CFD-DEM Investigation on the Biomass Fast Pyrolysis: The Influences

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Thermodynamics, Transport, and Fluid Mechanics

CFD-DEM Investigation on the Biomass Fast Pyrolysis: The Influences of Shrinkage Patterns and Operating Parameters Chenshu Hu, Kun Luo, Shuai Wang, Liyan Sun, and Jianren Fan Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b05279 • Publication Date (Web): 24 Dec 2018 Downloaded from http://pubs.acs.org on January 3, 2019

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Industrial & Engineering Chemistry Research

CFD-DEM Investigation on the Biomass Fast Pyrolysis: The Influences of Shrinkage Patterns and Operating Parameters

By

Chenshu Hu, Kun Luo*, Shuai Wang, Liyan Sun, Jianren Fan

State Key Laboratory of Clean Energy Utilization

Zhejiang University, Hangzhou 310027, P.R. China

Submitted to


*

Author for correspondence. Fax: +86-0571-87991864; E-mail: [email protected]

ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 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

Abstract In this study, we present a CFD-DEM framework for the biomass fast pyrolysis. With model validations against experiments, the effects of the particle shrinkage and superficial gas velocity on the pyrolysis process in the fluidized bed reactor are investigated. The results show that the shrinkage pattern has minor effect on the product yields, but it greatly affects the entrainment behaviors. It is found that the constant density shrinkage pattern leads to the longest residence time and the constant size shrinkage pattern results in the shortest residence time. Interestingly, the time that biomass particles stay in the dense region shows a reverse trend. It is demonstrated that the gas velocity has minor effect on the heat transfer rate and chemical conversion rate of biomass particles, but it has a profound impact on the distributions of solid concentration and heterogeneous reactions.

Key words: fluidized bed; biomass fast pyrolysis; particle shrinkage; CFD-DEM; superficial gas velocity.


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1.Introduction With the urgent need to find the alternatives of fossil energy, the biomass, which is an environmentally neutral and abundant energy source, has attracted great interests. Among different biomass utilization technologies, the biomass fast pyrolysis process 1 is a promising way to convert the biomass into liquid fuel. The fluidized bed is the most commonly used reactor for the biomass fast pyrolysis. With efforts to validate and improve the numerical models, CFD simulations have been extensively applied to investigate the fluidized bed under various conditions. Basically, the modeling methods of the dense gas-solid flow can be divided into two categories, namely the Eulerian-Eulerian and Eulerian-Lagrangian methods. Specifically, the twofluid model (TFM)

2

under the Eulerian-Eulerian framework and the CFD-DEM

3, 4

under the Eulerian-Lagrangian framework are two mainstream modeling approaches. Comparatively, the CFD-DEM simulation often requires more computation cost to deal with inter-particle interactions and particle tracking, but it provides more details at particle-level, which is helpful to better understand the underlying mechanisms. In comparisons, the CFD-DEM generally gives a better prediction of the gas-solid fluidization behaviors than the classical TFM. Goldschmidt et al.

5

compared the

predictions of the TFM and CFD-DEM in a bubbling fluidized bed. The results showed that the CFD-DEM provides superior resemblance with the experimental measurements, in terms of the bubble structures, average solid volume fraction and bed expansion. It was pointed out that the differences are mainly due to that the classical TFM neglects the particle rotation. Almohammed et al.

6

compared the performances of different



numerical methods in a jetting fluidized bed. The results demonstrated that the TFM fails to reproduce the realistic pattern of bubble formation, while the CFD-DEM gives reasonable results. Moreover, researchers found that the classical TFM tend to overestimate the segregation speed in polydisperse fluidized bed. Chao et al. 7 found that the TFM underpredicts the interactions between binary mixtures, which results in a high segregation rate, while an extra semi-empirical frictional binary particle drag term can help improve the predictions. van Sint Annaland et al. 8 also showed that the classical TFM fails in predicting the correct segregation rate, and a new kinetic theory of granular flow for polydisperse mixtures were proposed 9, which can improve the predictions in binary fluidized bed greatly. It is noted that most of the previous numerical studies on the reactive fluidized bed were based on the two-fluid model (TFM) under the Eulerian-Eulerian framework 10, 11, while limited CFD-DEM studies were focused on the fluidized bed with chemical reactions 12, 13. In the reactive fluidized bed, fuel particles undergo continuous changes in the size and density, which has great influences on the behaviors of fuel particles. While it is difficult for the classical TFM to accurately describe the evolution of particle size/density, unless additional models (e.g., the population balance model (PBM) 14) are adopted. Overall, further CFD-DEM simulations on reactive fluidized beds are required, to give results with high resolution and accuracy, which is helpful to better understand the underlying mechanisms. Also, the CFD-DEM results are useful for validating the Eulerian-Eulerian models. A series of numerical simulations have been carried out to investigate the biomass


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fast pyrolysis in fluidized beds, and most of these studies were focused on the model validation and the effect of the model parameters and operating conditions. Xue et al. 15

validated the Eulerian-Eulerian models against the measured predicted yields and

reasonable agreement was obtained. Papadikis et al.

16

numerically investigated the

effect of drag force model on modeling of biomass fast pyrolysis, and the results showed that the use of Wen-Yu drag model leads to the highest interphase heat transfer coefficient and the shortest residence time. While the selection of the drag force model has only minor effect on the product yields. Ranganathan and Gu 17 compared different chemical kinetic schemes in predicting the biomass fast pyrolysis and found that the advanced kinetic scheme shows better agreement with experimental data. Sharma et al. 18

conducted simulations to explore the effect of bed temperature, superficial gas

velocity and particle size on the biomass fast pyrolysis process. It was found that the bed temperature higher than 500 ℃ will leads to a significant increase of conversion from the tar to the non-condensable gas. The results also showed that the fraction of product tar yields increases with the increase of superficial gas velocity. However, there is still a lack of thorough understanding on the interplays among the gas-solid hydrodynamics, heat transfer, and chemical reactions. As mentioned before, the particle shrinkage should be considered in the modeling of conversions of solid fuels. Two shrinkage models were most commonly adopted in previous numerical studies 19, 20, namely the constant size model and constant density model. Other shrinkage patterns were also proposed or employed by researchers

21, 22

.

Among different particle shrinkage models, the model proposed by Di Blasi 23 gives a



comprehensive description of the particle shrinkage for biomass pyrolysis process. In the Di Blasi’s model, the volume of the biomass particle is divided into two parts, namely the volume occupied by the solid and the volume occupied by the volatile, and three parameters should be specified to define the shrinkage patterns for different types of biomass. Li et al.’s 24 compared the performances of different shrinkage models in predicting biomass devolatilization, and simulation results showed that the Di Blasi model 23 gives a better agreement with experimental data than the constant size model and constant density model. Papadikis et al.

25

evaluated the effect of the shrinkage

parameters of the Di Blasi model 23 in the biomass fast pyrolysis process. They found that the shrinkage parameter has minor effect on the product yields and pyrolysis time. Liu et al. 26 also investigated the influence of the shrinkage parameters in the Di Blasi model. The results demonstrated that the shrinkage parameters greatly affect the residence time distributions (RTD) as well as the bio-char yields. Nevertheless, the knowledge of influences of the particle size evolution on the gas-solid behaviors in reactive fluidized beds are still quite limited. Moreover, in most previous numerical studies, the simulation results were commonly analyzed with the profiles as a function of the run time, but the characteristics as a function of the ‘particle residence time’ is also of great significance, which has rarely been mentioned, partly due to the limitation of the Eulerian-Eulerian method. In the present study, a CFD-DEM framework for the biomass fast pyrolysis process is presented. The influences of the particle size evolution and superficial gas velocity on the gas-solid behaviors are investigated. By the virtue of the CFD-DEM


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coupling method, the spatial and temporal characteristics of biomass particles are analyzed, in terms of the movements, entrainment behaviors, heat transfer and chemical reactions.

2.Mathmatical method 2.1 Fluid phase Based on the open-source code package MFiX-DEM 27, a CFD-DEM framework for biomass fast pyrolysis process has been developed and applied in this study. The fluid phase in the dense gas-solid reactive flow are described by four governing equations, namely the continuity equation, momentum balance equation, energy balance equation, and species balance equation, which are formulated as:

 ( f  f ) t

+   ( f  f u f ) = R f

 ( f  f u f ) t  f  f C p, f [

(1)

+   ( f  f u f u f ) = − f p f − F fp +  f  f g +   ( f  f ) + I pf

T f

+ u f T f ] =   [ f k f T f ] − Q fp − H rf t  (  f  f Y fk ) +   (  f  f u f Y fk ) =   (  f  f D fk Y fk ) + R fk t

where

f

represents the fluid volume fraction,

chemical reactions,

F fp

Rf

represents the momentum exchanges due to the interphase mass transfer,

kf

C p, f

(3) (4)

represents the source term due to

represents the interphase momentum exchanges,

represents the viscous stress tensor,

(2)

I pf

f

represents the specific heat of the fluid phase,

represents the thermal conductivity of fluid phase,

Q fp

represents the interphase

energy exchanges, H rf represents the heat source term due to chemical reactions,



Y fk

represents the mass fraction of fluid species k,

coefficient of fluid species k, and

R fk

D fk

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represents the diffusion

represents the source term for species k due to

chemical reactions. The fluid volume fraction is calculated as:

 f = 1− s s = where

1 Vcell



(5)

n i =1

(6)

Wp ,iVp ,i

 s is the solid volume fraction, W is the portion of the particle volume p ,i

occupied by the current cell, and V p ,i is the particle volume. Specifically, the divided particle volume method (DPVM) is adopted 28 to map the Lagrangian data to Eulerian cell. The momentum and energy exchanges are calculated as:

where

F fp =

1 Vcell



Q fp =

1 Vcell



f d ,i

n i =1

n

W p ,i ( f d ,i )

(7)

Wp（ ,i q fp ,i）

(8)

i =1

represents the drag force and

q fp ,i

represents the interphase heating rate

of particle i. In the calculations of the interphase momentum exchange, interphase energy exchange and heterogeneous reaction, the Eulerian fluid data is required to be interpolated into the Lagrangian particles, which is also through the DPVM algorithm. The correlations of the drag force and convective heat transfer adopted in simulations will be discussed in the later sections. The effects of the turbulence were ignored in most of previous numerical studies under different operating conditions, e.g., bubbling fluidized beds 29-32, spouted beds 33,


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34

, and circulating fluidized beds

. In several studies

35

36-41

, the LES models were

adopted to account for the turbulence of fluid phase. Nevertheless, there is still a lack of thorough understanding on the role of turbulence in the fluidized bed. Regarding the use of the turbulence model, it could be controversial to use the classical LES model in CFD-DEM simulations. As pointed out by Berrouk and Wu 42, the genuine LES requires a fine grid, which is often not feasible for CFD-DEM simulations since the grid size should be larger than the particle size 43. They suggested that the adoption of turbulence model is helpful for fluidized bed modeling, but it requires a modification for dense gas-solid flow. Hence, the turbulence model is not employed in the present simulation. 2.2 Solid phase Basically, the solid phase is solved in the Lagrangian framework. The evolutions of the mass, velocity, temperature and species of each particle is calculated based on the following formulas: dmi = Ri dt mi

Ii

(9)

dvi = f d ,i + f p ,i + f c ,i +mi g dt

(10)

di = Ti dt

miC p ,i

dTp ,i

dmiYi ,k dt

dt

(11)

= q fp ,i + q pp ,i + qr ,i

(12)

= Ri ,k

(13)

f where Ri represents the mass source term due to reactions, c ,i represents the

interparticle contact force, I i represents the momentum of inertia, Ti represents the torques exerted on the particle i,

q fp ,i

and

q pp ,i

represent the interphase and



q r ,i

interparticle heat transfer,

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represents the heat from reactions, and

Ri ,k

represents

the source term for solid species k due to reactions. 2.2.1 Models for particle motion The soft sphere contact model 3 is employed to describe the inter-particle collisions. Specifically, the linear spring-dashpot contact model is adopted, in which the spring stiffnesses are constant during collisions. The contact forces between particle i and j are calculated as: f c ,ij = f cn ,ij + f ct ,ij

(14)

f cn,ij = −(kn,ij n ,ij )ni − n ,ij (vr ,ij  ni )ni

(15)

f ct ,ij = min{| −kt ,ij t ,ij ti − t ,ij [(vr ,ij  ti )ti + (i  ri −  j  rj )] |,  fcn ,ij }

(16)



n ,ij



t ,ij =

mp =

=

2 mp kn,ij ln en,ij

(17)

 2 + ln 2 en,ij 2 m p kt ,ij ln et ,ij

(18)

 2 + ln 2 et ,ij mi m j

(19)

mi + m j

where

f cn ,ij

and

f ct ,ij

are the normal and tangential components of the collisional

force,

kn ,ij

and

kt ,ij

are the spring constants,

damping coefficients,

 n ,ij

and

 t ,ij

 n ,ij

and

t ,ij

are the dashpot

t are the overlaps of two particles, ni and i are

the unit vectors in the normal and tangential directions, and

vr ,ij

is the relative velocity

between particles. The tangential contact force is restricted by the maximum friction force

 f cn ,ij

18), where

. The calculations of the damping coefficients are given in Equation (17en ,ij

and

et ,ij

are the restitution coefficients in the normal and tangential


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directions, and

mp

is the effective mass and can be calculated by Equation (19).

With respect to the interphase momentum transfer, the drag force and pressure gradient force are considered, which are calculated as: f d ,i =

V p ,i 

 p ,i

( u f − v p ,i )

(20)

f p ,i = −Vp ,i p f

(21)

where  is the drag force coefficient. In the present study, the drag correlation proposed by Gidaspow et al. 44 is adopted, which is formulated as: 3  f  f (1 −  f ) u f − v p −2.65 f  CD d 4  p   = 2 150 (1 −  f )  f 1.75 f (1 −  f ) u f − v p +   f d p2 dp  

 24 0.687  Re (1 + 0.15Re p ) CD =  p 0.44 

 f  0.8 (22)

 f  0.8

Re p  1000

(23)

Re p  1000

2.2.2 Models for heat transfer Due to the relatively low temperature in the biomass fast pyrolysis reactor, the radiative heat transfer is neglected, while the interphase heat transfer and interparticle heat transfer are considered in the numerical models. The sub-models for heat transfer are summarized as:

q fp ,i = hpf ,i Ai (T f − Tp ,i )

(24)

hpf ,i = Nu p ,i k f / d p

(25)

0.33 0.33 Nu p,i = (7 − 10 f ,i + 5 2f ,i )(1.0 + 0.7 Re0.2 ) + (1.33 − 2.4 f ,i + 1.2 2f ,i ) Re0.7 p ,i Pr p ,i Pr

q pp ,ij = q pc ,ij + q pfp ,ij

(26) (27)



q pc ,ij =

4 k p ,i k p , j k p ,i + k p , j

Rc (Tp , j − Tp ,i )

q pfp ,ij = k f (Tp , j − Tp ,i ) 

lij − ( ri 2 − r 2 + rj2 − r 2 )

Rin

i, j out

R

= (r + d ) − ( k p

 0 Rini , j =   Rc ,ij

k 2

(28)

2 r

Rout

(rpk + d k )2 − rpl + li2, j 2li , j

dr

)2

li , j  2rpk li , j  2rpk

Rc ,ij = (rpk ) 2 − (

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(29)

(30)

(31)

(rpk )2 − rpl + li2, j

)2

(32)

rpk = max(ri , rj ) , rpl = min(ri , rj )

(33)

2li , j

To calculate the interphase convective heat transfer, the Gunn’s empirical correlation 45 for Nusselt Number is employed, as presented in Equation (26). Two mechanisms of conductive heat transfer are considered, namely the direct contact conduction and particle-fluid-particle conduction. For the calculation of the direct contact conduction rate

q pc ,ij

, a modified Batchelor and O’Brien contact

conduction model 46 is used, as presented in Equation (28). According to Lu et al.’ study 47

, the direct contact conduction took up only a very small portion in the total heat

transfer, while the particle-fluid-particle conduction accounted for approximately 34% of the total heat transfer in a bubbling fluidized bed. To model the particle-fluid-particle indirect conduction, the Rong and Horio’s model 48 is adopted. In Equation (29-33),

lij

i, j i, j is the distance between the centroids of two particles, Rout and Rin are the outer and

k inner bounds of the heat transfer region, and d is the thickness of gas phase layer,

respectively. In previous DEM studies, it is common to adopt a low spring constant to reduce


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the computational cost. Bakshi et al.

49

found that a spring constant higher than 100

N/m has minor effect on the prediction of gas-solid hydrodynamics. However, a low spring constant leads to the overestimation of the inter-particle heat transfer rate. Hence, the correction model proposed by Zhou et al 50 is adopted, which is formulated as: Rc ,0

where

k R = n c  k  n ,0

Rc ,0

  

2/3

(34)

is the corrected contacting radius and

kn ,0

is the real spring constant

obtained from solid properties, which is calculated as 47:

4 kn ,0 = Y R* 3

(35)

* where Y is the particle Young’s modulus and R is the effective radius of collision

particles. In the simulation, the specified Young’s modulus of the sand 51 and biomass 52

particles are 5GPa and 20MPa, respectively. Further discussions on the influence of

the spring constant is provided in the Supporting Information. 2.2.3 Pyrolysis and shrinkage model In the simulation, a competitive kinetic scheme 53, which takes the secondary tar cracking into consideration, is adopted to model the biomass fast pyrolysis. As presented in Equation (36-39), in the first step, the biomass is converted to the activated state, and then it undergoes two competitive reactions. In the meanwhile, the tar is decomposed into the volatile gas. k1 Biomass ⎯⎯ → Activated Intermediates

(36)

k2 Activated Intermediates ⎯⎯ → Tar

(37)



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k3 Activated Intermediates ⎯⎯ → (Y ) Char + (1 − Y ) Volatile gas

(38)

k4 Tar ⎯⎯ → Volatile gas

(39)

The calculations of reaction rates k1-k4 follow the Arrhenius’s Law, which is given as:

k = Ae − E / RT

(40)

where A is the pre-exponential coefficient, E is the activation energy, and R is the gas constant. To account for the shrinkage of biomass particles during the pyrolysis reactions, the Di Blasi’s shrinkage model 23 is employed, which is calculated as:

V = Vs + Vg

(41)

Vs M Mc = w + Vw0 M w0 M w0

(42)

Vg =

Mw M Vgi + (1 − w ) Vgi +  (Vw0 − Vs ) M w0 M w0

(43)

In Equation (41), the particle volume V is composed of the solid structure Vs and porous structure

Vg

. The Vw0 represents the initial volume of the solid structure,

M w represents the current biomass mass, M w0 represents the initial biomass mass, Mc

represents the current char mass, and

Vgi

porous structure. In the Di Blasi’s model,

represents the initial volume of the



, 

and

 are the numerical

parameters which define the different shrinkage patterns. Overall, in the CFD-DEM modeling, every biomass particle is individually tracked to obtain particle-level information of particle-particle/particle-wall collisional forces, fluid-particle interactions, heat transfers and chemical reactions. In each time step, after


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calculating the reaction rates, one can acquire the changes of biomass mass ( M w )and char mass ( M c ) for a particle. Then the volume of particle can be updated according to Equation (41-43). A brief outline of the CFD-DEM framework for modeling biomass fast pyrolysis is illustrated in Table 1. Basically, the particle shrinkage is affected mainly by chemical reactions, but the particle shrinkage in turn influences particle collisions and fluid-particle interactions. Table 1 The implementation of the CFD-DEM algorithm for the biomass fast pyrolysis Step

Details

1

Compute the quantities (velocity, pressure, temperature, species) of fluid phase

2

Calculate the exchange weights for fluid-particle interactions and search for neighboring particles

3

Compute the inter-particle/particle-wall collisional forces, fluid-particle momentum exchanges, heat transfers for particles, and chemical reaction rates

4

Update the position, velocity, temperature and species of particles

5

Update the particle volume based on Di Blasi’s model

3.Simulation setup The present simulations are based on Xue et al.’s experiment 54. The computational domain of the fluidized bed is a pseudo-2D rectangular column which measures 38.1 × 5 × 342.9mm. The fast pyrolysis of pure cellulose is focused and the kinetic parameters 55

are detailed in the Supporting Information. Initially, the fluidized bed is filled with

approximately 84,000 sand particles at the bottom with temperature of 773K. The nitrogen flow is introduced from the bottom with the velocity of 0.36m/s and the biomass particles are fed from the left side with the mass flowrate of 16.7g/h. The temperatures of the fluidization gas and feedstock are 300K. The boundary walls are



set as adiabatic. The physical parameters of the gas and solid phase are provided as the Supporting Information. The numerical parameters are given in Table 2. The total simulation time for each case is 20s and the solid time step satisfies the criterion proposed by Silbert et al. 56 Basically, the selection of the Eulerian-grid in the CFD-DEM simulations follows the suggestions from previous CFD-DEM studies. In general, the grid size in CFDDEM simulations is confined by the particle size. According to Peng et al.’s study 43, the ratio of cell size (Sc) to particle size (dp) in previous CFD-DEM studies were commonly greater than 2, and their simulation results recommended that the ratio (Sc/dp) should be greater than 1.63. Clarke et al. 57 also found that the the ratio (Sc/dp) less than 1.6 leads to a poor prediction in bubbling fluidized bed. Boyce et al. 58 suggested that a ratio (Sc/dp) of 3-4 gives the best agreements with experimental data in a bubbling fluidized bed. The grid sizes in X and Z dimensions are in the range of 3-4, while a relatively smaller size is adopted for the y direction (thickness direction). According to our experience, we specified a relatively small grid size for the y direction (Ny=4) to better resolve the fluid field with the influences of back and front wall. It is known that the CFD-DEM simulations require a relatively high computational cost, while the simulation of reactive fluidized bed is even more expensive due to the additional calculation of the heat transfer and chemical reactions. Specifically, the processors of Xeon(R) CPU E5-2699 v4 are employed for the simulations. The computational domain is decomposed into 418 (xyz) blocks for the parallel computation, which means that 32 CPU cores are utilized for each simulation case. The


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computation time for one simulation (20s) is approximately 25 days. To evaluate the influence of shrinkage patterns, four shrinkage patterns are employed and the model parameters are listed in Table 3. It is assumed that in the pattern 1-3, initially both the solid structure and porous structure occupy half of the particle volume. While for pattern 4, it is assumed that there’s only solid structure, which refers to the pattern of constant density. Table 2 Numerical parameters for the CFD-DEM modeling Parameter

Value

Grid allocation (x×y×z)

20×4×176 1.0×10-5 (fluid) 1.0×10-6 (solid) 1,600 0.95 0.1 1.04 Dp

Time step (s) Spring constant (N/s) Restitution coefficient (-) Friction coefficient (-) Gas layer diameter (m)

Table 3 Parameters for the shrinkage model Shrinkage parameters Pattern 1 Pattern 2 Pattern 3 Pattern 4

Value

 =1  = 0.3  = 0.5  =1

 =1  =0  =0  =0

 =1

 = 0.3  = 0.5  =0

In this study, the residence time has a two-fold meaning. The residence time is usually defined as the period of time from a particle enters the reactor to it exits, which is a fixed value for each particle. This definition is applied to Figure 6, Figure 7 and Table 5. Besides, for a biomass particle in the fluidized bed, the residence time also indicates how long it has stayed in the system, and thus the residence time will increase until the particle exits the reactor.



4.Result and discussion 4.1 Validation

A cold flow bubbling fluidized bed experiment

59

is used to validate the CFD-

DEM in term of predicting gas-solid hydrodynamics. Based on the experimental condition (3D geometry, U0=2.3Umf), the predicted solid mass flux profiles are compared with the experimental data, as shown in Figure 1. It can be observed that the CFD-DEM results are in reasonable agreement with measurements.

(a)

0.2

0.1

εsus,z(m/s)

εsus,z(m/s)

0.2

0.0 Exp CFD-DEM

-0.1

-0.2 0.00

0.05

0.10

(b)

0.1

0.0 Exp CFD-DEM

-0.1

-0.2 0.00

0.15

0.05

X (m)

0.2

(c)

0.2

0.1

0.0 Exp CFD-DEM

-0.1

-0.2 0.00

0.05

0.10

0.15

X (m)

εsus,z(m/s)

εsus,z(m/s)

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 18 of 42

0.10

(d)

0.1

0.0 Exp CFD-DEM

-0.1

0.15

-0.2 0.00

X (m)

0.05

0.10

0.15

X (m)

Figure 1 Comparison between predicted solid flux and experimental data 59 at different heights: (a) H=0.05m, (b) H=0.10m, (c) H=0.15m, (d) H=0.20m (3D bubbling fluidized bed with superficial fluid velocity U0=2.3Umf)


Page 19 of 42

Moreover, the reactive simulation results are validated against Xue et al.’s experiment 54, and the results are shown in Figure 2. The predictions are found to agree well with experimental data.

0.9 Exp. CFD-DEM

Product yields

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


0.6

0.3

0.0 Char

Tar

Volatile gas

Figure 2 Comparison of product gas yields between simulation prediction and experiment measurements

4.2 General gas-solid behaviors Firstly, the instantaneous snapshots of gas-solid behaviors are presented in Figure 3. Figure 3(a) shows the Eulerian distribution of the volume fraction. It is worth noting that, since biomass particles are fed from the left side and the pyrolysis reactions take place rapidly, small bubbles near the solid inlet region can be observed. The Lagrangian description of solid particles is shown in Figure 3(b), in which sand particles are marked as grey point and biomass particles are marked as spheres colored by the velocity. In comparison, the biomass particles in the freeboard are be clearly shown in Figure 3(a), due to the small volume fraction of biomass particles. The visualization results also show the uniform distribution of biomass particles in the freeboard, despite



Page 20 of 42

they are fed from the one side. An enlarged view of the inlet region is presented in Figure 3(c), in which the sphere size is proportional to the real size of the biomass particles and they are colored with particle temperature. It is revealed that the biomass particles near the inlet region get heated rapidly and the particles shrink quickly due to chemical reactions. Moreover, the results show the back-mixing of biomass particles, which were not observed in relevant studies

18, 60

based on the TFM. This can be

attributed to that the TFM tends to over-predict the segregation speed 7, 9, 61.

Inlet region

(a)

(b)

(c)

Figure 3 Instantaneous gas-solid behaviors in fluidized bed (shrinkage pattern 3): (a) gas volume fraction distribution, (b) solid distributions with sand particles colored in grey and biomass particles marked with velocity vectors, (c) size and temperature distributions of biomass particles in the inlet region.

4.3 Effect of particle shrinkage patterns The predicted product yields with different shrinkage patterns are presented in


Page 21 of 42

Table 4. The results show only minor difference among four cases, and it indicates that the shrinkage pattern can hardly affect the chemical reactions, which is consistent with Papadikis et al.’s study 25. Table 4 The predicted product yields with different shrinkage patterns

Char Tar Volatile gas

Pattern 1

Pattern 2

Pattern 3

Pattern 4

0.0227 0.8147 0.1627

0.0233 0.8125 0.1643

0.0231 0.8112 0.1657

0.0233 0.8129 0.1638

Figure 4 shows the evolution of particle size versus particle residence time. Different particle size evolution profiles can be observed. The shrinkage pattern 4, namely the constant density model, results in the smallest size after the pyrolysis, which is about one-quarter of the initial diameter. Also, it is shown that the average time to complete the pyrolysis reactions is approximately 1.0s.

0.4

Diameter (10-3m)

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


Pattern_1 Pattern_2 Pattern_3 Pattern_4

0.3

0.2

0.1 0

1

2

3

4

5

Residence time (s) Figure 4 Evolutions of biomass particle size under different shrinkage patterns

The profiles of the average vertical velocity and drag force of biomass particles are plotted in Figure 5. The results demonstrate that the shrinkage patterns greatly influence the vertical movements and entrainment behaviors of biomass particles. With shrinkage pattern 1, i.e., the constant size volume, the velocity of biomass particles is



higher than other patterns. While shrinkage pattern 4 leads to the slowest vertical movements of biomass particles. It can be inferred from the drag force model that the drag force increases with the increase of particle size. Therefore, a large particle size leads to a stronger drag force and higher vertical velocity, which explains the influences of different shrinkage patterns shown in Figure 5. Moreover, the peaks of the drag force curves occur in the near-inlet region, which can be due to the large particle size and high relative velocity in this region. It can be observed that, when the biomass particles rise and speed up, the gap between the particle velocity and gas velocity reduces so the drag force decreases greatly. 10

0.3

0.2 Pattern_1 Pattern_2 Pattern_3 Pattern_4

0.1

0.0

0.0

0.1

0.2

0.3

Drag force (10-7N)

0.4

Velocity (m/s)

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 42

8 6 4 Pattern_1 Pattern_2 Pattern_3 Pattern_4

2 0

0.0

0.1

0.2

Height (m)

Height (m)

(a)

(b)

0.3

Figure 5 Spatial distributions of biomass particles’ (a)vertical velocity and (b) drag force

To quantitatively describe the entrainment behaviors of biomass particles, the residence time distributions (RTD) are plotted in Figure 6. The results show that the life cycle of a biomass particle in the fluidized bed varies from 1s to 8s. It can be observed that the distribution patterns and the mean residence time are similar in shrinkage pattern 1-3, which is the typical early-peak and long tail distribution. The mean residence time is the shortest with the shrinkage pattern 1, which is due to the


Page 23 of 42 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


highest entrainment velocity as discussed before. Notably, the shrinkage pattern 4 leads to the highest residence time. Also, the RTD curve of the shrinkage pattern 4 is distinguished from the other patterns, which shows a bimodal distribution. To explain the difference, the scatter plot of the residence time of biomass particles as a function of the horizontal position is shown in Figure 7. It can be observed that the particles exiting from the near-wall region takes a much longer residence time than from the central region, which explains the bimodal distribution of residence time very. Such distinction can be caused by the solid back-mixing. Compared with the results of the shrinkage pattern 1-3, it can be inferred that the lower entrainment velocity may lead to the solid back-mixing and a wide distribution of residence time. Furthermore, the averaged residence time of biomass particles escaping from the dense bottom bed are calculated and listed in Table 5. Contrary to the residence time in the whole reactor, the residence time in the dense bottom bed is the shortest for pattern 4 and longest for pattern 1. This can be attributed to that the biomass particles with larger size are more easily obstructed by sand particles. It also implies that the differences of RTD in Figure 6 are mainly owning to the movements in the freeboard.



Pattern_1

0.8

Pattern_2

PDF

RTmean=2.28s

RTmean=2.32s

0.6 0.4 0.2 0.0 Pattern_3

0.8

Pattern_4

RTmean=2.32s

PDF

RTmean=3.20s

0.6 0.4 0.2 0.0

1

2

3

4

5

6

7

8 1

2

Residence time (s)

3

4

5

6

7

8

Residence time (s)

Figure 6 Probability density function (PDF) of biomass particles residence time in the fluidized bed with different shrinkage patterns. 8 Pattern_4

Residence time (s)

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 24 of 42

6

4

2

0

1

2

3

4

X (m) Figure 7 Scatter plot of biomass particle residence time as a function of horizontal position (shrinkage pattern 4) Table 5 Mean residence time of biomass particles in dense bed region

Mean residence time (s)

Pattern 1

Pattern 2

Pattern 3

Pattern 4

1.472

1.243

1.269

1.174


Page 25 of 42

4.4 Effect of superficial velocity Three simulations are carried out under different superficial gas velocities. The product yields as a function of the superficial velocity are shown in Figure 8. It can be observed that the mass fraction of the tar increases and the fraction of volatile gas decreases with the increase of the superficial velocity. This is owning to that the residence time of the tar in the fluidized bed decreases with higher superficial velocity, which leads to a lower level of tar cracking reaction (k4). 1.0 0.8

Product yields

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


0.6 Char Tar Volatile gas

0.4 0.2 0.0 2.2

2.6

3.0

U/Umf

Figure 8 Effect of the superficial gas velocity on the product gas yields

The contour plots of the tar concentration and tar cracking reaction rate with different superficial velocities are shown in Figure 9. It can be observed that the tar becomes dilute with higher superficial velocity, which results in a lower tar cracking rate.



(a)

(b)

(c)

(d)

(e)

Page 26 of 42

(f)

Figure 9 Contour plots of the tar concentration ((a)U0/Umf=2.2, (b) U0/Umf =2.6, (c) U0/Umf =3.0) and the tar cracking reaction rate ((d) U0/Umf =2.2, (e) U0/Umf =2.6, (f) U0/Umf =3.0)

It is found that the homogeneous reaction (k4) is greatly affected by the superficial gas velocity. Moreover, the effect on the heterogeneous reactions in the pyrolysis process is analyzed. The particle-average temperature and conversion profiles are shown in Figure 10. It is found that the superficial gas velocity has quite limited effect on the heat transfer and heterogeneous reactions. It is demonstrated that the particle temperature increases quickly at the beginning (0-0.2s). After the particle temperature approaches the bed temperature, the rise of temperature becomes much slower. It can be inferred that the slower heating rate after 0.2s is partly due to the endothermic pyrolysis reactions. In Figure 10 (b), the conversion ratio is defined as the ratio of the products mass to the initial particle mass. It can be found that there’s no conversion at first and then the conversion ratio increases linearly until the end of the pyrolysis process.


Page 27 of 42

1.0 800

0.8 700

Conversion

Particle temperature (K)

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


600 500 U0/Umf=2.2 U0/Umf=2.6 U0/Umf=3.0

400 300 0.0

0.2

0.4

0.6

0.8

1.0

0.6 0.4 U0/Umf=2.2 U0/Umf=2.6 U0/Umf=3.0

0.2 0.0 0.0

Residence time (s)

0.5

1.0

1.5

2.0

Residence time (s)

(a)

(b)

Figure 10 Particle-averaged temperature and conversion evolutions under different superficial gas velocity

Based on the CFD-DEM, the particle-level heat transfer, including the interparticle heat conduction, interphase heat convection and chemical reaction heat, are recorded for analysis. Since the effect of the superficial velocity is minor, only the results of the base case are presented. The profiles of particle heating rates are depicted in Figure 11(a). and the ratios of different heating rates as a function of particle residence time are plotted in Figure 11(b). It is observed that the conduction heating rate first increases and then decreases with residence time. The relatively lower conduction rate in the initial phase can be due to that the entering particles are neighbored with other lowtemperature biomass particles. The convection heating rate continuously decreases with the increase of residence time. The heating rate from chemical reaction start to increase from the beginning of pyrolysis reactions and then decrease until the end of conversion. The peak of reaction heating rate indicates that the highest reaction rate occurs at approximately 0.3s. Comparatively, it can be found that, in the initial phase (0-0.25s), the convective



heat transfer plays a major role. Afterwards, the ratio of the reaction heating rate increases greatly and the heating rate of chemical reactions nearly offset the heating rates of the conduction and convection, which results in a low temperature rising rate shown in Figure 10. It is also noted that, after 0.4s, the ratio of the reaction heating rate maintains nearly constant, which shows a dynamic balance between the heat transfer and chemical reaction. In the whole heating up process, except for the heat of chemical reactions, the conductive heat transfer contributes approximately 33% of total heat transfer and the convective heat transfer contributes the remaining 67%. 0.10 Conduction Convection Heat of reaction(Negative)

0.08 0.06 0.04

Conduction Convection Heat of reaction

0.6

Ratio

Heating rate (W)

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 28 of 42

0.4

0.2 0.02 0.00 0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.0

Residence time (s)

(a)

0.5

1.0

Residence time (s)

(b)

Figure 11 Particle-average profiles of (a) heating rates, (b) ratios of different heat transfer mechanisms (U0/Umf=2.6)

The temporal characteristics of heterogeneous chemical reactions are shown in Figure 12. The results show that the peak of curve k1 occurs earlier than that of k2 and k3. The peaks of curve k2 and k3 occur at similar time, which is consistent with the peak of heating rate of chemical reaction in Figure 11(a).


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


Particle reaction rate (10-6mol/s)

Page 29 of 42

3 k1 k2 k3*10 2

1

0 0.0

0.5

1.0

1.5

Residence time (s)

Figure 12 Particle-averaged profile of pyrolysis reaction rate (U0/Umf=2.6)

Moreover, the spatial characteristics of heterogeneous reactions are shown in Figure 13. From Figure 13(a), it can be found that under 2.2-fold and 2.6-fold of the Umf, the distributions of the biomass concentration in vertical direction are similar. While under a higher superficial gas velocity (U0/Umf=3.0), the biomass particles tend to stay at a lower position compared to other conditions. Such differences could be attributed to the solid back mixing in the near-wall region with higher gas velocity. As a result, the vertical distribution of the heterogeneous reaction rate is also affected by the superficial gas velocity. From Figure 13(b), it is observed that a higher level of pyrolysis reaction (k2) occurs at lower position under the high gas velocity (U0/Umf=3.0).



PDF of Biomass

0.30

U0/Umf=2.2 U0/Umf=2.6 U0/Umf=3.0

0.25 0.20 0.15 0.10 0.05 0.00 0.00

0.05

0.10

0.15

0.20

0.25

0.30

Height (m)

(a)

0.25

k2 Rate (10-6mol/s)

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 30 of 42

U0/Umf=2.2 U0/Umf=2.6 U0/Umf=3.0

0.20 0.15 0.10 0.05 0.00 0.00

0.05

0.10

0.15

0.20

0.25

0.30

Height (m)

(b) Figure 13 Spatial distributions of (a) biomass concentration, (b) pyrolysis reaction rate

5.Conclusion In the present study, CFD-DEM simulations are conducted to investigate the biomass fast pyrolysis process in the fluidized bed. By the virtue of the CFD-DEM coupling method, particle-scale analysis is conducted focusing on the spatial and temporal properties of the gas-solid hydrodynamics, heat transfer and chemical reactions. The influences of shrinkage patterns of the biomass particle are evaluated. The


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results show that the shrinkage pattern has minor effect on the product yields, but it greatly affects the entrainment behaviors of biomass particles. The results show that the entrainment velocity increases with the particle size after the shrinkage. Specifically, the constant density shrinkage pattern leads to the longest residence time and the constant size shrinkage pattern leads to the shortest residence time. Nevertheless, the residence time in the dense bottom bed shows a reverse trend with varying shrinkage patterns. Notably, the residence time under the constant density shrinkage pattern shows a bimodal distribution, which reflects the severe solid back-mixing in the near-wall region. Furthermore, the effects of the superficial gas velocity are analyzed. The results show that the tar yield increases and the volatile gas yield decreases with the increase of the gas velocity. It is revealed that the superficial gas velocity can hardly affect the temporal characteristics of the biomass pyrolysis process, in terms of heat transfer and chemical conversion, but it greatly influences the spatial distributions of fuel particles and heterogeneous reactions. In addition, the roles of different heat transfer mechanisms in the heating-up procedure of biomass particles are explored. The results show that the convective heat transfer contributes as twice as the heating rate of conductive heat transfer.

Supporting Information Effect of the spring constant; Chemical kinetics parameters; Physical parameters of gas and solid materials



Acknowledgements Financial supports from the National Key Research and Development Program of China (2017YFB0601805) and the National Natural Science Foundation of China (Grant Nos. 51390493, 51621005) are sincerely acknowledged

NOMENCLATURE Latin letters

A

Surface of particles, m2

A

Pre-exponential coefficient, s-1

Cp

Specific heat, J/(kg·K)

D

Diffusion coefficient of gas/solid species, kg/ms

dk

Gas phase layer thickness, m

dp

Particle diameter, m

E

Activation energy, J/mol

e

Restitution coefficient

F fp

Volumetric inter-phase momentum exchange rate, N/m3

fc

Contact force, N

f cn

Normal contact force, N

f ct

Tangential contact force, N

fd

Drag force, N

fp

Pressure gradient force, N

g

Gravitational acceleration, m/s2


Page 32 of 42

Page 33 of 42 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


h pf

Inter-phase heat transfer coefficient, W/(m2K)

I

Particle moment of inertia, kg·m2

I pf

Momentum exchanges due to the interphase mass transfer, N/m3

k

Thermal conductivity, J/(m K s)

kn

Spring coefficient in normal direction, N/m

kt

Spring coefficient in tangential direction, N/m

lij

Distance between particles, m

M

Torque on particle, N·m

m

Particle mass, kg

N p ,cell

Number of particles in a fluid cell

Nu

Nusselt number

n

Unit vector in the normal direction during collision

pf

Fluid pressure, Pa

Pr

Turbulent Prandtl number

R

Gas constant, J/(mol K)

r

Vector from the particle centroid to the contact point, m

r

Particle radius, m

Q fp

Volumetric heat transfer rate, W/m3

q

Particle heat transfer rate, W

Rc

Contact radius, m

Rf

Mass source term due to chemical reactions for fluid



phase, kg/(m3s)

Ri

Mass source term due to chemical reactions for solid phase, kg/s

Rin , Rout

Bounds of the particle-fluid-particle heat conduction region, m

Re

Particle Reynolds number

T

Torques on particles, N·m

Tf

Fluid temprature, K

Tp

Particle temprature, K

t

Time, s

t

Unit vector in the tangential direction during collision

Ug

Superficial gas velocity, m/s

U mf

Minimum fluidized velocity, m/s

uf

Fluid velocity, m/s

us

Solid velocity (Eulerian description), m/s

Vcell

Volume of the current cell, m3

Vp

Particle volume, m3

v

Particle velocity, m/s

Wp

Weight of particle i to the current cell

Y

Mass fraction of gas or solid species

Y

Young’s modulus, Pa

Greek letters


Page 34 of 42

Page 35 of 42 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




Inter-phase momentum transfer coefficient, kg/(m3·s)

H rf

Source term of energy due to chemical reaction, W/m3



Overlap between particles, m

f

Fluid volume fraction

s

Solid volume fraction



Damping coefficient, kg/s



Friction coefficient

f

Gas dynamic viscosity, kg/(m·s)

f

Gas density, kg/m3

f

Viscous stress tensor, Pa



Particle angular velocity, 1/s

Subscript f

Fluid properties

fp

Interphase exchanges

i

Particle i

ij

Interactions between particle i and j

n

Normal direction

p

Properties of particle

pc

Particle-particle contact

pp

Interparticle interactions

pfp

Particle-fluid-particle non-contact

t

Tangential direction



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