Filtration Performance of Coal Pyrolysis Flying Char Particles in a

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Filtration Performance of Coal Pyrolysis Flying Char Particles in a Granular Bed Filter Minshu Zhan, Guogang Sun, Shen Yan, Jiaqing Chen, and Minghao You Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b02307 • Publication Date (Web): 01 Jan 2018 Downloaded from http://pubs.acs.org on January 2, 2018

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Energy & Fuels

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Filtration Performance of Coal Pyrolysis Flying Char Particles in a Granular Bed Filter

3

Minshu Zhana,b,c,1, Guogang Sunb,c,,1, Shen Yanb,c,, Jiaqing Chena, Minghao Youa

1

4

a

5

102617, China

6

b

7

Beijing102249, China

8

c

9

Petroleum, Beijing 102249, China

10

School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing

State Key Laboratory of Heavy Oil Processing, China University of Petroleum,

Beijing Key Laboratory of Process Fluid Filtration and Separation, China University of

ABSTRACT

11

The filtration of flying char particles from coal pyrolysis vapors plays a very important role

12

in enhancing yields and quality of pyrolysis oil. In this work, the performance of coal

13

pyrolysis flying char particles in a granular bed filter (GBF) was studied in a cold model

14

experiments. A filtration model was developed using macroscopic phenomenological method

15

which describes the filtration of the GBF. The polynomial expression of the relative filter

16

coefficient F and the nonlinear expression of the relative pressure drop ratio G were applied in

17

the new model. The unsteady state of granular filtration was captured, demonstrating that the

18

GBF performance could be predicted by the new model. Effects of superficial gas velocity,

19

thickness of granular layer, and dust mass concentration on collection efficiency and pressure

20

drop were analyzed. An excellent performance of the GBF was obtained and the total

21

collection efficiency could reach a span between 98% and 99.9%. In the case of lower dust

22

mass concentration, the total collection efficiency and pressure drop were little affected by the

23

increasing of dust mass concentration. The optimal operating conditions of the GBF were

24

obtained: superficial gas velocity of 0.2 m/s to 0.6 m/s and granular layer thickness of 0.07 m

25

to 0.11 m.

26

Keywords: granular bed filter; collection efficiency; pressure drop; filtration model 

Corresponding author. Tel: +86 10 89734820. E-mail:[email protected] (Guogang Sun). Dr. Zhan and Prof. Sun contribute equally to this work.

1

ACS Paragon Plus Environment

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1. INTRODUCTION

2

Pyrolysis of coal could make efficient utilization of valuable chemical structures inherent in

3

coal to produce liquid fuels and chemicals. 1-3 Coal pyrolysis has been studied and reported by

4

many researchers, however the technology is still in the developing stage due to some

5

challenges. One of the key challenges in the coal pyrolysis process is the removal of flying

6

char particles in the complex pyrolysis gases products of hundreds of compounds existing at a

7

temperature above 400℃. 4 The pyrolysis vapors are typically corrosive, viscous, easy-coking

8

and high-temperature. Simultaneously, char particles less than 10μm in size in the pyrolysis

9

vapors present a high concentration.

5

It is demonstrated that the secondary reactions

10

occurring to the high-temperature primary pyrolysis vapors by char particles are significant

11

decrease the yields and quality of pyrolysis oil or tar. 6, 7 The dust content in the pyrolysis oil

12

causes difficulties in the downstream processing of the pyrolysis oil. Moreover, the presence

13

of char particulate matter in the pyrolysis vapors erodes components used for thermal energy

14

conversion, clogs the transport system, and even threatens the steady running of the pyrolysis

15

process. Therefore, the rapid and efficient filtration of flying char particles from high

16

temperature pyrolysis vapors is of crucial importance.

17

Several attempts, such as cyclone 8-10 , barrier type filter

11-13

and electrostatic precipitator

14

18

(ESP.)

19

temperature pyrolysis vapors. Among them, granular bed filter (GBF) as an engineering

20

solution for pyrolysis vapors seems to be paid more attention by many researchers.15, 16 Hsiau

21

et al.

22

pyrolysis vapors filtration. They studied the flow characteristics and performance of the

23

moving granular bed filter with different geometry louvers. They were able to realize a

24

10-fold decrease in pyrolysis oil solids content when using a MBGF as opposed to traditional

25

cyclone technology. 24 Brown et al. 26, 27 designed and constructed a counter-current MGBF for

26

hot gas streams filtration of fine char particles produced during biomass pyrolysis. The high

27

efficiency granter than 94% on 1~10μm particles and the constant pressure drop operated as

28

moving bed were obtained. Liang et al.

29

hot vapors filtration in coal pyrolysis. Most of the GBFs made a good filtration performance

30

of removal char particles.

, have been made to more completely remove char particles from the high

17-25

developed a cross flow moving granular bed filter (MGBF) for hot biomass

28, 29

and Xu et al.

30, 31


developed different GBFs for

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The description and prediction of granular bed filtration performance play an important role

2

in understanding filtration process. In general, there are two kinds of theory to describe the

3

granular bed filtration process. One of them is focus on the basic filtration mechanisms of

4

inertial impaction, interception, gravitation, electrostatic attraction, and Brownian diffusion,

5

namely microscopic method. Tardos et al.

6

basic filtration mechanisms and simulated the granular bed filtration. These works developed

7

a two-dimensional filtration model to describe the transport and deposition of particles in

8

porous granular media. Zhao et al. 34 built a two-dimensional model for the cross-flow MGBF.

9

The process optimization for the combined hot gas desulfurization and dust removal were

10

investigated. Murphy et al.35 and Guan et al.36 proposed a three-dimensional model based on

11

the microscopic method to simulate granular bed filtration process. These studies mainly

12

focused on the collection mechanisms of single granule. The total collection efficiency was

13

obtained by some correlations. However, the transfer of information from micro-scale to

14

macro-scale remains beyond a clear and thorough understanding among researchers. It was

15

difficult to quantitatively identify the basic collection mechanisms effect on macro-scale

16

filtration performance. The other one is the phenomenological method which describes the

17

macroscopic dynamic behavior of granular bed filtration. Tien et al.37, 38 summarized the

18

governing equations of the macroscopic phenomenological method. The solution and

19

application of these equations were discussed in their works. On the basis of the macroscopic

20

equations, Sulaymon et al.39 and Wenzel et al.40 developed different filtration model for

21

fixed-bed and cross-flow moving bed respectively. These works revealed that the parameters

22

which describe the effect of deposition on the filtration rate are important factors. As opposed

23

to the model of basic collection mechanisms, the macroscopic phenomenological model was

24

close to the filtration process.

32

and Boccardo et al.

33

considered parts of the

25

Granular bed filtration is an unsteady state in nature. During actual filtration, pores in the

26

granular media become filled when the accumulation of deposited particles contact and

27

adhere to the surfaces. Dust may agglomerate at the entrance to the filter. A filter cake which

28

increase of filtration efficiency and pressure drop is formed. However, the deposited particles

29

entrained in the gas flow decrease filtration efficiency. In this study, a filtration model was

30

developed using macroscopic phenomenological method which describes the unsteady state


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1

of granular bed filtration. A cold model GBF experimental system was built. The filtration

2

process and influences of operating parameters on performance of coal pyrolysis flying char

3

particles in the GBF were analyzed.

4

2. THEORY

5

2.1. Macroscopic Description of Granular Filtration

6

Figure 1 shows the schematic representation of the fixed-bed granular filtration. The

7

governing equations, basic assumptions and the parameters of the macroscopic behavior of

8

granular filtration have been defined by Tien and other researchers38-40. Dispersion or

9

diffusion is a mass transfer process driven by the particles mass concentration gradient at the

10

surface of the granular media. The dispersion effect (both axial and radial) is negligible in

11

granular filtration.

12

within the bed and monodisperse particle suspensions, the governing equations of granular

13

filtration can be simplified as

38

In the case of one-dimensional and the velocity profile flat of flow

us

14

15

c  m  0 z t

（1）

c   c z

（2）

16

In the governing equations of Eqs. (1) and (2), us is the superficial gas velocity, c is the

17

mass concentration of particles in the gaseous flow, z is the axial direction, σm is the specific

18

mass deposit, t is the time and λ is known as the filter coefficient. The specific mass deposit

19

σm is used for describing the extent of deposition, which represents the mass of the particles

20

deposited per unit filter volume.

21

The filtration rate Eq. (3) can be obtained by combination of Eqs. (1) and (2). The filtration 𝜕𝜎𝑚 𝜕𝑡

22

rate

23

time. The filter coefficient λ is first order with respect to the mass concentration of particles in

24

the fluid.

25 26

is expressed as the mass of the particles collected per unit filter volume per unit

 m  us c0 F t

（3）

In the Eq. (3), the filter coefficient λ is defined by the initial filter coefficient λ0 and the


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correcting factor to account for the deviation from the logarithmic law for the concentration

2

profile F. The formula F is function of specific mass deposit σm.

F

3

  F  m  0

（4）

4

Based on the conservation principle, a relationship of Eq. (5) was found in the fixed-bed

5

granular filtration by Tien 38. Where cin and σm,in are the mass concentration of particles and

6

the specific mass deposit at the filter inlet respectively. In other words, the mass concentration

7

of particles in the fluid phase at any given time, c, can be calculated in the previous

8

knowledge of cin , σm and σm,in.

 c  m cin  m,in

9

（5）

10

In the case of uniform deposition assumption, Eq. (6) can be obtained. In the Eq. (6), the

11

local mass concentration of particles, c is replaced by an average mass concentration of

12

particles, 𝑐̅, between the inlet and the outlet of the bed. In this way, the specific mass deposit

13

̅̅̅̅. is replaced by an average specific mass deposit 𝜎 𝑚

c

14 15

m

18

 m,in



c

m

 m  m c  t c t

（6）

（7）

Comparing the above two expressions of Eqs. (3) and (7), it is found that the right term of these two partial equations are equivalent. Accordingly, Eq. (8) can be obtained.

 m c

19 20

cin

The partial differential equation of Eq. (7) can be derived by Eq. (6)

16 17



c t

 us c0 F

（8）

The partial differential equation can be rewritten as

c c  us 0 F t c m

21

（9）

22

Integrating the above equation of Eq. (9) between the inlet and outlet of the filter by

23

applying the initial boundary condition cin=0 at t=0 and the boundary condition c=cin at z =0,

24

yields


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 ln c 

1 2

5

m

us 0 Ft

（10）

The mass concentration of particles in outlet at any given time is solved as

cout  e

3 4

c

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

c

m

us 0 Ft

（11）

Finally, the total collection efficiency of the fixed-bed granular filtration can be expressed as an analytical solution of Eq. (12). 

  1

6

c us 0 Ft m

cout e  1 cin cin

（12）

7

2.2. Determination of λ0 and F

8

According to Eq. (12), the average mass concentration of particles 𝑐̅, the average specific

9

mass deposit 𝜎 ̅̅̅̅, 𝑚 the superficial gas velocity us, and the mass concentration of particles at the

10

filter inlet cin can be easily obtained from experimental data. If the initial filter coefficient λ0

11

and the relative filter coefficient F are known, the total collection efficiency η can be solved.

12 13

If assuming uniform particle deposition with a filter depth L, the average specific mass deposit at any time, ̅̅̅̅, 𝜎𝑚 can be determined for overall mass balance consideration

m =

14 15

1 L us (cin  cout )dz L 0

（13）

The corresponding average filter coefficient, 𝜆̅, is given



16

1 ln(cin cout ) L

（14）

17

from which the values of 𝜆̅⁄𝜆0 can be obtained and the relationship of 𝜆̅⁄𝜆0 versus 𝜎 ̅̅̅̅ 𝑚

18

established.

19

The relative filter coefficient F is a function of specific mass deposit 𝜎 ̅̅̅̅ 𝑚 and it describes

20

the effect of deposition on the filtration rate. Firstly, a particular expression of F is chosen

21

based on the consideration of general filtration process. Then, the undetermined constants of

22

F can be determined from experimental filtration data.

23

2.3. Pressure Drop Model in Granular Filtration

24

For a GBF in operation, the accumulation of deposited particles influence not only on the


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Energy & Fuels

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collection efficiency, but also on the pressure drop. Alternatively, the change in the pressure

2

drop is attributed to the extent of specific mass deposit. A function G of the average specific

3

mass deposit 𝜎 ̅̅̅̅ 𝑚 is defined to describe the ratio of the current and initial pressure drop in the

4

GBF with depth L.

G( m ) 

5 6 7

(P / L)0

(p / L) (p / L)0

（15）

is the pressure drop corresponding to a clean filter and it can be estimated by the

Ergun’s equation of Eq. (16).

1   0   u  1.75  1   0  u 2  P  s    150  03d g2  03 d g s  L 0 2

8 9 10 11

（16）

In the Eq. 16, dg is the diameter of granular media, ρ is the density of the fluid, μ is the dynamic viscosity of the fluid, ε0 is the porosity of the clean granular bed filter. If the specific functional form of pressure drop ratio G( m ) is known, the dynamic

12

behavior of pressure drop can be obtained from the solutions of Eq. (15).

13 14

3. MATERIALS AND METHODS

15

3.1 Experimental Apparatus

16

A schematic of the cold model experimental apparatus used in this work is shown in Figure

17

2. It consists of three parts: an air flow control system, the granular bed filter (GBF), and a

18

measurement system.

19

The air flow control system generates gas flow by a centrifugal blower. During filtration,

20

valves V1 and V3 are switched on while the other valves are turned off. Dust-laden gas is

21

sucked off to pass through the GBF from the top to the bottom. During regeneration, valves

22

V2 and V4 are switched on while the other valves kept closed. Clean air is blew into the GBF

23

from the bottom to the top.

24

The GBF is fixed in the middle of a vertical tube with a diameter of 0.286 m and a height

25

of 1.5 m. The dust is added by a screw conveyor to simulate the pyrolysis vapors. The gas

26

contained dust flow across it and part of the dust is collected by the GBF. The escaped dust

27

from GBF is finally collected by a bag filter.

28

The velocity distributions in the cross section of the air inlet pipe and regeneration air inlet


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1

tube are measured by a L-type Pitot tube. The pressure drop caused by the GBF is measured

2

by the U-tube differential pressure gauge. The mass concentration and particle size

3

distribution of the dust are measured by an aerosol spectrometer (Welas® digital 3000, Palas

4

Gmbn, Germany).

5

3.2 Particulate Systems

6

The dust used in this study is flying char particles from coal topping technology, which is

7

collected by cyclones in series. According to the cumulative volume percentage of char

8

particles in Figure 3, the size is mainly concentrated in the range of 1 μm to 100 μm. The

9

median size of char particles is 11.1 μm, while the density of the particles is 1300 kg/m3.

10

In order to study the filtration performance of flying char particles in pyrolysis process

11

industry, binary granules in the GBF are chosen as the experimental materials. Expanded

12

perlite with high dust-loading capacity and low bulk density is used as one of the media.

13

Ceramic ball with regular shape and uniform granule size distribution is chosen as another

14

media. The properties of materials are shown in Table 1.

15

3.3 Experimental Procedure

16

The granular media was loaded in GBF and the dust storage hopper was filled with enough

17

particulate matter to conduct each experiment. The centrifugal blower was turned on and then

18

the screw-feeder was initiated to adjust the feeding mass concentration of particulate matter.

19

At this moment, the Welas® digital 3000 aerosol spectrometer was started to measure the

20

mass concentration and particle size distribution of the dust in effluent. The data about

21

pressure drop in the GBF was recorded at an interval of 5 to 10 minutes. In order to obtain

22

isokinetic samplings of the output particulate matter, a bypass gas flow was arranged to adjust

23

the velocity. During each filtration, the granular media was regenerated with clean fluidization

24

air. The granular regeneration efficiency was measured by comparing the pressure drop of

25

regenerated granular media with initial clean granular media.

26

In these experiments, the pressure drop of clean granular media with different thickness of

27

granular layer were recorded first. Then several filtration experiments were conducted with

28

different superficial gas velocity, thickness of granular layer, dust mass concentration, and

29

filtration time (see Table 2). In tests 1~4, monodisperse ceramic ball and polydisperse

30

expanded perlite were used as granular media to verify the rationality and accuracy of driven


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Energy & Fuels

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model in this study. The influences of operating parameters on filtration performance of coal

2

pyrolysis flying char particles in the GBF were investigated in tests 5~8.

3

The ability to collect particulate matter was expressed in terms of total collection efficiency,

4

η, calculated by Eq. (17). The average filter coefficient 𝜆̅ was defined as Eq. (14) which can

5

be calculated from the outlet particles mass concentration history data. Several methods can

6

be used for the determination values of λ0. The limiting value of

7

t0 gives the value of λ0. According to Wenzel

8

empiric equation. The relationship between 𝜆̅⁄𝜆0 versus ̅̅̅̅ 𝜎𝑚 can be considered as an

9

approximation of F. The value of G can be calculated from the pressure drop history data in

10

the value of λ0 was also obtained from the

experiments.

  1

11

12

40

1 ln(cin / cout ) or cin / cout as L

cout cin

（17）

4. RESULTS AND DISCUSSION

13

4.1 The Model Calculation and Process Analysis

14

The macroscopic description of the GBF filtration ability is represented by the function F

15

which depends on the extent of dust deposition. According to the study by Tien38, several

16

specific formations can be used to represent the relationship between F and the average mass

17

specific deposit ̅̅̅̅. 𝜎𝑚 On account of an unsteady state during actual granular bed filtration, the

18

surfaces and pores in the granular media are clogged by the deposited particles which improve

19

filtration efficiency. However, the accumulation of deposited particles will be entrained in the

20

gas flow which decreases filtration efficiency. Hence, the dust deposition exhibits principally

21

a mixed behavior, namely, F first increases with the increasing of 𝜎 ̅̅̅̅ 𝑚 and then decreases

22

after reaching a maximum value. In the present work, the polynomial expression of F is

23

chosen to represent the dynamic behavior of granular filtration. In the Eq. (18), k1, k2 and k3

24

are empirical constants in the relative filter coefficient F.

25

      F  F   m   1  k1  m  k2  m  k3  m   

26

In most cases, polydisperse particles are presented in granular filtration. It may be

27

necessary to calculate the efficiency of particles separately according to their sizes. Therefore,

2


3

（18）

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1

it implies extra difficulty in the application in industrial processes. In practice, we use the

2

average particle diameter or median particle diameter instead of size distribution of particles.

3

So, the char particles median size of 11.1 μm is applied to calculate the model efficiency and

4

pressure drop. This is beneficial and convenient for predicting GBF performance in industry.

5

Based on experimental data obtained (tests 1~4), the results of initial filter coefficient λ0

6

and the parameters of function F are summarized in Table 3. Because both experimental and

7

empiric methods are used, the different values of λ0 are obtained. It is noted that there is a big

8

difference between the values, a range values of λ0 obtained as the adjustment parameters for

9

the filtration model. The parameters of cubic polynomial expression for the relative filter

10

coefficient F are given in this filtration model.

11

Based on the results in Table 3 and the proposed filtration model (Eq. (12), the total

12

collection efficiency was calculated. The total collection efficiency of model prediction is

13

shown in Figure 4. In test 1 (see Figure 4a), the total collection efficiency was improved from

14

the beginning to 40 mins as the deposition of dust captures more particles. From 40 mins to

15

110 mins, the total collection efficiency decreased which might be attributed to the deposited

16

particles entrained into the gas flow. The same tendency was observed in Figure 4b and 4c.

17

The difference in total collection efficiency between the tests 1 and 2 might be due to the

18

different thickness of granular layer. The GBF in test 2 (with a granular layer thickness of 0.2

19

m) might obtain higher initial collection efficiency than that of test 1 (with a granular layer

20

thickness of 0.11 m). It needs to be noted that this initial period when collection efficiency

21

increases was short in time length which causes the increases of total collection efficiency

22

difficult to be detected. However, a comparison between the prediction results of test 2 and 3

23

reveals a significant difference in filtration performance (see Figure 4b and 4c). The collection

24

efficiency of expanded perlites with large granular size of 2~3 mm and large porosity of 0.44

25

was lower than that of small ones (ceramic balls of 1 mm in diameter and porosity of 0.38).

26

The prediction results demonstrated that the approximate expression based on the uniform

27

deposition assumption was acceptable for the polynomial expression of F. A mixed effect of

28

the average mass specific deposit ̅̅̅̅ 𝜎𝑚 on function F was taken into account in the cubic

29

polynomial expression. It first increased with the augment of 𝜎 ̅̅̅̅ 𝑚 and then decreased. The

30

tendency of prediction collection efficiency matched well with experimental data. The


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unsteady filtration process in the GBF as the deposition of dust could be described well in the

2

new model.

3

The increase of pressure drop in clogged GBF might be attributed to the accumulation of 40

and Ives 41, a nonlinear expression

4

deposited particles. According to the studies by Wenzel

5

adapted for average specific mass deposit was chosen as the function of model G. The

6

function of pressure drop ratio G can be expressed as Eq. (19).

7

 σ  G  1  d m   ε0 ρp  

m1

 σ  1  m   ε0 ρp   

m2

（19）

8

In the Eq. (19), d, m1 and m2 are empirical constants of the pressure drop ratio G and ρp is

9

the density of particles. The constants of function G and the correlation coefficient (R2) are

10

presented in Table 4. Based on the experimental data of tests 1~3, the coefficient of

11

determination value R2 was more than 0.93 in all cases. It indicated that the model fitted the

12

data well. It should be, however, noted that the values of m2 is 0 and m1 is 1 in test 1 and 2.

13

Correspondingly, the function G linearly increased with the increasing of average specific

14

42 ̅̅̅̅. mass deposit 𝜎 𝑚 The model was actually reduced to the empirical form proposed by Mints .

15

The deposition formed by flying char particles afforded a more significant increase in the

16

pressure drop as a function of the specific deposit.

17

Figure 4 illustrates the pressure drop predicted by the model along with the experimental

18

results. The initial pressure drop of a clean GBF was estimated by the Ergun’s equation. The

19

GBF pressure drop was calculated by using Eqs. (16) and (19). According to the results, the

20

model can predict that the pressure drop varies at different points in the experiment time.

21

Figure 4a and 4b showed that the pressure drop almost linearly increased with time. It

22

indicated that the deposition changed the porosity of the filter media. The greater thickness of

23

granular layer was, the larger pressure drop achieved. It was also noted that the pressure drop

24

in test 2 was almost twice than that of in test 1. However, the total collection efficiency was

25

almost the same. The detailed reason for the difference would be discussed in the later section.

26

Figure 4c shows that the curve of pressure drop almost monotonously increased with time.

27

The increasing rate of pressure drop in test 3 was slower than that of linear ones (tests 1 and

28

2). The reason of this phenomenon in test 3 might be due to filter media in unsaturated

29

condition. The coarse and porous structure of expanded perlite surface would capture more


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

pyrolysis flying char particles in filtration. The GBF with expanded perlite reached saturation

2

with a long period. It should be noted that the pressure drop of expanded perlite with granular

3

size of 2~3 mm and porosity of 0.44 was only one third of ceramic ball’s. A better

4

performance of pressure drop and capacity of capture pyrolysis flying char particles were

5

observed.

6

To evaluate the effectiveness of the new model, a comparison on the predictions of

7

collection efficiency was conducted between the present experimental data (test 4) and those

8

obtained in models of other researchers. Table 5 shows several equations of collection

9

efficiency model for the GBF. According to the basic theory of macroscopic

10

phenomenological method, a model of collection efficiency was developed by Tien 38. In this

11

model, a linear equation of relative filter coefficient F was used. It assumed that particle

12

deposition results principally in filter clogging 43. On the basis of the theory mentioned above,

13

Wenzel 40 proposed a collection efficiency model by using a different expression of function F.

14

A empirical model based on the microscopic basic filtration mechanisms was also listed in

15

Table 5. In this equation of microscopic model, ηs was the single collector efficiency. In theory,

16

the single collector efficiency could be calculated by different collection mechanisms. Table 6

17

presents efficiency equation of inertial impaction, interception, gravitational sedimentation,

18

and Brownian diffusion. 44

19

Figure 5 illustrates the comparison of the predicted results from different collection

20

efficiency models. It was found that the prediction results of collection efficiency with Tien’s

21

model were 100%. Ornatski

22

and monotonically decreasing relationship between F and average specific mass deposit 𝜎 ̅̅̅̅ 𝑚

23

was applied. If deposition leads to exhibit a mixed behavior, F must display similar behavior.

24

According to the experimental data in our study, the GBF performance first improved with

25

time then deteriorated. F must first increase with ̅̅̅̅, 𝜎𝑚 reaching a maximum, and then

26

decrease. However, a linear function of F (F=1-k𝜎 ̅̅̅̅, 𝑚 where k is an arbitrary positive constant)

27

was used in Tien's model. It implies that the filtration rate in GBF is deteriorated as the filter

28

becomes progressively clogged. The specific functional form of F results in undetectable

29

enhanced filtration behavior by the clogged pores of the GBF media. Accordingly, the

30

collection efficiency calculated by Tien's model might not vary with time. The expression of F

43

assumed that deposition results principally in filter clogging


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1

proposed by Ives 41 that capable of describing mixed behavior was applied in Wenzel’s model.

2

The prediction results matched well with experimental data within 30 mins. However, the

3

prediction results were not good enough after 30 mins. There was a gap between microscopic

4

basic filtration mechanisms model and experimental data. It might be due to negligence of the

5

fact that filter performance varies with time. So the calculation values from microscopic basic

6

filtration mechanisms model could not predict the unsteady filtration process in the GBF. It

7

should be noted, however, that predicted plot by the new model (lines) displayed a good

8

agreement with the experimental mixing data (dispersed symbols). It was observed that better

9

prediction results were obtained by the new model than the other models. The results

10

indicated that the new filtration model captured the evolutional features (the filter

11

performance first improved with time then deteriorated) of the GBF. Therefore, it was

12

demonstrated that the model proposed was able to describe the GBF filtration ability

13

quantitatively.

14

4.2 Influence of Operating Parameters on Filtration Performance

15

In order to investigate the filtration performance of coal pyrolysis flying char particles in

16

the GBF, the effects of superficial gas velocity, thickness of granular layer, and dust mass

17

concentration on collection efficiency and pressure drop were examined in cold model

18

experiments. The conditions of these experiments (test 5~8) are shown in table 2. Although

19

the unsteady state filtration model and predicted values variation with time, the proposed

20

model could also be applied for predicting collection efficiency and pressure drop at the fixed

21

time point of 10 minutes.

22

4.2.1 Superficial Gas Velocity

23

Figure 6 illustrates the effect of superficial gas velocity on filtration performance. In test 5

24

(see Figure 6a), with the velocity increasing from 0.2 m/s to 0.6 m/s, the collection efficiency

25

increased slightly before reaching the point in the vicinity of 99.9%. When the velocity

26

exceeded 0.6 m/s, the collection efficiency rapidly decreased from 99.9% to 98.8%. The

27

reason for this phenomenon might be due to effect of the inertial impaction collection

28

mechanism. Lower superficial gas velocity would be of benefit to capture more particles by

29

inertial impaction of granular media. When the velocity exceeded 0.6 m/s, re-entrainment of

30

deposited particles would occur and the collection efficiency declined. The pressure drop


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

monotonically increased with increasing of velocity. It seemed that the porosity of the GBF

2

was not under significant influence exerted by the increases of superficial gas velocity.

3

As shown in Figure 6b, the same tendency of both collection efficiency and pressure drop

4

was obtained in test 6. Under the same conditions, the pressure drop of expanded perlite was

5

only one third of the pressure drop of ceramic ball. It should be noted that the collection

6

efficiency was insensitive to superficial gas velocity and the values were more than 99.6%. It

7

was suggested that the GBF with expanded perlite as granular media would obtain high

8

efficiency and low pressure drop.

9

4.2.2 Thickness of Granular Layer

10

Figure 7 depicts the effect of the thickness of granular layer on filtration performance. As

11

shown in Figure 7a, when the superficial gas velocity at the range of 0.2 m/s to 0.6 m/s, those

12

collection efficiency were almost the same for both the granular layer thickness of 0.11 m and

13

0.2 m, the number could exceed 99.9%. The lower collection efficiency was observed at the

14

granular layer thickness of 0.07 m. When the velocity exceeded 0.6 m/s, the collection

15

efficiency saw a rapid decrease despite the variances in granular layer thickness. The effect of

16

granular layer thickness on collection efficiency was manifested. With the increasing of

17

granular layer thickness, the collection efficiency maintained rising trends. The influence of

18

the thickness of granular layer on pressure drop is illustrated in Figure 7b. The pressure drop

19

curves were almost monotonously increased.

20

When the superficial gas velocity varies in the range of 0.2 m/s to 0.6 m/s, the collection

21

efficiencies were almost the same with the increasing of granular layer thickness. However,

22

the pressure drop nearly tripled since the thickness of granular layer increased from 0.07 m to

23

0.2 m at the same superficial gas velocity. It seemed that the pressure drop increases with the

24

growing of the granular layer thickness. The results revealed the proper conditions for the

25

GBF might be: superficial gas velocity of 0.2 m/s to 0.6 m/s and granular layer thickness of

26

0.07 m to 0.11 m.

27

4.2.3 Dust Mass Concentration

28

In general practice, GBF was applied as a fine filtering equipment. Therefore, it is operated

29

under low dust mass concentration to avoid frequent regeneration of GBF. The particles mass

30

concentration within the range of 3 g/m3 to 8 g/m3 was considered and used to investigate the


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Energy & Fuels

1

effect on filtration performance in test 8. It was found that the collection efficiency and

2

pressure drop were little affected by the increasing of dust mass concentration. The increase

3

of dust mass concentration might result in more deposition which in turn contributes to the

4

capture of more particles. However, the collection efficiency was around 99.9%. There was no

5

significant change in pressure drop with the variations of dust mass concentration. A

6

noteworthy operating flexibility in the case of low dust mass concentration of the GBF was

7

observed.

8

5. CONCLUSIONS

9

A macroscopic phenomenological filtration model was developed to describe the filtration

10

process of the GBF. The average mass concentration of particles 𝑐̅ and the average specific

11

mass deposit 𝜎 ̅̅̅̅ 𝑚 were defined as the characteristic parameters of the model. A third-order

12

polynomial expression of the relative filter coefficient F was applied to describe the dynamic

13

behavior of granular filtration efficiency. The nonlinear expression was applied for the

14

relative pressure drop ratio G. That the filtration performance varies with time was also

15

detected. The filtration model has the capacity to describe the GBF performance

16

quantitatively and a better predictive performance was obtained. As opposed to other models,

17

the model proposed in our study captured the unsteady state of granular bed filtration: the

18

collection efficiency first improved with time then deteriorated.

19

The filtration performance of coal pyrolysis flying char particles in the GBF was evaluated

20

in the cold model experiments. Effects of superficial gas velocity, thickness of granular layer,

21

and dust mass concentration on total collection efficiency and pressure drop were analyzed.

22

The total collection efficiency could reach a span between 98% and 99.9%. It demonstrated

23

that the GBF was a high efficiency technology for coal pyrolysis flying char particles

24

filtration. Some important features of the GBF might be drawn: with the increasing of

25

superficial gas velocity, the total collection efficiency slightly increased at first and then

26

decreased, while the pressure drop increased. The total collection efficiency and pressure drop

27

were increased with increasing of the thickness of granular layer. In the case of lower dust

28

mass concentration, the total collection efficiency and pressure drop were little affected by the

29

increasing of dust mass concentration. The optimal operating conditions of the GBF were

30

obtained: superficial gas velocity of 0.2 m/s to 0.6 m/s and granular layer thickness of 0.07 m


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

to 0.11 m.

2 3

ACKNOWLEDGEMENT

4

The authors acknowledge the financial support by National Natural Science Foundation

5

of China (Grant No. 21606022), the National Basic Research Program of China (973 Program,

6

Grant No. 2014CB744304) and the Open Project Program of State Key Laboratory of

7

Multiphase Complex Systems (MPCS2014D11).

8 9 10

AUTHOR INFORMATION Corresponding author: Prof. Guogang Sun, [email protected]

11 12

NOMENCLATURE

13

c——mass concentration of particles in the gaseous flow, kg·m-3

14

𝑐̅——average mass concentration of particle, kg·m-3

15

C——Cunningham correction factor, dimensionless

16

dg——granular media diameter, m

17

dp——particle diameter, m

18

b, k, k1, k2, k3, n1, n2,——parameter of the relative filter coefficient, dimensionless

19

d, m1, m2,——parameter of the pressure drop ratio, dimensionless

20

F——relative filter coefficient, dimensionless

21

G——pressure drop ratio, dimensionless

22

L——filter depth, m

23

ΔP——pressure drop, Pa

24

t——time, s

25

us——superficial gas velocity, m·s-1

26

z——axial direction, m

27

R——Interception number, dimensionless

28

Re——Reynolds number, dimensionless

29

Stk——Stoke number, dimensionless


Page 16 of 33

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Energy & Fuels

1

G’——Gravitational number, dimensionless

2

Pe——Peclet number, dimensionless

3

μ——dynamic viscosity of the fluid, Pa·s

4

ρ——density of the fluid, kg·m-3

5

ρp——density of particles, kg·m-3

6

σm——specific mass deposit, kg·m-3

7

𝜎 ̅̅̅̅——average specific mass deposit, kg·m-3 𝑚

8

λ——filter coefficient, m-1

9

𝜆̅——average filter coefficient, m-1

10

ε0——porosity of the clean granular bed filter, dimensionless

11

η——total collection efficiency, dimensionless

12

ηs——efficiency of the individual collectors, dimensionless

13

Subscripts

14

0——indicates the initial state, i.e. the clean granular bed filter media conditions

15

in——indicate the condition on the inlet of the filter device

16

out——indicate the condition on the outlet of the filter device

17 18

REFERENCES

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

1. Miura, K., Mild conversion of coal for producing valuable chemicals. Fuel Process. Technol. 2000, 62, 119-135. 2. Zhang, J.; Wu, R.; Zhang, G.; Yao, C.; Zhang, Y.; Wang, Y.; Xu, G., Recent Studies on Chemical Engineering Fundamentals for Fuel Pyrolysis and Gasification in Dual Fluidized Bed. Ind. Eng. Chem. Res. 2013, 52, (19), 6283-6302. 3. Liu, Z.; Guo, X.; Shi, L.; He, W.; Wu, J.; Liu, Q.; Liu, J., Reaction of volatiles – A crucial step in pyrolysis of coals. Fuel 2015, 154, 361-369. 4. Zhang, C.; Wu, R.; Hu, E.; Liu, S.; Xu, G., Coal Pyrolysis for High-Quality Tar and Gas in 100 kg Fixed Bed Enhanced with Internals. Energy Fuels 2014, 28, (11), 7294-7302. 5. Whitmer, L. E., Removal of particulate matter from condensable vapors using a moving bed granular filter (M.S. Thesis). Ames: Iowa State University 2011. 6. Zhang, C.; Wu, R.; Xu, G., Coal Pyrolysis for High-Quality Tar in a Fixed-Bed Pyrolyzer Enhanced with Internals. Energy Fuels 2014, 28, (1), 236-244. 7. Franklin, H. D.; Peters, W. A.; Howard, J. B., Mineral matter effects on the rapid pyrolysis and hydropyrolysis of a bituminous coal. 1. Effects on yields of char, tar and light gaseous volatiles. Fuel 1982, 61, (2), 155-160. 8. Agblevor, F. A.; Besler, S., Inorganic Compounds in Biomass Feedstocks. 1. Effect on


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the Quality of Fast Pyrolysis Oils. Energy Fuels 1996, 10, (2), 293-298. 9. Wang, B.; Liu, Y.; Liu, J.; Sun, G., Experimental study on separation performance of a cyclone separator for oil shale processes. Petro. Process. Petrochem. 2011, 42, (10), 59-62. 10. Wang, W.; Wang, Y.; Ma, Q.; Sun, G., Contrast experiments on cyclone separator performances of shale ash and FCC fine catalysts. CN. Powder Sci. Technol. 2012, 18, (04), 70-72. 11. Peukert, W.; Wadenpohl, C., Industrial separation of fine particles with difficult dust properties. Powder Technol. 2001, 118, (1-2), 136-148. 12. Kang, B.-S.; Lee, K. H.; Park, H. J.; Park, Y.-K.; Kim, J.-S., Fast pyrolysis of radiata pine in a bench scale plant with a fluidized bed: Influence of a char separation system and reaction conditions on the production of bio-oil. J. Anal. Appl. Pyrolysis 2006, 76, (1-2), 32-37. 13. Hoekstra, E.; Hogendoorn, K. J. A.; Wang, X.; Westerhof, R. J. M.; Kersten, S. R. A.; van Swaaij, W. P. M.; Groeneveld, M. J., Fast Pyrolysis of Biomass in a Fluidized Bed Reactor: In Situ Filtering of the Vapors. Ind. Eng. Chem. Res. 2009, 48, (10), 4744-4756. 14. Pollard, A. J. S., comparison of bio-oil produced in a fractionated bio-oil collection system (M.S. Thesis). Ames: Iowa State University 2009. 15. Xiao, G.; Wang, X.; Zhang, J.; Ni, M.; Gao, X.; Luo, Z.; Cen, K., Granular bed filter: A promising technology for hot gas clean-up. Powder Technol. 2013, 244, 93-99. 16. Smid, J.; Hsiau, S.-S.; Peng, C.-Y.; .Lee, H.-T, Granular moving bed filters and adsorbers (GM-BF/A) — patent review: 1970–2000. Adv. Powder Technol. 2005, 16, (4), 301–345. 17. Hsu, C.-J.; Hsiau, S.-S., A study of filtration performance in a cross-flow moving granular bed filter: The influence of gas flow uniformity. Powder Technol. 2015, 274, 20-27. 18. Kuo, Y.-M.; Huang, S.-H.; Lin, W.-Y.; Hsiao, M.-F.; Chen, C.-C., Filtration and loading characteristics of granular bed filters. J. Aerosol Sci. 2010, 41, (2), 223-229. 19. Hsu, C.-J.; Hsiau, S.-S.; Chen, Y.-S.; Smid, J., Investigation of the gas inlet velocity distribution in a fixed granular bed filter. Adv. Powder Technol. 2010, 21, (6), 614-622. 20. Hsu, C.-J.; Hsiau, S.-S., Experimental study of the gas flow behavior in the inlet of a granular bed filter. Adv. Powder Technol. 2011, 22, (6), 741-752. 21. Chou, C.-S.; Lee, A.-F.; Yeh, C.-H., Gas-Solid Flow in a Two-Dimensional Cross-Flow Moving Granular Filter Bed with a Symmetric Boundary. Part. Parti. Syst. Charact. 2007, 24, (3), 210-222. 22. Chen, Y.-S.; Hsu, C.-J.; Hsiau, S.-S.; Ma, S.-M., Clean coal technology for removal dust using moving granular bed filter. Energy 2017, 120, 441-449. 23. Chen, Y.-S.; Hsiau, S.-S.; Smid, J.; Wu, J.-F.; Ma, S.-M., Removal of dust particles from fuel gas using a moving granular bed filter. Fuel 2016, 182, 174-187. 24. Chen, J.-Y.; Hsiau, S.-S., Removal of Char Fines from Biomass Fast Pyrolysis Vapors by Moving Granular Bed Filter. The 13th Asia Pacific Confederation of Chemical Engineering Congress, APCChe: Taipei. 2010. 25. Smid, J.; Hsiau, S.-S.; Peng, C.-Y.; Lee, H.-T., Moving bed filters for hot gas cleanup. Filtr. Sep. 2005, 42, (6), 34-37. 26. Brown, R. C.; Shi, H.; Colver, G.; Soo, S.-C., Similitude study of a moving bed granular filter. Powder Technol. 2003, 138, (2-3), 201-210. 27. El-Hedok, I. A.; Whitmer, L.; Brown, R. C., The influence of granular flow rate on the performance of a moving bed granular filter. Powder Technol. 2011, 214, (1), 69-76.


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28. Liang, P.; Qu, X.; Bi, J., Study on the low temperature coal pyrolysis by solid heat carrier in a moving bed pyrolyzer. J. Fuel Chem. Technol. 2008, 36, (04), 401-405. 29. Liang, P.; Wang, Z.; Dong, Z.; Bi, J., Hot dust removal in the process of low temperature coal pyrolysis. J. Fuel Chem. Technol. 2006, 34, (01), 25-29. 30. Gao, S.; Xu, S.; Wei, S.; Xia, J.; Ren, Y., Experimental study on moving granular bed filter for removing particulate at ambient temperature and high pressure. J. Fuel Chem. Technol. 2001, 29, (06), 532-536. 31. Xia, J.; Xu, S.; Gao, S.; Ren, Y., Experimental research on moving granular bed filter for hot gas cleanup. Power Eng. 2003, 23, (02), 2337-2341. 32. Tardos, G. I.; Abuaf, N.; Gutfinger, C., Dust Deposition in Granular Bed Filters: Theories and Experiments. J. Air Pollut. Control Assoc. 1978, 28, (4), 354-363. 33. Boccardo, G.; Marchisio, D. L.; Sethi, R., Microscale simulation of particle deposition in porous media. J Colloid Interface Sci. 2014, 417, 227-37. 34. Zhao, J.; Huang, J.; Wu, J.; Fang, Y.; Wang, Y., Modeling and optimization of the moving granular bed for combined hot gas desulfurization and dust removal. Powder Technol. 2008, 180, (1-2), 2-8. 35. Kolakaluri, R.; Murphy, E.; Subramaniam, S.; Brown, R. C.; Fox, R. O., Filtration model for polydisperse aerosols in gas-solid flow using granule-resolved direct numerical simulation. AIChE J. 2015, 61, (11), 3594-3606. 36. Guan, L.; Gu, Z.; Yuan, Z.; Yang, L.; Zhong, W.; Wu, Y.; Sun, S., Numerical study on the penetration of ash particles in a three-dimensional randomly packed granular filter. Fuel 2016, 163, 122-128. 37. Jung, Y.; Walata, S. A.; Tien, C., Experimental Determination of the Initial Collection Efficiency of Granular Beds in the Inertial-Impaction-Dominated Region. Aerosol Sci. Technol. 1989, 11, (2), 168-182. 38. Tien C.; Ramarao, B. V., Granular Filtration of Aerosols and Hydrosols (2nd edition). Elsevier Science & Technology Books 2007. 39. Sulaymon, A. H.; Mustafa, Y. A., Aerosol Filtration Using Quartz Sand Filter. American J. Environ. Sci. 2012, 8, (4), 385-395. 40. Wenzel, B. M.; Porciúncula, C. B.; Marcilio, N. R.; Menegolla, H. B.; Dornemann, G. M.; Godinho, M.; Martins, C. B., Filtration of dust in an intermittent moving granular bed filter: Performance and modeling. Sep. Purif. Technol. 2014, 133, 108-119. 41. Ives, K. J., Theory of filtration, in: International Water Supply Congress and Exhibition, Special Subject No. 7. Vienna 1969. 42. Mints, D. M., Modern theory of filtration, in: International Water Supply Congress, Special Report No. 10. Barcelona 1966. 43. Ornatski, N. V.; Sergeev, E. V.; Shekhtman, Y. M., Investigation of the Process of Clogging of Sands (PhD. Thesis). Moscow: University of Moscow, 1955. 44. Zhan, M., Research on the mixing of oil shale and solid heat carriers and high temperature pyrolysis gas dust removal technology (PhD. Thesis). Beijing: China University of Petroleum 2015.

42 43


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1

Page 20 of 33

Table 1. Properties of granular materials Property

Ceramic ball

Size range/mm Shape coefficient Particle density/kg•m-3 Bulk density/kg•m-3 Porosity of bed

1 1.00 2700 1670 0.38

2 3


Expanded perlite 2~3 0.79 70 0.44

Page 21 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1

Table 2. Conditions of experiment Test

Superficial gas velocity us/m•s-1

Thickness of granular layer L/m

Filter medium

Inlet particle concentration cin/g•m-3

Filtration time t/min

1 2

0.2 0.2

0.11 0.2

3 3

120 120

3

0.2

0.2

3

180

4 5

0.4 0.2~1

0.11 0.2

3 3

60 10

6

0.2~1

0.2

3

10

7 8

0.2 0.2

0.07~0.2 0.11

ceramic ball ceramic ball expanded perlite ceramic ball ceramic ball expanded perlite ceramic ball ceramic ball

3 3~8

10 10

2 3


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 33

Table 3. Parameters values of initial filtration coefficient λ0 and function F

1 Test

λ0 /m-1

Parameter values of F expression k1

1 2 3 4

67~115 51~115 31~73 54~91

k2 -2

5.24×10 6.27×10-2 3.89×10-2 6.16×10-2

R2

k3 -3

-2.61×10 -5.05×10-3 -1.95×10-3 -4.24×10-3

2 3


3.37×10-5 9.23×10-5 2.43×10-5 6.19×10-5

0.74 0.79 0.88 0.96

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Energy & Fuels

1 2

Table 4. Parameter values of the function G Test 1 2 3

Parameter values of G expression d 4.65

m1 1.00

7.49 83.86

R2

m2 0

0.93

1.00

0

0.98

0.19

1.04

0.97

3 4


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Page 24 of 33

Table 5. Summary of collection efficiency model for GBF Model

Efficiency equation 

New model

Chi Tien

Bruno M. Wenzel Microscopic basic filtration mechanisms model

c us 0 Ft m

  1

e

 =1 

eus 0cin kt e0 L  eus 0cin kt  1

cin

  1 e  =1  e



 0 FL

3 LS 2(1 ) d g

2 3


F experission       F  F   m   1  k1  m  k2  m  k3  m   2

3

F  1  k m , k＞0

 σ  F  1  b m   ε0 ρp  

n1

 σ  1  m   ε0 ρp   

n2

S =1  (1 R )(1 l )(1 G ' )(1 D )

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Energy & Fuels

1

Table 6. Single collector efficiency with different mechanisms Model

Collection mechanism

Efficiency equation p d p usC 0.75ln(4Stk ) 2 Stk = ] 9 d g 2Stk  1.214 , 2

Langmuir Bloggett

Inertial impaction

I  [1 

Langmuir

Interception

R  (1  R)2  (1  R) 

3 2

2/3 D  4.18Re1/6 D Pe

dp 1 R dg 2(1  R) ,

ReD  1, Pe

Filtration Performance of Coal Pyrolysis Flying Char Particles in a

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