Prediction of Activated Carbon Injection Performance for Mercury

Ind. Eng. Chem. Res. 2010, 49, 3603–3610

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Prediction of Activated Carbon Injection Performance for Mercury Capture in a Full-Scale Coal-Fired Boiler Wei Zhou,*,† Gilles Eggenspieler,‡ Abu Rokanuzzaman,† Vitali Lissianski,† and David Moyeda† General Electric Energy, 1831 Carnegie AVenue, Santa Ana, California 92705, and ANSYS, Inc., Southpointe, 275 Technology DriVe, Canonsburg, PennsylVania 15317

Activated carbon injection (ACI) is an effective mercury control technology demonstrated in both short-term and long-term full-scale tests. The effectiveness of mercury capture by activated carbon depends on the mercury speciation, total mercury concentration, flue gas composition, method of capture, and activated carbon properties, such as pore size, type of carbon impregnation, and surface area, etc. It is also desired that an ACI system be designed to produce good mixing between the activated carbon and the flue gas. In recent years, General Electric Energy has conducted both short-term and long-term tests in large-scale coal-fired boilers for ACI mercury capture demonstration. The programs consisted of (1) combustion optimization to improve natural mercury capture by fly ash, (2) computational fluid dynamics (CFD) modeling of activated carbon injection to design ACI lances, (3) a short-term test to select the activated carbon type, and (4) a long-term test to evaluate the mercury capture performance. This paper presents the CFD modeling for an ACI demonstration in Sundance Station Unit 5. The CFD model developed describes the film mass transport, pore diffusion, and carbon surface adsorption and desorption phenomena for the prediction of the mercury capture rate. The model was applied to evaluate the lance design and to calculate the mercury capture rate. The test data were also presented for comparison with the model results. Introduction With the potential of a more stringent mercury control requirement worldwide, technologies for removing mercury from coal-fired boiler emissions are in active development and demonstration. Sorbent injection is one of the technologies that have been demonstrated leading to a moderate to high rate of mercury removal.1,2 The technology injects activated carbon (AC) to a postcombustion duct prior to the particulate removal device, such as the electrostatic precipitator (ESP) and or the fabric filter (FF). The activated carbon mixes with flue gas in the duct, adsorbs mercury, and is then removed by the particulate control equipment. The effectiveness of the activated carbon injection (ACI) technology depends on (1) coal type, which impacts mercury speciation and flue gas composition; (2) activated carbon properties, which include active surface sites and pore size; and (3) the type of downstream particulate control device (usually a fabric filter is more effective than an ESP). Activated carbon injection is a mature technology with more than 130 installed or in the process of installation commercial systems in the U.S. and Canada.3 In the past decade, a number of studies were undertaken to understand the mercury speciation in coal-fired boilers.4-13 Typically, mercury evaporates from coal in an elemental form in the high-temperature region of the furnace. As the flue gas cools, elemental mercury (Hg0) is oxidized to oxidized mercury (Hg2+) and/or is bound to fly ash forming particulate mercury (HgP). Since the mercury oxidation process is kinetically rate limited, while the mercury adsorption on ash surface is diffusion limited, at the boiler exit, elemental, oxidized, and particulate mercury coexist in flue gas. The studies also found that the majority of gaseous mercury in bituminous-coal-fired boilers was Hg2+ and the majority of gaseous mercury in subbituminous- and lignite-coal-fired boilers was Hg0.14 * To whom correspondence should be addressed. Tel.: 949-794 2628. E-mail: [email protected]. † General Electric Energy. ‡ ANSYS, Inc.

Activated carbon injection has been demonstrated in both short-term and long-term tests for mercury control.1,15,16 Studies were also done to understand the mechanism of mercury adsorption on activated carbon. Scala17-19 proposed a theoretical model to describe the surface diffusion, surface adsorption, pore diffusion, and surface desorption phenomena for mercury capture on activated carbon. The governing equations were then solved in one-dimensional space using the fifth-order Runge-Kutta method for predicting the mercury capture. Scala’s work provided a sound basis for the modeling approach adopted in this paper. General Electric Energy (GE) has conducted several pilotscale and full-scale ACI demonstration programs in recent years. The demonstration programs evaluated the effectiveness of different types of activated carbon for mercury control in both wall-fired and tangentially fired boilers that fire bituminous or sub-bituminous coals. Combustion optimization was also applied to improve the intrinsic mercury capture to reduce the activated carbon consumption rate.2 One of such demonstrations took place in Sundance Station Unit 5 operated by TransAlta. Sundance Unit 5 is a 365 MW twin furnace tangentially fired boiler with a rated capacity of 2600000 lb/h of main steam at a pressure of 2350 psig and a temperature of 538 °C. The unit was originally installed in 1977 by CE Canada Ltd. In this ACI demonstration program, boiler combustion optimization, CFD modeling for lance design, short-term sorbent evaluation, and 30 days of sorbent injection tests were included for developing a sound understanding of the activated carbon mercury control performance. Continuous 30 day DARCO Hg-LH injection demonstrated that 70% Hg removal could be achieved at a sorbent injection rate of 1.2 lb/MMACF. Data on combustion optimization, sorbent evaluation, and 30 day tests in Unit 5 are described elsewhere.20 This paper reviews the CFD study for optimization and design of sorbent injection lances. The mercury capture model was first developed by Madsen21 and is now integrated with the CFD

10.1021/ie901967p  2010 American Chemical Society Published on Web 03/22/2010

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Ind. Eng. Chem. Res., Vol. 49, No. 8, 2010

model to calculate the mercury capture rate. The modeling results are compared with the test data in the paper. CFD Modeling Theory and Approach The CFD model solves transport equations for mercury species, i.e., Hg0 and Hg2+, in continuous phase, and activated carbon particle trajectories in the discrete phase. The mass exchanges of Hg0 and Hg2+ on the particle surface are described by the particle surface diffusion, surface adsorption and desorption, and pore diffusion mechanisms as proposed by Scala.17-19 Sorbent Particle Trajectories. Sorbent particle trajectories are solved by the Lagrangian model, which tracks a large number of particles through a calculated flow field.22 The momentum, mass, and energy exchanges between the continuous and the discrete phases are described by drag, and mass- and heat-transfer laws. The sorbent particle velocity is calculated by the following equation: duP gx(FP - F) ) Fd(u - uP) + dt FP

18µ CDRe FPdP2 24

(2)

where Re is the relative Reynolds number, µ is the flue gas molecular viscosity, CD is the drag coefficient, FP is the particle density, and dP is particle diameter. The dispersion of particles due to turbulence is predicted by using the stochastic tracking model, which includes the instantaneous turbulent velocity fluctuation on particle trajectories.22 The particle temperature change can be calculated by mPCP

dTP ) hAP(T∞ - TP) + εPAPσ(TR4 - TP4) dt

the particle surface. The film mass-transfer coefficient kf is obtained by the following formula:

(1)

where uP is the particle velocity, FP is the particle density, and Fd(u-uP) is the drag force per unit particle mass. The coefficient Fd can be expressed as Fd )

Figure 1. Schematic of mercury capture model.

(3)

where εP is the particle emissivity; σ is the Stefan-Boltzmann constant (5.67 × 108 W/(m2 · K)); h is the heat-transfer coefficient; AP is the particle surface area; and TP, T∞, and TR are particle, ambient, and radiation temperatures, respectively. The radiation temperature is calculated by (G/σ)1/4, where G is the incident radiation (W/m2). Since the flue gas temperature in the area where sorbent is injected is usually low (∼150 °C), the radiation term can be neglected or dropped in the simulation. The amount of captured mercury is negligible compared to the mass of the sorbent particles; therefore, the sorbent particles are treated as inert particles in the model and are assumed to have no mass change after injection. Mercury Capture by Sorbent. A schematic model illustrating mercury capture by sorbent is shown in Figure 1. The capture process consists of (1) film mass transfer from the bulk flow to the particle surface, (2) mercury adsorption on the particle surface, (3) mercury diffusion inside the particles, and (4) mercury desorption into the continuous phase. The decrease of Hg concentration from the bulk phase, C∞, to the particle surface, C0, can be described by the film layer effect. The mass-transfer flux across the film can be calculated by the multiplication of the film mass-transfer coefficient and the mercury concentration difference between the bulk flow and

kf )

Sh × Dmol dP

(4)

in which Sh is the Sherwood number and Dmol is the Chapman-Enskog diffusivity; i.e., Dmol ) 1.86 × 10-3

T3/2√1/MW1 + 1/MW2 Fδ122Ω

(5)

where Ω is the collision integral,23 δ12 is the collision diameter (angstrom)24 and MWn is the molecular weight of the particle. From eq 4, the film mass-transfer coefficient increases with the decrease of the sorbent particle size. The particle surface adsorption and desorption are described by the Langmuir theory, in which the net adsorption rate (R) equals the difference between the local surface adsorption and desorption rate: R ) k1ωmax(1 - θ)c0 -

b ω θ k1 max

(6)

Here, ωmax is the maximum number of the available sites, i.e., sorbent capacity, and θ is the sorbent utilization (ω/ωmax), i.e., fraction of the occupied sites. The ωmax, k1 (adsorption rate), and b (equilibrium ratio) are temperature-dependent and are usually obtained from experiments. The adsorption rate decreases with the increase of the occupied number of sites. Mercury diffusion inside the particle is limited by the rate of molecular diffusion (Dmol) or Knudsen diffusion (DKN), whichever is slower. Therefore, the effective diffusivity (cm2/s) is expressed as Deff )

ΦP

(

1 1 + D D KN √ΦP mol

)

-1

(7)

where ΦP is the sorbent porosity.24 The Knudsen diffusivity is defined as DKN ) 9.7e-4

dpore 2

T MW

(8)

where dpore is the pore size (nm).23 The Knudsen diffusivity does not depend on gas composition or pressure. It depends on particle temperature and pore size.


Figure 2. Schematic of numerical procedure.

Figure 3. Duct CFD model geometry.

Numerical Implementation. Figure 2 shows the steps for numerical implementation of the mercury capture model. A CFD model was first developed for a duct or an application domain to solve gas-phase governing equations. Then the sorbent trajectories were solved using the Langrangian model. The adsorption and desorption modeling described above were applied to provide the mass-transfer rate for the scalar equations of mercury species. The equations were iterated until the convergence criteria are met. For example, the residuals of the continuity, momentum, energy, and species transport equations are smaller than specified values. CFD Modeling of Sorbent Injection Performance Prior to sorbent injection tests, CFD was applied to facilitate the design of the sorbent injection lances and to evaluate the sorbent mixing performance for Sundance Station Unit 5. The CFD study consisted of three steps: (1) baseline evaluation, (2)

injection lance design, and (3) development of the sorbent performance model after completion of the test program. Baseline Study. For the baseline study, a three-dimensional CFD model was developed to characterize flow patterns and flue gas velocity distributions in the ducts, upstream of ESP in Sundance Unit 5. Unit 5 was equipped with two ducts exiting out of the air heaters and entering the ESP (North and South sides). The two ducts were independent and geometrically symmetrical, and, therefore, the CFD model was developed only for the North side of the ductwork where sorbent was injected. As shown in Figure 3, the model inlets were the outlets of the air heaters. The primary air heater outlet was located down stream of the secondary air heater outlet. Three sets of turning vanes are in the duct to help distribute the flow and minimize the flow separation. The model geometry was discretized into high quality computational cells using GAMBIT 2.0, a mesh generation tool. The mesh consists of hybrid elements (a mix of quadrahedral and tetrahedral elements) to account for the irregular shape of the geometry, such as the vanes and the lances in the duct. The mesh contains about 750000 cells for the baseline model and over two million cells for the lance studies to resolve the small holes on the lances. The mesh was read into FLUENT 6.2 for model setup and iteration to a solution. Physical models such as the standard k-ε turbulence model and the discrete phase model were applied to account for sorbent particle mixing in the turbulent duct flow. The boundary conditions for the studied cases are summarized in Table 1. The flow rates in the table are for the single duct at a nominal unit load. The flue gas temperature at the exit of the secondary air heater is linearly biased from side to side from 107 °C on the North to 154 °C on the South. The biased profile (shown later in Figure 5) was estimated from GE’s previous experience with the Lungstrom type air heaters. The flue gas temperature at the exit of the primary air heater was defined at 107 °C. The sorbent injection system consisted of four lances, each of which had 12 holes. The lances were located at the down stream of the primary air heater outlet before the duct splitting to two legs. The baseline case represents the duct condition before the installation of the sorbent injection system. The flow distribution at the model inlet was assumed to be uniform coming out of the air heater. Figure 4 shows a three-dimensional view of the predicted velocity profiles in the duct. The flue gas flow enters the computational domain and turns 90° before the duct splits into two legs. The flue gas flow is biased to the bottom of the duct after the turn, where the injection lances are located. The two legs bend downward about 30° with one of the legs yawing about 30° to the North. The directional change of the duct results in a low-velocity recirculation zone in the North leg. The two legs then turn 90° upward to ESP. A stratified flow distribution is observed in the upward duct with a low-velocity recirculation zone at the inner corner of the turn. The flow split between the two legs is shown in Figure 4, and the flow split ratio is about 53 (South)/47(North).

Table 1. CFD Model Inputs parameter flue gas flow rates per duct mass of primary flue gas, kg/h mass of secondary flue gas, kg/h total flue gas flow, SCFM sorbent flow, kg/h

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value

parameter

245,250 749,400 382,254 27.2

dilution air, kg/h temperature primary flue gas, °C secondary flue gas, °C dilution air, °C

value 8000 107 107-154 51

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Figure 4. Baseline velocity profiles in the duct.

Figure 5. Temperature profiles in the duct.

Figure 6. Sorbent trajectories colored by residence time.


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Figure 7. Sorbent particle concentrations. Table 2. Modeling Parameters for Sorbent Performance Modeling parameters sorbent sorbent sorbent sorbent sorbent

particle size, mean, µm particle size, 95% less than, µm porosity pore size, nm

value

comments

18 45 0.67 15

from vendor ref 24 ref 24

Temperature profiles in the duct are shown in Figure 5. The temperature linearly varies from 127 to 132 °C at the model outlet. The average temperature in the South leg is predicted to be 5 °C lower than that in the North leg because the majority of the cooler flue gas from the primary air heater goes to the South leg. Injection Lances Design and Performance Predictions. Four sorbent injection lances were placed off-center of the duct to avoid the interference with the vanes at the outlet of the primary air heater (as shown in Figures 6 and 7). Each lance had 12 holes with less number of holes at the top part of the lance and more number of holes at the lower part of the lance. The average diameter of the holes is 20 mm. The biased distribution was to deliver the sorbent to the bulk of the flue gas, which was biased to the bottom of the duct at the injection location as discussed in the previous section. Sorbent (activated carbon) is premixed with transport air and enters the computational domain at the top of each lance. The mixture flow then leaves the lances at a high speed through the holes in an alternate pattern to improve mixing between the sorbent and the flue gas. Figure 6 shows representative sorbent trajectories in the duct, colored by the residence time. Each of the two legs receives about half of the sorbent flow. The residence time of the sorbent in the duct ranges from 2 to 4 s. The distribution of sorbent concentration is nonuniform as shown in Figure 7. The sorbent concentration profiles are stratified and concentrate at the center of the main duct and the corners of the two legs. Further down stream from the duct, the mixing is improved at a longer residence time. The particle concentration appears to be more evenly distributed at the exit of the North leg than that at the exit of the South leg. The mercury capture model developed in this paper was applied to calculate the mercury capture rate by the sorbent.

parameters

value

comments

initial mercury concentration particulate, µg/m3 elemental, µg/m3 oxidized, µg/m3 total, µg/m3

0.125 6.76 1.215 8.1

Ontario Hydro measurement Ontario Hydro measurement Ontario Hydro measurement from coal analysis calculation

Figure 8. Sorbent particle size distribution.

The modeling parameters were mainly obtained from the test data and are listed in Table 2. Sorbent size distribution is described by Rosin-Rammler distribution, which is derived on the basis of a mean size of 18 µm and 95% of particles smaller than 45 µm as shown in Figure 8. The capture model was calibrated depending upon the types of sorbent (DARCO Hg and DARCO Hg-LH). Three parameters in the model influence the mercury capture: the sorbent capacity (ωmax), the adsorption rate (k1), and the equilibrium constant (b). The calibration process used ad-hoc sorbent capacity (ωmax) deduced by comparing numerical results to field test results for the lowest sorbent injection rate case. The calibrated adsorption rates are summarized in Table 3. The adsorption rate parameters are functions of particle temperature. The maximum available sites, ωmax, are larger for the oxidized mercury than that for the elemental mercury. The adsorption rate used for the DARCO Hg-LH sorbent is twice the one used for the DARCO Hg sorbent. The calibrated adsorption rates were then fixed and

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Table 3. Adsorption Rate Parameters for DARCO-Hg elemental mercury, Hg0 temperature, K sorbent capacity, ωmax equilibrium constant, b, m3/g adsorption rate, k1, m3/(g s)

363 100 × 103 1200 0.415

393 27.7 × 103 668 0.505

applied to different sorbent injection rates and design conditions for evaluation of sorbent injection performance. The predicted incremental mercury removal rate as a function of the sorbent injection rate is compared with test data in Figure 9. Numerical results obtained for the DARCO Hg-LH sorbent are in good qualitative and quantitative agreement with the field results. The numerical study predicts the reduction in relative mercury capture efficiency as the sorbent injection rate is increased. Comparison between numerical and field mercury removal is not as favorable when the DARCO-Hg sorbent is considered although still in overall agreement. Both the test data and the modeling results show that more than 40% incremental mercury removal was achieved with the sorbent injection rate of 2 lb/MMACF, when injecting DARCO Hg-LH in this application. When the sorbent flow rate was greater than 5 lb/ MMACF, more than 70% incremental removal rate was

oxidized mercury, Hg2+ 423 15.5 × 103 420 0.630

363 365 × 103 166.7 0.25

393 125.8 × 103 476.2 0.2

obtained. Considering 15-20% of native fly ash capture measured during the test, close to 90% of mercury can be captured by the ACI technology in this application. The predicted elemental, oxidized, and total mercury concentrations for the DARCO Hg-LH at 4.2 lb/MMACF injection rate are shown in Figure 10-Figure 12, respectively. As shown in the figures, the inlet mercury concentrations are assumed to be uniform. After injection of activated carbon, the mercury concentration starts decreasing. The mercury reduction was more uniform in the North leg than that in the South leg. The lower corner of the South leg had a poorer mercury reduction, which is consistent with the AC distribution shown in Figure 7. To further improve the mercury removal, the activated carbon

Figure 11. Elemental mercury, Hg0, concentration. Figure 9. Comparison of CFD predictions with measurements.

Figure 10. Oxidized mercury, Hg2+, concentration.

423 82.2 × 103 588.2 0.1

Figure 12. Total mercury concentration.


should be injected in a manner that increases AC concentration in that corner of the south leg. Conclusions CFD modeling has been applied to optimize lance design to maximize mercury reduction in the activated carbon injection tests in Sundance Station Unit 5. A performance calculation model for mercury capture was integrated with a CFD model for quantitatively predicting efficiency of mercury reduction. The integrated modeling approach can be used for predictions of the mercury capture rate as a function of the sorbent injection rate. The modeling results are compared reasonably well with the test for lance injection of DARCO Hg-LH sorbents. Over 40% incremental mercury reduction was obtained on site and predicted by the model for injection of 2 lb/MMACF DARCO Hg-LH sorbent, and over 70% incremental mercury reduction was obtained on site and predicted by the model for injection of 5 lb/MMACF DARCO Hg-LH sorbent. The developed performance calculation model is a good tool for predicting the mercury removal rate by activated carbon injection. The adsorption rates in the model need to be calibrated for different applications. Notation ACI ) activated carbon injection b ) equilibrium ratio co ) ambient mercury concentration Cd ) drag force coefficient CP ) specific heat, J/(kg · K) dP ) sorbent particle diameter, µm dpore ) pore size, nm DKN ) Knudsen diffusivity Dmol ) molecular diffusivity Dmol ) molecular diffusivity ESP ) electrostatic precipitator Fd ) drag force per unit mass G ) radiation incident, W/m2 h ) heat-transfer coefficient, W/(m2 · K) Hg0 ) elemental mercury Hg2+ ) oxidized mercury HgP ) particulate mercury IR ) incremental removal rate k1 ) adsorption rate kf ) film mass-transfer coefficient NR ) native removal rate mP ) particle mass, kg MW ) molecular weight, kg/kmol TP ) particle temperature, K TR ) total removal rate uP ) sorbent particle velocity, m/s Re ) Reynolds number Sh ) Sherwood number Greek Letters δ12 ) collision diameter εP ) particle emissivity θ ) sorbent utilization µ ) molecular viscosity σ ) Steven-Boltzmann constant, W/(m2 · K4) ΦP ) sorbent porosity ωmax ) maximum number of available surface sites Ω ) collision integral

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ReceiVed for reView December 11, 2009 ReVised manuscript receiVed February 25, 2010 Accepted February 26, 2010 IE901967P

Prediction of Activated Carbon Injection Performance for Mercury

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