Numerical Study on Fast Pyrolysis of Lignocellulosic Biomass with

Research Article pubs.acs.org/journal/ascecg

Numerical Study on Fast Pyrolysis of Lignocellulosic Biomass with Varying Column Size of Bubbling Fluidized Bed Ji Eun Lee, Hoon Chae Park, and Hang Seok Choi*

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Department of Environmental Engineering, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon-do, Republic of Korea ABSTRACT: A fundamental understanding of the complex hydrodynamic characteristics in a reactor and its pyrolysis reaction field is required to elucidate the physical phenomena occurring in a bubbling fluidized bed (BFB) pyrolyzer. Specifically, bubble behavior is a very important factor for hydrodynamics, solid mixing, and consequent pyrolysis reaction. Accordingly, this study performed modeling and simulation of the BFB pyrolyzer using the computational fluid dynamics method with an Eulerian−Eulerian approach with kinetic theory. The hydrodynamic and pyrolysis characteristics of the reactor were also investigated. A two-stage semiglobal kinetics was applied to model the fast pyrolysis reaction of the lignocellulosic biomass. The dimensions of the fluidized bed column were varied to examine how the bubble size and the column shape affect the pyrolysis reaction. Changing the dimension physically confined the size and number of gas bubbles. Moreover, the primary and secondary reaction rates for the pyrolysis reaction were computed. The different column sizes caused variations in the size and number of gas bubbles, which resulted in the changes of the solid mixing and the heat transfer rate from the heat source to the biomass particles. The fast pyrolysis reaction was influenced by the characteristics of the bubbles, which were varied by the column dimension. KEYWORDS: Biomass, Biocrude oil, Bubbling fluidized bed, Fast pyrolysis, CFD

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INTRODUCTION The fast pyrolysis method has attracted considerable interest because it can provide a high yield of liquid fuel that can be directly used in various applications or as an efficient energy carrier.1 The product yield and quality for biomass pyrolysis differ depending on the pyrolysis process conditions, including reaction temperature, heating rate, and residence time of vapor, among others. The fast pyrolysis of biomass has been studied by many researchers. Bridgewater et al.1 reviewed the overall fast pyrolysis technologies. Patwardhan et al.2 investigated the product distribution resulting from the fast pyrolysis of lignin derived from a corn stover. Meanwhile, Greenhalf et al.3 performed an analytical and lab-scale pyrolysis to characterize and compare fast pyrolysis product yields from various biomass feedstocks. Mourant et al.4 examined the effect of temperature on the bio-oil yields and properties from the fast pyrolysis of a bark. Bok et al.5,6 also studied the effect of the heating rate on the yield and quality of the biocrude oil using two different fluidized bed reactors. One of the key technologies for the fast pyrolysis is a reactor. Various reactor types, including the bubbling or circulating fluidized bed, rotating cone, twin screw, ablative, and rotary cylinder, are presently used.7 Among these types, the bubbling fluidized bed (BFB) is widely utilized for the fast pyrolysis of biomass because of its higher heat and mass transfer and simple design.1,7 The bubble behavior in the bubbling fluidized bed © 2017 American Chemical Society

pyrolyzer is an important factor, especially for the particle mixing, scalar transport, and final thermal decomposition of biomass. Thus, understanding the hydrodynamic characteristics and the consequent pyrolysis reactions is essential to an efficient reactor design. However, studying the gas−solid reacting flow field in detail using experimental methods is difficult because the experiment is under time and space limitations. Moreover, measuring and observing the gas−solid flows and transport phenomena in the bed are very restricted because of the structure of the reactor and the operating conditions. The computational fluid dynamics (CFD) method can be an effective alternative to overcome these problems because the CFD can provide comprehensive data for the complex multiphase reacting flows. Hence, various studies involving the CFD simulation concerning the hydrodynamics and consequent fast pyrolysis of biomass in fluidized bed reactors were reported.8−18 Most of the numerical and experimental studies regarding the influence of hydrodynamics on the fast pyrolysis particularly mainly focus on process parameters, such as reaction temperature, heating rate, bed height, superficial gas velocity, and sample species. Accordingly, Xiong et al.17 studied the Received: September 30, 2016 Revised: January 3, 2017 Published: January 13, 2017 2196

DOI: 10.1021/acssuschemeng.6b02360 ACS Sustainable Chem. Eng. 2017, 5, 2196−2204

Research Article

ACS Sustainable Chemistry & Engineering

Figure 1. Three-dimensional CAD features of the computational domains for the four different column shapes: (a) case 1, (b) case 2, (c) case 3, and (d) case 4. Momentum equations:

impact of the bubbling bed hydrodynamics on the product yield considering the superficial gas velocity, particle size, and bed height. Choi et al.19 also conducted a fast pyrolysis experiment varying the flow rate, particle size, reaction temperature, cooling temperature, and biomass feeding rate. However, the in-depth study for the influence of the column size or the effects of the bubble size and population on the pyrolysis reaction is very rare. The size and number of gas bubbles and their behavior are expected to differ depending on the column shape of the bubbling fluidized bed. It should also influence the gas−solid flow and the consequent particle mixing or segregation related to heat and mass transfer. Finally, the pyrolysis reaction of biomass would be affected. The secondary volatile cracking reaction could also be affected by the bubble size, number, and behaviors. Accordingly, the present study performs the modeling and simulation of bubbling fluidized beds using CFD with different column shapes. For the computational simulation, an Eulerian−Eulerian approach with kinetic theory is applied to examine the hydrodynamic and pyrolysis characteristics in the reactor. A two-stage semiglobal kinetic model is then applied to model the fast pyrolysis reaction of the lignocellulosic biomass. The model includes the secondary cracking mechanism of volatiles. Condensable gas (volatile), noncondensable gas, and N2 are included for the gas phase, while sand, char, and biomass particles are used for the solid phases. Accordingly, the effects of the size and number of gas bubbles and their behavior on the pyrolysis reaction are scrutinized by changing the dimension of the fluidized bed column. The transient hydrodynamic characteristics of the bubbles and the consequent solid particles are examined. Furthermore, their statistics are calculated. The product yields and the primary and secondary reaction rates for the pyrolysis reaction are also computed.

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∂ (εg ρg νg ) + ∇·(εg ρg νg νg ) = − εg ∇Pg + ∇· τg + ∂t + εg ρg g −

(3)

∂ (εsjρsj νsj ) + ∇·(εsjρsj νsj νsj ) = − εsj∇Pg + ∇·Ssj + Fgsj(νsj − νg ) ∂t n

+

∂ (εsjρsj ) + ∇·(εsjρsj νsj ) = ∂t

k=1

n

−

′ νsj ) ∑ MR sksj(ζsksjνsk + ζsksj k=1

(4) where τg is the gas phase stress tensor; Ssj is the jth solid phase stress tensor; Fgsj is the coefficient of the interphase drag force between the gas and solid phases; Fsksj is the coefficient of the interphase drag force between the solid phases; MRxy is the mass transfer term from the xth to the yth phase. Energy equations:

⎛ ∂Tg ⎞ εg ρg CPg ⎜ + νg ·∇Tg ⎟ = −∇·q g + ⎝ ∂t ⎠

n

∑ γgsj(Tsj − Tg ) − ΔHrg j=1

(5)

⎛ ∂Tsj ⎞ εsjρsj CPsj⎜ + νsj ·∇Tg ⎟ = −∇· q sj + γgsj(Tsj − Tg) − ΔHrsj ⎝ ∂t ⎠ (6) where T is the temperature of each phase; q is the heat flux by conduction of the gas or solid phase; γgsj is the heat transfer coefficient between the gas and solid phases; ΔHrsj is the heat of the reaction according to each reaction for the gas or solid phase. Species equations:

(1)

n

∑ Rβ β=1

∑ Fsksj(νsk − νsj) + εsjρsj g

′ νg ) + MR gsj(ζgsjνsj + ζgsj

n α=1

∑ MR gsj(ζgsjνsj + ζgsj′ νg ) j=1

NUMERICAL METHOD

∑ Rα

j=1

n

Governing Equations. The following governing equations, which were rearranged by Wachem et al.,20 were used for the gas−solid multiphase flow: Continuity equations: ∂ (εg ρg ) + ∇· (εg ρg νg ) = ∂t

n

∑ Fgsj(νsj − νg )

(2)

∂ (εg ρg Ygα) + ∇·(εg ρg Ygανg ) = ∇· Dgα ∇Ygα + R gα ∂t

(7)

∂ (εg ρg Ysjβ) + ∇· (εg ρg Ysjβνg ) = ∇· Dsjβ ∇Ysjβ + R sjβ ∂t

(8)

where Y is the mass fraction of the chemical species, and D is the diffusion coefficient for each chemical species. The MFIX code for the CFD simulation is used. The details of the computational theory and procedure can be found in the MFIX theory guide.21

where subscripts “g” and “sj” denote the gas and jth solid phases, respectively. Here, v is the velocity vector, and ε stands for the volume fraction of the gas or solid phase. 2197


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ACS Sustainable Chemistry & Engineering Calculation Conditions. As shown in Figure 1, the reactor was initially filled with sand to the height of 0.159 m. Nitrogen gas flowed into a bubbling fluidized bed through the inlet at the bottom of the reactor and fluidizes the sand bed. The nitrogen temperature was 753 K. The inlet gas velocity was 0.12 m/s, which was selected from the experimental condition for the maximum yield of biocrude oil.5,6 At the same time, a fixed quantity of wood particles was put into the reactor through a feeder installed on the front surface at the height of 0.02 m from the bottom. The mass flow rate of the biomass for all the cases was 0.1 kg/h. The fast pyrolysis of the biomass then occurred in the hot bed. Gaseous products from the pyrolysis reaction flowed out the reactor through the exit with nitrogen, while the solid product remained in the bed. Table 1 presents the computational domains and boundary

Figure 2. Two-stage semiglobal reaction mechanism. tar, noncondensable gas1 (NCG1), and char1 by the primary reaction of the fast pyrolysis. However, the secondary reaction was the tar decomposition produced by the primary reaction, which produced noncondensable gas2 (NCG2) and char2. The noncondensable gas and char produced from the primary and secondary reactions were distinguished by number. An Arrhenius-type reaction model was adopted [e.g., Ki = Ai exp(−Ei/RuT)], where Ai is the pre-exponential factor, and Ei is the activation energy for each species. Here, Ru is a universal gas constant. Their values can be found in Table 1.

Table 1. Calculation Conditions

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Specifications for the computational domains X (m)

case

Y (m)

Z (m)

grid allocation

RESULTS AND DISCUSSION The hydrodynamics and pyrolysis reaction field for the four different column shapes were scrutinized in the present study. All the CFD data were chosen for the analysis of the hydrodynamics and pyrolysis reaction when the steady state of the bed was reached. The steady state here was assumed to be attained when random bubble formation and distribution were examined in the bed. Validation of the Computational Method. Before performing the main calculation, the bed density was calculated for the three-dimensional square bed of case 1 with the increasing superficial gas velocity to validate the present numerical method. The CFD results were compared with the experimental one.26 Subscript “mf ” represents the minimum fluidization of the bed. Figure 3(a) shows the result of the grid dependency test for two different superficial gas velocity cases. The value of the bed density for the two cases was almost saturated after the grid number 15 × 15 × 75. Therefore, the grid number 20 × 20 × 100 was selected for the main calculation, considering the calculation cost and accuracy. The Eulerian−Eulerian approach adopted in the present study considered the solid phase as the continuum. Hence, the minimum grid size should be greater than the solid phase diameter. Figure 3(b) shows the bed density change with respect to the superficial gas velocity. The bed density increased, then almost saturated, with the increasing superficial gas velocity. The phenomena for the Geldart B particles were well understood. The CFD results in Figure 3 showed good agreement with the experimental data. Hence, the present numerical procedure was expected to obtain reasonable predictions for the main pyrolysis reaction calculations. Hydrodynamic Characteristics in BFBs. Figure 4 shows the isosurface of the instantaneous gas volume fraction (at 0.52) in the reactors. As shown in the figure, the bubbles formed in the bed for all the cases. The number of the gas bubble increased with the change of the aspect ratio from cases 1 to 4. The number of gas bubbles was the largest in case 4. However, most of them overlapped each other and horizontally lined up around the middle of the bed. Approaching the top region of the bed, the horizontally longer bubbles region is broken into to smaller bubbles. Considering the unit volume of the bed, smaller bubbles seemed to form more in case 3. As regards the location of the onset of bubble, small bubbles were generated near the middle of the bed in cases 1 to 3. However, the bubbles were generated in the lower region close to the bottom in case 4. An image-processing technique was adopted

1 0.1 0.1 0.5 20 × 20 × 100 2 0.2 0.05 0.5 40 × 10 × 100 3 0.4 0.025 0.5 80 × 5 × 100 4 0.8 0.0125 0.5 160 × 5 × 100 Boundary conditions for the four aspect ratio cases gas inlet Uinlet = 0.12 m/s, Tnitrogen = 753 K biomass inlet ṁ inlet = 0.1 kg/h, Tbiomass = 300 K wall no-slip, Twall = 753 K outlet Neumann Pre-exponential factor E1 2.69 × 1004 cal/mol E2 2.12 × 1004 cal/mol E3 2.54 × 1004 cal/mol E4 2.58 × 1004 cal/mol E5 2.58 × 1004 cal/mol Activation energy A1 2.0 × 1008 s−1 A2 1.3 × 1008 s−1 A3 1.0 × 1007 s−1 A4 2.6 × 1006 s−1 A5 1.0 × 1006 s−1

conditions of the bubbling fluidized bed pyrolyzer used in the study. Figure 1 shows the three-dimensional CAD features for the four cases of the bubbling fluidized bed reactors. For reference, the aspect ratio of the bed column was changed to examine the influence of the bubble size and the column shape on the pyrolysis reaction. This change physically confined the diameter and the number of gas bubbles. The cross-sectional areas of the reactors were the same, but the aspect ratios of the reactors were varied. Hence, the volumes of the reactors were similar. The aspect ratios of cases 1 to 4 were 1, 0.25, 0.0625, and 0.0156, respectively. Case 4 was very close to the two-dimensional bed, as discussed by the other researchers.22,23 Regular hexahedrons were used for the calculation grid. Table 1 shows the total numbers of grids, while Table 2 presents the physical properties of the solid particles. The numerical procedure adopted in the present study can be found and was fully validated in Choi and Meier24 as well as in the Results and Discussion section. A two-stage semiglobal reaction model was used for the fast pyrolysis of the lignocellulosic biomass.25 As shown in Figure 2, the biomass (wood particle) was mainly decomposed into

Table 2. Physical Properties of Solids solid particle

density (kg/m3)

mean diameter (μm)

volume fraction

Geldart classification

sand biomass

2500 650

200 400

0.58 0.3

B A 2198


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Figure 3. Validation of the present computational procedure: (a) bed density with respect to the grid number and (b) bed density with respect to the superficial gas velocity.

Figure 4. Isosurface of the instantaneous gas volume fraction for the four cases (gas volume fraction = 0.52): (a) case 1, (b) case 2, (c) case 3, and (d) case 4.

Figure 5. Image processing procedure for the bubble size calculation: (a) step 1, (b) step 2, and (c) step 3.

tended to decrease as the column aspect ratio decreased from cases 1 to 3. The column aspect ratio then further decreased from cases 3 to 4. The bubble diameter rapidly increased, except at the height of 130 mm in case 4. For the height of 130 mm in case 4, the horizontally longer quasi-two-dimensional bubbles were broken into smaller ones upon approaching the top of the bed. Hence, the bubble size decreased. Comparing the results with respect to the column shapes, the average bubble size was the biggest in case 4 at a height of 80 mm and the smallest in case 3 in most heights of the bed. The number of smaller bubbles was the largest in case 3, and its size was the smallest. For cases 3 and 4, the small bubbles burst. Consequently, the solid particles splashed along the longer

(Figure 5) to calculate the mean bubble diameter. In step 1, using a threshold value for gas volume fraction, bubbles were identified from the CFD results. In step 2, the gray image in step 1 was converted into a binary image. In step 3, the area of the black bubble (Ab) in the binary image was calculated. The area-equivalent diameter (Db) of the bubbles was also calculated using Db = 4Ab/π .27 Figure 6 presents the bubble sizes according to the bed height, especially for the middle and top regions of the bed. Generally, relatively smaller bubbles agglomerate, and the bubbles experience a lower static pressure when they go up. Therefore, the bubbles get bigger. The calculation results generally coincided with the common phenomena in Figure 6. In the said figure, the bubble diameter 2199


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The power spectral density of the pressure fluctuation was calculated in Figure 7 to scrutinize the bubble size effect on the hydrodynamics. As discussed by previous works,28,29 the amplitude of the pressure fluctuation was higher when the larger bubble rose in the bed. The period of the pressure fluctuation for the one-bubble rising cycle also became longer, which may influence the solid movement, especially for the solid rotating flow around the bubble and the consequent solid mixing or segregation. The phenomena can be stochastically identified by using the power spectral density (PSD) of the pressure fluctuation in eq 9. The PSD was calculated using MATLAB R2016. The instantaneous pressure fluctuation is defined as the instantaneous pressure minus the time−mean pressure. Figure 6. Bubble size distribution with respect to the reactor height.

1 SXX (ω) = N

cross section at the top of the bed. This result may show the peculiar feature of the solid movement, such as mixing or segregation and consequent fast pyrolysis reaction.

2

N−1

∑ x(n)e n=0

jω n

(9)

The band of the dominant frequencies in Figure 7 was located between 1 and 4 Hz. The profound peaks were around

Figure 7. Power spectral density for the middle and top positions of the bed: (a) case 1, height = 80 mm, (b) case 2, height = 80 mm, (c) case 3, height = 80 mm, (d) case 4, height = 80 mm, (e) case 1, height = 130 mm, (f) case 2, height = 130 mm, (g) case 3, height = 130 mm, and (h) case 4, height = 130 mm. 2200


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Figure 8. Time- and area-averaged contours: (a) gas volume fraction and (b) solid bulk density.

Figure 9. Contour of the instantaneous primary reaction rate for the tar formation: (a) case 1, (b) case 2, (c) case 3, and (4) case 4.

Figure 10. Contour of the instantaneous secondary reaction rate for the NCG2 formation: (a) case 1, (b) case 2, (c) case 3, and (4) case 4.

1.75 to 2.25 Hz for all cases. This finding indicated that the flow regime was in a multiple-bubbling fluidization regime. For reference, the present PSD results were well matched with those of the other studies.30,31 In case 3, the peak increased and its magnitude became maximum at the height of 80 mm as the aspect ratio increased from cases 1 to 3. The peak decreased and the band of the dominant frequencies broadened when the aspect ratio further increased. The peak frequency at that height was the highest in case 3 (i.e., 2.25 Hz). The higher peak frequency was closely related to the movement of the smaller bubbles. Furthermore, considering its maximum magnitude, the small-sized bubbles were more populated in case 3 compared with the other cases. However, in case 4, the magnitude of the second peak was very similar to that of the first one. In addition, the frequency was less than 2 Hz. These findings denoted that the bubbles were slowly rising and consequently reduced the intensity of the solid fluctuating. The frequency patterns of the PSDs at the height of 130 mm were very similar to those at the height of 80 mm, even though the magnitudes decreased. In cases 1 to 4, the magnitude of the peak increased, but the frequency slightly decreased from 2.25 to 2 Hz.

Meanwhile, in cases 3 and 4, the peak has a higher value than in cases 1 and 2. The width between the front and rear walls of the bed column also became shorter. In cases like these, solid segregation may enhance at the top region of the bed, which can be explained to be associated with the wall effect in Figure 8. Here, Hw,lb was calculated using eqs 10 and 11 and the hydraulic diameter (Dh) as suggested by Agarwal to investigate the wall effect.32 For reference, Hw,lb represents the upper limit of the bed height, where the bubble hydrodynamics can be independent of the geometry. Here, Hw,lb/Dh decreased from 0.43 to 0.3 as the aspect ratio decreased from cases 1 to 4, which indicated that the wall effect intensified as the aspect ratio decreased. The higher Hw,lb was preferred for the bed scale-up. Even though case 4 was designed for the twodimensional bed, it can provide a theoretical insight and a better understanding of the effects of the bubble behavior and the wall effect, as discussed by Julian et al. and Wu et al.33,34 H w,lb D 2201

⎛ = 0.77D0.25⎜⎜1 − ⎝

db0 D

⎞2.5 ⎟⎟ ⎠

(10)


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ACS Sustainable Chemistry & Engineering db0 = 0.00376(U − Umf )2

(11)

Figures 8(a) and (b) show the time- and area-averaged contours of the gas volume fraction and the solid bulk density at the (y,z) cross-sectional plane, respectively. The gas volume fraction decreased and the solid bulk density of the biomass increased at the top region of the bed as the aspect ratio decreased from cases 1 to 4. In case 4, the region of the higher gas volume fraction was located near the bottom that was very close to the side walls. The bed was fluidized by the nitrogen gas that flowed into the bed from the bottom of the reactor. The bubbles then formed, rose, and finally burst. These phenomena resulted in the active motions of the particles, such as the rise or descent of the particles in the bed. The segregation between the biomass and the sand enhanced as the wall width became shorter. However, the bubble behavior was very active near the top region (e.g., bubble rising velocity and burst), especially for cases 3 and 4 as shown in Figure 7(g) and (h). Finally, the segregation and bubble behavior at the top region affected the fast pyrolysis. For reference, the segregation phenomena were fully studied in our other work and will appear in the literature. Pyrolysis Reaction Fields in the BFB. Figures 9 and 10 show the contours of the instantaneous primary and secondary reaction rates in the beds, respectively. Figures 11 and 12

Figure 12. Time-averaged distribution of the secondary reaction rate for the NCG2 formation.

hydrodynamic results, the primary reaction rate in case 4 was the lowest. However, the primary reaction rates at the top region of the bed sharply increased over the height of 100 mm in cases 3 and 4. As discussed in the hydrodynamic subsection, the wall width affected the segregation phenomena, which resulted in the increase of the primary reaction rate, because the amount of reactant (biomass) at the top region was more distributed and the bubble motion was more active. Considering the hydrodynamics, the primary reaction rate was affected by the differences in the number and size of the bubble and the wall width according to the column sizes. When small bubbles formed more along the longer cross section, the local regions of the highly fluctuating solid flow seemed to also increase more. The solid particles could then be evenly mixed, and the heat transfer and final reaction can occur more vigorously. Moreover, at the top region of the bed, the gas volume fraction was decreased and the solid bulk density of the biomass was increased by the segregation phenomena as the wall width decreased, which resulted in the enhancement of the primary reaction rate at that region. In Figure 12, the secondary reaction rate also increased as the height increased. The graphs showed no significant difference in cases 1 to 3 until 150 mm. However, the overall magnitude was the smallest in case 4 because the generation rate of the gas-phase tar was lower in the bed. The secondary reaction was the gas-phase tar decomposition. Therefore, it is considered that the hydrodynamic characteristics of the solid particles did not substantially influence the secondary reaction in the bed, where the gas fraction was low. However, the secondary reaction rate increased with the changing aspect ratio from cases 1 to 3 at the freeboard. Figure 13 presents the time-averaged mass flow rate of the product gas passing through the reactor exit. The product gas consisted of tar, noncondensable gas 1, and noncondensable gas 2. The mass flow rate of each species was divided by the maximum mass flow rate of gas for nondimensionalization. The largest amount of product gas was generated in case 3. In cases 1 and 2, the mass flow rates of the product gas were almost the same. Case 4 showed the lowest value. These data corresponded to the reaction rate data in Figures 11 and 12. The tar can be quenched into biocrude oil in the condenser after discharging from the reactor. Hence, the reactor in case 3 was the most efficient for the maximum biocrude oil yield because the tar yield was the highest. Figure 14 shows the time-

Figure 11. Time-averaged distribution of the primary reaction rate for the tar formation.

present the time-averaged distributions of the primary and secondary reaction rates, respectively. For reference, the black solid line in the figures represents the bubbles. As shown in Figure 9, the primary reaction for the tar formation occurred throughout the solid bed. Its rate showed a higher magnitude at the lower gas volume fraction and increased as the height went up. However, in the freeboard region, the rate dramatically dropped down to zero because the primary reaction was the biomass decomposition, which was a solid phase. On the contrary, the secondary reaction rate for the NCG2 formation was still higher at the freeboard and the bubbles in Figure 10 because the secondary reaction was the gas-phase tar decomposition. In Figure 11, the primary reaction rates of cases 1 and 2 at the lower region of the bed were very similar. However, the magnitude of case 2 was slightly higher than that of case 1. In case 3, the reaction rate was slightly smaller than in cases 1 and 2 in the lower part of the bed. As expected from the 2202


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Figure 13. Time-averaged mass flow rate of the product gases for the four cases: (a) tar, (b) NCG1, and (c) NCG2.

In summary, the size and number of gas bubbles were affected by the different dimensions of the fluidized bed columns. These resulted in the increase or decrease in the particle mixing or segregation and the heat transfer rate from the heat source to the biomass particles. The number of smaller bubbles generated in case 3 was higher than the others over the whole bed. In addition, the number of bubbles was the largest. The local regions of the highly fluctuating solid flow increased when more small bubbles formed and the number of bubbled increased. The contact of solid particles was then enhanced, and the heat transfer and the final pyrolysis reaction can occur more vigorously. In case 3 and close to the top of the bed, active smaller bubbles were distributed along the longer crosssection of the bed. Furthermore, the bulk density of the biomass increased by segregation between the sand and the biomass particles, which resulted in a dramatic increase in the primary reaction rate. Hence, the tar yield was considered as the maximum. In other words, the higher yield of the biocrude oil was expected when using the column in case 3. In addition, the total amount of the generated char was the highest in case 4 and lowest in case 3.

Figure 14. Time-averaged mass of char 1 and 2 in the reactor for the four cases: (a) char1 and (b) char2.

averaged total amounts of the produced char in the reactors. The data were described as the ratio of the mass of char 1 or 2 to the maximum mass of the total char. The total amount was the largest in case 4 but was the smallest in case 3. A low oil yield was expected when the amount of produced char was large. Char could act as a negative catalyst that elevates a tarcracking secondary reaction. Therefore, rapidly removing the char from the reactor was required. 35 Moreover, the accumulation of char in the reactor caused heat loss, and disruption of heat transfer as well as continuous operation of the fast pyrolysis. Considering the char formation, case 3 was preferred for the fast pyrolysis reaction.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +82-33-760-2485. Fax: +82-33-760-2571. ORCID

Ji Eun Lee: 0000-0001-5197-7688 Hoon Chae Park: 0000-0003-0124-4894 Hang Seok Choi: 0000-0002-0282-9360

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CONCLUSIONS This study investigated the numerical simulations of a bubbling fluidized bed pyrolyzer. An Eulerian−Eulerian approach with kinetic theory was applied to solve the hydrodynamic and pyrolysis reaction field in the reactor. A two-stage semiglobal kinetic model was also used to model the fast pyrolysis reaction of the lignocellulosic biomass that included the secondary cracking mechanism of volatiles. The study focused on the effect of the size and number of generated bubbles and its hydrodynamics on the fast pyrolysis reaction depending on the dimension of the fluidized bed column. The hydrodynamic characteristics in the reactor were examined accordingly. The characteristics of the primary and secondary reaction rates and the final yields of the products were computed.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT, and Future Planning (MSIP) of Korea (NRF2014R1A2A2A03003812). This work was also supported by a New & Renewable Energy of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government Ministry of Knowledge Economy (20143010091790). 2203


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