Modeling of the Devolatilization of Nonspherical Wet Pine Wood

3642

Ind. Eng. Chem. Res. 2002, 41, 3642-3650

SEPARATIONS Modeling of the Devolatilization of Nonspherical Wet Pine Wood Particles in Fluidized Beds Luis F. de Diego, Francisco Garcı´a-Labiano, Alberto Abad, Pilar Gaya´ n, and Juan Ada´ nez* Department of Energy and Environment, Instituto de Carboquı´mica (CSIC), Miguel Luesma Casta´ n 4, 50015 Zaragoza, Spain

The release of volatiles of pine wood particles was analyzed by means of continuous measurements of the CO2 and O2 concentrations obtained after the complete combustion of the volatiles and from flame extinction times. A relatively simple mathematical model was used to predict the O2 consumed, as a function of time, during the devolatilization and further volatiles combustion of wood chips in fluidized beds. In this model, the drying and pyrolysis of wood particles is considered as a coupled process controlled by the kinetics of devolatilization as well as heat transfer to and through the particles. A distributed activation energy model is adopted for the kinetics of devolatilization of the wood particles. The wood chips are characterized by an equivalent particle diameter and a shape factor and transformed into spherical shape. The model was in satisfactory agreement with experimental data over a wide range of operating conditions, particle sizes and shapes, and particle moisture contents of practical importance for wood combustion in fluidized beds. Introduction In recent years, fluidized-bed boilers have increased the number of practical applications of burning coal, biomass, organic waste, and mixtures of them. Biomass and organic waste are becoming interesting for energy production because they are renewable fuels that reduce CO2 and sulfur emissions. Designs of the existing fluidized-bed boilers for biomass and organic waste combustion are mainly based on experience from coal combustion1 because the complex mechanism of combustion of these solids in fluidized beds is insufficiently known. Devolatilization and volatiles combustion are the main steps in the combustion of biomass. The volatile fraction of biomass particles contributes a significant proportion to the total amount of heat released during combustion. Hence, a study of the processes leading to volatiles combustion is of vital significance to an understanding of fluidized-bed combustion (FBC). In some cases, moisture contents as high as 50% can be found; as a consequence the drying of the particles is very important in the global process of devolatilization. However, the mechanisms by which devolatilization of biomass and combustion of volatiles take place in fluidized beds are insufficiently known. Previous work has concentrated on the combustion of coal particles. It is generally accepted that heat transfer to and within the particle, chemical kinetics of pyrolysis, and mass transfer of volatile products within the particle may control the solid fuel devolatilization. It is not clear, * Corresponding author: Tel.: 34-976-733977. Fax: 34-976733318. E-mail: [email protected].

however, under what conditions it would be reasonable to consider only one of these mechanisms in any evaluation. Juntgen and van Heek2 have reported that the coal particle size has to be 0.3 mm. Agarwal et al.4 indicated that a chemical-kineticscontrolled regime depends on the Biot number as well as the interaction of kinetic parameters with bed temperature. For typical operating conditions of fluidized beds, the devolatilization was controlled by the chemical reaction for coal particle sizes of less than 0.10.3 mm. As the size increased, the rate-limiting step was in a mixed regime between chemical reaction and heattransfer control. Similar results were found by Di Blasi5 and Maa and Bailie6 working with cellulose at the typical conditions of fluidized beds. Several researchers have developed detailed numerical models of wood pyrolysis.7-13 In general, the thermal degradation of wood in these models involves the interaction in a porous media of heat, mass, and momentum transfer with chemical reactions. However, mathematical models available in the literature1,14,15 lead to the conclusion that the shrinking-core model with control by heat transfer with overlapping drying and devolatilization is reasonable for describing the behavior of large wet solid fuel particles under FBC conditions. Palchonok et al.1 modeled this process and found analytical solutions under certain simplifications. A single particle model for the coupled drying and devolatilization in fluidized beds was also proposed by Agarwal et al.16 This model combines the devolatiliza-

10.1021/ie0201922 CCC: $22.00 © 2002 American Chemical Society Published on Web 06/26/2002

Ind. Eng. Chem. Res., Vol. 41, No. 15, 2002 3643

Figure 2. Typical O2 concentration profile obtained during devolatilization tests using air as the fluidizing gas: (s) measured; (‚‚‚) corrected.

Figure 1. Experimental setup used for devolatilization tests: (a) visual; (b) continuous gas analysis.

tion kinetics with the processes of heat transfer to and through the solid fuel particle. Traditional methods of particle combustion analysis apply only to spherical particles; however, the majority of biomass particles are highly nonspherical. Only a few attempts have been made to determine the effect of shape on the burning rates. Stubington et al.17 found no effect of the particle shape on devolatilization times when the equivalent-mass diameter of the particle is used and the shape factor is greater than 0.7. Austin et al.18 determined that the volatile burning times for nearly-spherical corncob particles obeyed a d2 law. The nonspherical particles obeyed a volume to the two-thirds power law. They used several d2 definitions to demonstrate a linear relationship between d2 and the burning times of a particle, indicating the necessity of more research to determine the effectiveness of these correlations. In this work, the drying and devolatilization process of pine wood particles of regular and irregular forms has been studied in a fluidized bed by means of continuous measurements of CO2 and O2 concentrations obtained after the complete combustion of the volatiles and from visual observation of the flame of volatiles. A model for the coupled drying and devolatilization of single particles of wood chips in fluidized beds has been used to compare experimental data and model predictions over a wide range of operating conditions and particle moisture contents. Experimental Section The devolatilization of biomass was studied in a fluidized-bed reactor of 50 mm i.d. and 0.5 m height, with a perforated steel plate distributor, as shown in Figure 1. The fluidizing gas (air or N2) was preheated in a ceramic fixed bed below the distributor plate. The entire system was inside an electrically heated furnace. The fluidized bed was composed of 300 g of silica sand with a particle size of 0.50-0.63 mm. The superficial

gas velocity inside the fluidized bed was kept constant at 40 cm/s in all experiments, and four temperatures between 650 and 950 °C were used. The experiments were carried out with debarked pine wood chips of different sizes and irregular shapes (dp,eq ) 7-37 mm and φ ) 0.4-0.75). Moreover, to better compare the model predictions with the experimental data, several pine wood particles with well-defined size and shape (dp,eq ) 6-45 mm and φ ) 0.5-0.8) were prepared. Elemental analysis showed that the pine (Pinus Sylvestris) wood contained 52.9% C, 6.8% H, and 0.1% N with respect to dry mass. The moisture content was 8.3 wt %. The devolatilization times were determined by two methods: (1) visual observation of the volatiles flame and (2) continuous gas analysis of O2 and CO2. For the visual technique, the devolatilization experiments were recorded on video through a mirror located over the reactor. The devolatilization times (tv) were measured as the time elapsed between the sample addition and complete extinction of the flame of volatiles. The reproducibility of these measurements was around 5%. The second technique consisted of measuring the combustible volatiles evolution as a function of time by means of gas analyzers. For these experiments, the fluidized bed was sealed with a lid and the individual particles were dropped into the bed through a ball valve connected vertically to the lid. The combustible volatiles evolution was followed by means of the continuous measurement of the CO2 and O2 concentrations obtained after the complete combustion of the volatiles. The CO concentration was negligible in all of the experiments. A nondispersive infrared analyzer for CO2 (and CO) and a paramagnetic analyzer for O2 were used. The gas concentrations were logged as a function of time via a PC-based data-logging system. When N2 was used as the fluidizing gas, the volatiles were burned with O2 addition in a fixed-bed reactor (900 °C) placed downstream from the fluidized bed. This technique allowed us to determine both the total devolatilization times and the evolution of the devolatilization rate with time. For better data analysis, the CO2 and O2 concentrations versus time profiles were corrected for gas flow and dispersion through the sampling line and analyzers. The system dispersion was determined by adding CO2 to the bed. Figure 2 shows the measured and corrected evolution of the O2 concentration for a typical devolatilization

3644

Ind. Eng. Chem. Res., Vol. 41, No. 15, 2002

Figure 3. Plot showing the reproducibility of tests carried out at identical operating conditions.

test using air as the fluidizing gas. The end of the devolatilization was considered as the time when the O2 or CO2 concentration reached the first local minimum. After this time, the CO2 and O2 concentrations remained almost constant for a long time while the remaining char burnt. Figure 2 also illustrates the point for the end of devolatilization, which was defined as the devolatilization time, tv. For each experiment, one particle was dropped into the hot fluidized bed. For each temperature and particle size, three to five separate experiments were performed with the well-defined particles. Figure 3 plots a typical series of three experiments at identical operating conditions showing the good reproducibility of the tests. This was not possible with the chips because each chip had a different size and shape factor. In addition, to analyze the effect of the moisture content over the devolatilization rate, pine wood particles of the same size (well-defined size and shape) and different moisture content were devolatilized under oxidizing conditions at the same operating conditions (650 and 850 °C) in the fluidized bed. To obtain particles with different moisture content, water was added to the original samples (moisture ) 8.3 wt %) and each wood particle was kept for 1 week in a sealed vessel to allow its stabilization. The moisture content was determined by weighing the wood particle before water addition and after stabilization. Some original samples were also dried in an oven at 108 °C to obtain particles without moisture (moisture content ) 0%). Results and Discussion In the visual test, it was observed that most of the volatiles of the large wood particles were burnt in the freeboard region of the fluidized bed and not around the wood particle. Only at the end of the devolatilization process was the flame observed to be formed around the particle, which was floating on the bed surface. It was also observed that the release of volatiles was not uniform with time. The release of volatiles was higher in the first seconds of the devolatilization process and decreased with time. This volatiles evolution was determined by measuring the CO2 produced and the O2 consumed during different devolatilization tests with further volatiles combustion. This method of determination of the devolatilization rates was developed to determine the evolution of combustible volatiles. These

Figure 4. Photograph showing wood particles before and after devolatilization.

kinds of devolatilization rates are useful for modeling the evolution of volatiles in combustion environments. Figure 2 shows an example of these tests. It can be observed that the combustible volatile releasing rate was not uniform during the devolatilization time, confirming the visual observations. In some tests, wood chars were obtained by introducing well-defined wood particles, in a wire mesh basket, into a fluidized bed in a nitrogen atmosphere. In these tests, there was not attrition of the wood particles because the use of the wire mesh basket avoided the contact of the wood particles with the inert material. After devolatilization, the basket with the sample was removed from the bed. The particles were weighed and measured before and after devolatilization to know the char yield and the change in volume of the particles due to devolatilization. Figure 4 shows wood particles, with different sizes and shape factors, before and after devolatilization. It can be observed that the wood samples underwent structural changes with significant shrinkage and crack formation. Similar results were observed by Di Blasi et al.19 and Davidsson and Pettersson.20 The final volume of the particles at 850 °C was about 50 ( 5% of the initial volume. These effects should be taken into account in the devolatilization models to obtain realistic results. Reduction in the particle diameter causes faster internal heat-transfer rates.21 Cracks or structural failures decrease the intraparticle residence time of volatiles, and so the resistance to mass transfer could be negligible.19 Different experimental techniques have been involved in the study of devolatilization rates.22-28 Most workers have correlated their results using the typical powerlaw expression

tv) adpn

(1)

with n ranging from 0.32 to 2.6, although most data fell between 1 and 2.17 The variability of the experimental results has been attributed to the differences in the types of apparatuses employed, operating conditions, definition of devolatilization time, etc. Figure 5a shows the devolatilization times measured under identical operating conditions with different particle sizes at 850 °C and using the two techniques mentioned above. It can be observed that the devolatilization times measured with both techniques were almost the same. The devolatilization times increased with an increase in the equivalent particle diameter,


Figure 5. Devolatilization times measured with particles of different sizes and well-defined geometries (a) as a function of the equivalent particle diameter and (b) as a function of the equivalent particle diameter multiplied by the shape factor. Tb ) 850 °C.

Figure 6. Devolatilization rates of wood particles of different sizes and shapes but the same factor dp,eqφ. Tb ) 850 °C.

dp,eq, but there was an important dispersion in the results. However, as shown in Figure 5b, this dispersion decreased when the devolatilization times were plotted versus the equivalent particle diameter multiplied by the shape factor, dp,eqφ, defined as dp,eq ) diameter of a sphere having the same volume as the particle and φ ) surface area of a sphere having the same volume as the initial particle/initial surface area of the particle. Similar results were observed at other temperatures (650950 °C), with the devolatilization times decreasing slightly with increasing temperature. The devolatilization times were fitted to eq 1, replacing the particle diameter by the equivalent particle diameter multiplied by the shape factor.

tv ) a(dp,eqφ)n

(2)

The values of the constants a and n at each temperature were determined from linear regression of the logarithmic data. The values of n were between 1.5 and 1.6, which is in the same range as the values in the literature.17 The a values decreased (1.69, 1.38, 1.03, and 0.89) as the bed temperature increased (650, 750, 850, and 950 °C), which is also in good agreement with the literature data.24 Figure 6 shows the devolatilization profiles obtained for samples of different shape and equivalent diameter

but with the same value for the product of both. As can be seen, both the devolatilization times and the variation with time of the volatiles evolution rates are nearly identical. Taking into account these findings, it is possible to conclude that nonspherical wood particles can be modeled as spherical particles, characterized by an equivalent particle diameter and a shape factor. In this way, the models developed for spherical particles can be applied to particles of different shapes, replacing, in all of the equations of the devolatilization model, the particle dimensions by the equivalent particle diameter multiplied by the shape factor (dp,eqφ). Devolatilization Model On the basis of the findings previously shown, a model to predict the O2 consumed as a function of time during the wood devolatilization and volatiles combustion was developed. In the model the following assumptions were used: (1) The drying and pyrolysis of biomass particles is considered as a coupled process controlled by the kinetics of devolatilization as well as heat transfer to and through the particles (mass transfer does not play an important role in the volatile evolution rate). (2) The biomass particle is initially isotropic and homogeneous. (3) The water vapor and volatiles do not affect the heat

3646


transfer inside the particle because of the existence of cracks. (4) The gas (vapor water and volatiles) leaving the particle does not heat to the temperature of the wood char through which it could flow because most of the gas leaves the particle through the cracks. It is assumed that the gas heats to the temperature of the bed around the particle surface. Thus, the reaction products leaving the particle lower the heat-transfer rate from the bed to the particle. (5) The volatiles are assumed to have the properties (thermal conductivity and specific heat) of CO and water vapor. (6) The devolatilization process is assumed to be thermally neutral, and no chemical interaction takes place between the char and the moisture and volatile species escaping from the particle. (7) Particle fragmentation does not occur. (8) The particles are characterized by an equivalent particle diameter and a shape factor and transformed into spherical shape. When a biomass particle is introduced into a hot fluidized bed, drying commences almost immediately, with the drying front moving inward. When the particle temperature increases, the devolatilization starts and the particle will dry and devolatilize simultaneously. In these conditions, the temperature profile inside the particles may be obtained by solving the heat conduction equation:

∂T 1 ∂ 2 ∂T ) 2 rR ∂t ∂t r ∂r

(

)

(3)

with the following initial and boundary conditions:

T(r,0) ) T0

(4)

Nu ) (N1 - N∞)(dp/di)1/3 + N∞(dp/di) N1 ) 6 + 0.117Ari0.39Pr0.33 N∞ ) 0.85Ari0.19 + 0.006Ari0.5Pr0.33

For a large fuel particle, dp/di > 7, of low density, floating on the surface of the bed, a factor of 0.85 was introduced in eq 8.1 The radiative heat-transfer coefficient was calculated as

Rr ) σ(Ts2 + Tb2)(Ts + Tb)/(1/p + 1/b - 1)

dT dr

|

r)R0

) h(Tb - Ts) - Q

(9)

where the emissivity was chosen to be 0.8 for both the biomass particle and the fluidized-bed inert solid.1 It was assumed that the thermal conductivity of wood and char did not change with the temperature, and values of 0.14 W/m‚K for wood and 0.11 W/m‚K for char were used.1 The equations proposed by Wenzl29 and by Perry and Green30 were used to calculate the wood and char specific heats, respectively. For the properties related to the partially devolatilized particles, a linear variation between the virgin wood and char was considered. Because it is very difficult to choose a reaction model with proper rate constants for the thermal decomposition of wood, the kinetics of volatiles released from the nonisothermal wood decomposition was described using the distributed activation energy model proposed by Anthony et al.,31 which assumes f(E) to be a Gaussian distribution with mean activation energy Eo and standard deviation σE.

V0 - V ) V0

at the particle’s surface ks

(8)

(5)

∫0∞exp(-k0∫0te-E/RT dt)f(E) dE

(10)

f(E) ) [σE(2π)1/2]-1 exp[-(E - E0)2/2σE2]

(11)

at the wet/dry interface dT ks dr

|

dT ) ks r)re+ dr

|

dre + FpC0 λ r)redt

(6)

r ) re f T ) 100 °C

(

dT dr

|

r)0

)0

)

V0 - V V0

in the center of the particle ks

The kinetic model integrated over the temperature profile along the particle diameter allows us to obtain the fractional volume-average devolatilization at any given time:

(7)

Analytical solutions have been proposed using certain simplifications,1,16 such as, for example, that the thermophysical properties are constant with the temperature and that the radius of the particle is constant during the process of drying as well as during devolatilization. However, as was previously shown, the final volume of pine wood particles at 850 °C was about 50% of the initial volume. This volume reduction must be taken into account in the devolatilization models to obtain realistic results. In this work, it was assumed that the volume reduction was proportional to the fraction of volatiles released. The overall heat-transfer coefficient to a fuel particle submersed in a fluidized bed was calculated as the sum of a convective and a radiative constituent. The former was calculated, as recommended by Leckner et al. (cited in ref 1), with the following equations:

)

avg

∫0R (t)R 33(t)[∫0∞exp(-k0∫0te-E/RT dt)f(E) dE]r2 dr 0

(12)

0

The set of differential and nonlinear equations was solved simultaneously by a finite difference method, starting from the outer part of the particle. Modeling Results One of the main problems with modeling the processes happening in wood chips is the measurement and definition of their geometry because of their irregular forms. Then first, to test the validity of the model, several samples of well-defined geometry and size were prepared. With these samples the best kinetic parameters (k0, Eo, σE) of the devolatilization model were obtained by curve fitting to the experimental O2 consumed versus time data at different temperatures and pine wood particle sizes. The Nelder and Mead32 method was used to optimize the k0, Eo, and σE parameters by


Figure 7. Experimental (symbols) and predicted (continuous line) devolatilization rates of wood particles with well-defined geometries and sizes (14.5 × 14.5 × 14.5 mm).

minimizing the mean deviation between calculated and experimental data. The fitting procedure used in this work permitted observation of the behavior of the error function when the optimum values of the parameters are reached. Very different Eo-k0 couples gave a mean error similar to the optimum. This behavior was also reported by Anthony and Howard.33 Determination of kinetic parameters in a pyrolysis model becomes somewhat uncertain when conduction is the dominant process because influences from imperfections in the description of the conductive heat transport may become included in the pyrolysis parameters. However, these kinetic parameters may be very useful when modeling fluidized beds because they have been obtained with particle sizes and operating conditions of practical importance for wood combustion in these boilers. From the transition state theory, the frequency factor, k0, was calculated to have a value34 of around 1013 s-1, and this value has been used in much modeling work.33,35 When this value (k0 ) 1013 s-1) id adopted for our model, the best Eo and σE parameters obtained were Eo ) 200 ( 6 kJ/mol and σE ) 25 ( 3 kJ/mol. A comparison between experimental and calculated data is shown in Figure 7, using the optimum kinetic parameters. As can be seen, a reasonable fit was achieved in all ranges of temperatures studied. It can be also observed that the amount of combustible volatiles released in the first moments of the reaction increased with increasing temperature and the devolatilization time increased

slightly with decreasing temperature, which it was reasonably well predicted by the model. The main objective of the model was to simulate the behavior of pine wood chips, for which the particle dimensions are more difficult to define. Figure 8 shows a comparison, using the optimum kinetic parameters previously obtained, between experimental and predicted data of O2 consumed using wood chips of different sizes and shapes. In general, a good agreement between experimental results and model predictions was observed in all cases, especially taking into account that the model used is relatively simple and the wood chips are characterized by an equivalent particle diameter and a shape factor and transformed into spherical shape. The distribution of volatiles throughout the bed depends on the rate of devolatilization and the rate of particle mixing within a fluidized bed. The relative rates of these two processes determine where the volatiles are released into the bed. In the combustion of wet solid fuels, a marked extension of the solid pyrolysis period has been observed.19,22-24,36,37 So, combustion of wet fuels may prevent an abrupt release of volatiles near the feeding point, thus enhancing the uniformity of the distribution of combustible volatiles over the crosssectional area of fluidized beds; however, it is also widely recognized that moisture largely affects the reactor efficiency and product quality. In this work, the effect of the moisture content over the devolatilization rate of pine wood particles was

3648


Figure 8. Experimental (symbols) and predicted (continuous line) devolatilization rates of wood chips.

studied. It was observed that the devolatilization times increased with increasing moisture content of the wood particles, which agrees with previous observations.19,22-24,36,37 Figure 9 shows the effect of the moisture content on the variation of devolatilization rates with time. As the moisture content of the wood particles increased, more water had to be evaporated and therefore the devolatilization rate of combustible volatiles decreased and was more uniform along the devolatilization time. The model described in this work was used to predict the effect of the moisture content on the devolatilization rates. Although moisture in the biomass can exist in three forms1 (as free water and water vapor in the pores

and as bound water absorbed in the solid matrix), in the model all of the moisture content was considered as free water. In Figure 9, a comparison between experimental and predicted devolatilization rates for particles with moisture contents up to 50% is shown. A good agreement between experimental data and model predictions was obtained in all cases. Conclusions Visual observations of flame produced by the combustion of the volatiles released by pine wood particles showed that the release of volatiles was not uniform with time. The release of volatiles was higher in the first


multiplied by the shape factor). Taking into account this finding, it was suggested that the models developed for spherical wood particles can be used to predict the behavior of nonspherical wood particles with different shapes. On the basis of this conclusion, a relatively simple model was used to predict the coupled drying and devolatilization of wet pine wood particles in fluidized beds. The model gave a good prediction of the experimental devolatilization rates of irregular wood chips at different operating conditions and moisture contents. An increase in the moisture content of the wood particles produced an increase in the devolatilization time and a more uniform devolatilization rate along the devolatilization time, which was well predicted by the model. Acknowledgment This research was carried out with financial support from the European Commission in the framework of the Non-Nuclear Energy Program (JOULE III), Contract No. JOR3-CT98-0306. Nomenclature

Figure 9. Effect of the particle moisture content on the devolatilization rate. Symbols ) experimental data; continuous lines ) model predictions.

seconds of the devolatilization process and decreased with time. The volatiles evolution was determined by measuring the CO2 produced and the O2 consumed during different devolatilization tests with further volatiles combustion. During the devolalization process in the fluidized bed, the pine wood particles underwent structural changes with significant shrinkage and crack formation. The final volume of the particles at 850 °C was about 50 ( 5% of the initial volume. These effects increase the internal heat-transfer rates and decrease the resistance to mass transfer and also the intraparticle residence time of volatiles. The devolatilization times and the volatiles evolution rates of nonspherical pine wood particles of different sizes were nearly identical when these particles had the same value of dp,eqφ (equivalent particle diameter

a ) constant in the power-law expressoin (s/mmn) Ari ) inert particle Archimedes number C0 ) initial moisture content (g/gdrywood) dp ) particle diameter (m or mm) dp,eq ) equivalent particle diameter (m or mm) di ) inert particle diameter (m) E ) activation energy (J/mol) Eo ) mean of the activation energy distribution (J/mol) f(E) ) Gaussian distribution function, eq 11 h ) heat-transfer coefficient (W/m2‚K) ks ) thermal conductivity (W/m‚K) k0 ) preexponential factor (s-1) n ) exponent in the power-law expression Nu ) Nusselt number Pr ) Prandtl number Q ) heat absorbed by the volatiles leaving the particle (W/ m2) r ) radial position within a particle (m) re ) radial position of the wet-dry interface (m) rv ) devolatilization rate (mmol of O2 consumed/gsample‚s) Ro ) external radius of the wood particle (m) R ) gas constant (J/mol‚K) t ) time (s) tv ) devolatization time (s) tvo ) devolatization time of oven-dried wood particle (s) T ) temperature (K) Tb ) bed temperature (K) T0 ) initial temperature of the wood particle (K) Ts ) temperature at the particle surface (K) V ) Volatiles evolved (g/gwood) V0 ) initial volatiles content (g/gwood) Greek Symbols R ) thermal diffusivity (m2/s) Rr ) radiative heat-transfer coefficient (W/m2‚K) b ) bed emissivity p ) particle emissivity φ ) shape factor Fp ) wood particle density (g/m3) σ ) Stefan-Boltzmann constant (W/m2‚K4)

3650


σE ) standard deviation in the activation energy distribution function (J/mol)

Literature Cited (1) Palchonok, G. I.; Dikalenko, V. A.; Stanchits, L. K.; Borodulya, V. A.; Werther, J.; Leckner, B. Kinetics of the Main Stages of Fluidized Bed Combustion of a Wet Biomass Particle. In Proceedings of the 14th International Conference on Fluidized Bed Combustion; Preto, F. D. S., Ed.; ASME: Fairfield, NJ, 1997; Vol. 1, p 125. (2) Juntgen, H.; van Heek, K. H. An Update of German Nonisothermal Coal Pyrolysis Work. Fuel Process. Technol. 1979, 2, 261. (3) Essenhigh, R. H. The Influence of Coal Rank on the Burning Times of Single Captive Particles. J. Eng. Power 1963, 85, 183. (4) Agarwal, P. K.; Genetti, W. E.; Lee, Y. Y. Model for Devolatilization of Coal Particles in fluidized beds. Fuel 1984, 63, 1157. (5) Di Blasi, C. Kinetic and Heat Transfer Control in the Slow and Flash Pyrolysis of Solids. Ind. Eng. Chem. Res. 1996, 35, 37. (6) Maa, P. S.; Bailie, R. C. Influence of Particle Sizes and Environmental Conditions on High-Temperature Pyrolysis of Cellulosic Material-I (Theorical). Combust. Sci. Technol. 1973, 7, 257. (7) Kansa, E. J.; Perlee, H. E.; Chaiken, R. F. Mathematical Model of Wood Pyrolysis Including Internal Forced Convection. Combust. Flame 1977, 29, 311. (8) Chan, W. 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, 1505. (9) Bilbao, R.; Millera, A.; Murillo, M. B. Temperature Profiles and Weight Loss in the Thermal Decomposition of Large Spherical Wood Particles. Ind. Eng. Chem. Res. 1993, 32, 1811. (10) Miller, R. S.; Bellan, J. Analysis of Reaction Products and Conversion Time in the Pyrolysis of Cellulose and Wood Particles. Combust. Sci. Technol. 1996, 119, 331. (11) Di Blasi, C. Influences of Physical Properties on Biomass Devolatilization Characteristics. Fuel 1997, 76, 957. (12) Grønli, M. G.; Melaaen, M. C. Mathematical Model for Wood PyrolysissComparison of Experimental Measurements with Model Predictions. Energy Fuels 2000, 14, 791. (13) Bryden K. M.; Ragland, K. W.; Rutland, C. J. Modeling Thermally Thick Pyrolysis of Wood. Biomass Bioenergy 2002, 22, 41. (14) Agarwal, P. K.; La Nauze, R. D. Transfer Processes Local to the Coal Particle: A Review of Drying, Devolatilization and Mass Transfer in Fluidized Bed Combustion. Chem. Eng. Res. Des. 1989, 67, 457. (15) Saastamoinen J. J. Fundamentals of Biomass Drying, Pyrolysis and Combustion. In IEA Biomass Combustion Conference; International Energy Agency: Cambridge, U.K., 1994. (16) Agarwal, P. K.; Genetti, W. E.; Lee, Y. Y. Coupled Drying and Devolatilization of Wet Coal in Fluidized Beds. Chem. Eng. Sci. 1986, 41, 2373. (17) Stubington, J. F.; Chui, T. Y. S.; Saisithidej, S. Experimental Factors Affecting Coal Devolatilization Time in Fluidized Bed Combustion. Fuel Sci. Technol. Int. 1992, 10 (3), 397. (18) Austin, P. J.; Kauffman, C. W.; Sichel, M. Combustion of Single Nonspherical Cellulosic Particles. Dynamics of Heteroge-

neous Combustion and Reacting Systems; Kuhl, A. L., Leyer, J. C., Borisov, A. A., Sirignano, W. A., Eds.; Progress in Astronautics and Aeronautics; American Institute of Aeronautics and Astronautics: Reston, VA, 1993; Vol. 152, p 136. (19) Di Blasi, C.; Gonzalez-Hernandez, E.; Santoro, A. Radiative Pyrolysis of Single Moist Wood Particles. Ind. Eng. Chem. Res. 2000, 39, 873. (20) Davidsson, K. O.; Pettersson, J. B. C. Birch Wood Particle Shrinkage During Rapid Pyrolysis. Fuel 2002, 81, 263. (21) Di Blasi, C. Heat, Momentum and Mass Transport through a Shrinking Biomass Particle Exposed to Thermal Radiation. Chem. Eng. Sci. 1996, 51, 1121. (22) Urkan, M. K.; Arikol, M. Burning Times of Volatiles from Turkish Coals During Fluidized Bed Combustion. Fuel 1994, 73, 768. (23) Prins, W.; Siemons, R.; van Swaaij, W. P. M.; Radovanovic, M. Devolatilization and Ignition of Coal Particles in a TwoDimensional Fluidized Bed. Combust. Flame 1989, 75, 57. (24) Ross, D. P.; Heidenreich, C. A.; Zhang, D. K. Devolatilization Times of Coal Particles in a Fluidized Bed. Fuel 2000, 79, 873. (25) Jung, K.; Stanmore, B. R. Fluidized Bed Combustion of Wet Brown Coal. Fuel 1980, 59, 74. (26) Agarwal, P. K.; Genetti, W. E.; Lee, Y. Y. Devolatilization of Large Coal Particles in Fluidized Beds. Fuel 1984, 63, 1748. (27) Stubington, J. F.; Huang, G.; Scaroni, A. W. Devolatilization Times of mm-size Coal Particles. Fuel 1991, 70, 1105. (28) Stubington, J. F.; Sumaryono. Release of Volatiles from Large Coal Particles in a Hot Fluidized Bed. Fuel 1984, 63, 1013. (29) Wenzl, H. The Chemical Technology of Wood; Academic Press: New York, 1970. (30) Perry, R. H., Green D., Eds. Perry’s Chemical Engineers’ Handbook, 6th ed.; McGraw-Hill: New York, 1984. (31) Anthony, D. B.; Howard, J. B.; Hottel, H. C.; Meissner, H. P. Rapid Devolatilization of Pulverized Coal. In Fifteenth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, PA, 1975; p 1303. (32) Nelder, J. A.; Mead, R. A Simplex Method for Function Minimization. Comput. J. 1964, 7, 308. (33) Anthony, D. B.; Howard, J. B. Coal Devolatilization and Hydrogasification. AIChE J. 1976, 22, 625. (34) Benson, S. Thermochemical Kinetics; Wiley: New York, 1968. (35) Agarwal, P. K.; Agnew, J. B.; Ravindran, N.; Weimann, R. Distributed Kinetic Parameters for the Evolution of Gaseous Species in the Pyrolysis of Coal. Fuel 1987, 66, 1097. (36) Chan, W. R.; Kelbon, M.; Krieger-Brockett, B. SingleParticle Biomass Pyrolysis: Correlations of Reaction Products with Process Conditions. Ind. Eng. Chem. Res. 1988, 27, 2261. (37) Ouedraogo, A.; Mulligan, J. C.; Cleland, J. G. A QuasiSteady Shrinking Core Analysis of Wood Combustion. Combust. Flame 1998, 114, 1.

Received for review March 12, 2002 Revised manuscript received May 17, 2002 Accepted May 25, 2002 IE0201922

Modeling of the Devolatilization of Nonspherical Wet Pine Wood

Recommend Documents