Energy & Fuels 2004, 18, 1021-1031
1021
Effects of Intraparticle Heat and Mass Transfer on Biomass Devolatilization: Experimental Results and Model Predictions Anshu Bharadwaj Center for Energy and Environmental Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Larry L. Baxter Chemical Engineering Department, Brigham Young University, Provo, Utah 84602
Allen L. Robinson* Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 Received July 22, 2003. Revised Manuscript Received February 9, 2004
This paper examines the effects of intraparticle heat and mass transfer on the devolatilization of millimeter-sized biomass particles under conditions similar to those found in commercial coalfired boilers. A computational model is presented that accounts for intraparticle heat and mass transfer by diffusion and advection during particle heating, drying, and devolatilization. To evaluate the model, devolatilization experiments under high-temperature and high-heating rate conditions were conducted using the Multifuel Combustor at Sandia National Laboratories. Measurements of mass-loss and changes in particle size for millimeter-sized alfalfa and wood particles are presented as a function of reactor residence time. For millimeter-sized particles, both fuels completely devolatilized in approximately 1 s with rapid initial mass loss. The total volatile yield of the wood was 92% on a dry, ash-free basis, significantly higher than that reported by a standard ASTM test, indicating dependence of the ultimate yield on local conditions. Particles for both fuels shrink significantly and become less dense during devolatilization. The comprehensive model accurately predicts the devolatilization behavior of millimeter-sized biomass particles; these measurements could not be reproduced with a simple lumped model that ignores intraparticle transport effects. The comprehensive model is used to examine the effects of particle size and moisture content on devolatilization under conditions representative of those found in coal boilers. Biomass particles of radii up to 2 mm and moisture content up to 50% are considered. As expected, intraparticle heat and mass effects are more significant for larger particles. These effects can significantly delay particle heating and devolatilization; for example, intraparticle effects delay the heating and devolatilization of millimeter-size particles by as much as several seconds for a particle with a 1.5-mm radius compared to predictions of a lumped model. This delay is significant considering the short residence times of commercial boilers and should be accounted for in computational models used to evaluate the effects of biomass-coal cofiring on boiler performance.
Introduction The threat of global change due to anthropogenic emissions of greenhouse gases has stimulated significant interest in biomass energy, a CO2-neutral energy source. An attractive near-term option for utilizing biomass is cofiring biomass with coal in existing coalfired utility boilers.1-4 However, there are significant * Corresponding author. Phone: (412) 268-3657. E-mail: alr@ andrew.cmu.edu. (1) Robinson, A. L.; Rhodes J.; Keith, D. W. Environ. Sci. Technol. 2003, 37, 5081-5089. (2) Sami, M.; Annamalai, K.; Wooldridge, M. Prog. Energy Combust. Sci. 2001, 27, 171-214. (3) Hughes, E. Biomass Bioenergy 2000, 19, 457-465.
differences between the physical and chemical properties of coal and biomass that may create problems when cofiring. Biomass particles are typically much larger than pulverized coal particles because biomass cannot be economically processed to the same sizes as coal; the average size of a pulverized coal particle is ∼50 µm, whereas a biomass particle can be up to 200 times as large. Biomass also has a much greater volatile content and often much higher moisture level than coal. However, biomass particles have considerably lower densities than coal particles, commonly differing by a factor of 4-7. Considering all of these factors, the devolatil(4) Tillman, D. A. Biomass Bioenergy 2000, 19, 365-384.
10.1021/ef0340357 CCC: $27.50 © 2004 American Chemical Society Published on Web 06/29/2004
1022 Energy & Fuels, Vol. 18, No. 4, 2004
ization time scale of millimeter-sized biomass particles typically exceeds that of pulverized coal particles under similar conditions. Recent research has used computational fluid dynamics (CFD)-based simulations of pulverized-coal boilers to examine the effects of biomass-coal cofiring on boiler performance.5-9 These simulations seek to predict the effects of cofiring on heat release, pollutant generation, carbon conversion, boiler efficiency, ash deposition, and fouling. To simulate devolatilization, these CFD models have used a relatively simple framework that treats a fuel particle as a lumped system for predicting devolatilization and combustion. This lumped approach ignores intraparticle heat and mass transfer effects and assumes the particle undergoes sequential heating, drying, devolatilization, and char combustion as the particle is transported through the boiler. This lumped approach is valid for small pulverized coal particles, but may not be a realistic model for biomass particles because of the large particle size and high volatile/ moisture content. Intraparticle resistance to heat and mass transfer can significantly affect the devolatilization rate of millimeter-sized particles such as those typically used in cofiring applications. Particle size is a major factor in determining the role of transport limitations on devolatilization.10,11 A theoretical framework for incorporating intraparticle heat and/or intraparticle mass transport has been developed for large coal particles in fluidized bed combustors and gasifiers.10,12-17 It is generally agreed that devolatilization of pulverized coal particles (1 mm), thermal and transport limitations dominate over the reaction time.10 Several papers have examined the effects of intraparticle heat and mass transfer on biomass heating and devolatilization. Kanury19 presents the governing equations for heating, devolatilization, and combustion of biomass considering temperature gradients within the particle. Saastamoinen20 examined intraparticle effects (5) Gera, D.; Mathur, M.; Freeman, M.; O’Dowd, W. Combust. Sci. Technol. 2001, 172, 35-69. (6) Kaer, S. K.; Rosendahl, L.; Overgaard, P. Numerical analysis of cofiring coal and straw. In Proceedings of the 4th European CFD Conference, Athens, Greece, September 7-11, 1998; pp 1013-1018. (7) Koufopanos, C.; Papayannakos, N. Can. J. Chem. Eng. 1991, 69, 907-915. (8) Williams, A.; Pourkashanian, M.; Jones, J. M. Prog. Energy Combust. Sci. 2001, 27, 587-610. (9) Gera, D.; Mathur, M. P.; Freeman, M. C.; Robinson, A. L. Energy Fuels 2002, 16, 1523-1532. (10) Saxena, S. C. Prog. Energy Combust. Sci. 1990, 16, 55-94. (11) Solomon, P. R.; Serio, M. A.; Suuberg, E. M. Prog. Energy Combust. Sci. 1992, 18, 133-220. (12) Gavalas, G. R.; Wilks, K. A. AIChE J. 1988, 26, 201-211. (13) Antal, M. J. Fuel 1985, 64, 1483-1485. (14) Devanathan, N.; Saxena, S. C. 1985, 605-624. (15) Agarwal, P. K.; Genetti, W. E.; Lee, Y. Y. Fuel 1984, 63, 11571165. (16) Wildegger-Gaissmaier, A. E.; Agarwal, P. K. Fuel 1990, 69, 4451. (17) Zhao, Y.; Serio, M. A.; Solomon, P. R. Twenty-Sixth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, Pa, 1996; pp 3145-3151. (18) Russel, W. B. S., P. A., Greene, M. I. AIChE J. 1979, 25. (19) Kanury, A. M. Combust. Sci. Technol. 1994, 97, 469-491.
Bharadwaj et al.
on heating, drying, and devolatilization of large wood logs. Several researchers21-25 have theoretically examined the effects of intraparticle heat and mass transfer for large biomass particles (cm) when subject to convection and radiation heat transfer at the surface. However, it is difficult to draw conclusions from this previous research to the conditions found in utility power generation systems used for cofiring that expose millimetersized biomass particles to rapid heating rates and high temperatures. In this paper, we develop a theoretical model to examine the effects of intraparticle heat and mass transfer on particle heating, drying, and devolatilization. The model is evaluated by comparing model results with experimental data collected using the Multifuel Combustor (MFC) at the Sandia National Laboratories. The model is then used to examine the effects of particle size and moisture content on the devolatilization rate under conditions representative of those found in large utility boilers. Comprehensive Model Formulation This section develops a model to predict the heating, drying, and devolatilization of a wet and porous spherical biomass particle subject to convection and radiation heat transfer accounting for intraparticle heat and mass transfer. The model considers three products of thermal heating of a biomass particle: water vapor, volatiles, and solid residues (char and ash). The particle undergoes an initial drying during which the moisture vaporizes and enters the particle pore space. The evaporation front progresses radially into the particle and at any instant of time there is no moisture behind the front, but the particle contains residual moisture ahead of the front. At higher temperatures, the pyrolysis of the particle begins and the volatiles are released into the particle pore space. The water vapor and volatiles are then transported through the particle’s matrix to the surface by diffusion and advection where they escape to the surrounding gas. The model allows for heating, drying, and devolatilization to occur simultaneously within a particle. For example, the outer region of the particle can be undergoing devolatilization while the core of the particle is only beginning to heat up. Both advection and diffusion contribute to the heat and mass transport within the particle. Advection is driven by pressure gradients created by the vaporization of water and release of gaseous volatiles at high temperatures. The approach is based on the framework developed by earlier researchers.10,21,23-25 The core of the model is the coupled mass and energy transport equations used to predict temperature and composition within a spherical particle, (20) Saastamoinen, J. J. Fundamentals of biomass drying, pyrolysis and combustion, Presented at IEA Biomass Combustion Conference Cambridge, UK, 1994. (21) Di Blasi, C. Prog. Energy Combust. Sci. 1993, 19, 71-104. (22) Di Blasi, C. Chem. Eng. Sci. 1996, 51, 1121-1132. (23) Di Blasi, C. Fuel 1997, 76, 957-964. (24) Gronli, M. Ph.D. Thesis, Norwegian University of Science and Technology, Norway, 1996. (25) Gronli, M. G.; Melaaen, M. C. Energy Fuels 2000, 14, 791800.
Intraparticle Effects in Biomass Devolatilization
∂(F) 1 ∂ 2 + 2 (r uF) ) ω′′′ ∂t r ∂r
(
Energy & Fuels, Vol. 18, No. 4, 2004 1023
)
∂Yv ∂(Fv) 1 ∂ 2 1 ∂ D*Fr2 + ω′′′ (2) + 2 (r uFv) ) 2 v ∂t ∂r r ∂r r ∂r ∂ [(F C + FcharCp,char + ∂t biomass p,biomass (FvCp,volatiles + FwCp,vapor))T] )
)
(The nomenclature table provides a complete listing of the symbols and subscripts.) Equation 1 is the overall conservation of mass for the particle and describes the transport of both gaseous species (water vapor and volatiles) within the particle. Equation 2 describes the transport of volatiles within the particle. Equation 3 describes energy transport, neglecting the effects of radiation, but including conduction, convection, and heats of reaction. The model assumes that the particle is spherically symmetricsvariations in mass and temperature occur only in the radial direction and with time. Most biomass samples ultimately shrink when devolatilizing, although many go through a transient swelling stage. The shrinkage produces a corresponding change in the surface area and density. Particle shrinkage is incorporated in the model as follows:
( ) m m0
R
)
Fbiomass Fbiomass,0
(4)
where R is the mode of burning: R )1 corresponds to constant volume devolatilization and R ) 0 corresponds to constant density devolatilization. Particle mass and density used in this expression are both on dry, ashfree bases. A moving grid was used in the simulations to account for the changes in particle size for a given value of R. The particle porosity is assumed to be constant. This assumption rarely corresponds to reality, but sophisticated pore models are beyond the scope of this effort. There is no condensation or reaction of volatiles in the pores, and volatiles and water vapor are treated as ideal gases. Local thermal equilibrium is assumed between the gas phase in the pores and the solid matter (ash and char). Tars are not tracked separately from total volatiles. Bulk flow driven by pressure gradients created from the vaporization of water and devolatilization is estimated using Darcy’s law. B is the average permeability of the solid substrate, and µ is the average fluid viscosity (volatiles and water vapor). B is obtained by a linear interpolation between the unconverted biomass and char.25
u)-
B ∂p µ ∂r
B ) ηBbiomass + (1 - η)Bchar
Fbiomass Fbiomass,0
The total volumetric rate of generation of volatiles and water vapor is then given by
ω′′′ ) ω′′′ v + ω′′′ w
1 ∂ 1 ∂ 2 ∂T kr2 (r FvuCp,volatilesT) 2 ∂r ∂r r ∂r r 1 ∂ 2 (r FwuCp,vaporT) + ω′′′ v ∆H (3) r2 ∂r
(
η)
(1)
(5)
(6)
The rate of generation of volatiles (ω′′′ v ) is predicted by a first-order Arrhenius rate expression and is directly proportional to the amount of volatile matter left in the solid biomass particle.
(
ω′′′ v ) A exp -
E (F - (1 - fv - fw)F0,biomass) RT biomass (7)
)
where F0,biomass is the initial bulk density of the biomass particle and fv and fw are the volatile and moisture contents, respectively. A disadvantage of this model is that it requires a priori knowledge of the ultimate extent of devolatilization, as expressed in fv. This parameter will likely vary as a function of local conditions and rate of devolatilization. However, our focus is on particle size effectsssophisticated devolatilization models are outside the scope of this investigation. When the temperature at a radial location of the particle reaches 100 °C, the temperature is held constant until all of the moisture present has evaporated. Mathematically, this effect is accounted for in the model by dropping the time-dependent portion of the energy equation.
ω′′′ w L )
1 ∂ 1 ∂ ∂T kr2 - 2 (r2FvuCp,volatilesT) 2 ∂r ∂r r r ∂r 1 ∂ 2 (r FwuCp,vaporT) (8) r2 ∂r
(
)
The model is solved numerically for a particle traveling through a one-dimensional plug flow reactor. The system of coupled partial differential equations is solved using a finite-volume discretization (50 concentric shells) with first-order upwind scheme for the convection terms, implicit scheme for unsteady terms, and tri-diagonal matrix algorithm. The grid size and time step were chosen sufficiently small and varied to ensure that the results were stable. Boundary and Initial Conditions. The surface of the particle is subject to a connective and radiative heat flux. The heat and mass flux at the particle surface is defined as
q′′ ) h(Tg - T) + eσ(Tr4 - T4) m′′ ) hm(Fv - Fv,0)
(9)
where Tg is the surrounding gas-phase temperature and Tr is the radiation temperature. Note that we neglect the effects of Stefan flow on the heat and mass transfer. Heat and mass transfer coefficients are based on correlations.26 (26) Incropera, F. P.; DeWitt, D. P. Fundamentals of Heat and Mass Transfer; John Wiley and Sons: New York, 1996.
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Table 1. Thermo Physical and Kinetic Data variable e D Bbiomass Bchar Cp,biomass Cp,char Cp,volatiles Cp,vapor µ k Pr A E ∆H
value emissivity porosity of biomass diffusivity (m2/s) permeability of biomass (m2) permeability of char (m2) specific heat of biomass [J/(kg K)] specific heat of char [J/(kg K)] specific heat of volatiles [J/(kg K)] specific heat of vapor [J/(kg K)] dynamic viscosity (kg/ms) thermal conductivity of biomass (W/(m K)) Prandtl number of air preexponential factor (s-1) Arrhenius rate constant (J/mol) heat of pyrolysis reaction (J/kg)
Nu ) 2 + (0.4Re0.5 + 0.06Re0.66)Pr0.4 Sh ) 2 + (0.4Re0.5 + 0.06Re0.66)Sc0.4
(10)
The particle Reynolds number is defined by the relative velocity (or slip velocity) between the particle and the surrounding gas phase. The pressure at the particle surface is atmospheric, and the particle loses mass by advection and diffusion at the surface. The particle is initially at ambient temperature, and the pores contain air at atmospheric pressure. Lumped Model A lumped, single-temperature model that ignores the effects of intraparticle heat and mass transfer is commonly used to simulate biomass devolatilization, especially in CFD simulations5,6,8 This lumped approach assumes that the entire particle is isothermal and that that the water vapor and volatiles generated leave the particle instantaneously. The lumped model consists of two coupled ordinary differential equations:5,9
∂T ) hAp(Tg - T) + eσAp(Tr4 - T4) ∂t
(11)
∂m E ) A exp (m - (1 - fv - fw)m0) ∂t RT
(12)
mCp
(
)
Equation 11 is the overall energy balance accounting for heat transfer to the particle by convection and radiation. Equation 12 is the devolatilization rate from the particle. Biomass Properties. Evaluation of the model requires values for a large number of physical and chemical properties, in particular the porosity, permeability, diffusivity, thermal conductivity, heat of pyrolysis, volatile yield, and kinetic parameters for devolatilization (A and E). The full set of values is summarized in Table 1 and is based on a review of the literature. Some of the properties are likely to be a function of particle temperature and mass loss. These effects are largely beyond the scope of this paper, which is focused on effects of particle size. We do account for effects of mass loss on permeability and thermal conductivity, as described below. Given the uncertainty and variability of kinetic parameters for biomass devolatilization reported in the literature, we use the model to examine a range of values. It is generally accepted that the cellulose
0.85 0.4 10-6 10-12 10-11 (1500 + T) 420 + 2.09T + 6.85T2 -100 + 4.4T - 0.00157T2 1872.3 3 × 10-5 0.25 0.7 3.9 × 1017 234 × 103 210 000
ref
39 25 25 24 24 24 21 27,29 27,29 29
component of biomass undergoes pyrolysis according to a high activation energy step27,28 and that the kinetic parameters (A and E) reported in the literature vary over a wide range, depending on the type of biomass sample and experimental conditions.27-34 Therefore, we chose three different kinetic schemes as reported in the literature.29 The numbers in parentheses are the A and E values, respectively, for the three kinetic schemes (3.3 × 107 s-1, 100000 J/mol), (3.9 × 1017 s-1, 234000 J/mol), and (1.1 × 1030 s-1, 400000 J/mol). The endothermic nature of pyrolysis reactions is well established, and we chose the heat of pyrolysis reaction as 210000 J/kg.29 The thermal conductivity and permeability of biomass are extremely variable properties and depend on the type of biomass and also on whether the heating is perpendicular or parallel to the grain.22 Parallel grain thermal conductivity (0.255 W/(m K)) is estimated to be about 2.5 times that of perpendicular grain thermal conductivity.22 The corresponding ratio for char thermal conductivity is lower.35 We use a value for the thermal conductivity of biomass and char as 0.25 W/(m K) and 0.10 W/(m K), respectively.25 For permeability, we chose a base value of 10-12 m2 (1.013 darcys) and varied it between 10-11 m2 (10.13 darcys) and 10-14 m2 (0.01 darcys)25 to examine the sensitivity of the predictions. The permeability of char was kept constant at 10-11 m2 (10.13 darcys).25 For both the effective thermal conductivity and permeability for each location within the particle is a weighted average based on the unconverted biomass and char. Experimental Methods We compare model predictions with results from experiments conducted at the Multifuel Combustor (MFC) at Sandia National Laboratories. The MFC is a pilot-scale (∼30 kW at (27) Antal, M. J.; Varhegyi, G. Ind. Eng. Chem. Res. 1995, 34, 703717. (28) Gronli, M.; Antal, M.J.; Varhegyi, G. Ind. Eng. Chem. Res. 1999, 38, 2238-2244. (29) Narayan, R.; Antal, M. J. Ind. Eng. Chem. Res. 1996, 35, 17111721. (30) Lede, J. Biomass Bioenergy 1994, 7, 49-60. (31) Kothari, V.; Antal, M. J. Fuel 1985, 64, 1487. (32) Lede, J.; Li, H. Z.; Villermaux, J. J. Anal. Appl. Pyrolysis 1987, 10. (33) Lede, J.; Panagopoulos, J.; Li, H. Z.; Villermaux, J. Fuel 1985, 64, 1514. (34) Bradbury, A. G. W.; Sakai, Y.; Shafizadeh, F. J. Appl. Polym. Sci. 1979, 23, 3271. (35) Lee, C. K.; Chaiken, R. F.; Singer, J. M. Sixteenth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, PA, 1976; pp 1459-1470.
Intraparticle Effects in Biomass Devolatilization Table 2. Results from Proximate, Ultimate, and Ash Analysis of Fuel Samples red oak
alfalfa
Proximate Analysis (% Mass, As Received) moisture 5.17 7.445 ash 0.27 7.485 BTU/LB 7899.50 7140.5 Ultimate Analysis (% Mass, Dry, Ash-Free Basis) volatile matter 82.09 80.00 fixed C 17.90 20.00 C 49.01 48.52 H 6.07 6.06 N 0.07 2.39 S 0.01 0.23 Cl 0.01 0.57 O (diff) 44.82 42.23 Al2O3 CaO Fe2O3 MgO MnO P2O5 K2O SiO2 Na2O TiO2 SO3 sum
Ash Analysis (% Mass, Ash Basis) 0.09 10.19 0.26 5.88 0.02 8.37 44.73 0.56 1.79 0.00 0.00 71.89
0.65 40.94 2.17 0.74 0.13 0.40 28.58 1.27 0.23 0.00 0.00 75.11
full-load), 4.2-m-high, down-fired, turbulent flow combustor designed to simulate gas temperature, gas composition, and residence times experienced by particles in pulverized-fuel combustion systems.36 The combustor consists of 7 modular sections, which are electrically heated, each equipped with separate temperature controllers. A 25-cm-long unheated exit tube leads to the open test section portion of the reactor. The combustor liner is constructed of a 15-cm-ID silicon carbide tube. Devolatilization experiments are conducted while operating the MFC as a flow reactor under inert conditions (100% N2). Size-classified fuel particles are pneumatically injected using N2 through water-cooled lances inserted horizontally through the side of the furnace. Particle loadings are low, less than 1 particle cm-3. Particles are iso-kinetically sampled at the exit of the reactor using a water-cooled probe, rapidly quenched in N2, and then collected using a cyclone followed by 1 µm filter. Pyrolyzed fuel particles (chars) are collected in the cyclone; tars and fine particles are collected on the filter. The particle residence time is varied by changing the distance between the fuel injection and particle collection points. For these experiments the distance between the injection and collection point varied between 0.2 and 4.6 m. The particle residence time is estimated on the basis of the distance between the injection lance and the sampling probe, assuming a plug flow velocity profile, and that the particles are traveling at the gas velocity. All of the fuels were examined with an isothermal wall temperature profile of 1250 °C. Experiments were conducted using samples of red oak and alfalfascommon woody and herbaceous fuels, respectively. Results from standard analyses of these fuels are listed in Table 2. Biomass samples are processed using a knife mill and then size-classified using sieves. The results reported here are for particles with a nominal size of 0.707-0.841 mm. Biomass particles are often irregularly shaped and therefore sieving classifies particles by their smallest dimensions. For these experiments, the typical raw fuel particles had an aspect ratio of 2 to 3. (36) Mitchell, R. E.; Hurt, R. H.; Baxter, L. L.; Hardesty, D. R. Compilation of SANDIA coal char combustion data and kinetic analysis, SAND92-8208, Sandia National Laboratories, Livermore, CA, 1992.
Energy & Fuels, Vol. 18, No. 4, 2004 1025 Mass loss rates are calculated using tracer techniques applied to the results from the chemical analyses of the char samples.36 At least 100 mg of char is collected at each location and analyzed for total ash and major ash elements. Complete duplicate (and in some cases triplicate) chemical analyses are performed on each sample. The fraction mass remaining in the particles is calculated on an as-received basis,
( ) ( ) xi,o m ) mo xi
(13)
tracer
where xi and xi,o are the mass fractions of element i that is used as a tracer in the partially devolatilized fuel particle and the raw fuel, respectively. To be a good tracer for mass loss, a species must be both nonreactive and nonvolatile, and, therefore to remain inside the particle during devolatilization. The fractional mass loss on a dry, ash-free basis is
( ) ( )( m mo
)
daf
)(
)
m 1 - xa 1 - xm mo 1 - xa,o 1 - xm,o
(14)
where xa and xa,o are the mass fractions of ash in the char sample and raw fuel, respectively, and xm and xm,o are the mass fractions of moisture in the char sample and raw fuel, respectively. Essentially no moisture is present in the char samples. One must be careful when identifying the tracer species used to determine mass loss rates of biomass fuels. The traditional tracer species commonly used in coal research (Si, Al, and Ti) are often not present in sufficient quantity in many biomass fuels. We employed two criteria to identify tracer species: (1) the tracer had to represent at least 5% of the ash (to avoid large errors associated with detection limits), and (2) the tracer cannot be volatile (no K, Na, S, Cl, etc.). For fuels with three potential tracers (no fuel had more than three), inter comparisons were performed to determine if any one of the tracers appeared to produce biased results; if so, the tracer was discarded. The acceptable tracers were then used to calculate an average overall mass loss, m/mo. For the wood sample, Ca and Mg were used as tracers; P and Mg were used as tracers for the alfalfa. The shape of both the raw fuel and char particles are examined using digital images taken with a low-power optical microscope. The images are examined manually to identify distinct particles. NIH image (a standard image analysis software package) is used to fit an ellipse to each particle. We assume that the aspect ratio of this ellipse is the same as the aspect ratio of the particle. We assume that the volume of the particle is equal to the volume swept by the ellipse when it is rotated around its major axis. More than 100 particles are analyzed for each sample to ensure statistically representative results.
Experimental Results Figures 1 and 2 present the measured mass loss of the red oak and alfalfa, respectively, as a function of residence time. The total volatile yield of the red oak sample is 92% on a dry, ash-free basis which is much higher than the 82% volatile matter measured using standard ASTM procedures (Table 2). The total volatile yield of the alfalfa is 82% on a dry, ash-free basis which is comparable to the 80% dry, ash-free mass measured by the ASTM procedures. The higher yield observed here for the red oak samples are likely due to the rapid heating and high-temperature conditions of these experiments. These conditions are more representative of those found in commercial boilers than the lowertemperature, slower-heating conditions used for the
1026 Energy & Fuels, Vol. 18, No. 4, 2004
Bharadwaj et al.
Figure 1. Mass loss rate of red oak wood particles as a function of residence time in the reactor. Vertical bars indicate variability of mass loss estimate based on the different tracerssCa and Mg. This variability was small for the points with no vertical bars.
Figure 3. (a) Average aspect ratio, (b) volume, and (c) relative density of particles of the red oak wood as a function of mass loss. The vertical bars are the standard deviation of the measurements, and are not shown on all data for clarity. The lines connecting points are intended for visual guidance. Figure 2. Mass loss rate of alfalfa particles as a function of residence time in the reactor. Vertical bars indicate variability of mass loss estimate based on the different tracerssP and Mg. This variability was small for the points with no vertical bars.
ASTM test. It is well established that some coals have higher volatile yields under higher-temperature and higher-heating-rate conditions than the ASTM test.37 These results indicate the total volatile yield of biomass fuels also depends on operating conditions. Somewhat unexpected was the significantly lower volatiles yield of the alfalfa sample compared to wood. This difference may be due to the higher alkali content of the alfalfa (Table 2), which may promote char reactions.38 The dependence of volatile yield on local conditions such as heating-rate and peak temperature must be accounted for if one is to estimate accurately the carbon burnout of a biomass fuel. For example, the higher volatile yield of the wood observed here will significantly increase carbon burnout compared to estimated based on results from the ASTM procedures. When evaluating carbon burnout, char yield is a more useful parameter than total volatile yield because char oxidation is (37) Kobayashi, H.; Howard, J. B.; Sarofim, A. F. Sixteenth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, PA, 1977; pp 411-425. (38) Raveendran, K.; Ganesh, A.; Khilart, K. C. Fuel 1995, 74, 18121822. (39) Chan, W. R.; Kelbon, M.; Krieger, B. B. Fuel 1985, 64, 15051513.
typically the rate-limiting step for particle combustion. To first order, particle combustion times scale with char yield. From a char yield perspective, the differences between the slow-heating, low-temperature conditions of the ASTM test and the rapid-heating, high-temperature conditions of these experiments are dramatic. For example, the ASTM test measures a char yield of 17.9% (dry, ash-free mass basis) for the wood sample; the rapid heating, high-temperature conditions of these experiments decreased this yield to 7% (dry, ash-free mass basis). This represents a factor of 2.5 decrease in char yield and, to first order, a similar reduction of char burnout time. The experimental results show a rapid initial mass loss followed by a relatively slower subsequent stage. Under the conditions of these experiments, the alfalfa sample is devolatilized more quickly than the wood sample: 0.7 s for the alfalfa versus 1 s for the wood. The devolatilization rate is discussed in more detail below in conjunction with the model. Figures 3 and 4 present the average aspect ratio, volume, and relative density of the wood and alfalfa particles, respectively, as a function of mass loss. Each point shown in Figures 3 and 4 is the average of more than 100 individual particles analyzed for a given residence time. Figures 3a and 4a show only modest reductions in the particle aspect ratio with mass loss. In the case of the wood fuel, this reduction is from 2.5 to 2. Similar changes are observed with the alfalfa. Figures 3b and 4b present measurements of the
Intraparticle Effects in Biomass Devolatilization
Energy & Fuels, Vol. 18, No. 4, 2004 1027
Figure 4. (a) Average aspect ratio, (b) volume, and (c) relative density of particles of the alfalfa as a function of mass loss. The vertical bars are the standard deviation of the measurements, and are not shown on all data for clarity. The lines connecting points are intended for visual guidance.
average particle volume as a function of mass loss. There are significant reductions in particle volume during devolatilization. The change in volume of the wood particles is almost linear with mass loss. The volume of the alfalfa particles changes in two steps: the first during the initial stages of devolatilization (mass loss between 0 and 20% on an as received basis), and the second step in the final stages (mass loss between 70 and 80% on an as received basis). These steps may be due to the evolution of the structural component of the alfalfa. Figures 3c and 4c show the changes of normalized particle density as a function of mass loss. The normalized density is the density of devolatilized sample normalized by the density of the raw fuel,
Fbiomass m Vo ) Fbiomass,o mo V
(15)
where m/mo is the fractional mass loss calculated from eq 15, and Vo/V is the ratio of the particle volumes determined from the image analysis. The results indicate that the density of red oak particles decreases with mass loss, and that the final red oak chars have a density of approximately 30% that of the wood particles. The data for the alfalfa indicate a much smaller change of particle density, and that the final char density is around 75% of that of the raw fuel. These data were fit using eq 4 to determine values for the mode of burning parameter, R. An R of 0.6 and 0.13 describes the changes in density with mass loss for the red oak and alfalfa particles, respectively.
Figure 5. Model-measurement comparisons for red oak wood. (a) Predictions of the three models and comparison with experimental results; (b) sensitivity of the model predictions to particle’s permeability; and (c) sensitivity of model predictions for three kinetic schemes of devolatilization. In all figures, symbols are experimental data and lines are model predictions. The three models are the comprehensive model that accounts for both intraparticle heat and mass transfer, a model that only accounts for intraparticle heat transfer, and a lumped model that neglects intraparticle effects and treats the particle as isothermal.
Model-Measurement Comparison Figures 5 and 6 compare predictions of the model to the measured mass loss rate of the red oak and alfalfa, respectively, as a function of the residence time in the MFC. For these calculations the conditions of the model were matched to the experimental conditions in the MFC. The gas and radiative temperatures in eq 9 were set at 1250 °C, and the fuel particles traveled in the direction of gravity with a gas velocity equal to the plug flow velocity in the MFC. The ultimate extent of devolatilization in the model, fv, was set at the measured values, 93% and 82% for wood and alfalfa, respectively. Initial particle volume is from the image analysis data shown in Figures 3b and 4b; values for R in eq 4 are based on the previously discussed fits of the data in Figures 3c and 4c. The rest of the parameters used to evaluate the model for comparison with the experimental data are listed in Table 1. Figures 5a and 6a show that the comprehensive model that accounts for intraparticle heat and mass transfer predicts both the total time required for devolatilization and the shape of the measured mass loss curve. Predictions of the simple lumped model (eqs 11 and 12) that neglects intraparticle effects are also shown in Figures 5a and 6a. By comparing the experimental data to predictions of both the comprehensive model and the
1028 Energy & Fuels, Vol. 18, No. 4, 2004
Figure 6. Model-measurement comparison for alfalfa. (a) Predictions of the three models and comparison with experimental data; (b) sensitivity of the model predictions to particle permeability; and (c) sensitivity of model predictions for three kinetic schemes of devolatilization. In all figures, symbols are experimental data and lines are model predictions. The three models are the comprehensive model that accounts for both intraparticle heat and mass transfer, a model that only accounts for intraparticle heat transfer, and a lumped model that neglects intraparticle effects and treats the particle as isothermal.
lumped model we can evaluate the relative importance of intraparticle heat and mass transfer on devolatilization. The lumped model significantly underestimates the total time required for devolatilization and does not predict the shape of the mass loss curves. The lumped model predicts a short lag (∼0.2 s) followed by extremely rapid devolatilization. The combination of the excellent agreement between the experiments and the comprehensive model and the lack of agreement between the experiments and the lumped model provides strong evidence that intraparticle effects play an important role in determining the devolatilization rate of large biomass particles. By comparing experimental data to predictions of both the comprehensive model and a model that only accounts for intraparticle heat transfer we can evaluate the relative importance of intraparticle mass transfer versus intraparticle heat transfer. To consider only intraparticle heat transfer the comprehensive model is modified by assuming that the water vapor and the volatiles generated leave the particle instantaneously. Under these assumptions, eq 3 becomes the Fourier equation for heat conduction in a sphere. A comparison of the predictions of the different models is shown in Figures 5a and 6a. Accounting for intraparticle heat transfer improves the predictions relative to the lumped model, but the overall predicted devolatilization rate is
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still too fast compared to the experimental data. The conclusion is that both intraparticle heat and mass transfer must be accounted for to reproduce the experimental results. A potential issue in the model calculations shown in Figures 5 and 6 is the large uncertainties in the kinetic parameters and other properties used by the model. As previously discussed, a wide range of values has been reported in the literature for these properties. Are these uncertainties responsible for the poor agreement between, for example, the lumped model and the experiments shown in Figures 5a and 6a? To examine these issues, model predictions using different sets of published kinetics data and a range of values of permeability are shown in Figures 5 and 6. Predictions using different kinetic schemes are compared in Figures 5c and 6c. The different schemes have little effect on the predictions of the comprehensive model, and the comprehensive model reproduces the experimental data for all three kinetic schemes within experimental variability. More significantly, the different kinetic schemes have only a minor influence on the predictions of the lumped modelsthe shape of the mass loss curve does not change; it shows a rapid mass loss regardless of the kinetic scheme. Therefore, uncertainty in biomass devolatilization kinetic parameters is not the cause for the poor performance of the lumped model, and that one must include intraparticle effects to reproduce the experimental data. Figures 5b and 6b evaluate the effect of variations in permeability on predictions of the comprehensive model. The permeability is the property that defines the internal resistance of the particle to bulk flow. Three different values of permeability were considered (10-10, 10-12, and 10-14 m2). This large variation in permeability had little effect on the predictions of the comprehensive model. Increasing the permeability reduces the time for devolatilization (Figures 5b and 6b) because of reduced resistance to advective flow; however, the predictions of the comprehensive model over this wide range of permeability values are within the experimental variability. The above results indicate that the measured mass loss data for the devolatilization of millimeter-sized biomass particles cannot be explained either by the lumped capacitance model or by only accounting for intraparticle heat transfer effects. In addition, sensitivity analysis shows that the differences between the predictions of the different models and the data are significantly greater than what can be attributed to uncertainty in the model input parameters. Effects of Particle Size and Moisture Content We now consider the impact of fuel moisture and particle size on the total time for drying and devolatilization under conditions representative of those found in existing large-scale coal-fired utility boilers. For these calculations we use the velocity and temperature profiles shown in Figure 7 which are representative of those found in commercial utility boilers. The effective radiative temperature is assumed to be 150 K greater than the gas temperature. The particles travel upward (against gravity) through the boiler, and we assumed a
Intraparticle Effects in Biomass Devolatilization
Figure 7. Temperature and velocity fields used in the onedimensional plug flow reactor model to simulate conditions in a typical utility scale boiler.
Figure 8. Predictions of the total time required for drying and devolatilization for particles as a function particle size using both the comprehensive model that accounts for intraparticle heat and mass transfer and the lumped model. Calculations are shown for two different moisture levels: 5% and 50%, on an as-received mass basis.
total mass loss of 90% on a dry, ash-free basis. The rest of the thermophysical properties used are listed in Table 1. Figure 8 plots model predictions of the total time for drying and devolatilization as a function of particle size. Predictions are shown for both the lumped and the comprehensive to model. For small particles (radii less than 0.25 mm), the predictions of the two models converge and thus support the previously reported results that intraparticle effects can be ignored for pulverized coal particles.10,11 The total drying and devolatilization time increases with the increase in particle size, as expected. For large particles (radii greater than 1 mm), the total time for drying and devolatilization predicted by the comprehensive model is several seconds longer than the lumped model. This delay is significant considering the short residence times of commercial boilers and should be accounted for in computational models used to evaluate the effects of biomass-coal cofiring on boiler performance. Figure 8 also presents results for two different moisture contents, 5% to 50% on a mass basis. As
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Figure 9. Predicted temperature profiles at different instances of time for a spherical biomass particle of radius 1 mm, 15% moisture, and 75% volatile matter on a mass basis.
expected, increasing the moisture content increases the time required for drying and devolatilization. For large particles this increase can be substantial; for example, for a particle of radius 1.5 mm, 50% moisture delays the devolatilization by almost 1 s compared to a particle with 5% moisture. Figure 8 only shows predictions for particles with radii up to 2-mm because larger particles could not be suspended in the flow considered here (they rapidly dropped out the bottom of the plug flow reactor due to the influence of gravity). The results shown in Figure 8 can be used to assess the extent of devolatilization for different size particles in cofiring applications. Typical commercial boilers have residence times on the order of several seconds. Small biomass particles (radii less than 1 mm) will likely completely devolatilize in a commercial boiler. Large biomass particles (radii greater than 1.5 mm) with high moisture content (50% on a mass basis) may not completely devolatilize in a commercial boiler. However, even for large particles devolatilization will likely be largely complete given the shape of the mass loss curves shown in Figures 5 and 6 which show high initial mass loss raise followed by slowing rates at high mass loss. Our model predicts that very large biomass particles (radii greater than 2 mm) will drop rapidly into the bottom ash, and therefore release very little volatiles into the boiler. The specific size cutoff for very large particles depends on the local velocity profile in the boiler and also the shape of the particle. The drag on nonspherical particles is higher than that on a spherical particle with the same mass.9 Our calculations are based on the velocity profile shown in Figure 7 and spherical particles. Finally, we use the comprehensive model to examine the spatial and temporal variations of temperature and mass loss rates within a particle. For this discussion we consider a spherical biomass particle with a radius 1 mm and moisture content of 15% and a volatile content of 75% (particle composition is expressed on an as-received mass basis). Figure 9 shows that intraparticle heat and mass transfer resistance creates a significant temperature gradient in the particle. The surface of the particle (r/R ) 1) heats rapidly since it is exposed to the convection and radiation heat transfer, while the heating of the center is delayed. The rapid heating of the particle surface significantly slows the
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Figure 10. Rate of generation of volatiles in the biomass particle at different radial locations. Radial locations have been normalized by total particle radius. (Particle radius 1 mm, moisture content 15%, and volatile content 75%.)
overall heat transfer rate from the surroundings to the particle compared to an isothermal particle. The temperature gradient inside the particle causes a radial variation in the devolatilization rate. Figure 10 shows the rate of generation of volatiles as a function of time for three radial different locations. Devolatilization occurs in stages with the outer layers devolatilizing first, followed by the inner layers. A similar sequencing occurs for vaporization of moisture within the particle. It is interesting that the devolatilization curves shown in Figure 10 for inner regions are broader compared to the outer layers, indicating a slowing volatile generation rate. Conclusions This paper presents a model to simulate the devolatilization of millimeter-sized biomass particles under conditions found in commercial coal-based power generation systems. The model accounts for intraparticle heat and mass transfer by diffusion and advection during particle heating, drying, and devolatilization. As a reference, we also examined predictions of a simple lumped model that neglects intraparticle transport effects and assumes the particle is isothermal. This lumped formulation is commonly used to predict devolatilization of pulverized coal particles, and has been recently used in CFD simulations for modeling biomass devolatilization in biomass-coal cofiring applications.5,6 To evaluate the model, devolatilization experiments under high-temperature and high-heating rate conditions were conducted using the Multifuel Combustor at Sandia National Laboratories. Results are presented for millimeter-sized particles for two fuels, red oak wood and alfalfa. For millimeter-sized particles, both fuels completely devolatilized in approximately 1 s with rapid initial mass loss followed by a relatively slower subsequent stage. The total volatile yield of the wood measured here is significantly higher than reported by standard ASTM tests, indicating dependence of ultimate yield on local conditions. Particles for both fuels shrink significantly and became less dense during devolatilization. No evidence of particle swelling was observed. The comprehensive model accurately predicts both the total time required for devolatilization and the mass loss
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curve for millimeter-sized biomass particles. The measured mass loss data cannot be explained either by the lumped capacitance model or by only accounting for intraparticle heat transfer effects. Sensitivity analysis revealed that the poor performance of the lumped model could not be attributed to uncertainty in chemical and physical properties. The conclusion is that both intraparticle heat and mass transfer must be accounted for to reproduce the experimental results. The comprehensive model is used to examine the effects of particle size and moisture content on devolatilization under conditions representative of those found in coal boilers. Biomass particles of radii up to 2 mm and moisture content up to 50% on a mass basis are considered. As expected, intraparticle heat and mass effects are more significant for larger particles. These effects can significantly delay particle heating and devolatilization; for example, intraparticle effects delay the heating and devolatilization of millimeter-size particles by as much as several seconds for a particle with a 1.5-mm-radius compared to predictions of a lumped model. This delay is significant considering the short residence times of commercial boilers and should be accounted for in computational models used to evaluate the effects of biomass-coal cofiring on boiler performance. These effects have not been accounted for in recent CFD studies of cofiring that use a lumped approach to model biomass devolatilization.5,6 Accounting for intraparticle heat and mass transfer effects will delay predictions of volatile release and combustion, which will influence the heat release and stoichiometry within the boiler, potentially impacting boiler performance (fouling and slagging, pollutant emissions, etc.). Acknowledgment. The experiments at Sandia National Laboratories were sponsored by the U.S. DOE Federal Energy Technology Center, Advanced Research and Technology Development Coal Utilization Program, and the U.S. DOE Office of Energy Efficiency and Renewable Energy’s Biomass Power Program. The authors thank G. Sclippa for his assistance operating the MFC, and K. Cassavant and H. Lee for performing the image analysis of the char particles. Nomenclature List of Symbols u ) bulk advection velocity of volatiles and vapor (m/s) Tg ) gas-phase temperature (K) Tr ) radiation temperature (K) T0 ) ambient temperature (K) Bbiomass ) permeability of biomass (m2) Bchar ) permeability of char (m2) p ) gauge pressure (N/m2) R ) universal gas constant (J/(K mol)) m ) instantaneous mass of the particle (kg) m0 ) initial mass of the particle (kg) e ) emissivity fv ) initial mass fraction of volatile matter (ultimate yield) fw ) initial mass fraction of moisture xi ) instantaneous mass fractions of element i xi,o ) initial mass fractions of element i xa ) instantaneous mass fractions of ash xa,o ) initial mass fractions of ash xm ) instantaneous mass fraction of moisture xm,o ) initial mass fractions of moisture
Intraparticle Effects in Biomass Devolatilization
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Cp ) specific heat of the particle (J/(kg K)) Cp,volatile ) specific heat of volatiles (J/(kg K)) Cp,char ) specific heat of char (J/(kg K)) Cp,biomass ) specific heat of biomass (J/(kg K)) Cp,vapor ) specific heat of water vapor (J/(kg K)) T ) instantaneous temperature (K) t ) time (s) Yv ) mass fraction of volatiles D ) diffusivity (m2/s) D* ) effective diffusivity (m2/s) A ) preexponential factor for devolatilization reaction (s-1) E ) activation energy for devolatilization (J/mol) Re ) Reynolds number Sh ) Sherwood number Nu ) Nusselt number Pr ) Prandtl number V ) instantaneous particle volume Vo ) initial particle volume
R ) mode of burning ) porosity of the particle σ ) Stefan-Boltzmann’s constant F ) total concentration of tar and water vapor (kg/m3) Fv ) concentration of volatiles (kg/m3) Fw ) concentration of water vapor (kg/m3) Fa ) density of air (kg/m3) Fbiomass ) instantaneous density of biomass (kg/m3) Fbiomass,0 ) initial density of biomass (kg/m3) Fv0 ) ambient concentration of volatiles (kg/m3) Fw0 ) ambient concentration of water vapor (kg/m3) µ ) viscosity (kg/(m s)) ω′′′ ) total volumetric generation rate (volatiles and vapor) (kg/(m3 s)) 3 ω′′′ v ) volatile generation rate (kg/(m s)) 3 ω′′′ ) water vapor generation rate (kg/(m s)) v
List of Greek Symbols
EF0340357
