numerical simulation of the in-line pressure jig unit in coal preparation minerals engineering

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Numerical simulation of the in-line pressure jig unit in coal preparation

K.J. Dong a, S.B. Kuang a, A. Vince b, T. Hughes c, A.B. Yu a,*

a Lab. for Computer Simulation and Modelling of Particulate Systems, University of New South Wales, NSW, Australiab Elsa Consulting Group Pty Ltd., Queensland, Australiac Gekko Systems, Victoria, Australia

a r t i c l e i n f o

Article history:Received 18 August 2009

Accepted 21 October 2009

Keywords:

Gravity concentration

Classification

Coal

Computational fluid dynamics

Discrete element method

a b s t r a c t

This paper presents a numerical study of the multiphase flow in an in-line pressure jig (IPJ), which is ahigh yield and high recovery gravity separation device widely used in ore processing but may have poten-

tial in coal preparation. The mathematical model is developed by use of the combined approach of com-

putational fluid dynamics (CFD) for liquid flow and discrete element method (DEM) for particle flow. It is

qualitatively verified by comparing the calculated and measured results under similar conditions. The

effects of a few key variables, such as vibration frequency and amplitude, and the size and density of rag-

ging particles, on the flow and separation performance of the IPJ are studied by conducting a series of

simulations. The results are analyzed in terms of velocity field, porosity distribution and forces on parti-

cles. The findings would be helpful in the design, control and optimisation of an IPJ unit.

2009 Elsevier Ltd. All rights reserved.

1. Introduction

The use of jigging machinery for the classification and benefici-

ation of ore has a long history. Classic jigging units characteristi-

cally dilate the particle bed by an upward blast of water caused

by the movement of a remote piston through a screen. Particles

of different densities are then likely to segregate when they settle.

Repeating such an operation makes the lighter particles remain on

the top layer and the heavier particles drop down to the bottom

layer. These particles can then be collected at either end to meet

specific product requirements. These units were popular during

and prior to the 1980s. In the 1990s, the jigging unit was improved

by incorporating a centrifugal action in the unit (Beniuk et al.,

1994). However, recent technological developments have resulted

in jigging technology becoming an even more sophisticated tool of

classification. For example, the invention of the in-line pressure

jig (IPJ) resulted in a more sophisticated classifier and can achieve

even higher levels of efficiency. When using this method, a screen

is moved up and down in a cyclic manner by means of a hydrauli-

cally powered servo that is mechanically linked to the screen.

Moreover, the entire process occurring in a confined pressurized

environment, adding a new dimension of security to the unit.

During the last decade, the IPJ has grown extensively in its tech-

nology in applications in the metalliferous industry. More recently,

it is being considered as an alternative means to the dense medium

cyclone for processing coal particles in large size ranges (0.25

30 mm). Some pilot scale tests have been performed to investigate

the effects of the operational conditions for optimization of the

control of IPJ in such separations (Vince et al., 2007). However,

due to the complicated nature of the system and the number of

the parameters involved, the full optimization through experimen-

tal studies is not an easy task. The lack of the fundamental under-

standings of such processes is the key motivation for a theoretical

study.

There are few fundamental studies on the classification mecha-

nism of the jigging devices in the current literature. Steiner (1996)

studied the classical jigging device with only bare basics being de-

bated. Galvin et al. (2002) and Mishra and Adhikari (1999) investi-

gated the water flow in the jigging process in a simple geometry.

Nesbitt et al. (2005) discussed only the effects of vibrating condi-

tions on the jigging process in IPJ, although other parameters such

as the properties of the ragging particles on the screen are also very

critical.

In principle, the bulk behavior of particles in a system depends

on the collective outcome of the interactions between individual

particles, particles and boundary walls, and particles and fluid.

Therefore, an investigation of the particle flow inside an IPJ on a

particle scale should provide insight into the classification mecha-

nism of the unit. Experimentally, such an investigation is challeng-

ing because the access to an IPJ is difficult being a confined

pressurized unit. However, numerical simulation based on the

so-called discrete element method (DEM) (Cundall and Strack,

1979) provides an effective away to perform such studies. This

method has been applied in the study of particlefluid flow pro-

cesses in various industrial processes and is shown to be very

0892-6875/$ - see front matter 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.mineng.2009.10.009

* Corresponding author. Tel.: +61 2 93854429; fax: +61 2 93855956.

E-mail address: [email protected] (A.B. Yu).

Minerals Engineering 23 (2010) 301312

Contents lists available at ScienceDirect

Minerals Engineering

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / m i n e n g
http://dx.doi.org/10.1016/j.mineng.2009.10.009mailto:[email protected]://www.sciencedirect.com/science/journal/08926875http://www.elsevier.com/locate/minenghttp://www.elsevier.com/locate/minenghttp://www.sciencedirect.com/science/journal/08926875mailto:[email protected]://dx.doi.org/10.1016/j.mineng.2009.10.009


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useful in understanding the fundamentals (Zhu et al., 2007, 2008).

In particular, it has been adapted for modeling the vibrating

screening process (Dong et al., 2009).

In this work we present a three-dimensional CFDDEM model,

which is capable of simulating an IPJ unit. The model is validated

by comparing the calculated and measured results under similar

conditions. The effects of a few key variables on the flow and sep-

aration performance of the IPJ are studied by conducting a series of

controlled numerical experiments, the key variables being the

vibration conditions and properties of ragging particles. The

numerical results are analyzed in terms of forces on particles, par-

ticle and fluid velocities and porosities of the particle bed, which

present a better understanding on the particlefluid flow in the

IPJ unit.

2. Model description

Fig. 1a shows the working principle of the IPJ schematically. For

confidential reasons, the detailed dimensions are not given here.

The whole unit is sealed, hutch water and slurry (including water

and coal particles) is pumped in. Ragging particles are put onto

the screen. The upper part of the IPJ, including the upper part of

the inner chamber with ring shape apertures on the wall, the

screen and the feeding bowl, is continuously vibrated with jig-

saw motions. Coal particles are fed from the top tube into the IPJ,

and they either flow out through the apertures on the inner wall

and then to the product outlet, or pass through the screen and dis-

charged from the tails or reject outlet.

A coupled CFDDEM model is developed here to model the sys-

tem. In DEM, the particle flow is treated as a discrete phase, and

the translational and rotational motions of particles are deter-

mined by Newtons law of motion, which can be written as

midvidt

fpf;i Xki

j1

fc;ij fd;ij mig 1

and

Iidxidt

Xki

j1

Tij 2

where mi, Ii, ki, vi, and xi are, respectively, the mass, momentum of

rotational inertia, number of contacting particles, translational and

rotational velocities of particle i; ffp,i and migare the force between

particle and fluid and gravitational force, respectively; and fc,ij and

fd,ij, and Ti,j are the contact force, viscous contact damping forceand torque between particles i and j. These individual interaction

forces and torques are summed over the ki particles in interaction

with particle i. The particleparticle or particlewall contact force

is calculated according to non-linear models commonly used in

DEM, as recently reviewed by Zhu et al. (2007). The particlefluid

interactions include the buoyancy force and the drag force. The drag

force is calculated according to Di Felices correlation (1994). The

equations used to calculate the forces and torques involved in

Eqs. (1) and (2) can be found elsewhere (Dong et al., 2008; Kuang

et al., 2008).

In CFD, the water flow is treated as a continuous phase and

modeled in a way similar to the one in the conventional two-fluid

modeling. Thus, its governing equations are the conservation of

mass and momentum in terms of local mean variables over a com-putational cell, given by

r qfu 0 3

and

r qfuu rP r s qfg Fpf 4

where q, u, P and Fpf are, respectively, the fluid density, velocity,

pressure, and the volumetric forces between particle and fluid; s

is fluid viscous stress tensor, calculated according to standard ke

turbulent model.

DEM is solved by an object-oriented-programming based in-

house code which can handle dynamic and complex boundaries

and calculate the fluidparticle forces with the fluid flow field

introduced from CFD simulation (Dong et al., 2008). The model

has been successfully used in the simulation studies of complicated

(a) (b)

Fig. 1. (a) Schematic representation of in-line pressure jig and (b) the mesh used in CFD.

302 K.J. Dong et al./ Minerals Engineering 23 (2010) 301312


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screening processes (Dong et al., 2009). CFD is solved by the com-

mercial package fluent. The IPJ unit contains a big number of par-

ticles and CFD meshes and generally takes about 30 s to achieve

steady-state coal separation. Because of computational limit at this

stage of development, only 1/8th sector of the region around the

actual jigging area of the in-line pressure jig is considered in the

DEM simulations as shown in Fig. 2. However, the full unit is sim-

ulated in CFD, assuming that the water flow is a steady-state flow

and is not affected by screen vibration. The water flow over screen

is largely axisymmetric. To eliminate inconsistencies between CFD

and DEM due to the use of different computational domains, fluid

field is first averaged over all the sectors before being used to cal-

culate particlefluid forces. In addition, porous medium instead of

two-way coupling of CFD and DEM is used here to account for ef-

fects of particles on the fluid flow. In this work, porous medium is

only applied to the region over the screen as shown in Fig. 3, where

the solid loading is relatively high and its water flow is crucial to

the coal separation. The porous medium considers the presence

of particles in fluid by the addition of volumetric particlefluid

forces to the standard momentum equations of fluid. Here, the vol-

umetric forces are estimated according to Ergun equation, which

are composed of two parts: a viscous loss term and an inertial loss

term:

Fpf lf

au C

1

2qfjuju 5

where a d2p

150

e3f

1ef2; C

3:5dp

1ef

e3f

; ef is porosity, which is set to 0.45

in Zone I and 0.7 in Zone II according to the particle configuration, in

a preliminarily numerical experiment; and dp is the maximum par-

ticle size in each zone.

3. Simulation conditions

In this work, the simulation is based on a laboratory-scale IPJ

unit and the conditions used in the related work (Vince et al.,

2007). The water flow rates at the inlets and outlets are specified

according to the experimental measurements. The actual size dis-

tribution of coal particles (from 2 to 6 mm) is simplified to 3 differ-

ent sized particles (2, 4 and 6 mm) and their density distribution is

assumed to be uniformly distributed from 1.2 RD to 1.9 RD. Note

that relative density (RD) is used here as the density unit in this

work asit is often used intheIPJ studies(Nesbitt et al., 2005; Vince

et al., 2007). A summary of the conditions used in the simulation is

listed in Table 1. The variables have their base values correspond-

ing to the experiment conditions. The vibration frequency and

amplitude and ragging density and size are varied as shown in

the range column when studying their effects on the separationperformance. When one variable is changed, other variables are

all kept to their base values. Table 2 lists the parameters used in

the DEM simulation, which are generally based on the properties

of the coal.

When a simulation run begins, the IPJ is full of water, and the

screen is kept stationary. The ragging particles are first placed on

the screen. The screen vibration is then switched on, and the coal

particles begin to be generated simultaneously in the top part of

the feed tube. Coal particles then flow in the IPJ unit, and those that

Fig. 2. Schematic representation of the region (under shadows) considered in the DEM simulation: (a) top view and (b) side view.

Zone I

Zone II

Zone I

Zone II

Fig. 3. Illustration of porous media zones.

Table 1

List of conditions used in this work.

Variables (Unit) Base Value Range

Hutch Water(l/s) 5 -

Reject Flow (l/s) 3.3

Vibration frequency (Hz) 2 1, 2, 4

Vibration Amplitude (mm) 10 5, 10, 20

Solid Feed Rate (tph) 1.0 -

Size of Coal Particles (mm),

and the volume fraction

2.0, 20% -

4.0, 30%

6.0, 50%

Density of Coal Particles (RD),

and the volume fraction

1.2, 12.5% -

1.3, 12.5%

1.4, 12.5%

1.5, 12.5%

1.6, 12.5%

1.7, 12.5%

1.8, 12.5%

1.9, 12.5%

Ragging Density (RD) 1.6 1.4, 1.6, 1.8

Ragging Diameter (mm) 18 16, 18, 20

K.J. Dong et al./ Minerals Engineering 23 (2010) 301312 303


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exit through the apertures on the inner wall are collected in the

product flow, while those that exit from the bottom of the simu-

lated zone are collected in the reject flow. A simulation run needs

to be carried for a certain time to achieve the steady state, which

means the total outflow rate (the product flow plus the reject flow)

is equal to the feed rate for all kinds of particles. The data used in

the analyses are obtained after 30 s simulations. For most cases,

the steady state is achieved at this stage. However for several cases

the steady state is not even achievable after 50 s. These cases are

not continued, while the data obtained from them is used in a lim-

ited range in the latter discussions.

4. Results and discussion

4.1. Water flow

Firstly, we discuss the water flow field modeled by CFD. Fig. 4

shows the representative flow field in the IPJ, along with details

around the feed bowl, the region considered in DEM. The figure

shows that the water velocity in the unit overall is small except in-

side the feed bowl and near the bottom area. The trajectory of

water exiting the feed well covers a significant part of the screen

surface. This flow would tend to push coal particles away from

the region close to the inner surface of the screen towards the out-

er surface. In addition, a significant recirculation zone on the main

chamber is found, which is believed to result from the strong hor-izontal flow of hutch water. This recirculation zone may hinder the

settling down process of tails in the inner chamber, and is thus not

favored in an IPJ.

In order to further understand the flow in the IPJ, we trace the

flow paths of water using massless particles separately. The results

are shown in Fig. 5. It can be seen from Fig. 5a that the feeding

water always exits via the products outlet, and none of it is found

to exit from the tails outlet. On the other hand, the pathways fol-

lowed by hutch water as shown in Fig. 5b show that most of the

hutch water report to tailings with a small proportion passing

through the particle bed and over the screen and discharge with

the product. Some hutch water also circulates in the unit before

exiting via the tails and product outlets. Evidently, the strong hor-

izontal flow of hutch water helps push tails towards the tails out-

let, and its portion flowing over the screen may raise coal particles

towards the screen surface, and thus improving separation perfor-

mance. Note that the hutch water also results in harmful circulat-

ing flows, as discussed above. This two-sided role of hutch water

explains why, in the previous experiments (Vince et al., 2007),

the increase of hutch water cannot always increase the separation

efficiency of the unit.

Fig. 6 shows the flow fields of water in the feed bowls with dif-

ferent depths. Even with the effects of particles ignored, it can be

seen from the figure that water flow is very sensitive to the geom-

etry of feed bowl, which is used to distribute slurry into the screen.

The water flow in the unit with a deep bowl has a greater circulat-

ing flow, which may trap particles and cause difficulty for coal sep-

aration. This effect has not been observed in the experiments, and

deserves further investigation in the future.

In the following particle flow simulations, to simplify the stud-

ies of the first stage work, we focus our studies on the cases using

the water flow field modeled with base conditions; although other

cases are also studied, they will be reported elsewhere.

4.2. Particle flow

To validate the numerical model, the simulation results of the

base case are compared with those measured in terms of partition

number of particles in the reject flow as a function of particle den-

sity; partition number is defined as the ratio of the mass of parti-

cles found in the reject flow to the mass of the fed particles for

particles of a certain density. The results are shown in Fig. 7a.The predicted results are qualitatively comparable to the experi-

mental results. In addition, in the following parametric studies,

changes of the product collection rate with the changes of each

variable are also found to be qualitatively comparable with the

experimental findings. These agreements demonstrate the validity

of our numerical model, although no effort has been made to tune

Table 2

List of parameters used in DEM simulations.

Youngs modulus of particles (N/m2) 1 107

Youngs modulus of walls (N/m2) 1 107

Damping coefficient (inter-particle) 1 108

Damping coefficient (particlewall) 2 105

Sliding friction coefficient 0.4

Rolling friction coefficient 0.01

Fig. 4. Flow field obtained by CFDcalculation on theyzplane atx = 0: (a)spatial distribution of water velocity, (b) streamline with velocity contours, and (c)velocity field inthe amplified section in (b), when hutch water flowrate = 5 l/s, reject flowrate = 3.3 l/s, and slurry flowrate = 4.61 l/s.



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parameters for better quantitative agreement. Moreover, Fig. 7b

shows that the partition curves for different sized particles are

similar to that of the sum of all sized particles, indicating that

the IPJ is suitable for different sized particles as well, although itcan also be noted that the increase of the particle size decreases

the cut point and sharpens the partition curve.

Before the parametric studies by a series of controlled numeri-

cal simulations, we first focus our discussion on the base case.

Fig. 8 shows the snapshots of the flow of coal particles in the IPJ.

The particle bed on the screen is a mixture of ragging particles

and coal particles of different densities and sizes. It is thought that

the particle bed is critical to the segregation of particles and the

subsequent separations (Vince et al., 2007; Nesbitt et al., 2005).

We therefore investigate the bed structure in terms of the time

averaged spatial distributions of volume fractions of particles for

different kinds of particles. The results are shown in Fig. 9. Since

the particle flow is largely axisymmetric, we show the averaged re-

sults in the radial and axial directions (donoted as r and z,respectively).

It can be seen from Fig. 9 that in the particle bed, particles of dif-

ferent sizes have similar distributions, while particles of different

densities have different distributions. The lightest particles mostly

distribute away from the center and stick to the inner wall. In par-ticular, those at the top are close to the apertures on the inner wall,

therefore they are easy to report to the product flow through these

apertures. Contrary to this, the heaviest particles are more likely to

accumulate close to the center, close to the feeding bowl, and in a

much lower position, away from the apertures on the inner walls.

The medium dense particles distribute more sparsely in all regions.

The segregation among particles of different densities but not of

different sizes could be related to the similar partition curves of

different sized particles in Fig. 7b. This further indicates the appli-

cability of the IPJ to separate particles according to their densities.

The segregation of the particles with different densities should

occur because of their different motions in the IPJ. From Fig. 8a, we

can see general differences of their trajectories. When particles are

fed into the feeding bowl, they follow similar paths. However, thedifference emerges when particles are bumped out from the feed-

Fig. 5. Flow paths of water entering at the feed (a) and hutch water inlets (b), corresponding to Fig. 4.

Fig. 6. Effect of feed bowl geometry on water flow without considering particles: (a) real feed bowl geometry and (b) modified feed bowl geometry.



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ing bowl by the vibration and water flow. It can be seen that the

trajectories of the lighter particles are higher than those of the hea-

vier particles. Since the water flow on the top part of the IPJ show

high outwards radial velocity in Fig. 4c, some of the lighter parti-

cles are likely to be drawn directly to the product flow through

the apertures on the inner wall and by-pass the particle bed on

the screen. These particles appear to be separated by an elutria-tion-based mechanism. Other particles, however, fall on the parti-

cle bed first. And then with the jigging of the particle bed, they

either flow up and are drawn to the product flow through the aper-

tures, or percolate through the jigging bed and report to the reject

flow. Hence, in the simulated system, there are two mechanisms

for the separation of lighter particles to the product flow, corre-

sponding to different flow paths.

In order to see the behavior of different particles more clearly in

simulations, we traced a group of particles which were generated

at the same time. From the tracing of this group of particles, four

particles with typical trajectories are chosen for discussion. These

are indexed as particle A, B, C and D, respectively. The sizes of all

of them are 4 mm while their densities are 1.2 RD, 1.5 RD, 1.6 RD

and 1.8 RD, respectively. The same sized particles have been cho-sen because different sized particles give similar relationships be-

tween the partition number and density as seen from Fig. 7b, and

the partition numbers of 4 mm particles are closest to the summed

partition number. In the following analysis, we consider only the

movements of particles along the radial and axial directions, as

those along the tangential direction are negligible.

Fig. 10a shows the entire trajectory of particle A in IPJ, and the

trajectories of particles B and D before they fall onto the jiggingbed. The trajectory of particle C is not shown here for it is very sim-

ilar to that of particle B, as their densities are close. From the figure

we can see that particle A directly reports to the product flow

while the other two particles (B and D) fall down onto the particle

bed first. In this stage, particles have few collisions with other par-

ticles; therefore liquidparticle interactions should dominate the

motion of the particle. From Fig. 10c wecan see that when particles

are bumped out from the feeding bowl, the axial liquidparticle

forces for particle A are upwards (indicating positive value), while

those for particles B and C are downwards (indicating minus value)

or slightly upwards. Fig. 4c shows that the fluid velocities in this

region flow mainly along the radial direction, so the drag force

on the particles in the axial direction should be rather small.

Hence, the buoyancy force should be the main axial force on theparticles. As the buoyancy force is relatively larger for lighter par-

(a) (b)

Fig. 7. Partition number of particles in the reject flow as a function of density: (a) comparison between simulation and experimental results (from Vince et al., 2007) and (b)

numerical results for different sized particles and the sum of all sized particles.

Fig. 8. Snapshots of particles in IPJ when screen is at: (a) the highest position and (b) the lowest position. The rectangle in (a) indicates the region investigated in Fig. 9.



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ticles, particle A obtains a relatively large upwards force to support

its gravity and has the chance to flow directly out of the inner

chamber.

After particles fall onto the particle bed, the situation becomesmore complicated. From Fig. 11 we can see that although at the

end, particle B reports to the product flow, while particles C and

D flow down towards the reject flow, their trajectories in the pro-

cess are rather random, especially for particles with medium den-

sities (B and C). Their velocities and forces also show very

disordered patterns with random variations of the direction along

both radial and axial directions in Fig. 12. Nevertheless, the main

difference in the movement of these particles is along the axial

direction, since it determines whether they report to the productflow or to the reject flow. Thus we mainly focus our analysis on

the axial direction. The right column of Fig. 12 shows the compar-

isons between the particlefluid forces, particleparticle forces and

total forces on a particle along the axial direction. We can see that

the total axial forces oscillate about a value a bit lower than zero

250

270

290

310

330

350

370

390

410

430

0 50 100 150 200 250

Z(mm)

r (mm)

Particle A

Particle B

Particle D-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

20.2 20.4 20.6 20.8 21.0 21.2 21.4

Force(mg)

Time (sec)

ParticleA

Particle B

Particle D

(a) (b)

Fig. 10. (a) Trajectories and (b) particleliquid forces along axial direction for particle A, B and D.

Fig. 9. Time averaged space distributions of the volume fraction of different kinds of particles: (a) d = 2 mm, (b) d = 4 mm, (c) d = 6 mm, (d)q = 1.2 RD, (e) q = 1.5 RD, and (f)

q = 1.9 RD. Coordinates are transferred to radial dimension (r) and axial dimension (z).



8/12

and particlefluid axial forces oscillate a bit lower than 1 mg, i.e.

particle weight. The axial forces due to particleparticle collision,

on the other hand, only show some sharp peaks either upwards

or downwards. Briefly speaking, the liquidparticle interactions

seem to continuously counteract gravity, while the collision forces

make the total forces more disperse.

A clearer picture about the forces can be seen from Fig. 13, in

which we plot the distributions of the total force and particlefluid

force in the axial direction for the three particles. Note, although

there are large forces occasionally, their probabilities are rather

small. So here we only show the force distributions in the high

probability range. For the total force, it can be seen in Fig. 13a that

the distributions are all bell-shaped. But the peak shifts to the right

(at higher forces) and broadens with the decrease of particle den-

sity (D>C>B). For the heavy particle D, the position of the peak is

negative with a sharpshape, while the distribution of positive force

is almost nil. This indicates that the forces on particle D in the jig-

ging process are mainly downwards; hence it falls fastest as shown

in Fig. 11. For particle C, the peak of the force shifts to the right

although it is still negative, and the peak widens. A certain amount

of forces are positive values in the distribution. Hence particle C

has some upwards movements in the jigging process in Fig. 11.

For particle B, the peak is at almost zero, while it can be noticed

that the distribution of positive values cover a larger area than that

of minus values. Thus the particle gets a better chance to drift up-

wards and finally to be drawn to the product flow. These patterns

are similar to the distribution of the fluidparticle forces in

Fig. 13b, although all the curves shift to right for 1 mg. This shows

that the fluidparticle forces and gravity are almost balanced indi-

cating that the collision forces, acting as pulse forces, are responsi-

ble for the jigging and the separation of coal particles in the

particle bed.

4.3. Effects of some operational parameters

By means of the numerical model, we have studied the effects of

some key operational parameters on the separation performance of

IPJ, including the vibration conditions and properties of the ragging

particles.

Fig. 14 shows the partition curve for the reject flow at different

vibration frequencies or amplitudes. We also performed the simu-

lation when vibration amplitude is at 5 mm, while the system can-

not achieve a steady state for a long simulation beyond 50 s due to

the chocking of the screens. Consequently this case is not included

here. From the figure, it can be seen that at higher frequencies or

amplitudes, a lower cut point and a slightly sharper separation

are achieved.

240

260

280

300

320

340

360

240

260

280

300

320

340

360

240

260

280

300

320

340

360

150 170 190 210 230 250

Z(m)

r (m)150 170 190 210 230 250

r (m)150 170 190 210 230 250

r (m)

Particle B Particle C Particle D

Fig. 11. Trajectories in jigging process for particles B, C and D. The arrows show the entrance and exit of particles.

-0.20

0.00

0.20

0.40

0.60

0.80

Vr

(m/s)

-0.50

-0.30

-0.10

0.10

0.30

0.50

Vz

(m/s)

-5.00

-3.00

-1.00

1.00

3.00

5.00

20.50 21.50 22.50 23.50 24.50 25.50 26.50 27.50 28.50 20.50 21.50 22.50 23.50 24.50 25.50 26.50 27.50 28.50

Ftotal,r

(mg)

Time (sec)

-2.00

-1.00

0.00

1.00

2.00

F

total,z

(mg)

-1.00

0.00

1.00

2.00

3.00

Fliquid,z

(mg)

-2.00

-1.00

0.00

1.00

2.00

Fcontact,z

(mg)

Time (sec)

Fig. 12. Velocities and forces in the radial and axial directions as a function of time for particle B.



9/12


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sharpness of the partition curve. The case of ragging particles of

16 mm has also been simulated, but the partition curve is not ob-

tained as the steady state is not achievable after 50 s of simulation.

However, it has been included in the analysis of particle velocity

and porosity of the particle bed in Fig. 17b. It can be seen from

Fig. 17b that larger ragging particles are more difficult to agitate

with vibration and liquid flow, leading to a denser packing bed

which makes coal particles harder to pass through the bed to reject

flow. For smaller sized ragging particles (16 mm), even the velocity

of ragging particles is greater than that of the larger ragging parti-

cles, the particle bed still becomes denser. The reason for this is

that the smaller the ragging particles, the smaller the gaps between

the ragging particles, which makes coal particles harder to pass

through the screen. Thus the screen is chocked and the steady state

is not reached even after a long period of simulation. The compli-

cated effects of the ragging size may need further studies as cur-

rently there are no comparable experimental studies available.

For all the cases studied, it is found that the sharpening of the

partition curve is always accomplished with the decrease of the

cut point. Fig. 18 summarizes the relationships between the cut

point and Ep, which is the shape factor of the partition curve de-

fined as Ep = (q75 q25)/2, where q75 and q25 are the densities

when the partition numbers are 75%and 25% in the partition curve,

respectively. The figure shows that Ep is strongly dependent on the

cut point for the conditions simulated in this work. This indicates

that to obtain good separation performance, there cannot be a high

cut point. This relationship may need further confirmation under

different conditions.

(a) (b)

Fig. 15. Average velocities of ragging particles and porosity of the center part in the particle bed at: (a) different vibration frequencies and (b) different vibration amplitudes.

(a) (b)

Fig. 16. Partition number as a function of particle density for the particles in the reject flow with ragging particles of: (a) different densities and (b) different sizes.



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5. Conclusions

A one-way coupled CFD and DEM model has been developed to

simulate the flow and separation of an IPJ. The model can generate

results qualitatively comparable to the experimental data, and

yield microscopical information helpful to the improvement of

the fundamental understandings and the industrial design of IPJ.

The separation mechanisms are studied by analyzing the spatial

distributions of particles in the IPJ and tracing typical particles in

the process. It is found that particles of different densities distrib-

ute differently while particles of different sizes distribute similarly

in the IPJ, which confirms the ability of the IPJ to separate particles

according to their densities. Two separation mechanisms are

found. Some very light particles are drifted directly to the product

flow mainly due to the buoyancy force and fluid drag force. On the

other hand, most particles fall on the packed bed and are separated

in the jigging process, in which the particlefluidforces and gravity

are nearly balanced, while the particleparticle collision forces act

as pulse forces and are responsible for the jigging and the

separation.

The effects of variables such as the vibration amplitude and fre-

quency, and the size and density of ragging particles have been

studied by the model. The increase of vibration frequency or ampli-

tude introduces more mechanical energies to the particle bed, and

the decrease of the density of ragging particles increases the effects

of fluidparticle interactions. Consequently they all increase the

velocities of ragging particles and the porosity of the jigging bed,

resulting in the decrease of the cut point and the sharpening of

the partition curve. On the other hand, the decrease of the size ofthe ragging particles leads to the increase of the velocity of ragging

particles but also the decrease in the porosity of the jigging bed,

which shows a more complicated effect on the partition curve.

For all the cases studied, there is a linear correlation between the

cut point and Ep of the partition curve.

It is also found that the flow in IPJ is very sensitive to opera-

tional conditions and material properties of particles, which may

lead to unsteady state operation. There is a need to study this issue

further.

Acknowledgement

The authors are grateful to Australian Coal Association ResearchProgram (ACARP) for the financial support of this work.

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