Integrated Particle- and Reactor-Scale Simulation of Pine Pyrolysis in

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Biofuels and Biomass

Integrated Particle- and Reactor-Scale Simulation of Pine Pyrolysis in a Fluidized Bed Brennan Pecha, Emilio Ramirez, Gavin M. Wiggins, Daniel L Carpenter, Branden Kappes, Stuart Daw, and Peter N. Ciesielski Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b02309 • Publication Date (Web): 04 Sep 2018 Downloaded from http://pubs.acs.org on September 7, 2018

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Integrated Particle- and Reactor-Scale Simulation of Pine Pyrolysis in a Fluidized Bed

M. Brennan Pecha1, Emilio Ramirez2, Gavin M. Wiggins2, Daniel Carpenter1, Branden Kappes3, Stuart Daw2, Peter N. Ciesielski1*

1

National Renewable Energy Laboratory, Biosciences Center, 1503 Denver W. Parkway, Golden, CO 80401, United States

2

Oak Ridge National Laboratory, 2360 Cherahala Boulevard, Knoxville, Tennessee 37932, United States

3

Department of Mechanical Engineering, Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, United States

1

ACS Paragon Plus Environment

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Abstract

We report results from a multiscale computational modeling study of biomass fast pyrolysis in an experimental laboratory reactor that combined the hydrodynamics predicted by a two-fluid model (TFM) with predictions from a finite element model (FEM) of heat and mass transfer and chemical reactions within biomass particles. The experimental pyrolyzer consisted of a 2-inch (5.1 cm)diameter bubbling fluidized bed reactor (FBR) fed with milled pine pellets. The predicted FBR hydrodynamics included estimates of the residence times that the gas and biomass particles spend in the reactor before they exit. A single-particle FEM model was constructed based on geometry and heat transfer properties determined from optical and X-ray Computed Tomography (XCT) measurements of wood and char particles collected from the experimental FBR, along with previously proposed pyrolysis reaction kinetics. Taken together, the combined TFM and FEM simulation results predicted net bio-oil yields at the reactor exit that agree well with experimental observations, without any arbitrary fitting parameters. The combined computational models also provided practical information about the most important reactor and feedstock parameters.

Keywords Multiscale modeling, biomass pyrolysis, pine, single particle model, reactor model

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Table of contents graphic

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1 Introduction

Today, pyrolysis is recognized in the renewable energy sector as a viable pathway for renewable biofuels, chemicals, and high value carbonaceous materials 1. Although the technology is very popular, it is difficult to implement at industrial scales due to the complex, heterogeneous nature of biomass feedstocks and multiphase reactor operations 2. Nearly every aspect of pyrolysis impacts the product yields: temperature, heat transfer mechanism, collection system, biomass composition and microstructure, ash content, particle size, and others 3. The goal of fast pyrolysis is to produce bio-oil via rapid heat transfer (>100 oC s-1) to biomass particles (like wood) in the presence of an inert carrier gas which quickly removes condensable vapors and non-condensable product gases. Once condensed, the vapors produce high yields of liquefied bio-oil. Many different types of biomass pyrolysis reactors have been proposed and tested over the years, but one of the most common reactor designs used in laboratory studies is the fluidized bed reactor (FBR), in which biomass particles are injected into a preheated bed of denser particles (such as sand) that are ‘fluidized’ by an upward flowing gas into a state that is similar to a boiling liquid 4. To determine optimal reactor configurations and operating conditions, many researchers have developed reasonable models to predict for heat transfer, chemical reactions, and mass transfer for this type of reactor5-6, but often experimental correlations are used in scaleup due to the complexity and unreliability of these models.

1.1 Challenges to modeling biomass pyrolysis phenomena

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There are essentially three length scales at which pyrolysis models have been developed: the reactor scale 7, the single particle scale 8, and the chemical species level 9. Global or semi-global scale models (i.e., empirical kinetics devoid of spatial parameters) have also been developed 10-11. Global kinetic modeling is the simplest to perform because of the lack of coupling to transport phenomena, which are critical in determining process outcomes in many pyrolysis scenarios. Coupling models of transport phenomena to detailed chemical reaction mechanisms often require large computational resources which can surpass capabilities of current day high performance computing resources, although some progress has been made recently with lumped-species mechanisms and particle-scale physical models 12-14.

1.2 Simulating realistic particle geometries

Single particle models have emerged as a useful approach for studying feedstock-specific effects and other physical phenomena commonly observed in the laboratory. Given sufficient feedstock characterization data such as particle size, shape, composition, density, and porosity, such models may be parameterized for specific biomass feedstocks. This facilitates a more accurate representation of the inherent biocomplexity and heterogeneity of real biomass feedstocks and can thereby account for species-specific variations in conversion outcomes 15. In addition, modeling pyrolysis at the single-particle level allows for insights into the interconnected phenomena of heat transfer, mass transfer, and conversion kinetics in the context of physically accurate geometries using well-studied engineering approaches.

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One of the most important findings from laboratory studies of biomass fast pyrolysis is that the highest yields of bio-oil are achieved when the feed particle size is less than 0.5 mm 16. For each reactor, there is an optimal particle size which achieves the highest yields within the respective operating envelope. It has been proposed that this trend is due to the faster heat and mass-transfer rates that can be achieved with the relatively large surface-area to volume ratios of the smaller biomass particles

17-18

. Similarly, due to the anisotropic exterior geometry which is typical of

biomass particles, higher particle aspect ratios (D/L) provide a higher yield of bio-oil 19.

Recent technological advances have allowed for the use of X-ray computed tomography (XCT) to characterize µm-scale macroporosity in materials like wood and char. While the technique has been used for coal as early as 1996 (as reviewed by 20), only recently has it been used for biomass. The development of XCT, starting in the early ’70s

21

, did not evolve to the low-micrometer

resolution until the late 1980s, when the use of the current scintillation-type detector was coupled to a charge-coupled device to achieve resolutions of 2.5 µm

22-23

. Improvements in computing

power, lab source brilliance, and data collection techniques have steadily improved the resolution of lab-scale XCT to 50 nm (e.g., Zeiss Ultra-series X-ray microscopes) and 350 nm (e.g. Zeiss Versa-series X-ray microscopes). 24 used XCT to study porosity characteristics of various biochars, finding that pore size did not change dramatically with temperature but that the original properties of the biomass were strong predictors of the properties of the char. Similar findings were reported by 25. XCT of chars from gasification by 26 showed that they preserved the same pore structure as its biomass starting material, and that this anisotropy forced reaction progression to primarily occur along the direction of the grains rather than perpendicular to them. Thus, it is critical to represent

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the anisotropic porosity and permeability in pyrolysis simulations to realistically represent experimental observations.

Biomass particles typically exhibit anisotropic exterior geometries. In one early study, Di Blasi implemented a model that includes particle multi-dimensional shrinking, convection, conduction, and radiation, multi-step kinetics, and moisture evaporation 27. However, the kinetic scheme was not well-verified. Bellais et al. developed a cylinder model for slow pyrolysis with secondary reactions, but did not include gas species 28. Another work studied the effect of particle size and aspect ratio on the yields of products from pyrolysis, though it did not include water evaporation or mass transfer by diffusion and particle shrinking is not included 19. Paulsen et al. developed an interesting model to track carbohydrate degradation inside the particle, but used an isotropic model, ignored secondary reactions, and did not include diffusive mass transfer 29. Some studies even hypothesized a liquid-phase pyrolysis intermediate that bubbled as it vaporized 30-33.

1.3 Multiscale CFD modeling approach

Fluid bed pyrolyzers are commonly used in laboratory and industrial settings to take advantage of the constant flow of gas over all particles which allows for high heat transfer from gas to solids 4

. The gas and solids mixing and contacting patterns in fluidized bed reactors are extremely

complex. Computational fluid dynamics (CFD) provides a way to simulate the spatiotemporal details about pressures, velocities, flows, and concentrations that are either impossible or extremely difficult to obtain experimentally 34. CFD has also been utilized in numerous studies of gas-solid fluidized beds 35, but very limited number of biomass fast pyrolysis CFD studies have 7


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addressed hydrodynamic effects 7, 36-39. It is generally not feasible to directly simulate the motion of all the individual particles in FBRs due to limits of current computers and the high computational cost of tracking the thousands or millions of particles present in typical reactors. Thus more approximate methods, such as the two-fluid modeling (TFM) approach, have been pursued as a way to reduce computational overhead 40. A recent TFM modeling work illustrated the importance of using multiple particle sizes for accurate pyrolysis conversion times, though it did not account for intraparticle gradients advance pyrolysis reactor simulations

41

42-43

. In addition, recent reviews have detailed efforts to

, specifically methods for linking the physics and

chemistry at different spatial and temporal scales to achieve the correct global reactor performance 44

. However, one remaining topic that has received little attention is the mixing behavior of non-

spherical biomass particles and their residence times in FBRs.

In this work we present a multiscale integration approach similar to that previously employed by Wurzenburger and colleagues 45 wherein reactor- and particle-scale modelling approaches are combined to provide an approximate description of the key physics and chemistry while reducing the computational overhead. In this work, TFM predictions of the hydrodynamics were used to estimate particle and gas residence times, which were then combined with single-particle simulations of realistic biomass particle evolution during pyrolysis to estimate the overall yield of bio-oil for typical laboratory FBR conditions. We expect that this type of approach to integrated, multiscale modelling represents an exemplar computational framework that will enable many types of rapid screening simulations to estimate and interpret the effects of key experimental pyrolyzer parameters such as geometry, carrier gas flow rate, temperature, and residence times as well as realistic biomass feedstock parameters like species and size-shape distributions. 8


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2 Experimental Context

2.1 Fluid bed pyrolyzer

The experimental 2-inch fluid bed reactor (2FBR) at the National Renewable Energy Laboratory (NREL) was used as the basis for the computational simulations of biomass fast pyrolysis studied here. The key relevant features of this reactor are illustrated in Figure 1. While the 2FBR is part of a more extensive experimental research reactor system that includes two FBRs: an initial one for pyrolysis and a second one for catalytic vapor phase upgrading, the focus of this paper is on the first reactor (pyrolyzer) . In this reactor, biomass particles and fluidizing gas are fed into a bubbling fluidized bed of hot sand, and the wood is thermally decomposed into gas, tar, and char products, which exit from the top of the reactor. Figure 1 provides general information about the reactor geometry and typical operating conditions. More information about the reactor system is available in the online documentation at https://ccpcode.github.io/docs-2fbr/ and Howe et al. 46.

In the experiments of interest here, the biomass under study was pine wood that was initially pelletized and then crushed and sieved through a 2 mm screen before being fed into the FBR. Each pyrolysis experiment was run at a reactor temperature of 500 °C, with the wood feed rate set at 1 kg/h for 2.5 h to achieve stable operation so that steady-state mass balances could be made. The initial sand charge remained in the reactor throughout each experimental run.

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Figure 1: (Left) Schematic illustration of geometry and operating conditions of the 2-inch fluidized bed reactor (FBR) system for thermochemical conversion of biomass. As biomass particles are conveyed through the pyrolyzer and elutriate from the top, they are converted into light gases, bio-oil, and char to varying degrees depending on their residence times. The sand particles comprising the bulk of the fluidized bed, are large and dense enough to remain in the reactor. (Right) Example of conceptual control volume and boundary conditions described below for use in the 2D axisymmetric single particle simulations.

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2.2 Biomass feedstock properties

Statistical properties for the external size and shape of the as-fed biomass particles and char particles collected from the FBR experiments were optically determined by MicroTrac, Inc. using their PartAn 3D analyzer, which makes 3-dimensional images of particle samples and tabulates relevant parameters for up to hundreds of thousands of particles

47

. The data provided by

MicroTrac was processed and binned in Matlab to summarize statistics that could be used to construct representative ‘nominal’ particles in the particle-scale simulations described below.

Internal microstructures of the biomass feed particles were characterized with X-ray microcomputed tomography (CT) using a ZEISS Versa 520 X-ray microscope (XRM) or X-ray CT (XCT) operating at 30 kV, 2 W (67 micro amps) using the 4x lens, no filter. This produced threedimensional CT images of the internal structure with a voxel size of 3.4 µm such as those depicted in Figure 5 by convolving the information from 1600 to 2400 two-dimensional X-ray images as particle samples were rotated through 360 degrees.

3. Computational Approach and Methodology

3.1 Reactor hydrodynamics simulations

The global three-dimensional hydrodynamics of the NREL 2FBR pyrolyzer were simulated with MFIX (Multiphase Flow with Interphase eXchanges)

48

, which is an open source computational 11


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fluid dynamics (CFD) package specifically developed for modeling multiphase reacting flows. In this case, the specific objective was to use the two-fluid approximation to estimate the residence times of biomass and char particles, respectively. Values of the MFIX input parameters used for these simulations are summarized in Table 1. The Sauter mean diameters calculated from the 3D particle Feret lengths were 58, 278, 344, 426, and 543 E-4 cm. When simulations were run with a density of 500 kg/m3, the fresh wood particles were found to have an infinite residence time (they elutriated to the bottom of the reactor) until they convert to char with a density of 80 kg/m3. Thus, running a larger wood density would not change the effective particle residence time, which was determined using Eq 18, where Uwood is 0 and Uchar was calculated to have a known value so the results from initial density of 500 kg/m3 were used.

Table 1. Key input parameters for two-fluid reactor scale simulation in MFiX Property Value Units particle diameter (sand)

500 × 10-6

m

particle density (sand)

2500

kg/m3

particle density (wood)

500

kg/m3

particle density (char)

80

kg/m3

temperature

773

K

pressure (inlet)

133

kPa

fluidizing N2 (range)

0.249

m/s

minimum fluidization

0.0565

m/s

coefficient of restitution

0.9

-

angle of repose

55

°

friction coefficient

0.1

-

12


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The two-fluid approach approximates the gas and solid phases as interpenetrating continua. The conservation equations solved for the gas and solid phases for mass, momentum, and species in the fluid domain are respectively given by:   g  g      g  g ug   0 t

(1)

   s  s       s  s us   0 t

(2)

  g  g ug      g  g ug ug     g   g Pg   g  g g   ug  usm  t

(3)

  sm smusm      sm smusmusm     sm   smPg   sm sm g    ug  usm  t

(4)

  g  g X g ,n      g  g X g ,nug     Dg ,nX g ,n t

(5)

  sm sm X sm,n      sm sm X sm,nusm     Dsm,nX sm,n t

(6)

where g, s, sm, gn, smn describe, respectively, gas, solid, solid phase m, gas phase species n, and solid phase m species n and where ε is the volume fraction, ρ is the density, u is the velocity vector, τ is the stress tensor, P is the pressure, X is the mass fraction, D is diffusion coefficient, and β is the coefficient for the interphase force between solid and gas phases. The gas-solid momentum transfer, in the interphase force, utilized the Syamlal-OBrien49 correlation for the drag model. To model solids transport properties, such as solids pressure and viscosity, the kinetic theory of granular flow48 together with the Schaeffer frictional stress tensor formulation50 and the sigmoidal blending stress51 function relate the computed solids temperature with solids transport properties. Furthermore, the numerical simulation utilized the species equation for tracking purposes and there were no chemical reactions. Separate mass inlet boundary conditions were

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applied for the gas distributor and the biomass inlet. A pressure outlet boundary condition defined the outlet at the top of the reactor and wall boundary conditions were treated as no slip.

MFIX is capable of simulating extremely complex systems of multiphase reacting flows. The specific conditions in the 2FBR pyrolyzer make some important simplifications possible that reduce the computational demand. Specifically, the 2FBR is operated such that temperature gradients are minimized and the large excess of diluent fluidizing gas relative to the biomass vapors makes it possible to neglect volume changes in gas flow through the reactor.

Biomass and char residence time distributions and average velocities in the reactor were determined in this study by seeding the simulated biomass inflow stream with tracer particles having the density of either raw biomass or devolatilized char. Runs were completed using five different particle sizes (at a single fluidizing gas flow) to determine how the average particle residence times were affected by particle size. For estimating drag properties, the biomass and char particles were treated as having spherical shapes corresponding to the Sauter mean diameter (d32) calculated from the Feret lengths from the MicroTrac particle analyses. The exit times out the top of the reactor were used to create biomass particle residence time distribution (RTD) curves, which were then averaged to generate mean transit times.

In order to determine steady-state particle residence times, it was necessary to continue MFIX computations for ~8 seconds beyond initial start up reach a condition where the statistics were stationary. Figure 2 shows a cross section of the 3D reactor simulation for char flowing through

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the fluidized bed with sand. Based on this result, total simulation times of 20 s were utilized to generate statistics for which transition effects could be eliminated 34.

Figure 2: Reactor hydrodynamic simulation: Cross sectional image of char flowing through the fluidized bed reactor with sand

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3.2. Particle-scale pyrolysis simulations

Our particle-scale biomass pyrolysis simulations accounted for previously proposed reaction kinetics coupled to transient, intra-particle differential energy, species, and momentum balances for the particle characteristics described above. Darcy’s Law was used to describe the anisotropic fluid transport within porous biomass particles, in which porosity is represented as a continuum. The resulting differential balances are summarized by:

 CP

T    (k T )   CP u  T  Q t

ci      Di ci   u ci  Ri t

(7)

(8)

 u u  2 T      u     p      u   u       u  I     p  3    t

(9)

     u   0 t

(10)





The above differential balances can be restated as:

 CP eff p

T  keff   (T )   CP u T  Q t

ci      Di ci   u ci   Ri t

  u u    u       p  t p   1  2   u  T    p       u   u       u  I     κ ij 1  2  u 3  p       p 





(11)

(12)

(13)

16


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 p  t

     u    Ri

(14)

where ρ is the density, CP is the heat capacity at constant pressure, T is the temperature, k is the thermal conductivity, u the velocity vector, Q is the heat generation term, ci is the concentration species i, Di is the diffusion coefficient for species i, ∑Ri is the sum reaction rates for species i, εp is the porosity, µ is the viscosity, I is the identity matrix, κij is the permeability tensor, and the superscript T in equation 3 denotes the transpose operator. The mass source term ∑Ri represents the reactions from solid wood to vapor products throughout the porous medium. Radiation at the particle surface followed the Stefan-Boltzmann equation with surface emissivity 0.9 and ambient temperature 500 °C. Heats of reaction (Table 2) were applied through the volume of the particle. At the particle surface, the boundary condition is simply equal flux such that heat, mass, and species flux is the same on both sides. A fixed pressure outlet boundary condition follows the equation:



 p   u   u 

T

  32    u    p

0

(15)

The intraparticle generation and reaction of pyrolysis vapors were simulated with finite element integration of the differential balances assuming a relative gas flow traveling over each particle surrounded by a control volume as illustrated in Figure 3. As explained below, the kinetics used in this study included both ‘primary’ reactions for the initial release of vapors from the solid phase and ‘secondary’ gas-phase reactions of the released vapors to create the final products. To account for secondary vapor-phase reactions outside the particles with minimal computational effort, the released vapors were assumed to have an average total residence time in the reactor based on the time required for the fluidizing gas to transit from the middle of the bed through the entire

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freeboard. This was determined to be about 1.4 s from the fluidizing gas conditions used for the experiments of interest.

Figure 3. Illustration of the particle-scale modeling approach to account for both primary and secondary pyrolysis reactions.

Integration of differential biomass particle balances is complicated by the change in each particle’s properties as it transits through the reactor and converts from a raw state to char as illustrated in Figure 1. The limiting average rise velocities and associated residence times for particles of raw biomass and fully devolatilized char in the depicted computational control volume (denoted U) were determined from the reactor-scale model for each particle size of wood and char as described above. As wood is converted to char, the effective rise velocity increases as the particle density decreases. Thus for particle-scale simulations, Ui can be effectively estimated by scaling between the limiting rise velocities for each particle size i based on conversion (such that the smallest wood size was converted to the smallest char size, etc.), as summarized by: U i ,t  (1   )U i ,wood  U i ,char

  1

 wood  wood

(16) (17)

0

where ρwood is the density of partially converted biomass and ρwood0 is the initial density of biomass before it reacts. 18


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The varying average particle rise velocities can be related to the average particle residence times in the reactor (tR) by numerically integrating the particle velocity with respect to time until the particle moves completely through the reactor to reach the exit at the top (Lt): tR

Lt   U i ,t dt

(18)

0

Thus when the limiting particle rise velocities for raw biomass and fully devolatilized char are available (e.g., from reactor CFD simulations) , it is possible to use the above relationship to relate the average residence time of each particle to its average conversion at the reactor exit.

Table 2: First order Arrhenius reaction rate parameters for pyrolysis reactions Ai (s-1) Ei (kJ/mol) ΔH (kJ/kg)

1 (bio-oil)

2 (gas)

3 (char)

4 (2ndary char)

5 (2ndary gas)

1.08E10 11 148 11 255 55

4.38E9 11 152.7 11 -20 55

3.75E6 11 111.7 11 -20 55

1.0E5 52 108 53 -42 56

4.28E6 53 108 53 -42 56

Moisture desorption 5.13E6 54 87.9 53 2700

The reaction rates used to track particle mass loss and complete the differential particle balances utilized the Arrhenius kinetics developed by Di Blasi

11, 52

, which track the conversion of pine

wood into char and lumped product classes of light gases and condensable vapors (often referred to as “tar” or “bio-oil”). These kinetics also account for secondary reactions in which condensable vapors crack to light gases and recondense to form additional char

53

. Moisture desorption is

represented as an Arrhenius reaction for bound water 54. The Arrhenius reaction rate parameters utilized in the present study are provided in Table 2.

The initial water content in all the simulations was assumed to be 1%, based on compositional analysis of the feedstock. The porosity, permeability, and density were calculated at each time step 19


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and varied with the conversion of wood into condensable vapors (bio-oil), light gas, and char. Other properties were held constant, including gas viscosity, thermal conductivity of solid, and specific heat capacity. The viscosity of the gas and any vapor was assumed to be 1E-5 Pa s, the viscosity of H2 at 300 °C. Thermal conductivity of wood cell wall (also for char in this study) is 0.41 W / m K 57. The heat capacity of wood was taken as (0.1031+0.003867*T[1/K]) kJ / kg K 58.

Table 3: Parameters used in the single particle simulations of pine pyrolysis Parameter Cpwood Cpchar Dbio-oil DN2 ε0 ksolid kgas kH2O,L kH2O,V Kchar,axial Kchar,radial Kwood,axial Kwood,radial Mc0 P0 Ti Tr ρwood

Description Heat capacity of wood cell wall Heat capacity of char wall Diffusion coefficient of naphthalene at 500 °C Diffusion coefficient of nitrogen at 500 °C Porosity of wood Thermal conductivity of cell wall Thermal conductivity of gas (steam at 500 °C) Thermal conductivity of liquid water Thermal conductivity of water vapor at 500 °C, 1.44 atm Axial permeability of char (5x wood) Radial permeability of char (5x wood) Axial permeability of wood, scaled by porosity factor (εwood/ 0.65) Radial permeability of wood (Kwood,axial/17600) Initial moisture content Average pressure above reactor sand bed Initial temperature of particle at t = 0 Temperature of reactor and carrier gas Density of wood/char cell wall

Value 0.1031+0.003867*T kJ kg-1 K-1 1003.2+2.09*(T-273.15) J kg-1 K-1 0.3 cm2 s-1

Reference

1 cm2 s-1

60

0.276 0.41 W m-1 K-1 0.067 W m-1 K-1

57

59 58 60

0.61 W m-1 K-1 0.067 W m-1 K-1 1.17E-10 m2 6.67E-15 m2 4.69E-12 m2

61

2.67E-16 m2

62

61 62

1% 109 kPa 298 K 773 K 1500 kg m-3

The value for gas permeability in non-pelletized wood was 11.2 darcy units in the longitudinal direction and 4.6E-4 darcy units in the radial/tangential direction 62. Permeability scales with pore size to the power of 2 63-64. For char, the permeability was taken as that for wood multiplied by a

20


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factor of five 61. The permeability was reduced by a factor of 4 for the pelletized wood based on pore size analysis. The permeability at a given time at a point in the particle is determined by: Kt 

K wood ,t wood

wood



Kchar ,t (1  wood )

wood

0

(19)

0

where K is permeability, wood is the density of wood evolving with conversion, and  wood is the 0

initial density of wood in the particle. Additional key parameters used in the single particle model are shown in Table 3.

The bulk diffusivity (Di) of bio-oil was taken to be the diffusivity of naphthalene in nitrogen at 500 °C, and gases and nitrogen to be nitrogen at the same temperature 60. Inside the particle, the effective diffusivity Deff,i is based on the bulk diffusivity and porosity65: Deff ,i 

 D  i

(20)

where ε is porosity and τ is tortuosity calculated by the Millington and Quirk model 66 to be 

1/3

.

The initial porosities of non-densified pine and poplar are 0.65 and 0.80, respectively, based on cell wall density of 1500 kg/m3 and reported bulk wood densities

58

. Based on the X-ray

tomography data, the initial porosity of our densified pine is 0.270. The porosity at a given time at a point in the particle follows Eq. 1.15. The impact of moisture content on the porosity was ignored because the rate of pyrolysis reactions are negligible below ~350 °C.

  1

 0 (  wood  char )  wood

(21)

0

where ε is porosity and char is the density of char. The density of the solid porous matrix was calculated by the following equation: 21


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solid  wood  char  H O 0

2

(22)

L

The density of the vapor/gas was calculated by the following equation (Dalton’s law):

 gas 



p Cgas  Ctar  CN2  CH 2OV RT



(23)

where R is the universal gas constant, p is pressure, T is temperature, and Ci is the molar concentration of each species. Reaction-coordinate dependent shrinking was accounted for in the reactor-scale model, but not in the single particle model because it has been previously shown to have little impact at the particle scale 38.

3.3 Canonical ensemble yield calculations

As discussed above, the as-fed biomass particles had a relatively wide range of sizes. To account for this, five representative particle sizes were identified to span the range of experimental sizes for reactor and particle-scale simulations. Results for each size were then used to compute volumeweighted averages for the expected bio-oil, gas, char, and unconverted biomass yields predicted by the models that could be compared with experimental measurements. This is summarized below, where y is the yield of bio-oil, char, gas, secondary char, or biomass (wood), V is the volume fraction, and i is the bin number. i 1:5

y   Vi yi

(24)

bins

22


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4. Results and discussion

4.1. Experimental feedstock analysis

The as-fed biomass particles have a relatively wide range of sizes, even for the milled pellets which have been screened to 2 mm prior to feeding, as can be seen in Figure 4. It also appears that the char particles exiting the reactor are typically much smaller than the as-fed wood particles, with the most populous particle size dropping from 2.3 mm to 0.4 mm in length. We speculate that the harsh environment of the sand fluidized bed likely causes significant attrition and fragmentation of the biomass particles as they transit the reactor.

Figure 4: A. Biomass and char particle size distributions from the MicroTrac measurements. For convenience in the simulations described below, these distributions were split into five bins spanning the sizes and shapes present in the feedstock. B. Mean width and thickness of particles for their respective particle ferret lengths used to construct single particle representations.

The XCT results provided porosity and pore structure, as well as a basis for estimating the effective thermal conductivity of the milled and pelletized biomass particles because the thermal conductivity of woody biomass is linearly dependent upon its specific gravity, which is dictated 23


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by the presence of void space resulting from the cellular tissue structure of biomass. 58 This dependence is illustrated for example woody materials in . Thus we were able to estimate the original particle thermal conductivity from the XCT images using the interior void fraction (denoted fv) that was revealed by dividing the filled tomographic volume of the particle interior by the total volume encapsulated by the exterior particle boundary. Since the specific gravity of cell wall is largely constant at ~1.5 among wood species 𝑔

estimated as 𝜌 = (1 − 𝑓𝑣 )1.5 [

𝑐𝑚3

67

, the total density of the particle was

]. The thermal conductivity was then estimated using the linear

expression shown below the fitted line in Figure 6 by generating a weighted average of the void space domain (kgas) and the solid domain (ksolid) in the particles.

Figure 5. X-Ray computed tomographic data comparing the structure of milled (left) and pelletized/crushed (right) pine feedstock particles.

24


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Energy & Fuels

Another useful result from the XCT analyses is that the feed preparation steps used in the experiments increased the effective feed particle density from ~540 kg/m3 for the milled pine to ~1096 kg/m3 for pelletized pine. This corresponds to changes in the effective thermal conductivity from 0.12 to 0.23 W/(m·K) for the milled and pelletized feedstock, respectively. While it has been speculated that densification of biomass during pelletization can impact the particle microstructure and internal particle transport properties, this is the first time to our knowledge that the phenomenon has been experimentally verified.

Figure 6. Linear dependence of thermal conductivity on specific gravity 58.

Milling also appears to have an impact on the distribution of biomass particle aspect ratio. Specifically, the milling process used in the NREL experiments produces relatively high-aspect 25


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particles that preserve the inherent directional microstructure. In contrast, XCT images of the pelletized feed particles appear to suggest more of a random agglomeration of smaller particles, which has less net directional character.

4.2. Reactor scale modeling

CFD modeling of the 2FBR with MFIX was used to estimate the residence times of biomass and char particles to provide heating and reaction time scales that could be applied to particle-scale pyrolysis simulations. Specifically, the simulated behavior of selected sizes of raw biomass and char particles were used to determine the RTDs and mean reactor transit times for each particle class. Table 4 and Figure 7 summarize the results for both raw biomass (pine wood) and char particles at various sizes, based on the particle size distribution from the particle analyses described above.

Table 4: Wood and char particle residence times for the five representative particle sizes. Note that for all but the smallest wood particle size, the particle is predicted to not leave the reactor, as represented by “-”. Feret Particle length residence (mm) time (s) 0.76 2.67 Wood 1.52 2.27 3.03 4 0.11 2.45 Char 0.44 3.31 0.78 3.70 1.08 4.22 1.44 4.69

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As expected, the MFIX simulation predicted that larger and more dense particles had increased residence times in the reactor. In cases where the as-fed biomass particles were sufficiently large, it was not even possible to determine finite reactor residence times within the limits of the current MFIX simulations. This should not be surprising, since the physics of particle elutriation require that there is a critical size limit for biomass particles such that they are never able to elutriate from the reactor unless the gas flow is increased. Conversely, the smallest particles are almost able to attain the velocity of the gas (near zero slip velocity), resulting in very short residence times nearly equivalent to the gas phase. The presence of such critical limits illustrates the kind of strong nonlinear relationships involved in selecting of design and operating parameters like particle size and gas flow that need to be considered in optimizing pyrolysis FBRs.

Figure 7: Superficial gas-particle slip velocity of gas around relevant particle sizes (dash) and average limiting particle rise velocity through the fluidized bed reactor (solid) 27


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4.3. Pyrolysis yields from coupled reactor- and single particle-scale models The particle-scale models captured intraparticle heat conduction, mass diffusion, mass convection, pressure, density, chemical reactions, and solid-gas phase change. Particle-scale simulations were run for each of the 5 representative pine particles over a range of reaction times, to produce the conversion snapshots after 4s illustrated in Figure 8. One notable observation revealed by these simulations is that rather high velocities (up to 0.25 m/s) were calculated for the pyrolysis vapors that were ejected from the ends of the porous pine particle. The average particle temperature and yields predicted by the particle-scale model as a function of exposure time in the reactor for different size particles are summarized in Figure 9. The temperature results illustrate that initial heat-up is very rapid for the smallest particles (0.76 mm long), and they reach 95% of their final temperature after only 0.7 s. However, the largest particles (3.8 mm long) take 4.9 s to achieve the same temperature, revealing how dramatic the effect of feed particle size is on heating rate. This effect is due to a combination of the lower thermal mass and higher surface area to volume ratio for the smaller particles.

Figure 8: Predicted wood conversion and exit gas velocities for 5 different size particles after 4s. 28


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Based on the mean residence time predictions from the reactor-scale simulations combined with the residence time-conversion relationship represented by Eqn. 18 and the assumptions concerning secondary vapor-phase reactions described above, the product yields at the reactor exit were estimated for these same particle sizes as summarized in Table 5. Notably, the 0.76 mm particle, which has a residence time of 2.6 s, only achieves 87 % conversion because its small size makes it pass very quickly through the reactor before the primary reactions can be completed. This also results in a reduced predicted bio-oil yield of 49.6% by weight. On the other hand, the 3.79 mm particle has a 9.3 s residence time, allowing the primary reactions to be about 97% completed and a higher predicted bio-oil yield of 55-56% by weight. Again, these differences illustrate the importance of particle size on pyrolysis performance. Of course size effects should be weighted by the relative abundance of each size. For example, although the conversion of the 0.76 mm particles is relatively high, these particles only account for about 1.1% of the feedstock on a weight basis, and thus they do not dramatically affect the overall biomass conversion.

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Figure 9: Yields (wt./wt. from dry wood) of pyrolysis products from five selected particle sizes vs. reaction time.

The predicted exit product yields from our simulations for the 2FBR appear to agree well with the available experimental values (within the estimated error limits), as also shown in Table 5. This suggests to us that the basic computational modeling approach we have proposed for combining computational simulations of reactor and particle-scale processes is sufficiently detailed to capture the major effects associated with feed particle variations, without the addition of arbitrarily fit parameters.

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Table 5. Summary of simulation results compared to experimental results. Yields in wt./wt. % from dry wood. Experimental error represents population standard deviation. Bins

Feret length wood (mm) Volume % Feret length char (mm) Effective particle residence time (s) Gas residence time (s) Bio-oil yield Char yield Gas + water vapor yield Wood conversion

0.76 1.1 0.11 2.6 1.4 49.6 7.85 29.5 87.2

1.52 34.3 0.44 5.4 1.4 55.9 9.3 33.2 97.6

2.27 50.7 0.78 6.6 1.4 55.6 9.77 33.0 97.3

Volume Experimental weighted yields average 3.03 12.0 1.08 7.9 1.4 55.3 10.1 32.7 97.0

3.79 1.9 1.44 9.3 1.4 55.2 10.5 32.7 97.1

55.6 9.2 33.0 97.2

55.6 ± 1.3 11.3 ± 0.5 30.9 ± 1.7

4.4. Additional Particle-Scale Simulations

To evaluate the practical value of the particle-scale model implemented here for process development, we also used it to explore the predicted impact of changes in specific pyrolysis parameters. Initially, we did this by considering the impact of changes in reactor temperature and particle residence time. Assuming a single feed particle size of 2.27 mm and a constant gas residence time of 1.4 s, the trends in bio-oil yield and the relationship between reactor temperature and particle residence time illustrated in Figure 10 were generated. These results imply that there is an optimum combination of temperature and particle residence time in pyrolysis reactors that should produce the maximum bio-oil yield. In the specific example depicted, this optimal bio-oil yield is predicted to approach 69.5% by weight at a reactor temperature of 430 °C (703 K). Such a yield is considerably higher than that achieved to date in experiments with this FBR, indicating that significant potential remains for additional process improvement.

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Figure 10: Effect of particle residence time and temperature on the yield of bio-oil with varying particle residence times (Left) and optimal temperature vs particle residence time (right) with fixed 1.4 s gas residence time for the 2.27 mm particle.

As another example, we considered the joint impact of gas residence time and temperature changes on bio-oil yield, shown in Figure 11. The impact of gas residence time is enhanced as temperature increases, particularly above 560 °C (840 K), where the rate of secondary reactions is very high and over half of the initially generated bio-oil is decomposed to light gases in less than 0.1 s. Above this temperature and with gas residence times greater than 2 s, nearly all the bio-oil is predicted to be converted into gas. Interestingly, these results also suggest that there is a regime with high bio-oil yields (greater than 50 wt/wt%) that is less sensitive to the gas residence time between 400 and 450 °C (673-723 K).

32


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Figure 11: Bio-oil yield (left) and gas yield (right) with temperature and gas residence time with a fixed particle residence time of 6.6 s for a 2.27 mm particle.

Clearly, these above examples illustrate the usefulness of such particle-scale models for planning experiments, designing reactors, and assessing the technoeconomic potential of candidate processes. While the design and operating limitations of the reference reactor considered in this study may prohibit the realization of the predicted optimal conditions, the computational simulations make it possible to recognize important opportunities for future research.

5. Conclusions

The results reported here demonstrate that combined implementation of separate reactor-scale and particle-scale models can lead to new insights and opportunities for understanding and improving biomass fast pyrolysis in fluidized beds. At the reactor scale, it is feasible to simulate the biomass and char particle residence times with sufficient accuracy using two-fluid computational fluid dynamics (CFD) at a reasonable cost in time and computing resources. At the particle scale, it is feasible to integrate the differential intra-particle heat, mass, and momentum 33


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Page 34 of 41

balances with finite element methods (FEM) for selected classes of particles while still including realistic assumptions about feedstock particle size variations and microstructure. Taken together, the combined information generated by independent simulations with each of these models can be used to accurately predict the experimentally observed product yields from the NREL 2-inch fluidized bed pyrolyzer within measurement accuracy. As additional kinetic details for the pyrolysis reactions of different types of biomass become available, we expect that additional experimental verifications for using this modeling approach will be made.

For future work, we recommend continued utilization of this type of multi-model simulation strategy for simulating multiscale biomass conversion processes. Combining information generated by separate models that approximate the dominant physics at different time and length scales would appear to offer significant advantages in computational efficiency over models that attempt to address all the scales in detail simultaneously (e.g., such as CFD simulations that directly couple hydrodynamics with chemical reactions

37-38

). For other more complex process

contexts, it may be necessary to account more fully for multidirectional coupling among the scales (rather than the one-way coupling implemented in this work), but the potential advantages of making the information transfer process more selective between separate, independently run models would appear to be considerable.

34


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AUTHOR INFORMATION Corresponding author Peter N. Ciesielski, [email protected], Phone: 1-303-384-7691, National Renewable Energy Laboratory, Biosciences Center, 1503 Denver W. Parkway, Golden, CO 80401, United States

Notes and Funding Sources

This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

ACKNOWLEDGEMENT Steve Deutch and Kellene Orton for information and discussions about the FBR experimental setup.

35


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ABBREVIATIONS CFD, computational fluid dynamics; DEM, discrete element model; FBR, fluidized bed reactor; FEM, finite element model; MFIX, Multiphase Flow with Interphase eXchanges, a reactor modeling software package; NREL, National Renewable Energy Laboratory; XCT, X-Ray computed tomography

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