Energy & Fuels 1995,9, 870-879
870
Model Comparisons with Drop Tube Combustion Data for Various Devolatilization Submodels Brandon S. Brewster,* L. Douglas Smoot, and Stephen H. Barthelson Advanced Combustion Engineering Research Center, Brigham Young University, 45 CTB, Provo, Utah 84602
David E. Thornock ABB Power Plant Laboratories, A Division of Combustion Engineering, Inc, Windsor, Connecticut 06095 Received March 16, 1995@
Predictions of a two-dimensional, axisymmetric combustion model, using various devolatilization submodel options, are compared with new experimental data from a near-laminar, drop-tube furnace. Included in the devolatilization submodels that were tested are the commonly used empirical one- and two-step models and a chemical, coal network model with parameters based on coal structure. The goals of this work were t o evaluate the latter approach as compared with the simple, empirical approach usually used in such calculations and to assess the role of turbulence in a near-laminar reacting flow. Comparisons were made for carbon conversion, radially averaged oxygen and near-effluent NO, concentrations, for a range of coal types and equivalence ratios. The predictions quantify an ignition delay which is consistent with the measurements. Computations with the fundamental, chemical devolatilization submodel gave superior predictions of mass loss when the coal type was within the interpolation range of the submodel parameter database. Accuracy declined significantly when the coal type was outside the interpolation range. Inclusion of the effects of turbulence was required to account for the observations. Near-effluent NO predictions with the chemical submodel agreed with measured NO, values t o within an average of about 20 percent.
Introduction Comprehensive models for combustion and gasification of pulverized coal in boilers, reactors, gasifiers, and pyrolyzers rely heavily on accurately predicting coal devolatilization processes.1)2A recent sensitivity study of a CFD-based coal combustion model has shown that uncertainty in the devolatilizatiodoxidation parameters has a dominant effect on uncertainty in model predict i o n ~ .Volatiles ~ released during devolatilization can account for as much as half of the coal mass and heating value.4 Although a complete description of the chemistry of devolatilization is not practical, owing to the complexity and heterogeneity of coal, models based on simplified mechanisms have been proposed. Early models were based on single reactions, multiple reactions, and series decomposition.5 Such models are kinetically simplistic, do .not account for chemical structure, and do not attempt to describe the physics of the process.6 In such Abstract published in Aduance ACS Abstracts, August 15,1995. (1)Fletcher, T.H. “Sensitivity of combustion calculations to devolatilization rate expressions”; SAND85-8854; Sandia National Laboratories: Livermore, CA, 1985. (2) Brewster, B. S.; Baxter, L. L.; Smoot, L. D.Energy Fuels 1988, 2,362-370. (3)Smith, J. D.;Smith, P. J.; Hill, S. C. AICHE J. 1993,39,16681679. (4)Saxena, S.C. Prog. Energy Combust. Sci. 1990,16, 55-94. (5) Smoot, L. D.In Fossil fuel combustion; Bartok, W., Sarofim, A. F., Eds.; John Wiley: New York, 1991;Chapter 10,pp 653-781. (6)Smith, K. L.;Smoot, L. D.;Fletcher, T. H.In Fundamentals of coal combustion: For clean and efficient use; Smoot, L. D.,Ed,; Elsevier: New York, 1993;Chapter 3,pp 131-298. @
models, empirical, coal-specific rate parameters are required. Most current-generation, comprehensive models for coal combustion use devolatilization submodels based on the simple empirical formulation^.^ Recent advances in understanding coal structure have led to more fundamental approaches for modeling devolatilization behavior. Among the more promising of these approaches are the network models.8-10 Devolatilization submodels based on coal network concepts can improve the accuracy and precision of comprehensive model predictions and can increase the generality of the model for application to a wider range of coal types and process conditions. Newer, generalized models of devolatilization require more structure-specific information. There is another potential advantage to incorporating the newer coal network models into comprehensive combustion calculations. The earlier, empirical models were based on experimental data where the particle temperature was assumed or calculated rather than measured.ll This practice led to a wide variation in reported devolatilization rates, as much as 3 orders of (7)Brewster, B. S.;Hill, S. C.; Radulovic, P. R.; Smoot, L. D. In Fundamentals of coal combustion for clean and efficient use; Smoot, L. D., Ed.; Elsevier: New York, 1993;pp 567-706. (8)Solomon, P.R.; Hamblen, D.G.; Serio, M.A.; Yu, Z.; Charpenay, S.Fuel 1993,72, 469-488. (9)Niksa, S.Energy Fuels 1991,5, 673-683. (10) Grant, D.M.; Pugmire, R.J.; Fletcher, T. H.; Kerstein, A. R. Energy Fuels 1989,3,175-186. (11)Fletcher, T.H.Comb. Sci. Technol. 1989,63,89-105.
0887-0624/95/2509-0870$09.00/0 0 1995 American Chemical Society
Energy & Fuels, Vol. 9,No. 5, 1995 871
Drop Tube Combustion Data Table 1. Coal and Char Properties ~
coal properties moisture (mass %, as-received basis) proximate volatile matter (mass %, daf) ash (mass %, dry basis) hydrogen (mass %, dry, ash-free) carbon (mass %, dry, ash-free) organic sulfur (mass %, dry, ash-free) nitrogen (mass %, dry, ash-free) oxygen (mass %, dry, ash-free) higher heating value (J kg-l) swelling ratio DTFS" volatiles fraction (dry, ash-free)
_
_
_
Preheater
1
_
coalA coalB coal C (hvAb) (hvBb) (subC) 13.4 1.7 9.8 33.4
40.7
55.0
8.3 6.0 84.0 0.6 1.8 7.7 3.51E7b 1.39 46.6
9.3 5.9 80.0 3.4 2.1 8.7 3.3937 1.49 40.2
8.0 5.5 72.6 0.6
I
II I Coal feeder
Flow straighteners
Test section
1.1
20.1 2.9837 1.00 62.0
Sampling probe Filter --c
To gas analyzer
Figure 1. Drop-tube reactor system. char properties activation energy (J kmol-l) 8.7237 1.07E8 8.0437 Arrhenius preexponential parameter 1.16 0.70 3.01 (m s-l K-l) a
Coal + Carrier gas
Sheath gas
Flow straightener
0.84 mm ID hypodermictube
DTFS = drop-tube furnace system (experimental apparatus).
bEn
~10". Table 2. Coal Particle Size Distributions
screen size bm) 75 63 53 45 38 fines
coal A 1.5 14.2 29.7 16.1 34.8 3.7
mass % retained coal B 4.0 25.2 24.4 14.1 27.1 5.1
coal C 2.3 22.2 23.3 15.7 31.5 5.0
magnitude at some temperatures.12 This controversy has been largely reso1ved,12and recent models are more consistent in their predictions. Devolatilization models are usually validated with data from drop-tube furnaces or other laminar flow reactors. Laminar conditions are desirable in evaluating devolatilization submodel predictions in order to avoid obscuring the reaction rates with effects of turbulence, which are difficult to model. Brewster et al.13 reported predictions made with a comprehensive combustion model incorporating a new, chemical coal network devolatilization submodel. Predictions were compared with detailed data from a near-laminar coal jet flame. The purpose of the present study is to make further comparisons with recent drop-tube furnace data for three coals of varying rank at conditions of interest in practical furnaces. Predictions using the coal network devolatilization submodel and conventional modeling techniques (i.e., one- and two-step models) were also made for comparison. Brewster et al.13 also found previously that turbulent mixing effects were important in modeling pulverized coal combustion in a "laminar" transparent wall reactor. Turbulent mixing effects were therefore considered in this study, even though the flow was designed to be laminar.
Experimental Section The experiments were performed in a near-laminar, droptube furnace. Three k l s were used: a high-volatile A bituminous (hvAb) coal, a high-volatile B bituminous (hvBb) (12)Solomon, P. R.;'Serio,M. A.; Suuberg, E. M. Prog. Energy Combust. Sci. 1992,18,133-220. (13)Brewster, B. S.; Smoot, L. D.; Solomon, P. R.; Markham, J. R. Energy Fuels 1993,7, 884-890.
Water-cooled injector 2.67 mm ID
Figure 2. Coal injector and mixing section. coal, and a subbituminous C (subC) coal. These three coals are referred to herein as A, B, and C, respectively. Coal and char properties are shown in Table 1. The particle size distribution is shown in Table 2. The reactor system was comprised of a coal feed system, a secondary gas preheater, a heated vertical test section, and a water-cooled sampling probe. A diagram is shown in Figure 1. Pulverized coal was entrained in carrier gas and introduced into the heated section through a hypodermic tube. The hypodermic tube was enclosed in a water-cooled sleeve until the injection point. Sheath gas flowed through the annulus around the hypodermic tube. A preheated secondary gas stream was introduced around the coal injector through flow straighteners. Mixing of the coal particles with the hot secondary gas resulted in heating rates typical of pulverized coal combustion applications (> lo4 Ws).Details of the coal injector and mixing section are shown in Figure 2. The reaction section was electrically heated with silicon carbide elements surrounding the outer surface of the ceramic center tube and was capable of obtaining a gas temperature of 1920
K.
A water-cooled sampling probe was inserted into the reaction section from below. The elevation of the sampling probe determined the length of the reaction zone and, hence, the residence time. Reaction products were quenched by aspiration into the water-cooled probe. The area between the probe and the reactor tube was sealed to prevent leakage around the probe. Therefore, all of the gas and solids were collected by the probe. Solids were removed by filtration, and the gas sample was sent to an analyzer to measure NO, (NO NO& SOz, 0 2 , C02, CO, and THC (total hydrocarbons).
+
Brewster et al.
872 Energy & Fuels, Vol. 9,No. 5, 1995 Table 3. Inlet G a s Flow Rates, Compositions, a n d Temperature flow rate (kg/s) 8.21E-7(' 4.11E-6 4.11E-4
inlet stream sheath gas carrier gas secondary gas ('
E-n
coal type A
B C
composition (mol % 02 in Ar) 6.5 6.5 6.5
temp (K) 293 293 1700
~10-".
Table 4. Coal Flow Rates coal flow rate 7.45
equivalence ratio 0.82 1.27 1.61 0.76 1.17 1.54 0.78 1.19 1.53
kgh)
11.5 14.6 7.29 11.3 14.9 9.78 14.8 19.1
Parametric tests were conducted by furing the inlet gas concentration and flowrates a n d varying t h e coal feed rate to obtain equivalence ratios (4) of approximately 0.8,1.2, and 1.5, for each of the three coals. These 4 values are typical of commercial, standard, and low-NO, firing conditions. The gas flow rate was kept constant to maintain repeatable residence times and flow conditions. Gas flow rates, compositions, and temperatures are shown in Table 3. Table 4 shows the coal flow rates and equivalence ratios.
Modeling Devolatilization Submodels. The complexity of devolatilization models varies widely.14 At one end of the spectrum are the simple, empirical, weight loss models which correlate rates of particle weight loss with temperature. These correlations are usually in Arrhenius form with a preexponential factor, an activation energy, and a volatiles fraction.15 Single-step models are the simplest, having only one set of parameters: raw coal
kl
Y,(volatiles)
+ (1- Y,)(char)
(1)
where Y1 is the volatiles fraction and k l is given by
Multiple-step models have the capability of predicting overall volatiles fraction as a function of heating rate by including additional equations, similar in form to eqs 1and 2, each with their own Arrhenius parameters and volatiles fractions: Y,(volatiles) + (1 - Y,)(char) raw coal
(3)
Y2(volatiles) + (1 - Y2)(char)
The overall volatiles fraction then becomes a function of the heating rate, which determines how much of the devolatilization occurs by each reaction (i.e., step). Distributed-activation energy models are more complex and are better able to correlate data by having an ~~
(14) Solomon, P. R.; Hamblen, D. G.; Carangelo, R. M.; Serio, M. A.; Deshpande, G. V. Energy Fuels 1988,2, 405-422. (15) Smoot, L. D.; Smith, P. J. Coal combustion and gasification; Plenum: New York, 1985.
additional model parameter, namely the standard deviation of the activation energy.15 As the complexity of devolatilizationmodels increases, so does their generality and ability t o correlate experimental data for a wider range of conditions. The simple, one-step models are highly empirical and narrow in their applicability. They cannot be extrapolated with confidence to coal types or conditions that differ significantly from those for which they were specifically derived. Two-step models have increased generality and can be extrapolated over limited ranges of temperature and heating rate, but they are still empirical and must be used with caution. Because of their empirical nature, simple weight-loss models do not have the potential of being applied generally with confidence t o a wide range of coal types and conditions. At the opposite end of the spectrum from the simple weight-loss models are the generalized devolatilization models, such as tar formation models (also called network models of coal thermal decomposition), species evolutiodfunctional group models, and chemical network models which consider the evolution of gas species and also describe the composition of the tar and char.14 These generalized models are based on chemicallphysical descriptions of the structure and processes of the coal particle as it heats up and pyrolyzes. Examples of tar formation models are the chemical percolationdevolatilization (CPDP and FLASHCHAIN17 models. For the CPD model, coal-dependent chemical structural coefficients come principally from NMR data.18 An example of a chemical network model is the functional group-depolymerizationvaporization cross-linking (FGDVC) model.a Generalized models for devolatilization have been recently reviewed by Smith et a1.6 The input data for generalized devolatilizationmodels are based on coal structure and include fundamental measurements of coal properties. Because the models attempt to represent the fundamental processes at a greater level of detail, they are more generally applicable to a wider range of coal types and process conditions. The level of detail appropriate for a model depends, in part, on its intended use. The primary role of the devolatilization submodels in this study has been to predict the rate of particle weight loss due to devolatilization. Solomon et al.l4J9 give several reasons for including generalized coal devolatilization models in simulations of coal combustors and gasifiers. These include predicting (1)tar and gas yields, (2) the distribution and nature of evolved nitrogen species, (3) secondary pyrolysis reactions and soot formation from tar, (4) combustion characteristics of pyrolysis products, and (5) properties of coal char, including reactivity. In this study, model predictions using the FG-DVC generalized devolatilization submodel are compared with measurements. Calculations with the two-step ~~
~
(16)Fletcher, T. H.; Kerstein, A. R.; Pugmire, R. J.; Grant, D. M. Energy Fuels 1990,4,54-60. (17) Niksa, S.; Kerstein, A. R. Energy Fuels 1991, 5 , 647-665. (18) Fletcher, T. H.; Kerstein, A. R.; Pugmire, R. J.; Solum, M. S.; Grant, D. M. Energy Fuels 1992, 6, 414-431. (19) Solomon, P. R.; Serio, M. A,; Carangelo, R. M.; Bassilakis, R.; Yu, Z. 2.; Charpenay, S.; Whelan, J. J. Anal. Appl. Pyrol. 1991, 19, 1-14.
Drop Tube Combustion Data Table 5. Devolatilization Parameters coalA coalB coalC (hvAb) (hvBb) (SubC) single stepa 0.40 0.62 VI (daf) 0.47 Ai (6-l) 4.303+14 4.303+14 4.303+14 E1 (Jkmol) 2.29Ef08 2.293+08 2.29E+OS two-stepb 0.41 0.55 Vi (daf) 0.33 Ai (8-l) 3.703+05 3.703+05 3.703+05 E1 (Jkmol) 7.36Ef07 7.363+07 7.363+07 0.8 0.8 V2 (daf) 0.8 A2 (8-l) 1.50ES13 1.50E+13 1.503+13 E2 (Jkmol) 2.513+08 2.513+08 2.51Ef08 FG-DVC major functional group amounts (mass fraction, d a V aliphatic C 0.187 0.157 0.075 aliphatic H 0.027 0.016 0.017 aromatic H 0.013 0.017 0.013 carbon 0.562 0.550 0.520 0.025 0.014 CH4 0.038 char N 0.009 0.014 0.007 char S 0.002 0.002 0.003 co 0.052 0.030 0.115 0.016 0.010 0.060 c02 0.069 0.102 H2O 0.040 0.002 0.034 0.003 H2S HCN 0.014 0.011 0.006 NHdSOdCOS 0.003 0.001 0.001 olefins 0.015 0.027 0.028 paraffins 0.020 0.025 0.027 0.000 0.006 0.004 CZH4 0.006 0.002 C2H6 0.000 a Arrhenius parameters were taken from ref 23. Volatile yields were based on measurements in the drop tube. bArrhenius parameters were taken from ref 21. The low-temperature volatile yield was taken as the ASTM value, while the high-temperature volatile yield was taken as 0.8, consistent with ref 21. None of the parameters were based on the drop-tube measurements reported in this study. Coals B and C were modeled using FGDVC parameters for similar ANL coals as described in the text, with appropriate changes (see ref 25) for the differing elemental compositions. Parameters for coal A were interpolated from the parameters for Blind Canyon (Utah), Pittsburgh Seam, and PSOC 1448 coals, using a procedure provided by AFR. Only a few parameters are shown. A more complete list of FG-DVC submodel parameters, including functional group kinetic parameters, can be found in refs 12 and 14. None of the parameters were based on the drop-tube measurements reported in this study.
modelz0with rates from Ubhayakar et al.,zl and with the one-step using parameters from Solomon et al.,23are also shown for comparison. Devolatilization parameters are shown in Table 5. With the exception of the volatile yields used in the single-step model, none of the devolatilizationparameters were determined from data obtained in the drop-tube. For the FG-DVC submodel, three data files are required: a coal composition data Me, a functional group kinetics data file, and a polymer network data file. The FG-DVC database includes files for all eight of the coals in the Argonne National Laboratory (ANL) Premium Coal Sample Program.* A computerized interpolation (20) Kobayashi, H.; Howard, J. B.; Sarofim, A. F. Sixteenth Symposium (International) on Combustion [Proceedings];The Combustion Institute: Pittsburgh, PA, 1976; pp 411-425. (21)Ubhayakar, S. K.; Stickler, D. B.; von Rosenberg, C. W.; Gannon, R. E. Sixteenth Symposium (Internationalj on Combustion [Proceedings]; The Combustion Institute: Pittsburgh, PA, 1976; pp 427-436. (22) Badzioch, S.;Hawksley, P. G. W. Ind. Eng. Chem. Process Des. Dev. 1970, 9,521-30. (23) Solomon, P. R.; Serio, M. A.; Carangelo,R. M.; Markham, J. R. Fuel 1986, 65,182-194.
Energy & Fuels, Vol. 9, No. 5, 1995 873
’ tw
1
0.65 O.’
1
0.6
0
0.05
1
0.1
0.15
0.2
0.25
Oxyp.Wcarbn alwnlc rlltlo
Figure 3. Interpolation mesh for FG-DVCmodel parameters and mapping of coals A, B, a n d C on that mesh. (Figure adapted from Figure 2 in ref 24.)
procedure is available for coals that fall in the triangular region formed by any three of the library coals on a plot of WC vs OK atomic mass ratio.z4 Only six of the ANL coals are used in the interpolation scheme because of the similarities in composition among some of the coals. Three Penn State (PSOC)coals are also included in this procedure t o provide for additional variation in coal properties. One of the purposes of this work was to assess the accuracy of the FG-DVC submodel when applied t o coals other than those in the FG-DVC database. Parameters for coal A were determined using the computerized interpolation procedure. It may have been possible to avoid the interpolation procedure, because coal A is similar to one of the coals used in the interpolation procedure (see Figure 3); however, the procedure was used to obtain as much accuracy as possible. The atomic mass ratios of WC and OK for coals B and C were outside the range of the interpolation procedure as shown in Figure 3. Following the recommendations of Z h a ~parameters ,~~ for these coals were estimated as follows: coal B is similar in composition to the ANL Blind Canyon, Utah coal, while coal C is similar to the ANL Wyodak-Anderson coal. Thus, the kinetics and polymer data files for these two ANL coals were used directly without modification. The coal composition files were modified t o reflect the slightly different elemental compositions of coals B and C. In addition, the functional group amounts were slightly modified in accordance with the recommendations of Z h a ~ All . ~ other ~ parameters remained the same. Volatile yields for the one- and two-step models were taken as follows: For the one-step model, the experimental volatile yields on a dry, ash-free basis were used. For the two-step model, the dry, ash-free ASTM yields were used for the low-temperature reaction and a value of 0.8 was used for the high-temperature reaction. Kinetic rates of Ubhayakar et aLZ1were used in the twostep model, and the value of 0.8 is consistent with the value used by them. NO Submodel. The NO submodel in the comprehensive model was originally developed by Hill et a1.z6 for fuel NO and recently revised and extended to include thermal NO and t o include the effects of fuel-rich (24) Zhao, Y.; Serio, M. A.; Bassilakis, R.; Solomon, P. R. Twentyfifth Symposium (International) on Combustion [Proceedings] ; The
Combustion Institute: Pittsburgh, PA, 1994. (25)Zhao, Y.,personal communication, Advanced Fuel Research, Inc., East Hartford, CT, 1993. (26) Hill, S. C.; Smoot, L. D.; Smith, P. J. Twentieth Symposium Ilnternatwnal) on Combustion [Proceedingsl; The Combustion Institute: Pittsburgh, PA, 1984; pp 1391-1400.
Brewster et al.
874 Energy & Fuels, Vol. 9, No. 5, 1995 a)
4 = 0.82,
l a t s p (Case 1A)
ow
0 10 Anal dManse (m)
b)
Figure 4. Joint thermal- and fuel-NO mechanism. (Reprinted with permission from ref 34. Copyright 1993 Elsevier.)
4 = 0.82,2-rtlep (Case 4A)
c) 4
0.82, FG-DVC (Cars 6A)
,
Table 6. Summary of Drop-Tube Furnace Simulationsa
caselcoal
~ _ _ _ _
1IA,B,C 2lA,B,C 3lA,B,C 4lA,B,C 5lA,B,C GIA,B,C 7lA,B,C
approx equivalence ratio _ 0.8 1.2 1.5 0.8 0.8 0.8 1.5
uuruose base case: single devolatilization step effect of equivalence ratio effect of equivalence ratio two-step devolatilization scheme fully laminar flow FG-DVCdevolatilization submodel effect of equivalence ratio with FG-DVC
All seven cases repeated for each of coals A,B and C. conditions on nitrogen c h e m i ~ t r y . ~ The ' , ~ ~submodel is decoupled from the generalized combustion model and is executed after the flame structure has been predicted. A schematic diagram of the submodel mechanism is shown in Figure 4. The submodel combines the thermal NO mechanism of ZeldovichZ9with a fuel NO mechanism assuming that nitrogen evolving from the coal is instantaneously converted to either HCN or NH3. The fraction of evolving nitrogen which comes off as HCN was an input parameter (ain Figure 4). A value of 1.0 was used in this study, consistent with observation for bituminous coals and for high heating rates.30 Nitrogen was assumed to evolve from the coal at a rate proporFormation of tional to the total coal weight prompt NO, N20, and NO2 was neglected in the model, as were NO recycle and reburning reactions. Combustion Model and Computations. The twodimensional, axisymmetric furnace model (pulverized coal gasification or combustion-2dimensional, 93-PCG-C2) is described in detail el~ewhere.~It solves the Eulerian, reacting gas flow field with Lagrangian reacting particles coupled with radiation. Laminar, nearlaminar, and turbulent flows can be treated. Effects of gas buoyancy, various devolatilization submodel options, and the NO, submodel described above are included. A set of seven computer runs was completed for each coal type, making a total of 21 computations. Table 6 provides a summary of these computations. Variables in the computations, besides three coal types, consisted of three devolatilization submodels, three equivalence ratios, and a laminar flow case. Cases l/A-C (-0.8 equivalence ratio, near-laminar flow, one-step devolatilization submodel) were used as the base cases for coals A, B, and C, respectively. (27)Boardman, R. D.;Eatough, C. N.; Germane, G . J.; Smoot, L. D.Combust. Sci. Technol. 1993,X X , 1-18. (28) Smoot, L. D.; Boardman, R. D.; Brewster, B. S.; Hill, S. C.; Foli, A. K. Energy Fuels 1993,7 , 786-795. (29) Zeldovich, Y. B.;Sadovnikov, P.Y.;Frank-Kamentskii, D. A. Acta Physicochim. U.R.S.S. 1946,21,577. (30)Bassilakis. R.:Zhao. Y.: Solomon. P. R.: Serio. M. A. Energy -_ Fuels 1993,7; 710-720. (31)Harding, N.S.;Smoot, L. D.; Hedman, P. 0. AIChE J . 1982, 28,573-578.
I
d)
4 = 1.27,l-step (Care 2A)
e) 4 = 1.61,l -step (Case 3A)
Figure 5. Predicted gas temperature for coal A.
Near-laminar flow requires appropriate extensions to the K - E turbulence model t o make it applicable to lowReynolds-number These runs were designed to be in the laminar regime ( N b % 14001, but turbulence effects are present around the injector inlet and are predicted by the model to persist to the exit of the reactor. The near-laminar (i.e., relaminarized) k-E model appropriately reduces in the limit of low Reynolds number to fully laminar flow and is therefore an appropriate submodel to use for these calculations. In order to investigate the magnitude of the turbulence effects, calculations were also performed assuming fully laminar flow without any allowance for turbulence (cases 5/A-C). These cases were identical to cases l/A-C in all other respects. The computational grid consisted of 40 nodes in the axial direction and 34 nodes in the radial direction. The nodes were concentrated near the axis of symmetry where they were used to resolve the primary gas inlet, which is bounded by the injection probe wall.
Results and Discussion Results are shown and discussed below for all three coals, using both simple and detailed devolatilization submodels. The effects of equivalence ratio are shown. The one-step model was deemed adequate for representing the simple approach. However, one calculation was also performed with each coal using the two-step model at baseline conditions for comparison. Contour plots of predicted gas temperature for the inlet region of the drop tube are shown in Figure 5 for three equivalence ratios and three devolatilization submodels for coal A. The top of each figure is the reactor wall, and the bottom is the centerline. The left of each figure is the feed location, and the flow is from left to (32)Jones, W.P.;Launder, B. E. Int. J . Heat Mass Transfer 1973, 16,1119-1130.
Energy & Fuels, Vol. 9,No. 5, 1995 876
Drop Tube Combustion Data
+ = 0.82
1 ,
1
I
I
4
I
I
I
0.82
a)
0.8
-
2 -
'
d) I
0 2
0
0
3
0
4
0
5
0
0
10
Axlal distsm, cm
Coal A
I
I
I
20
30
40
50
Axial distance, cm
4 = 1.27 1-step (Case 2A)
P
0
-" o.2
-i
I / tI 0
4
c
-I
\o 0
0
-1
2 e)
Coal A I
0
5
10
I
I
I
20
30
40
1
0 50
0
10
0 = 1.61
0
10
20
30 Axial dlstllnce, cm
I
20
30
40
0
Coal A
50
40
1.61 1-step Case 3A
1-step (Case 3A) FG-DVC Case 7A) Measured
0.8
I
M a l distsm, cm
Me1dlstam. cm
1
I
- _ - - - FG-DvL (Case 1A) Measured
5
50
0
I
I
10
20
I
30 Axle1 dlstance, cm
I
I
40
50
Figure 6. Predicted and measured carbon conversion and oxygen concentration for coal A. right. The particles are being fed from the left near the centerline. The model predicts an ignition delay, and the ignition distance can be clearly seen in the plots. This predicted ignition delay results from the time required for the hot secondary gas to sufficiently mix with, heat up, and devolatilize the coal. There is no predicted delay due to reaction in the gas phase since the particles are transported by a gas stream containing oxygen and since homogeneous reaction of coal volatiles is assumed in the model to be infinitely fast relative to mixing. The ignition distance is slightly shorter when using the FGDVC submodel. As equivalence ratio increases, the cold, fuel-rich zone on the centerline persists farther into the reactor. The data, shown subsequently, do not definitively show this ignition delay, but the test results support this delay at about the magnitude predicted. Also, observations of the particle cloud show that there is a short delay in ignition. Carbon conversion efficiency (CCE) and radially averaged oxygen concentration are compared with experimental measurements in Figure 6 for coal A. Average
predicted particle residence time is approximately 350 ms in all of the plots. There is little variation in ignition distance for the various devolatilization submodel options, as shown in the carbon conversion plots. Devolatilization is a rapid process and is limited by the particle heat-up rate, in addition to kinetics. Particle heat-up is controlled by the mixing rate of the particles with the hot secondary gas together with radiation from the very hot walls. Fully laminar flow slows down the mixing, as expected, by reducing the mixing rate. Carbon conversion is lower for the laminar case because of the reduced rate at which oxygen diffises toward the centerline of the reactor to react with the char particles. The near-laminar cases all have the same rate of carbon Conversion during char oxidation, as expected, since the same char oxidation kinetics were used for all of the cases and since they have similar rates of mixing and oxygen diffusion. The fully laminar case has a slower rate of carbon conversion during char burnout due to the limitation of oxygen diffusion. The rate of carbon conversion during char burnout for the nearlaminar cases better matches the rate indicated by the measurements, as opposed to the fully laminar cases.
Brewster et al.
876 Energy & Fuels, Vol. 9, No. 5, 1995
4 1 ,
1
0.8
-
0.6
-
0.4
-
0.2
-
9 = 0.76
0.76
I
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1
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---.
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Coal6 I
Measured
o I
I
1
I
Measured 10
0
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Coal B I
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Axial distance. cm
4 = 1.17
4 = 1.17 I
I
I
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I
b)
i
0.8 -
1-step (Case 28) 5
0.60.4
-
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-
/
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2
h iB I
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I
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I [ 0 0
Coal E
e) I
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Axial distance, cm
Axial dlstance. cm
,)
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e---. 1 coal E
0 0
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Axial dldance, cm
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Axial distam, cm
Figure 7. Predicted and measured carbon conversion and oxygen concentration for coal B. The major discrepancy between the predicted and measured carbon conversion efficiencies (CCE) is the amount of volatiles that are formed during devolatilization. The prediction with the FG-DVC submodel clearly gave the best comparison with the measurements for coal A. The one-step model used the experimental value of volatiles fraction, but overpredicted the carbon conversion curve. This discrepancy could be due, at least in part, to the modeling assumption of all elements, including carbon, evolving at rates proportional to the total organic coal weight loss. Since a higher precentage of the carbon in the original coal tends to remain in the char instead of being evolved with the volatiles, it is reasonable for the measured carbon conversion during the early stages of burnout to be lower than the predicted carbon conversion efliciency. The two would be expected to approach agreement as carbon conversion approaches 100%;however, this does not seem to be occurring in Figure 6. Also, this effect would not be expected to be significant for a high-rank coal which has relatively little organic oxygen and hydrogen content. The two-step model substantially overpredicted the volatiles fraction.
Comparisons for coal B are shown in Figure 7. Again, the FG-DVC submodel gives slightly earlier ignition and the laminar option gives a later ignition. Also, the laminar carbon conversion curve gives a lower rate of char oxidation, at least initially, than the near-laminar cases. Again, the reported experimental value of volatiles fraction appears t o be inconsistent with the experimental data, since the one-step model underpredicts the carbon conversion. The FG-DVC submodel prediction of volatiles fraction seems t o give a reasonable prediction of the volatiles fraction. Again, the two-step model predicts the highest volatiles fraction of all the submodel options. Figure 8 shows comparisons for coal C. This time, the FG-DVC submodel substantially underpredicts the volatiles fraction and correspondingly overpredicts oxygen concentration. The kinetics and model parameters for the ANL Wyodak-Anderson coal, to which coal C is most similar in composition, apparently do not adequately model the behavior of coal C. Of the two coals that were outside the interpolation range of the FGDVC submodel database (e.g., coals B and C), coal C was the farthest outside. Other submodel options,
Drop Tube Combustion Data
0.8
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Energy & Fuels, Vol. 9,No. 5, 1995 877
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Coal c
d) 1
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Axld diatom, om
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L
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0
Measured
I
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li
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0.2
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4 = 1.53
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Axkl distance, cm
4
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O
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' I
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_ _ _ - - - - _- _ _ _ _ _ _ _ - _ - - - - -
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"
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Axial dktonce. cm
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Axial d!atam, cm
Figure 8. Predicted and measured carbon conversion and oxygen concentration for coal C .
+
including the two-step model and the one-step model, which utilizes the reported experimental value, are in somewhat better agreement with the measured CCE, but still underpredict it. The lack of agreement of the code predictions for coal C may result from several factors. Coal C has a very high proximate volatile matter content (55% daf, Table 1)when compared with the ANL Wyodak-Andersoncoal (49% daf)33that was used as the "equivalent" coal for the FGDVC simulations. The yields from the drop tube tests are also quite high (62% daf, Table 1)for coal C, and it is possible that this coal has a low alkali metal cation content and/or a high polymethylene content, both of which can be quite variable €or low-rank coals and which could lead to a high volatile matter content. Measured near-effluent nitrogen oxide (NO NO21 concentrations a t the lowest sampling location (40cm) for all three coals are shown with NO predictions in Figure 9 as functions of equivalence ratio. While total
NO NO2 was measured, only NO was considered in the predictions. Generally, NO is substantially greater than NO2 in high temperature, pulverized coal syst e m ~ Predictions .~~ using the two-step devolatilization submodel (cases 4A,B, and C) and neglecting turbulent mixing (cases 5A,B, and C) are shown as single points, since simulations were performed only for the fuel-lean equivalence ratio (-0.8). Predictions with the FG-DVC submodel are shown as straight lines since simulations were performed for only two equivalence ratios (-0.8 and -1.5-1.6). The comparisons can be explained by reference to the carbon conversion profiles shown in Figure 6 and the contour plots of predicted oxygen concentration shown for coal A in Figure 10. Similar explanations could be made for the comparisons for coals B and C. The concentration of NO, is a function of how much nitrogen is evolved from the coal and whether that fuel-nitrogen forms NO, or N2. If the nitrogen evolves in the presence
(33) Vorres, K. S. "Users handbook for the Argonne premium coal sample program"; prepared under DOE contract no. W-31-109-ENG38, 1989.
(34)Boardman, R. D.; Smoot, L. D. In Fundamentals of coal combustion for clean and efficient use; Smoot, L. D., Ed.;Elsevier: New York, 1993; Chapter 6, pp 433-509.
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Brewster et al.
878 Energy & Fuels, Vol. 9,No. 5, 1995
I
Coal A 250
a) $ = 0.82,1-8t.p (Caw 1A)
I B
0.020 0.010 0.000
$
._ P 2
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Axial distance (m)
b) $ E 1.27,14ep (CaW 2A)
0 01 C)
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--. --_ -_
- 11
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I
1
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Figure 10. Predicted oxygen concentration for coal A.
1 IfMeasured I +
200
1-step (Cases 1,2,3/C) , 2-ste (C se 4C LamiRar (%are C) ---_ FG-DVC . (Cases 6,7/C)
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Equlvalenw ratio. $
250
E
Axial disleumce = 40 cm
I
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I
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1.2
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1.6
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Figure 9. Predicted near-effluent NO and measured NO,
concentrations. of oxygen, it will likely form NO,. If it evolves in a fuelrich region, it will more likely form N2. At 0.8 equivalence ratio, the fully laminar option (case 5A) predicts near-effluent NO concentration lower than measured NO, while the other three submodel options (cases lA, 4A, 6A) predict near-effluent NO concentration higher than measured NO,. Case 5A predicts the near-effluent NO less than measured NO,, probably because carbon conversion is underpredicted (see Figure 6a) and because the fuel-rich zone a t the centerline is probably overpredicted (see Figure 10e). The fuel-rich zone for the case 5A is larger than is predicted for the other three cases. Hence, less fuel-nitrogen is evolved and a larger percentage of it forms N2 instead of NO,. Carbon conversion is overpredicted for cases lA, 4A, and 6A at the 0.8 equivalence ratio, which contributes to the predicted near-effluent NO being predicted greater than measureed NO, as well. The one-step model (case 1A) gave the highest near-effluent NO
prediction; however, the two-step model gave the highest carbon conversion since the total volatiles fraction predicted with the two-step model was higher than that assumed for the one-step model. This resulted in a slightly larger fuel-rich zone, and more of the nitrogen in the coal was evolved in this fuel-rich region, forming N2 rather than NO. Although the final carbon conversion was approximately the same for both simple models, more of the nitrogen was evolved during char oxidation, in the presence of oxygen, with the one-step model, and more near-effluent NO was predicted as a result. It is interesting to note that the concentrations of near-effluent NO predicted by the two-step model (case 4A) and FG-DVC (case 6A) were about the same, even though the carbon conversion was quite different for the two submodels. Even though the two-step model evolved more nitrogen, this nitrogen evolved in a fuel-rich environment where it formed N2. The rates of nitrogen evolution during char oxidation were similar for the two submodel options, and the amounts of predicted neareffluent NO are, therefore, similar. Contrary to the data, the predicted near-effluent NO decreases with increasing equivalence ratio for coal A, due to the decreased availability of oxygen. It is interesting to note that predicted near-effluent NO with the one-step model decreases more quickly with increasing equivalence ratio than with FG-DVC. The reason for this rapid decrease is that oxygen was depleted early in the reactor for the 1-step model (Figure 1Oc) due to overpredictingthe carbon conversion (Figure 6a), whereas oxygen continued to be available throughout most of the reactor length for the FG-DVC submodel option (Figure log). Predicted near-effluent NO for fully laminar flow is consistently very low for all three coals, due to the limiting rate at which oxygen diffuses to the centerline.
Drop Tube Combustion Data
This result is consistent with the above observations, which all support the observation that the flow was not fully laminar. Predictions and measurements of near-effluent NO are of the same order of magnitude, but the trends are not very well predicted for coals A and B. At higher equivalence ratio, less NO, would ordinarily be expected for a constant coal flowrate due the lower availability of oxygen. This is the principle of air-staging for NO, control. However, in these experiments, the gas flow rate was held constant and higher equivalence ratio was achieved by increasing the coal flow rate, resulting in decreased carbon conversion a t the same axial location. However, more nitrogen was released from the coal at a given axial location in the fuel-rich case due the higher coal flow rate. It is possible that the increased nitrogen evolution and decreased availability of oxygen could offset each other, resulting in flat profiles of neareffluent NO vs equivalence ratio. In addition, it is known that nitrogen evolves preferentially compared to both carbon loss and total mass loss in higher rank bituminous c0als,3~and this could account for the flatness of the experimental profiles relative t o the predictions for coals A and B. Nitrogen which evolves early is less likely t o form NO,, and the model does not take preferential evolution of nitrogen into account. This effect would be most pronounced at lower equivalence ratios, where the fuel-rich devolatilization zone is small, and there is oxygen available downstream to react with nitrogen that evolves later during char oxidation. In addition to assuming that nitrogen evolves from the coal at the same rate as all other elements, the model used in this study has limitations of assuming that evolves as HCN, N H 3 , or some prespecified mixture of both. NO, production is clearly a function of how nitrogen is bound in the coal, what is liberated with the volatile matter, and what remains in the solid phase to be liberated under heterogeneous combustion conditions. Such detailed information is available with the FG-DVC model but was not utilized in the current neareffluent NO predictions to the fullest extent possible because of model limitations. Such limitations may have contributed t o the inability of the model to predict the trends in near-effluent NO, production with equivalence ratio for these cases. In addition, the near-effluent NO, data may have some error due to continued reaction upon mixing and finite rate of quenching in the water-cooled sampling probe. However, quenching rate may not have been a significant factor since more rapid quenching (e.g., water-quenched) would have decreased the measured NO, values a t the lower equivalence ratios, where oxygen was not depeleted, and this would have resulted in worse agreement between the data and the predictions.
Conclusions Simple one- or two-step devolatilizationmodels cannot be applied with a high degree of confidence without coalspecific information. However, when their parameters are known, these models can be used to predict weight loss. Chemical and coal network devolatilization models have many potential advantages that make them worth (35)Baxter, L. L.; Mitchell, R. E.; Fletcher, T. H.; Hurt,R. H., to be submitted for publication in Energy Fuels.
Energy & Fuels, Vol. 9, No. 5, 1995 879 considering for inclusion in CFD-based combustion models. These models provide a methodology for removing some of the iterative process of fine-tuning parameters. Using coal structural information, devolatilization rates and temperatures can be more accurately predicted. These models also predict the volatiles composition, which will be useful for future detailed chemistry and NO, submodels. The FG-DVC model examined in this study gave improved predictions of mass loss with the more commonly used two-step model for coal A, where required coal parameters were well-known. For coal B, where required coal parameters for FG-DVC were estimated and where these parameters were slightly outside the interpolation range, the predictions were comparable. For coal C, where these parameters were farther outside the interpolation range, the predictions were inferior. The FG-DVC submodel generally predicted a slightly shorter ignition distance. Although the reactor in this study was designed for laminar flow, the data and predictions clearly support the role of turbulence. Particles spread more quickly into the oxygen-rich layer around the coal stream, and the oxygen-rich gas diffuses more quickly into the particle stream at the centerline.
Acknowledgment. Funding for model computations performed a t the Advanced Combustion Engineering Research Center (ACERC) at Brigham Young University were provided by the National Science Foundation's Engineering Educational Centers Division (Dr. Tappan Mukherjee, project officer). Other ACERC participants, including 35 industrial companies, the US. Department of Energy (METC, PETC),the Environmental Protection Agency, and the State of Utah provide additional support to the Center. Joint work to further develop and evaluate the FG-DVC submodel at Advanced Fuel Research, Inc. (AFR),to improve the combustion model and to combine with the FG-DVC devolatilization submodel at this Center was supported by the US. Department of Energy, Morgantown Energy Technology Center (Contract No. DE-AC21-86Mc23075)(Dr. Norman Holcombe, project officer). Close cooperation of several colleagues at AFR, including Drs. Peter Solomon, Yuxin Zhao, and Michael Serio, is also appreciated. Data for experimental verification were provided by ABB Power Plant Laboratories and were obtained with internal funds. Nomenclature preexponential factor activation energy kinetic rate coefficient Reynolds no. universal gas constant particle temperature volatiles coefficient, particle starting location fraction fractional amount of functional group i equivalence ratio EF9500548
