COMBUSTION MODELING AND
OPTIMIZATION
Application of Computational Fluid Dynamics and Systematic
Optimization in Emission Reduction of Biomass Fluidized Bed
Boiler
A. Saario, A. Oksanen
Tampere University of Technology, Finland
M. Ylitalo, J. Roppo
Metso Power Oy, Finland
Acknowledgments:
K. Miettinen – JYU, J. Koski – TUT, L. Kjäldman / P. Jukola – VTT
Combustion Modeling and Optimization
Introduction
Combustion Modeling and Optimization
Bubbling fluidized bed boiler
Bubbling fluidized bed boiler burning biomass is studied using
computational fluid dynamics (CFD)
Special attention is paid to NOx reduction by ammonia injection
(SNCR process)
NH3 + O2 → NO + H2 O +
1
H2
2
(1)
NH3 + NO → N2 + H2 O +
1
H2
2
(2)
Nitric oxide emission (NOx ) limits get more stringent ⇒ new and
improved design tools are required
Combustion Modeling and Optimization
Boiler sketch
1
DEPTH (m)
3
5
SUPERHEATERS
17
15
NH3 INJECTION
LEVELS
9
NH3 + air
NH3
+
air
NH3 + air
x
7
HEIGHT (m)
4. Side
left B
FREEBOARD
REAR WALL
3
Primary air (fluidization air),
supporting fuel
Combustion Modeling and Optimization
3. Side
left A
1
500 00
4
1000500
3
RIGHT
WALL
10
10
5
5. Side
left C
5
SPLASH ZONE
DENSE BOTTOM BED
z
LEFT 6
WALL
11
FRONT WALL
Secondary air
Fuel mixture + air
REAR WALL
13
Outlet
Depth (m)
SECOND
PASS
BULL-NOSE
100 00
5
10
100
9. Side
right C
100
8. Side
right B
500
10
100
2
10
10
7. Side
right A
500
500
100
1
x
1
8
FRONT WALL
7
6
5
4
3
Width (m)
2. Front left
2
6. Front right
1
y
CFD modeling
1450
1400
1350
1300
1250
1200
1150
1100
Goals in CFD modeling:
1050
1000
950
900
850
(i) Detailed study on the effect of computational grid
(ii) Realistic description of bubbling bed and fuel feed
(iii) Study nitrogen chemistry and turbulence - chemistry interaction
modeling
(iv) Improve radiative heat transfer modeling
(v) Model validation with full-scale experimental data
Combustion Modeling and Optimization
CFD and optimization
CFD model must be able to predict correct
emission formation trends before the
application of optimization algorithm
makes sense !
Combustion Modeling and Optimization
Model validation
Combustion Modeling and Optimization
Effect of local grid refinement
Baseline grid (102 524 cells)
Once refined grid (105 968 cells)
Twice refined grid (109 412 cells)
Three times refined grid (112 856 cells)
Dense grid (2 067 424 cells)
Very dense grid (4 264 650 cells)
0.004
2
k / u0
0.003
0.002
0.001
0
0.05
Combustion Modeling and Optimization
0.10
0.15
r/x
0.20
0.25
0.30
Predicted v. measured temperature
REAR WALL
6
LEFT
WALL 5
RIGHT
WALL
1200
1143
1203
0
1253
1350
8
1218
0
135
1233
1
1300
1238
1223
1288
1233
0
125
1158
00
14
130
125
1200
0
Width (m)
50
130
2
1100
0
12
20 0
1
1238
1250
1168
115
00
4
3
1203
12
1150
1168
1158
1200
1183
1188
1100
1138
0
7
6
5
4
Depth (m)
3
2
1
FRONT WALL
Combustion Modeling and Optimization
Boiler height (m)
Model validation as function of SNCR injection
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Without injection
50% injection
100% injection
150% injection
100% injection
5.5 m level
Without injection exp.
50% injection exp.
100% injection exp.
150% injection exp.
100% injection
5.5 m level exp.
20
40
60
80 100 120 140
Average nitric oxide concentration (ppmvol)
Combustion Modeling and Optimization
160
Optimization
Combustion Modeling and Optimization
Optimization - CFD interaction
Design
V ariables
@
OPTIMIZATION
ALGORITHM
@
I
@
@
Objective
F unction
Combustion Modeling and Optimization
@
R
@
CFD
Multiobjective optimization problem
Objective function vector f (x) is given in the present study by
f (x) = (f1 (x) , f2 (x))
T
where f1 (x) and f2 (x) measure the concentrations of NO and NH3
(ppmvol ) in flue gas, respectively. The design variable vector, x, is
given by
x = (X1 , X2 , . . . , X9 )
T
where Xi stands for the concentration of NH3 (vol-%) in the ith
injection.
Combustion Modeling and Optimization
Feasible set
The feasible set, S, is defined as
S=
(
x ∈ Rn | 0 ≤ Xi ≤ 6.60 for all i ∈ {1, 2, . . . , 9},
MNH3
g (x) = ṁflow
Mflow
Combustion Modeling and Optimization
9
X
i=1
)
Xi − ṁNH3 ,max ≤ 0
Achievement function
The reference point method applied minimizes the maximum
normalized difference between the objective function vector f (x) and
the reference point vector zref :
minimize
x∈S ′
max
i=1,2
fi (x) − ziref
zinad − ziid
!
+ρ
2
X
i=1
fi (x) − ziref
zinad − ziid
!
+ r [max (0, g (x))]2
!
Above optimization problem is solved using a combination of genetic
algorithm (global search) and Powell’s conjugate direction method
(local search).
Combustion Modeling and Optimization
Points in objective and design space
SOO NO
SOO NH3
MOO I
MOO II
Current design
Reference point I
Reference point II
40
30
20
10
0
40
7
NH3 concentration (vol−%)
Ammonia concentration (ppmvol)
50
50
60
70
80
90
100
Nitric oxide concentration (ppmvol)
Combustion Modeling and Optimization
110
6
5
4
3
Side
left C Front
right
Rear
Side
left B
2
1
Side
right A Side Side
right B right C
Front
left
Side
left A
Injection
All predicted points in objective space
Ammonia concentration (ppmvol)
50
SOO NO
SOO NH3
MOO I
MOO II
40
30
20
10
0
40
50
60
70
80
90
100
Nitric oxide concentration (ppmvol)
Combustion Modeling and Optimization
110
Predicted and experimental points
Ammonia concentration (ppmvol)
50
SOO NO
SOO NH3
MOO I
MOO II
50% injection exp.
100% injection exp.
150% injection exp.
50% injection pred.
100% injection pred.
150% injection pred.
40
30
20
10
0
40
50
60
70
80
90
100
Nitric oxide concentration (ppmvol)
Combustion Modeling and Optimization
110
Conclusions
Combustion Modeling and Optimization
Some conclusions on CFD modeling
• Grid independent solution is consistently approached using
local grid refinement
• Over 2 000 000 cells are required to achieve grid
independency in modeling a single jet
• The conditions in the different parts of the boiler are
different =⇒ the combination of two global ammonia
chemistry mechanisms can perform well
Combustion Modeling and Optimization
Some conclusions on optimization
• 12% decrease in NO emission obtained, while maintaining
NH3 emission at an acceptable level
• Interactive multiobjective optimization used to generate
Pareto optimal solutions
• Genetic algorithm used to explore design space, after
which Powell’s method used for local refinement near the
optimum
• Modeling and optimization becomes frequently an iterative
loop (experimental data)
Combustion Modeling and Optimization
Selected publications
SAARIO, A., OKSANEN, A. (2008). Computational Fluid Dynamics and
Interactive Multiobjective Optimization in Design of Low-Emission Industrial
Boilers. Under consideration for publication in Engineering Optimization
SAARIO, A., OKSANEN, A. (2008). Effect of Computational Grid in
Industrial-Scale Boiler Modeling. Accepted for publication in International
Journal of Numerical Methods for Heat & Fluid Flow.
SAARIO, A., OKSANEN, A. (2008). Comparison of Global Ammonia Chemistry
Mechanisms in Biomass Combustion and Selective Noncatalytic Reduction
Process Conditions. Energy & Fuels, Vol. 22, pp. 297–305.
SAARIO, A., OKSANEN, A., YLITALO, M., ROPPO, J. (2007). Application of
Computational Fluid Dynamics and Multi-Objective Optimization in Design of
Low-Emission Combustion Equipment, 15th IFRF Member’s Conference, Pisa,
Italy, June 13–15.
SAARIO, A., OKSANEN, A., YLITALO, M. (2006). Combination of Genetic
Algorithm and Computational Fluid Dynamics in Combustion Process Emission
Minimisation. Combustion Theory and Modelling, Vol. 10, pp. 1037–1047.
SAARIO, A., OKSANEN, A., YLITALO, M. (2006). NO Emission Modeling in
Bubbling Fluidized Bed Furnace for Biomass. Clean Air, Vol. 7, pp. 1–22.
Combustion Modeling and Optimization