Simulation of Biomass Pyrolysis in a Fluidized Bed Reactor Using

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Thermodynamics, Transport, and Fluid Mechanics

Simulation of biomass pyrolysis in a fluidized bed reactor using thermally thick treatment Xiaoke Ku, Fengli Shen, Hanhui Jin, Jianzhong Lin, and Heng Li Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b04778 • Publication Date (Web): 08 Jan 2019 Downloaded from http://pubs.acs.org on January 11, 2019

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Industrial & Engineering Chemistry Research

Simulation of biomass pyrolysis in a fluidized bed reactor using thermally thick treatment Xiaoke Kua,b,*, Fengli Shena, Hanhui Jina, Jianzhong Lina,*, Heng Lia aDepartment

bState

of Engineering Mechanics, Zhejiang University, 310027 Hangzhou, China

Key Laboratory of Clean Energy Utilization, Zhejiang University, 310027 Hangzhou, China

ABSTRACT A thermally thick particle model is proposed and combined with the Euler-Lagrange model to study the biomass pyrolysis process in a fluidized bed reactor. Besides model validation, both a single biomass particle and a batch of particles are simulated. The large differences between thermally thick and thermally thin models are also highlighted by several indicators: particle surface and core temperature evolutions, mass loss history, particle trajectory, and product gas distributions. Results show the importance and necessity of thermally thick treatment for large particles because there exist big intra-particle temperature gradients, which in turn make different parts of the particle experience different conversion stages. Such behavior cannot be predicted by thermally thin model. In addition, effects of particle size and operating temperature are also explored, revealing that smaller particle size and higher temperature promote the pyrolysis process and reduce the time period during which the core temperature approaches the surface temperature. Keywords: Thermally thick model, CFD-DEM, Fluidized bed, Biomass pyrolysis

1. INTRODUCTION Biomass has the potential to become an important energy resource due to the shortage of fossil fuels and the severe air pollution. Its wide utilization can reduce the emissions of CO2 and other polluted gas into the atmosphere.1-2 Among the different conversion technologies for biomass, pyrolysis characterized as the

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thermal degradation of organic materials in the absence of oxygen, has attracted much attention because it can convert raw biomasses with a low energy density into various products with a high energy density such as bio-oil.3 Meanwhile, fluidized bed (FB) reactor has been a promising choice for biomass pyrolysis because of its high rates of heat and mass transfer, superior temperature and flow control, and incomparable mixing ability.4 CFD modelling has become a very powerful tool for the study of thermochemical conversion of biomass, in which the particle temperature is a critical parameter to predict because it determines the reaction rates of the fuel. Generally, for very tiny fuel particles (e.g., pulverized coal powder), the thermally thin model is frequently chosen for simplification purpose and the particle is treated as an entirety in which every part of it has the same temperature and reaction rate. However, biomass particles are normally big and hard to be ground because of its fibrous property. As a result, huge temperature and property differences between particle surface and particle core can be observed when it experiences heating-up and conversions. Thus, thermally thin treatment may cause great error for large fuel particles and an accurate resolution of the interior temperature distribution is important for a better understanding of the biomass conversion process. Thermally thick particle model has started to attract attention in the past decade.5-8 Wurzenberger et al.9 discretized the particle along the radial direction and the fixed bed was also divided into several parts. Babu and Chaurasia10 studied the effect of particle shrinkage on the pyrolysis behavior of large biomass particles with a diameter up to 50 mm. Yang et al.11 used three types of mesh cells (i.e., void mesh cell, boundary cell and inner cell) to represent a thermally thick particle and different cells could have different kinds of reactions and governing equations. Komatina et al.12 put forward a thermally thick model in which the specific heat capacity and heat transfer coefficient were a function of particle temperature. They found that the temperature gradient and devolatilization rate were mostly controlled by the intra-particle heat transfer.

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Papadikis et al.13 split the particle into a few grids to capture the temperature distribution inside the particle. Lu et al.14 discussed the influence of shapes and sizes on the devolatilization behavior of thermally thick biomass particles. Ström and Thunman15 described the heat of devolatilization as a linear function of temperature to better predict the particle temperature. Ciesielski et al.16 developed a 3D model for large biomass particles with morphology and microstructure obtained from quantitative image analysis. Gómez et al.17 partitioned the particle into layers to obtain the interior information of a large biomass particle. More recently, Remacha et al.18,19 experimentally and theoretically studied the devolatilization and combustion of millimeter-sized (3-15 mm) biomass particles at high temperatures and heating rates. They found that the thermally thick model performed much better than the thermally thin model for the entire range of particles sizes and operating conditions tested. However, all of them focused on the conversion of a single stationary particle or a batch of particles in fixed bed, and the thermally thick particle model has been rarely studied for freely moving biomass particles in fluidized bed. In the previous work,20 we have successfully developed a CFD-DEM (Discrete Element Method) model for biomass conversion in a FB reactor. The integrated model consists of various submodels which resolve the hydrodynamics, heat and mass transfer, turbulence, particle collision, radiation, and homogeneous and heterogeneous reactions, respectively. However, a thermally thin model was adopted for fuel particles. In order to more precisely predict the temperature for large biomass particles, here, the objectives of the current paper are thus to: (1) propose a new thermally thick model to resolve the temperature gradient inside the large biomass particle, (2) consider the contributions of heat conduction, heat convection, radiation and reaction heat, (3) combine the proposed thermally thick model with the already developed CFD-DEM model to investigate the biomass pyrolysis process in a FB reactor, and (4) highlight the differences between thermally thick and thermally thin models and also explore the sensitivity of the thermally thick model to the

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particle size and operating temperature. Such information is quite important for deeply understanding the conversion process of large biomass particles in FB reactor. The rest of this paper is organized as follows. In Section 2, a brief overview of the already developed CFD-DEM model applied for biomass conversion is first presented. Then, the thermally thick model for large biomass particles is described in detail. Section 3 validates the proposed model by comparing the predicted results with the experimental data reported in the literature. In Section 4, the simulation results for the pyrolysis process of a single big biomass particle and a batch of particles in FB reactor are provided and analyzed both qualitatively and quantitatively. Moreover, the significant differences between thermally thick and thermally thin models are also highlighted. Finally, a concise conclusion is given in Section 5.

2. MATHEMATICAL MODEL

2.1. CFD-DEM model The CFD-DEM model used which has the capability to well describe dense and reactive multiphase flows, is based on the Eulerian-Lagrangian concept. Correspondingly, various transport equations are solved for the continuous gas phase and discrete particles are individually tracked. It was already developed and implementation details were systematically documented in our earlier publications.20-21 Here, for concision purpose, the governing equations for both particle and gas phase are summarized in Table 1 and a brief description is also provided. Table 1 For biomass pyrolysis in a FB reactor, the particle phase consists of sand and biomass particles which are assumed to be spherical. The discrete particles are tracked in the Lagrangian way and a simple Euler time integration procedure is adopted to solve the particle motion equations. Sand particles are bed material and

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act as heat carrier during pyrolysis. Inter-particle and particle-wall collisions are taken into account by a soft-sphere model in which particles are allowed to slightly overlap so that multiple contacts encountered in fluidized bed can be dealt with.22 At every time-step, the particle temperature is calculated by considering the convective heat transfer, radiation, and the latent heat of moisture evaporation. Furthermore, the intra-particle heat conduction is also resolved under the thermally thick treatment. Meanwhile, the evolution of the continuous gas phase is evaluated by solving the mass, momentum, energy, and species transport equations, in which time discretization is accomplished using an Euler scheme and spatial discretization is implemented by a finite-volume technique. Turbulence is modelled with a modified k-ε approach which includes the volume fraction of gas and is suitable for the dense gas-particle flows in fluidized bed.20 Note that the effective mass diffusion coefficient for species Deff is determined by Deff=Dg+ μt/Sct where μt, Dg and Sct are the turbulent viscosity, mass diffusion coefficient for species in the mixture, and turbulent Schmidt number (i.e., 0.9), respectively. In addition, the inter-phase coupling is considered by adding different source terms in the transport equations.

2.2. Thermally thick model In order to obtain the temperature distribution within the large biomass particles, a thermally thick model is proposed and combined with the CFD-DEM model mentioned above. Generally speaking, the thermally thin model assumes that the temperature is constant throughout the particle’s volume and the whole particle has only one time-varying temperature as it is heated up. In contrast, the thermally thick model has no such assumption and can provide the non-spatially-uniform and time-varying temperature distribution within the particle by solving intra-particle heat transfer equations. Whether particles are thermally thick or thermally thin can be broadly determined by Biot number which is defined by Bi=heffdp/λ. heff, dp and λ are the convective heat transfer coefficient, particle size, and thermal conductivity, respectively. If Bi is quite small 5



(e.g., 0.1), obvious temperature gradient appears inside the particle during its heating up and only thermally thick models are applicable.15 Note that, because the convective heat transfer coefficient heff is dependent on the material type and local flow conditions such as velocity, viscosity and other temperature-dependent parameters, a constant value (i.e., 20 W m-2 K-1) is used to calculate the Biot number for simplification purpose.23 Figure 1 The thermally thick model proposed here is basically based on the discretization method of Papadikis et al.13 and the layer concept of Ström and Thunman15 and Gómez et al.17 The schematic is shown in Figure 1. It is seen that the biomass particle is divided into three different material layers, which are wet wood layer (Lm), dry wood layer (Ld) and char layer (Lc), respectively. These three layers may exist at the same time during pyrolysis and each layer has different physical properties and conversion rates. Between these layers there are two boundaries. The one between Lm and Ld is called drying front (fe) where moisture evaporation takes place. The other one between Ld and Lc is called devolatilization front (fd) which is the outermost boundary of Ld. Furthermore, due to the heat exchange with the atmosphere, the particle surface is treated as a special front (fs). In addition, as shown in Figure 1b, each material layer is further discretized by many grids which can be used to resolve the temperature gradient in the layer. The number of total grids inside the particle is set to 50 which can ensure both the accuracy and efficiency of the simulation after testing a few grid numbers. In addition, the following assumptions are also adopted: (i) The whole volume of the particle is fixed and does not change with time during conversion; (ii) The thermal conductivity and specific heat capacity of every material layer are constant; (iii) The evaporation of the moisture occurs at a constant temperature of 100 °C. With the drying front (fe) advancing to the center, the wet wood layer becomes

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thinner and thinner; (iv) The gases produced during the processes of evaporation and devolatilization are assumed to exhaust immediately; (v) In order to reduce the computational efforts, the devolatilization kinetics is modelled by a single-step first-order Arrhenius reaction equation although more complex pyrolysis schemes are also possible. Moreover, the devolatilization process is assumed to be heat-neutral (i.e., neither exothermic nor endothermic) because the devolatilization heat (qdevol) is generally small and has an uncertain value.24

2.2.1. Energy balance This model discretizes the spherical particle along the radial direction, which splits the particle into concentric spheres. Hence, the heat conduction equation is,

 (  cpT ) t



1  T ( r 2 ) 2 r r r

(1)

where cp is the specific heat capacity and λ is thermal conductivity. Under the assumption that cp and λ are constant within each material layer, substituting α = λ/(ρcp) into Eq. (1), it can be reduced to another form,

T 1  T   2 (r 2 ) t r r r

(2)

The heat balance at boundary fe where the drying occurs, is,15

QLd  Lm  Ld Afe

Ffe 

T r

Ffe

(3)

fe

8.071311730.63/(T fe 39.724)

10

760

(4)

where Afe is the surface area of boundary fe. The boundary condition at fe is,

T fe  373 K The heat balance at fd is,15

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(5)


L A f d

d

T r

 Lc Afd

f d

T r

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(6) f d

The energy equation at fs is,

m fs cp,fs

dT fs dt

ep Afs

 hAfs (Tg  T fs ) 

4

(G  4 T f4s )  Qp

(7)

where h is the heat transfer coefficient and determined by the Ranz-Marshall expression;13 ep, σ, G, and Qp are the particle emissivity, Stefan-Boltzmann constant, incident radiation, and source term, respectively. Note that G is obtained by solving the transport equation, i.e., ·(γG)-aG+4aσT4=0, where γ and a are model parameters. Because of symmetry, the boundary condition at the core is,

T r

0

(8)

r 0

2.2.1. Mass balance During pyrolysis, the drying front fe moves from the particle surface to the core and the wet wood becomes dry wood. As a result, the wet wood layer Lm shrinks and its mass decreases as:15

dmLm dt



 fw

(9)

where fw is the mass fraction of water and γ is the drying rate which is related to the heat transferred to boundary fe by QLd-Lm,



QLd  Lm

(10)

H

where ΔH is the heat of vaporization of water. When mLm is known, it is easy to obtain the position of fe,

 3mLm rf e    4π L m 

1/3

  

(11)

The mass change of layer Ld includes both the generation by the drying of wet wood and the consumption 8


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by the devolatilization. In order to model the devolatilization process, a single-step first-order Arrhenius reaction model is adopted for simplification purpose although there are a number of complex reaction models available in the literature (e.g, multi-step devolatilization model, distributed activation energy model, chemical percolation devolatilization model).25 The devolatilization rate is also dependent on the amount of volatiles remaining in particle and the total contribution of devolatilization is determined by an integration from boundary fe to fd.

dmLd dt

 

fd fe

K f i

v,i

dm 

dmLm

i

dt

(1  f w )

(12)

where Ki=Aie-Ei/RT and it is different among different grids in layer Ld; fv,i is the mass fraction of volatile species i. Note that the specific values of Ai and the activation energy Ei are adopted from the literature which are derived from the experimental data and suitable for woody biomasses.26 After devolatilization, the position of fd is: 1/3

 3mLd  rf d    rf3  e  4π L  d  

(13)

Finally, the mass change of layer Lc is,

dmLc dt



dmLd dt

(1  f v )

(14)

3. VALIDATION Table 2 Table 3 To validate the proposed thermally thick model, we compare our simulation results with the experimental data of Wurzenberger et al.9 and Lu et al.27 In the experiments of Wurzenberger et al.9, a spherical beech particle with a diameter of 20 mm and a moisture content of 20% (based on total wet particle mass) is exposed to a nitrogen atmosphere at 1098 K. In the experiments of Lu et al.27, biomass particles with two 9



different shapes are adopted. The first one is a spherical poplar particle with a diameter of 9.5 mm and a moisture content of 6%. The other one is a cylindrical poplar particle with a cross-sectional diameter of 9.5 mm and an aspect ratio of 4.0 as well as a moisture content of 40%. They are both exposed to a nitrogen environment at 1050 K and the reactor wall temperature is fixed at 1276 K. Main parameters and their values used in simulation are given in Table 2 and the proximate and ultimate analyses of the biomasses are shown in Table 3. 30,31 Note that the elemental analysis in Table 3 is relevant to the compositions of the pyrolysis products and each product yield is calculated using the elemental conservation relationships. Figure 2 Figure 3 Figure 2 presents the comparisons of the evolutions of surface and core temperatures and solid mass fraction with the experimental data reported by Wurzenberger et al.9 Note that the solid mass fraction is defined as the ratio of the particle mass at time t to its initial mass. Generally, for the temperature evolutions at the surface and core of the particle, a good agreement has been obtained although minor differences can be observed in some local parts. Furthermore, for the solid mass fraction evolution, the simulation results almost coincide with the experimental data, demonstrating the validity and accuracy of the proposed thermally thick model for resolving the internal temperature distribution and particle mass loss during biomass pyrolysis. As described in subsection 2.2, the devolatilization process is assumed to be heat-neutral, i.e., the devolatilization heat (qdevol) is set to 0 J/kg in the above simulations. In order to explore the effect of qdevol on the predicted results, three other values of qdevol (i.e., 1×104 J/kg, 1×105 J/kg, 2×105 J/kg) have also been tested. Figure 3 depicts the evolutions of surface and core temperatures and solid mass fraction for different values of qdevol. Clearly, including the devolatilization heat will decelerate the particle heating process and

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such retarding effect is insignificant within the tested range of qdevol. Thus, the devolatilization heat is still assumed to be 0 J/kg in the following simulations. Figure 4 Figure 5 Figures 4 and 5 show the comparisons of the evolutions of surface and core temperatures and solid mass fraction with the experimental data reported by Lu et al.27 Again, for both the temperature and solid mass fraction evolutions, the predicted results are in good agreement with the experimental data although relatively large differences are seen for the cylindrical particle.

4. RESULTS AND DISCUSSIONS For the validation cases, the biomass particle is fixed. In order to further evaluate the predictive ability of the model for freely moving biomass particles in flows, in this section, the pyrolysis processes of one large biomass particle and a batch of biomass particles in a FB reactor are simulated.

4.1. One large biomass particle Figure 6 The biomass particle is fed into a small-scale FB reactor with dimensions of width (4 cm) × height (16 cm) × thickness (3 mm). Figure 6 shows the schematic diagram of the reactor. Because the CFD-DEM model which is very time-consuming is adopted, the simulations are restricted to quasi-3D (reactor thickness is one diameter of the largest biomass particle) solutions which means that the bed contains only one layer of biggest fuel particles. As a result, the front and back walls of the reactor are not treated as real walls at which the boundary conditions are set zero-gradient. Furthermore, the motion of biomass particles in the thickness direction are not resolved.

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Table 4 Most of the model parameters are the same as those of the validation case and other parameters with the default values involved in the fluidized bed are listed in Table 4. As shown in Table 4, the biomass diameter ranges from 0.5 mm to 3 mm. The reason why millimeter-sized fuel particles are adopted is that the biomass particles in range of 0.25-5 mm are usually used in fluidized bed reactors while larger particles (e.g, >=10 mm) is normally utilized in fixed bed reactors.32,33 Initially, sand particles make up a packed bed with a height of approximately 20 mm. For fluidization and pyrolysis, nitrogen is introduced at a constant flow rate from the bottom of the bed. Biomass particle is fed from the position located at 6 cm above the reactor bottom rather than the top outlet to save the dropping time. Furthermore, before feeding biomass, nitrogen is injected for half second to eliminate the influence of the start of the fluidization. Simulation cases are carried out on the Dell Precision Workstation (3.00 GHz Intel Xeon processors) and each run will take about 7 days to finish the 10 s real time of biomass pyrolysis process simulation. Figure 7 Figure 8 Figure 7 shows the biomass particle position and its core temperature at different time instants. Furthermore, Figure 8 presents the evolutions of surface and core temperatures of the particle as well as the solid mass fractions during pyrolysis. Note that the solid mass fraction evolutions for the surface and core sublayers are also included for comparison purpose and the sublayer solid mass fraction is defined as the ratio of the sublayer mass at time t to its initial mass. Apparently, it can be seen that the surface temperature sharply increases from the initial temperature 298 K to the operating temperature 1098 K, while the core temperature stays at the initial temperature 298 K for 1.5 s and then slowly rises to the boiling temperature 373 K. When the moisture evaporation finishes at the core, the core temperature dramatically surges to the

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high temperature. Moreover, the surface temperature has slight fluctuations during the first 2 seconds because of the competition between the contributions from different heat transfer mechanisms at the surface. In addition, from the evolving curves of solid mass fraction as shown in Figure 8b, the particle completes pyrolysis at about 8 s when the core temperature still does not reach the operating temperature. Furthermore, as expected, the evolving curve for the whole particle lies between the curves for the surface and core sublayers. Figure 9 Figure 10 In order to emphasize the importance of the thermally thick treatment for large biomass particles, Figures 9 and 10 provide the comparisons of temperature and solid mass fraction evolutions during biomass pyrolysis, respectively, between thermally thick and thermally thin models. Note that the curves end at the time instant when the particle is finally entrained out of the reactor and the thermally thin model predicts a faster particle entrainment than the thermally thick model. As shown in Figure 9, for thermally thin model, the whole particle has a single temperature and it cannot resolve the temperature gradient inside the particle. In contrast, for thermally thick model, the large temperature differences at the particle surface and core are properly predicted and such differences play a key role in distinguishing the reaction rates within the particle. In addition, for the solid mass fraction curves as presented in Figure 10, there are two plateaus for thermally thin model which represent, respectively, the heating-up stages before drying and devolatilization. However, for thermally thick model, the solid mass fraction continually and smoothly reduces when the biomass particle is heated up and there exist no plateaus. Such evolving trend is more practical and expected, considering the particle surface can release matters even if the interior temperature is still very low. But for thermally thin model, only when the temperature of the whole particle exceeds a certain temperature, can the

13



drying and devolatilization come into effect, leading to the two plateaus in its mass fraction evolution curve. Figure 11 Figure 12 Figure 11 shows the evolution of particle temperature along the moving trajectory before it is entrained out of the reactor for both thermally thick and thermally thin models. Although particle circulation in the bed section is observed for both models, large differences in the trajectory and particle temperature still appear in both the bed and freeboard sections. Note that these differences are only caused by the different models used even the other simulation conditions are exactly the same. Figure 12 provides the mass loss rate along the moving trajectories. Clearly, different trajectories will cause different local heating up processes during pyrolysis in fluidized bed reactor, making the fuel particle experience distinct heat and mass transfer characteristics and in turn cause different conversion behaviors. Furthermore, the particle residence times for thermally thick and thermally thin models are 9.0 s and 6.6 s, respectively. Figure 13 Figure 14 In order to explore the effect of particle size on the difference between thermally thick and thermally thin models, Figure 13 presents the surface and core temperature evolutions for four different particle sizes (i.e., dp = 0.5, 1, 2, and 3 mm). As expected, when the particle size decreases, the time period during which the core temperature approaches the surface temperature reduces. Note that, for dp < 3 mm, the core temperature still does not reach the surface temperature when the particle escapes from the reactor. Figure 14 shows the evolution of difference between particle surface and core temperatures for various particle sizes. Obviously, the maximum difference between the surface and core temperatures enhances with increasing particle size, which are 668.8 K, 702.7 K, 711.3 K, and 716.6 K for 0.5 mm, 1 mm, 2 mm, and 3 mm particles,

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respectively. Another important factor is the duration of the intra-particle temperature gradient which also rises nonlinearly with an increase in dp, which are about 0.4 s, 1.1 s, 3.2 s, and 9.0 s for 0.5 mm, 1 mm, 2 mm, and 3 mm particles, respectively. Figure 15 The proposed thermally thick model can provide not only the surface and core temperatures of the particle but also the temperature evolution at any location inside the particle, while only one approximate particle temperature is offered by the thermally thin model which treats the particle as an entirety. Figure 15 displays the temperature evolutions for eleven locations within the particle which cover the entire particle size ranging from particle surface to core. The spacing between neighboring positions is 0.05dp. Obviously, there are great temperature deviations inside the particle, especially during the first 4 s. Moreover, within the first 2.5 s, half of the particle is below the boiling temperature which means that about half of the particle starts devolatilization and the other half is still in drying process. Thus, different parts inside the particle undergo different conversion processes. In contrast, the thermally thin model cannot predict this behavior. In addition, it is also seen that the surface temperature quickly approaches the operating temperature after the particle is injected, while the interior parts of the particle fall behind. After drying process, the temperatures of the interior parts also increase rapidly to the operating temperature. After 4 s, when the entire particle completes drying, the temperature deviations inside the particle become smaller and smaller. Finally, the whole particle reaches the ambient temperature at 9 s. Since the operating temperature is the key parameter for biomass FB pyrolysis, three values (998 K, 1098 K and 1198 K) are chosen to test its effect on the simulation results. Figure 16 Figure 17

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Figures 16 and 17 present the effect of operating temperature on the temperature evolution and solid mass fraction. Clearly, the operating temperature has a significant impact on both the surface and core temperatures as well as the solid mass fraction. As expected, the higher the operating temperature, the faster the particle temperature increases because of the higher heating rate and the faster mass loss. It is also seen that the particle residence time is shorter for higher operating temperature, i.e., the particle takes less time to finish pyrolysis and fly out of the reactor when the operating temperature is enhanced.

4.2 A batch of biomass particles Besides the case of one single biomass particle, the pyrolysis behavior of a batch of biomass particles with a diameter of 2 mm in fluidized bed is also explored. Correspondingly, 20 fuel particles with an initial temperature of 298 K are fed into the reactor at the same time. Again, before feeding biomass, nitrogen is introduced for 0.5 s to eliminate the effect of the start of the fluidization. Figure 18 Figure 18 illustrates the biomass particle positions and their core temperatures at different time instants. After feeding the particles, a majority of them sink to the bed bottom due to the strong mixing ability of the fluidized bed. It can also be observed that biomass particles circulate in the bed and their core temperatures increase at different rates owing to different trajectories experienced by the particles. Furthermore, the temperature of sand particles close to cold biomass particles decrease apparently, revealing that a batch of particles influences the bed temperature greater than a single biomass particle (see Figure 7 for comparison). All biomass particles approach the operating temperature (i.e., 1098 K) at about 4.5 s. Figure 19 Figure 19 displays the distributions of temperature, gas porosity and CH4 mass fraction in the reactor at 2.5 s. Temperature is low in the region around fuel particles because the heating-up and moisture 16


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evaporation processes absorb heat from gas phase. Since there is no cavity in the fluidized bed at this time instant, the porosity of the bed seems nearly uniform. In addition, CH4 concentration is high near the side walls. The reason is that biomass particles tend to move towards the wall and then descend along the wall when bubbles burst at the top of the bed, which is a typical feature in the bubbling fluidized bed reactor (see Figure 18). Figure 20 Figure 20 shows the CO mass fraction distributions for both thermally thick and thermally thin models. Again, there exist big differences in the CO concentration between the two models. At 2.5 s, biomass already experiences devolatilization for thermally thick model while there is no CO released for thermally thin model. The reason is that, for thermally thick model, the devolatilization will begin as long as the particle surface temperature reaches the high temperature. However, for thermally thin model, the devolatilization can come into play only when the whole particle temperature gets to the high temperature and this takes a longer heating time. As a result, the thermally thick model predicts an earlier devolatilization. At 3.0 s, the devolatilization is intensive for thermally thick model whereas it just starts for thermally thin model. Finally, devolatilization almost completes at 4.5 s for thermally thick model but it will take a little longer time for thermally thin model. Figure 21 Figure 22 Quantitatively, Figure 21 shows the evolutions of average particle temperature and solid mass fraction of the biomass particles. Note that the averaging is carried out on the biomass particles remaining in the fluidized bed reactor because some of them will escape from the reactor during pyrolysis. Moreover, Figure 22 presents the volume fractions of the four main components in the product gas as a function of time t.

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Obviously, the evolving curves for the four gas species have quite similar shapes. For each gas species, the pyrolysis of thermally thick model starts at 1.5 s which is 1 s earlier than that of thermally thin model. Furthermore, the peak of thermally thick model appears earlier and lower than that of thermally thin model. In addition, the pyrolysis processes of thermally thick and thermally thin models last for 3.5 s and 2.8 s, respectively, implying that the thermally thin model predicts a shorter biomass pyrolysis process.

5. CONCLUSION A new thermally thick particle model is proposed and combined with the CFD-DEM model already developed to investigate the biomass pyrolysis process in a FB reactor. Using this model, detailed internal temperature information of the particle can be obtained. Both a single biomass particle and a batch of particles are simulated. The large differences between thermally thick and thermally thin models are highlighted by several indicators: temperature evolution, mass loss history, particle trajectory, and product gas distributions. Results show that the thermally thick model is more accurate for large biomass particles in fluidized bed than the thermally thin model because there exist large temperature gradients within the particle during its heating up, which in turn make different parts of the particle experience different conversion stages during pyrolysis. Such behavior cannot be predicted by the thermally thin model. Moreover, the particle mass continually and smoothly reduces during pyrolysis for the thermally thick model while two plateaus appear for the thermally thin model. In addition, effects of particle size and operating temperature are also tested, revealing that smaller particle size and higher temperature promote the pyrolysis process and also reduce the time period during which the core temperature approaches the surface temperature.

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


AUTHOR INFORMATION

Corresponding Authors *(X. Ku) Tel.: +86 57187952221. E-mail: [email protected]. *(J. Lin) Tel.: +86 57187952882. E-mail: [email protected]. Notes The authors declare no competing financial interest. 

ACKNOWLEDGEMENTS

The authors thank the National Natural Science Foundation of China (Grant Nos. 91634103, 51876191, and 11632016) and the startup fund from the ‘‘hundred talents program’’ of Zhejiang University for financial support. 

REFERENCES

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Table 1 Governing equations for particle and gas phase. Phase

Equations Mass:

Particle

Momentum: Energy:

Gas phase

dmp dt



dmvapor dt



dmdevol dt

dv p

 fg  fc  mp g dt dT eA mp cp p  hAp (Tg  Tp )  p p (G  4 Tp4 )  Qp dt 4 mp

Mass:

 (  )    ( g g u g )  S p,m t g g

Momentum:

 (  u )    ( g g u g u g )  p    ( g τ eff )   g g g  S p, mom t g g g

Energy:

 ( g g E )    ( gu g ( g E  p))    ( g eff hs )  Sp,h  Srad t E  hs 

Species:

p

g



ug2 2

 ( g gYi )    ( g gu gYi )    ( g g Deff Yi )  Sp,Yi t

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Table 2 Model parameters used in the validation cases. Parameters

Value

Time step (s) Initial particle temperature (K) Heat of vaporization of water (J/kg) Density (kg m-3)

10-3

Pre-exponential factor (s-1) Activation energy (kJ/mol) Thermal conductivity (W m-1 K-1)

Thermal diffusivity (m2 s-1)

Ref.

298 2.262 ×106 ρLd = 684 (beech) ρLd = 580 (poplar) Ai= 5.0 ×106 Ei=120 λLm = 0.09 λLd = 0.12 λLc = 0.06 αLm = 1.0 × 10-7 αLd = 7.0 × 10-8 αLc = 3.0 × 10-7

9 28 29 27 26 26 15 15 15 15 15 15

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Table 3 Wood properties. Beech

Poplar

Proximate analysis (wt %, dry basis) Moisture 0 0 Ash 0.4 4.09 Fixed carbon 14.3 12.34 Volatile 85.3 83.57 Elemental analysis (wt %, dry-ash-free basis) C 49.2 45.5 H 6.0 6.3 O 44.1 47.2 Others 0.7 1.0

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Table 4 Model parameters used in the fluidized bed simulations. Parameters

Value

Reactor size (m) Operating temperature (K) CFD grid size (m) Time step (s) Sand number (-) Sand diameter (m) Sand density (kg m-3) Sand specific heat (J kg-1 K-1) Sand emissivity (-) Biomass diameter (m) Biot number (-) Stiffness (N m-1) Coefficient of restitution (-) Coefficient of friction (-) N2 flow rate at inlet (g/s)

0.04 × 0.16 × 0.003 1098 0.004 × 0.004 × 0.003 1 × 10-5 2223 0.001 2600 860 0.9 0.0005-0.003 0.11-0.67 8.19×104 0.9 0.3 0.065373 (2.5Umf)

26


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(a)

(b)

Figure 1. (a) Schematic of biomass particle and (b) grids layout. fe and fd are the drying front and devolatilization front, respectively; fs is particle surface; Lm Ld, and Lc are wet wood layer, dry wood layer, and char layer, respectively.

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(a)

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(b)

Figure 2. Evolutions of (a) surface and core temperatures and (b) solid mass fraction for the spherical particle. dp = 20 mm (Bi = 4.44) and the moisture content is 20%.

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(a)

(b)

Figure 3. Effect of devolatilization heat on the evolutions of (a) surface and core temperatures and (b) solid mass fraction for the spherical particle. dp = 20 mm (Bi = 4.44) and the moisture content is 20%.

29



(a)

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(b)

Figure 4. Evolutions of (a) surface and core temperatures and (b) solid mass fraction for the spherical particle. dp = 9.5 mm (Bi = 2.11) and the moisture content is 6%.

30


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(a)

(b)

Figure 5. Evolutions of (a) surface and core temperatures and (b) solid mass fraction for the cylindrical particle. dp = 9.5 mm, the aspect ratio is 4.0 and the moisture content is 40%. Note that Bi=3.84 which is calculated based on the effetive diamater of the cylindrical partilce.

31



Figure 6. Schematic diagram of the fluidized bed reactor.

32


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Figure 7. Biomass particle position and its core temperature at different time instants. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

33



(a)

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(b)

Figure 8. Evolutions of (a) surface temperature, core temperature and (b) solid mass fraction of the biomass particle. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

34


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Figure 9. Comparison of temperature evolution betweem thermally thick and thermlly thin models. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

35



Figure 10. Comparison of solid mass fraction evolution between thermally thick and thermlly thin models. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

36


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(a)

(b)

Figure 11. Evolution of particle temperature along the moving trajectory before it is entrained out of the reactor. (a) Thermally thick model; (b) Thermally thin model. dp = 3 mm (Bi = 0.67) and the moisture content is 20%. Note that the partice core temperaure is used for thermally thick model.

37



(a)

(b)

Figure 12. Evolution of normalized mass loss rate along the moving trajectory. (a) Thermally thick model; (b) Thermally thin model. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

38


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(a)

(b)

(c)

(d)

Figure 13. Temperature evolution for different biomass particle sizes. (a) dp = 3 mm (Bi = 0.67); (b) dp = 2 mm (Bi = 0.44); (c) dp = 1 mm (Bi = 0.22); (d) dp = 0.5 mm (Bi = 0.11). The moisture content is 20%.

39



Figure 14. Evolution of difference between particle surface and core temperatures for different biomass particle sizes. The moisture content is 20%.

40


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Figure 15. Temperature evolution throughout the particle. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

41



Figure 16. Temperature evolution at different operating temperatures. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

42


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Figure 17. Evolution of solid mass fraction at different operating temperatures. dp = 3 mm (Bi = 0.67) and the moisture content is 20%.

43



Figure 18. Biomass particle positions and their core temperatures at different time instants. The moisture content is 20%.

44


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(a)

(b)

(c)

Figure 19. Contour results at 2.5 s. (a) Gas temperature, K; (b) Gas porosity; (c) CH4 mass fraction. The moisture content is 20%.

45



(a)

(b) Figure 20. CO mass fraction at different time instants. (a) Thermally thick model; (b) Thermally thin model. The moisture content is 20%. 46


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Figure 21. Evolutions of (a) average surface temperature, core temperature and (b) solid mass fraction of the biomass particles. The moisture content is 20%.

47



(a)

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(b)

Figure 22. Temporal evolution of product gas species at the reactor outlet. (a) Thermally thick model; (b) Thermally thin model. The moisture content is 20%.

48


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This figure displays the temperature evolutions for eleven locations within the particle during pyrolysis, which are predicted by the proposed thermally thick model and cover the entire particle size ranging from particle surface to core. Obviously, there are great temperature gradients inside the particle. Thus, different parts inside the particle undergo different conversion processes. In contrast, the thermally thin model cannot predict this behavior.

1


Simulation of Biomass Pyrolysis in a Fluidized Bed Reactor Using

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