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3-Dimensional Simulation of Air Mixing in the MSW Incinerators
Article in Combustion Science and Technology · October 1996
DOI: 10.1080/00102209608951997
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Changkook Ryu
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Sungkyunkwan University
Korea Advanced Institute of Science and Technology
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3-Dimensional Simulation of Air Mixing in the MSW Incinerators
Chang Kook Ryu and Sangmin Choi’
Department of Mechanical Engineering,
Korea Advanced Institute of Science and Technology, Taejon, Korea
Abstract
Combustion control strategies to minimize the pollutants emission from the municipal solid waste (MSW)
incinerators are formulated based on the improved mixing of air with the products of incomplete combustion and the
subsequent increase in oxidative destruction. Secondary air injection into the combustion chamber plays the key role in this
mixing process. However, design variables of the air jet into the combustion gas stream are not clearly identified, and the
performance of mixing and reaction is not fully understood. Three-dimensional flow simulation was performed to study the
mixing performance of a full-scale incinerator combustion chamber according to the secondary air nozzle configuration. A
detailed flow was analyzed and the degree of mixing was quantitatively evaluated by introducing a statistical parameter
based on the chemical species distribution. The gas residence time distribution was analyzed using the particle trajectory.
The overall flow field was strongly influenced by the nozzle configuration. It was demonstrated that the degree of mixing
could be improved by selecting larger inter-jet spacing and stronger jet velocity. The staggered arrangement of two opposite
nozzle arrays was found to be more effective in terms of mixing and gas residence time distribution.
Keywords: Incinerator, Numerical simulation, Secondary air, Mixing, Residence time
INTRODUCTION
To meet the increasingly higher level of performance standards, incinerator designers and operators focus their
attention on the combustion control along with the more conventional flue gas cleaning processes. The Good
Combustion Practices (Kilgroe et al., 1990) and the Combustion Strategy (Shapiro, 1995) have been formulated
and some of the technical features have been introduced as guidelines or legal requirements. Basic idea of the
combustion control is well summarized in the 3T's (temperature, time, and turbulence). By providing an
adequate combustion environment, formation of the incomplete combustion products can be minimized and the
destruction of pollutants can be maximized.
Combustion control guarantees sufficiently long residence time at
a high enough temperature and the good mixing with fresh air.
In the mass-burning municipal solid waste (MSW) incinerators, the combustion control strategies can be
implemented by optimizing the furnace chamber geometry, the method of air supply and the grate movement.
Previous experimental and computational studies have identified the importance of the secondary air injection.
’
Corresponding author
It was shown that the strong air jet injection, or the change in injection angle can improve the efficiency of
mixing (Bette et al., 1994). The effects of the secondary air injection are also shown by the residence time
distribution, and the pathline shapes or the size of the recirculation zones (Nasserzadeh et al., 1994). A scheme
to quantify the degree of mixing was also proposed (Choi et al., 1994) and applied to the 2-dimensional
evaluation of the incinerator designs (Kim et al., 1995). The authors recently investigated a unitized jet air
mixing in the simplified geometry where the flow was inevitably treated as 3-dimensional. It was shown that the
flow characteristics such as the jet trajectory, the recirculation zone and the mixedness were controlled by the
momentum flux ratio of the jet to the main gas stream and the inter-jet spacing (Ryu and Choi, 1995). Design
variables of the secondary air injection include ①the air distribution ratio supplied by the secondary air, ②the
number, location and flow rate distribution of nozzle arrays, ③the number, size and injection angle of nozzles
in each nozzle arrays, ④nozzle arrangement of two opposed jets which determines the jet interaction. However,
current understanding about these variables is limited while the environmental regulations become tighter. It
would be desirable to quantitatively evaluate the incinerator performance, while the design variables are
optimized for the various types and shapes of incinerators.
The purpose of this paper is to investigate the effect of the secondary air nozzle configuration (which
includes ③ and ④ listed above) by numerical flow simulations on the 3-dimensional geometry. The
quantitative evaluation of mixing performance in the MSW incinerator is also proposed.
INCINERATOR AND COMPUTATIONAL MODEL
INCINERATOR MODELING
Shown in Fig.1 is the combustion chamber of a typical MSW incinerator of 300 ton/day capacity. It is a
relatively simple geometry of the central-flow type having a uniform depth, and only the half volume is shown
with a plane of symmetry. Primary air is supplied through the grate and the secondary air jets are injected from
the wall. Three secondary air nozzle arrays are located in the designated location, SA1, SA2, and SA3,
respectively. The combustion chamber is conceptually divided into two sub-chambers. In the primary
combustion chamber, the bed of solid waste burns on the grate. Combustion gas products as well as some
excess air are released from the solid waste bed, and then allowed to pass through the furnace. Inevitably, some
amount of the incomplete combustion products is also released, which may lead to the pollutant emission. The
secondary combustion chamber is intended to provide an adequate environment so that the incomplete
combustion products and the pollutants should be destructed, while transferring heat to the water-wall of the
boiler.
Following assumptions and simplifications are applied for the numerical model of the incinerator.
• Combustion gases of CO2, H2O, O2, and N2 are released from the burning waste bed over the grate.
Velocity, species concentration and temperature on each grate segment are calculated based on the waste
composition and the corresponding combustion rate (Santos et al., 1991). Boundary conditions of the
combustion gas on the grate are listed in Table 1.
• For a geometric simplicity, waste feeder and ash hopper are not considered in the computational model, and
the nozzle cross-sections are assumed to fill the rectangular grid spacing.
• Since the mixing is dominated by the flow field, thermal radiation is not considered and the water-wall is
assumed as adiabatic.
These simplifications do not warrant sufficient accuracy to predict the physical phenomena. However the
present study places emphasis on the comparative evaluation based on the relative merits of different nozzle
configurations.
FIGURE 1. Schematic of a typical 300 ton/day incinerator. (Width : 7.5m, Height : 14m, Full depth : 5.7m)
waste feed rate
12.5 ton/h
excess air ratio
1.85
primary air / secondary air
65 / 35
waste constitution
C: 0.20, H: 0.03, O: 0.14. N: 0.01, Water: 0.47, Ash:0.15
inlet on the grate
#1
#2
#3
#4
primary air distributions
25%
35%
30%
10%
combustion ratio
25%
40%
32%
3%
water vaporization ratio
75%
14%
11%
0.0%
temperature
927K
2004K
1928K
909K
mass fraction of CO2
0.0806
0.1368
0.1293
0.0423
TABLE 1. Operating conditions and boundary conditions of the combustion gas on the grate in the model incinerator.
CASE SELECTION
Four sets of nozzle configuration were selected as test cases as shown in Fig. 2. Case 1 has the flat slot jets,
while Cases 2∼4 have multiple arrays of nozzles. For the Case 3, the SA1 and SA2 nozzles are arranged in the
staggered format, while Case 2 and Case 4 have in-line nozzles. The inter-jet spacings are set identical for the
Case 3 and the Case 4. Operating conditions of the secondary air are summarized in Table 2. Dimensionless
inter-jet spacing, S is defined as S=s/d, where s is inter-jet spacing and d is diameter of the nozzle.
fixed
injection points
SA1, SA2 and SA3 as shown in Fig. 1
a) Case 1
b) Case 2
c) Case 3
d) Case 4
FIGURE 2. Selected nozzle arrangements for 4 test cases.
variable
nozzle diameter [m]
0.06 m
flow rate distribution
SA1 : SA2 : SA3 = 2 : 2 : 1
injection angle from x-axis
SA1: -45 , SA2: 210 , SA3: 210
Case 1
Case 2
Case 3
Case 4
1
6.5
13
13
nozzle arrangement (SA1-SA2)
in-line
in-line
staggered
in-line
velocity of SA1 [m/s]
13.4
51.1
89.4
89.4
number of nozzles in SA1
1 (flat)
14
8
8
S (s/d)
determined
TABLE 2. Operating conditions of the secondary air and selected test cases
COMPUTATIONAL MODEL
Computational grid pattern was constructed as in Fig. 3. The volume is divided into 38×54×24 elements, and
the front surface of the chamber is the symmetry plane. Concentrating on the jets, they are not sufficiently fine
to predict the entire characteristics of jets. Preliminary investigation showed that coarse grid could result in the
decrease of the penetration depth of jets and the increase of the mixedness within the range of 5% or so.
However, it does not affect the relative performance of the given cases.
For the prediction of the non-reacting thermal turbulent flow field, equations of mass, momentum and
enthalpy conservation along with the standard k-ε
turbulence model were employed.
Conservation of mass :
∂
( ρ ui ) = 0
∂
Conservation of momentum :
⎛ ∂u
∂ u j ⎞ 2 ⎛ ∂ ui ⎞ ⎤
∂
∂ ⎡
⎟⎟ + μ ⎜⎜
⎟⎟ δ ij ⎥
( ρ ui u j + ρ ui' u 'j ) = −
⎢ p δ ij − μ ⎜⎜ i +
∂ xi
∂ xi ⎢⎣
⎝ ∂ x j ∂ xi ⎠ 3 ⎝ ∂ x j ⎠ ⎥⎦
k-ε
turbulence model :
∂
∂ ⎛ μ ∂k ⎞
( ρ ui k ) = ⎜ t ⎟ + Gk − ρε
∂ xi
∂ xi ⎝ σ k ∂ xi ⎠
∂
∂ ⎛ μ ∂ε ⎞
ε
ε2
( ρ uε ) = ⎜ t ⎟ + C1ε Gk − C2ε ρ
∂ xi
∂ xi ⎝ σ ε ∂ xi ⎠
k
k
μ t = Cμ ρ
k2
ε
FIGURE 3. Grid pattern for the combustion chamber : 38×54×24 cells
where Gk is the rate of production of turbulent kinetic energy :
⎛ ∂u j ∂ui ⎞ ∂ui
⎟⎟
Gk = μ t ⎜⎜
+
⎝ ∂xi ∂x j ⎠ ∂x j
The coefficients in the k-ε model :
C1ε = 144
. , C2 ε = 192
. , Cμ = 0.09 σ k = 10
. σ ε = 13
.
Conservation of energy :
∂
∂ ⎡⎛ μ + μ t ⎞ ⎛ ∂ h ⎞ ⎤
ρ ui h) =
⎟⎜
⎟⎥
(
⎢⎜
∂ xi
∂ xi ⎢⎣⎝ σ h ⎠ ⎝ ∂ xi ⎠ ⎥⎦
Conservation of chemical species :
∂ ⎡⎛ μ + μ t ⎞ ⎛ ∂mi ' ⎞ ⎤
∂
ρ ui mi ' ) =
⎟⎜
⎟⎥
(
⎢⎜
∂ xi ⎢⎣⎝ σ s ⎠ ⎝ ∂xi ⎠ ⎥⎦
∂ xi
The equation of state :
p
ρ=
m
RT ∑ j
Mj
Using the FLUENT 4.25 code, a typical run needed 60 hours of CPU time on an Indigo2 workstation.
PARTICLE TRAJECTORIES
On each grate, 200 injection points are selected and 10 particles per a position are generated (total 8000
particles are traced). Physical properties of the particles are chosen as air at 1000℃. Since these particles are
nearly massless, their trajectories are determined by the given flow field as :
dx i
= u p ,i ≈ u ∞ ,i
dt
where up means the velocity of a particle and u∞ means the velocity of surrounding gas media. To incorporate
the instantaneous values of the fluctuating components of the gas phase velocity, a stochastic calculation
method is used :
u ∞ ,i = ui + ui'
ζ
where
ui' = ζ u /'2
is a normally distributed random number(0≤ ζ ≤ 1), obeying the Gaussian probability distribution. After a
is chosen, ui' remains constant during the characteristic lifetime of the eddy defined as :
new value of ζ
3
τ=
Cu4 k
2 ε
The kinetic energy of turbulence is known for turbulent flow calculations and the value of ui' can be
obtained as :
ui' = ζ
2k
3
When a particle reached the wall, it is assumed to be reflected with a restitution factor of unity. Maximum
number of steps in trajectory calculation is 100,000 when a characteristic length factor in a control volume
related to the integration time step is 1/10. Some particles do not escape the chamber until the integration step
reaches its maximum. It takes typically 15 hours of CPU time to predict the trajectories of 8000 particles on an
Indigo2 workstation.
RESULTS
FLOW FIELDS
Basic flow fields can be interpreted using the velocity vector plots and the streaklines. Shown in Fig. 4 are the
streaklines of each case : 30 seed points are released from the grate and traced for 5 seconds with time step of
0.0005 second. The streaklines of Case 1 (Fig.4-(a)) and Case 2 (Fig.4-(b)) show very strong 2-dimensionality
except for the recirculation in the primary chamber. The combustion gas in Case 1 is affected by the secondary
air but flows nearly in straight line toward the outlet. Large recirculation zone appears along the front wall in
the in-line nozzle arrangement, Case 1, 2 and 4. The recirculation zone decreases the gas residence time, and
subsequently the overall mixing time. Flow field of Case 3 (Fig. 4-(c)) shows 3-dimensional characteristics. The
streaklines are crossing, since the jets of SA2 on the rear wall penetrate through the space between jets of SA1
on the front wall. It causes swirling around the center y-axis of the secondary chamber, and the recirculation
zone near the front wall disappear. Case 4 (Fig. 4-(d)) shows weak 2-dimensional flow characteristics. Case 4
also shows the stronger recirculation in the primary chamber which could improve mixing and could increase
the residence time of the combustion gas in high temperature region. Qualitative observation suggests that the
Case 3 is the most effective flow field and the Case 1 the worst.
a) Case 1
c) Case 3
b) Case 2
d) Case 4
FIGURE 4. Streaklines of the particles released from the grate
a) Case 3
b) Case 4
c) Case 3
d) Case 4
FIGURE 5. Predicted velocity vectors on the center plane and on the selected x-z cross-sections for Case 3 and Case 4
a) Case 3
b) Case 4
FIGURE 6. Predicted temperature contours on the center-plane and on selected x-z cross-sections for Case 3 and Case 4.
Fig. 5 shows the velocity vectors on the center plane and on the selected x-z planes in the secondary
combustion chamber for Case 3 and Case 4.
Comparing the velocity vectors on the x-z planes in the
secondary chamber for Case 3 (Fig.5-(c)) and Case 4 (Fig.5-(d)), the effect of the nozzle arrangement can be
discussed. In Case 3, swirling is easily seen along the y-direction and the recirculation is weak. The swirling can
improve the mixing efficiency in the secondary combustion chamber. In Case 4, the jet streams of SA1 and SA2
conflicts with each other so that the high v-velocity zone is produced.
Fig. 6 shows the temperature contours for Case 3 and Case 4. It can be easily seen that the shape of high
temperature region in the primary combustion chamber is very different. In Case 3, the high temperature region
inclines toward SA2 but toward SA1 in Case 4. It reflects the flow field in the primary combustion chamber that
is determined by the jets in SA1 and SA2 as shown in Fig. 4. In the secondary air chamber, the temperature
profile becomes more uniform as the gas flows downstream.
MIXING PARAMETER
To compare the mixing performance, a parameter α is used. A local variable α is defined as
α=
∑ ( X i − X o ,i )
2
i
2
where Xi is the mass fraction of chemical species i and Xo,i is its average mass fraction (Choi et al., 1994). The
FIGURE 7. Mass-averaged mixing parameter α at the cross-sections along the secondary combustion chamber.
parameter α uses the concept of the standard deviation based on the chemical species distribution. α
becomes 0 for the complete mixing.
Fig. 7 shows the mass-averaged values of α on the cross-section of the secondary combustion chamber
(y=0 is the starting point of the secondary chamber as shown in Fig. 1). The quantitative comparison shows
consistent results with the qualitative judgement based on the mixing characteristics inferred from the flow
fields. In Case 1, the mixedness in the primary combustion chamber is poor so that initial value of α is larger.
And the value of α does not decrease as the gas flows downstream, since the weak secondary air jet cannot
penetrate into the flow field. The values of α in Case 1 and Case 2 are higher than in Case 3 and Case 4. It
suggests that the larger inter-jet spacing and stronger jets improve mixing. According to the results in a unitized
jet analysis (Ryu and Choi, 1995), the penetration of the jet and the mixedness between the fresh air jet and the
combustion gas increases as the jet momentum and inter-jet spacing increase. The difference in α between
Case 3 and Case 4 is not apparent but Case 3 is slightly more effective.
Since the mixing parameter α does not include the direct effect of the gas residence time, comparing the
gas residence time distribution is helpful to complete the mixing analysis.
GAS RESIDENCE TIME DISTRIBUTION
Gas residence time inside the combustion chamber is considered as one of the key parameters for destruction of
products of incomplete combustion. Minimum residence time of 2.5∼3 seconds is sometimes required by law.
In calculating the residence time, however, an overall average concept is often employed: by dividing the
chamber volume by the volumetric flow rate of the combustion gas. However, turbulent nature of the
combustion gas stream results in a distributive pattern of the residence time of volume elements.
FIGURE 8. Residence time distribution of combustion gas for Case 3
Residence time distribution is analyzed using the particle trajectories. Shown in Fig. 8 and 9 are the
probability density functions of residence time of the volume elements. Fig. 8 shows the residence time
distribution for Case 3. The gas (or the particles) generated from the drying zone (the grate zone #1) shows
slightly longer residence time than from the burning zone (the grate zone #2 and #3). Gas from the grate zone
#4 (after-burning zone) shows a wide distribution of residence time. The particles whose residence times are
longer, say, longer than 8 seconds are the ones that have been partly trapped in the recirculation zones.
To evaluate the overall performance, particle numbers are averaged over the grate zone #1 through #4, while
the relative mass flow rate of each zone is taken into consideration. Fig. 9 shows the Case 1 through 4. The
effects of secondary air injection patterns are clearly shown. Weak 2-dimensional slot jets of Case 1 do not
interact with the combustion gas stream, and as a result, particles can escape the combustion chamber more
freely. Case 3 and 4, whose jet nozzle velocities are higher than those for Case 1 and 2, show better
performance in terms of residence time distribution. However, distinction between Case 3 and Case 4 is not as
evident as in other comparison.
Cumulative number density diagrams are shown in Fig. 10. The number of particles which escape the
combustion chamber within 3 seconds are 56.0% for Case 1, 33.8% for Case 2, 27.1% for Case 3 and 36.7% for
Case 4. One can easily evaluate that Case 3 shows the most desirable flow pattern. Based on the cases above,
we can conclude that the secondary air jet arrangements can indeed affect the residence time
FIGURE 9. Residence time distribution of total combustion gas
FIGURE 10. Cumulative percentage of mass escaping the chamber within the set residence time
distribution. By comparing the Cases 3 and 4, the nozzle arrangements can also influence the flow pattern
significantly, even though the number of jets and the jet speed are set identical.
CONCLUSION
3-dimensional hot-flow simulations have been performed for the model geometry of a MSW incinerator. It was
demonstrated that the secondary air jet did play a major role in mixing and the phenomena was indeed 3dimensional. Flow simulation results were evaluated in terms of the velocity vectors, flow path (streaklines), the
temperature distribution, the mixing parameter and the residence time distribution. The mixing parameter α
based on the chemical species distribution is successfully used to quantitatively evaluate the degree of mixing.
Gas residence time distribution from particle trajectories is also helpful to analyze the mixing performance.
By comparing the alternatives of nozzle configuration, it was suggested that the nozzles with larger inter-jet
spacing and stronger jet would result in better mixing, because of the stronger jet penetration. It was also
suggested that the staggered nozzle arrangements would minimize the gas portion having a short residence time
and improve mixing efficiency.
ACKNOWLEDGMENTS
This study was funded by the Korean Ministry of Environment, Daewoo Corporation, Daewoo Heavy Machinery and
Samsung Heavy Industries.
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