Modeling Effects of Operating Conditions on Biomass Fast Pyrolysis in

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Modeling Effects of Operating Conditions on Biomass Fast Pyrolysis in Bubbling Fluidized Bed Reactors Qingang Xiong, Soroush Aramideh, and Song-Charng Kong* Department of Mechanical Engineering, Iowa State University, Ames, Iowa 50011, United States ABSTRACT: This numerical study investigates the effects of operating conditions on the product yields of a biomass fast pyrolysis reactor. A numerical approach that combines a multifluid model and pyrolysis reaction kinetics was applied to simulate the biomass fast pyrolysis process in a bubbling fluidized-bed reactor. The model was first validated using experimental data, and a parametric study was conducted. Operating parameters were varied to characterize their effects on the final product yields. For the reactor studied, it was found that the maximum tar yield could be obtained by maintaining both the wall temperature and the inlet temperature of nitorgen at approximately 800 K. The inlet velocity of nitrogen at about 0.6 m/s also produced favorable results. Simulations indicated that the optimal biomass particle diameter and the feeding rate were 900 μm and 1.92 kg/h, respectively, for tar production. Larger sand particle diameter and deeper initial sand bed also favored the tar yield.

1. INTRODUCTION Biomass as an energy resource has received much attention in recent years because of the increased concerns of the fossil fuel availability and greenhouse gas emissions. In comparison to the biological approach that uses microbes or enzymes to convert biomass to desired products, thermochemical conversion of biomass has the advantages of short residence time, large-scale application, and convenient reactor design.1 Thermochemical conversion of biomass can be classified into three categories: combustion, gasification, and fast pyrolysis. Different from the other two counterparts, fast pyrolysis is mainly aimed to produce the high energy density, easily transportable liquid bio-oil for further utilization.2 Fast pyrolysis has become increasingly important in recent years. As fast pyrolysis becomes an emerging technology, however, the understanding of its underlining mechanisms is far from satisfactory. This has undoubtedly limited the application of fast pyrolysis of biomass to produce energy products. Experiments are a direct approach to study the characteristics of biomass fast pyrolysis; however, high operating costs and harsh reactor conditions often hinder experimental studies. On the other hand, because of the rapid advancement in computing power and numerical algorithms, numerical simulation has become an effective tool for both scientific studies and engineering applications.3−6 By using accurate models, reactor performance can be predicted in a cost-effective fashion.7,8 Computational modeling is being used to help understand biomass fast pyrolysis for harnessing biorenewable energy.9−14 Because of its relatively high heat transfer rate and mature operating technology, a bubbling fluidized-bed reactor is often used for biomass fast pyrolysis. A detailed understanding of the effects of operating conditions on the product yield is of vital importance to the optimization of reactor operations. Numerous computer simulations have been conducted for this purpose. A laboratory-scale bubbling fluidized-bed reactor was simulated by Lathouwers and Bellan,15,16 in which the inlet temperature and velocity of nitrogen, biomass feeding rate, and biomass particle diameter were varied. It was found that the inlet temperature of nitrogen and the diameter of raw biomass have significant influences on the tar yield. Tar achieves its © 2013 American Chemical Society

maximum yield for the inlet temperature of nitrogen around 750 K, while increased biomass diameter has a negative effect on the tar yield. A similar laboratory-scale bubbling fluidized bed reactor was modeled by Xue et al.17 using both twodimensional (2-D) and three-dimensional (3-D) domains. In this study, the effects of biomass particle diameter and the inlet temperature and velocity of nitorgen were investigated. It was concluded that the tar yield reached its maximum for the inlet gas temperature of around 793 K and the increase in the inlet gas velocity was beneficial to the tar yield. Xue et al.17 also found that increased biomass diameter has a positive effect on the tar yield, contradictory to the finding by Lathouwers and Bellan.15,16 The effects of inlet velocity of nitrogen and reactor temperature were investigated by Mellin et al.,18 in which the tar yield was found to decrease with the increase in inlet velocity and reactor temperature. In the simulation of a larger fluidized bed by Luo et al.,19 the influences of the bed temperature, biomass particle size, and feed rate were investigated. An optimal bed temperature of 773 K was proposed for maximum tar yield, but the effects of particle size and feed rate were not straightforward. The sensitivity of the product yields to the biomass particle size was also studied numerically,20 and a complex behavior was seen. For a bed temperature below 750 K, the increase in particle size was found to have a negative effect on the tar yield; however, above 750 K, the tar yield first increased then decreased with the increase in biomass particle size. Using 3-D discrete particle modeling,21,22 the bed temperature and fluidization velocity were varied to investigate the effects of operating conditions. The tar yield at 758 K was found to be significantly higher than that at 699 K, but the effects of nitrogen velocity to the product yield were not characterized. By considering a number of individual particles in a 3-D simulation, the effects of biomass particle size and particle sphericity were investigated.23,24 The product yields were found Received: July 9, 2013 Revised: September 15, 2013 Published: September 16, 2013 5948

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Table 1. Governing Equations in the Present Multi-fluid Model25 solid phases (m = 1, 2, ..., M)

gas phase continuity equation

∂αgρg ∂t

∂αsmρsm

+ ∇(αgρg Ug) = R g

∂t

+ ∇(αsmρsm Usm) = R sm

momentum equation

∂(αgρg Ug) ∂t

M

+ ∇(αgρg UgUg) = ∇τg − αg∇p +

M

∂(αsmρsm Usm)

∑ βgsm(Usm − Ug) + ∑ ψgsm m=1

∂t

m=1

+ ∇(αsmρsm UsmUsm) = ∇τsm − αsm∇p + βgsm(Ug − Usm)

M

+ αgρg g

∑

+

βslm(Usl − Usm) + ψgsm + αsmρsm g

l = 1, l ≠ m

stress−strain tensor

τg = 2αgμg Dg + αgλg tr(Dg )I

τsm = − psm I + 2αsmμsm Dsm + αsmλsmtr(Dsm)I

momentum transfer due to chemical reactions

solid physical properties computed by KTGF

ψgsm = R gsm[ζUsm + (1 − ζ )Ug] ⎧ 0 R gsm < 0 ⎪ ζ=⎨ ⎪ ⎩ 1 R gsm ≥ 0 energy equation

∂(αgρg CpgTg) ∂t

M

+ ∇(αgρg CpgTgUg) = ∇qg +

∂(αsmρsm CsmTsm)

∑ hgsm(Tsm − Tg) + ΔHg

∂t

m=1

+ ∇(αsmρsm CpsmTsmUsm) = ∇qsm + hgsm(Tg − Tsm) + ΔHsm

conductive heat flux

qsm = αsmκsm∇Tsm

qg = αgκg∇Tg species equation

∂αgρg Ygk ∂t

∂αsmρsm Ysmk

+ ∇(αgρg YgkUg) = R gk

∂t

+ ∇(αsmρsm YsmkUsm) = R smk

coefficient and convective heat transfer coefficient were incorporated in the momentum and energy conservation equations. Based on our previous validation study,26 the EMMS drag model30 for gas−solid momentum exchange and Gunn heat transfer model31 for gas−solid heat transfer were employed. All the conservation equations and interphase transport correlations are summarized in Tables 1 and 2. To accurately solve the convection terms, a limited linear total variation vanishing (TVD) scheme was used. To simulate the reaction kinetics of biomass fast pyrolysis, the modified Shafizadeh−Chin decomposition kinetics32 was used. This model simulates biomass materials using three components: cellulose, hemicellulose, and lignin. During the reaction process, all the components undergo the same devolatilization pathway with different rates. The activation reaction promotes virgin biomass to an active state (eq 1). Two primary reactions describe the conversion of active biomass to tar (eq 2a) and to biochar and gas (eq 2b). A secondary reaction is also formulated to simulate the cracking of tar to produce gas. The end products of the reactions include tar, biochar, and gas. Tar is the vapor form of condensable hydrocarbons that will lead to bio-oil when it is cooled. Therefore, high concentration of tar exiting the reactor will imply a high bio-oil yield. In addition, all reactions are assumed to be first-order irreversible reactions with Arrhenius rates. All the reaction constants are listed in Table 3. Details of the rate constants can be found in the original literature.15,29,32

to be insensitive to the biomass particle size but greatly affected by the shape of biomass particles. From the aforementioned literature, it can be seen that most of the studies only varied a very few number of operating variables. In addition, the combined effects of biomass particle size and inlet nitrogen velocity were inclusive. In the present study, biomass fast pyrolysis in a bubbling fluidized bed was numerically studied using an approach combining a multifluid model for in-bed hydrodynamics and multistep chemical kinetics for fast pyrolysis reactions. This numerical approach was first validated using experimental data. Major operating variables, including external temperatures, velocity of nitrogen gas, and diameter of biomass particles, were varied within reasonable ranges to investigate their effects on the product yields. Finally, guidelines for reactor operation were discussed for achieving desired outcomes.

2. NUMERICAL MODELS Following our previous studies,25,26 a comprehensive multifluid model (MFM) that can describe a mixture of one gas phase and an arbitrary number of solid phases, with each phase containing an arbitrary number of species, was chosen to simulate the hydrodynamics in a bubbling fluidized bed reactor. This model is able to capture the tempo-spatial variations using continuum descriptions for both gas and solid phases. Solid stress is computed by the so-called kinetic theory of granular flows (KTGF).27 Both collisional and frictional stresses in KTGF were computed using the expressions proposed by Lun et al.28 Additionally, the diffusion term in the species equations was not included because its effects are negligible compared to the convection.17,18,29 To account for the momentum and heat transfer between the gas and solid phases, the validated drag

Activation: Virgin biomass → Active biomass

5949

(1)

Primary: Active biomass → Tar

(2a)

Active biomass → Y Biochar + (1 − Y )Gas

(2b)

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Table 2. Gas−Solid Drag Force and Heat Transfer Coefficient Correlations ⎧ 24 (1 + 0.15Re0.687 ⎪ m ) (Rem < 1000) C Dm = ⎨ Rem ; ⎪ 0.44 (Rem > 1000) ⎩

base equations

hgsm = 30

EMMS drag model

βgsm =

6αsmκgNum 2 dsm

;

Pr =

Rem =

ρg dsm|Ug − Usm| μg

Cpgμg κg

ρg αgαsm|Ug − Usm| 3 C Dm αg−2.65HDm 4 dsm

HDm = a(Rem + b)c ⎧ 0.5174 ⎪ a = 0.7008 − 1 + (αg /0.437)19.8015 ⎨ ⎪ ⎩ c=0 ⎧ ⎪ ⎪ ⎪ ⎪b ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩

a = 0.01786 +

(αg < 0.465)

0.6252 1 + (αg /0.5069)32.3483

19.6031 1 + exp((0.4393 − αg)/0.00575) ⎛ ⎞ (0.465 < αg < 0.61) 1 ⎜⎜1 − ⎟ 1 + exp((0.6701 − αg)/0.00999) ⎟⎠ ⎝

= 19.5897 −

c = 0.4036 −

0.4358 1 + (αg /0.5216)21.1039

⎧ 1 − 0.2923 (0.61 < αg < 0.9898) ⎪a = 61.9321 − 622783αg 6.7883 ⎪ 1.5321 ⎪ + ⎪ ⎪ 1 + exp((0.9703 − αg)/0.2682) ⎨ ⎛ ⎞ ⎪ 1 ⎜⎜1 − ⎟⎟ ⎪ + − α 1 exp((0.9703 )/0.0322) ⎝ ⎠ g ⎪ ⎪ 0.1037 ⎪ c = (0.00029 − 0.00029αg) ⎩ ⎧ 1.9134 ⎪ a = 0.00657 + ⎪ 1 + exp((0.9966 − αg)/0.00399) ⎪ ⎞ ⎛ ⎪ 1 ⎟ ⎜1 − ⎪ ⎜ 1 + exp((0.9999 − αg)/0.00057) ⎟⎠ ⎝ ⎪ ⎪ ⎨ (0.9898 < αg < 0.9997) αg − 0.9912 ⎪b = ⎪ 0.05377 − 15.9492(αg − 0.9912) + 1444.8906(αg − 0.9912)2 ⎪ ⎪ ⎛ ⎛ αg − 0.9975 ⎞2 ⎞ ⎪ c = 13.08817 − 13.01786exp⎜⎜ − 0.5⎜ ⎟ ⎟ ⎪ ⎝ 0.0533 ⎠ ⎟⎠ ⎪ ⎝ ⎩

a = 1, c = 0 (αg > 0.9997) 31

Gunn heat transfer model

1/3 2 Nu m = (7 − 10αg + 5αg2)(1 + 0.7Re 0.2 m Pr ) + (1.33 − 2.4αg + 1.2αg ) 1/3 Re 0.7 m Pr

Secondary: Tar → Gas

in diameter and 50 cm in height. Silica sand with nominal diameter of 655 μm was initially packed at a height of 0.17 m with a porosity of 0.45. The feedstock, composed of 42% cellulose, 34% hemicellulose, and 24% lignin, was injected from the injector at the left-hand side at a height of 5 cm at a fixed rate of 2.22 kg/h in batch operation. The nominal diameter of biomass particle is approximately 500 μm. The fluidization nitrogen gas was supplied from the bottom of the reactor at a fixed rate of 4.81 kg/h. The wall temperature was maintained constant at 773 K to sustain the heat of biomass fast pyrolysis. The exit of the reactor was maintained at atmospheric pressure. Biomass feedstock was introduced when the reactor temperature became stable at 773 K, about 1800 s after the beginning of nitrogen injection. Data acquisition started after another 1800 s. The amounts of tar and biochar were obtained by gravimetric analysis, while the flow streams were measured by volumetric flow meter and the gas components were determined by gas chromatography analysis. Detailed information about the experiment can be found in the original literature.34

(3)

To efficiently solve the coupled partially differential equations, the popular time-split procedure33 was utilized to divide the actual integration into three steps. Within each time step, the hydrodynamic equations first evolved into an intermediate state. Variables such as concentration, temperature, and species mass fraction were used to calculate the fast pyrolysis process. Finally, all variables within each computational cell were updated by considering the postreaction source terms.

3. EXPERIMENTAL VALIDATION To validate the numerical simulation, a laboratory-scale bubbling fluidized-bed reactor for biomass fast pyrolysis, located at the Agricultural Research Service of the U.S. Department of Agriculture,34 was simulated. The reactor vessel was a stainless steel pipe with 7.8 cm 5950

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Table 3. Reaction Constants in the Modified ShafizadehChin Decomposition Kinetics32 components

reaction

mass fraction of biochar in reaction 2b Y

k1c k2c k3c k1h k2h k3h k1l k2l k3l k4

cellulose

hemicellulose

lignin

tar

prefactor A (s−1) 2.8 3.28 1.3 2.1 8.75 2.6 9.6 1.5 7.7 4.28

0.35

0.6

0.75

× × × × × × × × × ×

reaction energy E (J/mol)

1019 1014 1010 1016 1015 1011 108 109 106 106

2.424 1.965 1.505 1.867 2.024 1.457 1.076 1.438 1.114 1.08

× × × × × × × × × ×

reaction heat per unit mass ΔH (J/kg)

105 105 105 105 105 105 105 105 105 105

0 2.55 × 105 2.55 × 105 0 2.55 × 105 2.55 × 105 0 2.55 × 105 2.55 × 105 −4.2 × 104

Figure 1. Schematic of the system (adapted from Ref [34]) and computational domain (wall temperature 773 K, biomass flow rate 2.22 kg/h, nitrogen flow rate 4.81 kg/h and inlet velocity 0.55 m/s, inlet biomass diameter 500 μm, and sand diameter 655 μm).

Table 4. Physical Properties of Each Species in the Solid and Gas Phases species tar gas N2 biomass char sand

density ρ (kg/m3)

molecular weight W (g/mol) 100 30 28

400 2333 1258

heat capacity Cp (J/(kg·K)) dynamic viscosity μ (kg/(m·s)) thermal conductivity κ (J/(m·s·K)) diameter d (m) 3 × 10−5 3 × 10−5 3.58 × 10−5

2500 1100 1121 2300 1100 800

In the following parametric study, the aforementioned operating condition was used as a reference state. The geometrical configuration and boundary conditions used in the numerical simulation are shown in Figure 1. Fixed wall boundary conditions were applied to both solid and gas phases. All physical properties of the materials used in computer simulations are listed in Table 4. The minimum fluidization velocity was calculated based on the following formula Umf =

3 αmf d2 g (ρs − ρg ) 150μg 1 − αmf

2.577 × 10−2 2.577 × 10−2 5.63 × 10−2 0.1 0.1 0.1

5 × 10−4 5 × 10−4 6.55 × 10−4

the simulation data (e.g., product yields, temperature) were recorded and averaged. For this purpose, the predicted temporal evolution of the outflux of solid biomass, including unreacted biomass and biochar, at the reactor exit was monitored and shown in Figure 2. The results, obtained using 2-D mesh, indicated that the solid biomass outflux has reached a stable condition around 100 s after the start of the simulation. Consequently, the predicted outcomes from 150 to 200 s were used for averaging and reporting. A grid dependence study was conducted using 2-D mesh with different grid sizes, including 5.2 m, 2.6, and 1.3 mm. The predicted axial temperature distributions at the reactor centerline are shown in Figure 3. It can be seen that there is no significant difference between the results using the 2.6 and 1.3-mm meshes. Thus, the 2.6-mm mesh was used in this study for saving computational efforts. Results obtained using 2-D mesh were verified by comparing with those using 3-D mesh. The initial and boundary conditions were physically mapped between these two simulations based on the phase volume fraction and species mass fraction, especially for the feeding rates of biomass and nitrogen. This means that in 2-D simulations, the corresponding biomass volume fractions and velocity of nitrogen at the inlet were calculated using the mass of sand as a reference. In 3-D simulations, the boundary conditions for biomass and nitrogen were

(4)

where αmf is the minimum gas volume fraction for bubbling fluidization. Under the current conditions, Umf is about 0.23 m/s. In the simulation, biomass particles, with uniform size, were fed continuously into the reactor. The simulation time was 200 s, and the predicted data of the last 50 s were averaged and reported. Although the experimental operation required a considerable time for the system to reach steady state conditions, the purpose of this modeling study is to characterize the effects of operating parameters on product yields but not to model the transient behaviors. Therefore, when the predicted flow field in the reactor reached a relatively stable condition, 5951

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Figure 4. Comparison of bed temperatures among experiment, 2-D modeling, and 3-D modeling.

Figure 2. Temporal evolution of predicted solid biomass outflux at the reactor exit.

Table 5. Comparisons of Product Yields (%) among Experiment, 2-D Modeling, and 3-D Modeling experiment 2-D modeling 3-D modeling

tar

gas

biochar

unreacted biomass

60.7 61.8 58.9

11.3 12.9 15.8

12.9 14.8 15.7

15.1 10.5 9.6

t = 200

ηtar = {

∫t =150 ∫outlet (αgρg UgYtar)dAdt } t = 200

/{

∫t =150 ∫outlet [αgρg Ug(Ytar + Ygas) + αbiomassρbiomass Ubiomass

(Ybiochar + Yunreacted ‐ biomass)]dA dt }

(5)

4. RESULTS AND DISCUSSION 4.1. Effects of Reactor Temperature. In practice, there are two ways to control the reactor temperature for biomass fast pyrolysis. One is to vary the temperature of the side walls, and the other is to heat the fluidization nitrogen prior to entering the bed. The variations of the product yields to the side wall temperature are presented in Figure 5. It can be seen that the initial increase of wall temperature elevates the tar and gas yields by causing more biomass to devolatilize and react. However, if the wall temperature exceeds 793 K, tar yield will decrease. This is consistent with the results from literature because the secondary tar cracking reaction (eq 3) is favored at high temperature, causing more tar to decompose to form gas. The relationships between the product yields and the temperature of the fluidization nitrogen are shown in Figure 6. Because the flow rate of nitrogen remained unchanged, the velocity of nitrogen will change because of the variation in nitrogen temperature. A similar conclusion on the temperature effects can be seen from Figure 6 which is that the initial increase in nitrogen temperature will increase the tar and gas yields by causing more biomass to devolatilize and react. However, further increase in nitrogen temperature will decrease the tar yield. The inlet nitrogen temperature for maximum tar yield is about 813 K. The higher temperature for inlet nitrogen to obtain maximum tar yield may be due to the relatively lower thermal conductivity of gas phase compared to sand.

Figure 3. Axial distributions of bed temperature at statistically steady state predicted using different meshes with grid sizes of 5.2, 2.6, and 1.3 mm. derived directly form their feeding rates. The temporal evolutions of the domain-averaged reactor temperature in both 2-D and 3-D simulations are shown in Figure 4 where the experimental measurement is also presented. It can be seen that the predicted reactor temperature using 2-D simulation is slightly closer to the experimental measurement than that using 3-D simulation. The maximum discrepancies of these two simulations are about 10 and 20 K, respectively. Both simulation results can be regarded similar. It should be mentioned that the comparison between the experimental and predicted bed temperature is qualitative because of the complexity of the reactor conditions in which it is impossible to measure/obtain the average bed temperature. The predicted statistically steady state tar yields are 61.8% in 2-D simulation and 58.9% in 3-D simulation, in comparison to the experimental data of 60.7%, as summarized in Table 5. It is concluded that 2-D simulations are adequate for modeling the present reactor. Also, because of its reduced computational effort, 2-D simulations were conducted in the following parametric study. The product yields were monitored at the outlet of the reactor. For example, the yield of tar, ηtar, was calculated as follows. 5952

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Figure 5. Variation of product yields with respect to the wall temperature (with other parameters same as those in Figure 1).

Figure 7. Variation of product yields with respect to the nitrogen velocity (with other parameters same as those in Figure 1).

the tar yield to the variation of nitrogen velocity can be explained by two competing mechanisms. The increase of inlet nitrogen velocity will decrease the residence time of biomass and reduce tar production (eq 2a). However, high gas velocity in the reactor also decreases the residence time of tar and reduce the opportunity of tar being decomposed into gas via the secondary reaction (eq 3), causing tar production to increase. These two mechanisms compete with each other, and simulations show that if the inlet nitrogen velocity is below 0.6 m/s, the net effect is the increase in the tar yield. Above 0.6 m/s, the reduced production of tar via eq 2a is compensated by the decreased consumption of tar to form gas via eq 3, resulting in the same net tar yield. It should be noted that high inlet nitrogen velocity implies high pumping power and also increases the risk of blowing sand out of the reactor. The inlet nitrogen velocity ought to be limited to a reasonable range, although it has a positive effect on the tar yield in general. 4.3. Effects of Biomass Particle Diameter. The relationship between the product yields and biomass particle size is shown in Figure 8. In this study, it is assumed that within the range of the biomass particle diameter considered, the intraparticle temperature gradient does not have significant effects on the reaction kinetics. In the range studied, when the particle diameter is below 900 μm, tar, gas, and biochar yields are found to increase with the increase in particle diameter. Beyond this value, tar, gas, and biochar yields appear to be almost insensitive. On the other hand, the percentage of unreacted biomass decreases as the biomass particle size increases for the range studied. These results imply that biomass materials do not need to be milled to extremely fine particles, thus reducing preprocessing efforts. With the increase in biomass particle diameter, the particle Reynolds number becomes larger, and it is more difficult for biomass particles to be blown out. The longer residence time means that more biomass will be converted into products via the primary reactions. The initial increase in the product yields with respect to the increase of biomass particle size can be explained in this way. However, beyond 900 μm, tar, gas, and biochar yields are found to be insensitive to the variation in biomass particle diameter. This is because beyond 900 μm almost all of the biomass was decomposed. It should be noted

Figure 6. Variation of product yields with respect to the temperature of inlet nitrogen (with other parameters same as those in Figure 1).

4.2. Effects of Nitrogen Velocity. The variation of product yields with respect to the nitrogen velocity is shown in Figure 7. With the increase in nitrogen velocity, the yields of biochar and gas decrease, while the amount of unreacted biomass increases. These phenomena are consistent with our previous studies25 and results from literature.17 With the increase in nitrogen velocity, the residence time of tar becomes shorter and less tar is converted to gas via eq 3. At the same time, more unreacted biomass will be carried out of the reactor before it is fully decomposed. More unreacted biomass means that less biochar and gas are produced via the primary decomposition reaction (eq 2b). As a result, gas yield decreases noticeably as the inlet nitrogen velocity increases. The relationship of tar yield to the increase of nitrogen velocity is not linear. Before the nitrogen velocity is increased to 0.6 m/s, tar yield increases monotonically. Beyond this critical velocity, tar yield appears to be insensitive to the variation of nitrogen velocity, which is consistent with the results of Lathouwers and Bellan.15,16 This complex response of 5953

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Figure 10. Variation of product yields with respect to the sand particle diameter (with other parameters same as those in Figure 1).

Figure 8. Variation of product yields with respect to the biomass particle diameter (with other parameters same as those in Figure 1).

that, with the increase in biomass particle size, the intraparticle temperature gradient becomes significant. Such intraparticle temperature gradient will increase the heating time for a biomass particle, which in turn will affect the fast pyrolysis process. To more accurately describe the temperature heterogeneity inside biomass particles, a detailed submodel that can consider the temperature distribution below the particle scale will be required in future studies. 4.4. Effects of Biomass Feed Rate. The relationship between product yields and the biomass feed rate is shown in Figure 9. It can be seen that below 1.92 kg/h the biomass feed

Figure 11. Variation of product yields with respect to the initial height of sand bed (with other parameters same as those in Figure 1).

effect of biomass feed rate on tar and biochar was found. The percentage of unreacted biomass increases as the biomass feed rate increases. At high biomass feed rate, sufficient heat cannot be provided to biomass in a limited time before biomass particles exit the reactor. Consequently, biomass feed rate needs to be maintained at a reasonable level for ensuring appropriate product yields. 4.5. Effects of Sand Diameter and Initial Sand Height. In the existing literature, the effects of sand particle diameter and initial height of sand bed on the pyrolysis product yields have not been characterized. An increase in sand particle size will result in higher minimum fluidization velocity, meaning that the bed is more difficult to be fluidized. Thus, with the increase in sand particle diameter, the bed expansion will decrease if the nitrogen velocity is fixed. The relationship between product yields and the sand diameter predicted by the present model is shown in Figure 10. It can be seen that with the increase in sand diameter, a trend in the product yields similar to that caused by the nitrogen velocity is observed. This

Figure 9. Variation of product yields with respect to the biomass feeding rate (with other parameters same as those in Figure 1).

rate has negligible effect on all product yields. This can be understood that when the biomass feeding rate is not too high, because of the adequate heat supply from the side walls, the heat for biomass decomposition is sufficient, and thus, increasing biomass feed rate does not slow down the reactions. This result is consistent with the finding by Lathouwers and Bellan.15 However, beyond 1.92 kg/h, a slightly negative 5954

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h = convective heat exchange coefficient between gas and solid phases, J/(m3·s·K) ΔH = heat source from chemical reactions, J/(m3·s) Y = species mass fraction d = particle diameter, m CD = single particle drag coefficient Re = Reynolds number Nu = Nusselt number Pr = Prandtl number HD = coefficient due to flow heterogeneity in β

phenomenon can be explained by considering the effects of gas phase velocity. With the increase in sand diameter, the in-bed gas phase velocity also increases because a higher interphase slip velocity is needed to fluidize sand. Thus, the residence times of tar and biomass particles will decrease. The shorter residence time of tar appears to have a more dominant influence and prevents tar from further cracking into gas. As shown in Figure 11, in the range studied, tar, gas, and biochar see their yields increase as the initial height of the sand bed increases. The reason can be attributed to the high heat conductivity of sand. With more sand in the reactor, more heat can be transferred from side walls such that more biomass is decomposed into tar, gas, and biochar. However, it needs to be mentioned that the quantity of sand needs to be within a reasonable range because of the practical consideration. Excessive sand loading can deteriorate the fluidization, causing more tar to be cracked into gas.

Greek Letters

α = volume fraction ρ = material density, kg/m3 τ = stress tensor, kg/(m·s2) β = momentum exchange coefficient between gas and solid phases, kg/(m3·s) ψ = momentum exchange due to reactions, kg/(m2·s2) μ = dynamical viscosity, kg/(m·s) λ = bulk viscosity, kg/(m·s) κ = thermal conductivity, J/(m·s·K)

5. CONCLUSION Numerical simulations were conducted to characterize the effects of operating conditions on the product yields of a bubbling fluidized-bed reactor for biomass fast pyrolysis. Both solid and gas phases were simulated using an Euler−Euler multifluid model, and a generalized biomass decomposition kinetic scheme was applied to simulate fast pyrolysis reactions. Validations were performed for both 2-D and 3-D simulations, and it was found that 2-D simulation was adequate in reproducing the experimental results of the reactor studied. Operating variables, including bed temperature, nitrogen velocity, biomass particle diameter, feed rate, sand particle diameter, and initial height of sand bed, were varied to investigate their effects on the product yields. It was found that maximum tar yield could be obtained by maintaining both the wall temperature and the inlet temperature of nitrogen at approximately 800 K. The inlet nitrogen velocity at about 0.6 m/s also produced favorable results. Simulations indicated that the optimal biomass particle diameter and the feeding rate were 900 μm and 1.92 kg/h, respectively, for tar production. Larger sand particle diameter and deeper initial sand bed also favored the tar yield.

■

Subscripts

■

g = gas s = solid l, m = solid phase m, l k = species k

REFERENCES

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +1 515-294-3244. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Science Foundation under the Grant Number EPS-1101284. NOMENCLATURE U = velocity, m/s R = Reaction rate, kg/(m3·s) t = time, s p = pressure, Pa g = gravitational acceleration, m/s2 D = stain tensor, s−1 I = unit matrix T = tmeprature, K Cp = heat capacity, J/(kg·K) q = heat flux, J/(m2·s) 5955

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Article

NOTE ADDED AFTER ASAP PUBLICATION Figure 1 and caption were replaced in the version of this paper published September 27, 2013. The corrected version published October 9, 2013.

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