Energy Fuels 2010, 24, 4689–4692 Published on Web 03/04/2010
: DOI:10.1021/ef901254g
Biomass Fast Pyrolysis: Experimental Analysis and Modeling Approach† M. Al-Haddad, E. Rendek, J.-P. Corriou, and G. Mauviel* Laboratorie R eactions et G enie des Proc ed es (LRGP) - CNRS-Nancy Universit e, 1 rue Grandville, BP 20451, 54001 NANCY Cedex, France Received October 30, 2009. Revised Manuscript Received February 8, 2010
A single particle model able to predict the evolution of the product yields during biomass fast pyrolysis is developed. Mass balance equations based on a kinetic scheme of solid-phase pyrolysis are coupled to heattransfer equations. This two-dimensional model is solved by the finite volume method. The model results are compared to experimental data obtained in an image furnace, where biomass pellets are submitted to a controlled and concentrated radiation. Heat flux densities that are available at the biomass surface are similar to those encountered in fluidized beds (0.2-0.8 106 W m-2). The comparison between the experimental data and simulated results shows that the kinetic parameters need to be further optimized to accurately represent the final product yields.
out in an image furnace, which easily permits us to reach fast pyrolysis conditions similar to those encountered in fluidized beds, to measure the particle surface temperature and, furthermore, to prevent the secondary cracking reactions, because the vapor produced is instantaneously quenched in cold inert gas. The biomass pellets are submitted to a concentrated radiation providing heat flux densities in the range of 0.2-0.8 106 W m-2.
Introduction The pyrolysis of biomass has been widely studied in the literature. Different mathematical models have been published1-6 to simulate the biomass fast pyrolysis in fluidized beds. The three parallel reactions scheme, which considers the production of gas, vapors, and char, has been widely used for its simplicity (Figure 1). The reaction kinetics parameters generally used are those obtained by Chan et al.,7 Thurner and Mann,8 Di Blasi and Branca,9 and Wagenaar et al.10 Nevertheless, it is important to note that there is no consensus concerning the sets of kinetic parameters relative to these three competitive reactions scheme (Table 1). Therefore, the goal of this study is to assess these kinetic parameters through the comparison of experimental data and simulation results obtained from a two-dimensional model using the abovementioned kinetic parameters. The experiments are carried
Image Furnace Apparatus The descriptions of the image furnace apparatus (Figure 2) and the experimental procedures have been already described in detail by Boutin et al.11,12 and Lede et al.13 A 5 kW xenon arc lamp (Osram XBO 5000 W/H CL OFR) is settled at the first focus F1 (Figure 2) of an elliptical mirror M1 (height, 3.8 10-1 m; depth, 2.2 10-1 m). The concentrated beams reach the target sample placed at the second focal zone F (Figure 2). The heat flux density is controlled by the use of metallic grids that intercept a known percentage of the radiant flux. The time during which the sample is subjected to the concentrated radiation is checked by the use of a sensor integrated to a specific device based on the use of a pendulum.11 This specific device is placed between the metallic grid and the reactor, where it intercepts the incoming radiation. The biomass samples (diameter, 10-2 m; thickness, 3 -3 10 m), settled at the second focus F of the elliptical mirror (Figure 2), are placed inside a cylindrical quartz reactor (diameter, 3 10-2 m; height, 5 10-2 m) fed by a controlled flow of nitrogen [8.3 10-5 m3 s-1, standard temperature and pressure (STP)] at ambient temperature. The carrier gas is injected by the mean of two different nozzles (3.175 10-3 m)
† This paper has been designated for the Bioenergy and Green Engineering special section. *To whom correspondence should be addressed. Telephone: þ333-8317-5207. Fax: þ33-3-8332-2975. E-mail: guillain.mauviel@ensic. inpl-nancy.fr. (1) Kersten, S. R. A.; Wang, X.; Prins, W.; van Swaaij, W. P. M. Biomass pyrolysis in a fluidized bed reactor. Part 1: Literature review and model simulations. Ind. Eng. Chem. Res. 2005, 44 (23), 8773–8785. (2) Luo, Z.; Wang, S.; Cen, K. A model of wood flash pyrolysis in fluidized bed reactor. Renewable Energy 2005, 30 (3), 377–392. (3) Di Blasi, C. Modelling the fast pyrolysis of cellulosic particles in fluid-bed reactors. Chem. Eng. Sci. 2000, 55 (24), 5999–6013. (4) Saastamoinen, J. J. Simplified model for calculation of devolatilization in fluidized beds. Fuel 2006, 85 (17-18), 2388–2395. (5) Sreekanth, M.; Kolar, A. K. Progress of conversion in a shrinking wet cylindrical wood particle pyrolyzing in a hot fluidized bed. J. Anal. Appl. Pyrolysis 2009, 84 (1), 53–67. (6) Sreekanth, M.; Ajit Kumar, K.; Leckner, B. Transient thermal behaviour of a cylindrical wood particle during devolatilization in a bubbling fluidized bed. Fuel Process. Technol. 2008, 89 (9), 838–850. (7) 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. (8) Thurner, F.; Mann, U. Kinetic investigation of wood pyrolysis. Ind. Eng. Chem. Process Des. Dev. 1981, 20 (3), 482–488. (9) Di Blasi, C.; Branca, C. Kinetics of primary product formation from wood pyrolysis. Ind. Eng. Chem. Res. 2001, 40 (23), 5547–5556. (10) Wagenaar, B. M.; Prins, W.; van Swaaij, W. P. M. Flash pyrolysis kinetics of pine wood. Fuel Process. Technol. 1993, 36 (1-3), 291–298.
r 2010 American Chemical Society
(11) Boutin, O.; Ferrer, M.; Lede, J. Radiant flash pyrolysis of cellulose;Evidence for the formation of short life time intermediate liquid species. J. Anal. Appl. Pyrolysis 1998, 47 (1), 13–31. (12) Boutin, O.; Ferrer, M.; Lede, J. Flash pyrolysis of cellulose pellets submitted to a concentrated radiation: Experiments and modelling. Chem. Eng. Sci. 2002, 57 (1), 15–25. (13) Lede, J.; Blanchard, F.; Boutin, O. Radiant flash pyrolysis of cellulose pellets: Products and mechanisms involved in transient and steady state conditions. Fuel 2002, 81 (10), 1269–1279.
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Energy Fuels 2010, 24, 4689–4692
: DOI:10.1021/ef901254g
Al-Haddad et al.
Table 1. Arrhenius Law Parameters for the Three Competitive Reactions Scheme reference
biomass
Ag (s-1)
Eg (kJ mol-1)
Av (s-1)
Ev (kJ mol-1)
Ac (s-1)
Ec (kJ mol-1)
Chan et al.7 Thurner and Mann8 Di Blasi and Branca9 Wagenaar et al.10
cellulose, wood oak beech pine
1.30 108 1.43 104 4.38 109 1.11 1011
140.3 88.6 152.7 177.0
2.00 108 4.12 106 1.08 108 9.38 109
133.1 112.7 148.0 149.0
1.1 107 7.38 105 3.27 106 3.05 107
121.3 106.5 111.7 125.0
positioned at the bottom of the reactor. It quenches the volatiles released by the wood sample and, thus, prevents secondary cracking reactions in the gas phase. Experimental Procedures
Figure 1. Simplified three competitive reactions kinetic pathway.18
The fast pyrolysis experiments are carried out in the image furnace, where the biomass pellets are submitted to four different mean heat flux densities: 0.2 106, 0.37 106, 0.55 106, and 0.8 106 W m-2. The distribution of the heat flux densities over the irradiated surface has been determined by the use of a specific device relying on the use of a flux sensor (OPHIR 1000 W Thermal Head). To study the time-dependent evolution of the mass loss, biomass pellets are weighted before and after being exposed for a certain time to the concentrated radiation. The final product yields, Yi, are obtained from the complete pyrolysis of the entire biomass samples, where all of the products are recovered. These experiments are repeated 3 times for each heat flux density. The mean value and the standard deviation of the final product yields are then calculated. A part of the condensable vapors is recovered in a cold trap, and another part is trapped in a cylindrical glass tube containing a packed bed of zeolite particles and glass wool. The gases are recovered in sampling bags. At the end of each experiment, the masses of condensable vapors and char are measured. The gas composition is determined using gas chromatography [Varian 3800 GC/FID, CP-Poraplot U-type capillary column with silica packing, 27.5 0.63 10-3 m for light hydrocarbons (i.e., CH4, C2H6, C2H4, C2H2, and C3H8), GC/ TCD Carbosphere column, 2 2 10-3 m for CO and CO2, and Varian 3900 GC/TCD Carbosphere column, 2 2 10-3 m for H2]. All the products yields Yi are calculated on the basis of the initial mass of the dried biomass pellet as Δmcold trap þ Δmzeolite trap Yv ¼ minitial dried pellet mmfully pyrolyzed pellet Yc ¼ minitial dried pellet P Mi yi mg PQN2 Δt gas species i Yg ¼ ¼ ð1Þ Rg T minitial dried pellet minitial dried pellet
cellulose and extended to wood by Lede et al.,15-17 a simple scheme based on three competitive reactions is used in this study for the sake of simplification (Figure 1). Each kinetic constant (kg, kv, and kc) is computed by the Arrhenius law. The four set of Arrhenius parameters, which are used in this study, are given in Table 1. Governing Equations The different assumptions of the model are as follows: no particle shrinkage; vapors and gases formed during the pyrolysis instantaneously escape from the particle; dry biomass pellets are considered; and enthalpies of the three reactions are considered to be the same and equal to zero (actually, these values have low impact on the results). The mass and energy balances are written on an elementary volume, where composition and temperature can be considered uniform. These differential equations are integrated to describe evolutions of composition and temperature gradients inside the cylindrical pellet. Mass Balance. The local concentration evolution of the different products i can be written as dci ¼ Ai e-Ei =Rg T cw ðtÞ ð2Þ dt The time-dependent evolution of the local wood concentration is given by dcg dcv dcc dcw ¼þ þ ð3Þ dt dt dt dt Energy Balance. The dynamic energy conservation equation is given in cylindrical coordinates DT DT 1DT DT X dci FCp ¼ λr þ þ λz ð4Þ ΔHi Dt Dr r Dr Dz dt i
Biomass Samples The fast pyrolysis experiments are performed on cylindrical beech wood pellets of approximately 170 10-6 kg (diameter, 10-2 m; thickness, 3 10-3 m). The pellets are dried in an oven at 378 K for 24 h just before pyrolysis experiments. The ultimate analysis of this dried beech wood is 47% carbon, 6% hydrogen, 43% oxygen, and 2% nitrogen. Its physical properties are presented in Table 2.
Boundary Conditions. (1) For the non-irradiated surface (z = 0) (Figure 2), where the heat transfer is controlled by convection and radiation DT ¼ -σεðT 4 -Tamb 4 Þjz ¼0 -hðT -Tamb Þjz ¼0 ð5Þ λz Dz z ¼0
Kinetic Scheme of Biomass Pyrolysis (15) Lede, J. Comparison of contact and radiant ablative pyrolysis of biomass. J. Anal. Appl. Pyrolysis 2003, 70 (2), 601–618. (16) Lede, J.; Li, H. Z.; Villermaux, J.; Martin, H. Fusion-like behaviour of wood pyrolysis. J. Anal. Appl. Pyrolysis 1987, 10 (4), 291–308. (17) Lede, J. Solar thermochemical conversion of biomass. Sol. Energy 1999, 65 (1), 3–13.
Even though the presence of a short lifetime intermediate liquid compound (ILC) has been identified in the case of (14) Gronli, M. G. A theoretical and experimental study of the thermal degradation of biomass. The Norwegian University of Science and Technology, Trondheim, Norway, 1996.
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Energy Fuels 2010, 24, 4689–4692
: DOI:10.1021/ef901254g
Al-Haddad et al.
Table 2. Physical Properties of the Cylindrical Beech Wood Pellets and Char parameters -3
F (kg m ) Cp (J kg-1 K-1) λr (W m-1 K-1) λz (W m-1 K-1) ε
value for wood
reference
value for char
reference
720 1500 0.21 0.35 0.26
measured Gronli14 Gronli14 Gronli14 measured
computed by model 420 þ 2.09T - 6.85 10-4T2 0.1 0.1 0.95
Gronli14 Gronli14 Gronli14 measured
Figure 2. Scheme of the whole experimental setup including the image furnace.
Figure 3. Vapor yields versus the heat flux density.
where the convective heat-transfer coefficient, h, is determined from the following Nusselt correlation given by Perry and Green19 for flat plate surfaces: hD Nu ¼ ¼ 0:829Re0:5 Pr0:33 ð6Þ λG (2) For the irradiated surface (z = L) (Figure 2) receiving the concentrated radiant flux DT ¼ -σεðT 4 -Tamb 4 Þjz ¼L -hðT -Tamb Þjz ¼L þ λz Dz z ¼L
ð1 -RÞjðrÞ
ð7Þ
The measured heat flux density distribution is described by jðrÞ ¼ jr ¼0 ð0:18 þ 0:78eð-0:35r Þ Þ 2
Figure 4. Char yields versus the heat flux density.
ð8Þ
(3) At the center of the cylindrical particle (r = 0) and at the lateral surface (r = R) DT ¼0 Dr r ¼0 DT λr ¼ -σεðT 4 -Tamb 4 Þjr ¼R -hðT -Tamb Þjr ¼R ð9Þ Dr r ¼R
Initial Conditions. The initial temperature of the biomass pellet is supposed to be uniform and equal to the ambient temperature. The initial pellet is only dry wood (no moisture content). During the pyrolysis process, the physical properties of the biomass at any point of the sample (thermal conductivity, heat capacity, and emissivity) are computed by linear interpolations of the intrinsic properties of wood and char (Table 2). The heat and mass conservation equations are solved numerically using the finite volume method,20 and a Fortran
Figure 5. Gas yields versus the heat flux density.
program is developed. The yields of the different products are computed at any time by integration of their local concentration. Results and Discussion The experimental results show that the char and vapor yields decrease (from 21 ( 2 to 12 ( 1 wt % and from 63 ( 6 to 49 ( 6 wt %, respectively) when the mean available heat flux density increases from 0.2 to 0.8 106 W m-2, whereas gas yields vary from 9 ( 1 to 36 ( 2 wt %. The mass balances for complete pyrolysis experiments range between 95 and 100%.
(18) Shafizadeh, F.; Chin, P. P. S. Thermal deterioration of wood. ACS Symp. Ser. 1977, 43, 57–81. (19) Perry, R. H.; Green, D. W. Perry’s Chemical Engineers’ Handbook, 8th ed.; McGraw-Hill: New York, 2007; p 2640. (20) Patankar, S. V.; Karki, K. C.; Kelkar, K. M. Finite volume method. In The Handbook of Fluid Dynamics; CRC Press: Boca Raton, FL, 1998; Chapter 5.
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Energy Fuels 2010, 24, 4689–4692
: DOI:10.1021/ef901254g
Al-Haddad et al. 7-10
For each set of kinetic parameters available in the literature, the final product yields are simulated by the model as a function of the heat flux density. The comparison between the experimental data and the simulated results shows that all sets of the previously mentioned kinetic parameters for the three competitive reactions scheme are unable to predict the final product yields for the vapors, the char, and the gases, as illustrated in Figures 3-5. These different product yield predictions were already noted by Kersten et al.1 This disparity seems to be mainly due to the wide variety of biomasses used, the lack of reliable experimental data, and especially the measurement of the sample temperature. Besides, most of the kinetic parameters have been determined under low heat fluxes (TGA) and/or in conditions where the secondary vapor cracking is uncontrolled.
Nomenclature A = kinetic pre-exponential factor (s-1) c = local mass concentration (kg m-3) Cp = heat capacity (J kg-1 K-1) D = characteristic length, D = 2R (m) E = activation energy (J mol-1) h = heat-transfer coefficient (W m-2 K-1) L = thickness (m) m = mass (kg) M = molar mass (kg mol-1) Nu = Nusselt number P = total pressure (Pa) Pr = Prandtl number, Pr = (μGCpG)/λG Q = volumic flow rate (m3 s-1) r = radial coordinate (m) R = radius (m) Re = Reynolds number, Re = (FGuGD)/μG Rg = gas constant (8.314 J mol-1 K-1) t = time (s) T = temperature (K) uG = carrier gas velocity reaching the biomass surfaces (m s-1) V = volume (m3) y = volumic fraction Y = final product yield z = axial coordinate (m)
Conclusion A single biomass particle pyrolysis model has been developed. The evolution of the products and mass losses of biomass pellets submitted to concentrated radiant flux have been determined in an image furnace able to deliver high heat flux densities close to those encountered in fluidized beds. The comparison between the experimental data and simulated results for a two-dimensional particle shows that none of the different kinetic parameters sets, which are available in the literature for the three competitive reactions scheme, is able to predict the final yields of the different products. It is important to note that several conclusions of this work are similar to those obtained with another modeling approach,21,22 relying on the concept of the existence of two distinct layers inside the pyrolyzing sample: unreacted wood and char. Therefore, the kinetic parameters have to be further optimized and validated with other fast pyrolysis experimental data.
Greek Letters R = reflectivity ΔH = reaction enthalpy (J kg-1) Δt = sampling time (s) ε = emissivity j = incident heat flux density (W m-2) λ = thermal conductivity (W m-1 K-1) μ = dynamic viscosity (kg m-1 s-1) F = mass density (kg m-3) σ = Stefan-Boltzmann constant (5.67 10-8 W m-2 K-4)
Acknowledgment. The research was funded by the French National Research Agency (ANR) in the framework of the National Research Program on Bioenergies (PNRB) (ANR-05BIOE-008). The authors gratefully acknowledge Dr. J. Lede (LRGP, CNRS-Nancy Universite) for his valuable consultancy and B. Monod (LEMTA, CNRS-Nancy Universite) for the reflectivity measurements.
Subscripts amb = ambient w = wood c = char exp = experiment g = gas G = carrier gas i = component i (g, gases; v, vapor; or c, char) init = initial mod = model r = radial s = solid v = vapor z = axial
(21) Le Dirach, J.; Hesse, B.; Mauviel, G.; Corriou, J.-P.; Ferrer, M.; Valle Marcos, J. C.; Khalfi, A.-E.; Lede, J. In Wood fast pyrolysis: Experimental study and modelling in thermal conditions close to those encountered in a FICFB gasification process. In Success and Visions for Bioenergy: Thermal Processing of Biomass for Bioenergy, Biofuels and Bioproducts; Bridgwater, A. V., Ed.; CPL Press: Salzburg, Austria, 2007. (22) Authier, O.; Ferrer, M.; Mauviel, G.; Khalfi, A.-E.; Lede, J. Wood fast pyrolysis: Comparison of Lagrangian and Eulerian modeling approaches with experimental measurements. Ind. Eng. Chem. Res. 2009, 48 (10), 4796–4809.
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