CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

Contents lists available at ScienceDirect

Chemical Engineering Research and Design journal homepage: www.elsevier.com/locate/cherd

CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems Zhengbiao Peng, Elham Doroodchi ∗ , Yusif A. Alghamdi, Kalpit Shah, Caimao Luo, Behdad Moghtaderi Discipline of Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia

a r t i c l e

i n f o

a b s t r a c t

Article history:

In a chemical looping combustor (CLC) system, the solid circulation rate (SCR) is a key

Received 17 June 2014

parameter that determines the design, operating conditions and the overall efficiency of the

Received in revised form 22

system. In the present work, the gas–solid flow of a CLC cold flow model (10 kWth ) has been

September 2014

simulated by the computational fluid dynamics–discrete element method (CFD–DEM). The

Accepted 11 November 2014

results showed that the SCR at different locations of the system fluctuates with time with

Available online xxx

different amplitude, and the variation of SCR is periodically stable. The turbulent gas–solid flow regime in the air reactor was found to be the main mechanism driving the fluctuation

Keywords:

of SCR and determined the fluctuation frequency and amplitude. The SCR increased with

CFD–DEM

the flow rates of air/fuel reactors and loop seals, and the total solid inventory. Changes in

Chemical looping combustor

operating conditions directly induced the change in the mass of solids that were entrained

Solid circulation rate

into the riser from the air reactor and how fast the solids were transported therein. A corre-

Parcel

lation was subsequently proposed to describe the SCR as a function of solid hold-up and gas

Particle residence time

flow velocity in the riser. The particle residence time decreased in a power law as the SCR

Pressure profile

increased. Reasonable agreements were obtained between simulations and experiments in terms of solid distribution, gas–solid flow patterns, pressure drop profiles and SCR. © 2014 Published by Elsevier B.V. on behalf of The Institution of Chemical Engineers.

1.

Introduction

Carbon dioxide is released into atmosphere through the fossil fuel combustion and land-use changes. From 1960 to 2010, atmospheric concentration of carbon dioxide has monotonically increased from 310 to 390 ppm (Pieter, 2014; Ralph, 2014). About 60% of the heat contained in the atmosphere is attributed to carbon dioxide (Kenarsari et al., 2013). Several measures including energy production with higher efficiency, more conservation of energy, replacing current fossil fuel based energies with renewable energies, and carbon capture and storage (CCS) can be taken to reduce the greenhouse gas discharge to the atmosphere. Several technologies are available for CCS which can be classified as post-combustion, pre-combustion and

oxy-combustion systems (Rubin et al., 2012). However, most methods are energy intensive, resulting in a significant decrease of the overall thermal efficiency, and therefore increase the cost of electricity generation. Chemical-looping combustion (CLC) is a combustion technology where due to in situ oxygen production capability, CO2 enriched flue gas stream can be produced at possible lowest energy penalty (Hossain and de Lasa, 2008). A typical CLC system is comprised of an air reactor, a fuel reactor, a cyclone, two loop seals and connecting pipes. There are numerous experimental and numerical studies reported for the identification of suitable metal–metal oxides, CLC operating conditions, and oxidation and reduction kinetics for single metal or mixtures of metal oxides combined with different solid and gaseous fuels (see e.g., Kolbitsch et al., 2009; Kronberger et al., 2004; Mattisson

∗

Corresponding author. Tel.: +61 2 4033 9066; fax: +61 2 4033 9095. E-mail address: [email protected] (E. Doroodchi). http://dx.doi.org/10.1016/j.cherd.2014.11.005 0263-8762/© 2014 Published by Elsevier B.V. on behalf of The Institution of Chemical Engineers.

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

2

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

Nomenclature Symbols A Ai Ar C0 dp fc ff fd fsg Gs I MTSI m Np Nf p Q tres ug Usf Umf Ut Scr Vg Vp v vsz

area, m2 area of cell face i, m2 cross-section area of riser, m2 fitting constant cell face area, m2 collision contact forces, N total fluid forces, N fluid drag force, N forces acting on gas by the solid particles, N solid mass circulation rate, g/s moment of inertia total solid inventory, kg mass, kg number of particles number of cell face pressure, Pa flow rate, L/min particle residence time, s gas velocity vector, m/s superficial gas velocity, m/s particle minimum fluidisation velocity, m/s particle terminal velocity, m/s solid mass circulation rate per unit area, kg/(s m2 ) gas vertical velocity, m/s volume of single particle, m3 particle velocity vector, m/s particle vertical velocity, m/s

Greek letters ˛ gas leakage ratio particle fractional volume in a cell ı  scaling factor pressure drop, Pa p  viscosity of fluid, kg/(m s) density, kg/m3  gas flow flux, g/s ˚s solid hold-up Subscripts AR air reactor f fluid fuel reactor FR g gas phase index i LS loop seal particle p res residence time s solid phase total solid inventory TSI

et al., 2009; Shah et al., 2012; Song et al., 2014). By and large, it has been concluded that the solid circulation rate (SCR) is a key parameter that determines the conversion of oxygen carriers, heat transfer between the two reactors, reactor size and total solid inventory of the system (Song et al., 2014). However, modelling studies focusing on the SCR of CLC systems are very limited to date. It is also known that accurate estimation of the SCR/solid flow is a key problem

in various solids handling equipment such as pneumatic conveying and coal pulveriser units. Some empirical correlations for the prediction of SCR for CLC systems have been reported in the literature (see e.g., de Diego et al., 1995; Kronberger et al., 2004, 2005). The most common method was the one proposed by Johnsson and Leckner (1995) which however is not straightforward to apply to direct the CLC design (Kronberger et al., 2005; Markstorm and Lyngfelt, 2012). Other correlations developed are often complicated, somewhat inaccurate or too simple with many assumptions on the gas–solid flow (e.g., gas/solid flow velocities) (de Diego et al., 1995; Kronberger et al., 2004, 2005). As reported with a number of studies in other research areas (see e.g., Chu and Yu, 2008; Deen et al., 2007; Hoomans et al., 1996; Kafui et al., 2002; Peng et al., 2014; Tsuji et al., 1993; Xu and Yu, 1997), the computational fluid dynamics–discrete element method (CFD–DEM) may provide more accurate predictions of the CLC hydrodynamics, related SCR and particle residence time, based on which improved empirical correlations could be delivered. Though, to the best of our knowledge, there have been very limited numerical studies using CFD–DEM for simulating the hydrodynamics and thermodynamics of a real CLC system. In one-dimensional (1D) CLC models, Kaushal et al. (2007) and Abad et al. (2009, 2013) developed the fuel reactor model using the two-phase theory to calculate the bubble fraction, solid concentration and gas species concentration at the bottom and inside the upper freeboard combined with the reduction kinetics of the metal oxide. In this model the SCR was used as an important input parameter to determine the conversion rate of particles assuming that the full combustion can be achieved. The authors presented the axial profiles of gas components and solid fractions along the reactor height. In two- and three-dimensional (2D/3D) CLC models, Kruggel-Emden et al. (2010) developed a 2D interconnected multi-phase CFD model to characterise the transient behaviour of a coupled chemical looping combustion systems. The particles of the CLC system were modelled using continuity, momentum and energy equations of the solid phase. In each computational cell, there existed solid fraction, solid velocity and solid temperature. The outputs of the software included concentrations of all gas components, gas volume fraction, gas (solid) velocity, and gas (solid) temperature in the computational domain. Because the air reactor and the fuel reactor were separate computational zones with the same SCR as the input parameter, the model presented contours of solid volume fraction, solid temperature, degree of reduction, and mass fractions of fuel in the fuel reactor and oxidant in the air reactor. Wang et al. (2014, 2011), Deng et al. (2009) and Mahalatkar et al. (2011) employed similar governing equations to obtain contours of solid fractions and gas species concentrations under different conditions. Seo et al. (2011) used the 2D multiphase Eulerian model to study the effects of gas velocities of riser, bubbling fluidized bed, supply chamber and recycle chamber as well as vertical aeration rate on the SCR, concluding that the SCR increased with increasing the gas velocities into loop-seals and the riser. Except for characterising the particles using the governing equation as shown by Kruggel-Emden et al. (2010), the particles have also been characterised by the discrete element method (DEM). In this technique, each particle is tracked individually thus accounting for the dynamics due to particle–particle, particle–wall and particle–fluid interactions.

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

Compared to the two-fluid model (Deng et al., 2009; KruggelEmden et al., 2010; Mahalatkar et al., 2011; Wang et al., 2014, 2011), no closure is required for the solid phase stress tensor in the DEM model, as the motion of individual particles is solved directly. Therefore the detailed local information on individual particles can be obtained, such as position, velocity, forces and species reaction rates at any given time whilst the fluid flow profile is captured using CFD. The CFD–DEM approach has been successfully applied to describe the fluidization characteristics of gas–solid flows, as summarised by Deen et al. (2007) and Zhu et al. (2008). Recently, Parker (2012) reported a threedimensional model of a CLC system based on the CFD–DEM approach, which is implemented in Barracuda VR 15.0 (CPFD Software, LLC, Albuquerque, NM). Results detailing the fluidization and transport behaviour, temperature profiles, and reaction conversion were presented. The objective of this work is to provide insight into the characteristics of SCR in the cold flow model of CLC systems. A CFD–DEM model has been employed to solve the gas–solid flow and the parcel methodology (Chu et al., 2009; Huber and Sommerfeld, 1998) was used to deal with the computationally inhibitive particle number in a real CLC system. Characteristics of SCR in a 10 kWth CLC system were extensively described. Effects of operating conditions including inlet flow rates of air/fuel reactors and loop seals, and the total solid inventory on the SCR were investigated. The relationship between SCR and particle residence time has also been discussed. The validity of simulation results was verified against the experimental data.

2.

Mathematical model

2.1.

Governing equations of DEM–CFD model

In the loop seals and fuel reactor of a CLC system, the gas–solid flow often exhibits the typical bubbling fluidization regime, in which the local solid concentration is very high and the influence of the presence of solid particles on the gas flow cannot be neglected. The presence of solid particles in the two-phase flow is often taken into account through the introduction of local void fraction and gas–solid interacting terms into the Navier–Stokes equations of the single phase flow. The local averaged continuity and momentum equations of the gas flow thus are (Anderson and Jackson, 1967): ∂(εg ) + ∇ · (εg ug ) = 0 ∂t

(1)

∂(εg ug ) + ∇ · (εg ug ug ) = −ε∇p + ∇ · ε␶g + fsg + εg g ∂t

(2)

where ε is the volume fraction of the discrete phase in a computational cell; g and ug are fluid density and velocity, respectively; fsg is the local mean particle–fluid interaction force. In a dense fluid–solid flow, a single particle is interacting with neighbouring particles, surrounding fluid and the geometry of computational domain. The equations describing the motion of particle i are mi

dvi = fc,i + ff,i + mi g dt

(3)

where mi , Ii , and vi are mass, moment of inertia, translational velocities of particle i, respectively. fc,i is the total collision

3

contact force. A soft-sphere model, specifically the linear spring-dashpot model (Deen et al., 2007; Peng et al., 2014; Tsuji and Tanaka, 1998; Xu and Yu, 1997), was employed in the present study to solve the contact mechanics of collisions. ff,i is the total fluid force acting on the particle including drag force, pressure gradient force and Saffman lift force (ANSYS Inc, 2014). The Gidaspow drag law was used to calculate the momentum exchange coefficient between the solid and the fluid phases (Peng et al., 2014).

2.2.

Parcel concept

A conventional DEM model is very computationally demanding as each particle is tracked individually. As the particle size becomes smaller, the total particle number increases exponentially, which renders the computation prohibitive. For example, in a lab-scale CLC system, the particle number is around 7 × 1011 , which is far beyond the capacity of current computational resources. To circumvent this problem, the parcel methodology developed by Patankar and Joseph (2001) has been adopted in this work. Similar concepts or treatments have been used by other researchers to simulate industrial systems (see e.g., Chu et al., 2009; Huber and Sommerfeld, 1998; Sakai et al., 2004; Tsuji and Tanaka, 1998). According to the concept, a group of real particles with the same properties (e.g. size and density) can be represented by one parcel-particle. The mass used in collisions is that of the entire parcel, not the one of a single particle. The total mass (mp ) and volume (Vp ) of the parcel-particle is equal to the sum of the mass and volume of the real particles it represents. The radius of the parcel is thus determined by the mass of the entire parcel and the particle density. At a given point in the fluid flow, the acceleration of a parcel due to fluid forces is assumed to be the same as the sum of fluid force acting on the group of real particles it represents:

ff,p =

Np 

ff,i

(4)

i=1

where Np is the number of real particles contained in a parcel, and ff,i is the total fluid force acting on a real particle. The acceleration due to inter-particle collision forces and particle–wall collisions forces are calculated according to the properties of the parcel-particle. To determine the properties of the parcel-particle a priori is very challenging. In this study, for simplicity, the material properties of a parcel-particle were assumed to be the same as those of real particles it represents, including density, spring stiffness and the friction and damping coefficients following the work by Chu et al. (2009).

3.

Numerical strategy and methodologies

The commercial software ANSYS® FLUENT 14.5 (ANSYS, Inc., USA) has been used to assist with the numerical simulations. Parallel user-defined functions (UDFs) have been programmed to customise the simulations including, non-uniform inlet velocity profiles, particle drag laws, particle-fluid interaction forces, particle tracking, and data post-processing and analysis. The phase coupled Semi-Implicit Method for PressureLinked Equation (SIMPLE) algorithm (Patankar, 1980) was used to solve the pressure–velocity coupling equations of the fluid flow, namely the continuity and momentum conservation

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

4

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

system. An area-weighted averaging has been applied to obtain the velocity of computational faces on the inlets of loop seals and fuel reactor.

Table 1 – Simulation conditions and parameters. Fluid phase Gas density, kg/m3 Gas viscosity, Pa s Fluid time step, s

1.125 1.8 × 10−5 1 × 10−3

Solid phase Particle diameter, mm Parcel diameter, mm Particle density, kg/m3 Total solid inventory, kg Normal spring-stiffness, N/m Restitution coefficient Solid time step, s

0.138 2 2462 1.8–2.75 1000 0.9 1 × 10−4

equations (i.e., Eqs. (1) and (2)). The diffusion term was discretised by a central-differenced scheme that always is of second-order accuracy. The Green-Gauss Node Based method (Holmes and Connel, 1989; Rauch et al., 1991) was employed to calculate the variable gradients for constructing values of a scalar at cell faces and also for computing secondary diffusion terms and velocity derivatives. The validation of simulation results was carried out using the experimental data that were obtained on a lab-scale cold flow model of a CLC system (10 kWth ). For the purpose of consistent comparison, the geometry of the simulated domain and particle (polystyrene) properties were kept exactly the same as those used in experiments. The computational geometry is shown in Fig. 1. Hex meshes (6000 meshes) are used throughout the whole computational domain, as shown in Fig. 1(b). In the simulation, the local pressure at 18 positions, the same as those in experiments (see Fig. 2(a)) as to be detailed below, has been monitored and recorded. The solid circulating rate (SCR) at the periodically steady state has been calculated. As it was very time-consuming to scan over all parcels in the domain, SCR was calculated at a time interval of 0.02 s, in which the parcel motion was monitored once with a time interval of 0.005 s. Moreover, a number of cross-sections throughout of the system have been set and used to monitor data of the gas–solid flow including local solid hold-up, gas (solid) flow flux, and gas (solid) vertical velocity. The simulation conditions and parameters are listed in Table 1. For all simulations, a sufficiently long period of time was completed until the gas–solid flow reaches the periodically steady state under a certain set of conditions. The data in the last 10 s were used for the averaging calculation to avoid start-up transients. A hybrid scheme of parallel computation (MPI for fluid flow and shared memory OpenMP for particle flow) was employed to improve the computational efficiency. For a typical parallel simulation with 241 685 parcels on 4 computational nodes (clock speed 2.8 GHz, Smart Cache 12 M, and QPI speed 6.4 GT/s), it expends 25¼ days to complete the simulation of 20 s gas–solid flow. As the hole of the perforated distributor is relatively large (1 mm and 0.5 mm in diameter for loop seals and the fuel reactor, respectively), the influence of perforated distributors on the gas–solid two phase flow has been considered in this study. Separate simulations of single gas phase flow have been conducted to obtain the gas flow profile through the distributor. The gas flow velocity profile of the cross-section just above the distributor (0.002 m) was extracted and imported by UDF into the simulation of the gas–solid two phase flow in the CLC

4.

Experiments

The experimental studies were carried out using two interconnected fluidized beds reactors shown in Fig. 2. This setup was a cold flow model of 10 kWth chemical looping combustion (of natural gas) plant based on the scaling law of Kronberger et al. (2005) and Lyngfelt et al. (2004) and the simplified scaling relationships for fluidised beds by Glicksman et al. (1993). The cold flow model of CLC apparatus consisted of five major components namely the air reactor, riser, fuel reactor, two loop seals and a cyclone. The air reactor was a vertical tube with an inner diameter of 80 mm and height of 300 mm connected to a riser with inner diameter of 40 mm and height of 1150 mm. A cyclone was used to capture the particles transported by the upward flow of the fluidising fluid through the riser and return them to the system. Two fluidised loop seals were used in the experimental setup with the upper loop seal located before the fuel reactor to prevent leakage of air to the fuel reactor and the lower loop seal located after the fuel reactor to prevent any leakage of fuel to the air reactor. The particles captured in the cyclone fell under the force of gravity to the upper loop seal and then fluidised to the fuel reactor. The fuel reactor with an inner diameter of 85 mm and height of 196 mm was operated at a range 4–8 times the minimum fluidisation velocity of the particle mixture. Fluidisation was used to guarantee a smooth transport of particles through the loop seals. The air reactor could be operated at flow rates up to QAR = 200 L/min, while flow rates up to QFR = 50 L/min and QLS = 25 L/min could be reached in the fuel reactor and loop seals, respectively. The cold flow model of CLC process was run continuously using nitrogen gas as the fluidising media in the air reactor, while compressed air was used in the fuel reactor and the loop seals. It is worth noting that the cold flow system was designed for chemical looping combustion of natural gas. The fuel reactor was designed for 50–120 L/min of gas flow rate. Based on the stoichiometry, the flow rate ratio of CH4 to flue gas required for CLC is 1/5, which defies the system rating of 5–10 kWth . The solid inventory used in experiments ranges from 1 to 3 kg. This represents the specific inventory norms of 100–300 kg/MWth , which is in line with the reported literature (Cho et al., 2004). Flow rates of the feed streams for the air reactor, the fuel reactor and loop seals were controlled using feedback control systems. The computer program interface for the air reactor was FlowDDE-2nd v4.58 whilst LabView 8.5 was employed for the fuel reactor and loop seals. The pressure of the nitrogen stream entering the air reactor was maintained between 5 and 6 bars, and the pressure of the compressed air supply to the fuel reactor and loop seals was kept at 4–5 bars using a gas regulator. The system was initially operated at a low flow rate in the air reactor and increased gradually until the desired flow rate for the experiment was reached. This approach prevented pressure build up within the CLC system and ensured good quality fluidisation. The pressure distribution inside the system was obtained by measuring the gauge pressure along the vessel using Honeywell pressure transducers. The numbers shown in Fig. 2(a) refer to these pressure ports on the apparatus. Particle total mass circulating rate was obtained by stopping the aeration of the upper loop seal during a steady state operation and

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

5

Fig. 1 – Computational geometry: (a) domain geometry; (b) mesh. 1, air reactor; 2, riser; 3, connection between riser and cyclone; 4, upper loop seal; 5, fuel reactor; 6, lower loop seal.

Fig. 2 – Experimental set-up: (a) schematic drawing; (b) photo.

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

6

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

Table 2 – Hydrodynamic properties of particles used in experiments. Parameter Mean Diameter (arithmetic averaging), dp Density, s Terminal velocity, Ut a Minimum fluidization velocity, Umf b a b

Unit ␮m kg/m3 m/s m/s

Glass Ballotini 138 2462 0.87 0.018

Calculated by the correlation of Haider and Levenspiel (1989). Calculated by Ergun equation (Ergun, 1952) based on the packed bed voidage of 0.4.

measuring the time required to reach a particle bed height accumulation of 10 mm. The particles mass in the packed bed was calculated based on a packed bed void fraction of 0.4 in the upper loop seal at steady state conditions. The measurements for each case were repeated at least 3 times. Glass beads (GB) with a mean diameter of dp = 138 ␮m and the apparent density of p = 2462 kg/m3 were used in the experiments. The particle terminal velocity, Ut , and minimum fluidisation velocity, Umf , are shown in Table 2. The experiments were performed using systems with a total solid inventories of MTSI = 2 kg, 2.25 kg, 2.5 kg and 2.75 kg.

5.

Results and discussion

5.1.

Model validation

5.1.1. profile

Comparison on solid flow pattern and pressure

The solid flow pattern in the critical components is directly determined by the characteristics of the gas–solid two-phase flow in the CLC system. Fig. 3 shows the comparisons between simulations and experiments on the solid flow pattern and the pressure profile in each component. In each group, from left, experimental snapshots, DEM-CFD prediction results, and comparison on the pressure drop profile, are shown. The data of simulation results were collected after the system reaches the periodically steady state. Excellent agreements on the solid flow pattern between prediction results and experimental snapshots are observed in all components of a CLC system for air reactor flow rate of 112 L/min. Specifically, in the air reactor (Fig. 3(a)), the gas–solid flow exhibits typical features of turbulent fluidisation regime due to the high superficial gas velocity (i.e. Usf /Umf = 18.5). Significant local solid circulations are observed and the particles are sporadically splashed upwards. A portion of particles that are lifted to the contraction section are accelerated and entrained into the riser section, through which the particles are circulated throughout the system. The model predicts the same fluidisation features as those observed in experiments (i.e., the left image in Fig. 3(a)). It is a good indication that the model is capable of capturing the major gas–solid flow characteristics in the air reactor. After the gas–solid separation in the cyclone, the particles are separated from the flue gas and fall down into the upper loop seal. As the superficial gas velocity of the loop seal is just above the minimum fluidisation velocity of the particles (Usf /Umf = 1.5), a bubbling fluidisation regime is observed (Fig. 3(b)). As particles accumulate in the loop seal, the bed expands over the height of the weir (on the right hand side of the loop seal). Subsequently, a portion of particles are split out and conveyed downwards into the fuel reactor for the

reduction reaction. Notably, the bed expansion height in the recycle chamber (right hand side) is higher than that in the supply chamber (left hand side) due to the lower pressure in the recycle chamber. This feature predicted by the model agrees well with the experimental snapshot. In the fuel reactor, the typical bubbling fluidisation regime is observed (Fig. 3(c)) due to the small fluidisation number (i.e. Usf /Umf = 4.5). The bubbling fluidisation regime was designed to allow for the sufficiently long gas–solid contact time to maximise the fuel conversion. Particles in the central region are continuously lifted upwards and splashed around in the freeboard and fall down to side regions. The side particles then move downwards due to the low gas velocity close to the wall. As such, solid circulations are formed close to the wall. Some particles that are splashed into the funnel at the left side of the fuel reactor are transported into the lower loop seal. It can be seen that the above fluidisation behaviour from the model prediction is verified by the experiment observation. The visual difference is due to the parcel-particles shown in the simulation image as one parcel-particle represents a number of real particles. Likewise, the particles in the bottom loop seal (Fig. 3(d)) are expanded due to the small fluidisation number (i.e. Usf /Umf = 1.5). The particles in the recycle chamber (left hand side) move out of the weir and circulate into the air reactor for re-oxidation. The system pressure drop profiles predicted in simulations and those measured in experiments for air reactor flow rates of 81 L/min, 112 L/min and 127 L/min are also shown in Fig. 3 alongside the snapshots. The pressure is the averaged value of the transient pressure collected at each port over 10 s of flow at the steady state. In the desired operation of a CLC system, there should be no gas exchange between the fuel reactor and the air reactor. The majority of the gas entering from the bottom of air reactor should flow out of the system via the outlet on the top of the cyclone (i.e. passing port p10 in Fig. 2(a)). The majority of the gas entering from the bottom of the fuel reactor flows out via the outlet on the top of the fuel reactor (i.e. passing port p14 in Fig. 2(a)). Therefore, the loops of pressure drop balance for the gas flow throughout the system can be expressed as, p2−3 + p3−4 + p4−5 + p5−6 + p6−7 + p7−8 + p8−9 + p9−10 + p10−2 = 0

(5)

p12−11 + p11−10 + p10−12 = 0

(6)

p13 = p14

(7)

p17−16 + p16−15 + p15−14 + p14−17 = 0

(8)

In ideal simulations, the pressure at the outlet of cyclone and that at the outlet of fuel reactor were set to be at a fixed pressure of 0 Pa, as these two outlets are open to the atmosphere. The pressure at the two outlet ports, namely port p10 close to the outlet of cyclone and port p14 close to the outlet of fuel reactor (Fig. 2(a)), should be therefore close to the atmospheric pressure. However, the measured values of pressure at these two ports in experiments were always much greater than the atmospheric pressure as there is a long exhaust pipe connected to the two outlets to collect the exhausting particles

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

7

Fig. 3 – Comparisons on solid flow pattern and pressure profile in components of CLC system for MTSI = 2.25 kg, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m: (a) air reactor; (b) upper loop seal; (c) fuel reactor; (d) lower loop seal. In both experiments and simulations, the solid flow patterns are shown for QAR = 112 L/min, and the pressure profiles are shown for QAR = 81 L/min, QAR = 112 L/min and QAR = 127 L/min.

in experiments. For consistent comparison, reference points were used to ensure the pressure at these two outlets was kept equal in both simulations and experiments. In this work, the pressure values at ports p10 and p14 have been selected as the reference points and the profile of pressure drop, i.e., local pressure minus the value at reference points was plotted for comparison. Specifically, the pressure at port p10 was used as the reference point for pressure drop balance loops of Eqs. (5) and (6), and the pressure at port p14 was used for loops of Eqs. (7) and (8).

It can be seen that the predicted pressure drop profiles agree satisfactorily with the experimental data. However, the local pressure inside the air reactor and the riser is slightly over-predicted by the model. This over-prediction of local pressure becomes more pronounced as the flow rate of air reactor increases. Moreover, deviation of pressure drop profiles inside the fuel reactor is also observed. The deviations between the simulation and experiment might be primarily attributed to the Gidaspow drag law employed in the simulations, which has been recognised to overestimate the particle–fluid

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

8

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

0.0005

a

φs (%)

0.0004 0.0003 0.0002 0.0001 0

4

5

6

7

8

9

10

Time (s) 0.012

b

0.01

φs (%)

Fig. 4 – Comparison on the mass circulation rate of the CLC system between simulations and experimental measurements for MTSI = 2.25 kg, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m. interactions (Zimmermann and Taghipour, 2005). Also, as the model neglects the angular motion of particles, the energy dissipation during the collision of two particles would be significantly under predicted. This simplification of the model also contributes to the deviation. Mono-sized particles used in the numerical model might partially contribute to the over-prediction of the pressure. The small particles act as a lubricant between large particles and facilitate the fluidisation of particles, which decreases the pressure drop of the fluidised bed (Chew and Hrenya, 2011; Hoomans et al., 1996).

0.008 0.006 0.004 0.002 0

4

5

6

7

8

9

10

Time (s) 0.032

c 0.024

Comparison on SCR

Fig. 4 shows the SCR measured in experiments for air reactor flow rates of 81 L/min, 112 L/min and 127 L/min under the total solid inventories of 2.0 kg, 2.25 kg and 2.5 kg, respectively. Good agreements have been observed for high air reactor flow rates (i.e., 112 L/min and 127 L/min). However, for the lowest flow rate of 81 L/min, the deviation has been observed. This might be attributed to the instability of the system induced by the low flow rate of air reactor for the total solid inventories (i.e., 2.25 kg). Fig. 5 demonstrates the evolution of the averaged solid hold-up of the cross-section monitored in the middle of the riser (h = 0.75 m) during a sampled time period of 4–10 s. It can be seen that for 81 L/min the evolutional line is non-continuous. The solid hold-up is zero over a significant portion of the entire sampled time. On the contrast, continuous evolutional lines of solid hold-up are obtained for 112 L/min and 127 L/min. The air reactor flow rate of 81 L/min is not high enough to continuously transport the particles upwards into the contraction section hence to the riser for circulation. The non-continuous transport of particles and the resultant instability of the system have also been observed in the experimental operation for the low inlet flow rate of air reactor (i.e., 81 L/min). Under such conditions, the data acquisition time needs to be sufficiently long to attain a statistically time-averaged value. As the simulations are very computationally expensive, the limited simulation time might lead to the deviation between the predicted SCR and the measured value. Moreover, as discussed above, the model uses virtual parcels instead of real particles and ignores the rotation of the parcel-particles, hence underestimates the energy dissipation when particles collide with each other. As a result, the model over predicts the SCR compared to the experimental data.

φs (%)

5.1.2.

0.016

0.008

0

4

5

6

7

8

9

10

Time(s) Fig. 5 – Evolution of solid hold-up monitored on a cross-section in the middle of the riser for MTSI = 2.25 kg, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m: (a) QAR = 81 L/min; (b) QAR = 112 L/min; (c) QAR = 127 L/min.

5.2.

Justification of operating conditions

As the focus of this study is on the SCR of CLC systems, it is necessary to ensure that the operating conditions (i.e., flow rates of air/fuel reactors and loop seals, and total solid inventory) of the simulated cases are reasonable and thus the system is functioning well without operating problems such as blockage and gas leakage. Two quantities were thus monitored in the simulations as the indication of continuous operation of the CLC system: the local solid hold-up in the middle of riser (h = 0.75 m, see Fig. 1) and gas leakages through the two loop seals. The local solid hold-up is the averaged value on the horizontal cross-section of h = 0.75 m over a time period of 20 s. The minimum value of the monitored local solid hold-up was used as the indication of continuous solid circulation to verify the reasonability of operating conditions that are related to the

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS 9

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

0.005

a 0.004

εp, min[-]

solid phase, i.e. particle size and total solid inventory. Moreover, the gas leakage is an issue commonly encountered in the operation of a CLC system. If operating conditions are not reasonable, severe gas leakage would occur and ultimately leads to the downtime of a CLC system. In simulations, the gas leakage through the two loop seals was monitored to ensure the operating conditions that are related to the fluid flow, i.e. flow rates in air/fuel reactors and loop seals, are selected appropriately. The gas leakage ratio ˛ is defined as the ratio of total gas flow flux from outlets to that at the inlet of each loop seal, and calculated by,

0.003

0.002

0.001

0

Nout,i Nf,j ˛=

i=1

Nf,j j=1

 j=1 f,ij

0

2

4

6

8

(9)

f,j

10

12

Loop seal 1 Loop seal 2

b 1

α[%]

where Nf ,j is the face number on the inlet or outlet faces; Nout ,i is the number of outlets and f ,j is the gas flow flux of face j of inlet or outlet. Ideally, ˛ is equal to 1 if there is no gas leakage between the two reactors. However, more or less gas leakage is expected in a real CLC system (Kronberger et al., 2005; Lyngfelt et al., 2004, 2001; Wang et al., 2014), thus ˛ is never equal to 1. Fig. 6 shows the validity of operating conditions of simulation cases investigated in the present study, as detailed in Table 3. Fig. 6(a) shows the minimum value of solid holdup on the horizontal cross section in the middle of the riser (h = 0.75 m). It can be seen that the solid hold-up in the majority of simulated cases is greater than zero. The data in cases with a minimum solid hold-up of zero indicate the solids were not transported continuously throughout the system, as discussed above. It is also worth noting that all the values of solid hold-up are very small, below 0.005. This is because in the riser the sparse gas–solid flow is present due to the solid conveying regime, in which the solid concentration is extremely low (Alghamdi et al., 2013). The gas leakage ratios through two loop seals were shown in Fig. 6(b). The gas leakage ratio in all cases is below 2% (the maximum value is 1.3% from the upper loop

10

Simulated cases [-]

0.1

0.01

0

2

4

6

8

10

12

Simulated cases [-] Fig. 6 – Validity of simulation conditions of the CLC system: (a) minimum solid hold-up in the middle of riser and (b) gas leakage ratios through two loop seals.

seal). The gas leakage ratio in the majority of cases is below 1%, which indicates that the operating conditions designed for the fluid flow are reasonable and the system is functioning well with negligible gas leakage.

Fig. 7 – Circulation of solid particles in the CLC system for MTSI = 2.25 kg, QAR = 112 L/min, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m. Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

10

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

0.08

0.08

a

0.06 0.04

0.04

Gs (g/s)

Gs (g/s)

b

0.06

0.02 0.00

0.02 0.00

-0.02

-0.02

-0.04

-0.04

-0.06

-0.06

-0.08

-0.08 15

16

17

18

15

16

t (s)

c

0.06

d

0.06 0.04

Gs (g/s)

0.04

Gs (g/s)

18

0.08

0.08

0.02 0.00

0.02

0.00

-0.02

-0.02

-0.04

-0.04

-0.06

-0.06 -0.08

-0.08

15

16

17

15

18

16

t (s)

17

18

t (s)

0.08

0.08

e

0.06

f

0.06 0.04

Gs (g/s)

0.04

Gs (g/s)

17

t (s)

0.02 0.00

0.02 0.00

-0.02

-0.02

-0.04

-0.04

-0.06

-0.06 -0.08

-0.08 15

16

17

16

15

18

17

18

t (s)

t (s)

Fig. 8 – Time series of solid mass circulating rate at cross sections of: (a) inlet of riser; (b) outlet of riser; (c) outlet of cyclone; (d) outlet of upper loop seal; (e) outlet of fuel reactor; (f) outlet of lower loop seal for MTSI = 2.25 kg, QAR = 112 L/min, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m. Table 3 – Simulation cases. Case 1 2 3 4 5 6 7 8 9 10 11

5.3.

QAR (L/min) 81 112 127 112 112 112 112 112 112 112 112

QLS (L/min)

QFR (L/min)

3 3 3 3 3 4.5 6 7.5 3 3 3

SCR in a CLC system

The SCR was often used as an important input parameter with a constant value in the theoretical analysis of chemical looping systems (Abad et al., 2009, 2013). The transient value of SCR in a CLC system has been monitored to investigate the time dependant characteristics of SCR. Subsequently, effects

30 30 30 40 50 30 30 30 30 30 30

dp (␮m)

MTSI (kg)

138 138 138 138 138 138 138 138 138 138 138

2.25 2.25 2.25 2.25 2.25 2.25 2.25 2.25 2 2.5 2.75

of operating conditions on SCR were discussed and the relationship between particle residence time and SCR has also been explored.

5.3.1.

Time dependent characteristics of SCR

The SCR of a CLC system is closely related to the solid flow pattern in the each component of the system, which

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

11

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

1.4E-05

1.2E-07

a

8.0E-06 6.0E-06

8.0E-08 6.0E-08 4.0E-08

4.0E-06

2.0E-08

2.0E-06 0.0E+00 0

b

1.0E-07

1.0E-05

Gs PSD (-)

Gs PSD (-)

1.2E-05

10

20

30

40

50

0.0E+00 0

60

10

Frequency(1/s

2.5E-06

c Gs PSD (-)

Gs PSD (-)

d

1.6E-06

1.5E-06 1.0E-06 5.0E-07

1.2E-06 8.0E-07 4.0E-07

10

20

30

40

50

0.0E+00 0

60

10

8.0E-06

30

40

50

60

50

60

1.2E-05 1.0E-05 Gs PSD (-)

e

6.0E-06 Gs PSD (-)

20

Frequency(1/s)

Frequency(1/s)

4.0E-06 2.0E-06 0.0E+00 0

60

2.0E-06

2.0E-06

0.0E+00 0

20 30 40 50 Frequency(1/s)

f

8.0E-06 6.0E-06 4.0E-06 2.0E-06

10

20

30

40

50

60

0.0E+00 0

10

20

30

40

Frequency (1/s)

Frequency (1/s)

Fig. 9 – Fourier analysis of local SCR fluctuation at: (a) inlet of riser; (b) outlet of riser; (c) outlet of cyclone; (d) outlet of upper loop seal; (e) outlet of fuel reactor; (f) outlet of lower loop seal for MTSI = 2.25 kg, QAR = 112 L/min, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m. determines the pressure distribution and pressure drop balance of the entire system. The evolution of solid flow pattern in the CLC system over 23.3 s–27.3 s is shown in Fig. 7. A periodically steady state of the system was observed over the monitored 4 s. In the air reactor, as the turbulent fluidisation regime was present, the solid distribution is periodically changing and the surface of the emulsion phase is pulsating with time, as highlighted in the red box. Due to the significant variation of solid flow pattern in the air reactor, the solid distribution in the adjacent lower loop seal, as highlighted in the black box, also varies. Specifically, the heights of the fluidised bed in the supply chamber and the recycle chamber are changing over time. According to the solid mass conversation law, we have ∂ ∂t

CV (˚s p )dV

= Ain Scr,in − Aout Scr,out

(10)

where Ain and Aout are the areas of inlet and outlet of the controlled volume, respectively; Scr is the solid mass flow rate per area, unit kg/(s m2 ). As a result of the variation of solid

mass flow rate (i.e., Scr , in and Scr ,out ) induced by the turbulent gas–solid flow in the adjacent air reactor, the solid mass, thus the bed height, in the supply chamber and the recycle chamber are changing with time. Similarly, the significant variation of solid distribution is observed in the riser (in the violet box). There is slight variation in the flow pattern in the fuel reactor (highlighted in the green box), where a bubbling fluidisation regime is present. Noticeably, the solid flow pattern in the upper loop seal (in the blue box) is very stable with almost unchanged solid distribution. The evolution of local SCR monitored at different locations of the system over a time period of 15 s–18 s is shown in Fig. 8. The value of SCR at a time instant was the total solid flux through a cross section. It can be seen that the SCR at different locations of the system fluctuates with time with different amplitude and the variation of SCR is periodically stable. The SCR fluctuates most intensely at the cross-section that is located at the inlet of the air riser (Fig. 8(a)). There are several peaks of amplitude, followed by a period of nearly zero solid flux through the cross section. This is due to the turbulent gas–solid fluidisation regime and the resultant intense

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

12

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

Fig. 10 – Snapshots of solid distribution for different air reactor flow rates: MTSI = 2.25 kg, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m: (a) QAR = 81 L/min; (b) QAR = 112 L/min; (c) QAR = 127 L/min. local solid circulation and the pulsation of emulsion phase in the air reactor (as shown in the red box of Fig. 7). The SCR at the outlet of the riser (Fig. 8(b)) is quite stable without any abrupt peaks of high amplitude. The particles inside the riser adjust velocity when they flow through the riser by the local solid circulation close to the wall. Hence the riser serves as a buffer to uniformalise the particle momentum. However, there are a couple of large peaks of SCR at the cross-section located at the outlet of cyclone (Fig. 8(c)). From Fig. 7, it can be seen that some particles are piling up in the top channel connecting the riser and the cyclone. Due to the mechanical imbalance of the pile, some particles on the right hand side are prone to collapse and slip off the pile and fall into the cyclone when excessive particles are accumulating there. Correspondingly, there are a couple of small peaks at the outlet of the cyclone (c-5, Fig. 8(c)). Afterwards, i.e., from upper loop seal to lower loop seal (Fig. 8(d)–(f)), the values of SCR are relatively stable, fluctuating with smaller amplitude. To identify the dominant frequency for the time series of the SCR, Fourier analysis was carried out for the data of SCR at the six locations shown in Fig. 8. The results of Fourier analysis are shown in Fig. 9. It can be seen that the dominant frequency of the local SCR fluctuation keeps consistent at around 50 Hz. The power spectra density (PSD) amplitude at the peak dominant frequency depends majorly on the perturbation of local flowing velocity. Accordingly, the PSD amplitudes at the inlet of riser (Fig. 9(a)), outlet of fuel reactor (Fig. 9(e)) and outlet of lower loop seal that is close to the air reactor ((Fig. 9(f)) are relatively large due to the local higher flowing velocity perturbation, compared to those at other locations such as outlet

of riser (Fig. 9(b)), outlet of cyclone (Fig. 9(c)) and outlet of the upper loop seal (Fig. 9(d)). The results shown above simply suggest that the turbulent flow regime of the gas–solid flow in the air reactor is the main mechanism that drives the fluctuation of SCR and determines the frequency and amplitude of the fluctuation.

5.3.2. Effects of operating conditions on SCR 5.3.2.1. Effect of air reactor flow rate. The gas flow in the air reactor supplies oxygen to the particles therein and also serves as the major driving force to circulate the oxygen carrier particles throughout the system. If the gas flow velocity is too low, it allows for complete oxidation between air and metal particles, but cannot lift the particles upwards and circulate particles. In particular the particles cannot be circulated into the fuel reactor for the reduction process, which is the core of a CLC system. Hence the gas flow rate should be high enough to circulate particles whilst also providing a good conversion rate between metal and metal oxides particles. The snapshots of solid distribution for different air reactor flow rates at the periodically steady state (t = 10 s) are depicted in Fig. 10. Distinct gas–solid flow patterns have been observed for different values of the inlet flow rate QAR . For QAR = 81 L/m (Fig. 10(a)), the fluidised bed height in the air reactor is very small, and very few particles are observed in the riser. As the air reactor flow rate increases, more particles are lifted upwards and subsequently entrained into the riser for circulation. Consequently, more particles are transported to other parts of the system. At the highest flow rate of QAR = 127 L/m (Fig. 10(c)), more particles can be seen piling up in the top

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS 13

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

10

Gs (g/s)

channel that connects the riser and the cyclone, and the bed height in the fuel reactor is higher, indicating more particles are fluidised there. Moreover, particles are observed accumulating before the supply chamber of the lower loop seal. Fig. 11 shows the variation of SCR as a function of the air reactor flow rate. It can be seen that the SCR increases in a logarithmic law as the air reactor flow rate increases. As indicated above, the air reactor provides the driving forces to circulate the particles. As the air reactor flow rate increases, the lift forces on the particles increase, hence the particle circulation rate increases. Therefore, an increase in the flow rate of air reactor directly leads to the increase in the SCR of the system.

1

0.1 70

5.3.2.2. Effect of fuel reactor flow rate. Fig. 12 shows the snapshots of particle position at different flow rates of fuel reactor at the periodically steady state (t = 10 s). It can be seen that as the flow rate QFR increases, the height to which the number of particles are splashed in the fuel reactor significantly increases, which in turn increases the solid mass transferred into the lower loop seal. As shown in Fig. 12(c), more particles are supplied from the fuel reactor to the lower loop seal which subsequently are transported into the air reactor. As the particle mass increases, the expanded bed height in the air reactor increases and more particles are conveyed into the air riser. Accordingly, the mass of particles resting on the top channel connecting the riser and cyclone also increases as the fuel reactor flow rate increases. The variation of SCR as a function of fuel reactor flow rate is shown in Fig. 13. A linear relationship is observed between SCR and QFR for the range of QFR investigated in this study.

80

90

100

110

120

130

140

QAR (L/min) Fig. 11 – Effect of air reactor flow rate on the mass circulation rate of the CLC system for MTSI = 2.25 kg, QLS = 3 L/min, QFR = 30 L/min and dp = 138 ␮m.

As QFR increases from 30 L/min to 50 L/min, the value of SCR increases from 1.74 g/s to 3.3 g/s. As shown in Fig. 12, more particles are transported from the fuel reactor to the air reactor as QFR increases. Subsequently, more particles are lifted into the riser and circulated in the system. Therefore, SCR of the system increases as QFR increases. This is similar to the case when the air reactor flow rate increases, as shown in Figs. 10–11.

5.3.2.3. Effect of loop seal flow rate. The snapshots of solid distribution for different loop seal flow rates are shown in

Fig. 12 – Snapshots of solid distribution for different fuel reactor flow rates: (a) QFR = 30 L/min; (b) QFR = 40 L/min; (c) QFR = 50 L/min for MTSI = 2.25 kg, QLS = 3 L/min, QAR = 112 L/min and dp = 138 ␮m. Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

14

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

5.0

5.5

4.5

5

4.0

4.5 4

3.0

G s (g/s)

Gs (g/s)

3.5

2.5 2.0 1.5

3.5 3 2.5

1.0

2

0.5

1.5

0.0 25

30

35

40

45

50

55

Q FR (L/min)

2.5

3.5

4.5

5.5

6.5

7.5

Q LS (L/min)

Fig. 13 – Solid mass circulating rate as a function of fuel reactor flow rate for MTSI = 2.25 kg, QLS = 3 L/min, QAR = 112 L/min and dp = 138 ␮m.

Fig. 15 – Solid mass circulating rate as a function of loop seal flow rate for MTSI = 2.25 kg, QFR = 30 L/min, QAR = 112 L/min and dp = 138 ␮m.

Fig. 14. As the loop seal flow rate increases, the average bed voidage in the loop seals increases. Accordingly more particles are released from the upper loop seal to the fuel reactor, where the particles are transported into the lower loop seal. The excessive particles in the lower loop seal are subsequently conveyed into the air reactor for circulation. It can be seen the solid mass in the top horizontal channel connecting the riser and the cyclone increases as QLS increases, indicating more particles are circulated throughout the system. The quantitative variation of SCR as a function of loop seal flow rate is shown in Fig. 15. As QLS increases from 3 L/min to 7.5 L/min, SCR increases from 1.74 g/s to 5.04 g/s. Loop seals are primarily used to transport particle from different reactors by building up the necessary pressure drop so that particles could flow from a zone of low pressure to a high pressure zone

without undesirable inverse gas flow. As the flow rate of loop seals are much smaller compared to those of the fuel reactor and the air reactor, the increase in the loop seal flow rate only increases the bed expansion height of particles, thus more particles are split out from the loop seals. Therefore, the increase in the flow rate of loop seals fastens the transport of particles and thus increases the total solid mass circulated through the system.

5.3.2.4. Effect of total solid inventory. The influence of total solid inventory on solid distribution in the system is depicted in Fig. 16. It can be seen that as the total solid inventory increases, more particles are observed in each component of the system. A larger value of total solid inventory allows for more particles to be transported in the system. However,

Fig. 14 – Snapshots of solid distribution for different loop seal flow rates: (a) QLS = 3 L/min; (b) QLS = 4.5 L/min; (c) QLS = 6 L/min; (d) QLS = 7.5 L/min for MTSI = 2.25 kg, QFR = 30 L/min, QAR = 112 L/min and dp = 138 ␮m. Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

15

Fig. 16 – Snapshots of particle position for different total solid inventories: (a) MTSI = 2 kg; (b) MTSI = 2.25 kg; (c) MTSI = 2.5 kg; (d) MTSI = 2.75 kg, for QFR = 30 L/min, QAR = 112 L/min and QLS = 3 L/min. the circulation throughout the system. However, it should be noted that MTSI cannot go over a certain value for a specific system; otherwise the system will function abnormally with severe particle accumulation and piping blockage.

5 4.5 4

Gs (g/s)

3.5

5.3.2.5. Further discussion on effects of operating conditions on SCR. As indicated above, changes in operating conditions (i.e.,

3 2.5 2 1.5 1 0.5 0 1.9

2

2.1

2.2

2.3

2.4

2.5

2.6

MTSI(kg)

Fig. 17 – SCR as a function of total solid inventories for QFR = 30 L/min, QAR = 112 L/min and QLS = 3 L/min. under certain flow rates of air/fuel reactors and loop seals, a too large total solid inventory causes the occurrence of local particle accumulations, as shown in Fig. 16(c) and (d), due to the limited cross-section area and the insufficient time to transport particles. As the system is not functioning properly for MTSI = 2.75 kg, the data of this case is not used in the analysis. Fig. 17 shows the variation of SCR as a function of total solid inventory. It can be seen that as the total solid inventory increases, SCR increases in an exponential law. This result is in line with our previous experimental findings (Alghamdi et al., 2013). Specifically, as the total solid inventory increases from 2 kg to 2.5 kg, SCR significantly increases from 0.83 g/s to 6.5 g/s. As Fig. 16 shows, as MTSI increases, the mass of particles in each component of the system increases. In particular, the increase in the solid mass in the air reactor directly leads to the increase in the total solid mass lifted into the riser for

flow rates of air/fuel reactors and loop seals, and total solid inventory) ultimately led to the change in the mass of solids that were entrained into the riser from the air reactor and how fast the solid particles were transported therein. It thus seems reasonable to relate the SCR of the CLC system to the solid hold-up (Фs ) and the gas flow velocity (Vg ) in the riser. The dependence of solid hold-up (Фs ) and gas flow velocity (Vg ) in the riser on operating conditions has been summarised, as shown in Fig. 18. It can be seen that increasing the air reactor flow rate directly increases both the solid hold-up and gas flow velocity in the riser (Fig. 18(a)). However, when the fuel reactor/loop seal flow rates and the total solid inventory increase, there is not much increase in the gas velocity, but the solid hold-up increases significantly. This result is consistent with those shown in Figs. 10, 12, 14 and 16. When the fuel reactor/loop seal flow rates, and the total solid inventory increase, the total mass of particles that are being transported through the riser increases, hence the solid hold-up increases. However, as the components of a CLC system are functioning in an isolate way, i.e., ideally there is no gas exchange among the air reactor, loop seals and the fuel reactor, changes in the fuel reactor/loop seals flow rates and the total solid inventory do not influence the gas velocity in the riser too much. The dependence of SCR on the combined effect of solid hold-up (i.e. Фs ) and gas flow velocity (i.e. Vg ) in the riser has been plotted in the Fig. 19. It can be seen that as the combined value (i.e. Фs × Vg ) increases, SCR increases. As Фs is related to the solid mass that is being circulated through the riser and Vg is related to the time that is required to circulate the

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

ARTICLE IN PRESS

CHERD-1736; No. of Pages 19

16

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

0.02 Solid hold- up

Solid hold-up

1.7

Gas velocity

1.7

Gas velocity

0.015

0.015

1.6

1.5 0.01

a

1.4

Φs (-)

1.6

Vg (m/s)

Φs (-)

1.8

1.8

c

1.4 1.3

0.005

1.3

0.005

1.5 0.01

Vg (m/s)

0.02

1.2 1.2 0 1.1 150

0

50

100

1.1

0

2

4

1.8 Solid hold-up

1.8 Solid hold- up

1.7

Gas velocity

0.015

0.015

1.5 0.01

b

1.4

Φs (-)

1.6

Vg (m/s)

Φs (-)

0.02

1.7

Gas velocity

8

Q LS (L/min)

Q AR (L/min) 0.02

6

1.6 1.5

0.01 1.4

d 1.3

0.005

1.3

0.005 1.2 1.1

0

0

10

20

30

40

50

60

1.2 0

1.1 0

Q FR (L/min)

Vg (m/s)

0

0.5

1

1.5

2

2.5

3

MTSI(kg)

Fig. 18 – Dependence of solid volume fraction and gas velocity in the riser on operating conditions: (a) air reactor flow rate; (b) fuel reactor flow rate; (c) loop seal flow rate; (d) total solid inventory. 0.006

10 9

Gs predicted by correlations(g/s)

0.005

Gs (kg/s)

0.004

0.003

0.002

y = 0.185x 0.001

0

8 7 30%

6 5 4

15%

3 Eq. (12) DEM- CFD Johnsson&Leckner (1995)

2 1

0

0.005

0.01

0.015

0.02

0.025

0.03 0

Φs (-) × Vg (m/s)

0

Fig. 19 – Dependence of SCR on the solid hold-up and gas velocity in the riser. solid particles, an increase in the combined value of Фs × Vg therefore corresponds to a greater value of SCR. A linear function was applied to fit the scattered data and relate SCR to the combined effect of Фs × Vg . A correlation is subsequently obtained as, Gs = C0 Ar s (˚s V)

(11)

where Ar is the cross-section area of the riser and s is the density of the solid phase; C0 is a fitting constant (C0 = 0.06 in this study). For extreme cases in which the gas velocity is zero or no particles are transported (i.e. when the system is not functioning properly), the value of Фs × Vg is equal to 0, the solid circulating rate (i.e., Gs ) is equal to 0.

1

2

3

4

5

6

7

8

9

10

Gs in experiments(g/s)

Fig. 20 – Comparisons between the prediction results by Eq. (11) and those predicted by Johnsson and Leckner correlation and CFD–DEM simulations. The values of SCR predicted by Eq. (11), and those predicted by the Johnsson and Leckner correlation (Johnsson and Leckner, 1995) and the CFD–DEM model are compared against the experimental data, as shown in Fig. 20. The prediction results by CFD–DEM agree best with the experimental data (i.e., the maximum deviation around 15%). Relatively large deviations are observed between the experimental data and the data predicted by the Johnsson and Leckner correlation due to the assumptions employed in the development of the correlation (Johnsson and Leckner, 1995). The data predicted by Eq. (11) reasonably agree with the experimental data with all data points falling in the range of 30%. As the correlation

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS 17

chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

1  pi n s ghi

250

200

150

tres (s)

was derived from the simulation data, the deviation between the prediction results by Eq. (11) and the experimental data might be due to the fitting error as well as the inconsistencies between ideal (theoretical) conditions used in the simulations (e.g. particle size and shape, boundary conditions and computational geometry), which are different to some extent from practical or actual cases in experiments. Moreover, as the entire CLC system is large and dynamically stable, the limited operating error and inherent flaws of methodologies for data acquisition and processing in experiments, and the limitations of the employed CFD–DEM model as discussed above might have also contributed to the deviation. Compared to the correlations reported in the literature, Eq. (11) allows the SCR to be predicted directly based on the operating conditions and simple measurements. Specifically, in the operation of a CLC system, the average solid hold-up in the riser (i.e. Фs ) can be calculated by the values of pressure drop measured along the riser height (Alghamdi et al., 2013; Markstorm and Lyngfelt, 2012),

100

y = 90x -1.5 50

0 0

5

10

15

20

G s (g/s) Fig. 21 – Relationship between SCR and particle residence time. SCR and particle residence time needs to be explored in reality to maximise the performance of CLC system.

n

˚s =

(12)

6.

Conclusion

i=1

where p and h is the pressure drop and height between two pressure ports, respectively. The mean gas velocity (i.e., Vg ) in the riser can be either measured directly or estimated by the mass conservation law, i.e., Vg = QAR /Ar . However, the correlation needs to be used with due care and can be only applied when the system is functioning well and operating continuously. For systems with running problems, e.g., too much solid mass loaded in the system, too low flow rates of reactors and subsequent occurrence of gas leakages, the correlation is supposed to be invalid.

5.3.3. time

Relationship between SCR and particle residence

The particle residence time (RTD) is an important parameter that dictates the conversion rate of the oxidised particles, thus provide vital information for system designer and operators. In this work, the particle residence time in the riser was monitored and calculated by:

 h i

ncs,riser

tres =

i=1

vsz,i

(13)

where i is the cross-section along the height of the riser. h is the vertical distance between cross sections i and i + 1; vsz , i is the vertical velocity of the solid phase through the cross section i. ncs,riser is the number of cross sections selected along the riser height. The relationship between SCR and the particle residence time in the riser is shown in Fig. 21. It can be seen that the particle residence time decreases in a power law (i.e., y = 90x−1.5 ) as SCR increases. As indicated above when the circulated particle mass or the gas velocity in the riser increases, SCR increases. As the gas velocity increases, the particles that are carried and circulated by the gas move faster. Therefore a shorter particle residence time is obtained. The shorter residence time due to higher SCR may reduce the solid conversion which further increases the total solid inventory and size of the reactors. The higher solid circulation though is desired to improve the heat transfer between the two reactors. From the results shown in Fig. 21, it seems impractical to maintain both the above two aspects. Therefore, a reasonable combination of

In this paper, a CFD–DEM model implemented in ANSYSFLUENT has been employed for the simulation of a lab-scale (10 kWth ) chemical looping cold flow model. Parallel UDFs have been programmed to customise the simulations. The parcel concept was employed to circumvent the difficulty in computational efficiency due to the enormous particle number. Experiments including measurements of pressure profile and SCR have been conducted to validate the established model. Reasonable agreements were obtained between the simulation and the experiment in terms of solid distribution, gas–solid flow pattern, pressure drop profile and SCR. The SCR at different locations of a CLC system fluctuated with time with different amplitude, and the variation of SCR was periodically stable. The turbulent gas–solid flow regime in the air reactor was found to be the main reason for the fluctuation of SCR and determined the frequency and amplitude of the fluctuation. Effects of the CLC operating conditions (including the flow rates of air reactor, fuel reactor and two loop seals, and the total solid inventory) on the varying SCR have also been investigated. As the air reactor flow rate increased, more particles were transported to other parts of the system. As a result, the SCR increased in a logarithmic law as the air reactor flow rate increased. As the fuel reactor flow rate increased, more particles were supplied from the fuel reactor to the lower loop seal and subsequently transported into the air reactor for circulation. Therefore, a linear relationship was observed between SCR and QFR for the range of QFR investigated in this study. The increase in the flow rate of loop seals fastened the transport of particles and ultimately increased the SCR throughout the system. A larger value of total solid inventory allowed for more particles to be transported in the system. However, a too large total solid inventory led to the local particle accumulation due to the limited cross-section area and the insufficient time to transport particles. Changes in operating conditions ultimately led to the change in the mass of solids that were entrained into the riser from the air reactor and how fast the particles were transported therein. Subsequently, the SCR was described as a function of solid hold-up (Фs ) and gas flow velocity (Vg ) in the riser, which can be obtained readily from pressure drop

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

18

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

measurements and the flow rate of air reactor, respectively. A correlation was proposed to correlate SCR with Фs and Vg with a maximum deviation of 30% when compared to the experimental data. The particle residence time decreased in a power law as SCR increased. A reasonable combination of SCR and particle residence time needs to be explored in reality to maximise the performance of CLC system.

Acknowledgements Thanks to R. Dear for his assistance with the usage of high performance cluster (HPC) facilities at the University of Newcastle. The authors also wish to acknowledge the financial support of The University of Newcastle (Australia), the NSW Clean Coal Council, Xstrata Coal Pty Ltd, Moits Pty Ltd, and the Australian Research Council for the work presented in this paper.

References Abad, A., Adanez, J., Garcia-Labiano, F., de Diego, L.F., Gayan, P., 2009. Modeling of the chemical-looping combustion of methane using a Cu-based oxygen carrier. Energy Procedia 1, 391–398. Abad, A., Gayan, P., de Diego, L.F., Garcia-Labiano, F., Adanez, J., 2013. Fuel reactor modelling in chemical-looping combustion of coal: 1. Model formulation. Chem. Eng. Sci. 87, 277–293. Alghamdi, Y.A., Doroodchi, E., Moghtaderi, B., 2013. Mixing and segregation of binary oxygen carrier mixtures in a cold flow model of a chemical looping combustor. Chem. Eng. J. 223, 772–784. Anderson, T.B., Jackson, R., 1967. A fluid mechanical description of fluidized beds. I&EC Fundam. 6, 527–539. ANSYS Inc., 2014. FLUENT Theory Guide. ANSYS Fluent 14.5 Help. ANSYS Inc., USA. Chew, J.W., Hrenya, C.M., 2011. Link between bubbling and segregation patterns in gas-fluidized beds with continuous size distributions. AIChE J. 57, 3003–3011. Cho, P., Mattisson, T., Lyngfelt, A., 2004. Comparison of iron-, nickel-, copper- and manganese-based oxygen carriers for chemical-looping combustion. Fuel 83, 1215–1225. Chu, K.W., Wang, B., Yu, A.B., Vince, A., Barnett, G.D., Barnett, P.J., 2009. CFD–DEM study of the effect of particle density distribution on the multiphase flow and performance of dense medium cyclone. Miner. Eng. 22, 893–909. Chu, K.W., Yu, A.B., 2008. Numerical simulation of complex particle–fluid flows. Powder Technol. 179, 104–114. de Diego, L.F., Gayan, P., Adanez, J., 1995. Modelling of the flow structure in circulating fluidized beds. Powder Technol. 85, 19–27. Deen, N.G., van Sint Annaland, M., van der Hoef, M.A., Kuipers, J.A.M., 2007. Review of discrete particle modeling of fluidized beds. Chem. Eng. Sci. 62, 28–44. Deng, Z.Y., Xiao, R., Jin, B.S., Song, Q., 2009. Numerical simulation of chemical looping combustion process with caso4 oxygen carrier. Int. J. Greenh. Gas. Con. 3, 368–375. Ergun, S., 1952. Fluid flow through packed columns. Chem. Eng. Prog. 48, 89–94. Glicksman, L.R., Hyre, M., Woloshun, K., 1993. Simplified scaling relationships for fluidized beds. Powder Technol. 77, 177–199. Haider, A., Levenspiel, O., 1989. Drag coefficient and terminal velocity of spherical and nonspherical particles. Powder Technol. 58, 63–70. Holmes, D.G., Connel, S.D., 1989. Solution of the 2D Navier–Stokes equations on unstructured adaptive grids. In: Presented at the AIAA 9th Computational Fluid Dynamics Conference, June 1989. Hoomans, B.P.B., Kuipers, J.A.M., Briels, W.J., van Swaaij, W.P.M., 1996. Discrete particle simulation of bubble and slug

formation in a two-dimensional gas-fluidised bed. A hard-sphere approach. Chem. Eng. Sci. 51, 99–118. Hossain, M.M., de Lasa, H.I., 2008. Chemical-looping combustion (CLC) for inherent CO2 separations—a review. Chem. Eng. Sci. 63, 4433–4451. Huber, N., Sommerfeld, M., 1998. Modelling and numerical calculation of dilute-phase pneumatic conveying in pipe systems. Powder Technol. 99, 90–101. Johnsson, F., Leckner, B., 1995. Vertical distribution of solids in a CFB-furnace. In: 13th International Conference on Fluidized Bed Combustion, Orlando. Kafui, K.D., Thornton, C., Adams, M.J., 2002. Discrete particle-continuum fluid modelling of gas–solid fluidised beds. Chem. Eng. Sci. 57, 2395–2410. Kaushal, P., Proll, T., Hofbauer, H., 2007. Model development and validation: co-combustion of residual char, gases and volatile fuels in the fast fluidized combustion chamber of a dual fluidized bed biomass gasifier. Fuel 86, 2687–2695. Kenarsari, S.D., Yang, D., Jiang, G., Zhang, S., Wang, J., Russell, A.G., Wei, Q., Fan, M., 2013. Review of recent advances in carbon dioxide separation and capture. RSC Adv. 3, 22739–22773. Kolbitsch, P., Proll, T., Bolhar-Nordenkampf, J., Hofbauer, H., 2009. Design of a chemical looping combustor using a dual circulating fluidized bed (DCFB) reactor system. Chem. Eng. Technol. 32, 398–403. Kronberger, B., Johansson, E., Loffler, G., Mattisson, T., Lyngfelt, A., Hofbauer, H., 2004. A two-dimensional fluidized bed reactor for CO2 capture by chemical-looping combustion. Chem. Eng. Technol. 27, 1318–1326. Kronberger, B., Lyngfelt, A., Loffler, G., Hofbauer, H., 2005. Design and fluid dynamic analysis of a bench-scale combustion system with CO2 separation-chemical-looping combustion. Ind. Eng. Chem. Res. 44, 546–556. Kruggel-Emden, H., Rickelt, S., Stepanek, F., Munjiza, A., 2010. Development and testing of an interconnected multiphase CFD-model for chemical looping combustion. Chem. Eng. Sci. 65, 4732–4745. Lyngfelt, A., Kronberger, B., Adanez, J., Morin, J.X., Hurst, P., 2004. Development of oxygen carrier particles for chemical looping combustion design and operation of a 10kW chemical combustor. In: 7th International Conference on Greenhouse Gas Control Technologies, Canada. Lyngfelt, A., Leckner, B., Mattisson, T., 2001. A fluidized-bed combustion process with inherent CO2 separation; application of chemical-looping combustion. Chem. Eng. Sci. 56, 3101–3113. Mahalatkar, K., Kuhlman, J., Huckaby, E.D., O’Brien, T., 2011. Computational fluid dynamic simulations of chemical looping fuel reactors utilizing gaseous fuels. Chem. Eng. Sci. 66, 469–479. Markstorm, P., Lyngfelt, A., 2012. Designing and operating a cold-flow model of a 100 kW chemical-looping combustor. Powder Technol. 222, 182–192. Mattisson, T., Lyngfelt, A., Leion, H., 2009. Chemical-looping with oxygen uncoupling for combustion of solid fuels. Int. J. Greenh. Gas. Con. 3, 11–19. Parker, J.M., 2012. Computer simulation of a full chemical looping combustion system. In: AIChE Annual Meeting 28 Oct–2 Nov, Pittsburgh, PA. Patankar, N.A., Joseph, D.D., 2001. Modeling and numerical simulation of particulate flows by the Eulerian–Lagrangian approach. Int. J. Multiphase Flow 27, 1659–1684. Patankar, S.V., 1980. Numerical Heat Transfer and Fluid Flow. Taylor & Francis, ISBN 978-0-89116-522-4. Peng, Z., Doroodchi, E., Luo, C., Moghtaderi, B., 2014. Influence of void fraction calculation on fidelity of CFD–DEM simulation of gas–solid fluidised beds. AIChE J. 60, 2000–2008. Pieter, T., 2014. NOAA/ESRL. www.esrl.noaa.gov/gmd/ccgg/trends/ Ralph, K., 2014. Scripps Institution of Oceanography. scrippsco2.ucsd.edu/ Rauch, R.D., Batira, J.T., Yang, N.T.Y., 1991. Spatial Adaption Procedures on Unstructured Meshes for Accurate Unsteady

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005

CHERD-1736; No. of Pages 19

ARTICLE IN PRESS chemical engineering research and design x x x ( 2 0 1 4 ) xxx–xxx

Aerodynamic Flow Computations. Technical Report AIAA-91-1106. AIAA. Rubin, E.S., Mantripragada, H., Marks, A., Versteeg, P., Kitchin, J., 2012. The outlook for improved carbon capture technology. Prog. Energ. Combust. 38, 630–671. Sakai, M., Yamamoto, T., Murazaki, M., Miyoshi, Y., 2004. Effect of particulate behavior on criticality evaluation in agitating powder of nuclear fuel. Powder Technol. 148, 67–71. Seo, M.W., Nguyen, T.D.B., Lim, Y.I., Kim, S.D., Park, S., Song, B.H., Kim, Y.J., 2011. Solid circulation and loop-seal characteristics of a dual circulating fluidized bed: experiments and CFD simulation. Chem. Eng. J. 168, 803–811. Shah, K., Moghtaderi, B., Wall, T., 2012. Selection of suitable oxygen carriers for chemical looping air separation: a thermodynamic approach. Energy Fuels 26, 2038–2045. Song, H., Shah, K., Doroodchi, E., Wall, T., Moghtaderi, B., 2014. Reactivity of Al2 O3 - or SiO2 -supported Cu-, Mn-, and Co-based oxygen carriers for chemical looping air separation. Energy Fuels 28, 1284–1294. Tsuji, Y., Kawaguchi, T., Tanaka, T., 1993. Discrete particle simulation of two-dimensional fluidized bed. Powder Technol. 77, 79–87.

19

Tsuji, Y., Tanaka, T., 1998. Cluster patterns in circulating fluidized beds predicted by numerical simulation (discrete particle model versus two-fluid model). Powder Technol. 95, 254– 264. Wang, S., Lu, H.L., Zhao, F.X., Liu, G.D., 2014. CFD studies of dual circulating fluidized bed reactors for chemical looping combustion processes. Chem. Eng. J. 236, 121–130. Wang, S., Yang, Y.C., Lu, H.L., Wang, J.X., Xu, P.F., Liu, G.D., 2011. Hydrodynamic simulation of fuel-reactor in chemical looping combustion process. Chem. Eng. Res. Des. 89, 1501–1510. Xu, B.H., Yu, A.B., 1997. Numerical simulation of the gas–solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics. Chem. Eng. Sci. 52, 2785–2809. Zhu, H., Zhou, Z., Yang, R., Yu, A., 2008. Discrete particle simulation of particulate systems: a review of major applications and findings. Chem. Eng. Sci. 63, 5728–5770. Zimmermann, S., Taghipour, F., 2005. CFD modeling of the hydrodynamics and reaction kinetics of FCC fluidized-bed reactors. Ind. Eng. Chem. Res. 44, 9818–9827.

Please cite this article in press as: Peng, Z., et al., CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem. Eng. Res. Des. (2014), http://dx.doi.org/10.1016/j.cherd.2014.11.005