Urea Injection Design Analysis for NOx Control using CFD · 5 Urea Injection Design Analysis for NO x Control using CFD EQUIPMENT & METHODS General Application Requirements In the

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Urea Injection Design Analysis for NOx Control using CFD

Kathleen Brown*, Wojciech Kalata and Rudolf J. SchickSpraying Systems Co.

Wheaton, IL 60187 USA

Abstract

There are many industrial pollutants produced from coal fired power generation plants. Recent legislation is pushing to reduce these emissions. Greenhouse gasses as well as nitrous oxides (NOX) and sulfur dioxide (SO2) are the targeted pollutants. Governmental legislation outlined by Environmental Protection Agency (EPA) through its Maximum Achievable Control Technology (MACT) guideline is a major thrust.

One method of NOx reduction is selective non-catalytic reduction (SNCR) which is implemented prior to gases venting to the atmosphere. In SNCR systems a reagent is injected into the flue gas, in an effort to reduce NOx levels by up to 50%. The operation of this equipment is highly dependent on the temperature and distribution of the gases requiring careful control of the system. Spray nozzles are an important factor in this process; spray nozzles add value by providing controlled volumes of liquid

with predictable drop size and consistent spray coverage. Knowing these values remains vital for the optimization of gas conditioning processes.

In addition, the all-important placement of nozzles is often times misunderstood and therefore inaccurate. While immense care is taken to optimize placement, this often leads to failure due to the inability to predict spray performance in the application environment. This process can be facilitated by using computational fluid dynamics (CFD).

In this case study, CFD is used to simulate NOx levels produced in the coal combustion process and estimate the NOx reduction that can be achieved with the urea injection. In the end, adjustments were made to original nozzle placement suggestions in order to optimize gas conditioning process while minimizing the slip through the system.

As presented at: 10th International Conference on Combustion and Energy Utilization, Mugla, Turkey, May 2010

*Corresponding author

2 www.sprayconsultants.com


Introduction

An important concern in air pollution control is the treatment of exhaust gases from power plants and industrial plants, so as to drastically limit the amount of pollutants entering the atmosphere. One of these pollutants is NOx, which is defined as the collective term for nitrogen oxide gases. There are several methods of NOx formation including: thermal NOx, prompt NOx, and fuel NOx. The most prevalent form is thermal NOx - which is formed at the areas of highest temperature during the combustion process. NOx is a complex process which occurs in pre-combustion, combustion and post-flame regions. There are several approaches to NOx abatement and reductions. This study focuses solely on emission control by flue gas treatment using selective non-catalytic reduction (SNCR).

For SNCR systems, a reagent such as urea or ammonia is injection into the flue gas. NOx reductions of 30%–50% can be achieved. The reaction between reagent and NOx should occur within a temperature range of 900°C and 1,100°C depending on the reagent and condition of SNCR operation [1]. Above this range there may be reductions in efficiency due to the thermal decomposition of ammonia. Below this temperature range, slip or carryover may increase. The aim is to injection the reagent with droplets that provide a residence time in excess of one second. However a minimum residence time of 0.3 seconds is recommend for adequate effectiveness [1].

Ammonia slip or carryover is a prevalent issue with SNCR control techniques. This occurs from

injections at temperatures below the aforementioned temperature window or from too much mass per injector leading to uneven distribution.

Current methods rely on using spray injectors to deliver the urea or ammonia fluid into the flue gas stream. Spray injectors add value by providing controlled quantities of liquid and predictable drop size and spray coverage. This data is critical for efficient NOx control with minimal slip or carryover. While this method has yielded some success, it has presented a series of problems due to the inability to accurately predict spray performance in the complex environment as required. Today, computational fluid dynamics (CFD) is well suited to predict flow conditions and aid in the optimization of these exhaust systems.

Today, there is no standard method in use for selecting and optimizing fluid sprays for gas conditioning applications. This paper will outline a case study in which injectors were selected and positioned based on the critical elements of combustion chamber design and injector characteristics. This method consists of injector selection and location considerations based on known laboratory measurements of drop size, velocity and spray distribution. Furthermore, this method will also evaluate the effects of “normal” operating parameters such as flue gas flow rate, flue gas and combustion chamber temperature, and flue gas velocity on exhaust uniformity.

Approach

The process of designing the gas conditioning process begins with determining the amount of liquid required for reaction with NOx levels measured. In addition the optimal drop size is also determined and the drop size in this case will be a function of the dwell time.

Drop Size Specification

Proper chemical interaction is dependent on liquid

atomization and a controllable drop size distribution to ensure a rapid evaporation rate and eliminate a wall wetting problem causing down time (1). Wall wetting problems can be caused by atomizers that produce large drops that do not achieve full evaporation within the allowable dwell time in the duct. The process to determine the maximum allowable drop size is governed by the following equation:

(1) tDtD o λ−= 2)(

EQUIPMENT & METHODS



• t is dwell time in seconds • D0 is the initial diameter of the drop in microns

•

liq

wb

LTT

.

)(8ρ

κλ −=

• ĸ is the Thermal Conductivity of the gas.• T is the gas temperature• Twb is the Wet-Bulb Temperature of the system• L is the Latent Heat of Vaporization of the liquid • ρliq is the density of the liquid.

Nozzle Specification

Generally, large volume, two fluid atomizers are used in large scale industrial processes. These types of nozzles are preferred because they are able to control large and/or frequent variations in gas temperature or volume, provide consistent predictable drop size and are energy efficient.

Based on the required flow rate and drop size values determined above, a Spraying Systems Co. WhirlJet® BD-5 nozzle was used in this case study. The WhirlJet® nozzle is a hydraulic atomizer, featuring an inline hollow cone design for ease of installation and reduced footprint. It consists of a main liquid inlet in line with the orifice. The flow is diverted into a swirl chamber and the liquid is discharged tangential to the orifice. The orifice is relatively large minimizing the risk for clogging.

The principal of operation is as follows. The liquid enters the swirl chamber and exits tangential to the orifice. The liquid experiences a high shear and is broken into sheets. The sheets experience turbulence and additional shear causing further atomization. All testing was performed with each WhirlJet® injector mounted on a single lance body. A schematic of the WhirlJet® BD-5 nozzle used in this test is shown in Fig. 1. Spray characterization testing was performed for several nozzles to determine optimal injection properties for the CFD model, based on the theoretical requirements.

Figure 1. Nozzle – Inline WhirlJet® BD5

For drop sizing, the nozzles were mounted on a 3-axis traverse. Drop size testing was performed in a single plume of the spray. Drop size measurements were executed at multiple locations, based on nozzle performance.

A two-dimensional Artium Technologies PDI-200MD instrument was used to make drop size and velocity measurements, as shown in Fig. 2, the test setup is shown in Fig. 3. The solid state laser systems (green 532 nm and red 660 nm) used in the PDI-200 MD are Class 3B lasers and provide about 50-60mW of power per beam. This is an intense enough laser power to help offset dense spray effects. [2, 3, 4]. The test results are shown in Table 1 and Fig. 4.

Figure 2. Artium PDPA

Figure 3. Empirical Setup

Figure 4. Injection Location

EQUIPMENT & METHODS



Inline WhirlJet® Nozzles (hollow cone)

units BD5

Velocity magnitude m/s 30

Q gpm 1.6

ṁa kg/s 0.1008

Pressure psig 100

Temperature C 20

Spray Angle 76

DV0.01 - minimum μm 23

DV0.50 - average μm 200

DV0.99 - maximum μm 298

N 3.0

Lance Insertion Depth m 0.1524

a. Spray drops treated as inserted urea/water particles. Wet combustion modeling used with urea as devolatizing species and water as evaporating species

Table 1. Injector properties as acquired empirically

The DV0.5 and D32 diameters were used to evaluate the drop size data. The drop size terminology [5, and 6] is as follows:

DV0.5: Volume Median Diameter (also known as VMD or MVD). A means of expressing drop size in terms of the volume of liquid sprayed. The VMD is a value where 50% of the total volume (or mass) of liquid sprayed is made up of drops with diameters larger than the median value and 50% smaller than the median value. This diameter is used to compare the change in drop size on average between test conditions.

DV0.1: is a value where 10% of the total volume (or mass) of liquid sprayed is made up of drops with diameters smaller or equal to this value.

DV0.9: is a value where 90% of the total volume (or mass) of liquid sprayed is made up of drops with diameters smaller or equal to this value.

All pressures were monitored immediately upstream of the nozzle body using a 0-7 bar, class 1A pressure gauge. Liquid flow to the nozzle was delivered using a positive displacement pump. The flow rate was measured previously using a MicroMotion D6 flow meter and was correlated to nozzle pressure settings.

The MicroMotion flow meter is a Coriolis Mass flow meter that measures the density of water to determine the volume flow. The meter is accurate to ±0.4% of reading.

The Rosin-Rammler distribution function is used to convert raw measured drop data into a drop size distribution function for CFD. The Rosin-Rammler distribution function (2) is a representation of the drop population and size in a spray. The exact size for every volume fraction F(D) in the spray can be calculated using the X and N parameters.

(2) F(D) = 1- exp - D

X

N

CFD Analysis Method

Computational Fluid Dynamics (CFD) is the science of predicting fluid flow, heat and mass transfer, chemical reactions, and related phenomena by solving numerically the set of governing mathematical equations. For the computation the general CFD code Fluent is applied. Fluent solvers are based on the finite volume method. The fluid region is broken into a finite set of control volume (mesh).

The general conservation equations are applied. Each volume within the mesh is simultaneously solved to render the solution field. The conservation equations are shown below (3–5)[7].

Conservation of mass:

(3) ∑=⋅∇+∂∂

=

n

ppqqqqqq mu

t 1

ραρα

Conservation of momentum:

(4) )()(

1pqpq

n

ppqqqqqqqqqqqqqq umRFPuuu

t

+++⋅∇+∇−=⊗⋅∇+∂∂ ∑

=

ρατααραρα

Conservation of enthalpy:

(5) )(.:)()(

1pqpq

n

ppqqqqk

qqqqqqqqq hmQsqu

dtdp

huht

+++∇−∇+−=⋅∇+∂∂ ∑

=

ταραρα

EQUIPMENT & METHODS



EQUIPMENT & METHODS

General Application Requirements

In the framework of the Eulerian-Lagrangian modeling approach, coal particles are inserted using the non-premixed model. The parameters of the coal and gas conditions are shown in tables 1-2. Meshing was performed with a GAMBIT pre-processor. Dense mesh was incorporated in the areas very near the injection locations and coal injection locations. Size functions were used to further refine mesh size. The 3D mesh consisted of mixed elements with approximately 3.2 million cells. Figure 5 provides a schematic of the CFD model setup and defines the coordinate system referenced in both the computational and experimental results.

Ultimate ProximateCarbon 79.92 Ash 10.6

Hydrogen 5.13 Volatile Matter 32.0

Oxygen 8.78 Fixed Carbon 46.2

Nitrogen 1.29 Moisture 11.2

Sulfur 2.13

Table 2. Chemical and Thermo Physical Properties of the Fuel

Gas parameters Symbol Units Total

Primary Air (mass flow) ṁ1 lb/hr 135648

Secondary Air (mass flow) ṁ2 lb/hr 424637

Operating pressure P psia 14.7

PA oper. Temperature T1 °F 160

SA oper. Temperature T2 °F 550

Coal (mass flow) ṁ3 tons/hr 37.68

Specific heat capacity Cp J/kg•K PWL (6)

Thermal conductivity k W/m•K 0.0454

Table 3. Physical Properties of the Gas

Figure 5. Geometry Overview

Figure 6. Mesh Detail



EQUIPMENT & METHODS

The CFD model was set up with mass flow inlet boundary conditions at the primary and secondary air inlets. The outlet side of the chamber was defined with a constant pressure boundary condition. The combustion chamber and lance walls were specified as rigid with no-slip and adiabatic conditions. Throughout all simulations the following models were included: k-ε Realizable Turbulence Model, DPM for LaGrangian tracking of coal particles and urea droplets, and Species Transport Model to include combustion of coal based on volumetric reaction rates and urea reaction with NOx species created during combustion. Spray pattern is predicted by tracing droplets’ trajectories in the simulation. In the DPM model, a droplet’s trajectory is obtained by numerical integration of Newton’s second law:

(6)

g/MU

+−= pp

dtd

ρ

Interfacial force density accounts for the coupling between the movement of gas and the liquid droplet; and g is the gravitational acceleration. In the flow analyzed there, the drag force is a primary mechanism of gas-droplet interaction, which can be computed as:

(7)

)UU(24Re18M 2 −= p

pD

p

CDµ

where U is the gas viscosity, Dp is the droplet diameter, Rep is the droplet Reynolds number and CD is the drag coefficient. The drag coefficient was computed from the law of Moris and Alexander [7]. After a droplet’s velocity is solved, its location can be integrated out from the trajectory equation:

(8)

pp

dtd

UX

=

Initial states (velocity, injection direction) of liquid droplets from the spray nozzle are determined by lab experiments and are given as inputs in the simulation. The Rosin-Rammler distribution is used in the simulation to represent the droplet size variation [8].

The gas phase, combustion and particle tracking were performed in steady state. The P-1 radiation model was used due to the high temperatures associated with the combustion. This is the simplest formulation of the more general P-N radiation model, which is based on the expansion of the radiation intensity I into an orthogonal series of spherical harmonics [9].

RESULTS & DISCUSSION

Results

Many different simulations have been made to determine where and how to place the injectors to achieve optimal NOx reduction. Fig. 7 shows the gas flow pathlines and expected travel of the coal particulate. From this, area of recirculation and high swirl can be avoided as potential injection locations.

Figure 7. Gas Flow Pathlines




Gas velocity is another important factor in determining locations of injections inside of a large system. Velocity is expected to control particle tracks due to the relatively high velocity of the gas flow compared to the momentum of the droplet injected into the system. Gas velocity is shown in Fig. 8 below.

Gas Velocity m/s

0

20

10

Figure 8. Velocity Profiles

Velocity peaks are found at the primary and secondary air inlets. The velocity magnitude and turbulence remain fairly low above the burners. There is one elevated velocity area with recirculation directly above the burners (gas flow inlet). For this reason the injectors were located outside of the recirculation zone.

Another important factor for efficient NOx removal is temperature. The reaction between reagent and NOx should occur within a temperature range of 900°C and 1,100°C depending on the reagent and condition of SNCR operation [1]. Above this range there may be reductions in efficiency due to the thermal decomposition of ammonia. Below this temperature range, slip or carryover may increase. Temperature profiles are shown in Fig. 9.

Temperature °K

533

1480

1006

Figure 9. Temperature Profiles

The injection locations were selected based on temperature profiles. Injector locations were located in areas of desirable temperature and velocity profiles. Some restrictions on injector locations were set based on physical constraints and accessibility.

A simulation was performed to evaluate the NOx levels prior to the reagent injection. Fig. 10 contains the NOx profiles calculated by the simulation. The outlet surface is used to determine the approximate NOx levels that could be expected to be exhausted. The average NOx level across the outlet is 156.3ppmvd.

NOx levels ppmvd

0

160

80

Figure 10. NOx Level Profiles




Fig. 11 shows the injection locations and particle tracking. Particles are colored based on drop size. In this simulation, the injections are comprised of 93% water and 7% urea. Liquid water concentration contours and particle tracking results indicated that all spray particles are evaporating without a contact with walls. Evidence of this can be seen from evaluation of the injection tracking as shown in fig. 11. Though the injections are all operating at the same parameters, injection plumes vary based on variations in temperature and velocity profiles of the environment.

Particle Size (µm)

0

100

50

Figure 11. Injection Tracking – Drop Size

Post analysis was performed to determine the impact of the injected reagent on temperature and NOx profiles. The results of these simulations are contained in fig.12-13.

Temperature °K

533

1480

1006

Figure 12. Temperature Profiles – Post Urea Injection

NOx levels ppmvd

0

160

80

Figure 13. NOx Profiles – Post Urea Injection

The resulting temperature profile indicates a slight reduction in temperatures in the injection region. The water injected was found to be 100% evaporated prior to the turn into the exit. There was found to be no contact with the walls in the combustion chamber, from both the water and urea agent. No foreseeable areas of recirculation indicate that there is essential no potential issues with wall wetting or wall buildup/damage.

The resulting NOx values indicate a favorable result in NOx reduction. The outlet surface is used to determine the approximate NOx levels that could be expected to be exhausted with urea injections. The average NOx level across the outlet is 87.5ppmvd with urea injections. This would indicate an approximate 44% reduction in NOx levels. This would indicate a fairly efficient reduction based on the input level of the urea.

The outlet surface was also examined to determine the expected carryover of urea. The value of carryover was calculated to be 2.325e-21kg/s through the outlet. This would indicate that carryover is negligible.



Conclusion

Computational Fluid Dynamics (CFD) was used to optimize injection location and orientation in a coal combustion application. Through a case study, many different factors which impact the NOx reduction were

quickly investigated and prioritized. The aim of this study was to demonstrate effectiveness of applying CFD tools in such applications to reduce risk and evaluate environmental impact of designs.

References

1. IEA Clean Coal Centre, www.iea-coal.co.uk, Mar. 2010.

2. Abrams, J.Z., Baldwin, A.L., Higgins, S., Rubin A.G., “Demonstration of Bechtel’s Confined Zone Dispersion Process at Pennsylvania Electric Company’s Seward Station,” International Power Generation Conference, October 1991, San Diego, CA. ASME Paper No: 91-JPGC-EC-5.

3. Bachalo, W.D. and Houser, M.J., “Phase Doppler Spray Analyzer for Simultaneous Measurements of Drop Size and Velocity Distributions,” Optical Engineering, Volume 23, Number 5, September-October, 1984.

4. Bachalo, W.D. and Houser, M.J., “Spray Drop Size and Velocity Measurements Using the Phase/Doppler Particle Analyzer”, Proceedings of the ICLASS (3rd Intl.), July 1985.

5. R. J. Schick, “A Guide to Drop Size for Engineers,” Spraying Systems Co. Bulletin 459.

6. E1296-92: Standard Terminology relating to liquid particle statistics. “1996 Annual Book of ASTM Standards, General Methods and Instrumentation, Volume 14.02 pp. 810-812”.

7. Morsi, S.A. and Alexander, A.J., “An Investigation of Particle Trajectories in Two-Phase Flow Systems,” Journal of Fluent Mechanics, Vol. 55, pp. 193-208, 1972.

8. Li, G.L., Messah, H., Schick, R.J., “Numerical Simulation of Spray Pattern in a Liquid Flashing Column”, ILASS Americas 20th Annual Conference, May 2007, Chicago, IL.

9. Fluent Inc. Fluent 6.3 User’s Guide, Lebanon, NH, 2009.

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Urea Injection Design Analysis for NOx Control using CFD · 5 Urea Injection Design Analysis for NO x Control using CFD EQUIPMENT & METHODS General Application Requirements In the