Cfd Simulation of Pollutant Emission in Power Plant Boilers

February 21, 2018 | Author: Rajneesh Vachaspati | Category: Computational Fluid Dynamics, Combustion, N Ox, Boiler, Turbulence
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Proceedings Proceedingsof ofthe theASME ASME 2011 2011 Power Power Conference Conference POWER2011 POWER2011 July July12-14, 12-14,2011, 2011, Denver, Denver, Colorado, Colorado, USA USA

POWER2011-55110 POWER2011-55110 CFD SIMULATION OF POLLUTANT EMISSION IN POWER PLANT BOILERS Iván F. Galindo-García, Ana K. Vázquez-Barragán, Alejandro G. Maní-González, and Miguel Rossano-Román Electrical Research Institute of México (IIE), Reforma 113, Cuernavaca, México, 62490

ABSTRACT A computational model is developed in order to investigate pollutant emissions from power plant boilers to the atmosphere. A well-known method of pollutant reduction is the modification of the combustion conditions to prevent their formation, and 3D computational fluid dynamics (CFD) codes provide an effective tool for the analysis of the combustion process. In this paper CFD calculations were performed to analyze the effect of the amount of combustion air on the production and emission of nitrogen oxides, one of the main pollutants produced during the combustion process. For this analysis the appropriate modeling of the chemical and physical phenomena involved is important, because the production and transport of pollutant species strongly depend on the flow and temperature distributions in the furnace. Two case studies are presented: a pulverized coal-firing tangential boiler and a fueloil frontal boiler. The CFD calculations adopt a 3Dformulation of the mean flow equations in combination with the standard high-Reynolds-number k-ε turbulence model. The model domain consists of the whole boiler, from the burner nozzles up to the exit of the economizer. Due to their complex geometrical features and computational limitations bank tubes are not modeled individually, but are grouped in a total volume. A porous media region approach is then undertaken to model gas flow and heat transfer in each heat exchanger. Model validation is a difficult task due to the lack of available data from commercial utilities. Validation has been done using routinely measured global parameters. Relatively good agreement is obtained. Results show that increasing the amount of air reduce nitrogen oxides formation for the case of the tangential boiler, however for the frontal boiler case this behavior is not as evident. These results demonstrate that CFD simulations are a viable tool to study the effect some combustion parameters have on the production of pollutants. CFD results may help to establish trends that, in turn, may help to reduce pollutant emissions from power plant boilers.

1. INTRODUCTION One of the main challenges facing the power generation industry that use fossil fuels is to meet the increased electricity demand while maintaining the emission of pollutants according to environmental regulations. Some of the air pollutants formed in high temperature combustion processes where the nitrogen present in the fuel or air combines with oxygen are nitrogen oxides (nitric oxide NO, nitrogen dioxide NO2 and nitrous oxide N2O, grouped as NOx), which, along with sulfur oxides (SO2 and SO3) and particulate matter, contribute to the formation of acid rain and ozone degradation, with the potential to affect visibility and in general deteriorate human health [1]. In consequence, in order to diminish their effect most combustion systems are regulated, monitored and required to have some type of control. This control is closely related to the combustion process. In utility boilers, the socalled primary techniques for reduction of nitrogen oxides consist in the modification of combustion conditions to avoid their formation. Depending on the fuel, and also on the design of furnace and combustion system, diverse technologies are available: low NOx burners, fuel/air staging, overfire air, reburning, and flue gas recirculation. In many instances these primary techniques are not enough to meet the new, very stringent regulations, so that secondary techniques, i.e. NOx selective chemical reduction, are unavoidable. Useful tools to help in the control of the emission of contaminants, with either primary or secondary techniques, are simulation tools such as the Computational Fluids Dynamics (CFD) codes. The method involves the use of mathematical and numerical models to solve fluid flow, mass and heat transfer, chemical reactions and phase change. CFD simulations help to analyze the combustion process through a virtual prototype that provides qualitative and quantitative results. In this work simulations have been performed in order to demonstrate that CFD simulations are a viable tool to study the


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effect some combustion parameters have on the production of pollutants. The appropriate modeling of the chemical and physical phenomena involved is important, because the production and transport of pollutant species strongly depend on the flow and temperature distributions in the furnace. CFD results may help to establish trends that, in turn, may help to reduce pollutant emissions from power plant boilers. The present CFD simulations analyze the effect of increasing the amount of combustion air available for combustion by 10%. Two 350 MW generating units are simulated; one is a coalfired tangential-design boiler and the other a heavy oil-fired frontal-design boiler. Flow, energy and the global kinetics of NOx formation in the furnace have been simulated by standard CFD techniques. 2. NOX FORMATION OVERVIEW Combustion of a fossil fuel invariably produces NOx due to the high temperature and the availability of oxygen and nitrogen in the air and fuel. NOx formation results from three main mechanisms: thermal NOx, fuel NOx and prompt NOx [1-3]. Thermal NOx is produced by oxidation at high temperature of the nitrogen present in the combustion air and its formation rate is function of temperature (significantly increases after 1204°C) and the residence time at that temperature. Since the typical factors for an efficient combustion are high temperature, long residence time, and high turbulence, i.e. characteristics that tend to increase the formation rates of thermal NOx, a balance has to be sought between an effective combustion and production of NOx. Typical control of thermal NOx consists of reducing mean and maximum flame temperature. Fuel NOx is formed through two mechanisms: oxidation of volatile species in the nitrogen present in the fuel during the initial phase of combustion and the formation of nitrogen radicals during char combustion. The volatiles consist of hydrogen, carbon monoxide, carbon dioxide, and methane, principally, while the remaining particle, following devolatilization, consists of char residue and inert ash. In the first mechanism nitrogen reacts to form intermediate compounds in the rich fuel region, therefore NO and NO2 formation strongly depends on the local air-fuel stoichiometry ratio. Conversion can be controlled by reducing the available oxygen during the initial stages of combustion. In the second mechanism, char-oxidant reactions, a fraction of the nitrogen in the char is directly converted to NO. Additionally, heterogeneous reduction to N2 by interaction with the char particles can contribute to slightly decrease the total amount of fuel NOx. Prompt NOx is formed by reactions of N2 with fuel derived radicals such as CH and CH2 in regions near the flame zone of a hydrocarbon fuel. The contribution of prompt NOx to total NOx is usually very small. In order to model NOx formation, kinetic rate models detailed enough to characterize the process, but consisting of

only a few global steps, to facilitate their interaction with the computation of velocity, temperature and concentration fields are employed. In CFD modeling, NOx simulation is usually decoupled from the main calculation. Simulations consist of two stages. First velocity, temperature and concentration of major species are calculated, and second NOx calculations are executed after as a post-processing task. This is expected, because the presence and evolution of trace species hardly affect the overall flame evolution, controlled by comparatively faster fuel combustion mechanisms [2]. 3. NUMERICAL SIMULATION MODEL The general purpose CFD code FLUENT [4] has been adopted to simulate chemical reactions, fluid and particle flow, and heat and mass transfer inside the furnace. The discretized conservation equations for mass, momentum and energy are solved. Turbulence is accounted for by means of the standard k–ε model [5]. For the simulation of the tube bundles (superheaters, reheaters, economizers and hanger tubes) located downstream of the furnace, it is not feasible to model each tube individually due to the different length scales, instead a porous media approach was adopted. The porous media model adds two source terms to the momentum equations, a viscous term and an inertial loss term, which depend on the molecular viscosity and the square of velocity, respectively. For the case of the heat absorbed inside the porous zone, the energy equations are modified in the heat conduction terms, using an effective conductivity that takes into account the fluid and solid conductivities and the porosity of the medium. The porosity is the volume fraction of fluid within the porous region (i.e., the open volume fraction of the medium). The formula for porosity factor β is, β =1−

πDo2 4ST SL


where Do is heat exchanger tube diameter, ST is the transversal length (pitch), and SL is the axial length. The porous model is employed to model three superheaters (SH1, SH3 and SH4), two reheaters (RH2 and RH3), two economizers (ECOSUP and ECOINF) and hanger tubes for SH1. Superheater SH2 is modeled as plates and reheater RH1 is not modeled. The geometric data used for the calculation of the porosity of the heat exchangers are given in Table 1.


Panel s 26 40 80 80 54 27 27 54

Tubes 33 20 8 8 34 12 12 5

Diam. mm 48.6 63.5 60.3 48.6 54 45 45 48.6

ST, mm 522 348 174 174 130.5 130.5 130.5 130.5

SL, mm 58.1 115 115 100 100 115 115 100

Porosity 0.938 0.920 0.857 0.893 0.824 0.894 0.894 0.857



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Total heat absorbed in each exchanger is modeled by adding an energy source term to the energy equation. The value of the source term is calculated based on the percentage of heat absorbed in each heat exchanger. In Table 2, the percentage of heat absorbed in each heat exchanger is shown.

Heat exchanger Boiler Walls (%) Superheaters (%) Reheaters (%) Economizer (%) Air Pre-heater (%)(not modeled) Total absorbed heat (%)

Coal 34 33 14 9 10 100

Heavy Oil 36 31 14 10 9 100


Coal combustion involves homogeneous (or gas-phase) combustion and heterogeneous reaction of fuel particles. During gas phase combustion species products of combustion are formed. The main objective of a combustion model is to determine the mean production rates of those species; the kinetic rates then appear in the species transport equations. Here the Eddy-dissipation model with a one step heat-release mechanism, available in FLUENT, was employed. For combustion of fuel particles the process involves drying, devolatilization, and char oxidation. The devolatilization model is applied to a combusting particle after the particle reaches the vaporization temperature (i.e. after drying). The single kinetic rate model, where the yielding kinetic rate k is defined by an Arrhenius expression, was adopted. The Arrhenius expression is given by:

k = A1 e −(E RT )

the weighted-sum-of-gray-gases model (WSGGM). The impact of reacting particles or droplets on the continuous phase can be examined using heat and mass transfer relationships, available in FLUENT. For coal particles the model includes particle heating, evolution of volatiles and swelling, char reaction and cooling of the particle. For droplet combustion the droplet evolution includes heating to vaporization temperature, evaporation, boiling and cooling. Interaction between the turbulence and chemistry is modeled with the eddy dissipation model in which reaction rates are assumed to be controlled by the turbulence. All models mentioned above have been extensively used for an efficient modeling of the complex phenomena found in large scale boilers. Detailed formulations of the models are not given here since they can be found in the ANSYS FLUENT Theory Guide [3]. A parametric analysis was accomplished to confirm that numerical results were grid independent. Fig. 1 shows the velocity in a horizontal line inside the boiler at 23 m from the bottom and going from one corner to the opposite corner (position 0-12.5m) using grids with different total number of cells of 2, 3 and 5 million cells. It can be observed that velocity is similar for the grids with 3 and 5 million cells, which indicates that increasing the number of cells in the mesh does not significantly affect results.


where A1 is the pre-exponential factor, E is the activation energy, T is the temperature and R is the universal gas constant. Following devolatilization the remaining particle consist of char residue and inert ash. Char combustion is modeled by the kinetic/diffusion-limited rate model, which assumes that the surface reaction rate is determined either by kinetics or by a diffusion rate. The time averaged conservation equations are solved for predicting the flow, temperature and concentration of gas species within the boiler. Turbulent quantities are calculated using the standard high-Reynolds-number k-ε turbulence model. Lagrangian particle trajectories of the pulverized coal particles or heavy oil droplets are calculated throughout the computational domain. The dispersion of particles due to gas turbulence is predicted using the stochastic tracking model which includes the effect of instantaneous turbulent velocity fluctuations of the gas on the particle trajectories. The P1 radiation model is used to simulate radiation heat transfer. Absorption coefficients of the gas phase are calculated using


4. CASE-STUDY BOILER The boiler under consideration is part of a 350 MW commercial power plant operating in a subcritical steam cycle. The combustion chamber is rectangular in shape (dimensions 12.7 x 14.15 x 45.6 m), and is fired tangentially using either five pulverized coal burners or four heavy oil fuel burners in each corner. The burner nozzles discharge a mixture of coal and air into the furnace, while auxiliary nozzles discharge secondary air. For the case of the boiler firing pulverized coal it has two types of coal burners, one is a fuel rich burner


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(CONC) and the other is a fuel lean burner (WEAK). The furnace geometry and burner arrangement are shown in Fig. 2.


The model domain consists of the combustion chamber, from the burner nozzles at the furnace corners, up to the exit of the economizer. A mesh for the model was created using tetrahedral and hexahedral elements and has approximately 3.5×106 elements of unequal size. The regions close to the burners, where the fluids are injected and the combustion processes take place, were assigned a denser mesh, Fig.3.

Boundary conditions were obtained from the plant design data sheets. The air and fuel nozzles were the inlets and the region after the economizers was the outlet. The boiler walls were assigned wall boundary conditions for flow and thermal properties. Table 3 presents the boundary conditions for the simulation cases. Properties of fuels, proximate and ultimate analyses, as well as heating value are given in Table 4.

Parameter Case 1: Coal firing Load Elevations in service Coal flow rate Primary air flow rate Primary air temperature Total secondary air flow rate Secondary air temperature OFA flow rate Outlet pressure relative to atmospheric pressure Case 2: Heavy oil firing Load Elevations in service Heavy oil flow rate Heavy oil temperature Air flow rate Air temperature Gas recirculation flow rate OFA flow rate Outlet pressure relative to atmospheric pressure

Boundary condition value 100% (350 MW) A, B, C, D 33.786 kg/s 86.666 kg/s 70 °C (343 K) 256.388 kg/s 321 °C (594 K) 21.04 % of secondary air -1000 Pa

100% (350 MW) A, B, C, D 21.98 kg/s 117.4°C (390 K) 305.55 kg/s 325 °C (598 K) 30.8 kg/s 13.78 % of total air -1000 Pa


Coal Properties Proximate analysis (as received) Moisture (%) Ash (%) Volatile (%) Fixed carbon (%) Ultimate analysis (as received) Carbon (%) Hydrogen (%) Oxygen (%) Nitrogen (%) Sulfur (%) Chloride (%) Heating value (kJ/kg) Heavy oil Properties Proximate analysis (as received) Carbon (%) Hydrogen (%) Sulfur (%) Oxygen + Nitrogen (%) Heating value (kJ/kg)

values 9.5 12.2 31 47.3 82.5 5.6 8.96 1.8 1.1 0.04 26,497.27 values 83.64 11.3 4.2 0.86 41,868




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5. MODEL VALIDATION The data needed for model validation is usually not available in commercial utilities. As stated in [6], it is impractical and unlikely that enough experimental data could be collected to provide the detailed information needed for combustion modeling. Therefore the global parameters measured routinely by plant operators may be taken as a guide for model validation. In this context validation refers more to agreement in trends than comparison of absolute values. 5.1. Validation results firing coal The boiler was operated with the lower A–B–C–D levels firing coal and the upper E level out of service. The tilt angles of the A–E burner levels were assumed to be 0°. The combustion simulations are compared to some key global design parameters available from boiler data, including the average temperature and average O2 and CO2 mass fraction at the furnace exit. Table 5 presents data from calculations and from the reference boiler. It should be noted that the exact location for measurements inside the boiler was not known. Therefore calculated values in Table 5 are given as a range limited by the maximum and minimum values in a transversal plane at the indicated location, where the minimum and maximum temperature values are greatly influenced by the cold temperature at the walls (377 °C) and by the hot gases at the center of the furnace, hence the wide range of values in Table 5. These results show that, except for flue gas temperature at furnace exit and O2 concentration at economizer exit, which are higher and lower than plant data, respectively, the ranges of calculated values enclose the respective reference value. Discrepancies are difficult to analyze because of the uncertainties about the exact location of the measurement point. Despite these differences the general trend in flue gas temperature as the gas flows through the furnace is similar for calculation and plant data. Recall, however, that this comparison is only a rough approximation towards model validation and that more plant data is needed for better analysis.

Variable Flow rate Flue gas (economizer outlet) kg/s Temperature Flue gas at furnace exit, °C Reheater outlet, °C Superheater outlet, °C Flue gas at economizer inlet, °C Flue gas economizer outlet, °C Flue gas concentration O2 economizer exit (dry vol %) CO2 economizer exit (dry vol %)


Plant data



1433 – 1711 511 – 1235 584 – 907 487 – 907 252 – 610

1007 779 527 524 343

0.73 – 2.58 15.5 – 18.1

3.6 15.2–17.7


5.2. Validation results firing heavy oil For the condition of 100% load and the boiler firing heavy oil the four elevations of oil burners are used. Similarly to the case for coal-firing, results in this case also show that plant data is within the ranges given by the maximum and minimum calculated values, exceptions are temperature at the furnace exit and, only slightly, species concentrations at the exit. Model predictions and plant data are shown in Table 6. Comparing these data it is clear that more reference data is needed for a comprehensive validation; however the available plant data helps to roughly corroborate results from these simulations.

Variable Flow rate Flue gas at economizer outlet, kg/s Temperature Flue gas at furnace exit, °C Reheater outlet, °C Superheater outlet, °C Flue gas at economizer inlet, °C Flue gas economizer outlet, °C Flue gas concentration O2 economizer exit (dry vol %) CO2 economizer exit(dry vol %)


Plant data



819–1484 445 – 1081 336 – 680 336 – 668 171 – 400

1017 773 517 546 352

0.08 – 0.9 12.7 – 13.39

1.1 13.59


6. MODEL RESULTS CFD simulations have been performed in order to analyze the effect of changing the boiler operating conditions on the combustion process and in particular on pollutant emissions. Here the effect of varying the amount of combustion air is investigated. It is assumed that variation of the combustion air can have a significant influence on the NOx formation process. Two cases are presented: a case with a tangentially fired boiler burning coal and a case of a frontal burner boiler using fuel oil. For both cases results were obtained for 100% load and increasing the combustion air by 10%. 6.1 Tangentially fired Boiler Figure 4 shows a plot of the average temperature in horizontal cross-sections at different distances in the path of the gas. It shows the influence of the amount of air supplied for the combustion process on average temperature. Temperature is lower when combustion air was increased. Figure 5 shows temperature contours in a horizontal crosssection at the height of the elevation D burner (10 m) for the two cases. Even if the profiles are very similar, it appears that the temperature for the base case is higher.


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The significant difference between the two cases can be observed in Fig. 7, where a comparison of the distributions of predicted NO mole fraction for the two cases is shown at vertical and horizontal planes inside the furnace.


Base case

Overfire air (10% increase)

Base case


As for the production of NOx, the simulations show that the decrease in average temperature causes a reduction in NO formation. Figure 6 shows a plot of the volume fraction of NO averaged over horizontal planes at different distances from the path of the gases.

Overfire air (10% increase) FIG. 7. CONTOURS OF NITRIC OXIDE (NO) MASS FRACTION.



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The difference between the two images is evident, because while the base case shows several areas at the top of the scale (0.02) and an average at the outlet of 0.005, the case with 10% more air has a maximum fraction of 0.0028 and an average at the outlet of 0.001. It should be noted that due to uncertainty in the validation, the assessment of trends, i.e. the variation between different states of operation, is of greater value than the absolute values of the simulations. In our case the trend exhibited by the results can be considered adequate according to the NOx formation processes both thermal and fuel, as described above. Although the formation of fuel NOx is greater with increased availability of air, the temperature decrease due to more air dominates the formation of NOx and has a lower yield for the case where the secondary air was increased by 10%. 6.2 Frontal burner boiler In a second case study the same scenario of 10% increase of combustion air, but now in a boiler that burns fuel oil with frontal burners distributed in four levels of three wall burners with a total of 24 burners was also investigated. Unlike the previous case, it sought to analyze the formation of NOx mainly at the furnace and excluding the effect of heat transfer on the walls and on the banks of tubes. Results show that increasing the combustion air NO formation is reduced in the furnace region, however, at the outlet both have similar values, even the base case is slightly higher (Fig. 8).


Figure 9 shows the contours of the mole fraction of NO for the two scenarios. As can be seen, although at the top and at the outlet the profile is similar, in the furnace itself differences in the profiles of NO can be noticed. The case in which there is more available air appears to have less NO produced in the burner region.

Base case

10% air increase


6. CONCLUDING REMARKS Computations of a utility boiler have been undertaken using a CFD-based model that solves the 3D-equations for massdiffusion, momentum, and energy in combination with models for turbulence (standard high-Reynolds-number k-ε model), combustion (eddy dissipation model) and radiation (P1 model). The aim was to demonstrate that CFD simulations are a viable tool to study the effect that combustion parameters have on the production of pollutants. CFD results may help to establish trends that, in turn, may help to reduce pollutant emissions from power plant boilers. The CFD calculations presented here investigate the effect that changing the operating conditions of a boiler would have on NOx formation. The model has been validated comparing simulation results to design parameters from the reference plant, where validation refers more to agreement in trends than comparison of absolute values Two case studies have been presented in which numerical simulations were conducted varying the amount of oxygen available for combustion, which changes the conditions for the production of NOx. Results show that NO production is reduced when the amount of available air for combustion is increased by 10%. This result is justified because temperature decreases by increasing air flow. However, in the case of the frontal boiler firing fuel oil this trend was not observed as clearly, so further simulations are required varying this parameter further. As a final conclusion CFD is recommended as a viable computational tool to evaluate techniques aimed at reducing emissions in boilers, bearing in mind that validation is a key part in the simulation process.


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ACKNOWLEDGMENTS Financial support for this work was provided by CFE (the Mexican utility, Laboratorio de Pruebas a Equipos y Materiales, LAPEM), CONACYT (National Council for Science and Technology of México) and IIE (Electrical Research Institute of México).

NOMENCLATURE Roman letters A1 pre-exponential factor (kgmol/m3-s) Do tube diameter (m) E activation energy (J/kgmol) R universal gas constant (J/kgmol-K) ST transversal length (m) SL axial length (m) T temperature (K) k kinetic reaction rate (kg/m3-s) Greek letters β porosity factor

REFERENCES [1] Babcock and Wilcox, 2005, Steam/its generation and use, 41 ed, eds. J.B. Kitto and S.C.Strultz, The Babcock and Wilcox Company, Ohio, USA. [2] L. I. Dıéz, C. Cortés, and J. Pallarés, 2008, “Numerical Investigation of NOx Emissions From a Tangentially-Fired Utility Boiler Under Conventional and Overfire Air Operation,” Fuel, 87, pp 1259-1269. [3] ANSYS, 2009. ANSYS FLUENT 12.0 Theory Guide, ANSYS, April 2009. [4] ANSYS, 2009. ANSYS FLUENT 12.0 User’s Guide, ANSYS, April 2009. [5] Launder BE, Spalding DB., 1974, “The Numerical Computation of Turbulent Flows”, Comput Meth Appl Mech Eng, 3, pp. 269–289. [6] Fiveland, W.A., Wessel RA, 1988, “Numerical Model for Predicting Performance of Three Dimensional Pulverize-Fuel Fired Furnaces”. J Eng Gas Turb Power, 110, pp.117–126.


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