally, the catalyst deactivation rate of the HDV reaction is comparatively faster than that of the HDNi reaction. 3.3. Prediction of a Multibed Pilot Plant with Catalyst Deactivation. After the excellent comparison as discussed in Section 3.2, the model was used to simulate the pilot-plant performance and predict the catalyst deactivation beyond the 4000 hours of pilotplant test. The precision of this prediction could only be determined after the completion of the long-life pilotplant test. The predicted sulfur and asphaltenes concentrations in the product oil beyond the 4100 hours (and up to 9000 hours of pilot-plant operations in the second reactor) are illustrated in Figure 9. The pilot-plant sulfur and asphaltenes data clearly follow the model predictions well, which were presented to our laboratories seven months before the test run was completed.50 Further comparison of changes in the vanadium and nickel concentration with processing time between (50) Kam, E. K. T.; Al-Shamali, M.; Juraidan, M. Studies of ARDS Catalyst Systems and Reactor PerformancesModel Development and Applications. Kuwait Institute for Scientific Research, Final Report, Vol. IV, Kuwait, 2003.


Figure 9. Comparison of mechanistic model predictions and pilot-plant data of sulfur and asphaltenes with processing time from the second reactor.

Figure 10. Comparison of the mechanistic model predictions and pilot-plant data of vanadium and nickel product concentrations with processing time from the second reactor.

model predictions and pilot-plant data from the second reactor beyond 4100 hours is shown in Figure 10. The model predictions on nickel matched the pilot-plant data very well. However, predictions of the vanadium content were slightly higher than the pilot-plant data. 3.4. Parametric Study on Process Performance. From the aforementioned favorable comparison and prediction, it will be useful to determine if the model simulations can predict the known reactor performance and catalyst deactivation behavior under different processing conditions and catalyst properties. In the following simulations, the processing temperature, the LHSV value, and the Mmax value are made and results are discussed. The sulfur specification is usually the most important parameter in the refining industries; therefore, the discussions will focus only on the sulfur in the product oils. 3.4.1. Process Temperature. Figure 11 shows the comparison of the sulfur concentration in the product oil with processing time at three different processing temperatures. The LHSV, feedstock, catalyst system, and Mmax value are the same. The pilot-plant data (symbols) and simulated values (thick solid line) at 643 K are used as the base case for comparison. At a lower temperature of 633 K (thin solid line), the simulated sulfur concentration is well above 0.5 wt % at the SOR and MOR. However, at the EOR (over 9500 hours of operation), there is better sulfur conversion than at 643 K. Nevertheless, the sulfur concentration is still well over the sulfur target. At a higher temperature of 663

762


Kam et al.

Figure 11. Effect of process temperature.

Figure 13. Effect of maximum metal on catalyst (Mmax), in constant-temperature mode.

Figure 12. Effect of liquid hourly space velocity (LHSV). Figure 14. Simulated sulfur content and exit temperature with processing time, in constant-sulfur-concentration mode.

Table 5. The Four Sets of Mmax Values Mmax (g/100 g catalyst) graph type thick dotted line thick solid line thin dotted line thin solid line

HDM HDS HDS/HDN weight bed catalyst catalyst catalyst average, WBA 60.0 60.0 65.0 49.0

39.0 60.0 30.0 29.0

39.0 60.0 10.0 29.0

45.4 60.0 37.5 35.1

K (dotted line), the sulfur target is well below the required specifications at ∼0.3 wt % initially. However, the sulfur target value deteriorates very rapidly, because of the rapid catalyst deactivation. The catalyst life has shortened. Hence, it is not a good practice to start the ARDS unit at a higher processing temperature. 3.4.2. Liquid Hour Space Velocity. The effect of the LHSV is presented in Figure 12 for three LHSV values: 0.1, 0.28, and 0.5 h-1. The processing temperature, feedstock properties, catalyst system, and the Mmax value are the same. The pilot-plant data (symbols) and the simulated values (thick solid line) obtained from an LHSV value of 0.28 are used as the basis for comparison. At a lower LHSV of 0.1, better sulfur conversion and longer catalyst life (thin solid curve) are observed, as expected, However, an inverse trend was observed at a higher LHSV of 0.5 (thick dotted line), at which the conversion is always over the sulfur target and the catalyst deactivated faster. 3.4.3. Maximum Capacity of Metal on Catalyst. Four sets of Mmax values are considered in the simulation (see Table 5). The values shown in the second, third, and fourth columns are the Mmax values for the HDM and two HDS catalysts, respectively. The weight bed average (WBA) Mmax values of the three catalysts are presented in the last column. To demonstrate the versatility of the

model, simulations are made in two operation modes: constant-temperature and constant-sulfur-concentration. A constant-sulfur-concentration mode requires increases in the processing temperature, to compensate for the loss in catalyst activity whenever the sulfur in the product oil is over the target. The effect of Mmax values on catalyst activity under the constant temperature mode of operations is shown in Figure 13, in which the sulfur concentration in the product oil is plotted against the processing time. It clearly shows that the catalyst system, which HDM catalyst has the highest Mmax values (as shown by the thick dotted line at Mmax ≈ 60, 60, 60) provides better performance than those which have lower Mmax values. When comparing the catalyst performance with the WBA Mmax values, those with a WBA Mmax value of >45 g/100 g catalyst performs better. This may be due to the higher Mmax values for the HDM catalyst, which can capture more metals before the metals spill to downstream reactors to deactivate the HDS catalysts. After metals spillage trickles down to the HDS catalyst beds, rapid deactivation occurs. 3.4.4. Application to Constant-Sulfur-Mode Simulations. To demonstrate the flexibility of the model, the changes in process temperature to compensate for the loss of catalyst activity are presented in Figure 14 as a constant-sulfur-mode operation. An optimal temperature policy to increase the temperature 1 K per day in all reactors whenever the sulfur in the product oil was off-target was assumed. The dotted line indicates the sulfur concentrations in the product oil. It is fairly constant at 0.5 wt % for ∼6000 hours of operation. The


corresponding temperature profile, relative to processing time, shown by the solid line, increases sharply initially, to compensate for the initial rapid activity loss. After 150 h, the temperature increase becomes gradual, up to 5500 hours of operation. A very steep temperature increase is observed, and a temperature of 673 K is attained at the 6000th hour. The typical deactivation characteristics of the hydrotreating catalyst from which catalyst activity is compensated by a temperature increase are clearly shown in Figure 1. 4. Conclusions A model to study the hydrotreating reactor performance and catalyst deactivation was developed. The model is based on heat and mass conservation, and its associated process parameters, covering the apparent rate constants of HDS, HDV, HDNi, and HDAsph reactions, processing conditions, catalyst system configurations, feed properties, and maximum metal on catalyst (MOC) values. From model simulations, the catalyst activity enhancement of the HDM catalyst in the start of the run (SOR) and a rapid deactivation period in the HDS catalyst in the SOR are the two unique characteristics usually found in the multireactor atmospheric residue desulfurization (ARDS) unit. These features are clearly shown from the pilot-plant data and also simulated from the model. Its capability of predicting the trend of catalyst deactivation in the middle of the run (MOR) and end of the run (EOR) are also demonstrated. Although several model parameters cannot be measured or obtained directly from the pilot-plant data, their optimal values can be determined by a parameter estimation procedure. The model was verified well with data from a longlife pilot-plant test run experiment, in a constanttemperature-mode, including the HDS, HDV, HDNi, and HDAsph reactions, and catalyst deactivation. The model predictions of the same long-life test run covered the HDS, HDV, HDNi, and HDAsph reactions, catalyst deactivation mechanisms (according to the specific deactivation characteristics of the ARDS units), and catalyst activity. Its predictions on the pilot-plant unit performance and catalyst deactivation matched the pilot-plant data very well. The concentrations of sulfur, vanadium, nickel, and asphaltenes in the product oil from each reactor, as well as the reactor exit temperatures, were also determined. Simulations of varying processing conditions, catalyst system loadings, operational parameters, feed properties, product specifications, and/or catalyst properties can be made. These features enable the model to perform catalyst and processing predictions, evaluation, and optimization in ARDS units, in either constanttemperature or constant-sulfur mode. However, there is still room for improvement in the areas of the interparticle mass-transfer determination, coking deactivation, thermal cracking contribution in the overall conversion, and hydrogen mass balance to provide better prediction. Hence, further experimental and theoretical studies are required to fine-tune the parametric values for better simulation.


Notation a ) normalized activity A ) deactivation correlation coefficients cf ) concentration of feed (wt % or wt-ppm) cp ) concentration of product (wt % or wt-ppm) C ) the actual amount of carbon on the catalyst C1, C2 ) concentration of asphaltenes and products from the cracked asphaltenes species Cmax ) the maximum carbon deposition of a complete monolayer CF ) the concentration of the foulant CL ) concentration in the oil feed (wt % or wt-ppm) CAsph ) asphaltenes concentration (wt %) CS ) sulfur concentration (wt %) CNi ) nickel concentration (wt-ppm) CV ) vanadium concentration (wt-ppm) Cf,i ) feed concentration (wt % or wt-ppm) Cp,i ) product concentration (wt % or wt-ppm) C0 ) the amount of coke on the catalyst (wt %) CpG ) heat capacity of gas (kcal/kmol) CpL ) heat capacity of liquid (kcal/kmol) Dz ) diffusivity in the deposited layer (m2/h) Efi ) activation energy for parallel or series coking reaction (kcal/kmol) fi(‚‚‚) ) function of FG,j ) mass flow rate of gas (kg m-2 h-1) FL,j ) mass flow rate of liquid (kg m-2 h-1) k0 ) initial reaction rate constant in the active sites of fresh catalysts kz ) reaction rate constant in the deposited layer k0,i ) apparent rate constant for species i in the initial rapid deactivation period k′0,i ) apparent rate constant for species i in the reported kinetics kf1 ) apparent rate constant for parallel coking kf2 ) apparent rate constant for series coking, K ) adsorption constant LHSV ) liquid hour space velocity (h-1) Mi ) metal on catalyst (g/(100 g-cat.)) M ) maximum metal on catalyst (g/(100 g-cat.)) n ) correlation coefficient or reaction order N ) total number of reactors Qj ) heat flux (kcal/h) rp ) initial pore radius (m) r(‚‚‚) ) rate of reaction function of R ) gas constant (kcal kmol-1 K-1) s ) catalyst activity Sf ) sulfur content in feed and in product (wt-ppm) Sp ) sulfur content in feed and in product (wt-ppm) t ) on-stream time (h) T ) process temperature (K) T0 ) initial reaction temperature (K) uG ) linear gas flow rate (m/h) uL ) linear liquid flow rate (m/h) w ) weight of deposited metal (kg) W ) total weight of the catalyst system (kg) z ) reactor axial coordinate (m) Z ) parameter for unconverted metals ZRj ) dimensionless axial coordinate of the jth reactor Suffixes i ) species index (1 for sulfur, 2 for vanadium, 3 for nickel, and 4 for asphaltenes) j ) reactor index, between 1 and N Greek Symbols R ) the deactivation constant -∆H ) heat of reaction (kcal/mol) G ) gas holdup in catalyst bed (m3/kg) L ) liquid holdup in catalyst bed (m3/kg)

764


η ) relative catalyst activity Φ ) normalized Φ0, as defined in eq 14 Φ0 ) Thiele modulus based as on the bulk conditions Φz ) Thiele modulus based on conditions in deactivated pores φ ) deactivation function FG ) gas density (kg/m3) FL ) density of oils (kg/m3) Ψi ) a decay function for species i ω ) cumulative feed-to-catalyst ratio (m3/kg)

Acknowledgment. The authors would like to acknowledge the financial support of the Kuwait Institute for Scientific Research (KISR), the Petroleum Energy

Kam et al.

Center (Japan), Japan Cooperation Center, Petroleum (JCCP), and the Ministry of Economics, Trade, and Industry (Japan). The authors would like also to acknowledge the support of Kuwait National Petroleum Company in supplying the required petroleum feedstocks for the pilot-plant study on the performance of selected catalysts, as well as for their useful discussion of the results. Finally, special appreciation is due to the Petroleum Research and Studies Center pilot-plant staff for supplying the experimental data. This publication bears a KISR reference number of KISR7488. EF049843S

A Hydroprocessing Multicatalyst Deactivation and ... - ACS Publications

Recommend Documents