Numerical Simulation of Hydrogen Production via Chemical Looping

Article pubs.acs.org/IECR

Numerical Simulation of Hydrogen Production via Chemical Looping Reforming in Interconnected Fluidized Bed Reactor Shuai Wang, Liming Yan, Feixiang Zhao, Huilin Lu,* Liyan Sun, and Qinghong Zhang School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China S Supporting Information *

ABSTRACT: A multiphase reactive fluid dynamic model has been applied to simulate a complete chemical looping reforming (CLR) system, including an air reactor (AR), a fuel reactor (FR), a cyclone, and a loop seal. A two-fluid model with consideration of frictional stress between particles at high solid concentrations is used. A bubble-structure-dependent drag coefficient model is proposed and incorporated into the computational fluid dynamics (CFD) code to account for the effect of multiscale structures in the bubbling fluidized reactor. The predictions by the model are in good agreement with the experimental results. Flow pattern, profiles of bubbles, and distributions of gas compositions and temperatures are obtained. In addition, the influence of reaction temperature, operating velocity, and H2O/CH4 molar ratio on the gas concentration is also investigated.

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INTRODUCTION Hydrogen is regarded as the most promising clean energy and plays a key role in the reduction of greenhouse gas emissions. The production of hydrogen has attracted great attention in recent years.1,2 Among these methods of hydrogen production, methane steam reforming on solid catalysts is a well-known industrial route.3,4 However, the hydrogen production by means of methane steam reforming will lead to appreciable amounts of CO2 released. Hence, developing a new technology for hydrogen production with lower cost and less CO2 emission has become desirable. Chemical looping reforming (CLR), a new hydrogen production process integrated with CO2 capture technology, utilizes oxygen carriers to transport oxygen instead of direct combustion of fuel and air, which avoids the contact of fuel gas and air.5 During the CLR process, the hydrogen production depends on partial oxidation of fuel by oxygen carriers and steam reforming of fuel. Hence, the air-to-fuel ratio is limited at a lower level to avoid the fuel complete oxidation. The CLR advantage lies in that the product CO2 is not diluted by N2 and can easily be recovered and the heat needed for hydrogen production can be supplied by the oxygen carrier flow from the exothermic air reactor (AR) to endothermic fuel reactor (FR), without costly oxygen combustion.6 The feasibility of oxygen carriers for the CLR was evaluated by experiments.7 NiO/SiO2 showed a high reactivity in combination with high selectivity to H2 at 800 °C, which makes it a suitable candidate as an oxygen carrier for the CLR. Both NiO/NiAl2O4 and Mn3O4/Mg−ZrO2 showed a stable reactivity for the CLR of biomass tar.8 It was also pointed out that shortening the reduction time and reducing the operating temperature could restrain carbon deposition. Berguerand et al.9 conducted experiments in a bench-scale CLR reactor with a manufactured NiO catalyst and studied the tar-reforming performance and the effects on the gas composition. It can be concluded that the Nibased catalyst displayed a strong tar-reforming ability and a capacity to eliminate tars in the raw gas. Pröll et al.10 tested a CLR process with natural gas as fuel at a 140-kW pilot plant. The © 2014 American Chemical Society

oxygen in the AR could be completely absorbed by oxygen carriers when operating temperature reached a higher level and there was no carbon formation for a larger global excess air ratio although no steam was added to fuel. The effects of different operating conditions on the CLR performance, such as H2O/ CH4 molar ratio and operating temperature were investigated.11 An increase in the H2O/CH4 molar ratio would lead to a reduction of the carbon deposition. Perovskite LaFeO3-based oxygen carriers show a high activity and selectivity to syngas and have potential application for the CLR process.12 Dai et al.13 used Al2O3−kaolin as an addition to investigate the reactivity of perovskite LaFeO3-based oxygen carriers. The results indicated that the addition of Al2O3−kaolin could improve the fuel conversion and oxidize CH4 selectively to syngas, which was beneficial for the CLR process. Computational fluid dynamics (CFD), as a developing technique, can offer more insights into complex fluid dynamics, which has become a fundamental method to study the multiphase system. Several hydrodynamic studies on chemical looping applications by means of CFD have been published in the literature. Tabib et al.14 developed a 3D CFD-DEM methodology to simulate an industrial-scale packed-bed chemical looping combustion reactor. The pressure drop and heat-transfer coefficients predicted by this methodology were validated for the spherical packed bed by a comparison of available correlations. Shuai et al.15 investigated hydrodynamic characteristics in a dual circulating fluidized bed system with a two-fluid model and analyzed the effects of solids inventory and locations of loop seals. A three-dimensional simulation of chemical looping combustion was performed using CuO/Al2O3 as oxygen carriers.16 The effects of operating velocity and particle diameter on fuel conversion were evaluated. Mahalatkar et al.17 tested the ability of CFD approach via an investigation of the influence of Received: Revised: Accepted: Published: 4182

August 24, 2013 February 3, 2014 February 18, 2014 February 18, 2014 dx.doi.org/10.1021/ie402787v | Ind. Eng. Chem. Res. 2014, 53, 4182−4191

Industrial & Engineering Chemistry Research

Article

Table 1. Constitutive Correlations Used in the Modela

different parameters such as reaction temperatures and solid concentrations. Kruggel-Emden et al.18 studied a coupled chemical looping combustion system using an interconnected CFD model. Both the FR and AR are solely implemented and coupled through boundary conditions and data exchange after each time step. However, the influence of the loop seal and cyclone is necessary and should be represented in the interconnected framework. Regarding to the reactor in the form of bubbling fluidized beds, bubble phases as mesoscale structures play an important role. Especially for CFD simulations, the existence of bubble phases will lead to a reduction of the drag interaction, which makes the traditional drag model limited. Hence, a drag coefficient model with consideration of bubble effects for the heterogeneous flow is required. Shi et al.19 proposed a multiscale drag model to account for the influence of bubbles, which was based on the energy minimization multiscale (EMMS) method.20 Zhao et al.21 applied this model to perform a three-dimensional simulation of a large-scale methanol to olefins fluidized bed reactor. Hongzhong et al.22 proposed a new drag model for bubbling fluidized beds with Geldart-B particles, where empirical equation of bubble velocity was adopted and gas velocity in the emulsion phase was assumed to be constant. In these models, the global operating parameters were used to obtain the relations between drag coefficient and solid concentration. However, the local drag coefficient depends not only on local solid concentration but also on local velocity.23 For this purpose, a bubble-structure dependent drag coefficient model is developed and incorporated into the CFD code to consider multiscale structures in the reactor. A complete CLR system including the AR, FR, and cyclone is simulated by means of CFD model with heterogeneous reactions. Flow pattern, distributions of gas components, profiles of temperature and the effects of operating temperature, operating velocity, and H2O/CH4 molar ratio on reaction performance are studied.

equation 1. Stress tensor T⎤ ⎡ τg = μg ⎢∇ug + ∇ug ⎥ − ⎣ ⎦

{

τs = μs ⎡⎣∇us + (∇us)T ⎤⎦ −

∂ (εsρs ) + ∇·(εsρs us) = Ssg ∂t

(2)

2 3

(T1-1)

(∇·u )I} g

(T1-2)

(∇·us)I} + ξs ∇us

2. Solid pressure ps = (1 − φ1(εs))ps,k + φ1(εs)(ps,k + ps,f )

(T1-3)

ps,k = εsρθ + 2ρs (1 + e)εs 2g0θ s

(T1-4)

n

(T1-5)

(εs − εs,min)

ps,f =

(εs,max − εs) p arctan⎡⎣25(εs − εs ,min)(εs ,max − εs ,min)−2 ⎤⎦

φ(εs) =

(T1-6)

+ 0.5

π

3. Solid shear viscosity

(

)( ))

( )(

μs = 1 − φ2 εs μs,k + φ2 εs μs,k + μs,f 4

μs = 5 εs 2ρs dsg0(1 + e)

θ π

+

(T1-7)

)

⎡1 + 4 g ε (1 + e)⎤2 ⎦ 5 0 s

10ρs ds πθ

96(1 + e)εsg0 ⎣

(T1-8) (T1-9)

ps,f sin(ψ )

μs,f =

2 I2D

φ2(εs) =

arctan⎡⎣96(εs − εs,min)⎤⎦ π

(T1-10)

+ 0.5

4. Thermal conductivity of particles

ks =

25ρs ds πθ

⎡1 + 6 (1 + e)g ε ⎤2 + 2ε 2ρ d g (1 + e) s s s 0 0 s⎦ 5

1/2

( πθ )

64(1 + e)g0 ⎣

(T1-11)

5. Dissipation of fluctuation kinetic energy

(

)

(

γs = 3 1 − e 2 εs 2ρs g0θ

4 ds

θ π

)

− ∇·us

(T1-12)

6. Rate of energy dissipation per unit volume

Dgs =

dsρs 4 πθg0

2 ⎛ 18μg ⎞2 ⎜ ⎟ u − u s ⎝ ds2ρs ⎠ g

7. Exchange of fluctuating energy between gas and particles φs = − 3βgsθ

(T1-13)

(T1-14)

a

Note that a complete listing of the parameters, variables, and terms used in this paper is given in the Supporting Information.

MATHEMATICAL MODEL A two-fluid model is adopted in this study and assumed as follows: (1) The particles are spherical and uniform in density and size. (2) The particles are assumed to be smooth, inelastic spheres. (3) The shrinking-core model, which is controlled by chemical reaction, is used to determine the global reaction rates in oxygen carriers. The kinetic theory of granular flow is used to close the governing equations for each phase.24 Detailed relations are listed in Table 1. Gas−Solid Hydrodynamics. Continuity equations: (1)

2 3

{

■

∂ (εgρg ) + ∇·(εgρg u g) = Sgs ∂t

( )

No.

∂ (εsρs us) + ∇·(εsρs usus) ∂t = −εs∇p − ∇ps + εs∇·τs + εsρs g + β(u g − us) + Ssg us (4)

where τ is the stress tensor and β represents the gas−solid drag coefficient. In eq (T1-1) in Table 1, μg represents gas viscosity, which can be solved by a variety of turbulence models. From the study of Cloete et al.,25 even the subgrid closure model of large eddy simulation had less significant influence on predictions. Hence, for simplification, laminar flow is assumed and the constant gas viscosity is adopted in current simulation.The state equation of gas phase is used to account for variations of gas density with temperature and given by R gTg 1 = p ρg

where Sgs= −Ssg is mass source term due to heterogeneous reactions. Momentum conservation equations:

n

∑ j=1

Yg, j Mg, j

(5)

Energy conservation equations:

∂ (εgρg u g) + ∇·(εgρg u gu g) ∂t

∂ (εgρg Hg) + ∇·(εgρg u gHg) ∂t

= −εg∇p + εg∇·τg + εg ρg g − β(u g − us) + Sgs u g

= ∇(λg ∇Tg) − hgs(Tg − Ts) + ΔHg

(3) 4183

(6)

dx.doi.org/10.1021/ie402787v | Ind. Eng. Chem. Res. 2014, 53, 4182−4191


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Figure 1. Dense phase, dilute phase and interface in the grid cell.

Table 2. A Bubble-Structure-Dependent Drag Coefficient Modela equation

No.

1. Balance equations fεden + (1 − f ) = εg

(T2-1)

ug = ⎡⎣fUg,den + (1 − f )Ub⎤⎦/εg

(T2-2)

us = fUs,den /(1 − εg)

(T2-3)

2. Superficial slip velocity of emulsion phase and interphase

Uden = Ug,den −

εdenUs,den

(T2-4)

1 − εden

(T2-5)

Uint = f (Ub − Ue) 3. Momentum equation of particles in the emulsion phase ndenFden + nint Fint = f (1 − εden)∇pg + f (1 − εden)(ρs − ρg )(g + as ,den)

(T2-6)

4. Balance equations of pressure drop (1 − f ) n F fεden den den

(T2-7)

= nint Fint − (1 − f )ρs (ag,den − ag,dil)

5. Equation for superficial slip velocity of dense phase ⎡ ⎤ (1 − εden)μg 1 7 1 0.125πds 2ρg ⎢200 + 3 3 ⎥Uden 2 εden3dsρg Uden εden ⎦ ⎣

=

εdenπds3 ⎡ ⎢f (1 6(1 − εden)εg ⎣

(T2-8)

− εden)∇pg + f (1 − εden)(ρs − ρg )(g + as,den) − (1 − f )

⎤ ρg (ag,den − ag,dil)⎥ ⎦ 6. Equation for superficial slip velocity of interphase

0.125πdb2ρe f −0.5 38Re int −1.5 Uint Uint =

πd b3 [f (1 6εg

(ρs − ρg )(g + as,den) + fεdenρg (ag,den − ag,dil)] 2

0.125πdb ρe f

−0.5

(T2-9)

− εden)∇pg + f (1 − εden) Re int ≤ 1.8

⎡⎣2.7 + 24Re int ⎤⎦ Uint Uint

(T2-10)

−1

3

=

πd b 6εg

[f (1 − εden)∇pg + f (1 − εden)(ρs − ρg )(g + as,den)

+ fεdenρg (ag,den − ag,dil)]

Re int > 1.8

7. Stability criterion by minimization of the energy dissipation by drag force ⎡ ⎤ 1 Ndf = (1 − ε )ρ ⎢ndenFdenUg,den + nint FintUb(1 − f )⎥ → minimum ⎦ g s⎣ a

(T2-11)


∂ (εsρs Hs) + ∇·(εsρs usHs) ∂t = ∇(λs∇Ts) + hgs(Tg − Ts) + ΔHs

Species transport equations: ∂ (εgρg Yg, j) + ∇·(εgρg u gYg, j) ∂t ⎡ ⎛ ⎤ μ⎞ = ∇·⎢εg ⎜ρg Dj + t ⎟∇Yg, j ⎥ + Sg, j ⎢⎣ ⎝ ⎥⎦ Sc ⎠

(7)

where hgs and ΔH represent the interphase heat-transfer coefficient and the heat released due to chemical reactions, respectively. 4184

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Table 3. Correlations and Parameters Used in the Modela equation

No.

1. Number density of particles in the emulsion phase and bubbles

nden = n int =

(T3-1)

f (1 − εden) πds3 / 6 1−f

(T3-2)

πd b3 / 6

2. Equations for drag force of dense phase and interphase ⎡ ⎤ (1 − εden)μg 1 7 1 1 Fden = 8 πds 2ρg ⎢200 + 3 3 ⎥Uden 2 εden3dsρg Uden εden ⎦ ⎣

(T3-3)

⎧ 0.125πd 2ρ f −0.5 38Re −1.5 U U Re int ≤ 1.8 ⎪ b e int int int Fint = ⎨ − − 2 0.5 1 ⎪ 0.125πd ρ f [2.7 + 24Re int ] Uint Uint Re int > 1.8 ⎩ b e 3. Mean density in the emulsion phase ρe = ρg εden + ρs (1 − εden)

(T3-4)

(T3-5)

4. Viscosity in the emulsion phase (T3-6)

μe = μg [1 + 2.5(1 − εden) + 10.05(1 − εden)2 + 0.00273 exp(16.6 (1 − εden))] 5. Superficial velocity in the emulsion phase Ue =

ρg Ug,den + ρs Us,den

(T3-7)

ρg εden + ρs (1 − εden)

6. Accelerations

(

as,den = ∂

fUs,den

fUs,den

1 − εden 1 − εden

(T3-8)

)/∂z

ag,dil = ∂((1 − f )Ub*(1 − f )Ub)/∂z

(

ag,den = ∂ a

fUg,den fUg,den εden

εden

(T3-9) (T3-10)

)/∂z


∂ (εsρs Ys, j) + ∇·(εsρs usYs, j) = Ss, j ∂t

coefficient with consideration of bubble-emulsion structures can be expressed as follows:

(9)

Granular temperature conservation equation: βbubble‐based =

⎤ 3⎡ ∂ ) + ∇·(εsρθ )us⎥ ⎢ (εsρθ s s ⎣ ⎦ 2 ∂t

εg 2 Fgs Uslip

=

εg 2 Uslip

[nden Fden + n int Fint ]

(11)

where nden and nint represent number densities of particles in the emulsion phase and bubbles. Fden and Fint are the interaction forces in the emulsion phase and acting on the bubbles. These parameters can be obtained by solving a series of nonlinear equations, which are summarized in Tables 2 and 3. In this model, the global operating parameters (Ug, Gs) in the original bubble-based EMMS model are substituted by the local variables (ug, us, εg) in the grid. For the unsteady flow, the local dynamic parameters should be more reasonable to determine the drag coefficient. Meanwhile, the pressure gradient and accelerations in the grid are considered. The procedure of the revised bubblestructure-dependent drag coefficient model is displayed in Figure 2. For high voidage (εg > 0.8), the expression of βWen‑Yu is adopted:28

= ( −∇ps I + τs): ∇us + ∇·(ks∇θ ) − γs − 3βθ + Dgs (10)

where θ is granular temperature and defined as θ = ⟨C2⟩/3, which is used to compute the parameters, such as solid pressure and viscosity. For the reactors and loop seal in the form of a bubbling fluidized bed, the friction stress between particles dominates at higher solid concentrations. Hence, a frictional−kinetic model proposed by Johnson and Jackson26 is applied to account for the frictional and kinetic contribution. A Bubble-Structure-Dependent Drag Coefficient Model. To model bubbling fluidized beds, the local flow in the grid cell is resolved into three subsystems: the dilute phase that characterizes the bubbles, the dense phase that characterizes the emulsion, and the interphase between dense phase and dilute phase, as is displayed in Figure 1. For the dense phase and dilute phase, gas and particles are accelerated or decelerated by complex interactions. Here, as a first approximation, particles in the bubble phase is assumed negligible, so, εdil = 1.0. Bubbles as mesoscale structures in the bubbling fluidized bed system can be treated like clusters in the fast fluidized bed. Hence, the clusterstructure-dependent drag model for fast fluidized beds proposed by Shuai et al.27 can be extended to bubbling fluidized beds, although there is difference between the two systems. The drag

βWen ‐ Yu =

3 ρg (1 − εg)|u g − us| −2.65 Cd εg 4 ds

(12)

Reaction Kinetics Model. de Diego et al.29 evaluated the behaviors of Ni-based oxygen carriers for the CLR process by means of experiments in an interconnected fluidized bed reactor. The reducing reactions of oxygen carriers with fuel gas in the FR are considered as follows: 4185



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CH4 + H 2O → 3H 2 + CO

ΔHr4 = 206.0 kJ/mol (R5)

CH4 + 2H 2O → 4H 2 + CO2

ΔHr5 = 164.9 kJ/mol (R6)

CO + H 2O → H 2 + CO2

ΔHr6 = − 41.0 kJ/mol (R7)

From the study of de Diego et al.,32 the kinetic model developed by Xu and Froment33 can be used to represent the methane steam reforming and water-gas shift reactions during the chemical looping process, and the corresponding reaction rate equations are expressed as

Figure 2. Procedure of a bubble-structure-dependent drag coefficient model.

CH4(g ) + NiO(s) → Ni(s) + 2H 2(g ) + CO(g ) ΔHr1 = 203.75 kJ/mol

(R1)

H 2(g ) + NiO(s) → Ni(s) + H 2O(g ) ΔHr2 = −2.1 kJ/mol

(R2)

(R3)

(16)

⎡ (P P 2/P 3.5) − (P P 0.5/K K ) ⎤ CH4 H 2O H2 CO2 H 2 1 2 ⎥ r3 = k 3⎢ 2 ⎢⎣ ⎥⎦ DEN

(17)

de Diego et al. found that the catalytic activity of oxygen carriers was much lower than those from conventional catalyst and obtained the corresponding kinetic parameters in this model for two Ni-based oxygen carriers by experiments and multivariable regression, which was selected in this work. Moreover, the catalytic activity depended on the oxygen-carriers conversion. A catalytic activity factor of oxygen-carriers for methane steam reforming rates was introduced, which was related to oxygencarrier conversion. In this work, the reaction rate is corrected by a factor of oxygen-carrier conversion. Initial and Boundary Conditions. A schematic diagram of a 900 Wth continuous atmospheric CLR experimental facility is displayed in Figure 3, which was tested by de Diego et al.29 and is chosen as the reference case in this simulation. The CLR system consists of two fluidized beds. Both the AR and FR include a bubbling fluidized bed with a bed height of 0.1 m and a bed diameter of 0.05 m. The AR is connected to a riser with a bed height of 1 m and a bed diameter of 0.02 m. The loop seal is located between the AR and FR to avoid the leakage of fuel. The main geometries and operating conditions of the FR and AR are summarized in Table 5. The above equations are solved using suitable initial and boundary conditions. At the beginning, solid oxygen carriers are loaded with an initial solid concentration of 0.5 in the AR and FR, respectively. It is assumed that all Ni in oxygen carriers are in the form of free NiO and the initial fraction of oxidation state is 0.1. The velocity-inlet conditions are set at the bottom of reactors and loop seal. At the top of separator, the pressure is set to be 1 atm. For the wall, no slip condition is applied and the wall temperature is considered as a constant. The simulation is performed using a modified K-FIX program, which was applied to the simulation of circulating fluidized beds in advance.34 The maximum convergence residual is set to be 10−3 and the time step varies between 10−4 s and 10−6 s. The variables are time-averaged after reaching the quasi-equilibrium

ΔHr4 = − 479.4 kJ/mol (R4)

The mean particle diameter of 0.2 mm is selected in this simulation. From previous studies, chemical reaction can be assumed as the main resistance to determine the global reaction rate when the particle size locates in the range of 0.09−0.5 mm.30 In this study, the shrinking-core model is adopted to determinate the reaction rates, which takes the following form:31,32 dX 1 dt M O

(13)

⎛ E ⎞ dX = k 0 exp⎜ − 0 ⎟Cg nfNiO−1/3 ⎝ RT ⎠ dt

(14)

where Cg and n represent the concentration of the gas reactant and the reaction order. The detailed kinetic parameters are given in Table 4.33 Table 4. Kinetic Parameters for Ni-Based Oxygen Carriers CH4

H2

CO

O2

0.2 5 0.2

0.15 5 0.4

0.059 5 0.6

0.84 22 0.7

(18)

32

The reduced particles are transferred to the AR and reoxidized:

k0 (mol1−n m3n−2 s−1) E0 (kJ/mol) n

⎡ (PCOPH O/PH ) − (PCO /K 2) ⎤ 2 2 2 ⎥ r2 = k 2⎢ ⎢⎣ ⎥⎦ DEN2

+ K H2OPH2O/PH2

ΔHr3 = −43.3 kJ/mol

−rOC, i = R 0

(15)

DEN = 1 + K CH4PCH4 + K H2PH2 + K COPCO

CO(g ) + NiO(s) → Ni(s) + CO2 (g )

O2 (g ) + 2Ni(s) → 2NiO(s)

⎡ (P P /P 2.5) − (P P 0.5/K ) ⎤ CH4 H 2O H 2 CO H 2 1 ⎥ r1 = k1⎢ ⎢⎣ ⎥⎦ DEN2

During the methane steam reforming process, there will be carbon formation due to the decompositions of hydrocarbons. However, according to the literature,34 the following three reactions are only considered to represent methane steam reforming. 4186



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Figure 3. Schematic diagram of a chemical looping reforming system.

state from 10 s to 15 s. The computational time is ∼3−4 weeks on a 2.8 GHz workstation.

■

RESULTS AND DISCUSSION To verify the fluid dynamic model, a simulation of a bubbling gas−solid fluidized bed is performed in comparison with the experimental results of Laverman et al.,35 as shown in Figure 4a. Meanwhile, the predicted results by Ergun/Wen-Yu drag model are also given. From time-averaged axial solids velocity profiles, it can be observed that the predictions by bubble-based drag model are in better agreement with the experimental data than that by Ergun/Wen-Yu drag model. There is a slight overprediction on the profile of solid velocity between the center and the wall by Ergun/Wen-Yu drag model. A comparison of reaction performance is implemented between the predictions and hot experimental data on a CLR reactor,29 as shown in Figure 4b. It can be found that molar fractions of CO and H2 at the FR exit are overestimated by the model. Accordingly, molar fraction of H2O obtained by the model is underpredicted, which may be attributed to the selectivity of the reaction rate equations. Overall, the predictions by the simulation are consistent with the measured data.

Figure 4. Comparisons between predictions and experimental data.

The pressure balance for the chemical looping system plays an important role in the gas leakage. If there is an obvious pressure deviation between the FR and the rest of the system, large gas leakages will take place. Figure 5 displays the profile of gas pressure in the CLR system. It can be observed that the pressure in the FR is lower than that in the AR and pot-seal, and higher than that in the cyclone. From the gas pressure variation along the height, it can be seen that there is an evident decay profile of gas pressure at the bottom of the AR and FR. For the riser of the

Table 5. Physical Properties and Model Parameters Used in the Simulation description

AR

FR

particle diameter (μm) particle density (kg m−3) reactor height (m) reactor diameter (m) initial concentration of particles initial mass fraction of solid species initial static bed height (m) initial temperature (K) inlet gas flow (m s−1) inlet gas composition (molar ratio) restitution coefficient wall restitution coefficient specularity coefficient

200 2500 0.15/1 0.05/0.02 0.5 Ni/NiO:Al2O3 (0.18:0.82) 0.1 1223 0.46/0.59 O2:N2 (0.21:0.79) 0.97 0.9 0.5

200 2500 0.25 0.052 0.5 Ni/NiO:Al2O3 (0.18: 0.82) 0.1 1073, 1123, 1173 0.07, 0.1, 0.15 CH4:H2O:N2 0.5:0.05:0.45, 0.5:0.15:0.35, 0.5:0.25:0.25 0.97 0.9 0.5

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the reactions are greatly influenced, which leads to the unsteady H2 yield. Figure 7 shows the instantaneous contour plots of solid concentration and gas species molar fraction at the quasiequilibrium state. The bubble phase in the reactor can be clearly observed, which leads to the nonuniformity of oxygen carriers in the bed and has a great impact on the fuel conversion. The distribution of O2 molar fraction in the AR reflects the regeneration process of oxygen carriers. With the bed height increased, the oxygen is continually transferred to oxygen carriers and shows a descending trend. Above the bed of the AR, there is a local increase in the O2 molar fraction due to the inlet of the secondary air. For the FR, there is a higher CH4 concentration at the center region of bed, which is due to that the bubble passing results in gas leakage. At the top area, CH4 concentration closes to zero, which means that nearly complete fuel conversion can be achieved. The H2 molar fraction near the wall has a slight low value where the higher concentrations of oxygen carriers promote the conversion of H2 to H2O. Figure 8 displays the profile of gas and solid temperatures along the axial direction of the FR. It can be observed that there is a global decay trend for both gas and solid temperatures as the reactor height increases, which can be attributed to the fact that the reducing reactions of oxygen carriers and methane steam reforming are endothermic reactions and dominate the temperature distribution of the FR. Because of the interphase heat transfer, the discrepancy between gas temperature and solid temperature is not evident. However, at the bottom inlet of the reactor, a reverse change occurs on the profile of gas temperature as a consequence of the inlet temperature of fuel gas. The axial distribution of solid concentrations is also given in Figure 8. There are local minimum values at the bottom dense region, which is due to the effects of the fuel gas inlet and the positions connected with the loop-seal and the separator. The solid concentration shows a sharp reduction near the bed surface and closes to zero in the freeboard space. The operating temperature is an important factor for the CLR process. To evaluate the effect of temperatures on the reaction, the FR temperature is changed from 1073 K to 1173 K. From Figure 9, it can be found that a higher temperature promotes the CH4 conversion and more CO2 and H2O are captured. Accordingly, the H2 yield is restrained. This may be attributed to the fact that the reactivity of oxygen carriers is enhanced with the increasing temperature and more fuel gas can be completely oxidized. The trends predicted by the model are consistent with that obtained by experiments.29 To investigate the effect of operating velocity on the CLR performance, the FR inlet velocity is varied from 0.07 m s−1 to 0.15 m s−1. The gas reactant and product concentrations at the FR exit, as a function of the FR operating velocity, are shown in Figure 10. An increase in the FR inlet velocity leads to a slight reduction of H2O and CO2 concentrations, while there is a reverse influence on H2 and CO concentrations. The fuel gas flow rate increases as the FR inlet velocity increases, which means more fuel gas is available in the reactor and methane steam reforming reactions are enhanced. Hence, the yield of H2 is increased. On the other hand, with increasing fuel flow, there are not enough oxygen carriers to react with fuel gas, which will lead to the reduction of fuel conversion and more CH4 is obtained at the exit of FR. The inlet gas composition of the FR includes of 50 vol % of CH4 and 50 vol % of H2O + N2. To study the influence of the H2O/CH4 molar ratio, the molar fraction of the feeding CH4 was

Figure 5. Pressure profiles in the reactor system.

AR, the variation of gas pressure becomes less significant with the height increased. This is related to the distribution of solid concentration. The gas compositions at the outlet of FR as a function of time are shown in Figure 6. At the initial stage, the outlet

Figure 6. Molar fractions of gas species (CH4, H2,CO,CO2,H2O) and solid conversion as a function of time at the outlet of FR.

concentrations of gas species get increased with fuel gas injected into the reactor. The molar fractions of the reactants CH4 and H2O display a high value due to that the fluidized state in the reactor is incompletely reached. In addition, the lack of catalytic activity of oxygen carrier particles for reforming reaction is a factor that influences the performance of reaction. The evolution of the solids conversion with time is also shown in Figure 6. It can be seen that the solids conversion is increased, which means an increase of the fractional reduction of metal oxygen carrier and leads to a change of reaction rate. With the reactions in progress, large amounts of reactants are consumed and the reactant concentrations begin to decrease while the products are gradually generated. After 4 s, the reactions reach a quasi-equilibrium state. The concentrations of gas species vary around a constant. However, there is an intense oscillation of H2 molar fraction. The reducing reactions of oxygen carriers associated with methane steam reforming control the H2 yield. Because of the disturbance of the bubble phase in the bed and circulation of oxygen carriers, 4188



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Figure 7. Instantaneous solid volume fraction and molar fraction of gas species at 10 s (H2O/CH4 = 0.3; uFR = 0.1m/s; TFR = 1173 K; TAR = 1223 K).

Figure 8. Axial profiles of gas and solid temperatures and solid volume fraction in the FR.

Figure 10. Effect of FR inlet velocities on gas reactant and product concentrations at the outlet of FR.

maintained at a constant of 0.5 and the molar fraction of H2O is varied from 0 to 0.25. N2 is used as a balanced gas. The effect of the H2O/CH4 molar ratio on the gas reactant and product concentrations at the FR exit is shown in Figure 11. An increase in the H2O/CH4 molar ratio means more water vapor available for methane reforming reaction. It can be observed that the yield of H2 is enhanced when the H2O/CH4 molar ratio is increased. Accordingly, the concentrations of CO and CH4 decrease and the molar fractions of CO2 and H2O are increased. This may be attributed to that a higher H2O/CH4 molar ratio promotes the water-gas shift reaction. However, compared with the previous two factors, H2O/CH4 molar ratio has less influence on the performance of FR.

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CONCLUSION Flow behavior and reaction performance in a chemical-looping reforming reactor is investigated by means of CFD simulation. A multiphase reactive kinetic model is incorporated into the CFD code. A two-fluid model with the kinetic theory of granular flow is applied. To determine the effect of mesoscale structures on the

Figure 9. Effect of FR temperature on gas reactant and product concentrations at the outlet of FR.

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(3) Lee, C. B.; Lee, S. W.; Lee, D. W.; Ryi, S. K.; Park, J. S.; Kim, S. H. Hydrogen production from methane steam reforming in combustion heat assisted novel micro-channel reactor with catalytic stacking. Ind. Eng. Chem. Res. 2013, 52, 14049−14054. (4) Izquierdo, U.; Barrio, V. L.; Cambra, J. F.; Requies, J.; Güemez, M. B.; Arias, P. L.; Kolb, G.; Zapf, R.; Gutiérrez, A. M.; Arraibi, J. R. Hydrogen production from methane and natural gas steam reforming in conventional and microreactor reaction systems. Int. J. Hydrogen Energy 2012, 37, 7026−7033. (5) Lyngfelt, A.; Leckner, B.; Mattisson, T. A fluidized-bed combustion process with inherent CO2 separation; application of chemical-looping combustion. Chem. Eng. Sci. 2001, 56, 3101−3113. (6) Ortiz, M.; de Diego, L. F.; Abad, A.; García-Labiano, F.; Gayán, P.; Adánez, J. Hydrogen production by auto-thermal chemical-looping reforming in a pressurized fluidized bed reactor using Ni-based oxygen carriers. Int. J. Hydrogen Energy 2010, 35, 151−160. (7) Zafar, Q.; Mattisson, T.; Gevert, B. Integrated hydrogen and power production with CO2 capture using chemical-looping reforming-redox reactivity of particles of CuO, Mn2O3, NiO, and Fe2O3 using SiO2 as a support. Ind. Eng. Chem. Res. 2005, 44, 3485−3496. (8) Mendiara, T.; Johansen, J. M.; Utrilla, R.; Geraldo, P.; Jensen, A. D.; Glarborg, P. Evaluation of different oxygen carriers for biomass tar reforming (I): Carbon deposition in experiments with toluene. Fuel 2011, 90, 1049−1060. (9) Berguerand, N.; Lind, F.; Israelsson, M.; Seemann, M.; Biollaz, S.; Thunman, H. Use of Nickel Oxide as a Catalyst for Tar Elimination in a Chemical-Looping Reforming Reactor Operated with Biomass Producer Gas. Ind. Eng. Chem. Res. 2012, 51, 16610−16616. (10) Pröll, T.; Bolhàr-Nordenkampf, J.; Kolbitsch, P.; Hofbauer, H. Syngas and a separate nitrogen/argon stream via chemical looping reformingA 140 kW pilot plant study. Fuel 2010, 89, 1249−1256. (11) de Diego, L. F.; Ortiz, M.; Adánez, J.; García-Labiano, F.; Abad, A.; Gayán, P. Synthesis gas generation by chemical-looping reforming in a batch fluidized bed reactor using Ni-based oxygen carriers. Chem. Eng. J. 2008, 144, 289−298. (12) Mihai, O.; Chen, D.; Holmen, A. Catalytic consequence of oxygen of lanthanum ferrite perovskite in chemical looping reforming of methane. Ind. Eng. Chem. Res. 2011, 50, 2613−2621. (13) Dai, X.; Li, J.; Fan, J.; Wei, W.; Xu, J. Synthesis gas generation by chemical-looping reforming in a circulating fluidized bed reactor using perovskite LaFeO3-based oxygen carriers. Ind. Eng. Chem. Res. 2012, 51, 11072−11082. (14) Tabib, M. V.; Johansen, S. T.; Amini, S. A 3D CFD-DEM methodology for simulating industrial scale packed bed chemical looping combustion reactors. Ind. Eng. Chem. Res. 2013, 52, 12041− 12058. (15) Wang, S.; Yang, Y.; Lu, H.; Xu, P.; Sun, L. Computational fluid dynamic simulation based cluster structures-dependent drag coefficient model in dual circulating fluidized beds of chemical looping combustion. Ind. Eng. Chem. Res. 2012, 51, 1396−1412. (16) Wang, X.; Jin, B.; Zhong, W.; Zhang, Y.; Song, M. Threedimensional simulation of a coal gas fueled chemical looping combustion process. Int. J .Greenhouse Gas Control 2011, 5, 1498−1506. (17) Mahalatkar, K.; Kuhlman, J.; Huckaby, E. D.; O’Brien, T. Computational fluid dynamic simulations of chemical looping fuel reactors utilizing gaseous fuels. Chem. Eng. Sci. 2011, 66, 469−479. (18) Kruggel-Emden, H.; Rickelt, S.; Stepanek, F.; Munjiza, A. Development and testing of an interconnected multiphase CFD-model for chemical looping combustion. Chem. Eng. Sci. 2010, 65, 4732−4745. (19) Shi, Z.; Wang, W.; Li, J. A bubble-based EMMS model for gas− solid bubbling fluidization. Chem. Eng. Sci. 2011, 66, 5541−5555. (20) Li, J.; Kwauk, M. Particle−Fluid Two Phase Flow; Metallurgical Industry Press: Beijing, 1994. (21) Zhao, Y.; Li, H.; Ye, M.; Liu, Z. 3D Numerical Simulation of a Large Scale MTO Fluidized Bed Reactor. Ind. Eng. Chem. Res. 2013, 52, 11354−11364. (22) Wang, Y.; Zou, Z.; Li, H.; Zhu, Q. A new drag model for TFM simulation of gas−solid bubbling fluidized beds with Geldart-B particles. Particuology, DOI: 10.1016/j.partic.2013.07.003.

Figure 11. Effect of H2O/CH4 molar ratio on gas reactant and product concentrations at the outlet of FR.

flow behavior and reactive characteristic, a bubble-structuredependent drag coefficient model is proposed to characterize the influence of bubbles. A complete CLR system including the loop seal and separator is simulated, where the effect of the unsteady mass flow can be characterized. By comparison with the traditional drag model, it can be observed that the predictions by bubble-based drag model are in better agreement with the experimental results. The distribution of gas concentrations and temperature is given. The influence of different operating conditions on the outlet gas concentrations is also analyzed and the trends predicted by the model are in accordant with that obtained by experiments. It can be seen that the hydrogen yield depends on the oxygen-carrier-to-fuel ratio. Overall, the CFD approach with bubble-based drag model is suitable to evaluate the CLR performance. In further work, this approach is required to be further verified and extended to an industrial CLR system including the water-gas shift reactor.

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ASSOCIATED CONTENT

S Supporting Information *

A listing of the nomenclature used in this paper is provided as Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +0451 8641 2258. Fax: +0451 8622 1048. E-mail address: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was financially supported by the Project supported by the National Natural Science Foundation of China (Nos. 51176042 and 20490202).

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REFERENCES

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Numerical Simulation of Hydrogen Production via Chemical Looping

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