COMPUTATIONAL METHODS IN ENGINEERING AND SCIENCE
EPMESC X, Aug. 21-23, 2006, Sanya, Hainan, China
©2006 Tsinghua University Press & Springer
Computational Modeling of Coal Water Slurry Combustion Processes
in Industrial Heating Boiler
L. J. Zhu, B. Q. Gu *
College of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing, 210009 China
Email: [email protected], [email protected]
Abstract Coal water slurry (CWS) has been developed over the last 20 years as an alternative to fuel oil mainly in
industrial and utility boilers. In this paper, a commercial computational fluid dynamics (CFD) software is utilized to
investigate numerically the flow field, heat transfer and the combustion processes in a CWS-fired industrial heating
boiler. The modeling results show that there exists swirl flow field in the furnace; the swirl gases expand outward to
form the recirculation zones near the burner areas; from the front wall to the back wall, the swirl intensity firstly
strengthens and then weakens; the temperature drops with the increase of the furnace length, and the highest
temperature appears near the burners area in the combustion chamber; the distribution of CO, O2 and CO2
concentration is strongly related to the temperature distributions; the average exit temperature, the maximum furnace
temperature and the burnout rate are affected by the boiler load. The obtained results provide a helpful reference for
the design, the running and the retrofit of the CWS-fired industrial heating boiler.
Key words: coal water slurry (CWS), industrial heating boiler, computational modeling
INTRODUCTION
At the present, energy and environment are the two most stringent problems. Along with the current upward trend of
world oil price in the spot market, continuously increasing demands for energy have led scientists to seek for ways
of finding new energy sources. Thus, researchers have directed their attention towards various methods of burning
coal water slurry (CWS) for energy generation [1-3]. CWS is typically composed of 60-70% coal, 30-40% water,
and 1% chemical additives. It has been developed over the last 20 years as an alternative to fuel oil mainly in
industrial and utility boilers. The attraction of CWS is its complete independence of an oil supply. There are two
main reasons for investigating the suitability of CWS as a fuel. Firstly, CWS can be stored without the danger of a
coal dust explosion and burned in a similar way to heavy fuel oil in the existing oil-fired equipment with a few
retrofits, and secondly CWS can be transported in pipelines, leading to reduction in transportation costs compared to
coal. There are also some disadvantages to be overcome, such as the increase of wear and blockage of mechanical
components, flame instability and changes in heat transfer in the combustion chamber, and stability and flow
problems during storage and pumping [4-6].
Computational fluid dynamics (CFD) modeling is now widely applied as an industrial plant development and
process optimization tool. The development and increased reliability of commercial CFD codes allow the modeling
of complex geometries and physically challenging problems using state-of-the-art physical models. As a result, the
number of applications of CFD on industrial processes is also growing rapidly and increasing in sophistication [7-9].
In the present work, the finite volume based commercial CFD-code is utilized to investigate numerically the flow
field, heat transfer and the combustion processes in a CWS-fired industrial heating boiler.
COMPUTATIONAL MODELING
The combustion process of CWS is a complex physical and chemical process, including turbulent fluid mechanics,
gaseous combustion, particle dispersion and reactions (i.e. water evaporation, coal devolatilization, and char
burnout) and radiation. The predictions are obtained by numerical solution of conservation equations for the gas and
particle phases. The gas phase is solved in an Eulerian domain, while the particle phase is tracked in a Lagrangian
fashion.
⎯ 291 ⎯
In this paper, the gas phase turbulence is represented by the realizable k-ε two-equation model [10], which is
believed to give more accurate results for swirl flow than the standard k-ε model. The combustion process is
modeled by the mixture-fraction/probability density function (PDF) approach [11], coupled with the P-1 radiation
model. Chemical equilibrium is assumed to relate instantaneous mass fractions and temperature with the conserved
scalar, taken as the mixture fraction. The P-1 radiation model [12] is the simplest case of P-N model, which is based
on the expansion of the radiation intensity in an orthogonal series of spherical harmonics. This approach has the
advantage of having only a simple diffusion equation, which is easy to solve with little CPU demand and also allows
particulate and anisotropic scattering. The size distribution of coal is separated into discrete particle groups,
assuming a Rosin-Rammler distribution. Coal devolatilization is described by the single kinetic rate devolatilization
model, which assumes the rate of devolatilization is first-order dependent on the amount of volatiles remaining in the
particle [13]. For char combustion, the kinetic/diffusion-limited rate model is used following Field [14] and Street
[15]. The model assumes that the surface reaction is determined either by kinetics or by a diffusion rate.
The gas-phase turbulent flow is predicted by solving the steady-state conservation equations for continuity,
momentums, turbulence kinetic energy and its dissipation. Numerical solution methods for these have been
developed by Patankar and Spalding [16]. The Eulerian equations for the gas phase are given in the following
general form:
div(ρ v φ ) − div(Γgradφ ) = Sφ + S pφ
(1)
where φ represents the variables: fluid pressure ( p ), three velocity components ( u , v, w ), turbulent kinetic energy
( k ), and its dissipation rate ( ε ); v the velocity vector; ρ the fluid mass density; Γ the exchange coefficient in the
transport law; Sφ the gas-phase source term and S pφ the particle-phase source term.
Table 1 identifies the Eulerian or gas phase variables together with the corresponding expressions for the exchange
coefficients Γ and the source terms Sφ , S pφ used in Eq. (1).
Table 1 Eulerian conservation equations and identification of terms in Eq. (1)
φ
Γ
Sφ
S pφ
Mass (continuity)
1
0
0
S p ,m
x-Direction momentum
u
μ eff
y-Direction momentum
v
μ eff
z-Direction momentum
w
μ eff
Turbulent kinetic energy
k
μ eff σ k
Dissipation rate of
turbulent kinetic energy
ε
μ eff σ ε
Conservation equation
μ eff = μ + μ t , μ t = Cμ ρk 2 ε , C μ =
∂p ∂ ⎛
∂u ⎞ ∂ ⎛
+ ⎜ μ eff
⎟ + ⎜ μ eff
∂x ∂x ⎝
∂x ⎠ ∂y ⎝
∂p ∂ ⎛
∂u ⎞ ∂ ⎛
⎟ + ⎜ μ eff
−
+ ⎜⎜ μ eff
∂y ∂x ⎝
∂y ⎟⎠ ∂y ⎜⎝
−
−
A0 + ASU
∂w ⎞
∂v ⎞ ∂ ⎛
∂p ∂ ⎛
∂u ⎞ ∂ ⎛
+ ⎜ μ eff
⎟
⎟ + ⎜ μ eff
⎟ + ⎜ μ eff
∂z ⎠
∂z ⎠ ∂z ⎝
∂z ∂x ⎝
∂z ⎠ ∂y ⎝
Gk − ρε
(
ρC1Sε − ρC2ε 2 k + με ρ
1
*
k
,
ε
~ ~ ~
U = S ij Sij + Ω ij Ω ij , Ω ij = Ω ij − 2ε ijk ω k , Ω ij = Ω ij − ε ijkω k , A0 = 4.04 , AS = 6 cos φ
*
1
3
(
)
φ = arccos 6W , W =
∂v ⎞ ∂ ⎛
∂w ⎞
⎟ + ⎜ μ eff
⎟
∂x ⎠ ∂z ⎝
∂x ⎠
∂v ⎞ ∂ ⎛
∂w ⎞
⎟⎟ + ⎜⎜ μ eff
⎟
∂y ⎠ ∂z ⎝
∂y ⎟⎠
S ij S jk S ki ~
1 ⎡ ∂u ∂u j ⎤
, S = S ij S ij , S ij = ⎢ i +
⎥
~
2 ⎢⎣ ∂x j ∂xi ⎥⎦
S
Gk = μ t S 2 ， S ≡ 2 Sij Sij
k
⎡
η ⎤
C1 = max ⎢0.43,
, η=S
⎥
η + 5⎦
ε
⎣
C2 = 1.9 , σ k = 1.0 , σ ε = 1.2
⎯ 292 ⎯
)
S p ,u
S p ,v
S p ,w
S p ,k
S p ,ε
where μ eff is the effective viscosity; μ t the eddy viscosity; Gk the production of turbulence kinetic energy; S
the modulus of the mean rate-of-strain tensor; σ k and σ ε the turbulent Prandtl numbers for k and ε ,
~
respectively; Ωij the mean rate-of-rotation tensor viewed in a rotating reference frame with the angular velocity; ω k ,
C2 , A0 , AS , σ k , σ ε are the model constants.
The particle phase flow is treated by solving Lagrangian equations for the trajectories of a statistically significant
sample of individual particles, which represent a number of real particles with the same properties. Particle
trajectories are tracked throughout the computational domain, and interactions between the particles and gas are
incorporated by an exchange of source terms for mass, momentum and energy.
The conservation equations for particles are different in form from Eq. (1). The equation of motion of a particle is
du p
dt
= FD (u − u p ) + g x (ρ p − ρ ) + Fx
(2)
where the first term on the right is the resistance of particle; the second one the gravity (including buoyancy) of
particle; the third one the other applied forces.
The conservation of particle energy is
mpc p
dTp
dt
= hAp (T∞ − Tp ) + Apε pσ (θ R4 − Tp4 ) +
dm p
dt
h fg − f h
dm p
dt
H react
(3)
where m p is the quality of particle; c p specific heat of particle; T p temperature of particle; h connective heat
transfer coefficient; Ap superficial area of particle; T∞ temperature of gas phase; ε p emissivity of particle; σ
Boltzmann constant; θ R temperature of radiation; h fg latent heat; f h quotient; H react reaction heat.
BOILER GEOMETRY AND NUMERICAL ALGORITHMS
The computations are performed on a full-scale CWS-fired boiler belonging to Beijing Yanshan Petrochemical Co.,
Ltd. CEHF. The system is the retrofit of 14MW oil-fired industrial heating boiler to fire CWS. The schematic
diagram of industrial heating boiler is given in Fig. 1, and the schematic diagram of adjustable swirl burner shown in
Fig. 2. Two swirl burners are installed in the front wall of the boiler. The property of CWS used in the modeling is
summarized in Table 2. The coal particle size distribution is presented in Table 3.
Figure 1: Schematic diagram of industrial heating boiler
Figure 2: Schematic diagram of adjustable swirl burner
⎯ 293 ⎯
Table 2 Property of CWS (wt %, as-received)
Fixed Carbon
46.28
Volatile matter
11.36
Ash
5.45
Carbon
42.54
Hydrogen
2.51
Nitrogen
0.47
Sulfur
0.41
Oxygen
11.71
Water
36.91
LHV (MJ/kg)
16.37
Table 3 The size distribution of coal particle size distribution
Diameter (μm)
Mass fraction (%)
0-45
81.34
45-75
10.31
75-150
5.76
150-300
1.52
300-500
0.61
500-710
0.30
710-1000
0.16
The boiler geometry is modeled with a three-dimensional mesh having a non-uniform grid with higher concentration
of cells near the burner areas. The calculation modeling starts with solving the gas flow field equations assuming that
the particles are absent, that is cold flow. Using the flow field, the temperature，component concentrations and
burnout histories of particles are determined. The mass, momentum and energy source terms for each cell is
calculated. The source terms are included in the gas-phase equations and the flow field is then recalculated. The
process is repeated until further repetition fails to change the solution. Thus, the mutual interaction of the gas and
particles is accounted for.
RESULTS AND DISCUSSIONS
In this paper, cold flow and combustion process at five different loads (60%, 70%, 80%, 90%, and 100% load) in the
CWS-fired industrial heating boiler are modeled by CFD respectively. The velocity field, temperature distribution
and gaseous phase component fraction distribution are analyzed.
1. Cold flow analysis Fig. 3 shows the contours of velocity magnitude and Fig. 4 the vectors of axial velocity in the
central vertical plane of furnace (i.e. y = 0 m) for cold flow. It is observed that there exists inner recirculation zone
near the exit area of each swirl burner, which the axial velocity at the axial line of burner exit is negative. The
magnitude of axial velocity is not positive until the recirculation zone is over. The length of upper inner recirculation
zone is about 0.7 m, the width about 0.35 m, however, the length of lower inner recirculation zone is about 1.1 m, the
width about 0.4 m. The results indicate that the quantity of recirculation of lower swirl burner is lager than that of
upper swirl burner. It will be helpful to improve the burnout rate of CWS. Since the negative pressure is produced
between the airflow and fire wall, outer recirculation zones are also formed in the furnace.
Figure 3: Contours of velocity magnitude at y = 0 m for cold flow (m/s)
Figure 4: Vectors of axial velocity at y = 0 m for cold flow (m/s)
The vectors of velocity magnitude at x = 0.5, 1.0, 2.0 m for cold flow are given in Fig. 5. It is shown that airflow is
injected into the furnace from burner; strong circle swirl is formed in the center of furnace. Rotation motion of
airflow is intensive near the burner exit (i.e. x = 0.5m). As the increase of furnace length (i.e. x = 1.0 m), the rotation
⎯ 294 ⎯
motion of airflow is weakened gradually. When x = 2.0m, the rotation motion of airflow disappears basically,
however, the velocity difference is still existent. Thus, from the front wall to the back wall of furnace, the swirl
intensity firstly strengthens and then weakens.
x = 0.5 m
x = 1.0 m
x = 2.0 m
Figure 5: Vectors of velocity magnitude at x = 0.5, 1.0, 2.0 m for cold flow (m/s)
2. Hot flow analysis Combustion process at five different cases (60%, 70%, 80%, 90%, and full load) in the
CWS-fired industrial heating boiler is modeled numerically. Fig. 6 presents contours of temperature and distributions of
CO, O2, and CO2 mass fraction in the central vertical plane for full load. These figures show that there is a strong
relation between the CO, O2, CO2 concentration and temperature distribution. As expected, the higher temperature
regions correspond to lower O2, CO2 concentrations and higher CO concentration. The O2 concentration decreases
greatly because of char combustion. A high temperature region with intensive combustion activity is formed in the
combustion chamber. It leads to very low levels of O2 concentration in that region as can be seen in Fig. 6(b). The
maximum furnace temperature is about 1670K, which is in agreement with measurements of actual boiler under
normal operating conditions.
(a) Contours of temperature (K)
(b) Contours of mass fraction of O2
(c) Contours of mass fraction of CO2
(d) Contours of mass fraction of CO
Figure 6: Contours of temperature and CO, O2, and CO2 mass fractions for full load at y = 0 m
Figs.7-10 show contours of the temperature and the distributions of O2 mass fraction in the central vertical plane for
90%, 80%, 70% and 60% load, respectively. Although the flow presents the same overall characteristics as in the
⎯ 295 ⎯
case of the full load, the highest temperature of those loads that occurs in the combustion chamber like that of full
load, are all lower than that of full load. Combustion is controlled in its early stages by the rates of reaction of the
solid residues. Although the O2 concentration distribution is similar to that of full load, it is observed that the values
of the O2 concentration for 90%, 80%, 70%and 60%load, which are higher than full load near the outlet region, are
affected by the load.
(a) Contours of temperature (K)
(b) Contours of mass fraction of O2
Figure 7: Contours of temperature and O2 mass fraction for 90% load at y = 0 m
(a) Contours of temperature (K)
(b) Contours of mass fraction of O2
Figure 8: Contours of temperature and O2 mass fraction for 80% load at y = 0 m
(a) Contours of temperature (K)
(b) Contours of mass fraction of O2
Figure 9: Contours of temperature and O2 mass fraction for 70% load at y = 0 m
(a) Contours of temperature (K)
(b) Contours of mass fraction of O2
Figure 10: Contours of temperature and O2 mass fraction for 60% load at y = 0 m
⎯ 296 ⎯
The relations between the average exit temperature, the maximum furnace temperature, and the burnout rate of CWS
at each swirl burner with the boiler load are summarized in Fig. 11. It is observed that with the increase of load, the
average exit temperature and the maximum furnace temperature increase, however, the burnout rate of CWS at both
swirl burners decrease. That is, the average exit temperature, the maximum furnace temperature are directly
proportional to the boiler load, however, the burnout rate of CWS is inversely proportional to the boiler load.
Meanwhile, it can be observed the burnout rate of CWS from upper swirl burner is lower than that of CWS from
lower one. It is validated that the quantity of recirculation of lower swirl burner is lager than that of upper swirl
burner, which improve the burnout rate of CWS efficiently. Therefore, the boiler load is chosen considering both the
burnout rate of CWS and the temperature distribution in the furnace.
Burnout rate (%)
Average exit Maximum furnace
temperature
temperature
110
105
100
95
90
85
80
75
70
65
60
55
50
45
40
(K)
Upper injection nozzle
Lower injection nozzle
Average exit temperature
Maximum furnace temperature
60
70
80
90
100
(K)
1300
1900
1250
1800
1200
1700
1150
1600
1100
1500
1050
1400
1000
1300
Load (%)
Figure 11: Change of some parameters with boiler load
CONCLUSIONS
The flow, heat transfer and combustion process of CWS in industrial heating boiler equipped with two swirl burners
are numerically investigated by CFD software. The main results can be summarized as follows:
(1) There exists swirl flow field in the furnace; the swirl gases expand outward to form the recirculation zones near
the burner areas; from the front wall to the back wall of the furnace, the swirl intensity firstly strengthens and then
weakens;
(2) The temperature drops with the increase of the furnace length, and the highest temperature appears near the area
of the burners in the combustion chamber;
(3) The distributions of CO, O2 and CO2 concentration are strongly related to the temperature field, that is, the higher
the temperature, the higher the CO concentration is and the lower the O2 and CO2 concentration;
(4) The average exit temperature, the maximum furnace temperature are directly proportional to the boiler load,
however, the burnout rate of CWS is inversely proportional to the boiler load.
These obtained results provide a helpful reference for the design, the running and the retrofit of the CWS-fired
industrial heating boiler.
Acknowledgements
The support of Beijing Yanshan Petrochemical Co., Ltd. CEHF is gratefully acknowledged.
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