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A modeling study of the effect of reactor configuration on the cycle length of heavy oil fixed-bed hydroprocessing
Fuel 90 (2011) 3551–3560
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A modeling study of the effect of reactor configuration on the cycle length of heavy oil fixed-bed hydroprocessing Anton Alvarez a,⇑, Jorge Ancheyta a,b, Guillermo Centeno a, Gustavo Marroquín a a b
Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas 152, Col. San Bartolo Atepehuacan, México, DF 07730, Mexico Escuela Superior de Ingeniería Química e Industrias Extractivas (ESIQIE-IPN), México, DF 07738, Mexico
a r t i c l e
i n f o
Article history: Received 19 August 2010 Received in revised form 3 March 2011 Accepted 4 March 2011 Available online 11 April 2011 Keywords: Modeling Residue hydroprocessing Fixed-bed reactor Catalyst deactivation
a b s t r a c t This article analyses how the configuration of an industrial fixed-bed reactor affects the cycle length of a heavy oil hydroprocessing unit. It is well-known that during the hydroprocessing of heavy feeds, catalyst aging is counterbalanced by continuously increasing reaction temperature. In addition, the exothermality of the reaction provokes a huge temperature rise along the reactor, which is why quenching is necessary. Thus, there is an increasing temperature profile that evolves with time until a maximum allowable temperature is reached and then the operation is shut down. For this reason, there is an optimum reactor configuration (i.e. number of quenches and their positions) that must be established when designing new processes in order to maximize unit run length. To evaluate this problem, a reactor performance model with time varying catalyst activity was constructed. Kinetic and catalyst aging data were obtained from bench-scale tests. The model showed to reproduce sufficiently well the experimental data set. The analysis of various reactor designs indicated that for this process the use of single-bed or double-bed reactors is unpractical in terms of cycle length. A more complex configuration consisting of multiple beds of increasing lengths is necessary to delay shut down. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Most hydrotreating and hydrocracking (HCR) commercial units employ fixed-bed reactors. The exothermic nature of hydroprocessing reactions provokes a temperature rise along the reactor that can exceed 100 °C, when processing heavy feeds [1]. Temperature runaways beyond acceptable limits boost catalyst deactivation, change selectivity towards undesirable products, and sometimes even damage the reactor vessel, which affects process profitability [2,3]. This is why controlling this variable is essential for these processes. Hydroprocessing of heavy fractions in single-bed reactors would be very unpractical and unsafe due to the huge temperature increase. To obtain a more favorable and controllable temperature profile, the total heat release is limited to smaller portions by dividing the total catalyst volume into several beds, commonly of increasing depth, separated by quench zones for introducing relatively cold hydrogen-rich streams [4]. It is well-known that the main disadvantage of using fixed-bed reactors for the hydroprocessing of heavy feeds is the catalyst aging during time-on-stream, caused by metals (Ni + V) and coke deposits. Coking generally occurs during the first few hours of ⇑ Corresponding author. Fax: +52 55 9175 8429. E-mail address: [email protected] (A. Alvarez). 0016-2361/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2011.03.043
operation and then apparently reaches a pseudo steady-state, in contrast to that, metals build-up continues through the whole cycle [5]. In industrial practice, the gradual loss in catalytic activity is offset by increasing reaction temperature. The process is shut down upon the attainment at any position of the reactor of a maximum allowable temperature (MAT) [6], which is established by the metallurgical characteristics of the reactor vessel. However, at end-of-run conditions the catalyst is not necessarily completely deactivated because metals tend to accumulate in the front-end of the catalyst system [7]; this is reflected in a descending-type axial profile of metals-on-catalyst (MOC) [5]. In this sense, front-end hydrodemetallization (HDM) catalysts with high-metal uptake capacity (layered catalyst systems) were introduced to make better use of the catalyst inventory [8]. Nevertheless, proper features of the fixed-bed reactor (i.e. number of quenches and their local positions) strongly contribute delaying the moment in which MAT is attained as well. The problem of reactor design in this type of systems arises from the uneven axial activity and temperature profiles, which also evolve with time. For this reason, a suitable configuration must be established when designing new units in order to maximize cycle length. Perhaps the works of Shah et al. [6] and Mhaskar et al. [9] in the seventies are the only ones that addressed this issue. They studied the dynamics of an industrial double-bed reactor for residue hydrodesulfurization (HDS). Their general conclusion
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Nomenclature A aL aMOC aS AS Asph B Ci Cp g GL Hi H2 H2S H2/oil kapp kint kp L ki S
ki
K H2 S l MOC nj N Ni Ni NH3 pi P q rj R1
fitting parameter of Eq. (13) gas–liquid interfacial area (cm1) unit conversion factor of Eq. (17) liquid–solid interfacial area (cm1) sectional area of the reactor (cm2) asphaltene fitting parameter of Eq. (13) molar concentration of compound i (cm3 mol1) mass heat capacity (J g1 K1) gas mass rate (g s1) superficial liquid mass velocity (kg m2 s1) Henry’s law constant for compound i (MPa cm3 mol1) hydrogen hydrogen sulfide hydrogen-to-oil ratio (std m3 m3) apparent rate coefficient intrinsic rate coefficient particle rate coefficient gas–liquid mass-transfer coefficient for compound i (cm s1) gas–liquid mass-transfer coefficient for compound i (cm s1) adsorption equilibrium constant for H2S (cm3 mol1) liquid mass rate of the j stream (g s1) concentration of metals-on-catalyst (wt.%) order of reaction j nitrogen nickel molar flow of compound i (mol s1) ammonia partial pressure of compound i (MPa) total pressure (MPa) quench fluid mass flowrate (g s1) rate of jth reaction (mol cm3 s1) first reactor
was that for each system there is an optimal reactor configuration, more specifically, a quenching location, that maximizes the cycle length or time to reach MAT. This ideal arrangement depends on numerous variables such as feed temperature, concentrations of metals in the feed, activation energies, and desired conversion level. Recently, the Mexican Institute of Petroleum (IMP) developed a hydroprocessing technology for upgrading heavy and extra-heavy crude oils (Fig. 1) [10]. The heart of the process is a system of fixed-bed reactors in series loaded with a graded catalyst system for hydrotreating and hydroconversion. The IMP technology has been already demonstrated at semi-industrial scale (10 bpd feed flowrate) with excellent results, and is ready for commercial application. During its development the aspects reviewed above were considered important to address. Therefore, it is the purpose of this study to analyze in detail the influence of reactor configuration on the cycle length of the IMP process. To meet this goal, a reactor performance model considering time varying catalyst activity was constructed, based on experimental data obtained from benchscale tests. 2. Experimental 2.1. Kinetic measurements A series of bench-scale hydroprocessing experiments were carried out to obtain kinetic data. The bench-scale plant consists of
R2 S SOR t T uG uL V xMOC
second reactor sulfur start-of-run time (h) temperature gas superficial velocity (cm s1) liquid superficial velocity (cm s1) vanadium fractional concentration of metals-on-catalyst
Greek symbols a fitting parameter of Eq. (16) b fitting parameter of Eq. (16) c fitting parameter of Eq. (16) DYR overall heat of reaction (kJ kg sulfur1) g0 effectiveness factor gCE external catalyst wetting efficiency qG gas density at process conditions (g cm3) qL liquid density at process conditions (g cm3) /j deactivation function of jth reaction deactivation function for coking reactions /Coke /Metals deactivation function for metals deposition Subscripts HDM hydrodemetallization HDS hydrodesulfurization in inlet to the following catalytic bed out outlet of the previous catalytic bed q quench stream Superscripts G gas phase L liquid phase Q quench fluid S solid phase
two fixed-bed reactors in series (500 cm3 of catalyst in each reactor), which were loaded with a system of commercial size catalysts: a front-end HDM catalyst, a midsection catalyst with balanced HDM/HDS activity, and a tail-end highly active catalyst for HDS and HCR. Catalysts were in situ activated by sulfiding with straight-run gas oil (1.46 wt.% sulfur) containing 1.0 wt.% dimethyl disulfide. The feedstock was an atmospheric residue (343 °C+) from a Mexican heavy crude oil (13°API). Physical and chemical properties of the feed (AR 1) are presented in Table 1. Before the tests, the fresh catalyst system was stabilized at low temperature (360 °C) by processing the feed during 100 h. After this initial deactivation period, the experiments were conducted by varying liquid hourly space velocity (LHSV) (0.25–1.0 h1) and temperature (380–420 °C). Pressure and H2/oil ratio were kept constant at 9.81 MPa and 891 std m3/m3, respectively. For each experiment, operating conditions were adjusted in a period of 5 h, followed by a stabilization period to obtain representative products. Under steady operation, two consecutive mass balance runs were performed by collecting the product during 3 h. The tests were carried in less than 250 h of operation so as to minimize the influence of catalyst deactivation on the kinetic analysis. After the experiments, catalyst activity was monitored with a checkback test using the conditions of the first experiment. Physical and chemical properties of the collected liquid samples from the bench-scale plant were analyzed with standard methods. A more detailed description of this set of experiments is provided elsewhere [11].
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H2 make-up
Purge
H2 recycle Light fraction
Heavy crude oil
Sweetening Lean DEA Heavy fraction
Rich DEA
Sour gas
HDT reactors High pressure separator
Stripping Upgraded oil
Fig. 1. Basic representation of the IMP heavy oil upgrading technology.
Table 1 Physical and chemical properties of the feedstock. Property 3
Density at 15.6 °C, g/cm API Gravity Kinematic viscosity at 121 °C, cSt S, wt.% Metals, wppm Ni V Ni + V Asphaltenes in C7, wt.% Distillation IBP, °C 5 vol.%, °C 10 vol.%, °C 15 vol.%, °C 20 vol.%, °C 30 vol.%, °C 40 vol.%, °C vol.% recovery at 538 °C
AR1
AR2
1.0326 5.4 1637.9 5.74
1.0475 3.2 1832.4 6.21
102 620 722 21.77 ASTM D-1160 296 372 401 438 475 521 541 35.25
117 578 695.6 25.10 ASTM D-1160 380.3 415.4 447 474 504 551 – 26.90
2.2. Catalyst aging Catalyst stability was evaluated under process conditions in a 5.2 month-long test. The study was carried out in another benchscale plant with two fixed-bed reactors in series (900 cm3 of catalyst in each reactor). The total reactor volume was loaded with the same catalyst system than that used for generating the kinetic data. The feedstock was a similar atmospheric residue coming from another batch of 13°API heavy crude oil, which properties are also exhibited in Table 1 (AR 2). One important aspect of catalyst life testing is the selection of the operation mode [12]: fixed-performance or fixed-temperature. The former mode, in which reaction temperature is increased periodically to compensate for catalyst deactivation, is more suitable for representing commercial operation. For this reason, in the present work catalyst aging was studied under those conditions.
Unlike most of the residue upgrading processes which typically operate under HDS iso-performance, the target in this process is to keep constant the API Gravity of the product, as this parameter is a good measure of the overall character of oils. The evaluation was carried out under the following operating conditions: LHSV of 0.25 h1, initial temperature of 380 °C, pressure of 9.81 MPa, and H2/oil ratio of 891 std m3/m3. The mass balance runs were performed consecutively by recovering the products every 12 h. Inter-reactor samples were taken every 24 h in order to monitor the behavior of the first reactor. The sample size was kept at less than 2% of the total feed rate so as to reduce the disturbance of the system. This small amount was sufficient enough to carry out some analyses, particularly to determine metals content in the products for estimating the metals uptake in the first reactor. 3. Modeling approach The performance model is a three-phase plug-flow reactor model reported in our previous publication [11]. The model incorporates mass-transfer at the gas–liquid and liquid–solid interfaces, in accordance with the two film theory, and uses correlations to estimate mass-transfer coefficients and fluid properties at process conditions [13]. The feedstock and products are represented by chemical lumps (S, N, Ni, V, asphaltenes (Asph), and 538 °C+ residue) defined by the overall elemental and physical analyses. Thus, the model accounts for the corresponding hydroprocessing reactions such as HDS, hydrodenitrogenation (HDN), HDM (nickel (HDNi) + vanadium (HDV) removals), hydrodeasphaltenization (HDAs) and HCR of the 538 °C+ residue, termed conversion. The following assumptions were considered to develop the model equations: The reactor operates in plug flow mode; there is no axial or radial dispersion. Liquid velocity is constant through the reactor, since liquid expansion caused by hydrocracking is between 3 and 5 vol.% under the experimental conditions.
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Vaporization of the feed does not take place. Gas velocity changes along the reactor due to hydrogen consumption. Pressure is constant along the reactors. Coke build-up occurs during the first 100 h-on-stream and then reaches equilibrium, whereas metals are deposited during the entire operation period. Since catalyst deactivation is relatively slow with respect to the duration of the mass balance runs, a quasi-steady state approximation can be applied to simulate the performance of the process [14]. 3.1. Mass and heat balances The model incorporates reactant mass-transfer phenomena at the gas–liquid and liquid–solid interphases. Hydrogen is dissolved in the liquid and then travels to the catalyst surface in order to react, whereas the gaseous products, such as H2S, NH3 and light hydrocarbons (constituted mainly by CH4), are released to the gas phase. Thus, the change in molar flow of gaseous compounds along the reactor is equal to the gas–liquid transport rate: G
dNi P NGi L L ¼ AS ki aL P G Ci Hi dz Ni
! ð1Þ
where i = H2, H2S, NH3 and CH4. The change in the concentration of gaseous compounds in the liquid phase is owed to gas–liquid equilibrium and mass-transfer to the solid phase: L
dC i 1 P NGi L L ki aL ¼ P G Ci uL Hi dz Ni
!
! S
ki aS ðC Li C Si Þ
ð2Þ
where i = H2, H2S, NH3 and CH4. The chemical species are transferred from the liquid bulk to the catalyst surface, thus the mass balance equation for these compounds is the following: L
dC i 1 S ¼ ki aS ðC Li C Si Þ uL dz
ð3Þ
where i = S, N, Ni, V, Asph and 538 °C+ residue. The compounds transported between the liquid–solid interphase are either consumed or produced by chemical reaction:
Once the quench rate is known, the actual rates of gas and liquid entering the following bed (lin + gin) can be determined by mixing the preceding bed effluent (lout + gout) with the quench fluid:
q þ lout þ g out ¼ lin þ g in
ð7Þ
3.2. Reaction kinetics Kinetic rates of each chemical lump were represented by power-law expressions of nth order: app
rj ðz; tÞ ¼ kt;j ðT; z; tÞðC Si ðz; tÞÞnj
ð8Þ
In the case of HDS, additionally it was considered that the reaction is inhibited by H2S adsorption [13]: app
rHDS ðz; tÞ ¼ kt;HDS ðT; z; tÞ
ðC SS ðz; tÞÞnHDS ðC SH2 ðz; tÞÞ0:5 ð1 þ K SH2 S C SH2 S ðz; tÞÞ2
ð9Þ
Eqs. (8) and (9) represent the local (with respect to the axial poapp sition z along the reactor) rates of reaction j at time t, where kt;j is the time-evolving local apparent rate coefficient of reaction j, with an Arrhenius type temperature dependence, C Si is the local concentration of lump i at the catalyst particle at time t, and nj is the order app of reaction j. The time dependence of kt;j is given by the activity of the catalyst, which can be represented as follows: app
app
kt;j ðT; z; tÞ ¼ kj ðTÞ/ðz; tÞ
ð10Þ
app
where kj is the temperature-dependant apparent rate coefficient of reaction j and /j is the local deactivation function of reaction j app at time t. It is important to notice that by definition kj is a rate coefficient distorted by hydrodynamic and mass-transfer phenomena, which are typically present when obtaining kinetic data from bench-scale trickle-bed reactors. Therefore, it is necessary to correct these apparent coefficients in order to predict industrial scale performance, as it is discussed in the next section. H2 consumption and H2S generation are accounted for through overall stoichiometric coefficients, whereas CH4 is considered to be produced exclusively from residue HCR [17]. The rate parameters were estimated with the Levenberg–Marquardt’s algorithm from the kinetic data, as presented in our previous work [11]. 3.3. Scale-up of kinetic data
S
ki aS ðC Li C Si Þ ¼ rj
ð4Þ
where i = H2, H2S, NH3, CH4, S, N, Ni, V, Asph and 538 °C+ residue, and j = HDS, HDN, HDNi, HDV, HDAs and HCR. The ‘‘’’ sign is for the reactants, while the ‘‘ + ’’ sign is for the products. The following heat balance is included for representing adiabatic operation of the commercial scale reactors:
dT 1 ¼ ½ðDHR ÞrHDS dz uG qG cGP þ uL qL cLP
ð5Þ
The heat balance incorporates an overall heat of reaction for the atmospheric residue HDS process, reported in the literature [15]. This is a fitting parameter originated from several heat balances of similar processes, which accounts for the contribution of all reactions (HDS, hydrogenation, etc.) [16]. It is also necessary to include equations for describing the quench zones. The following heat balance can be employed to estimate the quench rate (q) for a desired cooled mixture temperature (Tin):
Z
T in
T out
lout CpL dT þ
Z
T in
T out
g out CpG dT þ
Z
T in
TQ
qCpQ dT ¼ 0
ð6Þ
The accuracy when scaling-up reaction kinetics strongly depends on the quality of the experimental data. The experience indicates that bench-scale trickle-bed reactors produce apparent kinetics because they suffer from several deviations from ideal plug flow behavior, as a result of low liquid velocities [18]. Having this in mind, apparent kinetics can be translated to intrinsic kinetics by considering these effects as follows [19]: app
in
kj ðTÞ ¼ g0 gCE kj ðTÞ
ð11Þ
Eq. (11) correlates the experimentally obtained apparent rate in coefficient of reaction j with the intrinsic rate coefficient kj , through intraparticle diffusion and reactor hydrodynamics, represented by the catalyst effectiveness factor (g0) and the external catalyst wetting efficiency (gCE), respectively. When the experiments are carried out using commercial size catalysts, g0 is already included in the apparent rate coefficients. This allows for describing industrial performance [20], but only if the catalyst used in the tests is exactly the same as the one in the commercial unit [21]. Considering that in the present work the experiments were carried in out using commercial size catalyst, g0 can be grouped with kj , and thus Eq. (11) becomes:
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ð12Þ
p
where kj is the ‘‘particle’’ rate coefficient. This simple expression correlates bench-scale and industrial reactor performances through a single scale-up parameter. The term gCE is a measure of the extent of catalyst utilization, which tends to become complete (gCE 1) at industrial scale [22]. In the present work, this effect was handled with the empirical approach proposed by Bondi [23], of the linear form:
1 k
app
¼
1 A p þ B k GL
ð13Þ
Equation (13) is a relationship in which the apparent rate coefficient approaches that of particle kinetics as superficial liquid mass velocity (GL) goes to infinity, with fitting parameters A and B. For a number of systems the value of exponent B typically falls in the range of 0.5 to 0.7, more commonly 2/3. This approach is based on the fact that kapp tends to increase with liquid rate in trickle-bed reactors. By plotting 1/kapp vs. 1/GBL it is possible to approximate the value of the particle rate coefficient at infinite GL, which is the intercept on the y-axis (1/kp). For this reason, it is necessary to perform the tests at various liquid rates and if possible, as the author recommended [23], using two reactor scales in order to reduce the uncertainty of the extrapolated kp. In this case, wetting efficiency was estimated from the LHSV bench-scale experiments and also from semi-industrial scale data (10 bpd plant), using the same feedstock, operating conditions, and catalysts. 3.4. Catalyst deactivation During hydroprocessing of heavy feeds, coking and metals deposition reduce catalytic activity with time-on-stream. Our previous experimental observations [24,25] indicate that coke formation is the main cause of the substantial activity loss during the few first hours of operation (generally the first 100 h), but at long-term, metals build-up is more relevant. It was also noticed that coking reaches equilibrium after this initial stage; hence the assumption of considering that coke formation occurs only during the first 100 h. The deactivation function /j mentioned earlier is defined as the ratio between the local apparent rates of reaction j at time t and at t = 0 (fresh catalyst):
/j ðz; tÞ ¼
r j ðz; tÞ rj ðz; t ¼ 0Þ
/j ðz; tÞ ¼
1
ð15Þ
ðxMOC ðz; tÞÞcj
ð1 þ aj tÞbj
ð16Þ
The first term of Eq. (16) represents the initial activity decline (/Coke ) during the first 100 h, as function of time t with fitting j parameters aj and bj for reaction j [28]. At t = 100 h it is considered that this initial period is terminated and the value of /Coke is kept j constant for the rest of the operation cycle. It must be stressed out that the proper way for describing this process is to link this function to a coking agent instead of time, unfortunately there was no experimental information available that provided insight into this mechanism, and therefore, there was no other possible way to represent this behavior. This is why the application of such functionality is restricted to the experimental operating conditions. Nevertheless, SOR data obtained with several heavy crudes and residues at different operating conditions [29] indicated that the HDS and HDM activity decay in this period interestingly follows the same pattern even in such a wide range of conditions. This behavior is represented in Fig. 2 as the change in the deactivation functions of HDM and HDS with respect to the change in time (D/j =Dt). It is observed also that in all cases the catalyst system is rapidly stabilized at t = 100 h. On this basis, it can be considered that it is reasonable to use this approach and it can be assumed that the shape of the SOR curve is not affected by the variable temperature profile when running the adiabatic simulations. The second term of Eq. (16) describes /Metals . In this case, this j function is directly linked to the deactivating agent: the local amount of metals build-up, with fitting parameter cj for reaction j, which also is specific for each of the three catalysts. The quantity of metals deposition is expressed in terms of xMOC, defined as the ratio between the local concentration of metals-on-catalyst (MOC) at time t and the specific maximum metals uptake of each catalyst. Such functionality allows for establishing a deactivation profile with respect to time t and axial position z, which is a determining factor for the catalyst cycle life. MOC is obtained by solving the metals mass balance equation:
0.03
ð14Þ
The values of /j range between 1 (fresh catalyst) and 0 (totally spent catalyst). This function is essentially similar to the concept of active surface area of the catalyst, employed in other deactivation models [26,27]. The formulation of the deactivation function requires measuring rj at t = 0, however, in these cases this is not possible because the liquid product is accumulated during several hours to obtain enough sample to carry out all the required analyses. For practical reasons, it has been proposed to assume that the initial activities are those obtained from the first sample [12], which is collected after the first 10–12 h of operation. In this study however, initial activities are obtained from the start-of-run (SOR) aging curve by extrapolation [28], which seems to be more accurate. As already mentioned, it is considered that catalyst aging is provoked by coking reactions at SOR conditions and metals deposition during the whole cycle. The contribution of these two processes to the overall catalyst deactivation can be expressed as follows:
/j ðz; tÞ ¼ /Coke ðtÞ þ /Metals ðz; tÞ j j
where /Coke and /Metals stand for the deactivation functions for cokj j ing at time t 6 100 h and local metals build-up (Ni + V) at time t, respectively. The following empirical relationship is proposed for describing the deactivation process:
ΔφHDS /Δt
p
¼ gCE kj
0.02 0.01 0.00 0
10
20
30
40
50
60
70
80
90 100
80
90 100
0.04
ΔφHDM /Δt
app
kj
0.03 0.02 0.01 0.00 0
10
20
30
40
50
60
70
time-on-stream, h Fig. 2. HDS and HDM deactivation curves at SOR conditions [29]. Heavy crude oil (13°API) at LHSV = 0.5 h1, P = 6.86 MPa, and H2/oil = 891 std m3/m3: (d) 380 °C, (N) 400 °C, () 420 °C; (j) atmospheric residue (5.4°API) at 360 °C, LHSV = 0.25 h1, P = 9.81 MPa, and H2/oil = 891 std m3/m3; Maya crude oil (21°API) at LHSV = 0.5 h1, P = 6.86 MPa, and H2/oil = 891 std m3/m3: (s) 380 °C, (D) 400 °C, (}) 420 °C.
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A. Alvarez et al. / Fuel 90 (2011) 3551–3560
dMOCðz; tÞ ¼ aMOC r HDM ðz; tÞ dt
4.0
ð17Þ
3.5
where MOC is expressed in wt.%, rHDM is the local rate of HDM at time t, and aMOC is a unit conversion factor.
4.1. Bench-scale unit simulations
70
N
6000 5000 4000 3000
538°C+
600
Content, wt%
60 50
500 20
Asph
400
15
300
V 10
200
5
Ni
0
S 0.2
0.4
Content, wppm
The reactor performance model was applied to simulate the behavior of the bench-scale reaction system at various LHSV and temperatures under steady catalyst activity (kinetic tests). The results presented in Fig. 3 show that the evolution of the various chemical lumps along the reactor is well predicted. The model is also able to describe H2S and CH4 yields, and H2 consumption along the reactor, as shown in Fig. 4. In general, the model predictions are in good agreement with the experimental observations. A deeper analysis on the behavior of this reaction system can be found in our previous work [11]. Fig. 5 illustrates the levels of HDS, HDM, and HCR of 538 °C+ residue along the catalyst stability test. It can be observed that reaction temperature was increased periodically to keep the target oil quality at 23°API (the upgraded oil is obtained by blending the bypassed light fraction with the reactor liquid effluent, as shown in Fig. 1). The deactivation curves followed different patterns under the selected operation mode, for instance, HDS and HDNi decreased progressively with time-on-stream, whereas HDV and HCR responded well to the gradual temperature increase. Such a behavior is owed to diverse factors such as the properties of the catalysts, activation energies, feed type, and the thermal effect on the overall performance. There are two discontinuities at around 1500 h of time-on-stream and one close to 2500 h, which are owed
100 0
0.6
0.8
1.0
Relative reactor length Fig. 3. Evolution of the chemical lumps along the reactor at T = 380 °C and LHSV = 0.25 h1: (symbols) experimental, (–) simulated.
3
2.5
H2/oil, std m /m
3
850
2.0
800
H2S
1.5 1.0
750
CH4
0.5 0.0
700 0.0
0.2
0.4
0.6
0.8
1.0
Relative reactor length Fig. 4. Evolution of gas yields at T = 380 °C and LHSV = 0.25 h1: (symbols) experimental, (–) simulated.
510 500 490 HDV 480 470 460 450 440 430 420 API 410 400 390 T 380
100 90
HDS
80 70 60 50
HDNi
40
HCR
30 20 10 0 0
T, °C
4. Results and discussion
Yield, wt%
The reactor model consists of a set of coupled ordinary differential and algebraic equations, which was coded in Fortran. The mass balances for the gas and liquid phases, as well as the heat balance, are solved simultaneously using a fourth-order Runge–Kutta method, whereas the solid phase equations are solved with a Newton– Raphson method, in order to obtain the concentration and temperature profiles along the reactor. With the concentration profiles, the local amount of metals build-up is estimated and then the deactivation functions for each reaction are evaluated. The time step is increased and all the calculations are updated. The industrial simulation is terminated when a MAT of 420 °C is reached at any point of the reactor length.
3.0
Performance % or API Gravity
3.5. Solution technique
0.0
900
500 1000 1500 2000 2500 3000 3500
time-on-stream, h Fig. 5. Process performance during time-on-stream: (symbols) experimental, (–) simulated.
to operational problems caused by high pressure drops between the reactors and the separation system. In those cases, reaction temperature was decreased and the heavy feed was switched to light gasoil in order to wash off the plugs. This represented a 297 h interruption (in total) of normal operation; however, these periods are not included in Fig. 5. Once the pressure drop was within acceptable limits, the feed was switched back to residue and temperature was increased again until the target performance was achieved. The test demonstrated that the IMP hydrotreating technology is capable of keeping the product within specification during the 3440 h-on-stream (5 months of operation). The activity levels at the end of the test also indicated that the catalyst was still active; however, the substantial loss in HDS activity implies that at this stage the process is mainly driven by thermal effect. The operation was terminated due to the high temperature level (398 °C) in order to avoid excessive coking, which eventually would produce even more severe fouling problems. The aging data presented in Fig. 5 were employed to fit the deactivation parameters in order to simulate the performance of the process during time-on-stream. The parameters of the deactivation functions are listed in Table 2. From Fig. 5 it can be observed that the model predicts reasonably well the deactivation curves. The parity plot shown in Fig. 6 indicates that most of the data randomly falls into the ±10% interval, which confirms the validity of the model. Nickel removal exhibits the highest deviations, which is owed to a relatively wide dispersion of the experimental data. Such level of scattering is most likely to be caused by experimental errors along with the effect of relatively low analyte concentration. As mentioned in the experimental section, inter-reactor samples were taken to monitor the behavior of the first reactor. The
3557
Table 2 Deactivation parameters.
3.81 2.70 4.57 101 2.80
0.06 0.08 0.10 0.05
HDM (R1 + R2)
70
c
b
Front-end
Mid-end
Tail-end
0.34 0.96 3.00 1.15
0.35 0.75 0.52 1.49
0.46 0.45 0.31 2.70
60
HDM, %
a HDS HDNi HDV HCR
50 40 HDM (R1)
30
R1 R1 + R2
20
R2
10 100
HDV HDNi HDS HCR
90 80
0 0
500 1000 1500 2000 2500 3000 3500
time-on-stream, h
70
Predicted, %
260 240 220 200 180 160 140 120 100 80 60 40 20 0
80
MOC , wt%
A. Alvarez et al. / Fuel 90 (2011) 3551–3560
Fig. 7. HDM performance and metals-on-catalyst (MOC): (symbols) experimental, (–) simulated, R1 (first reactor), R2 (second reactor). The experimental values (s) of MOC at the end of the run were estimated based on the metals balance between feed and products.
60 50 40 30 20
R1
0.0
R2
0.0
0 0
10
20
30
40
50
60
70
80
90 100
Experimental, % Fig. 6. Comparison between experimental data and model predictions. (- - -) ±10%.
HDM performance of the first reactor, along with the global HDM level, is presented in Fig. 7. The performance data of the first reactor are widely scattered but the model is still able to capture the general trend. It is evident that inter-reactor sampling does not produce reliable information for kinetic analysis, which is why these data were not considered for parameter estimation. However, those samples are acceptable enough for a rough estimation of the metals uptake in the first reactor and to verify the model capability. As for the overall HDM, the model works sufficiently well since parameter estimation was carried out using the overall aging data presented earlier. The same figure shows the evolution of the MOC in the both reactors. It is clear that MOC accumulation follows a linear behavior. Apparently, there is a good match between the model results and the observed MOC, which was estimated from the metals balances between the feed and products. In the case of the first reactor, the dispersion of HDM activity does not affect substantially the linear MOC trend because the experimental data are uniformly scattered around the simulation. These results actually confirm the usefulness of inter-reactor sampling in this kind of studies. It is noteworthy that metals build-up in the first reactor is as high as 120 wt.% (in fresh-catalyst basis), whereas in the second reactor is only 20 wt.%, which indicates that the front-end catalyst indeed is protecting downstream catalysts. The model is also capable of describing the evolution of axial MOC profiles, as illustrated in Fig. 8. It can be observed that initially (500 h) the MOC profile is relatively uniform along the whole reactor length. As time-on-stream increases, the MOC profile acquires the typical descending shape, particularly in the first reactor. Clearly, it can be noticed that the front-end HDM catalyst, located in the first 60% of the first reactor (0–0.6), exhibits the highest metals accumulation, followed by the mid-end catalyst, which is distributed in the remaining space of the first reactor and the first 20% of the second reactor (0–0.2). Such a layered catalyst arrangement reduces significantly the metals content in the stream entering to the highly-active HDS catalyst bed located in the tail-end of the second reactor.
Relative reactor length
10 0.2
0.2
0.4
0.4
0.6
0.6 500 h 1500 h 2500 h 3440 h
0.8 1.0
0.8 1.0
0
5
10 15 20 25
MOC, wt%
0
1
2
3
4
5
MOC, wt%
Fig. 8. Simulated axial MOC profiles in the first (R1) and second (R2) reactors. The MOC units are referred to the total amount of fresh catalyst in each reactor.
4.2. Scale effect analysis Ideally, in trickle-bed processes sustaining liquid-limited reactions, the whole catalyst bed contributes to the overall conversion because every catalyst particle is covered by a flowing film of liquid [21]. However, the behavior of bench-scale reactors deviates significantly from ideal behavior as a result of low liquid velocities that cause incomplete catalyst wetting. For this reason, it is essential to estimate wetting efficiency to determine the extent of catalyst utilization and thus correcting kinetic data. Fig. 9 provides the analysis of the scale effect on catalyst wetting efficiency, defined earlier as the ratio between the apparent and particle rate coefficients. The theoretical curve was obtained from Eq. (13) with A = 1.79 103 and B = 2/3, using the benchand semi-industrial scale data at 380 °C. The shape of the curve indicates that wetting efficiency is a weak function of superficial liquid mass velocity in this system. In fact, the values of gCE for all experiments fall in the range of commercial operation (0.7–1.0); for instance, at the lowest GL (0.06 kg/(m2s)), gCE is already higher than 0.7 (more than 70% catalyst utilization), whereas for the semiindustrial test gCE is very close to 1, as this plant has roughly 230 times more processing capacity that the bench-scale unit operating at LHSV = 0.25 h1. Apparently, this indicates that the semi-industrial scale reactor behaves ideally in terms of catalyst utilization and therefore, this test can be considered representative of commercial operation. The semi-industrial scale data are compared against benchscale performance in Fig. 10. From the previous analysis, it was
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LHSVsemi-ind = 0.2 h
1.0
-1
0.9
LHSVbench = 1.0 h LHSVbench = 0.75 h
ηCE
0.8
LHSVbench = 0.5 h 0.7
LHSVbench = 0.25 h
-1
-1
-1
-1
0.6
0.5 0.1
1
10
100
2
GL, kg/(m s) Fig. 9. Catalyst wetting efficiency as function of superficial liquid mass velocity: (d) bench-scale, (s) semi-industrial scale, (–) theoretical.
determined that a wetting efficiency of 0.7 at those operating conditions correlates sufficiently well bench-scale and semi-industrial reactor performances. It is evident that the semi-industrial scale outperforms the bench-scale because the latter uses only 70% of its catalyst inventory. 4.3. Industrial reactor simulations The performance model was applied to analyze the effect of reactor design on the behavior of the process. The main target was to construct an arrangement that maximizes the time to reach the permissible temperature limit (420 °C) at any point of the reactor length or at least that equals the cycle length obtained during the aging test (5 months). To meet this goal, it was necessary to apply a sequential analysis of several reactor configurations that evolved from one to another. This allowed identifying the flaws of a given design and then improving it in terms of cycle length. From this sequential procedure the following reactor arrangements were studied: Case 1: two single-bed reactors of equal length, with one hydrogen quench. Case 2: one reactor with two beds of equal length followed by a second single-bed reactor, with two hydrogen quenches. Case 3: one reactor with four beds of increasing length followed by a second reactor with two beds of increasing length, with two hydrogen quenches and three heat exchangers.
The three reactor configurations are illustrated in Fig. 11. The first arrangement (Case 1) is identical to that of the bench-scale plant and it was used as a starting point design, whereas the other two are its successors. It can be noticed also from Fig. 11 that the configuration of Case 3 uses a combination of hydrogen quenching and heat exchange. The amount of hydrogen quenching in this system is restricted by a low H2/oil ratio, which is meant to reduce compression requirements. In previous studies [11,30], it was determined that in this process it is not possible to quench with hydrogen the total heat release and at the same time to keep the required average H2/oil ratio (891 std m3/m3) along the reactors. In fact, no more than two hydrogen quenches are allowed to keep the required H2/oil ratio, because each extra hydrogen quench decreases significantly this ratio, especially in the first reactor. Having this in mind, the amount of hydrogen quenching in Cases 1 and 2 was also optimized to meet this criterion. The time-evolving axial temperature profiles are illustrated in Fig. 12. It can be observed that in all cases the temperature profile is progressively displaced upwards because the feed temperature is increased so as to keep API Gravity of the product constant (HCR at 35%). The configuration of Case 1 is characterized by a large and continuous heat release in both reactors. The considerable delta-T produces high temperature zones at the outlet of each reactor. The temperature rise in the second reactor slowly decreases with process time because the tail-end catalyst is less tolerant to metals deposition and loses activity more rapidly, whereas the catalysts in the first reactor are more stable. Eventually, the run would have to be terminated earlier, as the axial temperature at the outlet of the first reactor reaches the permissible limit (420 °C) at 2748 h (3.8 months). It is evident from this simulation that the drawback of the design of Case 1 is the absence of quenching in the first reactor where it is more necessary. In Case 2, the hydrogen quench in the first reactor slightly increases the time to reach MAT to 2964 h (4.1 months) of operation, however, now the high temperature zone is shifted towards the outlet of the second reactor. In both cases, a substantial amount of catalyst is underused and the cycle length is lower than that of the aging test. Additionally, the high temperature zones at the outlet of both
Case 1
Case 2
Case 3
AR/H2
AR/H2
AR/H2
H2 H2 H2
Reactor1 Semi-industrial (model) Bench-scale (model) Semi-industrial data Bench-scale data
100 90
Performance, %
80
H2
H2
70 60 50
Reactor2
40 30 20 10 0 HDS
HDN
HDNi
HDV HDAs
HCR
H2 cons.
Fig. 10. Comparison of bench-scale and semi-industrial plant performances.
Product
Product
Fig. 11. Proposed industrial reactor configurations.
Product
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420
0.6
Case 1
400
Case 1 Case 2 Case 3
0.5 0.4
360 340
1h 500 h 2000 h 2748 h
320 300
MOC, wt%
T, °C
380
0.3 0.2 0.1
Case 2
420
0.0
T, °C
400
0.0
0.4
0.6
0.8
1.0
Relative reactor length
380
Fig. 13. Metals deposition profiles at 2700 h.
360
1h 500 h 2000 h 2964 h
340 320 420 400
T, °C
0.2
lengths because the heat release decreases with bed depth. Quench rate also must be carefully selected to obtain the desired temperature profile. It is possible to increase further the number of beds in order to flatten even more the temperature profile, thus reducing the dependency on temperature constraints; however, this increases the height of the reactor vessel and consequently internal hardware investment.
380
1h 500 h 2000 h 3000 h 4500 h
360 340
Case 3
0.0
0.2
0.4
0.6
0.8
1.0
Relative reactor length Fig. 12. Evolution of the axial temperature profiles.
reactors may induce premature thermal cracking leading to pressure drop problems as the ones observed during the aging test. Case 3 overcomes these disadvantages by the use of multiple catalyst beds of increasing lengths. In this case there is a better temperature distribution along the reactor: the temperature levels in all catalyst beds are very similar and there are no high temperature zones. With such an arrangement, it is possible to operate 4500 h (6.3 months) without reaching MAT at any point of the reactor length. Fig. 13 exhibits the axial MOC pattern of each case at 2700 h. Clearly, the MOC profile of Case 3 has the typical descending shape. This means that the majority of the metals are deposited in the front-end HDM catalyst as it should be. An improper reactor design as those of Cases 1 and 2, shifts metals deposition towards the mid and tail-end section. In those cases, the feed temperature is considerably lower than in Case 3, which reduces the HDM rate in the front-end section, whereas the higher temperatures in the remaining reactor length increase metals accumulation and consequently deactivation in the mid and tail-end catalysts. This also explains why the temperature rise in the second reactor decreases with time more rapidly in Cases 1 and 2 than in Case 3. These results indicate that an appropriate reactor configuration is essential for maximizing cycle length and for making better use of the catalyst inventory. For highly exothermic processes, the key factor is to limit the total heat release into smaller portions (i.e. small delta-Ts) in order achieve a more even temperature distribution along the reactor length. To meet this goal, it is preferable to use multiple beds of increasing length rather than equal bed
5. Conclusions The time-evolving uneven axial temperature and activity profiles add a new dimension to the design of industrial reactors. In this work, a hydroprocessing reactor model coupling catalyst deactivation was developed in order to analyze the influence of reactor configuration on cycle length. The model showed to predict reasonably well the deactivation curves obtained from a long-term bench-scale test. The industrial reactor simulations revealed that for this kind of process, single-beds and double-beds are inadequate reactor designs due to two main reasons: (a) they exhibit wide axial temperature differences as result of large bed length, leading to the presence of high temperature zones, which eventually are the main cause of premature shut down; (b) the uneven temperature distribution adversely displaces HDM reactions from the front-end towards the mid and tail-end sections. The configuration with multiple beds of increasing length is characterized by a more regular temperature profile and therefore, corrects the deficiencies of the other arrangements, thereby increasing significantly cycle length. References [1] Robinson PR, Dolbear GE. Hydrotreating and hydrocracking: fundamentals. In: Hsu CS, Robinson PR, editors. Practical advances in petroleum processing, vol. 1. New York: Springer; 2006. [2] Yan TY. Can J Chem Eng 1980;58:259–66. [3] Furimsky E, Massoth FE. Catal Today 1999;52:381–495. [4] Satterfield CN. AIChE J 1975;21:209–28. [5] Sie ST. Appl Catal A 2001;212:129–51. [6] Shah YT, Mhaskar RD, Paraskos J. Ind Eng Chem Process Des Dev 1976;15:400–6. [7] Kam EKT, Al-Shamali M, Juraidan M, Qabazard H. Energy Fuels 2005;19:753–64. [8] Furimsky E. Appl Catal A 1998;171:177–206. [9] Mhaskar RD, Shah YT, Paraskos JA. Ind Eng Chem Process Des Dev 1978;17:27–33. [10] Ancheyta J, Betancourt G, Marroquín G, Centeno G, Muñoz JAD, Alonso F. US patent 7651,604 B2 January; 2010. [11] Alvarez A, Ancheyta J. Appl Catal A 2008;351:148–58. [12] Marafi A, Maruyama F, Stanislaus A, Stanislaus A, Kam E. Ind Eng Chem Res 2008;47:724–41. [13] Korsten H, Hoffmann U. AIChE J 1996;42:1350–60.
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[14] Lababidi HMS, Shaban HI, Al-Radwan S, Alper E. Chem Eng Technol 1998;21:193–200. [15] Shah YT, Paraskos JA. Chem Eng Sci 1975;30:1169–76. [16] Döhler W, Rupp M. Chem Eng Technol 1987;10:349–52. [17] Sánchez S, Rodríguez MA, Ancheyta J. Ind Eng Chem Res 2005;44:9409–13. [18] Bej SK. Energy Fuels 2002;16:774–84. [19] Al-Dahhan MH. Recent advances and scale-up of trickle bed reactors for energy and environmental applications. In: Proc international symposium on advances in hydroprocessing of oil fractions, Morelia (Mexico); June 26–29, 2007. [20] Al-Dahhan MH, Dudukovic MP. AIChE J 1996;42:2594–606. [21] Sie ST. Revue de L’Institute Francais du Pétrole 1999;46:501–15. [22] Mederos FS, Ancheyta J, Chen J. Appl Catal A 2009;355:1–19.
[23] Bondi A. Chem Technol 1971;185–8. [24] Ancheyta J, Betancourt G, Centeno G, Marroquín G, Alonso F, Garciafigueroa E. Energy Fuels 2002;16:1438–43. [25] Ancheyta J, Betancourt G, Centeno G, Marroquín G. Energy Fuels 2002;17:462–7. [26] Kodama S, Nitta H, Takatsuka T, Yokoyama T. J Jpn Petrol Inst 1980;23:310–20. [27] Toulhoat H, Hudebine D, Raybaud P, Guillaume D, Kressmann S. Catal Today 2005;109:135–53. [28] Froment GF, Bischoff KB. Chemical reactor analysis and design. New York: John Wiley & Sons; 1979. [29] Centeno G. Catalyst deactivation during hydroprocessing of heavy oils. Ph.D. dissertation, Instituto Tecnológico de Ciudad Madero, Mexico; 2008. [30] Alvarez A, Ancheyta J, Muñoz JAD. Appl Catal A 2009;361:1–12.
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A modeling study of the effect of reactor configuration on the cycle length of heavy oil fixed-bed hydroprocessing Anton Alvarez a,⇑, Jorge Ancheyta a,b, Guillermo Centeno a, Gustavo Marroquín a a b
Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas 152, Col. San Bartolo Atepehuacan, México, DF 07730, Mexico Escuela Superior de Ingeniería Química e Industrias Extractivas (ESIQIE-IPN), México, DF 07738, Mexico
a r t i c l e
i n f o
Article history: Received 19 August 2010 Received in revised form 3 March 2011 Accepted 4 March 2011 Available online 11 April 2011 Keywords: Modeling Residue hydroprocessing Fixed-bed reactor Catalyst deactivation
a b s t r a c t This article analyses how the configuration of an industrial fixed-bed reactor affects the cycle length of a heavy oil hydroprocessing unit. It is well-known that during the hydroprocessing of heavy feeds, catalyst aging is counterbalanced by continuously increasing reaction temperature. In addition, the exothermality of the reaction provokes a huge temperature rise along the reactor, which is why quenching is necessary. Thus, there is an increasing temperature profile that evolves with time until a maximum allowable temperature is reached and then the operation is shut down. For this reason, there is an optimum reactor configuration (i.e. number of quenches and their positions) that must be established when designing new processes in order to maximize unit run length. To evaluate this problem, a reactor performance model with time varying catalyst activity was constructed. Kinetic and catalyst aging data were obtained from bench-scale tests. The model showed to reproduce sufficiently well the experimental data set. The analysis of various reactor designs indicated that for this process the use of single-bed or double-bed reactors is unpractical in terms of cycle length. A more complex configuration consisting of multiple beds of increasing lengths is necessary to delay shut down. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Most hydrotreating and hydrocracking (HCR) commercial units employ fixed-bed reactors. The exothermic nature of hydroprocessing reactions provokes a temperature rise along the reactor that can exceed 100 °C, when processing heavy feeds [1]. Temperature runaways beyond acceptable limits boost catalyst deactivation, change selectivity towards undesirable products, and sometimes even damage the reactor vessel, which affects process profitability [2,3]. This is why controlling this variable is essential for these processes. Hydroprocessing of heavy fractions in single-bed reactors would be very unpractical and unsafe due to the huge temperature increase. To obtain a more favorable and controllable temperature profile, the total heat release is limited to smaller portions by dividing the total catalyst volume into several beds, commonly of increasing depth, separated by quench zones for introducing relatively cold hydrogen-rich streams [4]. It is well-known that the main disadvantage of using fixed-bed reactors for the hydroprocessing of heavy feeds is the catalyst aging during time-on-stream, caused by metals (Ni + V) and coke deposits. Coking generally occurs during the first few hours of ⇑ Corresponding author. Fax: +52 55 9175 8429. E-mail address: [email protected] (A. Alvarez). 0016-2361/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2011.03.043
operation and then apparently reaches a pseudo steady-state, in contrast to that, metals build-up continues through the whole cycle [5]. In industrial practice, the gradual loss in catalytic activity is offset by increasing reaction temperature. The process is shut down upon the attainment at any position of the reactor of a maximum allowable temperature (MAT) [6], which is established by the metallurgical characteristics of the reactor vessel. However, at end-of-run conditions the catalyst is not necessarily completely deactivated because metals tend to accumulate in the front-end of the catalyst system [7]; this is reflected in a descending-type axial profile of metals-on-catalyst (MOC) [5]. In this sense, front-end hydrodemetallization (HDM) catalysts with high-metal uptake capacity (layered catalyst systems) were introduced to make better use of the catalyst inventory [8]. Nevertheless, proper features of the fixed-bed reactor (i.e. number of quenches and their local positions) strongly contribute delaying the moment in which MAT is attained as well. The problem of reactor design in this type of systems arises from the uneven axial activity and temperature profiles, which also evolve with time. For this reason, a suitable configuration must be established when designing new units in order to maximize cycle length. Perhaps the works of Shah et al. [6] and Mhaskar et al. [9] in the seventies are the only ones that addressed this issue. They studied the dynamics of an industrial double-bed reactor for residue hydrodesulfurization (HDS). Their general conclusion
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Nomenclature A aL aMOC aS AS Asph B Ci Cp g GL Hi H2 H2S H2/oil kapp kint kp L ki S
ki
K H2 S l MOC nj N Ni Ni NH3 pi P q rj R1
fitting parameter of Eq. (13) gas–liquid interfacial area (cm1) unit conversion factor of Eq. (17) liquid–solid interfacial area (cm1) sectional area of the reactor (cm2) asphaltene fitting parameter of Eq. (13) molar concentration of compound i (cm3 mol1) mass heat capacity (J g1 K1) gas mass rate (g s1) superficial liquid mass velocity (kg m2 s1) Henry’s law constant for compound i (MPa cm3 mol1) hydrogen hydrogen sulfide hydrogen-to-oil ratio (std m3 m3) apparent rate coefficient intrinsic rate coefficient particle rate coefficient gas–liquid mass-transfer coefficient for compound i (cm s1) gas–liquid mass-transfer coefficient for compound i (cm s1) adsorption equilibrium constant for H2S (cm3 mol1) liquid mass rate of the j stream (g s1) concentration of metals-on-catalyst (wt.%) order of reaction j nitrogen nickel molar flow of compound i (mol s1) ammonia partial pressure of compound i (MPa) total pressure (MPa) quench fluid mass flowrate (g s1) rate of jth reaction (mol cm3 s1) first reactor
was that for each system there is an optimal reactor configuration, more specifically, a quenching location, that maximizes the cycle length or time to reach MAT. This ideal arrangement depends on numerous variables such as feed temperature, concentrations of metals in the feed, activation energies, and desired conversion level. Recently, the Mexican Institute of Petroleum (IMP) developed a hydroprocessing technology for upgrading heavy and extra-heavy crude oils (Fig. 1) [10]. The heart of the process is a system of fixed-bed reactors in series loaded with a graded catalyst system for hydrotreating and hydroconversion. The IMP technology has been already demonstrated at semi-industrial scale (10 bpd feed flowrate) with excellent results, and is ready for commercial application. During its development the aspects reviewed above were considered important to address. Therefore, it is the purpose of this study to analyze in detail the influence of reactor configuration on the cycle length of the IMP process. To meet this goal, a reactor performance model considering time varying catalyst activity was constructed, based on experimental data obtained from benchscale tests. 2. Experimental 2.1. Kinetic measurements A series of bench-scale hydroprocessing experiments were carried out to obtain kinetic data. The bench-scale plant consists of
R2 S SOR t T uG uL V xMOC
second reactor sulfur start-of-run time (h) temperature gas superficial velocity (cm s1) liquid superficial velocity (cm s1) vanadium fractional concentration of metals-on-catalyst
Greek symbols a fitting parameter of Eq. (16) b fitting parameter of Eq. (16) c fitting parameter of Eq. (16) DYR overall heat of reaction (kJ kg sulfur1) g0 effectiveness factor gCE external catalyst wetting efficiency qG gas density at process conditions (g cm3) qL liquid density at process conditions (g cm3) /j deactivation function of jth reaction deactivation function for coking reactions /Coke /Metals deactivation function for metals deposition Subscripts HDM hydrodemetallization HDS hydrodesulfurization in inlet to the following catalytic bed out outlet of the previous catalytic bed q quench stream Superscripts G gas phase L liquid phase Q quench fluid S solid phase
two fixed-bed reactors in series (500 cm3 of catalyst in each reactor), which were loaded with a system of commercial size catalysts: a front-end HDM catalyst, a midsection catalyst with balanced HDM/HDS activity, and a tail-end highly active catalyst for HDS and HCR. Catalysts were in situ activated by sulfiding with straight-run gas oil (1.46 wt.% sulfur) containing 1.0 wt.% dimethyl disulfide. The feedstock was an atmospheric residue (343 °C+) from a Mexican heavy crude oil (13°API). Physical and chemical properties of the feed (AR 1) are presented in Table 1. Before the tests, the fresh catalyst system was stabilized at low temperature (360 °C) by processing the feed during 100 h. After this initial deactivation period, the experiments were conducted by varying liquid hourly space velocity (LHSV) (0.25–1.0 h1) and temperature (380–420 °C). Pressure and H2/oil ratio were kept constant at 9.81 MPa and 891 std m3/m3, respectively. For each experiment, operating conditions were adjusted in a period of 5 h, followed by a stabilization period to obtain representative products. Under steady operation, two consecutive mass balance runs were performed by collecting the product during 3 h. The tests were carried in less than 250 h of operation so as to minimize the influence of catalyst deactivation on the kinetic analysis. After the experiments, catalyst activity was monitored with a checkback test using the conditions of the first experiment. Physical and chemical properties of the collected liquid samples from the bench-scale plant were analyzed with standard methods. A more detailed description of this set of experiments is provided elsewhere [11].
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H2 make-up
Purge
H2 recycle Light fraction
Heavy crude oil
Sweetening Lean DEA Heavy fraction
Rich DEA
Sour gas
HDT reactors High pressure separator
Stripping Upgraded oil
Fig. 1. Basic representation of the IMP heavy oil upgrading technology.
Table 1 Physical and chemical properties of the feedstock. Property 3
Density at 15.6 °C, g/cm API Gravity Kinematic viscosity at 121 °C, cSt S, wt.% Metals, wppm Ni V Ni + V Asphaltenes in C7, wt.% Distillation IBP, °C 5 vol.%, °C 10 vol.%, °C 15 vol.%, °C 20 vol.%, °C 30 vol.%, °C 40 vol.%, °C vol.% recovery at 538 °C
AR1
AR2
1.0326 5.4 1637.9 5.74
1.0475 3.2 1832.4 6.21
102 620 722 21.77 ASTM D-1160 296 372 401 438 475 521 541 35.25
117 578 695.6 25.10 ASTM D-1160 380.3 415.4 447 474 504 551 – 26.90
2.2. Catalyst aging Catalyst stability was evaluated under process conditions in a 5.2 month-long test. The study was carried out in another benchscale plant with two fixed-bed reactors in series (900 cm3 of catalyst in each reactor). The total reactor volume was loaded with the same catalyst system than that used for generating the kinetic data. The feedstock was a similar atmospheric residue coming from another batch of 13°API heavy crude oil, which properties are also exhibited in Table 1 (AR 2). One important aspect of catalyst life testing is the selection of the operation mode [12]: fixed-performance or fixed-temperature. The former mode, in which reaction temperature is increased periodically to compensate for catalyst deactivation, is more suitable for representing commercial operation. For this reason, in the present work catalyst aging was studied under those conditions.
Unlike most of the residue upgrading processes which typically operate under HDS iso-performance, the target in this process is to keep constant the API Gravity of the product, as this parameter is a good measure of the overall character of oils. The evaluation was carried out under the following operating conditions: LHSV of 0.25 h1, initial temperature of 380 °C, pressure of 9.81 MPa, and H2/oil ratio of 891 std m3/m3. The mass balance runs were performed consecutively by recovering the products every 12 h. Inter-reactor samples were taken every 24 h in order to monitor the behavior of the first reactor. The sample size was kept at less than 2% of the total feed rate so as to reduce the disturbance of the system. This small amount was sufficient enough to carry out some analyses, particularly to determine metals content in the products for estimating the metals uptake in the first reactor. 3. Modeling approach The performance model is a three-phase plug-flow reactor model reported in our previous publication [11]. The model incorporates mass-transfer at the gas–liquid and liquid–solid interfaces, in accordance with the two film theory, and uses correlations to estimate mass-transfer coefficients and fluid properties at process conditions [13]. The feedstock and products are represented by chemical lumps (S, N, Ni, V, asphaltenes (Asph), and 538 °C+ residue) defined by the overall elemental and physical analyses. Thus, the model accounts for the corresponding hydroprocessing reactions such as HDS, hydrodenitrogenation (HDN), HDM (nickel (HDNi) + vanadium (HDV) removals), hydrodeasphaltenization (HDAs) and HCR of the 538 °C+ residue, termed conversion. The following assumptions were considered to develop the model equations: The reactor operates in plug flow mode; there is no axial or radial dispersion. Liquid velocity is constant through the reactor, since liquid expansion caused by hydrocracking is between 3 and 5 vol.% under the experimental conditions.
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Vaporization of the feed does not take place. Gas velocity changes along the reactor due to hydrogen consumption. Pressure is constant along the reactors. Coke build-up occurs during the first 100 h-on-stream and then reaches equilibrium, whereas metals are deposited during the entire operation period. Since catalyst deactivation is relatively slow with respect to the duration of the mass balance runs, a quasi-steady state approximation can be applied to simulate the performance of the process [14]. 3.1. Mass and heat balances The model incorporates reactant mass-transfer phenomena at the gas–liquid and liquid–solid interphases. Hydrogen is dissolved in the liquid and then travels to the catalyst surface in order to react, whereas the gaseous products, such as H2S, NH3 and light hydrocarbons (constituted mainly by CH4), are released to the gas phase. Thus, the change in molar flow of gaseous compounds along the reactor is equal to the gas–liquid transport rate: G
dNi P NGi L L ¼ AS ki aL P G Ci Hi dz Ni
! ð1Þ
where i = H2, H2S, NH3 and CH4. The change in the concentration of gaseous compounds in the liquid phase is owed to gas–liquid equilibrium and mass-transfer to the solid phase: L
dC i 1 P NGi L L ki aL ¼ P G Ci uL Hi dz Ni
!
! S
ki aS ðC Li C Si Þ
ð2Þ
where i = H2, H2S, NH3 and CH4. The chemical species are transferred from the liquid bulk to the catalyst surface, thus the mass balance equation for these compounds is the following: L
dC i 1 S ¼ ki aS ðC Li C Si Þ uL dz
ð3Þ
where i = S, N, Ni, V, Asph and 538 °C+ residue. The compounds transported between the liquid–solid interphase are either consumed or produced by chemical reaction:
Once the quench rate is known, the actual rates of gas and liquid entering the following bed (lin + gin) can be determined by mixing the preceding bed effluent (lout + gout) with the quench fluid:
q þ lout þ g out ¼ lin þ g in
ð7Þ
3.2. Reaction kinetics Kinetic rates of each chemical lump were represented by power-law expressions of nth order: app
rj ðz; tÞ ¼ kt;j ðT; z; tÞðC Si ðz; tÞÞnj
ð8Þ
In the case of HDS, additionally it was considered that the reaction is inhibited by H2S adsorption [13]: app
rHDS ðz; tÞ ¼ kt;HDS ðT; z; tÞ
ðC SS ðz; tÞÞnHDS ðC SH2 ðz; tÞÞ0:5 ð1 þ K SH2 S C SH2 S ðz; tÞÞ2
ð9Þ
Eqs. (8) and (9) represent the local (with respect to the axial poapp sition z along the reactor) rates of reaction j at time t, where kt;j is the time-evolving local apparent rate coefficient of reaction j, with an Arrhenius type temperature dependence, C Si is the local concentration of lump i at the catalyst particle at time t, and nj is the order app of reaction j. The time dependence of kt;j is given by the activity of the catalyst, which can be represented as follows: app
app
kt;j ðT; z; tÞ ¼ kj ðTÞ/ðz; tÞ
ð10Þ
app
where kj is the temperature-dependant apparent rate coefficient of reaction j and /j is the local deactivation function of reaction j app at time t. It is important to notice that by definition kj is a rate coefficient distorted by hydrodynamic and mass-transfer phenomena, which are typically present when obtaining kinetic data from bench-scale trickle-bed reactors. Therefore, it is necessary to correct these apparent coefficients in order to predict industrial scale performance, as it is discussed in the next section. H2 consumption and H2S generation are accounted for through overall stoichiometric coefficients, whereas CH4 is considered to be produced exclusively from residue HCR [17]. The rate parameters were estimated with the Levenberg–Marquardt’s algorithm from the kinetic data, as presented in our previous work [11]. 3.3. Scale-up of kinetic data
S
ki aS ðC Li C Si Þ ¼ rj
ð4Þ
where i = H2, H2S, NH3, CH4, S, N, Ni, V, Asph and 538 °C+ residue, and j = HDS, HDN, HDNi, HDV, HDAs and HCR. The ‘‘’’ sign is for the reactants, while the ‘‘ + ’’ sign is for the products. The following heat balance is included for representing adiabatic operation of the commercial scale reactors:
dT 1 ¼ ½ðDHR ÞrHDS dz uG qG cGP þ uL qL cLP
ð5Þ
The heat balance incorporates an overall heat of reaction for the atmospheric residue HDS process, reported in the literature [15]. This is a fitting parameter originated from several heat balances of similar processes, which accounts for the contribution of all reactions (HDS, hydrogenation, etc.) [16]. It is also necessary to include equations for describing the quench zones. The following heat balance can be employed to estimate the quench rate (q) for a desired cooled mixture temperature (Tin):
Z
T in
T out
lout CpL dT þ
Z
T in
T out
g out CpG dT þ
Z
T in
TQ
qCpQ dT ¼ 0
ð6Þ
The accuracy when scaling-up reaction kinetics strongly depends on the quality of the experimental data. The experience indicates that bench-scale trickle-bed reactors produce apparent kinetics because they suffer from several deviations from ideal plug flow behavior, as a result of low liquid velocities [18]. Having this in mind, apparent kinetics can be translated to intrinsic kinetics by considering these effects as follows [19]: app
in
kj ðTÞ ¼ g0 gCE kj ðTÞ
ð11Þ
Eq. (11) correlates the experimentally obtained apparent rate in coefficient of reaction j with the intrinsic rate coefficient kj , through intraparticle diffusion and reactor hydrodynamics, represented by the catalyst effectiveness factor (g0) and the external catalyst wetting efficiency (gCE), respectively. When the experiments are carried out using commercial size catalysts, g0 is already included in the apparent rate coefficients. This allows for describing industrial performance [20], but only if the catalyst used in the tests is exactly the same as the one in the commercial unit [21]. Considering that in the present work the experiments were carried in out using commercial size catalyst, g0 can be grouped with kj , and thus Eq. (11) becomes:
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A. Alvarez et al. / Fuel 90 (2011) 3551–3560
ð12Þ
p
where kj is the ‘‘particle’’ rate coefficient. This simple expression correlates bench-scale and industrial reactor performances through a single scale-up parameter. The term gCE is a measure of the extent of catalyst utilization, which tends to become complete (gCE 1) at industrial scale [22]. In the present work, this effect was handled with the empirical approach proposed by Bondi [23], of the linear form:
1 k
app
¼
1 A p þ B k GL
ð13Þ
Equation (13) is a relationship in which the apparent rate coefficient approaches that of particle kinetics as superficial liquid mass velocity (GL) goes to infinity, with fitting parameters A and B. For a number of systems the value of exponent B typically falls in the range of 0.5 to 0.7, more commonly 2/3. This approach is based on the fact that kapp tends to increase with liquid rate in trickle-bed reactors. By plotting 1/kapp vs. 1/GBL it is possible to approximate the value of the particle rate coefficient at infinite GL, which is the intercept on the y-axis (1/kp). For this reason, it is necessary to perform the tests at various liquid rates and if possible, as the author recommended [23], using two reactor scales in order to reduce the uncertainty of the extrapolated kp. In this case, wetting efficiency was estimated from the LHSV bench-scale experiments and also from semi-industrial scale data (10 bpd plant), using the same feedstock, operating conditions, and catalysts. 3.4. Catalyst deactivation During hydroprocessing of heavy feeds, coking and metals deposition reduce catalytic activity with time-on-stream. Our previous experimental observations [24,25] indicate that coke formation is the main cause of the substantial activity loss during the few first hours of operation (generally the first 100 h), but at long-term, metals build-up is more relevant. It was also noticed that coking reaches equilibrium after this initial stage; hence the assumption of considering that coke formation occurs only during the first 100 h. The deactivation function /j mentioned earlier is defined as the ratio between the local apparent rates of reaction j at time t and at t = 0 (fresh catalyst):
/j ðz; tÞ ¼
r j ðz; tÞ rj ðz; t ¼ 0Þ
/j ðz; tÞ ¼
1
ð15Þ
ðxMOC ðz; tÞÞcj
ð1 þ aj tÞbj
ð16Þ
The first term of Eq. (16) represents the initial activity decline (/Coke ) during the first 100 h, as function of time t with fitting j parameters aj and bj for reaction j [28]. At t = 100 h it is considered that this initial period is terminated and the value of /Coke is kept j constant for the rest of the operation cycle. It must be stressed out that the proper way for describing this process is to link this function to a coking agent instead of time, unfortunately there was no experimental information available that provided insight into this mechanism, and therefore, there was no other possible way to represent this behavior. This is why the application of such functionality is restricted to the experimental operating conditions. Nevertheless, SOR data obtained with several heavy crudes and residues at different operating conditions [29] indicated that the HDS and HDM activity decay in this period interestingly follows the same pattern even in such a wide range of conditions. This behavior is represented in Fig. 2 as the change in the deactivation functions of HDM and HDS with respect to the change in time (D/j =Dt). It is observed also that in all cases the catalyst system is rapidly stabilized at t = 100 h. On this basis, it can be considered that it is reasonable to use this approach and it can be assumed that the shape of the SOR curve is not affected by the variable temperature profile when running the adiabatic simulations. The second term of Eq. (16) describes /Metals . In this case, this j function is directly linked to the deactivating agent: the local amount of metals build-up, with fitting parameter cj for reaction j, which also is specific for each of the three catalysts. The quantity of metals deposition is expressed in terms of xMOC, defined as the ratio between the local concentration of metals-on-catalyst (MOC) at time t and the specific maximum metals uptake of each catalyst. Such functionality allows for establishing a deactivation profile with respect to time t and axial position z, which is a determining factor for the catalyst cycle life. MOC is obtained by solving the metals mass balance equation:
0.03
ð14Þ
The values of /j range between 1 (fresh catalyst) and 0 (totally spent catalyst). This function is essentially similar to the concept of active surface area of the catalyst, employed in other deactivation models [26,27]. The formulation of the deactivation function requires measuring rj at t = 0, however, in these cases this is not possible because the liquid product is accumulated during several hours to obtain enough sample to carry out all the required analyses. For practical reasons, it has been proposed to assume that the initial activities are those obtained from the first sample [12], which is collected after the first 10–12 h of operation. In this study however, initial activities are obtained from the start-of-run (SOR) aging curve by extrapolation [28], which seems to be more accurate. As already mentioned, it is considered that catalyst aging is provoked by coking reactions at SOR conditions and metals deposition during the whole cycle. The contribution of these two processes to the overall catalyst deactivation can be expressed as follows:
/j ðz; tÞ ¼ /Coke ðtÞ þ /Metals ðz; tÞ j j
where /Coke and /Metals stand for the deactivation functions for cokj j ing at time t 6 100 h and local metals build-up (Ni + V) at time t, respectively. The following empirical relationship is proposed for describing the deactivation process:
ΔφHDS /Δt
p
¼ gCE kj
0.02 0.01 0.00 0
10
20
30
40
50
60
70
80
90 100
80
90 100
0.04
ΔφHDM /Δt
app
kj
0.03 0.02 0.01 0.00 0
10
20
30
40
50
60
70
time-on-stream, h Fig. 2. HDS and HDM deactivation curves at SOR conditions [29]. Heavy crude oil (13°API) at LHSV = 0.5 h1, P = 6.86 MPa, and H2/oil = 891 std m3/m3: (d) 380 °C, (N) 400 °C, () 420 °C; (j) atmospheric residue (5.4°API) at 360 °C, LHSV = 0.25 h1, P = 9.81 MPa, and H2/oil = 891 std m3/m3; Maya crude oil (21°API) at LHSV = 0.5 h1, P = 6.86 MPa, and H2/oil = 891 std m3/m3: (s) 380 °C, (D) 400 °C, (}) 420 °C.
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A. Alvarez et al. / Fuel 90 (2011) 3551–3560
dMOCðz; tÞ ¼ aMOC r HDM ðz; tÞ dt
4.0
ð17Þ
3.5
where MOC is expressed in wt.%, rHDM is the local rate of HDM at time t, and aMOC is a unit conversion factor.
4.1. Bench-scale unit simulations
70
N
6000 5000 4000 3000
538°C+
600
Content, wt%
60 50
500 20
Asph
400
15
300
V 10
200
5
Ni
0
S 0.2
0.4
Content, wppm
The reactor performance model was applied to simulate the behavior of the bench-scale reaction system at various LHSV and temperatures under steady catalyst activity (kinetic tests). The results presented in Fig. 3 show that the evolution of the various chemical lumps along the reactor is well predicted. The model is also able to describe H2S and CH4 yields, and H2 consumption along the reactor, as shown in Fig. 4. In general, the model predictions are in good agreement with the experimental observations. A deeper analysis on the behavior of this reaction system can be found in our previous work [11]. Fig. 5 illustrates the levels of HDS, HDM, and HCR of 538 °C+ residue along the catalyst stability test. It can be observed that reaction temperature was increased periodically to keep the target oil quality at 23°API (the upgraded oil is obtained by blending the bypassed light fraction with the reactor liquid effluent, as shown in Fig. 1). The deactivation curves followed different patterns under the selected operation mode, for instance, HDS and HDNi decreased progressively with time-on-stream, whereas HDV and HCR responded well to the gradual temperature increase. Such a behavior is owed to diverse factors such as the properties of the catalysts, activation energies, feed type, and the thermal effect on the overall performance. There are two discontinuities at around 1500 h of time-on-stream and one close to 2500 h, which are owed
100 0
0.6
0.8
1.0
Relative reactor length Fig. 3. Evolution of the chemical lumps along the reactor at T = 380 °C and LHSV = 0.25 h1: (symbols) experimental, (–) simulated.
3
2.5
H2/oil, std m /m
3
850
2.0
800
H2S
1.5 1.0
750
CH4
0.5 0.0
700 0.0
0.2
0.4
0.6
0.8
1.0
Relative reactor length Fig. 4. Evolution of gas yields at T = 380 °C and LHSV = 0.25 h1: (symbols) experimental, (–) simulated.
510 500 490 HDV 480 470 460 450 440 430 420 API 410 400 390 T 380
100 90
HDS
80 70 60 50
HDNi
40
HCR
30 20 10 0 0
T, °C
4. Results and discussion
Yield, wt%
The reactor model consists of a set of coupled ordinary differential and algebraic equations, which was coded in Fortran. The mass balances for the gas and liquid phases, as well as the heat balance, are solved simultaneously using a fourth-order Runge–Kutta method, whereas the solid phase equations are solved with a Newton– Raphson method, in order to obtain the concentration and temperature profiles along the reactor. With the concentration profiles, the local amount of metals build-up is estimated and then the deactivation functions for each reaction are evaluated. The time step is increased and all the calculations are updated. The industrial simulation is terminated when a MAT of 420 °C is reached at any point of the reactor length.
3.0
Performance % or API Gravity
3.5. Solution technique
0.0
900
500 1000 1500 2000 2500 3000 3500
time-on-stream, h Fig. 5. Process performance during time-on-stream: (symbols) experimental, (–) simulated.
to operational problems caused by high pressure drops between the reactors and the separation system. In those cases, reaction temperature was decreased and the heavy feed was switched to light gasoil in order to wash off the plugs. This represented a 297 h interruption (in total) of normal operation; however, these periods are not included in Fig. 5. Once the pressure drop was within acceptable limits, the feed was switched back to residue and temperature was increased again until the target performance was achieved. The test demonstrated that the IMP hydrotreating technology is capable of keeping the product within specification during the 3440 h-on-stream (5 months of operation). The activity levels at the end of the test also indicated that the catalyst was still active; however, the substantial loss in HDS activity implies that at this stage the process is mainly driven by thermal effect. The operation was terminated due to the high temperature level (398 °C) in order to avoid excessive coking, which eventually would produce even more severe fouling problems. The aging data presented in Fig. 5 were employed to fit the deactivation parameters in order to simulate the performance of the process during time-on-stream. The parameters of the deactivation functions are listed in Table 2. From Fig. 5 it can be observed that the model predicts reasonably well the deactivation curves. The parity plot shown in Fig. 6 indicates that most of the data randomly falls into the ±10% interval, which confirms the validity of the model. Nickel removal exhibits the highest deviations, which is owed to a relatively wide dispersion of the experimental data. Such level of scattering is most likely to be caused by experimental errors along with the effect of relatively low analyte concentration. As mentioned in the experimental section, inter-reactor samples were taken to monitor the behavior of the first reactor. The
3557
Table 2 Deactivation parameters.
3.81 2.70 4.57 101 2.80
0.06 0.08 0.10 0.05
HDM (R1 + R2)
70
c
b
Front-end
Mid-end
Tail-end
0.34 0.96 3.00 1.15
0.35 0.75 0.52 1.49
0.46 0.45 0.31 2.70
60
HDM, %
a HDS HDNi HDV HCR
50 40 HDM (R1)
30
R1 R1 + R2
20
R2
10 100
HDV HDNi HDS HCR
90 80
0 0
500 1000 1500 2000 2500 3000 3500
time-on-stream, h
70
Predicted, %
260 240 220 200 180 160 140 120 100 80 60 40 20 0
80
MOC , wt%
A. Alvarez et al. / Fuel 90 (2011) 3551–3560
Fig. 7. HDM performance and metals-on-catalyst (MOC): (symbols) experimental, (–) simulated, R1 (first reactor), R2 (second reactor). The experimental values (s) of MOC at the end of the run were estimated based on the metals balance between feed and products.
60 50 40 30 20
R1
0.0
R2
0.0
0 0
10
20
30
40
50
60
70
80
90 100
Experimental, % Fig. 6. Comparison between experimental data and model predictions. (- - -) ±10%.
HDM performance of the first reactor, along with the global HDM level, is presented in Fig. 7. The performance data of the first reactor are widely scattered but the model is still able to capture the general trend. It is evident that inter-reactor sampling does not produce reliable information for kinetic analysis, which is why these data were not considered for parameter estimation. However, those samples are acceptable enough for a rough estimation of the metals uptake in the first reactor and to verify the model capability. As for the overall HDM, the model works sufficiently well since parameter estimation was carried out using the overall aging data presented earlier. The same figure shows the evolution of the MOC in the both reactors. It is clear that MOC accumulation follows a linear behavior. Apparently, there is a good match between the model results and the observed MOC, which was estimated from the metals balances between the feed and products. In the case of the first reactor, the dispersion of HDM activity does not affect substantially the linear MOC trend because the experimental data are uniformly scattered around the simulation. These results actually confirm the usefulness of inter-reactor sampling in this kind of studies. It is noteworthy that metals build-up in the first reactor is as high as 120 wt.% (in fresh-catalyst basis), whereas in the second reactor is only 20 wt.%, which indicates that the front-end catalyst indeed is protecting downstream catalysts. The model is also capable of describing the evolution of axial MOC profiles, as illustrated in Fig. 8. It can be observed that initially (500 h) the MOC profile is relatively uniform along the whole reactor length. As time-on-stream increases, the MOC profile acquires the typical descending shape, particularly in the first reactor. Clearly, it can be noticed that the front-end HDM catalyst, located in the first 60% of the first reactor (0–0.6), exhibits the highest metals accumulation, followed by the mid-end catalyst, which is distributed in the remaining space of the first reactor and the first 20% of the second reactor (0–0.2). Such a layered catalyst arrangement reduces significantly the metals content in the stream entering to the highly-active HDS catalyst bed located in the tail-end of the second reactor.
Relative reactor length
10 0.2
0.2
0.4
0.4
0.6
0.6 500 h 1500 h 2500 h 3440 h
0.8 1.0
0.8 1.0
0
5
10 15 20 25
MOC, wt%
0
1
2
3
4
5
MOC, wt%
Fig. 8. Simulated axial MOC profiles in the first (R1) and second (R2) reactors. The MOC units are referred to the total amount of fresh catalyst in each reactor.
4.2. Scale effect analysis Ideally, in trickle-bed processes sustaining liquid-limited reactions, the whole catalyst bed contributes to the overall conversion because every catalyst particle is covered by a flowing film of liquid [21]. However, the behavior of bench-scale reactors deviates significantly from ideal behavior as a result of low liquid velocities that cause incomplete catalyst wetting. For this reason, it is essential to estimate wetting efficiency to determine the extent of catalyst utilization and thus correcting kinetic data. Fig. 9 provides the analysis of the scale effect on catalyst wetting efficiency, defined earlier as the ratio between the apparent and particle rate coefficients. The theoretical curve was obtained from Eq. (13) with A = 1.79 103 and B = 2/3, using the benchand semi-industrial scale data at 380 °C. The shape of the curve indicates that wetting efficiency is a weak function of superficial liquid mass velocity in this system. In fact, the values of gCE for all experiments fall in the range of commercial operation (0.7–1.0); for instance, at the lowest GL (0.06 kg/(m2s)), gCE is already higher than 0.7 (more than 70% catalyst utilization), whereas for the semiindustrial test gCE is very close to 1, as this plant has roughly 230 times more processing capacity that the bench-scale unit operating at LHSV = 0.25 h1. Apparently, this indicates that the semi-industrial scale reactor behaves ideally in terms of catalyst utilization and therefore, this test can be considered representative of commercial operation. The semi-industrial scale data are compared against benchscale performance in Fig. 10. From the previous analysis, it was
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A. Alvarez et al. / Fuel 90 (2011) 3551–3560
LHSVsemi-ind = 0.2 h
1.0
-1
0.9
LHSVbench = 1.0 h LHSVbench = 0.75 h
ηCE
0.8
LHSVbench = 0.5 h 0.7
LHSVbench = 0.25 h
-1
-1
-1
-1
0.6
0.5 0.1
1
10
100
2
GL, kg/(m s) Fig. 9. Catalyst wetting efficiency as function of superficial liquid mass velocity: (d) bench-scale, (s) semi-industrial scale, (–) theoretical.
determined that a wetting efficiency of 0.7 at those operating conditions correlates sufficiently well bench-scale and semi-industrial reactor performances. It is evident that the semi-industrial scale outperforms the bench-scale because the latter uses only 70% of its catalyst inventory. 4.3. Industrial reactor simulations The performance model was applied to analyze the effect of reactor design on the behavior of the process. The main target was to construct an arrangement that maximizes the time to reach the permissible temperature limit (420 °C) at any point of the reactor length or at least that equals the cycle length obtained during the aging test (5 months). To meet this goal, it was necessary to apply a sequential analysis of several reactor configurations that evolved from one to another. This allowed identifying the flaws of a given design and then improving it in terms of cycle length. From this sequential procedure the following reactor arrangements were studied: Case 1: two single-bed reactors of equal length, with one hydrogen quench. Case 2: one reactor with two beds of equal length followed by a second single-bed reactor, with two hydrogen quenches. Case 3: one reactor with four beds of increasing length followed by a second reactor with two beds of increasing length, with two hydrogen quenches and three heat exchangers.
The three reactor configurations are illustrated in Fig. 11. The first arrangement (Case 1) is identical to that of the bench-scale plant and it was used as a starting point design, whereas the other two are its successors. It can be noticed also from Fig. 11 that the configuration of Case 3 uses a combination of hydrogen quenching and heat exchange. The amount of hydrogen quenching in this system is restricted by a low H2/oil ratio, which is meant to reduce compression requirements. In previous studies [11,30], it was determined that in this process it is not possible to quench with hydrogen the total heat release and at the same time to keep the required average H2/oil ratio (891 std m3/m3) along the reactors. In fact, no more than two hydrogen quenches are allowed to keep the required H2/oil ratio, because each extra hydrogen quench decreases significantly this ratio, especially in the first reactor. Having this in mind, the amount of hydrogen quenching in Cases 1 and 2 was also optimized to meet this criterion. The time-evolving axial temperature profiles are illustrated in Fig. 12. It can be observed that in all cases the temperature profile is progressively displaced upwards because the feed temperature is increased so as to keep API Gravity of the product constant (HCR at 35%). The configuration of Case 1 is characterized by a large and continuous heat release in both reactors. The considerable delta-T produces high temperature zones at the outlet of each reactor. The temperature rise in the second reactor slowly decreases with process time because the tail-end catalyst is less tolerant to metals deposition and loses activity more rapidly, whereas the catalysts in the first reactor are more stable. Eventually, the run would have to be terminated earlier, as the axial temperature at the outlet of the first reactor reaches the permissible limit (420 °C) at 2748 h (3.8 months). It is evident from this simulation that the drawback of the design of Case 1 is the absence of quenching in the first reactor where it is more necessary. In Case 2, the hydrogen quench in the first reactor slightly increases the time to reach MAT to 2964 h (4.1 months) of operation, however, now the high temperature zone is shifted towards the outlet of the second reactor. In both cases, a substantial amount of catalyst is underused and the cycle length is lower than that of the aging test. Additionally, the high temperature zones at the outlet of both
Case 1
Case 2
Case 3
AR/H2
AR/H2
AR/H2
H2 H2 H2
Reactor1 Semi-industrial (model) Bench-scale (model) Semi-industrial data Bench-scale data
100 90
Performance, %
80
H2
H2
70 60 50
Reactor2
40 30 20 10 0 HDS
HDN
HDNi
HDV HDAs
HCR
H2 cons.
Fig. 10. Comparison of bench-scale and semi-industrial plant performances.
Product
Product
Fig. 11. Proposed industrial reactor configurations.
Product
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A. Alvarez et al. / Fuel 90 (2011) 3551–3560
420
0.6
Case 1
400
Case 1 Case 2 Case 3
0.5 0.4
360 340
1h 500 h 2000 h 2748 h
320 300
MOC, wt%
T, °C
380
0.3 0.2 0.1
Case 2
420
0.0
T, °C
400
0.0
0.4
0.6
0.8
1.0
Relative reactor length
380
Fig. 13. Metals deposition profiles at 2700 h.
360
1h 500 h 2000 h 2964 h
340 320 420 400
T, °C
0.2
lengths because the heat release decreases with bed depth. Quench rate also must be carefully selected to obtain the desired temperature profile. It is possible to increase further the number of beds in order to flatten even more the temperature profile, thus reducing the dependency on temperature constraints; however, this increases the height of the reactor vessel and consequently internal hardware investment.
380
1h 500 h 2000 h 3000 h 4500 h
360 340
Case 3
0.0
0.2
0.4
0.6
0.8
1.0
Relative reactor length Fig. 12. Evolution of the axial temperature profiles.
reactors may induce premature thermal cracking leading to pressure drop problems as the ones observed during the aging test. Case 3 overcomes these disadvantages by the use of multiple catalyst beds of increasing lengths. In this case there is a better temperature distribution along the reactor: the temperature levels in all catalyst beds are very similar and there are no high temperature zones. With such an arrangement, it is possible to operate 4500 h (6.3 months) without reaching MAT at any point of the reactor length. Fig. 13 exhibits the axial MOC pattern of each case at 2700 h. Clearly, the MOC profile of Case 3 has the typical descending shape. This means that the majority of the metals are deposited in the front-end HDM catalyst as it should be. An improper reactor design as those of Cases 1 and 2, shifts metals deposition towards the mid and tail-end section. In those cases, the feed temperature is considerably lower than in Case 3, which reduces the HDM rate in the front-end section, whereas the higher temperatures in the remaining reactor length increase metals accumulation and consequently deactivation in the mid and tail-end catalysts. This also explains why the temperature rise in the second reactor decreases with time more rapidly in Cases 1 and 2 than in Case 3. These results indicate that an appropriate reactor configuration is essential for maximizing cycle length and for making better use of the catalyst inventory. For highly exothermic processes, the key factor is to limit the total heat release into smaller portions (i.e. small delta-Ts) in order achieve a more even temperature distribution along the reactor length. To meet this goal, it is preferable to use multiple beds of increasing length rather than equal bed
5. Conclusions The time-evolving uneven axial temperature and activity profiles add a new dimension to the design of industrial reactors. In this work, a hydroprocessing reactor model coupling catalyst deactivation was developed in order to analyze the influence of reactor configuration on cycle length. The model showed to predict reasonably well the deactivation curves obtained from a long-term bench-scale test. The industrial reactor simulations revealed that for this kind of process, single-beds and double-beds are inadequate reactor designs due to two main reasons: (a) they exhibit wide axial temperature differences as result of large bed length, leading to the presence of high temperature zones, which eventually are the main cause of premature shut down; (b) the uneven temperature distribution adversely displaces HDM reactions from the front-end towards the mid and tail-end sections. The configuration with multiple beds of increasing length is characterized by a more regular temperature profile and therefore, corrects the deficiencies of the other arrangements, thereby increasing significantly cycle length. References [1] Robinson PR, Dolbear GE. Hydrotreating and hydrocracking: fundamentals. In: Hsu CS, Robinson PR, editors. Practical advances in petroleum processing, vol. 1. New York: Springer; 2006. [2] Yan TY. Can J Chem Eng 1980;58:259–66. [3] Furimsky E, Massoth FE. Catal Today 1999;52:381–495. [4] Satterfield CN. AIChE J 1975;21:209–28. [5] Sie ST. Appl Catal A 2001;212:129–51. [6] Shah YT, Mhaskar RD, Paraskos J. Ind Eng Chem Process Des Dev 1976;15:400–6. [7] Kam EKT, Al-Shamali M, Juraidan M, Qabazard H. Energy Fuels 2005;19:753–64. [8] Furimsky E. Appl Catal A 1998;171:177–206. [9] Mhaskar RD, Shah YT, Paraskos JA. Ind Eng Chem Process Des Dev 1978;17:27–33. [10] Ancheyta J, Betancourt G, Marroquín G, Centeno G, Muñoz JAD, Alonso F. US patent 7651,604 B2 January; 2010. [11] Alvarez A, Ancheyta J. Appl Catal A 2008;351:148–58. [12] Marafi A, Maruyama F, Stanislaus A, Stanislaus A, Kam E. Ind Eng Chem Res 2008;47:724–41. [13] Korsten H, Hoffmann U. AIChE J 1996;42:1350–60.
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