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Effects of fluidized parameters on capture of heavy metal in various bed-material size distributions
Fuel Processing Technology 106 (2013) 149–159
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Effects of fluidized parameters on capture of heavy metal in various bed-material size distributions Chiou-Liang Lin ⁎ Department of Civil and Environmental Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan
a r t i c l e
i n f o
Article history: Received 6 April 2012 Received in revised form 3 July 2012 Accepted 16 July 2012 Available online 18 August 2012 Keywords: Defluidization Combustion Fluidized bed Heavy metal Bottom ash
a b s t r a c t Artificial waste was used to simulate agglomeration/defluidization in a fluidized bed and to examine the heavy-metal distribution in bottom ash for various bed-material particle-size distributions (narrow, flat, and Gaussian). Heavy-metal concentration in most particles increased with Na addition, and the largest (>1.000 mm)- and smallest (b0.500 mm)-particle-size fractions had the highest metal concentrations. Additionally, Cr had the highest retention rate within the bed particles, followed by Pb and Cd. Comparison of the heavy-metal concentrations and retention rates for different particle-size distributions showed that the total retention rates were higher for narrow and Gaussian distributions than for the flat distribution. © 2012 Elsevier B.V. All rights reserved.
1. Introduction A fluidized bed has many advantages and is widely used for urban residue treatment, biofuel and coal combustion, gasification, pyrolysis, and other processes [1–4]. However, during the operation of fluidized bed reactors, the presence of certain elements such as alkali metals causes the bed material to agglomerate into large blocks. These agglomerated materials affect the operating conditions of the fluidized bed, such as the minimum fluidization velocity, bubble size, bubble frequency, and rise velocity [5], and in some cases, may lead to shutting down of the fluidized bed (defluidization) [6,7]. Therefore, agglomeration has been identified as one of the chief issues in a fluidized bed reactor. Previous studies show that many types of elements can cause agglomeration; the corresponding results are summarized in Table 1 [5,8–15]. In those studies, alkali group elements such as Na and K were among the major elements that caused agglomeration. Knight et al. [16] postulated that the mechanism of agglomeration depends on two factors: (1) whether the particle substances flow to generate bridges between particles, and (2) the quantity of liquidphase materials generated during the reaction process. Agglomerates form when the cohesive forces in the system exceed the separation forces, and the probability of agglomeration increases with an increase in the operating temperature and operating time. Liquidphase materials can act as bridges between particles and hence lead to agglomeration/defluidization. Lin and Wey [7] pointed out that Na contained in the urban waste can react with the bed material or ⁎ Tel.: +886 7 5919722; fax: +886 7 5919376. E-mail address: [email protected]. 0378-3820/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.fuproc.2012.07.015
other elements in the fluidization chamber to produce liquid eutectic materials. These liquid eutectics are formed on the surfaces of the bed materials, leading to an increase in cohesive forces and thereby inducing agglomeration/defluidization. The operating parameters for the fluidized bed are important factors causing agglomeration/defluidization. Moseley and O'Brien [17] and Wank et al. [18] found that the occurrence of agglomeration and defluidization is influenced by attributes of the bed such as bed surface area, operating temperature, and gas velocity, as well as the attributes of bed particles such as density and size distribution. Of these factors, particle-size distribution (PSD) is the most important because it affects the quality of fluidization and the conversion efficiency of the reaction. Ray et al. [19] and Pell et al. [20] showed that particle-size distribution influences the minimum fluidization velocity, terminal velocity, elutriation velocity, chemical reaction rate, and hydrodynamic characteristics of fluidization. Gauthier et al. [21] showed that a narrow particle size range can increase the operational stability of a fluidized bed (for instance, by preventing bed material separation), while a broad particle size range can increase the bed material mobility and chemical conversion rates. Waste and coal often include some trace elements such as heavy metals, which vaporize or leach out into the environment during combustion and pose a serious risk to human health [4,22]. Therefore, it is essential to control heavy-metal emission during combustion processes. These heavy metals may vaporize because of the high combustion temperatures and pass into the environment along with the exhaust gas or fly ash. Alternatively, they may get trapped in the bottom ash. The mode of transport for different heavy metals depends on the corresponding heavy-metal compounds present and their respective boiling points. Generally, heavy-metal compounds with low
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
Table 1 Elements that possibly induce agglomeration in fluidized beds. Na Tardos and Pfeffer [5] Conn [8] Mikami et al. [9] Steenari et al. [10] Wang et al. [11] Arvelakis et al. [12] Lin et al. [13] Atakül et al. [14] Lin et al. [15]
● ● ● ● ●
K
Mg
Ca
●
● ●
● ● ● ●
●
●
Si
S
Cl
● ●
V
Ni
● ● ●
●
●
Fe
● ●
● ● ●
●
●
●
●
boiling points are mainly distributed in the fly ash or flue gas, while heavy-metal compounds with high boiling points are mainly found in the bottom ash or the larger fly ash particles [23,24]. In addition to the properties of the heavy metals, major factors affecting the distribution of heavy metals in the combustion waste are combustion conditions such as temperature, feeding load, waste properties, and chlorine species present [25–27]. The fluidization behavior of the reactor is influenced by the particle-size distribution of the bed material. If the bed material is not well mixed, particle movement is restricted, and thus, bridges are easily formed between particles. The agglomerate thus formed affects the fluidized-bed operating parameters and indirectly affects the distribution of heavy-metal pollutants in the ash and flue gas particles. However, the effects of particle size of the bed material on the agglomeration/defluidization and heavy-metal distribution during fluidization have rarely been examined in previous studies. Lin et al. [28] had discussed the emission trends of three heavy metals (Pb, Cr, and Cd), which were added to the bed material, during agglomeration/defluidization. When the sand bed defluidized, the amount of heavy metals emitted into the flue gas or fly ash increased significantly. This result showed that the fluidized bed conditions do affect the distribution of heavy metals. However, the effect of fluidized bed conditions on the distribution of heavy metals in bottom ash was not discussed in these studies. Additionally, silica sand usually functions not only as a bed material but also as an adsorbent of heavy metals during the combustion. The formation of a eutectic during agglomeration also affects the distribution of heavy metals, but few studies currently address the effect of agglomeration/defluidization on the distribution of heavy metals in the bottom ash. Therefore, in the present study, artificial waste was used to simulate agglomeration/defluidization, and the distribution of heavy metals in bottom ash was examined during the process. Different bed-material particle-size distributions (narrow, flat, and Gaussian) were considered in this study. In order to understand the effects of bed-material particle-size distribution on the deposition of heavy metals in bottom ash during agglomeration/defluidization, the experimental parameters, including the bed-material particlesize distribution, operating gas velocity, operating temperature, and Na addition, were controlled. The results of this study can be employed as reference to decide the optimum operating conditions for fluidized bed combustors. 2. Materials and methods In order to reduce the differences in the composition of the feeding waste, artificial waste was prepared in this study. Real waste could not used because its composition is very complex and bound to vary. Since most waste contains alkali metals (especially Na), which may cause agglomeration/defluidization during the combustion process, Na was added to the artificial waste to form lowmelting-point eutectic compounds during the experiments. The artificial waste contained sawdust, polypropylene, polyethylene and metal solution. Sawdust was used in place of the cellulose found in
urban residues, and polypropylene and polyethylene were used in place of the plastics. Elemental analysis of sawdust, polypropylene and polyethylene used were performed using Elemental Analyzer (EA), and these results are listed in Table 2. Concentration of sodium in sawdust was found to be 0.0023%. The concentration of Na in sawdust was reported to be very low in earlier studies as well [29,30]; therefore, the effect of the concentration of alkali metal inherent in sawdust was neglected. Three metals (Cd, Pb, and Cr, characterized by high, medium, and low volatility, respectively) were selected to simulate the presence of heavy metals in waste. For the metal additives in this study, metal nitrates (NaNO3, Pb(NO3)2, Cr(NO3)3, and Cd(NO3)2) were dissolved in deionized water and added to the artificial waste. The artificial waste was prepared using 1.6 g sawdust, 0.35 g polypropylene, and 1 mL metal nitrate solution, all enclosed in a polyethylene (PE) bag weighing 0.29 g. A standard bag of the artificial waste weighed 3.24 g, which was used as the standard mass in the calculations of heavy-metal distribution. The concentration of heavy metals (Pb, Cd, and Cr) was maintained at 0.7% in each case. Table 3 lists the operating conditions used in different experiments. Silica sand comprising SiO2 (97.80%), Al2O3 (2.01%), and Fe2O3 (0.07%) and having a density of 2.6 g/cm 3 was used as the bed material in the experiments. The natural concentration of the heavy metals in silica sand was analyzed. According to the analysis result, Cd and Cr were absent in the sand, while the Pb concentration was 0.024 mg/g (i.e., 0.0024%). The sand bed was 18 cm in height and 9 cm in diameter (H/D = 2). The silica sand was sorted by standard ASTM sieves, and segregated into three set of fractions with three different PSDs, namely, narrow, flat, and Gaussian. The preparation method of Gauthier et al. [21] was used. The dsv values for all three PSDs were maintained at about 0.725 mm using the following equation: dsv ¼
1 ∑ i
xi dpi
where xi is the weight ratio (%) and dpi is the average diameter (mm). Table 4 lists the particle size and weight proportion for each type of PSD. Before the experiments, the minimum fluidization velocity was measured using the method proposed by Lin et al. [31]. The gas velocities were maintained at 0.137, 0.163, or 0.187 m/s during the combustion process, and the operating temperature was maintained at 800 °C. The experimental parameters monitored were bed-material particle-size distribution, operating gas velocity, operating temperature, and Na addition. Table 3 lists the operating conditions used for these experiments. A bubbling fluidized-bed incinerator system was employed, as shown in Fig. 1. The reactor was made of stainless steel (AISI 310) sheets of 3 mm thickness, and it had a main chamber measuring 120 cm in height and 10 cm in inner diameter. A stainless steel porous plate with an open area of 15.2% was fixed at the bottom of the system to separate the gas flow. The reactor was enclosed by electrically resistant material and connected to two thermocouples with a programmable logic controller (PID controller) to regulate the experiment temperature. In order to control the emission of pollutants, a cyclone separator fitted with a bag filter was used. The experimental procedure followed is as follows. First, the sand bed was heated to the desired temperature and then cooled by cool Table 2 Elemental analysis of different compositions by weight.
Sawdust Polyethylene (PE) Polypropylene (PP)
N (%)
C (%)
O (%)
H (%)
5.01 0.86 1.12
43.12 85.71 86.16
46.07 0.39 0.52
5.80 13.04 12.20
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
151
Table 3 Operating conditions for each experiment. Run
T (°C)
Gas velocity (m/s)
Na (wt.%)
Narrow
1 2 3 4 5 6 7 8 9
800 800 800 700 700 700 800 800 800
0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163
0.7 0.7 0.7 0.7 0.7 0.7
●
Flat
Gaussian
● ● ● ● ● ● ● ●
air from a blower adjusted using a float-type flow meter. Then, the artificial waste was fed into the chamber at a rate of one bag (3.24 g) per 20 s. Both pressure-versus-time profiling and manual observations were employed to evaluate the defluidization time during the combustion. Two pressure probes were used to measure the pressure difference between the sand bed and the freeboard. These probes were connected to a differential pressure transmitter, and the range of measurement was 0–1000 mm H2O. When defluidization occurred, feeding of artificial waste was stopped. When the sand bed cooled to room temperature, the silica sand was collected and then sorted by standard ASTM sieves into eight fractions on the basis of particle size (>1.180 mm, 1.180–1.000 mm, 1.000–0.850 mm, 0.850–0.710 mm, 0. 710–0.600 mm, 0.600–0.500 mm, 0.500–0.355 mm, and b0.355 mm) to evaluate the changes in particle size after agglomeration/defluidization. Each fraction was first weighed and then sampled in order to analyze the concentrations of heavy metals. The solid sample was treated by microwave digestion before analysis by inductively coupled plasma mass spectrometry (ICP-MS) for heavy-metal concentrations. The agglomerates were also analyzed by field-emission scanning electron microscopy/ energy-dispersive spectrometry (FESEM/EDS) to observe the particle surfaces and identify the species of agglomerates. Additionally, the Brunauer, Emmett, and Teller (BET) method was used to determine surface area and pore size of silica sand.
3. Results and discussion 3.1. Changes defluidization
in
particle-size
distribution
with
agglomeration/
Fig. 2 shows the particle-size distributions of the bed material, with and without Na addition, after agglomeration/defluidization. According to Fig. 2, the distribution was different from the original distribution. The range of bed-material particle sizes widened,
Table 4 Particle size distributions of different silica sand bed materials. Type
Weight (%) Xi
Size range (mm)
Average diameter di (mm)
Narrow Flat
100 17 17 19 23 24 8 25 35 23 9
0.600–0.850 0.355–0.500 0.500–0.710 0.710–0.850 0.850–1.000 1.000–1.180 0.355–0.500 0.500–0.710 0.710–0.850 0.850–1.000 1.000–1.180
0.725 0.428 0.605 0.780 0.925 1.090 0.428 0.605 0.780 0.925 1.090 dsv = 0.725
Gaussian
The dsv calculation formula is: dsv ¼
xi : dpi
1
∑ i
Run
T (°C)
Gas velocity (m/s)
Na (wt.%)
Narrow
10 11 12 13 14 15 16 17 18
900 900 900 800 800 800 800 800 800
0.163 0.163 0.163 0.137 0.137 0.137 0.187 0.187 0.187
0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7
●
Flat
Gaussian
● ● ● ● ● ● ● ●
especially in the case of the narrow distribution. In the absence of Na, the number of particles with sizes smaller than 0.500 mm increased considerably. These results suggested that the sand particle size decreased during fluidized operation, because of collisions between the particles as well as the attrition with the bed material and the chamber wall. Moreover, the combustion reaction resulted in a high-temperature environment inside the chamber, and hence, the decrease in particle size could be attributed to the thermal stress inside the particles [32,33]. In result, the bed-particle size tended to decrease during thermal fluidization. Addition of Na to the artificial waste led to complex variations in the particle size due to the generation of agglomerates during combustion. Fig. 2 reveals that besides the small particles, some of the large particles (> 1.000 mm) increased in size. In general, the proportion of large particles increased with the addition of Na. These results indicated that during agglomeration, Na probably reacts with the silica sand or its impurities to generate low-melting-point eutectics that melt at high temperatures. These viscous molten eutectics come into contact with other particles by collision, and if the cohesive forces are larger than the separation force, these particles agglomerate, thereby increasing the bed-material particle size [34]. In our previous study [35], we analyzed the agglomerate by thermogravimetric analysis and differential scanning calorimetry (TGA/DTA) and estimated two melting points, 575 °C and 782 °C. Since the melting point of SiO2 is 1650 °C, the two temperatures may be the melting points of the eutectics formed by Na and Si in the agglomerate. 3.2. Distribution of heavy-metal concentration with particle size Fig. 3 shows the distribution of heavy-metal concentration against particle size with and without Na addition. According to a previous study [36], the heavy metal is adsorbed on silica sand because of the high surface area of the latter. Therefore, in the absence of Na, the heavy metal within the bed material is entrapped by adsorption on silica sand. However, when Na was added, the heavy-metal concentration in the bottom ash increased significantly. Hence, the molten eutectics increased the amount of heavy metals in the sand bed, especially in a bed consisting of coarse particles (> 1.000 mm). Generally, smaller particles were generated by the attrition of coarse particles during fluidization, and these particles had a large surface area that allowed for easy adsorption of heavy metals. Larger particles were formed by the agglomeration of bed particles caused by lowmelting-point eutectic compounds, and these particles had a small surface area. The surface areas of small (b 0.355 mm) and coarse (>1.180 mm) silica sands were found to be 3.98 m 2/g and 3.05 m 2/g, respectively, by BET surface area analysis. Although the surface area of a large particle was relatively small in this case, the concentration of heavy metals was still high. Therefore, we speculated that adsorption may not be the only mechanism to entrap heavy metals within the sand bed. Heavy metals and Na may react to form low-melting eutectic compounds, or alternatively, heavy metals in the feed can attach to molten Na-containing eutectic compounds formed at high temperatures.
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8
10
1 9
4 6
11 5 7 3
2
Fig. 1. The bubbling 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.
Fig. 4 illustrates the concentration distribution of heavy metals at different temperatures. Fig. 5 plots the concentration distribution of heavy metals at different gas velocities. According to these results, the heavy-metal concentrations were high for the fractions with the largest as well as smallest particle sizes. Generally, the heavy-metal concentration increased when the particle size decreased below 0.500 mm and when the particle size increased above 1.000 mm.
50
50
50
Gaussian
Flat
Narrow 40
30
20
40
Percentage (wt%)
Percentage (wt%)
40
30
20
30
20
10
0
0
0
Particle size distribution (mm)
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
10
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
10
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Percentage (wt%)
In order to determine the heavy-metal composition of the agglomerate, SEM/EDS analysis was performed. The results presented in Fig. 6 show that in addition to Na, heavy metals (Cd, Pb, and Cr) also existed in the agglomerate. This demonstrates that heavy metals get entrapped in the bed either by reacting with Na to form low-melting-point eutectics or just by attaching themselves to the molten eutectics formed. In the case of small particles, adsorption is
Particle size distribution (mm)
no-Na 0.7%Na
Fig. 2. Particle size distributions of bed material after agglomeration/defluidization with and without added Na.
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
no-Na-Pb
no-Na-Cr 40
15
10
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
20
15
10
30
20
5
10
0
0
0
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
5
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
Particle size distribution (mm)
Particle size distribution (mm) 50
25
25
0.7%Na-Cd
0.7%Na-Pb
0.7%Na-Cr 40
20
Heavy metal conc. (mg/g)
20
15
10
Heavy metal conc. (mg/g)
15
10
30
20
10
0
0
0
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
5
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
5
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Heavy metal conc. (mg/g)
20
Heavy metal conc. (mg/g)
50
25
no-Na-Cd
153
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 3. Concentration distributions of heavy metals with and without added Na to the bed material.
the chief mechanism for heavy-metal entrapment. However, in the case of large particles, entrapment of heavy metals in the bed can also occur through the formation of low-melting-point eutectics. 3.3. Heavy-metal retention rates of bed particles under different conditions Fig. 7 shows the heavy-metal retention rates for different particle sizes with and without Na addition. The retention rate for each particle size fraction was obtained by dividing the total quantity of heavy metals retained by each size fraction by the total quantity of heavy metal fed in. According to the results, in the absence of Na, agglomeration did not occur. Further, entrapment of Na by the formation of low-melting-point eutectics did not occur during the combustion.
The retention of heavy metals in bed material was entirely due to adsorption on silica sand. Naturally, large particles showed a lesser degree of adsorption than did small particles. When the artificial waste had Na, the total quantity of heavy metals retained in small particles (b0.500 mm) was similar to that retained without Na. However, the total quantity of heavy metals retained by other particle sizes (>0.500 mm) seemed to increase. In particular, the amount of heavy metals retained in the largest-particle-size fraction (>1.000 mm) increased significantly. Therefore, adsorption remained the chief mechanism of heavy metal retention for small particles. For larger particles, the formation of low-melting-point eutectics became the important mechanism for heavy-metal retention. Fig. 8 shows the ratio of the total quantity of heavy metals retained by the bed particle to the total quantity of heavy metals
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
25
10 5
20 15 10 5 0
5 0
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
800oC-Pb
800oC-Cr
20 15 10 5
Heavy metal conc. (mg/g)
900oC-Cd
5 0
900oC-Cr
20 15 10 5
40 30 20 10 0
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
900oC-Pb
0
Particle size distribution (mm)
10
50 Heavy metal conc. (mg/g)
25
10
20
Particle size distribution (mm)
25
15
30
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
40
0
0
20
10
50
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
10
20
0
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
25 800oC-Cd
15
30
Particle size distribution (mm)
Particle size distribution (mm) 25 20
40
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
15
700oC-Cr
Heavy metal conc. (mg/g)
20
0
Heavy metal conc. (mg/g)
50
700oC-Pb
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
700oC-Cd
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 4. Concentration distributions of heavy metals for different temperatures.
fed in, under various operating conditions. Of the three heavy metals studied, Cr had the highest retention rate in the bed particles, followed by Pb and Cd. Cd, Pb, and Cr have high, intermediate, and low volatility, respectively. Cr had the highest retention rate within the sand bed because of its low volatility. In a previous study, silica
sand was used as a bed material to adsorb large amounts of heavy metals during incineration; the adsorption efficiency of the three metals followed the order Cr > Pb > Cd [36]. The order of adsorption efficiency of these metals corresponded to the order of their boiling points.
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
25
15 10 5
20 15 10 5
Particle size distribution (mm)
40 30 20 10 0 0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 6 0 .6 0. 0-0 00 71 . 0. 0-0 710 8 5 .8 1. 0-1 50 0 0 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 5 0 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 0 0 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 5 0 .5 0. 0-0 00 60 . 0. 0-0 600 7 1 .7 0. 0-0 10 85 . 1. 0-1 850 0 0 .0 0- 00 1. 1 >1 80 .1 80
0
Particle size distribution (mm)
25
Particle size distribution (mm) 50
25
0.163 m/s-Pb
20 15 10 5 0
0.163 m/s-Cr
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
0.163 m/s-Cd 20 15 10 5
30 20 10
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
40
0
0
Particle size distribution (mm)
Particle size distribution (mm)
25
25
0.187 m/s-Cd
60 0.187 m/s-Cr
20 15 10 5 0
20 15 10 5
Particle size distribution (mm)
40 30 20 10 0
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
0
50
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Heavy metal conc. (mg/g)
0.187 m/s-Pb
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
0.137 m/s-Cr
Heavy metal conc. (mg/g)
20
0
Heavy metal conc. (mg/g)
50 0.137 m/s-Pb
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
0.137 m/s-Cd
155
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 5. Concentration distributions of heavy metals for different gas velocities.
Figs. 3–5 point out that the largest particles had higher heavymetal concentrations. However, the proportion of the larger particles was lower than that of other particles, and hence, the total heavymetal retention rate within the larger particles was also lower than that in the case of other particles (b 1.000 mm). The heavy-metal
retention rate for particles with intermediate sizes was lower than those of the smaller- and larger-sized particles. However, the particles with intermediate sizes were greater in quantity, and consequently, their total heavy-metal retention rate was the highest.
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
Element Atomic (%) Element Atomic (%) Na
28.93
Cd
1.56
Si
57.56
Pb
4.34
Cr
7.60
Fig. 6. FE-SEM/EDS results for agglomerate.
Comparison of the heavy-metal retention rates at different temperatures revealed that the retention rate of Cd within the bed material gradually decreased as the temperature increased from 800 °C to 900 °C. On the other hand, since Pb and Cr have lower volatility, they do not vaporize easily at the operating temperature. Although the heavy-metal retention rate seemingly decreased at 900 °C, the reason is that the low-melting-point eutectic was easily generated to form agglomeration/defluidization as operating temperature increased. The defluidization time decreased at 900 °C and in turn decreased the total incoming heavy-metal content to decrease the heavy-metal retention rate. Additionally, comparison of the heavy-metal retention rates at different gas velocities indicated that the retention rate at 0.137 m/s was lower than that at 0.163 m/s. Because of the poor mixing of bed materials at low gas velocities, the probability of contact between the bed material and the waste material decreased. When the gas velocity was increased to 0.187 m/s, the heavy-metal retention rate decreased. This was probably because small particles with higher heavy-metal concentrations are easily passed into the flue gas stream at a high gas velocity, and hence, the heavy-metal retention rate of the bed material decreased. 3.4. Heavy-metal distribution in different bed-particle-size distributions Figs. 3–5 show the heavy-metal concentrations for the three particle-size distributions used: narrow, flat, and Gaussian. Generally, the average concentrations in the large and small particles with the narrow size distribution were higher than those in the particles with flat and Gaussian distributions. Under most conditions, the heavy-metal retention rates for the narrow and Gaussian particle-size distributions
were higher than that for the flat distribution. According to a previous study [21], particles with a flat size distribution tended to disperse, and those with a Gaussian distribution tended to mix well. This was because in a Gaussian distribution, the proportion of average-sized particles was greater than that of the extreme-sized particles, and hence, the influence of the extreme-sized particles became less significant. Therefore, the behavior of the particles with a Gaussian distribution was similar to that of the particles with a narrow distribution, and the particles exhibited better mixing efficiency. The particles with narrow and Gaussian distributions showed less uneven mixing and did not easily form agglomerates. Because the mixing of particles with narrow and Gaussian distributions is better than that of particles with a flat distribution, bed materials with the former type of particles show increased probability of contacting the waste particles during combustion, and this increased contact results in increased adsorption rates of heavy metals. However, the differences among the adsorption rates for the particles with the three types of size distributions were not significant in some experiments, probably because the proportion of small particles with the flat distribution was higher than that with narrow and Gaussian distributions. Since small particles have a larger surface area for the adsorption of heavy metals, the higher proportion of smaller-sized particles in the flat distribution greatly enhanced the rate of heavy-metal adsorption. To sum up, the probability of contact between the bed material and the waste particles is lower in the case of particles with a flat distribution, owing to the poor mixing; however, the heavy-metal adsorption rate is on par with that in the case of the particles with narrow and Gaussian distributions, because of the higher proportion of smaller-sized particles.
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
20
15
10
Percentage (wt%)
Percentage (wt%)
20
15
10
15
10
5
5
0
0
0
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
Particle size distribution (mm)
25
0.7%Na-Cd
Particle size distribution (mm)
25
20
20
Percentage (wt%)
20
15
10
15
10
15
10
0
0
0 0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. >1 180 .1 80
5
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
5
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
5
Particle size distribution (mm)
Particle size distribution (mm) 0.7%Na-Cr
0.7%Na-Pb
Percentage (wt%)
25
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. >1 180 .1 80
5
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
Percentage (wt%)
no-Na-Cr
no-Na-Pb
20
Percentage (wt%)
25
25 no-Na-Cd
157
Particle size distribution (mm)
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 7. Heavy metal retention rates with and without added Na.
4. Conclusions In this research, artificial waste was used to simulate agglomeration/ defluidization in a fluidized bed and examine the distribution of heavy metals in bottom ash with different bed-material size distributions (narrow, flat, and Gaussian). In the absence of Na in the artificial waste, the bed-material particle size gradually decreased owing to attrition and thermal shock developed during incineration. However, addition of Na led to an increase in the proportion of the larger particles through agglomeration induced by the formation of low-melting-point eutectics. For different operating parameters, the heavy-metal concentration for most particles increased with Na addition. The largest- and smallest-particle-size fractions had the highest heavy-metal concentrations. Generally, the heavy-metal concentrations were highest
when the particle size of bottom ash was smaller than 0.500 mm or larger than 1.000 mm. The low-melting-point eutectic compound produced by the combination of heavy metals with Na or with the liquid eutectic material was likely a more important mechanism of heavy metal retention. The heavy metal retention rates in bottom ash with different particle-size distributions showed that Cr had the highest retention rate within bed particles, followed by Pb and Cd. The degree of mixing of the particles with a narrow or Gaussian distribution was higher than that of the particles with a flat distribution. Comparison of the heavy-metal concentrations and retention rates for different particle-size distributions revealed that under most of the operating conditions adopted, the heavy-metal concentrations and total retention rates in the largest- and smallest-particle-size fractions were higher in the case of narrow and Gaussian distributions than in the case of the flat distribution.
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
100
(A) Cd
60 40 20
80
60 40
20
Na
0 no-Na
Additive
Na
no-Na
Additive
100
(B) Cd
100
(B) Pb
40 20
60 40 20
0 800
900
800
900
700
o
Temperature ( C)
100
(C) Cd
40 20
60 40 20
0
(C) Cr
80 Percentage (wt%)
80 Percentage (wt%)
80
900
Temperature ( C)
100
(C) Pb
800
o
Temperature ( C)
100
60
40
0 700
o
60
20
0 700
(B) Cr
80 Percentage (wt%)
60
Na
Additive
80 Percentage (wt%)
80 Percentage (wt%)
40
0 no-Na
Percentage (wt%)
60
20
0
100
(A) Cr
80 Percentage (wt%)
Percentage (wt%)
80
100
(A) Pb Percentage (wt%)
100
60 40 20
0
0
0.137 0.163 0.187
0.137 0.163 0.187
0.137 0.163 0.187
Gas velocity (m/s)
Gas velocity (m/s)
Gas velocity (m/s)
Narrow Flat Gaussian Fig. 8. Heavy metal content ratio (bed/incoming) under various operating conditions (A) with and without added Na, (B) temperatures, (C) gas velocities.
Acknowledgments The authors thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract NSC 100-2628-E-390-001-MY3.
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Contents lists available at SciVerse ScienceDirect
Fuel Processing Technology journal homepage: www.elsevier.com/locate/fuproc
Effects of fluidized parameters on capture of heavy metal in various bed-material size distributions Chiou-Liang Lin ⁎ Department of Civil and Environmental Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan
a r t i c l e
i n f o
Article history: Received 6 April 2012 Received in revised form 3 July 2012 Accepted 16 July 2012 Available online 18 August 2012 Keywords: Defluidization Combustion Fluidized bed Heavy metal Bottom ash
a b s t r a c t Artificial waste was used to simulate agglomeration/defluidization in a fluidized bed and to examine the heavy-metal distribution in bottom ash for various bed-material particle-size distributions (narrow, flat, and Gaussian). Heavy-metal concentration in most particles increased with Na addition, and the largest (>1.000 mm)- and smallest (b0.500 mm)-particle-size fractions had the highest metal concentrations. Additionally, Cr had the highest retention rate within the bed particles, followed by Pb and Cd. Comparison of the heavy-metal concentrations and retention rates for different particle-size distributions showed that the total retention rates were higher for narrow and Gaussian distributions than for the flat distribution. © 2012 Elsevier B.V. All rights reserved.
1. Introduction A fluidized bed has many advantages and is widely used for urban residue treatment, biofuel and coal combustion, gasification, pyrolysis, and other processes [1–4]. However, during the operation of fluidized bed reactors, the presence of certain elements such as alkali metals causes the bed material to agglomerate into large blocks. These agglomerated materials affect the operating conditions of the fluidized bed, such as the minimum fluidization velocity, bubble size, bubble frequency, and rise velocity [5], and in some cases, may lead to shutting down of the fluidized bed (defluidization) [6,7]. Therefore, agglomeration has been identified as one of the chief issues in a fluidized bed reactor. Previous studies show that many types of elements can cause agglomeration; the corresponding results are summarized in Table 1 [5,8–15]. In those studies, alkali group elements such as Na and K were among the major elements that caused agglomeration. Knight et al. [16] postulated that the mechanism of agglomeration depends on two factors: (1) whether the particle substances flow to generate bridges between particles, and (2) the quantity of liquidphase materials generated during the reaction process. Agglomerates form when the cohesive forces in the system exceed the separation forces, and the probability of agglomeration increases with an increase in the operating temperature and operating time. Liquidphase materials can act as bridges between particles and hence lead to agglomeration/defluidization. Lin and Wey [7] pointed out that Na contained in the urban waste can react with the bed material or ⁎ Tel.: +886 7 5919722; fax: +886 7 5919376. E-mail address: [email protected]. 0378-3820/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.fuproc.2012.07.015
other elements in the fluidization chamber to produce liquid eutectic materials. These liquid eutectics are formed on the surfaces of the bed materials, leading to an increase in cohesive forces and thereby inducing agglomeration/defluidization. The operating parameters for the fluidized bed are important factors causing agglomeration/defluidization. Moseley and O'Brien [17] and Wank et al. [18] found that the occurrence of agglomeration and defluidization is influenced by attributes of the bed such as bed surface area, operating temperature, and gas velocity, as well as the attributes of bed particles such as density and size distribution. Of these factors, particle-size distribution (PSD) is the most important because it affects the quality of fluidization and the conversion efficiency of the reaction. Ray et al. [19] and Pell et al. [20] showed that particle-size distribution influences the minimum fluidization velocity, terminal velocity, elutriation velocity, chemical reaction rate, and hydrodynamic characteristics of fluidization. Gauthier et al. [21] showed that a narrow particle size range can increase the operational stability of a fluidized bed (for instance, by preventing bed material separation), while a broad particle size range can increase the bed material mobility and chemical conversion rates. Waste and coal often include some trace elements such as heavy metals, which vaporize or leach out into the environment during combustion and pose a serious risk to human health [4,22]. Therefore, it is essential to control heavy-metal emission during combustion processes. These heavy metals may vaporize because of the high combustion temperatures and pass into the environment along with the exhaust gas or fly ash. Alternatively, they may get trapped in the bottom ash. The mode of transport for different heavy metals depends on the corresponding heavy-metal compounds present and their respective boiling points. Generally, heavy-metal compounds with low
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
Table 1 Elements that possibly induce agglomeration in fluidized beds. Na Tardos and Pfeffer [5] Conn [8] Mikami et al. [9] Steenari et al. [10] Wang et al. [11] Arvelakis et al. [12] Lin et al. [13] Atakül et al. [14] Lin et al. [15]
● ● ● ● ●
K
Mg
Ca
●
● ●
● ● ● ●
●
●
Si
S
Cl
● ●
V
Ni
● ● ●
●
●
Fe
● ●
● ● ●
●
●
●
●
boiling points are mainly distributed in the fly ash or flue gas, while heavy-metal compounds with high boiling points are mainly found in the bottom ash or the larger fly ash particles [23,24]. In addition to the properties of the heavy metals, major factors affecting the distribution of heavy metals in the combustion waste are combustion conditions such as temperature, feeding load, waste properties, and chlorine species present [25–27]. The fluidization behavior of the reactor is influenced by the particle-size distribution of the bed material. If the bed material is not well mixed, particle movement is restricted, and thus, bridges are easily formed between particles. The agglomerate thus formed affects the fluidized-bed operating parameters and indirectly affects the distribution of heavy-metal pollutants in the ash and flue gas particles. However, the effects of particle size of the bed material on the agglomeration/defluidization and heavy-metal distribution during fluidization have rarely been examined in previous studies. Lin et al. [28] had discussed the emission trends of three heavy metals (Pb, Cr, and Cd), which were added to the bed material, during agglomeration/defluidization. When the sand bed defluidized, the amount of heavy metals emitted into the flue gas or fly ash increased significantly. This result showed that the fluidized bed conditions do affect the distribution of heavy metals. However, the effect of fluidized bed conditions on the distribution of heavy metals in bottom ash was not discussed in these studies. Additionally, silica sand usually functions not only as a bed material but also as an adsorbent of heavy metals during the combustion. The formation of a eutectic during agglomeration also affects the distribution of heavy metals, but few studies currently address the effect of agglomeration/defluidization on the distribution of heavy metals in the bottom ash. Therefore, in the present study, artificial waste was used to simulate agglomeration/defluidization, and the distribution of heavy metals in bottom ash was examined during the process. Different bed-material particle-size distributions (narrow, flat, and Gaussian) were considered in this study. In order to understand the effects of bed-material particle-size distribution on the deposition of heavy metals in bottom ash during agglomeration/defluidization, the experimental parameters, including the bed-material particlesize distribution, operating gas velocity, operating temperature, and Na addition, were controlled. The results of this study can be employed as reference to decide the optimum operating conditions for fluidized bed combustors. 2. Materials and methods In order to reduce the differences in the composition of the feeding waste, artificial waste was prepared in this study. Real waste could not used because its composition is very complex and bound to vary. Since most waste contains alkali metals (especially Na), which may cause agglomeration/defluidization during the combustion process, Na was added to the artificial waste to form lowmelting-point eutectic compounds during the experiments. The artificial waste contained sawdust, polypropylene, polyethylene and metal solution. Sawdust was used in place of the cellulose found in
urban residues, and polypropylene and polyethylene were used in place of the plastics. Elemental analysis of sawdust, polypropylene and polyethylene used were performed using Elemental Analyzer (EA), and these results are listed in Table 2. Concentration of sodium in sawdust was found to be 0.0023%. The concentration of Na in sawdust was reported to be very low in earlier studies as well [29,30]; therefore, the effect of the concentration of alkali metal inherent in sawdust was neglected. Three metals (Cd, Pb, and Cr, characterized by high, medium, and low volatility, respectively) were selected to simulate the presence of heavy metals in waste. For the metal additives in this study, metal nitrates (NaNO3, Pb(NO3)2, Cr(NO3)3, and Cd(NO3)2) were dissolved in deionized water and added to the artificial waste. The artificial waste was prepared using 1.6 g sawdust, 0.35 g polypropylene, and 1 mL metal nitrate solution, all enclosed in a polyethylene (PE) bag weighing 0.29 g. A standard bag of the artificial waste weighed 3.24 g, which was used as the standard mass in the calculations of heavy-metal distribution. The concentration of heavy metals (Pb, Cd, and Cr) was maintained at 0.7% in each case. Table 3 lists the operating conditions used in different experiments. Silica sand comprising SiO2 (97.80%), Al2O3 (2.01%), and Fe2O3 (0.07%) and having a density of 2.6 g/cm 3 was used as the bed material in the experiments. The natural concentration of the heavy metals in silica sand was analyzed. According to the analysis result, Cd and Cr were absent in the sand, while the Pb concentration was 0.024 mg/g (i.e., 0.0024%). The sand bed was 18 cm in height and 9 cm in diameter (H/D = 2). The silica sand was sorted by standard ASTM sieves, and segregated into three set of fractions with three different PSDs, namely, narrow, flat, and Gaussian. The preparation method of Gauthier et al. [21] was used. The dsv values for all three PSDs were maintained at about 0.725 mm using the following equation: dsv ¼
1 ∑ i
xi dpi
where xi is the weight ratio (%) and dpi is the average diameter (mm). Table 4 lists the particle size and weight proportion for each type of PSD. Before the experiments, the minimum fluidization velocity was measured using the method proposed by Lin et al. [31]. The gas velocities were maintained at 0.137, 0.163, or 0.187 m/s during the combustion process, and the operating temperature was maintained at 800 °C. The experimental parameters monitored were bed-material particle-size distribution, operating gas velocity, operating temperature, and Na addition. Table 3 lists the operating conditions used for these experiments. A bubbling fluidized-bed incinerator system was employed, as shown in Fig. 1. The reactor was made of stainless steel (AISI 310) sheets of 3 mm thickness, and it had a main chamber measuring 120 cm in height and 10 cm in inner diameter. A stainless steel porous plate with an open area of 15.2% was fixed at the bottom of the system to separate the gas flow. The reactor was enclosed by electrically resistant material and connected to two thermocouples with a programmable logic controller (PID controller) to regulate the experiment temperature. In order to control the emission of pollutants, a cyclone separator fitted with a bag filter was used. The experimental procedure followed is as follows. First, the sand bed was heated to the desired temperature and then cooled by cool Table 2 Elemental analysis of different compositions by weight.
Sawdust Polyethylene (PE) Polypropylene (PP)
N (%)
C (%)
O (%)
H (%)
5.01 0.86 1.12
43.12 85.71 86.16
46.07 0.39 0.52
5.80 13.04 12.20
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
151
Table 3 Operating conditions for each experiment. Run
T (°C)
Gas velocity (m/s)
Na (wt.%)
Narrow
1 2 3 4 5 6 7 8 9
800 800 800 700 700 700 800 800 800
0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163
0.7 0.7 0.7 0.7 0.7 0.7
●
Flat
Gaussian
● ● ● ● ● ● ● ●
air from a blower adjusted using a float-type flow meter. Then, the artificial waste was fed into the chamber at a rate of one bag (3.24 g) per 20 s. Both pressure-versus-time profiling and manual observations were employed to evaluate the defluidization time during the combustion. Two pressure probes were used to measure the pressure difference between the sand bed and the freeboard. These probes were connected to a differential pressure transmitter, and the range of measurement was 0–1000 mm H2O. When defluidization occurred, feeding of artificial waste was stopped. When the sand bed cooled to room temperature, the silica sand was collected and then sorted by standard ASTM sieves into eight fractions on the basis of particle size (>1.180 mm, 1.180–1.000 mm, 1.000–0.850 mm, 0.850–0.710 mm, 0. 710–0.600 mm, 0.600–0.500 mm, 0.500–0.355 mm, and b0.355 mm) to evaluate the changes in particle size after agglomeration/defluidization. Each fraction was first weighed and then sampled in order to analyze the concentrations of heavy metals. The solid sample was treated by microwave digestion before analysis by inductively coupled plasma mass spectrometry (ICP-MS) for heavy-metal concentrations. The agglomerates were also analyzed by field-emission scanning electron microscopy/ energy-dispersive spectrometry (FESEM/EDS) to observe the particle surfaces and identify the species of agglomerates. Additionally, the Brunauer, Emmett, and Teller (BET) method was used to determine surface area and pore size of silica sand.
3. Results and discussion 3.1. Changes defluidization
in
particle-size
distribution
with
agglomeration/
Fig. 2 shows the particle-size distributions of the bed material, with and without Na addition, after agglomeration/defluidization. According to Fig. 2, the distribution was different from the original distribution. The range of bed-material particle sizes widened,
Table 4 Particle size distributions of different silica sand bed materials. Type
Weight (%) Xi
Size range (mm)
Average diameter di (mm)
Narrow Flat
100 17 17 19 23 24 8 25 35 23 9
0.600–0.850 0.355–0.500 0.500–0.710 0.710–0.850 0.850–1.000 1.000–1.180 0.355–0.500 0.500–0.710 0.710–0.850 0.850–1.000 1.000–1.180
0.725 0.428 0.605 0.780 0.925 1.090 0.428 0.605 0.780 0.925 1.090 dsv = 0.725
Gaussian
The dsv calculation formula is: dsv ¼
xi : dpi
1
∑ i
Run
T (°C)
Gas velocity (m/s)
Na (wt.%)
Narrow
10 11 12 13 14 15 16 17 18
900 900 900 800 800 800 800 800 800
0.163 0.163 0.163 0.137 0.137 0.137 0.187 0.187 0.187
0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7
●
Flat
Gaussian
● ● ● ● ● ● ● ●
especially in the case of the narrow distribution. In the absence of Na, the number of particles with sizes smaller than 0.500 mm increased considerably. These results suggested that the sand particle size decreased during fluidized operation, because of collisions between the particles as well as the attrition with the bed material and the chamber wall. Moreover, the combustion reaction resulted in a high-temperature environment inside the chamber, and hence, the decrease in particle size could be attributed to the thermal stress inside the particles [32,33]. In result, the bed-particle size tended to decrease during thermal fluidization. Addition of Na to the artificial waste led to complex variations in the particle size due to the generation of agglomerates during combustion. Fig. 2 reveals that besides the small particles, some of the large particles (> 1.000 mm) increased in size. In general, the proportion of large particles increased with the addition of Na. These results indicated that during agglomeration, Na probably reacts with the silica sand or its impurities to generate low-melting-point eutectics that melt at high temperatures. These viscous molten eutectics come into contact with other particles by collision, and if the cohesive forces are larger than the separation force, these particles agglomerate, thereby increasing the bed-material particle size [34]. In our previous study [35], we analyzed the agglomerate by thermogravimetric analysis and differential scanning calorimetry (TGA/DTA) and estimated two melting points, 575 °C and 782 °C. Since the melting point of SiO2 is 1650 °C, the two temperatures may be the melting points of the eutectics formed by Na and Si in the agglomerate. 3.2. Distribution of heavy-metal concentration with particle size Fig. 3 shows the distribution of heavy-metal concentration against particle size with and without Na addition. According to a previous study [36], the heavy metal is adsorbed on silica sand because of the high surface area of the latter. Therefore, in the absence of Na, the heavy metal within the bed material is entrapped by adsorption on silica sand. However, when Na was added, the heavy-metal concentration in the bottom ash increased significantly. Hence, the molten eutectics increased the amount of heavy metals in the sand bed, especially in a bed consisting of coarse particles (> 1.000 mm). Generally, smaller particles were generated by the attrition of coarse particles during fluidization, and these particles had a large surface area that allowed for easy adsorption of heavy metals. Larger particles were formed by the agglomeration of bed particles caused by lowmelting-point eutectic compounds, and these particles had a small surface area. The surface areas of small (b 0.355 mm) and coarse (>1.180 mm) silica sands were found to be 3.98 m 2/g and 3.05 m 2/g, respectively, by BET surface area analysis. Although the surface area of a large particle was relatively small in this case, the concentration of heavy metals was still high. Therefore, we speculated that adsorption may not be the only mechanism to entrap heavy metals within the sand bed. Heavy metals and Na may react to form low-melting eutectic compounds, or alternatively, heavy metals in the feed can attach to molten Na-containing eutectic compounds formed at high temperatures.
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
8
10
1 9
4 6
11 5 7 3
2
Fig. 1. The bubbling 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.
Fig. 4 illustrates the concentration distribution of heavy metals at different temperatures. Fig. 5 plots the concentration distribution of heavy metals at different gas velocities. According to these results, the heavy-metal concentrations were high for the fractions with the largest as well as smallest particle sizes. Generally, the heavy-metal concentration increased when the particle size decreased below 0.500 mm and when the particle size increased above 1.000 mm.
50
50
50
Gaussian
Flat
Narrow 40
30
20
40
Percentage (wt%)
Percentage (wt%)
40
30
20
30
20
10
0
0
0
Particle size distribution (mm)
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
10
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
10
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Percentage (wt%)
In order to determine the heavy-metal composition of the agglomerate, SEM/EDS analysis was performed. The results presented in Fig. 6 show that in addition to Na, heavy metals (Cd, Pb, and Cr) also existed in the agglomerate. This demonstrates that heavy metals get entrapped in the bed either by reacting with Na to form low-melting-point eutectics or just by attaching themselves to the molten eutectics formed. In the case of small particles, adsorption is
Particle size distribution (mm)
no-Na 0.7%Na
Fig. 2. Particle size distributions of bed material after agglomeration/defluidization with and without added Na.
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
no-Na-Pb
no-Na-Cr 40
15
10
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
20
15
10
30
20
5
10
0
0
0
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
5
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
Particle size distribution (mm)
Particle size distribution (mm) 50
25
25
0.7%Na-Cd
0.7%Na-Pb
0.7%Na-Cr 40
20
Heavy metal conc. (mg/g)
20
15
10
Heavy metal conc. (mg/g)
15
10
30
20
10
0
0
0
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
5
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
5
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Heavy metal conc. (mg/g)
20
Heavy metal conc. (mg/g)
50
25
no-Na-Cd
153
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 3. Concentration distributions of heavy metals with and without added Na to the bed material.
the chief mechanism for heavy-metal entrapment. However, in the case of large particles, entrapment of heavy metals in the bed can also occur through the formation of low-melting-point eutectics. 3.3. Heavy-metal retention rates of bed particles under different conditions Fig. 7 shows the heavy-metal retention rates for different particle sizes with and without Na addition. The retention rate for each particle size fraction was obtained by dividing the total quantity of heavy metals retained by each size fraction by the total quantity of heavy metal fed in. According to the results, in the absence of Na, agglomeration did not occur. Further, entrapment of Na by the formation of low-melting-point eutectics did not occur during the combustion.
The retention of heavy metals in bed material was entirely due to adsorption on silica sand. Naturally, large particles showed a lesser degree of adsorption than did small particles. When the artificial waste had Na, the total quantity of heavy metals retained in small particles (b0.500 mm) was similar to that retained without Na. However, the total quantity of heavy metals retained by other particle sizes (>0.500 mm) seemed to increase. In particular, the amount of heavy metals retained in the largest-particle-size fraction (>1.000 mm) increased significantly. Therefore, adsorption remained the chief mechanism of heavy metal retention for small particles. For larger particles, the formation of low-melting-point eutectics became the important mechanism for heavy-metal retention. Fig. 8 shows the ratio of the total quantity of heavy metals retained by the bed particle to the total quantity of heavy metals
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
25
10 5
20 15 10 5 0
5 0
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
800oC-Pb
800oC-Cr
20 15 10 5
Heavy metal conc. (mg/g)
900oC-Cd
5 0
900oC-Cr
20 15 10 5
40 30 20 10 0
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
900oC-Pb
0
Particle size distribution (mm)
10
50 Heavy metal conc. (mg/g)
25
10
20
Particle size distribution (mm)
25
15
30
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
40
0
0
20
10
50
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
10
20
0
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
25 800oC-Cd
15
30
Particle size distribution (mm)
Particle size distribution (mm) 25 20
40
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
15
700oC-Cr
Heavy metal conc. (mg/g)
20
0
Heavy metal conc. (mg/g)
50
700oC-Pb
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
700oC-Cd
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 4. Concentration distributions of heavy metals for different temperatures.
fed in, under various operating conditions. Of the three heavy metals studied, Cr had the highest retention rate in the bed particles, followed by Pb and Cd. Cd, Pb, and Cr have high, intermediate, and low volatility, respectively. Cr had the highest retention rate within the sand bed because of its low volatility. In a previous study, silica
sand was used as a bed material to adsorb large amounts of heavy metals during incineration; the adsorption efficiency of the three metals followed the order Cr > Pb > Cd [36]. The order of adsorption efficiency of these metals corresponded to the order of their boiling points.
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
25
15 10 5
20 15 10 5
Particle size distribution (mm)
40 30 20 10 0 0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 6 0 .6 0. 0-0 00 71 . 0. 0-0 710 8 5 .8 1. 0-1 50 0 0 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 5 0 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 0 0 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 5 0 .5 0. 0-0 00 60 . 0. 0-0 600 7 1 .7 0. 0-0 10 85 . 1. 0-1 850 0 0 .0 0- 00 1. 1 >1 80 .1 80
0
Particle size distribution (mm)
25
Particle size distribution (mm) 50
25
0.163 m/s-Pb
20 15 10 5 0
0.163 m/s-Cr
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
0.163 m/s-Cd 20 15 10 5
30 20 10
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Particle size distribution (mm)
40
0
0
Particle size distribution (mm)
Particle size distribution (mm)
25
25
0.187 m/s-Cd
60 0.187 m/s-Cr
20 15 10 5 0
20 15 10 5
Particle size distribution (mm)
40 30 20 10 0
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. 1 >1 80 .1 80
0. <0 35 . 0. 5-0 355 50 .5 0. 0-0 00 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
0
50
Particle size distribution (mm)
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. 1 >1 80 .1 80
Heavy metal conc. (mg/g)
0.187 m/s-Pb
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
0.137 m/s-Cr
Heavy metal conc. (mg/g)
20
0
Heavy metal conc. (mg/g)
50 0.137 m/s-Pb
Heavy metal conc. (mg/g)
Heavy metal conc. (mg/g)
0.137 m/s-Cd
155
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 5. Concentration distributions of heavy metals for different gas velocities.
Figs. 3–5 point out that the largest particles had higher heavymetal concentrations. However, the proportion of the larger particles was lower than that of other particles, and hence, the total heavymetal retention rate within the larger particles was also lower than that in the case of other particles (b 1.000 mm). The heavy-metal
retention rate for particles with intermediate sizes was lower than those of the smaller- and larger-sized particles. However, the particles with intermediate sizes were greater in quantity, and consequently, their total heavy-metal retention rate was the highest.
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C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
Element Atomic (%) Element Atomic (%) Na
28.93
Cd
1.56
Si
57.56
Pb
4.34
Cr
7.60
Fig. 6. FE-SEM/EDS results for agglomerate.
Comparison of the heavy-metal retention rates at different temperatures revealed that the retention rate of Cd within the bed material gradually decreased as the temperature increased from 800 °C to 900 °C. On the other hand, since Pb and Cr have lower volatility, they do not vaporize easily at the operating temperature. Although the heavy-metal retention rate seemingly decreased at 900 °C, the reason is that the low-melting-point eutectic was easily generated to form agglomeration/defluidization as operating temperature increased. The defluidization time decreased at 900 °C and in turn decreased the total incoming heavy-metal content to decrease the heavy-metal retention rate. Additionally, comparison of the heavy-metal retention rates at different gas velocities indicated that the retention rate at 0.137 m/s was lower than that at 0.163 m/s. Because of the poor mixing of bed materials at low gas velocities, the probability of contact between the bed material and the waste material decreased. When the gas velocity was increased to 0.187 m/s, the heavy-metal retention rate decreased. This was probably because small particles with higher heavy-metal concentrations are easily passed into the flue gas stream at a high gas velocity, and hence, the heavy-metal retention rate of the bed material decreased. 3.4. Heavy-metal distribution in different bed-particle-size distributions Figs. 3–5 show the heavy-metal concentrations for the three particle-size distributions used: narrow, flat, and Gaussian. Generally, the average concentrations in the large and small particles with the narrow size distribution were higher than those in the particles with flat and Gaussian distributions. Under most conditions, the heavy-metal retention rates for the narrow and Gaussian particle-size distributions
were higher than that for the flat distribution. According to a previous study [21], particles with a flat size distribution tended to disperse, and those with a Gaussian distribution tended to mix well. This was because in a Gaussian distribution, the proportion of average-sized particles was greater than that of the extreme-sized particles, and hence, the influence of the extreme-sized particles became less significant. Therefore, the behavior of the particles with a Gaussian distribution was similar to that of the particles with a narrow distribution, and the particles exhibited better mixing efficiency. The particles with narrow and Gaussian distributions showed less uneven mixing and did not easily form agglomerates. Because the mixing of particles with narrow and Gaussian distributions is better than that of particles with a flat distribution, bed materials with the former type of particles show increased probability of contacting the waste particles during combustion, and this increased contact results in increased adsorption rates of heavy metals. However, the differences among the adsorption rates for the particles with the three types of size distributions were not significant in some experiments, probably because the proportion of small particles with the flat distribution was higher than that with narrow and Gaussian distributions. Since small particles have a larger surface area for the adsorption of heavy metals, the higher proportion of smaller-sized particles in the flat distribution greatly enhanced the rate of heavy-metal adsorption. To sum up, the probability of contact between the bed material and the waste particles is lower in the case of particles with a flat distribution, owing to the poor mixing; however, the heavy-metal adsorption rate is on par with that in the case of the particles with narrow and Gaussian distributions, because of the higher proportion of smaller-sized particles.
C.-L. Lin / Fuel Processing Technology 106 (2013) 149–159
25
20
15
10
Percentage (wt%)
Percentage (wt%)
20
15
10
15
10
5
5
0
0
0
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
Particle size distribution (mm)
25
0.7%Na-Cd
Particle size distribution (mm)
25
20
20
Percentage (wt%)
20
15
10
15
10
15
10
0
0
0 0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. >1 180 .1 80
5
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 .6 0. 0-0 00 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
5
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 .7 0. 0-0 10 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
5
Particle size distribution (mm)
Particle size distribution (mm) 0.7%Na-Cr
0.7%Na-Pb
Percentage (wt%)
25
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 .8 1. 0-1 50 00 .0 0- 00 1. >1 180 .1 80
5
0. <0 35 . 0. 5-0 355 50 . 0. 0-0 500 60 . 0. 0-0 600 71 . 0. 0-0 710 85 . 1. 0-1 850 00 .0 0- 00 1. >1 180 .1 80
Percentage (wt%)
no-Na-Cr
no-Na-Pb
20
Percentage (wt%)
25
25 no-Na-Cd
157
Particle size distribution (mm)
Particle size distribution (mm)
Narrow Flat Gaussian Fig. 7. Heavy metal retention rates with and without added Na.
4. Conclusions In this research, artificial waste was used to simulate agglomeration/ defluidization in a fluidized bed and examine the distribution of heavy metals in bottom ash with different bed-material size distributions (narrow, flat, and Gaussian). In the absence of Na in the artificial waste, the bed-material particle size gradually decreased owing to attrition and thermal shock developed during incineration. However, addition of Na led to an increase in the proportion of the larger particles through agglomeration induced by the formation of low-melting-point eutectics. For different operating parameters, the heavy-metal concentration for most particles increased with Na addition. The largest- and smallest-particle-size fractions had the highest heavy-metal concentrations. Generally, the heavy-metal concentrations were highest
when the particle size of bottom ash was smaller than 0.500 mm or larger than 1.000 mm. The low-melting-point eutectic compound produced by the combination of heavy metals with Na or with the liquid eutectic material was likely a more important mechanism of heavy metal retention. The heavy metal retention rates in bottom ash with different particle-size distributions showed that Cr had the highest retention rate within bed particles, followed by Pb and Cd. The degree of mixing of the particles with a narrow or Gaussian distribution was higher than that of the particles with a flat distribution. Comparison of the heavy-metal concentrations and retention rates for different particle-size distributions revealed that under most of the operating conditions adopted, the heavy-metal concentrations and total retention rates in the largest- and smallest-particle-size fractions were higher in the case of narrow and Gaussian distributions than in the case of the flat distribution.
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100
(A) Cd
60 40 20
80
60 40
20
Na
0 no-Na
Additive
Na
no-Na
Additive
100
(B) Cd
100
(B) Pb
40 20
60 40 20
0 800
900
800
900
700
o
Temperature ( C)
100
(C) Cd
40 20
60 40 20
0
(C) Cr
80 Percentage (wt%)
80 Percentage (wt%)
80
900
Temperature ( C)
100
(C) Pb
800
o
Temperature ( C)
100
60
40
0 700
o
60
20
0 700
(B) Cr
80 Percentage (wt%)
60
Na
Additive
80 Percentage (wt%)
80 Percentage (wt%)
40
0 no-Na
Percentage (wt%)
60
20
0
100
(A) Cr
80 Percentage (wt%)
Percentage (wt%)
80
100
(A) Pb Percentage (wt%)
100
60 40 20
0
0
0.137 0.163 0.187
0.137 0.163 0.187
0.137 0.163 0.187
Gas velocity (m/s)
Gas velocity (m/s)
Gas velocity (m/s)
Narrow Flat Gaussian Fig. 8. Heavy metal content ratio (bed/incoming) under various operating conditions (A) with and without added Na, (B) temperatures, (C) gas velocities.
Acknowledgments The authors thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract NSC 100-2628-E-390-001-MY3.
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