Effect of Large Aspect Ratio of Biomass Particles on Carbon Burnout

Energy & Fuels 2002, 16, 1523-1532

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Effect of Large Aspect Ratio of Biomass Particles on Carbon Burnout in a Utility Boiler D. Gera,*,† M. P. Mathur,‡ M. C. Freeman,‡ and Allen Robinson§ Fluent, Inc./NETL, 3647 Collins Ferry Road, Morgantown, West Virginia 26505, United States Department of Energy National Energy Technology Laboratory, 626 Cochans Mill Road, Pittsburgh, Pennsylvania 15236, and Carnegie Mellon University, Mechanical Engineering Department, Pittsburgh, Pennsylvania 15213 Received April 18, 2002

This paper reports on the development and validation of comprehensive combustion sub models that include the effect of large aspect ratio of biomass (switchgrass) particles on carbon burnout and temperature distribution inside the particles. Temperature and carbon burnout data are compared from two different models that are formulated by assuming (i) the particles are cylindrical and conduct heat internally, and (ii) the particles are spherical without internal heat conduction, i.e., no temperature gradient exists inside the particle. It was inferred that the latter model significantly underpredicted the temperature of the particle and, consequently, the burnout. Additionally, some results from cofiring biomass (10% heat input) with pulverized coal (90% heat input) are compared with the pulverized coal (100% heat input) simulations and coal experiments in a tangentially fired 150 MWe utility boiler.

1.0. Introduction Because of the concern over global warming, there is considerable worldwide interest in increased utilization of renewable energy sources, including biomass fuels, that are essentially CO2 neutral. Biomass not only has considerable potential as a fuel source, it also shows a reasonable cost level in comparison to other renewable energies. Biomass cofiring in large industrial and utility coal-fired boilers is a practical approach for increasing renewable energy given the wide availability, existing capital investment, and established performance of coalfired boilers for providing efficient, low cost power. Cofiring biomass in pulverized coal-fired boilers has been considered to be a particularly attractive option because these units can accept particle topsizes that are an order of magnitude larger than the pulverized coal, thus reducing fuel preparation costs. In fact, cofiring biofuels with coal has been tested and demonstrated on various types of combustion technologies, including cyclone boilers, wall-fired and tangentially fired pulverized coal boilers, fluidized bed boilers, stoker fired boilers, and Kraft black liquor paper mills with a thermal capacity ranging from 50 to 500 MW.1-3 Biofuels offer important advantages as a combustion feedstock due to their low ash content, the high volatility

of the fuel and the high reactivity of both the fuel and the resulting char. High volatile biomass, if used as a reburning fuel in coal-fired boilers, has a promising future for reducing NOx to benign N2 via HCN and NHi reduction reaction mechanisms (e.g., NO + NH2 f N2 + H2O).4-6 During the past decade, substantial progress has been made in developing technologies to reduce NOx in combustion systems. There are numerous numerical/ experimental studies conducted to develop and validate the models for predicting thermal NOx, reburning-NOx, and fuel decomposition to produce NH3 in combustors.7 Recently, Abbas et al.8 found that the amount of energy required for the grinding of biomass (2-3% of the heating value) was almost double compared to the energy required for coals (0.9-1.2% of the heating value). The energy requirements increased significantly (>20% of the heating value) to reduce the fibrous/moist biomass to a diameter of less than 1 mm. Hence, cofiring large biomass particles (>1 mm) make economically viable option in the existing pulverized coal boilers, but may raise additional concerns over unburned carbon in terms of boiler operability and the marketing of ash. Thus, minimizing the unburned carbon in ash is a high priority in evaluating biomass fuels for utility boilers, where variations in furnace temperature, flow profiles, and load swings may significantly impact the residence

* To whom correspondence should be addressed. Phone: (304) 5987934. Fax: (304) 598-7185. E-mail: [email protected]. † Fluent, Inc./NETL. ‡ United States Department of Energy National Energy Technology Laboratory. § Carnegie Mellon University. (1) Tillman, D. A. Biomass Bioenergy 2000, 19, 365-384. (2) Ekmann, J. M.; Winslow, J. C.; Smouse, S. M Fuel Process. Technol. 1998, 54, 171-188. (3) Demirbas, A. Energy Convers. Manage. 2002, 43, 877-884.

(4) Williams, A.; Pourkashanian, M.; Jones, J. M.; Rowlands, L. J. Inst. Energy 1997, 70, 102-13. (5) Salzmann, R.; Nussbaumer, T. Energy Fuels 2001, 15, 575-582. (6) Maly, P. M.; Zamansky, V. M.; Ho, L.; Payne, R. Fuel 1999, 78, 327-334. (7) Smoot, L. D. Prog Energy Combust. Sci. 1997, 23, 203-32. (8) Abbas, T.; Costen, P. G.; Lockwood, F. C. Solid Fuel Utilization: From Coal to Biomass. Invited Plenary Lecture. In Proceedings of the 26th International Symposium on Combustion; The Combustion Institute: Pittsburgh, PA, 1996; pp 3041-58.

10.1021/ef0200931 CCC: $22.00 © 2002 American Chemical Society Published on Web 09/12/2002

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time requirements for the combustion of large biomass particles. The accurate prediction of unburned carbon in such a highly fluctuating environment requires advanced combustion models to be used in the computational fluid dynamics (CFD) simulations [e.g., refs 9 and 109-10]. A key concern in developing an accurate model for biomass combustion is accounting for a large length/ diameter ratio of biomass particles, which plays a key role in char burnout. A large length/diameter ratio of biomass particles results in a temperature gradient from the surface to the center of the particle. Hence, the heat transfer to the biomass particles from the surrounding gas and furnace must be accounted for by considering the combination of conduction, convection and radiation modes of heat transfer. On the basis of dimensionless Biot number (the ratio of convective to conductive heat transfer) and the Pyrolysis number, Pyle and Zaror11 showed that the temperature is fairly uniform inside the small pulverized coal particles where the combustion kinetics is so fast that carbonization is uniform throughout the particle. Hence, the conduction effects in small pulverized coal particles can be neglected. For large biomass particles, however, the conduction effects cannot be ignored. The objective of the current investigation is to quantify the effect of large biomass (switchgrass in the current study) particle aspect ratios on the oxygen diffusion mass flux on the surface of the particle and the internal heating of the particle. A number of theedimensional (3D) exploratory CFD simulations, which are related to the tangentially fired utility boiler, are presented in this article to examine the effects of switchgrass particle size and aspect ratio on residence times and carbon burnout. The mathematical formulation incorporating the effect of switchgrass aspect ratio on carbon burnout is described in detail in the next section. The next section also describes the gas and dispersed phase models used in the current simulations. 2.0. Theoretical Formulation and Governing Equations An Eulerian/Lagrangian approach is employed to numerically investigate the flow characteristics, heat transfer, and species transport by solving the time-averaged equations of global mass, momentum, energy, and species mass fractions (see, e.g., Smoot and Smith;13 Kuo14). The particle-phase equations are formulated in a Lagrangian frame of reference, and the coupling between the phases is introduced through particle sources in the Eulerian gas-phase equations. The sources of mass, momentum and energy transfer from the coal and biomass are based on particle-source-in-cell (PSI-cell) algorithm of Crowe et al.,12 and are computed separately from their respective densities, flow rates and the chemical composition. The standard k- turbulence closure, finite rate chemistry controlled using eddy dissipation model (for details (9) Hein, K. R. G.; Bemtgen, J. M. Fuel Process. Technol. 1998, 54, 159-69. (10) Lockwood, F. C.; Mahmud, T.; Yehia, M. A. SFuel 1998, 77, 1329-37. (11) Pyle, D. L.; Zaror, C. A. Chem. Eng. Sci. 1984, 39, 147-58. (12) Crowe, C. T.; Sharma, M. P.; Stock, D. E. J. Fluids Eng., Trans. ASME 1977, 99, 325-332 (June, ser. 1., no. 2). (13) Smoot, L. D.; Smith, P. J. Coal Combustion and Gasification; Plenum Press: NY, 1985. (14) Kuo, K. K. Principles of Combustion; John Wiley & Sons: New York, 1986.

Gera et al. see Gera et al.15), and the discrete ordinate radiation models are used for the gas phase in the present simulations. The standard FLUENT 5.6 solver is used for computing the gas phase species, flow field, and the temperature variations inside the combustor, and the conservation equations for the same can be viewed directly from Gera et al.15 Particle combustion related submodels, which were not the part of standard FLUENT code but were developed for the accurate treatment of initial heating, devolatilization, and char oxidation for coal and switchgrass, are described in this section. These submodels are coupled to the gas phase via externally defined user functions. The thermal annealing module of the stand-alone char burnout kinetics (CBK) model developed by Hurt et.al.16 was coupled with the gas-phase reactions in FLUENT 5.6 software through a user defined function (UDF). 2.1. Dispersed Particle Phase. The heat and mass transfer to/from the particles is described in a sequence of four stages, where the first stage accounts for the initial heating of the particles to 400 K, which is followed by devolatilization (second stage), and char oxidation (third stage). The fourth stage is the heating of ash. The trajectories and the heat and mass transfer for these discrete particles are coupled with the continuous phase. The governing equations for these four stages for coal combustion are described in details in Gera et al.,15 and the equations for switchgrass combustion are described below. 2.1.1. Initial Heating of the Switchgrass Particles. To model the effect of large length/diameter ratio of switchgrass particles on temperature gradients, the heat transfer to the switchgrass particles from the surrounding gas and furnace is accounted for by considering the combination of conduction, convection, and radiation modes of heat transfer. The temperature distribution for the large switchgrass particles is determined by solving the following heat conduction equation inside the particles:

cp

(

)

( )

∂Fp ∂ ∂2T 1 ∂T ∂2T (FpT) ) K 2 + + 2 + (-q) ∂t r ∂r ∂t ∂r ∂z

(1)

where cp, Fp, and K are the particle’s specific heat capacity, density, and effective thermal conductivity respectively; q is the apparent enthalpy change associated with the set of reactions and physical changes; T is the local temperature; r is the radial position; and z is the axial location. During the initial heating stage, there is no change in mass, (∂F/∂t ) 0), and the heat of reaction q is zero. The current work is an extension of Jalan and Srivastava’s17 work, which did not include the temperature variation in the axial direction. Here, the above eq 1 is solved using an alternating direction implicit (ADI) scheme (Carnahan et al.18) inside the cylindrical particle with the following boundary conditions:

|

∂T ) hc(Tf - T) + σ(Tf4 - T4); t > 0 ∂r r)R

K

|

|

∂T ∂T ) 0; K ) hc(Tf - T) + σ(T4f - T4); ∂r r)0 ∂z z)0;z)L

(2a) t>0 (2b)

where hc is the convective heat transfer coefficient, and and σ are the particle emissivity and Stefan-Boltzmann constant, respectively. 2.1.2. Switchgrass Devolatilization. A single rate kinetic devolatilization model is used to predict a volatile yield from (15) Gera, D.; Mathur, M.; Freeman, M.; O’Dowd, W. Combust. Sci. Technol. 2001, 172, 35-68. (16) Hurt, R.; Sun, J. K.; Lunden, L. A. Combust. Flame 1998, 113, 181-197. (17) Jalan, R. K.; Srivastava, V. K. Energy Convers. Manage. 1999, 40, 467-94. (18) Carnahan, B.; Luther, H. A.; Wilkes, J. O. Applied Numerical Methods; Krieger Publishing Co.; Malabar, FL, 1990; p 508.

Carbon Burnout in a Utility Boiler

Energy & Fuels, Vol. 16, No. 6, 2002 1525

the switchgrass and coal. This simple devolatilization model assumes that the rate of devolatilization is dependent on the amount of volatiles remaining in the particle via a first-order reaction:

-

dmp ) k(mp - (1 - fv0)mp0) dt

(3)

where mp is the particle mass at any time t, k is the kinetic rate, fv0 is the fraction of the volatiles that are initially present in the particle, and mp0 is the initial particle mass. The kinetic rate k is defined by the input of an Arrhenius-type, preexponential factor and an activation energy expression:

k ) A1 exp(- E/RTP)

(4)

where A1 and E are assumed to be 1.0× 106 s-1 and 7.48 × 107 J/Kg mol, respectively, for switchgrass.19 The change in the switchgrass particles' temperature is calculated from eq 1. The diameter of the particle during devolatilization process is tracked from the following relation:

dp mp0 - mp ) 1 + (Csw - 1) dp0 fvmp0

(5)

where dp and dp0 are the current and initial diameters of particle with mass mp and mp0, respectively; Csw is the swelling coefficient, and fv is the initial volatile fraction. The density of the particle during the devolatilization process changes as F ) (6mp/πdp3). The species released during the devolatilization process included CO, CO2, SO2, H2O, and N2 among others, determined by performing a mass balance from ultimate and proximate analyses of the fuel. 2.1.3. Switchgrass Char Combustion. After the volatile component of the particle is completely evolved, a surface reaction begins, which consumes the combustible fraction of the particle. Because of the large size of the switchgrass char, the surface reaction proceeds at a rate determined by the diffusion of the gaseous oxidant to the surface of the particle:

dmp ) - qΦenAp dt

(6)

where the overall particle burning rate q per unit external area Ap can be expressed in terms of a diffusion rate coefficient kd as follows:20

equivalent spherical particle (Grow22): φen )

Shellipsoid ) Shspherical

(

d

1 d2 d L 1 + sin -1(e) 2 4 22e

)∫ x[ ( ) ][ ( ) ][ ( ) ] (9) ∞

0

1/

ξ+

L 2

2

ξ+

d 2

2

ξ+

d 2

2

dξ

where d and L are the ellipsoidal particle’s minor and major axes, respectively, and e is the eccentricity. Using the above approach, the mass flux at the surface of the ellipsoidal particle can be written as22 hm,ell )

( )(

(2 + 0.6Re0.5Pr0.33)Dox

)∫ x[

y2 d2 L x2 + 4 2 (d/2)4 (L/2)4

∞

0

1/

ξ+

(L2) ][ξ + (d2) ][ξ + (d2) ]dξ 2

2

2

(10)

and the mean mass flux transfer coefficient for an equivalent spherical particle may be written as eq 11, which will be used for normalizing the mass transfer coefficient in eq 10.

h h m,sph )

(2 + 0.6Re0.5Pr0.33)Dox dsph

(11)

Switchgrass char is assumed to have a constant density, and the change in particle diameter during the char combustion process is calculated from the current mass of the particle. The heat convective transfer coefficient was corrected with “blowing factor φ” to account for high rates of mass transfer during devolatilization.23 The corrected convective heat transfer coefficient h′ was written as h′ ) hφ/[exp(φ) - 1], where h is the convective heat transfer coefficient without any mass loss. An additional key consideration in modeling biomass and pulverized coal combustion is the accurate estimation of various external forces, such as drag, buoyancy, and gravitational forces acting on the particles. The trajectories of the discrete phase particles are calculated by integrating the force balance on the particle.15

3.0. Results and Discussion

where Dox is the diffusion coefficient for oxidant in the bulk, R is the universal gas constant, Tm is the mean film temperature, φm is a mechanism factor based on the stoichiometric oxygen coefficient for the combustion reaction (φm ) 1 if the product of combustion is CO2, and (φm ) 2 if the product of combustion is CO), and φen is an enhancement factor in eq 6 that accounts for the nonspherical shape of the switchgrass. It is calculated by solving the ratio of the average oxygen mass flux at the surface of an oblate (ellipsoidal) particle to an

The effect of a large length/diameter ratio of switchgrass particles on internal temperature gradients and mass flux of oxygen over the surface of the particles is discussed in the first phase of this discussion. An alternating-direction-implicit (ADI) algorithm developed for solving the nonisothermal model presented in eq 1 is tested with the Pyle and Zaror’s11 experimental data, which was obtained from the controlled study of pyrolysis of cylindrical samples of wood. The thermophysical properties of the cylindrical wood samples used in the above test are given in Table 1. Pyle and Zaror11 made temperature measurements in the radial direction at 2.5 cm from the bottom of the cylinder, which is compared with the numerical solution of eq 1 in Figure 1. An excellent agreement is observed between the measured and numerically predicted values. It may be seen from Figure 1 that the temperature gradient is very large during the initial heating period (time equal

(19) Bech, N.; Wolff, L.; German, L. Energy Fuels 1996, 10, 27683. (20) Mitchell, R. E. Combust. Sci. Technol. 1987, 53, 165-86. (21) Baum, M. M.; Street, P. J. Combust. Sci. Technol. 1971, 3, 23143.

(22) Grow, D. T. Combust. Flame 1990, 80, 209-13. (23) Eisenklam, P.; Arunachalam, S. A.; Wetson, J. A. Evaporation Rates and Drag Resistance of Burning Drops. In Proceedings of the 11th International Symposium on Combustion; The Combustion Institute: Pittsburgh, PA, 1967; pp 715-28.

q ) kd(Pg - Ps)

(7)

where Pg is the partial pressure of oxygen in the free stream and Ps is the partial pressure of oxygen at the surface, and the diffusion rate coefficient are defined by21

kd )

McΦmDox RTm(dp/2)

(8)

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Gera et al.

Figure 1. Temporal variation of temperature (K) along the radial coordinate. (s) CFD model. (9) Experimental Data taken at 2.5 cm from the bottom of cylinder. Table 1. Thermophysical Properties of Cylindrical Samples of Wood thermal conductivity (K) bulk density (F) specific heat (Cp) initial temperature (T0) surrounding fluid temperature (Tf) convective heat transfer emissivity of solid heat of reaction (q)

0.12 W/mK 550 Kg/m3 1670 J/Kg/K 303 K 643 K 0.322 W/m2K 0.95 -2.1 × 10+5 J/Kg

to 2 min), but the temperature becomes fairly constant at a time equal to 11 min. In an utility boiler, where the surrounding gas temperatures are of the order of 1600-1800 K, it is important to evaluate the temperature distribution from the center to the surface of the particle for residence times ranging from 4 to 8 s. There are two exploratory cases evaluated for an aspect ratio of 6 and two different radii of the cylindrical pellets (0.5 and 1.5 mm) at Tf ) 1643 K. The radial temperature distribution is presented in Figure 2a,b. It may be seen from Figure 2a that, for a small particle diameter, the temperature inside the particle becomes almost constant within 1.5 s. For large particles (1.5 mm radius) however, it takes approximately 8.5 s for the temperature to become uniform. Hence, it may be inferred that the nonisothermal model must be used for accurately predicting burnout of large (aspect ratio) switchgrass particles in utility boilers.

For a given volume of the particle, sphere has a minimum surface area among any other volume shapes. Biomass particles that are typically cylindrical or ellipsoidal, therefore, have a larger surface area when compared with the spherical particle of an equivalent volume. The large surface then essentially results in enhancement of the (diffusion) mass flux of oxygen on the surface of the particle. In addition, aspect ratio plays an important role in determining the ignition behavior because burning could begin at points where the mass flux of oxygen is greatest. To this end, a local normalized mass transfer coefficient (hm,ell/hm,sph), deduced from the local Sherwood number along the surface of the particle is plotted in Figure 3. It may be seen that the mass flux of oxygen at the corner end points increases rapidly with the increasing aspect ratio, suggesting that the “end corners” of the large aspect ratio particles are going to ignite first. It is also worth mentioning that the ratio of the local mass flux of oxygen at the end corner (θ ) 90°) and the waist (θ ) 0°) is exactly the same as the length-to-diameter ratio of the particle. Hence, the change in the aspect ratio in the burning of biomass particles is not likely to deviate from its original aspect ratio, and can be assumed to be constant in numerical simulations. An overall burning enhancement factor φen (eq 9), which was calculated by solving the ratio of the average oxygen mass flux at the surface of the switchgrass particle and an equivalent spherical particle, is pre-



Figure 2. (a,b) Temporal Variation of Temperature (K) along the Radial Coordinate.

sented in Figure 4. It may be observed from Figure 4 that the increase in surface area of the particle is approximately 44%, but the enhancement in the overall burning rate is 31% only when compared with the spherical particle of an equivalent volume. A simple algebraic enhancement factor φen is used in evaluating the burning rate of switchgrass particle in a utility boiler, which is the second part of the discussion of this article. The simulations are performed using an eastern bituminous coal and switchgrass (SWG) as the biomass fuel, which were fired with thermal input of 100% from coal in the first configuration, and 90% coal and 10% switchgrass in the second configuration. Initial benchmarking and validation of combustion submodels was carried out on a lab scale drop tube furnace (30 kW) and a 110 kW pilot scale combustor at NETL (for details,

see Gera et al.15,24). The current models are extended on a tangentially fired, 150 MWe utility boiler (shown in Figure 5) cofiring coal and biomass switchgrass. There are four levels of burners on the boiler with four burners located off diagonal at each level to create a strong swirling flow in the middle of the boiler. The composition of coal and SWG used in the simulations is given in Table 2. The coal particles used in this simulation were utility ground with the sizes ranging from 10 to 150 µm and a mean diameter of 53 µm, and they were modeled using the Rosin-Rammler distribution. The coal was fired at 52317 kg/h (115236 lbs/h) for 100% thermal input by (24) Gera, D.; Freeman, M. C.; O’Dowd, W.; Mathur, M. P.; Walbert, G.; Robinson, A. Computational Fluid Dynamics Modeling For Biomass Cofiring Design in Pulverized Coal Boilers BioEnergy, October 1519, Buffalo, NY, 2000.

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Figure 3. Variation of local normalized Sherwood number along the surface of an ellipsoidal particle for three different aspect ratios.

Figure 4. Variation of an overall mass diffusion flux enhancement and surface area enhancement for a cylindrical particle with different aspect ratios.

coal case. The coal was fired at 47085 kg/h (103,712 lbs/ h) in cofiring simulations where 90% of the thermal input came from coal and the remaining 10% from the switchgrass. The biomass fuel used in these simulations was switchgrass (SWG) with a broad particle size distribution of about 5% plus 6 mm, 14% plus 4 mm, 37% plus 2 mm, 60% plus 1 mm, and 23% minus 0.5 mm, with a mean particle size of 2 mm. The aspect ratio of the particles used in this study was 4. The switchgrass size distribution was also modeled using the Rosin-Rammler distribution. The switchgrass was fired at 10208 kg/h (22,484 lbs/h). The total airflow rate used in this study was 592,066 kg/h (1,304,111 lbs/h). The heating value for the dry coal and switchgrass were 32 MJ/Kg (13,643 BTU/lb) and 16.4 MJ/Kg (7,002 BTU/ lb), respectively. It is worth mentioning that the proximate analysis performed in accordance with ASTM gave a volatile yield for coal and switchgrass as 38.5% and 72.85%, respectively. It has been well established that the volatile yields under commercially relevant high temperature environments are typically 10-20% higher than those reported by the standard ASTM test.25-26 (25) Kobayashi, H.; Howard, J. B.; Sarofim, A. F. Coal Devolatilization at High Temperatures. In Proceedings of the 16th International Symposium on Combustion; The Combustion Institute: Pittsburgh, PA, 1976; pp 411-25.

Gera et al.

Therefore, for the current simulations, volatile yield of 42.8% and 85% was used for coal and SWG, respectively. The wall boundary conditions are accounted for by prescribing a flux of 236 kW/m2 (75,000 Btu/h/ft2) in the radiant section as a simplifying assumption to help establish reasonable temperature profiles in the boiler during the initial simulations. The other relevant flow parameters are given in Table 3. The temperature predicted by the CFD model for 100% coal and 90%/10% (coal/ switchgrass) firing is compared with the experimentally measured values of 100% coal firing at 2.26 m (nearly 7.5 feet) from the rear wall in Figure 6. Figure 6 shows reasonable agreement, with a slight (75-100K) deviation of predicted and measured temperature at a given location, considering various factors associated with measurements of high temperatures inside boilers. For example, in the absence of perfect shielding where suction pyrometers may indicate lower values than true gas temperatures due to heat transfer considerations. Of significance in evaluating the results of CFD simulation versus experimental results is to evaluate the distance (from the experimental port location) where the 75-100 K increase would be shown in the CFD temperature profiles of the furnace. In this case, the CFD results show increases of 75-100K at distances of only 0.46 m (about 18 in.) toward the center at the same elevation, or about 0.30 m (1 foot) down in the elevation (i.e., toward the burners) from the exact experimental location inside the boiler. Given the large dimensions of the boiler, such 75-100 K uncertainty at short distances may be considered reasonable given the grid spacing in the initial CFD simulations. The difference in the temperature at the exit of this utility boiler with 100% coal and 90%coal/ 10% switchgrass is less than 20 K. From Figure 6, it is worth mentioning here that rapid burning of highly volatile switchgrass results in a slight increase in temperature near the burner area. The slight increase in temperature may not pose a big concern for the utility companies to cofire typically less than 10% biomass (switchgrass) in the existing power plants. A typical variation in temperature and the burnout fraction of a particle as it traverses in the combustor is presented in Figure 7. It may be seen from Figure 7 that there is a significant difference in temperature when the particle is assumed to be spherical without conducting heat internally (isothermal) than when the particle is assumed to be cylindrical and conducting heat internally. This difference is due partly to the fact that the cylindrical particle has a large surface area compared to the spherical particle, and hence it heats quickly. Also, the diameter (1.65 mm) of the cylindrical particle (with an aspect ratio of 4) is smaller than the equivalent diameter (3 mm) of the sphere; consequently it burns out quickly. It is worth mentioning here that if the burnout is not predicted correctly, the aerodynamic drag on the particle will not be sufficient to sustain the weight of the particle. As a result, the particle may fall into the bottom ash hopper instead of exiting the combustor as fly ash. This phenomenon is demonstrated (26) Williams, A.; Backreedy, R.; Habib, R.; Jones, J. M.; Poukashanian. Fuel 2002, 81, 605-18.



Figure 5. Schematic of a utility T-fired 150 MWe boiler. Table 2. Composition of Coal and Switchgrass (Moisture Free Basis) C H O N S ash fixed carbon volatile matter (ASTM)

coal

switchgrass

76.39 4.61 5.34 1.50 0.74 11.42 50.08 38.5

44.07 6.56 40.16 0.82 0.12 8.27 18.88 72.85

Table 3. Summary of Inlet Conditions for the Utility Boiler zone

velocity

flow area

temperature

primary air secondary air close coupled coal and SWG inflow no. of burners

15.3 m/s 37.8 m/s 35.5 m/s 15.3 m/s 16

0.65 m2 0.96 m2 0.37 m2 0.65 m2

322 K 572 K 572 K 322 K

for the “spherical” particle in Figure 7. Additionally, the small spike in temperature for the cylindrical particle around 1 s in Figure 7 is due to the fact that the particle has hit the pockets of lower oxygen concentration and temperature.

Figure 6. Temperature (K) variation on a line 2.27 m (7 ft 5 in.) from the rear wall. (s) Predicted CFD model for 100% coal. (- -) Predicted CFD Model for 90% coal and 10% biomass.(9) Experimental Data for 100% coal.

The fate of the switchgrass particle burnout when fired from one of the 4 corners of level A is presented in Table 4. In the current CFD simulation, pulverized coal achieved an average residence time of over 3 s with the combustion efficiencies of 99.9%. Although switchgrass

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Gera et al.

Figure 7. A typical temporal Variation of (a) temperature (K) and (b) burnout fraction (m/m0) of a 3 mm equivalent diameter particle in an industrial boiler.

particles are an order of magnitude larger than pulverized coal, they achieve high combustion efficiencies due to their highly volatile content and large residence time. Table 4 shows that the small switchgrass particles (∼1 mm), which are formulated in “spheres without internal conduction” or “cylindrical with internal heat conduction” behave in an expected fashion, resulting in complete burnout. However, the larger switchgrass particles behave quite differently because the relative contributions of gravity, buoyancy, and drag forces alter particle trajectories significantly. The CFD simulations show that if larger switchgrass particles were modeled (or

actually physically injected in the form of) spheres larger than 2 mm, they would fall into the bottom ash hopper due to their poor burnout. In contrast, switchgrass modeled as cylindrical particles, because of their high burnout, experience enough aerodynamic drag to leave the combustor as fly ash. Thus, the CFD modeling shows that the likely shredding or grinding of switchgrasssa fibrous fuelsinto slivers which would approximate cylinders for computational purposes, would be much more favorable from a cofiring perspective relative to processing requirements. Interestingly, CFD simulations may also help explain why plant nodes



Table 4. Effect of Switchgrass Particle Sizes on Residence Time, Combustion Efficiency, and Fly Ash/ Bottom Ash Partitioning for Switchgrass Cofiring at Full Load in a 150 MWt Tangentially Fired Boiler equivalent diameter

spherical particle

cylindrical particle (aspect ratio ) 4)

1 mm

residence time: 5.8 s combustion efficiency: 99.9% fly ash: 100% bottom ash: 0% residence time: 5.39 s combustion efficiency: 95.3% fly ash: 20.4% bottom ash: 79.6% residence time: 3.56 s combustion efficiency: 4.0% fly ash: 0% bottom ash: 100% residence time: 2.97 s combustion efficiency: 1.0% fly ash: 0% bottom ash: 100% residence time: 2.65 s combustion efficiency: 1), and the height of cylinder to disk to be β (.1), then the ratio of surface area of the disk Ad to cylinder Ac can be readily written as

Ad (β1.5 + R) ) 0.5 Ac β (1 + R)

(12)

It may be inferred from the eq 12 that the surface area of disk will always be greater than the surface area of an equivalent cylinder, and consequently it will burn faster than the long thin cylinder. This phenomenon can easily be explained by imagining the burning of a thin sheet of a scribble paper. Table 4 also clearly demonstrates the effect of surface area on burnout for the cylindrical particles. It may be extrapolated from Table 4 and eq 12 that any irregular shaped switchgrass particle (but not spherical) with an equivalent diameter of 5 mm can be easily burned in the current utility boiler configuration. The limitation on use of the large aspect

ratio of 5 mm particles will come from the handling and transport of these particles in the ducts and the burners but not from the thermodynamics or chemistry side. Additionally, a slight decrease in the residence of cylindrical particles of 5 mm is due to the fact that 28% of these particles are falling into the bottom ash hopper, where it may take ∼2 s to fall in the bin. The time to converge these simulations ranged from two weeks to five weeks per simulation on a cluster of eight Intel Pentium (866 MHz) processors. The computational time can be significantly reduced to less than a week if a cluster of 24 Pentium 4 (2.0 GHz) processors is used. This time estimate is based on the benchmark studies performed on a problem of 1 million cells, where a speed up of 18 was achieved with the 24 P4-2 GHz processors in a cluster using Fluent software. The time spent on modeling/tracking particle trajectories was about 30% of the total computational time. The models were refined to check the grid independence and as well as independent of number of particle injection locations. 4.0. Conclusions This study examined the impact of the large aspect ratio of biomass particles on carbon burnout in cofiring switchgrass/coal simulations. Two models were presented to predict the temperature and burnout fraction of large aspect ratio biomass (switchgrass) particles. These models assumed that (i) the particles can be represented by an equivalent sphere without any internal heat conduction, and (ii) the particles were cylindrical with internal heat conduction. CFD simulation results suggest that switchgrass particles that naturally occur or may be processed (e.g., using shedders or grinders) in cylindrical shapes with large aspect ratios have considerable advantages when cofiring relative to switchgrass particles that would be more spherical in shape. CFD simulation results clearly indicate how important the shape factor is in specifying suitable sizing criteria for biomass handling/processing schemes for cofiring applications. This is particularly important given that traditional sieving and classification equipment is typically based on a singular dimension. Thus, while the specification of a 6 mm topsize switchgrass, which would be expected have large aspect ratios based on the types of shredding/grinding equipment available, may be appropriate for cofiring applications in a given boiler, a smaller topsize specification should be considered for other biomass feedstocks that are more spherical in shape. These CFD results also serve to illustrate how important the shape factor is in predicting particle trajectories, residence time, and carbon burnout, as well as the inclusion of heat conduction in the case of larger biomass particles to accurately design and operational issues, such as carbon burnout in utility boilers. The simulation results clearly show that it is possible to cofire much larger biomass (switchgrass) particles than coal in the utility boilers without increasing any unburned carbon amount in the fly ash. This phenomenon may be attributed to the high volatile content of switchgrass, as well as aerodynamic factors that can allow large switchgrass particles to achieve considerably longer residence times inside the boiler.

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Nomenclature A1 ) Reactivity in Arrhenius equation (1/s) Ap ) surface area of the particle (m2) d ) ellipsoidal particle’s minor axis (m) Dox ) diffusion coefficient for oxidant in the bulk gases (m2/s) dp ) particle diameter (m) e ) eccentricity of ellipse E ) activation energy (J/Kgmol) fv0 ) initial fraction of volatiles h ) convective heat transfer coefficient (W/m2 K) heac ) heat of reaction (J/Kg) hm,ell ) local mass transfer coefficient on the surface of elliptical particle (m/s) hm,sph ) average mass transfer coefficient on the surface of spherical particle (m/s) I ) radiation intensity (W/m2) k∞ ) thermal conductivity of the surrounding gas phase (W/m K) L ) ellipsoidal particle’s major axis (m) mp ) mass of the particle (Kg) p ) gas pressure (Pa) Po ) partial pressure of oxidant (Pa) r ) radius of the particle (m) R ) universal gas constant (J/Kg mol K) Re ) Reynolds number ()FDp|up - u|/µ) Sb ) stoichiometric coefficient Sh ) Sherwood number t ) time (s) T∞ ) local temperature of the surrounding gas phase (K) Tp ) particle temperature (K) Greek Symbols δij ) 1 for i ) j; δij ) 0 when i * j ) dissipation rate

Gera et al. p, ) emissivity of the particle φ ) mechanism factor φen enhancement factor µ ) laminar gas viscosity Fg ) gas density (kg/m3) Fp ) particle density (kg/m3) σ ) Boltzman constant

Acknowledgment. The authors thank Professor Robert H. Hurt at Brown University (Providence, RI) for providing access to the CBK module. This standalone CBK module was coupled with the gas phase reactions in FLUENT software through a user defined function (UDF). The authors also thank Mark Sheldon and staff at the General Electric Energy and Environmental Research (GE-EER) Corporation for their assistance in providing geometry and experimental data for a utility boiler. The authors thank the DOE Office of Fossil Energy (FE) for supporting NETL combustion/ environmental research for coal-fired boilers, including biomass and opportunity fuels cofiring studies, as well as Dr. Raymond Costello of the DOE Office of Energy Efficiency and Renewable Energy (EERE) Biomass Power Program. Finally, the authors acknowledge Dr. Robert Romanosky, NETL Advanced Research/Power Systems Product Manager, and Mr. Donald Bonk, NETL Combustion Systems Product Manager. Any reference in this paper to specific commercial products, processes, or services is to facilitate understanding only and does not necessarily imply its endorsement or favoring by the U.S. Department of Energy. EF0200931

Effect of Large Aspect Ratio of Biomass Particles on Carbon Burnout

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