Fuel Processing Technology 92 (2011) 556–562

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Fuel Processing Technology

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Numerical modelling of the carrier gas phase in a laboratory-scale coalclassifier model

Lanre Afolabi ⁎, Abdelwahab Aroussi, Nora Mat IsaThermal and Fluids Engineering, Department of Engineering, The University of Leicester, University Road, LE1 7RH, United Kingdom

⁎ Corresponding author. Tel.: +44 116 252 2558; faxE-mail address: oja3@le.ac.uk (L. Afolabi).

0378-3820/$ – see front matter © 2010 Elsevier B.V. Adoi:10.1016/j.fuproc.2010.11.011

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 March 2010Accepted 18 November 2010Available online 28 December 2010

Keywords:Computational fluid dynamicsCoal classificationTurbulence modellingSwirling flow

The pneumatic transport of fine ideally combustible coal dust to the burner furnace is an important process incoal fired power plants. The strongly swirling air phase responsible for the particle separation and transport in acoal pulverising mill was characterised experimentally and numerically. Measurements of the swirl velocitycomponent were taken in a scaled laboratory model of the device and compared to CFD model. In particular, anevaluation of the turbulence models used to describe the flow was performed. The modified isotropic k-epsilonturbulencemodels (RNG k-ε and Realizable k-ε) were compared to the anisotropic Reynolds stressmodel (RSM)and their ability to predict the bulk flow structure present in the classifier was assessed. The experimentsshowed that the swirling flow structure, responsible for coarse-fine particle classification, has several flowregimes which are governed by the areas it is bounded by. The numerical model predictions generallycorroborate the results. However, a distinction in performance between the three models can bemade based onaccuracy, solution generation time and numerical stability. The RSM model predicted both the trends andmagnitude the most accurately when compared to the isotropic models. However, the Realizable k-epsilonmodel, with its relatively low solution generation time, shows potential when using CFD as a classifier designoptimization tool. The investigation has given some insight on single phase classifier flow and suggests a designimprovement based on the results.

: +44 116 252 2619.

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© 2010 Elsevier B.V. All rights reserved.

1. Introduction

The reduction of pollutant emissions in energy generation hasbecome principally important. In particular the combustion of coalcontributes to a large percentage of global emissions. Efforts inimproving the emissions performance in pulverised coal combustionare continually being made with much success. The introduction oflow NOx burners into existing furnace systems for example, hasyielded impressive results. However, the performance of the burnersand downstream processes are hinged on the quality of the pulverisedfuel delivered by the grinding system.

The complex multi stage process of delivering fuel to the furnaceincludes the grinding, pneumatic conveyance and classification of thesolid fuel, all of which takes place in the pulveriser. Although the twophase flow, heat transfer, combustion and pollutant formation in theburner–furnace system are the key processes in firing pulverised fuel(PF), the size classification of the PF is no less important and candictate the performance of the entire plant [1].

The complexity of the system is exemplified by the necessity tobalance several performance parameters which can sometimes beinversely proportional. An increased mill capacity or “throughput”

will increase mill efficiency but if too high, it will lead to a reducedproduct sharpness of cut and wide particle size distribution which areboth detrimental to efficient combustion. A result of coarse or “abovecut” PF being burned is the production of NOx and loss-on-ignition(LOI), unburned coal particles contaminating the ash by-productproduced in the combustion chamber. It is accordingly important tomaintain close control over the fineness of the pulverised coal fed intothe combustion system [2]. This is the scope of the present study.

Coal classification devices are usually housed above the grindingmill (pulveriser) and depending on the mill type they will havedifferent design features and operating techniques. However, theygenerally possess the same fundamental separation medium knownas: centrifugal-counterflow classification [3]. One of the most popularmodels is the vertical spindle mill static classifier. In this device, rollerbearings crush the coal coming in from a central chute against a bowl.The particles, which have a wide size distribution, are displaced by thebowl's rotation to the outer periphery of the grinding plate. Upwardswirling air induced by angled vanes entrains the particles and directsthem to the classifier. Additional swirl to the air flow is impartedupstream of the internal separation cone by variable circumferentiallyspaced guide vanes. The vanes also control the coal throughput,collection efficiency and pressure drop across the mill. The tangentialvelocity or “swirling” velocity controls the particle separation via thecentrifugal force and its magnitude determines the outlet productsize. The larger particles with a higher momentum take an outer path

Fig. 1. Section view of laboratory classifier model highlighting the key geometricalfeatures and coordinate system.

Table 1Comparison of the dynamic scaling parameters of a typical classifier and the scaledlaboratory model.

Parameter Equation Power station Laboratory

Reynolds number Re = ρf uDμ 113,000 218,059

Stokes number St = ρpd2u18μD 0.632 0.7

Froude number Fr = u2

gD 13.48 8.5

557L. Afolabi et al. / Fuel Processing Technology 92 (2011) 556–562

and fall back to the grinding bed, while the finer particles follow thehot air out through the outlets and into the burner delivery pipenetwork. There is a lack of scientific studies into the optimization ofthe classifier flow and its performance parameters. Therefore in orderto improve the design and operation of classifiers, a betterunderstanding and control of both the single phase and two phase,gas–solid flow within the device is essential. In the present study,single phase experimental and numerical studies are carried out in alaboratory, static-vane coal classifier.

Air flow in a coal classifier, due to its entry conditions (swirlingblades and tangential injection) is normally a strongly swirling flow.Experimental studies have shown that for inert jets, swirl has largescale effects on flow fields such as jet growth, entrainment and decay.However, most of the literature on swirling flows is limited toapplications such as swirl burners, helical tubes, and cyclones. Thereare several studies where CFD has been applied to a coal pulveriser[1,4–6] but none has focussed on the single phase swirl aspect of theflow. Benim et al. [1] simulated the gas–solid flow in a 2D model coalpulveriser, however focussing on the effect of the particle motion onthe gas flow. Parham and Easson [6] studied the aerodynamiccharacteristics inside a vertical spindle mill static classifier geometryat different vane angles. In the work, a similar tangential velocitydistribution identified in a cyclone was found in the lower half of theclassifier known as a Rankine vortex. This profile has also beenidentified experimentally in studies of confined swirling flow in [7–9],where hot wire measurements of the Reynolds stress showedanisotropy in the eddy viscosity. This anisotropy mainly in theannular region between the wall and core region, is highlighted in thestudies of the multiphase flow in gas cyclones [10–13]. The threedimensionality of the shear stress presents a problemwhenmodellingconfined swirl flows and flow in a coal classifier using the popularisotropic turbulence closure models. Numerical modelling studies ofcyclones [10–12] discovered that the isotropic turbulence modelssuch as the k-epsilon and its variations are incapable of providing anaccurate depiction of the flow structure. Typical problems encoun-tered by [10–12] when applying the standard KEM to (strongly)swirling flow include the inability to predict the correct tangentialvelocity profile due to strong radial diffusion of momentum and theover-prediction of the shear stresses. The near wall regions are alsodifficult to model precisely. However, the choice of turbulence closuremodel is case specific and its use should depend on the objectives ofthe computational modelling effort. In this study, the bulk air flowcharacteristics and tangential velocity trends are the areas of interestbecause a good understanding of these will lead to the optimisation ofa coal classifier. However, the choice of turbulence closure model hasbeen found to be crucial in obtaining a correct flow prediction. Theanisotropic Reynolds Stress Model (RSM) handles the additionalturbulence effects induced by swirling flow (streamline curvature)but is almost twice as computationally expensive than the isotropic Kepsilon models. The effect of the isotropic turbulence assumption onpredicting the bulk flow features appears an untouched area inclassifier devices and will therefore be investigated in this work.

2. Laboratory coal classifier description

The laboratory model developed is a one third scale model of atypical classifier housed above the pulveriser in a coal power plant asshown in Fig. 1. The simplified design is based on a four outlet highperformance static classifier with the grinding bed and rollersomitted. The vortex finder has also been excluded in the design inorder to study the effects of its absence on performance, which will becarried out in a later study. The outer diameter of the model is 1.2 m,the diameter of the classifier cone at the inner edge is 395 mm and thediameter of the central pipe is 150 mm. The angle of the classifier coneis 70°. The classifier consists of eight vanes of height 150 mm flatpanels pivoted at top and bottom on a pitch circle diameter of

790 mm. The vane angle, relative to the radial line through the centreof the classifier model can be varied from 0 to 60°. However, in thisstudy the angle was kept constant 30°, a setting typically used in coalmills.

The dynamic scaling parameters appropriate to classifier air floware the Reynolds number (Re) Stokes number (St) and Froudenumber. These parameters are similar in quantity to those in a coalfired power station [14] see (Table 1). The Stokes number is based on aparticle average diameter of 50 μm. The model is operated underpositive pressure so the air is blown by a centrifugal fan through thetangential inlet creating an upward swirling flowwithin the classifier.Measurements of the tangential component of velocity Vθ were takenradially across the diameter using a direction sensitive Pitot-statictube positioned at a tangent to the axis of rotation. To ensure that thecorrect velocity was beingmeasured, several data points were verifiedusing a high response hot-wire anemometer. The measurement areasof interest are the regions where swirl is expected to be high, as this isa good way of testing the ability of the turbulence models to predictthe strongly swirling flow characteristic of the device. The areas areshown in Fig. 2 and Table 2.

3. Modelling

The swirling gas phase flow was resolved by assuming anincompressible and steady flow. The Reynolds averaged Navier–Stokes (RANS) method was used in the simulations. The RANSequations govern the transport of the averaged flow quantities, withthe whole range of the scales of turbulence being modelled [15]. Thisinvolves decomposing the solution variables of the instantaneousNavier–Stokes equations into ensemble averaged and fluctuatingcomponents. The expressions are substituted into the instantaneouscontinuity andmomentum equations and taking an ensemble average(for a steady flow) gives the ensemble-averaged momentumequations shown below in Eq. (1) [16]. The additional terms(−ρu′iu′j) generated in Eq. (2), represents the effects of turbulenceand must be modelled to provide closure.

∂ρ∂t +

∂∂xi

ρuið Þ = 0 ð1Þ

Table 2Measurement locations relative to the model base, and their respective zones.

Position ID A B C D

Axial location (mm) 98 550 890 1047Zone Near inlet Mid section Guide vane area Outlet region

Fig. 3. (A) Computational grid of the separation zone 3 (B) close up view of theseparation zone inlet.

558 L. Afolabi et al. / Fuel Processing Technology 92 (2011) 556–562

∂∂t ρuið Þ + ∂

∂xjρuiuj

� �= − ∂p

∂xi+

∂∂xj

μ∂ui

∂xj+

∂uj

∂xi−2

3δij

∂ul

∂xl

!" #

+∂∂xj

−ρu′iu′j� �

:

ð2Þ

The turbulence specifications used for all three turbulence models,were the turbulence intensity (TI) and hydraulic diameter (HD). Theturbulence intensity was measured at both inlet and outlet, using ahot wire anemometer. The voltage reading from the electronicmonitor was correlated with TI using Eq. (3) below where e´ is thefluctuating voltage corresponding to the fluctuating velocity u´ and

e′ð Þ2h i1=2� �

is the RMS value of the fluctuating voltage.

Ti =uð Þ2

h in o1=2

U× 100% = e′ð Þ2

h i1=2×

4EE2−E20

× 100%: ð3Þ

The classifier air flow was simulated using the commercial CFDsoftware FLUENT V6.3 in a full three-dimensional model using thepressure based solver. The second order upwind interpolation schemewas used for all convective fluxes but the PRESTO discretization forthe pressure. The SIMPLE segregated algorithm was implemented forthe pressure–velocity coupling. This uses a relationship betweenvelocity and pressure corrections to enforce mass conservation andobtain the pressure field [16]. Preliminary convergence studiesshowed that the SIMPLE algorithm was equally as accurate with lesscomputational expense than the other pressure based segregatedalgorithms (SIMPLEC, PISO, and Fractional step).

3.1. Grid independence study

A grid independence study was performed on a benchmark casefor all three turbulence models. Three grids of cell number 800,000,1.5 million and 3 million cells were used in the study. The minimumtotal cell count that the computational domain required in order to

Fig. 2. Front view cross section of classifier showing measurement locations and flowschematic.

obtain a grid independent solution was 1.5 million tetrahedral cells. Asize functionwas usedwhenmeshing the geometry to ensure a highergrid density at regions of particular interest and potentially highvelocity gradients. These regions were the vane area, the cone, inletand outlet faces and the central feed pipe. Fig. 3 shows a picture of thegrid. The Grid convergence metric (εc) [17] used for the two finestgrids is given by Eq. (4)

εc =Φ1−Φ2

Φtð4Þ

where φ1 is the point variable (max mean velocity) for the finer grid(3 million cells) and φ2 is the point variable for the coarser grid(1.5 million cells). Table 3 shows the convergence metric and griduncertainty for different turbulence models and inlet velocities used.

3.2. Turbulence closure models

Three turbulence models were used to simulate the flow in theclassifier model. These were the improved k-ε RNG model (Renorma-lisation group), the Realizable k-ε model and the Reynolds StressTurbulence model (RSM).

Table 3Convergence metric and grid uncertainty for different turbulence models and inletvelocities used.

Turbulence model Convergence metric (ε) Grid uncertainty% (USG)

RKE 0 0RNG 6×10−3 0.4RSM 0.01 0.6

559L. Afolabi et al. / Fuel Processing Technology 92 (2011) 556–562

3.2.1. Realizable k-εThe Realizable k-ε model [18] is a relatively recent development

and an improvement on the original “Standard” k-ε model. TheRealizable k-ε (RKE) model has been improved by the newformulation of the transport equation for the dissipation rate ε byderiving it from an exact equation for the transport of mean-squarevorticity fluctuation. In addition the RKE model contains a newformulation for the turbulent viscosity. The Realizable model satisfiesthe Schwarz inequality for shear stress by making Cμ variable bysensitising it to the mean flow (mean deformation) and theturbulence (k,ε). RKE includes the effects of mean rotation in thedefinition of the turbulent viscosity. This extra rotation effect has beentested on a single rotating reference frame system and showedsuperiority over the standard k-ε model [16].

The modelled transport equations for k and ε in the RKE model aregiven in Eqs. (5) and (6).

∂∂t ρkð Þ + ∂

∂xjρkuj

� �=

∂∂xj

μ +μtσk

� � ∂k∂xj

" #+ Gk + Gb−ρε−Ym + Sk

ð5Þ

and

∂∂t ρ�ð Þ + ∂

∂xjρ�uj

� �=

∂∂xj

μ +μtσ�

� � ∂k∂xj

" #

+ ρC1S�−ρC2�2

k +ffiffiffiffiffiν�

p + C1��

kC3�Gb + S�

ð6Þ

where C1 = max 0:43; ηη + 5

h i; η = S k

ε ; S =ffiffiffiffiffiffiffiffiffiffiffiffi2SijSij

q:

3.2.2. The RNG k-εThe RNG k-ε model was derived from the instantaneous Navier–

Stokes equations using a statistical technique called renormalisationgroup theory [19]. This closure model accounts for the effect of swirlon turbulence and for rapidly strained flows. Turbulence, in general isaffected by swirl in the mean flow so the RNG model in FLUENTaccounts for this by modifying the turbulent viscosity appropriately.The Prandtl numbers are formulated analytically — an improvementon the standard k-εmodel's use of user-specified, constant values. Theeffective viscosity term is also analytically derived, thereby allowingthe model to be used to resolve low Reynolds number flow effects atthe near wall region. The transport equations for the RNG k-ε modeland standard are different due to the extra terms incorporated in theRNG.

∂∂t ρkð Þ + ∂

∂xiρkuið Þ = ∂

∂xjαkμeff

∂kδxj

!+ Gk + Gb−ρ�−Ym + Sk ð7Þ

and

∂∂t ρ�ð Þ + ∂

∂xiρεuið Þ = ∂

∂xjαεμeff

δδxj

!+ C1θ

εk

Gk + C3εGbð Þ−C2ερε2

k−Rε + Sε

ð8Þ

whereαk andαε are the inverse effective Prandtl numbers for k and ε,respectively. Sk and Sε are user defined source terms.

Rε =Cμρη

3 1−η=η0ð Þ1 + βη3

ε2

kð9Þ

where η≡Sk.� , η0=4.38, β=0.012.

3.2.3. The RSM modelThe Reynolds stress model (RSM) closes the Navier–Stokes

equation by solving transport equations for the Reynolds stresses,

together with an equation for the dissipation rate [20]. This model ismore elaborate than the two-equation isotropic RNG and Realizable k-εmodel as its anisotropic formulation gives seven additional transportequations in 3D. Clearly the additional rigour of this model means thatit can resolve complex flow structures such as streamline curvature,swirl, rotation and rapid strain with more precision than the twoequation eddy viscosity models. However, there are limitations to thefidelity of the RSM. The modelling of the pressure-strain anddissipation-rate terms is difficult and is usually the cause ofinaccuracies in RSM predictions [10,11].

The Reynolds stress transport equations are derived by takingmoments of the exact momentum equation. This is the Reynoldsaveraging of a product of the exact momentum equations and afluctuating property. The exact transport equations for the transportof Reynolds stresses ρu′iu′j may be written as Eq. (9).

δδt

ρu′iu′j� �

+δδxk

ρuku′iu′j� �

= − δδxk

ρu′iu′ju′k + p δkju′i + δiku′j

�� +

δδxk

μδδxk

u′iu′j� �

−ρ�u′iu′j

δuj

δxk+ u′ju′k

δuδx

giu′jθ�

+ p� ∂̇ui

∂xj+

∂̇uj

∂xi

�−2μ

δu′iδxk

δu′jδxk

P

−2ρΩk u′ju′mεikm + u′iu′mεjkm� �

+ S

ð10Þ

4. Results and discussion

As the tangential velocity governs the particle separation in theclassifier, the data from the experimental and numerical model arepresented and discussed in Section 4.1. The performance of theturbulence models have also been evaluated in the context of thetangential velocity.

4.1. Tangential velocity

The analysis of the tangential velocity profiles in Figs. 4 and 5provides an insight into the air flow characteristics at discrete axiallocations along the height of the classifier. The characterisation can bebest performed by splitting the classifier into geometric boundariesacross which large flow gradients exist. Fig. 6 illustrates the location ofthese regions with respect to the classifier radius. The core region (CR)is bounded by the central pipe wall and an imaginary surfaceextending from the outlet orifice. The outlet region (OR) is boundedat both ends by the inner and outer control surfaces surrounding theoutlet orifice. The outer cone region (OCR) is bounded by the outeroutlet region boundary and the cone inner wall. And lastly the annularregion (AR) is bounded by the cone outer wall and the inner wall ofthe classifier enclosure.

It should be noted that measurements along each profile havebeen taken at a maximum proximity of 75 mm from the wall due tolarge instrument inaccuracies experienced beyond this point. Mea-surement positions A and B are taken solely in the annular regionwhile C and D encompass all the stated regions. It can be seen from theprofiles (Fig. 5a–d) that the flow is generally uniform in the annularregion and is characterised by an increasing tangential velocity fromthe cone wall to the near enclosure wall region. The velocity isexpected to drop after this zone owing to boundary layer viscouseffects and a reduction in dynamic pressure or a transformation intostatic pressure at the wall as observed by [9].The flow in the annularregion can be identified as a forced vortex flow or a solid body rotationswirling flow where Vθ=Ωr where Ω is a constant angular velocity.This flow structure is present in other flow regions such as the coreand outer cone regions of position D. The forced vortex profile is also

Fig. 4. Comparison of numerical and experimental data of themean tangential (Vθ) normalised by the inlet velocity, Vin=10 ms−1. Results for axial positions (a) A, (b) B, (c) C, (d) D.

560 L. Afolabi et al. / Fuel Processing Technology 92 (2011) 556–562

present in the core of position C and as a combination of a forced andfree vortex in the outer cone region. This combined vortex has beenidentified in the separation region of cyclones [9,11–13]. The freevortex flow can be defined by Vθ = C

r where C is a constant and r isradius. However, the location of the combined vortex in a cyclone isdifferent from that in the classifier investigated here. The reason forthis is the difference in geometry between the two devices, inparticular, the absence of a vortex finder in the studied classifiermodel. The resulting profile is a shift in the combined vortex awayfrom the core region. The profiles at positions C and D at the outletregion illustrate the effect of the outlet proximity on the flowstructure. At position C the flow in this region has an increasingtangential velocity trend whereas at position D the velocity decreasesdue to the swirl and dynamic pressure dissipation. In the outer coneregion at axial positions C and D, the tangential velocity distribution ischaracterised by a solid body rotation, where the velocity is propor-tional to the radius. This is a positive flow structure for good classifica-tions as the heavier particles (with higher terminal velocity) will beconstrained to an outer position—near the wall and not contaminatethe fine coal product exiting the classifier. The particles are expectedto collide with the cone wall or vane wall and fall to the bottom of thedevice where roller bearings are located, which will then re-crush theparticles to a more acceptable size. Because the air flow velocity islower in the inside regions it will be more efficient in terms ofsharpness of cut to have the classifier outletsmore centrally positioned.

The tangential velocity profile in the classifier is not affected byinlet velocity as shown by a comparison of Figs. 4 and 5. Aproportional increase in tangential velocity is experienced but theshape of the graphs generally remains the same.

4.2. Turbulence models

Referring to Figs. 4 and 5, the performance of the differentturbulence models in predicting the air flow characteristics in aclassifier can be evaluated. Tangential velocity is the velocitycomponent governing separation in a classifier; therefore it alone isa suitable reference comparison parameter to base the performance ofthe turbulence models on. In the core regions, for both velocity cases,the RKE and RSM models closely predict the tangential velocity trendas well as its magnitude. The RNG model consistently over-predictsthemagnitudes in both positions C and D. Although the RSM is slightlymore comparable to the experimental results than the RKE, thedifference is negligible considering the experimental errors them-selves are comparable to the error difference between the twoturbulence models. The major discrepancy in the RNG results could bedue to the under-predicted stress term in the tangential direction ofthe transport equations as well as the gross unsteadiness in thesimulation residuals. However, all CFD turbulence models are able topredict the forced vortex flow identified by experiment in the coreregion. In the outlet zone of positions D for the high and low velocity,the RKE and RSMmodels produce a realistic trend but the RNG modelfails to resolve the velocity profile in this zone by overestimating thedrop inmagnitude. It also fails to predict the sharp velocity recovery atthe OCR identified by experiment and predicted by the RKE and RSM.Generally in the OCR the CFD models under-predict the tangentialvelocity magnitude. In the annular regions of positions A and B; thenear inlet and mid section regions respectively, the prediction s nearthe cone outer wall are good, but the discrepancy grows as the radiusincreases towards the enclosure wall. The RNG largely over estimates

Fig. 5. Comparison of numerical and experimental data of themean tangential (Vθ) normalised by the inlet velocity, Vin=19 ms−1. Results for axial positions (a) A, (b) B, (c) C, (d) D.

561L. Afolabi et al. / Fuel Processing Technology 92 (2011) 556–562

while the RKE and RSM deviate only slightly from the experimentalmeasurements. Reasons for the errors or uncertainty in the RSMresults can be attributed to the difficulty in modelling the pressure-strain and dissipation-rate terms. Uncertainty in the experimentalmeasurements also exists due to the reduced precision of the Pitottube at low velocities.

Fig. 6. Characterised flow regions and their locations within classifier model. Outletregion (OR), core region (CR), outer cone region (OCR) and annular region (AR).

5. Concluding remarks

Tangential velocity measurements of the air flow in a laboratoryclassifier model have been used to evaluate the performance of threeturbulence models available in commercial CFD package FLUENT. Thework sought to determine the most efficient model to apply whenperforming parametric studies in a classifier to optimise its separationefficiency or pressure drop characteristics. It has been shown that CFDcan be a valuable tool in classifier design optimisation.

The Reynolds Stress Model (RSM) generally predicted the flow tomore accuracy than the RNG and RKE models. However, the extracomputational expense of using this model could not be fully justified.The application of CFD as a classifier optimisation design tool is mostvaluable when the solution generation time is low as extensiveparametric cases are often required. CFD assists in eliminating on amacro scale, design or operating changes that are adverse or produceno improvement to the benchmark case. The RSM is the mostcomputationally demanding turbulence model as it has an additionalseven equations to compute per simulation in comparison to the RNGand RKEmodels. The results showed that the RKEmodel, which is lesscomputationally expensive than the RNG and RSM, predicts the flowin all four regions to an acceptable error margin. The RNG modelappears to be very unstable for flow in a complex geometry such asthe classifier. Further detailed studies are required to ascertain thereason for this which is beyond the scope of this study.

This study has also shown that the tangential velocity profile in theclassifier is dependent upon the geometrical structures it is boundedby. The flow was split into a core region, outlet region, outer coneregion and annular region. The flow trends were repeated in some

562 L. Afolabi et al. / Fuel Processing Technology 92 (2011) 556–562

regions but their magnitudes always differed. The flow in position C,which encompasses the main separation region, has a core of acombined vortex (free and forced)—a profile identified in gas andhydrocyclones. Because the flow velocity in the outer region wasgenerally higher than the core region, due to higher momentum, thelarger coal particles will be collected in this region. As a result, theauthor suggests that the classifier design can be improved bycentralising the outlets, where the flow velocity is lower hence areduction in fine product contamination and an increase in sharpnessof cut. In summary, a validated computational methodology formodelling flow in a classifier has been presented. This sets a basis forimplementing two phase computations using the Eulerian–Lagrang-ian approach, where the coal particle trajectory can be tracked withinthe initial continuous phase solution at discrete points throughout themodel.

Acknowledgements

Support for the research was provided by The Greenbank GroupUK Limited. The authors are grateful for their financial and technicalassistance in developing the experimental facility at the University ofLeicester.

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