Ind. Eng. Chem. Res. 2004, 43, 6027-6034
6027
Simulation of Catalytic Cracking in a Fixed-Fluidized-Bed Unit Roberta C. Vieira,† Jose´ Carlos Pinto,† Evaristo C. Biscaia, Jr.,† Claudia M. L. A. Baptista,‡ and Henrique S. Cerqueira*,‡ Programa de Engenharia Quı´mica, Cidade Universita´ ria, CP 68502, Universidade Federal do Rio de Janeiro, COPPE, Rio de Janeiro, 21945-970 Rio de Janeiro, Brazil, and Petrobras, Centro de Pesquisas e Desenvolvimento Leopoldo A. Miguez de Mello (Cenpes), Pesquisa e Desenvolvimento do Abastecimento, Tecnologia em FCC, Ilha do Funda˜ o, Av. Jequitiba´ 950, 21949-900 Rio de Janeiro, Brazil
This paper shows that reliable mechanistic models that take into account coke formation and the amount of strippable hydrocarbons can be built based on fixed-fluidized-bed laboratory tests if proper experimental designs and oil characterizations are provided. A methodology was implemented and used here to evaluate and to compare the performance of three distinct equilibrium fluid catalytic cracking (FCC) catalysts (e-cat) using different feedstocks. It is shown that phenomenological models (through their kinetic parameter estimates) can provide useful insights about the different catalyst performances during both the FCC and the catalyst stripping processes. It was observed that the deactivation parameter correlates well with the metal content of e-cat and that kinetic constants associated with the cracking of heavy components correlate well with the surface area and the microactivity of the e-cat. 1. Introduction Fluid catalytic cracking (FCC) is one of the key processes in the petroleum industry, aiming at the conversion of heavy feedstocks into lighter more valuable products such as liquefied petroleum gases (LPGs) and gasoline. Besides the desired cracking reactions, the formation of coke (hydrocarbons dragged or retained in the pore structure of the catalyst before regeneration) also occurs in these systems. This coke temporarily deactivates the catalyst, which continuously circulates between the riser (FCC reactor) and the regenerator. In the regenerator, the coke is converted into CO, CO2, H2O, SOx, and NOx compounds. Under the regenerating conditions, at temperatures close to 710 °C, the water vapor (combined with the action of vanadic acid) causes permanent degradation of the zeolite structure. Fresh catalyst must be added continuously in order to maintain the activity of the inventory (1400 t/day for 350 FCC units), which makes the FCC process the most important catalyst market.1,2 For the reasons described above, the prediction of the commercial catalyst performance is one of the most important research activities in the oil refining industry. This is particularly true for the FCC process, where the impact of the FCC catalyst on the performance of the commercial unit justifies continuous efforts to ensure the use of the best available catalyst. The prediction of the catalyst performance is usually accomplished with the help of pilot-plant and/or laboratory-scale facilities. Standardized tests have been developed to allow for comparison of different catalysts in different laboratories and companies. Laboratory-scale facilities are generally preferred for catalyst testing because of the much lower costs of investment, operation, and analysis.3 A good review of * To whom correspondence should be addressed. E-mail:
[email protected]. † Universidade Federal do Rio de Janeiro. ‡ Petrobras, Centro de Pesquisas e Desenvolvimento Leopoldo A. Miguez de Mello (Cenpes).
laboratory reactor types used in catalytic cracking was presented elsewhere.4 The most common test used for characterization of catalyst cracking is the fixed-bed microactivity test (MAT)5 with some modifications.6-8 Among many reactor types currently in use for FCC catalyst evaluation, one may cite the pulse reactor,9-12 the microbalance pulse reactor,13 the riser simulator,14 the microdowner,15 the transport type continuous reactor,16,17 and the fixed-fluidized-bed ACE unit.18,19 In this latter test unit, a vaporized stream of a heavy oil fraction is forced to flow through a fluidized catalyst bed and is partially cracked into lighter oil fractions, particularly into gasoline. Conversions into the different cracking products, including coke and stripped hydrocarbons, are then used to compare the performance of different catalysts. Empirical mathematical models are normally built to correlate the results with the catalystto-oil (CTO) ratio, and afterward, these models are used to evaluate the catalyst. In the present paper, a simple and reliable mechanistic (lump) model20-24 is built based on fixed-fluidizedbed ACE data in order to evaluate and compare the performance of three distinct industrial catalysts using different oil feeds. It is shown that a phenomenological model (through their kinetic parameter estimates) can provide useful insights about the different catalyst performances during both the catalytic cracking and the catalyst stripping steps. 2. Materials and Methods Three different equilibrium catalysts (e-cats), with distinct characteristics (Table 1) and used in different commercial FCC units, were selected for this study. Each e-cat was tested with its corresponding commercial feedstock, but the experimental design also included testing of catalysts B and C with alternative commercial feedstocks. The following pairs of e-cat,feedstock were evaluated: A,A; B,B; B,C; B,D; C,B; and C,C. The used feedstocks present differences in density, carbon residue, and composition (Table 2). For each pair of feedstock/ catalyst, at least three different CTO ratios were used
10.1021/ie049781t CCC: $27.50 © 2004 American Chemical Society Published on Web 08/10/2004
6028 Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004 Table 1. Catalyst Properties e-cat
A
B
C
MAT at CTO ) 5.0 (wt %) bulk density (g/mL) surface area (m2/g) micropore volume (cm3/g) AAIa RE2O3 (%) Fe (%) Na (%) Ni (ppm) V (ppm)
58 0.94 121 0.041 6.0 2.20 0.45 0.38 3365 2555
70 0.84 145 0.055 6.2 2.20 0.35 0.43 2495 1872
63 0.86 125 0.043 8.3 2.94 0.42 0.36 5066 5845
a
Akzo Nobel accessibitity index.25
Table 2. Feedstock Properties feedstock
A
B
C
D
density at 20/4 °C (g/cm3) aniline point (°C) RCR (wt %) saturates (%) monoaromatics (%) diaromatics (%) triaromatics (%) polyaromatics (%) viscosity (cSt) at 100 °C at 80 °C @ 60 °C simulated distillation (°C) IBP 5% 10% 20% 30% 50% 70% 80% 90% PFE total sulfur (ppm) total nitrogen (ppm) basic nitrogen (ppm)
0.9395 88.2 1.8 52.2 17.2 18.3 7.2 5.1
0.9410 86.0 1.4 45.1 13.5 13.2 4.5 2.1
0.9476 84.3 3.2 41.8 13.4 13.4 4.5 2.2
0.9595 92.0 6.9 36.6 10.4 11.4 3.8 1.9
23.6 45.6 n.a.
20.2 38.0 n.a.
23.5 43.2 140.4
39.0 n.a. 292.8
224.5 338.5 374.5
290.5 341.5 368.5 405.5
275.0 343.5 374.0 410.0
215.0 305.0 349.5 406.5
476.5 530.5 568.5 632.5 743.0 7830 3250 1116
469.5 509.5 536.0 590.5 732.0 6449 4088 1425
505.0 587.0 637.0 720.5 750.0 5781 4858 1743
442.0 498.5 574.5 715.0 6900 3735 1019
for evaluation of the catalyst performance, in the range between 3 and 9. The experimental runs were performed in an automated fixed-fluidized-bed ACE unit.18 For each experimental run, 9 g of e-cat without any pretreatment was loaded into the ACE reactor, which has an effective height of 29 cm and a volume of about 90 cm3. After reaction, the catalyst was stripped with nitrogen for 10150 s in order to recover entrapped hydrocarbons. After stripping, the reactor temperature was increased to 690 °C for 4 min in order to completely burn the coke with air. A CO2 infrared detector quantified the total amount of coke (coke I + coke II). Liquid and gaseous effluents were collected in a receiver and a gas collection bottle, respectively. The gaseous effluent consists of a mixture of fuel gas (H2, C1, and C2), LPG (C3 and C4), and gasoline (C5 plus). The gaseous effluent was analyzed by online injection into a Agilent 6890N gas chromatograph, equipped with two thermal conductivity detectors and two columns: a Porapak (20% sebaconitrile/80% chromosorb PAW Q) and a molecular sieve, both maintained at 50 °C. The liquid effluent was analyzed by simulated distillation in a Agilent 6890 gas chromatograph equipped with a flame ionization detector and a HP-1 methyl silicon column. The amounts of gasoline, light cycle oil (LCO), and heavy cycle oil (HCO) were quantified considering the temperature ranges of 35-221, 221-343, and 343+ °C, respectively. The temperature program was the following: 40 °C for 2 min, heating at 10 °C/min up to
Figure 1. Schematic representation of the ACE unit: reactor and receiver. Table 3. Experimental Error yield (wt %)
experimental error (wt %)
hydrogen fuel gas LPG gasoline
0.02 0.20 1.30 2.15
yield (wt %)
experimental error (wt %)
LCO bottoms coke
0.72 1.93 0.26
200 °C, and heating at 20 °C/min up to 325 °C followed by 10 min at that temperature. All ACE runs were performed at a constant temperature of 550 °C with a constant feedstock flow rate of 1.2 g/min. For a given pair of feedstock/catalyst, the CTO was varied by means of the injection time (catalyst time on stream, TOS), which was varied in the range of 75-150 s. To quantify the amount of strippable hydrocarbons, two stripping times were considered: 10 s (operational minimum) and 150 s (full stripping). The yields were calculated as the weight percent of reactant. Conversion is defined as X ) 100 (wt %) - LCO (wt %) - HCO (wt %). The experimental errors were considered to be equal to 2 times the standard deviation, evaluated with 15 replicate runs performed with a standard catalyst and feedstock at CTO ) 5.0. As shown in Table 3, experimental errors are small for all yields, indicating that experimental data were obtained with fair precision. Similar values have been previously reported.3 Extra experiments with e-cat and feedstock A (Tables 1 and 2) were conducted with a modified procedure. In those experiments, an inert solid (spray-dried silica) was mixed with the active catalyst in different amounts, to quantify the importance of the thermal cracking during the ACE test operating conditions. 3. Mathematical Model 3.1. Hypothesis of the Model. Figure 1 depicts a schematic representation of the ACE unit. For modeling purposes, it is assumed that liquid and gaseous effluents are collected in the same receiver. The feed rate F0, the pressure P, and the temperature T of the reaction vessel are assumed to be constant throughout the tests. The molar outlet rate F is variable because of the loss of catalyst activity that results in a variation in the product number of moles during the cracking reactions. The feed composition a0(t) is a discontinuous function representing the different stages of the experiment: aI is the composition of the inert gas (100% N2) and aF is the composition of the feedstock.
Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004 6029
a0(t) ) aI t < 0 catalyst fluidization aF 0 e t < TOS reaction stage aI TOS e t < TOS + TSCAT stripping stage
{
The heterogeneously catalyzed cracking is modeled as a pseudohomogeneous process. The traditional lump approach20-24 is adopted to represent the six chemical pseudocomponents of interest: H2, C1-C2, C3-C4 (LPG), C5-C12 (gasoline), C13-C20 (LCO), and C20+ (HCO). In the receiver, the inert gas N2 is also monitored. It is assumed in the model that each lump can be adsorbed either on the catalyst surface or in the gas phase. Although it may be difficult to quantify precisely the amount of each lump adsorbed on the catalyst surface, the total amount of adsorbed products can be easily estimated by the amount of CO2 gas detected after regeneration of the catalyst. It is important to emphasize that the model does not take diffusion and masstransfer limitations explicitly into consideration. Therefore, the calculated desorption parameters certainly combine adsorption, diffusion, and mass-transfer effects. On that basis, it is assumed in the proposed pseudohomogeneous model that there are two different types of coke inside the catalyst: coke I, adsorbed hydrocarbons that can be totally removed by stripping; coke II, which cannot be removed by stripping. Coke I can be converted into coke II via dehydrogenation reactions.26-28 The proposed model can represent three important characteristics of the FCC process: (i) a significant amount of entrapped products is recovered during stripping; (ii) part of the coke present in the catalyst cannot be removed from the catalyst by stripping; (ii) although the cracking reaction does not favor hydrogen production, expressive amounts of H2 may be observed in the bottom of the strippers of industrial units.29,30 The complete phenomenological model comprises 22 equations. The state vector can be represented as [N, a, η, S, F]T, where N and a represent the composition of the vapor phase of the reactor in different units, η represents the number of moles of each pseudocomponent collected in the receiver, and S represents the mass of coke (types I and II) retained in the catalyst particles. N, a, η, and S are vectors of dimensions 6, 6, 7, and 2, respectively. The removal of coke is considered to follow a firstorder kinetics. In other words, the rate of coke consumption (by either desorption or chemical reaction) is dependent only on the amount of coke present in the catalyst. This hypothesis seems adequate to represent the ACE unit, where the gas-phase concentration is low. 3.2. Mathematical Description. Equations 1-3 describe the mass balances of the different pseudocomponents in the gas phase of the reactor, in the catalyst particles, and in the receiver, respectively.
d (N ) ) F0F0a0i(t) - FFai + (V)Ri(t) dt i dSi dt
6
) (V)φ(t) F(
∑ j)1
i ) 1, ..., 6 (1)
2
K6+i,jPMjaj +
K6+i,jSj) ∑ j)1
i ) 1, 2 (2)
Representation of gas-phase concentrations in moles per mass of the gas phase (ai) may be convenient for the determination of reaction rates. Equation 4 shows
dηi ) FFai dt
i ) 1, ..., 7
(3)
the correspondence between the concentration ai and the number of moles, Ni.
Ni
ai )
i ) 1, ..., 6
7
(4)
NjPMj ∑ j)1 Because P and T are assumed to be constant during the experiment, the molar outlet rate F can be calculated according to eq 5. 6
P RT
Ri ) 0 ∑ i)1
(F0 - F) + (V)
(5)
The following auxiliary equations are needed to complete the mathematical model:
[(∑
Ntot )
P(V) RT
(6)
Nj ∑ j)1
(7)
∑
7
)
)]
2 PMj Ki,j aj + Ki,6+jSj PMi j)1 j)1 6
Ri(t) ) φ(t) F
∑ j)1
6
Nj
NI ) Ntot -
7
Ni ∑ i)1
7
ai ) ∑ i)1
F)
Mtot V
)
7
) PMm
PMm )
NjPMj ∑ j)1
1
(8)
7
aj ∑ j)1
Ntot P P 1 PMm ) PMm ) V RT RT 7 aj
∑ j)1
F)
P RT
φ(t) ) exp[-R(S1 + S2)]
1 7
(9)
aj ∑ j)1 (10)
Equation 10 represents the catalyst deactivation. It is assumed that catalyst deactivation depends on the overall coke concentration. Although the authors are aware that modeling of the catalyst deactivation is a subject of great interest in the literature,31-33 the validity of the exponential law of eq 10 is assumed a priori for modeling purposes. The developed mathematical model is a set of coupled differential and algebraic equations, a DAE system. In the beginning (t ) 0 s), it is assumed that the reactor is filled with inert gas and fresh catalyst (S ) 0, N ) 0, and a ) 0) and that the receiver is empty (η ) 0). This is a consistent initial set for the DAE system. The software DASSL34 was used to perform the numerical integration, using the operational conditions and constants presented in Table 4. It must be emphasized that the proposed model only takes into account the composition of the feedstocks. The authors are aware that it is
6030 Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004 Table 4. Operating Conditions and Constants Used for Model Simulation F0 ) 3.178 × 10-6 m3/s feedstock A: 7% LCO and 93% HCO feedstock B: 4% LCO and 96% HCO feedstock C: 6% LCO and 94% HCO feedstock D: 10% LCO and 90% HCO
Mcat ) 9 × 10-3 kg V ) 10-4 m3 P ) 101 300 Pa T ) 823 K
) 0.89 PMN2 ) 28 PMH2 ) 2 PMC1/C2 ) 28
PMC3/C4 ) 56 PMgasoline ) 112 PMLCO ) 196 PMHCO ) 224
constants for each of the proposed reaction steps (k1k15), the stoichiometric constant of the coke dehydrogenation reaction (w), and the deactivation constant (R). The parameters were estimated with the help of a maximum likelihood procedure implemented in the computer code Estima.35 The objective function was defined as follows.
F)
Figure 2. Proposed kinetic scheme.
necessary to define a much larger number of characteristics for proper characterization of a FCC feedstock. However, despite that, the model has been capable of describing the experimental results. 3.3. Kinetic Scheme. As a first approach, both catalytic and thermal cracking were included in the adopted kinetic scheme. It was considered that each lump can be cracked into the immediately lower molecular weight lump and that only the two heavier lumps (HCO and LCO) contribute to the coke I (Figure 2). The rate of catalytic cracking depends on the mass of catalyst present and on the temperature, while the rate of thermal cracking depends only on the temperature. Hence, kinetic constants would be represented as the sum of two contributions, as represented below. To use a reasonable amount of experimental data, a limited number of reactions should be adopted.
Ki,j ) KTi,j + McatKCi,j
(11)
The resulting matrices KC and KT are shown below. It must be emphasized that, although this model could be easily modified to include additional reaction steps by simply modifying the matrix (12), a too large number of parameters may severely impact the parameter estimation step.
[
KC ) 0 0 0 0 0 0 0 0
(1 - w)k7 0 0 0 0 0 0 k4 0 0 0 0 k8 0 -k4 k3 0 0 k2 k9 0 0 -k3 0 k1 k10 0 0 0 -k2 - k6 k6 -k1 - k5 k11 0 0 0 k5 -k7 - k8 - k9 - k10- k11 0 0 0 0 wk7 0 0 0 0 0
[
0 0 0 0 KT ) 0 0 0 0
0 0 0 0 0 0 0 0 0 k15 0 -k15 k14 0 0 0 0 -k14 k13 0 0 0 0 -k13 k12 0 0 0 0 -k12 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
]
0 0 0 0 0 0 0 0
]
(12)
(13)
3.4. Parameter Estimation. The proposed model has 17 parameters, which correspond to the 15 kinetic
(xei - xci )2
∑
σ2
(14)
The input variables used for all e-cats were the feed composition a0, the feed injection time TOS, and the catalyst stripping time TSCAT. In the specific case of e-cat A data, the inert to e-cat ratio was also an input variable, and eq 11 was modified accordingly. For e-cats B and C, the inert gas flow rate was also an input variable. The output variables were the yields of the different pseudocomponents at the end of the experiment. Experiments corresponding to each particular e-cat were analyzed separately. The simultaneous estimation of all parameters led to a very slow decrease of the objective function (and therefore to very slow rates of convergence). Hence, a sequential strategy was adopted: each parameter was estimated separately, assuming that all of the remaining parameters were constant and equal to the best numerical value available so far. The procedure was repeated until no further improvement of the objective function could be observed. Typically, two or three iterations over the entire parameter set were sufficient to achieve a good correspondence between experimental and simulated results. Parameter values obtained were then used as initial guesses for the simultaneous reestimation of all of the parameters. Final parameter estimates were then used for computation of parameter variances and parameter correlations. 4. Results and Discussion 4.1. Parameter Estimation of e-cat A Data. The model was insensitive to the stoichiometric parameter w, probably because of experimental errors associated with hydrogen measurement. Simulations were then conducted assuming a constant value of 0.05 for w. It was possible to estimate all 16 remaining parameters, and the numerical values obtained are shown in Table 5. The computational costs of the parameter estimation and numerical simulation were affordable because a typical estimation session lasts for no longer than a few minutes on a Pentium III 256 Mb RAM personal computer. Figure 3 presents an example of the evolution of the objective function (eq 14) during the initial individual parameter estimation. Figure 4 shows a comparison of the simulated and experimental results. It can be observed that, despite the model simplicity, the model is capable of well representing the experimental results. Only data obtained with feedstock A were used for model fitting. Because in this data set the stripping time was constant and equal to 120 s, only cracking (kinetic and thermal)
Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004 6031
Figure 3. Typical evolution of the objective function during sequential parameter estimation for e-cat A data.
Figure 5. Comparison of experimental data and simulated results for e-cat C data. Table 6. Parameter Estimates and Standard Deviations for Data Set 2
Figure 4. Comparison of experimental data and simulated results for e-cat A data. Table 5. Parameter Estimates and Standard Deviations for Data Set 1 parameter
value
σ
k1 k2 k3 k4 k5 k6 k7 R
3.425 × 2.153 × 101 2.349 × 10 1.100 × 10 1.715 × 10-6 2.878 × 10 3.433 × 10-2 2.249 × 10-2
5.467 × 10-1 2.202 × 10-1 3.949 × 10-2 2.251 × 10-2 1.867 × 10-7 1.894 × 10-2 1.439 × 10-3 8.784 × 10-3
101
parameters are estimated. The obtained thermal cracking parameters (k12-k15) were orders of magnitude smaller than the kinetic cracking parameters for the same reactions (k1-k4). This fact indicates that the catalytic cracking is the main cracking mechanism at the adopted operating conditions in the ACE unit. This can be explained in terms of the differences between the industrial and laboratory FCC units. In an industrial riser, the feed at approximately 300 °C is contacted with the regenerated catalyst at 700 °C. The mixture enters the riser at a temperature of around 560 °C. At these conditions, thermal cracking is favored, yielding preferably lighter components (C1 and C2 groups). In the ACE unit, both the feed and catalyst are heated to 550 °C, so that the feed never is submitted to temperatures as high as 700 °C. This observation suggests that an additional simplification can be introduced into the proposed model: the thermal cracking mechanism can be neglected, reducing the number of model parameters from 16 to 12. The remaining parameters were reestimated assuming null values for k12-k15. The numerical values of the new parameters (Table 5) were not significantly modified, which confirms the adequacy
parameter
value
σ
k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 R
5.572 × 2.689 × 101 3.719 × 10 1.566 × 10 1.785 × 10-3 1.365 × 101 1.311 × 10-1 1.038 × 10-5 4.120 × 10-6 2.946 × 10-10 3.526 × 10-1 1.440 × 10-1
5.805 × 10-1 1.582 × 10-1 3.115 × 10-2 1.361 × 10-2 1.764 × 10-4 9.782 × 10-2 1.387 × 10-3 1.070 × 10-7 1.036 × 10-7 4.952 × 10-12 4.864 × 10-3 1.510 × 10-3
101
of the proposed approach. The elimination of the thermal cracking mechanism simplifies the model and reduces the estimation computational cost of parameter estimation, without compromising the representation of the physical phenomena. For the reasons described above, all estimations presented hereafter neglect the thermal cracking mechanism. 4.2. Parameter Estimation of e-cat B Data. The 12 parameters of the simplified model could be estimated based on experimental runs performed with e-cat B and three different feedstocks (B-D in Table 2). The numerical values obtained for the model parameters are shown in Table 6. A comparison of the experimental and simulated results is presented in Figure 5. When Figure 4 is compared with Figure 5, it can be noted that the dispersion is higher in Figure 5, probably because of the differences in feedstock quality. Despite that, although feedstock characteristics were not explicitly taken into account in the model (only changes of the overall lump compositions were considered), the predictive capacity of the model may be regarded as satisfactory. The parameter estimates indicate that coke formation from HCO (k5) and the desorption constants (k8-k10) are not relevant. Therefore, the reaction scheme might be simplified even further in this case. 4.3. Parameter Estimation of e-cat C Data. The 12 parameters of the simplified model (k1-k11 and R) were estimated based on the ACE data obtained with feedstocks B and C (Table 2). The obtained numerical values of the model parameters and a comparison between the simulated and experimental results are shown in Table 7 and Figure 6, respectively. The good agreement between the model and experiments indicates that the estimated set of parameters is sufficiently good to allow for a proper description of the behavior of
6032 Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004
Figure 7. Gasoline yield vs conversion. Simulated results × experimental data for feedstock B. Figure 6. Comparison of experimental data and simulated results for e-cat B data. Table 7. Parameter Estimates and Standard Deviations for Data Set 3 parameter
value
σ
k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 R
4.828 × 2.559 × 101 3.487 × 10 1.746 × 10 1.294 × 10-4 8.883 × 10 7.475 × 10-4 8.000 × 10-5 9.118 × 10-6 1.299 × 10-4 2.112 × 10-1 2.676 × 10-1
6.76 × 10-1 2.67 × 10-1 5.62 × 10-2 3.23 × 10-2 4.11 × 10-6 6.52 × 10-2 5.61 × 10-5 8.91 × 10-7 4.11 × 10-7 5.50 × 10-6 1.59 × 10-3 4.10 × 10-3
101
e-cat C. As previously observed for e-cat B, desorption parameters were relatively lower when compared to kinetic parameters; this indicates that the kinetic effect is more important than adsorption effects. Besides, desorption parameters are smaller for e-cat C than e-cat B. As explained before, desorption parameters combine adsorption, diffusion, and mass-transfer effects. This result is in agreement with the high accessibility observed for this e-cat (Table 1). 4.4. Data Analysis and Simulation Results. It is important to say that in all previous cases parameter correlations were low (which explains why the correlation matrices are not shown). Absolute correlations were almost always below 0.7, characterizing a very good degree of independence among the parameter estimates (which is rare in the field of chemical kinetics) and the appropriateness of the proposed experimental design. The only exceptions were the correlations among k5k7, which describe the rates of formation of coke I, which were around 0.85. This was probably caused by the difficult task of quantifying the total amounts of coke and defining the coke composition, as discussed previously. After estimation of the model parameters, the phenomenological model can be used to simulate the ACE test, to provide analysts with information that sometimes cannot be obtained experimentally, and to extend (through interpolation and extrapolation) the available experimental data. Typical examples are the analysis of catalyst stripping conditions and the analysis of lump concentrations as functions of the CTO ratio. Figure 7 compares experimental data and simulation results for e-cats B and C with feedstock B. The simulation results agree well with the experimental
Figure 8. Coke yield vs stripping time. Simulated results × experimental data for e-cat B and feedstock B.
data, evidencing that the e-cat C has a higher gasoline yield than e-cat B (Figure 8). Despite that, e-cat B is more active than e-cat C, which can be related with the higher microactivity and surface area (Table 1) observed for e-cat B. Furthermore, the deactivation rate (parameter R, Tables 5-7) is lower for e-cat B compared to e-cat C. This result could be explained by the significant higher amount of metals (Ni and V) observed for e-cat C (Table 1). Finally, Figure 8 presents the effect of the stripping time on the reduction of the coke yield. The experimental trend is very similar to the simulated model results. The same accordance between simulated results and experiments was obtained for different nitrogen stripping gas rates. 5. Conclusions In this paper, an alternative methodology is presented to evaluate the performance of FCC catalysts. It is shown that it is possible to devise a phenomenological model that accurately represents experimental data obtained in a standardized ACE unit. On the basis of a relatively low number of experimental data, the computational costs of the parameter estimation and numerical simulation were affordable because a typical estimation session lasts for no longer than a few minutes on a personal computer. Using the proposed mathematical model, it was possible to reproduce experiments with a single set of kinetic constants for typical FCC catalysts. On the basis of the kinetic parameter estimates obtained for different e-cats, insights that could not be provided by the traditional methodology used for catalyst characterization were observed. The deactivation parameter correlates well with the metal content of e-cat; the kinetic constants associated with the cracking of heavy com-
Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004 6033
ponents correlate well with the surface area and microactivity of the e-cats. Acknowledgment The authors thank Raul Rawet for the careful revision and suggestions to improve this work. Nomenclature a ) concentration of lump i in the gas phase of the reactor [mol of i/mass of gas] F ) flow rate [kg/s] kj ) individual parameter to be estimated KC ) kinetic constants for catalytic cracking [1/s] ∀ i, j ) 1, 6 and [mol/(s‚kg)] ∀ i, j ) 7, 8 KT ) kinetic constants for thermal cracking [1/s] ∀ i, j ) 1, 6 and [mol/(s‚kg)] ∀ i, j ) 7, 8 Mcat ) mass of catalyst added [kg] N ) number of moles of lump i in the gas phase of the reactor [mol of i] P ) pressure [atm] PM ) molecular weight R ) universal gas constant Ri ) reaction rate of lump i [mol/(s‚m3)] S ) mass of coke per mass of catalyst [kg of i/kg of catalyst] T ) temperature [K] t ) time [s] TOS ) time on stream [s] TSCAT ) time of catalyst stripping [s] V ) volume of the reactor [m3] w ) stoichiometric constant of the coke dehydrogenation reaction x ) overall mass fraction of lump I in the product steam Greek Letters R ) catalyst deactivation constant ) porosity of the reactor φ ) catalyst deactivation function η ) number of moles of lump i in the receiver [mol of i] F ) density [kg/m3] σ ) standard deviation Subscripts 0 ) inlet feed F ) feedstock I ) inert (nitrogen) i ) pseudocomponent (lump) j ) individual parameter m ) mean value tot ) total value (summation) Superscripts c ) calculated e ) experimental
Literature Cited (1) Biswas, J.; Maxwell, I. E. Recent process- and catalystrelated developments in fluid catalytic cracking. Appl. Catal. 1990, 63, 197. (2) Guisnet, M.; Mignard, S. Le craquage catalytique, unite cle´ d’une raffinerie. Actual. Chim. 2000, Feb, 14. (3) Cerqueira, H. S.; Rawet, R.; Pinto, J. C. The influence of experimental errors during laboratory evaluation of FCC catalysts. Appl. Catal. A 1999, 181, 209. (4) Sedran, U. A. Laboratory testing of FCC catalysts and hydrogen transfer properties evaluation. Catal. Rev.sSci. Eng. 1994, 36, 3, 405. (5) 1989 Annual Book of ASTM Standards; ASTM-D3907; ASTM: West Conshohocken, PA, 1989; section 5, p 733. (6) O’Connor, P.; Hartkamp, M. B. A microscale simulation test for FCC development. Symposium on the preparation and char-
acterization of catalysts, Los Angeles, Sept 25-30, 1988; Prepr.s Am. Chem. Soc., Div. Pet. Chem. 1989, 33 (4), 656. (7) Wallenstein, D.; Harding, R. H.; Witzler, J.; Zhao, X. Rational assessment of FCC catalyst performance by utilization of micro-activity testing. Appl. Catal. A 1998, 167, 141. (8) Wallenstein, D.; Seese, M.; Zhao, X. A novel selectivity test for the evaluation of FCC catalysts. Appl. Catal. A 2002, 231, 227. (9) Nace, D. M. Catalytic crackling over crystalline aluminosilicates II. Application of microreactor technique to investigation of structural effects. Ind. Eng. Chem. Prod. Res. Dev. 1969, 8 (1), 24. (10) Corella, J.; Bilbao, R.; Molina, J. A.; Artigas, A. Variation with time of the mechanism, observable order, and activation energy of the catalyst deactivation by coke in the FCC process. Ind. Eng. Chem. Process Des. Dev. 1985, 24, 625. (11) Lipia¨inen, K.; Hagelberg, P.; Aittamaa, J.; Eilos, I.; Hiltunen, J.; Niemi, V. M.; Krause, A. O. I. Novel equipment for testing catalytic cracking and catalyst regeneration with short contact times. Appl. Catal. A 1999, 183, 411. (12) Hagelberg, P.; Eilos, I.; Hiltunen, J.; Lipia¨inen, K.; Niemi, V. M.; Aittamaa, J.; Krause, A. O. I. Kinetics of catalytic cracking with short contact times. Appl. Catal. A 2002, 223, 73. (13) Hershkowitz F.; Madiara, P. D. Simultaneous measurements of adsorption, reaction and coke using a pulsed microbalance reactor. Ind. Eng. Chem. Res. 1993, 32, 2969. (14) The University of Western Ontario. Riser simulator. U.S. Patent 5,102,628, 1988. (15) Corma, A.; Martinez, C.; Melo, F. V.; Sauvanaud, L.; Carriat, J. Y. A new continuous laboratory reactor for the study of catalytic cracking. Appl. Catal. A 2002, 232, 247. (16) den Hollander, M. A.; Makkee, M.; Moulijn, J. A. Fluid catalytic cracking (FCC): Activity in the (mili)seconds range in an entrained flow reactor. Appl. Catal. A 1999, 187, 3. (17) Dupain, X.; Gamas, E. D.; Madon, R.; Kelkar, C. P.; Makkee, M.; Moulijn, J. A. Aromatic gas oil cracking under realistic FCC conditions in a microriser reactor. Fuel 2003, 82 (13), 1559. (18) Kayser Technology, Inc. Versatile fluidized bed reactor. U.S. Patent 6,069,012, 2000. (19) Kelkar, C. P.; Xu, H.; Patel, H.; Madon, R. J. FCC catalyst evaluation in a small fluid bed. 2nd International Symposium on Deactivation and Testing of Catalysts, Proceedings of the 219th ACS Preprints meeting, San Francisco, CA, Mar 26-31, 2000; American Chemical Society: Washington, DC, 2000; Vol. 290. (20) Weekman, V. W., Jr. A model of catalytic cracking conversion in fixed, moving and fluid bed reactors. Ind. Eng. Chem. Process Des. Dev. 1968, 7, 90. (21) Weekman, V. W., Jr.; Nace, D. Kinetics of catalytic cracking selectivity in fixed, moving and fluid bed reactors. AIChE J. 1970, 16, 397. (22) Lee, L. S.; Chen, Y. W.; Huang, T. N. Four-lump kinetic model for fluid catalytic cracking process. Can. J. Chem. Eng. 1989, 67, 615. (23) Cerqueira, H. S.; Sousa-Aguiar E. F.; Biscaia, E. C., Jr. Mathematical modeling and simulation of catalytic cracking of gasoil in a fixed bed: Coke formation. Appl. Catal. 1997, 164, 35. (24) den Hollander, M. A.; Makee, M.; Moulijn, J. A. Coke formation in fluid catalytic cracking studied with the microriser. Catal. Today 1998, 46, 27. (25) Humpries, A.; Fletcher, R.; Pearce, J. A unique development in FCC catalyst technology maximizes refier profitability. NPRA Annual Meeting, San Antonio, TX, Mar 1999; Paper AM99-63. (26) Royo, C.; Ibarra, J. V.; Monzo´n, A.; Santamaria, J. Regeneration of coked catalysts: The effects of aging upon the characteristics of the coke deposits. Ind. Eng. Chem. Res. 1994, 33, 2563. (27) Moljord, K.; Magnoux, P.; Guisnet, M. Coking, aging, and regeneration of zeolites XV. Influence of the composition of HY zeolites on the mode of formation of coke from propene at 450 °C. Appl. Catal. A 1995, 122, 21. (28) Magnoux, P.; Cerqueira, H. S.; Guisnet, M. Evolution of coke composition during aging under nitrogen. Appl. Catal. A 2002, 235, 93. (29) Baptista, C. M. L. A.; Cerqueira, H. S.; Moreira, E. M.; Mansur, A. L. Diagno´stico do retificador da unidade de FCC da REMAN. Bol. Tec. PETROBRAS 2002, 45 (3/4), 284.
6034 Ind. Eng. Chem. Res., Vol. 43, No. 19, 2004 (30) Cerqueira, H. S.; Baptista, C. M. L. A.; Fusco, J. M. What happens in the FCC stripper? NPRA Annual Meeting, San Antonio, TX, Mar 2004; Paper AM-04-53. (31) Froment, G.; Bischoff, K. B. Kinetic data and product distributions from fixed bed catalytic reactors subject to catalyst fouling. Chem. Eng. Sci. 1962, 17 (4), 105. (32) Martin, M. P.; Derouin, C.; Turlier, P.; Forissier, M.; Wild, G.; Bernard, J. R. Catalytic cracking in riser reactors: Coreannulus and elbow effects. Chem. Eng. Sci. 1992, 47, 2319. (33) Delattre, C.; Forissier, M.; Pitault, I.; Schweich, D.; Bernard, J. R. Improvement of the microactivity test for kinetic and
deactivation studies involved in catalytic cracking. Chem. Eng. Sci. 2001, 56 (4), 1337. (34) Petzold, L. DASSL code, 1989. (35) Pinto, J. C. ESTIMAsA Software for Parameter Estimation and Experimental Design. User’s Guide; Programa de Engenharia Quı´mica/COPPE, Universidade Federal do Rio de Janeiro: Rio de Janeiro, Brazil, 1999.
Received for review March 19, 2004 Revised manuscript received May 21, 2004 Accepted July 1, 2004 IE049781T
