Development of Advanced Simulator for Desulfurization in the

Oct 18, 2012 - An advanced simulator is newly developed to predict desulfurization behavior by direct injection of Ca fine particles in coal combustio...
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Development of Advanced Simulator for Desulfurization in the Pulverized Coal Combustion Process Akira Suzuki,*,† Akira Ohnaka,‡ Tatsumi Tano,‡ Ryunosuke Itokazu,§ Masato Tamura,§ and Takahiko Terada‡ †

IDAJ Co., Ltd., Yokohama Landmark Tower 37F, 2-2-1-1 minato Mirai, Nishi-ku, Yokohama, 220-8137, Japan UBE Industries, Ltd., 1978-96, Kogushi, Ube, Yamaguchi, 755-8633, Japan § IHI Corporation, Toyosu IHI Building 1-1, Toyosu 3-chome, Koto-ku, Tokyo, 135-8710, Japan ‡

ABSTRACT: An advanced simulator is newly developed to predict desulfurization behavior by direct injection of Ca fine particles in coal combustion furnaces. The Ca fine particles, the main component of which is CaCO3, are a byproduct of cement manufacturing process. This simulator is built on the coal combustion/gasification module based on the commercial computational fluid dynamics (CFD) software of STAR-CD V3.26. Regarding the desulfurization reaction model, it is based on two physical models of thermal decomposition reaction of CaCO3 and of desulfurization reaction by CaO. We improve both models in order to simulate desulfurization behavior of Ca fine particles in coal combustion furnaces. A case study is carried out with a bench scale experimental furnace. According to the comparison between calculated result and experimental one, this simulator can predict the tendency of the increase of desulfurization ratio with an increase of feeding rate of Ca fine particles and the influence of the temperature at injection part of Ca fine particles. Moreover, the calculated result shows that the desulfurization reaction does not finish in the boiler and it continues at the exhaust pipe, since the Ca fine particle’s temperature in the pipe is still higher than 1073 K. As a consequence, this simulator well predicts the tendency of the desulfurization process successfully and can be used for improvement of the operation condition of the furnaces.

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150 °C. Therefore, the furnace operation with higher exhaust gas temperature is necessary, and it is a major cause of decreasing the energy efficiency of the furnaces. The reduction of SO3 in the exhaust gas decreases the risk of the erosion of the furnace body, and it is possible to decrease the exhaust gas temperature of 120−100 °C. Therefore, this technology contributes to not only reducing the pollutant of SOx emission but also improving energy efficiency. To enhance the effectiveness of in-furnace desulfurization, 20% of SOx reduction is decided as a goal, since 20% of SOx reduction provides the selective reduction of SO3, especially in usage of our market, which contributes decrease of exhaust gas temperature. On the other hand, the desulfurization reaction in furnaces depends on the operating condition of the furnaces. Therefore, the experimental study is carried out with a bench scale experimental furnace to examine characteristics of this reaction behavior.1−3 However, it is not realistic to optimize the various operation conditions for practical furnaces by means of experimental method only. Therefore, the object of this study is the development of the numerical simulator for the desulfurization process in order to evaluate SOx reduction behavior effectively. According to previous studies, the SOx reduction technology has already been established.4−8 However, the material for SOx reduction in previous studies are mainly limestone (CaCO3) or Ca(OH)2, and it is the first trial that the utilization of the byproduct of Ca fine particles from cement manufacture process for the material, which has

INTRODUCTION Coal fired boilers have been faced with the task of improving energy efficiency and flue gas cleanliness because of they have high CO2, NOx, and SOx emissions and contain ash. There are several technologies under development and demonstration such as oxy-fuel for CO2 recovery, high steam temperature for increasing power generating efficiency, and so on. Most of these approaches are completed in one factory or one business unit. On the other hand, it occasionally happens that the byproduct from one factory can be a material or valuable component for other factories. The combination of two different facilities and the use of each byproduct become a solution for achievement of energy and cost saving. This energy saving leads the reduction of CO2 emission. In this study, microground calcium compound (Ca fine particles) is directly injected into coal combustion furnaces in order to reduce SOx emission. The Ca fine particles are a byproduct collected from cement manufacturing processes, and the major component of the Ca fine particles is CaCO3. This technology can be called in-furnace desulfurization. The merits of in-furnace desulfurization technology are indicated as follows. First, this technology can decrease SOx emission without large-scale additional equipment to practical power plants. This facilitates the use of sulfur-rich coal, especially for small plants that have no desulfurization equipment. Second, the enhancement of the utilization of sulfur rich coal contributes the decrease of fuel cost. Finally, the SOx reduction directly contributes the improvement of the energy efficiency. In general, the exhaust gas including SO3 component in SOx damages the furnace body caused by erosion, since the SO3 changes to H2SO4, especially at low temperature less than 200− © 2012 American Chemical Society

Received: July 8, 2012 Revised: September 21, 2012 Published: October 18, 2012 6749

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different property from limestone (CaCO3) and Ca(OH)2. The operation temperature of previous studies is lower than that of this study. Moreover, there is no simulator to predict in-furnace SOx reduction behavior quantitatively. Therefore, the reaction model of Ca fine particles at high temperature with the validation of the simulation result by means of the comparison between simulation results and experimental ones should be considered. To achieve this target, various submodels for the simulator are evaluated. Regarding the reaction of the Ca fine particles, the study of limestone is referenced and the process is distinguished between thermal decomposition and desulfurization because the desulfurization reaction occurs after thermal decomposition of CaCO3 in Ca fine particles. According to previous studies of calcination reactions of limestone,9−11 the thermal decomposition reaction rate depends on CO 2 concentration around limestone particles. On the other hand, in this study, the CO2 concentration is decided by coal combustion and by thermal decomposition of Ca fine particles itself. Therefore, it is necessary to calculate the coal combustion simulation and the desulfurization simulation, including thermal decomposition reaction, simultaneously in order to express the desulfurization behavior of the in-furnace desulfurization. Regarding the pulverized coal combustion simulation, a number of studies have already been carried out and the mathematical models developed by them have been applied for a practical furnace.12,13 This simulator also references those studies, especially for reaction models of devolatilization model and char reaction model. In this study, to estimate the effectiveness of this simulator, the validation process is divided into two steps. First, regarding coal combustion results without any Ca fine particle’s injection, temperature distribution and O2 and SO2 concentration at exhaust of the furnace are compared between the calculation results and the experimental ones, because the coal combustion result becomes the basis of the desulfurization calculation. Second, the thermal decomposition ratio of CaCO3 and the desulfurization ratio are compared between calculation results and experimental ones regarding the variation of particle injection parts in the experimental furnace and the variation of the Ca fine particle’s feeding rate. Finally, we discuss the desulfurization behavior based on these validation processes.

Figure 1. Schematic diagram of the calculation mesh for the bench scale vertical experimental furnace. A, B are indicated the injection part of Ca fine particles in Table 3.

dissipation and the gravity force of the thick arrow direction, as shown in Figure 1. The two-way coupling method is applied for the interaction between the flow phase and the particle one. The representative particle in a parcel is calculated because the calculation of all particles in the furnace is unrealistic for the calculation time. In this study, the total number of coal parcels is 1440 and that of Ca fine parcels is 540. Regarding the reaction process of coal, the mathematical model consists of two submodels: one is heterogeneous solid phase reaction model for the moisture evaporation process, the devolatilization process, and the char combustion process and the other is the homogeneous gas phase volatile reaction model. Regarding the solid phase reaction model, the Ranz−Marshall equation is applied for the moisture evaporation process, the first order reaction model is applied for the devolatilization process, and the reaction model controlled by both of the first order kinetic reaction rate and the diffusion rate of oxygen to the particle surface is applied for the char combustion process. We assume that the solid phase reaction progresses in order of the moisture evaporation process, the devolatilization process, and the char combustion process. Regarding the gas phase volatile reaction model, the combined rate model between the eddy dissipation model and the Ahrrenius type kinetic reaction model is applied. Regarding the sulfur behavior, combustible sulfur in the coal particles released as S in the volatile matter during devolatilization process. This S becomes SO2 during the gas phase volatile reaction process as follows:

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MATHEMATICAL MODELING The mathematical model for the in-furnace desulfurization consists of two submodels: one is the coal combustion model and the other is the desulfurization model. Coal Combustion Modeling. The coal combustion/ gasification module, which is developed by the first author,14,15 based on commercial computational fluid dynamics (CFD) code STAR-CD V3.26, is applied for the base of the simulation model. The control volume method is applied for solving transport equations for mass, momentum, enthalpy, and chemical species (CH4, H2, C, CO, O2, H2O, CO2, S, SO2, N2). The Reynolds stress model is applied for the turbulence model to represent strong swirl flow caused by swirl banes, as shown in Figure 1. To predict radiative heat transfer behavior with particle radiation, the radiative transfer equation is solved by the discrete ordinate method. The SIMPLE algorithm is applied for the calculation algorithm. The Lagrangian multiphase flow model is applied for the coal and Ca fine particle’s tracking model. The momentum equation of particles consists of the drag force with turbulence

volatile S + O2 → SO2

(1)

In general, sulfur is released to gas phase via volatile S and char S, and the release rate of char S is slower than that of volatile S because char S is released with char combustion. However, in this study, Ca fine particles are injected in the furnace near the exhaust part of the furnace. This means that the SOx reduction occurs after the majority of sulfur is released from coal particles. Therefore, we consider that the distinction of the combustible sulfur’s release way (volatile S or char S) has a small impact for desulfurization behavior within the scope of this study. However, it is important that the amount of SO2 release of simulation result is reasonable compared with that of experimental result. To confirm the accuracy of the amount 6750

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of SO2 release, it is discussed at following section of validation of coal combustion. Desulfurization Modeling. The desulfurization process of Ca fine particles is composed of two subprocesses. One is thermal decomposition of CaCO3 and the other is the desulfurization reaction of CaO as follows: CaCO3 → CaO + CO2

(2)

CaO + SO2 + 0.5O2 → CaSO4

(3)

Thermal Decomposition Modeling of CaCO3. According to the previous studies of the calcination of limestone,9−11 the thermal decomposition of CaCO3, the reaction rate depends on the CO2 partial pressure around the Ca fine particles. In this case, Hu and Scaroni’s model16 is applied as follows: rate = ksA [mol/s]

(4)

where A is particle surface area [m2] and ks is modified rate coefficient [mol/(m2 s)]. According to the model, the rate coefficient ks′ [mol/(m2 s)] is expressed as follows: ks′ = 6.078 × 107 exp(− 205 000/RT )

Figure 3. Image of desulfurization reaction model for a Ca fine particle.

(5)

However, the generated CaSO4 becomes an obstruction to the diffusion of SO2 and O2. Therefore, we apply the unreacted shrinking model to represent this behavior. According to Manovic et al.,17 the desulfurization reaction rate is expressed as follows:

where R is conditional constant [J/(mol K)] and T is temperature [K]. The modified rate coefficient depends on the partial pressure of CO2, PCO2 [Pa], as follows: ks = ks′ for PCO2 < Pe × 10−2

dCSO2

(6) −2

ks = ks′(Pe − PCO2)/Pe for Pe × 10

dt ≤ PCO2 < Pe

(7)

(10)

where CCaO is CaO molar concentration [mol/m3] and CSO2 is SO2 molar concentration [mol/m3]. The reaction rate coefficient ks [m4/(mol s)] is expressed as follows:

where the equilibrium criterion of partial pressure of CO2, Pe [Pa], is expressed as follows: Pe = 1.826 × 107 exp(− 19680/T )

= −ksSCaOCCaOCSO2

(8)

ks = 7.7 × 10−3 exp(− 67000/RT )

Equation 7 shows that the absorption of CO2 by Ca fine particles happens if PCO2 ≥ Pe. The key of this model is the estimation method of particle surface area of Ca fine particles, since the surface area changes during heating condition. Figure 2 shows the variance of scanning electron microscopy (SEM)

(11)

Also, the relative surface area of CaO in the Ca fine particles SCaO [m2/m3] is expressed as follows: SCaO = 6fCaO

ρ ρCaO

d2 d03

(12)

where d is diameter of Ca fine particles [m], d0 is initial diameter of Ca fine particles [m], f CaO is mass fraction of CaO in Ca fine particles [kg/kg-all], ρCaO is CaO density (=3350) [kg/m3], and ρ is the density of Ca fine particles [kg/m3]. The CaO concentration is expressed as follows: CCaO =

Figure 2. Variation of SEM images (×17 000) of the particle surface with heating condition.

DSO2 ρ × MCaO DSO2 + 0.5dksCSO2

(

d d2

)

−1

(13)

where MCaO is molecular weight of CaO [kg/mol] and internal diffusion coefficient of SO2 DSO2 [m2/s] is expressed as follows:

images of the particle surface during the heating history. By increasing the temperature, the particle surface area decreases caused by the decrease of inner particle pore. In this simulator, the change of particle surface area A [m2] is modeled as follows: 2 A new = Aold for T ≥ 1573 K (9) 3

DSO2 = 1.0 × 10−10 exp(− 125 000/RT )

(14)

and unreacted shrinking core diameter of CaO d2 [m] is expressed as follows: d 2 = 2 3 d3/8 − 3.09xCaOd03/3

Desulfurization Reaction Modeling. Figure 3 shows the image of desulfurization reaction on a Ca fine particle surface. To progress the desulfurization reaction, the diffusion process of SO2 and O2 to the CaO surface is very important.

(15)

where xCaO is reaction ratio of CaO [kg/kg-all]. The progression of the desulfurization reaction causes a decrease in the reaction rate itself. According to the eq 15, unreacted shrinking core diameter d2 decreases with the 6751

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between cases with and cases without injection of Ca fine particles. The SO2 concentrations in the exhaust gas are measured by means of nondispersive infrared analysis (ENDAseries, HORIBA Ltd., Japan). Table 3 shows the test cases for this study. Ca/S means feeding mass ratio of Ca fine particles compared with

progression of desulfurization reaction. It causes an increase in the second term of the denominator of eq 13, and then, the CaO concentration decreases.

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CASE STUDY Figure 1 shows the calculation mesh of the bench scale vertical experimental furnace (water cooling jacket type, combustion capacity: 160 kg coal/h). The furnace has a diameter of 1300 mm and a length of 7000 mm. This mesh also includes the exhaust pipe to examine the desulfurization reaction at this part. The number of cells is about 300 000. Figure 1 also shows the flow of the primary, the secondary, the OAP (Over fired Air Port) combustion air, and injection ports for Ca fine particles in the furnace. The coal particles are fed together with the primary air flow. The calculation domain includes swirl vanes of the secondary air to predict the swirl flow in the furnace accurately. The excess air ratio is 1.13 and 20 wt % of the total air flows in the OAP, as shown in Figure 1. Tables 1 and 2 show properties of coal and Ca fine particles, respectively. The initial particle size of coal particles is

Table 3. Test Cases Ca/S injection port

ash

C

H

2.5 μm 12 wt %

O

2 B

VALIDATION OF THE COAL COMBUSTION MODEL Since the desulfurization model is a complex model including various physical models, the validation of the model is important. To validate the coal combustion modeling, the case of coal combustion without any Ca fine particle’s injection is compared between experimental results and calculated ones. Figure 4 shows variation of center gas temperature with

41 N

5.9 13.3 1.5 initial particle size distribution 10 μm 18 wt %

case 4

3 A

fixed carbon

volatile

11.8 43.7 ultimate analysis (wt % daf)

78.6

case 3

2 A

■

proximate analysis (wt % db) 3.5

case 2

1 A

equivalence mass for desulfurization of sulfur included in coal particles. The characters of the injection port (A, B) mean the injection port for Ca fine particles in Figure 1.

Table 1. Properties of Coal moisture

case 1

27.5 μm 55 μm 28 wt % 24 wt % heating value (high)

85 μm 13 wt %

S 0.7 150 μm 5 wt %

28.4 MJ/kg particle density 1200 kg/m

Table 2. Properties of Ca Fine Particles Figure 4. Variation of center gas temperature of the case without any Ca fine particle injection. Line plot and dot plot indicate calculated result and experimental one, respectively.

composition of Ca fine particles Ig-Loss SiO2 36 wt %

18 wt %

0.25 μm 11 wt %

0.75 μm 12 wt %

Fe2O3

A12O3

CaO

2.6 wt % 6.2 wt % 69 wt % initial particle size distribution 2 μm 4.5 μm 25 wt % 22 wt % particle density

8 μm 15 wt %

others 4.2 wt %

distance from burner. The experimental temperature is measured by thermocouple.3 The calculated temperature shows good agreement with experimental one. The difference of O2 concentration at outlet part of experimental result and that of calculated one is about 5 vol % (experiment, 2.9 vol %; calculation, 3.05 vol %). The O2 concentrations in the exhaust gas are measured by means of magnetic pressure method (ENDA-series, HORIBA Ltd., Japan). This means that the total char reaction ratio between experimental result and calculated one is almost same. Also, the relative difference of SO2 concentration at the outlet part between experimental result and that of calculated one is about 11 vol % (experiment, 316 ppm; calculation, 353 ppm). This difference is small because the exhaust SO2 concentration is much smaller than that of O2. Since these calculated results showed good agreement with experimental ones, the calculation with Ca fine particle’s injection is examined.

30 μm 15 wt %

2690 kg/m

distributed between a few micrometers and hundreds of micrometers. On the other hand, that of Ca fine particles is distributed between submicrometers and tens of micrometers. The particle size distribution of the experiment is measured by the laser diffraction particle size analyzer (SALD-3000S, SHIMADZU Co. Ltd., Japan). The thermal decomposition ratio (CO2 release ratio) of Ca fine particles is calculated by the mass loss of Ca fine particles, since the mass loss of Ca fine particles is caused by CO2 release. The experimental method for the thermal decomposition ratio is discussed in the Result and Discussion section. The desulfurization rate is decided by the comparison of SO2 concentration in the exhaust gas 6752

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Figure 5. Distribution of the SO2 mass fraction (left) and the tracking of Ca fine particles (right) of case 3 without the reverse reaction of the desulfurization reaction.

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VALIDATION OF DESULFURIZATION REACTION MODEL

dCSO2 dt

Figure 5 shows the distribution mass fraction of SO2 and the Ca fine particle’s tracking in case 3. According to the results, it is very clear that the correspondence of the position where the SO2 mass fraction decreases and the Ca fine particles tracks, since the desulfurization behavior occurs on the surface of Ca fine particles. On the other hand, the desulfurization ratio of the calculated result and the experimental one are 99.0 wt % and 57.5 wt %, respectively. This difference shows that the calculated result of desulfurization ratio is too high compared with the experimental one, since calculated desulfurization reaction rate is too fast. According to the Manovic et al.,17 the desulfurization model is applied to the lower temperature field compared with this study. Therefore, this model is not enough to represent desulfurization reaction in this study, and it is necessary to improve. According to the viewpoint of the chemical equilibrium result with FactSage V.6.118 of Figure 6, the CaSO4 is not stable at over 1300 K and is decomposed to CaO, SO2, and O2. Therefore, we introduce the reverse reaction of desulfurization to eq 10, and the desulfurization reaction rate is expressed as follows:

⎛ ⎞ 1 SCaSO4CCaSO4⎟⎟ = ks⎜⎜ −SCaOCCaOCSO2 + Keq ⎝ ⎠

(16)

where Keq is the equivalence coefficient calculated with enthalpy and entholopy of each chemical species based on the thermophysical data of NASA [mol/m3]; the relative surface area of CaSO4 SCaSO4[m2/m3] and the concentration of CaSO4 CCaSO4 [mol/m3] are expressed as follows:

SCaSO4 = 6

CCaSO4 =

d2 d03

(17)

ρCaSO

4

MCaSO4

(18)

where MCaSO4 is the molecular weight of CaSO4 [kg/mol] and ρCaSO4 is the density of CaSO4 [kg/m3].

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RESULTS AND DISCUSSION Figure 7 shows the comparison of the thermal decomposition ratio of CaCO3 between the experimental result (averaged value among cases 1−4) and the calculated ones. Although the experimental result is only the average value of each case, it

Figure 7. Comparison of thermal decomposition ratio of CaCO3 between experimental result (averaged value among cases 1−4) ■ and calculated results ◩.

Figure 6. Chemical equilibrium result between CaSO4 (solid line) and SO2 (dotted line) with FactSage V.6.1. 6753

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becomes a reference for the validation of calculated results. The experimental result is based on the collected ash, which includes Ca fine particles and fly ash distributed in the furnace. The decomposition ratio is calculated from mass loss of Ca fine particles caused by CO2 release. However, the collected ash includes unreacted char in fly ash. Therefore, it is necessary to remove this influence, and the experimental result of thermal decomposition ratio ϕCO2 [wt %] is calculated as follows: φCO = 2

fCaCO 1 − fCaCO 2 3

3

fCaCO 1 3

× 100 (19)

where f CaCO31 is initial mass fraction of CaCO3 of Ca fine particles in the collected ash [kg/kg-all] and f CaCO32 is residual mass fraction of CaCO3 of Ca fine particles in the collected ash [kg/kg-all]. f CaCO31 is decided by the measurement of XRF analysis of Ca concentration of collected ash. f CaCO32 is decided by the relation between total carbon mass in the collected ash and mass loss of collected ash caused by firing with CO2 release. The difference of the experimental result and calculated results is about 10%. This difference is first based on the difference of history time of Ca fine particles, because the Ca fine particles in collected ash are exposed in the furnace gas during the experiment of cases 1−4. According to the experimental results of cases 1−4, the exhaust temperature of furnace is over 1073 K. Therefore, it is considered that the temperature of the deposited part of the collected ash is high enough and the thermal decomposition progresses after the deposition. Therefore, it is reasonable that the experimental result of thermal decomposition is higher than the calculated result, which does not take into account the decomposition reaction for deposited ash. Second, the thermal decomposition reaction parameter of eq 5 is originally for limestone. Therefore, one of the causes of this difference is the difference of surface property and component between limestone and Ca fine particles. Also, according to the previous study of thermal decomposition of limestone,11 the range of decomposition rate is distributed with 1000 order with each study. To improve the accuracy of the prediction, it is important that the improvement of accuracy of experiment and decision of the reaction rate parameter of eq 5 based on the experiment. Figure 8 shows the comparison of desulfurization ratio between experimental results and calculated ones. Figure 9 shows the distribution of temperature and SO2 mass fraction at the center section plane and Ca fine particle’s tracking. According to the results of Figure 8, the tendency of the calculation results shows good agreement with experimental results. Regarding the comparison between Figure 5 and case 3 of Figure 9, the SO2 mass fraction in the furnace of case 3 in Figure 9 is higher than the result of Figure 5, and the desulfurization ratio with the reverse reaction shows good agreement with experimental result in Figure 8. As the consequence, it is found that the reverse reaction is very important for in-furnace desulfurization. Regarding temperature distribution in Figure 9, the variation of the injection of Ca fine particles has almost no influence to the gas temperature field, since the injection amount of Ca fine particles is small. However, the accuracy of prediction of gas temperature distribution is very important, since the temperature of Ca fine particles, which influence the thermal decomposition reaction of CaCO3 and desulfurization reaction,

Figure 8. Comparison of desulfurization ratio between experimental result ■ and calculated one ◩.

mainly depends on the gas temperature. Regarding the experimental results among cases 1−3 in Figure 8, the desulfurization ratio increases with an increase of feeding rate of Ca fine particles. The calculated results show good agreement with these experimental results. This trend also can be confirmed from the SO2 distribution at exhaust pipe part of case 1, case 2, and case 3 in Figure 9. According to the track of Ca fine particles in Figure 9, the particle temperature in the furnace is mainly over 1300 K. Therefore, the SOx reduction reaction in the furnace is not so much active compared with that in the exhaust pipe. This is more obvious in case 4. Figure 10 shows variation of temperature of Ca fine particles with particle’s tracking time. According to Figure 10, Ca fine particles of all cases are exposed to the high temperature atmosphere over 1300 K. However, the exposed time for case 4 is longer than that for cases 1−3. Therefore, even if the desulfurization reaction progresses at the injection part of Ca fine particles, where the temperature of Ca fine particles is still less than 1300 K, CaSO4 is decomposed during the tracking of Ca fine particles in the furnace and the ratio of desulfurization is suppressed small compared with case 2 with same Ca/S ratio. This means that the temperature of the injection port of Ca fine particles is very important to maximize the efficiency of the desulfurization. In this case, it is found that port A is more suitable than port B in Figure 1. The simulation result well predicts this trend. It is clear that all cases achieve the target desulfurization ratio (=20 wt %) of this study. However, in case of applying this technology to the practical furnaces, further improvement of the efficiency of SOx reduction is important. According to the result of this study, the increase of Ca fine particle injection is effective, though the influence of deposition of Ca fine particles in the furnace also should be considered. Also, the injection port of Ca fine particles is important in order to keep the Ca fine particles’ temperature at the suitable temperature for desulfurization and to increase the tracking time of Ca fine particles for the enhancement of mixing with exhaust gas including SOx. According to Figure 10, the temperature of Ca fine particles is less than 1573 K. Therefore, the change of particle surface area based on eq 9 does not occur. However, this behavior is also important if the temperature of injection part of Ca fine particles is higher than that in this study. Because of the good correspondence between experimental results and calculated ones in this study, this simulator has the 6754

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Figure 9. Distribution of temperature (left), SO2 mass fraction (center) at center section plane, and Ca fine particle tracking (right).

of desulfurization reaction to represent in-furnace SO x desulfurization behavior. According to the experimental result, the desulfurization ratio increases with an increase of amount of Ca fine particles. Also, the desulfurization ratio is influenced by the temperature of Ca fine particles. Therefore, the injection position of Ca fine particles has big impact on the desulfurization behavior. The calculation result also shows these trends, and the desulfurization reaction progresses in the exhaust pipe since the temperature is higher than 1073 K. As a consequence, this simulator is effective not only for the

potential to predict these behaviors quantitatively and it is possible to utilize this simulator to the practical furnaces operation through the evaluation of the simulation result.

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CONCLUSIONS The simulator to estimate desulfurization behavior of the exhaust gas of pulverized coal combustion process is newly developed. To evaluate the simulator, a case study is carried out to compare between experimental result and calculated one. According to the validation, it is necessary the reverse reaction 6755

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Figure 10. Variation of temperature of Ca fine particles with particle’s tracking time. (6) Dennis, J. S.; Hayhurst, A. N. Chem. Eng. Sci. 1987, 42, 2361− 2372. (7) Zijlma, G. J.; Geritsen, A. W.; van den Bleek, C. M. Proc. 15th Int. Conf. Fluidized Bed Comb., Savannah, May, 1999. (8) Zevenhoven, R.; Yrjas, P.; Hupa, M. Fuel 1998, 77, 285−292. (9) Silcox, G. D.; Kramlich, J. C.; Pershing, D. W. Ind. Eng. Chem. Res. 1989, 28, 155−160. (10) Infan, Ar; Dogu, Gulsen Chem. Eng. J. 2001, 83, 131−137. (11) Stanmore, B. R.; Gilot, P. Fuel Process. Technol. 2005, 86, 1707− 1743. (12) Williams, A.; Pourkashanian, M.; Jones, J. M. Proc. Combst. Inst. 2000, 28, 2141. (13) Kurose, R.; Makino, H.; Suzuki, A. Fuel 2004, 83, 693. (14) Suzuki, A. Proc. Combust. Inst. (Japan) 2007, 45, 454. (15) Suzuki, A.; Ohnaka, A.; Tano, T.; Itokazu, R.; Tamura, M.; Terada, T. Proc. Combust. Inst. 2011, 33, 2811−2819. (16) Hu, N.; Scaroni, A. W. Fuel 1996, 75, 177. (17) Manovic, V.; Grubor, B.; Ilic, M. Therm. Sci. 2002, 6, 29−46. (18) FactSage V.6.1; GTT Technologies: Herzogenrath, Germany, 2009.

prediction of the desulfurization ratio, but also for improvement of the operation condition. To improve this simulator’s accuracy, it is necessary to improve each submodel such as SOx release reaction model from coal and thermal decomposition reaction model of CaCO3. Furthermore, it is important to apply this simulator to practical furnaces and accumulate the knowledge by the comparison between experimental results and calculated ones.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +81-45-683-1907. Fax: +81-45-683-1999. E-mail: [email protected], [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was subsidized by New Energy and Industrial Technology Development Organization (NEDO) “Strategic Development of Energy Conservation Technology Project”, JFY 2007-2009.

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REFERENCES

(1) Ohnaka, A.; Tano, T.; Terada, T.; Itokazu, R.; Tamura, M. Proceeding of 26th Annual International Pittsburgh Coal Conference, Pittsburgh, PA, 2009. (2) Itokazu, R.; Tamura, M.; Ohnaka, A.; Tano, T.; Terada, T. 10th International Conference on Technologies and Combustion for a Clean Environment, Lisbon, 2009. (3) Yamaguchi University, UBE Industries, Ltd. Research and development on the technology and management system for a high efficient accommodation of energy and materials in an industrial complex, P03033. New Energy and Industrial Technology Development Organization: Japan, 2010. (4) Muñoz-Guillena, M. J.; Linares-Solano, A.; Salinas-Martínez de Lecea, C. Appl. Surf. Sci. 1994, 81, 409−415. (5) Muñoz-Guillena, M. J.; Linares-Solano, A.; Salinas-Martínez de Lecea, C. Appl. Surf. Sci. 1996, 99, 111−117. 6756

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