New Pyrolysis Model for Biomass Particles in a Thermally Thick

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Biofuels and Biomass

A new pyrolysis model for biomass particles in thermally thick regime Tao Chen, Xiaoke Ku, Jianzhong Lin, and Liwu Fan Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b01261 • Publication Date (Web): 30 Jul 2018 Downloaded from http://pubs.acs.org on July 31, 2018

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Energy & Fuels

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A new pyrolysis model for biomass particles in thermally thick regime

2

Tao Chen†, Xiaoke Ku*,†,‡, Jianzhong Lin† and Liwu Fan‡

3

†

4

‡

5

ABSTRACT

6

In order to get a deep understanding of biomass pyrolysis and gasification with particle size

7

ranging from several millimeters to centimeters, detailed modeling of thermochemical conversion

8

of thermally thick particles is required. In this paper, a new pyrolysis model for large biomass

9

particle is established and the porous particle is modeled with two systems. In the continuous

10

system, a uniform multi-phase CFD algorithm is implemented to resolve both internal and external

11

flow fields and the solid temperature is solved by considering heat conduction, heat convection,

12

and radiation. In the discrete system, the large particle is regarded as a porous media and

13

discretized into a cluster of small virtual particles which are used to model the devolatilization and

14

particle shrinkage. The evolutions of porosity and internal specific surface area during pyrolysis

15

are also taken into account. The proposed model is first validated with the experiments in literature

16

and good agreements have been obtained. Moreover, the temperature contour, gas species

17

distribution and streamlines both inside and outside the particle, which cannot be provided by the

18

experiment, are also presented and analyzed. In addition, effects of particle size, initial porosity,

19

pore structure parameter, inflow velocity, tar generation, devolatilization heat and particle shape

20

on pyrolysis behavior are also explored. It is found that, increasing initial porosity, specific surface

21

area, inflow velocity, particle length-to-width ratio and decreasing particle diameter will reduce

22

the intra-particle temperature gradient, which is also greatly influenced by the Stefan flow effect.

Department of Engineering Mechanics, Zhejiang University, 310027 Hangzhou, China State Key Laboratory of Clean Energy Utilization, Zhejiang University, 310027 Hangzhou, China

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ACS Paragon Plus Environment

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Tar generation influences the thermophysical properties of the released gas and the interphase

2

convective heat transfer. All the simulation results demonstrate that the current model is capable of

3

capturing the detailed evolution of a large biomass particle during pyrolysis and providing deeper

4

insight into the interaction between the particle and the surrounding gas.

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Keywords: Biomass pyrolysis; Thermally thick; Porous media; Multi-phase flow; Numerical heat

6

transfer

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1. INTRODUCTION

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The exploration of renewable energy has become an urgent global focus during the past few

9

decades. Biomass is one of these promising energy sources due to its huge amount of reserves and

10

CO2-neutral characteristic.1 Two common ways of high efficient utilization of biomass are

11

pyrolysis and gasification where a series of complicated physical and chemical conversion

12

processes will undertake.2-4 In order to improve the conversion efficiency, biomass material is

13

usually smashed into small pieces. However, the size of biomass particles can still be large in

14

practice considering both its fibrous structure and the limited mechanical energy consumed in the

15

milling process. Taking the fluidized-bed gasifier for example, the feedstock diameter can range

16

from a few millimeters to several centimeters.5 As a result, the large size of biomass particles

17

brings new challenges for the understanding of their pyrolysis and gasification characteristics and

18

thus inhibits the expansion of laboratory exploration to large-scale industrial applications.

19

Thermally thick particles are characterized by Biot number. When it is larger than 0.1, apparent

20

temperature gradient can be caused inside the particle during the heating process. According to the

21

work of Di Blasi,6 large particle size has a significant impact on the conversion process of biomass

2


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particles. In the last decade, a few pyrolysis and gasification models were developed to cope with

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thermally thick particles.7-10 Thunman et al.11 established an advancing layer approach to model

3

the thermochemical conversion inside the particle. Their method divided the particle into four

4

layers: moist wood, dry wood, char and ash. Each layer evolved with the variation of temperature.

5

Gómez et al.12 also used the concept of layers for thermally thick particles. Drying was assumed to

6

take place at all layers while gasification only occurred at the outer layer of the particle, resulting

7

in a shrinking core condition. It is noted that in both Thunman's and Gómez's studies, radial

8

symmetry assumption is adopted for the particle, leading to one-dimensional models which can

9

only deal with cylindrical or spherical particles. Lu et al.13 extended the application of

10

one-dimensional models to arbitrary shaped particles. Their model gave a reasonable prediction

11

for both internal and external temperature histories of large size particles. They also found that for

12

aspherical particles, isothermal spherical assumption could give a poor representation of the

13

combustion process when particle size exceeded several hundred microns.

14

One-dimensional thermally thick models might keep a balance between computational accuracy

15

and efficiency. However, the demand for a comprehensive understanding of the conversion

16

process inside the particles drives researchers to construct more accurate models. Mehrabian et

17

al.14 coupled a four-layer thermally thick model with a CFD framework to study the interaction

18

between the gasified particle and its surrounding flow. Yang et al.5 also used a similar strategy to

19

investigate the gasification of particle clusters in a packed bed. It was found that the temperature

20

gradient reached over 400 °C inside the particle with a diameter of 35 mm. Kwiatkowski et al.15

21

developed a three-dimensional pyrolysis and gasification model based on the conservation laws of

22

gaseous and solid phases. The internal flow field of the particle governed by Darcy's law was also 3


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considered. However, there was a noticeable discrepancy between the simulated gasification time

2

and experimental data due to the neglect of heat convection and radiation in energy equation. In

3

recent years, Gentile et al.16 developed a detailed thermochemical conversion model for large

4

biomass particles with arbitrary shapes. The model was capable of simulating the evolution of the

5

particle's physical properties, such as material anisotropy and deformation. However, the external

6

flow field of the particle was not resolved.

7

Note that in all of the above-mentioned works, the change in specific surface area inside the

8

porous particle is not taken into account, which is actually an important influential factor

9

controlling the pyrolysis rate. Observations using scanning electron microscopy or x-ray have

10

shown that the pore radius inside a char particle after devolatilization varies from several to

11

hundreds nanometer, causing a significant increase in reactive area.17 Studies for coal particles

12

indicated that the change in reactive area highly depended on particle's physical properties and that

13

the value of surface area to volume ratio after devolatilization could increase by several orders of

14

magnitude to 1×107.18 Currently, there are mainly three kinds of models to simulate the specific

15

surface area of a porous particle: volumetric model,19 grain model,20 and random pore model

16

(RPM).21 The common feature of these models is that char reactivity is a function of carbon

17

conversion. Compared with the volumetric model and the grain model, the random pore model

18

which characterizes porous media with a system of growing and collapsing pores, provides a

19

better approximation for the evolution of reactive area.22

20

In this work, we aim to establish a new pyrolysis model for thermally thick biomass particles

21

with arbitrary shapes. The intra-particle heat conduction, the heat/mass exchange between particle

22

and surrounding gas, and effects of several key parameters are all taken into account. The rest of 4


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the paper is structured as follows. In Section 2, the mathematical formulations used for the

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pyrolysis of a porous biomass particle are introduced. Section 3 gives the implementation of the

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integrated algorithm. In Section 4, we first present the validation of the model by comparing the

4

simulation results with the experimental data. Then extensive simulation results which include the

5

temperature contour, gas species distribution and streamlines as well as influences of particle size,

6

initial porosity, pore structure parameter, inflow velocity, tar generation, devolatilization heat and

7

particle shape on pyrolysis are also provided. Finally, conclusions are drawn in Section 5.

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2. MATHEMATICAL MODEL

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The proposed model focuses on the simulation of the internal and external flow fields of a porous

10

particle with a uniform algorithm and the heat/mass exchange between solid and surrounding gas.

11

In the gas phase simulation, the particle is discretized into a cluster of smaller particles by

12

computational grids. Therefore, the already developed multi-phase algorithms in our group such as

13

CFD-DEM/DPM models can be used directly.23-25 The cluster system also provides a convenient

14

way to model the pyrolysis process. In the simulation of solid phase temperature, the particle is

15

treated as continuous media and existing heat transfer models can thus be utilized. Afterwards, the

16

solid phase temperature in each finite element grid is passed to the corresponding virtual particle.

17 18 19 20 21

Gas phase conservation law includes the mass, momentum, energy and species transport equations.25

∂ (ε g ρ g ) + ∇ ⋅ (ε g ρ g ug ) = S p,m ∂t ∂ (ε g ρ g ug ) + ∇ ⋅ (ε g ρ g ug ug ) = −∇p + ∇ ⋅ (ε gτ eff ) + ε g ρ g g + S p,mom ∂t ∂ (ε g ρ g E ) + ∇ ⋅ (ε g ug ( ρ g E + p) ) = ∇ ⋅ (ε gα eff ∇hs ) + Sh + S p ,h + Srad ∂t 5


(1) (2) (3)

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E = hs − p / ρ g + ug2 / 2

1

(4)

2

∂ (ε g ρ gYi ) + ∇ ⋅ (ε g ρ g ugYi ) = ∇ ⋅ (ε g ρ g Deff ∇Yi ) + S p,Yi + SYi ∂t

3

Note that, in the momentum equation, a Gidaspow model is used to calculate the interaction

4

between the flow and the virtual particles.26 Besides, a k-ε model is adopted to solve turbulence.27

5

For further details on the communication between the continuous flow model and the discrete

6

particle model, the reader is referred to our previous work.25

(5)

7

With the heat exchange between solid phase and surrounding gas, the temperature inside the

8

particle will change according to the local thermal condition. The energy equation for the solid

9

particle is constructed as follows.

ρ s cs

10

∂Ts e S′ = ∇ (κ∇Ts ) + hS ′ (Tg − Ts ) + s ( G − 4σ Ts4 ) + Q 4 ∂t

(6)

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where the first term on the right-hand side of Eq. (6) is the heat conduction between adjacent

12

elements. The second term is the heat convection between solid element and gas phase. The third

13

term represents the heat radiation. Q is the source term concerned with moisture vaporization and

14

devolatilization. In contrast to the existing pyrolysis models, we solve the energy equations for gas

15

and solid phases separately, allowing the current model to cope with local thermal non-equilibrium

16

conditions.

17

The biomass pyrolysis occurs when particle temperature reaches a certain value, during which

18

different products will be generated. The pyrolysis compositions released from biomass are given

19

by Eq. (7) and each product yield is determined by the elemental conservation analysis.

20

ms = ∑ α i ms ,

∑α

i

=1

(7)

21

where, i=H2O, H2, CO, CO2, CH4, tar, char, and ash. In Eq. (7), reactions with sulfur and nitrogen

22

are not included because of their little amount. Besides, only light gases (e.g., H2, CO, CO2, CH4 6


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and H2O) are considered in the product gas. High-molecular-weight hydrocarbons (i.e., tar) are

2

non-stable at high temperature and thus are not taken into account in the simulation except in

3

subsection 4.7 where the effect of including tar species on pyrolysis is explored. This simplified

4

mechanism has been widely adopted in the literature.28-29 A single step first-order Arrhenius

5

reaction model is chosen to calculate the devolatilization rate,

 E dms = − A exp  − dt  RTs

6

  mdevol 

(8)

7

where mdevol is the mass of the volatile matter left in the particle, A= 5×104 s-1, and E=1.2×108

8

J/kmol.

9 10

With the progress of devolatilization, biomass particle loses its weight gradually. As a result, the density and porosity in each element can be computed as follows.

ρst =

11

mst mt , ε gt = 1 − 0s (1 − ε g0 ) V ms

(9)

12

Besides the change of particle density, the pore structure inside the particle will also evolve with

13

devolatilization. For the calculation of intra-particle specific surface area, we adopt the widely

14

accepted random pore model.30 Since the original model is only valid in combustion stage, here,

15

we extend it as follows:

16

S ′ = S0′ (1 − x ) 1 −Ψ ln (1 − x ) , x = f ( X ), X =

ms0 − mst ms0

(10)

17

where Ψ is the dimensionless pore structural parameter and represents initial biomass structure. Its

18

value depends on the nature of the biomass particle. X is the fraction of mass conversion during

19

pyrolysis. S0′ is the initial specific surface area inside the particle. f(X) is a map function which

20

can be chosen as a polynomial, so that the value of S′ evolves in accordance with the experimental

21

measurements of a specific biomass particle. 7


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3. COMPUTATIONAL FRAMEWORK

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The mathematical equations described in Section 2 are implemented in the framework of

3

OpenFOAM.31 A finite volume algorithm is used to discretize these equations and a uniform time

4

step (i.e., 2.0E-05 s) is adopted. Moreover, an implicit Euler method is chosen for time

5

discretization and the gradient terms are discretized with a linear Gauss method. At every time

6

step, a sub-iteration strategy is conducted to ensure the convergence of the overall solution

7

algorithm, whose convergence criterion is set to 1.0E-06.

8

The pyrolysis simulation is carried out on a 10-core workstation with a parallelization scheme.

9

During each time step, the CFD solver passes flow field variables such as ug and Tg to the heat

10

transfer solver, and the latter will send back the solid temperature and inter-phase interaction force.

11

The temperature field computed from the heat transfer solver is transferred to the pyrolysis solver,

12

in which each particle undergoes drying and devolatilization sequentially.

13

4. RESULTS AND DISCUSSIONS

14

4.1 Validation

15

Table 1

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To validate the proposed model, both the experiments of Lu et al.13 and Gauthier et al. 32 are

17

employed. The former provides detailed information on the particle temperature and mass loss

18

history during pyrolysis, while the latter mainly gives the yield of product gas species.

19

In the experiment of Lu et al.13, a cylindrical poplar wood particle with an aspect ratio of 4 and

20

a cross-sectional diameter of 9.5 mm is exposed to a nitrogen atmosphere at 1050 K. The wall

21

temperature of the reactor is maintained at 1276 K. Table 1 presents the biomass properties.33 8


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Figure 1

2

Fig. 1 presents the computational grids. To reduce the computational cost, the cylindrical

3

particle is simulated with a two-dimensional model which has been frequently adopted by other

4

researchers.12,13 There are totally 6120 elements in the CFD mesh and the grid near the particle

5

surface is refined to capture the boundary layer and high temperature gradient. The heat transfer

6

grid within the particle is identical to the CFD grid with a total number of 1600. Furthermore, in

7

order to model devolatilization, each element of the heat transfer grid is also equivalent to a

8

spherical particle (Fig. 1c).

9

Figure 2

10

Fig. 2 gives the comparison of the simulated temperature and mass loss histories with the

11

experimental data of Lu et al.13 Note that in our integrated model, a continuous system and a

12

discrete system are used to solve for the temperature and devolatilization of the particle,

13

respectively. The temperature fields within the particle are calculated by the continuous system in

14

which the contributions of heat conduction, heat convection, radiation, and reaction heat are all

15

taken into account by the finite element solver. However, the devolatilization model is constructed

16

based on the individual virtual particle within the particle, implying that the mass loss results are

17

obtained from the discrete system.

18

As shown in Fig. 2, the measured surface temperature of the particle is well predicted by our

19

model and the calculated center temperature is also in reasonable agreement with the experimental

20

result. Furthermore, the predicted center temperature follows the experimental data pretty well

21

before 22 s, where a small plateau about 373 K corresponding to the water boiling point appears.

22

This indicates that the center temperature stays constant until the end of drying. After 22 s, a sharp 9


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increase in the temperature is observed, resulting in an over prediction of center temperature until

2

51 s. The discrepancy is probably attributed to the inaccurate devolatilization parameters chosen

3

from the literature, which are obtained under particular thermal conditions that are different from

4

those of the pyrolysis experiment.12 Meanwhile, the assumption of constant volume of the biomass

5

particle may also cause a certain impact on the results. After 51 s, there is a rise in the heating rate,

6

although the trend is a little slower than that measured in the experiment. The predicted particle

7

mass loss is slightly faster than the experimental one before the end of drying. After moisture

8

evaporation, the mass loss rate of the simulation slows down, resulting in a delay of about 10 s in

9

the overall pyrolysis time.

10

Table 2

11

In order to further evaluate the predictive ability of the proposed model, the experiment of

12

Gauthier et al.32 is also employed. Correspondingly, a beech wood particle with a diameter of 20

13

mm is exposed to a nitrogen atmosphere at 1073 K. Table 2 shows the wood properties.24

14

Figure 3

15

Figure 4

16

Fig. 3 presents the comparison of the simulated temperature with the experimental data of

17

Gauthier et al.32 Obviously, both the calculated surface and center temperatures of the particle

18

agree quite well with the experimental data. At the end of pyrolysis, the heating rate near the

19

center of the particle increases rapidly due to a reduced amount of dry wood left. This feature is

20

well captured by the simulation. In addition, Fig. 4 compares the yield history of the main gas

21

species (i.e., CO, CO2, CH4, C2H4). The predicted results, such as the amplitudes and shapes of the

22

evolving curves, correspond fairly well with the measurements, except that the first peak for each 10


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species appears a little earlier than the experimental data. The underestimated releasing rate

2

between 50 s and 125 s is mainly caused by the lower heating rate inside the particle, which in

3

turn results in a higher heating rate and an overestimate of the yields after 125 s.

4

Considering the complexity of the biomass pyrolysis process, the match between our

5

predictions and the experimental results of Lu et al.13 and Gauthier et al.32 is thought to be

6

satisfactory, which demonstrates the validity of the proposed model for modelling the conversion

7

of biomass particle in thermally thick regime.

8

4.2 Pyrolysis phenomena

9

Figure 5

10

The model can provide lots of details on the biomass pyrolysis process. In this subsection, some

11

qualitative results of the poplar wood particle are first presented. Fig. 5 shows the temperature

12

contour of the internal and external flow fields (note that the black circle only denotes the particle

13

surface). At the beginning of pyrolysis, the surface temperature of the particle reaches the ambient

14

temperature very quickly, causing a large temperature gradient inside the particle. The high

15

temperature results in a fast pyrolysis of the dry wood at the outer layer. Besides, the porosity and

16

the specific surface area at the outer layer also increase very quickly, which further accelerates the

17

convection of the internal flow and the heat transfer inside the particle. This also explains the high

18

heating rate in the temperature curve after 22 s (see Fig. 2). With the progress of devolatilization, a

19

thick char layer forms gradually and causes an inhibition of the heat transfer in the core region.

20

This corresponds to a decreased heating rate in the temperature curve after about 40 s (also see Fig.

21

2). At the end of pyrolysis, the core region temperature rises very quickly due to a reduced amount

11


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of dry wood left in the particle. Finally, the center area reaches the ambient temperature and then

2

the pyrolysis completes.

3

Figure 6

4

Figure 7

5

The released gas species distributions are presented in Fig. 6. It can be clearly seen that

6

moisture evaporation finishes at around 20 s, which corresponds to the drying process in Fig. 2. It

7

is interesting to find that the species contours inside the particle show nonuniform distributions

8

along the circumferential direction at the beginning of pyrolysis. The reason is that there are slight

9

differences in the circumferential temperatures inside the particle. Fig. 7 shows the circumferential

10

temperature distribution inside the particle at t=1s. Obviously, two local maximum temperatures

11

appear in the 45° and 135° directions, respectively, which correspond to the locations of the high

12

gas yield regions in Fig. 6a. In addition, the convection and diffusion of the released gas inside the

13

particle also contribute to the formation of these high gas yield regions. This phenomenon

14

indicates that the pyrolysis models without considering the interaction of internal and external

15

flows may lose accuracy in the prediction of pyrolysis and gasification of large biomass particles.

16

After a few seconds of pyrolysis, the outer layer of the particle loses moisture very quickly.

17

Moreover, the thermal decomposition of dry wood following the evaporation causes a

18

concentration of CO and CH4 near the leeward side of the particle under the influence of inflow.

19

As time evolves, devolatilization develops towards the core region, which can be observed by the

20

species distribution. When pyrolysis is completed, the internal particle is filled with nitrogen.

21

Figure 8

22

To analyze the interaction between internal and external flows, streamlines along with the 12


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temperature distributions are drawn in Fig. 8. Fig. 8a reveals that the Stefan flow caused by the

2

diffusive mass transport from the rapid devolatilization remarkably widens the boundary layer.

3

Inside the particle, streamlines start from some source points, which is mainly caused by the slight

4

temperature difference along the circumferential direction (see Fig. 7). Furthermore, the

5

nonuniform distributions of the product gas (see Fig. 6) also contribute to the formation of these

6

source points. The value of the internal flow velocity is very small, which is normally two orders

7

of magnitude lower than the inflow velocity. This indicates that the internal flow mainly functions

8

as a carrier of mass transfer. As time evolves, the sources of the internal flow move towards the

9

core region following the evolution of devolatilization areas (Fig. 8b, c, d, e, f). The Stefan flow

10

also has a strong impact on the wake flow. During pyrolysis, the vortex pair behind the particle is

11

blown away from the particle by the Stefan flow. After pyrolysis, the vortex pair reattaches to the

12

particle surface (Fig. 8i).

13

The above analysis concludes that the interaction of the internal and external flow fields plays

14

an important role in the biomass pyrolysis process. In the following, effects of some key

15

parameters on the pyrolysis behavior of the poplar wood particle will be further explored.

16

4.3 Effect of Particle Diameter on Pyrolysis

17

Figure 9

18

The effect of particle diameter (d) on biomass pyrolysis behavior is studied in this subsection. It

19

is implemented by varying d while keeping the other parameters the same as those of the base case

20

as shown in Fig. 2. Fig. 9 shows the calculated results for different particle diameters. It can be

21

clearly observed that the variation of particle diameter causes great influence on the heating and

13


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devolatilization processes. The temperature histories denote that the particle surface temperature

2

reaches the ambient temperature (1276 K) at 42.4 s, 60.4 s and 114.4 s, respectively, for d=7 mm,

3

9.5 mm and 12 mm. The delay of the heating process at the surface can be explained from two

4

aspects: (i) A larger intra-particle temperature gradient caused by the increase of particle diameter;

5

(ii) The Stefan flow caused by mass transfer forms an isolation layer near the particle surface

6

during pyrolysis, prohibiting the particle surface from contacting with the ambient flow directly.

7

With increasing d, the isolation layer becomes thicker and thus the heat transfer between the

8

inflow and the particle can be slowed down. In addition, the center temperature histories

9

demonstrate that the change in d also makes a significant impact on both the evaporation and

10

devolatilization processes inside the particle. With enhancing d, the total moisture and dry wood

11

content of the particle become larger, resulting in a nonlinear increase in the total pyrolysis time.

12

The mass loss histories also confirm this conclusion. The above analysis indicates that the

13

devolatilization of biomass particle with a large diameter may experience different chemical

14

reaction kinetics due to different heating processes inside the particle, which will cause variation

15

in the final products.

16

Figure 10

17

Figure 11

18

Figs. 10 and 11 present the streamlines around the particles with two different diameters.

19

Qualitatively, the streamline pattern outside the particle with a diameter of 7 mm (Fig. 10) is

20

similar to that of the base case (Fig. 8). However, for the largest particle with a diameter of 12 mm,

21

big difference in streamline configuration appears. As shown in Fig. 11, at the beginning of

22

pyrolysis, the Stefan flow around the large particle is not strong enough to blow away the vortex 14


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Energy & Fuels

1

pair in the wake region (see Fig. 11a). Moreover, at the end of moisture evaporation, the vortex

2

pair appears again and locates at a position closer to the particle (Fig. 11g). With the progress of

3

devolatilization, the vortex pair grows larger and moves further closer to the particle (Fig. 11g-k).

4

Finally, at the end of pyrolysis, the vortex pair attaches to the particle (Fig. 11l). In addition, inside

5

the particle, the overall characteristics of the streamlines are similar for the particles with different

6

sizes, i.e., the streamlines go from the relatively higher temperature regions where moisture

7

evaporation and devolatilization first occur to the outer layer of the particle.

8

4.4 Effect of Particle Initial Porosity on Pyrolysis

9

Figure 12

10

In this subsection we investigate the influence of initial porosity (εg0) on the pyrolysis behavior

11

of a large biomass particle. Fig. 12 presents the comparison of temperature and mass loss histories

12

for different initial porosities. The temperature curves indicate that the change of εg0 does not make

13

a significant impact on the particle surface temperature. However, the center temperature is

14

sensitive to its variation. It can be seen that the moisture evaporation inside the particle accelerates

15

with an increase in εg0, which finishes at 17.8 s, 21.6 s and 25.2 s for εg0= 0.5, 0.4 and 0.3,

16

respectively. After evaporation, the center temperatures for εg0= 0.3 and 0.5 show a similar

17

evolving trend to that of the base case (εg0= 0.4). Therefore, the difference in the whole pyrolysis

18

time for the three tested εg0 is mainly caused by the asynchronous evaporation. The mass loss

19

histories reveal that the particle pyrolysis becomes faster with increasing the initial porosity,

20

because larger εg0 will result in stronger mass and heat exchange between surface and central area

21

of the particle. This is helpful in reducing the intra-particle temperature gradient.

15


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

4.5 Effect of Pore Structure Parameter on Pyrolysis

2

Figure 13

3

Figure 14

4

In this subsection the effect of pore structure parameter (Ψ) on pyrolysis is explored. Fig. 13

5

shows the specific surface area (S′) evolution with different values of Ψ. Note that the initial S′ is

6

same for all the tested Ψ. It is seen that, for the three values of Ψ (100, 200 and 400) tested, the

7

specific surface area increases from 9.04×104 m2/m3 to 3.90×105 m2/m3, 5.48×105 m2/m3 and

8

7.73×105 m2/m3, respectively, at the end of pyrolysis. The increase in specific surface area will

9

accelerate the heat transfer process and thus the pyrolysis rate inside the particle will also be

10

promoted. As shown in the particle energy equation (i.e., Eq. 6), the second and third terms on the

11

right-hand side of Eq. (6), which represent the contributions from heat convection and heat

12

radiation, respectively, are proportional to S′. When S′ increases, these two terms will have a

13

larger value, meaning that the heat convection and radiation processes are accelerated and the

14

particle temperature becomes higher. In addition, the computed specific surface area is different

15

from that measured in the experiment. The reason is that the shrinkage of the whole biomass

16

particle is not directly taken into account, instead a mass-proportional shrinkage scheme is used

17

for the virtual particles inside the particle. Consequently, a lower specific surface area should be

18

adopted.

19

Fig. 14 presents the corresponding temperature and mass loss histories. With increasing Ψ, the

20

temperature gradient inside the particle decreases. Both moisture evaporation and devolatilization

21

stages are accelerated. Therefore, for large biomass particles, the change of specific surface area

22

after devolatilization is great and its influence on the pyrolysis behavior cannot be ignored. 16


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Energy & Fuels

1

4.6 Effect of Inflow Velocity on Pyrolysis

2

Figure 15

3

The effect of inflow velocity (Vin) on the pyrolysis of large biomass particle is also checked.

4

Cases with three inflow velocities (0.2 m/s, 0.5 m/s and 0.8 m/s) are compared with each other.

5

Fig. 15 denotes that the variation in Vin causes little influence on the heating process near the

6

particle surface and the slight difference in particle surface temperature for the three cases is

7

mainly caused by the interaction of internal and external flows. Furthermore, the temperature

8

history inside the particle shows a decreased sensitivity to the uniform increase of inflow velocity.

9

It is observed that, when Vin increases from 0.2 m/s to 0.5 m/s, the heating rate inside the particle

10

appears a significant increase, which results in a much faster mass loss after about 12 s and the

11

total pyrolysis time is decreased by 15 s. However, when Vin increases from 0.5 m/s to 0.8 m/s, the

12

pyrolysis time is only reduced by about 3 s. As has been analyzed in subsection 4.3, the Stefan

13

flow will form an isolation layer between the inflow and particle surface. With an increase in Vin,

14

the isolation layer becomes weaker, suggesting that the effect of Stefan flow on pyrolysis is

15

remarkable only when the inflow velocity is within low to medium range.

16

4.7 Effect of Tar Generation on Pyrolysis

17

Figure 16

18

Tar generation is common in biomass pyrolysis process. In the above subsections, tar is

19

neglected for simplification purpose because tar species vary a lot for different applications.

20

However, it is straightforward to include tar species in our proposed model. In this subsection, we

21

choose four typical tar species (i.e., CH3OH, HCHO, HCOOH, and CH3COOH) and explore the 17


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

effect of mass fraction of tar on the pyrolysis behavior. Note that these four tar species were

2

mainly observed in another pyrolysis experiment of poplar wood,33 whereas the operation

3

temperature was much lower than that in the experiment of Lu et al.13

4

Fig. 16 presents the temperature and mass loss histories of the particle for different mass

5

fractions of tar released during pyrolysis. Compared with the base case (i.e., mass fraction of tar is

6

0%), the moisture evaporation stage is shortened by 1.8 s and the total pyrolysis time is reduced by

7

3.8 s when the mass fraction of tar accounts for 20% of the product gas. Moreover, the pyrolysis

8

time is further reduced with increasing the mass fraction of tar to 40%. Such differences may

9

probably be caused by the different thermophysical properties of tar species compared to the light

10

gases (e.g., N2, H2, CH4, CO and CO2). As a result, the inclusion of tar species will affect the heat

11

convection and diffusion of the product gas, which in turn influence the particle heating process

12

and its pyrolysis behavior. However, tar species generated during pyrolysis in real situations are

13

more complex than those tested here. Further experimental and modelling work needs to be

14

carried out in the future.

15

4.8 Effect of Devolatilization Heat on Pyrolysis

16

In the above simulations, the devolatilization process is assumed to be energetically neutral, i.e.,

17

the devolatilization heat (q) is set to 0. In this subsection, the impact of devolatilization heat on the

18

pyrolysis characteristics is studied.

19

Figure 17

20

Fig. 17 provides the temperature and mass loss histories for different values of q. Obviously, the

21

devolatilization heat has no significant influence on the moisture evaporation process, although

18


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Energy & Fuels

1

large differences appear in the devolatilization stage. Generally, the inclusion of devolatilization

2

heat slows down the particle heating process in the center area and thus raises the temperature

3

gradient inside the particle. Furthermore, the total pyrolysis time shows a nonlinear increasing

4

trend with an increase in q. Considering the devolatilization heat is artificially specified and kept

5

constant in the model, more accurate simulation results will be obtained if the values of

6

devolatilization heat can be provided as a function of temperature by experimental measurements.

7

4.9 Effect of Particle Shape on Pyrolysis

8

The current pyrolysis model is capable of resolving biomass particles with different shapes. In

9

this subsection, the pyrolysis of square and rectangular particles are compared with the base case

10

in which the particle has a circular shape. The length-to-width ratio of the rectangular particle is 2

11

and the short side is set perpendicular to the inflow. In order to isolate the effect of particle shape,

12

initial physical properties such as the particle volume, porosity and specific surface area have the

13

same values for the three shaped particles.

14

Figure 18

15

Fig. 18 displays the temperature and mass loss histories for the three particles. It can be seen

16

that the center temperature of the square particle goes up slightly faster than that of the circular

17

particle during the whole pyrolysis. The mass loss of the square particle is very similar to that of

18

the circular particle until 40 s after which its rates becomes larger than that of the circular particle,

19

making the total pyrolysis time reduced by 10 s. For the rectangular particle, the surface

20

temperature history shows a similar trend to that of the square particle although it always has a

21

higher value than those of the square and circular particles. Moreover, the center temperature rises

19


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

much faster than both the square and circular particles due to the shortest distance between particle

2

center and the nearest surface. Meanwhile, the rectangular particle has the largest interphase

3

contact surface area among the three particles which also contributes to the higher heating rate

4

inside the particle. As a result, the total pyrolysis time of the rectangular particle reduces to two

5

thirds of the circular particle.

6

Figure 19

7

Figure 20

8

Figs. 19 and 20 present the temperature contours for the square and rectangular particles,

9

respectively. It is interesting to find that the configuration of the temperature contour inside the

10

square particle changes gradually from a square shape to a circular shape and the configuration

11

inside the rectangular particle progressively shrinks to an elliptic shape, indicating that the dry

12

wood area left in the core region of irregular biomass particles tends to become a nearly round

13

shape during pyrolysis.

14

5. CONCLUSION

15

A comprehensive pyrolysis model for thermally thick biomass particle is established in the present

16

paper. To capture the detailed pyrolysis process, a uniform multi-phase CFD algorithm is utilized

17

to resolve both internal and external flow fields of the particle. Meanwhile, a continuous system

18

and a discrete system are also adopted to model the temperature and devolatilization of the particle.

19

The model is first validated with the pyrolysis experiments reported in the literature. The predicted

20

temperature, mass loss and species yield histories agree well with the experimental data. Besides,

21

it is also observed that, during devolatilization, the released gas species concentrate near the

20


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Energy & Fuels

1

leeward side of the particle. Moreover, the Stefan flow forms an isolation layer around the particle

2

surface, which plays a key role in the interaction between internal and external flows.

3

Effects of seven parameters (particle size, initial porosity, pore structure parameter, inflow

4

velocity, tar generation, devolatilization heat and particle shape) on particle pyrolysis behavior are

5

also studied. Results show that enhancing particle diameter makes larger intra-particle temperature

6

gradient and stronger Stefan flow effect, which will decrease the heating rate near both the particle

7

surface and center area. With increasing the initial porosity, the mass transfer inside the particle is

8

strengthened and thus the moisture evaporation is accelerated. The evolution of specific surface

9

area mainly affects the heating process inside the particle and must be taken into account for large

10

particles. Furthermore, a moderate increase of inflow velocity will be helpful for the pyrolysis of

11

large particles. However, when it is further increased, such promotion effect will be weakened due

12

to the reduced Stefan flow effect.

13

In addition, the impact of tar generation on pyrolysis is also studied. Simulation results indicate

14

that the heating process inside the particle is apparently changed when the mass fraction of tar

15

accounts for 40% of the total product gas. The difference is mainly caused by the different

16

thermophysical properties of tar species, which not only change the temperature distribution but

17

also affect the convection and diffusion processes both inside and outside the particle. The

18

devolatilization heat slows down the particle heating process in the center area and thus raises the

19

intra-particle temperature gradient. Finally, regarding the particle shape, it is found that the

20

intra-particle heating rate has a positive correlation with the length-to-width ratio of the particle

21

and the dry wood region left in irregular particles tend to shrink to a nearly round shape during

22

pyrolysis. 21


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

Nomenclature

A

pre-exponential factor, 1/s

cs

specific heat of particle, J/(kg K)

Deff

effective mass diffusion coefficient for gas, m2/s

es

particle emissivity, -

E

parameter in Eq. (3), J/kg or activation energy, J/kmol

G

incident radiation, kg/s3

h

heat transfer coefficient, W/(m2 K)

hs

sensible enthalpy, J/kg

ms

particle mass, kg

Q

energy source term, W/m3

R

universal gas constant, J/(kmol K)

Sh

enthalpy source term due to homogeneous reactions, W/m3

Sp,m

mass source term from particle, kg/(m3 s)

Sp,h

enthalpy source term from particle, W/m3

Srad

radiation source term, W/m3

Sp,Yi

species source term from particle, kg/(m3 s)

SYi

species source term due to homogeneous reactions, kg/(m3 s)

Sp,mom S′ Tg , Ts

momentum source term, N/m3 specific surface area, m2/m3 temperature of gas phase and solid phase, K

ug

gas velocity, m/s

V

particle volume, m3

X

mass conversion fraction, 22


Page 22 of 48

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Energy & Fuels

Yi

mass fraction of species i, -

αeff

effective thermal diffusivity, kg/(m s)

εg

volume fraction, -

κ

heat conduction coefficient, W/(m K)

ρg , ρs

gas and solid phase density, kg/m3 Stefan-Boltzmann constant, W/(m2 K4)

σ τeff

effective stress tensor, Pa

Ψ

pore structural parameter, -

1

2

Corresponding Author

3

*Telephone: +86 57187952221. E-mail: [email protected].

4

Notes

5

The authors declare no competing financial interest.

6

7

The present work is financially supported by the National Natural Science Foundation of China

8

(Grant Nos. 91634103 and 11632016) and the China Postdoctoral Science Foundation (Grant No.

9

2018M632469).

AUTHOR INFORMATION

ACKNOWLEDGEMENTS

10

11

(1) World Energy Council, World Energy Resources 2016.

12

(2) Ku, X.; Lin, J.; Yuan, F. Energy Fuels 2016, 30, 4053-4064.

REFERENCES

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(3) Sikarwar, V.S.; Zhao, M.; Clough, P.; Yao, J.; Zhong, X.; Memon, M.Z.; et al. Energy Environ.

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Sci. 2016, 9, 2939-2977.

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(4) Wang, S.; Dai, G.; Yang, H.; Luo, Z. Prog. Energy Combust. Sci. 2017, 62, 33-86.

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(5) Yang, Y.B.; Sharifi, V.N.; Swithenbank, J. Process Saf. Environ. Prot. 2005, 83, 549-558.

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(6) Di Blasi, C. Fuel 1997, 76, 957-964.

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(7) Haberle, I.; Skreiberg, Ø.; Lazar, J.; Haugena, N.E.L. Prog. Energy Combust. Sci. 2017, 63,

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204-252.

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(8) Larfeldt, J.; Leckner, B.; Melaaen, M.C. Fuel 2000, 79, 1637-1643.

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(9) Babu, B.; Chaurasia, A. International symposium and 56th annual session of IIChE 2003,

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19-22.

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(10) Babu, B.; Chaurasia, A. Chem. Eng. Sci. 2004, 59, 1999-2012.

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(11) Thunman, H.; Leckner, B.; Niklasson, F.; Johnsson, F. Combust. Flame 2002, 129, 30-46.

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(12) Gómez, M.A.; Porteiro, J.; Patiño, D.; Míguez, J.L. Energy Convers. Manage. 2015, 105,

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30-44.

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(13) Lu, H.; Robert, W.; Peirce, G.; Ripa, B.; Baxter, L. L. Energy Fuels 2008, 22, 2826-2839.

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(14) Mehrabian, R.; Zahirovic, S.; Scharler, R.; Obernberger, I.; Kleditzsch, S.; Wirtz, S.; et al.

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Fuel Process. Technol. 2012, 95, 96-108.

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(15) Kwiatkowski, K.; Bajer, K.; Celińska, A.; Dudyński, M.; Korotko, J.; Sosnowaka, M. Fuel

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2014, 132, 125-134.

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(16) Gentile, G.; Debiagi, P.E.A.; Cuoci, A.; Frassoldati, A; Ranzi, E.; Faravelli, T. Biochem. Eng.

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J. 2017, 321, 458-473.

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(17) Daud, W.M.A.A.; Ali, W.S.W. Bioresour. Technol. 2004, 93, 63-69. 24


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1

(18) Sadhukhan, A.K., Gupta, P., Saha, R.K. Fuel Process. Technol. 2009, 90, 692-700.

2

(19) Heesink, A.B.M.; Prins, W.; van Swaaij, W.P.M. Chem. Eng. J. 1993, 53, 25-37.

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(20) Gómez-Barea, A.; Ollero, P. Chem. Eng. Sci. 2006, 11, 3725-3735.

4

(21) Bhatia, S.K.; Perlmutter, D.D. AIChE J. 1980, 26, 379-385.

5

(22) Witting, K.; Nikrityuk, P.A.; Schulze, S.; Richter, A. AlChE J. 2017, 63, 1638-1647.

6

(23) Ku, X.; Li, T.; Løvås, T. Chem. Eng. Sci. 2013, 95, 94-106.

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(24) Ku, X.; Li, T.; Løvås, T. Energy Fuels 2014, 28, 5184-5196.

8

(25) Ku, X.; Li, T.; Løvås, T. Chem. Eng. Sci. 2015, 122, 270-283.

9

(26) Gidaspow, D. Multiphase Flow and Fluidization, Academic Press 1994, San Diego.

10

(27) Kumar, M.; Ghoniem, A.F. Energy Fuels 2012, 26, 464-479.

11

(28) Ergüdenler, A.; Ghaly, A.E.; Hamdullahpur, F.; Altaweel, A.M. Energy Sources 1997, 19,

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1065-1084.

13

(29) Gerber, S.; Behrendt, F.; Oevermann, M. Fuel 2010, 89, 2903-2917.

14

(30) Nikrityuk, P.A.; Meyer, B. Gasification processes: modeling and simulation, Wiley 2014,

15

143-169.

16

(31) OpenCFD Ltd, OpenFOAM-The open source CFD toolbox user guide (Version 2.1.1) 2012.

17

(32) Gauthier, G.; Melkior, T.; Grateau, M; Thiery, S.; Salvador, S. J. Anal. Appl. Pyrol 2008, 104,

18

521-530.

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(33) Bennadji, H.; Smith, K.; Shabangu, S.; & Fisher, E.M. Energy Fuels 2013, 27, 1453-1459.

20

25


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1

Tables

2

Table 1. Biomass properties of poplar wood particle Proximate analysis (wt.%, as-received basis)

Elemental analysis (wt.%, dry and ash-free basis)

moisture ash volatiles fixed carbon

C H O others

6.0 1.5 84.0 8.5

47.4 8.8 43.7 0.1

3 4

26


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Energy & Fuels

1

Table 2. Biomass properties of beech wood particle Proximate analysis (wt.%, dry basis)

moisture ash volatiles fixed carbon

Elemental analysis (wt.%, dry and ash-free basis)

0.0 0.7 84.3 15.0

C H O others

49.9 6.4 43.6 0.1

2

27


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

Page 28 of 48

Figures

2

(b)

(a)

(c)

3

Fig. 1. Computational grids. (a) CFD grid, (b) heat transfer grid within the particle, and (c)

4

discretized cluster.

5

28


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Energy & Fuels

1 2 3

(a)

(b)

Fig. 2. (a) Temperature and (b) mass loss (1-mt/m0) histories of the poplar wood particle.

4

29


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

2

Fig. 3. Temperature history of the beech wood particle.

3

30


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Energy & Fuels

1

(a)

(b)

(c)

(d)

2

Fig. 4. Species yield histories of the beech wood particle (values are normalized by the initial

3

particle mass). (a) CO, (b) CO2, (c) CH4, and (d) C2H4.

4

31


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 32 of 48

1

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

2

Fig. 5. Temperature contour of the poplar wood particle. (a) 1 s, (b) 5 s, (c) 10 s, (d) 20 s, (e) 30 s,

3

(f) 40 s, (g) 50 s, (h) 60 s, and (i) 70 s.

4

32


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Energy & Fuels

1 H2O

CO

(a)

(b)

(c)

(d)

(e)

(f)

33


CH4

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

(g)

(h)

(i)

1

Fig. 6. Gas species distribution of the poplar wood particle. (a) 1 s, (b) 5 s, (c) 10 s, (d) 20 s, (e) 30

2

s, (f) 40 s, (g) 50 s, (h) 60 s, and (i) 70 s.

3

34


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Energy & Fuels

1 2

Fig. 7. Circumferential temperature distribution inside the poplar wood particle at t=1s (δd is the

3

distance to the particle surface).

4

35


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Page 36 of 48

1

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

2

Fig. 8. Streamlines of the poplar wood particle with a diameter of 9.5 mm. (a) 1 s, (b) 5 s, (c) 10 s,

3

(d) 20 s, (e) 30 s, (f) 40 s, (g) 50 s, (h) 60 s, and (i) 70 s.

4

36


Page 37 of 48 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

Energy & Fuels

1 2

(a)

(b)

3

Fig. 9. (a) Temperature and (b) mass loss (1-mt/m0) histories for different particle sizes (d=9.5mm

4

is the base case).

5

37


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 38 of 48

1

(a)

(b)

(c)

(d)

(e)

(f)

2

Fig. 10. Streamlines of the poplar wood particle with a diameter of 7 mm. (a) 1 s, (b) 5 s, (c) 10 s,

3

(d) 20 s, (e) 30 s, (f) 40 s.

4

38


Page 39 of 48 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

Energy & Fuels

1

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

2

Fig. 11. Streamlines of the poplar wood particle with a diameter of 12 mm. (a) 1 s, (b) 5 s, (c) 10 s,

3

(d) 20 s, (e) 30 s, (f) 40 s, (g) 50 s, (h) 60 s, (i) 70 s, (j) 80 s, (k) 100 s, and (l) 120 s.

4

39


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 40 of 48

1 2

(a)

(b)

3

Fig. 12. (a) Temperature and (b) mass loss (1-mt/m0) histories for different initial porosities

4

(εg0=0.4 is the base case).

5

40


Page 41 of 48 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

Energy & Fuels

1 2

Fig. 13. The evolution of specific surface area with different Ψ (Ψ =200 is the base case).

41


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 42 of 48

1 2

(a)

(b)

3

Fig. 14. (a) Temperature and (b) mass loss (1-mt/m0) histories for different Ψ (Ψ =200 is the base

4

case).

5

42


Page 43 of 48 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

Energy & Fuels

1 2

(a)

(b)

3

Fig. 15. (a) Temperature and (b) mass loss (1-mt/m0) histories for different inflow velocities

4

(Vin=0.5 m/s is the base case).

5

43


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 44 of 48

1 2

(a)

(b)

3

Fig. 16. (a) Temperature and (b) mass loss (1-mt/m0) histories for different mass fractions of tar

4

(0% is the base case).

5

44


Page 45 of 48 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

Energy & Fuels

1 2

(a)

(b)

3

Fig. 17. (a) Temperature and (b) mass loss (1-mt/m0) histories under different values of

4

devolatilization heat (0 J/kg is the base case).

5

45


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 46 of 48

1 2 3

(a)

(b)

Fig. 18. (a) Temperature and (b) mass loss (1-mt/m0) histories for different particle shapes.

4

46


Page 47 of 48 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

Energy & Fuels

1

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

2

Fig. 19. Temperature contour of the square particle. (a) 1 s, (b) 5 s, (c) 10 s, (d) 20 s, (e) 30 s, (f)

3

40 s, (g) 50 s and (h) 60.

4

47


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 48 of 48

1

(a)

(b)

(c)

(d)

(e)

(f)

2

Fig. 20. Temperature contour of the rectangular particle. (a) 1 s, (b) 5 s, (c) 10 s, (d) 20 s, (e) 30 s

3

and (f) 40 s.

4

48


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