High-Resolution Particle-Scale Simulation of Biomass Pyrolysis - ACS

Research Article pubs.acs.org/journal/ascecg

High-Resolution Particle-Scale Simulation of Biomass Pyrolysis Qingang Xiong and Song-Charng Kong* Department of Mechanical Engineering, Iowa State University, 2025 Black Engineering Building, Ames, Iowa 50011, United States ABSTRACT: Pyrolysis is an important process encountered in direct combustion, gasification, and fast pyrolysis of solid biomass materials. This paper describes a computational study of biomass pyrolysis at the particle scale, considering the intraparticle transport phenomena and flow fields surrounding the particles. The lattice Boltzmann method, chosen to solve the conservation equations, was coupled with a chemical reaction mechanism to simulate the pyrolysis kinetics. The flow field was directly resolved at a scale smaller than the particle size, and Darcy’s law was employed to simulate the gas flow in the porous biomass particle. The present numerical method was validated by predicting the pyrolysis process of a single biomass particle. The numerical results agreed with experimental data on the temperature history and extent of conversion of the biomass particle under pyrolysis conditions. A small-scale fast pyrolysis reactor was also simulated. The numerical results indicated that the pyrolysis process may be divided into three stages: rapid heating and decomposition of the raw biomass, moderate decomposition, and full completion. The results further indicated that the modeling approach in the literature, using a constant convective heat transfer coefficient, has the potential to cause numerical errors because it neglects the rapidly changing conditions of the particle and the flow field. Parametric studies were also conducted to characterize the effects of the biomass particle concentration and the reactor inlet nitrogen velocity on pyrolysis. The results showed that increasing the biomass particle concentration significantly decreases the tar production and increases the syngas and char and that increasing the inlet nitrogen velocity moderately increases the tar production. KEYWORDS: Biomass pyrolysis, Lattice Boltzmann method, Intraparticle transport phenomena

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INTRODUCTION Pyrolysis of solid biomass is an important step in initializing the combustion process (relevant in power generation utilizing biomass and in wildland fires)1 and a critical step in the thermochemical conversion of biomass, via gasification or fast pyrolysis, to produce renewable gaseous or liquid fuels. Biomass fast pyrolysis is used increasingly for producing high-density liquid bio-oil (or pyrolysis oil). Bio-oil can be used directly in combustion devices or upgraded to liquid transportation fuels (e.g., green gasoline or green diesel).2 Despite these important applications, developing methods capable of predicting the detailed processes of pyrolysis remains challenging.3 The challenges of accurately predicting biomass fast pyrolysis arise mainly from the complex composition of biomass material and the complexity of reactor conditions.4 Biomass particles usually develop temperature and species gradients inside the particle when subjected to heat. This feature causes intraparticle transport to have a significant effect on reaction rates because the temperature and density fields within a biomass particle are not uniform. In a typical fast pyrolysis reactor, biomass devolatilization is strongly coupled with the multiphase fluid dynamics.5,6 The surrounding gas affects the particle dynamics, which ultimately influences the process of chemical reactions. Therefore, to obtain accurate information, biomass particles cannot be lumped with uniform properties, and their interactions with the environment should be taken into consideration under realistic conditions. Few experiments7−10 have been done to investigate the mechanisms of biomass pyrolysis. The inability to accurately © 2016 American Chemical Society

measure the processes of intraparticle transport and nearparticle gas flow has limited the understanding of the detailed mechanisms of particle-scale biomass pyrolysis. However, computational fluid dynamics (CFD) has been an effective approach to study complex processes, such as those associated with biomass pyrolysis. Relative to particle-scale biomass pyrolysis, the most often used CFD approach is the so-called single-particle model.11 The single-particle model is most often one-dimensional.12−16 A few two-dimensional (2D)17−19 and three-dimensional20 simulations have been reported. In these models, the boundary conditions are artificially prescribed, either by giving a constant temperature or a constant heat transfer coefficient. These types of boundary conditions generally cannot be met under realistic reactor conditions because the boundary conditions at the particle surface are dynamic and highly dependent on the surrounding gas flow. A few studies have attempted to couple intraparticle transport with the surrounding flow field (see, e.g., refs 21−23). Consideration of a group of particles is required to characterize the overall system behaviors. In this study, high-resolution particle-scale direct numerical simulation (DNS) of biomass pyrolysis, with consideration given to chemical reactions, was conducted. The numerical results were first validated using experimental data for a single Received: May 10, 2016 Revised: August 14, 2016 Published: September 1, 2016 5456

DOI: 10.1021/acssuschemeng.6b01020 ACS Sustainable Chem. Eng. 2016, 4, 5456−5461

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ACS Sustainable Chemistry & Engineering particle. Then a small-scale reactor was simulated, and the results were analyzed.

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Θeq 5,6,7,8 =

NUMERICAL METHODS

1 eq [Pi (ρ , u , T , Y ) − Pi(x , t )] τP (1)

∂ρg

where P represents f, Θ, or Ω, ei is the lattice propagation velocity in the ith direction, dt is the time increment, τ is the dimensionless relaxation time related to the fluid transport properties (e.g., kinematic viscosity ν, heat diffusivity α, and molecular diffusivity D), and Peq i (ρ, u, T, Y) is the equilibrium distribution derived from the equilibrium Boltzmann distribution. τ relates to the fluid transport properties as follows:

∂t

τΩ =

∂t

∂t ∂ρg Ygk

3 dt α + 0.5 (dx)2

∂t

3 dt D + 0.5 (dx)2

∑ fi ,

ρu =

∑ fi ei,

ρT =

∑ Θi ,

ρY =

∑ Ωi

i

i

i

(3) The D2Q9 method was used in the present 2D simulations. In D2Q9, the equilibrium distributions for f and Θ are expressed as

⎡ 5T f 0eq = ρ⎢1 − ⎢⎣ 9 eq = f1,2,3,4

eq f 5,6,7,8

∑ k

Yk 4u 2 ⎤ − 2⎥ Wk 9c ⎥⎦

Y e ·u 9(ei ·u)2 1 ⎡⎢ 3u 2 ⎤⎥ ρ T ∑ k + 3 i2 + − 9 ⎢⎣ k Wk 2c 4 c 2c 2 ⎥⎦

+ ∇·(ρg Ygk u g) = ρg Dg ∇2 Ygk

(5)

κ = pbiomass κbiomass + (1 − pbiomass )κchar (6) where pbiomass is the local mass fraction of biomass and κbiomass and κchar are the thermal conductivities of pure biomass and char, respectively. (d) Local thermal equilibrium at the gas−solid interface within particle is assumed, meaning that there is no temperature difference between the gas and the solid at the interface because of the high heating rate during the pyrolysis process.30 Biomass Fast Pyrolysis Kinetics. Because of the extremely complex nature of the chemical reactions associated with biomass

Y e ·u 9(ei ·u)2 1 ⎡⎢ 3u 2 ⎤⎥ = ρ T ∑ k + 3 i2 + − 36 ⎢⎣ k Wk 2c 4 c 2c 2 ⎥⎦

2 u2 Θeq 0 = − ρT 2 3 c eq Θ1,2,3,4 =

+ ∇·(ρg CpgTg u g) = ρg Cpgαg∇2 Tg

Modeling of Intraparticle Transport. The DNS method, as applied in this study, aims to resolve as many details as possible; however, several processes still need to be modeled because of computational limitations or a lack of knowledge at the molecular level. The following assumptions/approximations were made for modeling of the intraparticle transport phenomena: (a) As the first step in simulating particle-scale DNS of biomass pyrolysis, the gas flow within the particle was modeled using Darcy’s law through the permeability parameter K and the local porosity ε because of computational restrictions. In future studies, spatial resolution will be refined to the subpore scale to include the effects of heterogeneous pore structures. (b) Particle shrinkage was not considered because it is assumed to be due to the intermolecular attractive forces and needs to be simulated using molecular dynamics theory, which is beyond the scope of this study. (c) Local solid properties, such as thermal conductivity and heat capacity, were determined as the arithmetic averages of the values for raw biomass and the resulting char, which coexist in the solid particle during the pyrolysis process. For example, the local thermal conductivity κ was computed as

(2)

i

+ ∇· (ρg u gu g) = −∇p + ρg νg∇2 u g + ρg g

∂(ρg CpgTg)

where dx is the lattice grid spacing. From these property distribution functions, macro variables such as density ρ, velocity u, temperature T, and species mass fraction Y can be obtained as ρ=

+ ∇·(ρg u g) = 0

∂(ρg u g)

3 dt τf = ν + 0.5 (dx)2 τΘ =

(4)

where c is the so-called lattice speed (defined as dx/dt), W is the molecular weight for a gas species, and u = |u|. The equilibrium distribution for Ω is analogous to that for Θ. It is worth noting that T, Y, and W are included in the density equilibrium distribution, which accounts for the relationships between pressure, temperature, and gas composition, through the ideal gas equation of state.27 The passive scalar model28 was utilized in this study for LBM modeling of the temperature and species mass fraction. This model is based on the assumption that the pressure work and viscous dissipation have a minor effect on the thermal transport, which is valid for gases in most cases. The standard procedure to solve the above LBM equations can be classified into two steps. First, eqs 3 and 4 are used to obtain the relevant equilibrium distributions. Relaxation is then performed using the right-hand side of eq 1 with the calculated τ from eq 2. Finally, the relaxed distributions propagate to their corresponding neighbors. More details on the LBM solution process can be found in the paper by Chen and Doolen.24 The Chapman−Enskog expansion has proven that the LBM expressions above are equal to the following gas-phase conservation equations to second-order accuracy:29

Lattice Boltzmann Method. For the purposes of this study, the lattice Boltzmann method (LBM)24,25 was chosen to simulate the transport phenomena of biomass particles during pyrolysis. Compared with traditional computational approaches, such as the finite volume method and the finite element method, LBM possesses a simpler mathematical formulation and lower computational costs. In addition, LBM is programming-friendly and versatile to treat complex geometries. In LBM, the simulation domain is discretized into a grid of regular lattices on which several sets of property distribution functions are solved (i.e., the density distribution f i(x, t), the internal energy distribution Θi(x, t), and the species mass distribution Ωi(x, t)), where x is the position of the lattice, t is the current time, and i is the discretized direction pointing to neighboring lattices. On the basis of the Bhatnagar−Gross−Krook approximation,26 the evolution of a property distribution function can be formulated as

Pi(x + ei dt , t + dt ) = Pi(x , t ) +

⎡ e ·u 9(ei · u)2 1 3u 2 ⎤ ρT ⎢3 + 6 i 2 + − 2⎥ 4 36 ⎣ 2c c 2c ⎦

9(ei · u)2 1 ⎡3 3 ei · u 3u 2 ⎤ ⎥ ρT ⎢ + + − 9 ⎣2 2 c2 2c 4 2c 2 ⎦ 5457


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Figure 1. Experimental and simulation results: (a) radial distributions of temperature at different times; (b) temporal evolution of biomass conversion extent. pyrolysis, a full and precise description of the actual chemical process is currently not available. As a first step toward the ultimate comprehensive understanding of the pyrolysis process, in this study a relatively simple reaction mechanism that considers the major steps was adopted,31 and more detailed kinetics32 will be left for future studies.

activation:

experimental results, the maximum error being around 7%. As shown in Figure 1b, the predicted history of particle conversion extent agrees with the experimental data. The conversion extent was defined as the mass ratio of the products to the raw biomass.

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REACTOR SIMULATION CONDITIONS A small-scale reactor with a width (W) of 4 × 10−3 m and a height (H) of 1 × 10−3 m was simulated in two dimensions. The geometrical and boundary conditions are shown in Figure 2. The initial temperatures were 773 K for the gas and 300 K

k1

virgin biomass → active biomass k2

decomposition: active biomass → tar k3

active biomass → Y char + (1 − Y )syngas (7) In this mechanism, both the virgin biomass and the active biomass consist of cellulose, hemicellulose, and lignin. Each component has its own chemical reaction rates.31 In this mechanism, virgin biomass is rapidly elevated to active biomass because of the heating from the hot gas stream. Then two competing decomposition reactions occur, one to produce tar and the other to produce char and syngas. Here tar represents condensable gaseous species that will eventually be condensed to produce bio-oil outside the reactor. In the numerical simulation at each time step, transport phenomena, without contributions from chemical reactions, were first solved. Then at each lattice the resulting intermediate field variables were used to calculate the chemical reaction rates. After the chemical reaction calculations were made, the field variables were updated.

Figure 2. Numerical setup for the small-scale biomass reactor.

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for the biomass particles. The temperature of the walls was maintained constant at 773 K. Nitrogen at 773 K with a parabolic velocity profile was given at the inlet to induce pyrolysis. The peak velocity, uc, was varied to investigate the effect of the superficial particle Reynolds number. Table 1 lists the physical properties of the species.34,35 As the first step to reveal the interaction between intra- and extraparticle fluid dynamics and heat transfer, only circular particles were modeled in this study. Ns biomass particles with a diameter (ds) of 1 × 10−4 m were randomly placed in the middle half of the reactor. The initial porosity and permeability inside biomass particles were 0.4 and 1 × 10−12 m2, respectively. The diameter of each particle was discretized into 20 lattices, and the time step used was 1 μs. This lattice density was found to yield grid-independent results. The lattice speed of 5 m/s guaranteed that the maximum Mach number was less than 0.12. On the basis of the maximum velocity, the particle Reynolds number was around 1, and the flow was within the laminar regime. To minimize the statistical error arising from the initial particle distribution, 10 random initial configurations were generated with the same Ns, and the results were arithmetically averaged. Each case was run until the biomass was completely decomposed.

VALIDATION A classical problem was simulated, in which a circular particle was immersed in a hot gas to induce pyrolysis.33 In the experiment, a cylindrical pellet with a diameter of 0.022 m and an initial temperature of 303 K was suddenly placed in hot nitrogen with a constant temperature of 643 K. The temporal evolutions of temperatures at different radial positions were recorded, and the extent of biomass conversion was also monitored. The present numerical study used the same conditions and material properties as were used in the experiment.33 The simulation was conducted in two dimensions. Because of the large ratio of the particle’s axial length to its radial diameter, the effects of axial inhomogeneity were small. The particle was placed in the middle of the simulation domain, the dimension of which was 10 times the particle diameter. Fully developed conditions were chosen for the domain boundary. The particle diameter was discretized into 40 lattices, and the time step was 1 × 10−4 s. The simulation was conducted until the raw biomass was completely converted to products. The numerical and experimental temperature profiles with respect to the particle radial position are shown in Figure 1a. It can be seen that the numerical results agree with the 5458


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ACS Sustainable Chemistry & Engineering Table 1. Species Properties species biomass (s) char (s) tar (g) syngas (g) nitrogen (g)

material density ρ (kg/m3) 4 × 102 1.4 × 102

molecular weight W (kg/mol)

1 × 10−1 3 × 10−2 2.8 × 10−2

heat capacity Cp (m2·s−2·K−1) 2.3 1.1 2.5 1.1 1.1

× × × × ×

103 103 103 103 103

kinematic viscosity ν (m2/s)

2 × 10−5 6.6 × 10−5 6.8 × 10−5

thermal diffusivity α (m2/s) 2 1 5 1.5 1.4

× × × × ×

10−6 10−5 10−5 10−5 10−5

diffusivity D (m2/s)

1 × 10−5 1.8 × 10−5 1.9 × 10−5

Figure 3. (a) Temporal evolution of product yields. (b) Instantaneous temperature distributions at different stages.

Figure 4. Spatial distributions of predicted (a) gas density and (b) gas velocity at different times.

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RESULTS AND DISCUSSION Physicochemical Characterization. The temporal evolutions of product yields with Ns = 30 and uc = 0.2 m/s are shown in Figure 3a. Figure 3a illustrates that conversion may be divided into three distinct stages. In the first stage, biomass particles decomposed rapidly at a steady rate because of the steady heating, causing thermal penetration toward the particle center. In the second stage (from approximately 1.4 to 5.5 s), the thermal front reached the particle center for most of the particles. The decomposition rate was reduced because of the reduction in the unreacted biomass. In the last stage, biomass was converted completely, and only char existed in the solid phase. Three instantaneous temperature distributions at each stage for the second half of the reactor are shown in Figure 3b. This figure shows that the temperature surrounding the biomass particles changed drastically with time. This phenomenon demonstrates that it is not appropriate to give a constanttemperature boundary condition to simulate biomass pyrolysis, as is often done in the literature. Figure 4 shows the temporal evolutions of the spatial distributions of gas density and velocity, both of which exhibit very dynamic structures. For the selected particle, Figure 5 indicates that the Prandtl number (Pr) and Reynolds number (Re) change with time. The commonly used simplified modeling approach, in which a constant convective heat transfer coefficient is used, will introduce noticeable errors in calculating the intraparticle transport phenomena. Thus, in order to accurately simulate biomass pyrolysis at the particle

Figure 5. Temporal evolutions of Pr and Re of the selected particle.

scale, the intraparticle transport and the surrounding gas flow need to be considered simultaneously. Effects of Solid Volume Fraction and Inlet Nitrogen Velocity. The performance of a biomass pyrolysis reactor is sensitive to the feed rate and the residence time of the biomass particles. In this study, the number of biomass particles Ns and the nitrogen inlet velocity uc were varied to simulate these two effects. For a fixed uc of 0.2 m/s, Figure 6a shows the effects of Ns on the product yields. It can be seen that with an increase in 5459


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Figure 6. Effects of Ns on pyrolysis outcome (uc = 0.2 m/s): (a) product yields; (b) conversion time.

Figure 7. Effects of uc on pyrolysis outcome (Ns = 30): (a) product yields; (b) conversion time.

Ns the tar yield decreased, while more syngas and char were produced. It should be noted that more external heat was required to facilitate biomass particle decomposition as Ns increased. As external heat was provided by the gas phase, an increase in the number of biomass particles resulted in lower temperatures in the biomass particles. From the reaction kinetics described in eq 7, at low temperatures the reaction producing tar is less effective than the reaction producing syngas and char. This is the case because more energy is required to break down biomass molecules to produce tar compared with devolatilization to produce syngas. Thus, more syngas and char were formed. The overall conversion time was also found to increase with increasing Ns, as shown in Figure 6b, because the heating rate was reduced, supporting the conclusion found earlier in this report that lower solid temperature for higher Ns can result in a lower rate of biomass decomposition and thus longer conversion times. It is worth noting that the standard deviations, caused by different initial biomass particle configurations, for both the product yields and conversion time decreased as Ns increased. This implies that at high biomass feed rates the biomass particles are already crowded in the reactor, possibly hindering the heat and mass transfer. Thus, the distribution of the particles becomes less influential on the product yields. The variations of the product yields and conversion time with respect to uc are shown in Figure 7 for a fixed Ns of 30. As uc increased, more tar and less syngas and char were produced. The conversion time decreased as uc increased. These results were due to the high convective heat transfer rate from gas to biomass particles at high uc. Furthermore, as uc increased, more heat was carried into the system by nitrogen, and the tar and syngas were quickly carried out of the system, reducing the accumulation of low-temperature gas products and further enhancing the conversion rate. The standard deviations, caused

by different initial biomass particle configurations, for both the product yields and conversion time were found not to be sensitive to uc.

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CONCLUSIONS High-resolution direct numerical simulation was conducted to study biomass pyrolysis at the particle scale. The lattice Boltzmann method for simulating the dynamics of solids and fluids was coupled with a chemical reaction mechanism for predicting the chemical kinetics of pyrolysis. The intraparticle phenomena were modeled by assuming that the solid biomass particles were homogeneous porous media. The numerical results agreed with experimental data in temperature history and conversion extent of the biomass particle under pyrolysis conditions. A small-scale fast pyrolysis reactor was simulated. The numerical results indicated that the conversion may be divided into three stages. In the first stage, the rapid heating caused biomass particles to decompose rapidly. In the second stage, the thermal front reached the particle center, and the decomposition rate was reduced. In the third stage, biomass was slowly but completely converted, and only char existed in the solid phase. Detailed analysis of the reactor conditions indicates that the common modeling approach using a constant convective heat transfer coefficient is likely to introduce numerical errors by neglecting the dynamically changing flow fields. The parametric study showed that for a fixed inlet nitrogen velocity, an increase in biomass particle concentration significantly decreased the tar production but increased the syngas and char yields. The conversion time also increased significantly. An increase in the inlet nitrogen velocity moderately increased the tar production but decreased the syngas and char yields as well as the conversion time. These 5460


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ACS Sustainable Chemistry & Engineering findings indicate that the biomass feed rate needs to be carefully controlled for desirable product yields.

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AUTHOR INFORMATION

Corresponding Author

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

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Support from the National Science Foundation under Grant EPS-1101284 is acknowledged.

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