Comparison of Global Ammonia Chemistry Mechanisms in Biomass

Energy & Fuels 2008, 22, 297–305

297

Comparison of Global Ammonia Chemistry Mechanisms in Biomass Combustion and Selective Noncatalytic Reduction Process Conditions A. Saario* and A. Oksanen Institute of Energy and Process Engineering, Tampere UniVersity of Technology, Korkeakoulunkatu 6, P.O. Box 589, 33101 Tampere, Finland ReceiVed May 11, 2007. ReVised Manuscript ReceiVed September 11, 2007

The success of computational fluid dynamics (CFD)-based combustion modeling is strongly dependent on several submodels which are required to close the Favre-averaged conservation equations for mass, momentum, and energy and transport equations for scalar quantities. Due to the requirement of high computer capacity, global mechanisms are frequently applied to model chemical reactions in industrial-scale CFD. The present study compares the performance of five global ammonia chemistry mechanisms in the conditions typical of the biomass combustion in fluidized beds. A special emphasis is given to the modeling of the selective noncatalytic reduction (SNCR) process. A modification of the standard k–ε model is used to model turbulence, the eddy dissipation combustion model and the eddy dissipation concept are used to model turbulence–chemistry interaction, and the finite-volume method (discrete ordinates) together with the weighted sum of gray gases model are used to model radiative heat transfer. A simplified approach is used to consider the bubbling bed in the overall CFD model. Experimental data available on the temperature and the concentrations of NH3 and NO are presented to validate the predictions to a certain extent. The results are shown to be strongly dependent on the chemistry model. Global chemistry models typically perform well under the conditions for which they were derived, but under other conditions, they may fail badly. Models suitable for modeling the SNCR process are identified, and it is shown that the correct emission trends can be predicted as a function of the SNCR process load. Due to the different conditions in the lower part near the bubbling bed and the upper section of the freeboard, a combination of more than one model may be a good approach in modeling the overall process.

Introduction One reaction may be dominant at a given temperature, whereas at different temperatures or concentrations other competitive reactions may also need to be considered.1 Hence, global reaction mechanisms are typically capable of yielding reasonable predictions only under conditions similar to those that they were derived for, which makes their selection and validation a critical issue. Here, the main interest is to identify a suitable global mechanism for computational fluid dynamics (CFD) modeling. This mechanism must be able to describe satisfactorily the nitrogen chemistry under biomass combustion conditions, including the selective noncatalytic reduction (SNCR) process. In an earlier study,2 the authors compared the performance of four global nitrogen chemistry mechanisms in modeling biomass combustion and the SNCR process. Most mechanisms were found to fail badly in predicting NO emission reduction. However, it was difficult to draw any definite conclusions, first, due to the deficiencies in the turbulence–chemistry interaction model applied,3 and second, due to the lack of experimental data. Here, a more suitable approach for emission modeling by * To whom correspondence should be addressed. Phone: +358 40 849 0879. Fax: +358 3 3115 3751. E-mail:[email protected]. (1) Kuo, K. K. Principles of Combustion; John Wiley & Sons: Hoboken, NJ, 2005. (2) Saario, A.; Oksanen, A.; Ylitalo, M. Clean Air 2006, 7, 105–126. (3) Magnussen, B. F.; Hjertager, B. H. Proc. Combust. Inst. 1976, 16, 719–729.

Magnussen4–6 is applied, and some experimental data are available for validation purposes. The five mechanisms compared here are those of Brink et al.,7 Brouwer et al.,8 DeSoete,9 Duo et al.,10 and Mitchell and Tarbell.11 Global mechanisms in biomass combustion have been compared earlier in only a few studies.2,12,13 They generally conclude that none of the global mechanisms have the potential to predict correctly the nitrogen chemistry over a wide range of conditions. In these studies, the mechanisms of Brouwer et al. and of Mitchell and Tarbell have yielded reasonable results under certain operating conditions, whereas the mechanism of DeSoete has constantly failed. (4) Ertesvåg, I. S.; Magnussen, B. F. Combust. Sci. Technol. 2000, 159, 213–235. (5) Magnussen, B. F. Modeling of NOx and Soot Formation by the Eddy Dissipation Concept; IFRF 1st Top. Oriented Tech. Meet., Amsterdam, The Netherlands, Oct 17–19, 1989. (6) Magnussen, B. F. The Eddy Dissipation Concept a Bridge between Science and Technology, ECCOMAS Themat. Conf. Comput. Combust., Lisbon, Portugal, June 21–24, 2005. (7) Brink, A.; Kilpinen, P.; Hupa, M. Energy Fuels 2001, 15, 1094– 1099. (8) Brouwer, J.; Heap, M. P.; Pershing, D. W.; Smith, P. J. Proc. Combust. Inst. 1996, 26, 2117–2124. (9) DeSoete, G. G. Proc. Combust. Inst. 1974, 15, 1093–1102. (10) Duo, W.; Dam-Johansen, K.; Østergaard, K. Can. J. Chem. Eng. 1992, 70, 1014–1020. (11) Mitchell, J. W.; Tarbell, J. M. AIChE J. 1982, 28, 302–311. (12) Kjäldman, L. Application of Different Chemical NO-Mechanisms to Numerical Flow Simulation of PulVerized Peat Combustion, 4th Int. Conf. Technol. Combust. Clean Environ., Lisbon, Portugal, July 7–10, 1997. (13) Norström, T.; Kilpinen, P.; Brink, A.; Vakkilainen, E.; Hupa, M. Energy Fuels 2000, 14, 947–952.

10.1021/ef700238a CCC: $40.75  2008 American Chemical Society Published on Web 11/22/2007

298 Energy & Fuels, Vol. 22, No. 1, 2008

Saario and Oksanen

Figure 2. Location of NH3 injections at 5.5 and 7.5 m levels (seen from boiler roof). Contours correspond to NH3 concentration (ppm vol).

Figure 1. Boiler sketch. Table 1. Chemical Properties of Biomass Sludge (d ) dry, daf ) dry ash free, ar ) as received) biomass sludge moisture content carbon (d) hydrogen (d) nitrogen (d) sulfur (d) oxygen (d) ash (d) volatile matter (daf) fixed carbon (daf) lower heating value (ar)

value 40.4 wt % 28.3 wt % 3.2 wt % 0.5 wt % 0.1 wt % 16.8 wt % 51.1 wt % 81.8 wt % 18.2 wt % 4.22 MJ/kg

Table 2. Boiler Operating Conditions [tds/d ) tons of dry solids per day (24 h)] operating parameter fuel mixture flow rate supporting fuel flow rate (CH4) combustion air flow rate single ammonia + air injection mass flow rate axial inlet velocity ammonia concentration in current design

value 315 tds/d 0.24 kg/s 23.0 kg/s 0.13 kg/s 115 m/s 0.66 vol %

Case Description. A sketch of the bubbling fluidized bed boiler studied is shown in Figure 1. The mixture burned in the boiler consists mainly of biomass sludge originating from the deinking and effluent treatment processes of a newsprint mill (for the chemical properties of biomass sludge, see Table 1). In addition, a moderate amount of plastic reject is mixed with the biomass sludge, and some natural gas (CH4) is added into the boiler as supporting fuel. The boiler has a capacity of 40 MWth, and the operating conditions are summarized in Table 2. The SNCR process is applied; a mixture of NH3 and air can be injected from three separate levels at heights between 5.5 and 7.5 m (see Figure 1). There are eight injections per level at the heights of 5.5 and 7.5 m (see Figure 2) and one injection at a height of 6.5 m on the rear wall. Selective Noncatalytic Reduction (SNCR). The SNCR process (NH3 injection) is a low-cost, effective, and retrofittable NO control strategy, which has been studied extensively.14–18 The injected NH3 initiates a sequence of reactions that converts (14) Alzueta, M. U.; Røjel, H.; Kristensen, P. G.; Glarborg, P.; DamJohansen, K. Energy Fuels 1997, 11, 716–723. (15) Kasuya, F.; Glarborg, P.; Johnsson, J. E.; Dam-Johansen, K. Chem. Eng. Sci. 1995, 50, 1455–1466.

NO formed in the lower part of the boiler into N2. Typically, the efficiency of NO reduction, operating temperature, and NH3/ NO molar ratio vary in the range 30–80%, 1073–1373 K, and 0.8–2.5, respectively.17 The principal chemical reactions taking place in the SNCR process can be found in Miller and Bowman.16 The NO reduction efficiencies in practical combustion systems are primarily dependent on the following factors: absolute temperature level as well as nonisothermal temperature profile, local flue gas conditions, mixing of NH3 and NO, NH3/NO molar ratio, and NH3 residence time. The reduction of NO is achieved only inside a relatively narrow temperature window centered approximately at 1250 K in the absence of other additives (see Figure 3a). At too-high temperatures, NH3 oxidizes to NO, and at too-low temperatures, NH3 passes unreacted through the reaction zone, causing NH3 emission (ammonia slip) in flue gas. Typically, industrial boilers have large cross-sectional areas over which the injection system must disperse NH3 and mix it with NO. Moreover, these boilers may have to operate with different loads, which may change the spatial location of the optimum temperature window for the NO reduction. It is known that the presence of CO (or other additives) shifts the optimal temperature window for the NO reduction toward lower temperatures (see Figure 3b). Higher NO reductions obtained by increasing the NH3/NO ratio have been observed,14,15,18 but also, a significantly higher ammonia slip at the optimum temperature was observed. Although the presence of O2 is necessary for the NO reduction by NH3, the variation of O2 concentration in the range 0.5–4 vol % at the typical SNCR process temperature 1273 K does not have a significant effect on the NO reduction potential.14,15 It has been found also that the O2 concentration in the jet carrier gas has no effect on the process and that an increase in the ratio of the momentum of the jet to the momentum of the main gas flow improves the NO reduction.18 Mathematical Modeling Assuming incompressible steady-state flow, the Favre-averaged (see, e.g., Poinsot and Veynante,19 (pp 140–142) continuity and momentum equations are written as ∂ (F¯ u˜ ) ) 0 ∂xi i

(1)

(16) Miller, J. A.; Bowman, C. T. Prog. Energy Combust. Sci. 1989, 15, 287–338. (17) Radojevic, M. EnViron. Pollut. 1998, 102, 685–689. (18) Østberg, M.; Dam-Johansen, K.; Johnsson, J. E. Chem. Eng. Sci. 1997, 52, 2511–2525. (19) Poinsot, T.; Veynante, D. Theoretical and Numerical Combustion; R. T. Edwards: Philadelphia, PA, 2005.

Comparison of Global Ammonia Chemistry Mechanisms

Energy & Fuels, Vol. 22, No. 1, 2008 299

Figure 3. SNCR process principle. Oxygen concentration 4 vol %. Experimental data from (a) Kasuya et al.15 and (b) Alzueta et al.14

[(

)]

∂u˜i ∂u˜j 2 ∂u˜l ∂ ∂p¯ ∂ (F¯ u˜ju˜i) ) + µ + - δ + ∂xj ∂xi ∂xj ∂xj ∂xi 3 ij ∂xl ∂ ''u'') F¯ gi + (Fu˜ ∂xj i j

(2)

The overbar denotes time averaging, the tilde denotes Favre averaging, and the double prime denotes the Favre-averaged fluctuating part. Symbol F is the density, ui is the ith component of the velocity vector, p is the pressure, µ is the viscosity, δij is the Kronecker delta (δij ) 1 if i ) j, and δij ) 0 if i * j), and gi is the ith component of the gravitational vector. Here, the ''u'' , are modeled using a modification Reynolds stresses, -F¯ u˜ i j of the widely applied standard k–ε model.20 The Favre-averaged conservation equation for the sensible enthalpy, hs, of a mixture is written as

(

)

∂ ∂ ∂T˜ ˜ + S˜ ''h'' + S (Fu˜ h˜ ) ) λ - F¯ u˜ i s rad comb ∂xi i s ∂xi ∂xi

(3)

where λ is thermal conductivity, T is temperature, and Srad and Scomb are the source terms from radiation and combustion, respectively. Turbulent enthalpy fluxes are closed using the gradient diffusion hypothesis: ''h'' ) -F¯ u˜ i s

cpµt ∂T˜ Prt ∂xi

(4)

where cp is the specific heat capacity of a gas mixture at constant pressure, which is calculated using a temperature-dependent polynomial, and µt is the turbulent viscosity. Turbulent transport of momentum and heat or mass is due to the same mechanism, eddy mixing, and thus, the values of turbulent diffusivities are likely to be of the same order.21,22 The turbulent Prandtl number, Prt, is an empirical constant for which values of 0.5–0.7 and 0.9 have been recommended for free shear layers and boundary layers, respectively.21,23 Here, the value of Prt is set at 0.7, except in the wall function equations where the value 0.9 is used. S˜ rad is obtained using the finite-volume method24 (discrete ordinates) to solve the radiative heat transfer equation. The gas-phase absorption coefficient in the radiative heat transfer equation is (20) Shih, T.-H.; Liou, W. W.; Shabbir, A.; Yang, Z.; Zhu, J. Comput. Fluids 1995, 24, 227–238. (21) White, F. M. Viscous Fluid Flow; McGraw-Hill: New York, 2006. (22) Pope, S. B. Turbulent Flows; Cambridge University Press: Cambridge, U.K., 2000. (23) Wilcox, D. C. Turbulence Modeling for CFD; DCW Industries: La Cañada, CA, 1998. (24) Raithby, G. D.; Chui, E. H. J. Heat Transfer-Trans. ASME 1990, 112, 415–423.

determined using the weighted sum of gray gases model.25 The calculation of S˜ comb is described later in the present section. The Favre-averaged species conservation equations are written as

(

)

Y˜k ∂ ∂ ˜ ''Y'' + S (Fu˜iY˜k) ) F¯ Dk - F¯ u˜ i k k ∂xi ∂xi ∂xi

(5)

where Yk is mass fraction, Dk is diffusivity, and Sk is the source term of species k. Turbulent species fluxes are closed using the gradient diffusion hypothesis: ''Y'' ) -F¯ u˜ i k

µt ∂Y˜k Sct ∂xi

(6)

Experimental information on the turbulent Schmidt number, Sct, is very scarce (see p 512 of ref 26). Here, the value of Sct is set at 0.7 (consistent with Prt). Assuming a sufficiently high turbulence level, the laminar diffusive fluxes in eqs 3 and 5 are usually small compared to turbulent transport (see p 143 of ref 19). Numerical Solution. A thorough grid refinement study was carried out before creating the computational grid applied here.27 On the basis of the findings, a structured grid consisting of 348 709 computational cells was built. The grid was refined locally twice in the vicinity of the NH3 injections, using velocity gradients as the refinement criterion. In both refinement steps, the number of cells was increased on average by 3444 per NH3 injection. The number of cells at the NH3 injection inlets is 40–52. A commercial finite-volume-based CFD solver Fluent28 is used to solve the discretized equations applying the Gauss–Seidel method together with the algebraic multigrid approach. The pressure field is calculated from the continuity equation using the SIMPLE algorithm. The discretization of convective terms is performed applying a second-order accurate upwinding scheme, and a second-order central difference discretization scheme is used for the diffusion terms. A more detailed description of the computational grid, the numerical solution procedure, and the turbulence model applied can be found elsewhere.27 (25) Smith, T. F.; Shen, Z. F.; Friedman, J. N. J. Heat Transfer-Trans. ASME 1982, 104, 602–608. (26) Kays, W. M.; Crawford, M. E. ConVectiVe Heat and Mass Transfer; McGraw–Hill: New York, 1993. (27) Saario, A.; Oksanen, A. Int. J. Numer. Methods Heat Fluid Flow,under consideration for publication. (28) Fluent Inc. Fluent 6.2 User’s Guide, 2005; http://www.fluent.com.


Saario and Oksanen

account the heat necessary to ignite the reactants. The values A ) 4 and B ) 0.5 are used as proposed in Magnussen and Hjertager.3 One should remember that the constants A and B may differ substantially for different flows.3,29 Moreover, in reality, a wide range of turbulent scales is likely to be involved, and not only the integral scale as proposed in eq 7.19 The reaction rate controlled by chemical kinetics, R¯KIN, is included in the definition of the effective reaction rate, R¯EFF, as R¯EFF ) min(R¯EDCM, R¯KIN)

Figure 4. Bubbling bed element balance. m˘ , L, M, R, prim, supp, vol, and prod stand for mass flow rate, left, middle, right, primary air, supporting fuel, volatiles, and products of char and supporting fuel combustion, respectively. i ) C, H, O, N.

Figure 5. Determination of boundary conditions on the bed surface for nitrogen-containing species. The quantity of fuel-N in each branch is determined on the basis of experiments with biomass sludge (see Figure 9b of ref 35). vol, r, and nr stand for volatiles, reactive, and nonreactive, respectively. Table 3. Boundary Conditions on the Bed Surface for Three Fuel Landing Areas (vol,L; vol,M; and vol,R) and for the Other Part of Bed (prod) mass flow rate (kg/s) temperature (K) CH4 (vol %) H2 (vol %) CO (vol %) O2 (vol %) CO2 (vol %) H2O (vol %) N2 (vol %) NH3 (ppmvol) NO (ppmvol)

vol,L

vol,M

vol,R

prod

3.95 1064 5.6 0.0 0.0 0.0 12.8 40.9 40.7 2063 0

3.62 1064 5.3 0.0 0.0 0.0 12.8 39.8 42.1 1980 0

3.10 1064 3.8 0.0 0.0 0.0 12.8 36.1 47.2 1687 0

10.51 1064 0.0 0.0 0.0 10.5 7.1 8.1 74.2 0 177

Eddy Dissipation Combustion Model. The turbulence–chemistry interaction of hydrocarbon species is modeled using a simple eddy dissipation combustion model (EDCM).3 The model assumes that the local reaction rate, R¯EDCM , is proportional to the dissipation rate of turbulent eddies containing reactants and products as well as to the mean concentration of the limiting species, as follows:

(

)

Y˜P Y˜O ε , B (7) R¯EDCM ) F¯ A min Y˜F, k s (1 + s) Here, F¯ is the mean density, A and B are model constants, ε/k is the inverse of the turbulent mixing integral time scale, Y˜F and Y˜O are the mean mass fractions of fuel and oxygen, respectively, and s is the stoichiometric coefficient. Y˜P is the mean mass fraction of combustion products, which takes into

(8)

where R¯KIN is calculated using the Arrhenius-type expression and the Favre-averaged values (i.e., neglecting the effect of turbulent fluctuations). Eddy Dissipation Concept. The turbulence–chemistry interaction of nitrogen-containing species (NH3, NO) is modeled using the advanced eddy dissipation concept (EDC) of Magnussen.4–6 The postprocessing technique is applied to solve the transport equations of NH2 and NO. The basic concept of the EDC model is the fine structure reactor, in which the reactants are mixed at the molecular level. A detailed description of the energy cascade establishing the connection between the large-scale characteristics of turbulence and the Kolmogorov-scale fine structures can be found elsewhere.4–6 Although the EDC is partly based on intuitive arguments, it nevertheless captures some important features of turbulence structural interaction and dissipation. The characteristic scales for the fine structure level can be written as

(

L/ ) 1.42

(µ ⁄ F¯ )3 ε

)

1⁄4

,

( µεF¯ )

u/ ) 1.74

1⁄4

(9)

where the asterisk denotes the level of fine structure scales and L and u are the characteristic length and velocity scales, respectively. The fine structure mass fraction, γ*, is expressed as γ/ )

() u/ u′

2

( )

) 4.54

µε F¯ k2

1⁄2

(10)

where the slanted prime denotes the level of largest turbulent scales. The mass transfer rate between the fine structures and the surroundings, m ˙ , is modeled as m ˙ )2

ε u/ / γ ) 11.11 / k L

(11)

The mass transfer rate, m˘ , can be interpreted as the mean rate of molecular mixing, and the mean reaction rate of chemical species, R¯i, is assumed to be a linear function of this quantity: F¯ m ˙ χη ˜ R¯i ) (Yi - Y/i ) 1 - χγ/

(12)

where χ is the reactive fraction of the fine structures and η is a parameter used to improve the predictions in the tale of the / / in eq 12 flame.5 The fine structure mass fractions YNH3 and YNO are solved from transport equations with appropriate source / / in terms.30 The consumption or the production of YNH3 and YNO the fine structures is calculated using the Arrhenius-type expressions for reactions r5 and r6 below. (29) Chomiak, J. Combustion - A Study in Theory, Fact and Application; Abacus Press/Gordon and Breach Science Publishers: New York, 1990. (30) Kjäldman, L. Numerical Simulation of Combustion and Nitrogen Pollutants in Furnaces. Ph.D. Thesis, VTT Technical Research Centre of Finland, Espoo, Finland, 1993.



Figure 6. Predicted average mass-weighted quantities as a function of boiler height.

Hydrocarbon Chemistry. The reactions of hydrocarbon species are modeled using a global mechanism:31 CH4 + 1⁄2O2 f CO + 2H2

(r1)

CH4 + H2O f CO + 3H2

(r2)

H2 + 1⁄2O2 h H2O

(r3)

CO + H2O h CO2 + H2

(r4)

The mechanism includes two competing fuel breakdown reactions, of which reaction r1 is dominant under fuel-lean conditions and reaction r2 is important under fuel-rich conditions. Fuel breakdown reactions are followed by two reversible reactions, of which reaction r3 is treated here as irreversible by virtue of the relatively low temperatures in the bubbling fluidized bed boiler. It should be noted that the application of EDCM with the above mechanism may lead to nonphysical situations in some computational cells.32 Global Ammonia Chemistry Mechanisms. The homogeneous reactions of NH3 are modeled using a global two-step reaction mechanism: NH3 + O2 f NO + H2O + 1⁄2H2

(r5)

NH3 + NO f N2 + H2O + 1⁄2H2

(r6)

Several sets of reaction-rate parameters for the above reactions are available in the literature. Here, the following five global mechanisms are applied: Brink et al.,7 Brouwer et al.,8 DeSoete,9 Duo et al.,10 and Mitchell and Tarbell.11 The mechanism of Brink et al. was developed for the conditions in biomass combustion. The rate parameters were extracted from the perfectly stirred reactor calculations with a detailed mechanism. In the calculations, the temperature was varied between 900 and 1900 K and the oxygen concentration between 1 and 10 vol %. The mechanism of Brouwer et al. was developed for the prediction of the SNCR chemistry. The rate parameters were determined using a detailed mechanism for the conditions spanning typical SNCR operation. The parameters varied included the molar ratio NH3/NO and the temperature, which was varied between 873 and 1423 K. An empirical adjustment is included in the mechanism to take into account the effect of CO. (31) Jones, W. P.; Lindstedt, R. P. Combust. Flame 1988, 73, 233– 249. (32) Brink, A.; Mueller, C.; Kilpinen, P.; Hupa, M. Combust. Flame 2000, 123, 275–279.

The mechanism of DeSoete was developed on the basis of premixed flame measurements. The temperature in the measurements was varied between 1874 and 2355 K, so the temperature range was clearly beyond that in the present study. In the past, this mechanism has been widely applied in industrial boiler modeling. The mechanism of Duo et al. was developed for the prediction of the SNCR chemistry. The rate parameters were fitted to reproduce a set of experimental data obtained using an isothermal plug flow reactor. The temperature in the reactor was varied between 1140 and 1335 K at 4 vol % oxygen concentration, and the molar ratio NH3/NO was set at 1.64. The complete mechanism of Mitchell and Tarbell was developed for the conditions of pulverized coal combustion. The rate parameters of the homogeneous reactions of NH3 were fitted to reproduce a set of experimental data obtained from a plug flow reactor under SNCR conditions.33 The temperature in the experiments was varied between 1080 and 1285 K at 4 vol % oxygen concentration, and the molar ratio NH3/NO was varied between 0.3 and 1.6. In the predictions of the present study, the average temperature, the maximum temperature, and the average oxygen concentration in the boiler at heights between 5 and 10 m are 1180 K, 1440 K, and 5 vol %, respectively. Boundary Conditions on the Bed Surface. The freeboard of the boiler is modeled using CFD, whereas a less sophisticated approach is applied to consider the dense bubbling bed. The interactions between the solids-free bubble phase and solidsladen emulsion phase taking place in the dense bottom bed and the splash zone (zone caused by the ejection of dense bed particles by the bubbles) are not considered in detail in the present approach. The boundary conditions for CFD on the dense bed surface are obtained on the basis of element and energy balances of the known fuel composition. These balances are supplemented by the knowledge of chemical properties of fuel and by reasonable assumptions regarding fuel supply and bed processes. In addition, laboratory experiments are exploited to obtain the boundary conditions for the nitrogen-containing species. This simplified approach applied for the bed modeling can be seen as a reasonable approximation in full-scale fluidized bed boiler CFD modeling, considering the complexity of bubbling bed phenomena and the limits of the computational capacity of current computers. (33) Muzio, L. J.; Arand, J. K.; Teixeira, D. P. Proc. Combust. Inst. 1976, 16, 199–208.


Saario and Oksanen

when determining the size and location of fuel landing areas. A fuel conveyor delivers fuel to the chutes from the left wall side, hence causing an uneven fuel distribution between the chutes. On the basis of practical experience obtained from the boiler, the division of fuel mass flow between the left, middle, and right fuel chutes is assumed to be 40, 35, and 25%, respectively. The time for char combustion is long compared with lateral dispersion time,34 and consequently, it is assumed that char combustion is evenly distributed inside the bed. On the basis of Figure 4, the balance for any element i (i ) C, H, O, N) can be written as ˙ i,fuel,M + m ˙ i,fuel,R + m ˙ i,prim + m ˙ i,supp ) m ˙ i,fuel,L + m m ˙ i,vol,L + m ˙ i,vol,M + m ˙ i,vol,R + m ˙ i,prod

(13)

where m˘ , L, M, R, prim, supp, vol, and prod stand for mass flow rate, left, middle, right, primary air, supporting fuel, volatiles, and products of char and supporting fuel combustion, respectively. The element balances are supplemented by a similar enthalpy balance which includes all of the fluxes shown in Figure 4. The enthalpy balance is complemented with terms which take into account the heat flux between the bed and the freeboard. This heat flux consists of the radiative heat transfer and the convective heat transfer associated with bed particles ejected from the dense bed. The radiative heat flux is obtained iteratively from the CFD predictions, whereas the convective heat flux is approximated. The temperature of the bed is known from the measurements. Laboratory experiments are exploited, and some additional assumptions are made in order to determine the boundary conditions for the nitrogen-containing species on the bed surface. Here, the oxidation of fuel-bound nitrogen accounts for practically all NO in flue gas, since the formation of thermal-NO is negligible due to the relatively low combustion temperature. The principle of fuel-N conversion in the bed is shown in Figure 5. Fuel-N is first split into volatiles and char, which are in turn split into reactive and nonreactive parts. The quantity of fuel-N in each branch in Figure 5 is determined on the basis of ultimate analysis of biomass sludge and detailed experiments in a smallscale fluidized bed combustor described elsewhere.35 It is assumed that N in volatiles is released as NH3 or N2 in the fuel landing areas and that N in char is released as NO or N2 in the other part of the bed (see Figures 4 and 5). Some possibly important species, such as HCN and N2O, are omitted. Omitting HCN can be considered reasonable in the case of biomass sludge, since it is known that the ratio NH3/HCN in volatiles is higher for biomass fuel than for coals.36 The resulting boundary conditions on the bed surface are given in Table 3. Figure 7. Predicted (contours) versus measured (boxes) temperature (K). Measurement points are located in the centers of the boxes.

Deinking sludge has a high moisture content, and the distance between the fuel chutes and the bed is relatively short. For these reasons, wet fuel particles are assumed to reach the bubbling bed before any significant in-flight devolatilization occurs. In this approach, fuel particle trajectories are not modeled, and hence, the uncertainties related to estimating the distribution of deinking sludge particle size can be ignored. Instead, the volatiles are assumed to be released on the bed surface from the three 2.75 m × 2.25 m fuel landing areas located below each fuel chute (see Figure 4). The size and location of these areas are based on experience and some visual evidence obtained from the boiler. The lateral dispersion of volatilizing fuel particles caused by the bubbling bed can be roughly estimated

Experimental Section Temperature Measurements. Temperature was measured from 18 points at a height of 6.5 m and from four points at heights of 3.2 and 10 m. These measurements were carried out using a k-type thermoelement supported by a metal pipe. For practical reasons, it was not possible to keep the thermoelement in a gas flow for more than a few minutes, during which period the measured temperature was observed to fluctuate no more than 20 K. The measurements were carried out with a bare unshielded thermocouple, the reading of which typically underestimates the true temperature. The general (34) Leckner, B. Prog. Energy Combust. Sci. 1998, 24, 31–61. (35) Konttinen, J.; Hupa, M.; Kallio, S.; Winter, F.; Samuelsson, J. NO Formation Tendency Characterization for Biomass Fuels, 18th Int. Conf. Fluid. Bed Combust., Toronto, Canada, May 22–25, 2005. (36) Glarborg, P.; Jensen, A. D.; Johnsson, J. E. Prog. Energy Combust. Sci. 2003, 29, 89–113.



Figure 8. Comparison of predicted reaction rates using different global chemistry mechanisms under typical SNCR process conditions: cO2 ) 0.614366 mol/m3, cNH3 ) 0.000574 mol/m3, cNO ) 0.000778 mol/m3, cH2 ) 0.012480 mol/m3, and cCO ) 0.016805 mol/m3.

Figure 9. Average mass-weighted NH3 and NO concentrations without ammonia injection.

Figure 10. Average mass-weighted NH3 and NO concentrations with current design ammonia injection.

order of magnitude of the error is roughly 100 K at temperatures prevailing at the NH3 injection level in the present study.37 In the boiler studied, the walls perceived by the thermoelement were refractory-lined up to a height of 7 m, which may decrease the radiation losses to the walls. All in all, it is very difficult to approximate the radiation losses from the thermocouple with much confidence, although these losses can certainly be significant. Ammonia and Nitric Oxide Measurements. The gas concentration in the flue gas channel was continuously monitored using Fourier transform infrared spectroscopy (FTIR). The injected NH3 mass flow was systematically varied in order to assess the SNCR process performance. In the first case, the NH3 injection was turned off. In the following three cases, the experiment was run using the (37) Stultz, S. C., Kitto, J. B., Eds. Steam: Its Generation and Use; American Boiler Manufacturers Association, Babcock & Wilcox Co: Barberton, OH, 1992.

injections at a height of 7.5 m with 50% of the current design mass flow, with 100% of the current design mass flow, and with 150% of the current design mass flow, respectively. In the fifth case, 100% of the current design mass flow was injected from the lower injection level at a height of 5.5 m instead of 7.5 m.

Results and Discussion Main Species Concentrations and Temperature. AVerage Quantities as a Function of Boiler Height. Figure 6a shows the average concentration of CH4, CO, and H2 as a function of boiler height. These species, which are known to shift the optimal SNCR temperature window toward lower temperatures, exist at the NH3 injection levels (Figure 6a). The evolvement of the species concentrations is consistent with the global mechanism applied for the hydrocarbon species. Figure 6b shows the


Saario and Oksanen

Figure 11. Average mass-weighted NH3 and NO concentrations as a function of SNCR process load. Nitrogen chemistry mechanism of Duo et al.10 used in the upper section of freeboard (at a height of 3.65 m and above) and nitrogen chemistry mechanism of Brink et al.7 used near the bed (at a height below 3.65 m). 100% load corresponds to current design. Exp. ) experimental (outlet).

average temperature as a function of boiler height. After the bull-nose level at a height of 13 m, the temperature quickly decreases (not shown in Figure 6b), and hence, it can be assumed that the reactions related to the SNCR process are quenched there. Predicted Versus Measured Temperature. At a height of 3.2 m (Figure 7a), the predicted temperature level is consistent with the measurements. The temperature gradient between the left and right walls is moderate according to both the predictions and measurements. The measured values are affected by the experimental uncertainty, and also by natural gas injected from the bottom of the bed, which is known to produce local temperature peaks above the bubbling bed. It is interesting to note the relatively large temperature gradients and the nonuniformity of the temperature distribution at a height of 6.5 m (Figure 7b). This makes it possible to improve the SNCR process performance by seeking a more efficient (uneven) distribution of NH3 between the injections. The predicted temperature near the front wall on the left wall side is over 100 K higher than that indicated by the measurements, but elsewhere, the predicted temperature level as well as temperature gradients are consistent with the measurements. Near the front wall, the temperature on the left wall side is clearly higher than that on the right wall side. As mentioned earlier, the distribution of fuel from the chutes is uneven, which explains the asymmetry of temperature distribution observed here both in the predictions and in the measurements. It can also be noted that the rear wall NH3 injection is clearly visible in Figure 7b. At a height of 10.0 m (Figure 7c), it is seen that according to both the predictions and measurements the maximum temperature is closer to the left wall side. However, the predicted temperature gradient is greater than the measured one. In general, the predicted temperature contours agree reasonably well with the measurements, although one must remember that the experimental uncertainty is significant, and that the definition of boundary conditions on the bed surface affects the predictions. NH3 and NO Concentrations. Predicted Reaction Rates as a Function of Temperature;SensitiVity Study. Reaction rates predicted by the global chemistry mechanisms as a function of temperature are shown in Figure 8. The flue gas composition used in Figure 8 is typical of the SNCR process conditions. Figure 8a shows that the predicted NO formation rate (reaction r5) is always from one to two orders greater than the predicted NO reduction rate (reaction r6) according to the mechanisms

of DeSoete and Brink et al., and hence, these mechanisms cannot be suitable for the SNCR process modeling. These two mechanisms also show weaker dependence on temperature than other mechanisms considered. The predicted NO formation and reduction rate curves cross each other according to the mechanisms of Brouwer et al., Duo et al., and Mitchell and Tarbell at temperatures of 1210, 1240, and 1270 K, respectively. Hence, these mechanisms have the potential to predict the SNCR process behavior. The mechanism of Brink et al. predicts considerably greater reaction rates than other mechanisms at temperatures below 1250 K. The NO formation rate is very low according to the mechanisms of Mitchell and Tarbell and Brouwer et al. at temperatures below 1050 K. Predicted Versus Measured NH3 and NO Concentrations. In general, great differences can be observed in the predicted NH3 and NO concentrations, depending on the chemistry mechanism. Also, none of the mechanisms give reliable results in all cases. In the case where NH3 injection is turned off, all of the mechanisms predict reasonably well the NH3 concentration at the bull-nose level at a height of 13 m (Figure 9a). As for the predicted NO concentration at the bull-nose level, the mechanism of DeSoete seriously overpredicts it (XNO ) 355 ppmvol, not inside the scale in Figure 9b), whereas the other four mechanisms underpredict it (Figure 9b). With the mechanism of Brink et al., the NO concentration is closest to the measured value. However, it can be observed that NH3 mainly reacts in the lower part of the boiler in the case where NH3 injection is turned off. Hence, considering that the complex phenomena taking place inside and above the bubbling bed require strongly simplifying approximations, it is obvious that the predictions of all the mechanisms near the bubbling bed are susceptible to large error. In the case of the current design NH3 injection, the mechanisms of Mitchell and Tarbell and Brouwer et al. predict too low reaction rates of NH3 in the upper section of the freeboard (Figure 10a). Consistent with the sensitivity study in Figure 8, the NH3 reaction rate predicted by the mechanism of Brink et al. is always far greater than that predicted by the other mechanisms (Figure 10a). Consequently, the mechanism of Brink et al. predicts that all of the injected NH3 reacts very quickly close to the injection points in a relatively small number of computational cells. As shown in Figure 10b, the mechanism of Duo et al. best predicts the measured NO concentration. The correct NO reduction trend is produced by the mechanisms of Duo et al., Brouwer et al., and Mitchell and Tarbell, as could be expected on the basis of the sensitivity study in Figure 8.


The mechanism of DeSoete produces an opposite trend (XNO ) 540 ppmvol at the bull-nose level, not inside the scale in Figure 10b), whereas the mechanism of Brink et al. predicts practically no change in the NO concentration at the bull-nose level. The failure of the mechanism of DeSoete is understandable, considering the fact that it was developed at temperatures far higher than those typically prevailing in biomass combustion. One must bear in mind that the CFD predictions are influenced also by several other submodels, not only by the chemistry mechanism. Hence, the chemistry mechanisms cannot be ranked with confidence on the basis of the present study alone. In the present study, the mechanism of Duo et al. seems to perform best. Also, other studies report encouraging results using this mechanism under the SNCR process conditions. Good agreement with the experimental data is reported.18,38 The mechanism provides a reasonable description of the experimental data in the oxygen concentration range 4–20 vol %.15 The mechanism follows reasonably well the results obtained with a detailed mechanism under oxidizing conditions.13 However, the mechanism overestimates the NO reduction in the low temperature region.15,39 Predicted Versus Measured NH3 and NO Concentrations Using Combined Mechanism. The use of different global chemistry mechanisms under different conditions has the potential to improve the results without adding the computational cost associated with more detailed chemistry mechanisms (see also Schwer et al.40). Here, this kind of combined mechanism is applied to predict emission formation as a function of the SNCR process load. On the basis of Figures 9 and 10, the mechanism of Duo et al. is used in the upper section of the freeboard at a height of 3.65 m and above. As mentioned earlier, the predictions of all the mechanisms near the bubbling bed are susceptible to large error. As the mechanism of Brink et al. was developed for conditions corresponding to those near the bubbling bed, and as it is found to give the closest match with the measurements in the case where the NH3 injection is turned off (Figure 9), it is used here near the bubbling bed at a height below 3.65 m. One should note that the selection of different mechanisms is not a straightforward matter. Moreover, determining on which basis to switch from one mechanism to another requires caseby-case judgement, and specifying general guidelines is difficult. Figure 11a shows that there is practically no NH3 (XNH3 < 5 ppmvol) at heights between 2.5 and 4.0 m, and hence, the exact height at which the mechanism is switched is less critical as long as it is between 2.5 and 4.0 m. Selecting the mechanism in each computational cell, for example, on the basis of temperature or concentration, would be a more sophisticated approach, but it might lead to numerical problems. (38) Østberg, M.; Dam-Johansen, K. Chem. Eng. Sci. 1995, 50, 2061– 2067. (39) Østberg, M.; Dam-Johansen, K. Chem. Eng. Sci. 1994, 49, 1897– 1904. (40) Schwer, D. A.; Lu, P.; Green, W. H. Combust. Flame 2003, 133, 451–465.


It is seen that the combined mechanism predicts a consistent increase in the NH3 concentration with an increasing amount of injected NH3 (Figure 11a). The uncertainty in the measured NH3 concentrations in Figure 11a may be large due to the low concentrations. The predicted NO concentration trend is consistent with the measurements, although in general the predictions are too insensitive to the changes made in the SNCR process load (Figure 11b). All in all, one should note that the validation of the model remains incomplete, since the NO reduction in Figure 11 occurs in a region for which there are no data available. Unfortunately, obtaining a complete and detailed experimental data set from a full-scale industrial fluidized bed boiler is almost impossible in practice. Conclusions The present study compares the performance of five global ammonia chemistry mechanisms in full-scale boiler CFD modeling. The conditions in the study correspond to those typical of the selective noncatalytic reduction (SNCR) process and the biomass combustion in fluidized beds. As a first task, the grid dependency of the CFD predictions was studied (reported elsewhere27), after which the predictions are validated to a certain extent against the measurements of temperature and of NH3 and NO concentrations obtained from an industrial-scale boiler. It is shown that the global chemistry mechanisms must be applied only under the conditions for which they are derived, since otherwise completely misleading results may be obtained. None of the mechanisms give reliable results in all cases. The mechanisms of Duo et al.,10 Brouwer et al.,8 and Mitchell and Tarbell11 are found to predict the correct NO emission reduction trend associated with the SNCR process. The mechanism of Brink et al.7 does not predict significant changes in flue gas NO emission, irrespective of whether NH3 is injected or not, whereas the mechanism of DeSoete9 fails by predicting increased NO emission when NH3 is injected. The mechanism of Duo et al. seems to give reasonable predictions under the present type of conditions. Finally, due to the different conditions in the vicinity of the bubbling bed and in the upper section of the freeboard, a combination of two mechanisms is applied; the mechanism of Duo et al. is used in the upper section of the freeboard, and the mechanism of Brink et al. is used near the bubbling bed. This approach gives qualitatively correct results in comparison with the measurements. It is concluded that a carefully performed CFD study may be able to yield information on the trends in boiler emissions with respect to changes in operational parameters. Acknowledgment. The numerous discussions with Mr. Matti Ylitalo and Mr. Juha Roppo from Metso Power Oy are greatly appreciated. The assistance provided by VTT Technical Research Centre of Finland for the application of the eddy dissipation concept is acknowledged. The financial assistance from the Finnish Funding Agency for Technology and Innovation (Tekes) (project 40250/ 06) and Metso Power Oy is acknowledged. EF700238A

Comparison of Global Ammonia Chemistry Mechanisms in Biomass

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