Computational Study of Sugarcane Bagasse Pyrolysis Modeling in a

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Computational Study of Sugarcane Bagasse Pyrolysis Modeling in a Bubbling Fluidized Bed Reactor Filipe L. Brandão, Gabriel Lisbôa Verissimo, Marco Antonio Haikal Leite, Albino J. K. Leiroz, and Manuel E. Cruz Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01603 • Publication Date (Web): 05 Jan 2018 Downloaded from http://pubs.acs.org on January 7, 2018

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Computational Study of Sugarcane Bagasse Pyrolysis Modeling in a Bubbling Fluidized Bed Reactor Filipe L. Brandão,1,♦ Gabriel L. Verissimo,1 Marco A. H. Leite,2 Albino J. K. Leiroz,1, and Manuel E. Cruz1 1

Department of Mechanical Engineering/Politecnica/COPPE, Federal University of Rio de Janeiro (UFRJ), PO Box 68503, Rio de Janeiro, RJ, 21941-972, Brazil

2

Petrobras Research and Development Center, Av. Horácio Macedo 3324, Rio de Janeiro, RJ, 21941-915, Brazil

KEYWORDS. Pyrolysis, sugarcane bagasse, fluidized bed, biomass, CFD.

ABSTRACT. This work investigates computationally the modeling of sugarcane bagasse pyrolysis in a bubbling fluidized bed reactor. An Euler-Euler multiphase approach, as invoked by

Currently at the Department of Aerospace Engineering and Mechanics on the University of Minnesota. ♦



Corresponding Author

ACS Paragon Plus Environment

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the open code MFIX (Multiphase Flow with Interphase eXchanges), is adopted, and the simulations are carried out in a two-dimensional Cartesian domain. While several pyrolysis kinetic models have been developed for wood, coal, and generic biomass, generally using TGA analysis, no such model has been specifically tested or adapted to simulate sugarcane bagasse pyrolysis in fluidized bed reactors through CFD. In the present study, seven pyrolysis kinetic models available in the literature are implemented as MFIX user-supplied routines, and evaluated within given operational temperature ranges. Initially, well established wood pyrolysis results are used to validate the implementations. Following validation, six kinetic schemes are employed to simulate sugarcane bagasse pyrolysis. Results for the products distribution, formation reaction rate profiles, and tar composition at different operating temperatures of the fluidized bed reactor are obtained for all models, and compared to published experimental results. Based on the assessed predictive performances of the models, indications are drawn for the most appropriate models to simulate the reactor under different operating conditions.

1. INTRODUCTION During the last twenty years, an increased global awareness about greenhouse effects caused by the usage of fossil fuels has motivated studies to find less environmentally aggressive manners to generate power, thus raising interest in the so-called renewable energy resources. Among all renewable resources, biomass is one of the most promising. In fact, it has been used successfully to yield fuels in the automotive sector, like biodiesel and ethanol. One of the greatest advantages of biomass is that it contains carbon, as opposed to solar and wind sources, which permits a wide range of applications, after an adequate thermochemical or biological treatment.1


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The thermochemical process is highly efficient to transform biomass in solid, liquid, or gaseous products under specified thermal conditions.2 In addition, thermochemical conversion of biomass uses residual materials, thus it has no influence over the food supply market.3 In many developing countries sugarcane is a major culture, such that every year tons of sugarcane bagasse are produced. The bagasse is commonly burned in boilers to produce electric power for sugar mills.4 Important examples of thermochemical processes are pyrolysis, gasification, and combustion. Fast pyrolysis is of particular interest in the conversion of a solid fuel into liquid products, which can be used as alternative liquid fuels.5 Also, pyrolysis is one stage of the biomass combustion and gasification processes. Therefore, the understanding of the pyrolysis phenomena is crucial for all thermochemical conversion routes. In spite of the potential of sugarcane bagasse gasification for energy generation, very little research work6-8 is found on sugarcane bagasse pyrolysis. When biomass pyrolysis occurs in a fluidized bed reactor, the combination of hydrodynamics, mixing/segregation, heat transfer phenomena, and chemical reactions hinders the development of models with comprehensive kinetic mechanisms. Therefore, the utilization of simplified kinetic schemes becomes important for obtaining practical results.9 Different models for biomass pyrolysis have been described in the literature.9-12 Three model formulations are typically employed for pyrolysis description: the global one-step reaction model, the competitive reactions model, and the parallel reactions model.11-18 The most promising appear to be the competitive and the parallel reactions models, which consider different reaction constants for each pyrolysis product, allowing for the quantification of the temperature effect on the yields of volatiles and char.


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Competitive kinetic schemes for wood pyrolysis are described in Gronli and Melaaen,13 Park et al.,14 and Sharma et al.15 Such models may not be directly used to describe pyrolysis of other biomasses, because the kinetic parameters will change.2,11 On the other hand, parallel reactions models are, in principle, applicable to any biomass, as long as its composition is known in terms of the principal constituents, i.e., cellulose, hemicellulose and lignin.11 Generally, parallel reactions models for pyrolysis consist in the extension of the Broido-Shafizadeh model, valid for cellulose and described in Bradbury et al.,16 for hemicellulose and lignin, as given by Miller and Bellan17 and Ranzi et al.18 It is remarked that parallel reactions models do not typically consider the interactions between biomass constituents, and the catalytic effects of inorganic matter. An exception is the model proposed by Anca-Couce et al.,12 which accounts for the effect of alkali metals in the biomass. Recently, due to increased computer speed and improved numerical schemes, computational fluid dynamics (CFD) has become an important tool for the analysis and development of thermochemical conversion reactors.19 Xue and co-workers5,20 and Xiong and colleagues21,22 have conducted 2D Euler-Euler simulations to study the pyrolysis of pure cellulose and unspecified bagasse,5 red oak,20,21 and a generic biomass22 in a bubbling fluidized bed using the parallel pyrolysis scheme proposed by Miller and Bellan.17 Additionally, Xue et al.20 and Xiong et al.22 indicated that the 2D simulations yield sufficiently accurate results with a much smaller computational cost than 3D simulations. Mellin and co-workers23,24 carried out 3D simulations to study biomass pyrolysis in a fluidized bed reactor, where the gas and sand phases were treated in an Eulerian approach, and the biomass particles were tracked in a Lagrangian approach. The authors used the kinetic scheme by Ranzi et al.18 to describe the primary pyrolysis, coupled with the scheme proposed by Blondeau and Jeanmart25 to describe the tar cracking reactions. Mellin


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et al.24 compared the bed operation using nitrogen and steam as the fluidization agent, and verified that the model could not detect but a small difference in overall formation rate and composition. More recently, Sharma et al.26 investigated the effect of reactor temperature and particle size on the product yield of red oak pyrolysis, using the competitive reactions scheme developed in Sharma et al.15 in 2D Euler-Euler simulations. Apparently, the computational approaches found in the literature5,20-24,26,27 coupling biomass pyrolysis kinetics with CFD models focus on more traditional sources, such as wood. Ideally, the kinetic parameters of a pyrolysis parallel kinetic model should not depend on the biomass considered, however this statement has been questioned in the literature.10,11 In the present work, the focus is on the simulation of the sugarcane bagasse pyrolysis process in a bubbling fluidized bed reactor. For that matter, seven pyrolysis kinetic models are implemented in the open code MFIX (Multiphase Flow with Interphase eXchanges) to perform 2D Euler-Euler simulations within given operational temperature ranges. Initially, well established wood pyrolysis results are used to validate the implementations. Following validation, six kinetic schemes are selected to simulate sugarcane bagasse pyrolysis. Results for the products distribution, formation reaction rate profiles, and tar composition at different operating temperatures of the fluidized bed reactor are calculated for all models, and compared to published experimental results.7 Based on the assessed predictive performances of the models, indications are drawn for the most appropriate models to simulate this complex phenomenon within the operating conditions tested.

2. MATHEMATICAL MODELING In the present work, the so-called Euler-Euler approach28 is employed to describe the multiphase reactive flow in the fluidized bed reactor, with the gas and all solid phases being


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assumed as interpenetrating continua. The model considers that the different phases cannot occupy the same spatial location at the same time, therefore the volume fractions must satisfy

(1)

where αg is the gas volume fraction, αm is the volume fraction of the mth solid phase, and M is the total number of solid phases.

Gas Phase Governing Equations

The Eulerian description for the gas phase uses volume averaged balance equations for mass, momentum, energy, and species. The continuity equation for the gas phase is given by

(2)

where ρg, ug and Ng are the gas phase density, velocity and species number, respectively, and Rgn is the net mass exchange between the solid phase n and the gas phase due to heterogeneous reactions. The gas phase momentum equation can be written as


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(3)

where pg, τg and τgt are the gas phase pressure, viscous stress tensor and turbulent stress tensor, respectively, g is the acceleration of gravity, and ψgm accounts for the momentum generation due to mass transfer induced by chemical reactions between the gas and mth solid phases. The drag coefficient between the gas and mth solid phases, βgm, is modeled using the correlation given by Gidaspow.29 Assuming Newtonian behavior for the gas phase,29 and that the Boussinesq hypothesis is valid, the viscous and Reynolds stress tensors are

(4) (5)

where λg is the bulk viscosity, and I is the unit tensor. The turbulent viscosity μgt is given as

(6)

The turbulent kinetic energy

g

and associated dissipation rate εg are accounted for using the -

ε model,30 such that

(7)


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(8)

The constants of the turbulence models are σκ = 1.0, σε = 1.3, Cμ = 0.09, C1ε = 1.44, C2ε = 1.92. Finally, Πκg and Πεg are the turbulence interaction terms written as30

(9) (10)

respectively, where βgs is the drag coefficient term, kgs is the cross correlation between the gas and solids fluctuating velocities, and C3ε = 1.22 is a constant. Neglecting variations in kinetic and potential energies, viscous dissipation and volumetric expansion effects, the internal energy conservation equation is given by28

(11)

where Tg, cpg and qg are the gas phase temperature, constant-pressure specific heat and conductive heat flux, respectively, and ΔHg is the energy generation/consumption due to chemical reactions occurring in the gas phase. The heat transfer between the gas and mth solid phases, Wgm, is modeled using the Ranz-Marshall correlation.31 The chemical species conservation equation for the gas phase is written as


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(12)

where Ygn, Jgn and Rgn represent the mass fraction, mass flux vector and production or consumption rate of the nth gas species, respectively.

Solid Phases Governing Equations

The mass and momentum conservation equations for the mth solid phase, m = 1,…,Nm, are given, respectively, by

(13)

(14) ψ 1

ψ 1

In Eq. (13), um and ρm are the mth solid phase velocity and density, respectively, and Rmn is the net mass exchange rate between the mth solid phase and gas phase due to heterogeneous reactions. In Eq. (14), τm is the mth solid phase stress tensor, modeled through the Newtonian hypothesis, and βml and ψlm are, respectively, the drag coefficient and mass transfer induced source of momentum between the solid phases l and m. In dense fluidized bed flows, the solid stress tensor may have a kinetic/collisional contribution, commonly calculated using the Kinetic Theory of Granular Flows (KTGF), and/or a frictional contribution, frequently obtained using


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rheological theories from soil mechanics.32 Here, the KTGF as reported in Agrawal et al.33 is used to describe the solid stress tensor in regions where the volume fractions are smaller than 0.42, while the Schaffer theory28 is used where the volume fractions are greater than 0.42. Since the heat transfers between the different solid phases are relatively small,34,35 and similarly to previous computational studies,5,20-22,36-38 these energy interactions are here neglected. Thus, the energy conservation equation for the solid phase m can be written as28

(15)

where Tm, cpm and qm are, respectively, the temperature, constant-pressure specific heat and conductive heat flux for the mth solid phase, and ΔHm is the heat generated/consumed due to the chemical reactions occurring in the solid phase m. The chemical species conservation equation for the solid phase m is written as

(16)

where Ymn, Jmn and Rmn represent the mass fraction, mass flux vector and production or consumption rate of the nth species in the mth solid phase, respectively. It must be remarked that, according to the KTGF, the solid stress tensor, τm, in Eq. (14) is a function of the so-called granular temperature,

, defined by28,39

(17)


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where EΘm is the granular energy, mathematically defined in terms of the instantaneous fluctuation of the mth solid phase velocity. Therefore, in addition to the classical conservation equations, the Euler-Euler description of gas-particle flows requires the solution of one additional equation for the granular temperature. In bubbling fluidized beds it is possible to consider28,39 that the granular energy is locally dissipated, that the convective and diffusive contributions can be neglected, and that only the production and consumption source terms are significant. Thus, the granular temperature transport equation is reduced to the simple algebraic equation28,39

(18)

where Dm is the rate of strain tensor, and the expressions for K1m, K2m, K3m and K4m are found in the literature.28,39

Numerical Solution Procedure

The CFD computational tool employed in the present work is the MFIX open source code, a general-purpose program developed by the National Energy Technology Laboratory capable of describing multiphase flows with chemical reactions and heat transfer in dense or dilute fluidsolids mixtures.40,41 The chemical kinetics models investigated in this work are implemented in user-defined routines.


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The MFIX program uses the finite volume method and the second-order TVD scheme to spatially and temporally discretize the unsteady mass, momentum, energy, and species balance equations for the gas and multiple solid phases.37 In order to solve the system of discretized equations, a semi-implicit modified SIMPLE algorithm is applied, with a correction equation for the volumetric fraction of each solid phase. For the gas phase, the pressure correction equation is applied. Additionally, a Partial Elimination Algorithm41 (PEA) is used due to the existence of interphase coupling terms.

3. PYROLYSIS MODELING Seven pyrolysis kinetic schemes are evaluated in this work: three competitive reactions models, denoted here by GMB,13,37,42 PAR,14 and SHA15 models, and four parallel reactions models, denoted here by MB,17 RAN,18 BJR,25 and ANC12 models. The competitive reactions models are represented in Figure 1. The GMB model consists in the pyrolysis scheme proposed by Gronli and Melaaen,13 coupled with the proposition by Boroson et al.42 As a result, wood degrades into gas, tar, and char by three parallel reactions, and tar cracks into 22% of inert tar and 78% of gas. The kinetic parameters of the primary reactions are calculated by Gronli and Melaaen13 for wood pyrolysis, while those for tar cracking are obtained by Boroson et al.42


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Figure 1. Competitive reactions pyrolysis schemes.

The PAR model consists in the pyrolysis scheme proposed by Park et al.14 As shown in Figure 1, the model assumes the thermal decomposition of wood in an intermediate solid, and its subsequent decomposition in char. Park et al.14 obtained the kinetic constants for the Arrhenius rates of those reactions at temperatures ranging from 365 °C to 606 °C. Additionally, the authors also considered the presence of a tar re-polymerization reaction competing with the tar cracking reaction, yielding gas. Kinetic parameters suggested by Park et al.14 are obtained from Di Blasi43 for tar re-polymerization, Di Blasi and Branca44 for the primary reactions, and Liden et al.45 for tar cracking. The SHA model is a biomass pyrolysis scheme advanced by Sharma et al.,15 and it is very similar to the GMB model. The difference between the SHA and GMB models is the inclusion in the former of a mechanism for the secondary heterogeneous reaction of tar, which leads to the


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formation of char by re-polymerization reactions at higher temperatures, as stated by Di Blasi.43 Kinetic parameters suggested by Sharma et al.15 are obtained from Di Blasi and Branca44 for the primary reactions, Chan et al.46 for tar cracking, and Di Blasi43 for tar re-polymerization. Miller and Bellan17 generalized the model developed for cellulose in Bradbury et al.16 to include hemicellulose and lignin, and proposed one additional reaction to account for the cracking of condensable tar into low-molecular-weight gases. The model, denoted by MB, is shown in Figure 2, and the kinetic parameters and char formation mass ratios (X fractions) for the corresponding reactions are given in Miller and Bellan.17 It is noteworthy that the noncondensable gases (NCG) and tar compositions are not given explicitly in the MB model.

Figure 2. Biomass pyrolysis scheme of the MB model.17

The RAN model has been proposed by Ranzi et al.18 with a relatively more complex set of reactions, including the formation of levoglucosan (LVG), xylose monomer (XYL), and sinapaldehyde (FE2MACR) from cellulose, hemicellulose, and lignin, respectively. As shown in Figure 3, the pyrolysis of the hemicellulose contained in the biomass yields two types of active hemicellulose, HCEA1 and HCEA2. The authors consider three types of lignin, LIG-C, LIG-H,


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and LIG-O, which are rich in carbon, hydrogen, and oxygen, respectively. The RAN model provides the compositions of NCG and tar, as well as the stoichiometries of the corresponding reactions. The kinetic parameters for the model are given in Ranzi et al.18

Figure 3. Biomass pyrolysis scheme of the RAN18 and BJR25 models.

The BJR model consists of the RAN model with the modifications proposed by Blondeau and Jeanmart25 to increase the accuracy at high temperatures. While the scheme of the BJR model does not change relative to that of the RAN model, represented in Figure 3, the modifications consist in changing some of the RAN’s kinetic constants by the respective ones given in the MB model. All tar cracking reactions are modeled using the kinetic parameters proposed by Blondeau and Jeanmart.25


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Anca-Couce et al.12 proposes a modification of the RAN model that includes the secondary char formation reactions and the catalytic effect of alkali metals on sugar formation. The ANC model, shown in Figure 4, ignores the activation reactions, and includes the parameters xC1 for cellulose, xH1 and xH2 for hemicellulose, and xL1 and xL2 for lignin. In general, the x parameters are functions of the gas retention time inside the particle, as well as the gas partial pressure and temperature, and the presence of minerals. In their simulations, however, Anca-Couce et al.12 simply adopt constant values for the x parameters. It is here remarked, that such simplification requires, that these parameters be calibrated for each different situation. An important difference between the RAN and ANC models is the formation of sugars, i.e., levoglucosan from cellulose and xylose monomer from hemicellulose. Ance-Couce et al.12 found that the RAN model overestimates the amounts of levoglucosan and xylose monomer, because the model does not account for the catalytic thermal decomposition of sugars. Consequently, in the ANC model, Ance-Couce et al.12 have assumed that all levoglucosan and xylose monomer are decomposed due to catalytic effects of mineral matter (AAEM).

Figure 4. Biomass pyrolysis scheme of the ANC12 model.


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It is important to remark that, as depicted in Figures 1-4, appropriate gas phase reactions are, indeed, explicitly included for the considered pyrolysis models in order to account for further evolution of the tars in the freeboard region and other downstream effects. In this sense, the present work follows in the footsteps of well established wood pyrolysis studies available in the literature5,20-24,26,37. Due to space limitations, the complete set of the considered gas-phase reactions is not reproduced here, but is readily available from the pyrolysis models in the original references.12,14,15,17,18,37,42

4. VALIDATION FOR WOOD PYROLYSIS First, to adequately prepare for the sugarcane bagasse simulations, well-known wood pyrolysis results reported in the literature are used to validate the computational implementations of the seven models described in the previous section. For this purpose, the pyrolysis system described in Xue et al.20 and Xiong et al.21 is considered. The modeled reactor possesses a height of 34.29 cm, and a diameter of 3.81 cm. A mesh with 10 x 90 volumes discretizes the domain, and a first order upwind scheme discretizes the conservation equations, as suggested by Xiong et al.21 The wood used in the simulations is composed of 41% of cellulose, 32% of hemicellulose, 27% of lignin, and is free of moisture and ash.20,21 Nitrogen enters at the bottom of the reactor with a superficial velocity of 36 m/s and a temperature of 500 °C. Wood enters the reactor through a lateral inlet located 5.5 cm above the gas inlet, with a temperature of 27 °C and a mass flow rate of 0.1 kg/h. From the reactor bottom up to a height of 8 cm, a wall constant temperature of 527 °C is employed to simulate the heating of the fluidized bed, while the remaining of the reactor is assumed adiabatic. The physical properties of the gases and solids are given in Table 1.


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Table 2 shows the wood pyrolysis results from the present simulations and from the experiments reported in Xue et al.20 Root Mean Square (RMS) deviations between the obtained numerical results and the experimental data are also shown in Table 2. In evaluating the RMS deviations for the considered pyrolysis models, the absolute difference between a numerical result for a given product species and the corresponding mean value of the experimental data range is used. The indicated numerical results are taken as time averages at the outlet section of the reactor. Based on numerical experimentations conducted by Brandão,47 the values of the x parameters to obtain the ANC model results are 0.0 for xC1, 0.7 for xH1 and xH2, and 0.0 for xL1 and xL2.

Table 1. Physical properties of the solid and gas phases. dp [mm]

Cp [J/kg.K]

μ [kg/ms]

κ [W/m.K]

-

-

2,500

3 x 10-5

2.577 x 10-2

Noncondensable gás

-

1,100

3 x 10-5

2.577 x 10-2

N2

-

-

1,121

3.58 x 10-5

5.63 x 10-2

Biomass (wood)

400.0

0.40

2,300

-

0.3

Sand

2,649.0

0.52

800

-

0.27

[kg/m3]

Species Tar

Based on the RMS deviations, Table 2 indicates that the MB model (RMS = 4.5%) provides the best results for the pyrolysis of wood, leading to good estimates of the composition of the process products. The MB model also best predicts the temperature of the products leaving the reactor. The RAN model is known to overestimate the NCG yields, and to underestimate the tar


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yields at high temperature conditions.25 The BJR model improves upon the RAN model with respect to the NCG and tar yields, but both models predict very similar results for the amount of char produced and for the temperature of the NCG leaving the reactor. It is noteworthy that the BJR pyrolysis model, aimed to improve the RAN model for higher temperatures, also leads to satisfactory predictions (RMS = 5.6%). The ANC model (RMS = 8.6%) proposed as a correction to the RAN model to account for the presence of mineral matter has a similar performance as the original RAN model (RMS = 9.7%).

Table 2. Wood pyrolysis products yielded in the present simulations and in the experiments.20

NCG [%]

Tar [%]

Char [%]

RMS Deviation [%]

Tout [ºC]

Exp.20

19.2 - 21.8

70.3 - 73.1

11.5 - 14.5

-

500.0

MB

19.5

64.5

16.0

4.5

500.1

RAN

31.7

60.1

8.2

9.7

476.8

BJR

26.3

65.3

8.4

5.6

480.1

ANC

24.1

58.0

17.9

8.6

482.4

GMB

13.6

56.3

30.1

13.8

497.2

PAR

26.2

65.5

8.3

5.6

500.5

SHA

15.2

77.7

7.1

5.7

501.1

It is further verified in Table 2 that the competitive reactions pyrolysis models GMB, PAR, and SHA predict similar temperatures for the NCG leaving the reactor. However, the predicted amounts of gas, tar, and char differ markedly (5.6%


RMS

13.8%). Some general

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observations about the numerical data can be stated. The GMB model significantly overestimates the amount of char yielded, resulting in the reduced production of NCG and tar. Both the PAR and SHA models predict similar amounts of char and NCG exiting temperatures. Nevertheless, while the PAR model overestimates the amount of NCG and underestimates the amount of tar, the SHA model has the opposite behavior.

5. COMPUTATIONAL SETUP FOR SUGARCANE BAGASSE The fluidized bed reactor geometry considered in this work for the sugarcane bagasse pyrolysis study is adapted from the one described by Hugo.7 The bed is in the bubbling regime, for which the 2D computational domain simplification is known to yield satisfactory results.20,21,48 For that matter, it must be assured that the superficial velocity of the fluidizing agent and the ratio of biomass and gas mass flow rates are the same for the 3D real domain and for the 2D computational domain. It is worth remarking that an axisymmetric model domain has not been considered since the biomass is fed through a non circumferential lateral port in the experiment.7 Besides, it has been reported in the literature that the use of axisymmetric domains for describing a gas-particle bubbling bed may lead to unphysical behavior of the solid volume fraction along the reactor centerline.49 As shown in Figure 5, the reactor has a total height of 64 cm, while the diameter varies axially. The biomass enters the reactor with a temperature of 50 °C at a mass flow rate of 0.9 kg/h by a lateral port of 52 mm in diameter and located 168.5 mm above the inlet of the fluidizing agent. Nitrogen is used as the fluidizing agent, which enters the reactor at the bottom with a temperature of 500 °C and a velocity of 37.19 cm/s.


20

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Figure 5. Two dimensional Cartesian domain for the fluidized bed reactor.

In the experimental setup reported by Hugo,7 the reactor is heated by a surrounding oven to control the bed temperature, such that the wall temperature is kept constant when the desired set point is reached. Consequently, in the present computations, a prescribed wall temperature is imposed along the reactor boundary. It has been seen that for the wood pyrolysis simulations, the MB model best predicted the gas outlet temperature. Therefore, for the sugarcane simulations, the MB model has been employed to estimate the reactor wall temperatures (Tw), that would lead to the outlet products temperatures (Tout,exp) for the three cases described by Hugo7 within the respective reported experimental ranges, as shown in Table 3. The moisture contents of the


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sugarcane bagasse samples as measured by Hugo7 before each experiment are also shown in Table 3.

Table 3. Temperature boundary condition for each experimental case.7

Case A

Case B

Case C

Tout,exp [ºC]

428 ± 5

495 ± 2

526 ± 3

Tw [ºC]

444

512

545

Ymoist [%]

8.1

6.7

7.6

Following the literature for bubbling fluidized beds,50 the no-slip condition is imposed for both phases. The density and diameter of the sugarcane bagasse particles are 200 kg/m3 and 2.0 mm, respectively. The density and diameter of the sand particles are 2650 kg/m3 and 0.5 mm, respectively. The specific heat and thermal conductivity of the sugarcane bagasse are assumed equal to 1.760 J/kg.K51 and 0.1 W/m.K,52 respectively. The values of the specific heat, dynamic viscosity, and thermal conductivity of the sand and gaseous products are the same as those used in the validation simulations, given in Table 1. For simulation purposes, the drying process is assumed instantaneous,37,48 so that a stream of water vapor is fed to the reactor with the biomass to consider the moisture content. The dry basis ultimate analysis of the sugarcane bagasse considered by Hugo7 indicates 50.48% of C, 6.27% of H, and 43.25% of O, while the proximate analysis indicates 11.90% of fixed carbon, 82.50% of volatiles, and an ash content of 5.60%. The amounts of cellulose, hemicellulose, and lignin of the biomass are not explicitly provided. A procedure developed by Ranzi et al.18 is thus used to estimate the amounts of cellulose, hemicellulose, and three different


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types of lignin from the ultimate analysis. The obtained mass concentration percentages of the lignocellulosic components of the bagasse in dry basis are shown in Table 4 It must be stressed that the MB model considers only a single and general lignin type. Therefore, the sum of the amounts of the different lignin types (i.e., LIG-C, LIG-H and LIG-O) obtained from the Ranzi et al. procedure18 is used as input for the simulations when the MB model is employed.

Table 4. Estimated lignocellulosic components of the sugarcane bagasse considered by Hugo.7 Lignocellulosic Cellulose component % dry basis

40.6

Hemicellulose

LIG-C

LIG-H

LIG-O

Ash

22.0

4.1

27.1

0.6

5.6

A Total Variation Diminishing (TVD) scheme is used for the spatial discretization of the convective terms, using the Smart53 flux limiter. The marching scheme for the time integration of the governing equations is the implicit Euler scheme, with adaptive time stepping to reduce computational cost. Three different meshes have been tested, 28 x 64, 38 x 128 and 74 x 256, at a wall temperature of 526.9 oC, which corresponds to the highest experimental gas outlet temperature. For each mesh, average results for the last 20 seconds of the stationary regime are shown in Table 5. Four variables are verified considering four significant digits, the outlet temperature Tout, char volume fraction αchar along the whole reactor, and the outlet mass fractions of NCG, YNCG, and tar, Ytar. Given the small deviations (less than 3%) encountered between the results for the two more refined meshes, the intermediate size mesh 38 x 128 is henceforth used in the simulations.

Table 5. Average values for the last 20 seconds of the stationary regime.


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Mesh

Tout [oC]

αchar

YNCG [%]

Ytar [%]

28 x 64

511.3

1.601 E-04

8.614

18.69

38 x 128

511.7

1.465 E-04

8.715

18.81

74 x 256

514.2

1.422 E-04

8.730

18.55

6. SUGARCANE BAGASSE PYROLYSIS RESULTS AND DISCUSSION In this section, six pyrolysis models are selected for the simulations of sugarcane bagasse pyrolysis. The GMB model has been discarded, since it provides predictions associated with the largest RMS deviation for the products of wood pyrolysis, as shown in Table 2. Three cases are considered, corresponding to the different operational temperatures indicated in Table 3. All the results presented in this section are obtained as time averages over 20 s of simulation, once pseudo-stationary conditions for the exiting gas temperature and char content within the reactor are obtained. The PAR, SHA, MB, RAN, and BJR models are used exactly as they are originally proposed by the respective authors, with no allowance for parameter tuning. For the ANC model, on the other hand, the values or a calculation method for the x parameters are not a priori indicated for a given biomass.12 In the present work, for sugarcane bagasse pyrolysis, they have been chosen based on numerical experimentations conducted by Brandão.47 Thus, for Cases A and B, xC1 = xL1 = xL2 = 0.0 and xH1 = xH2 = 0.2, and for Case C, xC1 = xL1 = xL2 = 0.0 and xH1 = xH2 = 0.6. The different values of the x parameters for Case C with respect to those for Cases A and B may be a consequence of a nonlinear effect of the bed temperatures on the gas retention times inside the particles, as well as the gas partial pressures and temperatures, which affect the x parameters.12


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Energy & Fuels

Comparison between simulations and experiments

Table 6 shows the results for Case A that corresponds to the lowest operational temperature considered for sugarcane bagasse pyrolysis. Results show, based on the RMS deviations, that the ANC model provides the best prediction to the experimentally obtained values (RMS = 5.8%), followed by the RAN and BJR models (respectively, RMS = 7.8% and RMS = 7.9%). All the models overestimated the amount of tar, and underestimated the NCG generated. With respect to the amount of char, only the SHA model is seen to predict a value within the experimental range. With respect to the gas outlet temperature, an analysis of the terms in the global energy balance equation applied to the reactor indicates that their magnitudes vary substantially among the pyrolysis models, since the predicted products compositions are very different. The latter influences the heats of reactions and, thus, the reactor wall heat flux to maintain constant temperature. It turns out, as observed in Table 6, that the models that best predict the gas outlet temperature are the MB and SHA models. As the operational temperature is increased, Case B, results in Table 7 indicate that the numerical predictions of all investigated models improve, with significant reductions in the RMS values, with exception of the SHA model. As in Case A, the RAN model gives the best prediction (RMS = 0.6%) for Case B, followed by the ANC model (RMS = 1.6%). Both models are the only ones to predict NCG, tar, and char contents within the respective experimental ranges, but underestimating the bio-oil content. It is also noted that the BJR and PAR models lead to similar results for Case B, while the SHA model fails to provide pyrolysis products contents within the respective experimental ranges.


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Results for Case C, with the highest operational temperature, are shown in Table 8. As in the previous Cases, the RAN (RMS = 1.8%) and ANC (RMS = 2.0%) models present the best predictive performances for the products distribution for Case C, providing the NCG and tar contents within their experimental ranges. The MB and PAR models also satisfactorily predict products distribution for Case C. It is noteworthy that the predictive performance of the MB model improves considerably as the operational temperature increases, a tendency reported in the literature.25 The RMS value for the SHA model remains larger than the other models, while the gas outlet temperature is accurately predicted. Results in Tables 7 and 8 suggest, that the lowcomputational-cost PAR model can be employed equivalently to the RAN and ANC models to simulate sugarcane bagasse pyrolysis at higher operational temperatures ( 495 oC), provided NCG and tar compositions are not being sought. As a final note, in the relatively moderate temperature range investigated, it is observed that the use of the BJR model does not significantly improve the model predictions with respect to the RAN model.

Table 6. Products of sugarcane bagasse pyrolysis and gas outlet temperature for Case A.

a

Exp.7

MB

RAN

BJR

ANC

PAR

SHA

NCG [%]

25–33

15.0

24.0

24.2

26.3

15.6

13.2

Tar [%]a

56–62

62.4

65.3

66.9

62.6

69.5

74.0

(Bio-oil)

(48-54)

(NA)b

(58.8)

(63.5)

(54.2)

(NA)b

(NA)b

Char [%]

10-12

22.6

10.7

8.9

11.1

14.9

12.8

RMS Deviation [%]

-

10.7

4.6

5.5

2.6

10.1

12.6

Tout [ºC]

423–433

424.5

395.1

394.2

388.9

416.2

423.1

including Bio-Oil not available for the model

b


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Table 7. Products of sugarcane bagasse pyrolysis and gas outlet temperature for Case B.

a

Exp.7

MB

RAN

BJR

ANC

PAR

SHA

NCG [%]

21 – 29

23.2

27.8

26.1

29.0

25.1

14.7

Tar [%]a

62 – 68

58.4

62.2

65.0

59.9

64.9

77.2

(Bio-oil)

(54 -60)

(NA)b

(57.4)

(61.9)

(51.4)

(NA)b

(NA)b

Char [%]

8 – 10

18.4

10.0

8.9

11.1

10.0

8.1

RMS Deviation [%]

-

6.7

2.4

0.6

3.9

0.6

9.2

Tout [ºC]

493 - 497

497.0

455.8

455.6

472.1

477.8

493.3


b

Table 8. Products of sugarcane bagasse pyrolysis and gas outlet temperature for Case C.

a

Exp.7

MB

RAN

BJR

ANC

PAR

SHA

NCG [%]

25 – 33

31.5

32.5

31.3

29.9

32.7

15.2

Tar [%]a

56 – 62

51.7

57.2

59.7

54.4

58.7

77.9

(Bio-oil)

(48 - 54)

(NA)b

(52.7)

(58.6)

(41.9)

(NA)b

(NA)b

Char [%]

10 - 12

16.8

10.3

9.0

15.7

8.6

6.9

RMS Deviation [%]

-

5.6

2.3

1.8

3.8

2.6

13.7

Tout [ºC]

523 - 529

528.7

490.6

499.4

503.1

508.5

528.7


b


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Experimental results in Tables 6, 7 and 8 suggest that a non-monotonic behavior of the product distribution may be expected as the operational temperature is raised. Experimental results show that the tar yield reaches a maximum for the intermediate Case B, while the char and NCG yields reach a minimum for the same case. The anomalous char fraction increase in Case C compared to Case B is explained by Hugo7 as being a consequence of a higher humidity content in the sugarcane bagasse particles for the former case. The higher humidity content, in turn, would lead to a lower temperature inside the particles, thus increasing char formation. It is interesting to note that, in spite of the consideration of the biomass moisture content and heterogeneous and gas phase reactions, none of the models studied has corroborated such non-monotonic behavior for sugarcane bagasse pyrolysis under the conditions considered. This mismatch between numerical and experimental data is probably due to the fact, that the Euler-Euler approach employed here does not consider the intra-particle transport phenomena related to char formation. Consideration of such phenomena is beyond the scope of this work.

Formation Reaction Rate Analysis

The obtained numerical results are also used to study the formation reaction rates and biomass volume fraction profiles along the reactor. In Figures 6-11, reaction rates for the formation of NCG, tar, and char are shown while biomass volume fraction profiles are depicted in Figure 12. The results in Figures 6-12 are obtained with the MB, RAN, BJR, PAR, SHA, and ANC models for the operational temperatures associated with Cases A, B and C. Results in Figures 6-11 show reaction rate peaks in the near vicinity of the particulate bed top at a height of 28 cm. These peaks are associated with the biomass volume fraction peaks shown


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Energy & Fuels

in Figure 12, thus indicating the existence of a segregated particulate bed. The segregated bed is composed of a top region with biomass and an inert material layer underneath, for all cases considered. While Hugo7 does not report on such bed segregated structure, it has been experimentally and numerically observed by Qiaoqun et al.54 for rice husk-sand mixtures. o

For the low-operational temperature case (Case A -

C), reaction rate results in

Figures 6 (MB model), 9 (PAR model) and 10 (SHA model) show similar profiles along the reactor and indicate preferential formation of char and tar over NCG for the conditions studied. This behavior is verified by the results in Table 6, where it is noted that the MB, PAR and SHA models underpredict the NCG content of the pyrolysis products. On the other hand, results in Table 6 also show that the use of the ANC model leads to the lowest RMS deviation. Indeed, a different behavior is observed from the results for Case A in Figures 7 (RAN model), 8 (BJR model) and 11 (ANC model), which show that the reaction rates for NCG, tar and char have analogous profiles, with peaks of similar intensities. While it would be expected that the reaction rates peaks in general depend on the biomass composition, reactor hydrodynamics and operating conditions, for the cases studied in the present work, it is observed that models with more equilibrated formation reaction rates are able to better predict the yield composition. For the mid-operational temperature case (Case B -

1

o

C), reaction rate peaks are

reduced in comparison with those for Case A, since pyrolysis becomes more intense in the region between the biomass injection height and the top of the particle bed for all models considered. Figure 12 corroborates this observation, displaying lower biomass volume fraction values at the particle bed upper limit. While the RAN and ANC models (Figures 7 and 11, respectively) lead to similar reaction rate formation profiles along the reactor for NCG, tar, and char, the PAR model predicted preferential tar formation (Figure 9b), especially near the particulate bed top


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end. Since the heterogeneous and gas phase reactions are considered in the present study, the similarity of the ANC, RAN and PAR models as predictors of the pyrolysis products, as shown in Table 7, can be explained by the secondary pyrolysis of the generated tar in the freeboard region for the PAR model. In fact, non-vanishing rates of NCG generation for Case B are shown in Figure 9a in the freeboard region. Therefore, from the results in Table 7 and in Figures 7, 9 and 11, it can be inferred that the amount of NCG generated within the particulate bed and by tar cracking in the freeboard region predicted by the PAR model is similar to the amount of NCG generated within the particulate bed predicted by the RAN and ANC models. Corroborating this statement, results depicted in Figure 12 show that the biomass volume fraction vanishes in the freeboard for the PAR model, implying that the NCG generation is related only to tar cracking in this region. The RMS deviation values shown in Table 8 indicate that the RAN (RMS = 1.8%), ANC (RMS = 2.0%), MB (RMS = 2.4%) and PAR (RMS = 3.5%) models provide the best predictions for Case C (Tw = 545 oC). Reaction rate peaks for the RAN and ANC models are less pronounced for Case C than they are for Case B, as primary pyrolysis reaction rates become significant at lower reactor heights. Enhanced primary pyrolysis consumes the injected sugarcane bagasse particles closer to the biomass injection height, and reduces biomass volume fraction at the solid bed upper boundary, as shown in Figure 12. Similarly to the discussion presented for Case B, the PAR model relies on temperature intensified secondary pyrolysis of the produced tar within the freeboard to yield satisfactory results for NCG and tar. The same intensification of secondary pyrolysis is also observed in the MB model, as observed in Figure 6. It is also noteworthy that, for the PAR model, reaction rates for tar show a weak dependence on temperature in the range between Cases B and C.


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Energy & Fuels

The effect of operational temperature variation on the secondary pyrolysis can be observed in Figures 6-11 in the regions after the peak of the reaction rates. As observed with the primary pyrolysis, the influence of increasing temperature on the secondary pyrolysis is stronger in the MB and PAR models, which produce more non-condensable gases in the freeboard region. Similarly for all temperatures, the secondary pyrolysis generates secondary char for the RAN, BJR, PAR, and ANC models. However, Figure 10 shows that the operational temperatures considered are not sufficiently high to activate the secondary pyrolysis of the SHA model, which may explain the corresponding high amounts of tar predicted as indicated in Tables 6, 7 and 8. Finally, as displayed in Figure 12, it is remarked that the MB model predicts lower biomass volumetric concentrations along the reactor than the other models. Indeed, a comparison of Figures 6-11 shows that the char formation reaction rate for the MB model is higher than for the other models. Therefore, a higher char yield is obtained with the MB model (Tables 6-8), what justifies the lower biomass volume fractions in Figure 12.

Figure 6. Formation reaction rates of the MB model for NCG (a), tar (b) and char (c).


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Figure 7. Formation reaction rates of the RAN model for NCG (a), tar (b) and char (c).

Figure 8. Formation reaction rates of the BJR model for NCG (a), tar (b) and char (c).


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Figure 9. Formation reaction rates of the PAR model for NCG (a), tar (b) and char (c).

Figure 10. Formation reaction rates of the SHA model for NCG (a), tar (b) and char (c).


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Figure 11. Formation reaction rates of the ANC model for NCG (a), tar (b) and char (c).

Figure 12. Biomass volume fraction along the reactor for all three cases and six models.


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Energy & Fuels

Computational costs information associated with the simulations performed for sugarcane bagasse pyrolysis is of interest for researchers enduring in this subject. An analysis of the computational costs associated with the numerical simulation of the different sugarcane bagasse pyrolysis models considered, indicate that the PAR model requires computer times of, approximately, 30 h in a machine with 2.4 GHz processors running 12 tasks simultaneously, leading to the fastest simulations. No significant differences in computational costs are observed when the ANC, RAN and BJR models are used. A simulation using one of these models exceeds the computer time required by the corresponding PAR model simulation by a factor of approximately 1.8. Finally, for the conditions considered, a simulation using the MB model requires approximately 1.3 more computer time than the corresponding PAR model simulation.

Comments on the Produced Tar Composition

Tar has been considered as an important feedstock for chemical processing to obtain higher value products such as gasoline and diesel.55 The carbon (C), hydrogen (H), and oxygen (O) contents of the biomass produced tar is recognized as important parameters in determining its utilization.55 The comparisons of present numerical predictions with experimental results obtained through direct measurements7 and calculated values7 based on experimentally obtained composition data is presented in Table 9 for Cases A, B, and C for the elemental composition and the lower heating value (LHV) of the tar generated from sugarcane bagasse pyrolysis. The predicted tar LHV values are obtained as a function of the carbon, hydrogen and oxygen mass contents, using the expression given in Channiwala and Parikh.56 It is noted that only the RAN,


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BJR and ANC models permit the direct numerical evaluation of the tar elemental composition by considering tar formation by each biomass component (cellulose, hemicellulose and lignin). For the MB model, the tar elemental composition is estimated from these biomass components formulae1,25 and the stoichiometry of the tar formation reactions (Fig. 2). Results in Table 9 show that, for the lower temperature Case A, the RAN and ANC are the only models capable of numerically predicting the tar carbon and oxygen contents within the respective experimental ranges, while the hydrogen mass fraction predictions for both models present deviations smaller than 6%. For Case B, on the other hand, the RAN model results marginally worsen, while the BJR model provides the best predictions (RMS = 1.9%), with the tar oxygen content within the experimental range For the higher operational temperature Case C, all four models under predict the carbon content of the produced tar, while they over predict the oxygen content. Regarding the LHV of the produced tar, results in Table 9 show that calculated values for all models overestimate the experimentally measured values over the considered temperature range. It is somewhat surprising that the models with the smaller RMS values for the tar elemental composition do not necessarily lead to the best predictions of the LHV. In fact, the calculated LHV values for the MB model, with the lowest H contents, best approach the experimental LHV. On the other hand, the models with the smaller RMS values for the tar elemental composition do lead to the best or second best values for the relative deviations with respect to the calculated values7 for the LHV, also using Channiwala and Parikh,56 based on measured composition data. Results in Table 9 reinforce the importance of accurate experimental and numerical determination of the elemental composition for the definition of tar global properties, such as LHV.


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Table 9. Elemental analysis and LHV of the produced tar. Case A Exp. C [%] H [%] O [%] RMS [%] LHV [MJ/kg] [%] C [%] H [%] O [%] RMS [%] LHV [MJ/kg]

7

MB

RAN

BJR

ANC

48.6 6.1 45.3 1.6

48.9 6.9 44.2 1.0

46.9 7.0 46.1 2.5

51.1 7.0 41.9 0.9

17.2

18.4

17.6

19.6

7.0 8.3 Case B 43.3 – 47.3 49.1 6.3 – 6.5 6.1 46.3 – 50.3 44.9 – 3.0

14.7 1.7

9.6 6.1

21.7 4.3

48.0 7.0 45.0 2.5

47.3 6.9 45.8 1.9

51.0 7.1 41.9 5.0

18.1

17.7

19.5

48.2 – 52.2 6.5 – 6.7 41.2 – 45.2 – 16.00 – 16.12a 17.7 – 19.7b –

16.28 – 16.40a 14.7 – 16.7b

17.4

6.3 10.7 8.1 19.6 10.4 14.9 12.2 24.1 Case C C [%] 52.3 – 56.3 49.2 47.5 47.4 50.2 H [%] 6.3 – 6.5 6.1 7.1 6.9 7.2 O [%] 37.3 – 41.3 44.8 45.4 45.7 42.6 RMS [%] – 4.3 5.3 5.5 3.1 a 15.16 – 15.27 LHV 17.4 18.0 17.7 19.3 [MJ/kg] 18.7 – 20.7b 14.4 18.2 16.3 26.9 – [%] 11.8 8.9 10.4 2.2 a 7 experimental results obtained through direct measurements by Hugo b calculated results by Hugo7 using the expression by Channiwala and Parikh56 [%]

–

shown in Table 9. No clear correlation has been obtained between good composition prediction (low RMS) and appropriate LHV values reinforcing the importance of precise determination of the elemental composition for the definition of tar global properties, such as LHV.


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7. CONCLUSIONS In the present work, a numerical investigation of the sugarcane bagasse pyrolysis in a bubbling fluidized bed reactor has been conducted, using a CFD Euler-Euler multiphase approach. Initially, seven pyrolysis kinetic models described in the literature are implemented as MFIX user-supplied routines, and validated against well-known wood pyrolysis results. Six kinetic models are then selected, and applied to sugarcane bagasse pyrolysis. Numerically obtained results are compared with experimental data available in the literature for three operational reactor temperatures, and deviation values are obtained for pyrolysis products distribution and for elemental composition of tar. A tentative qualitative correlation between the axial formation rate profiles and the pyrolysis products distribution is also discussed. Based on the numerical results, recommendations on the application of traditional biomass pyrolysis models to sugarcane bagasse can be offered. From the overall deviations between numerical and experimental data, the ANC and RAN models are recommended for the exiting gas temperature range from 428 oC to 526 oC. If a single model is to be implemented for sugarcane bagasse pyrolysis CFD studies in the range of operational temperatures considered, the RAN model may be selected for having the least maximum deviation, or the ANC model may be selected for having the best average deviation. Numerically obtained results for the axial formation rate profiles associated with NCG, tar and char are also analysed. For the conditions considered in this study and based on calculated deviations, the results suggest that, models predicting similar reaction rates for the different pyrolysis products lead to better product distribution predictions, as compared to models that predict very different formation reaction rates among the product species.


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Finally, the calculated results for tar composition and LHV for sugarcane bagasse pyrolysis are also compared with experimental data. Although the investigated models satisfactorily predicted the tar elemental compositions, with RMS deviation varying between 0.9% and 5.5%, the considered pyrolysis models fail to provide predictions within the experimental ranges. The tar composition predicting performance of the pyrolysis models could be evaluated for wood following a procedure similar to the one described in Section 4. Unfortunately, however, the experimental elemental tar composition is not available in the works used for validation.20,21 It is thus verified that the accuracies of the sugarcane bagasse pyrolysis models are yet to be improved for the prediction of the LHV of tar.

8. ACKNOWLEDGMENTS The authors would like to thank PETROBRAS (Project 0050.0096779.15.9) and the ANP/PRH-37 program for financial support. Prof. M.E. Cruz is grateful to CNPq–Brazilian Council for Development of Science and Technology for Grant PQ-303208/2014-7.

9. ABBREVIATIONS ANC = biomass pyrolysis model proposed by Anca-Couce et al.12 BJR = modification proposed by Blondeau and Jeanmart25 of the biomass pyrolysis model proposed by Ranzi et al.18 CFD = computational fluid dynamics GMB = biomass pyrolysis model composed of the wood pyrolysis model proposed by Gronli and Melaaen,13 and kinetic parameters for tar cracking obtained by Boroson et al.38 LHV = lower heating value MB = biomass pyrolysis model proposed by Miller and Bellan17 MFIX = multiphase flow with interphase exchanges


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NCG = non-condensable gas PAR = biomass pyrolysis model proposed by Park et al.14 RAN = biomass pyrolysis model proposed by Ranzi et al.18 SHA = biomass pyrolysis model proposed by Sharma et al.15

Nomenclature Cp = heat capacity, J/(kg·K) D = diffusion rate coefficient, kg/(m·s) d = particle diameter, mm EΘm = granular energy of solid phase m g = gravity acceleration, m/s2 h = reactor height, m k = thermal conductivity, J/(m·s·K) ki = Arrhenius constant of the reaction i, 1/s Km = expressions of the calculation of algebraic granular temperature ΔH = heat source from chemical reactions, J/(m3·s) I = unit matrix Jkn = mass flux vector of the nth species in the kth solid phase, kg/(m2·s) M = maximum number of solid phases N = maximum number of species p = pressure, Pa q = heat flux, J/(m2·s) rr = reaction rate, mol/(m3·s) Rkn = production or consumption rate of the nth species in the phase k, kg/(m3·s) T = temperature, K t = time, s u = velocity vector, m/s


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Wgm = heat transfer between gas and solid phase m, J/(K m3 s) xC1, xH1, xH2, xL1, xL2 = parameters of the ANC model for cellulose (C1), hemicellulose (H1 and H2), and lignin (L1 and L2) pyrolysis reactions Ykn = mass fraction of the nth species in the kth phase

Greek Letters αk = volume fraction of phase k βlk = momentum exchange coefficient between phases l and k, kg/(m3·s) ΔLHV = relative difference related to the tar LHV, % εg = dissipation rate of gas phase turbulent kinetic energy, m2/s3 γgm = energy exchange coefficient between gas and solid phases, J/(m3·s·K) Θm = granular temperature of solid phase m κg = gas turbulent kinetic energy, m2/s2 λ = bulk viscosity, kg/(m·s) μ = dynamic viscosity, kg/(m·s) μt = turbulence eddy viscosity, kg/(m·s) Π = turbulence interaction term ρ = density, kg/m3 σ

the cross correlation of gas and solids fluctuating velocities

τ = stress-strain tensor, kg/(m·s2) τt = Reynolds stress tensor, kg/(m·s2) ψgm = interphase momentum transfer between gas phase and solid phase m due to heterogeneous reactions, kg (m/s)/m3 s Subscripts efb = expanded bed height f = fuel, biomass g = gas k = any phase


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l,m = solid phases l,m n = chemical species n out = reactor outlet p = particle s = solid w = wall

10. REFERENCES

(1) Basu, P. Biomass Gasification and Pyrolysis: Practical Design and Theory; Elsevier Inc.: Oxford, UK, 2010. (2) Munir, S.; Daood, S. S.; Nimmo, W.; Cunliffe, A. M.; Gibbs, B. M. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour. Technol. 2009, 100(3), 1413-1418. (3) Zhang, W. Automotive fuels from biomass via gasification. Fuel Process. Technol. 2010, 91(8), 866-876. (4) The Thermoelectric Generation Through Burning of Sugarcane Bagasse in Brazil (in Portuguese); Report from the National Company of Supply (CONAB); Brazil Ministry of Agriculture: Brasília, DF, March 2011. (5) Xue, Q; Heindel, T. J.; Fox, R. O. A CFD model for biomass fast pyrolysis in fluidized-bed reactors. Chem. Eng. Sci. 2011, 66(11), 2440-2452.


42

Page 43 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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(6) Stubington, J. F.; Aiman, S. Pyrolysis kinetics of bagasse at high heating rates. Energy Fuels 1994, 8(1), 194-203. (7) Hugo, T. J. Pyrolysis of sugarcane bagasse. M.Sc. Thesis, Stellenbosch University: Stellenbosch, South Africa, 2010. (8) Maliger, V. R.; Doherty, W. O. S.; Frost, R. L.; Mousavioun, P. Thermal decomposition of bagasse: effect of different sugar cane cultivars. Ind. Eng. Chem. Res. 2011, 50(2), 791-798. (9) Morf, P. O. Secondary Reactions of Tar during Thermochemical Biomass Conversion. Ph.D. Thesis, Swiss Federal Institute of Technology Zurich: Zurich, Switzerland, 2001. (10) Gómez-Barea, A.; Leckner, B. Modeling of biomass gasification in fluidized bed. Prog. Energy Combust. Sci. 2010, 36(4), 444-509. (11) Di Blasi, C. Modeling chemical and physical processes of wood and biomass pyrolysis. Prog. Energy Combust. Sci. 2008, 34(1), 47-90. (12) Anca-Couce, A.; Mehrabian, R.; Scharler, R.; Obernberger, I. Kinetic scheme of biomass pyrolysis considering secondary charring reactions. Energ. Convers. Manage. 2014, 87, 687-696. (13) Gronli, M. G.; Melaaen, M. C. Mathematical model for wood pyrolysis – Comparison of experimental measurements with model predictions. Energy Fuels 2000, 14(4), 791-800. (14) Park, W. C.; Atreya, A.; Baum, H. R. Experimental and theoretical investigation of heat and mass transfer processes during wood pyrolysis. Combust. Flame 2010, 157(3), 481-494.


43

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 63

(15) Sharma, A.; Pareek, V.; Wang, S.; Zhang, Z.; Yang, H.; Zhang, D. A phenomenological model of the mechanisms of lignocellulosic biomass pyrolysis processes. Comput. Chem. Eng. 2014, 60, 231-241. (16) Bradbury, A. G. W.; Sakai, Y.; Shafizadeh, F. A kinetic model for pyrolysis of cellulose. J. Appl. Polym. Sci. 1979, 23(11), 3271-3280. (17) Miller, R. S.; Bellan, J. A generalized biomass pyrolysis model based on superimposed cellulose, hemicellulose and lignin kinetics. Combust. Sci. Technol. 1997, 126(1-6), 97-137. (18) Ranzi, E.; Cuoci, A.; Faravelli, T.; Frassoldati, A.; Migliavacca, G.; Pierucci, S.; Sommariva, S. Chemical kinetics of biomass pyrolysis. Energy Fuels 2008, 22(6), 4292-4300. (19) Singh, R. I.; Brink, A.; Hupa, M. CFD modeling to study fluidized bed combustion and gasification. Appl. Thermal Eng. 2013, 52(2), 585-614. (20) Xue, Q.; Dalluge, D.; Heindel, T. J.; Fox, R. O.; Brown, R. C. Experimental validation and CFD modeling study of biomass fast pyrolysis in fluidized-bed reactors. Fuel 2012, 97, 757769. (21) Xiong, Q.; Kong, S.-C.; Passalacqua, A. Development of a generalized numerical framework for simulating biomass fast pyrolysis in fluidized-bed reactors. Chem. Eng. Sci. 2013, 99, 305-313. (22) Xiong, Q.; Aramideh, S.; Kong, S.-C. Modeling effects of operating conditions on biomass fast pyrolysis in bubbling fluidized bed reactors. Energy Fuels 2013, 27(10), 5948-5956.


44

Page 45 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

(23) Mellin, P.; Kantarelis, E.; Yang, W. Computational fluid dynamics modeling of biomass fast pyrolysis in a fluidized bed reactor, using a comprehensive chemistry scheme. Fuel 2014, 117(Part A), 704-715. (24) Mellin, P.; Kantarelis, E.; Zhou, C.; Yang, W. Simulation of bed dynamics and primary products from fast pyrolysis of biomass: steam compared to nitrogen as a fluidizing agent. Ind. Eng. Chem. Res. 2014, 53(30), 12129-12142. (25) Blondeau, J.; Jeanmart, H. Biomass pyrolysis at high temperatures: Prediction of gaseous species yields from an anisotropic particle. Biomass Bioenergy 2012, 41, 107-121. (26) Sharma, A.; Wang, S.; Pareek, V.; Yang, H.; Zhang, D. Multi-fluid reactive modeling of fluidized bed pyrolysis process. Chem. Eng. Sci. 2015, 123, 311-321. (27) Bruchmüller, J.; van Wachem, B. G. M.; Gu, S.; Luo, K. H.; Brown, R. C. Modeling the thermochemical degradation of biomass inside a fast pyrolysis fluidized bed reactor. AIChE J. 2012, 58(10), 3030-3042. (28) Syamlal, M.; Pannala, S. Multiphase continuum formulation for gas-solids reacting flows. In: Computational Gas-Solid Flows and Reacting Systems: Theory, Methods and Practice; Pannala, S., Syamlal, M., O’Brien, T. J., Eds.; Engineering Science Reference: Hershey, PA, 2011; pp. 1-65. (29) Gidaspow, D. Multiphase Flow and Fluidization: Continuum and Kinetic Theory Descriptions; Academic Press: San Diego, CA, 1994. (30) Benyahia, S.; Syamlal, M.; O’Brien, T. J. Evaluation of boundary conditions used to model dilute, turbulent gas/solids flows in a pipe. Powder Technol. 2005, 156(2-3), 62-72.


45

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 46 of 63

(31) Ranz, W. E.; Marshall, W. R., Jr. Evaporation from drops. Chem. Eng. Progr. 1952, 48(141), 141-146. (32) van Wachem, B. G. M.; Almstedt, A. E. Methods for multiphase computational fluid dynamics. Chem. Eng. J. 2003, 96(1-3), 81-98. (33) Agrawal, K.; Loezos, P. N.; Syamlal, M.; Sundaresan, S. The role of meso-scale structures in rapid gas-solid flows. J. Fluid Mech. 2001, 445, 151-185. (34) Chang, J.; Yang, S.; Zhang, K. A particle-to-particle heat transfer model for dense gassolid fluidized bed of binary mixture. Chem. Eng. Res. Des. 2011, 89, 894-903. (35) Chang, J.; Wang, G.; Gao, J.; Zhang, K.; Chen, H.; Yang, S. CFD modeling of particleparticle heat transfer in dense gas-solid fluidized beds of binary mixture. Powder Technol. 2012, 217, 50-60. (36) Xiong, Q.; Kong, S. C. Modeling effects of interphase transport coefficients on biomass pyrolysis in fluidized beds. Powder Technol. 2014, 262, 96-105. (37) Gerber, S.; Behrendt, F.; Oevermann, M. An Eulerian modeling approach of wood gasification in a bubbling fluidized bed reactor using char as bed material. Fuel 2010, 89(10), 2903-2917. (38) Xue, Q.; Fox, R. O. Multi-fluid CFD modeling of biomass gasification in polydisperse fluidized-bed gasifiers. Powder Technol. 2014, 254, 187-198. (39) van Wachem, B. G. M.; Schouten, J. C.; van den Bleek, C. M.; Krishna, R.; Sinclair, J. L. Comparative analysis of CFD models of dense gas-solid systems. AIChE J. 2001, 47(5), 10351051.


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Page 47 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

( 0) Syamlal, M.; Rogers, W.; O’Brien, T. J. MFIX Documentation: Theory Guide; Technical Note DOE/METC-94/1004; Office of Fossil Energy, Morgantown Energy Technology Center, U.S. Department of Energy: Morgantown, WV, December 1993. (41)

Syamlal,

M.

MFIX

Documentation

Numerical

Technique;

Technical

Note

DOE/MC31346-5824; Office of Fossil Energy, Morgantown Energy Technology Center, U.S. Department of Energy: Morgantown, WV, January 1998. (42) Boroson, M. L.; Howard, J. B.; Longwell, J. P.; Peters, W. A. Product yields and kinetics from the vapor phase cracking of wood pyrolysis tars. AIChE J. 1989, 35(1), 120-128. (43) Di Blasi, C. Analysis of convection and secondary reaction effects within porous solid fuels undergoing pyrolysis. Combust. Sci. Technol. 1993, 90(5-6), 315-340. (44) Di Blasi, C.; Branca, C. Kinetics of primary product formation from wood pyrolysis. Ind. Eng. Chem. Res. 2001, 40(23), 5547-5556. (45) Liden, A. G.; Berruti, F.; Scott, D. S. A kinetic model for the production of liquids from the flash pyrolysis of biomass. Chem. Eng. Commun. 1988, 65(1), 207-221. (46) Chan, W.-C. R.; Kelbon, M.; Krieger, B. B. Modelling and experimental verification of physical and chemical processes during pyrolysis of a large biomass particle. Fuel 1985, 64(11), 1505-1513. (47) Brandão, F. L. Computational study of sugarcane bagasse and wood pyrolysis in a fluidized bed reactor (in Portuguese). M.Sc. Thesis, Federal University of Rio de Janeiro: Rio de Janeiro, Brazil, 2015.


47

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 48 of 63

(48) Verissimo, G. L.; Pinho, J. M.; Leiroz, A. J. K.; Cruz, M. E. A numerical study on bed temperature and gasifying agent effects on the sugarcane bagasse gasification process. In: Renewable Energy, Proceedings of the 15th International Heat Transfer Conference, Kyoto, Japan, August 10-15, 2014; Begell House: Danbury, CT. DOI: 10.1615/IHTC15.rne.009817. (49) Xie, N.; Battaglia, F.; Pannala, S. Effects of using two- versus three-dimensional computational modeling of fluidized beds: Part I, hydrodynamics. Powder Technol. 2008, 182, 1-13. (50) Zhong, H.; Gao, J.; Xu, C.; Lan, X. CFD modeling the hydrodynamics of binary particle mixtures in bubbling fluidized beds: Effect of wall boundary condition. Powder Technol. 2012, 230, 232-240. (51) Ensinas, A. V.; Nebra S. A. Exergy analysis as a tool for sugar and ethanol process. In: Handbook of exergy, hydrogen energy and hydropower research; Pélissier, G., Calvet, A., Eds.; Nova Science Publishers Inc.: New York, NY, 2009; pp 125-160. (52) Ayugi, G.; Banda, E. J. K. B.; D’Ujanga, F. M. Local thermal insulating materials for thermal energy storage. Rwanda J. 2011, 23, 21-29. (53) Gaskell, P. H.; Lau, A. K. C. Curvature-compensated convective transport: SMART, a new boundedness-preserving transport algorithm. Int. J. Numer. Meth. Fl. 1988, 8(6), 617-641. (54) Qiaoqun, S.; Huilin, L.; Wentie, L.; Yurong, H.; Lidan, Y.; Gidaspow, D. Simulation and experiment of segregating/mixing of rice husk-sand mixture in a bubbling fluidized bed. Fuel 2005, 84, 1739-1748.


48

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(55) Bridgwater, A. V. Review of fast pyrolysis of biomass and product upgrading. Biomass Bioenergy 2012, 38, 68-94. (56) Channiwala, S. A.; Parikh, P. P. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel, 2002, 81, 1051-1063.


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Table Captions Table 1. Physical properties of the solid and gas phases. Table 2. Wood pyrolysis products yielded in the present simulations and in the experiments.20 Table 3. Temperature boundary condition for each experimental case.7 Table 4. Estimated lignocellulosic components of the sugarcane bagasse considered by Hugo.7 Table 5. Average values for the last 20 seconds of the stationary regime. Table 6. Products of sugarcane bagasse pyrolysis and gas outlet temperature for Case A. Table 7. Products of sugarcane bagasse pyrolysis and gas outlet temperature for Case B. Table 8. Products of sugarcane bagasse pyrolysis and gas outlet temperature for Case C. Table 9. Elemental analysis and LHV of the produced tar.


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Figure Captions Figure 1. Competitive reactions pyrolysis schemes. Figure 2. Biomass pyrolysis scheme of the MB model.17 Figure 3. Biomass pyrolysis scheme of the RAN18 and BJR25 models. Figure 4. Biomass pyrolysis scheme of the ANC12 model. Figure 5. Two dimensional Cartesian domain for the fluidized bed reactor. Figure 6. Formation reaction rates of the MB model for NCG (a), tar (b) and char (c). Figure 7. Formation reaction rates of the RAN model for NCG (a), tar (b) and char (c). Figure 8. Formation reaction rates of the BJR model for NCG (a), tar (b) and char (c). Figure 9. Formation reaction rates of the PAR model for NCG (a), tar (b) and char (c). Figure 10. Formation reaction rates of the SHA model for NCG (a), tar (b) and char (c). Figure 11. Formation reaction rates of the ANC model for NCG (a), tar (b) and char (c). Figure 12. Biomass volume fraction along the reactor for all three cases and six models.


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Figure 1


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Figure 2


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Figure 4


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Figure 6


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Figure 8


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Figure 9


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Figure 10


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Figure 11


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Figure 12


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Computational Study of Sugarcane Bagasse Pyrolysis Modeling in a

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