Efficiency Circulating Air Classifier

Available online at www.sciencedirect.com

Chemical Engineering and Processing 47 (2008) 1887–1900

A reduced efficiency approach-based process modelfor a circulating air classifier

C. Eswaraiah, S.S. Narayanan, S. Jayanti ∗

Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India

Received 2 November 2006; received in revised form 20 October 2007; accepted 22 October 2007

Available online 21 February 2008

Abstract

The performance of a circulating air classifier has been studied systematically by conducting experiments over a range of process and design

variables. It is found that while the overall induced flow rate is proportional to the speed of rotation of the wheel, the circulation pattern inside

the classifier depends on the configuration of stationary guide vanes. This is found to have a significant effect on the range of operability of the

classifier. Results show that the cut size, sharpness of separation and the bottom and top size selectivity increments are influenced strongly by the

stationary guide vane configuration. Using experimental data from a dense material (fly ash) and a light material (rice husk), a model based on the

reduced efficiency curve approach, originally proposed for hydrocyclones, has been developed to predict the performance of the classifier.

© 2007 Published by Elsevier B.V.

Keywords: Size classification; Circulating air classifier; Grade efficiency; Circulation pattern; Reduced efficiency curve; Similarity

1. Introduction

Size classification is an important unit operation in which

particles of different sizes suspended in a fluid medium are

separated into coarse and fine fractions based on their settling

velocities or densities or other properties. The focus of the

present study is on the size classification of particles of the same

materials. A number of methods are available for classification.

Shapiro and Galperin [1] have reviewed the operational princi-

ples and features of a number of modern air classifiers. These

include gravitational and centrifugal classifiers of crossflow or

counterflow type; cascade classifiers; fluidized bed classifiers;

inertial air classifiers; centrifugal classifiers; rotor classifiers,

and circulating air classifiers. Of these, circulating air classi-

fiers are often used for large volume applications such as in the

power generation and cement industries where they are used

to classify coal particles and fly ash, respectively. The advan-

tage of this type of air classifiers is that it does not require

external air from a compressor or a blower for suspending the

particles; an internal fan itself induces the air flow. Varying

the rotational speed of the fan can change the strength of the

∗ Corresponding author.

E-mail address: [email protected] (S. Jayanti).

forced vortex created within the classifier and can thus be used

to change the cut size, i.e., the size of the feed particle which

has an equal probability of appearing in the coarse or the fine

fraction.

Rotating wheel air classifiers have been around for several

decades although a number of design improvements have taken

place. Rumpf and co-workers [2,3] led the early work on air

classifiers and introduced new ideas such as spiral classifiers

with rotating walls and recycling of the coarse fraction. These

enabled a better separation of the coarse and the fine fractions at

smaller cut sizes than what had been possible with other spiral

classifiers. The Alpine Mikroplex classifier incorporating these

features was the first industrial-scale classifier to achieve cut

sizes in the range of 10 �m with a reasonably high sharpness of

cut even at cut points below 5 �m [4]. Variations of the Mikro-

plex spiral classifier are available from other manufacturers. In

some, the wall rotational speed can be adjusted independently

of the fan speed, or a secondary air stream is supplied with the

feed stream to more evenly distribute the particles entering the

classification zone. In one design tested by Austin and Luckie

[5], the rotor vanes are located at the top of the classifier. The air

is supplied from the bottom. The feed along with a secondary

air stream enters the classifier in a direction perpendicular to

the primary air stream. The combined streams travel upward to

the rotor vanes. Air and the fine fraction pass through the vanes

0255-2701/$ – see front matter © 2007 Published by Elsevier B.V.

doi:10.1016/j.cep.2007.10.016

1888 C. Eswaraiah et al. / Chemical Engineering and Processing 47 (2008) 1887–1900

and exit at the top of the classifier. The coarse fraction is fed

back into the feed stream and is re-classified. In the Accucut

classifier [6,7], air is drawn into the classifier from the bottom

in the direction of the axis of rotation at a radius just outside

the vanes. Particles enter in a direction perpendicular to the air

stream and mix with the air before passing through the vanes.

The rotor vanes are centrally located in the classifier in the axial

direction and occupy nearly the entire height of the classifier.

Air and the fine fraction exit through a tube at the axis of the

classifier, while coarse particles exit tangentially. As in the case

of free vortex classifiers, many designs for rotor-vane classifiers

send the coarse fraction back into the incoming air to obtain a

sharper cut. The MikroCut MC classifier [8] is a new rotor-vane

classifier that can operate with a centrifugal acceleration of over

15,000 × g. Because of this, cut sizes of the order of 1–20 �m

for particle densities from 1000 to 4000 kg/m3 can be obtained.

Here, air and particles enter the classifier together tangentially.

A secondary air stream of varying flow rate can also be supplied

tangentially to the classifier to improve separation of the fine

fraction from the coarse fraction and to prevent particles from

being collected before entering the classification zone.

In the CFS-HD classifier [4], both free and forced vortex flows

are used for classification. Stationary guide vanes surrounding

the rotor vanes are used to create a flow of high shear in the region

where the particles are introduced so that these can be dispersed

before they enter the classification region. The coarse fraction

exits tangentially within the periphery of the guide vanes. Air

and the fines pass through the rotor vanes into an internal, rotor-

vane free area inside the rotor, where the flow is of free vortex

type. To achieve a more uniform velocity in the radial direction in

this area, the height of the inside of the rotor is made to increase

as the radius decreases. In addition, the air and the fine fraction

outlet tube is made to rotate in the same direction as the rotor and

is extended into the internal area to prevent coarse particles from

being carried to the outlet by high radial velocities near the rotor

walls. Nied [4] reported achieving cut sizes of 2 �m with this

classifier using limestone of d97 of 35 �m as the feed material.

In another class of rotating wheel air classifiers of more recent

origin such as the Sturtevant side draft classifier and the O-Sepa

separator [9] a forced draft of air is introduced to achieve a higher

efficiency of operation than what is possible without external air

supply.

While there has thus been a continuous evolution of the design

of air classifiers, systematic theoretical and modelling studies

are of recent origin. Johansen et al. [10] developed a two-phase

model for particulate flow through a continuous Accucut air

classifier. They solved for the phase fractions of both phases

using field equations in the Eulerian–Eulerian framework exist-

ing in the commercial computational fluid dynamics (CFD) code

FLUENT and included the effect of particles on the turbulence

field. The drag reduction associated with the presence of the par-

ticulate phase was incorporated into the boundary conditions.

Only a two-dimensional model of the classifier was considered

and the motion of the rotor blades was treated approximately

by assuming that they imposed a constant angular velocity

on the flow. They found that increased particle loading could

improve the performance of the classifier by damping the tur-

bulence but that high loading might alter the macroscopic flow

pattern due to momentum coupling between the two phases.

Johansen and de Silva [11] approached the problem using an

Eulerian–Lagrangian framework in which field equations were

used to obtain the carrier gas flow field and particle tracking cal-

culations were used to obtain the trajectories of particles through

the flow field subject to centrifugal, drag and other forces. A

full, three-dimensional model was constructed and the motion

of the rotor blades was taken into account exactly using a rotat-

ing coordinate system. They used the code FLUENT to simulate

the flow field, particle dispersion and grade efficiency for the

Accucut A12 classifier rotating at a rotor speed of 3200 rpm.

They found that both the flow field and the particle trajectories

were more complicated than what one obtained using simple

theories. The predicted grade efficiency was lower than the mea-

sured one for even the smallest particles and they attributed this

to particle agglomeration arising from long residence times in

the classifying zone.

Leschonski and Legenhausen [12] used similarity arguments

to measure the flow field in water flow through a deflector wheel

air classifier and showed that a free vortex sink flow was formed

in the inner bladeless region which they argued would be detri-

mental to the cut-size conditions of the classifier. Wang et al.

[13] considered the effect of the blade angle of a horizontal tur-

bine air classifier. Using a pneumatic mechanics model for the

gas velocity, they obtained an expression for the fluid speed dis-

tribution in the turbine passage ways and used this to estimate

the drag and centrifugal forces acting on the particle. Since the

gas speed was non-uniform, they argued that a range of particle

sizes would be classified and obtained an analytical expression

for the maximum classified particle cut size as a function of the

structural parameters of the classifier, the feed material and the

operating parameters. They conducted experiments in the max-

imum cut-size range of 4–10 �m with mica, calcium carbonate,

Fig. 1. Schematic line diagram of the circulating air classifier.

C. Eswaraiah et al. / Chemical Engineering and Processing 47 (2008) 1887–1900 1889

Fig. 2. Sketch of the observed secondary flow in the classifier with (a) radial vanes and (b) angular vanes.

carbon silicon and alumina as feed materials and showed that

their expression exhibited better sensitivity of the cut size to

the turbine blade angle. Bauder et al. [14] reported that CFD

simulations using a commercial code gave satisfactory results

for the cut size and the sharpness of cut for a deflector wheel

air classifier. They also conducted systematic experiments to

determine the effect of parameters (such as the wheel geome-

try, classifier size, rotor tip speed, gas throughput, product flow

rate, vortex finder diameter and test material) on the cut size and

the sharpness of separation. However, they did not present any

quantitative models.

Most of the recent studies have been conducted for classi-

fication of particle sizes of less than 10 �m [4,8,15]. Particle

sizes of the order of the 50–100 �m are of interest to cement

and power generation industries. For example, the mean size

of the coal particle used in pulverized coal boilers is typically

about 70 �m. The resulting fly ash, which is used in the cement

industry, will also be of similar size. Rotating wheel air classi-

fiers (also called circulating air classifiers [1]) are widely used in

these applications as they can handle high throughputs. Clerc et

al. [16] conducted a systematic study of pneumatic classification

of fly ash obtained from coal combustion in thermal power plants

using the Condux size classifier. They studied the effect of rota-

tional speed, ash feeding rate and air flow rate on the cut size in

a laboratory scale and in an industrial pilot scale classifier. They

found that in both classifiers the cut size depended primarily

on the rotational speed and that it was inversely proportional to

it. Benzer et al. [17] and Kolacz [15] reported on experimental,

modelling and simulation work on applications in grinding cir-

cuits. Bhasker [18] presented a CFD simulation model of a coal

classifier concentrating on the geometry creation and the treat-

ment of the flow problem. Recently, the present authors [19]

conducted a detailed study of the flow field in a circulating gas

type rotating wheel air classifier used in thermal power plants

for fly ash classification.

Circulating air classifiers offer an attractive alternative to gas

cyclones for a range of classification duties. Although a number

of designs have been studied, much of the reported work has

been focused on cut sizes less than 10 �m. Also, the linkage

between the flow and the resulting classification has not been

fully explored. CFD-based studies have been used primarily to

shed light on the classification process while process models

have concentrated on the determination of only the cut size. The

work reported in the present paper builds on the hydrodynamic

study of a rotating wheel air classifier investigated previously

[19] using computational fluid dynamics simulations. System-

atic classification experiments have been done over a range

of parameters, and their effect on the classification has been

interpreted in the light of the insight gained into the hydro-

dynamics of the flow. Using the present experimental data, a

model incorporating appropriate dimensionless groups has been

developed to predict the performance of a similar classifier.

Details of these studies and the results obtained are discussed

below.

Table 1

(a) Range of operating parameters for the fly ash experiments

Vane

configuration

Feed size,

d80 (�m)

Guide vane

angle, θ (◦)

Fan speed,

N (rpm)

Feed flow rate,

Wo (kg/h)

Radial 190 5 400 75

30 600 125

60 800 175

Angular 190 5 1200 75

30 1450 125

60 1700 175

(b) Range of operating parameters for the rice husk experiments

Radial 200 5 300 10

350 15 400 20

550 30 500 40

Angular 200 5 1200 10

350 15 1450 20

550 30 1700 40


2. Experimental set-up

A schematic line diagram of the rotating plate circulating air

classifier used in the present study is shown in Fig. 1. It con-

sists of a cylindro-conical tank of overall height of 0.95 m and

a cylindrical diameter of 0.51 m. Air circulation is induced with

the help of 16 fan blades mounted on its vertical axis towards

the top of the cylinder. A set of six semi-cylindrical guide vanes

and a horizontal circular plate are mounted further down on the

same axis and rotate along with the fan blades at the same speed.

The angle of the guide vanes measured from the tangent can be

changed from 13◦ to 89◦. The air flow within the classifier can

also be altered using bottom stationary vanes located at the top

of the conical section. Two types of these stationary vanes are

used: the radial vanes, in which the guide vanes make an angle of

90◦ with the circumferential direction, and the angular vanes, in

which the guide vanes make an angle of 30◦ with the circumfer-

ential direction. The solids are fed using a vibratory feeder into

an annular space between the stationary and the rotating shaft.

They fall on to the rotating plate and are discharged radially

into the cylindrical portion of the classifier. Coarse particles fall

against the entering air stream and then move downwards into

the conical section to be discharged through the air inlet pipe for

collection in a filter bag. Fine particles are carried along with the

air flow into the annular region of the classifier and are collected

at the air outlet in another filter bag. A continuously variable

voltage auto transformer can be used to control the speed of the

fan between 0 and 2400 rpm.

The flow field induced in this classifier by the rotation of the

fans (the wheel rotation itself induces little flow) has been studied

previously [19] with the help of CFD simulations and flow visu-

alization experiments. These indicate two major features of the

flow, namely that the velocity is predominantly circumferential

and that a strong recirculation is set up in the upper (cylin-

drical) part of the classifier. The overall velocity field scales

linearly with the fan speed. The direction of the recirculating

flow depends strongly on the type of bottom stationary guide

vanes used. For radial vanes, the secondary flow is such that it

flows upwards in the outer, annular part and comes down in the

inner cylindrical part (Fig. 2a). For angular vanes, the secondary

flow is downwards in the outer, annular section and upwards in

the inner, cylindrical section (Fig. 2b). CFD simulations show

that the volumetric flow rate in this recirculation is twice as much

as the net flow rate through the classifier. Due to the large flow

cross-sectional area, the induced axial velocity in the cylindri-

cal section (where the feed is introduced) is only of the order of

2–3% of the tip speed. However, even this small axial velocity

is sufficient to entrain small particles.

The mechanism of classification is as follows. The flow is

primarily circumferential and the particles move at speeds close

to the local gas velocity. Hence the inertia of the particles is

directly proportional to their size. Classification into coarse and

fine fractions is effected principally by the drag force arising

from the secondary flow. With a stationary angular vane config-

uration, the secondary flow is upwards in the inner cylindrical

section. Small particles thrown off by the rotating disc feeder

are entrained by this secondary flow and are taken into the outer

annulus and eventually to the fine fraction stream. Large parti-

cles which resist this upward drag will come down into the inner

conical section and fall against the incoming air to be collected

as the coarse fraction. For a radial stationary vane, the secondary

flow is downwards in the inner cylindrical section. Hence even

the small particles are brought down. As they reach the gap

between the cylindrical and the conical parts of the classifier,

they are entrained by the secondary flow and are carried into

the outer annulus and eventually into the fine fraction. Coarse

particles, which have a larger inertia, remain in the central part

and eventually report to the coarse fraction. However, the actual

behaviour is complicated by the presence of centrifugal force

and particle–wall collisions, as we shall see later.

Fig. 3. Typical results obtained from the classification experiments using fly

ash: (a) actual efficiency curves for radial vanes, (b) the corrected efficiency

curves, and (c) the mass flow split for radial and angular vanes.


Size classification experiments were conducted using fly ash

obtained from the Ennore Thermal Power Station, Chennai,

India, and rice husk obtained from the Research & Develop-

ment Division of Bharat Heavy Electricals Limited (BHEL),

Tiruchirapalli, India. Both were acquired in bulk quantity to

avoid day-to-day variations at the source. The fly ash had a d80

of 190 �m while the rice husk had a d80 of 550 �m. By further

size reduction using pin and ball mills, two further samples of

rice husk, having d80 of 350 and 200 �m, were also prepared.

The density of the feed material was measured using the stan-

dard specific gravity bottle method and by the Helium–Mercury

Pycnometer. While the density of the fly ash was found to be

independent of the particle size, the bulk density of the three rice

husk samples was found to vary by about 20% (presumably due

to variation in the silica content). The bulk density of rice husk

of d80 of 550 �m was 1050 kg/m3 while that of 200 �m was

1250 kg/m3. Therefore, the actual density based on the sample

size was used in the analysis.

In a typical classification experiment, the feed was sampled

using the coning and quartering method. One kilogram of the

material was fed to the classifier using a vibratory feeder at a

pre-determined feed rate. The classifier was set with the desired

configuration which is defined by the angle of the rotating guide

vanes, the configuration (angular or radial) of the stationary bot-

Fig. 4. Variation of the actual cut size d50a for (a) the radial and (b) the angular

vane configuration for fly ash at a rotating guide vane angle of 13◦.

tom vanes and the rotational speed of the wheel (which was

regulated by a variac). The rotational speed was maintained

for several minutes after the feeding was completed in order

to ensure that all the material was classified. The material col-

lected in the fine and the coarse streams was weighed separately

and three samples of each were analyzed for size using a vibra-

tory sieve shaker in the size range of 40–800 �m. The vibratory

feeder used in the present study has a capacity of 200 kg/h of

fly ash and 80 kg/h of rice husk. Typically, the mass fed and the

mass collected in the two streams agreed to within 1%. Similar

tallying was also found to apply in the sieve analysis. Thus, the

overall split into coarse and fine streams and the size distribution

of each stream were found to be repeatable to within a few per

cent. The range of parameters in which the size classification

experiments were conducted is summarized in Table 1.

3. Results from the classification experiments

From known size distributions of the feed, the fines and the

coarse fractions, the actual efficiency of classification can be

calculated [20] as

Ea =mc �φc

mc �φc + mf �φf(1)

Fig. 5. Variation of the mass split M for (a) the radial and (b) the angular vane

configuration for fly ash at a rotating guide vane angle of 13◦.


where mc is the mass of the material collected in the coarse

fraction, mf is the mass of material collected in the fine fraction

and �φc and �φf are the cumulative size distribution interval of

the coarse and the fines, respectively. The grade efficiency curve

obtained for a specific case, namely for fly ash at a feed flow rate

of 175 kg/h with radial stationary vanes and a rotating guide vane

angle of 13◦ is shown in Fig. 3a for rotational speeds of 400 and

800 rpm. It can be seen that the actual grade efficiency curve has a

distorted S-shape. The cut size, d50, is the size at which the actual

efficiency is 50%. For the above case, the cut size is found to be

73 and 89 �m for wheel speeds of 400 and 800 rpm. All particles

below this size can be termed as fine particles and those larger

can be termed as coarse particles. Fig. 3a shows that, contrary to

expectation, a significant fraction of the “fine” particles report

to the coarse stream and that the grade efficiency of the lowest

measured size is not zero. Similarly, not all the largest particles

report to the coarse stream and their collection efficiency is not

100%. These two non-ideal effects, namely the fraction of the

finest particles entering the coarse stream, and the fraction of the

largest particles not entering the coarse stream, are expressed as

the bottom size selectivity increment (�Sb) and the top size

selectivity increment (�St), respectively. For the specific cases

shown in Fig. 3a, these have values of 16 and 4%, respectively,

at a rotational speed of 400 rpm and 18 and 7% at a speed of

800 rpm, respectively. The grade efficiency curves in Fig. 3a

Fig. 6. Variation of the bottom size selectivity increment �Sb for (a) the radial

and (b) the angular vane configurations for fly ash at a rotating guide vane angle

of 13◦.

can be adjusted such that the grade efficiency varies between 0

and 100% by evaluating a “corrected grade efficiency” as

Ec =Ea − �Sb

(1 − �Sb − �St)(2)

and is plotted in Fig. 3b. The corrected cut size, d50c, based on

the corrected grade efficiency, is 80 �m for 400 rpm and 98 �m

for 800 rpm.

A further measure of classification is the mass flow split (M)

between the coarse and the fine streams defined as the ratio of

mass of material collected in the coarse stream to that collected

in the fines stream:

M =mc

mf(3)

When M is large, most of the feed reports to the coarse stream

and when M is small, most of it reports to the fines stream.

Classification is not effective when M is either too small or too

large. However, this does not mean that the material is wrongly

classified. Even an ideal classifier may give very large or very

small values of M if the feed size distribution is highly skewed

with respect to the cut size. The measured variation of M with

rotational speed is plotted in Fig. 3c for fly ash at a feed rate

of 175 kg/h for radial and angular vanes. For the given feed and

geometry of the classifier, the radial vane configuration can be

operated only below 800 rpm. Above this speed, nearly all the

Fig. 7. Variation of the top size selectivity increment �St for fly ash with (a)

the radial and (b) the angular vane configuration at a rotating guide vane angle

of 13◦.


material is collected in the fines stream, presumably due to the

combined, unilateral action of the drag and the centrifugal forces

in pushing the particles into the outer annular region. With the

angular vane configuration, the centrifugal and the drag forces

at the cylindro-conical section oppose each other and appear to

be finely balanced so that the mass split is not very sensitive to

the speed in the range of 1200–2400 rpm.

Details of the variation of the four parameters of classifica-

tion, namely d50a, M, �Sb, �St, are shown in Figs. 4–7. The

measured variation of the actual cut size (d50a) is plotted as a

function of the fan speed in Fig. 4 for the two stationary vane con-

figurations. The feed material was fly ash with a d80 of 190 �m

and the rotating guide vanes were fixed at an angle of 13◦ with

the tangent. It can be seen that, in both cases, the d50a increases

as the fan speed increases although the effect is more prominent

for the radial vanes. This can be attributed to the stronger sec-

ondary flow at higher wheel speeds which enables even larger

particles to be carried out into the annulus (and ultimately into

the fine fraction) through the gap between the cylinder and the

cone sections. This shifts the efficiency curve towards the right

leading to an increase in d50a. The effect is more pronounced

for the radial vanes where the drag and the centrifugal forces act

in the same direction (radially outward) at the cylindro-conical

section where the classification takes place. The effect of fan

speed on the mass split, M, is compared in Fig. 5. With radial

vanes, a stronger secondary flow, created by a higher fan speed,

would carry more particles into the fines stream and the mass

split therefore decreases (Fig. 5a) as the fan speed increases.

With the angular vane, although stronger secondary flow carries

more particles into the outer annular region, it would also drag

a larger number of them back into the inner cylindrical section.

Hence the net effect is more muted.

The effect of the wheel speed on the bottom and the top

size selectivity increments is subtler. The bottom size selectiv-

ity increment, �Sb, is due to the fine particles reporting to the

coarse stream. Increasing the wheel speed increases the drag

force which pushes the fine particles into the fine stream. Since

the fine particles would require only a small amount of drag force

to entrain them, it is to be expected that increasing wheel speed

would have only a little effect on �Sb (Fig. 6). For an angular

vane, the secondary flow is such that it would bring small parti-

cles into the inner cylindro-conical section. This is reflected in

the high values of �Sb for the angular vanes as compared to the

radial vanes. A reverse trend is seen in the top size selectivity

increment, �St, which is higher for radial vanes than for angular

vanes (Fig. 7). Non-zero values of �St arise because large par-

ticles report to the fines stream. Normally, one would not expect

the largest particles to be affected by the drag to such an extent

that they would report to the fines stream. This can therefore

be attributed to particle–wall interaction. With radial vanes, if a

large particle is deflected slightly from its circular path, then it

will hit the radial vane at such an angle that upon rebounding it

will move into the fines stream (Fig. 8a). For angular vanes, the

angle of impact is such that the rebounding large particle will

remain in the central section (Fig. 8b) and will eventually report

to the coarse fraction.

Fig. 8. Effect of particle–stationary vane interaction for (a) radial vanes and (b) angular vanes.


Fig. 9. Measurements of the induced air flow rate: (a) flow rate variation with fan speed at a rotating guide vane angle of 13◦, (b) effective gap width for secondary

flow as a function of the rotating guide vane angle, and (c) effect of rotating guide vane angle on induced flow rate.

Thus, the classifier exhibits different performance with the

two stationary vanes. The cut size as well as the bottom and top

selectivity increments (which determine how much of the very

coarse and of the very fine particles are being wrongly classified)

is affected by the specific values of the variables set in each run.

This influence can be directly related to the flow pattern inside

the classifier set-up by the geometrical features. In view of this

linkage, a model is developed below to predict the performance

of a geometrically similar classifier.

4. Development of a mechanistic model

The objective of the model is to predict the actual efficiency

curve for a geometrically similar machine for a given set of

parameters which have been shown to influence the perfor-

mance, namely the feed material, the feed size and flow rate,

the fan speed, the type of stationary vanes and the angle of

the rotating vanes. The model developed consists of two ele-

ments: a flow model and a classification model. In the flow

model, correlations in terms of dimensionless variables have

been developed based on flow rate measurements to determine

the induced flow rate and the power consumed to maintain

a constant flow rate. In the classification model, the reduced

efficiency curve approach [21] has been used, along with dimen-

sionless correlations to determine d50c, �Sb and �St, to predict

the actual grade efficiency. Details of these models are given

below.

4.1. Flow model

The dynamic forces acting on the particle, namely the cen-

trifugal force and the drag force, are closely related to the

velocity field. Hence the induced flow rate through the clas-

sifier was measured using a flow meter for radial and angular

vanes for different positions of the rotating guide vane angles.

The results are shown in Fig. 9. Since radial vanes offer more

resistance to circumferential flow, the induced flow rate is less as

compared to the angular vanes at the same fan speed. A smaller

rotational guide vane angle means that it offers more resistance

to flow by reducing the cross-section available to flow (Fig. 9b)

and the induced flow rate increases as the rotating guide vane

angle increases (Fig. 9c). Previous CFD simulations [19] have

shown that the velocity field in the classifier scales well with the

tip speed of the fan and that the pressure field scales with the

dynamic pressure based on the tip speed of the fan. Keeping in

mind the similarity variables used in turbomachinery applica-

tions, the induced volumetric flow is correlated to the fan speed,

N, and the angle of the rotating vane, θ, and the angle of the

stationary vane angle, ϕ, which is 90◦ for the radial vanes and

30◦ for the angular vanes. The effect of constriction of the flow


cross-sectional area by the rotating guide vanes (Fig. 9b) can be

included in the form of the dimensionless parameter (ge/gmax)

or (1 − (l sin θ/gmax)) where l is the length of the guide vane and

ge and gmax are the effective and maximum gap widths, the latter

being given by gmax = (Dc − Dfp)/2, where Dc and Dfp are the

diameters of the cylinder and the feed plate respectively. The

volumetric flow rate and the fan speed are non-dimensionalized

in the form of flow number, NQ, and Reynolds number, NRe,

defined as NQ = Qa/ND3 and NRe = ND2ρa/µa, where N is the

wheel speed in revolutions per second, D is the diameter of the

fan blade (0.35 m), Qa is the volumetric flow rate in m3/s and

ρa and µa are the density and viscosity of air. Regression anal-

ysis of the present data yields the following correlation for the

induced air flow rate:

NQ = K1(ϕ)(NRe)0.85

(

ge

gmax

)0.1

(4)

where

K1 = 4.8 × 10−7, for ϕ = 30◦ (angular vane)

K1 = 2.5 × 10−7, for ϕ = 90◦ (radial vane)

Fig. 10. Measured variation of power consumption with fan speed for radial and

angular vanes.

Fig. 11. Measured reduced efficiency curve for fly ash as a function (a) fan speed in rpm, (b) feed mass flow rate in kg/h, (c) type of stationary guide vanes, and (d)

rotating guide vane angle in degrees.


Fig. 12. Measured reduced efficiency curve for rice husk as a function of (a) fan speed in rpm, (b) feed mass flow rate in kg/h, (c) type of stationary guide vanes, (b)

angle of rotating guide vanes in degrees, and (e) feed size in microns.

The above correlations are valid in the following ran-

ges.

0.001 ≤ NQ ≤ 0.02;

50, 000 ≤ NRe ≤ 250, 000;

0.25 ≤ge

gmax≤ 0.75

4.2. Power consumption

Power consumption studies for the rotating wheel classifier

have not been reported in the literature. In view of this, exper-

iments were conducted in which the power consumed to keep

the fan rotating at a certain speed was measured directly from

an energy meter connected to the power supply unit. There was

no feed of the solid material during these measurements. The


fan speed was maintained constant, typically for 30 min, and the

energy meter readings were taken for every 10 min. A nearly

linear variation of the power consumption (Fig. 10) with the fan

speed was obtained for both stationary bottom vane configura-

tions. As expected, the power consumption (P) for the radial

vanes was higher by 20–30% over the angular vanes at the same

fan speed. Following the practice in turbomachinery, the follow-

ing non-dimensional correlation between the flow number (NQ)

and the power number (NP) has been developed:

NP = K2(NQ)−3.33 (5)

where

K2 = 2.2 × 10−8 for the radial vane

= 7.9 × 10−8 for the angular vane

and

NP =P

N3D5ρ

4.3. Classification model

Early studies by Lynch and co-workers [21–23] showed that

for a hydrocylone, an appropriate non-dimensionalization, from

the point of view of performance prediction, of the grade effi-

ciency curve is the reduced efficiency defined as

Ec

(

d

d50c

)

=

[

exp(αd/d50c) − 1

exp(αd/d50c) + exp(α) − 2

]

(6)

where α is the reduced efficiency curve parameter. It was found

(see, for example, Lynch and Rao [22]; Lynch and Narayanan

[23]) that the reduced efficiency curve was invariant with design

and operating conditions and that the parameter α was a func-

tion of the feed material. Since the rotating wheel classifier

shares some features of the hydrocyclone, the applicability of the

reduced efficiency curve approach to air classifiers was investi-

gated using the present data. The measured corrected efficiency

for fly ash is plotted in Fig. 11 for different fan speeds (Fig. 11a),

Fig. 13. Reduced efficiency curves obtained from the present data for fly ash

(thick line, α = 4.3) and rice husk (thin line, α = 2.5).

different feed rates (Fig. 11b), different stationary vane con-

figurations (Fig. 11c) and different rotating guide vane angles

(Fig. 11d). The corresponding data obtained for rice husk is

plotted in Fig. 12; in addition, the data obtained for different

feed sizes is plotted in Fig. 12e. These plots confirm that, within

the range of parameters varied in the present study and within

the bounds of experimental uncertainty, the reduced efficiency

curve is dependent largely on the feed material and not on other

parameters. In view of this, the consolidated results for fly ash

and rice husk were used to estimate the reduced efficiency curve

parameter, α, and it was found (Fig. 13) that values of 4.3 and

2.5 gave a good fit to the data for fly ash and rice husk respec-

tively. It is noted [21] that these values are consistent with those

expected for dense and light particles, respectively.

The corrected efficiency curve can now be calculated from

the reduced efficiency curve if d50c is known. The present exper-

Fig. 14. Algorithm for optimization using the present process model.


Fig. 15. Comparison between measured and predicted grade efficiency curve

for fly ash (a) for a radial vane configuration at a fan speed of 700 rpm and a

feed flow rate of 75 kg/h and (b) for an angular vane configuration at a fan speed

of 1300 rpm and a feed flow rate of 125 kg/h.

imental data have been used to develop a correlation for d50c in

terms of dimensionless numbers involving rotating guide vane

angle, stationary bottom vane configuration angle and feed mate-

rial. It was found that the data from different feed materials could

be combined using the dimensionless group ρs/ρa, i.e., the ratio

of the particle density to the fluid (air) density. Similarly, the data

from different particle size groups could be brought together by

including d80 of the feed size in the correlation. Regression anal-

ysis of the present data consisting of 188 data points resulted in

the following dimensionless correlation:

d50c

d80= 0.48

(

ρs

ρa

)0.10(ge

gmax

)0.40

(sinϕ)0.9

(

wo

ma

)−0.25

(7)

Here,wo and ma are the mass flow rates of the solid feed (which is

an independent variable) and of the air, respectively. The induced

air flow rate is calculated using the correlation developed earlier.

Comparison with the data shows that the correlation predicts the

data of d50c with a mean relative error of 25%.

Finally, the actual efficiency curve can be reconstructed if the

top and the bottom size selectivity increments are known. Using

the present data, the following correlations have been developed

to calculate these:

�Sb = 50

(

ρs

ρa

)−0.30(ge

gmax

)−0.10

(sinϕ)−0.8

(

d50c

d80

)−0.40

(NQ)−0.2 (8)

�St = 400

(

ρs

ρa

)−0.50(ge

gmax

)0.15

(sinϕ)0.50

×

(

d50c

d80

)0.70(wo

ma

)−0.10

(9)

These correlations are valid in the following range of dimen-

sionless groups:

850 ≤ρs

ρa≤ 2050; 0.25 ≤

ge

gmax≤ 0.75;

0.14 ≤wo

ma≤ 64; 0.5 ≤ sinϕ ≤ 1.0;

0.001 ≤ NQ ≤ 0.02; 0.27 ≤d50c

d80≤ 1.15

4.4. Verification of the model

The overall algorithm for the prediction of performance of a

geometrically similar machine is as follows. For given geometric

and feed information, namely D, d80 and ρs, a suitable value of

α (4.3 for a dense medium such as fly ash and 2.5 for a light

material such as rice husk) is chosen and the reduced efficiency

Table 2

Operating conditions for the additional experiments carried to verify the model

S. No. Feed material Feed rate (kg/h) Fan speed, N (rpm) Guide vane angle, θ (◦) Feed size, d80 (�m) Bottom vane angle, ϕ (◦)

1 Fly ash 75 500 13 190 90

2 Fly ash 75 600 13 190 90

3 Fly ash 75 700 13 190 90

4 Fly ash 125 1300 13 190 30

5 Fly ash 175 1300 13 190 30

6 Fly ash 125 1600 13 190 30

7 Fly ash 175 1600 13 190 30

8 Fly ash 125 1800 13 190 30

9 Rice husk 20 1300 13 200 30

10 Rice husk 40 1300 13 200 30


Fig. 16. Comparison with the verification data of (a) d50a, (b) �Sb, and (c) �St.

curve is plotted using Eq. (6). For specified operating parameters,

namely N, D, θ, φ and feed rate, correlations (7)–(9) are used to

calculate d50c, �Sb and �St. Using these, the actual efficiency

for a specific particle size, d, can be calculated as

Ea(d) = �Sb + Ec(d)(1 − �Sb − �St) (10)

From the actual efficiency curve, the parameters of practical

interest such as d50a, mass split, the fraction of coarse particles

(of size greater than a given size as per process interest) in the

fines stream and the fraction of fines (of a size less than a given

size of process interest) in the coarse stream, etc. can be calcu-

lated. The power consumption corresponding to these conditions

is calculated from Eq. (5). If the performance is not satisfactory,

then the selected operating conditions can be revised to obtain

a more desirable performance. This process is summarized in

Fig. 14.

The flow model for the classifier is based on similarity

principles established for turbomachinery applications and is

therefore expected to be applicable for a geometrically simi-

lar classifier. The reduced efficiency curve approach, which is

a non-dimensional representation, is also expected to be a dif-

ferently sized but similar machine. Although the correlations

used to quantify the classification parameters (Eqs. (7)–(9)) are

in terms of dimensionless groups, they are not strictly based on

similarity analysis. However, it has not been possible to conduct

experiments on a different classifier to validate the complete

model. In order to verify the accuracy of process model, addi-

tional experiments have been conducted on the same machine.

The operating conditions for these 10 runs are summarized in

Table 2 and these cover the variation in the principal variables,

namely fan speed, feed material, feed rate and the stationary vane

configuration. The speeds used in these experiments are differ-

ent from those used in the earlier experiments. Some of the feed

rates are also different. Thus, this constitutes an independent set

of data, although it is obtained from the same experimental set-

up. The actual efficiency curve calculated using the above model

is compared in Fig. 15 for two cases and Fig. 16 compares the

predicted d50a, �Sb, and �St. Reasonably good agreement is

obtained between the two. Since the model is formulated in terms

of important dimensionless groups, it can be recommended for

application to a geometrically similar machine in the following

range of dimensionless parameters:

850 ≤ρs

ρa≤ 2050; 0.25 ≤

ge

gmax≤ 0.75;

0.14 ≤wo

ma≤ 64; 0.5 ≤ sin ϕ ≤ 1.0; 0.001 ≤ NQ ≤ 0.02;

0.27 ≤d50c

d80≤ 1.15; 50, 000 ≤ NRe ≤ 250, 000.


Additional experiments will have to be conducted on a

different classifier with different materials and parameters to

completely validate the model.

5. Conclusions

Classification data have been obtained for a light, flaky mate-

rial (rice husk) and a dense material (fly ash) under two different

flow conditions in a circulating air classifier. The speed of rota-

tion of the fan, the feed flow rate and the angle of rotating guide

vanes have been varied over a range. The obtained grade effi-

ciency curves showed a systematic variation consistent with the

prevailing flow pattern.

Analysis of the classification data showed that the reduced

efficiency curve approach used to parametrize hydrocylone data

is applicable to the present classifier. The values of 2.5 and 4.3

have been obtained for the reduced efficiency curve parameter

(α) for fly ash and rice husk, respectively. A model based on

correlations in terms of relevant dimensionless groups has been

proposed; additional experiments using different size classifiers

and different materials will have to be conducted to obtain a

validated model to predict the performance of a geometrically

similar classifier.

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Documents

Efficiency Circulating Air Classifier