Numerical Simulation of Influence of Feed Injection on

Article pubs.acs.org/IECR

Numerical Simulation of Influence of Feed Injection on Hydrodynamic Behavior and Catalytic Cracking Reactions in a FCC Riser under Reactive Conditions Jiang Li,† Yi-Ping Fan,§ Chun-Xi Lu,§ and Zheng-Hong Luo‡,* †

Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China ‡ Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China § State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Changping, Beijing 102249, People’s Republic of China ABSTRACT: A comprehensive three-dimensional (3D) heterogeneous reactor model based on the Eulerian−Eulerian approach was applied to simulate the turbulent gas−solid flow and catalytic cracking reactions in a fluid catalytic cracking (FCC) riser reactor. In addition, the 14-lump reaction kinetic equations were incorporated into the reactor model to describe the FCC reactions. First, the model at cold- or hot-model conditions was verified by comparing against the open experimental data. Second, the effect of the nozzle jet velocity on the flow of the feedstock injection zone of the riser was investigated numerically using the model at the cold-model condition. Finally, the influences of nozzle position and angle in the riser on the performance in the feedstock injection zone under the reactive conditions were investigated numerically. The simulation results showed that the nozzle jet velocity plays an important role in determining the two-phase flow in the feedstock mixing zone while the nozzle position had a small influence on the flow field and the cracking reactions in the feedstock mixing zone. It also showed that the nozzle angle had a significant influence on both the flow field and the cracking reactions in the feedstock mixing zone. The reactor with a nozzle angle of larger than 30° was preferable.

1. INTRODUCTION The riser reactor, where hot regenerated catalyst particles come in contact with liquid vacuum gas oil (VGO) to vaporize and convert it into lighter products and coke by catalytic cracking, is very important for a fluid catalytic cracking (FCC) process.1 A riser reactor can be divided into four sections from bottom to top according to their functions: the prelift zone, the feedstock injection zone, the full-reaction zone, and the quenching zone.1 In the feedstock injection zone, feed oil is injected obliquely upward into the riser with a high velocity, from the feed nozzles uniformly arranged at the side wall. The feed oil contacts with the high-temperature catalysts and catalytic cracking reaction initiates rapidly. The contact and flow conditions of these two phases will directly affect the FCC unit. In addition, some researchers2−4 found that the directed feed nozzles introduced into a fluidized-bed reactor have a considerable effect on the hydrodynamic behavior of the reactor, resulting in high velocity, temperature, and concentration gradients, which can degrade the performance of the FCC reactions. Furthermore, the dynamics behavior in this area is also very complex.2−4 Accordingly, it is obvious that knowledge of the dynamics of feed jet and solids motion in this area is of considerable significance for improving reactor operation and design. Our current study is focused on investigating the effect of the feed injection on the hydrodynamic behavior in this area in order to optimize the nozzle position, angle and its jet velocity. So far, most of the open reports5−19 on FCC riser reactors focused on either reactor hydrodynamics or catalytic cracking © 2013 American Chemical Society

kinetics, while few on the CFD modeling of the effect of the feed injection in a FCC riser especially its feedstock injection zone. In addition, the experimental technology is adopted in most of the previous works.5−11 For instance, Fan et al.,15 examined the FCC riser performance in a large cold-riser model made of 0.186-m-ID plexiglass. They also experimentally investigated the diffusion pattern of feed spray and the flow features of catalysts in the feed injection zone in the same coldriser model.8,9 The experimental results indicated that a secondary flow of spray, which enhances the mixing of catalysts with feed oil, will occur in the feed injection zone with the addition of a feed spray into the riser. However, the extension of the secondary flow causes a violent catalyst backmixing. Xu et al.10 measured the distribution of pressure and particle concentration in the feedstock injection zone experimentally at a cold-riser model, which cannot completely describe the flow pattern in this zone. As a whole, none of the above-mentioned work has addressed the cracking reactions. The authors have not identified any published works on the experimental investigating of FCC riser or process with catalytic cracking reaction condition taken into account. On the other hand, it is also difficult to measure the FCC riser flow under reactive conditions due to the high temperature and operating difficulty, Received: Revised: Accepted: Published: 11084

January 22, 2013 July 7, 2013 July 18, 2013 July 18, 2013 dx.doi.org/10.1021/ie400250c | Ind. Eng. Chem. Res. 2013, 52, 11084−11098

Industrial & Engineering Chemistry Research

Article

which limits the experimental study on the FCC riser. Therefore, there is an urgent need to model the FCC riser, especially the influence of feed injection on hydrodynamic behavior in the FCC riser under reactive conditions since the cracking reactions have significant effects on the flow in the riser.12 Such a modeling work is also expected to give lights to the design of higher performance FCC risers and the optimization of nozzle structure and position. Generally, simulation has been extensively adopted in FCC riser reactors.20−36 Theologos et al.32 simulated a FCC riser reactor with considering the 10-lump reaction scheme. The effects of feed-injector geometry on overall reactor performance were predicted, and the simulated results showed that the selectivity of primary products was improved with increasing the number of feed-injection operating nozzles. Gao et al.2 suggested a 3D two-phase CFD flow-reaction model for predicting the performance of FCC riser reactors. Their simulation results showed that the gas−solid turbulent reacting flow regime in the riser reactor was extremely complex, especially in the feed inlet zone of the riser reactor. Nayak et al.34 used the Eulerian−Lagrangian approach to simulate the influence of some key operation parameters on the FCC riser performance. Recently, Li et al.35 applied a comprehensive 3D heterogeneous reactor model to simulate the turbulent gas− solid flow and reaction in a polydisperse FCC riser reactor. The reactor model combined the population balance model of the catalyst with the 14-lump reaction kinetic equations. However, as described above, the earlier works were not on the influence of feed injection on hydrodynamic behavior and catalytic cracking reactions in a FCC riser. In this work, a 3D two-phase turbulent flow-reaction model based on the Eulerian−Eulerian approach with incorporating the kinetic theory of granular flow and a 14-lump reaction kinetic equation is applied to describe the turbulent gas−solid flow and reaction in FCC riser reactors. The model at cold- or hot-model conditions is first evaluated by comparing against the open experimental data from Fan et al.1,5 and Gao et al.2 Next, the influence of the nozzle jet velocity on the flow of the feedstock injection zone is predicted numerically to select an appropriate feedstock injection velocity under reactive conditions. Finally, the model is used to predict the influence of nozzle position and angle in the riser on the performance in the feedstock injection zone.

Figure 1. Experimental setup and schematic of the 3D FCC riser.

3. MATHEMATICAL MODEL AND SIMULATION METHOD Numerical simulations are based on a 3D two-fluid EulerianEulerian two-fluid model (TFM), closed using the kinetic theory of granular flow. In the TFM, both phases are considered to be continuous and fully interpenetrating. Herein, the 3D two-fluid model is identical to that of our previous work.35 Therefore, due to the limited space, only main governing equations are summarized in Table 1.35−43 On the other hand, the 14-lump reaction kinetics model44 was used to represent the FCC reactions. The 14 lumps are listed in Table 2, and the reaction scheme is shown in Figure 2 (also refer to our previous work35). Reaction paths and their kinetic parameters are shown in Table 3. The reaction rate of a gaseous lump j (Wj in Table 1) is defined according to eq 1.44,45 ρg ρs 1 Wj = −A 0 φ(Cc) KjYj 1 + KhCAh αg (1) where φ(Cc) = (1 + 0.51Cc)−2.78

(2)

2

2. SIMULATION OF FCC RISER REACTOR In this work, the selected FCC riser is the same as that reported in Fan et al.’s work.5 The selected riser with diameter of 186 mm and height of 14 m are illustrated in Figure 1. As shown in Figure 1, at the height of 4.5 m above the gas distributor, four feedstock nozzles are evenly distributed around the riser with an angle of 30° (relative to the riser axis, the same as below). In order to simulate the practical nozzle used in the industrialscale reactor, a rectangular nozzle is used herein, which is also adopted in most of industrial devices. An unstructured grid was applied to the zone near the nozzles, whereas a structured grid was applied to save the storage and computational time. In addition, more cells were placed closer to the feedstock injection zone and near the wall. Grid sensitivity analyses were performed for five mesh densities with 60 000, 90 000, 120 000, and 135 000 control volumes.

In addition, Qr shown in Table 1 is calculated according to the mass of coke produced from these cracking reactions. It is reported that these cracking reactions in the FCC process are endothermic reactions, and the production of 1 kg coke needs the heat quantity of 9.127 × 103 kJ.45,46 It means that Qr = 9.127 × 103 kJ/kg coke.45 As discussed earlier, the CFD simulation with the Eulerian− Eulerian approach was employed to study the complex gas− solid flow in the riser reactor. The standard k−ε model was used to describe the turbulence. Simulations of the coupled model were performed with FLUENT 6.3.26 (Ansys Inc., U.S.A.). A commercial grid-generation tool, GAMBIT 2.3.16 (Ansys Inc., U.S.A.), was used to generate the 3D geometries and the grids. Source terms in different governing equations were specified by UDFs using the C programming language. The source term codes were then coupled and hooked into the FLUENT solver. The simulation was performed in a platform 11085

dx.doi.org/10.1021/ie400250c | Ind. Eng. Chem. Res. 2013, 52, 11084−11098


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Table 1. Main Governing Equations and Constitutive Relations for Gas/Solids Flows35−43 governing equations 1. continuity equations of gas and solid phases ∂ (αgρg ) + ∇(αgρg vg) = 0 (1) ∂t

∂ (αsρs ) + ∇(αsρs vs) = 0 (2) ∂t 2. momentum equations of gas and solid phases ∂ (αgρg vg) + ∇(αgρg vgvg) = ∇ τg + αgρg g − αg∇P − β(vg − vs) ∂t ∂ (αsρs vs) + ∇(αsρs vsvs) = ∇τs + αsρs g − αs∇Ps + β(vg − vs) ∂t where 2 τg = [∇vg + (∇vg)T ] − αgμg ∇vgI (5) 3 ⎛ 2 ⎞ τs = αsμs [∇vs + (∇vs)T ] + αs⎜λs − μs ⎟∇vsI ⎝ 3 ⎠

(3) (4)

(6)

3. energy equation of gas and solid phases ∂ (αgρg CpgTg) + ∇(αgρg CpgvgTg) = ∇[λ ·grad(Tg)] − Q r + Q ∂t

∂ (αsρs CpsTs) + ∇(αsρs CpsvsTs) = − Q (8) ∂t 4. component transport equation of gas phase ∂ (αgρg Yj) + ∇(αgρg vgYj) = ∇(αgρg D·grad(YS)) + Wj ∂t constitutive equations

(7)

(9)

1. solid pressure

Ps = ρα Θ + 2g0αs2ρs Θs(1 + e) s s s

(10)

2. solid shear viscosity

μs =

10dsρs Θsπ ⎡ ⎤2 4 4 αsρs dsgo(1 + e)(Θs /π )0.5 + ⎢⎣1 + (1 + e)αsg0⎥⎦ 5 96(1 + e)g0 5

(11)

6. solid bulk viscosity 4 λs = αsρs dsg0(1 + e)(Θs /π )0.5 (12) 3 7. granular temperature equation ⎤ 3⎡ ∂ ⎢ (αsρs Θs) + ∇(αsρs Θsvs)⎥⎦ 2 ⎣ ∂t

= (−∇PI s + τs): ∇vs + ∇(k Θs∇Θs) − γΘ − 3β Θs s

(13)

8. energy-transfer coefficient

k Θs =

150ρs ds π Θs ⎡ ⎤2 Θs 6 2 d g (1 + e) ⎢⎣1 + αsg0(1 + e)⎥⎦ + 2ρα s s s 0 π 384(1 + e)g0 5

(14)

9. collisional energy dissipation

γΘ = s

12(1 − e 2)g0 ds π

2 1.5 ρα Θ s s s

(15)

10. radial distribution function −1 ⎡ ⎛ α ⎞1/3⎤ s ⎥ ⎢ ⎟⎟ g0 = 1 − ⎜⎜ ⎢ ⎝ αs,max ⎠ ⎦⎥ ⎣

(16)

11. drag model ⎧ αsαgρg |vs − vg| ⎪ 3 CD ω αg ≥ 0.74 ⎪4 ds β=⎨ αsαsμg αsρg |vs − vg| ⎪ + 1.75 αg < 0.74 150 ⎪ 2 ⎪ ds αgds ⎩

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(17)



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Table 1. continued constitutive equations

⎧ 0.0214 0.74 ≤ αg ≤ 0.82 ⎪− 0.576 + 4(αg − 0.7463)2 + 0.0044 ⎪ ⎪ αg > 0.97 ω = ⎨− 31.8295 + 32.8295αg ⎪ 0.0038 ⎪− 0.0101 + 0.82 ≤ αg ≤ 0.97 ⎪ 4(αg − 0.7789)2 + 0.0040 ⎩ ⎧ 24 ⎪ (1 + 0.15Re0.687) Re ≤ 1000 C D = ⎨ Re ⎪ ⎩ 0.44 Re > 1000

Re =

(18)

(19)

αgρg ds|vg − vs| μg

(20)

Table 2. Lumps of the 14-Lump Kinetic Model44 lump symbols

lumps

boiling ranges

Ph Pm Pl Nh Nm Nl FAh Ah Am Al GO LPG DG CK

heavy paraffinics medium paraffinics light paraffinics heavy naphthenics medium naphthenics light naphthenics heavy aromatics in resin and asphaltene heavy aromatics expert FAh medium aromatics light aromatics gasoline liquid petroleum gas dry gas coke

500 °C+ 350−500 °C 221−350 °C 500 °C+ 350−500 °C 221−350 °C 500 °C 500 °C+ 350−500 °C 221−350 °C C5−221 °C C3+C4 C1+C2+H2

Figure 3. Calculated local density at Z = 5.575 m compared with the experiment (angle = 30°, Z0 = 4.5 m).

4. RESULTS AND DISCUSSION 4.1. Model Validation and Grid Independency. No numerical simulation is complete without a study of grid-size dependence. To confirm that the CFD results are independent of the mesh size, the simulations of the system with 60 000, 90 000, 120 000, and 135 000 grids are performed at the coldmodel conditions (see Table 4, i.e., case 2). The local density and axial particle velocity, which are compared with the experimental data obtained by Fan et al.,5 are used to monitor numerical errors and validate the CFD model. As observed in Figures 3 and 4, there is no obvious difference of simulated results when the number of grids is larger than 120 000, and the simulated results for the meshes with 120 000 and 135 000 are in good agreement with the experimental data. Relative to the coarser mesh, the fine mesh (135 000) case and medium mesh (120 000) case capture the more real information. However, the case with 120 000 grids and that with 135 000 grids obtain similar information about local density and axial particle velocity, which indicates that the grid number of 120 000 is sufficiently fine for providing reasonably mesh independent results. Therefore, the grid number of 120 000 is selected as a base case and applied in the rest of the article. As described in section 3, herein, the suggested model at the hot-model condition is similar to that reported in our previous work35 and is also verified via the comparison of the product yield at the riser outlet between the simulated results and the

Figure 2. 14-lump reaction network for the FCC reactions.

of Intel 2.83 GHz Xeon with 8 GB of RAM. Appropriate inlet and boundary conditions are crucial for solving these equations listed in Tables 4 and 5, respectively. The inlet velocity was set for both the gas phase and the particle phase. The inlet velocity of the catalyst particle was calculated based on the particle mass flux and its inlet volume fraction. “Pressure outlet” boundary was used at the outlet and exit pressure was specified. At the wall region, no-slip boundary conditions were set for the gas phase, and the Johnson and Jackson’s partial-slip boundary conditions were used for the particle phase.47 The specularity coefficient and the particle-wall restitution coefficient were set to 0.001 and 0.9, respectively. 11087



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Table 3. Kinetic Parameters of the 14-Lump Kinetic Model44

a

path no.

path

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Ph→Pm Nh→Nm Ah→Am FAh→Am Ph→Pl Nh→Nl Ah→Al FAh→Al Ph→GO Nh→GO Ah→GO FAh→GO Ph→LPG Nh→LPG Ph→DG Nh→DG Ph→CK Nh→CK Ah→CK FAh→CK Pm→Pl Nm→Nl Am→Al Pm→GO

k0a (m3/kgcat·s)

Eb (kJ/kmol)

path no.

path

k0a (m3/kgcat·s)

Eb (kJ/kmol)

18.77 19.25 1.817 0.5514 16.38 17.05 1.617 0.4342 0.2266 0.1374 0.0136 0.0054 5.77 6.20 63.18 64.99 3.366 4.754 41.82 40.50 833.90 944.00 773.80 1.263

7.21 × 10 7.21 × 104 7.21 × 104 7.21 × 104 7.21 × 104 7.21 × 104 7.21 × 104 7.21 × 104 3.04 × 104 3.04 × 104 3.76 × 104 3.76 × 104 4.32 × 104 4.32 × 104 6.32(e+4) 6.32 × 104 5.00 × 104 5.00 × 104 5.44 × 104 5.44 × 104 7.21 × 104 7.21 × 104 7.21 × 104 3.04 × 104

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Nm→GO Pm→LPG Nm→LPG Am→LPG Pm→DG Nm→DG Am→DG Pm→CK Nm→CK Am→CK Pl→GO Nl→GO Pl→LPG Nl→LPG Al→LPG Pl→DG Nl→DG Al→DG Pl→CK Nl→CK Al→CK GO→LPG GO→DG GO→CK

0.4092 1.506 0.5742 0.0438 5.491 2.013 0.0324 1.704 1.061 5.611 1.257 1.986 2.258 1.506 0.033 15.20 10.14 0.0498 0.1363 0.1491 0.8254 1374.00 343.40 0.066

3.04 × 104 4.32 × 104 4.32 × 104 2.76 × 104 6.32 × 104 6.32 × 104 3.76 × 104 5.00 × 104 5.00 × 104 5.44 × 104 3.04 × 104 3.04 × 104 4.32(e+4) 4.32 × 104 2.76 × 104 6.32 × 104 6.32 × 104 3.76 × 104 5.00 × 104 5.00 × 104 5.44 × 104 1.18 × 104 1.18 × 104 5.61 × 104

4

Frequency factor. bActivation energy.

Table 4. Operation Conditions Used in the Cold-Model Simulations description

case 1

case 2

case 3

prelift gas superficial velocity (m·s−1) nozzle jet velocity (m·s−1) particle mass flux (kg·m−2·s−1) inlet volume fraction of catalysts particle density (kg·m−3) mean particle size (μm)

3.28 41.7 113

3.28 62.5 113 0.6 1310 65

3.28 83.3 113

Table 5. Operation Conditions Used in the Hot-Model Simulations descriptions

values

flux of catalysts (kg·s−1) inlet temp. of catalysts (K) inlet velocity of catalysts (m·s−1) inlet vol. fraction of catalysts particle diam. (μm) particle density (kg·m−3) inlet temp. of prelift steam (K) inlet velocity of prelift steam (m·s−1) Flux of feed oil (kg·s−1) Inject velocity of gaseous feed oil (m·s−1) temp. of the gaseous feed oil (K) molecular weight of feed oil concn of paraffinic parts of feed oil: CPm concn of naphthenic parts of feed oil: CNm concn of aromatic parts of feed oil: CAm

6.6734 840.5 0.3938 0.4 66 1560 840.5 3 1.3932 60.0 733.15 420.0 0.5588 0.2663 0.1749

Figure 4. Calculated particle velocity at Z = 5.575 m compared with the experiment (angle = 30°, Z0 = 4.5 m).

data is still listed (see Figure 5). The simulated data shown in Figure 5 are in good agreement with the plant data. Therefore, the model at the hot-model conditions can be used to predict the hydrodynamic of the gas−solid flow and catalytic cracking reactions in a FCC riser reactor. 4.2. Determination of Nozzle Jet Velocity. In order to determine the optimal nozzle jet speed, three cases with different nozzle jet velocities were designed. The nozzle jet velocity for cases 1−3 is 41.7, 62.5, and 83.3 m/s, respectively. The detailed operating conditions for the three cases were listed in Table 4. For the three cases, both the prelift gas and nozzle jet gas are air. The simulations for the three cases were performed using the model at the cold-model conditions. The optimal feed speed was determined by comparing the particle concentration and particle velocity distribution in the feed mixing zone of the riser.

plant data in our previous work. It is convincing that the kinetic model used can be adequate to describe the catalytic cracking of heavy petroleum. Herein, in order to keep the integrity of the manuscript, the comparison between the simulated and plant 11088



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Figure 5. Gas-phase composition concentration and temperature distribution compared with Gao et al.’s data and plant data (see refs 2 and 35).

Figure 6. Cross-section profiles of particle velocity (angle = 30°, Z0 = 4.5 m). 11089



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Figure 7. Radial distribution of particle velocity at various axial sections for case 2: (a) across feed nozzle, (b) π/12 from feed nozzle, (c) π/6 from feed nozzle, (d) π/4 from feed nozzle (angle = 30°, uj = 62.5 m·s−1, Z0 = 4.5 m).

in all directions. On the other hand, it is well-known that an obvious core-annulus structure distribution for particle velocity can be observed in a typical riser reactor without any nozzles.5 With the addition of nozzles, the nozzle jet gas will change the above distribution. As illustrated in Figure 6, due to the small nozzle jet velocity for case 1, the effect of the feedstock jet on the particle phase is also less than that produced by the prelift gas. Thus, the maximum particle velocity still locates at the riser axis. However, the maximal particle velocity does not locate in the central region of the riser for cases 2 and 3. Accordingly, the particle velocity distribution is the most uniform for cases 2 than those for cases 1 and 3, as described Figure 6. Besides the particle velocity distribution profile, we also predicted the particle volume fraction distribution profiles in the riser reactor for cases 1−3. Since the predicted particle volume fraction distribution patterns for cases 1−3 are similar, herein, only the profile for case 2 is listed (see Figure 8). As shown in Figure 8, when the gas encounters the catalyst particles, the particles shift toward the center of the riser due to the push force of the feedstock injected with a high velocity. However, a small amount of particles moves toward the region far away from the nozzle area at the cross-section planes. Thus, high particle concentration gradients can be seen in the feed mixing zone of the riser, leading to undesirable thermal cracking and catalyst deactivation. Namely, the nozzle jet velocity has important influence on the particle volume fraction distribution in the feedstock injection zone of the riser, which is illustrated in Figure 9. Figure 9 gives the particle volume fraction distribution profiles in different cross-section planes corresponding to different heights in the riser reactor for cases 1−3. It is seen that only a small fraction of particles appear at

Figure 8. Particle volume fraction profiles at various axial sections of case 2: (a) across feed nozzle, (b) π/12 from feed nozzle, (c) π/6 from feed nozzle, (d) π/4 from feed nozzle (angle = 30°, uj = 62.5 m·s−1, Z0 = 4.5 m).

Figure 6 shows the particle velocity profiles in different crosssection planes corresponding to different heights in the riser reactor for cases 1−3. For all cases, the feed gas injected into the reactor gives rise to the flow inhomogeneities in all directions. In addition, for the three cases, the inhomogeneities are similar while their degrees are different. Take case 2 as an example: the inhomogeneity is highlighted in Figure 7. Indeed, from Figure 7, obvious flow inhomogeneities can be observed 11090



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Figure 9. Cross-section profiles of particle volume fraction (angle = 30°, Z0 = 4.5 m).

Table 6. Product Yields at the Riser Exit nozzle location (m)

heavy fuel oil (wt %)

diesel (wt %)

gasoline (wt %)

LPG (wt %)

dry gas (wt %)

coke (wt %)

3.5 4.5 5.5

41.23 41.81 42.72

16.78 16.51 16.59

30.23 30.07 29.30

5.48 5.43 5.31

2.26 2.22 2.19

4.02 3.96 3.89

radial direction. Until at Z = 7.295 m, the particle volume fraction distribution shows the annular-core structure feature for case 3. Indeed, the effect of the nozzle jet velocity decreases for the same case with the increase of the height, and its effect increases for the same height with the increase of the nozzle jet velocity. In conclusion, the particle volume fraction distribution shows sharper gradient for cases 1 and 3 than that for case 2, which lessens the gas−solid contact efficiency. Based on Figures 6−9, the nozzle jet velocity plays an important role in determining the two-phase flow behavior in the riser at the same prelift gas velocity. At the prelift gas velocity of 3.28 m·s−1, the better nozzle jet velocity is 62.5 m·s−1, which is helpful to improve the gas−solid phase contact efficiency. Thus, the feedstock injection velocity of 60 m·s−1 is suggested for the next investigation since we adopt the approximate prelift gas velocity applied in Fan et al. report.5 4.3. Effect of Feed Injection. In this section, the effects of nozzle position and injection angle on the hydrodynamic behavior in the feed mixing zone under reactive conditions are predicted numerically. The simulated conditions are listed in Table 5. 4.3.1. Effect of Nozzle Position. Herein, three cases with different nozzle positions (Z0 = 3.5, 4.5, and 5.5 m) were selected. The simulated results are shown in Figures 10−13. Figure 10 shows the particle volume fraction distributions in the axial plane with different nozzle positions. As described in Figure 10a, the particle volume fraction under the feedstock

Figure 10. Particle volume fraction profiles at axial section (angle = 30°, uj = 60 m·s−1).

the central region of the riser due to the small nozzle jet velocity for case 1 (Z = 4.875 m). Moreover, the particle volume fraction distribution has a typical annular-core structure at the height of 5.875 m. In addition, at the riser height of 4.875 m, the particle volume fraction at the riser center for case 3 is larger than that for case 1. It means that more particles move toward the riser center for case 3, and those particles are pushed and entrained by the feedstock with a high injection velocity. Unfortunately, the high nozzle jet velocity also impedes the diffusion and transport of the particles along the 11091



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Figure 11. Cross-section profiles of particle volume fraction (angle = 30°, uj = 60 m·s−1).

Figure 12. Gas-phase temperature profiles at axial section (angle = 30°, uj = 60 m·s−1).

Figure 13. Gasoline concentration profiles at axial section (angle = 30°, uj = 60 m·s−1). 11092



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Figure 14. Cross-section profiles of gas-phase velocity (uj = 60 m·s−1, Z0 = 4.5 m).

Figure 15. Gas-phase velocity vector profile at Z = 5.175 m (uj = 60 m·s−1, Z0 = 4.5 m).

nozzles is higher than that on the opposite side, while a symmetric of the particle volume fraction profile appears in Figure 10b and c. It is believed that the asymmetric and nonuniform particle volume fraction profile is disadvantageous to the catalytic cracking reaction. However, there are similar particle

volume fraction distribution patterns and weak difference of particle concentration over the nozzles for the three cases (Z0 = 3.5, 4.5, and 5.5 m). The main reason is that the twophase flow pattern coming from the prelift zone is changed when the feedstock nozzle is injected into the riser. Additionally, it reduces 11093



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Figure 16. Cross-section profiles of particle volume fraction (uj = 60 m·s−1, Z0 = 4.5 m).

Figure 17. Profile of particle volume fraction along riser height (uj = 60 m·s−1, Z0 = 4.5 m).

the influence of the asymmetric flow in the prelift zone on the flow pattern over the feedstock nozzles (see Figure 10a) and results in similar particle volume fraction distributions at the feed mixing zone in these three cases. In order to further observe the above distribution patterns, we also recorded the particle volume fraction distributions in different cross-section planes for the three cases, as shown in Figure 11. Indeed, from Figure 11, there

Figure 18. Gas-phase temperature profiles at axial section (uj = 60 m·s−1, Z0 = 4.5 m).

are similar particle volume fraction distributions over the nozzles for the three cases (Z0 = 3.5, 4.5, and 5.5m). 11094



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Figure 19. Cross-section profiles of gas-phase temperature (uj = 60 m·s−1, Z0 = 4.5 m).

Figure 14 describes the gas-phase velocity profiles in the cross-section planes at different nozzle angles. From Figure 14, along the circumferential and radial directions of the riser, both the gas-phase velocity gradients increase with the decrease of the nozzle angle. In addition, the axial distance affected by the injected feedstock via the nozzles increases with the decrease of the nozzle angle. Among them, the obvious velocity gradients at Z = 4.685 and 5.875 m caused by the feedstock injection can be observed at angle = 15° (see Figure 14a), while these gradients at Z = 5.875 m have disappeared at angle 45° (see Figure 14c). In practice, the radial velocity is larger at angle = 45° than that at angle= 15°, which enhances the interaction between the gas phase and the particle phase. Accordingly, both the gas-phase velocity and its gradient rapidly decrease in the feedstock injection zone. However, the high radial velocity is likely to cause serious vortex (see Figure 15). Herein, Figure 15 shows the gas-phase velocity vector profile at Z = 5.175 m. From Figure 15, one knows that the gas backmixing can be observed easily at the wall region, particular at angle = 45°. This phenomenon is likely to cause the backmixing of the catalyst at the wall region and reduce the gas−solid contact efficiency. Besides the gas-phase velocity, the particle volume fraction profiles were also recorded. Figure 16 describes the particle volume fraction profiles in the cross-section planes at different

Figure 12 shows the gas-phase temperature profiles with different nozzle positions. It is known that the catalytic cracking reaction is a spontaneous endothermic reaction and the heat transfers from the particle phase to the gas phase. Thus, the particle volume fraction has a major impact on the gas-phase temperature. As shown in Figure 12, the gas-phase temperature profiles in the feed mixing zone have slight difference although the nozzle positions are different for the three cases. Besides the temperature profile is linked to the catalytic cracking reactions, the species concentrations are also linked to them. Herein, the gasoline concentration profiles in the axial-plane are recorded and shown in Figure 13. From Figure 13, the gasoline concentration profiles show slight difference, which depends largely on the particle volume fraction and the gas−solid phase temperature distributions (see Figures 10 and 12). However, the conversion of heavy oil is higher when the feedstock nozzles located at the height of 3.5 m (see Table 6), which is due to the longer residence time for Z0 = 3.5 m, resulting in the higher conversion. 4.3.2. Effect of Nozzle Angle. In this section, three cases with different nozzle angles (angle = 15°, 30°, and 45°) were selected to study the effect of nozzle angle. The simulated results are shown in Figures 14−20. In addition, the feedstock nozzles are installed at a height of 4.5 m above the gas distribution. 11095



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is listed in Figure 20. It can be observed that all the gasoline concentrations for the three cases are nonuniform in the feedstock injection zone due to the combined effects of the nozzle jet and the cracking reactions. In addition, the rate of the gasoline yield increased for the cases with angle = 30° and 45° is faster than that for the case with angle = 15°. The main reason is that the high radial velocity enhances the efficiency of gas−solid contact and further increases the cracking reaction rate. In addition, the gasoline concentration profiles along riser height are shown in Figure 21. According to Figure 21, gasoline

Figure 20. Gasoline concentration profiles at axial section (uj = 60 m·s−1, Z0 = 4.5 m).

nozzle angles. From Figure 16, the volume fraction of the particle phase is high in the center and low near the wall in the vicinity of the feed nozzles. In addition, with the increase of the nozzle angle, the particle volume fraction in the center increases due to the high radial velocity (see Z = 4.685 m in Figure 16). However, at Z = 5.875 m, the particle volume fraction shown in Figure 16c is more uniform than that shown in Figure 16a. As discussed earlier, the high radial velocities push more particles move toward the riser center and enhance the turbulence effect of the gas phase and solid phase, which is helpful to accelerate the cracking reactions. Hence, the gas velocity increases due to the volumetric expansion in the riser caused by the cracking reactions. Meanwhile, the particle velocity increases owing to the drag force. Accordingly, the particle volume fraction decreases, which leads to the lower particle volume fraction at the injection angle of 45°. The above changes can be obviously observed in Figure 17, where the axial particle volume fraction along the riser height is described. Figure 18 shows the gas-phase temperature profiles with different nozzle angles. As described in Figure 18, the gas-phase temperature profiles under the nozzles for the three cases have similar patterns although the nozzle angles are different. In the upper areas of the nozzles, the gas-phase temperature is high in the riser center and low near the wall in all three cases. However, the high-temperature zone is different, and it decreases with the increase of the nozzle angle. The different patterns are determined by the two-phase flow (see Figures 14 and 16). As discussed above, the high radial velocity enhances the turbulence of the gas phase and solid phase and that also strengthens heart transfer of gas−solid phase and further increases the cracking reaction rate. Therefore, the high-temperature zone in Figure 18c is smaller than that in Figure 18a. In addition, the gas phase temperature profiles in the cross-section planes at different nozzle angles are given in Figure 19. From Figures 18 and 19, the temperature distribution for the case with the angle of 15° has a large gradient in all directions and it is disadvantageous to the catalytic cracking reactions. The particle volume fraction distribution and gas−solid phase temperature distribution are believed to have great effect on the yield of cracking products. Herein, the gasoline concentration profiles linked to the catalytic cracking reactions

Figure 21. Gasoline concentration profiles along riser height (uj = 60 m·s−1, Z0 = 4.5 m).

yield at the riser outlet for cases with angle = 30° and 45° have slight difference. In addition, the final gasoline yield for the case with angle = 15° is the lowest among those three cases due to the nonhomogeneous particle volume fraction and temperature distribution and ineffective gas−solid contact.

5. CONCLUSIONS In this study, a 3D CFD model coupled cracking reactions was used to describe the hydrodynamic behavior in the feedstock mixing zone of a FCC riser. The effects of nozzle position and angle on the flow field and the cracking reactions were investigated. The model was validated by comparing with the experimental data both in cold model and hot model. The results showed that the nozzle jet velocity played an important role in determining the two-phase flow in the feedstock mixing zone. Meanwhile, the simulation results indicated that the nozzle position had a small influence on the flow field and the cracking reactions in the feedstock mixing zone. Nevertheless, changing the injection angle had a significant influence on the flow field and the cracking reactions in the feedstock mixing zone. In addition, the injection angle of no less than 30° was appropriate.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +86-21-54745602. Fax: +86-21-54745602. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the National Natural Science Foundation of China (No. 21276213), the National Ministry of Science and Technology of China (No. 2012CB21500402), and the State-Key 11096



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(12) Behjat, Y.; Shahhosseini, S.; Marvast, M. A. CFD analysis of hydrodynamic, heat transfer, and reaction of three phase riser reactor. Chem. Eng. Res. Des. 2011, 89, 978−989. (13) Corma, A.; Sauvanaud, L.; Doskocil, E.; Yaluris, G. Coke steam reforming in FCC regenerator: A new mastery over high coking feeds. J. Catal. 2011, 279, 183−195. (14) Ravichander, N.; Chiranjeevi, T.; Gokak, D. T.; Voolapalli, R. K.; Choudary, N. V. FCC catalyst and additive evaluation: A case study. Catal. Today 2009, 141, 115−119. (15) Gao, H. H.; Wang, G.; Li, R.; Xu, C. M.; Gao, J. S. Study on the catalytic cracking of heavy oil by proper cut for higher conversion and desirable products. Energy Fuels 2012, 26, 1880−1891. (16) Zhu, C.; Jun, Y.; Patel, R.; Wang, D. Interactions of flow and reaction in fluid catalytic cracking risers. AIChE J. 2011, 57 (11), 3122−3131. (17) Zhou, X. W.; Chen, T.; Yang, B.; Jiang, X. D.; Zhang, H. L.; Wang, L. Y. Kinetic model considering reactant oriented selective deactivation for secondary reactions of fluid catalytic cracking gasoline. Energy Fuels 2011, 25, 2427−2437. (18) Gilbert, W. R.; Morgado, E.; Abreu, M. A. S.; Puente, G.; Passamontib, F.; Sedran, U. A novel fluid catalytic cracking approach for producing low aromatic LCO. Fuel Process. Technol. 2011, 92, 2235−2240. (19) Meng, X. H.; Xu, C. M.; Li, L.; Gao, J. S. Kinetics of catalytic pyrolysis of heavy gas oil derived from Canadian synthetic crude oil. Energy Fuels 2011, 25, 3400−3407. (20) Dutta, A.; Constales, D.; Heynderickx, G. J. Applying the direct quadrature method of moments to improve multiphase FCC riser reactor simulation. Chem. Eng. Sci. 2012, 83, 93−109. (21) Pashikanti, K.; Liu, Y. A. Predictive modeling of large-scale integrated refinery reaction and fractionation systems from plant data. Part 2: Fluid catalytic cracking (FCC) process. Energy Fuels 2011, 25, 5298−5319. (22) Wu, C. N.; Cheng, Y.; Ding, Y. L.; Jin, Y. CFD-DEM simulation of gas−solid reacting flows in fluid catalytic cracking (FCC) process. Chem. Eng. Sci. 2010, 65, 542−549. (23) Gao, J. S.; Lan, X. Y.; Fan, Y. P.; Chang, J.; Wang, G.; Lu, C. X.; Xu, C. M. CFD modeling and validation of the turbulent fluidized bed of FCC particles. AIChE J. 2009, 55, 1680−1694. (24) Chang, J.; Meng, F. D.; Wang, L. Y.; Zhang, K.; Chen, H. G.; Yang, Y. P. CFD investigation of hydrodynamics, heat transfer, and cracking reaction in a heavy oil riser with bottom airlift loop mixer. Chem. Eng. Sci. 2012, 78, 128−143. (25) Souza, J. A.; Vargas, J. V. C.; Ordonez, J. C.; Martignoni, W. P.; Meien, O. F. Thermodynamic optimization of fluidized catalytic cracking (FCC) units. Int. J. Heat Mass Transfer 2011, 54, 1187−1197. (26) Quintana-Solorzano, R.; Rodriguez-Hernandez, A.; Garcia de Leon, R. Study of the performance of catalysts for catalytic cracking by applying a lump-based kinetic model. Ind. Eng. Chem. Res. 2009, 48, 1163−1171. (27) Quintana-Solorzano, R.; Thybaut, J. W.; Galtier, P.; Marin, G. B. Simulation of an industrial riser for catalytic cracking in the presence of coking using single-event microkinetics. Catal. Today 2010, 150, 319− 331. (28) Das, A. K.; Baudrez, E.; Marin, G. B.; Heynderickx, G. J. Threedimensional simulation of a fluid catalytic cracking riser reactor. Ind. Eng. Chem. Res. 2003, 42, 2602−2617. (29) Guptal, A.; Rao, D. S. Effect of feed atomization on FCC performance: Simulation of entire unit. Chem. Eng. Sci. 2003, 58, 4567−4579. (30) Gan, J. Q.; Yang, C. H.; Li, C. Y.; Zhao, H.; Liu, Y. B.; Luo, X. Gas−solid flow patterns in a novel multi-regime riser. Chem. Eng. J. 2011, 178, 297−305. (31) Lopes, G. C.; Rosa, L. M.; Mori, M.; Nunhez, J. R.; Martignoni, W. P. Three-dimensional modeling of fluid catalytic cracking industrial riser flow and reactions. Comput. Chem. Eng. 2011, 35, 2159−2168. (32) Theologos, K. N.; Nikou, I.; Lygeros, A. I.; Markatos, N. C. Simulation and design of fluid catalytic cracking riser-type reactors. AIChE J. 1997, 43, 486−498.

Laboratory of Chemical Engineering of Tsinghua University (No. SKL-ChE-13A05) for supporting this work.

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NOMENCLATURE CC= content of coke on FCC catalysts, wt% CD= drag coefficient Cp= specific heat capacity, kJ·kg−1·K−1 ds= particle diameter, m e= particle−particle restitution coefficient g= gravitational constant, m·s−2 g0= radial distribution function H= height above feed nozzles, m I=̿ identity matrix P= pressure, Pa Ps= particulate phase pressure, Pa Qr= cracking reaction heat, J·m−3·s−1 Q= interphase heat transfer, J·m−3·s−1 r= radial position, m Re= Reynolds number T= temperature, K v= velocity vector, m·s−1 uj= superficial velocity of nozzle jet injection, m·s−1 Wj= reaction rate of lump j, kg·m−3·s−1 Yj= mass fraction of component j Z= axial height, m Z0= location of the feed nozzles, m

Greek Letters

α= volume fraction β= interphase exchange coefficient, kg·m−3·s−1 Θs= granular temperature, m2·s−2 ρ= density, kg·m−3 ω= drag force modify coefficient

Subscripts

g= gas phase s= solid phase j= lump

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REFERENCES

(1) Fan, Y. P. The gas-solid two-phase flow in the FCC riser. Ph. D thesis; China University of Petroleum: Beijing, China, 1996. (2) Gao, J. S.; Xu, C. M.; Lin, S. X.; Yang, G. H. Advanced model for turbulent gas-solid flow and reaction in FCC riser reactors. AIChE J. 1999, 45, 1095−1113. (3) Gan, J. Q.; Zhao, H.; Berrouk, A. S.; Yang, C. H.; Shan, H. H. Numerical simulation of hydrodynamics and cracking reactions in the feed mixing zone of a multiregime gas−solid riser reactor. Ind. Eng. Chem. Res. 2011, 50, 11511−11520. (4) Chen, L.; Weinstein, H. Shape and extent of the void formed by a horizontal jet in a fluidized bed. AIChE J. 1993, 39, 1901. (5) Fan, Y. P.; Ye, S.; Chao, Z. X.; Lu, C. X.; Sun, G. G.; Shi, M. X. Gas−solid two-phase flow in FCC riser. AIChE J. 2002, 48, 1869− 1887. (6) Needham, D. J.; Riley, N.; Smith, J. H. B. A jet in crossflow. J. Fluid Mech. 1988, 159, 188−194. (7) Sergio, L.; Coelho, V.; Hunt, J. C. R. The dynamic of the near field of strong jets in crossflows. J. Fluid Mech. 1989, 95, 200−207. (8) Fan, Y. P.; Cai, F. P.; Shi, M. X.; Xu, C. M. Two types of novel feedstock injection structures of the FCC riser reactor. Chin. J. Chem. Eng. 2004, 12, 42−48. (9) Fan, Y. P.; E, C. L.; Shi, M. X.; Xu, C. M.; Gao, J. S.; Lu, C. X. Diffusion of feed spray in fluid catalytic cracker riser. AIChE J. 2010, 56, 858−868. (10) Xu, G. W.; Yang, G. H. In 7th National Conference on Chemical Engineering; NCCE: Beijing, 1994; pp 991−994. (11) Cho, Y. J. Fluidized bed. J. Chem. Eng. Jpn. 1994, 27, 158−164. 11097



Article

(33) Theologos, K. N.; Markatos, N. C. Advance modeling of fluid catalytic cracking riser-type reactors. AIChE J. 1993, 39, 1007−1017. (34) Nayak, S. V.; Joshi, S. L.; Ranade, V. V. Modeling of vaporization and cracking of liquid oil injected in a gas−solid riser. Chem. Eng. Sci. 2005, 60, 6049−6066. (35) Li, J.; Luo, Z. H.; Lan, X. Y.; Xu, C. M.; Gao, J. S. Numerical simulation of the turbulent gas−solid flow and reaction in a polydisperse FCC riser reactor. Powder Technol. 2013, 237, 569−580. (36) Zhu, C.; Jun, Y.; Patel, R.; Wang, D. W.; Ho, T. C. Interactions of flow and reaction in fluid catalytic cracking risers. AIChE J. 2011, 57, 3122−3131. (37) Yan, W. C.; Luo, Z. H.; Lu, Y. H.; Chen, X. D. A CFD-PBMPMLM integrated model for the gas−solid flow fields in fluidized bed polymerization reactors. AIChE J. 2012, 58, 1717−1732. (38) Chen, X. Z.; Luo, Z. H.; Yan, W. C.; Lu, Y. H.; Ng, I. S. Threedimensional CFD-PBM coupled model of the temperature fields in fluidized bed polymerization reactors. AIChE J. 2011, 57, 3351−3366. (39) Yan, W. C.; Luo, Z. H.; Guo, A. Y. Coupling of CFD with PBM for a pilot-plant tubular loop polymerization reactor. Chem. Eng. Sci. 2011, 66, 5148−5163. (40) Gidaspow, D. Multiphase Flow and Fluidization: Continuum and Kinetic TheoryDescriptions; Academic Press: New York, 1994. (41) Neri, A.; Gidaspow, D. Riser hydrodynamics: Simulation using kinetic theory. AIChE J. 2000, 46, 52−67. (42) Lun, C. K.; Savage, S. B.; Jeffrey, D. J.; Chepurniy, N. Kinetic theories for granular flow: Inelastic particles in Couette flow and slightly inelastic particles in general flow field. J. Fluid Mech. 1984, 140, 223−256. (43) Yang, N.; Wang, W.; Ge, W.; Li, J. H. CFD simulation of concurrent-up gas−solid flow in circulating fluidized beds with structure-dependent drag coefficient. Chem. Eng. J. 2003, 96, 71−80. (44) Zhang, P. Numerical simulation on catalytic cracking reaction in two-stage riser reactors. Master thesis; China University of Petroleum: Beijing, China, 2005. (45) Lan, X. Y.; Xu, C. M.; Wang, G.; Wu, L.; Gao, J. S. CFD modeling of gas-solid flow and cracking reaction in two-stage riser FCC reactors. Chem. Eng. Sci. 2009, 64, 3847−3858. (46) Xu, C. M.; Gao, J. S.; Lin, S. X.; Yang, G. H. Analysis of Catalytic Cracking Reaction Process; China Petroleum Industry Press: Beijing, 2002. (47) Johnson, P. C.; Jackson, R. Frictional-collisional constitutive relations for granular materials, with application to plane shearing. J. Fluid Mech. 1987, 176, 67−93.

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