1-s2.0-s0009250914000396-main

Modelling and experimental investigation of the full-loop gassolidflow in a circulating fluidized bed with six cyclone separators

Yu Jiang a,b, Guizhi Qiu a,b, Haigang Wang a,n

a Institute of Engineering Thermophysics, Chinese Academy of Sciences, PO Box 2706, Beijing 100190, Chinab University of Chinese Academy of Sciences, PO Box 2706, Beijing 100190, China

H I G H L I G H T S

CPFD model is used to simulation the full-loop of a circulating fluidized bed with six cyclone separators. Simulation results are compared with Electrical capacitance tomography and pressure measurement. The CPFD model combined with ECT technology provides a possibility to optimize the design for large scale circulating fluidized bed.

a r t i c l e i n f o

Article history:Received 22 November 2013Received in revised form15 January 2014Accepted 25 January 2014Available online 31 January 2014

Keywords:Circulating fluidized bedCPFDCyclone SeparatorFull-loopProcess tomography

a b s t r a c t

In the literature, there are few reports on the full-loop gassolid flow in a circulating fluidized bed (CFB) withlarge scale and complex cyclone arrangement. In this paper, a new approach based on computational particlefluid dynamic (CPFD) method combined with electrical capacitance tomography (ECT) is used to investigatethe hydrodynamic behavior of gassolid flow in a CFB with six cyclone separators in order to improve thedesign and performance of a large scale CFB boiler. The full-loop CFB system for the simulation includes theCFB riser, cyclone, standpipe and U-loop. Two types of cyclone arrangement, i.e. axis and point basedsymmetric arrangement, are used for the CPFD simulation and ECT measurements. To validate the CPFDsimulation, ECT is applied to measure the solids concentration in the standpipe with eight electrodesmounted on the outside of the standpipe. Key parameters including pressure, solids recirculation flux andvelocity profile along different positions based on the CPFD simulation are analyzed and compared withexperimental results. The CPFD simulation shows that the gassolid flow is non-uniform among the sixparallel cyclones. The solids concentration of four cyclones at the corner of the riser is higher than that of theothers. The location of cyclone as well as the inlet angle of the cyclone needs to be optimized. The studyshows that the presented approach based on CPFD simulation and ECT measurements can be used tooptimize the arrangement of cyclone separators in a supercritical pressure circulating fluidized bed system.

& 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Circulating fluidized bed (CFB) is one kind of clean coalcombustion technologies which plays an important role in thepower generation and coal gasification industry (Reh, 2003). Largethermal capacity and high steam pressure is a tendency for thedevelopment of CFB boilers (Lv et al., 2007, Fan et al., 2008, Chenet al., 2008). To meet the demands for high steam parameter andlarge thermal capacity, high efficiency of gassolid separation is akey to achieving high combustion efficiency, reducing limestoneconsumption and NOx emission (Koornneef et al., 2007).

With the scaling up of a CFB boiler, the dimension of thecyclone is increased accordingly and the separation efficiencydecreases due to a reduction in the centrifugal force. To overcomethe above issue, a large cyclone is replaced by numbers of smallercyclone with the increase of boiler size to reduce the cyclone size.Different arrangement of cyclones on the top of the CFB riser isprovided and patented (Hack et al., 2008). Experimental researchhas been carried out and methods related with cyclone arrange-ment have been patented (Armistead et al., 2002, Lv et al., 2007,Zhou et al., 2012). However, there is a non-uniform solids massflux distribution among cyclones with a maximum difference of17% (Morin, 2003, Chen et al., 2008, Zhou et al., 2012). For a CFBboiler with multi-cyclone separators, it is important to investigatethe gassolid flow in the whole loop including the CFB riser,cyclones as well as standpipe and U-loop.

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Chemical Engineering Science

http://dx.doi.org/10.1016/j.ces.2014.01.0290009-2509 & 2014 Elsevier Ltd. All rights reserved.

n Corresponding author. Tel.: 0086 10 8254 3140.E-mail address: [email protected] (H. Wang).

Chemical Engineering Science 109 (2014) 8597

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Computational fluid Dynamics (CFD) simulation provides detailedinformation for the investigation of fluidization characteristics withlarge-scale CFB boilers (Reh, 2003). Research has been reported usingthe CFD approach to investigate the three dimensions gassolid flowin a CFB boiler (Zhang et al., 2008, 2010, Ahuja and Patwardhan,2008, Hartge et al., 2009). In dealing with gassolid fluidized beds,two approaches, i.e. the Eulerian-Eulerian two-fluid model (TFM)(Gidaspow and Ettehadieh, 1983, Lun et al., 1984) and the Eulerian-Lagrangian discrete particle method (DPM), are commonly used. TheTFM model treats solids as a continuous phase which interacts withthe gas phase by momentum exchange. Conservation equations foreach phase have similar terms and are solved together with a set ofconstitutive equation derived by experiment. The TFM model hasbeen widely used in multiphase flows simulation. However, it haslimitations, such as not applicable to particle size distribution andinter-particle forces (Makkawi et al., 2006). The DPM methoddescribes the discrete phase by tracking numerous particles trajec-tories which exchange mass, momentum and energy with the gasphase all through the whole simulation field. The DPM model takesinto account the particle size distribution as well as particleparticleinteractions. However, it is difficult to simulate dense gassolid flowwith solids volume fraction above 5% due to large amount of particlenumbers. In general, the particle number is under the order of 2105 in the DPM model and it is often applied to two-dimensionalsimulation. Recently, an EulerianLagrangian model called CPFD(computational particle fluid dynamics) has been used to modelthe gassolid flow in a fluidized bed (Abbasi et al., 2011, Chen et al.,2013). This methodology incorporates the multi-phase-particle-in-cell (MP-PIC) method for calculating a dense gassolid flow(Andrews and O'Rourke, 1996, Snider, 2001). In the CPFD approach,the gas phase is modeled as a continuous fluid and particles as adiscrete phase which can handle particle size distribution. Particlesare classed into numerous of computational parcels. Each parcelrepresents a number of physical particles which have a same velocityand material property in the computational domain. With thisscheme, billions of particles can be simulated much more efficiently.

To validate the CFD simulation results in a CFB boiler, it isnecessary to verify the results with measurements. Electricalcapacitance tomography (ECT) provides an option to investigatethe gassolid flow in a fluidized bed due to its no-intrusive andno-invasive nature (Dyakowski et al., 1997, 1999 , Makkawi andWright, 2004, 2006, Du et al., 2005, Wang et al., 2006).

To understand the hydrodynamic behavior of a gassolidcirculating fluidized bed, a cold CFB test facility with six cycloneseparators in the top of the riser and rectangular shape combus-tion chamber has been built in the Institute of EngineeringThermophysics, Chinese Academy of Sciences. CPFD is used tosimulate the whole circulating loop. CPFD simulation results arecompared with experimental results by ECT and pressure mea-surements. Two different arrangements of cyclones are usedto compare the gassolid fluid hydrodynamic behavior in themulti-cyclone CFB system. The objective of the research is toevaluate the applicability of CPFD method for the gassolid flowsimulation in a whole-scale CFB boiler and compare with experi-ment results. The CPFD simulation is based on a commercial codeBARRACUDA. ECT was used to measure the solids concentration inthe cross sections of the standpipe with eight electrodes mountedoutside of the pipe and to validate the CPFD simulation results.

2. CPFD mathematical model

2.1. Governing equations

The CPFD methodology takes an Eulerian-Lagrangian approach todescribe the gassolid flow in three dimensions. The gas phase is

described as a continuous phase with strong coupling to the discretesolids phase in mass and momentum equations. As the gas and solidsphases are isothermal and the gas phase is incompressible, novolume averaged fluid energy equations are needed. In the CPFDscheme, a concept of numerical particle is introduced, which is anumerical approximation similar to the numerical control volumewithin which the fluid has a common property. The solids phase ismodeled as numerical parcels each containing quite a number ofphysical particles with same properties (species, density, size, etc) inthe same location. The flow fields of gas and solids phase arecalculated by separated governing equations. For the gas phase, thegoverning equations are

tggggvg Sg 1

tggvgggvgvg PgggggF 2

where g represents the volume fraction of gas, g and vg stands fordensity and velocity of gas respectively, Sgis a source term, grepresents the gas stress tensor, p stands for the pressure of gas, gis the acceleration of gravity, F is the rate of momentum exchange pervolume between the gas and solids phases. The momentum equationpresented here neglects the viscous molecular diffusion in the fluidbut retains the viscous drag between particles and fluid through aninterphase drag force, F, which is

F fm DpvgvpPp

!dmdv 3

where Dp is the drag function, vp and p represents particle velocityand density respectively, f is the probability distribution functionwhich is calculated from Liouville equation as

ftf vpvp f

ddtvp

0 4

where ddtvp is the particle acceleration, which is obtained bycalculating all forces on the particles and is given by MP-PIC method(Andrews and O'Rourke, 1996, Snider, 2001) as following:

ddtvp DpvgvpPp

ppp

g 5

where p is inter-particle normal stress, p represents volumefraction of particles. The trajectory of a particle is solved by

dxpdt

vp 6

where x is the location of the tracing particle.

2.2. Drag model

The Wen-Yu drag model is applicable to gassolid flow withsolids volume fraction lower than 0.61 while the Ergun drag modelcovers the range of 0.470.7. As the volume fraction of solids in thepresent study is less than 0.65 at close packing limit, the inter-phase drag function is defined by Wen-Yu model (Wen and Yu,1966)

Dp Cd38gp

jvgvpj3Vp=41=3

7

where Cd is the drag coefficient. It depends on the Reynoldsnumber, i.e..

Cd 24Re

10:15Re0:687g 2:65 for Reo1000

Cd 0:44g 2:65 for ReZ1000 8

Y. Jiang et al. / Chemical Engineering Science 109 (2014) 859786

The Reynolds number is given by

Re 2gjvgvpjg

3Vp4

1=39

where g is the gas density, g is the gas viscosity, vg and vprepresent the gas and particle velocity respectively, and Vp is thevolume of a particle.

2.3. Solids stress model

In the CPFD scheme, unlike the DEM approach which modelsthe particle-particle interaction forces by a spring-damper model,the inter-particle collision forces are calculated as a spatialgradient. Because it is difficult to calculate particle stress gradientfor each particle in a dense flow, CPFD calculates particle forces asa gradient on the grid and maps it back to particles. The presentstudy adopts the particle normal stress model by Harris andCrighton (1994).

p PS

p

max cpp; 1p10

where is a constant suggested to be on the order of 107, cp isthe solids volume fraction at close packing. Ps has pressure unitsand is supposed to be 2rr5 (Auzerais et al., 1988).

2.4. CPFD simulation procedure

In the CPFD approach, each cell contains numbers of numericalparcels. The solids volume fraction in the cell (i, j, k) from mapping

particle volume to the grid is defined as follow

i;j;k 1

Vi;j;kNp

1VpnpSi;j;k 11

where Vi,j,k is the volume of cell (i, j, k), Np is the total number ofnumerical parcels in the cell, Vp is the particle volume, np is thenumber of real particles in a numerical parcel, and Si,j,k is theinterpolation operator. The interpolation operator in x direction tothe cell i is given by

Sxi xp 01 xi 1 Zxp Zxi 1

xp xi 12

The interpolation operators in y and z directions have a similarform. With particle volume fraction obtained, the gas volumefraction can be updated and it is used to solve fluid continuity andmomentum equations in the next time step.

The mass and momentum equations are approximated andsolved by finite volumes with staggered scalar and momentumnodes. The numerical particle velocity at the following time step isupdated by

vn1p vnpt Dpvn1f ;p 1pp

n1p 1pp

n1p g

h i1tDp

13

where, vn1p is the interpolated particle velocity, vn1f ;p ,p

n1p ,n1p

represent the gas phase velocity, the pressure gradient and thesolid stress gradient interpolated at the particle locationrespectively.

Based on Eq. (13), the particle location at the following timestep is obtained by

xn1p xnpvn1p t 14

Fig. 1. CFB model (units by mm). (a) Schematics of simulation geometry, (b) axis-symmetric, (c) point-symmetric.

Y. Jiang et al. / Chemical Engineering Science 109 (2014) 8597 87

The inter-phase momentum transfer at momentum cell (i, j, k) isthen

Fn1i;j;k 1

Vi;j;kNp

1Si;j;k Dpvn1g;p vn1p

1p

pn1p

" #npmp 15

where mp is the particle mass and Fn1i;j;k is the interpolated rate of

inter-phase momentum exchange per volume.

3. Circulating fluidized bed model and ECT sensors

The schematic diagram of a cold CFB model as shown in Fig. 1 isscaled down from a 600 MWe super-critical pressure circulatingfluidized bed following the scaling rules proposed by Glicksman,Hyre and Woloshun (1993). The main dimensions and parametersof the model are given in Table 1. Two types of arrangementfor cyclones are considered in the CPFD simulation and shown inFig. 1(b) and (c), they are named axis and point basedsymmetric in this research. As it is recommended by Barracuda'smanual, CPFD requires hundreds of thousands of grids for experi-mental scale apparatus. In the present study, the total number ofgrids is about 500,000 for two arrangements and the mesh isshown in Fig. 2.

The particle size distribution given in Fig.3 is the same withexperiment and analyzed using a Malvern particle size analyzer.Gas phase and particle properties used in the simulation are listedin Table 2. The simulation parameters are listed in Table 3 and twosimulation conditions are given in Table 4.

In the CPFD scheme, the interpolation operators which are bothlocally and globally conservative are used for the simulation, and

the sub-grid model for modeling the particle normal stress appliedto discrete particles is implemented in a robust and fast algorithm.Meanwhile, particles are implicitly coupled to the fluid phase, andthe fluid momentum and pressure equations are implicitly solved,which gives a robust solution. In the present study, the residual ofvelocity in each time step is less than 108 while the residual ofpressure is 109.

The inlet boundary for the gas phase is at the base of the riser andthe bed material is packed in the bottom of the CFB riser before thesimulation start. As the air distributor used in experiment is a porousplate and difficult to model directly, it is not included in the CPFDsimulation. Instead, a simplified inlet boundary with pressure drop of820 Pa is used in the simulation which is in accordance with theexperimental measurement. The initial packed bed height is 0.5 m andthe total weight is 200 kg. An air flow rate of 0.6 m/s is introducedfrom the bottom of the U-loop for delivering the recycled solids back

Table 1Dimensions of CFB boiler and cyclone separator.

Parameter Units (m)

CFB boiler Width 0.42Depth 0.92Height 5.8

Cyclone separator Vortex tube diameter 0.14Inlet port width 0.19Inlet port height 0.30Diameter of cylindrical part of cyclone 0.31Cylindrical part length 0.39Conical part diameter in the bottom 0.07Conical part length 0.20

Fig. 2. Mesh of the CFB. (a) Axis-symmetric, (b) point-symmetric.

Fig. 3. Particle size distributions.

Table 2Gas and solid properties.

Parameter Value

Solid density, s (kg/m3) 2620Gas density, g (kg/m3) 1.205Gas viscosity, vg (Pa s) 1.85E-05


into the CFB riser. The top of the cyclones is set as the out flowcondition with a pressure 1000 Pa lower than the atmosphericpressure, which is derived from the time-averaged pressure drop ofthe six cyclones in the experiment. The x-axis is along the front-to-back wall direction, the y-axis is along the side-to-side wall direction,and the z-axis is against the gravity direction.

Fig. 4 shows the ECT sensor used in the research. The framesof the sensors are the wall of the standpipes. The sensor isenclosed by copper shielding to eliminate external interference.Each electrode has a length of 5 cm and width 2.5 cm. Themeasurement region encompasses a height between 50 and55 cm above the U-loop distributor. Therefore, each pixel in theECT imaging area represents an axial average over this circularmeasurement volume. The measurement system is an AC-basedECT system with a data acquisition rate up to 250 frames/s for an8-electrode ECT sensor (Yang and York, 1999). Using a Linear BackProjection (LBP) algorithm, images can be reconstructed online(Xie et al., 1992). To improve the image quality and accuracy, theLandweber iteration is used in the research (Yang and Peng, 2003).

4. Results analysis for CPFD modeling

4.1. Solids concentration and velocity vectors distribution

In the CPFD simulation, the first 10 s were neglected todisregard the effect of the impulsive initialization effect. The realsimulation times for both cases are longer than 20 s to ensure acomplete circulation of particles. Figs. 5 and 6 give a snapshot ofthe particle flow field for case A and B respectively. From Fig. 5(b),it can be seen that the cyclone labeled as E in Fig. 1 has highersolids volume fraction closed to the inner wall of the inlet, whichwill negatively affect the efficiency of separation. In both cases, itcan be find that cyclones in the corner (cyclones labeled as A,C, D and F) have a similar distribution which particles tend topass by the outer wall of the inlets, which can improve theefficiency of separation. However, in the middle ones (cycloneslabeled as B and E), the solids moves through the middle of theinlet, which decreases the efficiency.

Fig. 7 shows the velocity field of gas phase at the top and thebottom of the CFB riser. From Fig. 7(a) and (b), it can be found out that

the gas flow changes its direction from vertical to horizontal as itapproaches the inlet of cyclones. The gas flow has a strong rotation inthe center of cyclone, which can also be observed in Fig. 7(e) and (f).The gas flow tends to flow downward along the wall of cyclone andthen turns around joining the central upward flow right below theoutlet of cyclone until it flows through the outlet. In the bottom of theCFB riser, the gas flow is not uniform with both upward and down-ward flows as shown in Fig. 7(c) and (d). From Fig. 7(c) and (d), it canalso be observed that the air flow used to fluidize the particles in theU-loop flows back into the CFB riser. Thus, particles in the standpipeare dominated by gravitational force.

4.2. Comparison of pressure drop

In the experiment, the measured points for pressure are locatedat five layers in the vertical direction and each layer includes

Table 3Input parameters in the CPFD simulation.

Particle-to-wall interaction Normal retention coefficient, en 0.89Tangential retention coefficient, et 0.68Diffuse bounce, Df 0

Solver settings Time step, t 4E-4 sTotal time 32 sgravitational acceleration, g 9.81Maximum volume iteration 1Volume residual 1.00E-05Maximum pressure iterations 2000Pressure residual 1.00E-08Maximum velocity iterations 50Velocity residual 1.00E-07Maximum momentum redirection from collision 40%close pack fraction limit 0.65

Table 4Simulation conditions.

Case U (m/s) Bed material (kg) priser (kPa) Separators layout

A 4 200 5 Axis-symmetricB 4 200 5 Point-symmetric

Fig. 4. ECT sensor design.


6 points at different locations in the same cross section in the CFBriser. Thus, pressure drops in the dense region, middle region,dilute region and cyclones are recorded after the flow becomessteady. The signals are connected to a PC with Agilent Dataacquisition system. The area-weighted pressures taken fromdifferent heights along the CFB riser obtained by simulation arecompared with measurements. Fig. 8 shows the axial profile oftime-averaged pressure drop for case A and B. The averagepressure in the top of the riser is about 100,520 Pa, whichconforms to the requirement of slightly negative pressure byexperiment. The total pressure drop in the CFB riser is about4 kPa, while in the experiment it is 5 kPa.

Tables 5 and 6 compare the pressure drops obtained from theCPFD simulation and measurements for case A. From Table 5 it canbe found out that CPFD has a lower pressure drop prediction forthe dense region, which will result in the underestimation ofsolids volume fraction in this region. The maximum relative errorin the dense region is 128.23% and much higher than the predictedresults by the TFM method combined with the EMMS drag model(Zhang et al., 2008). In the dilute region, the CPFD result has agood agreement with measurements. The calculated pressure dropacross the cyclones by CPFD is smaller than the experiment results.The main reason for the discrepancy is due to the inaccurateestimation of drag force both in the dense and dilute region forthe CPFD simulation. The dense gassolid flow in a CFB riser

is heterogeneity and the CPFD model should account for thisintrinsic characteristics. Therefore, it is necessary to consider ahybrid model to estimate the drag force for the whole-loop CPFDsimulation. The EMMS drag model is one of the options for thedense gassolid flow CPFD simulation (Zhang et al., 2008, Chenet al., 2013). However, the current CPFD code does not provide theuser defined functions (Chen et al., 2013). Another reasons for theabove discrepancy is due to the simplified inlet boundary used inthe CPFD simulation. In the experiment, one type of tuyere gasdistributor is used to fluidize the particles. It is well known thatthe fluidization is affected by the performance of the gas dis-tributor (Lombardi et al., 1997). However, it is difficult to simulatethe real air distributor in the CPFD approach due to the limitationof computer capacity. Therefore, the simplified inlet boundary willalso introduce predicted errors.

4.3. Particle re-circulation flux

Particle re-circulation flux into the riser from the U-loop is animportant parameter to indicate the performance of a CFB boiler.As the particle mass flow rate at the inlet of the cyclones fluctuatesrather intensely over time, a time-averaged estimation of the fluxis taken after the gassolid flow become steady. Fig. 9 showsthe particle flux distribution among the cyclones from the CPFDsimulation.

Fig. 5. Contours of particle volume fraction at t32.93 s for case A. (a) Particle tracks in the full-loop of CFB, (b) contour of cross-section at Z5.5 m, (c) contour of thebottom.


From the results it can be seen that the cyclone in the middlehas a lower particle mass flow rate than the rest in both cases for Aand B. It reveals that more particles go through cyclones located atthe corner. The total mass flow rates in both sides are almost thesame, which means that the distribution of particles in the twosides of the riser is quite uniform. It also can be seen that thecyclone arrangement for axis symmetry is better than pointarrangement in terms of uniform solids distribution.

4.4. Distribution of solid concentration and velocity in the CFB riser

The solids concentration in the boiler can be derived from themeasured pressure drop and defined as following (Kunii andLevenspiel, 1991).

1 p=pgl 16

where, is the area-averaged solids volume fraction in the crosssection and l is the distance between two measured points.

Fig. 10 gives the time-averaged solids concentration both fromCPFD simulations and pressure drop predictions in the CFB riser.As can be seen from Fig. 10, CPFD predicts a bottom-dense andupper-dilute structure for the solids concentration. In both cases,the CPFD results are in well agreement with experimental data inthe region of z41 m. However, the CPFD predictions for solidsconcentration are much smaller than the experiment data in theregion below z1 m, which means more particles are broughtupwards and thus the predictions for upper region are denser thanthe experiment. The results are quite contrary to the CPFDsimulation for a CFB riser with single cyclone separator (Chenet al., 2013). This is mainly due to the inaccurate estimation of dragforce in the dense region.

Fig. 11 shows the horizontal distribution of particle verticalvelocity at different heights along the riser. The CPFD simulation

Fig. 6. Contours of particle volume fraction at t21.07 s for case B. (a) Particle tracks in the full-loop of CFB, (b) contour of cross-section at Z5.5 m, (c) contour of thebottom.


result shows that the particle velocity is higher in center. Incontrast to the core upward flow, the particles form a downwardflow near the wall. This is the core-annulus structure or crown-like shape concentration which is congruent with the resultsreported from the TEM simulations (Pain et al., 2002 Malcus et al.,2002, Wang et al., 2006). In the upper region of the riser, twopeaks instead of one are observed in the distribution curve at

z5.5 m for case A, which may be caused by the existence ofhorizontal flows through cyclone inlets. In both cases, horizontalparticle velocity profile at z2.5 m and z3.5 m exhibits a steeperpeak than the rest areas.

Fig. 12 depicts the horizontal solids concentration profile in xdirection at different heights. It is observed from the curves thatthe solids concentration decreases slightly with the increases in

Fig. 7. Comparison of vector field obtained in two cases by CPFD. (a) Top of the riser at t32.92 s (case A), (b) top of the riser at t21.07 s (case B), (c) bottom of the riser att32.92 s (case A), (d) bottom of the riser at t21.07 s (case B), (e) Z5 m at t32.92s (case A), (f) Z5 m at t21.07 s (case B).


height. In the dense region of the riser, solids concentration nearthe wall is higher than that near the center, exhibiting a core-annulus flow as mentioned above. However, there is no clear

performance of the distribution of solids concentration in theupper region at z4.5 m and z5.5 m.

4.5. Distribution of solid flux on the cross-section of cyclone inlets

Fig. 13 shows the horizontal distribution of particle mass flowrate at the inlets of different cyclones. The x/L coordinate repre-sents the dimensionless distance from the outer wall of theinlet of cyclone. As can be seen, all the cyclone separators exceptB have more particles passing by the outer wall of the inlet (x/L0).

From the above CPFD simulation results, it can be concludedthat the axis based symmetric arrangement for cyclone is betterthan point arrangement in term of solids distribution as well asflow fields in the CFB riser. However, further CFPD simulations aswell as ECT measurements are necessary to investigate the effectof cyclone geometry shape and cyclone inlets angle on the gassolid flow characteristics.

4.6. Distribution of solids in the standpipe

Figs. 14 and 15 give the reconstructed images in the crosssections in the standpipe with different fluidization flow rate fromECT measurements. The solids concentration is in the range of02%, 23%, 36% when the flow rate is 4500 m3/h, 5500 m3/h and6500 m3/h respectively. As can be seen from those images, thesolid flow in the standpipe is a typical annular flow due to thestrong swirl effect in the cyclone separator. The solids concentra-tion increases with the increase in fluidization air through theU-loop.

Fig. 16 shows the distribution of time-averaged solids concen-tration in the cross sections calculated by CPFD simulationand ECT measurements. The simulation condition is 5500 m3/hin both cases. As a result of un-symmetrical structure of thearrangement of multi-cyclone separators, flow deviation incyclones is inevitable. Table 7 summarized the solids concentra-tion in the six standpipes from the CPFD simulation and ECTmeasurements. From Table 7 and Fig. 16, it can be seen thatparticle flow has a non-uniform distribution. The maximumrelative error is 28.55% and 24.97% for case A and B respectively.However, there is no obvious difference from the reconstructedimage for case A and B.

From the above simulation results analysis, it can be seen thatCPFD simulation can provide details of the gassolid hydrody-namics behavior in the full-loop of a circulating fluidized bedwith six cyclone separators and predict the effect of operationparameters on the circulating process. The results indicatethat the presented CPFD model combined with ECT measurementscan be applied to the design and optimization for a circulatingfluidized bed. Furthermore, the ECT measurement results indicatethat ECT can not only provide the instantaneous parameterdistribution in a cross section, but also provide accurate time-averaged solids concentration in well agreement with the CPFDsimulation.

5. Discussions and conclusions

The gassolid hydrodynamic behavior is one of the key issuesto scale up a circulating fluidized bed (CFB) from small scaleto large scale with multi-cyclone separators. To investigate thegassolid flow in a large scale CFB with six cyclone separators,Barracuda CPFD is used to simulate the full-loop of gassolid flowin a super-critical pressure circulating fluidized bed with six-cyclone separators and rectangular shape of combustion chamber.

Fig. 8. Axial pressure distribution calculated by CPFD.

Table 5Pressure drop of the riser (case A).

Dense region Middle region Dilute region

CPFD (Pa) 1689.8 1273.62 1069.13Experiment (Pa) 3856.58 522.1 483.08Relative error (%) 128.23 59.0 54.82

Table 6Pressure drop of cyclones (case A).

Cyclone A B C D E F

CPFD (Pa) 585.39 580.01 589.57 591.33 583.54 588.95Experiment (Pa) 1078.11 828.12 918.94 833.19 740.46 958.84Relative error (%) 84.17 42.78 55.87 40.90 26.89 62.81

Fig. 9. Time-averaged particle flux distribution in cyclones.


A circular ECT sensor has been designed and used to measure thesolids concentration in the cross sections in the standpipe. Themain conclusions are as follows.

(1) From the CPFD simulation, the gassolid flow is non-uniformamong the six parallel cyclones. The solids concentration offour cyclones at the corner of the chamber is higher than that

of the others. The location of cyclone as well as the inlet angleof the cyclone need to be optimized based on the CPFDsimulation and ECT measurements.

(2) The ECT results show that the solids distribution in the coldCFB model with different arrangement of cyclones can bemeasured in different bed inventories and superficial veloci-ties. The measurement results provide valuable information

Fig. 10. Comparison of axial solids concentration profile. (a) Case A and (b) case B.

Fig. 11. Horizontal distribution of particle vertical velocity. (a) Case A and (b) case B.

Fig. 12. Horizontal distribution of solids concentration. (a) Case A and (b) case B.


for the scaling up of a super-critical pressure circulatingfluidized bed with multi-cyclone separators.

(3) The gas phase is relatively uniform in the CFB riser as well as inthe cyclones. The pressure drops of cyclones accounts for alarge proportion of the whole boiler pressure drop. The solidsconcentration in the CFB riser from the CPFD simulation agreeswell with pressure drop measurements except in the bottomdense region.

(4) In terms of uniform distribution of solids flux in the cycloneand flow fields in the CFB riser, axis based symmetricarrangement for cyclone is better than point arrangement.

Circulating fluidized bed with multi-cyclone separators is acomplex gassolid flow system, the design as well as scaling up isextremely difficult and several key issues need to be addressedbefore the commercial success of the system, including thearrangement of the cyclone, the geometrical shape of the riserand the dimensions of cyclone. Further work is necessary toprovide valuable information for the design and process optimiza-tion based on CFPD simulations and ECT measurements. Inadditional, it is necessary to consider a hybrid model to estimatethe drag force for the whole-loop CPFD simulation to improve theprediction error.

Fig. 13. Solid flux along the x direction in the inlet of cyclone. (a) Case A and (b) case B.

4500 m3/h 5500 m3 m0056h/ 3/h

No.1

No.2

No.3

No.4

No.5

No.6

Fig. 14. Solids concentration in the cross-section in the standpipe (case A).


4500 m3 m0055h/ 3 m0056h/ 3/h

No.1

No.2

No.3

No.4

No.5

No.6

Fig. 15. Solids concentration in the cross-section in the standpipe (case B).

Fig. 16. Average solids concentration in the cross sections of standpipes by CPFD. (a) Case A and (b) case B.

Table 7Solids concentration in the standpipes.

Cyclone Case A Case B

CPFD (%) ECT (%) Error (%) CFPD (%) ECT (%) Error (%)

A 3.05 2.76 9.51 4.215 4.17 11.03B 2.95 2.54 13.89 3.885 4.58 15.08C 2.92 2.30 21.18 4.063 3.93 3.46D 3.08 2.26 26.57 4.193 4.22 7.10E 3.05 2.18 28.55 3.989 3.19 24.97F 2.99 2.68 10.37 4.458 4.03 10.59


Nomenclature

Cd Drag coefficient ()Dp Inter-phase drag function (s1)F Inter-phase momentum exchange rate per volume

(N=m3)f Probability distribution function ()g Gravitational acceleration (m=s2)l Location of pressure sensor point (m)m Particle mass (kg)Np Total number of numerical particles in a cell ()np Total number of real particles in a numerical particle ()p Pressure (Pa)PS Pressure constant (Pa)Re Reynolds number ()S Interpolation operator ()Sg Gas source term (kg=m3s)t Time (s)V Volume (m3)v Velocity (m=s)xp Particle displacement (m)x; y; z Orthogonal directions (m)

Greek symbols

Density (kg=m3) Volume fraction () Viscous stress tensor (N2=m2) Gas viscosity (kg=ms) Constant number () Average voidage () Constant number () Pi ()

Subscripts

cp Close packing limitg Gas phasei, j, k Coordinate directionn Time stepp Particle phase

Acknowledgment

The authors would like to thank the National Natural ScienceFoundation of China (No. 61072001) and Strategic PriorityResearch Program Demonstration of Key Technologies for Cleanand Efficient Utilization of Low-rank Coal (No. XDA07030100) fromthe Chinese Academy of Sciences for financially supporting thisresearch.

References

Ahuja, G.N., Patwardhan, A.W., 2008. CFD and experimental studies of solids hold-up distribution and circulation patterns in gassolid fludized beds. Chem. Eng. J.143, 147160.

Abbasi, A., Ege, P.E.G., de Lasa, H.I., 2011. CPFD simulation of a fast fluidized bedsteam coal gasifier feeding section. Chem. Eng. J. 174, 341350.

Andrews, M.J., O'Rourke, P.J., 1996. The multiphase particle-in-cell (MP-PIC) methodfor dense particulate flows. Int. J. Multiph. Flow 22, 379402.

Armistead, T.F., Schwieger, R., Reina, P., 2002. Boiler technology pursues goals ofefficiency and lower emissions-circulating fluidized-bed equipment pushes sizelimits. Power Ind. Powerpl., 2002

Auzerais, F.M., Jackson, R., Russel, W.B., 1988. The resolution of shocks and the effects ofcompressible sediments in transient settling. J. Fluid Mech. 195, 437462.

Chen, C., Werther, J., Heinrich, S., Qi, H.Y., Hartge, E.U., 2013. CPFD simulation ofcirculating fluidized bed risers. Powder Technol. 235, 238247.

Chen, Y.M., Zhou, X.L., Zheng, C.H., Wang, L.H., Fang, M.X., Shi, Z.L., Luo, Z.Y., Cen, K.F., 2008. Development of large-scale circulating fluidized bed boiler. J. PowerEng. 28, 817826.

Du, B., Warsito, W., Fan, L.S., 2005. ECT studies of gassolid fluidized beds ofdifference diameters. Ind. Eng. Chem. Res. 44, 50205030.

Dyakowski, T., Edwards, R.B., Xie, C.G., Williams, R.A., 1997. Application ofcapacitance tomography to gassolid flows. Chem. Eng. J. 52, 20992110.

Dyakowski, T., Luke, S.P., Ostrowski, K.L., Williams, R.A., 1999. On-line monitoring ofdense phase flow using real time dielectric imaging. Powder Technol. 104, 287295.

Fan, Z., Goidich, S., Robertson, A., 2008. 800 MWe circulating fluidized bed boilerwith 1300 1C superhigh steam. In: Proceedings of the 33rd InternationalTechnical Conference on Coal Utilization and Fuel Systems June 15, 2008,Clearwater, FL, USA.

Gidaspow, D., Ettehadieh, B., 1983. Fluidization in two dimensional beds with a jet:Part II. Hydrodynamic Modeling Ind. Eng. Chem. Fund. 22, 193201.

Glicksman, l.R., Hyre, M., Woloshun, K., 1993. Simplified scaling relationships forfluidized-beds. Powder Technol. 77, 177199.

Hack, H., Hotta, A., Kettunen, A., Eriksson, T., 2008. Ultra-Supercritical CFBTechnology to Meet the Challenge of Climate Change, Power_Gen International2008, Orlando, Florida, USA, December 2-4, 2008, pp 111.

Harris, S.E., Crighton, D.G., 1994. Solitons, solitary waves and voidage disturbancesin gas-fluidized beds. J. Fluid Mech. 266, 243276.

Hartge, E.U., Ratschow, L., Wischnewski, R., Werther, J., 2009. CFD-simulation of acirculating fluidzied bed riser. Particuology 7, 283296.

Koornneef, J., Junginger, M., Faaij, A., 2007. Development of fluidized bed combustionan overview of trends, performance and cost. Prog. Energy Combust. Sci. 33, 1955.

Kunii, D., Levenspiel, O., 1991. Fluidization Engineering. 1991. ButterworthHeine-mann, Boston p. 69

Lombardi, G., pagliuso, J.D., Jr. Goldstein, L., 1997. Performance of a tuyere gasdistributor. Powder Technol. 94, 514.

Lun, C.K.K., Savage, S.B., Jeffrey, D.J., Chepurniy, N., 1984. Kinetic theories forgranular flow: inelastic particles in coquette flow and slightly inelastic particlesin a general flow field. J. Fluid Mech. 140, 223256.

Lv, J.F., Yu, L., Zhang, Y.J., Yue, G.X., Li, Z.Y., Wu, Y.X., 2007. A 600 MW super-highcirculating fluidized bed boiler. J. Power Eng. 27, 497501.

Makkawi, Y.T., Wright, P.C., 2004. Electrical capacitance tomography for conven-tional fluidized bed measurements-remarks on the measuring technique.Powder Technol. 148, 42157.

Makkawi, Y.T., Wright, P.C., Ocone, R., 2006. The effect of friction and inter-particlecohesive forces on the hydrodynamics of gassolid flow: a comparative analysisof theoretical predictions and experiments. Powder Technol. 163, 6979.

Malcus, S., Cruz, E., Rowe, C., Pugsley, T.S., 2002. Radial solid mass flux profiles in ahigh-suspension density circulating fluidized bed. Powder Technol. 125, 59.

Morin, J.X., 2003. Recent Alstom power large CFB and scale up aspects includingsteps to superhigh. In: Proceedings of the 47th International Energy AgencyWorkshop on Large Scale CFB. Zlotnicki, Poland.

Pain, C.C., Mansoorzadeh, S., Gomes, J.L.M., de Oliveira Cr, E., 2002. A numericalinvestigation of bubbling gas solid fluidized bed dynamics in 2-D geometries.Powder Technol. 128, 5677.

Reh, L., 2003. Development potentials and research needs in circulating fluidizedbed combustion. Particulology 1, 185200.

Snider, D.M., 2001. An incompressible three-dimensional multiphase particle-in-cell method for dense particle flows. J, Comput, Phys, 170, 523549.

Wang, H.G., Yang, W.Q., Dyakowski, T., Liu, S., 2006. Study of bubbling and sluggingfluidized beds by simulation and ECT. AIChE J. 52, 30783087.

Wen, C.Y., Yu, Y.H., 1966. Mechanics of fluidization. Chem. Eng. Prog. Symp. Ser. 62,100111.

Xie, C.G., Huang, S.M., Hoyle, B.S., Thorn, R., Lenn, C., Snowden, D., Beck, M.S., 1992.Electrical capacitance tomography for flow imaging: system model for devel-opment of image reconstruction algorithms and design of primary sensors. IEEProc. GCircuits Dev. Syst. 139, 8998.

Yang, W.Q., York, T.A., 1999. New AC-based capacitance tomography system.IEE Proc.Sci. Meas. Technol. 146, 4753.

Yang, W.Q., Peng, L.H., 2003. Image reconstruction algorithms for electricalcapacitance tomography. Meas. Sci. Technol. 14, R1R13.

Zhang, N.B.L., Wang, W., Li, J.H., 2010. 3D CFD simulation of hydrodynamics of a150 Mwe circulating fluidized bed boiler. Chem. Eng. J. 162, 821828.

Zhang, N., Lu, B.N., Wang, W., Li, J.H., 2008. Virtual experimentation through 3Dfull-loop simulation of a circulating fluidized bed. Particuology 6, 529539.

Zhou, X.L., Cheng, L.M., Wang, Q.H., Luo, Z.Y., Cen, K.F., 2012. Non-uniformdistribution of gassolid flow through six parallel cyclones in a CFB system:an experimental study. Particuology 10, 170175.


http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref1http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref1http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref1http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref2http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref2http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref3http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref3http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref4http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref4http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref4http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref5http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref5http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref6http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref6http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref7http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref7http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref7http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref8http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref8http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref9http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref9http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref10http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref10http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref11http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref11http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref12http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref12http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref13http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref13http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref14http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref14http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref15http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref15http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref16http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref16http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref17http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref17http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref18http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref18http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref18http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref19http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref19http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref19http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref20http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref20http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref20http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref21http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref21http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref21http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref22http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref22http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref23http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref23http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref23http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref24http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref24http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref25http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref25http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref26http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref26http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref27http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref27http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref28http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref28http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref28http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref28http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref29http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref29http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref30http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref30http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref31http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref31http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref31http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref32http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref32http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref33http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref33http://refhub.elsevier.com/S0009-2509(14)00039-6/sbref33

Modelling and experimental investigation of the full-loop gassolid flow in a circulating fluidized bed with six cyclone...IntroductionCPFD mathematical modelGoverning equationsDrag modelSolids stress modelCPFD simulation procedure

Circulating fluidized bed model and ECT sensorsResults analysis for CPFD modelingSolids concentration and velocity vectors distributionComparison of pressure dropParticle re-circulation fluxDistribution of solid concentration and velocity in the CFB riserDistribution of solid flux on the cross-section of cyclone inletsDistribution of solids in the standpipe

Discussions and conclusionsNomenclatureAcknowledgmentReferences

1-s2.0-s0009250914000396-main

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