One dimensional steady-state circulating fluidized-bed reactor model for biomass fast pyrolysis

One dimensional steady-state circulating fluidized-bed reactor model for biomass fast pyrolysis

Fuel 133 (2014) 253–262 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel One dimensional steady-state ...

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Fuel 133 (2014) 253–262

Contents lists available at ScienceDirect

Fuel journal homepage: www.elsevier.com/locate/fuel

One dimensional steady-state circulating fluidized-bed reactor model for biomass fast pyrolysis Anna Trendewicz a, Robert Braun a,⇑, Abhijit Dutta b, Jack Ziegler b a b

Colorado School of Mines, 1500 Illinois Street, Golden, CO, USA National Renewable Energy Laboratory, 15013 Denver West Pkwy, Golden, CO, USA

h i g h l i g h t s  A 1-D steady-state model for fast pyrolysis of biomass is developed and verified.  Biomass particles are rapidly heated to pyrolysis temperature of 786 K within 0.3 s.  Biomass conversion of 99% is reached within 0.9 s from entering the reactor.  The 1-D model is validated with a 2-D model and experimental results.

a r t i c l e

i n f o

Article history: Received 7 March 2014 Received in revised form 9 May 2014 Accepted 9 May 2014 Available online 22 May 2014 Keywords: Biomass Pyrolysis CFB reactor Modelling

a b s t r a c t A one dimensional (1-D) steady-state biomass fast pyrolysis reactor model is developed for integration with a biomass pyrolysis plant system model. A state-of-the-art biomass pyrolysis kinetic mechanism is combined with the 1-D Eulerian fluid dynamics and heat transfer description. Simulations are performed for a small scale reactor (0.023 kg/s) with four different biomass feedstocks (pine, wheat straw, olive husks, organic fraction of MSW). Results show that biomass particles are heated to pyrolysis temperature of 786 K in 0.3 s and 99% biomass conversion is reached in 0.9 s from entering the reactor. Comparison of pyrolysis products yields against available literature data shows that the employed reaction mechanism generally gives good predictions. However, water yield is under predicted. Fluid dynamics and heat transfer results are compared with averaged results from a 2-D, transient reactor model developed in Multiphase Flow with Interphase eXchanges (MFIX). Comparison of the 1-D and the 2-D model results shows flow patterns and reasonably similar values of flow parameters, with the average relative error between the gas velocities of 10%. The solids velocity predictions from the 1-D model carry a larger error since particle clustering is neglected in the plug flow approximation. The 1-D model is still considered attractive because of a reasonable agreement with the averaged experimental results. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Biomass fast pyrolysis is a potentially attractive method for producing liquid fuels from solid biomass. Obtaining liquid fuels from renewable sources is of increasing interest due to concerns about economics and environmental impact of using depleting fossil fuels. Before implementing a biorefinery based on thermochemical conversion of biomass using fast pyrolysis reactor concepts, techno-economic analyses can provide critical plant technical and economic performance information to support technology development [1–3]. It is desired that system-level models provide reasonable predictions of pyrolysis oil yield and composition from ⇑ Corresponding author. Tel.: +1 7202806365. E-mail addresses: [email protected] (A. Trendewicz), [email protected] (R. Braun). http://dx.doi.org/10.1016/j.fuel.2014.05.009 0016-2361/Ó 2014 Elsevier Ltd. All rights reserved.

different feedstocks and at different reactor scales, as these results are used for optimization of plant size and operating conditions depending on biomass feedstock type. Most biorefinery system models are currently based on yields and use a static snapshot of experimental results [1,3]. This technique is computationally simple however, the results cannot be extrapolated to describe systems equipped with different reactors, operating under different conditions or supplied with alternate biomass feedstocks. There is a need to improve the state-of-the-art pyrolysis reactor models used within process flowsheet simulators by incorporating a description of reaction kinetics coupled with heat transfer and fluid dynamics equations. Biomass pyrolysis reactor models with varying complexity and focus are currently available in the literature, but do not meet the aforementioned requirements for process simulations. Single

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particle models provide detailed description of intraparticle heat and mass transfer which is coupled with a simplified kinetic mechanism [4]. Fluid dynamics and particle interactions in the reactor, which affect particle residence time, are not captured in these models. Existing 1-D pyrolysis reactor models [5,6] are also coupled with oversimplified reaction mechanisms, which do not provide any information about products speciation. Computational fluid dynamics (CFD) models provide a detailed description of fluid dynamics and heat transfer inside a reactor, and can be coupled with complex reaction kinetic mechanisms [7–9]. However, complex CFD models are too computationally expensive for the purpose of evaluating multiple, plant-wide techno-economic scenarios, as well as optimizing operating parameters of large scale reactors. The goal of this work is to bridge the gap between simple yield reactor models, single particle models and CFD reactor models by developing a 1-D, steady-state CFD reactor model for integration with a biorefinery process model. The proposed reactor model offers several advantages over currently existing 1-D fast pyrolysis reactor models: (i) products speciation is included, (ii) particle velocity is computed from a solids–gas momentum balance in place of empirical correlations, and (iii) average reaction temperature is computed from an energy balance in place of an isothermal assumption. As a result, the reactor model is computationally compatible with a biorefinery system model and still captures much of the chemistry and physics affecting product composition. A steadystate model is assumed to be a reasonable approximation of a pyrolysis reactor for the purposes of techno-economic analyses because a continuous operation is desired. Model results are verified by comparison with a 2-D, transient reactor model developed in MFIX [10], and available literature and experimental data. Simulation results could be used for optimization of operating parameters (biomass particle size, inert solids mass flux, fluidizing gas velocity, etc.) and reactor geometry (height and diameter) to achieve desired product yields and compositions within the feasibility limits of the process.

2. Background 2.1. Biomass fast pyrolysis system description A circulating fluidized-bed reactor (CFB) system for the fast pyrolysis of biomass is comprised of a biomass feeder, a riser reactor, cyclones, a condenser, and a burner, as shown in Fig. 1. Biomass, inert solids (typically sand) and fluidizing gas are supplied at the bottom of the CFB reactor. The thermal energy necessary for the endothermic pyrolysis reactions is supplied by the carrier gas and sand, where between these two, the dense, sand particle-laden stream serves as the primary heat source for the process. Pyrolysis gases and vapors are separated from the char and inert solids in a cyclone. Gases and vapors are directed to a condenser, where oil is separated from the gases and collected. Non-condensable gases are partially recycled and used as a carrier gas for the reactor. Char and sand are conveyed into a combustor, where the sand is heated with the heat of combustion and recycled to the riser reactor. The fast pyrolysis riser reactor is a long tube of a circular cross-section, as shown in Fig. 1b. Biomass particles typically ground to particle size of 1–2 mm and dried to approximately 10 wt% moisture for industrial applications [1,2]. Biomass particles enter the reactor after the drying process at approximately 100 °C. Intense momentum and heat transfer occur at the reactor inlet between solids and carrier gas. Biomass particles are heated to the optimal pyrolysis temperature of approximately 500 °C, pyrolyzed and transported by the fluidizing gas to the top of the reactor. Within a few seconds pyrolysis products, sand particles and fluidizing gas reach the reactor outlet.

Fig. 1. (a) Biomass fast pyrolysis system schematic, (b) circulating fluidized bed reactor schematic.

2.2. Reactor modelling The biomass fast pyrolysis reactor model can be comprised of the following sub-models: biomass conversion chemistry model, biomass particle model, fluid dynamics model and heat transfer model. A literature review on previous work related to each of the sub-models is provided in the following sections. 2.2.1. Reaction mechanism Biomass pyrolysis reaction mechanisms are usually derived from thermogravimetric analysis (TGA) experiments, which allow determination of the rate of products formation [11]. Unfortunately, the repeatability of experimental results is often poor even for the same biomass sample batch and the same experimental equipment because of differences in thermal lag, applied heating rates, and compositional differences within the biomass sample batch [11]. Despite a large number of experimental results reported in the literature, a general conclusion is that a robust and flexible mechanism for biomass pyrolysis is not available due to systematic errors [12]. The published data for activation energy and pre-exponential factors for simple, one component models vary over a wide range and it has been concluded that they

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are not reliable for quantitative predictions outside of the experimental range that they were derived from [11]. Multicomponent models were found to give better product predictions compared with single component models [13]. Therefore, one of the most sophisticated multicomponent models, developed by Ranzi et al. [14], was adopted as a first approximation for the proposed reactor model. Pyrolysis of major biomass building blocks: cellulose, hemicellulose and lignin is described with multi-step, competing reactions characterized by rate constants summarized in Ref. [15]. The primary reaction mechanism was found to generally give good predictions of product yields for feedstocks with low ash content [16,15]. However, the model does not account for the catalytic effect of alkali metals, which are known to reduce oil yield and alter products composition [17] and might become important for high ash content biomass feedstocks. 2.2.2. Biomass particle The goal of biomass particle models is to describe coupled effects of heat transfer, mass transfer and anisotropic biomass properties on pyrolysis reactions. After entering a CFB pyrolysis reactor biomass particles are subjected to heat transfer from the surrounding gas. As large biomass particles (Bi > 0.2) are being heated, temperature gradients form inside the particle due to a relatively slow conductive heat transfer. Therefore, drying and pyrolysis occur first near to particle surface and proceed toward the inside of the particle as the thermal wave propagates. The vapors leave the particle through the pores [18]. The most comprehensive particle models incorporate chemical kinetics, water evaporation, particle shrinkage, heat transfer (conduction, convection, radiation) and convective mass transfer inside the particle [13,19,20,12]. However, particle models were found unsuitable for reactor design efforts because they do not capture the effect of reactor operating conditions on product yields [11]. Therefore, it is reasonable to seek engineering approximations of single particle models for reactor models. Kersten et al. [11] found that intraparticle heat transfer can be approximated by using an average particle temperature for evaluating reaction rates. They also showed that intraparticle mass transport phenomena do not affect pyrolysis oil yields for particle sizes between 0.4 mm and 2.4 mm. Janse et al. [21] showed that the intraparticle transport phenomena do not affect the oil yields under the conditions typical for fluidized bed reactors, however the conversion time was dependent on particle size. Although these results were confirmed by several other modelling studies, they were assessed not to be conclusive due to simplifying assumptions used in the models [12]. In addition to reaction temperature and intraparticle mass transport, water evaporation, particle shrinkage and biomass physical properties need to be approximated in the reactor model. Water evaporation could be represented with an Arrhenius type of expression, as it has been found that water is chemically adsorbed on a biomass surface below the saturation point of 30 wt% of dry biomass [18]. There is no consistency in the literature with respect to describing particle shrinkage. Bryden and Hagge [20] assume shrinkage to be a parameter due to uncertainty about its actual value, while Haseli et al. [22] entirely neglect shrinkage. Moreover, Thunman and Leckner [23] found that biomass structure and physical properties are anisotropic, heterogeneous and temperature dependent. Reactor models typically adopt effective properties obtained by applying various averaging techniques [23,24]. 2.2.3. Fluid dynamics Engineering models are usually based on a very simplified fluid dynamics description [12]. Some are simply single particle models, where the reactor is represented by a changing boundary condition, as done by Hastaoglu and Hassam [4]. A slightly more advanced approach to describing hydrodynamics of CFB reactors

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is to use empirical correlations. The fluidization regime, drag coefficient and pressure drop are described with dimensionless particle diameter and dimensionless velocity expressed with the Archimedes and the Reynolds numbers [25]. Although computationally simple, this approach is not reliable or flexible beyond specific conditions for which the correlations were developed. A more detailed description of solid–gas flow can be obtained by solving Navier–Stokes and Newtonian equations. However, the huge number of particles (typically > 106 ) necessitates averaging the equations to reduce computational cost. Typically an Eulerian– Eulerian two phase model is used [26]. It is computationally less demanding compared to Eulerian–Lagrangian models or direct numerical simulations. This is because an Eulerian- Eulerian model assumes that both the gas and the solids are continuous. The solid–gas interactions are described with the drag models and averaged collision models. The cold flow investigations of CFB reactors hydrodynamics show that the flow is turbulent and unsteady with transient particle clusters and high speed jets forming inside the reactor [27]. The presence of these flow instabilities poses a challenge to using experimental data for determining coefficients and correlations describing the drag force, particle collisions, and heat transfer. Without validating simulation results for a particular reactor dimension and configuration, accuracy of the solution is also often problematic. This is due to a problem dependent simulation setup, including the selection of various drag models, collision, and wall boundary condition parameters. Also, coarse resolutions above a minimum of 10–50 particle diameters create large errors in particle drag [28]. Nevertheless, advanced 2-D CFD models can be useful in describing and understanding the physics of gas–solids flows inside CFB reactors, such as the one used for verification of our 1-D model. 2.2.4. Heat transfer In CFB reactors heat is transferred between gas–solid, solid– solid, solid-wall, gas-wall. In general, all three heat transfer modes (conduction, convection and radiation) coexist. The contribution of radiation to overall heat transfer was found to be approximately 1% in fast pyrolysis CFB reactors [29]; convection and conduction are the dominate heat transfer modes due to relatively low solids volume fraction in CFB reactors and relatively low temperatures required for fast pyrolysis ( 500  C) [29,30]. There are numerous empirical correlations for evaluating the heat transfer coefficients between the solid–gas phases in CFB reactors. In some cases, using the heat transfer coefficient for a single spherical particle is a reasonable approximation [25]. However, there also exist correlations for an average heat transfer coefficient for the entire solid phase. Yang [31] shows that the correlations are able to practically predict the heat transfer coefficient within ±25%. 3. Modelling approach 3.1. Assumptions The reaction mechanism chosen for implementation in this work is the mechanism developed by Ranzi et al. [14], as described in Section 2.2.1. It is the most detailed and comprehensive mechanism currently available in the literature. The reaction mechanism schematic is presented in Fig. 2. Biomass is represented by its three primary constituent building blocks (cellulose, hemicellulose and lignin), which allows to account for variability in biomass composition through changes in fractions of the three constituents. Another advantage of the adopted reaction mechanism is the speciation of products. As illustrated in Fig. 2, pyrolysis vapors are comprised of multiple representative compounds, which provide information about relative yields of different functional groups in bio-oil (acids, aldehydes, alcohols, etc.). The mechanism

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Fig. 2. Schematic of a reaction kinetic mechanism.

is is inclusive of both primary reactions and secondary reactions. Only the primary reactions were adopted for the purpose of this work because the secondary reactions have not yet been validated and proven to work correctly [9]. Moreover, secondary reactions were found not to be significant at temperatures optimal for fast pyrolysis (below 800 K) [32,15]. Physical properties of biomass and char species were obtained from ref [15]. The properties of remaining components were obtained from the Multiflash database in gPROMs [33]. Particle behavior was modeled based on the following simplifying assumptions: (i) particles are identical spheres, (ii) physical properties are isotropic, (iii) particles behave with a lumped heat capacity (uniform temperature), (iv) intraparticle mass transport is not rate limiting, (v) particle attrition and shrinkage are neglected. The simplifying assumptions are not realistic, since biomass particles are not spherical and the properties are heterogenous and anisotropic. In order to improve the approximation of the effective properties of biomass and include a correction for the intraparticle mass and heat transfer phenomena, the results from the detailed 3-D particle models developed at the National Renewable Energy Laboratory (NREL) will be averaged and incorporated into the reactor model in the future. 3.2. Governing equations The reactor model is comprised of equations representing 1-D steady state conservation of species, continuity, momentum, and energy for a solid–gas flow system. The change in mass flow rate of species i (M i ) is represented by: k X d ðM i Þ ¼ mi;j  Rj ðzÞ dz j¼1

ð1Þ

where mi;j is the stoichiometric coefficient for species i in reaction j, k = 19 is the number of reactions, and Rj is the reaction rate for reaction j, given by the following formula: Ea;j

Rj ðzÞ ¼ kj  e RT  Mi

ð2Þ

The reactions follow an Arrhenius form with the pre-exponent k and the activation energy Ea . Moreover, the reactions are first order, as commonly reported in the literature [13,14]. The continuity equation for the gas phase is represented by: N X k X d ðg qg v g Þ ¼ mi;j  Rj ðzÞ dz i¼1 j

ð3Þ

where g ; qg ; v g are the volume fraction, density ðkg=m3 Þ and velocity (m/s) of gas mixture comprised of the total number of N = 20 species. The mass flux and the volume fraction are related as follows:

g qg v g ¼

N X Mi

ð4Þ

i¼1

The continuity equation for the biomass phase is represented by: M X k X d ðb qb v b Þ ¼ mi;j  Rj ðzÞ dz i¼1 j

ð5Þ

where b ; qb ; v b are the volume fraction, density (kg/m3) and velocity (m/s) of biomass comprised of the total number of M = 13 species. The mass flux and the volume fraction are related as follows:

b qb v b ¼

M X Mi i¼1

ð6Þ

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The continuity equation for the inert sand is represented by:

d ðs qs v s Þ ¼ 0 dz

ð7Þ

where s ; qs ; v s are the volume fraction, density (kg/m3) and velocity (m/s) of sand. The momentum balance for the biomass phase is represented by:

d db dp d vb ðb qb v b v b Þ ¼ Gðb Þ  b þ ð  b lb Þ   b q b g dz dz dz dz dz 2f b b qb v b v b þ bðv g  v b Þ  Dh

ð8Þ

where G is the particle collision coefficient, which represents momentum loss due to particle collisions, l is the solid viscosity (Pa s), b is an interphase momentum exchange coefficient, fb – is the friction coefficient, and Dh is the reactor diameter (m). The convective term on the left hand side of the equation is due to the combined impact of the following forces: momentum loss due to solid– solid interactions Gðb Þ ddzb , pressure drop b dp , viscous shear dz d ðb lb vdzb Þ, gravity force b qb g, momentum exchange between solid dz and gas phase bðv g  v b Þ, and momentum loss due to collisions with the wall 2f b bDqb v b v b . The empirical coefficients in the momentum h equation can be calculated from empirical correlations [34]. Particle collision coefficient can be calculated from:

G ¼ 108:76g þ5:43

ð9Þ

Interphase momentum exchange coefficient can be calculated from [34]:

b ¼ 150

2s lg qg 2

ðg ds /s Þ ðqs  qg Þ

þ 1:75

qg qs jv g  v s js g ds /p ðqs  qg Þ

if

g 6 0:8 ð10Þ



3 qg qs jv g  v s js 2:65 Cd  4 ds /p ðqs  qg Þ g

if

g > 0:8

ð11Þ

where C d is the empirical drag coefficient, which can be calculated from the following correlations:

Cd ¼

24 ð1 þ 0:15Re0:687 Þ Re

C d ¼ 0:44

if Re 6 1; 000

if Re > 1; 000

ð12Þ ð13Þ

where Re is the Reynolds number expressed with:

Re ¼

jv g  v s jds qg g

ð14Þ

lg

Friction coefficients for the gas and solid phase can be calculated from [34] as follows:

fg ¼ fg ¼

16 Re

if Re 6 2; 100

0:0791 1

Re4

ð15Þ

if 2; 100 6 Re 6 105

 2 qffiffiffiffi fg ¼ 2logRe fg  0:8

if Re > 105

ð16Þ

ð17Þ

where Re is the Reynolds number for the gas phase given by:

g jv g jDh qg Re ¼ lg fs ¼

0:0025

vp

Similarly the momentum balance for sand is represented by:

d ds dp d vs ðs qs v s v s Þ ¼ Gðs Þ  s þ ðs ls Þ  s qs g dz dz dz dz dz 2f s qs v s v s þ bðv g  v s Þ  s Dh

ð20Þ

The momentum balance for the gas phase is calculated as follows:

d dg dp d vg ðg qg v g v g Þ ¼ Gðg Þ  g þ ðg ls Þ  g qg g dz dz dz dz dz 2f g g qg v g v g þ bðv g  v s Þ  Dh

ð21Þ

The empirical coefficients and correlations required for the momentum balance were obtained from [34]. The energy balance for biomass phase is calculated as follows:

b qb v b Ac cp;b

X dT b ¼ As;bio hbg ðT b  T g Þ þ Rj DHj dz j

ð22Þ

2

As;bio ¼ pdb Nb

ð23Þ 2

where Ac is the reactor cross-section area (m ), cp;b is the specific heat of biomass ðJ=kg KÞ; T b ; T gas the average biomass particle temperature (K) and the average gas temperature (K) respectively, As;bio is the overall surface area of biomass particles (m2), hbg is the overall heat transfer coefficient between biomass and gas (W/ m2 K) , db is biomass particle diameter (m), N b is the number of biomass particles, and DHj is the enthalpy of reaction for reaction j. The change in biomass temperature along the reactor b qb v b Ac cp;b dTdzb is due to heat transfer from the gas phase As;bio hbg ðT b  T g Þ and heat P of pyrolysis reactions j Rj DH j . Similarly the energy balance for sand is calculated as follows:

s qs v s Ac cp;s

dT s ¼ As;sand hs;g ðT s  T g Þ dz

ð24Þ

2

As;sand ¼ pds Ns

ð25Þ

where cp;s is the specific heat of sand (J/kg K), T s is the average sand particle temperature (K), As;sand is the overall surface area of sand particles (m2), hsg is the overall heat transfer coefficient between sand and gas (W/m2 K) , ds is sand particle diameter (m) and N s is the number of sand particles. The change is sand temperature along s the reactor s qs v s Ac cp;s dT is due to heat transfer to the gas phase dz As;sand hsg ðT s  T g Þ. Energy equation for gas phase is calculated as follows:

g qg v g Ac cp;g

dT g ¼ As;sand hsg ðT s  T g Þ þ As;bio hbg ðT b  T g Þ dz

ð26Þ

where cp;s is the specific heat of gas mixture (J/kg K). The change in dT

gas temperature along the reactor g qg v g Ac cp;g dzg is due to heat transfer from the sand As;sand hsg ðT s  T g Þ and heat transfer to the biomass As;bio hbg ðT b  T g Þ. The heat transfer coefficient between the solid and gas phase was calculated from the following correlations:

hsg ¼

6kg s Nus 2

dp

ð27Þ

where the particle Nusselt Nus number was calculated as follows:

ð18Þ

0:33 Nus ¼ ð7  10g þ 52g Þð1 þ 0:7Re0:2 Þ þ ð1:33  2:4g s Pr 0:33 þ 1:22g ÞRe0:7 s Pr

ð19Þ

ð28Þ

where kg is the thermal conductivity of gas, Pr is the Prandtl number, Res is the solids Reynolds number and dp is particle diameter.

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Correlations were obtained from MFIX Documentation Theory Guide [35]. 3.3. Model input parameters Four feedstocks were considered for this work: (i) pine, (ii) wheat straw, (iii) olive husks and (iv) MSW (organic fraction). These feedstocks were chosen so that different biomass categories were represented. The chemical composition information, shown in Table 1, was obtained from the Phyllis database [36]. The operating parameters used for the baseline simulations of a small scale reactor ð0:023 kg=sÞ are summarized in Table 2. The inlet pressure, inlet gas velocity, inlet sand and biomass velocity, and inlet gas volume fraction assumptions are based on cold flow (non-reactive) information available for a similar size CFB riser [34]. The inlet gas temperature, biomass temperature and sand temperature and sand-to-biomass ratio are assumed based on estimates from Ref. [1]. The one dimensional model was discretized by using the backward finite difference method with a constant step size of 0.05 m. The 2-D TFM (two-fluid method) microscopic equations (conservation of mass, momentum, and energy) [10] were solved using second-order accuracy in cartesian coordinates with two dimensions. The residuals for the implicit method were controlled by error tolerances of 0.001 for the fluids and species conservation. For the domain size of 0.08 by 4 m, 36 cells were used in the horizontal direction and 90 cells in the vertical direction, yielding vertically stretched cells of 0.002 by 0.044 meters. In the r-direction the cell size is equivalent to 4.44 particle diameters and in the z-direction 89 particle diameters. Using the guide of 10–50 diameters [28], the r-direction is resolved and the z-direction is unresolved. The Syamlal and Obrien drag model [35] was used to model the gas/solids interaction and particle–particle collisions were modeled with a coefficient of restitution of 0.9 and a collision angle of 30 degrees.

At the walls, no-slip boundary conditions were used for the gas and the partial-slip Johnson and Jackson boundary condition was used for the solids with a specularity coefficient of 0.05 and a wall coefficient of restitution of 1.0. A simple pressure outflow condition was utilized for the exit. The time averaged traces for the 2-D simulation were conducted from a simulation time of 20–55 s, as the mass accumulation stabilized at approximately 20 s. 4. Results Simulation results show that biomass and sand particles are rapidly accelerated at the reactor inlet by the drag force exerted (due to significant velocity differences) between the gas and solid phases, as illustrated in Fig. 3(a). An intense momentum exchange within the first 0.4 m from the reactor inlet causes the gas velocity to decrease to 4.2 m/s, and biomass and sand particle average velocities increase to 2.8 m/s and 0.8 m/s respectively. The particle drag force is reduced when the particle velocities increase due to the rapid initial acceleration. As the gravitational force on the

Table 1 Chemical composition of biomass feedstocks [36]. Feedstock

Pine

Straw

Olive husk

Waste

Cellulose Hemicellulose Lignin Ash Water

0.420 0.240 0.240 0.003 0.097

0.300 0.350 0.175 0.075 0.100

0.215 0.211 0.459 0.015 0.100

0.185 0.085 0.305 0.325 0.100

Table 2 Model input parameters. Input parameters Biomass M biomass (kg/s) Dp;biomass (m6) T biomass (K) v biomass (m/s)

0.023 500 373 0.15

Sand Rsand=biomass (–) Dp;sand (m6) T sand (K) v sand (m/s)

15 500 900 0.15

Gas T gas (K) P (bar) v gas (m/s) gas (–)

700 1.175 5 0.7

Reactor Dreactor (m) Hreactor (m)

0.08 4

Fig. 3. (a) Velocity profiles, (b) volume fraction, and (c) temperature profiles along the reactor height.

A. Trendewicz et al. / Fuel 133 (2014) 253–262

particles is balanced by the drag force, the velocities approach a nearly constant value. Gas velocity approaches 4.1 m/s after the initial deceleration. Sand and biomass particle velocities approach 0.8 m/s and 3 m/s, respectively, as shown in Fig. 3(a). The calculated sand particle velocity is lower than biomass particle velocity because the density of sand is four times higher than that of biomass. Therefore, for the same particle diameter, sand particles have a greater mass; a greater drag force (higher relative velocity with the gas) is thus required to overcome its gravitational force. Fig. 3(b) depicts the particle volume fractions as a function of distance from the reactor inlet. The biomass and sand volume fractions decrease sharply at the inlet in response to acceleration. This behavior is imposed by the continuity equation. Pressure drop is approximately 1 kPa/m. As shown in Fig. 3c, rapid heat transfer causes the gas temperature to equilibrate with the sand temperature within 0.05 m from the inlet. Biomass particles are rapidly heated by the surrounding gas and the calculated biomass temperature reaches 786 K within 0.15 m from the reactor inlet. The biomass pyrolysis process starts at approximately 0.05 m from the reactor inlet, when biomass reaches 700 K, as shown in Fig. 3c. Pyrolysis reactions proceed quickly at 786 K resulting in 99% conversion within the two meters from the reactor inlet as illustrated in Fig. 4(a) and (b). The residence time in the reactor is 1.5s. Predicted yields of organics, gases, water and char from pine at the reactor outlet are 65.5%, 21.7%, 4.7% and 8.2% by weight of the dry ash-free biomass. Biomass feedstock composition is known to have an effect on product yields and composition. Fig. 5 illustrates that the highest yield of organics is from pine (65.5%) followed by MSW (organic fraction) (61.2%), straw (59.6%) and olive husks (58.0%) on a dry ash-free biomass weight basis. It is important to note that waste and straw have ash contents of 32.5% and 7.5%, respectively. Therefore, the predicted yields of organics relative to the total biomass mass flow rate are significantly lower. Gas and water yields are similar for all feedstocks about 20% and 5%, respectively. Predicted char yield is the highest from olive husk and MSW feedstocks on a dry ash-free basis. This is because those feedstocks are rich in lignin, which is known to form more char in fast pyrolysis process.

259

Fig. 4. Pyrolysis products distribution: (a) in the entire reactor, (b) within the first 1.2 m.

5. Model validation 5.1. Fluid dynamics A comparison of the 1-D steady state model described above with a 2-D cylindrical, transient MFIX model was done for the purpose of evaluating the loss of fidelity introduced with dimensions reduction and loss of transient phenomenon. Chemical reactions were deactivated for these comparative simulations. 2-D MFIX model was ran until a statistical stationary state was reached. The obtained results were averaged radially and over time. The 1-D equivalent velocity profiles along the reactor height were obtained by first obtaining a an average mass flow rate for the gas and solids species, which is equivalent to using weighted averages of the vertical velocity profile with the local mass in each cell as a weight. The boundary conditions used for the simulations are provided in Table 2. Fig. 6(a) and (b) shows that there is general agreement between the models. When comparing the 1-D and 2D simulations, it is observed that the time-averaged velocity profile for the gas and solids are different in the transient 2-D simulation. The 1-D simulation is a steady-state approximation, and therefore does not model the transient multidimensional phenomenon of solids clustering. Additional factors, such as collisions, wall interactions, and shielding of clusters (reducing particle drag), lead to solids hold-up and back flow in the riser. The gas velocities are also affected by the formation of clusters. Moreover, it is noted that

Fig. 5. Pyrolysis products yield from different biomass feedstocks.

due to conservation of mass and using equivalent inlet boundary conditions, the mass flux in the simulations is the same. However, this flux is realized through different combinations of density and velocity. The mass hold-up in the 2-D simulation from clustering phenomenon increases the solids density and reduces the solids velocity. The gas phase interacts with the solids phase (which is now denser) and its velocity and density also change. For example, if a horizontal orientated cluster is present, the gas is compressed and accelerates as it flows around the cluster. The results comparison shows flow patterns and reasonably similar values when the 1-D model has it’s steady-state heat transfer and drag coefficients matched to the complex MFIX terms. In order to better assess the error of the 1-D flow approximation, additional simulations were compared against the experimental data from a similar cold flow CFB riser unit at National Energy Technology Laboratory (NETL). The experimental reactor

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Fig. 6. Validation of a 1-D model reactor with a 2-D MFIX model (a) velocity profiles, (b) solid volume fraction profiles, (c) temperature profiles.

was approximately 16 m tall and had a diameter of 0.3 m. The experiments were performed at ambient temperature and pressure with polyethylene particles and air. The average particle size was 0.8 mm. The velocity was measured with a high-speed particle velocimetry and fiber optic technologies. The inlet superficial gas velocity was 5.7 m/s and the solids mass flow was 5.5 kg/s. The experimental particle velocity measurements at three different heights above the reactor inlet (6.23 m, 8.88 m and 13.33 m) were depicted for comparison. A one-inlet two-dimensional configuration was selected for this comparison. While using two (rather than three dimensions) and one (rather than two inlets) leads to less accurate results, it shows a gage of the validity of the previously discussed pyrolysis 2-D/1-D comparison for a reactor of smaller dimension with two solids present. We assumed a solid volume fraction of 0.8 at the inlet, which leads to a solids velocity of 0.45 m/s to get the 5.5 kg/s solids mass flow rate. Initially a 2-D cylindrical coordinate system was used to more easily model the domain, however, it was abandoned due to unphysical solids clustering and negative solids flow at the symmetry boundary condition. Therefore, 2-D cartesian simulations were conducted for an approximation of a ’slice’ of physical reactor. A mesh with 48  192 cells were used with the Syamlal and

Obrien drag model [35] to model the gas/solids interaction. The residuals for the implicit method were controlled by error tolerances of 0.001. Particle–particle collisions were modeled with a coefficient of restitution of 0.9 and a collision angle of 30°. At the walls, no-slip boundary conditions were used for the gas and the partial-slip Johnson and Jackson boundary condition was used for the solids with a specularity coefficient of 0.6 and a wall coefficient of restitution of 0.9. A simple pressure outflow condition was utilized for the exit. The time averaged traces for the 2-D simulation were conducted from a simulation time of 20–31 s, as the mass accumulation stabilized at approximately 20 s. These simulations are under-resolved, however a comparison (shown in Fig. 7) is still demonstrative. As with most underresolved multi-phase gas–solid simulations, the expected pressure drop of 22.8 kPa is under predicted as 12 kPa. The time-averaged radial solids vertical velocity and mass flux profiles at three heights were compared to the experimentally measure data, as illustrated in Fig. 7. For the velocity profile, the magnitude and shape of the profile is similar, as is an increasing solids velocity from 6 to 7 m/s at the center when going from 6.23 to 13.33 m. For the solids mass flux, the value at the centerline is over-predicted (450 kg/ m2/s) and the simulation shows a large concentration of solids at the walls going downward. Also, these simulations over-predicted the total solids inventory, with the experimental value being 442 kg and the simulated value being approximately 1200 kg at the statistical stationary state. Also of note is a comparison to 3-D simulations, which modeled the same experimental conditions. In these results shown in [37], the ’EE5’ simulation which had the highest, yet, still underresolved grid, the mass flux profile had similar deficiences to our 2-D simulation, when compared to the experimental profile. For this solution, the solids mass flux peak was approximately 300 kg/m2/s at the centerline and 100 kg/m2/s at the walls. As compared to the experiment, all 3-D methods tested over estimated the solids mass flux at the center and showed negative values at the walls, and additionally under-estimated the pressure drop. In these 3-D simulations, the solids entered at the side of the reactor, 3.66 m above the gas inlet. With the addition of more physically correct boundary conditions as compared to our 2-D cartesian setup, improvements in the flow field are observed, however, the solids velocities and volume fractions the walls are still deficient, possibly due to a lack of resolution. This stresses the importance of comparing our 1-D steady-state solution to both experiment and transient multidimensional results. The comparison of the velocity profiles shows a parabolic distribution in the radial direction. This proves that the plug flow approximation is not a realistic assumption. The 1-D model only allows for a comparison of the radially averaged velocity. The results (presented in Table 3) show that the plug flow assumption is still a reasonable engineering approximation, as the average velocities are in general agreement. The results compare well at low and intermediate heights above the distributor, however, the 1-D model fails in predicting the increased solids velocity at 13.3 m. The pressure drop of 13 kPa is underpredicted, and the total solids inventory of 1243 kg is overpredicted. The 1D model results show similar deficiencies compared with the 2-D simulations. Therefore, 1-D model is still considered attractive for techno-economic analysis application because of the reduction of computational complexity compared with 2-D/3-D models. 5.2. Heat transfer A similar comparison of heat transfer results from the 1-D and 2-D models is done for purposes of assessment of validity and impact of simplifying assumptions in the 1-D model. As in the previous case, chemical reactions are deactivated for the purpose

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Fig. 7. Comparison of the experimental gas velocity profiles at 6.23 m, 8.88 m and 13.33 m above the cold flow riser inlet with the 2-D MFIX simulation results.

Table 3 Comparison of the average particle velocity from 1D model and experimental data. Average velocity

1D Model

Velocity @6.23 m Velocity @8.88 m Velocity @13.33 m

3.16 3.19 3.22

Experimental 3.66 (±0.77) 3.92 (±1.47) 5.25 (±0.52)

of these simulations. Heat transfer occurs because of temperature differences between sand, biomass and fluidizing gas at the reactor inlet. Since convective heat transfer dominates in CFB reactors, heat is transferred from hot sand to the fluidizing gas, and from the fluidizing gas to the biomass feed. Gas and biomass temperatures increase sharply at the inlet due to heat transfer from the sand as illustrated in Fig. 6c. In the 1-D model, heat transfer is faster because of the plug flow assumption. In the 2-D model, solids are injected at a small velocity at the bottom and they need be mixed with the surrounding gas. In the absence of endothermic pyrolysis reactions (for purposes of comparison with the 2-D model), the temperature remains constant after reaching equilibrium at 827 K. The final temperature calculated using a simplified 1-D model is in excellent agreement with the averaged results from a 2-D MFIX model. 5.3. Reaction mechanism Fast pyrolysis product yields are validated by comparing the results with literature data from Oasmaa and Peacocke [38]. In order to exclude heat and mass transfer limitations from the comparison, simulations are run under isothermal conditions at 500 °C and 2 s residence time. The results are given in Table 4 and show that there are differences in the oil compositions from simulations and experiments. The discrepancies may be due to a combination of uncertainties related to the actual temperature and residence times in the reactor, actual composition of biomass feedstock, errors related to characterization process and model prediction errors. Differences among different models and experimental results are commonly found in the literature [11,39]. The predicted yields of product classes are in the same order of magnitude with relative differences below 10%, which may be assessed as satisfactory. These observations are in accordance with other reactor models employing the same kinetic mechanism [9]. The above comparison refers to bio-oil composition only. No information about gas and char yield is provided. A more detailed comparison given in Table 5 shows a better agreement between the simulation results and literature data [39], with differences below 4% points. The simulations were performed at 500 °C and 1 s residence time. The simulation results were compared against the available litera-

Table 4 Comparison of pyrolysis oil composition obtained from 1D model and literature [38] for pine feedstock. Oil composition

Simulation

Literature [38]

Water Acids Alcohols Aldehydes, ketones Sugar derived Lignin derived Extractives

19% 3% 3% 23% 26% 26% 0%

23.9% 4.3% 2.2% 15.4% 34.4% 15.35% 4.35%

Table 5 Comparison of pyrolysis products yield obtained from simulation and literature [39] for pine feedstock. Detailed products yield

Simulation (%)

Literature (%) [39]

Gas Lignin derived Alcohols Sugar derived Ketones Aldehydes Phenol Acids Water Char

15 18 2 18 4 13 0 2 13 15

11 14 2 22 3 17 0 2 16 13

ture data (instead of using a particular experimental reactor for validation) because the goal is to develop a flexible, engineering reactor model for process simulation. Therefore, it is desired to be able to approximate the pyrolysis products at a different reactor scale instead of matching one particular experimental setup exactly. 6. Conclusions Simulation of a 1-D CFB reactor for biomass fast pyrolysis reveals that biomass particles are accelerated and rapidly heated to pyrolysis temperature (within 0.15 m from the reactor inlet). Pyrolysis reactions proceed quickly to 99% conversion after 0.9 s. The highest organics yield of 65.5 wt% on an ash-free basis is obtained from pine, followed by 61.2% from MSW (organic fraction), 59.6% from straw and 58.0% from olive husks. The reactor model provides a satisfactory engineering approximation of fluid dynamics, heat transfer and pyrolysis product yields from lowash content feedstocks. Future work will include scaling, optimization and integration of the reactor model with a biorefinery process model.

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Acknowledgements The authors would like to acknowledge Dr. Pejman Kazempoor for his advice on model development in gPROMS software. We thank the U.S. Department of Energy’s Bioenergy Technologies Office (DOE-BETO) for supporting this work under Contract No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory.

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