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Influence of turbulence–chemical interaction on CFD pulverized coal MILD combustion modeling
Fuel 101 (2012) 90–101
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Influence of turbulence–chemical interaction on CFD pulverized coal MILD combustion modeling M. Vascellari ⇑, G. Cau Department of Mechanical Engineering, Università degli Studi di Cagliari, 09123 Piazza D’Armi, Cagliari, Sardinia, Italy
a r t i c l e
i n f o
Article history: Received 30 September 2010 Received in revised form 22 July 2011 Accepted 26 July 2011 Available online 22 August 2011 Keywords: Coal MILD Combustion CFD Turbulence
a b s t r a c t MILD (Moderate and Intensive Low oxygen Dilution) combustion is a novel approach to reducing NOx emissions and improving combustion efficiency in fossil fuels power plants. It is characterized by elevated temperature and high dilution of reactants and strong recirculation inside the combustion chamber which produce a low temperature increase, thus reducing NOx formation. The main differences with conventional combustion concern the chemical reactions that take place in almost the entire volume of the combustion chamber and the uniformity of both temperature and the chemical species concentration. For this reason advanced turbulence-chemistry interaction models with detailed kinetic mechanisms are required to accurately simulate MILD by means of CFD calculations. The main aim of this work is to deepen the influence of turbulence-chemistry interaction on pulverized coal MILD combustion and to understand which models are more accurate and suitable to reproduce the process. In particular, two turbulence-chemistry interaction models are analyzed. On one hand, a conventional model based on infinitely fast chemistry Eddy Dissipation Model with a two-step global kinetic mechanism is considered. On the other hand, an advanced model based on finite rate chemistry Eddy Dissipation Concept is considered and used with both a global and detailed kinetic mechanisms. The results are finally compared with an experimental test-case. From the comparison, advanced turbulence-chemistry models used with complex kinetic mechanisms give, as expected, the best agreement with numerical results, despite the higher computational resources required. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction MILD combustion (Moderate and Intensive Low oxygen Dilution) is a new technology for reducing the production of pollutants, especially NOx, from combustion of fossil fuels. Cavaliere and de Joannon [1] define MILD combustion ‘‘when the inlet temperature of the reactant mixture is higher than mixture self-ignition temperature whereas the maximum allowable temperature increase with respect to inlet temperature during combustion is lower than mixture self-ignition temperature (in Kelvin)’’. According to this definition, reactants are introduced at temperature, higher than mixture self-ignition, and the mixture is strongly diluted in order to reduce the increase in temperature during combustion. Generally, reactants are preheated by means of regeneration or recirculation using the sensible heat of exhaust gas. Lastly, reactants are introduced in the combustion chamber at high velocity in order to increase the internal recirculation of the combustion products. ⇑ Corresponding author. Current address: ZIK Virtuhcon, TU Bergakademie Freiberg, Reiche Zeche Fuchsmuehlenweg 9, 09596 Freiberg, Germany. Tel.: +49 (0) 3731394555; fax: +49 (0) 3731394823. E-mail addresses: [email protected] (M. Vascellari), [email protected] (G. Cau). 0016-2361/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2011.07.042
These solutions have the aim of reducing the high gradients of temperature and species concentrations existing in conventional combustion, where the chemical reactions take place in a well-defined thin flame front. Consequently, thermal stress and pollutant emissions, such as NOx and soot, are strongly reduced considering the lower gradients existing in MILD combustion. Therefore, MILD combustion differs from conventional combustion because of the absence of a visible flame. As a result, it also defined as ‘‘flameless’’. Experimental works on pulverized coal MILD combustion were performed by the International Flame Research Fundation (IFRF) [2,3], where a 880 kW furnace was constructed to investigate the MILD combustion of gaseous, liquid and solid fuels. Experimental tests and numerical simulations were carried out at the RWTH laboratory of Aachen University and at the Institute of Process Engineering and Power Plant Technology (IVD) of Stuttgart University [4–8] in Germany. The conceptual design of high temperature air combustion (HTAC) supercritical pulverized coal boiler was recently proposed by Schaffel-Mancini et al. [9]. Numerical simulations of pulverized coal MILD combustion have been performed considering the experimental IFRF furnace by Kim et al. [10] and Schaffel et al. [11] using different CFD approaches.
M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
The different fluid dynamic behavior of MILD combustion with respect to conventional requires the use of advanced models to account for turbulence-chemistry interactions. Generally speaking, with conventional combustion, simplified models for turbulencechemistry interactions are considered for CFD simulations. These models are generally based on the hypothesis of infinitely fast chemistry, because the chemical reactions are generally faster than turbulent flow mixing [12]. Another hypothesis generally assumed considers that reactions occur in a thin sheet with inner structure generally defined as laminar flamelet [13], decoupling fluid dynamic and chemical problems. The hypotheses considered for these models are no longer valid with MILD combustion, where the chemical reactions occur in an extended region with chemical rates comparable to the reactants turbulent mixing rates. Therefore, advanced turbulence-chemistry interaction models are required to account for the chemistry of the flame and its interaction with turbulent flow in a detailed way. In fact, Christo and Dally [14] demonstrated that the Eddy Dissipation Concept (EDC) [15] with a simplified three-step reaction kinetic mechanism performed better than did a laminar flamelet model, considering MILD combustion of CH4/H2 mixtures. In addition, Parente et al. [16] demonstrated that using EDC model with detailed kinetic mechanisms gave more accurate results with respect to simplified mechanisms. Besides, Orsino et al. [17] demonstrated that turbulence-chemistry interaction models based on chemical equilibrium assumptions gave unsatisfactory results for natural gas MILD combustion. Considering the same test-case, Kim et al. [18] showed that the results are strongly affected by the kinetic mechanism considered. The main aim of this work is to deepen the influence of turbulence-chemistry interaction on pulverized coal MILD combustion and to understand which models are more accurate and suitable to reproduce the process. In particular, two turbulence-chemistry interaction models are analyzed: a conventional model based on infinitely fast chemistry Eddy Dissipation Model with a two-step global kinetic mechanism and an advanced model based on finite rate chemistry Eddy Dissipation Concept. The last is used with both a global and a detailed kinetic mechanisms. The results are finally compared with the experimental test-case on pulverized coal MILD combustion developed and executed by IFRF [2,3].
2. Mathematical models for pulverized coal combustion During coal combustion several chemical-physical phenomena take place. They require specific mathematical models implemented in a comprehensive CFD code [19]. The main models considered concern the following phenomena: turbulence, multiphase flow and interphase interactions, homogeneous and heterogeneous chemical reactions, radiation, etc. Simulations of MILD coal combustion were performed considering the commercial CFD code Fluent™ [20], version 6.3. The Reynolds Average Navier Stokes (RANS) equations are solved on an unstructured hybrid mesh using a finite volume discretization approach. The three-dimensional version of the pressure-based solver is considered. The SIMPLE [21] algorithm is used for velocity–pressure coupling. Convective fluxes in all transport equations are discretized with a second-order accurate upwind scheme and the pressure gradient with a second-order accurate scheme. The realizable k turbulence model [22] is considered for RANS equations closure. The P-1 radiation model [23] is considered for radiation heat transfer. The coal discrete phase is modeled considering a Eulerian– Lagrangian approach. The main gas phase is solved considering transport equations for continuous phase in the Eulerian frame of
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reference, while the secondary discrete solid coal phase is solved considering a Lagrangian frame. The trajectories of the particles are evaluated by integrating the force balance on them with respect to time. The continuous phase flow pattern is impacted by the discrete phase (and vice versa) and the calculation of the main phase is alternated with the discrete phase until a converged coupled solution is achieved. As the trajectory of a particle is computed, the heat, mass and momentum gained or lost by the particle are evaluated, and these interactions are taken into account in the Eulerian equations of the primary phase by means of source terms. The dispersion of particles due to turbulence is taken into account by considering the stochastic tracking model, including the effect of instantaneous turbulent velocity fluctuations on particle trajectories. Coal combustion is modeled according to the following sequence of phenomena: drying, pyrolysis, volatile combustion and char burnout. 2.1. Drying Moisture drying is governed by the difference of water concentrations between the particle surface and the bulk phase. The water concentration on the particle surface is evaluated by assuming that the partial pressure of vapor at the interface is equal to the saturated vapor pressure at the particle temperature. The mass transfer coefficient used for evaluating moisture evaporation is calculated by means of correlation of Ranz and Marshall [24,25]. 2.2. Pyrolysis Pyrolysis can be regarded as a two-stage process [26]. During primary pyrolysis, coal particles decompose and release volatile matter (devolatilization), composed by TAR, light hydrocarbons and gas. During secondary pyrolysis, TAR decomposes and produces soot, light hydrocarbons and gas. Devolatilization of coal is evaluated by considering the Chemical Percolation Devolatilization (CPD) model [27–29]. The CPD model characterizes the devolatilization behavior of rapidly heated coal based on physical and chemical transformation of coal structures. It describes the chemical and physical processes by considering the coal structure as a simplified lattice or network of chemical bridges that link the aromatic clusters. CPD is able to accurately predict volatiles rates, yield and composition in terms of high weight hydrocarbons (TAR) and light gases (CH4, CO, CO2, H2O, etc.). The model requires data from coal solid state Nuclear Magnetic Resonance (NMR) analysis as input. Since such kinds of data are not easily available, Genetti et al. [30] developed a correlation to obtain them from proximate and ultimate analysis of the coal. The CPD model is implemented in Fluent™, but it allows only the evaluation of the devolatilization rate, and not its yield, thus allowing the definition of only one pseudo-volatile species. To define volatile matter composition, the pseudo-volatile species is decomposed into the gas phase by means of an infinitely fast reaction. The composition of volatile matter is obtained directly from a standalone version of the CPD model in a pre-processing step. Heavy hydrocarbons (TAR) are approximated as an equivalent molecule CnHmOp, balanced to conserve species stoichiometry from ultimate analysis. Finally, TAR reacts in the gaseous phase with O2 producing CO and H2 [26]. 2.3. Heterogeneous reactions After volatile matter is completely released during primary pyrolysis, the char remaining in the coal particle reacts with the surrounding gas phase. Considering exhaust gas recirculation, high
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CO2 and H2O concentrations characterize coal MILD combustion. Therefore, gasification and Boudouard reactions are considered besides oxidation:
1 CðsÞ þ O2 ! CO 2 CðsÞ þ CO2 ! 2CO CðsÞ þ H2 O ! CO þ H2 O
ð1Þ ð2Þ ð3Þ
Heterogeneous reaction rates are governed by the diffusion of the oxidant species from the bulk phase to the particle surface and by the intrinsic kinetic [31]. The overall heterogeneous reaction rate is controlled by two mechanisms: the diffusion of the oxidant through the film layer around the coal particle and the intrinsic kinetic rate of the reaction on the particle surface. Constant parameters for oxidant diffusion and kinetic rate are taken from the work of Field [32], Smoot and Pratt [33]. 2.4. Turbulence–chemistry interaction models The interaction between turbulent flow and chemical reaction plays a fundamental rule in MILD combustion modeling, whether considering solid or liquid and gaseous fuels. Indeed, fluid dynamic behavior of MILD combustion strongly differs from conventional combustion. As previously described, gradients of temperature and chemical species concentrations are generally lower. In this way, a well-defined flame front can no longer be observed. Therefore, the assumptions used for conventional combustion are no longer valid. Indeed, it is impossible to define a limited region where chemical reactions take place and their rates are comparable to mixing vortex. In this work two different approaches are considered to model turbulence–chemistry interactions. The first, based on the infinite fast chemistry assumption, considers the Eddy Dissipation Model (EDM) [12] which is the conventional approach used by commercial CFD codes for combustion modeling. The rate of the reactions depends only by the behavior of the turbulent flow, and it is proportional to the dissipation rate of the turbulent vortices. For this reason, EDM can handle only global kinetic mechanisms with a maximum of two reactions, since the model cannot discriminate between slow and fast chemistry. The second approach is based on the Eddy Dissipation Concept (EDC) [15,34], which is an extension of the previous model to consider detailed kinetic mechanisms. According to EDC, combustion occurs in the region of flow where the dissipation of turbulent kinetic energy takes place (fine structures). These regions can be described as perfectly stirred tank reactors (PSR) at constant pressure, where the reactions advance according to a chemical residence time, defined as a function of turbulence parameters. 2.5. Kinetic mechanisms Three kinetic mechanisms are considered for simulating coal MILD combustion. EDM allows consideration only of simple mechanisms with a maximum of two reactions. Consequently, a pseudovolatile species is considered (CxHyOz), reacting according to the following simple two-step (2S) mechanism:
Cx Hy Oz þ
1 1 1 x z þ y O2 ! xCO þ yH2 O 2 2 2
1 CO þ O2 ! CO2 2
ð4Þ ð5Þ
At first, it considers the direct oxidation of the pseudo-volatile species which produces CO and H2O. Then, CO is completely oxidized in a second reaction, thus producing CO2.
On the other hand, EDC allows consideration of complex kinetic mechanisms. Dissociation of the pseudo volatile species through an infinitely fast reaction, which produces TAR and light gases according to the CPD results, is assumed. One additional reaction is then considered to account for TAR oxidation [26]. Light gases react according the global kinetic mechanism of Jones and Lindstedt [35] and DRM [36] respectively. The first is a global mechanism constituted by four reactions with 6 species:
1 CH4 þ O2 ! CO þ 2H2 2 CH4 þ H2 O ! CO þ 3H2 CO þ H2 O ! CO2 þ H2 1 H2 þ O2 ! H2 O 2
ð6Þ ð7Þ ð8Þ ð9Þ
The second is a detailed mechanism, reduced from GRI mechanism [37], which was developed to obtain the smallest set of reactions needed to reproduce closely the main combustion characteristics of the full mechanism. It consists of 103 reactions with 22 chemical species. Within the context of MILD combustion, the advanced EDC approach with the DRM mechanism is expected to provide better results due to the slow kinetics related to low flame temperature. However, it requires remarkable computing time and more numerical issues with respect to the EDM with the 2S mechanism and EDC with simpler mechanisms, such as Jones and Lindstedt. 2.6. NOx pollutant formations The formation of NOx is generally modeled considering four different mechanisms: thermal, prompt, fuel NOx formation and reburning reduction. These mechanisms are reviewed in detail by Hill and Smoot [38]. In the present work a post processing approach is considered to evaluate NOx production from coal MILD combustion, decoupling the flow solution from NOx evaluation. In fact, the low NOx concentration has a reduced influence on the other flow properties, such as temperature and main species concentrations, and therefore the NOx formation has a reduced impact on the balance of mass, momentum and energy. Consequently the flow solutions is ‘‘frozen’’ and after the transport equations for NO, HCN and NH3 are solved, maintaining the other flow properties constant. Turbulence effects are modeled by the probability density function (PDF), considering a b-PDF function. At first, the thermal-NOx formation follows the Zeldovich mechanism [39]. It is based on the oxidation reactions of the molecular nitrogen contained in the gas, which are favoured by high flame temperature and by the presence of radical species, such as O and OH. Therefore in MILD combustion the reduction of temperature generally has a positive effect on reduction of thermal-NO. Then, prompt-NO are formed by reactions of atmospheric N2 with hydrocarbon radicals in fuel-rich regions of the flame [40]. Generally speaking, it plays a secondary role in MILD combustion, because diluted reactants mixture are considered. Fuel-NOx are formed by oxidation of the nitrogen bound in the fuel. Considering coal, nitrogen is distributed between the volatiles and the char, and it is released as HCN and NH3 during devolatilization and char burnout, which reacts with oxygen and NOx, thus producing NOx and N2 respectively. It represents the main NOx formation mechanism in coal combustion, especially in MILD combustion, where the thermal-NOx mechanism is reduced by the low flame temperature. Finally, the recirculation of exhaust gas containing NOx produces a positive reburning consumption, thus contributing to
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Fig. 1. Geometry of the IFRF furnace.
Table 1 Experimental conditions of IFRF furnace [2].
Primary air Secondary air
Mass flow rate (kg/h)
Temp. (K)
Composition (vol%)
130 675
313.15 1623.15
O2 21%, N2 79% CO2 8.1%, O2 19.7%, N2 57.2%, H2O 15.1%, NO 100 ppm (dry)
Table 2 Proximate and ultimate analysis of Guasare coal [2]. Proximate analysis Volatile matter Fixed carbon Moisture Ash
37.1 56.7 2.9 3.3
Ultimate analysis (% daf) C H O N S
78.41 5.22 10.90 1.49 0.82
LHV
31.74 MJ/kg
Table 4 Parameters used for CPD model. Md
Mcl
p0
r+1
c0
33.9
351.2
0.52
5
0
Table 5 Volatile yield predicted by CPD model. Volatile yield (% daf) Char TAR H2O CO2 CH4 CO other
Table 3 Classification of particle size [10]. Class
Mean diameter (lm)
Mass fraction (%)
1 2 3 4 5 6
3 16 36 65 120 200
7.19 27.61 18.51 28.16 15.25 3.28
reduce the overall NOx emissions [41,42]. The reburning NOx mechanism is a pathway whereby NOx reacts with hydrocarbons and is subsequently reduced, according to the following reaction:
CHi þ NO ! HCN þ products
ð10Þ
Considering global kinetic mechanism, concentrations of hydrocarbons is obtained from the species mass fraction resulting from the combustion calculation. 3. Numerical simulations of MILD coal combustion Numerical simulations of pulverized coal MILD combustion are performed considering the geometry and the working conditions of the IFRF furnace No. 1 [2,3]. The IFRF test case was previously modeled by Kim et al. [10] and Schaffel et al. [11]. The first authors considered EDC with detailed kinetic mechanism, while the second considered infinitely fast chemistry EDM with two-step kinetic
Coal-N (% of coal-N) Char-N TAR-N HCN32.6
61.69 26.91 5.25 1.26 2.16 0.73 2.00 36.3 31.1
mechanism. In this work both EDM and EDC with global and detailed mechanisms are considered, focusing on understanding the influence of turbulence–chemistry interaction on coal MILD combustion. 3.1. IFRF furnace geometry and working conditions The geometry of IFRF furnace No. 1 is shown in Fig. 1. It is characterized by a square section (2 2 m) and by a length of 6.25 m. Primary air enters from the two lateral inlets, transporting pulverized coal particles. Secondary air is preheated by means of combustion with natural gas up to levels of 1350 °C before entering the furnace from the central inlet. Vitiated air is enriched with pure O2 in order to maintain the same concentration as atmospheric air. Therefore, secondary air contains about 100 ppm of NOx produced during combustion with natural gas. They are introduced into the furnace improving thus NOx reburning mechanism. Experimental conditions considered for the numerical simulations of coal MILD combustion are shown in Table 1. The furnace is fired with 66 kg/h of coal and 130 kg/h and 675 kg/h respectively of primary and secondary air, corresponding to a stoichiometric
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Fig. 2. Computational grid used for simulating MILD combustion on IFRF furnace No.1.
(a)
Axial velocity, m/s Axial velocity, m/s
z=0.15 m
80
80
80
40
40
Axial velocity, m/s
(b)
40
z=0.44 m
80
experimental EDM EDC-JL EDC-DRM
40
0
0 0
0.5
1
0
z=0.735 m
0.5
1
z=1.32 m
0
0 0
0.5
1
z=2.05 m
80
0
1
z=3.22 m
80
40
0.5
40
0
0 0
0.5
x, m
1
0
0.5
1
x, m
Fig. 3. Axial velocity: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
ratio of 1.2. The wall of the furnace is considered at the constant temperature of about 1000 °C. 3.2. Coal characteristics The furnace is fired considering a highly volatile bituminous coal ‘‘Guasare’’. Proximate and ultimate analysis of the coal is given
in Table 2. Coal is finely pulverized to give a particle size distribution with 80% less than 90 lm [2]. Classification of the particle size used for the simulation is shown in Table 3 [10]. CPD parameters used for coal de-volatilization modeling are obtained from Genetti et al. [30] correlation from proximate and ultimate analysis, and they are shown in Table 4. Volatile matter composition is therefore defined by means of the standalone
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(b)
Temperature, K
(a)
z=0.15 m
2000
1500
Temperature, K
1500
1000
1000 0
0.5
1
z=0.735 m
2000
0
0.5
1
z=1.32 m
2000
1500
1500
1000
1000 0
Temperature, K
z=0.44 m
2000
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
2000
0
1
z=3.22 m
2000
1500
0.5
1500
1000
1000 0
0.5
1
0
x, m
0.5
1
x, m
Fig. 4. Temperature: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
version of the CPD model, considering a heating rate of about 120 K/ms to bring the coal particle from 400 to 1200 K. Table 5 gives the volatile yield and the repartition of coal nitrogen between char, TAR and HCN predicted by the CPD model. Coal particles are injected from the primary air inlet, considering six particle diameters (Table 3). For each particle class, 300 tries are considered in order to account for the turbulent dispersion of the solid phase through the discrete random walk model. The discrete solid phase is updated every 50 iterations of the gas phase.
3.3. Computational grid The geometry of the test furnace is discretized considering a 3D computational grid composed of about 500,000 hexahedral cells (Fig. 2). The near wall cells are stretched in the normal wall direction to achieve y+ values close to 30, as required by the wall function approach. In order to reduce computational cost of the simulations only one quarter of the geometry was considered. The mesh is realized by means of pre-processing commercial software IcemCFD™, using a multi-block approach.
4. Results Results of numerical simulations of pulverized coal MILD combustion are reported and validated referring to the experimental data from IFRF furnace No. 1 [2] with the main objective of investigating the effect of turbulence–chemistry interaction models and kinetic mechanisms. Three numerical simulations are performed, named EDM-2S, EDC-JL and EDC-DRM respectively, denoting the turbulence–chemistry models and the kinetic mechanisms used. In particular, EDM2S considers the infinitely fast chemistry Eddy Dissipation Model (EDM) for the turbulence–chemistry coupled with a simple twostep (2S) mechanism. On the other hand, EDC-JL and EDC-DRM consider the Eddy Dissipation Concept (EDC) coupled with the Jones and Lindstedt kinetic mechanism (JL) and the Direct Reduced kinetic Mechanism (DRM) respectively. The 2S global mechanism assumes that the pseudo-volatile species reacts directly with O2, while JL and DRM mechanisms assume that the pseudo-volatile specie is decomposed to produce TAR and other light gases through an infinitely fast dissociation reaction, according to the composition predicted by the CPD model (see Table 4).
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(b)
CO dry mol. fct.
(a)
z=0.15 m
0.1
0.05
CO dry mol. fct.
0.05
0
0 0
0.5
1
z=0.735 m
0.1
0
0.5
1
z=1.32 m
0.1
0.05
0.05
0
0 0
CO dry mol. fct.
z=0.44 m
0.1
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
0.1
0
1
z=3.22 m
0.1
0.05
0.5
0.05
0
0 0
0.5
1
0
x, cm
0.5
1
x, cm
Fig. 5. CO dry molar fraction: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
It was assumed that convergence of the solution is reached when the temperature, the CO concentration and the axial velocity on a domain point and at the exit were stable. Numerical results are consistent with other works available in literature [10,11], in particular EDM-2S are close to Schaffel et al. [11], while EDC-DRM to Kim et al. [10]. Furthermore, they are compared with experimental data considering six radial traverses, placed at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner inlet.
ity in the central air jet is lower than expected velocity. The peak velocity measured at the first traverse was about 47 m/s, while it was expected a value very close to the inlet. The measured low velocity may be explained by considering poor LDA measurements in the central jet. Results of numerical simulations are very similar, and only minor differences can be observed considering the traverse at 1.32 m near the mixing zone existing between the two jets. In particular, EDC-DRM gives the best agreement with experimental data in this zone, consequently to the better evaluate of temperature.
4.1. Axial velocity 4.2. Temperature Fig. 3a shows the comparison of the axial velocity contour plot on the axial section for the numerical simulations. Fig. 3b shows the comparison between numerical results and experimental data for the axial velocity along the six radial traverses. A central and a lateral jet can be observed, corresponding to the inlet of the vitiated secondary air and of the primary air streams respectively. In general numerical axial velocity fits the experimental data very well, except for the central jet peak, which numerical results are higher. Orsino et al. [2] pointed out that the measured peak veloc-
Fig. 4a shows the comparison of the temperature contour plot on the axial section for the numerical simulations. Fig. 4b shows the comparison between numerical results and experimental data for the temperature along the six radial traverses. Numerical results are characterized by a small front of flame around the secondary flow jet, that cannot be observed from the experimental data. It is more pronounced where the lateral jet interacts with the central one. All the numerical simulations are
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(b)
CO2 dry mol. fct.
(a)
z=0.15 m
0.4
0.2
CO2 dry mol. fct.
0.2
0
0 0
0.5
1
z=0.735 m
0.4
0
0.5
1
z=1.32 m
0.4
0.2
0.2
0
0 0
CO2 dry mol. fct.
z=0.44 m
0.4
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
0.4
0
1
z=3.22 m
0.4
0.2
0.5
0.2
0
0 0
0.5
1
0
x, m
0.5
1
x, m
Fig. 6. CO2 dry molar fraction: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
characterized by a cold zone correspondent to the lateral jet, that is not observed by measurements. The cold zone, related to a delayed ignition of the flame, does not directly depend on turbulence– chemistry model, but on the poor performances of the drying and devolatilization models. EDM-2S produces a higher value of the flame temperature, especially considering the second and the third traverses, where large gradients are observed near the secondary jet, because of the higher reactions rate related to the infinitely fast chemistry assumption of EDM. EDC-JL estimates a reasonable temperature profile at 0.44 m from the burner, but its results upstream are similar to EDM-2S, overestimating the experimental results. In particular, after the traverse at 1.32 m, EDM-2S and EDC-JL show very similar behavior, because the gas phase reactions are dominated by the oxidation of CO produced by heterogeneous reactions (Eqs. (1)–(3)). In fact, CO follows similar paths in 2S and JL, directly producing CO2 according to reactions in Eqs. (5) and (8) respectively. Instead, considering DRM, CO follows a more complex path, producing several intermediate species, before being completely converted into CO2.
Finally, EDC-DRM is characterized by a better agreement with measurements, slightly overestimating the flame temperature. Its better behavior is generally related to the detailed kinetic mechanism considered, able to describe in detail the slow chemistry of the MILD combustion. Whereas, global kinetic mechanisms, such as 2S and JL, are unable to describe correctly the behavior of flameless regime of the MILD combustion. 4.3. Chemical species Fig. 5a shows the comparison of the contour plots of the CO dry molar fraction on the axial section for the numerical simulations. Fig. 5b shows the comparison between numerical results and experimental data for the CO dry molar fraction along the six radial traverses. A large difference among the numerical results can be observed from the contour plots and the radial profiles. In particular, EDC-DRM is characterized by the widest zone where the presence of CO can be observed. This zone is comprised between the second and fifth traverses. Large part of CO is produced from char burnout, according to the reactions (1)–(3). However, the
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(b)
O2 dry mol. fct.
(a)
z=0.15 m
0.3
experimental EDM EDC-JL EDC-DRM
0.2 0.1
0.2 0.1
0
0
O2 dry mol. fct.
0
0
z=0.735 m
0.3 0.2
0.2
0.1
0.1
0
z=1.32 m
0.3
0 0
O2 dry mol. fct.
z=0.44 m
0.3
0
z=2.05 m
0.3 0.2
0.2
0.1
0.1
0
z=3.22 m
0.3
0 0
x, m
0
x, m
Fig. 7. O2 dry molar fraction: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
differences among the simulations are mainly related to the different kinetic mechanisms considered for the reactions in the homogeneous phase. Indeed, as previously described, the global 2S and JL mechanisms are unable to describe the slow chemistry of the MILD combustion, evaluating lower CO concentrations. Instead, DRM mechanism predicts higher CO concentrations, related to the more detailed mechanism, able to better describe the behavior of the process. In fact, this detailed mechanism allows consideration of the formation of secondary species through intermediate reactions which cannot be accounted for by the global mechanisms. The higher CO concentrations of EDC-DRM is therefore related to the lower flame temperature, due to the slower heat release. With respect to experimental data, numerical simulations calculate larger concentrations di CO starting from the second traverse. This difference could be related to the high production of CO from the char burnout. In fact, considering the char oxidation reaction (Eq. (1)) only CO formation is considered. In addition, considering the first traverse at 0.15 m experimental data are characterized by the presence of CO, that is completely absent in the numerical simulations. This region corresponds to the cold zone
in the secondary air jet (Fig. 4) and depends on the ignition delay in the numerical simulations. As previously explained, it could be attributed to inconsistencies in drying and devolatilization models. Fig. 6a shows the comparison of the contour plots of the CO2 dry molar fraction on the axial section for the numerical simulations. Fig. 6b shows the comparison between numerical results and experimental data for the CO2 dry molar fraction along the six radial traverses. Numerical simulations predict very similar CO2 concentrations, especially considering the first two traverses. Differences can be observed considering the third and fourth traverses, where EDC-DRM predicts lower CO2 concentrations, related to the higher CO calculated and by the slower chemistry, as described previously. Considering the last two traverses, CO2 evaluated concentrations are very similar. Good agreement with experimental data can be generally observed for CO2 concentrations for all the simulations. Finally, Fig. 7a shows the comparison of the contour plots of O2 dry molar fraction on the axial section for the numerical simulations, while Fig. 7b shows the comparison between numerical results and experimental data for the O2 dry molar fraction along the six radial traverses. This time, numerical simulations predict
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(b)
NO dry ppmv
(a)
z=0.15 m
2000
1000
NO dry ppmv
1000
0
0 0
0.5
1
z=0.735 m
2000
0
0.5
1
z=1.32 m
2000
1000
1000
0
0 0
NO dry ppmv
z=0.44 m
2000
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
2000
0
1
z=3.22 m
2000
1000
0.5
1000
0
0 0
0.5
x, m
1
0
0.5
1
x, m
Fig. 8. NO dry ppm: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
very similar O2 concentrations for all the traverses, showing very good agreements with experimental data. In particular, the main difference can be observed for the first traverse, where the calculated O2 peak is absent in measurements. It may again be related to the delayed ignition of the flame predicted by the simulations. 4.4. NO concentration Fig. 8a shows the comparison of the contour plots of the NO dry molar fraction on the axial section for the numerical simulation. Fig. 8b shows the comparison between numerical results and experimental data for the NO dry molar fraction along the six radial traverses. Fig. 9 shows the NO production and destruction according to the main mechanisms considered: thermal-NO, fuelNO and reburning.1 Numerical simulations predict very similar NO concentrations and only minor differences can be observed. In fact considering coal and in particular MILD combustion, fuel-NO is the main mechanism (Fig. 9). On one hand, thermal-NO plays a second-
1
Prompt-NO are not showed because their contribute is negligible.
ary role, especially in MILD combustion where flame temperature is lower than conventional combustion. Therefore, the temperature differences existing among numerical simulations produce only small variation on NO concentrations. On the other hand, NO formation through the fuel mechanism gives very similar values because it depends mainly by devolatilization and burnout rates, which are similar for the numerical simulations. On the contrary, the main differences existing among numerical simulations may be attributed to the reburning mechanism (Fig. 9), which depends on the NO recombination with hydrocarbons. Considering the post-processing approach, an equivalent hydrocarbon molecule is evaluated from the species containing carbon and hydrogen atoms present in the simulation. The equivalent hydrocarbon molecule reacts with NO producing mainly HCN according to the Eq. (10). EDM-2S is characterized by the larger reburning effect, since the equivalent hydrocarbons is given only by the pseudo-volatile species CxHyOz. For this reason, the reburning is strongly concentrated in the devolatilization zone after the first traverse near the secondary jet, where the pseudo-volatile concentration is greater. Consequently this zone the NO concentration is strongly overestimated.
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NO formation, mg/s
2 EDM-2S EDC-JL EDC-DRM
1
5. Conclusions
0
-1 Thermal-NO
Fuel-NO
NO-reburning
Fig. 9. NO production and destruction rates according the main mechanisms.
char burnout, %
100 H2O CO2 O2
50
0
zone, improving gasification and Boudouard reactions with respect to EDM-2S. However, EDC-DRM is characterized by lower flame temperature that reduces the importance of these reactions compared to the oxidation one.
EDM-2S
EDC-JL
EDC-DRM
Fig. 10. Influence of the heterogeneous reactions on char burnout.
Considering the other simulations, reburning effect is less pronounced. Equivalent hydrocarbon is evaluated from TAR and CH4 for EDC-JL. Whereas, additional species, such as CH2 and CH3, are considered for EDC-DRM. Therefore, the reburning is not concentrated only in the devolatilization zone, as in the previous case, but it is spread out into a wider area, reducing its intensity. From comparison with measurements, numerical simulations predict acceptable NO concentrations, especially considering the last traverses. The NO peak is correctly described by the EDC-JL and EDC-DRM, while this behavior is not correctly reproduced by EDM-2S, as previously described. As previously described for temperature and chemical species concentrations, the delayed flame ignition can be observed at 0.15 m. 4.5. Heterogeneous reaction Fig. 10 shows the repartition of the char burnout among the heterogeneous reactions considered (Eqs. (1)–(3)), respectively with O2, CO2 and H2O. Contribution of gasification and Boudouard reactions is relevant, as already observed by Stadler et al. [6], because of the large amount of CO2 and H2O present in the furnace, due to the recirculation of the exhaust gas. In fact, from Figs. 6 and 7, the partial pressure of CO2 is generally larger than O2, producing a sensible increase of char converted through the gasification reactions with respect to the conventional combustion, where the influence of these reactions is almost negligible. Generally, gasification reactions alter CO/CO2 balance, increasing CO concentrations and reduces particle temperature because of their endothermic behavior. However, considering moderate and low O2 concentrations, similar burning rates and flame temperatures are observed, related to the higher CO concentrations that improve reactions rates in the gas phase [43]. Considering the different simulations, EDC-JL shows larger influence of gasification reactions. In fact, EDC-JL and EDC-DRM are characterized by direct production of CO2 and H2O during devolatilization, and consequently by higher concentrations in this
This work investigates the influence of turbulence–chemistry interaction on CFD pulverized coal MILD combustion modeling, with the aim of understanding which models are more accurate and suitable to reproduce the process. In particular, two turbulence–chemistry interaction models are examined. The first is the conventional Eddy Dissipation Model (EDM), based on infinitely fast chemistry assumption, that is used with a global two-step mechanism. The second is the advanced finite rate chemistry, Eddy Dissipation Concept, used with both global and detailed kinetic mechanisms. Numerical results are with IFRF pulverized coal MILD combustion test case. Generally, MILD combustion differs from conventional combustion because the chemical reactions take place in a wider zone with reduced gradients of temperature and chemical species concentrations. For this reason, infinitely fast chemistry turbulence–chemistry models, such as EDM, can not correctly reproduce the process. Therefore, advanced models, such as EDC, used with detailed kinetic mechanisms, are required to correctly simulate MILD combustion. From the comparison of numerical simulations with experimental measurements, advanced turbulence–chemistry interaction models used with detailed kinetic mechanisms allow to better reproduce the chemical and fluid dynamic behavior of coal MILD combustion, as expected, especially considering velocity, temperature and species concentrations. However, considering coal MILD combustion the influence of the turbulence–chemistry interaction model is less pronounced when considering gaseous fuels (see [14,16]), because only the volatile matter reacts in the gaseous phase, while the remaining char is burned through heterogeneous surface reactions. Therefore, global mechanisms may produce satisfactory results and could be preferred owing to their lower computational cost with respect to the detailed mechanisms if extreme accuracy is not required. On the other hand, coal conversion mechanisms, such as drying, devolatilization and burnout, are responsible for the delayed flame lift-off observed considering different turbulence–chemistry models and kinetic mechanisms. Therefore the influence of these models needs to be further investigated in order to increase the overall accuracy of the simulations. Another critical aspect requiring further studying is the influence of the radiation model and of the radiative properties of the gas and solid phases.
References [1] Cavaliere A, de Joannon M. Mild combustion. Prog Energy Combust Sci 2004;30(4):329–66. [2] Orsino S, Tamura M, Stabat P, Constantini S, Prado O, Weber R. Excess enthalpy combustion of coal. Tech. Rep. IFRF Doc. No. f46/y/3, IFRF; 2000. [3] Weber R, Smart J, von der Kamp W. On the (MILD) combustion of gaseous, liquid, and solid fuels in high temperature preheated air. Proc Combust Inst 2005;30(2):2623–9. [4] Stadler H, Tschunko S, Förster M, Kneer R, Fielenbach C. NOx reduction by flameless combustion of pulverised coal. VGB PowerTech 2008;88(3):90–4. [5] Stadler H, Ristic D, Förster M, Schuster A. NOx-emissions from flameless coal combustion in air, Ar/O2 and CO2/O2. Proc Combust Inst 2009;32(2):3135–83. [6] Stadler H, Toporov D, Förster M, Kneer R. On the influence of the char gasification reactions on NO formation in flameless coal combustion. Combust Flame 2009;156(9):1755–63. [7] Ristic D, Schuster A, Scheffknecht G, Stadler H, Foerster M, Kneer R, et al. Experimental study on flameless oxidation of pulverised coal in bench and pilot scale. VDI Berichte 2007(1988):45–51.
M. Vascellari, G. Cau / Fuel 101 (2012) 90–101 [8] Ristic D, Schuster A, Scheffknecht G. On the potential of flameless oxidation to reduce NOx emissions from pulverized coal combustion. IFRF Combust J 2010:1–16. [9] Schaffel-Mancini N, Mancini M, Szlek A, Weber R. Novel conceptual design of a supercritical pulverized coal boiler utilizing high temperature air combustion (HTAC) technology. Energy 2010;35:2752–60. [10] Kim JP, Schnell U, Scheffknecht G, Benim AC. Numerical modelling of mild combustion for coal. Prog Comput Fluid Dynam 2007;7(6):337–46. [11] Schaffel N, Mancini M, Szlek A, Weber R. Mathematical modeling of mild combustion of pulverized coal. Combust Flame 2009;156(9):1771–84. [12] Magnussen B, Hjertager B. On mathematical modeling of turbulent combustion with special emphasis on soot formation and combustion. Symp (Int) Combust 1977;16(1):719–29. [13] Peters N. Laminar flamelet concepts in turbulent combustion. Symp (Int) Combust 1988;21(1):1231–50. [14] Christo F, Dally B. Modeling turbulent reacting jets issuing into a hot and diluted coflow. Combust Flame 2005;142(1–2):117–29. [15] Gran I, Magnussen B. A numerical study of a bluff-body stabilized diffusion flame. Part 1: influence of turbulence modeling and boundary conditions. Combust Sci Technol 1996;119(1–6):171–90. [16] Parente A, Galletti C, Tognotti L. Effect of the combustion model and kinetic mechanism on the mild combustion in an industrial burner fed with hydrogen enriched fuels. Int J Hydrogen Energy 2008;33(24):7553–64. [17] Orsino S, Weber R, Bollettini U. Numerical simulation of combustion of natural gas with high-temperature air. Combust Sci Technol 2001;170(1):1–34. [18] Kim JP, Schnell U, Scheffknecht G. Comparison of different global reaction mechanisms for mild combustion of natural gas. Combust Sci Technol 2008;180(4):565–92. [19] Eaton A, Smoot L, Hill S, Eatough C. Components, formulations, solutions, evaluation, and application of comprehensive combustion models. Prog Energy Combust Sci 1999;25(4):387–436. [20] VVAA. Fluent 6.3 user’s guide; 2006. [21] Patankar S, Spalding D. A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. Int J Heat Mass Transfer 1972;15(10):1787–806. [22] Shih T, Liou W, Shabbir A, Yang Z, Zhu J. A new k-epsilon eddy viscosity model for high reynolds number turbulent flows. Comput Fluids 1995;24(3):27–238. [23] Cheng P. Two-dimensional radiating gas flow by a moment method. AIAA J 1964;2(9):1662–4. [24] Ranz W, Marshall W. Evaporation from drops. Part I. Chem Eng Prog 1952;48(3):141–6. [25] Ranz W, Marshall W. Evaporation from drops. Part II. Chem Eng Prog 1952;48(4):173–80. [26] Förtsch D, Kluger F, Schnell U, Spliethoff H, Hein K. A kinetic model for the prediction of no emissions from staged combustion of pulverized coal.
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Influence of turbulence–chemical interaction on CFD pulverized coal MILD combustion modeling M. Vascellari ⇑, G. Cau Department of Mechanical Engineering, Università degli Studi di Cagliari, 09123 Piazza D’Armi, Cagliari, Sardinia, Italy
a r t i c l e
i n f o
Article history: Received 30 September 2010 Received in revised form 22 July 2011 Accepted 26 July 2011 Available online 22 August 2011 Keywords: Coal MILD Combustion CFD Turbulence
a b s t r a c t MILD (Moderate and Intensive Low oxygen Dilution) combustion is a novel approach to reducing NOx emissions and improving combustion efficiency in fossil fuels power plants. It is characterized by elevated temperature and high dilution of reactants and strong recirculation inside the combustion chamber which produce a low temperature increase, thus reducing NOx formation. The main differences with conventional combustion concern the chemical reactions that take place in almost the entire volume of the combustion chamber and the uniformity of both temperature and the chemical species concentration. For this reason advanced turbulence-chemistry interaction models with detailed kinetic mechanisms are required to accurately simulate MILD by means of CFD calculations. The main aim of this work is to deepen the influence of turbulence-chemistry interaction on pulverized coal MILD combustion and to understand which models are more accurate and suitable to reproduce the process. In particular, two turbulence-chemistry interaction models are analyzed. On one hand, a conventional model based on infinitely fast chemistry Eddy Dissipation Model with a two-step global kinetic mechanism is considered. On the other hand, an advanced model based on finite rate chemistry Eddy Dissipation Concept is considered and used with both a global and detailed kinetic mechanisms. The results are finally compared with an experimental test-case. From the comparison, advanced turbulence-chemistry models used with complex kinetic mechanisms give, as expected, the best agreement with numerical results, despite the higher computational resources required. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction MILD combustion (Moderate and Intensive Low oxygen Dilution) is a new technology for reducing the production of pollutants, especially NOx, from combustion of fossil fuels. Cavaliere and de Joannon [1] define MILD combustion ‘‘when the inlet temperature of the reactant mixture is higher than mixture self-ignition temperature whereas the maximum allowable temperature increase with respect to inlet temperature during combustion is lower than mixture self-ignition temperature (in Kelvin)’’. According to this definition, reactants are introduced at temperature, higher than mixture self-ignition, and the mixture is strongly diluted in order to reduce the increase in temperature during combustion. Generally, reactants are preheated by means of regeneration or recirculation using the sensible heat of exhaust gas. Lastly, reactants are introduced in the combustion chamber at high velocity in order to increase the internal recirculation of the combustion products. ⇑ Corresponding author. Current address: ZIK Virtuhcon, TU Bergakademie Freiberg, Reiche Zeche Fuchsmuehlenweg 9, 09596 Freiberg, Germany. Tel.: +49 (0) 3731394555; fax: +49 (0) 3731394823. E-mail addresses: [email protected] (M. Vascellari), [email protected] (G. Cau). 0016-2361/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2011.07.042
These solutions have the aim of reducing the high gradients of temperature and species concentrations existing in conventional combustion, where the chemical reactions take place in a well-defined thin flame front. Consequently, thermal stress and pollutant emissions, such as NOx and soot, are strongly reduced considering the lower gradients existing in MILD combustion. Therefore, MILD combustion differs from conventional combustion because of the absence of a visible flame. As a result, it also defined as ‘‘flameless’’. Experimental works on pulverized coal MILD combustion were performed by the International Flame Research Fundation (IFRF) [2,3], where a 880 kW furnace was constructed to investigate the MILD combustion of gaseous, liquid and solid fuels. Experimental tests and numerical simulations were carried out at the RWTH laboratory of Aachen University and at the Institute of Process Engineering and Power Plant Technology (IVD) of Stuttgart University [4–8] in Germany. The conceptual design of high temperature air combustion (HTAC) supercritical pulverized coal boiler was recently proposed by Schaffel-Mancini et al. [9]. Numerical simulations of pulverized coal MILD combustion have been performed considering the experimental IFRF furnace by Kim et al. [10] and Schaffel et al. [11] using different CFD approaches.
M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
The different fluid dynamic behavior of MILD combustion with respect to conventional requires the use of advanced models to account for turbulence-chemistry interactions. Generally speaking, with conventional combustion, simplified models for turbulencechemistry interactions are considered for CFD simulations. These models are generally based on the hypothesis of infinitely fast chemistry, because the chemical reactions are generally faster than turbulent flow mixing [12]. Another hypothesis generally assumed considers that reactions occur in a thin sheet with inner structure generally defined as laminar flamelet [13], decoupling fluid dynamic and chemical problems. The hypotheses considered for these models are no longer valid with MILD combustion, where the chemical reactions occur in an extended region with chemical rates comparable to the reactants turbulent mixing rates. Therefore, advanced turbulence-chemistry interaction models are required to account for the chemistry of the flame and its interaction with turbulent flow in a detailed way. In fact, Christo and Dally [14] demonstrated that the Eddy Dissipation Concept (EDC) [15] with a simplified three-step reaction kinetic mechanism performed better than did a laminar flamelet model, considering MILD combustion of CH4/H2 mixtures. In addition, Parente et al. [16] demonstrated that using EDC model with detailed kinetic mechanisms gave more accurate results with respect to simplified mechanisms. Besides, Orsino et al. [17] demonstrated that turbulence-chemistry interaction models based on chemical equilibrium assumptions gave unsatisfactory results for natural gas MILD combustion. Considering the same test-case, Kim et al. [18] showed that the results are strongly affected by the kinetic mechanism considered. The main aim of this work is to deepen the influence of turbulence-chemistry interaction on pulverized coal MILD combustion and to understand which models are more accurate and suitable to reproduce the process. In particular, two turbulence-chemistry interaction models are analyzed: a conventional model based on infinitely fast chemistry Eddy Dissipation Model with a two-step global kinetic mechanism and an advanced model based on finite rate chemistry Eddy Dissipation Concept. The last is used with both a global and a detailed kinetic mechanisms. The results are finally compared with the experimental test-case on pulverized coal MILD combustion developed and executed by IFRF [2,3].
2. Mathematical models for pulverized coal combustion During coal combustion several chemical-physical phenomena take place. They require specific mathematical models implemented in a comprehensive CFD code [19]. The main models considered concern the following phenomena: turbulence, multiphase flow and interphase interactions, homogeneous and heterogeneous chemical reactions, radiation, etc. Simulations of MILD coal combustion were performed considering the commercial CFD code Fluent™ [20], version 6.3. The Reynolds Average Navier Stokes (RANS) equations are solved on an unstructured hybrid mesh using a finite volume discretization approach. The three-dimensional version of the pressure-based solver is considered. The SIMPLE [21] algorithm is used for velocity–pressure coupling. Convective fluxes in all transport equations are discretized with a second-order accurate upwind scheme and the pressure gradient with a second-order accurate scheme. The realizable k turbulence model [22] is considered for RANS equations closure. The P-1 radiation model [23] is considered for radiation heat transfer. The coal discrete phase is modeled considering a Eulerian– Lagrangian approach. The main gas phase is solved considering transport equations for continuous phase in the Eulerian frame of
91
reference, while the secondary discrete solid coal phase is solved considering a Lagrangian frame. The trajectories of the particles are evaluated by integrating the force balance on them with respect to time. The continuous phase flow pattern is impacted by the discrete phase (and vice versa) and the calculation of the main phase is alternated with the discrete phase until a converged coupled solution is achieved. As the trajectory of a particle is computed, the heat, mass and momentum gained or lost by the particle are evaluated, and these interactions are taken into account in the Eulerian equations of the primary phase by means of source terms. The dispersion of particles due to turbulence is taken into account by considering the stochastic tracking model, including the effect of instantaneous turbulent velocity fluctuations on particle trajectories. Coal combustion is modeled according to the following sequence of phenomena: drying, pyrolysis, volatile combustion and char burnout. 2.1. Drying Moisture drying is governed by the difference of water concentrations between the particle surface and the bulk phase. The water concentration on the particle surface is evaluated by assuming that the partial pressure of vapor at the interface is equal to the saturated vapor pressure at the particle temperature. The mass transfer coefficient used for evaluating moisture evaporation is calculated by means of correlation of Ranz and Marshall [24,25]. 2.2. Pyrolysis Pyrolysis can be regarded as a two-stage process [26]. During primary pyrolysis, coal particles decompose and release volatile matter (devolatilization), composed by TAR, light hydrocarbons and gas. During secondary pyrolysis, TAR decomposes and produces soot, light hydrocarbons and gas. Devolatilization of coal is evaluated by considering the Chemical Percolation Devolatilization (CPD) model [27–29]. The CPD model characterizes the devolatilization behavior of rapidly heated coal based on physical and chemical transformation of coal structures. It describes the chemical and physical processes by considering the coal structure as a simplified lattice or network of chemical bridges that link the aromatic clusters. CPD is able to accurately predict volatiles rates, yield and composition in terms of high weight hydrocarbons (TAR) and light gases (CH4, CO, CO2, H2O, etc.). The model requires data from coal solid state Nuclear Magnetic Resonance (NMR) analysis as input. Since such kinds of data are not easily available, Genetti et al. [30] developed a correlation to obtain them from proximate and ultimate analysis of the coal. The CPD model is implemented in Fluent™, but it allows only the evaluation of the devolatilization rate, and not its yield, thus allowing the definition of only one pseudo-volatile species. To define volatile matter composition, the pseudo-volatile species is decomposed into the gas phase by means of an infinitely fast reaction. The composition of volatile matter is obtained directly from a standalone version of the CPD model in a pre-processing step. Heavy hydrocarbons (TAR) are approximated as an equivalent molecule CnHmOp, balanced to conserve species stoichiometry from ultimate analysis. Finally, TAR reacts in the gaseous phase with O2 producing CO and H2 [26]. 2.3. Heterogeneous reactions After volatile matter is completely released during primary pyrolysis, the char remaining in the coal particle reacts with the surrounding gas phase. Considering exhaust gas recirculation, high
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CO2 and H2O concentrations characterize coal MILD combustion. Therefore, gasification and Boudouard reactions are considered besides oxidation:
1 CðsÞ þ O2 ! CO 2 CðsÞ þ CO2 ! 2CO CðsÞ þ H2 O ! CO þ H2 O
ð1Þ ð2Þ ð3Þ
Heterogeneous reaction rates are governed by the diffusion of the oxidant species from the bulk phase to the particle surface and by the intrinsic kinetic [31]. The overall heterogeneous reaction rate is controlled by two mechanisms: the diffusion of the oxidant through the film layer around the coal particle and the intrinsic kinetic rate of the reaction on the particle surface. Constant parameters for oxidant diffusion and kinetic rate are taken from the work of Field [32], Smoot and Pratt [33]. 2.4. Turbulence–chemistry interaction models The interaction between turbulent flow and chemical reaction plays a fundamental rule in MILD combustion modeling, whether considering solid or liquid and gaseous fuels. Indeed, fluid dynamic behavior of MILD combustion strongly differs from conventional combustion. As previously described, gradients of temperature and chemical species concentrations are generally lower. In this way, a well-defined flame front can no longer be observed. Therefore, the assumptions used for conventional combustion are no longer valid. Indeed, it is impossible to define a limited region where chemical reactions take place and their rates are comparable to mixing vortex. In this work two different approaches are considered to model turbulence–chemistry interactions. The first, based on the infinite fast chemistry assumption, considers the Eddy Dissipation Model (EDM) [12] which is the conventional approach used by commercial CFD codes for combustion modeling. The rate of the reactions depends only by the behavior of the turbulent flow, and it is proportional to the dissipation rate of the turbulent vortices. For this reason, EDM can handle only global kinetic mechanisms with a maximum of two reactions, since the model cannot discriminate between slow and fast chemistry. The second approach is based on the Eddy Dissipation Concept (EDC) [15,34], which is an extension of the previous model to consider detailed kinetic mechanisms. According to EDC, combustion occurs in the region of flow where the dissipation of turbulent kinetic energy takes place (fine structures). These regions can be described as perfectly stirred tank reactors (PSR) at constant pressure, where the reactions advance according to a chemical residence time, defined as a function of turbulence parameters. 2.5. Kinetic mechanisms Three kinetic mechanisms are considered for simulating coal MILD combustion. EDM allows consideration only of simple mechanisms with a maximum of two reactions. Consequently, a pseudovolatile species is considered (CxHyOz), reacting according to the following simple two-step (2S) mechanism:
Cx Hy Oz þ
1 1 1 x z þ y O2 ! xCO þ yH2 O 2 2 2
1 CO þ O2 ! CO2 2
ð4Þ ð5Þ
At first, it considers the direct oxidation of the pseudo-volatile species which produces CO and H2O. Then, CO is completely oxidized in a second reaction, thus producing CO2.
On the other hand, EDC allows consideration of complex kinetic mechanisms. Dissociation of the pseudo volatile species through an infinitely fast reaction, which produces TAR and light gases according to the CPD results, is assumed. One additional reaction is then considered to account for TAR oxidation [26]. Light gases react according the global kinetic mechanism of Jones and Lindstedt [35] and DRM [36] respectively. The first is a global mechanism constituted by four reactions with 6 species:
1 CH4 þ O2 ! CO þ 2H2 2 CH4 þ H2 O ! CO þ 3H2 CO þ H2 O ! CO2 þ H2 1 H2 þ O2 ! H2 O 2
ð6Þ ð7Þ ð8Þ ð9Þ
The second is a detailed mechanism, reduced from GRI mechanism [37], which was developed to obtain the smallest set of reactions needed to reproduce closely the main combustion characteristics of the full mechanism. It consists of 103 reactions with 22 chemical species. Within the context of MILD combustion, the advanced EDC approach with the DRM mechanism is expected to provide better results due to the slow kinetics related to low flame temperature. However, it requires remarkable computing time and more numerical issues with respect to the EDM with the 2S mechanism and EDC with simpler mechanisms, such as Jones and Lindstedt. 2.6. NOx pollutant formations The formation of NOx is generally modeled considering four different mechanisms: thermal, prompt, fuel NOx formation and reburning reduction. These mechanisms are reviewed in detail by Hill and Smoot [38]. In the present work a post processing approach is considered to evaluate NOx production from coal MILD combustion, decoupling the flow solution from NOx evaluation. In fact, the low NOx concentration has a reduced influence on the other flow properties, such as temperature and main species concentrations, and therefore the NOx formation has a reduced impact on the balance of mass, momentum and energy. Consequently the flow solutions is ‘‘frozen’’ and after the transport equations for NO, HCN and NH3 are solved, maintaining the other flow properties constant. Turbulence effects are modeled by the probability density function (PDF), considering a b-PDF function. At first, the thermal-NOx formation follows the Zeldovich mechanism [39]. It is based on the oxidation reactions of the molecular nitrogen contained in the gas, which are favoured by high flame temperature and by the presence of radical species, such as O and OH. Therefore in MILD combustion the reduction of temperature generally has a positive effect on reduction of thermal-NO. Then, prompt-NO are formed by reactions of atmospheric N2 with hydrocarbon radicals in fuel-rich regions of the flame [40]. Generally speaking, it plays a secondary role in MILD combustion, because diluted reactants mixture are considered. Fuel-NOx are formed by oxidation of the nitrogen bound in the fuel. Considering coal, nitrogen is distributed between the volatiles and the char, and it is released as HCN and NH3 during devolatilization and char burnout, which reacts with oxygen and NOx, thus producing NOx and N2 respectively. It represents the main NOx formation mechanism in coal combustion, especially in MILD combustion, where the thermal-NOx mechanism is reduced by the low flame temperature. Finally, the recirculation of exhaust gas containing NOx produces a positive reburning consumption, thus contributing to
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Fig. 1. Geometry of the IFRF furnace.
Table 1 Experimental conditions of IFRF furnace [2].
Primary air Secondary air
Mass flow rate (kg/h)
Temp. (K)
Composition (vol%)
130 675
313.15 1623.15
O2 21%, N2 79% CO2 8.1%, O2 19.7%, N2 57.2%, H2O 15.1%, NO 100 ppm (dry)
Table 2 Proximate and ultimate analysis of Guasare coal [2]. Proximate analysis Volatile matter Fixed carbon Moisture Ash
37.1 56.7 2.9 3.3
Ultimate analysis (% daf) C H O N S
78.41 5.22 10.90 1.49 0.82
LHV
31.74 MJ/kg
Table 4 Parameters used for CPD model. Md
Mcl
p0
r+1
c0
33.9
351.2
0.52
5
0
Table 5 Volatile yield predicted by CPD model. Volatile yield (% daf) Char TAR H2O CO2 CH4 CO other
Table 3 Classification of particle size [10]. Class
Mean diameter (lm)
Mass fraction (%)
1 2 3 4 5 6
3 16 36 65 120 200
7.19 27.61 18.51 28.16 15.25 3.28
reduce the overall NOx emissions [41,42]. The reburning NOx mechanism is a pathway whereby NOx reacts with hydrocarbons and is subsequently reduced, according to the following reaction:
CHi þ NO ! HCN þ products
ð10Þ
Considering global kinetic mechanism, concentrations of hydrocarbons is obtained from the species mass fraction resulting from the combustion calculation. 3. Numerical simulations of MILD coal combustion Numerical simulations of pulverized coal MILD combustion are performed considering the geometry and the working conditions of the IFRF furnace No. 1 [2,3]. The IFRF test case was previously modeled by Kim et al. [10] and Schaffel et al. [11]. The first authors considered EDC with detailed kinetic mechanism, while the second considered infinitely fast chemistry EDM with two-step kinetic
Coal-N (% of coal-N) Char-N TAR-N HCN32.6
61.69 26.91 5.25 1.26 2.16 0.73 2.00 36.3 31.1
mechanism. In this work both EDM and EDC with global and detailed mechanisms are considered, focusing on understanding the influence of turbulence–chemistry interaction on coal MILD combustion. 3.1. IFRF furnace geometry and working conditions The geometry of IFRF furnace No. 1 is shown in Fig. 1. It is characterized by a square section (2 2 m) and by a length of 6.25 m. Primary air enters from the two lateral inlets, transporting pulverized coal particles. Secondary air is preheated by means of combustion with natural gas up to levels of 1350 °C before entering the furnace from the central inlet. Vitiated air is enriched with pure O2 in order to maintain the same concentration as atmospheric air. Therefore, secondary air contains about 100 ppm of NOx produced during combustion with natural gas. They are introduced into the furnace improving thus NOx reburning mechanism. Experimental conditions considered for the numerical simulations of coal MILD combustion are shown in Table 1. The furnace is fired with 66 kg/h of coal and 130 kg/h and 675 kg/h respectively of primary and secondary air, corresponding to a stoichiometric
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
Fig. 2. Computational grid used for simulating MILD combustion on IFRF furnace No.1.
(a)
Axial velocity, m/s Axial velocity, m/s
z=0.15 m
80
80
80
40
40
Axial velocity, m/s
(b)
40
z=0.44 m
80
experimental EDM EDC-JL EDC-DRM
40
0
0 0
0.5
1
0
z=0.735 m
0.5
1
z=1.32 m
0
0 0
0.5
1
z=2.05 m
80
0
1
z=3.22 m
80
40
0.5
40
0
0 0
0.5
x, m
1
0
0.5
1
x, m
Fig. 3. Axial velocity: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
ratio of 1.2. The wall of the furnace is considered at the constant temperature of about 1000 °C. 3.2. Coal characteristics The furnace is fired considering a highly volatile bituminous coal ‘‘Guasare’’. Proximate and ultimate analysis of the coal is given
in Table 2. Coal is finely pulverized to give a particle size distribution with 80% less than 90 lm [2]. Classification of the particle size used for the simulation is shown in Table 3 [10]. CPD parameters used for coal de-volatilization modeling are obtained from Genetti et al. [30] correlation from proximate and ultimate analysis, and they are shown in Table 4. Volatile matter composition is therefore defined by means of the standalone
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
(b)
Temperature, K
(a)
z=0.15 m
2000
1500
Temperature, K
1500
1000
1000 0
0.5
1
z=0.735 m
2000
0
0.5
1
z=1.32 m
2000
1500
1500
1000
1000 0
Temperature, K
z=0.44 m
2000
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
2000
0
1
z=3.22 m
2000
1500
0.5
1500
1000
1000 0
0.5
1
0
x, m
0.5
1
x, m
Fig. 4. Temperature: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
version of the CPD model, considering a heating rate of about 120 K/ms to bring the coal particle from 400 to 1200 K. Table 5 gives the volatile yield and the repartition of coal nitrogen between char, TAR and HCN predicted by the CPD model. Coal particles are injected from the primary air inlet, considering six particle diameters (Table 3). For each particle class, 300 tries are considered in order to account for the turbulent dispersion of the solid phase through the discrete random walk model. The discrete solid phase is updated every 50 iterations of the gas phase.
3.3. Computational grid The geometry of the test furnace is discretized considering a 3D computational grid composed of about 500,000 hexahedral cells (Fig. 2). The near wall cells are stretched in the normal wall direction to achieve y+ values close to 30, as required by the wall function approach. In order to reduce computational cost of the simulations only one quarter of the geometry was considered. The mesh is realized by means of pre-processing commercial software IcemCFD™, using a multi-block approach.
4. Results Results of numerical simulations of pulverized coal MILD combustion are reported and validated referring to the experimental data from IFRF furnace No. 1 [2] with the main objective of investigating the effect of turbulence–chemistry interaction models and kinetic mechanisms. Three numerical simulations are performed, named EDM-2S, EDC-JL and EDC-DRM respectively, denoting the turbulence–chemistry models and the kinetic mechanisms used. In particular, EDM2S considers the infinitely fast chemistry Eddy Dissipation Model (EDM) for the turbulence–chemistry coupled with a simple twostep (2S) mechanism. On the other hand, EDC-JL and EDC-DRM consider the Eddy Dissipation Concept (EDC) coupled with the Jones and Lindstedt kinetic mechanism (JL) and the Direct Reduced kinetic Mechanism (DRM) respectively. The 2S global mechanism assumes that the pseudo-volatile species reacts directly with O2, while JL and DRM mechanisms assume that the pseudo-volatile specie is decomposed to produce TAR and other light gases through an infinitely fast dissociation reaction, according to the composition predicted by the CPD model (see Table 4).
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
(b)
CO dry mol. fct.
(a)
z=0.15 m
0.1
0.05
CO dry mol. fct.
0.05
0
0 0
0.5
1
z=0.735 m
0.1
0
0.5
1
z=1.32 m
0.1
0.05
0.05
0
0 0
CO dry mol. fct.
z=0.44 m
0.1
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
0.1
0
1
z=3.22 m
0.1
0.05
0.5
0.05
0
0 0
0.5
1
0
x, cm
0.5
1
x, cm
Fig. 5. CO dry molar fraction: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
It was assumed that convergence of the solution is reached when the temperature, the CO concentration and the axial velocity on a domain point and at the exit were stable. Numerical results are consistent with other works available in literature [10,11], in particular EDM-2S are close to Schaffel et al. [11], while EDC-DRM to Kim et al. [10]. Furthermore, they are compared with experimental data considering six radial traverses, placed at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner inlet.
ity in the central air jet is lower than expected velocity. The peak velocity measured at the first traverse was about 47 m/s, while it was expected a value very close to the inlet. The measured low velocity may be explained by considering poor LDA measurements in the central jet. Results of numerical simulations are very similar, and only minor differences can be observed considering the traverse at 1.32 m near the mixing zone existing between the two jets. In particular, EDC-DRM gives the best agreement with experimental data in this zone, consequently to the better evaluate of temperature.
4.1. Axial velocity 4.2. Temperature Fig. 3a shows the comparison of the axial velocity contour plot on the axial section for the numerical simulations. Fig. 3b shows the comparison between numerical results and experimental data for the axial velocity along the six radial traverses. A central and a lateral jet can be observed, corresponding to the inlet of the vitiated secondary air and of the primary air streams respectively. In general numerical axial velocity fits the experimental data very well, except for the central jet peak, which numerical results are higher. Orsino et al. [2] pointed out that the measured peak veloc-
Fig. 4a shows the comparison of the temperature contour plot on the axial section for the numerical simulations. Fig. 4b shows the comparison between numerical results and experimental data for the temperature along the six radial traverses. Numerical results are characterized by a small front of flame around the secondary flow jet, that cannot be observed from the experimental data. It is more pronounced where the lateral jet interacts with the central one. All the numerical simulations are
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
(b)
CO2 dry mol. fct.
(a)
z=0.15 m
0.4
0.2
CO2 dry mol. fct.
0.2
0
0 0
0.5
1
z=0.735 m
0.4
0
0.5
1
z=1.32 m
0.4
0.2
0.2
0
0 0
CO2 dry mol. fct.
z=0.44 m
0.4
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
0.4
0
1
z=3.22 m
0.4
0.2
0.5
0.2
0
0 0
0.5
1
0
x, m
0.5
1
x, m
Fig. 6. CO2 dry molar fraction: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
characterized by a cold zone correspondent to the lateral jet, that is not observed by measurements. The cold zone, related to a delayed ignition of the flame, does not directly depend on turbulence– chemistry model, but on the poor performances of the drying and devolatilization models. EDM-2S produces a higher value of the flame temperature, especially considering the second and the third traverses, where large gradients are observed near the secondary jet, because of the higher reactions rate related to the infinitely fast chemistry assumption of EDM. EDC-JL estimates a reasonable temperature profile at 0.44 m from the burner, but its results upstream are similar to EDM-2S, overestimating the experimental results. In particular, after the traverse at 1.32 m, EDM-2S and EDC-JL show very similar behavior, because the gas phase reactions are dominated by the oxidation of CO produced by heterogeneous reactions (Eqs. (1)–(3)). In fact, CO follows similar paths in 2S and JL, directly producing CO2 according to reactions in Eqs. (5) and (8) respectively. Instead, considering DRM, CO follows a more complex path, producing several intermediate species, before being completely converted into CO2.
Finally, EDC-DRM is characterized by a better agreement with measurements, slightly overestimating the flame temperature. Its better behavior is generally related to the detailed kinetic mechanism considered, able to describe in detail the slow chemistry of the MILD combustion. Whereas, global kinetic mechanisms, such as 2S and JL, are unable to describe correctly the behavior of flameless regime of the MILD combustion. 4.3. Chemical species Fig. 5a shows the comparison of the contour plots of the CO dry molar fraction on the axial section for the numerical simulations. Fig. 5b shows the comparison between numerical results and experimental data for the CO dry molar fraction along the six radial traverses. A large difference among the numerical results can be observed from the contour plots and the radial profiles. In particular, EDC-DRM is characterized by the widest zone where the presence of CO can be observed. This zone is comprised between the second and fifth traverses. Large part of CO is produced from char burnout, according to the reactions (1)–(3). However, the
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
(b)
O2 dry mol. fct.
(a)
z=0.15 m
0.3
experimental EDM EDC-JL EDC-DRM
0.2 0.1
0.2 0.1
0
0
O2 dry mol. fct.
0
0
z=0.735 m
0.3 0.2
0.2
0.1
0.1
0
z=1.32 m
0.3
0 0
O2 dry mol. fct.
z=0.44 m
0.3
0
z=2.05 m
0.3 0.2
0.2
0.1
0.1
0
z=3.22 m
0.3
0 0
x, m
0
x, m
Fig. 7. O2 dry molar fraction: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
differences among the simulations are mainly related to the different kinetic mechanisms considered for the reactions in the homogeneous phase. Indeed, as previously described, the global 2S and JL mechanisms are unable to describe the slow chemistry of the MILD combustion, evaluating lower CO concentrations. Instead, DRM mechanism predicts higher CO concentrations, related to the more detailed mechanism, able to better describe the behavior of the process. In fact, this detailed mechanism allows consideration of the formation of secondary species through intermediate reactions which cannot be accounted for by the global mechanisms. The higher CO concentrations of EDC-DRM is therefore related to the lower flame temperature, due to the slower heat release. With respect to experimental data, numerical simulations calculate larger concentrations di CO starting from the second traverse. This difference could be related to the high production of CO from the char burnout. In fact, considering the char oxidation reaction (Eq. (1)) only CO formation is considered. In addition, considering the first traverse at 0.15 m experimental data are characterized by the presence of CO, that is completely absent in the numerical simulations. This region corresponds to the cold zone
in the secondary air jet (Fig. 4) and depends on the ignition delay in the numerical simulations. As previously explained, it could be attributed to inconsistencies in drying and devolatilization models. Fig. 6a shows the comparison of the contour plots of the CO2 dry molar fraction on the axial section for the numerical simulations. Fig. 6b shows the comparison between numerical results and experimental data for the CO2 dry molar fraction along the six radial traverses. Numerical simulations predict very similar CO2 concentrations, especially considering the first two traverses. Differences can be observed considering the third and fourth traverses, where EDC-DRM predicts lower CO2 concentrations, related to the higher CO calculated and by the slower chemistry, as described previously. Considering the last two traverses, CO2 evaluated concentrations are very similar. Good agreement with experimental data can be generally observed for CO2 concentrations for all the simulations. Finally, Fig. 7a shows the comparison of the contour plots of O2 dry molar fraction on the axial section for the numerical simulations, while Fig. 7b shows the comparison between numerical results and experimental data for the O2 dry molar fraction along the six radial traverses. This time, numerical simulations predict
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
(b)
NO dry ppmv
(a)
z=0.15 m
2000
1000
NO dry ppmv
1000
0
0 0
0.5
1
z=0.735 m
2000
0
0.5
1
z=1.32 m
2000
1000
1000
0
0 0
NO dry ppmv
z=0.44 m
2000
experimental EDM EDC-JL EDC-DRM
0.5
1
z=2.05 m
2000
0
1
z=3.22 m
2000
1000
0.5
1000
0
0 0
0.5
x, m
1
0
0.5
1
x, m
Fig. 8. NO dry ppm: (a) distribution of numerical results along the axial section; (b) profiles of numerical and experimental results at 0.15, 0.44, 0.735, 1.32, 2.05 and 3.22 m from the burner.
very similar O2 concentrations for all the traverses, showing very good agreements with experimental data. In particular, the main difference can be observed for the first traverse, where the calculated O2 peak is absent in measurements. It may again be related to the delayed ignition of the flame predicted by the simulations. 4.4. NO concentration Fig. 8a shows the comparison of the contour plots of the NO dry molar fraction on the axial section for the numerical simulation. Fig. 8b shows the comparison between numerical results and experimental data for the NO dry molar fraction along the six radial traverses. Fig. 9 shows the NO production and destruction according to the main mechanisms considered: thermal-NO, fuelNO and reburning.1 Numerical simulations predict very similar NO concentrations and only minor differences can be observed. In fact considering coal and in particular MILD combustion, fuel-NO is the main mechanism (Fig. 9). On one hand, thermal-NO plays a second-
1
Prompt-NO are not showed because their contribute is negligible.
ary role, especially in MILD combustion where flame temperature is lower than conventional combustion. Therefore, the temperature differences existing among numerical simulations produce only small variation on NO concentrations. On the other hand, NO formation through the fuel mechanism gives very similar values because it depends mainly by devolatilization and burnout rates, which are similar for the numerical simulations. On the contrary, the main differences existing among numerical simulations may be attributed to the reburning mechanism (Fig. 9), which depends on the NO recombination with hydrocarbons. Considering the post-processing approach, an equivalent hydrocarbon molecule is evaluated from the species containing carbon and hydrogen atoms present in the simulation. The equivalent hydrocarbon molecule reacts with NO producing mainly HCN according to the Eq. (10). EDM-2S is characterized by the larger reburning effect, since the equivalent hydrocarbons is given only by the pseudo-volatile species CxHyOz. For this reason, the reburning is strongly concentrated in the devolatilization zone after the first traverse near the secondary jet, where the pseudo-volatile concentration is greater. Consequently this zone the NO concentration is strongly overestimated.
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M. Vascellari, G. Cau / Fuel 101 (2012) 90–101
NO formation, mg/s
2 EDM-2S EDC-JL EDC-DRM
1
5. Conclusions
0
-1 Thermal-NO
Fuel-NO
NO-reburning
Fig. 9. NO production and destruction rates according the main mechanisms.
char burnout, %
100 H2O CO2 O2
50
0
zone, improving gasification and Boudouard reactions with respect to EDM-2S. However, EDC-DRM is characterized by lower flame temperature that reduces the importance of these reactions compared to the oxidation one.
EDM-2S
EDC-JL
EDC-DRM
Fig. 10. Influence of the heterogeneous reactions on char burnout.
Considering the other simulations, reburning effect is less pronounced. Equivalent hydrocarbon is evaluated from TAR and CH4 for EDC-JL. Whereas, additional species, such as CH2 and CH3, are considered for EDC-DRM. Therefore, the reburning is not concentrated only in the devolatilization zone, as in the previous case, but it is spread out into a wider area, reducing its intensity. From comparison with measurements, numerical simulations predict acceptable NO concentrations, especially considering the last traverses. The NO peak is correctly described by the EDC-JL and EDC-DRM, while this behavior is not correctly reproduced by EDM-2S, as previously described. As previously described for temperature and chemical species concentrations, the delayed flame ignition can be observed at 0.15 m. 4.5. Heterogeneous reaction Fig. 10 shows the repartition of the char burnout among the heterogeneous reactions considered (Eqs. (1)–(3)), respectively with O2, CO2 and H2O. Contribution of gasification and Boudouard reactions is relevant, as already observed by Stadler et al. [6], because of the large amount of CO2 and H2O present in the furnace, due to the recirculation of the exhaust gas. In fact, from Figs. 6 and 7, the partial pressure of CO2 is generally larger than O2, producing a sensible increase of char converted through the gasification reactions with respect to the conventional combustion, where the influence of these reactions is almost negligible. Generally, gasification reactions alter CO/CO2 balance, increasing CO concentrations and reduces particle temperature because of their endothermic behavior. However, considering moderate and low O2 concentrations, similar burning rates and flame temperatures are observed, related to the higher CO concentrations that improve reactions rates in the gas phase [43]. Considering the different simulations, EDC-JL shows larger influence of gasification reactions. In fact, EDC-JL and EDC-DRM are characterized by direct production of CO2 and H2O during devolatilization, and consequently by higher concentrations in this
This work investigates the influence of turbulence–chemistry interaction on CFD pulverized coal MILD combustion modeling, with the aim of understanding which models are more accurate and suitable to reproduce the process. In particular, two turbulence–chemistry interaction models are examined. The first is the conventional Eddy Dissipation Model (EDM), based on infinitely fast chemistry assumption, that is used with a global two-step mechanism. The second is the advanced finite rate chemistry, Eddy Dissipation Concept, used with both global and detailed kinetic mechanisms. Numerical results are with IFRF pulverized coal MILD combustion test case. Generally, MILD combustion differs from conventional combustion because the chemical reactions take place in a wider zone with reduced gradients of temperature and chemical species concentrations. For this reason, infinitely fast chemistry turbulence–chemistry models, such as EDM, can not correctly reproduce the process. Therefore, advanced models, such as EDC, used with detailed kinetic mechanisms, are required to correctly simulate MILD combustion. From the comparison of numerical simulations with experimental measurements, advanced turbulence–chemistry interaction models used with detailed kinetic mechanisms allow to better reproduce the chemical and fluid dynamic behavior of coal MILD combustion, as expected, especially considering velocity, temperature and species concentrations. However, considering coal MILD combustion the influence of the turbulence–chemistry interaction model is less pronounced when considering gaseous fuels (see [14,16]), because only the volatile matter reacts in the gaseous phase, while the remaining char is burned through heterogeneous surface reactions. Therefore, global mechanisms may produce satisfactory results and could be preferred owing to their lower computational cost with respect to the detailed mechanisms if extreme accuracy is not required. On the other hand, coal conversion mechanisms, such as drying, devolatilization and burnout, are responsible for the delayed flame lift-off observed considering different turbulence–chemistry models and kinetic mechanisms. Therefore the influence of these models needs to be further investigated in order to increase the overall accuracy of the simulations. Another critical aspect requiring further studying is the influence of the radiation model and of the radiative properties of the gas and solid phases.
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