J. Cent. South Univ. (2013) 20: 3660−3666 DOI: 10.1007/s11771­013­1893­0

Insights in active pulsing air separation technology for coarse coal slime by DEM­CFD approach

DONG Liang(董良), ZHAO Yue­min(赵跃民), XIE Wei­ning(谢卫宁), DUAN Chen­long(段晨龙), LI Hao(李浩), HUA Cheng­peng(华成鹏)

School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou 221116, China

© Central South University Press and Springer­Verlag Berlin Heidelberg 2013

Abstract: To research a novel technology for dry coarse coal slime beneficiation and extend its application, active pulsing air separation technology was investigated by DEM­CFD coupling simulation approach. The results show that the ash content of feed is reduced by 10%−15% and the organic efficiency is up to 91.78% by using the active pulsing air separation technology. The gas−solid flow in the active pulsing air classifier was simulated. Meanwhile, the characteristics of particle motion and the separation process of different particles were analyzed, and the mechanical structure of the classifier was also modified to achieve high separation efficiency. Therefore, a novel high­efficiency dry beneficiation technique was advanced for coarse coal slime.

Key words: pulsing air; coarse coal slime; DEM­CFD; separation

1 Introduction

At present, the highly efficient separation technology study is focused on the coarse coal slime in coal preparation field. With increasing content of coarse coal slime in raw coal, it has great environmental issues and economic value to approach a highly effective separation technology. In the field of traditional wet coal preparation, many separation machines have been researched such as reflux classifier [1], teetered bed separator (TBS) [2−5], spiral separator [6] and heavy medium cyclone for coarse coal slime [7]. But the separation technology can not work effectively in the arid regions and for coals that tend to slime under wet separation processes. So far, there is few report about dry separating technology for coarse coal slime. Scholars of China University of Mining and Technology (CUMT) have engaged in the research of dry coal preparation using air dense medium fluidized beds (ADMFB) [8−12] for more than 20 years. A gas−solid fluidized bed separator has been made for 6−50 mm coal preparation. Currently, <6 mm coals including coarse coal slime are not separated effectively by the ADMFB. In order to promote the application of ADMFB and popularize high by efficient dry coal preparation technology, separation characteristics for coarse coal slime using an active pulsing air classifier [13−14] (APAC) have been

investigated by DEM­CFD coupling simulation approach [15−17].

In this work, EDEM (a commercial discrete element software for the simulation and analysis of particle handling and manufacturing operations) and FLUENT (a computational fluid dynamics software) were employed. The separation process of different particles, interaction between gas and particles and particle dynamics were studied intensively. Then, the mechanical structure of the classifier was modified to achieve high separation efficiency.

2 Experimental

2.1 Experiment system The experimental apparatus used is illustrated in Fig.

1. By the roots blower and a buffer tank, airflow was sent into the separation system. Rotation of a special butterfly valve was driven by an electric motor under the control of an inverter. So, an active pulsing air was generated, the velocity of which varied periodically. In the APAC, the superficial airflow velocity varied regularly. Coarse coal slime was sent into the APAC by a screw feeder, oscillated and loosed, and then stratified by density in the synergistic effect of gravity and pulsing air. A cyclone separator was used to separate cleaned coal from gas overflowed from top of the APAC and tailings sank into the collection tank at the bottom.

Foundation item: Projects(51221462, 51134022, 51074156) supported by the National Natural Science Foundation of China; Project(2012CB214904) supported by the National Basic Research Program of China; Project(20120095130001) supported by Specialized Research Fund for the Doctoral Program of Higher Education, China

Received date: 2012−07−27; Accepted date: 2012−11−29 Corresponding author: ZHAO Yue­min, Professor, PhD; Tel: +86−516−83590092; E­mail: ymzhao@cumt.edu.cn

J. Cent. South Univ. (2013) 20: 3660−3666 3661

Fig. 1 Schematic diagram of experimental apparatus: 1—Root blower; 2—Buffer tank; 3—Flowmeter; 4—Butterfly; 5—Electric motor; 6—Inverter; 7—Active pulsing air classifier; 8—Cyclone; 9—Screw feeder; 10—Electric motor; A—Feedings inlet; B— Floats outlet; C—Gas outlet; D—Tailings outlet

2.2 Separation theory of APAC The superficial airflow velocity in the APAC can be

described by

A 0 0 | cos(2π ) | v v f t = (1)

where vA is the instantaneous velocity of air, v0 is the maximum velocity of air, f0 is the pulsation frequency and t is time.

Generally, as a particle moves through an upward air current, forces such as gravity, air buoyancy and drag act on it. Because the fluid flow pattern in this work was the active pulsing airflow, not only the particle was affected by drag, but also energy transmission would replace the mass of the fluid, which might resist the particle moving through the flow. Equations (2) and (3) give the acceleration of particles [18] in the flow:

2 4 p g A p

p p A p

p g

d 3 ( ) 1 3.6 10 9 [ ]

1 d 4 ( ) 2 2

v v v g

t d d v v

ρ ρ

ρ ρ

− − × = × − + + +

− + ⋅

g A , 0 5 2

v Re

t

ρ ∂ × < ≤

∂ (2)

2 4 p g p

p p p A p

p g

d 3 ( ) 1 3.6 10 [

1 d 4 ( ) 2

A v v v g

t d d v v

ρ ρ

ρ ρ

− − × = × − + +

− + ⋅

g A

A p 5

6 0.4] ,

2 ( ) 1

1.5 10

v t d v v

ρ

−

∂ + + ×

∂ − +

× 5 0 2 10 Re < ≤ × (3)

where vp is the particle velocity, ρp is the particle density, dp is the particle diameter, g is the acceleration of gravity, ρg is the gas density and Re is Reynolds number.

It is clear that the acceleration of particles in the pulsing airflow is affected by gravity acceleration,

pulsing air velocity, particle density and diameters, etc. Under the same conditions, the higher the particle density is, the lower the particle acceleration is, and thus particles tend to sink. Conversely, the low­density particles tend to rise. After several pulsation periods, phenomenon of stratification and separation can be observed obviously in particles with different densities. Therefore, the APAC can be used to separate particles with different densities theoretically. Experiments of active pulsing air separation technology for coarse coal slime were conducted in this work.

2.3 Results and discussion The 1−3 mm fine coal with an ash content of

30.48% was used. The washability curves of feed shown in Fig. 2 were obtained by float­sink analysis, where β is the cumulative float, θ is the cumulative sink, λ is the characteristic ash, δ is the relative density and ε is the near density.

The results show that the contents of <1.3 g/cm 3 and

Fig. 2Washability curves of feed

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>2.0 g/cm 3 particles in feed are 31.43% and 26.57%, respectively. The separation efficiency of APAC is affected by the instantaneous velocity of air, the pulsation frequency, the feed speed and so on. The combustible material recovery and organic efficiency are employed to evaluate the separation efficiency. The results of single­factor research are presented in Table 1. In the case of v0=8.65 m/s and f0=1.86 Hz, the feed speed Q is 6.94 g/s, the combustible recovery is 85.90% and the organic efficiency is 89.32%. Based on the single­factor research, more experiments were designed. The results listed in Table 2 show that in the condition that v0=9.04 m/s, f0=1.86 Hz and Q=5.56 g/s, the organic efficiency is up to 91.78%. With the APAC, the ash content of feed is reduced by 10%−15%, calculated by the ash content of feed of 30.48% and the ash content of clean coal in Table 2 under different conditions. Because

of the significant misplaced phenomenon during the separation experiments, the ash content of clean coals is slightly higher and the recovery of clean coal is very low.

In order to investigate the separation process of different particles and modify the operating parameters and mechanical structure, DEM­CFD coupling method was used to simulate the gas−solid two phases flow in the APAC. And the dynamic characteristics of particles were analysed.

3 Numerical simulations

3.1 DEM­CFD coupling circuit In the DEM­CFD coupling circuit, parameters of

fluid come from FLUENT, and parameters of particles are from EDEM. As shown in Fig. 3, the flow field of fluid is calculated by FLUENT using values of particles

Table 1 Results of single­factor research

f0=2.33 Hz, Q=6.94 g/s vA=8.65 m/s,Q=6.94 g/s f0=1.86 Hz, vA=8.65 m/s

vA/(m∙s −1 ) Combustible material

recovery/%

Organic efficiency/%

f0/Hz Combustible material

recovery/%

Organic efficiency/%

Q/(g∙s −1 ) Combustible material

recovery/%

Organic efficiency/%

5.90 45.83 50.90 0.47 76.45 78.74 4.17 65.10 64.93

6.29 49.65 76.48 0.93 77.17 87.60 5.56 80.09 88.25

6.68 47.77 72.09 1.17 79.94 84.61 6.94 85.90 89.32

7.47 53.67 80.78 1.40 79.65 82.24 8.33 79.53 97.64

7.86 57.75 85.92 1.63 81.99 86.82 9.72 73.03 82.35

8.26 60.62 89.84 1.86 85.90 89.32 11.11 72.13 87.96

8.65 63.85 86.59 2.10 84.89 86.17 12.50 46.66 51.11

9.04 61.49 90.72 2.33 82.05 78.74 13.89 42.94 65.36

Table 2 Results of separation experiments vA/(m∙s −1 ) f0/Hz Q/(g∙s −1 ) Recovery of clean coal, γ/% Ash content of clean coal, Ad/% Organic efficiency/%

8.26 1.86 8.33 56.33 14.60 75.31

8.65 2.10 5.56 52.19 15.41 67.87

8.26 1.63 6.94 64.82 16.88 83.05

8.26 1.86 5.56 66.53 16.24 85.46

8.65 2.10 8.33 67.45 18.06 84.26

8.26 2.10 6.94 71.10 18.27 88.52

9.04 2.10 6.94 61.05 16.19 78.62

9.04 1.86 5.56 75.31 19.00 91.78

8.65 1.63 5.56 72.81 18.32 90.42

8.65 1.63 8.33 66.79 17.28 84.69

8.65 1.86 6.94 64.51 16.50 82.78

9.04 1.86 8.33 71.18 19.31 86.55

9.04 1.63 6.94 74.42 19.97 88.63

J. Cent. South Univ. (2013) 20: 3660−3666 3663

velocity and location given by EDEM. The drag force acting on particles can be computed. And then, the status of particles is updated by EDEM. The governing equations [19] in the DEM­CFD model are displayed. Figure 4 gives the Hertz­Mindlin model [20−21] to describe the particles interaction in the EDEM software.

Fig. 3 Circuit of DEM­CFD coupling simulation

1) Governing equations for gas phase flow (1) Continuity equation

g g ( ) ( ) 0 t

αρ αρ ∂

+ ∇ ⋅ = ∂

u (4)

(2) Conservation equations of momentum

g g g g ( ) ( ) ( ) p g t

αρ αρ ατ αρ ∂

+ ∇ ⋅ = −∇ − + ∇ ⋅ + ∂

u uu F (5)

(3) Conservation equation of energy

g p,g g p,g g sg ( ) ( ) ( ) c T c T k T Q t

αρ αρ α ∂

+ ∇ ⋅ = ∇ ⋅ ∇ + ∂

u (6)

(4) Conservation equations of species

t g g g

t

( ) ( ) ( ( ) ) j j j j j Y Y D Y S t Sc

µ αρ αρ α ρ

∂ + ∇ ⋅ = ∇ ⋅ + ∇ +

∂ u (7)

(5) Realizable k−ε turbulent model

t g g g ( ) ( ) ( ( ) ) k

k

k k k G t

µ αρ αρ α µ α αρ ε

σ

∂ + ∇ ⋅ = ∇ ⋅ + ∇ + −

∂ u

(8)

g g ( ) ( ) ( ( ) ) t

t ε

µ αρ ε αρ ε α µ ε

σ

∂ + ∇ ⋅ = ∇ ⋅ + ∇ +

∂ u

2

g 1 2 ( ) C S C k

ε ε

αρ ε

− + ν

(9)

where α is gas volume fraction, p is pressure, τg is stress tensor, F is the interaction term between gas and particles; cp, g is heat capacity (subscripts p is for particle phase, g is for gas phase), T is temperature, k is turbulent kinetic energy, Qsg is heat source in a unit volume, Yj is mass fraction of component j, Gk is generation of turbulence kinetic energy due to the mean velocity gradients, Sj is mass source of species j, SCt is turbulent

Schmidt number, μt is turbulent viscosity, σk and σε are turbulent Prandtl numbers for k and ε, μ is gas viscosity, ε is turbulent dissipation rate, C1 and C2 are constants in Realizable k−εmodel, ν is kinematic viscosity.

2) Governing equations for particle motion (1) Continuity equations

, , , 1

, , 1

d ( )

d

( )

i

i

n i

i D i i n ij t ij j

n i

i t ij r ij j

m F m t

d I dt

=

=

= + + +

= +

∑

∑

V g F F

ω T T

(10)

, , , , , n ij n n n n ij t ij i ij t ij k L η = − − = × F δ V T n F (11)

(2) Coulomb friction law

,

, , ,

,

, , ,

, if

/ ,

if (reset / )

t ij

t t t t ij t ij s n ij

s n ij t t

t ij s n ij t s n ij t t t

k f

f

f f k

η

=

− − ≤ −

> =

F

δ V F F

F δ δ

F F δ F δ δ

(12)

,

, ,

,

, , ,

, if

/ ,

if (reset / )

r ij

r r ij r ij r n ij

r n ij

r ij r n ij r n ij r

T

k f

f

f f k

η

=

− − ≤ −

> =

α ω T F

F α α

T F α F α α

(13)

(3) Interaction between gas and particles, F

D, 1

cell

n i i

V =

− =

∑ F F and ( 1)

D D0 β α − + = F F (14)

2 p 2

D0 f Ds

2 p

π 1 ( )

2 4

3.7 0.65exp[ (1.5 lg ) / 2]

d C

Re

ρ α

β

= − −

= − − −

F u v u v (15)

p p Ds 0.5 2

p p

24 / 1

(0.63 4.8 ) 1

Re Re C

Re Re −

≤ =

+ >

(16)

f p p

f

d Re

αρ

µ

− =

u v (17)

where mi is the mass of the i­th catalyst particle, FD is drag force between gas and particles, Fij is the contact force between the i­th and j­th particles, Ii is inertia of moment, ωi is angular velocity, Tij is the torque due to contact force between the i­th and j­th particles, nij is the normal unit vector between two contacted particles, Vij is the relative particle velocity between particle i and j, Li is the arm of force, k is spring constant, d is damping constant, g is aueleration of gravity, f is friction coefficient, δ is displacement vector, η is damping coefficient, CDs is standard drag coefficient, Vcell is the volume of computational cell, subscripts “n” is the normal direction, “r” denotes the rolling, “t” designates the tangential direction and “s” corresponds to the static

J. Cent. South Univ. (2013) 20: 3664

Fig. 4Model of Hertz­Mindlin

state. The geometric model used in simulation

experiments was designed based on the real active pulsing air classifier, as illustrated in Fig. 5. A is the inlet of feed, B is the inlet of pulsing air, C is the outlet of floats, and D is the outlet of tailings. The parameters of particles used in the experiments are listed in Table 5.

Fig. 5Model of active pulsing air classifier used in simulations (Unit: mm)

Table 5 Particle parameters in simulations

Particle index

Particle type Average

diameter/mm Density/ (g∙cm −3 )

Feeding speed/

(piece∙s −1 )

1­A Low­density particle

1 1.40 2 000

1­B High­density particle

1 1.80 2 000

3.2 Analysis of solid phase The curve of average z­velocity of particles versus

time obtained from simulations is described in Fig. 6. Obviously, it is seen that the difference in densities determinates the different velocities between low­density particles and high­density particles. The velocities along the z direction of low­density particles which change periodically are positive for most of the time. Therefore, the light particles tend to move to position A. The z­velocities of high­density particles increase at the beginning and then reduce with time. Consequently, high­density particles tend to move to position D because of the negative z­velocity. The kinetic properties of high­density particles are caused by the gravity and drag force. At the beginning, the dominant force is gravity and high­density particles are accelerated to a high velocity at −z axis. With the increase of high­density particle velocity, the fluid drag force becomes larger and larger, thus the direction of the acceleration changes so that the high­density particle velocity becomes lower.

Fig. 6 Particle z­velocity with time in simulations

Figure 7 shows the location of particles in the APAC. Figure 8 gives the change of particles versus time at z­height. Different particles can be separated in one or two pulsation periods after entering into the APAC from position A. Low­density particles (1­A particle 1, 1­A particle 2 and 1­A particle 4 in Fig. 8) in floats exist in the APAC for about four pulsation periods. High­density particles (1­B particle 1, 1­B particle 2 and 1­B particle 4 in Fig. 8) in tailings exist in the APAC for about six pulsation periods. It is significant to reduce the retention time of particles in the APAC to increase the capacity and separation efficiency. The mechanical structure of the separator is modified and the design parameters are listed in Table 6. After modification, different particles in the APAC have the similar retention time, and the capacity and separation efficiency can be improved.

In Fig. 8, the 1­A particle 3 is a low­density particle which moves to the tailings and the 1­B particle 3 is a high­density particle which moves to the floats.

J. Cent. South Univ. (2013) 20: 3660−3666 3665

Fig. 7 Separation process of different particles in EDEM

Fig. 8 Curves of particle z­height in EDEM (a) and gas velocity in FLUENT (b) with time

Table 6 Comparison of design parameters Particle D/mm d/mm L1/mm L2/mm L3/mm L4/mm Before

modification 160 60 300 700 350 150

After modification

160 60 200 400 350 150

According to the statistics, the retention time of misplaced particles in the APAC ranges from 8 to 12 pulsation periods. During these pulsation periods, misplaced particles collide with each other, and the interaction of different particles can be strengthened. Therefore, loose and stratification process of particles could be affected seriously and the separation efficiency could be reduced. In addition, the phenomenon that misplaced particles in tailings move to the bottom along the wall of the APAC could be observed in simulations.

The total mass of different particles in feed, floats and tailings were given by EDEM, as listed in Table 7. The total misplaced material [22] can be employed to

evaluate the separation characteristics as m0=mh+ml (18)

where m0 is the total misplaced material, mh is the content of heavy material in floats and ml is the content of light material in tailings.

The total misplaced material is 18.21%. Therefore, the active pulsing air classifier has the potentiality to achieve high separation efficiency. The 76.96% recovery of light particles is close to the separation performance of TBS [23], which indicates that it is possible to separate coarse coal slime with the active pulsing air classifier.

Table 7Mass and recovery of particles in simulations

Mass/g Particles type Feed Floats Tailings

Recovery/ %

ml/% mh/%

Light particle

100.93 77.67 23.26 76.96

Heavy particle

129.87 18.77 105.72 81.40

8.13 10.08

3.3 Analysis of gas phase Because the periodical change of superficial air

velocity is the main factor for the separation of coarse coal slime, the analysis of air velocity in the APAC is significant. Figure 9 gives the air velocity−time contours within 1 s. It is clear that the velocity near wall of the APAC is lower than that near the axis when the air

Fig. 9Air velocity contours in FLUENT

J. Cent. South Univ. (2013) 20: 3666

velocity fluctuates near the maximum value. Due to the velocity distribution, the drag force acting on low­density particles near wall of the column is not high enough, which causes low­density particles to sink to the bottom. In order to modify the velocity distribution and decrease the total misplaced material, a new gas distributor is employed in gas inlet. So, the recovery of clean coal can be increased during the separation process.

4 Conclusions

1) The coarse coal slime is separated at high efficiency by the active pulsing air separation technology. The ash content is reduced by 10%−15%. The organic efficiency is up to 91.78% under the conditions that v0=9.04 m/s, f=1.86 Hz and Q=5.56 g/s.

2) Different particles are separated in one or two pulsation periods. The misplaced particles retention time in the separation column is much longer than others. A modified separator is also designed, which reduces the particles retention time and increases the capacity. A new gas distributor is employed to modify the gas velocity distribution.

3) A novel high­efficiency dry beneficiation technique is advanced for coarse coal slime. The separation efficiency of APAC is close to TBS. Cooperating with the air­dense medium fluidized bed separator, coals with a wide range of sizes is separated efficiently.

4) Separation theory of the APAC should be researched further in the future. Firstly, the force acting on particles should be focused on in DEM­CFD coupling simulations. Secondly, a high­speed dynamic camera should be used to investigate particle dynamics in the modified separator.

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(Edited by FANG Jing­hua)