Effect of particle size distribution on agglomeration/defluidization during fluidized bed combustion

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(1)Effect of particle size distribution on agglomeration/defluidization during fluidized bed combustion Chiou-Liang Lin*, Tzu-Huan Peng, Wei-Jen Wang Department of Civil and Environmental Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, ROC. *Corresponding author. Tel.: +886-7-5919722; Fax: +886-7-5919376 E-mail address: [email protected]. Abstract This study focused on the effect of particle size distribution (PSD) on agglomeration and defluidization in a fluidized bed. The four PSDs studied were narrow, Gaussian, binary and flat. The experimental variables studied included the gas velocity, the operating temperature, the Na concentration and the addition of Ca. The defluidization time decreased with increasing operating temperature and Na concentration, and these effects were independent of the sand bed’s PSD. In contrast, the defluidization time increased with increasing gas velocity for all PSDs. Comparing the four PSDs, the narrow and Gaussian distributions had higher defluidization times when using operating temperatures of 700°C and 800°C, gas velocities of 0.163 m/s and 0.187 m/s, and Na concentrations of 0.5% and 0.7%. However, the flat and binary distributions had lower defluidization times under these conditions because they had poor fluidized quality. Thus, the PSD apparently affects the agglomeration and defluidization properties of a sand bed. However, if the system operates at extreme conditions (e.g., high operating temperature, poor mixing and high Na concentration), the PSD’s influence on agglomeration will decrease. According to TGA/DTA results, there were two melting points in the agglomerate: 575°C and 782°C. This result confirmed that the liquid-phase eutectic species were 1.

(2) formed at the incineration temperature (700-900°C). Keywords: Agglomeration, Defluidization, Combustion, Fluidized bed, Particle size.. 1. Introduction In Taiwan, the incineration of solid waste is a primary solution for waste treatment due to its many advantages, such as waste volume reduction and stability increases. The use of incineration also creates the potential for energy recovery. Among commercial incinerators, the fluidized-bed reactor has advantages due to its thermal conductivity, continuous operation and ability to add a sorbent into the combustion chamber. Sorbent addition is useful for adsorbing heavy metals, sulfur and other harmful substances to reduce the generation of pollutants. As a result, fluidized-bed incinerators have been widely applied to the disposal of municipal and industrial solid waste. However, the waste from an incineration process is complex and may contain adhesive substances, such as alkali and alkaline earth metals [1]. These adhesive substances will gradually accumulate during incineration, causing agglomeration that affects the operation conditions of fluidization. Fluidization properties that can be altered by agglomeration include minimum fluidization velocity (Umf), bubble size, bubble frequency and bubble rise velocity [2]. These changes can even shut down the fluidized bed (defluidization) [3, 4]. Therefore, the phenomena of agglomeration and defluidization frequently cause problems during the operation of fluidized beds. Many factors can induce bed materials to undergo agglomeration and defluidization. Knight et al. [5] describe two mechanisms responsible for agglomeration and defluidization. The first mechanism stems from substances in the particle flow that form a bridge between particles. As the adhering force is larger than the separating force, agglomerates are formed, and the opportunity for defluidization increases with increasing operating temperature and time. The second mechanism 2.

(3) arises from the quantity of liquid-phase materials generated during the reaction process. These liquid-phase materials can form a bridge between two particles and cause agglomeration and defluidization. Gluckman et al. [6] also indicated that the generation of agglomerations independently determines the cohesiveness of particles and frequency of surface collisions. Additionally, Skrifvars et al. [7, 8] observed that agglomeration and defluidization occurs as a result of having “sticky” bed materials. In addition to the intrinsic characteristics of the particles, they proposed two additional factors that determine the adhesion tendencies of particles. One factor is the extent that melting and chemical reactions generate liquid-phase materials; the other factor is the extent of glassy material formation. As the operating temperature increases, the viscosity of these materials decreases and they flow more readily. Upon flow, they contact the surfaces of other particles. According to previous results, the waste being treated usually contains alkali elements that easily form liquid-phase eutectics at high temperatures, and these can lead to agglomeration and defluidization [4, 9]. Although the presence of an adhesive substance is a major factor in agglomeration and defluidization, the operating parameters are also important factors. Langston and Stephens [10], Moseley and O’brien [11] and Wank et al. [12] have shown that agglomeration is associated with the particles’ density, size and surface; it is also associated with the system’s operating temperature, gas velocity, physical and chemical characteristics, reaction mechanism and particle size distribution. Of these parameters, the particle size distribution (PSD) is an important factor that affects the agglomeration and defluidization processes, the quality of fluidization and the conversion efficiency of the reaction. Ray et al. [13] and Pell et al. [14] have reported that the PSD influenced many parameters, including the following: the minimum fluidization velocity, the terminal velocity, the elutriation rate, the reaction efficiency 3.

(4) and the hydrodynamic behavior. For a wide PSD, Sun and Grace [15] showed that the conversion efficiency of the reaction in the turbulent and fast fluidized bed reactor was usually higher than that of a bubbling fluidized bed. Gauthier et al. [16] also pointed out that a wide PSD enhanced fluidity and conversion, but a narrow PSD reduced the segregation phenomenon. Therefore, the particle size distribution may affect the fluidization of bed materials. If bed materials mix poorly, the movement of particles decreases and easily allows the formation of bridges (made from adhesive substances) between particles. For poor mixing, the separating force will be smaller than the adhering force, resulting in agglomeration and defluidization. Lin and Wey [4] also showed that coarse particles led to agglomeration and defluidization more easily than did fine particles because the course material moved slowly and had poor fluidity. However, most research has focused on determining the effects of the particle size on agglomeration [4, 17]. The effects of the particle size distribution on agglomeration and defluidization have rarely been examined during fluidization. To understand the effects of PSD, artificial waste that simulates municipal waste was used to form agglomerates. Artificial waste was used for simplicity. The effects of four kinds of PSDs (narrow, Gaussian, binary and flat) on agglomeration/defluidization were measured. The experimental variables studied included the gas velocity, the operating temperature, the Na concentration and the addition of Ca. Experimental results determined the influence of PSD on agglomeration and were used as a reference for the operation of fluidized bed incinerators.. 2. Experimental Methods A lab-scale bubbling fluidized bed reactor system was used in this study (Fig. 1). The reactor was constructed of 9-cm thick stainless steel (AISI 310) and had a main chamber measuring 120-cm high with an inner diameter of 9 cm. One stainless steel 4.

(5) porous plate was positioned at the bottom of the reactor and had a 15% open area to allow for distribution of the gas. The reactor was surrounded by an electrically resistant material packed with ceramic fibers for thermal insulation. The operating temperature was controlled by a programmed logical control (PID) controller connected to two thermocouples. At the flue gas outlet, a cyclone was connected to a carbon filter to collect the fly ash emitted from the combustion chamber. Silica sand was used as the bed material and had nearly constant density across all sizes (ρp= 2,600 kg/m3). The static bed height was maintained at 18 cm (H/D=2). According to the method suggested by Gauthier et al. [16], four types of particle size distributions (a narrow powder, a binary mixture, a flat and a similar Gaussian distribution powder) were prepared after sieving. All particle samples had nearly the same mean diameter (dsv=719 μm). The components of the four PSDs used in this work are listed in Table 1. To simulate the generation of agglomeration and defluidization during combustion, an alkali metal (Na) was added to the artificial waste to form a low melting point eutectic. An alkaline earth metal (Ca) was also added to observe its impact on agglomeration. The alkali and alkaline earth metals were added to the feed mixtures as a standardized metal solution formed from metal nitrates dissolved in distilled water. The metal nitrates contained NaNO3 and Ca(NO3)2. Artificial waste was prepared in bags, each of which had a total mass of 3.24 g. Each sample included sawdust (1.6 g), polypropylene (0.35 g), metal solution (1 mL) and a polyethylene (PE) bag (0.29 g). Before the experiment, the minimum fluidization velocity (Umf) was measured by plotting the pressure drop versus the gas velocity, according to the method of Lin et al. [18]. The minimum fluidization velocities for 4 PSDs at the experimental temperatures were measured before experiments. According to results, the minimum fluidization velocities were changed by different PSDs and temperatures. 5.

(6) The different behaviors for the different size distributions confirm other observations that Umf is not only a function of mean size. The value of minimum fluidization velocity for all the 4 PSDs at the experimental temperatures are shown in Table 2. According to Table 2, the particle size distribution (PSD) will affect the minimum fluidization velocity in fluidized bed. However, this study focused on the effect of PSD on agglomeration/defluidization during the fluidized bed combustion. The stoichiometry air of combusted reaction must be also considered. If the gas velocity changes with different PSDs, the air quality will change to affect the combustion. The combusted efficiency of various tests don’t base on the same conditions. Therefore, the constant gas velocity was used to simplify the factors. In this study, the minimum fluidization velocity based on the result of narrow distribution at 800oC, and the gas velocity 0.137, 0.163 and 0.187 m/s (1.1, 1.3 and 1.5 U/Umf base on narrow PSD at 800oC) were used as operating gas velocities. The different gas velocities were employed to estimate the influence of PSD on agglomeration/defluidization during incineration. The experimental conditions explored included the Na concentration, the particle size distribution, the gas velocity, the operating temperature and the addition of Ca. Table 3 lists the parameters that were controlled during the experiment. The sand bed was preheated to the set point temperature, and then the blower was opened to deliver air into the combustion chamber. The artificial waste was fed into the combustion chamber via the feed inlet at a rate of one bag every 20 sec. A pressure-versus-time profile and visual observation were used to evaluate the defluidization time. Defluidization is defined as any condition where a well-fluidized bed loses fluidization, whether partial or total. The channeling will occur to decrease the pressure drop under defluidization condition [19, 20]. Additionally, Tardos et al. [21] also showed that the rapid pressure drop through the sand bed was associated 6.

(7) with defluidization. Therefore, the pressure fluctuation can be measured the defluidization time during experiment. The pressure fluctuation was measured with experimental time and the pressure-versus-time profile was plotted. The pressure fluctuation maintained stably before defluidization. As the defluidization occurred, a rapid decrease in the bed pressure drop was observed and this time was defluidization time. The typical pressure drop vs. time diagram was shown in Fig. 2. The pressure difference was measured with two pressure sensors located in the freeboard and in the preheat chamber. The detectors had a measurement range of 0-1000 mmH2O and were connected to a single pressure transmitter. For a detailed method of measuring defluidization time, refer to Arena and Mastellone [22]. When defluidization occurred, the experiment was stopped and the defluidization time was recorded. The combustion chamber was cooled to room temperature, and the bed material (bottom ash) was collected. Scanning electron microscopy and energy dispersive spectrometry (SEM/EDS), X-ray diffraction (XRD), and thermogravimetric analysis and differential thermal analysis (TGA/DTA) were used to analyze the surfaces of the agglomerates.. 3. Results and Discussion 3.1. Effect of different conditions on defluidization time 3.1.1. Effect of operating temperature Figure 3 shows the effects of different PSDs at various operating temperatures on the time required to reach defluidization. The defluidization time increased with decreasing operating temperature, and this effect was independent of the PSD of the sand bed. Thus, increased operating temperature enhanced the generation of agglomerates. According to previous studies [23], alkali metals in the waste may react with the silica sand or with other impurities in the sand to generate eutectics with low melting points. These eutectics melt to form a highly viscous liquid phase because the 7.

(8) operating temperature exceeds the melting points of these eutectics. These liquid species flow easily and cover the surface of the sand particles via particle collision. If there is insufficient force to segregate the particles, they adhere and form agglomerates. Therefore, as the operating temperature increases, more low melting point eutectics are generated, and they melt easily to form liquid species. Additionally, the viscosity of the liquid species decreases with increased temperature. The reduced viscosity enables flow to occur, thus decreasing the time to defluidization. Manzoori and Agarwal [24] also reported that the agglomeration rate increased with increasing temperature. 3.1.2. Effect of gas velocity Figure 4 shows that the defluidization time increased with increasing gas velocity for all four PSDs. Therefore, increasing the gas velocity can delay agglomeration and defluidization. In previous studies [12, 25, 26], the concept of balanced forces was used to discuss the mechanism of agglomerate generation. If the adhesive force of the liquid eutectics exceeds the segregation force, the bed experiences agglomeration and defluidization. The adhesive forces of agglomeration include the van der Waals forces, the cohesive force and the capillary force of the liquid materials. The segregation forces include the gravitational force, the collision force and the drag force. As the gas velocity increases, the collision force and the drag force of the particles increase, and these increases counter the effect of the adhesive forces. Rehmat and Aaxena [27] noted that as the gas velocity increased, the momentum of the particles increased. Manzoori and Agarwal [21] also showed that the probability of agglomeration is inversely proportional to the momentum of the particles, because increased particle momentum increases the segregation forces and decreases the risk of agglomeration. Therefore, the probability of agglomeration and defluidization decreases with increasing gas velocity. 8.

(9) 3.1.3. Effect of Na concentration Figure 5 illustrates the defluidization times for different Na concentrations for all four PSDs. As the Na concentration in artificial waste increases, the defluidization time rapidly decreases. Because the Na concentration increases, more eutectics with low melting points are generated and form liquid phase species. Additionally, He [28] showed that the particles’ tendency to agglomerate is inversely proportional to the momentum of the particles. When a softer and thicker viscous flow exists at the particle surface, the particles are more deformable and have less momentum. These conditions favor agglomeration and defluidization. 3.2. The effect of PSDs on agglomeration/defluidization As shown in Figs. 3-4, the defluidization time trends of the various PSDs were similar at different conditions (i.e., operating temperature, gas velocity and Na concentration). The defluidization time decreased with increasing operating temperature and Na concentration. On the other hand, the defluidization time increased with increasing gas velocity. However, there were some differences for the four PSDs at different conditions. The narrow and Gaussian distributions had generally longer defluidization times when using operating temperatures of 700°C and 800°C, gas velocities of 0.163 m/s and 0.187 m/s, and Na concentrations of 0.5% and 0.7%. However, the flat and binary distributions had shorter defluidization times. The sand bed had superior stability when a narrow particle distribution was used. If another particle size was added, the distribution of the sand bed would be changed to a binary or a multi-distribution. The tendency to mix will compete with the tendency to segregate, thus changing the fluidized behavior. Gauthier et al. [16] demonstrated that binary and flat distributions have a greater tendency to segregate, whereas Gaussian and narrow distributions tend towards more complete mixing. For a Gaussian distribution, the extreme-sized particles existed in small proportions, 9.

(10) resulting in an increased tendency to mix completely (as found in narrow distributions). Therefore, the narrow and Gaussian distributions exhibited good mixing and uniform distributions with low levels of agglomeration. The four types of PSDs all had nearly the same mean diameter (dp=719 μm). Except for the narrow distribution, they all had coarse particles, especially the flat and the binary distributions. Gauthier et al. [16] and Chiba et al. [29] showed that binary and flat distributions tend to segregate. Therefore, the behaviors of these distributions exhibit complete segregation or partial mixing behavior during fluidization. If narrow and Gaussian distributions were regarded as completely mixed, the minimum fluidization velocities of binary and flat distributions were higher than those of the Gaussian and narrow distributions. Thus, the fluidized qualities of binary and flat distributions were inferior to those of the Gaussian and narrow distributions for a given gas velocity. This difference arises because coarse particles move with greater difficulty, thus decreasing the segregation force and producing agglomerates. Comparing the narrow and Gaussian distributions, the Gaussian distribution still had a few coarse particles that provided partial mixing; therefore, the defluidization time of the Gaussian distribution was shorter than that of the narrow distribution. Therefore, under general operating conditions, the defluidization time of the four PSDs was in the following order: Narrow > Gaussian > Binary ≈ Flat. However, at the operating conditions of 900°C, 0.137 m/s and 0.9% Na, the four PSDs all had the lowest defluidization times, and each time was similar. This result illustrated that the influence of extensive liquid-phase eutectic generation and of poor mixing on defluidization was larger than the influence of the PSD. Under the conditions of 900°C and 0.9% Na, large liquid-phase eutectics were formed and led to rapid agglomeration and defluidization. Additionally, at 0.137 m/s, the operating gas velocity was similar to the minimum fluidization velocity. The sand bed was poorly 10.

(11) mixed, which easily led to rapid agglomeration and defluidization. Therefore, the PSD significantly affected the agglomeration and defluidization, but if the system operated at extreme conditions (e.g., high operating temperature, poor mixing and high Na concentration), the influence of the PSD on agglomeration decreased. 3.3. Effect of added Ca on agglomeration/defluidization According to a previous study [23], the added Ca will form eutectics with high melting points that inhibit agglomeration, thus maintaining the fluidized quality and prolonging fluidization. The experimental results agreed with the previous report (Fig. 6). The addition of Ca inhibited agglomeration and increased the defluidization time, but the influence of the PSD on agglomeration did not change. 3.4. Analysis of agglomerate species To identify the eutectics, XRD was used to analyze the dominant species in the agglomerates. However, only SiO2 was detected, and no compounds containing Na were found. SEM/EDS was also used to analyze the agglomerates. As shown in Fig. 7, Na and Si were the dominant species in the agglomerates. The eutectics may exist in agglomerates, but they may be too thin or not sufficiently crystallized to be detected by XRD. Additionally, there may be non-crystalline eutectics. According to previous studies [30, 31], Na compounds with low melting points (e.g., Na2O) were found on agglomerates. In addition, Liu et al. [32] also used Electron Spectroscopy for Chemical Analysis (ESCA) to analyze the species on agglomerates, and they determined that Na compounds with low melting points (e.g., Na2CO3, NaNO3, Na2C2O4, NaHCO3 and Na2O) existed on agglomerates. Most of the Na compounds’ melting points were lower than 1000°C, and they easily melted into the viscous liquid phase to form agglomerates at high temperatures. When Ca was added into the system, the compounds with high melting points (e.g., CaO) were formed [32]. These high melting point species may increase the melting point of the 11.

(12) system and inhibit the generation of agglomerates. To analyze the melting point of eutectic species, the agglomerate was analyzed by TGA/DTA (Fig. 8). There were two melting points in the agglomerate: 575°C and 782°C. This result confirms that the liquid phase eutectic species were formed at the incineration temperature (700-900°C). Additionally, the Na compounds may have reacted with SiO2 to form eutectic species such as Na2O and SiO2. The melting points of these species would decrease rapidly with increasing Na2O content. Therefore, the melting point of the eutectics changes with the concentration and composition of system components, and these changes can then affect the agglomeration and defluidization of the combustion system.. 4. Conclusions This. study. used. artificial. waste. to. simulate. the. generation. of. agglomeration/defluidization. The effects of four PSDs (narrow, Gaussian, binary and flat) on agglomeration and defluidization were investigated. The experimental variables studied included the gas velocity, the operating temperature, the Na concentration and the addition of Ca. The defluidization time decreased with increasing operating temperature and Na concentration, and these effects were independent of the PSD of the sand bed. On the other hand, the defluidization time increased with increasing gas velocity for all PSDs. Comparing the four PSDs, the narrow and Gaussian distributions had higher defluidization times at operating temperatures of 700°C and 800°C, gas velocities of 0.163 m/s and 0.187 m/s, and Na concentrations of 0.5% and 0.7%. However, the flat and binary distributions had lower defluidization times because they had poor fluidized quality. Thus, the PSD apparently affected the agglomeration and defluidization processes. However, if the system operates at extreme conditions, such as high operating temperature, poor mixing and high Na concentration, the influence 12.

(13) of PSD on agglomeration decreases. According to TGA/DTA results, there were two melting points in the agglomerate: 575°C and 782°C. This result confirms that the liquid-phase eutectic species will form at the incineration temperature (700-900°C).. References [1] S. Arvelakis, H. Gehrmann, M. Beckmann, E.G. Koukios, Agglomeration problems during fluidized bed gasification of olive-oil residue: evaluation of fractionation and leaching as pre-treatments, Fuel 82 (2003) 1261-1270. [2] G. Tardos, R. Pfeffer, Chemical reaction induced agglomeration and defluidization of fluidized beds, Powder Technol. 85 (1995) 29-35. [3] F. Scala, R. Chirone, An SEM/EDX study of bed agglomerates formed during fluidized bed combustion of three biomass fuels, Biomass Bioenergy 32 (2008) 252-266. [4] C.L. Lin, M.Y. Wey, The effect of mineral compositions of waste and operating conditions on particle agglomeration/defluidization during incineration, Fuel 83 (2004) 2335-2343. [5] P.C. Knight, J.P.K. Seville, H. Kamiya, M. Horio, Modelling of sintering of iron particles in high-temperature gas fluidization, Chem. Eng. Sci. 55 (2000) 4783-4787. [6] M.J. Gluckman, J. Yerushalmi, A.M. Squires, Defluidization characteristics of sticky or agglomerating beds, In D.L. Keairns, Fluidization Technology, Vol. ΙΙ, Washington: DC Hemisphere, 1976, pp. 395-422. [7] B.J. Skrifvars, M. Hupa, M. Hiltunen, Sintering of ash during fluidized bed combustion, Ind. Eng. Chem. Res. 31 (1992) 1026-1030. [8] B.J. Skrifvars, M. Hupa, R. Backman, M. Hiltunen, Sintering mechanisms of FBC ashes, Fuel 73 (1994) 171-176. [9] C.L. Lin, M.Y. Wey, C.Y. Lu, Prediction of defluidization time of alkali 13.

(14) composition at various operating conditions during incineration, Powder Technol. 161 (2006) 150-157. [10] B.G. Langston, F.M. Stephens, Self agglomerating fluidized bed reduction, J. Metals 12 (1960) 312-316. [11] J.L. Moseley, T.J. O’Brien, A model for agglomeration in a fluidized bed, Chem. Eng. Sci. 48 (1993) 3043-3050. [12] J.R. Wank, S.M. George, A.W. Weimer, Vibro-fluidization of fine boron nitride powder at low pressure, Powder Technol. 121 (2001) 195-204. [13] Y.C. Ray, T.S. Jiang, C.Y. Wen, Particle attrition phenomena in a fluidized bed, Powder Technol. 49 (1987) 193-206. [14] M. Pell, in J.C. Williams, T. Allen, T. (Eds.), Handbook of Powder Technology, Vol. 8, Elsevier, Amsterdam, 1990. [15] G. Sun, R. Grace, Effect of particle size distribution in different fluidization regimes, AIChE J. 38 (1992) 716-722. [16] D. Gauthier, S. Zerguerras, G. Flamant, Influence of the particle size distribution of powders on the velocities of minimum and complete fluidization, Chem. Eng. J. 74 (1999) 181-196. [17] C.L. Lin, M.C. Tsai, C.H. Chang, The effects of agglomeration/defluidization on emission of heavy metals for various fluidized parameters in fluidized-bed incineration, Fuel Process Technol. 91 (2010) 52-61. [18] C.L. Lin, M.Y. Wey, S.D. You, The effect of particle size distribution on minimum fluidization velocity at high temperature, Powder Technol. 126 (2002) 297-301. [19] J.H. Siegell, High-temperature de fluidization, Powder Technol. 38 (1984) 13-22. [20] H. Atakül, B. Hilmioğlu, E. Ekinci, The relationship between the tendency of lignites to agglomerate and their fusion characteristics in a fluidized bed 14.

(15) combustor, Fuel Process. Technol. 86 (2005) 1369-1383. [21] G. Tardos, D. Mazzone, R. Pfeffer, Destabilization of fluidized beds due to agglomeration part Ⅱ: experimental verification, Can. J. Chem. Eng. 63 (1985) 384-389. [22] U. Arena, M.L. Mastellone, The phenomenology of bed defluidization during the pyrolysis of a food-packaging plastic waste, Powder Technol. 120 (2001) 127-133. [23] C.L. Lin, J.H. Kuo, M.Y. Wey, S.H. Chang, K.S. Wang, Inhibition and promotion: the effect of earth alkali metals and operating temperature on particle agglomeration/defluidization during incineration in fluidized bed, Powder Technol. 189 (2009) 57-63. [24] A.R. Manzoori, P.K. Agarwal, Agglomeration and defluidization under simulated circulating fluidized-bed combustion conditions, Fuel 73 (1994) 563-568. [25] T. Mikami, H. Kamiya, M. Horio, The mechanism of defluidization of iron particles in a fluidized bed, Powder Technol. 89 (1996) 231-238. [26] J.P.K. Seville, H. Silomon-Pflug, P.C. Knight, Modeling of sintering in high temperature gas fluidization, Powder Technol. 97 (1998) 160-169. [27] A. Rehmat, S.C. Saxena, Agglomeration of ash in fluidized-bed gasification of coal by steam-oxygen (or air) mixture, Ind. Eng. Chem. Process Des. Dev. 19 (1980) 223-230. [28] Y. He, Acriterion for particle agglomeration by collision, Powder Technol. 103 (1999) 189-193. [29] S. Chiba, T. Chiba, A.W. Nienow, H. Kobayashi, The minimun fluidisation velocity, bed expation and pressure-drop profile binatry particle mixture, Powder Technol. 22 (1979) 225-269. [30] W. Lin, K. Dam-Johansen, F. Frandsen, Agglomeration in bio-fuel fired fluidized bed combustors, Chem. Eng. J. 96 (2003) 171-185. 15.

(16) [31] R. Yan, D.T. Liang, K. Laursen, Y. Li, L. Tsen, J.H. Tay, Formation of bed agglomeration in a fluidized mulit-waste incinerator, Fuel 82 (2003) 843-851. [32] Z.S. Liu, C.L. Lin, J.D. Chou, Studies of Cd, Pb and Cr distribution characteristics in bottom ash following agglomeration / defluidization in a fluidized bed boiler incinerating artificial waste, Fuel Process Technol. 91 (2010) 591-599. Table 1 The particle size distributions of different powder types. Average diameter Type of powder Weight (%) xi. Sieves No. Sieves (μm) dpi (μm). Narrow. 100. 30~20. 590~840. 715.0. Gaussian. 8. 45~35. 350~500. 425.0. 25. 35~24. 500~701. 600.5. 35. 24~20. 701~840. 770.5. 23. 20~18. 840~1000. 920.0. 9. 18~16. 1000~1190. 1095.0. 59. 20~18. 840~1000. 920.0. 41. 35~30. 500~590. 545.0. 17. 45~35. 350~500. 425.0. 17. 35~24. 500~701. 600.5. 19. 24~20. 701~840. 770.5. 23. 20~18. 840~1000. 920.0. 24. 18~16. 1000~1190. 1095.0. Binary. Flat. dsv=719.0 ※The dsv calculation formula is: d sv . 1 x i d i pi. 16.

(17) Table 2 Minimum fluidization velocity for different PSDs and temperatures. Minimum fluidization velocity (m/s) Temperature (K) Narrow Binary Flat Gaussian 973 0.129 0.151 0.143 0.132 1073 0.125 0.124 0.126 0.113 1173 0.135 0.130 0.13 0.117. Run. 1-4. 5-8. 9-12. 13-16. 17-20. 21-24. 25-28. 29-32. Table 3 The operating conditions for each experiment. Concentration Gas Velocity Type of powder T (oC) (%) (m/s) Na Ca Narrow Binary Flat Gaussian ● ● － 700 0.163 0.7 ● ● ● ● － 800 0.163 0.7 ● ● ● ● － 900 0.163 0.7 ● ● ● ● － 800 0.137 0.7 ● ● ● ● － 800 0.187 0.7 ● ● ● ● － 800 0.163 0.5 ● ● ● ● － 800 0.163 0.9 ● ● ● ● 800 0.163 0.7 0.7 ● ●. 17.

(18) 8. 10. 1 9. 4 6. 11 5 7 3. 2. Figure 1 The bubble fluidized bed reactor. (1) PID controller, (2) blower, (3) flow meter, (4) thermocouple, (5) pressure transducer, (6) electric resistance, (7) sand bed, (8) feeder, (9) cyclone, (10) filter, (11) induced fan.. Pressure drop (mmH2O). 350. 300. 250. 200. 150 400. 600. 800. 1000. 1200. 1400. 1600. 1800. 2000. Time (sec). Figure 2 The typical pressure drop vs. time diagram. 18. 2200.

(19) 2500 Narrow Binary Flat Gaussian. 1500. 1000. 500. 0 650. 700. 750. 800. 850. 900. 950. Temperature (oC). Figure 3 The effects of different PSD at various operating temperatures on the defluidization time.. 3000 2500 Defluidization time (sec). Defluidization time (sec). 2000. Narrow Binary Flat Gaussian. 2000 1500 1000 500 0 0.137. 0.163. 0.187. Gas velocity (m/s). Figure 4 The effects of different PSD at various operating velocities on the defluidization time. 19.

(20) 3000 Narrow Binary Flat Gaussian. Defluidization time (sec). 2500 2000 1500 1000 500 0 0.4. 0.5. 0.6. 0.7. 0.8. 0.9. 1.0. Na concentration (%). Figure 5 The effects of different PSD at various Na concentrations on the defluidization time.. 2500 Narrow Binary Flat Gaussian. Defluidization time (sec). 2000. 1500. 1000. 500. 0 Non Ca addition. Added Ca. Ca addition or non-addition. Figure 6 The effect of adding Ca on the defluidization time for different PSD. 20.

(21) Figure 7 The FE-SEM/EDS results for the agglomerate. (800°C, 0.163 m/s, 0.7%Na and Narrow distribution). Heat Flow Endo Down (mW). -10. -5. 0 o. 782 C 5. 10. 575oC. 15 400. 500. 600. 700. 800. 900. o. Temperature ( C). Figure 8 The TGA/DTA thermogram of agglomerate.. 21.

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