computational fluid dynamics modelling for the prediction

COMPUTATIONAL FLUID DYNAMICS MODELLING FOR THE PREDICTION OF NOXIN A WASTE GAS BOILER

Adam BlackmoreTechnologies

Hatch Ltd.

Mississauga, Ontario

Email: [email protected]

Jennifer WoloshynTechnologies

Hatch Ltd.



Duane BakerTechnologies

Hatch Ltd.



ABSTRACT

Recent results from CFD modelling of a waste gas boiler

(WGB) to predict Nitrogen Oxides (NOx) emissions are pre-

sented. A verification / validation discussion is highlighted com-

paring the computational fluid dynamics (CFD) modelling with

field measurements of NOx and flame characteristics. Chal-

lenges associated with the accurate prediction of NOx emissions

are discussed along with the ability of CFD to accurately model

emissions from an industrial scale WGB.

NOMENCLATURE

ρ density, m3/kg

f mixture Fraction

µt turbulent viscosity, Pa · s

µl laminar viscosity, Pa · s

u velocity, m/s

h,H enthalpy, J

P pressure, Pa

xi coordinate, m

g acceleration of gravity, m2/s

k turbulent kinetic energy, m2/s2

kt turbulent thermal conductivity,cpµt

Prt, W/m ·K

Y mass fraction

ε dissipation rate, m2/s3

cp specific heat capacity, J/kg ·K

T temperature, K

htc heat transfer coefficient, W/m2 ·K

φ fuel/air ratio

q′′

heat flux, W/m2

Sh energy source term, W/m3

Sm momentum source term, N/m3

a time average

a′ fluctuating component

Prt turbulent Prandtl number, 0.85

σt 0.85

Cg 2.86

Cd 2

1 INTRODUCTION

The computational modelling of combustion to predict NOx

emissions must be accompanied by measurements to ensure the

boundary conditions and sub-model selections of the simulations

reasonably match the actual operating conditions. The present

investigation concerns a WGB which fires a variety of fuels de-

pending on plant conditions, primarily waste gas (WG), which is

a mixture of carbon monoxide, hydrogen, water and carbon diox-

ide, or natural gas (NG), made up of methane with small amounts

of heavier gases (e.g. propane). Changes in regulatory allowable

emission levels required a NOx reduction strategy, and it was

decided that since the main NOx formation mechanism was hy-

pothesized to be Thermal NOx a Flue Gas Recirculation (FGR)

system could be used to reduce the NOx emission to acceptable

levels. Due to plant conditions there was insufficient supply of

Proceedings of the ASME 2016 Power Conference POWER2016

June 26-30, 2016, Charlotte, North Carolina

POWER2016-59115

1 Copyright © 2016 by ASME

WG during testing to perform a validation campaign on WG, and

NG was used instead.

A low NOx arch-fired boiler was studied by [1], the coal

boiler was modelled by using an Euler-Euler multiphase model

for the coal-gas interaction and demonstrated reasonable accu-

racy between measured and simulated NOx concentration at the

outlet of the boiler. The combustion field and NOx formation

within an industrial sized bubbling fluidized bed (BFB) boiler

was investigated by [2]. The simulations explored the effect of

secondary air on a variety of boiler performance metrics (NOx,

CO, temperature, etc.). Hydrogen combustor model validation

was studied by [3] who used FLUENT to validate a CFD model

of a Low-NOx hydrogen combustor, and demonstrated satisfac-

tory results ( 25% discrepancy between measured and simulated

results).

This paper will discuss the findings of the testing done on

an industrial size Waste Gas Boiler (WGB), and present work

done on the CFD validation study. For reasons related to confi-

dentiality some specific details have been purposefully omitted,

however the authors do not believe this detracts from the overall

message or usefulness of the present work.

2 TESTING METHODOLOGY AND RESULTS

True validation of a CFD model requires many properly

measured and quantified variables of interest to the flow being

simulated. In the present case, modelling of a WGB, and the

subsequent emission of NOx being of primary interest, a true

validation data set would contain detailed measurements of all

boundary flows (mass flow rate, composition, temperature, tur-

bulence intensity, velocity uniformity) in addition to measure-

ments of the flame (shape, temperature, velocity, temporal be-

haviour). The surrounding boundary conditions (pipe walls and

refractory, tube banks, etc) would also be instrumented and tested

so that emissivity, temperatures, heat fluxes, etc. are well known.

Measurement of these quantities on an industrial scale operating

boiler is typically impractical, therefore, accurately capturing the

quantities required for a ’true’ validation is typically not possible.

In the present work an attempt was made to capture the most im-

portant variables, understand their uncertainty and impact on the

simulation, and develop a dataset that can be used to verify key

findings from the simulation. The WGB geometry can be seen

in Figure 1. The main components consisted of two air inlets

feeding into a windbox, NG and WG inlets, the radiant section,

the main tube bank with a superheater section, a feedwater drum,

upper steam drum, and exit ducting, which was the boundary of

the model.

2.1 Selection of a Verification Test Case

Tests were performed using only NG as the fuel; this was

done primarily because the WG was cut off unexpectedly during

FIGURE 1. WGB Geometry

the testing due to operational conditions within the plant. The

boiler did not have plant information (PI) online measurement

of the combustion air mass flow. A measurement campaign was

designed to establish all important flows across the ’boundaries’

of the boiler system, i.e. all incoming mass flows, exhaust mass

flows and the NOx emission mass flows exiting the stack. The

combustion air mass flow was determined by performing pitot

tube measurements upstream of the combustion air windbox to

obtain the velocity profile across the duct. The velocity profile

was integrated to obtain the volumetric flow rate and tempera-

ture measurements were used to convert the volumetric flow rate

into a mass flow rate. The same process was used to obtain stack

mass flow rates. The time of day during which the combustion air

/ exhaust mass flow measurements were taken was recorded, and

compared to data retrieved from the plant data logging system to

compare with NG flow rates, which are measured using online

instrumentation. Multiple measurements were taken, however

it is ideal to have a steady set of conditions on which to base

a validation case, since the CFD analysis will be performed as-

suming a steady state condition. Test 1 consists of measurements

1-4 on Figure 2, and Test 2 consists of measurements 5-8 on

Figure 2. The plant data of boiler fuel flow rates, temperatures,

etc. was analysed to choose the measurement which could most

accurately be replicated in the CFD model, i.e. the measurement

taken at a point in time when the boiler was near a steady state op-

erating condition. Inspection of Figure 2 shows a variation in the

NG flow rate over time, however a steady condition is achieved

during Test 2. As mentioned, during Test 2 four(4) measurements

of stack exhaust mass flow rate were taken, in addition to one (1)

measurement of combustion air flow. Examination of the tem-

perature measurements taken during the stack exhaust mass flow

measurements were compared to the plant’s online stack thermo-


couple. It is apparent that the 6th and 7th measurements showed

reasonable agreement with the plant thermocouple measurement

and were taken during a relatively steady state. The 5th and

8th measurements will be excluded in the validation case. The

5th measurement coincided with a sharp increase of natural gas

flow. It is believed there was an error in the 8th measurement.

Therefore, the CFD validation study was based on the combus-

tion air measurements, stack measurements, and flame imaging

data from Test 2.

A linear propagation of uncertainties [4] was performed on

the measurement data assuming a +/- 1 Pa uncertainty in pres-

sure measurement. The resulting uncertainties for the combus-

tion and exhaust mass flow rates was 0.3 and 0.6 percent error

respectively.

3 COMPUTATIONAL METHODOLOGYThe simulations were conducted in ANSYS Fluent v.

15.0.7, which is an industry standard commercial CFD code.

Turbulence in the flow field was modelled using the Realizeable

k − ε model [5], with curvature correction to more accurately

simulate the highly swirling combustion air flow resulting from

the turning vanes in the burner. Combustion was modelled by

solving a transport equation for the mixture fraction and it’s

variance, using an assumed probability density function (PDF) to

account for variance in the mixture fraction caused by turbulent

fluctuations in the flow. There are two (2) options for an assumed

PDF shape available within FLUENT v. 15.0.7: the Double

Delta function, and the β -function. The current analysis utilized

the β -function, since it more closely represents experimentally

observed PDFs [5]. A conservation equation for the mixture

fraction can be derived to account for the species transport if the

diffusivity is assumed equal for each specie, which in a turbulent

flow is not a spurious assumption. Chemistry tabulation [5]

was based on an equilibrium, non-adiabatic assumption; the

species included: HOCO, HCOOH, HONO, CHO, HCO, H202,

HO2, O, H, C (solid), CO, H20, CO2, C3H8, C2H6, O2,

N2, CH4. The main tube bank and superheater regions were

modelled as porous media domains with momentum and energy

sinks. Transport equations solved in the model can be seen below

Continuity : ∇ · (ρu) = 0 (1)

Momentum : ∇ ·(ρuu)=−∇P+∇ ·

[

µt

((

∂ui

∂x j

+∂u j

∂xi

)

−2

3ρkδi j

)]

+ρg+Sm (2)

Mix.Frac : ∇ ·(

ρu f)

= ∇ ·

(

µl +µt

σt

∇ f

)

(3)

Mix.Frac.Var : ∇ ·(

ρu f′2)

= ∇ ·

(

µl +µt

σt

∇ f′2

)

+Cgµt

(

∇ f)2

−Cdρε

kf′2 (4)

Energy : ∇ · (ρuH) = ∇ ·

(

kt

cp

∇H

)

+Sh (5)

The enthalpy in the energy equation is mass fraction

weighted, based on the contribution of the enthalpy of formation

ho, the component specific heat and temperature of the mixture.

H = ΣYjH j (6)

H j =∫

cp, jdT +ho, j (7)

where xi, j is the spatial coordinate, ρ is the density, u is the

velocity, µ is the viscosity (laminar or turbulent), P is the

pressure, g is the acceleration of gravity, f , f ′ is the mixture

fraction and it’s variance, respectively, ε is the dissipation rate, k

is the turbulent kinetic energy, Sm is a momentum source term,

H is the enthalpy, Sh is an energy source term, and Yj is the mass

fraction of component j. Further information on the constants σ ,

Cg, and Cd used in the mixture fraction variance equation can be

found in [5].

Radiation was modelled using the Discrete Ordinate (DO)

approach, where the radiative transfer equation in a particular

direction is written as a field equation [5]. The weighted sum

of gray gases model was used to account for the wavelength

dependent absorption characteristics of the gas components. The

simulations were assumed steady state, and monitors of velocity

and temperature in the domain were used to judge convergence.

Thermal NOx production, by way of the Zeldovich mech-

anism was considered in the evaluation of pollutant emissions,

and was calculated as a post processing step, due to its limited


FIGURE 2. Boiler Gas Flow Trends and Stack Temperature During Test Campaign


influence on the flow field. Further information on the imple-

mentation of the Zeldovich mechanism within FLUENT can be

seen in [5]. Prompt NOx was not evaluated due to the fuel-lean

mixture ratios within the boiler, and the uncertainty of the steady

two-equation turbulence model to accurately predict localized

fuel rich regions accurately.

The continuity, momentum, radiation, energy, mixture frac-

tion and it’s variance were discretized using the 2nd order up-

wind scheme [5]. Testing demonstrated that turbulence (kinetic

energy and dissipation) could be discretized using a 1st order

scheme, without a significant difference in results, resulting in

better convergence stability.

3.1 Computational Domain and Boundary Conditions

A picture of the computational domain can be seen in Fig-

ure 3, which is a representation of the gas volume within the

boiler. The boiler contains a radiant section, a main tube bank,

a superheater, two multi-fuel burners and combustion air in-

lets. The computational model does not include individual tubes

within the banks since the resulting mesh size would have been

prohibitively computationally expensive. In addition, the flow

through the superheater region and main tube bank are of sec-

ondary importance, since the primary focus of the present inves-

tigation is the thermal NOx production, most likely to occur near

the burners. The tube banks are instead represented as porous

media domains with appropriate developed momentum and en-

ergy sinks to account for anisotropic momentum loss and energy

loss within the banks. The combustion air is pre-heated using

the exhaust flue gas at the rear of the boiler. Further details on

boundary conditions can be seen in Table 1. The computational

mesh is a hybrid mesh of hexahedral cells and polyhedral cells,

with prisms along walls to create inflation layers. The resulting

mesh size was approximately 6.8 million cells. A cross section

of the mesh can be seen in Figure 4.

4 RESULTS

The thermal imaging video provided the following details

regarding the structure / behaviour of the flame: The flame is

unstable and turbulent. This is shown by the large temporal fluc-

tuations in flame structure that indicate the presence instabilities

in the flame sheet. The flame impinges on the back wall. The

boiler is approximately 13 m long, which is significantly shorter

than a theoretical estimation of flame length ( 23 m) [6]. The

addition of combustion air swirl in the burner is not captured in

the theoretical estimation of flame length, and this would shorten

the flame, resulting in an over estimation of flame length based

on theory. However, the video analysis, in addition to observa-

tions from the technician performing the flame imaging shows

evidence of two large recirculation zones created by the burner

FIGURE 3. Computational Domain

FIGURE 4. Computational Mesh

flame impingement. This would indicate that the flame length

should reside somewhere between 13 and 23 m.

The flame attaches itself to the boiler refractory at the en-

trance of the burner.

Flame imaging was performed to get an approximation of

the temperature field within the boiler, although there is a limit

to the accuracy of the imaging since only the Infra-red band

is captured by the measurements. However, the imaging does

give an approximation of the temperature distribution, flame

shape/behaviour and provides some form of verification of the

modelling. A comparison of the imaging taken during testing

and modelling results can be seen in Figure 5. The testing results

and the simulation show good agreement in the overall tempera-

ture profile and distribution.

The velocity distribution within the boiler is dominated by

the swirling combustion air flow entering the radiant section of

the boiler. Streamlines of the flow are plotted in Figure 6; exam-

ination of the flow inside the boiler show multiple recirculating

flow structures. The large momentum from the combustion air

flow results in significant impingement of the jet on the back side

of the boiler, which causes a large centrally located recirculation


Boundary Condition Value

Main NG Mass flow inlet φ = 0.04

Pilot NG Mass flow inlet φ = 0.013

Tube Walls Constant Temperature w/ virtual conduction 480 K,0.003048 m

Boiler Top Natural Convection htc = 0.11 W/m2K,T∞ = 309 K

Upper Steam Drum Constant Temperature T = 549 K

Lower Feedwater Drum Constant Temperature T = 380 K

Air Pre-Heat Duct Forced Convection htc = 29 W/m2K,T∞ = 544.7 K

Boiler Floor Adiabatic q′′= 0 W/m2K

Main Tube Bank Porous Media Domain Source Terms for Momentum and Energy, Sh,Sm

Super Heater Tube Bank Porous Media Domain Source Terms for Momentum and Energy, Sh,Sm

TABLE 1. Summary of Boundary Conditions

zone within the boiler. This recirculation was also witnessed by

inspectors performing thermal imaging of the burner flow field

during the validation study. The swirl induced by the turning

vanes in the boiler windbox also create a pair of counter-rotating

flow structures at the exit of the burners, which mix at the cen-

terline of the boiler. In addition to large scale structures there

are smaller recirculation zones near the burner tip, created by the

burner shroud and abrupt entrance into the boiler. It can been

seen that this swirling pattern produces higher velocity regions

near the outer walls and center of the boiler.

Examination of an iso-surface of the stoichiometric mixture

fraction, as seen in Figure 7 demonstrates that the approximate

flame shape is not organized, consistent with the behaviour of

boiler during visual inspection and the flow patterns in the simu-

lation.

Contours of temperature are plotted in the center-planes of

the boiler in Figure 8. It can be seen that high regions of tem-

perature are present near areas of intense mixing, coinciding

with the main recirculating flow structures, i.e. the center of

the boiler where the counter-rotating flow structures mix, and

near the burner shroud tip. The large temperature found near the

burner shroud tip are due to the localized entrainment of fuel and

combustion air, combined with strong mixing from the recircu-

lating turbulent flow structures. The strong radial flow momen-

tum results in the transport of high temperature flow from the

shroud region downstream along the boiler wall. Iso-contours of

temperature at 1800 K are shown in Figure 9, which can be used

to estimate where thermal NOx production may occur within the

boiler. Temperatures near the burner shroud and center of the

boiler radiant section are shown to agree with regions of thermal

NOx production seen in Figure 10. A continuous emissions mon-

itoring system (CEMS) was operated during the testing phase to

collect emissions data on NOx at the outlet of the stack, which

was not included in the simulation. However, there should be no

sources of NOx between the outlet of the boiler and the exit of

the stack, therefore the NOx monitored at the outlet of the sim-

ulation can be directly compared with the measurements of the

CEMS on the stack. The simulations and measurements agreed

very well, and the discrepancy between the measured value of

NOx concentration and simulated value was within 16%, which

is well within similar published investigations mentioned earlier.

The simulations were further used to investigate flue gas recircu-

lation (FGR) as a means of the reducing the thermal NOx; the

simulations demonstrated that a significant reduction in thermal

NOx could be achieved by using FGR. The FGR system has since

been installed and the results demonstrate that the simulation pre-

diction for the reduction in thermal NOx was accurate.

5 SUMMARY AND CONCLUSIONS

A CFD validation exercise was conducted to investigate the

efficacy of CFD in predicting the NOx being produced in an

industrial WGB. The verification study demonstrated that the

model accurately replicated the operational conditions of the

boiler through visual inspection, flame imaging and emissions

monitoring. The investigation focused on one fuel, NG, since

WG was unavailable due to plant conditions. The model matched

the NOx measured with the CEMS within 16%. Although not

presented in this paper, the model was used to predict that FGR

will result in a significant reduction in the NOx emissions. A

FGR system has since been installed, and the field data of NOx

measurement with the CEMS demonstrated that the reduction ex-

pected with FGR was accurately predicted by the model. The

same approach was used to establish a baseline NOx emission


FIGURE 5. Comparison of Simulation and Testing Infa-red Imaging

FIGURE 6. Velocity Streamlines within the Boiler

level if the boiler was fired with 15% NG and 85% WG by en-

ergy content, and the results compared favourably with historical

plant data, although, as mentioned there was no verification test-

ing for operation with WG. The effect of FGR on NOx reduction

was also simulated in the event the boiler was fired with 15%

NG and 85% WG by energy content, and the model results com-

pared well with the historical plant operating data. The present

investigation demonstrated that CFD can be used to establish

confidence in NOx reduction strategies prior to implementation,

which can significantly reduce technical risk.

FIGURE 7. Stoichiometric Mixture Fraction within the Boiler

REFERENCES[1] Fueyo, N., Gambon, V., and Dopazo, C., 1999. “Compu-

tational Evaluation of Low NOx Operating Conditions in

Arch-Fired Boilers”. ASME Journal of Engineering for Gas

Turbines and Power, 121(October), pp. 1–6.

[2] Jukola, P., Huttunen, M., Dernjatin, P., and Heikkila, J.,

2013. “Methods for NOx Reduction in BFB Combustion


FIGURE 8. Temperature contours at Centerline Planes within the

Boiler

FIGURE 9. Iso-contour of Temperature at 1800 K

: A CFD Study”. Flame, International and Finnish, pp. 1–7.

[3] Riccardi, J., Gheri, P., Giorgiani, G., Schiavetti, M., and

Gigliucci, G., 2006. “CFD Simulations for the Development

of Gas Turbine Low-NOx Hydrogen Combustors ”. WHEC

16.

[4] Tropea, C., Yarin, A., and Foss, J., 2007. Handbook of Ex-

perimental Fluid Mechanics. Springer-Verlag, Berlin.

[5] ANSYS, 2009. FLUENT v.15 Theory Guide. ANSYS Inc.,

Canonsburg, PA.

[6] Turns, S., 1996. An Introduction to Combustion: Concepts

FIGURE 10. Iso-contour of NO Mass Fraction (YNO = 0.00016)

and Applications. McGraw-Hill, New York, NY.

ACKNOWLEDGMENTThe authors acknowledge and thank Hatch Ltd. for pro-

viding support and resources towards this project. The authors

would also like to thank members of the Iron and Steel Business

Unit, in particular Chester Dietrick and Mike Neceda, without

which, this paper would not have been possible.


computational fluid dynamics modelling for the prediction

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