Overview of Computational Fluid Dynamics Simulation of Reactor

Perspective pubs.acs.org/journal/ascecg

Overview of Computational Fluid Dynamics Simulation of ReactorScale Biomass Pyrolysis Qingang Xiong,† Yang Yang,‡ Fei Xu,§ Yaoyu Pan,§ Jingchao Zhang,∥ Kun Hong,*,† Giulio Lorenzini,*,⊥ and Shurong Wang*,# †

Jiangsu Provincial Engineering Laboratory for Biomass Conversion and Process Integration, Jiangsu Provincial Engineering Laboratory for Advanced Materials of Salt Chemical Industry, Huaiyin Institute of Technology, 1 East Meicheng Road, Huai’an, Jiangsu 223003, China ‡ Department of Civil Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland 21218, United States § Department of Mechanical Engineering, Iowa State University, 2025 Black Engineering Building, Ames, Iowa 50011, United States ∥ Holland Computing Center, University of Nebraska−Lincoln, 1400 R Street, Lincoln, Nebraska 68588, United States ⊥ Department of Industrial Engineering, University of Parma, Str. dell’Università 12, Parma, Parma 43124, Italy # State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang 310027, China ABSTRACT: Computational fluid dynamics (CFD) has been widely used in both scientific studies and industrial applications of reactor-scale biomass pyrolysis. In this Perspective, the stateof-the-art progress in CFD modeling of reactor-scale biomass pyrolysis was summarized and discussed. First, because of the importance of biomass pyrolysis reaction kinetics to the predictability of CFD, the commonly used pyrolysis reaction kinetics in CFD modeling of reactor-scale biomass pyrolysis were reviewed. The characteristics of each reaction kinetics were described. Then, the theoretical basis and practical applications of three main CFD modeling approaches, i.e., porous media model, multifluid model, and discrete particle model for simulating reactor-scale biomass pyrolysis were presented. The activities and progresses with respect to each CFD modeling approach for reactor-scale biomass pyrolysis were reviewed. Aspects such as experimental validation, modeling speed, and capability were discussed. Finally, the paper was concluded with comments on future directions in CFD modeling of reactor-scale biomass pyrolysis. KEYWORDS: Computational fluid dynamics, Biomass pyrolysis, Reactor-scale, Porous media model, Multifluid model, Discrete particle model

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identify fundamental intraparticle transport phenomena,7 and reactor scale to draw guidelines for practical operations.8 Because of the relevant spatiotemporal scales, studies at molecular and particle scales are very fundamental, whose scopes are mainly on microstructures aiming to provide baseline information under relatively simple conditions. Studies at reactor scale, however, are majorly driven by realistic conditions and oriented to industrial levels for reactor design, scale-up, and optimization. In this Perspective, we focus on the activities devoted to reactor-scale biomass pyrolysis. It has been largely recognized that reactor-scale biomass pyrolysis is very complicated as multiphase flows are inherently coupled with complex chemical reactions.10−12 The complexity of reactor-scale biomass pyrolysis can be virtually seen in Figure 1. On one hand, more than one phase exists in reactor-scale biomass pyrolysis, which includes complex interphase inter-

INTRODUCTION The increased depletion of fossil fuel reservation and continued concern on environmental pollution has led to the increase of searching for alternate clean energy sources in recent years. Biomass, with its abundance all over the world and nearly zero emission of greenhouse gases, has received substantial interest in past decades for energy security and sustainability.1 Pyrolysis, a thermochemical route that employs external heat to break down the chemical bonds within biomolecules and decompose biomass into different products for further utilizations, is an attractive approach to convert low energy-density raw biomass into high energy-density products such as bio-oil.2 Compared with biological approaches, pyrolysis can process a large amount of raw biomass in a very short period.3 Additionally, pyrolysis is also viewed as the first stage in other thermochemical conversion processes, e.g., gasification and combustion, in which the actual biomass decomposition process will have significant influence on the subsequent gasification or oxidation processes.4,5 In general, biomass pyrolysis is studied at three separate scales, i.e., molecular scale to reveal chemical kinetics,6 particle scale to © 2017 American Chemical Society

Received: October 31, 2016 Revised: January 2, 2017 Published: March 9, 2017 2783

DOI: 10.1021/acssuschemeng.6b02634 ACS Sustainable Chem. Eng. 2017, 5, 2783−2798

Perspective

ACS Sustainable Chemistry & Engineering

Figure 1. Schematic diagram of biomass pyrolysis in a typical chemical reactor9 (with permission from Elsevier for use in this paper). Copyright 2014 Elsevier.

scale biomass pyrolysis, are first summarized. Then, a review of activities on the use of the three main CFD models, i.e., porous media model (PMM), multifluid model (MFM), and discrete particle model (DPM) to reactor-scale biomass pyrolysis is presented. Finally, the paper is concluded with highlights on future directions for improvement and development of CFD modeling of reactor-scale biomass pyrolysis.

actions and phase transitions. On the other hand, reactor-scale biomass pyrolysis still involves processes occurring at molecular and particle scales, which make the integrated process highly multiscale. Intraparticle heterogeneous mass, flow, and temperature distributions interact with hydrodynamics in surrounding gas. Finally, for a typical biomass feedstock, considerable components are subject to a great number of interconnected reactions. These complexities have inherently hindered harnessing the power from biomass pyrolysis. Experiment13 and numerical simulation14 are the two main approaches in the investigation of reactor-scale biomass pyrolysis. Though experiment is indispensable to the ultimate design and optimization of reactors for biomass pyrolysis, its relatively high cost and long development cycle still face great challenges. In addition, under harsh conditions such as high temperature and pressure, accurate measurements of in-reactor quantities are rather difficult. With the rapid development of computer hardware, numerical simulation, also called “virtual experiment”, becomes increasingly popular in the studies of reactor-scale biomass pyrolysis to complement real experiment to reduce both development cost and time.15 Therefore, using numerical simulation, useful guidelines and trends can be obtained in a relatively economical manner. Computational fluid dynamics (CFD),16 a well-established branch of numerical simulation, has found extensive applications in various complex problems. As mentioned earlier, one of the key aspects of reactor-scale biomass pyrolysis is complex multiphase flows, which is very suitable to be studied by CFD. In fact, so far, a sheer volume of problems on reactor-scale biomass pyrolysis have been studied with CFD17 and the understanding of the underlying complicated mechanisms has been advanced to an unprecedented level. So far, almost all types of reactors for biomass pyrolysis, e.g., fixed beds, bubbling beds, risers, vortex reactors, and auger reactors, etc., have been investigated using CFD. Thus, it is highly necessary to review the state-of-the-art status of the activities on CFD modeling of reactor-scale biomass pyrolysis and highlight future directions. This Perspective, to the best of our knowledge, is the first time to summarize comprehensively the state-of-the-art progress in CFD modeling of reactor-scale biomass pyrolysis. In the following, different commonly used reaction kinetics that have been used with CFD to describe biomass devolatilization effectively, an important issue in CFD modeling of reactor-

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COMMONLY USED REACTION KINETICS FOR CFD SIMULATION OF REACTOR-SCALE BIOMASS PYROLYSIS For a typical biomass feedstock, the actual chemical reactions associated with biomass pyrolysis are very complex whose fundamental mechanisms are still far from comprehensive understanding.18 It has been supposed that thousands to millions of elementary reactions are involved in the overall biomass pyrolysis process.19 However, in a typical CFD simulation of reactor-scale biomass pyrolysis, it is very difficult, even if impossible, to consider such great amount of chemical reactions. Thus, in a practical CFD modeling of reactor-scale biomass pyrolysis, the selection of a proper reaction kinetics to describe the biomass devolatilization and subsequent secondary tar cracking is critical.20 It should be not only as accurate as possible because chemical reactions can influence the ultimate reactor performance significantly but also computationally affordable as solution of a reaction kinetics containing thousands to millions of elementary reactions is not realistic. So far, in most CFD modeling of reactor-scale biomass pyrolysis, the most common types of reaction kinetics employed are the so-called lumped global kinetics, which usually include a limited number of reactants and products and reaction steps. Meanwhile, in recent years, several relatively complex reaction kinetics aiming to reproduce the overall biomass pyrolysis reaction process have been proposed. In this section, we will summarize and discuss the characteristics of different categories of reaction kinetics. It is worth noting that in most CFD simulations of reactor-scale biomass pyrolysis, the solution of reaction kinetics is decoupled with the solution of hydrodynamics through the so-called timesplit approach.21 In such an approach, the partial differential equations of flow field are solved first without the source terms from chemical reactions. Then the intermediately obtained flow variables are used to solve the ordinary differential equations for chemical reactions to obtain reaction-related source terms. 2784


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ACS Sustainable Chemistry & Engineering Table 1. Popular Set of Kinetic Parameter Values and Stoichiometric Coefficients for Scheme D32 Component Cellulose

Hemicellulose

Lignin

Tar

Reaction

Stoichiometric coefficient for char formation (Y)

Pre-exponential factor (A (s−1))

Activation energy (E (J/mol))

0.35

2.8 × 1019 3.28 × 1014 1.3 × 1010

2.424 × 105 1.965 × 105 1.505 × 105

0.6

2.1 × 1016 8.75 × 1015 2.6 × 1011

1.867 × 105 2.024 × 105 1.457 × 105

0.75

9.6 × 108 1.5 × 109 7.7 × 106

1.076 × 105 1.438 × 105 1.114 × 105

4.28 × 106

1.08 × 105

Activation of biomass Biomass decomposition to tar Biomass decomposition to char and gas Activation of biomass Biomass decomposition to tar Biomass decomposition to char and gas Activation of biomass Biomass decomposition to tar Biomass decomposition to char and gas Tar cracking to gas

To account for the effects of initial biomass melting, the most popular way is to introduce the activated stage of biomass during the primary decomposition process, such as

Finally, the obtained source terms are employed to update corresponding flow variables. Single-Component Single-Step Reaction Kinetics. The single-component single-step reaction kinetics was first proposed by Shafizadeh and Chin,22 primarily used to account for the decomposition of wood. The single-step reaction to produce gas, tar, and char is modeled either by competitive or noncompetitive subreactions as follows. For the competitive reaction type, one example of the kinetics is

Ra

(2.3)

For the secondary cracking reactions, one example is Rg

wood → gas

Rg

wood → gas

Rt

wood → tar

Rt

Rc

wood → tar Rc

wood → char

wood → char Rs

(2.1)

tar → gas

where Rg, Rt, and Rc are the reaction rates for gas, tar, and char, respectively. In the following, this type of scheme will be named Scheme A1. Scheme A1 has been used by Lamarche et al.23 and Ratte et al.24 in their CFD simulations of reactor-scale biomass pyrolysis. For large particles, Scheme A1 is adjusted to a noncompetitive single-step reaction kinetics to simplify further the wood decomposition process as

(2.4)

In the following, the category of single-component multistep reaction kinetics will be named Scheme B. Scheme B has been used by many authors in their CFD simulations of reactor-scale biomass pyrolysis, e.g., Ghabi et al.29 Multicomponent Single-Step Reaction Kinetics. The multicomponent single-step reaction kinetics was majorly proposed to involve the effects of primary components, i.e., cellulose, hemicellulose, and lignin.30 Thus, multicomponent single-step reaction kinetics can be viewed as independent singlecomponent single-step reaction kinetics for each primary component. An example of multicomponent single-step reaction kinetics is

R

wood → νg gas + νt tar + νcchar

R

wood virgin → woodactivated → νg gas + νt tar + νcchar

(2.2)

where νg, νt, and νc are the so-called stoichiometric coefficients representing the mass fractions of products. In the following, this scheme will be named Scheme A2. Scheme A2 has been employed by many researchers in their CFD simulations of reactor-scale biomass pyrolysis, such as Liang and Kozinski25 and Hofmann and Antal.26 It can be seen that single-component single-step reaction kinetics is very simple and easy to couple with CFD. However, because of its oversimplification, the effects of biomass compositions and secondary decompositions are not involved, which can produce significant errors from the chemical side. Single-Component Multistep Reaction Kinetics. It has been widely accepted that during biomass pyrolysis, unignored amount of products will undergo secondary cracking resulting in structural repolymerization.27 Besides, evidence also has shown that during the process of primary decomposition, virgin biomass will first melt to form an intermediate stage called activated biomass.28 These two types of reactions can significantly affect the overall reaction rates and final products. Thus, efforts have been devoted to take consideration of these two additional reactions.

R m1

celloluse ⎯⎯⎯→ νg1gas + νt1tar + νc1char R m2

hemicelloluse ⎯⎯⎯→ νg2 gas + νt2 tar + νc2char R m3

lignin ⎯⎯⎯→ νg3gas + νt3tar + νc3char

(2.5)

In the following, the type of multicomponent single-step reaction kinetics will be named Scheme C. The use of Scheme C is rare. To the best of our knowledge, it was only used by Romagnosi et al.31 in their CFD simulation of reactor-scale biomass pyrolysis. Multicomponent Multistep Reaction Kinetics. Within the scope of lumped reaction kinetics for biomass pyrolysis, multicomponent multistep reaction kinetics is viewed as the most accurate and feasible for practical applications. It was first proposed by Miller and Bellan32 to differentiate the rate disparity among cellulose, hemicellulose, and lignin. It also accounts for the secondary cracking of tar. Thus, multicomponent multistep 2785


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Figure 2. Relatively complex reaction kinetics for biomass pyrolysis used by Mellin et al.36 (with permission from Elsevier for use in this paper). Copyright 2014 Elsevier.

reaction kinetics can be formulated directly through the combination of Scheme B and C, which will be named Scheme D in the following. Many studies have used Scheme D in their CFD simulations of reactor-scale biomass pyrolysis, for example, Xue et al.33 and Xiong et al.34 A popular set of values for kinetic parameters and stoichiometric coefficients for Scheme D can be seen in Table 1.32 Relatively Complex Reaction Kinetics. The predictability of CFD for reactor-scale biomass pyrolysis is highly dependent on the accuracy of pyrolysis reaction kinetics. Though lumped reaction kinetics are relatively easy to use, their accuracy is still questioned as too much extent of simplifications is introduced. Thus, in recent years, researchers such as Ranzi et al.35 proposed relatively complex but more comprehensive reaction kinetics for biomass pyrolysis. One of such relatively complicated reaction kinetics can be seen in Figure 2. In the following, the type of relatively complex reactions kinetics is named Scheme E.

In recent years, because of the increase of computational power, the number of studies9 using Scheme E in CFD simulation of reactor-scale biomass pyrolysis increases obviously. Though Scheme E considers components and reaction steps in more detail, its execution with CFD is rather complicated. Moreover, because of its high complexity, its generality is very limited.

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POROUS MEDIA MODELING OF FIXED-BED BIOMASS PYROLYSIS

In fixed-bed biomass pyrolysis reactors, biomass particles remain stationary or semistationary and gas phase flows majorly through the voids between biomass particles. External heats are usually supplied by the contacted heated walls and transferred to the inner region of the bed for biomass particles to decompose. Driven by the resulted pressure gradient, devolatilized gas leaves 2786


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ACS Sustainable Chemistry & Engineering biomass particles and merges into the mainstream flow toward the reactor exit. Based on the flow characteristics in fixed-bed biomass pyrolysis, porous media model37 has been widely employed to simulate the related gas flow. The general mathematical formulations of PMM for the gas phase are

Simulations without Solving Gas Momentum Equation. In the early stage of simulating fixed-bed biomass pyrolysis using PMM, the momentum equation was usually not included. The velocity was assumed to be in plug flow or zero, or calculated directly by gas pressure gradient. In the studies of Cozzani et al.41 and Park et al.,42 pyrolysis of biomass in cylindrical beds was modeled. In these studies, the bed was modeled in the radial and axial directions, respectively, using the plug-flow assumption and Crank−Nicholson discretized finite-difference method. The temporal variations of temperature and product yields were obtained and validated with a laboratory experiment.39 The numerical study showed that higher heating rate can enhance the temperature gradient and produce distinct thermal fronts for biomass to degrade. Simulations were extended to 2D by Ghabi et al.,29 Olaleye et al.,43 and Borello et al.,44 in which the pyrolysis of spherical biomass particles in cylindrical fixed beds was simulated with Scheme B. Convective and radiative heat transfers were given to the sidewalls, whereas the outlet was modeled as nongradient. The resulted partial differential equations were solved by the finite volume method with the biconjugate gradient stabilized method. The temporal evolutions of thermal and reacting fronts inside the bed were quantitatively discussed and lower pyrolysis temperature was found to result in higher tar yield. It was also reported that higher temperature favors gas production. A similar work was conducted by Lamarche et al.23 for simulation of an indirectly heated cylindrical fixed bed with both Scheme A1 and A2. Upwind and central difference schemes were used to discretize convective and diffusive terms, respectively. After validation with experiment, the influence of wall temperature, reactor size, moisture content and type of biomass on conversion was studied. It was found that wood pellets are easier to be pyrolyzed. Gas velocity was assumed to be zero by Sharma and Rao45 and Batra and Rao,46 where a fixed bed equipped with fins was simulated in 1D with Scheme A2. Fixed sidewall temperature was given, and the discretized equations were solved through finite difference approach. The simulated temperature distributions agree well with experimental measurements. The effects of the number of fins, heat rate, and wall heat transfer rate coefficient were investigated. It was found that with the increases in number of fins, the temperature variation decreases and the pyrolysis process completes faster. Increased heating rate and wall heat transfer rate coefficient were also found to be beneficial to completion of pyrolysis. The same modeling strategy was employed in the 2D simulation of an annular bed.47 In this work, reaction constants are the function of temperature and heating rate and physical properties were obtained directly from experiment. The inner wall was given fixed temperature whereas the outer wall was assigned as convective. Grid spacing along the radial direction is nonuniform, and the conservation equations were solved using the finite volume method. The predicted mass loss was found to agree qualitatively with experiment, and a sensitivity study on the effects of char conductivity, void conductivity, and reaction rate constant was conducted. In the work conducted by Liang and Kozinski, 25 a thermogravimetric system was simulated in 2D with Scheme A2 where the gas velocity was determined by the local pressure gradient. The predicted weight loss agrees well with experiment and the effects of heating rate and porosity were studied. The same approach was also applied to the 2D simulation of a cylindrical bed for wood pyrolysis48 with Scheme B and the

Mass equation ∂αgρg

+ ∇·(αgρg Ug) = R gs

∂t

(3.1)

Momentum equation ∂(αgρg Ug) ∂t

+ ∇·(αgρg UgUg)

= ∇·(αgτg) − αg∇pg −

μg K

Ug + ψgs + αgρg g

(3.2)

Energy equation ∂(αgρg CpgTg) ∂t

+ ∇·(αgρg CpgTgUg)

= ∇·(αgqg) + Hgs + ΔHg

(3.3)

Species equation ∂αgρg Ygk ∂t

+ ∇·(αgρg YgkUg) = ∇·(αgJgk ) + R gk

(3.4)

where αg, ρg, Ug, pg, Tg, and Ygk are the local volume fraction, density, velocity, pressure, temperature, and mass fraction of species k of gas, respectively. μg and Cpg are the viscosity and heat capacity, and g is the gravitational acceleration. τg, qg, and Jgk are the shear stress, conductive heat tensor, and diffusive tensor. Generally, τg, qg, and Jgk are modeled with the Newton’s, Fourier’s, and Fick’s laws. Rgs, ψgs, Hgs, ΔHg, and Rgk are the mass from heterogeneous gas−solid reactions, momentum transfer due to heterogeneous gas−solid reactions, convective gas−solid heat transfer, heat adsorbed by gas phase, and mass from reactions for species k, respectively. In PMM, the resistance between gas and solid phases is modeled using Darcy’s law through K. K can be obtained by empirical correlations such as the Ergun equation. 38 The mass, energy, and species conservation equations for solids can be formulated similarly without convection terms as solids remain stationary. During the derivation of PMM for simulation of fixed-bed biomass pyrolysis, usually the following hypothesis have been employed as model assumptions. First, solid spatial dimensions are considered constant during pyrolysis. This is based on the fact that during fixed-bed biomass pyrolysis, no significant shrinkage phenomena have been observed.39,40 The second, also the most important assumption is that porous structure within each computational grid is viewed as homogeneous. Though at microscale, a porous structure should be far from homogeneous, an accurate description of the effects of microscale porous heterogeneity is still highly challengeable for both experimental and numerical approaches. The third assumption that has been widely used in PPM simulation of fixed-bed biomass pyrolysis is that local solid physical properties such as thermal conductivity and heat capacity change only with local conversion extent. Such an assumption has been widely proved to yield acceptable predictions. 2787


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Figure 3. Fixed-bed biomass pyrolysis simulated using XDEM conducted by Mahmoudi et al.55 (a) Experimental validation of the temperature profiles; (b) spatiotemporal distribution of particle temperature (with permission from Elsevier for use in this paper). Copyright 2013 Elsevier.

with experiment and the effects of thermal conductivity, specific heat, specific surface area, and moisture content on the temperature distribution were investigated. Similarly, a 3D simulation on the temperature distribution of oil palm empty fruit bunches in a microwave pyrolyzer was done by Hussain et al.52 The variations of surface temperature of oil palm empty fruit bunches with respect to nitrogen flow rate and microwave power were studied. It was found that the surface temperature of biomass was insensitive to nitrogen flow rate but heavily dependent on the microwave power. Simulations Including Intraparticle Transport Phenomena. For thermally “thick” biomass particles pyrolyzed in fixed beds, because of the relatively high Biot number, the temperature distribution inside biomass particles is nonuniform and its impact on the reaction rate needs to be accounted for. Meanwhile, because of the rapid increase of the computational power, the intraparticle mass flow and heat transfer can be directly solved and coupled with PMM. Thus, in recent years, several attempts have been devoted to the multiscale modeling of thermally “thick” biomass particles pyrolysis in fixed beds. In general, the above conservation equations can be applied to simulate the intraparticle transport phenomena with prescribed boundary conditions on the particle surface, e.g., as

drying of wood was included. The results were validated with experiment and the details inside the reactor were provided. It is worth noting that in several simulations of reactors other than fixed beds, PMM without solving gas momentum equation was also applied. Hofmann and Antal26 and Gorton et al.49 used this approach along the axial direction in their simulation of entrained-flow biomass pyrolysis with Scheme B and A2, respectively. The finite difference method was used to solve the conservation equations with Gaussian elimination approach. Axial distributions of temperature and product yields were obtained and the effects of operating conditions such as feeding rate and biomass particle size were clarified. In summary, PMM without gas momentum is relatively fast to solve and able to be applied to industry-scale fixed-bed reactors. However, because it assumes a uniform distribution of gas velocity across the flow field, its application is largely limited to homogeneous fixed beds, whereas for heterogeneous fixed beds its modeling accuracy is relatively low. Thus, its modeling capacity can be greatly enhanced by introducing the gas momentum equation, as discussed in the following. Simulations Solving Gas Momentum Equation. A cylindrical packed bed for slow pyrolysis of wood wastes was conducted by Yang et al.50 using the complete set of PMM with Scheme B and solid shrinkage. The agreement between simulation and experiment was rather good, and the simulation discovered that packed beds can produce 30−100% more char than standard TGA and wood has higher tar cracking ability. A horizontal pyrolysis flow inside porous media with Scheme C31 was modeled similarly. The simulated overall pressure drop was validated with experiment and the detailed spatial distributions of velocity, temperature, and species mass fraction inside the porous media were discussed. A 1D simulation by Polesek-Karczewska and Kardas51 was conducted on the pyrolysis of wet wood biomass in a cylindrical packed bed. The complete PMM equations were solved with Scheme A2. A mixed explicit-implicit numerical scheme was applied to solve the conservation equations. Mass and momentum balances were solved explicitly whereas energy balance was solved by an implicit Crank−Nicolson algorithm. The obtained temperature was found in qualitative agreement

∂Ts(r ) |r = 0 = 0, ∂r

k

∂Ts(r ) |r = R = h(Tg − Ts(R )) ∂r

(3.5)

where k and h are the solid thermal conductivity and convective gas−solid heat transfer coefficient. A pioneering work under this direction was conducted by Ratte et al.24 in their simulation of biomass pyrolysis in a packed column using Scheme A1. The complete set of conservation equations was solved in 3D inside biomass particles whereas only the mass and energy conservation equations were solved in 1D in the axial direction of the reactor. The plug-flow assumption was applied to the gas phase axial velocity. The radial direction was discretized by the finite difference method whereas the axial direction was discretized the by finite volume method using upwind scheme. The axial distribution of gas temperature was validated with experiment. The axial distributions of temperature, moisture content, and wood weight were provided. The 2788


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ACS Sustainable Chemistry & Engineering impact of moisture content on the consumed power was investigated, indicating that more power is required at higher moisture content. In the work by Anca-Couce et al.,53 the gas momentum equation was included at the reactor scale. Both sets of conservation equations were solved in 1D and a quantitative agreement with experiment on temperature and mass loss was achieved. One remarkable contribution of this work is that the importance of intraparticle gradients was highlighted. A step further has been launched by Mahmoudi et al.54,55 in their simulation of heterogeneous packed-bed biomass pyrolysis by the so-called XDEM approach. Other than simulating a representative biomass particle, the intraparticle transport of all biomass particles is solved simultaneously with Scheme B and particle drying. Thus, detailed information on each individual particle can be obtained to study quantitatively the effects of particle configuration. By solving the intraparticle transport in 1D and external gas flow in 3D, XDEM was found to be able to reproduce experimental results very well, as shown in Figure 3. The effects of inlet temperature, particle size, and packing mode were investigated. It was reported that under the same solid loading, the overall product yields are greatly influenced by particle size and packing mode. It is worth noting that the coupled strategy of intraparticle transport and PMM was also employed to simulate a hightemperature high-heating-rate entrained flow reactor by Dupont et al.56 with Scheme E. 1D simulation was applied both to the particle and reactor scale and the gas velocity was assumed to be constant instead of solving the momentum equation. The comparison between simulation and experiment on axial distribution of species mass fraction was encouraging. The influence of particle size and reactor temperature was investigated and it was found that the particle size is most crucial. Overall, PMM involving the intraparticle heat transfer is the most comprehensive CFD model for simulating fixed-bed biomass pyrolysis. Because it simulates the whole reactor below the individual particle scale, very detailed and accurate information on biomass pyrolysis in fixed beds can be obtained. However, restricted by the current computational power, this approach has only been utilized in relatively small-scale systems, which still cannot meet the requirement for industry.

∂(αgρg Ug) ∂t

+ ∇·(αgρg UgUg)

= ∇·(αgτg) − αg∇pg + βgs(Us − Ug) + ψgs + αgρg g (4.1)

where βgs is the so-called drag coefficient, which is conventionally modeled by empirical correlations such as the Gidaspow58 or Symlal-O’Brien59 drag models. The conservation equations for solid phases can be expressed similarly. It is worth noting that contrast to the PMM, because of the free moving of solid particles and interparticle collisions, the solid stress needs to be included which is usually modeled using the so-called kinetic theory of granular flow (KTGF).60 KTGF was derived from the analogue to kinetic theory of nonuniform gas,61 where the socalled granular temperature is defined and used to formulate pseudo solid transport properties such as pressure and viscosity. It is worth noting that though KTGF has relatively solid mathematical foundation, formulating a closed equation for granular temperature requires considerable empirical closures, which highly restrict its generality in simulating various particulate flows. It can be clearly seen that like PMM, in MFM the computational requirement is independent of the actual number of solid particles and only relevant to the prescribed spatiotemporal resolution. So far, MFM is the only feasible CFD model that can simulate fluidized-bed biomass pyrolysis at industry scale. The main assumptions for MFM simulation of fluidized-bed biomass pyrolysis are as follows. First, biomass particle diameter is usually assumed constant and porosity of particle increases in time. In most cases, this assumption is valid if biomass particles do not break or erode. The second is that interphase momentum transfer is dominated by drag force, which is correct for most flow regimes in fluidized-bed biomass pyrolysis. Likewise, local solid physical properties such as thermal conductivity and heat capacity are assumed to change only with local conversion extent. Finally, gas phase is assumed to be opaque, where thermal radiation is not involved in the gas energy equation. This is a good approximation as gas temperature in fluidized-bed biomass pyrolysis is relatively low. MFM Simulation of Fluidized-Bed Biomass Pyrolysis. Since the mathematical formulation of MFM for simulating bubbling fluidized-bed biomass pyrolysis by Lathouwers and Bellan,62 plenty of activities using MFM have been devoted to simulate biomass pyrolysis in fluidized beds. Using their own developed MFM code with Scheme D, Lathouwers and Bellan62,63 conducted parametric studies on the effects of operating conditions. Operating temperature was found to be the most influential factor to tar yield. Zero-flux boundary conditions were imposed for solid energy equations whereas thermal radiation was included. The conservation equations were solved using the finite volume method with TVD scheme for convective terms. Realizing the capability of MFM to simulate biomass pyrolysis in large-scale fluidized beds, a sheer volume of CFD modeling using MFM has appeared in the past decade. Xue et al.33,64,65 used the same formulations as those of Lathouwers and Bellan62 to simulate a laboratory-scale bubbling fluidized-bed biomass pyrolysis with an open-source code named MFIX.59 The modeling results were validated by experiment and the effects of biomass type, operating conditions, and biomass particle polydispersity were clarified. With their own developed opensource code “BIOTC”,34,66 a series of MFM modeling were

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MULTIFLUID MODELING OF FLUIDIZED-BED BIOMASS PYROLYSIS In conventional fluidized beds for biomass pyrolysis, the solid phases (usually including biomass and sand) are fluidized by the gas phase (usually containing inlet nitrogen and produced volatiles) with external heat supplies (usually through wall). With the dynamic mixing of sand and biomass particles, external heat is transferred to biomass to be adsorbed for endothermal reactions. Produced volatiles and biochar are carried by the nitrogen toward the reactor exit. In addition, the friction between biomass and sand particles can attrite and break big biomass particles into smaller sizes. The multifluid model, an extension of two-fluid model,57 is the most widely used CFD model in simulating reactor-scale biomass pyrolysis. In MFM, all phases are modeled as interpenetrating continua and each phase can contain an arbitrary number of species. The conservation equations employed in MFM for the gas phase are quite similar to those used for PMM, except that the gas−solid interphase transfers are modeled by gas−solid interphase coefficients. For example, the momentum conservation equation for gas phase is 2789


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Figure 4. Multifluid modeling of biomass pyrolysis in a bubbling fluidized bed conducted by Lee et al.75 (a) Reactor configuration and operating conditions; (b) steady-state tar reaction rates at different reactor temperatures (with permission from Elsevier for use in this paper). Copyright 2015 Elsevier.

Figure 5. Multifluid modeling of biomass pyrolysis in an auger reactor conducted by Aramideh et al.92 (a) Steady-state volume fraction and velocity distributions of solids phases; (b) steady-state mass fraction distributions of tar and gas (with permission from Elsevier for use in this paper). Copyright 2015 Elsevier.

MFM. The effects of bed column size on lignocellulosic biomass pyrolysis were recently studied by Lee et al.77 with Scheme B using MFIX. Under different column size, the bubble behaviors are significantly distinct and finally result in very different pyrolysis reactions. Scheme E was coupled with MFM by Mellin et al.,36,78−80 Ranganathan and Gu,81 and Eri et al.82 to simulate fluidized-bed biomass pyrolysis. The finite volume method with SIMPLE algorithm was used to solve the governing equations. Free slipping wall was assigned to the solid phase. It was claimed that compared with other schemes, Scheme E can provide better reproductions of experimental results. The effects of biomass particle shrinkage were considered in the MFM modeling by Zhong et al.83 with Scheme A2. The particle shrinkage was modeled using the prescribed biomass and char material density. It was found that stronger shrinkage can lead to weaker char entrainment, smaller char yield and higher biomass conversion. The intraparticle heat conduction was indirectly included by

conducted by Xiong et al. using Scheme D to study systematically the effects of operating conditions,67,68 choice of submodels,69 and reaction kinetics.70,71 The popular open-source code OpenFOAM was used as the platform to solve the governing partial differential equations with the so-called PISO algorithm. Boundary conditions are similar to those proposed by Lathouwers and Bellan.62 The tar yield and its temporal fluctuations were found to be sensitive to the flow pattern, especially from bubbling to slugging. Similar simulations were done by Boateng and Mtui72 and Azizi and Mowla,73 and Sharma et al.74 and Lee et al.75 by Fluent, with Scheme A2 and B, respectively. In these studies, experimental validation and detailed description of the flow field and heat transfer characteristics were presented, as shown in Figure 4. A model reduction work was done by Trendewicz et al.9 and Humbird et al.76 in their 1D steady-state modeling of biomass pyrolysis in a circulating fluidized bed using Scheme E. It was attractive to find that the developed 1D model shows close predictability to 2D 2790


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ACS Sustainable Chemistry & Engineering Dong et al.84 through modifying the rate constants of reaction kinetics. It was shown that the modification of reaction rate constants can qualitatively describe the effects of intraparticle heat conduction. MFM Simulation of Biomass Pyrolysis in Other Types of Reactors. Besides fluidized beds, MFM has been employed to simulate other types of reactors. Biomass pyrolysis in entrainedflow reactors were simulated with Scheme A2 by Xiu et al.85 and Sun et al.86 The primary purpose of these two studies was to determine the relevant kinetic parameters with experimental validation. Gravity-driven reactors for biomass pyrolysis were modeled by Choi et al.,87 Yu et al.,88,89 and Lin et al.90 The popular finite volume method with SIMPLE scheme was used to solve the conservation equations. A partially slip wall condition was assigned to solids phase whereas the gas phase was modeled as nonslip. Very detailed information on hydrodynamics, heat and mass transfers, and reaction rates were obtained. With MFM, some complicated reactors were modeled for biomass pyrolysis. A vortex reactor was simulated by Ashcraft et al.91 with Scheme D. Only 2D periodical domain was simulated. The numerical results were validated by experiment and radial distributions of solid volume fraction and temperature were discussed. Using the so-called rotating reference framework, Aramideh et al.92 simulated biomass pyrolysis in an auger reactor. The flow pattern inside the reactor and product distributions at steady state were presented, as shown in Figure 5. A parametric study was also conducted to study the effects of operating conditions. It is worth noting that for vortex and auger reactors, because of the complex geometry, generating a satisfactory mesh is the most nontrivial work.

elaborated interparticle collision models can be developed for nonspherical biomass particles. Modeling All Solid Phases as Discrete Particle. To the best of our knowledge, the earliest work using DPM was done by Wagenaar et al.94 in their simulation of biomass pyrolysis in a rotating cone with Scheme B. The gas phase was not solved using CFD but by the so-called CISTR model and the interparticle interaction was not taken in account. The predicted variations of product yields with temperature were in good agreement with experiment. The radial distributions of wood conversion were analyzed. A step further was conducted by Simone et al.95 in their simulation of drop tube reactor to evaluate devolatilization kinetics parameters, where the gas phase field was solved by CFD. Johansen et al.96 also used this approach to derive kinetic parameters for Scheme A2 in an entrained reactor with high heating rate. The obtained kinetic constants were evaluated in the simulation of a bench-scale reactor. A similar work was done by Tchapda and Pisupati97 in the Euler−Lagrange simulation of biomass−coal pyrolysis in an high-temperature entrained reactor. The in-bed temperature and velocity were analyzed and the dependence of product yields on reactor temperature was studied. It was found higher temperature leads to lower tar yield. The effects of particle shrinkage were studied by testing two shrinking models in the DPM simulation of biomass pyrolysis in a falling reactor98 with Scheme A2. The results showed that the constant volume model predicts a faster pyrolysis and longer particle residence time than the constant density model. An Euler−Lagrange simulation was conducted by Li et al.99 for biomass pyrolysis in a gravity-driven reactor with nonspherical particles. The results were validated with experimental data on conversion rate and product yields. In later studies, the intraparticle transport phenomena were involved and coupled with the Lagrange motion of biomass particles. In the work by Hastaoglu and Hassam100 and Miller and Bellan,101 the intraparticle mass and heat transfer were solved and Scheme B and D were used for wood pyrolysis, respectively. The temporal evolution of conversion agreed very well with experiment and the effects of temperature, particle diameter, and particle size distribution, etc., were studied. The interesting trends found are that with the increases in gas velocity and wood feeding rate, both conversion and particle residence time decrease. A similar study was conducted by Luo et al.102 in their DPM study of pyrolysis in a fluidized reactor. The plug-flow assumption for gas velocity was adopted. The product yields along reactor height were presented and the effects of operating conditions were discussed. The intraparticle heat transfer was involved by an empirical model to differentiate the particle surface and mean temperature in the work by Rabinovich et al.103 The effects of temperature on the product yields of a single biomass particle were investigated. The dynamics of gas phase was solved using CFD in the DPM simulation of an entrained bed by Brown et al.,104 where the intraparticle heat transfer was still included. Through this approach, the temporal profiles of particle temperature and conversion were analyzed and validated with experiment. Similarly, in the discrete simulation of biomass pyrolysis in suspension between two walls, Russo et al.105 solved the gas turbulent flow using direct numerical simulation and the intraparticle heat transfer was obtained by a semianalytical model. The delay of pyrolysis due to the intraparticle heat transfer was revealed and the importance of two-way coupling was demonstrated.

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DISCRETE PARTICLE MODELING OF REACTOR-SCALE BIOMASS PYROLYSIS In the discrete particle model, each solid particle is modeled as a discrete object and tracked individually.93 Proper particle/ particle and particle/wall collision models are used to represent the interactions among solid particles and walls. The gas phase is still modeled in the same way as that in MFM. The movement of a solid particle in DPM can be expressed as mp

dUp dt

= −Vp∇pg + Fc + Ff + mpg

(5.1)

where mp, Up, and Vp are the mass, velocity and volume of a solid particle. Fc and Ff are the interparticle collision force and gas− solid interaction. The temporal evolutions of energy and species mass fraction of a solid particle can be derived similarly. It should be mentioned that in DPM, from the side of gas phase each solid particle is viewed as a point source with finite size and mass. As solid particles are individually tracked in DPM, the computational effort is majorly devoted to the solid phase and the overall computational requirement is proportional to the number of solid particles. But on the other hand, as in DPM the motion of each solid particle is directly solved, temporal evolution of every solid particle can be recorded and more measurements such as solid residence time can be easily obtained. As each solid particle is tracked in Lagrange way, some assumptions from MFM can be released for DPM simulation of biomass pyrolysis. For example, biomass particle diameter can vary with time, which means that shrinkage and breakage can be incorporated. It is worth noting that in most DPM simulations of reactor-scale biomass pyrolysis, solid particles are still assumed to be spherical. In the future, such assumption can be eliminated if 2791


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Figure 6. Discrete particle modeling of biomass pyrolysis in a bubbling fluidized bed conducted by Bruchmuller et al.106 (a) 3D reactor configuration and initial conditions; (b) temporal evolution of positions and temperatures of biomass particles (with permission from Elsevier for use in this paper). Copyright 2013 Elsevier.

Modeling Sand as Continuum. Though tracking all solid phases as discrete particles can provide more details on the inbed hydrodynamics, the required computational effort is still unaffordable for practical engineering applications. On the other hand, in a conventional fluidized bed for biomass pyrolysis, the number of sand particles is much higher than that of biomass particles and the major computational burden is on the tracking of sand particles. As sand is inert where no density and shape change will happen, it is postulated that modeling sand as continuum will not only save a large portion of time but also can produce engineering accurate description of sand dynamics. Therefore, Papadikis et al.109,110 proposed a novel approach that models both gas and sand as continua through the two-fluid model, whereas biomass particles are tracked individually with consideration of intraparticle heat transfer. The numerical formulations for gas and sand have been discussed previously and thus will not be repeated here. The movement of individual biomass particle is governed as

It can be seen in the above-mentioned DPM simulations of reactor-scale biomass pyrolysis, interparticle interactions were not considered because the overall solid volume fraction is very low. However, for reactors with solid concentration far from dilute, excluding interparticle interactions in DPM simulation can introduce significant errors. Rabinovich et al.107 modeled the interparticle collision using the spring-dash discrete element method (DEM) in their DPM simulation of pyrolysis of an ensemble of biomass particles in a fluidized bed. Through this approach, correlations of the residence time and conversion to the reactor height were first derived and then coupled with the numerical solution of the intraparticle transport to calculate the overall tar yield. A parametric study on the impacts of particle diameter, moisture content and temperature was conducted. A comprehensive CFD-DEM simulation of biomass pyrolysis in a laboratory-scale fluidized bed was done by Bruchmuller et al.106,108 with Scheme D. Around 0.8 million sand and biomass particles were tracked, and the drying process was involved. A very good agreement with experiment on product yields was achieved and a detailed description of hydrodynamics was provided, as shown in Figure 6. The influences of inlet nitrogen velocity, moisture content, and reactor temperature were quantified, and it was reported that the inlet nitrogen velocity is more important than the moisture content on the final tar yield. Such study provides us an unprecedented insight in the complex behavior of biomass pyrolysis in fluidized bed at particle level. In summary, as DPM tracks all individual solid particles, less empirical correlations on solids phases are needed and more detailed information at particle scale can be provided. Thus, compared with MFM, the modeling accuracy of DPM is obviously higher. At the same time, unlike MFM, systems with polydispersity and particle shrinkage can be modeled using DPM. However, currently using DPM to simulate an industryscale reactor, even a pilot-scale reactor, is still impossible as billions to trillions of solid particles need to be modeled discretely.

mp

⎛ f ρ⎞ du b = c (uc − ub) + g ⎜⎜1 − c ⎟⎟ + Fvc τc ρb ⎠ dt ⎝

(5.2)

where ub is the velocity of biomass particle, and uc and ρc are the velocity and superficial density of modeled continuum phase (either gas or sand). fc, τc, and Fvc are the so-called drag factor, velocity response time, and virtual mass force related to the modeled continuum phase. More details on the formulation choice of fc, τc, and Fvc can be found elsewhere.110 The impact of intraparticle heat transfer was considered by solving the 1D heat diffusion equation for an isotropic biomass particle. Scheme B was employed to model the wood pyrolysis. Through this Euler−Euler−Lagrange approach, the pyrolysis of one biomass particle inside a laboratory-scale fluidized bed was simulated.110,111 Temporal evolutions of velocity, density, and drag force, etc. were monitored until it was blown out of the reactor. Both 2D and 3D simulations were tested and compared, and a qualitative agreement between each other was achieved. 2792


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Table 2. Summarized Information on Computational Effort for each CFD Approach To Simulate Reactor-Scale Biomass Pyrolysis Approach

Reactor type

Reactor dimension

PMM

Fixed bed53

MFM

Fluidized bed33 Fluidized bed79 Downer reactor89 Vortex reactor91 Fluidized bed108

0.125 m in radius and 0.21 m in height 0.01905 m in radius and 0.3429 m in height a and 0.95 m in height

DPM a

0.0345 m in radius and 1.335 m in height 0.27 m in radius and 0.1 m in length 0.01905 m in radius and 0.3429 m in height

Physical time and time step

CPU time

4000 s and 2e−3 s

2500 s

100 s and 7e−4 s

7 days

a and 5e−3 s

133.7 h

2.5 s and 1e−3 s

20 days

Dual core 2.4 GHz AMD Opteron Dell Precision T7400 workstation 4-core HP Compaq 81100 Elite CMT 4-core 3.2 GHz

400 s and 5e−4 s

200 days

8-core AMD

12500

10 s and 2e−6 s

87 days

192-core 2.3 GHz HECToR Phase 2a

800000 particles

Computing node

Grid/particle number 16 control volumes for reactor and 8 control volumes for each particle 940 69294 30785

, not provided.

disadvantages. Using commercial software, researchers can spend more time to the physical analysis, but designing a user-specific case may be inconvenient. Performing CFD modeling of reactorscale biomass pyrolysis using open-source codes requires users to have a relatively deep background on programming and numerical algorithm, but a freedom to introduce user-specific functions and modifications can be achieved. Finally, this review also reveals that most CFD studies on reactor-scale biomass pyrolysis have been carried out in 1D or 2D, whereas very few efforts have been devoted to 3D. In 1D simulations, either the radial or axial direction is considered and hence the computational requirement is low. Because it overlooks the variations in other dimensions, the predictive accuracy is also relatively low. On the other hand, 1D simulation still has its value. If we are more concerned on the rough trend other than the accuracy, a quick 1D simulation may be still a good choice. 2D simulation improves the modeling accuracy compared to 1D as it is closer to the actual reactor geometry. Most recent CFD simulations of reactor-scale biomass pyrolysis have been performed in 2D as it is more computationally affordable compared with 3D. It is worth noting that for complex but real geometries, 3D simulation is still highly desirable. Moreover, from the physical point of view, the predictive capability for 2D is still below the level of 3D. Thus, accelerating the modeling speed to conduct full 3D simulations should be one of the main directions in the future.

The variations of product yields in the radial direction with respect to time were predicted. Following the above studies,110,111 a series of parametric studies were conducted on the effects of particle shrinkage,112 convective heat transfer coefficient,113 biomass particle size,114,115 particle sphericity,116 and gas−sand drag correlation.117 It was reported that for small-size biomass particles (less than 500 μm), shrinkage does not have a significant effect on both momentum transport and pyrolysis yields. An infinitely fast external heat transfer rate was found to result in faster biomass decomposition and shorter particle residence time than the Ranz−Marshall correlation.118 It was also found that the particle residence time of tetrahedral shape is lowest, whereas that of spherical shape is highest. In summary, treating the sand phase as continuum can significantly reduce the computational requirement as the amount of sand is usually much larger than biomass. However, a compromise to the saving of modeling time is the loss of details for the sand itself and biomass−sand interactions. It is worth noting that so far this approach has only been used to simulate systems with a few biomass particles, majorly oriented to fundamental investigations.

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CONCLUDING REMARKS In this Perspective, a comprehensive review of CFD modeling of reactor-scale biomass pyrolysis is given. The topic on the reaction kinetics to model the biomass devolatilization process is summarized. Activities using PMM, MFM, and DPM to simulate biomass pyrolyzers are discussed in detail. For each of these three CFD models, their strengths and weaknesses for CFD modeling of reactor-scale biomass pyrolysis are reviewed. Table 2 summarizes available information such as reactor dimension, type, CPU time, computing node number, grid number, etc. in the literature for PMM, MFM, and DPM, which is more convenient to evaluate each approach for CFD simulation of reactor-scale biomass pyrolysis. During the early stage of CFD modeling of reactor-scale biomass pyrolysis, most studies used programmed in-house codes with MATLAB, Fortran, or C. In the last 2 decades, with the rapid development of commercial software, nearly half of CFD simulations of reactor-scale biomass pyrolysis have been performed with the help of popular commercial software, such as ANSYS Fluent and CFX. At the same time, an increased number of open-source codes such as MFIX and OpenFOAM have been released, based on which a lot of CFD simulations of reactorscale biomass pyrolysis have been conducted. Commercial software and open-source codes both have their merits and

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PERSPECTIVE In the future, a very urgent direction for the development of CFD modeling of reactor-scale biomass pyrolysis is to improve modeling accuracy. For MFM, more studies are needed to consider properly the effects of subgrid structures such as particle clustering, size distribution, and temporal changes of particle size and shape. For DPM, developing engineering accurate but computationally economic submodels to include the effects of intraparticle transport phenomena is the main direction in the future. In additional, continued effort on accelerating the modeling speed for DPM is crucial as this model is highly computationally expensive. Advanced computer architectures such as graphical processing unit computing and many-in-core skill can be employed as these methods are very suitable to discrete modeling. It is worth noting that with the increased computational power in recent years, a few attempts have been oriented to simulate biomass pyrolyzers by the so-called particle-resolved direct numerical simulation approach.119−122 In such studies, the grid size to simulate gas flow is below the particle scale. Intraparticle 2793


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ACS Sustainable Chemistry & Engineering transport phenomena are directly coupled with surrounding gas flow to substitute artificial boundary conditions at particle surface. Thus, it is postulated that this modeling approach can provide the most accurate information within the border of CFD. However, because of the tremendous computational requirement, this approach seems infeasible in the foreseeable future to simulate a practical pyrolyzer. Nonetheless, this approach should be very useful to conduct fundamental studies to deepen our knowledge of biomass pyrolysis at fine scales as well as to derive accurate submodels for PMM, MFM, and DPM.

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Dr. Yang Yang is an engineer with Corning Incorporated, USA. He received his B.S., M.S.E., and Ph.D. degrees all in Civil Engineering from Tongji University, China (2008), Syracuse University, USA (2011), and Johns Hopkins University, USA (2016), respectively. His research focuses on topology optimization for eigenvalue problems.

AUTHOR INFORMATION

Corresponding Authors

*Email: [email protected] (K. Hong). *Email: [email protected] (G. Lorenzini). *Email: [email protected] (S. Wang). ORCID

Qingang Xiong: 0000-0002-8484-6163 Jingchao Zhang: 0000-0001-5289-6062 Shurong Wang: 0000-0001-6733-3027 Notes

The authors declare no competing financial interest. Biographies

Fei Xu is a Ph.D. candidate at the Department of Mechanical Engineering, Iowa State University. He received his B.S. (2011) from the Xi’an Jiaotong University, China, and M.S. (2014) from Beihang Univerisity, China. His current research interests include finite-element based fluid−structure interaction simulations, high-performance parallel computational fluid dynamics, isogeometric analysis for complex geometries, and heart valve modeling and analysis.

Dr. Qingang Xiong is a senior engineer with Corning Incorporated, USA and an adjunct professor of Huaiyin Institute of Technology, China. Dr. Xiong received his Ph.D. degree of Chemical Engineering in 2011 from Institute of Process Engineering, Chinese Academy of Sciences, majoring in high performance computing aided computational fluid dynamics (CFD) simulation of multiphase flows. After graduation, Dr. Xiong continued his academic career as postdoctoral research associate in Iowa State University and Oak Ridge National Laboratory, conducting multiscale CFD simulation of biomass pyrolysis and heterogeneous catalysis. Dr. Xiong has published more than 30 scientific

Yaoyu Pan is currently a Ph.D. student in Mechanical Engineering at Iowa State University. He received B.E. and M.E. degrees in the School of Energy and Power Engineering from Huazhong University of Science and Technology, China, in 2012 and 2014, respectively. His main research interest is the numerical study of multiphase and multiscale fluid flow coupled with heat and mass transfer.

articles in prestigious peer-reviewed journals. Dr. Xiong’s main research interests are CFD simulation on multiphase catalytic flows, CFD model and algorithm development, biomass thermochemical conversion, heat and mass transfer. 2794


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Prof. Dr. Giulio Lorenzini received a Master’s Degree in Nuclear Engineering at Alma Mater Studiorum-University of Bologna (Italy) in 1994 with a “cum laude” evaluation. Professional State Examination was awarded in 1995 with an evaluation equal to 120/120. In 1999, he obtained a Ph.D. in Nuclear Engineering. From 1996 to 2005, he was a researcher in “Fisica Tecnica Industriale” at Alma Mater StudiorumUniversity of Bologna. From 2005 to 2010, he was an associate professor of “Fisica Tecnica Industriale” at Alma Mater Studiorum-University of Bologna (Italy). From 2010 to today, he is a full professor of “Fisica Tecnica Ambientale” at the Department of Industrial Engineering of the University of Parma (Italy).

Jingchao Zhang received his B.S. (2010) from the Department of Thermal and Power Engineering of Shandong University of China. In 2013, he graduated with a Ph.D. degree from the School of Mechanical Engineering of Iowa State University. At present, he is an HPC applications specialist at University of Nebraska−Lincoln Holland Computing Center. His current research interests include 2D atomiclayer interface energy transport, energy transport in heterostructures, and new nanoscale thermal probing to achieve atomic-level resolution. He is a campus champion for Extreme Science and Engineering Discovery Environment (XSEDE) and a certified instructor for Software Carpentry (SWC). Prof. Shurong Wang received a Ph.D. in Engineering Thermal Physics from Zhejiang University in 1999. He is the first awardee of the First Class Prize of Natural Science Award by the Ministry of Education for the project “High-quality liquid fuels production from the directional thermo-chemical conversion of biomass followed by the graded upgrading of bio-oil” in 2016. He is also the first author of a monograph in English published by De Gruyter in 2016 and a monograph in Chinese published by Science Press in 2013, the publishing of both was supported by the Scientific Publishing Funds of Chinese Academy of Sciences. He has also been included on the list of “Most Cited Chinese Researchers” in the energy field. His research interests include biomass pyrolysis, biomass liquefaction, bio-oil upgrading, biochar utilization, and alternative fuel synthesis.

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Dr. Kun Hong is an associate professor of Huaiyin Institute of Technology, China. Dr. Hong received his Ph.D. degree of Chemical Engineering in 2013 from Institute of Process Engineering, Chinese Academy of Sciences, majoring in modelling and simulation of multiphase flows. Dr. Hong has published more than 10 scientific articles on multiphase flows. Dr. Hong’s main research interests are the formulation for gas−solid fluidization and its application in chemical reactor.

ACKNOWLEDGMENTS This work was financially supported by the Externally Collaborative Project from State Key Laboratory of Clean Energy Utilization, Zhejiang University, China under Grant No. ZJUCEU2017011, the National Natural Science Foundation of China under Grant No. 21406081, and the Natural Science Foundation of Jiangsu Province under Grant No. BK20130420.

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