Energy & Fuels 1994,8, 124-130
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Articles Comparison of Large Scale Boiler Data with Combustion Model Predictionst R. K. Boyd* Pacific Power, Box 5257 GPO Sydney, 2001, Australia
J. H.Kent Department of Mechanical and Mechatronic Engineering, University of Sydney, NSW 2006, Australia Received April 8, 1993. Revised Manuscript Received September 4 , 1 9 9 P
A three-dimensional comprehensive furnace model capable of predicting gas flow, coal combustion, and radiative heat transfer is described. The model has been validated using operational data from a tangentially coal fired 500-MW(e)boiler. The model validation and the results of two investigations which have been conducted by Pacific Power by applying the model to large coal-fired boilers are described. The results demonstrate that the model has practical application for both the solution of operational problems and the optimization of the combustion performance of utility furnaces. For a swirl burner wall-fired furnace the model was able to illustrate the aerodynamic mechanisms which contribute to burner throat ash deposition. A combustion optimization investigation on a tangentially fired furnace indicates that potentially significant improvements are possible via modification of boiler operating parameters.
1. Introduction A three-dimensional comprehensive furnace model developed by Pacific Power and The University of Sydney has been used extensively by Pacific Power for the optimization of the combustion and the investigation of various operational problems in large coal-fired utility boilers. The model was validated using operational data from tangentially-fired 500-MW(e) coal-fired furnaces. Problems currently under investigation using the model include the minimization of unburnt carbon in a tangentially-fired furnace and the reduction in ash deposition in the burner quarls of a660-MW(e) swirl burner fired boiler. Model validation studies performed on the 500-MW(e) furnaces will be broadly described along with preliminary results of a combustionoptimization investigation. Results of the present investigation on ash deposition around swirl burners of 660-MW(e) boilers will also be presented and discussed. 2. Background The furnace model used here has been described previouslyla and has common features with similar models developed elsewhere.'l* This model predicts the three-
* Author for correspondence.
t Presented at theAdvanced Combustion Engineering Research Centre Seventh Annual Conference "Clean and Efficient Use of Fossil Fuel and Toxic Wastes". March 2-5,1993, Park City, Utah. Thii article waa part of the reviewed papers presented at the conference that were published in Energy Fuek 1993, 7, Nov/Dec issue. Abstract published in Advance ACS Abstracts, October 15, 1993. (1) Chen, 1.-Y.;Mann, A. P.; Kent, J. H. Twenty-FourthInternational Symposium on Combustion; The Combustion Institute: Pittsburgh, 1992.
OSS7-0624/94/250S-0124$04.5QI~
dimensional flow patterns, the combustion and trajectories of coal particles, the temperatures, the concentrations of the major species,and the radiative heat fluxes in furnaces. The model uses fundamental data as input, including the furnace geometry, the operating flow rates of coal and air, the coal type, and the particle size distribution of the coal. In previous studies,'P2 this computational model was validated with test data from three power station furnaces. The study performed at Liddell Power Station in New South Wales (NSW) Australia involved model validation (2) Boyd, R. K.;Kent, J. H. Twenty-First Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, 1986; pp 2 6 6 274. (3) Luo, X.-L.; Boyd, R. K.; Kent, J. H. J. Inst. Energy 1991,64 230238. (4) Hjertager, B. H.; Magnwen, B. F. PCH, PhysicoChemical Hydrodyn. 1982,3, 231-250. (5)Lock, F. C.; Rizvi, S. M. A.; Lee, G. K.; Whaley, H. Twentieth Symposium (International)on Combustion;The Combustion Institute: P-itGburgh,19W, pp 513-520. (6) Truelove, J. S. Twentieth Symposium (International) on Combustion; 19W, pp 523-530. (7) Benesch, W.; Kremer, H. Twentieth Symposium (International) on Combustion; The Combustion Institute: Pittsburgh, 19% pp 549555. (8)Smoot, L. D.; Smith, P. J. Coal Combustion and Gasification; Plenum Press: New York, 1985. (9) Carvalho, M. G.;Oliveira, P.; Semiao,V. I . Inst. Energy 1988,143156. (10) Gomer, K.; Zinser, W. Combust. Sci. Technol. 1988,58,43-58. (11) Lockwood,F. C.; Papadopouloe, C.; Abba, A. S. Combust. Sci. Technol. 1988,58,5-24. (12)Truelove, J. S.; Williams, R. G. Twenty-Second Symposium (International) on Combustion;The Combustion Institute: Pittsburgh, 1988;pp 155-164. (13) Lockwood, F. C.; Mahmud, T. Twenty-Second Symposium (International)on Combustion;The Combustion Institute: Pittsburgh, 1988; pp 165-173. (14) Steward, F. R.; Trivic, D. N. J. Inst. Energy 1989,62, 138-146. (15) Bilger, R. W. Annu. Reu. Fluid Mech. 1989,21, 101-135.
0 1994 American Chemical Society
Energy & Fuels, Vol. 8, No. 1, 1994 125
Comparison of Boiler Data with Model Predictions Table I. Coal Properties and Combustion Parameters ultimate analysis (as received) Carbon Hydrogen Nitrogen Sulfur
mass %
62.0 4.2 1.4 0.6
specific energy (dry ash free) devolatilization Ai A2
Ei E2 Y1 (proximate analysis, daf) Yz char combustion A,
E,
ultimate analysis (as received)
mass %
Oxygen Water Ash
6.2 6.0 19.6
33.8 MJ kg-l 3.7 x 106 s-1 1.5 x 1013 s-1 74 X 103 kJ kmol-l 251 X 109 kJ kmol-1 0.28 2Yl 0.052 kg mg-2 s-l P a 4 61 X 109 kJ kmol-l
via comparison of cold air velocity fields as well as gas temperatures, oxygen concentrations, and incident wall heat flux under operating conditions. Data on measured carbon burnout in two power station furnaces at Wallerawang, NSW, Australia and Jiaozuo in Henan, China, was used to validate the ability of the model to predict coal combustion efficiency. The validated model has been used extensivelyby Pacific Power to investigate heat transfer and combustion efficiency aspects of furnace performance. The effect of the coal ash level on furnace heat transfer has been studied as well as the effect of water wall condition on furnace exit temperature. The sensitivity of the combustion efficiency to parameters such as excess air, particle size, furnace temperatures, and particle size distribution as well as burner angle has been investigated and will be described here. Most of the applications of the computational model, such as the work described above, have been to tangentially-fired furnaces. These feature a large-scale swirling flowpattern. By contrast, wall-firedfurnaces such as those of the Bayswater Power Station in New South Wales have a large number of small swirl burners located on the front and rear furnace walls. The important coal particle heat up and devolatilization processes occur in small spatial regions in the near-burner regions. Computational modeling of this requires the use of the fine grids in each burner region, resulting in a large total number of grid cells. Solutions of this type of furnace have required the use of a supercomputer to achieve acceptable model run times. 3. Computational Furnace Model
Numerical solutions of the gas-phase time-averaged transport equations for mass, momentum, enthalpy, and mixture fraction are obtained using the standard k-e turbulence model. Average major species concentrations using fast chemistry assumptions in the gas phase are predicted from mixture fraction fluctuations.16 Particle motion and combustion are described in a Lagrangian fashion with a stochastic treatment for particle dispersion. Radiant heat transfer is modeled by the discrete transfer method." (16)Spalding, D. B. Chem. Eng. Sci. 1971,26,95. (17)Lockwood, F. C.; Shah, N. G.Eighteenth Symposium (Znternotional) on Combustion;The Combustion Institute: Pittsburgh, 1981;pp 1405-1414.
Table 11. Particle Size Distribution for the Coal size (rm)
total fuel mass
fraction of
300 275 225 180 145
0.005 0.008 0.0194 0.0346 0.046
size (rm)
95 60 30 10
fraction of
total fuel mass 0.161 0.266 0.204 0.255
The coal devolatilizationrate is treated using two parallel first-order reactions of the following form?
where the rate k = k l + k2 = A1 exp(-ElIRT) + A2 exp(-EdRT). The total volatile mass release is dependent on the particle temperature history:
where the mass fraction of volatile Y1 is obtained from the coal proximate analysis and Y2 is set to 2Y1. Typical coal properties and rates used in the studies described here are shown in Table I. The coal has a proximate analysis volatile content of 28% (dry ash free). The relative volatile yield (actual volatile yieldlproximate yield) in furnace conditions was predicted to be around 1.26 for the coal. This is comparable with relative yields of 1.26-1.52 for similar coal types measured in laboratory combustion reactoP experiments. Char combustion is modeled as a surface reaction which produces CO which is subsequently oxidized to COz in the gas phase. The char reaction is controlled by the oxygen diffusion rate to the particle surface and by the chemical reaction rate. An apparent order of one-half in oxygen partial pressure is assumed20leading to
where the diffusion rate is k, =
fWMCDO* R Td,
(4)
and the Sherwood number Sh = 2. Constant particle diameter is assumed during char combustion. The reaction rate isgiven by k , = A, exp(-EIRT). The rate constants shown in Table I are obtained from measurements made in an entrainment combustion reactorlg for a range of coals. The data selected is for the coal types which most closely match the ultimate analyses of the NSW coals considered. Combustion of the volatiles in the gas phase takes place wherever the fuel and oxidant are predicted to meet in stoichiometricproportions. The time average and variance of the mixture fraction field, together with an assumed (18)Ubhayakar,S.K.;Stickler, D. B.; Von Rosenberg, C. W.; Gannon, R. E. Sixteenth Symposium (Internotional) on Combustion; The Combustion Institute: Pittsburgh, 1977;pp 427-436. (19)Smith, I. W.;Wall,T. F.; Baker, J. W.; Holcombe, D.; Harris, D. J.; Juniper, L. A.; Truelove, J. S. The Combustion Behaviour of Lowvolatile Australian Coals, National Energy Research, Development and Demonstration Promam, Report No. 900, Dept. Primary Industries and Energy, 1989. (20)Smith, I. W.Nineteenth Symposium (International) on Combustion; pp The Combustion Institute: 1982;1045-1065.
126 Energy & Fuels, Vol. 8, No. 1, 1994
0
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Boyd and Kent
,
~
1
di memio ns in mm
gas f l o w
G o u
8ti -50 v
8
& -100
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-150 -200 0
10
20
30
40
Time since Sootblow (hours) a
-
8860
Figure 2. Error in the predicted exit gas temperature plotted
m
against time since sootblowing.
--.
- 8 LEVELS - OF BURNERS---
0 0
0
w
1
\
m
I
I
/ t
Figure 1. Liddell boiler elevation.
Gaussian probability distribution function for fluctuations, lead to the predicted instantaneous and time-averaged temperatures and oxygen concentrations. The source of the mixture fraction is the spatially distributed devolatilization rate. Carbon monoxide, the product of char surface combustion, is treated as a gas-phase fuel in the same manner. Calculations were performed using first-order finite differenceswith hybrid upwind differencing.21The studies on the tangentially-fired furnaces involved the use of grids with around 20 000 cells. For the swirl burner wall-fired boiler (Bayswater, NSW) a finer grid was necessary to accurately model the burners. The nonuniform Cartesian grid used for the Bayswater furnace was 78 X 18 X 78 giving 109 512 cells. A finer grid was required relative to the previous studies in order to resolve the small regions where the combustion processes are most intense. (21)Patankar, S. V. Numerical heat transfer and fluid flow; Hemisphere: Bristol, PA, 1980. (22)Boyd, R. K.Furnace Computer Model Validation. Electricity Commission of NSW,Internal Report, 1987. (23) Lowe, A. The Instrumentation of a Large Utility Boiler for the Determination of the Effects of Mineral Matter on Heat Transfer. Slugging and Fouling Due t o Impurities in Combustion Gases; The Engineering Foundation: Copper Mountain, CO, 1984.
A converged Bayswater solution" typically required 600 iterations and a CPU time of approximately 5 h on a Fujitau VP2200 Supercomputer when commencing the solution process. To obtain a new solution using a previous solution as a starting point required about 200 iterations and a correspondingly lower CPU time. Coal particles are tracked from each primary port cell. The particle size distribution is represented by a RosinRammler function fitted to the measured distribution and discretized into 9 class sizes,as shown in Table 11. Tracking is repeated to achieve adequate random particle dispersion. The model tracks around loo00 particle trajectories depending upon the burner geometry. 4. Results and Discussion Liddell. The Liddell boiler is a 500-MW(e) unit with two tangentially-fired furnaces separated by a common central water wall (Figure 1). The furnace dimensions are 8.86 m by 10.69 m by 31 m high. Pulverized coal is fed to the boiler from up to eight levels of tilting burners, with seven being necessary for maximum output. An extensive validation program was undertaken which covered some 15 different operating conditions.22 The model predictions were compared with test dataz3which included burner and furnace exit plane gas temperatures and oxygen concentrations, wall incident radiative fluxes, and overall coal burnout. In general the wall heat fluxes were well predicted2 as were the temperature patterns in the burner zone; the peak temperatures predicted in the furnace were however high by around 100 "C. Coals from NSW are generally nonslagging and produce dry easily removable deposita on boiler walls. A significant result from the validation study was the effect of furnace wall condition on the furnace exit temperature. Comparison of the predicted and measured exit temperatures showed a strong correlation between the time since sootblowing and the error in the predicted exit gas temperature (Figure 2). This can be used to infer a relationship (Figure 3) between time since sootblowing and the apparent wall emissivity-the model at present does not consider particle deposition and assumes a constant wall temperature. This result demonstrated the importance of having accurate knowledge of furnace wall conditions in order to predict furnace exit gas temperatures. (24) Benyon, P.; Mann, A.; Boyd, R.; Kent, J.; Langrish, T.; Lowe, A.; Crawford, J.; Stuart, A.; Miller, A. Furnace Modelling Using Computational Fluid Dynamics on Supercomputers, 5th Australian Supercomputing Conference, Melbourne, 1992.
*
Comparison of Boiler Data with Model Predictions
Energy &Fuels, Vol. 8, No. 1, 1994 127
1
0.9
.-00.8 -
.
\
1
2 ,g0.7 w 20.6 -
e 90.5
-
0.4 -
0.3
'
10
0
20 Time since Sootblow (hours)
40
30
Figure 3. Apparent wall emissivity plotted against time since sootblowing. 2.5 h
E
I
-30
-10 0 10 -20 Bumer Tilt Angle (degrees) + Predicted Total + Predicted Flue + Predicted Hopper Measured Flue
20
30
Figure 6. Predicted flue, hopper,and total unburnt carbon and measured flue unburnt carbon loss plotted against burner tilt angle.
l
1 2 3 4 P.F. Size Fraction (% +300 micron) Measured ,Predicted
5
Figure 4. Predictedand measured flue gas unbumt carbon losses plotted against pulverized fuel fineness.
"'
$1
0
5
15
20
25
30
'Excess AX (a) Measured +Predicted Figure 5. Predictedand measured flue gas unburnt carbon losses plotted against furnace excess air level. Wallerang. The 500-MW(e) Wallerawang boiler is of a similar design to Liddell with identical furnace dimensions but with six rather than eight levels of burners. A current investigation being conducted at Wallerawang is concerned with optimization of coal burnout within the furnace. Preliminary aspects of this investigation were described previously.'J The earlier work found that the main factors contributing to incomplete carbon burnout are as follows: particles trapped in the ash hopper region, nonoptimum fuel flow distribution between burner levels, and large pulverized coal particle sizes and high particle concentrations near the furnace walls. Further test results have provided additional data for the validation of the model
-.
Figure 7. Bayswater boiler elevation.
performance over a range of operating conditions. Changes to burner tilt, particle size distribution, and excess air levels were examined for their impact on combustion efficiency and compared with the model predictions.
128 Energy & Fuels,
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Table 111. Proposed Modifications to Boiler Operating Parameters
modification bias of secondary air to lower burner levels (lowest level 20% greater than highest level) bias of coal flow to upper burner levels (highest level 20% greater than lowest burner level) increased airflow to 28' burners (50% greater than 40' burners) fuel nozzles angled toward furnace center
increase in combustion efficiency (percentage points)
-..
3.
0.28 0.18
0.20 0.48
L
Figure 9. Bayswater incident heat flux contours in kW/m3: (a) side wall and (b) rear wall.
i
b)
Figure 8. Bayswater gas temperature contours in degrees Kelvin: (a) elevation through furnace centerline parallel to front and rear walls, and (b) elevation through furnace centerline parallel to side walls.
Figure 4 showsthe predicted and measured relationship between pulverized fuel fineness and the flue gas unburnt carbon loss. As expected the boiler test results showed a monotonic increase in percent unburnt carbon loss with increasing +300 pm pulverized fuel size fraction. The model was able to reproduce this effect quite closely. The importanceof this result rests in the similarity of the slopes of the two relationships-the absolute level of unburnt carbon predicted is sensitiveto the particle size distribution discretization employed. The other parameter which in practice is found to have amajor impact on unburnt carbon loss is the level of excess air in the flue gas. The predicted and measured relationships between excess air level and flue gas unburnt carbon loss are illustrated in Figure 5. The slopes of the predicted relationship is very close to that measured. Experimental results obtained during trials of a flue gas online carbon in flyash analyser revealed a correlation between burner tilt position and unburnt carbon loss. As illustrated in Figure 6 the measured trend in flue gas unburnt carbon loss versus burner tilt is in good agreement with the predictions. A significant finding of the predictions was the considerable degree of unburnt carbon finding its way to the furnace ash hopper. The trend in the predicted furnace hopper unburnt carbon versus burner tilt as expected was the opposite of the flue gas
Figure 10. Plan views of particle trajectories in the Bayswater furnace for the three cases: (a, top) low swirl, mills A and C out of service, all other mills in service, (b, middle) high swirl, mills A and C out of service, all other mills in service, (c, bottom) high swirl, mills E and G out of service, all other mills in service.
unburnt carbon, with more negative tilt settings resulting in higher furnace hopper unburnt carbon. The ability of the model to respond to quite small
Comparison of Boiler Data with Model Predictions
Energy & Fuels, Vol. 8, No. 1, 1994 129
C
A
D B E
G F
Figure 11. Elevation views of particle trajectories in the Bayswater furnace for the three cases: (a, left) low swirl, mills A and C out of service, all other mills in service, (b,middle) high swirl, mills A and C out of service, all other mills in service, (c, right) high swirl, mills E and G out of service. all other mills in service.
changes in the major operating parameters has led to a high degree of confidence in the model. The model has been used to design a program of plant testing which is currently in progress to examine potential modifications to operating practice which are expected to increase combustion efficiency. A selection of the modifications and the expected percentage point improvement of that modification (in isolation) are described in Table 111. Modifications 1, 2, and 3 can be carried out with only changes to the operation of the unit; modification 4 which has the highest potential benefit would require modification to the burner nozzles. Bayswater. The Bayswater units are 660-MW(e) opposed wall swirlburner-fired boilers having furnace plan dimensions of 25 m X 13 m. Each boiler is fired by four levels of four burners on the front wall and three levels of four burners on the rear wall. Five of the seven burner levels are necessary for maximum output. Elevational views of the boiler are illustrated in Figure 7. The four units at the Bayswater Power Station are currently the focus of an investigation which is being conducted to minimize the extent of ash deposition in the quarl region of the burners. Previous computational modeling of the near-burner aerodynamics using an axisymmetric version of the code revealed that increased burner swirl should cause a decrease in ash deposition on the burner quarls. A preliminary burner retuning program conducted at the station substantiated this finding and prompted the use of the comprehensive furnace model to predict the overall impact of the increased burner swirl levels. Three operating cases were considered. In the base case, mills A and C were inactive and low swirl in the inlet air to the burners was used. This represents a typical operating condition. The variations on this base case
investigated in this study were (a) high swirl, with mills A and C again out of service and (b) high swirl, with mills A and C in service and mills E and G out of service. These variations were made to assess the effects of changing the degree of swirl and the operating strategy for the mills, particularly the burner levels in service, on the operation of the furnace. The furnace temperature patterns and wall heat flux contours are illustrated for the base case condition in Figures 8 and 9, respectively. The peak in the incident heat flux values is greater than 300 kw/m2 and occurs as expected in the center of the walls slightly above the top of the burners. The particle trajectories through the furnace were found to be sensitive to both the burner levels in service and the degree of swirl on the burners. Particle trajectories for the three different conditions considered are presented in the plan and elevation views of Figures 10 and 11. Comparison of the plan viewsfor the operating and highswirl conditions (Figure 10, parts a and b) illustrates significant differences. As expected, the high-swirl case shows greater divergence of the particle streams in the near-burner region; however, the most significant feature of the illustrations is the dramatically lower number of particle-wall collisions for the high-swirl condition. The elevation views (Figure 11,parts a and b) show that the particle impactation on the walls is occurring over the region of the furnace near and above the burners. The high-swirl case shows lesser penetration of the burner jets, a higher particle concentration in the center of the furnace, and fewer resultant collisions with the boiler front and rear walls. These results tend to support trends currently observed in the operation of these boilers where the increase in
130 Energy
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swirl of the burners is being used to reduce ash particle deposition in the regions surrounding the burners. As expected, comparison of the high-swirl conditions for the two different burner level configurations shows significant differences. A greater degree of particle-wall interaction is evident for the case with E and G level burners out of service. The elevation views (Figure 11, parts b and c) reveal that most of this interaction is caused by the unopposed B level, C level, and F level burner jets reaching the opposite wall. This effect is not evident with the level A and level C burners being out of service as only the level D is unopposed. Since levels A and C are the highest burners in service with this case, the bulk gas flow in the furnace is quite strong and predominantly verticle. This feature reduces the penetration of the jets.
5. Conclusions The comprehensive furnace model has been shown to have practical application in the solution of operational problems and the optimization of combustion on large coal-fired utility boilers. The results from the tangentiallyfired Wallerwang Power Station demonstrate the ability
Boyd and Kent
of the model to accurately account for subtle changes in operating parameters such as fuel fineness, excess air level, and burner tilt. The combustion optimization modifications suggested by the model promise to allow significant improvements in the combustion efficiency of the boiler to be made. For the swirl burner wall-fired Bayswater Power Station the model was able to elaborate on the aerodynamic mechanisms which lead to ash deposition around the burners. The results obtained by the model have enabled the burner retuning program to proceed with confidence and help solve an operating problem currently experienced at the station.
Acknowledgment. Most of the work described in this paper has been supported by the Australian Electricity Industry Research Broad and Pacific Power. The authors acknowledge the Warren Centre, The University of Sydney, for facilitating the Bayswater supercomputer study. The efforts of Mr. A. Mann of Sydney University and Mr. P. Benyon of Pacific Power are gratefully acknowledged.
