Two-dimensional computational fluid dynamics simulation of nitrogen and sulfur oxides emissions in a circulating fluidized bed combustor

Chemical Engineering Journal 173 (2011) 564–573

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Two-dimensional computational fluid dynamics simulation of nitrogen and sulfur oxides emissions in a circulating fluidized bed combustor W. Zhou, C.S. Zhao ∗ , L.B. Duan, X.P. Chen, C. Liang School of Energy and Environment, Southeast University, Nanjing 210096, China

a r t i c l e

i n f o

Article history: Received 20 February 2011 Received in revised form 25 July 2011 Accepted 29 July 2011 Keywords: Computational fluid dynamics Circulating fluidized bed Combustion NO emission SO2 emission

a b s t r a c t Based on the previously established two-dimensional computational fluid dynamics (CFD) model which described processes of coal devolatilization, volatile combustion and char combustion in circulating fluidized bed (CFB) combustors, nitrogen and sulfur oxides emissions are numerically simulated and investigated in the present paper. First of all, a more accurate heat transfer model was established by applying energy conservation equations to gas and solid phases separately, rather than one conservation equation of the mixture enthalpy in our previous model. Interphase heat transfer mechanism was considered as well as bed-to-wall heat transfer. For the constant wall temperature boundary condition, proportional heat sinks were adopted in the furnace to compensate the missing heat transfer surfaces from 3-D cylinder riser to 2-D planar model. Results of temperature distributions agreed well with the experimental data. Secondly, processes related to nitrogen and sulfur oxides emissions are included. Results from our previous studies showed that, no NH3 was detected during pyrolysis of this bituminous coal by TGFTIR (Thermogravimetry coupled with Fourier Transform Infrared) analysis. So it was assumed that fuel N and S partially released to the volatile as HCN and SO2 , and partially retained in the char. HCN converted to NO and N2 O quickly through homogenous reactions. Char N and S converted to NO and SO2 during char combustion. NOx was reduced to N2 by char carbon or CO. SO2 was retained by CaO calcined from CaCO3 . By converting reaction rate expressions to suitable forms for Eulerian–Eulerian modeling, sulfation reaction rates from two different literatures were compared. Performances of SO2 emission were evaluated for conditions with/without considering sulfur self-retention. Distributions of gas components in the furnace were predicted and the outlet gas concentrations were validated by the experimental data. Distributions of certain reaction rates in the riser were also illustrated. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved.

1. Introduction NOx and SO2 are major environmental concerns due to their contribution to acid rain. The CFB combustion technology has become one of the most important developing directions for coal-fired boilers [1] with its well-known benefits of fuel flexibility, high combustion efficiency and low emissions, which is because of low operating temperature and staged combustion for low NO and optimum conditions for SO2 removal with sorbent addition into furnace. Mathematical modeling and simulation are helpful to a better understanding of the combustion and pollutant emission processes [2–9]. On the one hand, uncertainties with CFB hydrodynamics,

∗ Corresponding author. Tel.: +86 25 83793453; fax: +86 25 83793453. E-mail address: [email protected] (C.S. Zhao).

combustion and pollutants formation mechanisms come into sight during scale up. On the other hand, in a great majority of combustion models for circulating fluidized beds, an empirical correlation to describe the fluid dynamics inside the reactor is used to avoid solving the momentum equations. CFD analysis plays crucial role providing further insight on the complex multiphase combusting flow occurring in CFB combustors. CFD modeling of fluid dynamics has already reached a high level while numerical modeling of reactive multiphase flows is still in an early stage [10]. Recently, Energy Minimization Multi-Scale (EMMS) analysis [11–18] is an emerging methodology to predict the solid clusters in CFB hydrodynamics. Literatures regarding the numerical simulation of combustion mechanisms in CFB risers are limited. Gungor et al. [7] and Myohanen et al. [9], respectively used 2-D and 3-D comprehensive model with empirical or semiempirical hydrodynamics model. Nikolopoulos et al. [8] performed a 3-D CFD simulation using an Euler (gas) – Euler (inert material)

1385-8947/$ – see front matter. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.cej.2011.07.083

W. Zhou et al. / Chemical Engineering Journal 173 (2011) 564–573

Nomenclature C dp g H h Ji K k m pCO2 pSO2 Ri R r Re S Sc Sh T t

v Yi

mole concentration (kmol m−3 ) particle diameter (m) gravity (m s−2 ) riser height (m) enthalpy (kJ kg−1 ) diffusion flux of species i (kg/m3 s−1 ) momentum exchange coefficient reaction rate constant mass flow rate (kg/m3 s−1 ) CO2 partial pressure (atm) SO2 partial pressure (bar) net production rate of species i (kg/m3 s−1 ) Universal gas constant (J kmol−1 K−1 ) reaction rates (kmol m−3 s−1 ) Reynolds number heat source (J m−3 s−1 ) Schmidt number Sherwood number temperature (K) time (s) velocity (m s−1 ) mass fraction of species i

Greek letters  density (kg/m3 )  thermal conductivity (w m−1 s−1 ) εg voidage εs solid volume fraction stress tensor (Pa)   viscosity (kg m−1 s−1 ) interphase heat transfer coefficient ˛sg

565

and volatile-S releasing during devolatilization and char-N and char-S releasing during combustion processes. Both homogenous and heterogenous reactions were considered for NOx reduction. SO2 was captured by CaO. Simulated gas temperature and gas compositions at the furnace outlet were also validated against experimental data from the 50 kW pilot CFB combustor at Southeast University, China. A detailed description of the experimental system can be found in literature materials [26,27]. Temperature distributions, composition profiles of gas product and reaction rate distributions were predicted. 2. Model description Main assumptions of the comprehensive model can be found in the referenced material [26]. In addition, energy transfer due to pressure stress work and viscous dissipation are not considered. Since the Lewis number is nearly unity, the effect of enthalpy transport due to species diffusion is excluded. For good computational convergence, species thermal diffusions are also ignored. 2.1. Governing equations To describe the complicated coal combustion processes, the mass, momentum and energy conservation equations are applied to each phase and the species conservation equation is solved for every species. They are shown as Eqs. (1)–(4) for gas phase and gas species, respectively. Equations for the solid phase and solid species have similar forms. The standard k–ε dispersed turbulence model is adopted. ∂  g ) = msg (εg g ) + ∇ (εg g  ∂t

(1)

∂  g ) + ∇ (εg g  g  g ) = −εg ∇ p + ∇ g + εg g g (εg g  ∂t – Euler (fuel) approach. It presents an advanced methodology for simulating multiphase combusting flow occurring in CFB combustors. Many researchers have done a lot of work on the pollutant emission modeling in the CFB combustors. Kilpinen et al. [19] studied oxidation of bituminous coal char-nitrogen to NO, N2 O, and N2 by single particle modeling. Chen et al. [20] simulate the NO and NOx emissions from combustion of char in fluidized bed. Li et al. [21] and Mattisson et al. [22] established different sulfur retention models in CFB boilers, which will be described in detail below. Gungor [23] predicted SO2 and NOx emissions for low-grade Turkish lignite. Liu and Gibbs [24] studied NO and N2 O emissions from biomass. Shuyan et al. [25] investigated particle clustering effects on desulphurization and NO emission with numerical simulations. Most of the researches in the literatures treated the char and the limestone particles with Lagrange approach and it is difficult to describe the chemical processes properly with Euler method. In our previous study [26], an Euler (gas) – Euler (solid) model using the Kinetic Theory of Granular Flow (KTGF) and EMMS/matrix drag force correction [14,17,18] was employed to simulate the hydrodynamics of gas–solid flow in a CFB riser. The EMMS software is provided by Institute of Process Engineering, Chinese Academy of Sciences. Coupled with sub-models of heat transfer and chemical reaction, the comprehensive model predicted distributions of temperature and gas compositions, which were validated by experimental data. In this paper, heat transfer model was improved by applying energy conservation equations separately to the gas and solid phases considering interphase heat transfer mechanism. The nitrogen and sulfur emissions were simulated by modeling volatile-N

s −   g ) + msg  s +Ksg (

(2)

∂  g hg ) = ∇ (g ∇ Tg ) (εg g hg ) + ∇ (εg g  ∂t + ˛sg (Ts − Tg ) + Sghe + Sgho + Sgvol

∂  g Y ) = −∇ Ji + Ri (εg g Yi ) + ∇ (εg g  ∂t

(3)

(4)

Compared with the previous study [26], the heat transfer model is improved by applying a separate energy conservation equation for each phase rather than one conservation equation for the mixture enthalpy. The interphase heat transfer and heat generation/extraction during chemical reactions are also considered. In Eq. (3), hg is the sensible enthalpy for the gas phase and g is the gas thermal conductivity. The interphase heat transfer coefficient ˛sg is calculated according to ˛sg = 6g εs εg Nus /ds2 , where the Nusselt number Nus is calculated according to Gunn [28]. As part of the comprehensive model, the complicated processes of chemical reactions are considered by setting the source terms of mass, momentum, energy and/or species transport equations. As for the heat source due to chemical reactions, the formation enthalpy of the reactants and products is considered. The net  r r fr  enthalpy of the reactants is given by Hnet = m (hi + hi )/ r mri , r i fr

where hi represents the formation enthalpy of species i in the reactants and mri represents mass of the reactants. It is assumed that Hnet is distributed to the products in the ratio of their mass produc-

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tion rates. The heat source of the gas phase from chemical reactions is calculated according to Sghe =



p

p

mgi Hnet −



r

mrgi hrgi −



p

p

fp

mgi hgi

p

where mgi represents the mass generation rate of gas species gi in the products from the chemical reaction.  p  r And for homogenous gas reactions, where m = m , the p gi r gi heat source is simplified as Sgho = mrgi



fr

r

hgi −



fp

p



hgi

2.2. Chemical reactions It is assumed that the solid phase consists of 7 components (dry coal C4.914 H3.650 O0.456 N0.048 S0.044 , moisture H2 O, carbon CN0.008 S0.008 , calcium carbonate CaCO3 , calcium oxide CaO, calcium sulfate CaSO4 and ash) and the gas phase consists of 13 components (oxygen O2 , nitrogen N2 , carbon dioxide CO2 , water vapor H2 O, methane CH4 , carbon monoxide CO, hydrogen H2 , tar CH2.685 O0.13 , nitric oxide NO, hydrogen cyanide HCN, cyanic acid CNO, nitrous oxide N2 O and sulfur dioxide SO2 ). Equivalent formula of the dry coal is deduced from the proximate and ultimate analyses of the coal tested, which is illustrated in Table 1. Equivalent formula of the tar and its fraction in the volatile matter are deduced using the Loison and Chauvin model [7]. Sulfur forms in the tested coal [29] are shown in Table 2. Since the sulfate sulfur does not combust in the circulating fluidized bed, sulfur considered for pollutant emission only includes pyrite and organic sulfur. The amounts of CaCO3 and CaSO4 in the coal ash were deduced based on the ash component analysis of the coal in Table 3 [30]. The simulated coal combustion processes include moisture evaporation, coal devolatilization, volatile combustion, char combustion, char gasification and pollutants (NO, N2 O and SO2 ) emission. Detailed descriptions of devolatilization and volatile combustion reactions (R1)–(R4) can be found in another paper [26], and (R5)–(R7) are improved as below. Since the volatile N and volatile S are released in the volatile as HCN and SO2 , equivalent formula of the tar in (R5) is changed from the old model. Char gasification by H2 O (R8) is added as a preparation for future simulation of oxy-fuel combustion. Rates of gasification (R7) and (R8) are introduced by Matsui et al. [31,32] and used in fluidized beds [33,34] as shown in Table 4. CH2.685 O0.13 + 1.60625O2 → CO2 + 1.3425H2 O

(R5)

CN0.008 S0.008 + (1/˚ + 0.012)O2 + nash → (2 − 2/˚)CO + (2/˚ − 1)CO2 + 0.008NO + 0.008SO2 + nash (R6)

CN0.008 S0.008 + 1.024CO2 → 2.024CO + 0.008NO + 0.008SO2 (R7) CN0.008 S0.008 + 1.224H2 O → 1.224H2 + 0.2CO2 + 0.8CO + 0.008NO + 0.008SO2

(R8)

According to the heat distribution principle described in Section 2.1, a portion fh of the heat generated from (R6) directly heat solid phase with fh =

For coal combustion, fh is usually recommended as 0.3 when the production is all CO2 and 1 when the production is all CO [35]. In our paper, 0.5 is adopted for simplification.

nMWAsh MWCN0.008 S0.008 + (1/˚ + 0.012)MWO2 + nMWAsh

2.2.1. Nitrogen emission Usually, the nitrogen and sulfur contained in the coal were partially released as volatile and partially retained in the char during coal devolatilization. In the present study, the nitrogen in the coal was considered to be partitioned between the volatiles and char such that its concentration in the volatiles is identical to that in the dry, ash-free parent coal. The sulfur was treated in the same way. The mechanism of NOx formation is complex. At low temperatures in a CFB combustor, the dominant source of NOx is fuel nitrogen oxidation [7]. For the bituminous coal studied in this paper, no NH3 was detected during pyrolysis by TG-FTIR analysis (Thermogravimetry coupled with Fourier Transform Infrared) [36]. So in the present model, the volatile N is released as only HCN and the corresponding reactions are considered as (R9)–(R17), as adopted by Gungor et al. [7]. Char N converts to NO during char combustion and gasification (R6)–(R8). Reaction rates [7,19,37] used in this paper are given in Table 4. HCN + 0.5O2 → CNO

(R9)

CNO + 0.5O2 → NO + CO

(R10)

CNO + NO → N2 O + CO

(R11)

1.016N2 O + CN0.008 S0.008 → 1.02N2 + CO + 0.008SO2

(R12)

N2 O + CO → CO2 + N2

(R13)

N2 O + 0.5O2 → O2 + N2

(R14)

1.016NO + CN0.008 S0.008 → 0.512N2 + CO + 0.008SO2

(R15)

1.008NO + 0.5CN0.008 S0.008 → 0.506N2 + 0.5CO2 + 0.004SO2 (R16)

NO + CO → CO2 + 0.5N2

(R17)

2.2.2. Sulfur emission Volatile S is released as SO2 during devolatilization. Char S is also released as SO2 during char combustion and gasification (R6)–(R8). Sulfur retention during atmospheric combustion obeys the indirect sulfation mechanism. It involves two consecutive steps, calcination of CaCO3 (R18) and SO2 retention by CaO (R19). The calcination proceeds only if the partial pressure of CO2 , pCO2 , in the gas above the solid surface is less than the decomposition pressure of CaCO3 pe . An Arrhenius law was adopted to describe the calcination reaction rate [38]. The specific surface area SCaCO3 of CaCO3 in the tested coal was set as 1.26 m2 /g, the same with that of a limestone, which will be tested in the future research. CaCO3 → CaO + CO2

(R18)

CaO + SO2 + 0.5O2 → CaSO4

(R19)

The sulfation reaction between CaO and SO2 has been studied extensively in the literatures [21,39] using structural properties such as pore size, density and active surface area of CaO. According to Borgwardt [40], the resulting calcines can be regarded either as an assemblage of nonporous CaO grains surrounded by intergranular voids or as a solid continuum that is randomly penetrated by pores. These so called ‘grain’ and ‘pore’ models often do an excellent job in explaining the sulfation reaction for a certain limestone and size, but often fail to be applicable over a wide range of limestone types and sizes [22].

W. Zhou et al. / Chemical Engineering Journal 173 (2011) 564–573

567

Table 1 Ultimate and proximate analyses of Xuzhou bituminous coal. Sample

Xuzhou coal

LHV/MJ kg−1

Ultimate analysis/wt.% Cad

Had

Oad

Nad

Sad

58.97

3.65

7.30

0.67

1.76

Proximate analysis/wt.%

23.54

FCad

Vad

Aad

Mad

47.33

25.02

25.55

2.10

Table 2 Sulfur forms in the parent coal. Sample

Total sulfur

Pyrite sulfur

Organic sulfur

Sulfate sulfur

Xuzhou coal

1.76

0.30

1.11

0.35

Table 3 Analysis of ash composition of Xuzhou bituminous coal (wt.%). SiO2

Al2 O3

Fe2 O3

CaO

MgO

PbO

SO3

K2 O

Na2 O

CuO

35.69

24.47

6.71

14.89

1.44

0.89

13.99

1.08

0.57

0.27

However, sulfur capture rates according to Mattisson and Lyngfelt r12-M and Borgwardt r12-B expressed in Table 4 show simple and clear description without needing knowledge of specific reaction rate, which is hard to know during the sulfation process. Mattisson and Lyngfelt [22] preferred a common assumption in sulfur capture model for fluidized bed combustion that the exponential decay function described the decreasing rate of limestone reactivity as a function of conversion. Borgwardt [41] also measured the sulfation rate as a function of SO2 concentration, particle size, and CaO conversion. Kinetics data for the limestone used in our study were chosen the same as the limestone 1343 in the literature [41] with the chemical composition similar to ours. In accordance with the experimental consideration, diffusion effect of SO2 from the bulk gas to the particle surface was neglected in our modeling study. Effect of sintering on the indirect sulfation rate was considered with an effectiveness factor [41,42]. It indicated that as the reaction progressed and CaO was consumed, the pre-exponential

factor would be expected to decrease in the manner described by related to the amount of sulfate formed. These two reaction rates mentioned above were adopted and compared in our modeling researches. 2.3. Numerical considerations The grid information is the same as that in the previous study. Parts of the important material parameters, boundary conditions and initial conditions used in the simulation are listed in Table 5. They are set in consistent with the experimental operation conditions to the largest extent. 2.3.1. Initial conditions The bed was initially filled with ash particles with static height of 0.4 m, where the volume fraction of the solids was 0.55. The maximum particle packing was limited to 0.63. The initial temperature

Table 4 Rates of the chemical reactions. Reaction rate r/kmol m−3 s−1 k C

7 CO2 C +K 7 C k CO2 CO2 k CO CO

−1 s Yc s−1 ) exp(−2.68 × 108 /RT ) MW (1 − X) c −1 7 3 Kk CO = 66.0(m kmol ) [31,33,34] 2 −1 Kk7 CO = 1.20 × 102 (m3 kmol ) exp(−2.55 × 107 /RT ) X = 0.35 −1 s Yc k8 = 2.39 × 105 (m3 kmol s−1 ) exp(−1.29 × 108 /RT ) MW (1 − X) c −1 Kk8 H O = 31.6(m3 kmol ) exp(−3.01 × 107 /RT ) [32,33] 2 −1 Kk8 H = 5.36(m3 kmol ) exp(−5.98 × 107 /RT ) 2 −1 −2 8 3 Kk CO = 8.25 × 10 (m kmol ) exp(−9.61 × 107 /RT ) X = 0.5 k9 = 2.14 × 108 (m3 kmol−1 s−1 )exp(− 8.314 × 107 /RT) [7,37] ka2 /ka1 = 1.02 × 1012 (m3 kmol−1 )exp(− 2.12 × 108 /RT)

k7 = 4.89 × 1010 (m3 kmol

(R7)

r7 =

(R8)

r8 =

(R9) (R10) (R11) (R12)

r9 = εg k9 CO2 CHCN r10 = εg k9 CO2 CHCN (ka1 /(ka1 + ka2 CNO )) r11 = εg k9 CO2 CHCN (ka2 CNO /(ka1 + ka2 CNO )) 6ε  Y r12 = ds s c k12 CN2 O

(R13) (R14) (R15)

r13 = εg k13 CN2 O CCO r14 = εg k14 CN2 O 6ε  Y r15 = ds s c k15 CNO

(R16)

r16 =

(R17)

r17 =

(R18)

r18 = εs s Yc SCaCO3 k18

(R19)

r19−M =

1+K 7

k8 CH O 2 C +K 8 C +K 8 C k H2 O H2 O k H2 H2 k CO CO

1+K 8

p c

k12 = 2.9 × 109 (ms−1 )exp(− 1.412 × 108 /RT) [7,37] k13 = 1.24 × 109 (m3 kmol−1 s−1 )exp(− 4.9163 × 107 /RT) [7,19] k14 = 1.50 × 1011 (s−1 )exp(− 1.676 × 108 /RT) [7,19] k15 = 5.85 × 107 (ms−1 )exp(− 9.977 × 107 /RT) [7,37]

p c

6εs s Yc k16 CNO dp c k C (k C +k ) εg k17 kb1 CNO +kb2 CCO +kb3 b1 NO b2 CO b3

dX dt

Reaction rate constant ka,b

pe −pCO

2

pe

εs (CCaSO4 + CCaO )

k16 = 1.3 × 105 (ms−1 )exp(− 1.423 × 107 /RT) [7,19] k17 = 1.952 × 107 (kmol m−3 s−1 )exp(− 1.58 × 108 /RT) [7,37] kb1 = 18.26(m3 kmol−1 ), kb2 = 7.86(m3 kmol−1 ), kb3 = 0.002531 k18 = 6.078 × 104 (kmol m−2 s−1 )exp(− 2.05 × 108 /RT) [38] SCaCO3 = 1.26 m2 g−1 pe = 4.137 × 107 exp(− 1.702 × 108 /RT)atm dX dt

= keff−M pSO2 keff−M = Ae−BX X = −1

r19−B = εs a b

YCaO s CaO

k19−B CSO2

−1

CCaSO

4

CCaO +CCaSO

4

[22]

A = 0.59 − 1.11 log(dp ) bar s B = 13.62 + 4.46 log(dp ) k19−B = 1.1 × 106 s−1 exp(− 0.595 × 108 /RT) [40–42] = exp(− 5.71X)

Unit of molar concentration C in this paper is kmol m−3 and this leads to some different coefficients with the references. Unit of all the activity energy is J kmol−1 .

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W. Zhou et al. / Chemical Engineering Journal 173 (2011) 564–573

Table 5 Parts of parameters used in simulations. Parameters

Value

Parameters

Value

Real density of char particles Real density of ash particles Specific heat capacity of ash Particle diameter Coal feed rate Excess air coefficient Inlet coal temperature

1280 kg/m3 2400 kg/m3 856 J/kg K 0.35 mm 8 kg/h 1.2 298 K

Primary air ratio Primary air temperature Secondary air temperature Furnace wall thermal conductivity Furnace wall thickness Initial furnace temperature Initial gas compositions

0.7 298 K 298 K 8 300 mm 1123 K 21% O2 /79% N2

2.3.2. Boundary conditions Most of the boundary conditions are the same as the previous model and could be found in Ref. [26]. It is assumed that no chemical reactions occur in the return leg, which is reasonable in our test facility with large temperature drop. So the mass fractions of the solid species remain the same from the furnace outlet to the solid recycle inlet. For combusting flows modeling, proper variation of mass fractions should be considered. To maintain constant bed inventory, mass flow rate of the recycled solids is adapted during iteration based on the principle that the mass flow rates entering the furnace and leaving it are equal. A more reasonable temperature boundary condition is used in the new simulation. In our real facility, the riser is wrapped with aluminum silicate fiber felt of 300 mm thick to reduce the heat loss during the experiments. So we set the outer wall temperature with 323 K and the wall thickness with 300 mm in the simulation. Heat loss from the riser is adjusted by changing thermal conductivity value of the wall. Since the riser is not tightly walled by the fiber felt and radiation also contributes to the heat loss, the thermal conductivity we adopt is a virtual value considering all these heat transfer processes. With simulation tests, we find out that 8 Wm−1 K−1 is proper for the results in this paper. Based on the heat and mass balance of experimental operation condition for our test facility, about 50% of the thermal input is absorbed by flue gas and bed materials. Rest is released along the riser and the return leg. It is assumed that heat loss through the return leg is 20%. Heat extraction from the return leg is modeled by setting the temperature value of the recycled solid. Because width of the 2-D model was determined based on the assumption of furnace depth of 0.1 m and the corresponding crosssection area in the 3-D riser, therefore, the 2-D planar model had only nearly half of the heat transfer surface area of the real 3-D cylindrical riser. Considering that the heat transfer surface of the riser was under-calculated in the 2-D planar model, a volumetric heat sink Sgvol in Eq. (3) is adopted to simulate the virtual heat transfer surfaces. Our last paper [26] did not have this problem because the thermal boundary condition adopted was constant heat fluxes, but now we use constant wall temperatures. The heat sink is calculated as proportional to the real heat flux through corresponding side walls Qgwall Sgvol

=−

Qgwall Qgwall Am

Am

(A − Am )

where, A and Am are the heat transfer surface area in real 3-D cylindrical riser and 2-D planar model, respectively. Three different heat sinks are applied to three different zones according to the variation of furnace cross-sectional dimensions. Energy conservation equation of the solid phase is added by Ssvol in the similar way. The time step was set as 1 × 10−4 . From 0 s to 0.01 s, air–ash fluidization was simulated. Then the coal and synthetic gas were fed into the furnace at full load simultaneously and continuously.

The simulation was conducted based on FLUENT 12 software for 70 s and it cost nearly 60 days with 3 processes parallel on an Intel w5580 workstation. Time-averaged distributions of flow and combustion characteristic variables were adopted for the period from 50 s to 65 s. What is worth mentioning is that, during our modeling using the FLUENT software, “volumetric” reaction in “Species Model” panel is not activated. The homogenous reactions are added through “Phase Interactions” panel with user-defined reaction rates. So the homogenous reaction rates in Table 4 are multiplied by volume fraction of the reaction phase, which is voidage in our case. 3. Results and discussions The sub-models of hydrodynamics, volatile combustion and carbon combustion were validated in our early study [26]. Attention is focused on the pollutant emission characteristics in this paper. 3.1. Steady state consideration Values of some representative variables were monitored and recorded every 0.01 s for the judgement of steady-state coal combustion processes. Fig. 1 shows the time series of peak flame temperature and outlet gas temperature. It is noticed during the simulation tests that if the heat transfer model is badly solved, the peak flame temperature will go very high (even more than 5000 K) and the energy equation would not converge during the iteration. The peak flame temperature during our simulation falls into the reasonable range. As shown in Fig. 5, high temperatures near the value of peak flame temperature generally appear in a very small region. Fig. 2 shows the time series of O2 , NO and SO2 concentrations in the flue gas at the furnace outlet. The time series of solid inventories and species (carbon, CaO, CaSO4 ) mass are illustrated in Fig. 3. It shows that the solid inventory is exactly constant because the mass balance of the furnace is modeled according to the principle described in Section 2.3.2. It seems that mass of outlet gas

1600 1400

Temperature / K

was 1123 K. The initial gas species was set to 21% O2 /79% N2 and solid species of 100% ash.

1200 1000 Peak flame temperature

800

Outlet gas temperature 600 400

0

10

20

30

40

50

60

70

Time / s Fig. 1. Time series of peak flame temperature and outlet gas temperature.

569

25

0.20

Outlet O2 concentration 20

Outlet NO concentration Outlet SO2 concentration

0.15

15

0.10 10 0.05

5

0.00 0

10

20

30

40

50

60

70

Outlet O2 concentraion / %

Outlet NO/SO2 concentration / %

W. Zhou et al. / Chemical Engineering Journal 173 (2011) 564–573

0

Time / s Fig. 2. Time series of flue gas concentrations at the furnace outlet.

components the unburned carbon in the furnace reach a steady state gradually. However, CaSO4 is increasing linearly from 0 to 0.0265 kg during the time period 0–70 s. It is a result of the simulation condition that the bed is initially filled with ash particles excluding components of CaCO3 and CaSO4 . Since CaCO3 and CaSO4 are considered in the feeding coal for self-desulfurization simulation, a conditioned bed material will be achieved at last with main fuel ash and gypsum. As we all know, it usually costs several to more than 10 h for a CFB bed material to reach a steady state. But it is a too long for the CFD modeling. Fortunately, as shown in Fig. 2, the unsteady CaSO4 mass fraction has less impact on the SO2 concentration because CaSO4 and ash are both inert bed material in our model. We can get some interesting information from result in Fig. 3. If we assume that a virtual steady state is achieved with CaSO4 mass outlet , the conservation equation of fraction in the outlet solid as YCaSO 4 CaSO4 in the furnace can be obtained as follow. recycle

inlet ˙ coal + YCaSO m ˙ recycle + MWCaSO4 YCaSO m 4



4

(r19 Vcell )

outlet ˙ outlet = YCaSO m 4

inlet m ˙ coal is the CaSO4 mass flow rate in the feeding coal YCaSO 4 recycle ˙ recycle is the recycled CaSO4 through the recyand YCaSO m 4 recycle outlet is set as boundary condition. cle inlet, where YCaSO = YCaSO 4 4



MWCaSO4 (r19 Vcell ) is the whole CaSO4 mass source in the furnace from desulfurization reaction. Then replace variables with the time averaged values obtained from CFD simulation results. outlet × 470.18 kg/h + 136 kg/kmol 0.015 × 8 kg/h + YCaSO 4

−4

× 2.1868 × 10

Fig. 4. Time averaged mass fraction of oxygen for periods of 50–60 s, 50–65 s and 50–70 s.

We find out the outlet mass CaSO4 fraction at the virtual steady outlet = 0.051. state YCaSO 4 As the similar trend of CaSO4 mass in the bed, mass fraction of CaSO4 in the outlet solid is also increasing linearly from 0 to 0.00047 during the time period 0–70 s, which is not shown here. If the CaSO4 mass fraction increases at this rate (in fact the increasing rate of CaSO4 will slow down with time), it will cost about 2.1 h to achieve the steady state, where the magnitude is the same with the real experiments. Since the 70 s simulation have already cost two months, we will set more suitable initial and boundary conditions for the bed material in further research. Time averaged oxygen contours for periods of 50–60 s, 50–65 s and 50–70 s are displayed in Fig. 4. It seems that the time averaged distribution of oxygen concentration does not change any more from 15 s to 20 s statistics. So we adopt averaged value of time period 50–65 s for describing primary characteristics for the simulated cases. The asymmetrical contour of O2 molar fraction is owing to the asymmetrical structure of the furnace. The coal feeding point is on the left side. The secondary air inlet, recycle inlet and furnace outlet are on the right side. The 2-D planar model also contributes to the asymmetric phenomena by suppressing the radial mixing. Asymmetrical distribution of O2 concentration also has an impact on asymmetrical SO2 concentration through sulfation reaction, which will be shown below.

outlet kmol/h = YCaSO × 473.14 kg/h 4

1250 60

0.3

0.2

40 30

0.1

20

mCN0.008S0.0078

10 0

mCaSO4 ×10 0.0 0

10

20

30

40

50

60

70

Time / s Fig. 3. Time series of solid inventories and species mass in bed.

Temperature / K

Solid inventory

Mass of solid species / kg

Solid inventory / kg

50

Experiments Mean gas temperature

1200

Mean solid temperature 1150

1100

1050

0

1

2

3

4

5

Height / m Fig. 5. Axial distributions of cross-sectional mean temperature averaged during period of 50–65 s.

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Fig. 6. A time series of gas temperature contours (K).

3.2. Temperature profiles Fig. 5 shows the predicted axial distributions of cross-sectional mean temperatures of gas and solid phases averaged from 50 s to 65 s. It shows that the time-averaged values agree reasonably well with experimental data measured by thermal couples and temperature differences between gas phase and solid phase are very small. Gas temperature fluctuates near the coal feeding point because the feeding coal absorbs heat at first for devolatilization and then volatile combustion releases heat. Gas temperature also changes dramatically near the secondary air inlet because of the cold secondary air. Since the bed materials have a larger heat capacity, solid temperature fluctuates in a narrower range. The ambient temperature of inlet air reduces gas temperature near the bottom and the secondary air nozzle a little. While near the coal feeding point, the gas mean temperature shows about 10 K higher than that of solid because of violent volatile combustion. Temperatures near top of the furnace decrease quickly because of contribution from top heat transfer surfaces. A time series of the gas temperature contours from 56 s to 65 s are illustrated in Fig. 6. The temperature range was clipped to a

narrower range from 1000 K to 1200 K to be displayed accurately. These contours show different temperature distributions from the results of previous study, which is based on one energy equation. In this model, heat from volatile combustion is firstly absolutely absorbed by the gas phase and then is partly transferred to the solid phase through interphase heat transfer. And a portion of heat generated from char combustion directly heat the solid phase. The newly predicted results are more reasonable and accurate. Some high temperature regions in cluster shapes are observed near the coal feeding point because of volatile and char combustion with coal feeding air. In the upper furnace, high temperature regions are detected near the center line of the furnace because heat is released through side walls. The regions of high temperature are very limited. Once the volatiles release during coal devolatilization, the volatile combusts quickly and a local high-temperature zone forms. As the hightemperature gas flows upward, volatiles are consumed and heat is transferred to the walls through conduction, convection and radiation mechanisms. A new high-temperature zone forms again near the coal inlet. Turbulent combustion of volatiles leads to the fluctuation of the peak flame temperature in Fig. 5. Numerical simulation

0.35

1600 1200

Experiments Simulation

0.30

Mass fractions / %

Molar fraction / ppm

2000

SO2 Simulation without sulfur self-retention

800 400

0.25

No limestone addition SO2

0.20 0.15

NO

0.10 0.05 0.00

0

NO

SO2

Gas compositions Fig. 7. Comparisons between simulated and experimental pollutant concentrations at the outlet on dry basis.

0

1

2

3

4

Height / m Fig. 8. Averaged distributions of mass fractions of pollutants on wet basis along the furnace height.

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Fig. 9. Contours of reaction rates in relation with pollutant emissions (kmol m−3 s−1 ).

can describe detail characteristics which are difficult or impossible to be obtained by experiments. 3.3. Pollutant emissions Fig. 7 shows comparisons between the simulation results and the experimental data of NO and SO2 concentrations in the flue gas at the furnace outlet on dry basis. The form of molar fraction averaged over outlet area and time period of 50–65 s is adopted for the simulation results. Fig. 7 also displays the predicted SO2 concentration at the outlet for a hypothetical case without considering sulfur self-retention by ash. The simulation results show that the intrinsic sulfur sorbent in the coal captures about a quarter of the sulfur release without self-retention. It reveals that the sulfur selfretention by coal ash plays an important role in the SO2 emission during coal combustion and should be considered in the modeling.

The results show a good agreement with experiments. The maximum error is less than 20%. It implies that the present 2D numerical simulations of pollutant emissions are reasonable and the validity of the present model is verified. Fig. 8 illustrates distributions of pollutant mass fractions averaged over time period of 50–65 s and cross sectional area. According to the pollutant emission model described in Sections 2.2.1 and 2.2.2, the dramatic increases of NO and SO2 concentrations at lower zone of the furnace indicate quick devolatilization of raw coal. Then the both concentrations decrease suddenly near the secondary air inlet owing to the dilution effect. In the upper zone, NO concentration decreases gradually as a result of reduction by char and CO. SO2 concentration does not change so much with the combined effects of formation from char combustion and retention by CaO. For the case without limestone addition, CaO is from CaCO3 calcination in the coal itself.

Fig. 10. Contours of NO concentration and related reaction rates (kmol m−3 s−1 ).

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Fig. 11. Contours of SO2 concentration and related reaction rates (kmol m−3 s−1 ) without adding limestone.

Reaction rate contours of coal devolatilization and char combustion are also displayed in Fig. 9. High values of coal devolatilization rate (1.2 kmol m−3 s−1 for maximum) are in a tiny region near the coal feeding point, and they are clipped to get proper display behavior. Distributions of these reaction rates can help us understand the trends shown in Fig. 8. SO2 is formed with these two reactions. Volatile-N releases as form of HCN during devolatilization. NO is formed with homogenous reaction r10 , the rate of which is also shown in Fig. 9. 3.3.1. Nitrogen reductions Filled contours of wet molar fraction of NO and unfilled contours of reaction rates of NO reductions at 60 s are illustrated in Fig. 10. The contours of reaction rates in Figs. 9 and 10 reveal the NO emission trend. Formation of NO occurs in the lower zone from homogenous CNO combustion and heterogeneous char combustion. Contour of NO molar fraction also shows that the NO concentration is apparently diluted by the second air injection. After that, NO concentration decreases because the reduction reaction rates become higher than the formation rates. It appears that the reduction reactions by char and CO occur in the whole furnace and the rates are in the same magnitude. 3.3.2. Sulfur retention Predictions based on two different sulfur capture models according to Mattisson and Lyngfelt and Borgwardt were compared. Distributions of the corresponding reaction rate r12-M and r12-B are displayed in Fig. 11. It seems that the distributions are exactly the same except that r12-B is a little higher than r12-M , as a result of different kinds of limestone used in their researches. Both of them are believed to be reasonable in the mechanism and r12-M was applied in our present research. Fig. 11 also shows that CaCO3 calcination occurs quickly once it enters the furnace, while the sulfation rate is much lower. As mentioned above, the asymmetrical distribution of SO2 molar fraction is owing to asymmetric O2 concentration, which is resulting from asymmetrical structure of the furnace and 2-D planar modeling method. A 3-D CFD simulation is expected to investigate this phenomenon. It appears that the 2-D CFD results are qualitatively

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