2296
Ind. Eng. Chem. Res. 1992,31, 2296-2303
Ross, I. B., Davidson, J. F. The Combustion of Carbon Particles in a Fluidised Bed. Trans. Inst. Chem. Eng. 1981,59, 108. Ross, I. B.; Patel, M. S.; Davidmn, J. F. The Temperature of Burning Carbon Particles in Fluidised Beds. Trans. Znst. Chem. Eng. 1981, 59, 83. Rowe, P. N. Prediction of Bubble Size in a Gas Fluidized Bed. Chem. Eng. Sci. 1976,31, 285. Sit, S. P.; Grace, J. R. Effect of Bubble Interaction on Interphase Mass Transfer in Gas Fluidized Beds. Chem. Eng. Sci. 1981,36, 325. Souza-Santos, M. L. Comprehensive Modelling and Simulation of Fluidized Bed Boilers and Gasifiers. Fuel 1989, 68 (12), 1507. Thonglimp, V.; Hiquily, N.; Laguerie, C. Vitesse Minimale de Fluidimtion et Expansion des Couches Fluidisge par un Gaz. Powder Technol. 1984,38, 233.
Tojo, K.; Chang, C. C.; Fan, L. T. Modeling of Dynamic and Steady-State Shallow Fluidized Bed Coal Combustors. Effecta of Feeder Distribution. Znd. Erg. Chem. Process Des. Deu. 1981,20, 411.
Tung, S. E.; Hodges, J.; Louis, J. F. Application of an Interim Fluidized Bed Combustor System Model and an Interim F l u i W Bed Combustor Data Base Management System to Plant Design. AZChE Symp. Ser. 1981,205 (77),127. Turnbull, E.; Davidson, J. F. Fluidized Combustion of Char and Volatiles from Coal. AIChE J. 1984,30 (6),881. Westby, T. S.; Dangtran, K., Edgar, T. F. Fluidized-Bed Combustion of Texas Lignite. Fuel 1990,69 (5),590. Received for review February 18, 1992 Accepted June 26, 1992
Modeling of Lignite Combustion in Atmospheric Fluidized Bed Combustors. 2. Model Validation and Simulation J u a n Adiinez,* J u a n
C.Abiinades, Luis F. de Diego, a n d Francisco Garcia-Labiano
Znstituto de Carboqulmica (CSIC), P.O.Box 589, 50080 Zaragoza, Spain
In this work, different strategies to fit the experimental carbon combustion efficiencies obtained in the combustion of different lignites in a AFBC pilot plant with a 200-mm internal diameter have been analyzed with a model of the system. Different fitting methods used in literature based on the crossflow factor, the elutriation constant, or the fuel reactivity, have been considered for this analysis. It has been found that a great majority of the high carbon combustion efficiencies obtained experimentally cannot be predicted with a model which considers perfect mixing of gas in the emulsion phase, even when an infiite crossflow is considered. In addition, it has been found that the optimum values of the elutriation constant for fitting the experimental efficiencies show deviations which cannot be justified when working under similar operation conditions and solids. Therefore, it has been concluded that the best way to explain the combustion behavior of the lignites in AFBC is that founded on reactivity differences. In this way, each lignite has been characterized with only one adjustable parameter, which permits a simulation of the system reaching useful conclusions to design these combustors. Introduction Coal combustion in a bubbling atmospheric fluidized bed is a widely developed technology for a great variety of fuels and installation scales. Its application to high-reactivity fuels such as Spanish lignites, characterized by high sulfur and ash contents, has shown their feasibility, from the point of view of combustion efficiencies and SO2retention. In a previous work (Adhez and Abhades, 1992),different versions of models applicable to lignite combustion in fluidized bed have been set forth and solved. In addition, the sensitivity analysis carried out showed which submodels exercise the most influence on the prediction of carbon combustion efficiency. Also, the importance of the kinetic term in the overall reaction rate, the gas flow pattern in the bed, and the elutriation constant on combustion efficiency can be highlighted. However, the final validation of the combustion model will depend on ita capacity to imterpret the experimental efficiencies obtained in the pilot plant, that is, to reasonably predict the effect of the operation variables on the experimental carbon combustion efficiencies in the system. In this work, different procedures will be examined to interpret experimental efficiencies through different adjustable parameters. In this sense, different fitting strategies will be considered and discussed, based on the crossflow factor X on the elutriation constant k,. or on the reactivity factor f,. In this way, after a satisfactory fitting of experimental efficiencies with the model, a useful and reliable tool for simulating the fluidized bed combustor will be available.
Fitting Experimental Efficiencies In a previous work (Adinez et al., 1992)an AFBC pilot plant with a reactor of 200-mm internal diameter was described. In that installation the combustion efficiencies of different Spanish lignites were obtained under different operation conditions. Table I shows the experimental results obtained together with other new ones obtained with Mequinenza and Andorra lignites at higher gas velocities. The size distributions, fuel analysis, and experimental method were also described in the same work. As seen in Table I, and as expected for this type of fuel, the experimental efficiencies obtained are very high, with values greater than 96% under any conditions considered. The experimental error was not strictly determined, but when two experiments were repeated, the differences found in carbon efficiencies were 0.1 and 0.2%. The experimentation with the Mequinenza lignite was interrupted from the point of view of combustion efficiency, on obtaining efficiencies greater than 99.5% even at 750 O C . In the first part of this work it has been concluded that the hypothesis assumed about the gas flow pattern in the emulsion exercises an important effect on the efficiency predictions. Therefore, and to choose a suitable version of the model to interpret experimental results, it is necessary to characterize the hydrodynamics in the bed under experimental operation conditions. In this work, the criteria proposed by Catipovic et al. (1978)to distinguish between bubble regimes have been used. Taking into account the mean sorbent particle size (1-1.3 mm) and density (1800-2100kg/m3) used in experimental work, a 0 1992 American Chemical Society
Ind. Eng. Chem. Res., Vol. 31, No. 10, 1992 100
Table I. Experimental Carbon Combustion Efficiencies Obtained in the Pilot Plant lignite Samca Samca Samca Samca Samca Samca Samca Samca Salom6 Salom6 Salomk Salom6 Salom6 Salom6 Salom6 Mom6 Pilar Pilar Pilar Pilar Pilar Pilar Pilar Pilar Samca Samca Samca Samca Samca Samca Samca Samca Samca Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Andorra Mequinenza Mequinenza Mequinenza Mequinenza
size range +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-2 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4 +0-4
T
("C) 800 850 900 850 800 850 850 850 800 850 900 850 850 850 850 850 800 850 900 850 850 850 850 850 800 850 900 850 850 850 850 850 850 800 850 875 850 850 850 850 850 850 850 850 850 850 850 850 800 900 750
u
(m/s) 1.1 1.1 1.1 1.0 1.0 0.9 1.0 1.2 1.1 1.1 1.1 1.0 1.0 1.0 0.9 1.2 1.1 1.1 1.1 1.0 1.0 1.0 0.9 1.2 1.1 1.1 1.1 1.1 1.1 0.9 1.0 1.2 1.3 1.1 1.1 1.1 0.9 1.0 1.2 1.1 1.1 1.3 1.3 1.5 1.5 1.8 1.8 1.3 1.3 1.3 1.3
Exc
(%I 30 30 30 20 10 30 30 30 30 30 30 30 20 10 30 30 30 30 30 30 20 10 30 30 30 30 30 20 10 30 30 30 30 30 30 30 30 30 30 20 10 30 30 30 30 30 10 30 30 30 30
99
E,
( I ) 98.5 99.2 99.4 99.0 98.5 99.4 99.3 98.7 97.8 98.8 99.2 99.0 98.8 98.3 99.1 98.5 96.7 98.1 99.1 98.5 98.1 97.6 99.0 97.7 99.0 99.5 99.7 99.3 98.9 99.6 99.6 99.1 98.5 97.5
99.0 99.5 99.5 99.4 98.8 98.7 98.5 98.8 (2.5)" 98.8 (3.3)" 98.8 (2.3)" 98.1 (2.9)" 97.7 (2.2)O 97.1 (2.3)" 99Ab 99.8b 99Bb 99.6b
a Ca/S ratio (value of 3 for the rest of experiments). feed of limestone (in coal Ca/S ratio = 2.25).
Without
majority presence of slow bubbles is predicted in the bed (Abhades, 1991). Therefore, the most suitable and realistic version of the model, from a conceptual point of view, would be that which considers an infinite exchange between phases with plug flow in both phases. The rapid bubble growth regime and the formation of slugs were avoided by the presence of heat exchangers. In fact, in the pilot plant work, the great pressure fluctuations typical of rapid bubble growth and slugging regimes were not observed at any time. However, the criteria to distinguish between bubbling regimes, and the implications of these regimes on the oxygen exchange between phases, must not be taken in a strict sense. In fact, as can be seen in the model revision included in the previous work (Adinez and Abhades, 1992),the gas perfect mixing hypothesis in the emulsion phase is still being used in numerous models to interpret experimental results in FBC systems. In these types of
s 98
.'I =.
91 96 9s 95
96
91
98
99
100
.,
E c (experimental)
Figure 1. Comparison between experimental carbon combustion efficiencies and the obtained with infinite crossflow factor. 0,Samca; +, SalomB; . , Pilar; 0, Andorra; Mequinenza. 100 99
f
g
98
Y Y v
u
91 96 95
95
96
91
98
99
100
E (experimental) Figure 2. Comparison between experimental and predicted carbon combustion efficiencies using optimun multipliers of the elutriation constant. 0,Samca (1.93); +, Salom6 (6.81);,. Pilar (34.2);0, Andorra (1.55);0 , Mequinenza (0.45).
models, the crossflow factor, X,is the critical parameter. Therefore, in a fmt fitting stage, the crossflow values which best fit the experimental efficiency obtained in the pilot plant have been sought. In Figure 1the experimental efficiencies obtained with each lignite are compared with the efficiencies predicted by the model when considering an infiiite c r d o w fador between phases. As can be seen, for the majority of the lignites considered, it is not possible to predict the experimental efficiencies with any crossflow value. These results qualitatively agree with those obtained by Donsi et al. (1979) in beds of sand with particle sizes between 0.65 and 1mm. In that case, it was not possible to predict the experimental bed carbon loading assuming perfect gas mixing in the emulsion phase. It must also be pointed out that this problem could not have been detected in this way if carbons with lower reactivity had been worked with. Obviously, with lower experimental efficiency values (greater overall resistance to the combustion process), there would be greater flexibility for the X values. That is, the experimental points represented in Figure 1would be displaced above the line, and it would be possible to search for optimum crossflow factors to fit the experimental efficiencies. As discussed in the previous work (Adinez and Abinades, 1992), some authors have used the elutriation constant as a basis to fit experimental efficiencies. This procedure is justified due to the uncertainty inherent in the calculationsof the solid flow rates in the freeboard and in the great sensitivity of the efficiency predictions to this parameter.
2298 Ind. Eng. Chem. Res., Vol. 31, No. 10, 1992
95
96
97
98
99
(1989) include 14 factors which affect the reactivity of pulverized coal. Tseng and Edgar (1984) reported lignite char reactivities different by 3.5 times depending on pyrolysis temperature. Chandran et al. (1989) and Daw et al. (1991) present different kinetic equations even for similar type fuels, and Lemcoff (1988) correlates the fuel reactivity as a function of the ash coal content, using the equation of Field et al. (1967) as a basis. In a previous work (Adanez et al., 1992) this equation has also been used as a basis for fitting experimental efficiencies using a reactivity factor f, as a multiplier of the apparent kinetic constant. As seen in Figure 3, the final fit of experimental efficiencies included in Table I has been satisfactory, with a mean deviation of fO.l8% and of *0.27% with a confidence interval of 95%. However, these very low deviations are affected by the high value of efficiencies worked with. Therefore, a more practical way of evaluating the fitting quality is the comparison between theoretical and experimental unburn loss in carryover flow, which mainly determine the combustion efficiency in the system. The mean deviation obtained in this comparison (Figure 4) is 25.1 %. These deviations are more representative of the experimental efficiency determination method and of the errors inherent in the hypotheses carried out for constructing and solving the model. Obviously, the reactivity factors obtained include different error sources which come mainly from attrition and fragmentation mechanisms in the bed and from a certain resistance to the oxygen exchange between phases. Concerning this last point, it is clear that the coal reactivity factor also permita the experimental efficiency adjustment when the additional resistance to the oxygen exchange between phases is considered. Therefore, in those situations where the type of gas flow cannot be accurately defined, the reactivity factors obtained, using the model which considers infinite exchange, include the resistance to the exchange between phases. However, as shown in Figure 5, the differences in efficiency predictions assuming both typea of flow, AEc,become smaller as the bed particle diameter increases (nearest to slow bubble conditions). Therefore, the maximum possible error in the obtained f,, arising from deviations of the infinite gas exchange hypothesis, will be small. This shows a certain similarity between both versions of the model as the slow bubble conditions are imposed, since on increasing the ratio &/ubr or ud/u, the gas fraction across the emulsion phase increases and the exchange quality between phases becomes less important.
100
.,
E (experimental) Figure 3. Comparison between experimental and predicted carbon combustion efficiencies using optimum reactivity factors. 0 , Samca (1.02); Salom6 (0.43); Pilar (0.10);0, Andorra (1.40);0 , Mequinenza (2.60).
+,
In Figure 2 the experimental efficiencies are compared with those predicted by the model when using an elutriation constant multiplier for each coal. The Highley and Merrick (1978) equation has been used as a basis in this analysis. Ae can be seen in this figure, the fitting obtained with this method is reasonable, with a mean deviation of *0.28% and of fl.l%with a confidence interval of 95%. However, there are significant differences between the values of the optimum multipliers of kej obtained when fitting the lignite efficiencies. Small differences between them would be justifiable considering different particle properties of the elutriated fines (sphericity and/or density), depending on the fuel they generate from. However, considering the physical sense of the elutriation constant, the great differences obtained under very similar operation conditions (especiaUyvelocity), and in the same pilot plant, cannot be explained in this way. Only great modiications in the original feed size distribution, due to the fragmentation (Chirone et al., 1989; Dakic et al., 1989) or attrition during combustion (Arena et al., 1983),could explain these results. However, in small-sized installations, the greater h e sourca generated by these mechanisms come8 from the feed system itself (Daw et al., 1989) and this effect was eliminated by measuring the size distributions &r passing through the screw feeders. As indicated above, the other possibility for adjusting and interpreting the experimental efficiencies included in Table I is by means of the possible reactivity differences between the lignites considered. These differences can be due to a great number of factors. Hampartsoumianet al. 0.15 I
/
0.06 0.05 0.04 0.03 I
0.02 0.01
0.00 0.00 0.01
..
0.00
0.05
0.10
0.15
F;
0.02
0.03
0.04
0.05
0.06
(experimental) (KgClh)
Figure 4. Comparison between experimental and predicted carbon carryover flow using optimum reactivity factors. (0.43);D, Pilar (0.10);0, Andorra (1.40);0 , Mequinenza (2.60).
0 , Samca (1.02);
*,Salom6
Ind. Eng. Chem. Res.,Vol. 31, No. 10,1992 2299 2.01
I00
I
98
-
5
96
wu 94
850'C
92
0.2
0.4
0.6
0.8
1.0
1.2
1.4
90 7 50
1.6
dp (mm)
Figure 6. Differences of carbon combustion efficiency predictions between the model considering infinite interchange and the model considering exchange between phases at different bed particle diameters and temperatures. u = 1 m/s, Exc = 20%.
Finally, it is clear that the attrition mechanisms in the bed (once the attrition in the feed system has been considered) can play an important role due to two factors (Arena et al., 1983): increasing the shrinking velocity of the coarse particles and generating fine particles which can leave the combustor without being totally burned in the bed and/or in the freeboard. The first effect would be reasonably included in the reactivity factor and would mean a slight increase in the apparent fuel reactivity. The second effect would influence to quite an extent the fines size distribution in the bed, and so the flow rate of unburned fines which leave the system. However, as can be seen in Figure 4, no special tendencies are observed in the comparison of elutriates even at higher velocities. Thus, it is not necessary to include attrition mechanisms in the model for these lignites. Therefore, it is possible to conclude that the attrition in the bed with these fuek is either not important or reduced to an increase of the average shrinking rate, generating fines which are small enough to completely burn in the bed and in the freeboard. Thus, one may conclude that each of the lignites used in this work is definitely characterized by the reactivity factors obtained in the fitting. That is, with a majority presence of slow bubbles, it is possible to simulate with enough accuracy the combustor behavior under different operation conditions with regard to the carbon efficiency predictions.
Simulation In the previous work (Adbez and Abbades, 1992) a simulation was made with the different versions of the model to perform the sensitivity analysis to some hyp o t h w , equations and parameters. Below, the reactivity factors obtained for each coal are used as a characteristic parameter in their combustion in a fluidized bed, to simulate their behavior depending on different design and operation conditions. Effect of Bed Height. The bed height in AFBC varies from 0.2 to 1.5 m, although normally it is approximately 1m. The bed height is one of the basic design variables from the combustion point of view. In addition, it is also an operation variable in utility scale combustors, as it is a tool to modify the heat-exchange areas immersed in the bed and to permit load changes in the system. In Figures 6-8 the effect of the bed height is shown using variables T,u, and Exc as basis of comparison. The slow bubble version, using Samca lignite ( + e 4 mm, f, = 1.02) has been used as a simulation basis. In all of them it can be seen that the bed height has an important effect on
800
850
900
T ("C) Figure 6. Effect of temperature on overall carbon combustion efficiency using bed height (m) as parameter. u = 1.3 m/s, Ekc = 20%.
7u
0.8
1.0
1.2
1.4
1.6
1.8
2.0
u (m/s) Figure 7. Effect of gas velocity on overall carbon combustion efficiency using bed height (m) as parameter. T = 850 "C,Exc = 20%.
w
94
gu96
r 0
IO
20
30
40
Exc (%) Figure 8. Effect of air excess on overall carbon combustion efficiency using bed height (m) as parameter. T = 850 O C , u = 1.3 m/s.
combustion efficiency predictions. Obviously, there is a reduction in the average residence times of all the particles in the bed proportional to the height. The effect of the bed height at different working temperatures can be seen in Figure 6. The differences in predictions are reduced in absolute terms when the temperature is increased, because the combustion rates are higher and high char conversions are reached in all the cases. As seen in Figure 7, the prediction differences, with the different heights, increase quickly with the velocity. At low air velocities, the coal flow rate to the system is smaller, and any one of the beds used implies average residence times which are long enough to obtain high efficiencies s i m i i to each other. However, at high working velocities, bed heights greater than 1 m are required to achieve high
2300 Ind. Eng. Chem. Res., Vol. 31, No. 10, 1992 r
11-10
95
I
"
750
I
I
I
800
850
900
T ("C) Figure 9. Effect of temperature on overall carbon combustion efficiency using particle density (g/cm3) aa parameter. u = 1.3 m/s, Exc = 20%.
95 750
I
I
800
850
I 900
T ("C) Figure 11. Effect of temperature on overall carbon combustion efficiency using bed particle diameter (mm) BB parameter. u = 1.3 m/s, Exc = 20%.
_08
I O
12
14
16
I S
20
11 (111ia)
Figure 10, Effect of gas velocity on overall carbon combustion efficiency using particle density (g/cm3) aa parameter. '2' 850 O C , Exc = 20%.
combustion efficiencies without recycling unburnt fines. In Figure 8 the different predictions have been plotted using the excess air on the stoichiometric as a comparison variable. The tendencies can be explained by the same reasons given above, pointing out that the increases in the Exc values do not compensate for the decreases in efficiency due to the smaller height of the bed, as occurred with the gas velocity and temperature. This is due to the fact that the excess air does not affect the residence time distribution in the same way the velocity does (through the coal feed rate). Neither are the combustion rates affected by the excess air to the same extent as the temperature. In view of Figures 6 8 it can be said that the bed height is a design or operation variable which is essential from the point of view of combustion efficiency in the system. Therefore, in the search for an optimum working height, the efficiency improvement represented by working with deep beds must be taken into account, opposed to the proportional increase of the pressure drop in the bed. Effect of Sorbent Propertier. The bed hydrodynamic characteristics, and therefore the environment where the char particle combustion takes place, are determined by the particle properties of the SO2sorbent present in the bed. The density of these particles in the bed is going to be determined by the sulfating degree of the limestone particles fed into the reactor. Therefore, it has been deemed advisable to analyze the effect of the sorbent particle density on the Combustion efficiency in the system. To do this, a density interval between 1500 and 2600 kg/m3, which includes the possible values in a combustor, have been used.
0.8
1.0
1.2
1.4
I
6
1.8
2.0
u (m/s) Figure 12. Effect of gas velocity on overall carbon combustion efficiency using bed particle diameter (mm) aa parameter. T = 850 "C, Exc = 20%.
In Figures 9 and 10 the results obtained with the simulation have been represented. The differences between predictions are noticeable in the majority of the conditions, these being reduced when working at high temperatures and at low gas velocities. In view of these figures it can be stated that, at increasing particle densities (more reactive limestone), the efficiences obtained are higher. At high densities, the minimum fluidization velocity umf(and therefore the interstitial in the emulsion) is higher, and then the oxygen-transfercoefficients around the particle increase. Although quantitatively the previous reason is the main one for explaining the changes, it must be taken into account that the modifications of umfdirectly or indirectly affect all the submodels which make up the global model. The efficienciespredicted in beds when sorbent particle size is changed have been plotted in Figures 11 and 12, where tendencies similar to those obtained when the particle density is changed are observed. In addition, the effect of the particle size can be explained qualitatively in a way similar to the above changes in umfdue to variations in density. Effect of Lignite Type. One of the main advantages of the fluidized bed combustion technology is the flexibility, as regards the type of fuel which can be processed. In order to analyze the effect of lignite type, a simulation has been made with the five lignites used in experimentation and with the lignite used by Halder and Saha (19911, with the following combustion kinetics: R, = 2.90 exp[-80170/RTp] (kg, m-2 s-l kPa-9 (1) When using this expression, where the reaction order is different from the unit, the shrinking velocity has to be
Ind. Eng. Chem. Res., Vol. 31, No. 10,1992 2301
.-
900
750
Figure. 13. Effect of bed temperature on overall carbon combustion efficiency using different lignites. -, this work 1, Mequinenza; 2, Andorra; 3, Samca; 4, SalomC; 5, Pilar. - - -,Halder and Saha. u = 1.3 m/s, Exc = 20%.
2
1
0
3
4
d (mm) Figure. 15. Normalized bin-Ramler feed particle size distribution used in eimulation. The n values used as parameter have been adopted from Yu and Standish (1990).
99
h
#
Y
wu
95 96
0.8
1.0
1.2
1.4
1.6
1.8
2.0
u (m/s) Figure 14. Effect of gas velocity on overall carbon cornbustion efficiency using different lignites. -, this work 1, Mequinenza; 2, Andorra; 3, Samca; 4, Salom(; 5, Pilar. - - -,Halder and Saha. u = 1.3 m/e, Exc = 20%.
calculated iteratively, acting on fractional approach to mass-transfer control (Halder and Saha, 1991). Some resulta of the simulation carried out are plotted in Figures 13 and 14. It must be pointed out that these curves have been obtained working under the same operation conditions, including the feed size distribution. Then, the differences between predictions are only due to changes in fuel reactivity and composition. As expected, the lower value of activation energy in eq 1 implies less effect of temperature on carbon efficiency predictions. However, in the normal range of working temperaturea (800-900 "C)there is a reactivity factor (1.78 in that range) that would predict resulta very similar to those obtained with the original kinetic equation. As can be seen in these figures, as the reactivity decreases, it would be necessary to recycle unburnt fines to the bed,or to increase the bed height substantially,because even at high temperatures and low gas velocities the drop in combustion efficiency becomes significant. However, for coals like lignites,with typical reactivity factors higher than 0.5, no complication in the system is required to reach very high efficiencies under normal working conditions. Effect of Coal Feed Size Distribution. In the experimentation carried out in the pilot plant, basically two type% of size distributions were used (+0-2 and +0-4 mm). For the case of Samca lignite, the model reasonably predicta the effect of changes in the size distribution maintaining the same reactivity factor (Adhez et al., 1992). However, the particle size distributions of the coal feed can be very different depending on a large number of d i d properties and on the type of grinding to ita preparation.
t /
750
800
850
900
T ("C) Figure 16. Effect of bed temperature on overall carbon combustion efficiency using fuels with different feed size distributions and n value as parameter. u = 1.3 m/s, Exc = 20%.
Therefore, in this section the combustor behavior has been simulated with several coal feed size distributions. Special attention has been paid to the effect of the frequency distribution curves in the efficiency prediction maintaining a constant size interval between 0 and 4 mm. To generate the different frequency distributions, the Rosin-Ramler equation has been used in a standardized way. f ( d ) = nb(dpm,/dpl-") exp[-bd"]
(2)
where n is the adjustable parameter which in this case will be used as a simulation parameter and b is a constant which, when f(d) is standardized, takes on the value
b = -In 0.001/dP",,
(3)
The previous equations and the values of n and b adopted have been taken from the work of Yu and Standish (1990),and they represent a full scan over the possible frequency distributions as shown in Figure 15. As can be Been in Figures 1618,the form of frequency particle size distribution hae a great effect on the efficiency predictions even working with high reactivity fuels such as lignites. The effect is much more apparent in those distributions where parameter n is nearer to 1, which as seen in Figure 15 correapond to the frequency distributions which are most biaaed toward the fines area. With higher n values there is a greater proportion of c o m e solids in the size distributions, theae solids W i n g thome which cannot leave the bed until they reach an elutriable size by shrinking. In these situations, high char conversions are ensured for the majority of the particles fed in, so the
2302 Ind. Eng. Chem. Res., Vol. 31, No. 10,1992
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utility and flexibility. The reactivity factors obtained include different sources of error related to some phenomena not considered in the model such as attrition, fragmentation, and a certain resistance t o the exchange between phases. In addition, they would comprise the inherent errors related to the model construction. However, the good quality of the adjustments obtained with this procedure indicates that the lignites used in the pilot plant are reliably characterized by these factors. The simulation carried out with the model has enabled information to be obtained on the critical variables for the design and operation of an atmospheric fluidized bed combustor from the point of view of carbon combustion efficiency. In this sense, the great effect of the bed height must be pointed out, and similarly the effect of the working temperature, the gas velocity, the fuel reactivity, and the amount of coal particles fed in with diameter under 1mm. In addition, the less significant effect of variables such as excess air on the stoichiometric and the density and diameter of sorbent particles must also be highlighted, providing they are within the normal values in these combustors. Acknowledgment We thank the DPT, DGA, and DGICYT (GEO-890732402-02) for financing this work.
I
,
I
.
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,
Nomenclature b = constant defined in eq 3 = particle diameter of coal (m) = maximum particle diameter of coal (m) ,$:overal carbon combustion efficiency (%) Exc = excess air (%) f r = reactivity factor f ( d ) = frequency size distribution of coal feed n = parameter in eq 2 R = gas constant (J mol/K) R, = combustion reaction rate (kg, m-28-l kPa+.6' ) T = bed temperature (K) Tp = superficial char particle temperature (K) u = superficial gas velocity (m/s) Ubr = rise velocity of single isolated bubble (m/s) unf = minimum fluidization velocity (m/s) X = crossflow factor
2
Literature Cited Abhades, J. C. Combustih de lignitos en lecho fluidizado atmosf6rico burbujeante. Modelado y simulaci6n de la eficacia de combustitin. Ph.D. Thesis, University of Zaragoza, 1991. Adhez, J.; Abirnades, J. C. Modeling of Lignite Combustion in Atmospheric Fluidized Bed Combustors. 1. Selection of Submodels and Sensitivity Analysis. Ind. Eng. Chem. Res. 1992, preceding paper in this issue. Adhez, J.; Abirnades, J. C.; Garda Labiano, F.; de Diego, L. F. Carbon Efficiency in Atmospheric Fluidized Bed Combustion of Lignites. Fuel 1992, 71, 41?. Arena, U.; DAmore, M.; Masaimilla, L. Carbon Attrition During the Fluidized Combustion of a Coal. AZChE J. 1983,29(1), 40; Catipovic, N. M.; Jovanovic, G. N.; Fitzgerald, T. S. Regimes of Fluidization for Large Particles. AIChE J. 1978, 24(3), 543. Chandran, R. R.; Duqum, J. N.; Perna, M.A.; Sutherland, D. D.; Rowley, D. R.; Pirkey, J.; Petrill, E. M. Ranking Fuels for Utility-Scale AFBC Application. R o c . Int. Conf.Fluid. Bed Combust. 1989, loth, I, 313. Chirone, R.; Salatino, P.; Masaimilla, L. Secondary Fragmentation of Char Particles During Combustion in a Fluidized Bed. Combust. Flame 1989, 77, 79. Dakic, D.; van der Honing,G.; Valk, M. Fragmentation and Swelling of Various Coals During Devolatilization in a Fluidized Bed. Fuel 1989,68,911.
Daw, C. S.; Chandran, R. R.; Duqum, J. N.; Perna, M. A.; Petrill, E. M. FBC Engineering Correlations for Estimating the Combustion
Ind. Eng. Chem. Res. 1992,31, 2303-2311 Efficiency of a Range of Fuels. h o c . Znt. Conf.Fluid. Bed Combust. 1989,loth, I, 305. Daw, C. S.; Rowley, D. R.; Perna, M. A.; Stallings, J. W.; Divilio, R. J. Characterizationof Fuels for Atmospheric Fluidized Bed Combustion. R o c . Znt. Conf.Fluid. Bed Combust. 1991,llth,I, 157. Donsi, G.;Maasimilla, I.; Miccio, M.; Russo, G.; Stecconi, P. The Calculation of Carbon Load and Axial Profiles of Oxygen Concentration in the Bed of a Fluidized Combustor. Combust. Sci. Technol. 1979,21,25. Field, M. A.; Gill, D. N.; Morgan, B. B.; Hawkseley, P. G. W. 'Combustion Pulverized Coal"; British Coal Utilization Research Association, Leatherhead; Cheney and Sons Ltd.: Banbury, 1967. Halder, S.; Saha, R. K. Combustion Kinetics of Lignite Char in a Fluidised Bed. J. Znst. Energy 1991,64,55.
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Hampartaoumian, E.; Pourkashanian, M.; Williams, A. Combustion Rates of Chars and Carbonaceous Residues. J. Zwt. Energy 1989, 62,48. Highley, J.; Merrick, D. Particle Size Reduction and Elutriation in a Fluidized Bed Process. AZChE Symp. Ser. 1978,70(137), 336. Lemcoff, N. 0.Fluidized Bed Combustion of High Ash Chars. Combust. Sci. Technol. 1988,62,131. Tseng, H. P.; Edgar, T. F. Identification of Combustion Behaviour of Lignite Char Between 350 and 900 OC. Fuel 1984,63(3), 385. Yu, A. B.; Standish, N. A Study of Particle Size Distributions. Powder Technol. 1990,62,101. Received for review February 18, 1992 Accepted June 26, 1992
Recovery of Neodymium and Ytterbium by Biopolymer Gel Particles of Alginic Acid Yasuhiro Konishi,* Satoru Asai, Junichi Shimaoka, Masanori Miyata, and Takeshi Kawamura Department of Chemical Engineering, University of Osaka Prefecture, 1-1, Gakuen-cho, Sakai, Osaka 593, Japan
Gel particles of alginic acid, which is a naturally occurring polymer found in brown seaweeds, were capable of collecting neodymium and ytterbium from aqueous solutions. The rare earth metals collected by the gel particles were completely eluted by using dilute HC1 solution of 0.1 kmol~m-~, suggesting that the sorption-desorption is a reversible phenomenon. The sorption capacity and the distribution equilibrium constants were determined by comparing experimental data with theoretical predictions made assuming that the sorption takes place with the ion-exchange reaction between trivalent metal ions and alginic acid. The rate process in the biopolymer system was found to be controlled by the intrinsic ion-exchange reaction, the resistance to mass transfer being insignificant. The experimentally observed rates of sorption and desorption were consistent with rate equations derived assuming that the rate-controlling step is the first step of the ion-exchange reaction. The reaction rate constants appearing in the associated rate equations were evaluated using the kinetic data.
Introduction There have been numerous studies on the sorption of metals from aqueous solutions by microbial biomass, as reviewed in the literature (Muzzarelli, 1973;Beveridge, 1986;Hunt, 1986;Volesky, 1986;Asai et al., 1986;Geesey and Jang, 1989). It was demonstrated that various microorganisms including algae, bacteria, and fungi were capable of collecting toxic metals (Cd(II), Hg(II), Zn(II), Pb(II)), precious metals (Au(III), Ag(I)), base metals (Co(II),Ni(II), Cu(II)),and radionuclides (U(VI),Th(IV)). It was further reported that many biopolymers making up cell walls of the microorganisms display an ion-exchange property and play a major role in the sorption of the metal ions. Such polymers derived from microbial biomass are potentially useful as biosorbent materials for collecting various metal ions in industrial and analytical applications, although synthetic polymers such as ion-exchange resins and chelating resins have been widely used as commercial sorbents. Since the biopolymers having ion-exchange property can be obtained from the enormous amount of natural raw materials, the use of the biopolymers would be a much cheaper means of collecting metal ions than that of the synthetic resins. Alginic acid is a biopolymer consisting of mannuronic acid and guluronic acid and occurs in the marine product brown algae. Commercially important brown algae generally contain alginic acid in the range of 13-40 w t 5% on a dry weight basis, as a structural component of the cell walls in the form of alginates. The ability of alginate to
form gels by ion-exchange reaction with multivalent metal ions is a suitable properly as a sorbent of metal ions. The ion-exchange properties of *tea have been investigated from several different viewpoints. Schweiger (1962)revealed that divalent metal ions connect to two carboxyl groups of both mannuronic and guluronic acid, suggesting that the carboxyl groups are responsible for the sorption of metal ions on alginates gels. Smidsrod and Haug (1968) demonstrated that, for the ion-exchange reactions for Ca(I1)-Mg(II), Ca(I1)-Sr(II), Sr(I1)-Mg(I1), and Co(I1)Ca(II), guluronic acid rich alginates exhibit a higher selectivity than mannuronic acid rich alginates. These investigators also examined the effect of the gel-sol state on the ion-exchange properties of alginates and found that, for the ion-exchange reaction for Ca(I1)-Mg(II), the gel state is more selective than the soluble form (Smidsrod and Haug, 1972). Cozzi et al. (1969)found that the affiiity of alginic acid for homologous ions of the periodic table can be correlated with the size of the hydrated ionic radius and decreases in the following order: Cs(1) > K(1) > Na(1) > Li(1); Ba(I1) > Sr(1I) > Ca(I1) > Mg(I1); Cd(I1) > Zn(I1). Muzzarelli (1973)reported that alginic acid has a high sorption capacity for transition metal ions compared with (carboxymethyl)cellulose,in accordance with the higher carboxyl group content of alginic acid. Recently, Kuyucak and Volesky (1989)studied the sorption mechanism of Co(I1) by nonliving algal biomass of Ascophyllum nodosum, and revealed that alginates (carboxyl groups) of the cell wall play an important role in the sorption of Co(I1)
oaa~-5aa5j92/2s3i-23o3~o3.oo/o0 1992 American Chemical Society
