Using CFD to Reduce Unburned Carbon during Installation ofLow NOx Burners

Lawrence D. Berg1, John Goldring2, Lyle Woodard3, and Joseph D. Smith, Ph.D.4

32nd International Technical Conference on Coal Utilization & Fuel Systems AgendaClearwater, Florida, USA

June 10 - 15, 2007

INTRODUCTION

To meet mandated NOx levels, coal plants are using a variety of reduction methods: low NOxburners, Over Fire Air (OFA), Selective Non-Catalytic Reduction (SNCR), Advanced Reburn,Selective Catalytic Reduction (SCR), etc. In general, low NOx burners and OFA systems areinstalled as a first step. Combustion modifications of this sort reduce NOx by decreasing theamount of air near the primary combustion region, resulting in conditions favorable to fuelnitrogen being converted to diatomic nitrogen (N2). Unfortunately, these conditions also tend toincrease the amount of unburned carbon in the flyash. In many locations, elimination of flyashwith high carbon content can become a significant economic liability.

European plants, in particular, are interested in maintaining a low Carbon-in-Ash (CIA) level asthis allows them to sell the ash, as opposed to having to pay for removal and disposal. Duringthe design phase for the low NOx equipment retrofit of a tangentially fired (T-fired) pulverizedcoal (PC) boiler in the UK, the client (RJM Corporation) requested Computational FluidDynamics (CFD) be used to verify the CIA guarantees. Previous CIA modeling attempts withFLUENT (versions 6.1 and earlier) had produced unsatisfactory results. The char burnout modelthat had been implemented only allowed one specie (either CO2 or CO) to be evolved from thechar oxidation. If CO only was employed, CIA levels were unrealistically low (~ 0.01% CIApredicted compared to ~ 6.2% actual) and predicted CO from the furnace was too high. If CO2was employed as the only product of char oxidation, the predicted CIA levels were too high (~25% CIA compared to ~6.2% actual) and the predicted CO levels were too low. Clearly abalance was needed.

Recent experience with coal gasification modeling indicated that the new multi-char reactionoption in FLUENT 6.2 provided better estimates of CIA. This option allows for multi-path andmulti-specie reactions with the char. As a commercial project with tight submission deadlines,prompt execution time was of the essence. This paper reports on a unique CFD methodology thatwas developed using FLUENT 6.2 for accurately predicting trends in flyash carbon. As will beseen, the model was able to predict CIA with remarkable accuracy.

CIA MODEL

Computational Fluid Dynamic modeling has been utilized for years [1, 2] on utility PC boilers tounderstand combustion dynamics and to predict NOx and CO trends. For example, Goldring and

1 Correspondence Author, Alion Science and Technology, Inc. Owasso, OK, USA lberg@alionscience.com2 RJM Corporation, Ltd.3 AES Kilroot Power Ltd.4 Alion Science and Technology, Inc. Owasso, OK, USA jdsmith@alionscience.com

Berg present some of RJMs previous successful application of CFD to various projects andapplications [3]. The CFD models (continuity, momentum, turbulence, species and reactions,etc.) that were utilized have been sufficiently documented elsewhere [4] and will not beaddressed in this paper. What is of interest, however, is the approach to coal modeling that wasemployed / developed for the current project.

For a T-fired PC coal furnace, experience has shown that the k- turbulence model, with eitherthe RNG or Realizable modification reasonably simulates the turbulent flow inside the furnace.Even though there is a strong tangential furnace circulation, experience shows that the full RSMturbulence model is not required. The combustion model includes species conservation and usesthe eddy-dissipation model. A single species is devolved from the coal, and spontaneouslydecomposes to form a combination of methane, CO, NO, SO3 and water. The specie split iscalculated to maintain a reasonable mass balance of the C, H, O, N, S and H2O from theproximate and ultimate analyses.

Boundary conditions (air mass flow rates, temperatures, coal particle size distribution, etc.) aredetermined from field measurements and data. The two furnace configurations that wereanalyzed will be discussed in greater detail in the next section.

The discrete phase model is used to model particle flow in the furnace. This model is based on aLagrangian particle tracking technique, which traces a particle trajectory through the phases ofcoal combustion. Figure 1 shows a cartoon that illustrates the steps which occur during generalcoal combustion. For the computer code utilized in the present study, these steps are sequential,and proceeds as follows:

1. Particle begins to heat up - particle achieves the boiling temperature of water, thetemperature does not change until enough heat has been absorbed to boil off all of theinherent water in the coal particle. Since specie conservation is used, the mass ofwater evolved from the coal is transferred to the gas phase.

2. Devolatilization - begins once water has been driven off coal particle. The particleheats up rapidly due to radiant interaction with the flame. Kobayshi et. al. [5]discusses volatile yield as a function of the conditions along the particle trajectory. Itis important that a path dependent devolatilization method is employed. The one usedfor this study is based on the Kobayashi model which has been modified to allow fordifferent volatile yields, depending on path conditions.

3. Char Burn-out - begins once devolatilization is complete. The multiple char reactionmodel was used. Bartok and Sarofim [6] discuss various competing reactions on page695 of their book. They provide applicability guidance and list references for specificrate data. The interested reader is highly encouraged to review this information priorto attempted implementation. From previous work in coal gasification, the followingreaction set was employed:

C + O2 => CO2 Reaction 1C + CO2 => 2CO Reaction 2C + H2O => CO + H2 Reaction 3

Reaction 1 is exothermic, and Reactions 2 & 3 are endothermic.

4. Leaving Domain - freezes unburned carbon in particle. In addition to leaving thedomain, the Lagrangian tracking methodology require a maximum residence time. Ifthe particle trajectory time exceeds this maximum time, the particle is dropped outof the calculation. A good practice is to have a residence time long enough so thatany carbon dropped out in this manner is at least one order of magnitude less thanthe total carbon leaving the furnace exit.

MODELING OF EXISTING FURNACE OPERATIONS

Figure 2 shows a wireframe of the furnace geometry with coal injection points identified. Thefurnace has four injection levels; A through D with D being the lowest injection level. Firingcoal the furnace has a nominal capacity of 220 MW gross power production. The furnace istypically operated with three of the four levels in service, with the fourth level kept in reserve.This allows the plant to maintain a high level of on-line availability. A previous modification tothe furnace included installation of offset secondary air buckets and Separated Over-Fire Air(SOFA) ports. The geometry of the furnace, existing SOFA ports, and burner corners weredeveloped from drawings supplied by the client.

As the plant performance is critical, AES-Kilroot Power Limited performed as series of baselinetests to help establish the existing performance of the plant. The furnace operates on both coaland oil, so baseline testing of various configurations for each fuel type was accomplished overabout a 5-day period. Of specific interest to this work were two 220 MW configurations firingpulverized coal through: 1) A-C level burners, and 2) B-D levels burners. During each test,continuous measurements of nearly two hundred set points collected process information overapproximately a four hour period.

The information gathered during the baseline testing was used to set the flow conditions(velocity and temperature) for each air or fuel injection location. Modest adjustments wereaccomplished to ensure excess O2 matched measured values. After the CFD model hadconverged, comparison of furnace exit values to measured data was accomplished to ensure thatthe CFD model reasonably reproduced field data. Table 1 compares the CFD model to actualdata for CO, NOx and Furnace Exit Temperature (FEGT) for upper mills (A-C) in service withexcess O2 of 4.3%.

Table 1 - Comparison of Data to CFD Upper Mills Operation

Data CFDCO 11 ppm 2200 ppmNOx 641 mg/Nm3

*649 mg/Nm3

*

FEGT 0C 1120 0C 1158 0CCarbon in Ash (CIA) 6.2% 22%

* To convert mg/Nm3 to lbs/MMBtu, multiply by 6.655E-04

Iso-surfaces of the predicted CO, NOx, and furnace temperatures are provided as Figures 3through 5. Since the CO was measured after the economizer, continued CO oxidation isexpected in the post furnace region (i.e., economizer, etc.). While it was not possible to directlycompare the CO numbers, the predicted values were typical of coal furnaces. NOx and FEGTmatch reasonably well. As discussed earlier in the paper the CIA prediction was so far off in

the initial CFD modeling that predicted trends were not expected to be meaningful. Thus, whatwas needed was a more accurate CIA model.

Using RJMs combustion expertise and building upon experience gained from recent workmodeling coal gasification, the three char reaction model discussed earlier was applied to thecurrent CFD simulation. Starting reaction rate parameters were taken from literature sourcesoutlined by Bartok and Sarofim [6]. Not all coals are identical, so it was anticipated that rationaladjustments to the kinetic parameters would be required to match the data. Since each rateparameter adjustment required a complete re-convergence of the CFD solution and since theexpected affect of the kinetic rate parameters have been shown to be nonlinear on CIA [7], it wasnot possible to completely match the predicted data. However, after just a few variations, aprediction of 6.48% CIA (compared to measured ~6.2% CIA) was accomplished.

This comparison is not particularly remarkable as it was accomplished by adjusting kineticparameters to achieve a good comparison. The second baseline test more critically tested themodels ability to match observed CIA for a completely different operational scenario. Using thesame methodology, a baseline CFD model of running the lower mills was executed. Results arepresented in Table 2 (excess O2 of 3.7%):

Table 2 - Comparison of Data to CFD Lower Mills Operation

Data CFDCO (ppm) 13 1770

NOx (mg/Nm3)* 554 560

FEGT (0C) 1246 1185Carbon in Ash (CIA) 4.4% 5.98%

* To convert mg/Nm3 to lbs/MMBtu, multiply by 6.655E-04

Again, the NOx and FEGT predictions are reasonable while the CO is high but anticipated giventhe relative location of the predictions. In this case, using the adjusted 3 char reactionmechanism accurately predicted the trend (CIA went down for this operating scenario). Thiswas especially encouraging, as the excess O2 was reduced from 4.3% to 3.7%.

In addition to the furnace exit predictions, the new CIA model also has the full range ofdiagnostic tools possible in a comprehensive CFD code. The following were particularly usefulduring the equipment design phase:

Identification of CIA sourcesIn addition to better accuracy, the new model allows for identification of carbon sources fromindividual injection locations. Table 3 shows the predicted Upper Mills Baseline CIA, brokendown by coal injection point. In this case, the first letter is for the mill (a, b, c, or d mill) and thenumber is for the particular furnace corner where the coal was injected. Using thisnomenclature, injection c1 means Corner 1, C-mill injection.5 Interestingly, 52% of thepredicted baseline Upper Mills CIA comes from the lowest level C mill.

5 Absolute location of each corner with respect to furnace nose and ash pit geometry can not be included due to theproprietary nature of the solution developed for the client.

Identification of C concentrationsVisual diagnostics are also available to supplement the quantitative information. Figure 6 givesan example of this type of diagnostic. It shows coal particle trajectories colored by carbonconcentration. The B4 injection shown starts off with maximum carbon concentration (red), andas carbon is oxidized, the path color becomes bluer, with dark blue representing nearly 0%carbon. This diagnostic not only shows where coal is going in the furnace, but where it is beingoxidized.

Table 3 - Upper Mills Baseline CIA by Coal Injectionc4 0.03877 18.5%b4 0.017 8.1%c3 0.02169 10.3%b3 0.00411 2.0%c2 0.0344 16.4%b2 0.0185 8.8%c1 0.0142 6.8%b1 0.00587 2.8%a4 0.0296 14.1%a3 0.00649 3.1%a2 0.014 6.7%a1 0.00514 2.5%

Particle / SOFA InteractionUsing a 10% iso-surface of oxygen, Figure 7 combines the iso-surface with coal path linescolored by carbon concentration. This unique view shows how coal particles interact withsurrounding air being supplied though either the offset or SOFA ports.

LOW NOX BURNERS AND CIA MINIMIZATION

After completion of baseline modeling, the proposed low NOx burners and modifications to theOFA system were modeled. Comparison to the baseline model is shown in Table 4.

Table 4 - Comparison of Baseline to Upgrade (CFD) Upper Mills Operation

Baseline UpgradeCO (ppm) 2200 4700Excess O2 4.3% 4.3%

NOx (mg/Nm3)* 649 430Carbon in Ash (CIA) 6.45% 14.70%

* To convert mg/Nm3 to lbs/MMBtu, multiply by 6.655E-04

As expected, lower NOx leads to higher CO and CIA. The roughly 33% NOx reduction predictedwas sufficient to provide confidence in RJMs solutions ability to meet the NOx guarantees. Thehigher CO number was not as much of a concern. This was due to previous baseline furnacemodeling results which indicated sufficient residence time to accomplish CO oxidation.However, the predicted increase in CIA number was troubling since it was critical that the CIAnot increase above the value for the existing operation. The following shows some of thediagnostic power of the new modeling tool.

Three methods were employed to reduce CIA and maintain the same NOx reduction:

1. Upgrade rotary class e,2. djust angle and dam ure high amounts of CIA, and3. Identify high CIA in ary air.

Option 1 was simple to incor particle size distribution in themodel input file. This was a value decreased to about 10%.This was encouraging, but sti

The SOFA ports were installe n and were installed fairly close(in vertical direction) to the e furnace was operated on theupper mills the residence tim e an issue especially with thelower furnace air level beincompares the carbon burnoutbe optimized.

Figure 9 exemplifies how thethat analysis had shown weretogether, in the back left co(top) port, virtually eliminatedports were fixed angle. The cangle control was required.operation. In this case a reminteract with the high concentnearly 28% less than the origi

Lower mill operation presentangles could not be varied, solower mill operation as well.When the upper mill SOFA7.5%. A good starting pointoperation using the optimized

Table 5 - Upgrifiers and reduce overall particle sizper positions of SOFA ports to captjections, and selectively add second

porate into the model by changingccomplished and the predicted CIAll not sufficient.

d as part of a previous modificatiofurnace exit. As a result, when the for carbon burnout could becom

g lowered to obtain a compliant NOx performance. Figure 8for the base case and the upgrade. Clearly the SOFA ports need to

SOFA ports were optimized. It shows a series of three injectionssignificant contributors to the CIA problem. As seen, they mergerner. Changing the angle of SOFA air injection on the back rightthe CIA. This was a significant finding since the existing SOFAlient, as part of the upgrade, now knew that SOFA ports with yawFigure 10 shows the virtual elimination of CIA with upper millaining SOFA port was angled into the center of the furnace to

ration of CIA in center furnace. The final predicted CIA is 4.65% -nal base line prediction.

ed a different set of problems. As a practical matter, SOFA yawthe angles that were set for upper mill operation had to be used forThis prevented using the SOFA angle optimization just outlined.

settings were used, the CFD prediction for lower mill CIA was, but still too high. Table 5 shows the CIA levels for lower millSOFA angles from the upper mill study.

ade CIA by Injection Source (Lower Mill Operation)

Injection Mass Flow % of CIAd4 0.0672 26.6%d3 0.0272 10.8%d2 0.0575 22.8%d1 0.0226 8.9%c4 0.0083 3.3%c3 0.00766 3.0%c2 0.0131 5.2%c1 0.0323 12.8%b4 0.00025 0.1%b3 0.0055 2.2%b2 0.0048 1.9%b1 0.00616 2.4%

Unexpectedly, almost 70% of the CIA came from the lowest injection level (D mill) in thisoperating scenario. When this row of nozzles were modified to a more optimum angle ofinjection, the CFD model predicted a CIA of 3.45% - with most of the reduction from thepredicted D mill injections.

SUMMARY

Table 6 shows the final CFD predictions for the upgrade.

Table 6 - Comparison of Baseline to Upgrade (CFD) Upper Mills Operation

Upper Mill Upgrade (CFD) Lower Mill Upgrade (CFD)CO 2400 ppm 2600 ppm

Excess O2 4.56% 4.3%NOx 430 mg/Nm3

*416 mg/Nm3

*

Carbon in Ash (CIA) 4.65% 3.45%* To convert mg/Nm3 to lbs/MMBtu, multiply by 6.655E-04

Often, these types of adjustments are made at start-up by experienced personnel which can betime consuming, labor intensive and costly. In this case, CFD analysis identified additionalequipment modification (yaw angles to the SOFA ports, and modification to the D mill injectors/ secondary air) and valuable operational settings prior to installation.

REFERENCES

1. Smoot , L. D. and Smith, P. J., Coal Combustion and Gasification, Plenum Press, NewYork, 1985.

2. Fiveland, W. A., Wessel, R. A., Numerical Model for Predicting Performance of Three-Dimensional Pulverized-Fuel Fired Furnaces, AIAA/ASME Thermophysics and HeatTransfer Conference, Boston MA, ASME paper 86-HT-35, 1986

3. Goldring, J. Berg, L.D., How Experienced use of CFD Analysis allow Compliance with strictLCPD NOx Emissions Requirements, International Power Generation Conference (IPG),Leipzig, Germany, November, 2006.

4. FLUENT Users Guide, Fluent Inc., Centerra Resource Park, 10 Cavendish Court,Lebanon, NH.

5. Kobayshi, H., Howard, J. B., and Sarofim, A. F., Coal Devolatilization at HighTemperatures, 18th Symposium (International) on Combustion, The Combustion Institute,Pittsburgh, PA (1977), p. 411.

6. Bartok, W. and Sarofim, A. F., Fossil Fuel Combustion A Source Book, Wiley-Interscience Publication, 1991.

7. Smith, J.D., Smith, P.J., Hill, S.C., "Parametric Sensitivity Study of a CFD-Based CoalCombustion Model," AIChE Journal, Vol. 39, No. 10, October (1993).

Figure 1Stages of Coal Combustion

Figure 2Furnace Wire Frame and Coal Injections

Coal particleO2

Leave DomainOr

Incomplete

Heat Up anddrive off H2O

Devolitization Char Burn-out

H2O

Volatile gas

CO CO2

Ash and UnburnedCarbon

ab

cd

CoalInjectionLevels

Figure 3Furnace 15,000 ppm CO Iso-Surface

Figure 5Furnace 1600 0C Iso-Surface

Figure 5Furnace 1600 0C Iso-Surface

Figure 6Identification of C Concentrations

Figure 7

Blue is 100% char Oxidation

Red is no char Oxidation

10% O2 Iso-Surface(from SOFA)

Interactions Increase Blue of Coal particles

Interactions also reduce O2 Volume

Figure 8Base Case to Upgrade Carbon Burn-out Comparison

Figure 9Three Level Contribution to CIA

Base

Upgrade

Initial Burnout not as complete

at ExitCoal Inj.C & B

Coal Inj.C

Coal Inj.C & B & A

Figure 10Elimination of Upper Mills CIA

High CIA at Corners2 & 4 Eliminated

High CIA inCenter

CIA Reduced by Aiming Left BackSOFA (lower) into center of furnace