Influence of Coal Blending Methods on Unburned Carbon and NO

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Influence of Coal Blending Methods on Unburned Carbon and NO Emissions in a Drop-Tube Furnace Byoung-hwa Lee,† Seoung-gon Kim,‡ Ju-hun Song,† Young-june Chang,† and Chung-hwan Jeon*,† † ‡

Pusan Clean Coal Center, School of Mechanical Engineering, Pusan National University, Busan 609-735, Republic of Korea Samtan Company, Seoul 135-280, Republic of Korea ABSTRACT: The influence of coal-blending methods, such as out-furnace and in-furnace methods, on unburned carbon and NO emissions of blending coal combustion in a drop-tube furnace (DTF) has been analyzed using experimental and numerical approaches for binary coals (sub-bituminous and bituminous coals) used by Korean power plants. In the out-furnace method, as the blending ratio of sub-bituminous coal based on bituminous coal (SBR) increases up to 50%, the unburned carbon fraction gradually decreases. The worst condition is found at a SBR of 75%, which is caused by the occurrence of highly oxygen-deficient conditions in the furnace. Furthermore, the emission index of NO (EINO) is proportional to the SBR. In addition, the in-furnace blending method is applied using a modified DTF to improve the efficiency of unburned carbon fractions and the involvement of NO emissions. The results show that unburned carbon fractions as well as the EINO decrease over time because of improvements in oxygen deficiency and NO reduction mechanisms. The numerical modeling strongly supports these phenomena and provides insights into the mechanisms that affect the blending methods during the combustion of coal blends.

’ INTRODUCTION In previous decades, extensive studies have been performed to characterize the combustion of coal blends.1,2 It has been recognized that the properties related to fuel composition (e.g., proximate and ultimate analysis data, heating value, etc.) remain additive after blending, while many characteristics related to combustion are non-additive; i.e., they exhibit reactive and unreactive effects. For example, ignition, combustion and flame stability, slagging, and fouling cannot be predicted by additivity.2 Experimental approaches have been employed to assess the combustion performance of coal blends fired in pulverized coalfired furnaces based on bench-scale,3,4 pilot-scale,5,6 and fullscale7 data. From the experimental data, some empirical indices, using volatile matter content, fuel ratios, and maceral compositions, were also derived to empirically predict the ignitability, flame stability, and combustion of coal blends.2 In addition, Biswas et al.8 studied the combustion behavior of blends of two Indian coals of high- and low-ash coal using a thermogravimeric analyzer (TGA) and drop-tube furnace (DTF). They showed that their observation of blends in a DTF was not similar to that reflected in a TGA because of non-addictive characteristics of coal blends. Another technique, which potentially can be an accurate and cost-effective tool in the analysis of coal blends, is numerical modeling. Over the last 20 years, numerical modeling has proven to be an effective tool in identifying and solving problems related to pulverized coal combustion. In particular, it can provide insights into the combustion characteristics of unfamiliar coals, and for this reason, it has been extensively applied to evaluate the combustion performance of a single coal and binary-coal blends in bench-, pilot-, and full-scale furnaces.9
13 Arenillas et al.12 evaluated the application of numerical modeling to model the combustion of binary-coal blends in a bench-scale DTF and to predict the NOx emissions and char r 2011 American Chemical Society

burnout. In their simulation, the blend was represented as a single coal, whose properties were obtained by the weighted averaging of the relevant properties of component coals, without adequate description of the chemical and physical interactions between components. Consequently, the non-additivity, particularly from widely different ranked coals, was not reproduced.12 Shen et al.13 carried out the simulation of flow and combustion for binary-coal blends, and under simplified blast-furnace conditions, the numerical blend modeling was validated with the experimental results. They showed that the interactions between component coals, in terms of particle temperature and volatile matter, are responsible for the synergistic effect and the modeling provides an effective tool for the design of coal blends. Ikeda14 focused on the investigation of coal blends using the in-furnace blending method in pilot-scale three-stage burners. He experimentally investigated the in-furnace blending method in comparison to the bunker blending method, namely, the outfurnace blending method, in three-stage burners. He successfully burned coal blends using the in-furnace blending method based on operational experiences and achieved reduction in both unburned carbon and NO emissions. Despite these studies, there is still a need to investigate coal-blend phenomena; most research has focused on the combustion characteristics of the out-furnace blending method, and the underlying mechanisms related to non-additive characteristics and the effect of the in-furnace blending method are, to date, poorly understood. Here, the out-furnace and in-furnace blending methods indicate that, when two types of coal are injected into a boiler, two different blending methods can be used. In the out-furnace blending method, the two types of coal are injected into a burner Received: May 27, 2011 Revised: October 9, 2011 Published: October 10, 2011 5055

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Figure 1. Schematic diagram of the modified DTF apparatus.

simultaneously, after being mixed together prior to entering the furnace. In the in-furnace blending method, each type of coal is injected by a separate burner without prior blending. They are then mixed in the furnace with different blending times. Therefore, this study was conducted as a fundamental examination of the non-additive phenomena that occur during the combustion of coal blends when using the out-furnace blending method, along with the mechanisms that affect the in-furnace blending method using a DTF. The DTF system was modified, so that the two coals were injected through separate injectors in the furnace, and their blending time was controlled by varying the distance between the two injectors to describe the out-furnace and in-furnace blending methods of a real power plant. In other words, two parameters were varied in this study: the mixing ratio of the two coals by regulating the mass flow rates of the two injectors and the mixing effectiveness of the two coals by varying the distance between the two injectors.

’ EXPERIMENTAL SECTION The experiments were conducted in an entrained flow DTF (60.0 cm long with an internal diameter of 7.0 cm), which was designed at the Pusan Clean Coal Center (Korea). The DTF consists of injection, reaction, and collection parts, along with a feeding system for the injection part that consists of a vibrator, syringe pump, dual tube, and vial. The vibrator shakes up the particles in the vial, and the carrier gas flows into the cylinder along the outer tube of the dual tube. In addition, the carrier gas moves the particles, which are being shaken in the vial, to the furnace through the inner tube. A syringe pump maintains a constant distance between the particles and the end of the dual tube, and the feeding rate should be kept constant during an experiment. The furnace of the reaction part uses an alumina tube that passes the particles and a SiC heater, which can be heated to 1500 °C. It is 60.0 cm long and has an internal diameter of 7.0 cm. Furthermore, the collecting part consists of a collecting probe, collecting cyclone, and filter holder. The collecting probe tip in the reaction zone is “V”-shaped, which increases the

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collection efficiency. The cyclone and filter holder are able to collect a burned particle sample.15 This DTF was modified to examine the effect of coal blending using out-furnace and in-furnace blending methods, as shown in Figure 1. Contrary to the previous coal feeding system of the DTF, the modified feeding system could inject binary coals simultaneously under high-temperature conditions. In addition, the mixing time between binary coals was varied, and two feeders were installed in the injecting section to feed different types of coal. This system was designed to mimic the in-furnace and out-furnace blending methods in actual burners in power plants. One coal was supplied directly into the side of the injection probe, and another coal was fed into the center tube installed with a cooling system. The feeding system was mounted above the injection probe, and the length between the feeders was adjustable from 0 to 10 cm along the axial direction of the injection probe, which means that the adjustment of the tube length controls the mixing time of the binary coals. The distance between the injection tubes in the outfurnace blending method is 0 cm, and in the in-furnace blending method, the distance is adjusted to 3, 5, and 7 cm, which corresponds to mixing times of 0.28, 0.45, and 0.63 s, respectively. These distances relate to the positions where the combustion by volatile matter content of one coal actively influences the adjacent coal.15 In the experiments, the total feeding rate for coal blending was 0.24 g/min, the blending ratios of sub-bituminous coal based on bituminous coal (SBR) were 0, 25, 50, 75, and 100% with the same amount of total coals, the coal particle size was 90
150 μm (on the basis of the Rosin
Rammler distribution), and the particle residence time was about 4.5 s. The total gas flow rate mixed with O2 and N2 was 5 L/min, and the O2 concentration prior to the reaction was 12%. The excess oxygen coefficient (λ) was somewhat varied from 1.3 to 1.5 with SBR because of coal elements. Here, the excess oxygen coefficient (λ) was defined as the ratio of the actual O2/coal ratio to stoichiometry for a given mixture. The stoichiometric O2/coal ratio was determined by considering all combustible elements in the coal, and the stoichiometric requirement of O2 volume flow for the combustion of 1 kg of coal was obtained as FO2,stoi = 18.6667C + 56H + 16N + 7(S
O), where C, H, N, S, and O were weight percent of carbon, hydrogen, nitrogen, sulfur, and oxygen in the coal on an as-received basis and were input parameters.16 The ambient temperature of the reacting coal was relative to a wall temperature of 1300 °C. A TGA was used to calculate the unburned carbon fraction with the ash tracer method, and the concentration of NO was measured by a portable gas analyzer (Eurotron gas analyzer). The coals used in this study were Yakutugol (Russia) for bituminous coal and Adaro (Indonesia) for sub-bituminous coal, which are commonly used in Korean power plants. The results of the proximate and ultimate analyses for each coal are shown in Table 1, and the input parameters considered in this study are given in Table 2.

’ NUMERICAL SECTION The furnace geometry was modeled with a two-dimensional (2D) mesh comprising 41 000 cells. Figure 2 presents the gas flow and injection point of the binary coals with geometry and boundary conditions of DTF used during the simulation. The injections of the binary coals with carrier gas were downward, and the internal gas temperature was constant. The conditions used in the numerical modeling are consistent with the experimental conditions. The numerical modeling was conducted using the commercial computational fluid dynamics (CFD) software FLUENT code version 6.17 The model was based on the Navier
Stokes equations for gas and particle phases. The gas phase was modeled in an Eulerian domain, whereas the particles were tracked in a Lagrangian fashion. The model comprised submodels for the 5056

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Table 1. Proximate and Ultimate Analyses for Coals Used in This Study proximate (wt %, as received)

a

ultimate (wt %, DAF)

coals

Ma

VMb

FCc

ash

Yakutugol (bituminous coal)

1.67

17.94

68.68

11.71

88.46

Adaro (sub-bituminous coal)

5.22

50.67

39.92

4.19

74.08

C

H

O

N

S

FR (FC/VM)

4.5

6.06

0.75

0.14

3.83

5.91

18.67

1.27

0.07

0.8

M = moisture. b VM = volatile matter. c FC = fixed carbon.

Table 2. Input Parameters Considered in This Study input parameters coal feeding rate (g/min)

0.24

total flow rate (lpm)

5

coal size (μm)

90
150 (on the basis of

bituminous coal

Yakutugol

the Rosin
Rammler distribution) sub-bituminous coal

Adaro

SBR (%)

0, 25, 50, 75, and 100 out-furnace blending method: 0

injection distance from inlet (cm) DTF setting temperature (°C)

in-furnace blending method: 3, 5, and 7 1300

vaporized after entering the furnace. The volatile matter comprises elements C, H, O, N, and S and enters the gas phase through devolatilization. The char, i.e., fixed carbon, enters the gas phase through the char combustion process. Modeling the coal particle reactions, particularly the devolatilization and char oxidation, is crucial for describing both the dispersed particle phase and the continuous gas phase. The equations for these phases are coupled through the particle mass loss rate by these reactions. Gas-Phase Combustion Model. The gas-phase combustion model was used to simulate the generation of volatile matter from coal and CO combustion. During the combustion process, a global single-step reaction was assumed, in which the volatile matter is converted into CO2, CO, and water vapor. In the species mass fraction transport equations, equal effective turbulent mass diffusion coefficients are set for the fuel, O2, and products. The Magnussen
Hjertager18 finite-rate/eddy-dissipation model is used to quantify the turbulent combustion rates of the volatile matter and CO. The net reaction rate is determined as R = min(REBU, RArr), where
 k Yox REBU ¼ CR F min YF , ε β

RArr

Figure 2. Gas flow and injection point of binary coals with geometry and boundary conditions of the DTF.

turbulent fluid mechanics, gaseous combustion, particle dispersion, reactions (i.e., moisture evaporation, coal devolatilization, and char burnout), and radiation. The standard two-equation k
ε model was chosen to model the turbulence. The discrete ordinate (DO) model was used to model the radiation heat transfer in the furnace, because it is suitable for a furnace with a short optical length. Two separate groups of coal particle injections were used, enabling the tracking of the two component coals during the simulation. The material properties, such as coal density, thermal capacity and conductivity, radiation characteristics, devolatilization kinetics, char swelling ratio, and char combustion kinetics can be specified for the two component coals. The initial conditions (including the velocities, temperature, and particle size distribution) of the two injection groups are defined separately. The coal components (moisture, volatile matter, fixed carbon, and ash) were assumed to behave as follows: The moisture is


 Es ¼ As F YF Yox exp
RT 2

Here, REBU and RArr are the reaction rates (kg m
3 s
1) for the EBU turbulent combustion model and Arrhenius reaction model, respectively; CR is an empirical constant of the gas-phase k
ε turbulence model, where k is the turbulence kinetic energy, ε is the dissipation rate, and accordingly, k/ε is the turbulent time scale; YF and Yox are the mass fractions of fuel and oxidizer, respectively; As is the pre-exponential factor (m3 kg
1 s
1); and Es is the activation energy for the gas phase (J kmol
1). Particle Devolatilization Model. The chemical percolation devolatilization (CPD) model was developed to describe coal devolatilization on the basis of the chemical structure of the parent coal. This model employs percolation statistics to describe the generation of light gas/tar precursors of finite size on the basis of the number of cleaved labile bonds in the infinite coal lattice. The model includes treatment of the vapor
liquid equilibrium and a cross-linking mechanism. Coal-independent kinetic parameters of the reaction rate are employed, and the coal-dependent chemical structure coefficients are set on the basis of correlations developed from 13C nuclear magnetic resonance (NMR) measurements of several coals.19 In this correlation, five parameters describe the chemical structure of each coal: (1) initial fraction of bridges in the coal lattice, po; (2) initial fraction of char bridges, co; (3) lattice coordination number, σ + 1; (4) cluster molecular weight, Mw,1; and (5) side-chain molecular weight, Mw,δ. Each parameter is used as an input for the CPD submodel.19 5057

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Table 3. Input Parameters for the CPD Model (Devolatilization) and the Kinetics for Char Oxidation input parameters for the devolatilization rate (calculated by 13C NMR)

kinetic parameters for char oxidation

po

co

σ+1

Mw,1

Mw,δ

A (g cm
2 s
1 Pa
1)

E (J kmol
1)

Yakutugol

0.7256

0.3048

4.333

240.99

20.31

0.0014

8.3  107

Adaro

0.4028

0

4.9749

457.95

39.68

0.2

8.2  107

Particle Char Oxidation Model. On the surface of coal particles, the overall reaction rate is simulated by a diffusionkinetic model.20,21 The heterogeneous char reaction rate is assumed to be first-order in terms of the O2 and CO2 concentrations. The diffusion rate coefficient R1 and the kinetic rate R2 are calculated and weighted to obtain the char combustion rate

R 1 ¼ C1

R2 ¼ η

½ðTp þ T∞ Þ=20:75 dp

dp F Ag k 6 p

dmp FRTmo R1 R2 ¼
Ap dt M o R1 þ R2 where Ap = πdp2 is the surface area of the coal particle, dp, Tp, and mp are the diameter, temperature, and mass of a particle, and η is the effectiveness factor or the ratio of the actual combustion rate to the rate attainable if no pore diffusion resistance existed. The intrinsic reactivity ki has the Arrhenius form ki ¼ Ai exp


ðEi =RTp Þ

where the pre-exponential factor Ai and the activation energy Ei can be measured experimentally. The kinetics for char oxidation was adopted from the results measured from the DTF given by Kang and Lee.22 In this study, the kinetic parameters of coals for devolatilization and char oxidation are shown in Table 3. NO Modeling. The NO post-processing package used in the coal combustion simulation can be subdivided into three main sections representing NO formation by thermal, prompt, and fuel NO pathways. Prompt NO is not applied in this study. In addition, the partitioning of the fuel N species between the char and volatile matter for each coal can be obtained for the coals using the CPD submodel, which was used in the NOx submodels in a post-processing mode. The average N partitioning with the ratio of blending was used as an input for the blends in the NO post-processing package used in the coal combustion simulation. Fuel NO was predicted using global reaction rates and included both volatile and char N.10 The volatile N is assumed to convert first into the intermediate species, that is, HCN (90%) and NH3 (10%), and then into N2 or NO according to the mechanism used within the model.23 Char N is oxidized at a rate proportional to the rate of char burnout by a factor related to the relative distribution of C and N in the char; it is assumed that the N remaining in the char after devolatilization is oxidized heterogeneously into NO.24 The reduction on char N to NO is considered in terms of a conversion factor, which is 0.6 in this paper.25 The partitioning of the fuel N species between the char and volatiles can be obtained for the coals using the CPD submodel. Thermal NO is the NO produced from the reaction of N2 and O2 from air at high temperatures. The amount of NO generated by

Figure 3. Schematic representations of the (a) fuel NO mechanism and (b) reburning mechanism.

this process increases with the temperature. This mechanism is well-known and is called the extended Zeldovich mechanism.26 In addition, the reburning mechanism is applied, which is based on the model proposed by Kandamby et al.27 The model adds a reduction path to the De Soetes global model28 that describes the NOx formation/destruction mechanism in a pulverized coal flame. The additional reduction path accounts for the NOx destruction in the fuel-rich reburn zone by the CH radical. The schematic representations of the fuel NO mechanism and reburning mechanism are shown in Figure 3. The models and assumptions used in numerical modeling of this study are given in Table 4

’ RESULTS AND DISCUSSION Effect of the Blending Ratio in the Out-furnace Blending Method. The numerical modeling of the char reaction rate with

SBR in the out-furnace blending method, shown in Figure 4, shows that the coal injected in the center tube is sub-bituminous and the coal injected from the second tube around the center is bituminous. The figure shows that the char reaction rate for subbituminous coal is faster than that of bituminous coal and the reaction time for bituminous coal is longer than that of subbituminous coal. The variation of char reaction phenomena for the two coals with SBR occurring in the furnace can be confirmed favorably from this numerical modeling. Unburned Carbon Fraction of the Out-furnace Blending Method. In Figure 5, the numerical results for the variations in the unburned carbon fraction in the out-furnace blending method is compared to the experimental results. The 5058

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Table 4. Models and Assumptions Used in Numerical Modeling models and assumptions governing equation

Navier
Stokes equation (gas phase f Eurian, particle f Lagrangian)

turbulence model

standard two-equation k
ε model

radiation model

DO model

gas combustion model

finite-rate/eddy-dissipation model18 (VM from coal and CO combustion)

particle (components: moisture, volatile

devolatilization model

CPD model19 (application of calculated 13C NMR for each coal)

char reaction model

diffusion-kinetic model20
22 (application of char oxidation kinetics for each coal)

matter, char, and ash) NO emission

fuel NO23
25 (application of VM N and char N calculated from the CPD submodel) thermal NO26 (extended Zeldovich mechanism) reburning mechanism27,28

Figure 5. Comparison of unburned carbon fractions obtained from experimental and numerical results at different SBRs. Figure 4. Contours of char reaction rates (kg/s) at different SBRs.

experimental results show that, as the SBR increases up to 50%, the unburned carbon fraction gradually decreases. This is explained by the fact that the high volatile coal, which is subbituminous, releases more volatile matter, forming a higher gas temperature field, which then heats the low volatile coal and promotes its devolatilization and combustion. Shen et al.13 also demonstrated that the interactions between component coals, in terms of particle temperature and volatile matter content, are responsible for the reactive effect of improving combustion efficiency. Experimentally, the highest percentage of unburned carbon fractions is at SBR 75% (see Figure 5). It is expected because there is competition between two opposite effects on the char burning rate in these conditions; a high oxygen-deficient environment is formed initially with increasing SBR, and this leads to inefficient combustion of the bituminous coal. Biswas et al.8 stated this phenomenon briefly in their paper. In our study, this is strongly supported by the observation of the greater oxygen concentration depletion with increasing SBR, as shown by the numerical modeling in Figures 6 and 7. That is, coal particles with faster devolatilization and combustion rates consume more oxygen and leave depleted oxygen concentrations for the combustion of less reactive particles. As a result, we can confirm that

there is competition between the reactive effect of the initial rising temperature and the unreactive effect of oxygen deficiency in the furnace. These observations suggest that the best condition for the SBR based on bituminous coal is 50% in a DTF, and this ratio should be used for the coal blends in actual power plants. In addition, the numerical modeling results also show that there is competition between the reactive and unreactive effects. However, the best conditions from the numerical modeling results are at SBR 25%, which is different from the experimental result of SBR 50%. As stated by Arenillas et al.,12 it is clear that more detailed mechanisms in the numerical modeling is required to accurately describe the phenomena and the development of numerical models that can fully describe the chemical and physical interactions between component coals in the binary blend are necessary. Nevertheless, it can be observed that unburned carbon fractions from numerical modeling are in reasonable agreement with the experimental values in terms of magnitude and tendencies. In addition, the results show that, under conditions of only sub-bituminous coal (SBR 100%), unburned carbon seldom exists because all of the sub-bituminous coal is oxidized. NO Emission of the Out-furnace Blending Method. In Figure 8, the experimental and numerical modeling results for the variation of emission index of NO (EINO) with SBR are shown. The dimensionless parameter, EINO, expresses the amount of NO emission formed per mass of fuel. The results show that, as 5059

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Figure 6. Contour of the O2 concentration at different SBRs.

SBR increases, EINO is higher; EINO is proportional to SBR, for the main cause of NO increases with SBR is due to the increase of fuel N input. Table 1 clearly shows that the fuel N content of the sub-bituminous coal is 60% higher than bituminous coal (0.75 versus 1.27 wt %). The results show that fuel N is the main contributor of the NO emission. In addition, the conversion ratio (CR) was introduced to investigate the effect of sub-bituminous coal on NO reduction during combustion of coal blending. The CR is the normalized NO concentration, as defined in the following equation, and represents the degree of conversion of fuel N to NO15 CR ¼ 0

Figure 7. Variation of the O2 concentration along the axial distance at different SBRs.

CNO

1 FN
2 2:24  10 B 1:4  10
2 C @ A Vdry

where CNO is the NO concentration at the furnace exit and Vdry is the flow rate of dry air for the unit coal feed rate (N m3 kg
1).The denominator represents the NO concentration when all fuel N is converted into NO. As seen in Figure 8, CR decreases with SBR. This finding confirmed that a high volatile matter contained in sub-bituminous coal with SBR results in low CR; in other words, high volatile matter content leads to high NO reduction. This effect increases with SBR, which is consistent with data by Lee et al.15 The numerical modeling results reasonably predict the experimental results within the error range. Effect of the In-furnace Blending Method. To improve the efficiency of unburned carbon and NO emission, the in-furnace blending method is applied, which affords control of the blending time between binary coals by moving the center tube (in-furnace blending feeder) along the axis of DTF, as mentioned in the previous section. A SBR 75%, in which predominantly oxygendeficient phenomena occur, is chosen to investigate the effect of the in-furnace blending method under three different feeder distances. Unburned Carbon Fraction of the In-furnace Blending Method. Figure 9 presents the experimental and numerical modeling results for unburned carbon fractions in the in-furnace blending method. The numerical modeling accurately shows the dynamic motion of the binary coals in the furnace; it is observed that the sub-bituminous coal is injected from the center tube and the bituminous coal is injected from the adjacent tube. The

Figure 8. Comparison of EINO values and CR obtained from experimental and numerical results at different SBRs.

different distances between the feeders (3, 5, and 7 cm) vary the injection position of the sub-bituminous coal and correspond to different blending times of 0.28, 0.45, and 0.63 s, respectively. The experimental results show that as the distance increases between the two feeders, the unburned carbon fraction gradually reduces, which means that a longer time is needed for the coals to be blended, leading to a reduced unburned carbon fraction. This is because the initial burning of bituminous coal is not affected by the oxygen-deficient conditions formed from the rapid combustion of sub-bituminous coal from more than 3
5 cm; therefore, each coal has enough oxygen to burn, which also indicates that the lack of oxygen entrainment required for the achievement of complete combustion is improved. Therefore, the unburned carbon fraction becomes smaller with an increasing feeder distance and also shows similar tendencies in the experimental and numerical modeling results (see Figure 10); however, the magnitudes obtained by numerical modeling are slightly greater than those obtained through experiments. NO Emission of the In-furnace Blending Method. Figure 11 shows the variation of EINO at different feeder distances in the in-furnace method. The results show that EINO decreases with 5060

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Figure 11. Comparison of EINO values obtained from experimental and numerical values at different feeder distances in the in-furnace blending method. Figure 9. Contour of char reaction rates (kg/s) in the in-furnace blending method.

Figure 10. Comparison of unburned carbon fractions obtained from experimental and numerical results at different feeder distances in the infurnace blending method.

an increasing feeder distance. The reason for the reduction of EINO is that NO formed from bituminous coal is decomposed by the pre-intermediate species (HCN and NH3) formed from the volatile matter of sub-bituminous coal. In out-furnace blending, the pre-intermediate species formed in binary coals are immediately oxidized directly to NO; on the other hand, in in-furnace blending, upon increasing the feeder distance, NO has time to decompose. In other words, as the NO emission from bituminous coal reacts with the pre-intermediate species formed from sub-bituminous coal, the NO emissions decompose; therefore, the amount of NO is reduced in the furnace. In addition, the numerical modeling results are showing the same NO reduction by a decomposition mechanism as experimental results. The difference in the amount of reduced NO has similar values between feeder distances of 5 and 7 cm, which indicates that the rate of NO decomposition, in this distance range, is similar. Glarborg et al.29 also demonstrated that reactions of NO with

pre-intermediate species lead to reduced amounts of NO by decomposition mechanisms. Furthermore, the numerical modeling incorporating the reburning mechanism better matches the experimental results to predict NO emission than that predicted without the reburning mechanism, which is in line with results by Hill et al.23 It means that this additional sub-bituminous coal with SBR creates a locally fuel-rich region, which provides CHi radicals that react with NOx to form HCN, which can then be reduced to N2 through the reaction for fuel NO. These results infer that the in-furnace blending method applied to actual power plants will reduce unburned carbon fractions and NO emissions. However, the practical applications of the in-furnace blending could be very limited because of the complex arrangement of pulverized fuel preparation, transportation, etc.

’ CONCLUSION This study was conducted to investigate the non-additive phenomena that occur in coal blends and to examine the mechanisms that affect the in-furnace blending method using a DTF. This study varied two parameters: the mixing ratio of the two coals by regulating the mass flow rates of the two injectors and the mixing effectiveness of the two coals by varying the distance between the two injectors. Experimental and numerical modeling approaches were employed to gain insights into the mechanisms involved in the blending methods. The conclusions are as follows: (1) It was confirmed that, in the out-furnace blending method, contrasting processes, such as reactive and unreactive effects, occur with the SBR. The worst condition was found at a SBR of 75% because of the oxygen-deficient environment. The EINO was relatively proportional to the SBR, and the CR of fuel N to NO decreased with the SBR because of the NO reduction effect. (2) The in-furnace blending method was suggested to mitigate the adverse effect of unburned carbon and reduce NO emissions. The in-furnace blending method resulted in an improvement in the efficiency of unburned carbon fractions and a further reduction in the EINO. These results indicate that the in-furnace blending method has great potential to improve the combustibility and reduce NO emissions in real power plants. (3) The numerical modeling was conducted using the commercial CFD software FLUENT code and provided in5061

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’ AUTHOR INFORMATION Corresponding Author

*Telephone: 82-51-510-7324. Fax: 82-51-582-9818. E-mail: [email protected].

’ ACKNOWLEDGMENT This work was supported by the second phase of the Brain Korea 21 Program in 2011 and the Pusan Clean Coal Center (PC3), Pusan National University, under the program of the University Electric Power Research Center, Ministry of Knowledge and Economy, Korea. ’ REFERENCES (1) Wall, T. F.; Elliott, L.; Sanders, D.; Conroy, A. A Review of the State-of-the-Art Coal Blending for Power Generation; Advanced Technology Centre, The University of Newcastle: Callaghan, New South Wales, Australia, May 2001; Project 3.16 of Cooperative Research Centre (CRC) for Black Coal Utilisation. (2) Su, S.; Pohl, J. H.; Holcombe, D.; Hart, J. A. Prog. Energy Combust. Sci. 2001, 27, 75–98. (3) Ulloa, C.; Borrego, A. G.; Helle, S.; Gordon, A. L.; Garcia, X. Fuel 2005, 84, 247–257. (4) Beeley, T.; Cahill, P.; Riley, G.; Stephenson, P.; Lewitt, M.; Whitehouse M. The Effect of Coal Blending on Combustion Performance; Department of Trade and Industry (DTI): London, U.K., 2000; DTI Report COAL R177, DTI/Pub URN 00/509. (5) Ikeda, M.; Makino, H.; Morinaga, H.; Higashiyama, K.; Kozai, Y. Fuel 2003, 82, 1851–1857. (6) Holcombe, D. Thermal Coal Blends: Test Results; Cooperative Research Centre (CRC): Canberra, Australian Capital Territory, Australia, May 2000; CRC Report for Project 5.7. (7) Shi, S.; John, H.; Don, H. Fuel 2003, 82, 1653–1667. (8) Biswas, S.; Chouhury, N.; Sarkar, P.; Mukherjee, A.; Sahu, S. G.; Boral, P.; Choudhury, A. Fuel Process. Technol. 2006, 87, 191–199. (9) Chaouki, G.; Isam, J. Energy Convers. Manage. 2010, 51 (8), 1694–1701. (10) Jones, J. M.; Patterson, P. M.; Pourkashanian, M.; Williams, A.; Arenillas, A.; Rubiera, F. Fuel 1999, 78, 1171. (11) Gera, D.; Mathur, M.; Freeman, M.; O’Dowd, W. Combust. Sci. Technol. 2001, 172, 35. (12) Arenillas, A.; Backreedy, R. I.; Jones, J. M.; Pis, J. J.; Pourkashanian, M.; Rubiera, F.; Williams, A. Fuel 2002, 81, 627–636. (13) Shen, Y. S.; Guo, B. Y.; Yu, A. B.; Zulli, P. Fuel 2009, 88, 255–363. (14) Ikeda, M. Development of Technology for Reducing NOx Emission and Unburned Carbon Concentration in Fly Ash Using In-furnace Blending Method; Central Research Institute of Electric Power Industry (CRIEPI): Tokyo, Japan, 2008; CRIEPI Report. (15) Lee, B. H.; Song, J. H.; Kim, R. G.; Kim, S. G.; Kim, Y. G.; Chang, Y. J.; Jeon, C. H. Energy Fuels 2010, 24, 4333–4340. (16) Liu, H.; Shao, Y. Appl. Energy 2010, 87, 3162–3170. (17) Fluent, Inc. FLUENT User’s Guide; Fluent, Inc.: Lebanon, NH, 2001.

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dx.doi.org/10.1021/ef200783q |Energy Fuels 2011, 25, 5055–5062