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Fluidization and reactor biomass characteristics of the denitrification fluidized bed biofilm reactor
War. Res. Vol. 21, No. 4, pp. 451--458, 1987 Printed in Great Britain. All rights reserved
0043-1354/87 $3.00+0.00 Copyright © 1987 Pergamon Journals Ltd
FLUIDIZATION A N D REACTOR BIOMASS CHARACTERISTICS OF THE DENITRIFICATION FLUIDIZED BED BIOFILM REACTOR LEO T. MULCAHYl and WEN K. SHIEH2. CBleached Board Division, Westvaco Corporation, Covington, VA 24426 and 2Department of Systems, University of Pennsylvania, Philadelphia, PA 19104, U.S.A. (Received October 1985)
Abstract--The fluldization and reactor biomass characteristics of the denitrification fluldized bed biofilm reactor (FBBR) were investigated. Experimental evidence obtained indicates that Richardson-Zaki correlation, which was developed for rigid solid particles, provides an excellent description of the fluldization mechanics of a denitrification FBBR. However, correlations for calculation of drag coefficient and expansion index should be modified to account for the FBBR characteristics that the degree of bed expansion increases with increased bioparticle size (i.e. increased biofilm thickness). The fluidization and reactor hiomass correlations developed in this investigation are capable of providing a direct and accurate prediction of biomass concentration and bed expansion in FBBRs designed for wastewater treatment applications. Engineering applications of these correlations are discussed. Key words--fluldized bed biofilm reactor (FBBR), fluldization, reactor biomass, bioparticle terminal settling velocity, expansion index, denitrification
p = Biofilm dry density (M L -3) Pt = Wastewater density (M L -3) Pm ffi Media density (M L -3) Ps = Bioparticle density (M L -3) /~ = Wastewater viscosity (M L - 1T - 1).
NOMENCLATURE
A = Cross-sectional area of the reactor perpendicular to the wastewater flow (L2) CD = Drag coefficient --Sauter mean diameter (L) d r = Diameter of particle i (L) dm= Media diameter (L) = Bioparticle diameter (L) -- Sauter mean diameter of media (L) dp --- Sauter mean diameter of bioparticle (L) g = Gravitational acceleration (L T -2) H s = Expanded bed height (L) H i = Expanded bed height after the sample was withdrawn (L) ~b = Trial expanded bed height (L) AHn = Change in the expanded bed height (L) mr = Number of particles with the diameter d~ n = Expansion index Np.,, -- Terminal Reynolds number AN = Number of bioparticles in the sample withdrawn from the reactor P = Biofdm moisture content U = Superficial velocity (L T -1) Ut = Bioparticle terminal settling velocity (L T- i) V,,--Media volume (L3) Vs = Total volume of bioparticles in the expanded bed (L 3) X = Biomass concentration (M L -3) X~ = Media concentration in the fluidized bed volume (gl -I) W~ = Weight of bioparticles withdrawn from the reactor
~
(M)
W, = Weight of sample withdrawn from the reactor (g) ffi Bed porosity 6 = Biofilrn thickness (L) = Trial biofilm thickness (L)
*To whom all correspondence should be addressed.
INTRODUCTION
The fluidized bed biofilm reactor (FBBR) is a recent process innovation in wastewater treatment technology. In contrast to conventional biofilm reactors in which media are fixed in space either by gravity or by direct attachment to the reactor wall, the media are retained in an F B B R by drag forces exerted by the upflowing wastewater. Immobilization o f microorganisms on the small, fluidized media as biofilms enables an F B B R to retain a high reactor biomass concentration and thereby operate at significantly reduced hydraulic retention times (HRTs) (Mulcahy and L a M o t t a , 1978; Atkinson et al., 1984). Fluidization also overcomes operating problems such as bed clogging and high pressure drop which would occur if small media were employed in conventional biofilm reactors. A further advantage is the possible elimination of the secondary clarifier (Sutton et al., 1981). F B B R s have been investigated for carbon oxidation, nitrification, denitrification, and anaerobic treatment, for a wide variety o f waters and wastewaters. A number of full-scale F B B R s are currently in operation. Detailed descriptions of these applications can be found elsewhere (Speece, 1983; Switzenbaum, 1983; Shieh and Keenan, 1986). Because all the claimed advantages of an F B B R are 451
452
LEO T. MULCAHYand WEN K. SmEH
virtually derived from its ability to retain a high reactor biomass concentration, therefore, an accurate prediction of reactor biomass concentration under different operating conditions becomes a vital design and operation parameter. F o r a given set of operating conditions, an analysis of the fluidization phenomena in an F B B R yields two critical pieces of information: equilibrium biofilm thickness and bed porosity, which in turn can be used to calculate the reactor biomass concentration using total mass balance principle (Mulcahy and LaMotta, 1978; Shieh et aL, 1981; Shieh and Chen, 1984). This paper describes and discusses a laboratory investigation aimed at obtaining fluidization and reactor biomass correlations for the denitrification FBBR. Engineering applications of these correlations are also discussed.
FLUIDIZATION AND REACTOR BIOMASS CORRELATIONS
The pertinent correlations for characterizing fluidization phenomena and predicting biomass concentration in an F B B R are summarized in Table 1. In essence, these correlations were derived based on the assumption that the bioparticle (i.e. biofilm-coated media) size is very small as compared to the dimensions of the F B B R (Lewis and Bowerman, 1952; Richardson and Zaki, 1954; Wen and Yu, 1962; Ngian and Martin, 1980; Vasalos et al., 1982; Hermanowicz and Ganczarczyk, 1983; Webb et al., t983; Chen and Pei, 1984; Patwardhan and Tien, 1984; Van der Meer, 1984). Furthermore, the individual bioparticle is assumed to have a smooth surface and a spherical shape. Once the coefficients in equations (3) and (4) are quantified with experimental data, the bioparticle terminal settling velocity (U,) can be evaluated by equation (2), the bed porosity (E) by equation (1), and the biomass concentration (X) by equation (7). Details involved in the derivation of these correlations shown in Table 1 can be found elsewhere (Mulcahy and LaMotta, 1978).
w
M
eel
Do Oo
.'l
sP eol**1
"I
• 01
~
F
Fp 1
Fp z
Fig. 1
Table 1. Fluidization and reactor biomass correlations of the FBBR Remarks
Correlation (I) U I U , = :
(2) Ut = [0.75(p,-p~)gdp/(Copl)]°.~ (3) Co = aNR~ (4) n = cNR~ (5) NRe,= U,a:,/U (6) p~ = p . ( d . / d p ) ~ + [p/(I
MATERIALS AND METHODS Laboratory-scale FBBR A laboratory-scale denitrification FBBR as illustrated in Fig. 1 was employed to obtain required experimental data. The reactor was fabricated from a 3.8 × 175cm Plexiglas column, with sampling ports (SP) (22 em apart) installed along the column length to remove liquid and bioparticles samples. Pea gravel (12 em deep) was placed at the reactor bottom to provide uniform flow distribution and prevent backflow of media. Spherical glass beads (density: 2.42gm1-1; Sauter mean dia: 680/am) were used as the media. The reactor feed consisted of the clarified effluent from an extended aeration unit treating a municipal wastewater; methanol and supplemental sodium nitrate were added. The resulting feed NO3-N concentrations varied from 16 to 57 mg 1-~. The feed (F) was pumped into a 750ml flask located in a feed vessel (FV) by a centrifugal pump (FP~). The intake of the FBBR feed pump (FP 2) was also located
- P)] x [I - (d./d,,) 3]
(7) X = p(l - ~)[I - (d, ldp)~l
Richardson-Zaki correlation which relates the bed porosity (¢) and superficial velocity (U) in a fluidized bed reactor. Correlation of bioparticle terminal settling velocity (U,) in an FBBR. Empirical correlation which relates the drag coefficient(Co) and terminal Reynolds number (NR¢,)in an FBBR. a and b are empirical coefficients. Empirical correlation which relates the expansion index (n) and terminal Reynolds number in an FBBR. c and d are empirical coefficients. Definition of terminal Reynolds number. Definition of bioparticle density. Correlation for biomass concentration calculation.
Characteristics of denitrification FBBRs in the feed vessel. Because the feed rate to the feed vessel was at least 50% greater than the outflow due to the FBBR feed pump, the contents of the feed vessel were not diluted by the bulk contents. However, in the event of feed interruptions, the FBBR would continue to operate in a total recirculation fashion. Temperature and pH of the FBBR were controlled at 22 _+ I°C and 6.9 _+0.1, respectively.
Seeding procedure Seeding of the FBBR was accomplished by expanding the clean media bed with feed solution at a superficial velocity of 0.7 o n s -~. Within a week, a growth interface, which separated clean from biofilm-coated media, appeared and propagated downward through the expanded bed. A mixer (M) was used to prevent the buildup of overgrown bioparticles at the top of the bed. The position of the mixer could be adjusted to obtain a specific biofilm thickness in the FBBR. This practice also insured the uniform distribution of biofilm thickness throughout the bed. Determination of relevant parameters Particle size and biofilm thickness. A microscope equipped with an ocular micrometer was used to determine bioparticle diameter, media diameter, and biofilm thickness. A volume surface mean or Sauter mean was used for interpretation of particle size in order to account for size distribution observed (Bailey and Ollis, 1977): d = Y~mid~ ]~mjd~
(8)
where d is the Sauter mean diameter, and mi is the number of particles with the diameter d,. The biofilm thickness is: 3 - d p - a~,~ 2
p = pm(X/Xm)(lO-3)/[(dm/dp) 3 -- 1]
(10)
and
(p/p~,)(Xm)[(dp/dm) 3 - l] w s - p, + x ~ { ( p , / p ~ , ) [ ( a , / d ~ )
3-
Bed expansion experimentation The basis for calculation of bed porosity in terms of the easily observable parameter, expanded bed height, was established by:
v~ H,A
w;
AN =
-3 (13) (p,)Otd,/6) where W~ is the weight of bioparticles in the sample. The total volume of bioparticles remained in the expanded bed could then be calculated as:
V. (H'B)(AN)(TE3~/6)
(14)
(AHB) where H i is the expanded bed height after the sample was withdrawn. Therefore, the bed porosity could be directly related to the expanded bed height by equation (12). The procedure outlined above was repeated for 20 different biofilm thicknesses ranging from 40 to 1200/~m. The biofilm thickness was varied by varying the degree of bed expansion maintained in the reactor. This was accomplished by changing the location of the mixer in the reactor. In general, the lower the mixer was, the thinner were the resultant biofilms.
Bioparticle terminal settling velocity experimentation During each bed expansion experimental run, a small portion of a bioparticle sample withdrawn from the expanded bed was allowed to settle through a 14 x 195 cm water-filled Plexiglas column. By timing bioparticle descent through a 120 cm segment of the column, the bioparticle terminal settling velocity was determined. By repeating the procedure outlined above for 20 different biofilm thicknesses, a data base for quantifying the coefficients in equation (3) was obtained. RESULTS The experimental results obtained are summarized in Tables 2 and 3. Both biofilm thicknesses and bioparticle terminal settling velocities reported are average values obtained from a number of measurements. The biofilm dry densities were calculated using equation (10).
Drag coefficient-terminal Reynolds number correlation F o r each experimental run, the terminal Reynolds number and the corresponding drag coefficient were determined. The least square analysis was used to obtain the best fit line. The resulting drag coefficientterminal Reynolds number correlation, which is specific for denitrification F B B R bioparticles, is: CD = 36.66N~ 2/3.
(l l)
l]}
where p is the biofilm dry density, g l-l; Pt is the wastewater density, g 1- i; p,, is the media density, g 1- ~; X is the biomass concentration, mg 1-~; X~ is the media concentration in the fluidized bed, g l-t; and Ws is the weight of the sample, g.
= 1- -
At a given superficial velocity, a small portion of bed materials was withdrawn from the reactor and the change in the expanded bed height (AHs) was noted. The number of bioparticles in the sample (AN) was calculated as:
(9)
where 3 is the biofilm thickness, ~p is the Sauter mean diameter of bioparticles, and dm is the Sauter mean diameter of media. 8ioma.ss concentration. The biomass concentration was determined as the total attached volatile solids concentration, which was determined as the difference between the total volatile solids concentration of the bed sample (i.e. bioparticlcs and liquid) and the total volatile solids concentration of the liquid sample. Details involved in this experimental procedure can be found elsewhere (Mulcahy and LaMotta, 1978; Shieh et al., 1981). 8iofilm dry density and moisture content. Both biofilm dry density and biofilm moisture content are required in order to predict the bioparticle density to be used for calculation of reactor biomass concentration. The mass balance principle was applied to calculate these two parameters:
P =1
453
(12)
where V~is the total bioparticle volume in the fluidized bed, HBA. V~ was experimentally determined as follows.
(15)
The correlation coefficient for the expression is -0.74. Combining this correlation with equation (2), an explicit expression for the bioparticle terminal settling velocity is obtained:
U, I_ 2 7 . S p ° 3036[ g7 " • Expansion relation
index-terminal
J
Reynolds
(16)
"
number
cor-
The experimental data summarized in Table 2 were used to prepare the plot of iog(~) vs log(U) to deter-
LEo T. MULCAHY a n d WEN K. SmEl4
454
Table 2. Data of bed expansion experimentation ~(um) 40 U* :0.361 :0.609 88 U :0.350 :0.605 167 U :0.380 E :0.560 207 U :0.300 :0.577 220 U :0.376 :0.615 268 U :0.394 :0.631 281 U :0.336 :0.581 318 U :0.431 :0.619 344 U :0.385 :0.651 420 U :0.379 :0.619 547 U :0.307 E :0.555 561 U :0.275 :0.595 590 U :0.302 :0.637 666 U :0.285 E :0.589 703 U :0.306 E :0.629 812 U :0.284 :0.591 858 U :0.461 E :0.658 1005 U :0.429 :0.630 1089 U :0.369 :0.659 1200 U :0.416 :0.655
0.440 0.625 0.406 0.607 0.430 0.589 0.341 0.604 0.466 0.635 0.460 0.654 0.391 0.605 0.4'75 0.621 0.424 0.677 0.405 0.639 0.332 0.560 0.313 0.611 0.362 0.668 0.316 0.611 0.345 0.639 0.361 0.600 0.512 0.660 0.482 0.655 0.418 0.661 0.471 0.678
0,500 0.640 0.463 0.631 0.494 0.598 0.391 0.607 0.571 0.669 0.514 0.670 0.438 0.620 0.533 0,642 0,460 0,679 0,525 0.661 0.372 0.596 0.360 0.621 0.426 0.679 0.350 0.630 0.406 0.662 0.402 0.621 0.579 0.685 0.565 0.680 0.477 0.685 0.541 0.691
0.566 0.659 0.537 0.651 0.600 0.629 0.484 0.641 0.692 0.680 0.618 0.680 0.514 0.639 0.615 0.655 0.539 0.705 0.610 0.682 0.426 0.600 0.431 0.645 0.506 0.702 0.416 0.638 0.489 0.700 0.450 0.669 0.671 0.702 0.674 0.712 0.561 0.710 0.659 0.720
0.660 0.670 0.635 0.660 0.681 0.641 0.579 0.662 0.781 0.709 0.671 0.701 0.619 0.661 0.718 0.688 0.600 0.719 0.720 0.686 0.459 0.618 0.531 0.675 0.590 0.729 0.499 0.665 0.570 0.722 0.522 0.680 0.759 0.725 0.772 0.735 0.638 0.721 0.732 0.734
0.760 0.691 0.760 0.690 0.779 0.659 0.681 0.687 0.850 0.710 0.760 0.719 0.738 0.691 0.790 0.695 0.679 0.740 0.822 0.725 0.520 0.635 0.672 0.710 0.681 0.750 0.560 0.679 0.658 0.750 0.612 0.705 0.890 0.750 0.880 0.750 0.710 0.739 0.810 0.751
0.970 0.720 0.908 0.715 0.890 0.673 0.770 0.706 0.939 0.736 0.829 0.720 0.845 0.708 0.908 0.720 0.810 0.760 0.949 0.740 0.610 0.651 0.789 0.724 0.759 0.768 0.615 0.692 0.779 0.761 0.690 0.718 1.090 0.780 0.975 0.779 0,789 0.760 0,916 0,762
1.300 0.760 1.130 0.740 1.000 0.690 0.881 0.730 1.130 0.759 0.960 0.755 0.990 0.731 1.120 0.749 0.922 0.791 1.010 0.751 0.738 0.688 0.890 0.758 0.861 0.779 0.719 0.710 0.835 0.779 0.839 0.759
1.140 0.714 1.080 0.750
1.460 0.745
1.110 0.770 1.180 0.760 1.370 0.780 1.040 0.805 1.240 0.790 0.850 0.701 0.955 0.762 0.979 0.801 0.846 0.740 0.940 0.795 0.980 0.780
0.938 0.719 1.160 0.800 1.230 0.819 0.975 0,759 1.190 0.839 1.140 0.818
1.270 0.777
1.230 0.800
0.965 0.785 1.050 0.795
*U in cm s ~ (experimentally measured using the stop watch and bucket approach).
mine whether or not the Richardson-Zaki correlation as defined by equation (1) is applicable for characterizing the FBBR fluidization phenomena. For 19 of the 20 sets of data, correaltion coefficients in excess of 0.99 were obtained, indicating that equation (1) provides an excellent description of denitrification Table 3. Data of bioparticle terminal settling velocity experimentation Biofilm thickness (/~m) 40 88 167 207 220 268 281 318 344 420 547 561 590 666 703 812 858 1005 1089 1200
Biofilm dry density (mgml -~) U, (cms ~) 57.6 70,6 66,8 62,1 73.6 32,6 62.0 56,1 30,8
52,9 45,7 37,0 26,6 20,8 36,6 26.8 34.7 31.1 24.7 21.1
6.17 5.15 5.41 4.75 5.02 4.87 4.16 4.50 3.24 4.23 4.12 3.67 3.09 3.45 3.03 2.89 3.50 3.17 2.87 2.95
FBBR fluidization phenomena. Typical examples of log(E)-log(U) plots are illustrated in Fig. 2. The least square analysis of the experimental data summarized in Tables 2 and 3 yields: n = 1 0 . 3 5 N ~ °'18.
(17)
The correlation coefficient for the expression is -0.43.
Performance of denitrification FBBR The denitrification FBBR was observed to be able to operate at significantly higher liquid throughputs while maintaining its excellent efficiency. Nitratenitrogen removal efficiencies in the range of 70-96% were achieved in this investigation when the denitrification FBBR was operated at NO3-N loadings of between I0.1 and 2 2 . 3 k g m - 3 d a y -t, HRTs of between 3 and 6 min, and feed NO3-N concentrations of between 16 and 57 mg 1-l. These denitrification efficiencies were obtained when the methanol supplied was excessive so that it was not a rate-limiting factor in the denitrification process. The equilibrium biomass concentrations observed in the denitrification FBBR ranged from 5 to 16 g 1-1, depending on the biofilm thickness maintained in the reactor.
Characteristics of denitrification FBBRs
describe the fluidization phenomena of the denitrification FBBR and therefore, provides a sound basis for biomass concentration prediction. An empirical correlation was developed which links the expansion index (n) in the Richardson-Zaki correlation to a measurable parameter, the terminal Reynolds number (N~,). Furthermore, an explicit expression, equation (16), was also developed which allows a direct calculation of the bioparticle terminal settling velocity (U,). Therefore, the biomass concentration in the denitrification FBBR under a given set of operating conditions can be independently predicted. These correlations are useful for determination of: (1) the expanded bed height to be maintained; (2) the equilibrium biofilm thickness (or biomass concentration) prevailed in the reactor; (3) the frequency of bioparticle wasting and biofilm separation; and (4) the amount of waste sludge generated.
2.0 8:88~m 1.0
I
0.8
0.6 0.5
i
I
0.3
I
E
o
]
455
]
2.0
8 : 2 0 7 g.m '1.0 0.8 0.6
Prediction of bioparticle terminal settling velocity
0.5
0.3 I 0.2
OO
I 0.4
I 0.6
0.8
I t.0
q[
Fig. 2 DISCUSSION
The fluidization characteristics of the denitrification FBBR has been defined in terms of measurable parameters in order to provide a convenient means for prediction of reactor biomass concentration. Experimental evidence obtained indicates that the Richardson-Zaki correlation, equation (1), is able to Table 4. Comparison of predicted and measured bioparticle terminal settling velocities d= (mm)
6 ~m)
U,p (cm s -t)
U~ (crn s -I)
% Error*
0.651 0.743 0.751 0.754 0.761 0.774 0.781 0.795 0.798 0.799 0.804 0,807 0.822 0.824 0.853 0.861 0.878 0.903 0.905 0.915 0.922 0.954
34 98 0 68 234 47 47 114 13 9 120 65 114 48 66 116 92 91 102 54 70 51
6.3 6.1 8.4 6.9 4.2 7.6 7.7 6.5 8.7 8.8 7.6 7.6 7.0 8.0 8.2 7.3 8.0 7.1 8.2 8.3 9.1 8.8
7.3 6.6 8.9 7.5 5.2 7.2 7.7 6.9 9.2 8.2 8.1 8.7 8.0 7.4 8.3 7.7 7.7 7.6 8.7 8.1 8.8 8.4
14 8 6 8 19 6 0 6 5 7 6 13 13 8 1 5 4 7 6 2 3 5
*O/oError= { I V , , - v . I } × 100/=.. U,p = predicted bioparticle terminal settling velocity. U~ = measured bioparticle terminal settling velocity as reported by Hermanowicz and Ganczarczyk 0983).
The ability of equation (16) for accurate prediction of the bioparticle terminal settling velocity was evaluated by comparing the predicted U, values with those measured in a denitrification FBBR as reported by Hermanowicz and Ganczarczyk (1983). The resuits are summarized in Table 4. A biofilm dry density of 65 mg ml -~ and a temperature of 20°C were used. In general, the relative errors observed between the predicted and measured Ut values are < 8% except for 4 sets of data, indicating that equation (16) is able to provide a good prediction of the bioparticle terminal settling velocity. The experimental approach used to obtain the bioparticle terminal settling velocity is simple and straightforward. The experimentation can be independently performed so that it is unaffected by the operating conditions of the FBBR concerned. However, because equation (16) is developed mainly on the basis of denitrification data, further refinement of this correlation with other types of data is recommended so that an unified correlation is available for a variety of applications.
Prediction of biomass denitrification FBBR
concentration
in
the
The prediction of biomass concentration in the denitrification FBBR using the fluidizaton correlations developed herein can proceed as follows: (1) Calculate p~ by substituting equations (10) and (11) into equation (6); (2) Calculate U, by substituting equation (6) into equation (16); (3) Calculate n by substituting equation (16) into equation (17); (4) Calculate E by substituting n into equation (1); and (5) Calculate X b y substituting ~ and equation (11) into equation (7).
456
LEo T. MuLc^mr and WEN K. SH[Eri o/
16--
(7) Calculate the resultant equilibrium bed porosity (E) at the specified superficial velocity (U) using equation (1); (8) Calculate the resultant trial expanded bed height (Hs) using a volume balance on solids within the reactor:
12--
i=1
s~
4
0 ~ *0' 0
#"-
/ I
t
I
I
4
8
12
16
Fig. 3 These predicted biomass concentrations were compared with those experimentally measured in this investigation as illustrated in Fig. 3. It is seen that the fluidization correlations developed herein provide a good prediction of biomass concentration in the denitrificaton FBBR. The relative errors are < 10%. The use of Sauter mean to account for particle size distribution is adequate because of the close agreement of predicted and measured biomass concentration values. The microscopic technique used for determination of particle size and biofilm thickness is adequate.
ENGINEERINGAPPLICATIONS The essential pieces of information for a specific application using the fluidization and biomass correlations developed herein include: (1) specifications of media; (2) volume of media; (3) characteristics of wastewater; and (4) superficial velocity. A prescribed expanded bed height is used as the design criterion. An iterative procedure has been developed to facilitate the application: (1) Estimate a biofilm thickness based on past experience, oe; (2) Calculate the bioparticle diameter:
(]8)
(3) Calculate the bioparticle density (p,) using equation (6); (4) Calculate the bioparticle terminal settling velocity (Us) using equation (16); (5) Calculate the terminal Reynolds number (NR<,): N~e, =
Utdppl #
(6) Calculate the expansion equation (17);
;
(20)
(9) Compare the calculated expanded bed height (/tn) with the prescribed expanded bed height (HB) and calculate the relative error; (10) If the relative error is unacceptable, return to step (1) and repeat the process with a new g; and (11) If the relative error is acceptable, use equation (7) to calculate the biomass concentration (X).
Xp (g~)
de = d~ + 2oe;
A(1 - ~) La,,j '
(19) index (n) using
The above iterative procedure can be repeated for different HB, U, and Vm values in order to select the design conditions which are most appropriate for a specific application.
Practical applications The fluidization and reactor biomass characteristics of a denitrification FBBR are affected by five parameters which are under direct control of the design engineer. They are: -----expanded bed height (Hn); --cross-sectional area of the reactor perpendicular to the wastewater flow (A); --media density (p,,); --media diameter (arm); and --media volume (V,,). A parameter which is indirectly controllable by the design engineer is the equilibrium biofilm thickness. For a given type of media, the equilibrium biofilm thickness is dependent on the superficial velocity, expanded bed height, and media volume. The biofilm thickness is the single most important parameter affecting the fluidization and reactor biomass concentration of a denitrification FBBR. The effect of biofilm thickness on the biomass concentration in a denitrification FBBR is illustrated in Fig. 4. Note that these curves are specific to the given superficial velocities and media characteristics. To the left of the maximum, the rate of biomass increase due to biofilm accumulation is faster than the rate of biomass decrease due to reduction of bioparticle number per unit fluidized volume caused by bed expansion. To the right, the opposite holds. If the objective was to maximize the biomass concentration for the given media characteristics, the FBBR should be operated so as to control the biofilm thickness at approx. 300 #m. However, thin biofilms in an FBBR are advantageous from the standpoint of bed stability against hydraulic shocks, as suggested in Fig. 5. The effect of media characteristics on the
Characteristics of denitrification FBBRs
15-
457
pm{orn1-1)
t20
t.4 80 io
40 2.4
5 I 1
I 2
I 3
U (cm s-~) I
I
O
200
4.00
I
600
Fig. 6
(p.m)
Fig. 4 fluidization phenomena of a denitrification FBBR can also be assessed, as illustrated in Fig. 6. In general, it appears that the use of larger or denser media in a denitrification FBBR improves its stability against hydraulic shocks. However, any such advantages should be weighed against the higher energy requirements for fluidization of these media.
CONCLUSIONS
An attempt has been made to develop the fluidization and biomass concentration correlations for the denitrification FBBR which are readily available for practical engineering applications. The fluidization
t20
correlation originally developed for rigid solid particles (i.e. Richardson-Zaki correlation) is equally applicable for description of fluidization characteristics of bioparticles. However, correlations for calculation of drag coefficient and expansion index should be specific for FBBR bioparticles. The fluidization and biomass concentration correlations developed herein are capable of predicting the bioparticle terminal settling velocity and biomass concentration in the denitrification FBBR with good accuracy. These correlations can be readily applied for prediction of biomass concentration and bed expansion of a denitrification FBBR operated under a variety of conditions. The experimental approach used in this investigation is simple and straightforward. Furthermore, the experimentation can be performed independent of the FBBR operation. Because the fluidization and biomass concentration correlations reported herein were developed mainly on the basis of denitrification data, further refinement of these correlations with other types of data is recommended. REFERENCES
80
.'3
(#.m)/ 40
I
I
I
2 U (cm s-~l
Fig. 5
I
3
Atkinson B., Cunningham J. D. and Pinches A. (1984) Biomass hold-ups and overall rates of substrate (glucose) uptake of support particles containing a mixed microbial culture. Chem. Engng Res. Des. 62, 155-164. Bailey J. E. and OUis D. F. (1977) Biochemical Engineering Fundamentals. McGraw-Hill, New York. Chen P. and Pei D. C. T. (1984) Fluidization characteristics of fine particles. Can. J. chem. Engng 62, 464-468. Hermanowicz S. W. and Ganczarczyk J. J. (1983) Some fluidization characteristics of biological beds. Biotechnol. Bioengng 25, 1321-1330. Lewis E. W. and Bowerman E. W. (1952) Fluidization of solid particles in liquids. Chem. Engng Prog. 48, 503-610. Mulcahy L. T. and LaMotta E. J. (1978) Mathematical model of the fluidized bed biofilm reactor. Report No. Env. E. 59-78-2, Department of Civil Engineering, University of Massachusetts at Amherst.
458
LEO T. MULCAHY and WEN K. SmEH
Ngian K. F. and Martin W. R. B. (1980) Biologically active fluidized beds: mechanical considerations. Biotechnol. Bioengng 22, 1007-1014. Patwardhan V. S. and Tien C. (1984) Distribution of solid particles in liquid fluidized beds. Can. J. chem. Engng 62, 46-54. Richardson J. F. and Zaki W. N. (1954) Sedimentation and fluidization: part I. Trans. lnstn chem. Engrs 32, 35-53. Shieh W. K. and Chen C. Y. (1984) Biomass hold-up correlations for a fluidized bed biofilm reactor. Chem. Engrs Res. Des. 62, 133-136. Shieh W. K. and Keenan J. D. (1986) Fluidized bed biofilm reactor for wastewater treatment. In Advances in Biochemical Engineering/Biotechnology (Edited by Fiechter A.), Vol. 33, pp. 131-169. Springer, Berlin. Shieh W. K., Sutton P. M. and Kos P. (1981) Predicting reactor biomass concentration in a fluidized-bed system. J. War. Pollut. Control Fed. 53, 1574-1584. Spcece R. E. (1983) Anaerobic biotechnology for industrial wastewater treatment. Envir. Sci. Technol. 17, 416A427A.
Sutton P. M., Shieh W. K., Kos P. and Dunning P. 1~.. (1981) Dorr-Oliver's Oxitron SystemTM fluidized-bed water and wastewater treatment process. In Biological Fluidized Bed Treatment of Water and Wastewater (Edited by Cooper P. F. and Atkinson B.), pp. 285-300. Ellis Horwood, London. Switzcnbaum M. S. (1983) Anaerobic fixed film wastewater treatment. Enzyme Microbiol. Technol. 5, 242-250. Van der Meer A. P., Blanchard C. M. R. J. P. and Wesselingh J. A. (1984) Mixing of particles in liquid fluidized beds. Chem. Engng Res. Des. 62, 214-222. Vasalos I. A., Rundell D. N., Megiris K. E. and Tjatjopoulos G. 3. (1982) Holdup correlations in slurry-solid fluidized beds A.I.Ch.E. Jl 28, 346-348. Webb C., Black G. M. and Atkinson B. (1983) Liquid fluidization of highly porous particles. Chem. Engng Res. Des. 61, 125-134. Wen C. Y. and Yu Y. H. (1962) Mechanics of fluidization. Chem. Engng Prog. Symp. Set. 62, 100-111.
0043-1354/87 $3.00+0.00 Copyright © 1987 Pergamon Journals Ltd
FLUIDIZATION A N D REACTOR BIOMASS CHARACTERISTICS OF THE DENITRIFICATION FLUIDIZED BED BIOFILM REACTOR LEO T. MULCAHYl and WEN K. SHIEH2. CBleached Board Division, Westvaco Corporation, Covington, VA 24426 and 2Department of Systems, University of Pennsylvania, Philadelphia, PA 19104, U.S.A. (Received October 1985)
Abstract--The fluldization and reactor biomass characteristics of the denitrification fluldized bed biofilm reactor (FBBR) were investigated. Experimental evidence obtained indicates that Richardson-Zaki correlation, which was developed for rigid solid particles, provides an excellent description of the fluldization mechanics of a denitrification FBBR. However, correlations for calculation of drag coefficient and expansion index should be modified to account for the FBBR characteristics that the degree of bed expansion increases with increased bioparticle size (i.e. increased biofilm thickness). The fluidization and reactor hiomass correlations developed in this investigation are capable of providing a direct and accurate prediction of biomass concentration and bed expansion in FBBRs designed for wastewater treatment applications. Engineering applications of these correlations are discussed. Key words--fluldized bed biofilm reactor (FBBR), fluldization, reactor biomass, bioparticle terminal settling velocity, expansion index, denitrification
p = Biofilm dry density (M L -3) Pt = Wastewater density (M L -3) Pm ffi Media density (M L -3) Ps = Bioparticle density (M L -3) /~ = Wastewater viscosity (M L - 1T - 1).
NOMENCLATURE
A = Cross-sectional area of the reactor perpendicular to the wastewater flow (L2) CD = Drag coefficient --Sauter mean diameter (L) d r = Diameter of particle i (L) dm= Media diameter (L) = Bioparticle diameter (L) -- Sauter mean diameter of media (L) dp --- Sauter mean diameter of bioparticle (L) g = Gravitational acceleration (L T -2) H s = Expanded bed height (L) H i = Expanded bed height after the sample was withdrawn (L) ~b = Trial expanded bed height (L) AHn = Change in the expanded bed height (L) mr = Number of particles with the diameter d~ n = Expansion index Np.,, -- Terminal Reynolds number AN = Number of bioparticles in the sample withdrawn from the reactor P = Biofdm moisture content U = Superficial velocity (L T -1) Ut = Bioparticle terminal settling velocity (L T- i) V,,--Media volume (L3) Vs = Total volume of bioparticles in the expanded bed (L 3) X = Biomass concentration (M L -3) X~ = Media concentration in the fluidized bed volume (gl -I) W~ = Weight of bioparticles withdrawn from the reactor
~
(M)
W, = Weight of sample withdrawn from the reactor (g) ffi Bed porosity 6 = Biofilrn thickness (L) = Trial biofilm thickness (L)
*To whom all correspondence should be addressed.
INTRODUCTION
The fluidized bed biofilm reactor (FBBR) is a recent process innovation in wastewater treatment technology. In contrast to conventional biofilm reactors in which media are fixed in space either by gravity or by direct attachment to the reactor wall, the media are retained in an F B B R by drag forces exerted by the upflowing wastewater. Immobilization o f microorganisms on the small, fluidized media as biofilms enables an F B B R to retain a high reactor biomass concentration and thereby operate at significantly reduced hydraulic retention times (HRTs) (Mulcahy and L a M o t t a , 1978; Atkinson et al., 1984). Fluidization also overcomes operating problems such as bed clogging and high pressure drop which would occur if small media were employed in conventional biofilm reactors. A further advantage is the possible elimination of the secondary clarifier (Sutton et al., 1981). F B B R s have been investigated for carbon oxidation, nitrification, denitrification, and anaerobic treatment, for a wide variety o f waters and wastewaters. A number of full-scale F B B R s are currently in operation. Detailed descriptions of these applications can be found elsewhere (Speece, 1983; Switzenbaum, 1983; Shieh and Keenan, 1986). Because all the claimed advantages of an F B B R are 451
452
LEO T. MULCAHYand WEN K. SmEH
virtually derived from its ability to retain a high reactor biomass concentration, therefore, an accurate prediction of reactor biomass concentration under different operating conditions becomes a vital design and operation parameter. F o r a given set of operating conditions, an analysis of the fluidization phenomena in an F B B R yields two critical pieces of information: equilibrium biofilm thickness and bed porosity, which in turn can be used to calculate the reactor biomass concentration using total mass balance principle (Mulcahy and LaMotta, 1978; Shieh et aL, 1981; Shieh and Chen, 1984). This paper describes and discusses a laboratory investigation aimed at obtaining fluidization and reactor biomass correlations for the denitrification FBBR. Engineering applications of these correlations are also discussed.
FLUIDIZATION AND REACTOR BIOMASS CORRELATIONS
The pertinent correlations for characterizing fluidization phenomena and predicting biomass concentration in an F B B R are summarized in Table 1. In essence, these correlations were derived based on the assumption that the bioparticle (i.e. biofilm-coated media) size is very small as compared to the dimensions of the F B B R (Lewis and Bowerman, 1952; Richardson and Zaki, 1954; Wen and Yu, 1962; Ngian and Martin, 1980; Vasalos et al., 1982; Hermanowicz and Ganczarczyk, 1983; Webb et al., t983; Chen and Pei, 1984; Patwardhan and Tien, 1984; Van der Meer, 1984). Furthermore, the individual bioparticle is assumed to have a smooth surface and a spherical shape. Once the coefficients in equations (3) and (4) are quantified with experimental data, the bioparticle terminal settling velocity (U,) can be evaluated by equation (2), the bed porosity (E) by equation (1), and the biomass concentration (X) by equation (7). Details involved in the derivation of these correlations shown in Table 1 can be found elsewhere (Mulcahy and LaMotta, 1978).
w
M
eel
Do Oo
.'l
sP eol**1
"I
• 01
~
F
Fp 1
Fp z
Fig. 1
Table 1. Fluidization and reactor biomass correlations of the FBBR Remarks
Correlation (I) U I U , = :
(2) Ut = [0.75(p,-p~)gdp/(Copl)]°.~ (3) Co = aNR~ (4) n = cNR~ (5) NRe,= U,a:,/U (6) p~ = p . ( d . / d p ) ~ + [p/(I
MATERIALS AND METHODS Laboratory-scale FBBR A laboratory-scale denitrification FBBR as illustrated in Fig. 1 was employed to obtain required experimental data. The reactor was fabricated from a 3.8 × 175cm Plexiglas column, with sampling ports (SP) (22 em apart) installed along the column length to remove liquid and bioparticles samples. Pea gravel (12 em deep) was placed at the reactor bottom to provide uniform flow distribution and prevent backflow of media. Spherical glass beads (density: 2.42gm1-1; Sauter mean dia: 680/am) were used as the media. The reactor feed consisted of the clarified effluent from an extended aeration unit treating a municipal wastewater; methanol and supplemental sodium nitrate were added. The resulting feed NO3-N concentrations varied from 16 to 57 mg 1-~. The feed (F) was pumped into a 750ml flask located in a feed vessel (FV) by a centrifugal pump (FP~). The intake of the FBBR feed pump (FP 2) was also located
- P)] x [I - (d./d,,) 3]
(7) X = p(l - ~)[I - (d, ldp)~l
Richardson-Zaki correlation which relates the bed porosity (¢) and superficial velocity (U) in a fluidized bed reactor. Correlation of bioparticle terminal settling velocity (U,) in an FBBR. Empirical correlation which relates the drag coefficient(Co) and terminal Reynolds number (NR¢,)in an FBBR. a and b are empirical coefficients. Empirical correlation which relates the expansion index (n) and terminal Reynolds number in an FBBR. c and d are empirical coefficients. Definition of terminal Reynolds number. Definition of bioparticle density. Correlation for biomass concentration calculation.
Characteristics of denitrification FBBRs in the feed vessel. Because the feed rate to the feed vessel was at least 50% greater than the outflow due to the FBBR feed pump, the contents of the feed vessel were not diluted by the bulk contents. However, in the event of feed interruptions, the FBBR would continue to operate in a total recirculation fashion. Temperature and pH of the FBBR were controlled at 22 _+ I°C and 6.9 _+0.1, respectively.
Seeding procedure Seeding of the FBBR was accomplished by expanding the clean media bed with feed solution at a superficial velocity of 0.7 o n s -~. Within a week, a growth interface, which separated clean from biofilm-coated media, appeared and propagated downward through the expanded bed. A mixer (M) was used to prevent the buildup of overgrown bioparticles at the top of the bed. The position of the mixer could be adjusted to obtain a specific biofilm thickness in the FBBR. This practice also insured the uniform distribution of biofilm thickness throughout the bed. Determination of relevant parameters Particle size and biofilm thickness. A microscope equipped with an ocular micrometer was used to determine bioparticle diameter, media diameter, and biofilm thickness. A volume surface mean or Sauter mean was used for interpretation of particle size in order to account for size distribution observed (Bailey and Ollis, 1977): d = Y~mid~ ]~mjd~
(8)
where d is the Sauter mean diameter, and mi is the number of particles with the diameter d,. The biofilm thickness is: 3 - d p - a~,~ 2
p = pm(X/Xm)(lO-3)/[(dm/dp) 3 -- 1]
(10)
and
(p/p~,)(Xm)[(dp/dm) 3 - l] w s - p, + x ~ { ( p , / p ~ , ) [ ( a , / d ~ )
3-
Bed expansion experimentation The basis for calculation of bed porosity in terms of the easily observable parameter, expanded bed height, was established by:
v~ H,A
w;
AN =
-3 (13) (p,)Otd,/6) where W~ is the weight of bioparticles in the sample. The total volume of bioparticles remained in the expanded bed could then be calculated as:
V. (H'B)(AN)(TE3~/6)
(14)
(AHB) where H i is the expanded bed height after the sample was withdrawn. Therefore, the bed porosity could be directly related to the expanded bed height by equation (12). The procedure outlined above was repeated for 20 different biofilm thicknesses ranging from 40 to 1200/~m. The biofilm thickness was varied by varying the degree of bed expansion maintained in the reactor. This was accomplished by changing the location of the mixer in the reactor. In general, the lower the mixer was, the thinner were the resultant biofilms.
Bioparticle terminal settling velocity experimentation During each bed expansion experimental run, a small portion of a bioparticle sample withdrawn from the expanded bed was allowed to settle through a 14 x 195 cm water-filled Plexiglas column. By timing bioparticle descent through a 120 cm segment of the column, the bioparticle terminal settling velocity was determined. By repeating the procedure outlined above for 20 different biofilm thicknesses, a data base for quantifying the coefficients in equation (3) was obtained. RESULTS The experimental results obtained are summarized in Tables 2 and 3. Both biofilm thicknesses and bioparticle terminal settling velocities reported are average values obtained from a number of measurements. The biofilm dry densities were calculated using equation (10).
Drag coefficient-terminal Reynolds number correlation F o r each experimental run, the terminal Reynolds number and the corresponding drag coefficient were determined. The least square analysis was used to obtain the best fit line. The resulting drag coefficientterminal Reynolds number correlation, which is specific for denitrification F B B R bioparticles, is: CD = 36.66N~ 2/3.
(l l)
l]}
where p is the biofilm dry density, g l-l; Pt is the wastewater density, g 1- i; p,, is the media density, g 1- ~; X is the biomass concentration, mg 1-~; X~ is the media concentration in the fluidized bed, g l-t; and Ws is the weight of the sample, g.
= 1- -
At a given superficial velocity, a small portion of bed materials was withdrawn from the reactor and the change in the expanded bed height (AHs) was noted. The number of bioparticles in the sample (AN) was calculated as:
(9)
where 3 is the biofilm thickness, ~p is the Sauter mean diameter of bioparticles, and dm is the Sauter mean diameter of media. 8ioma.ss concentration. The biomass concentration was determined as the total attached volatile solids concentration, which was determined as the difference between the total volatile solids concentration of the bed sample (i.e. bioparticlcs and liquid) and the total volatile solids concentration of the liquid sample. Details involved in this experimental procedure can be found elsewhere (Mulcahy and LaMotta, 1978; Shieh et al., 1981). 8iofilm dry density and moisture content. Both biofilm dry density and biofilm moisture content are required in order to predict the bioparticle density to be used for calculation of reactor biomass concentration. The mass balance principle was applied to calculate these two parameters:
P =1
453
(12)
where V~is the total bioparticle volume in the fluidized bed, HBA. V~ was experimentally determined as follows.
(15)
The correlation coefficient for the expression is -0.74. Combining this correlation with equation (2), an explicit expression for the bioparticle terminal settling velocity is obtained:
U, I_ 2 7 . S p ° 3036[ g7 " • Expansion relation
index-terminal
J
Reynolds
(16)
"
number
cor-
The experimental data summarized in Table 2 were used to prepare the plot of iog(~) vs log(U) to deter-
LEo T. MULCAHY a n d WEN K. SmEl4
454
Table 2. Data of bed expansion experimentation ~(um) 40 U* :0.361 :0.609 88 U :0.350 :0.605 167 U :0.380 E :0.560 207 U :0.300 :0.577 220 U :0.376 :0.615 268 U :0.394 :0.631 281 U :0.336 :0.581 318 U :0.431 :0.619 344 U :0.385 :0.651 420 U :0.379 :0.619 547 U :0.307 E :0.555 561 U :0.275 :0.595 590 U :0.302 :0.637 666 U :0.285 E :0.589 703 U :0.306 E :0.629 812 U :0.284 :0.591 858 U :0.461 E :0.658 1005 U :0.429 :0.630 1089 U :0.369 :0.659 1200 U :0.416 :0.655
0.440 0.625 0.406 0.607 0.430 0.589 0.341 0.604 0.466 0.635 0.460 0.654 0.391 0.605 0.4'75 0.621 0.424 0.677 0.405 0.639 0.332 0.560 0.313 0.611 0.362 0.668 0.316 0.611 0.345 0.639 0.361 0.600 0.512 0.660 0.482 0.655 0.418 0.661 0.471 0.678
0,500 0.640 0.463 0.631 0.494 0.598 0.391 0.607 0.571 0.669 0.514 0.670 0.438 0.620 0.533 0,642 0,460 0,679 0,525 0.661 0.372 0.596 0.360 0.621 0.426 0.679 0.350 0.630 0.406 0.662 0.402 0.621 0.579 0.685 0.565 0.680 0.477 0.685 0.541 0.691
0.566 0.659 0.537 0.651 0.600 0.629 0.484 0.641 0.692 0.680 0.618 0.680 0.514 0.639 0.615 0.655 0.539 0.705 0.610 0.682 0.426 0.600 0.431 0.645 0.506 0.702 0.416 0.638 0.489 0.700 0.450 0.669 0.671 0.702 0.674 0.712 0.561 0.710 0.659 0.720
0.660 0.670 0.635 0.660 0.681 0.641 0.579 0.662 0.781 0.709 0.671 0.701 0.619 0.661 0.718 0.688 0.600 0.719 0.720 0.686 0.459 0.618 0.531 0.675 0.590 0.729 0.499 0.665 0.570 0.722 0.522 0.680 0.759 0.725 0.772 0.735 0.638 0.721 0.732 0.734
0.760 0.691 0.760 0.690 0.779 0.659 0.681 0.687 0.850 0.710 0.760 0.719 0.738 0.691 0.790 0.695 0.679 0.740 0.822 0.725 0.520 0.635 0.672 0.710 0.681 0.750 0.560 0.679 0.658 0.750 0.612 0.705 0.890 0.750 0.880 0.750 0.710 0.739 0.810 0.751
0.970 0.720 0.908 0.715 0.890 0.673 0.770 0.706 0.939 0.736 0.829 0.720 0.845 0.708 0.908 0.720 0.810 0.760 0.949 0.740 0.610 0.651 0.789 0.724 0.759 0.768 0.615 0.692 0.779 0.761 0.690 0.718 1.090 0.780 0.975 0.779 0,789 0.760 0,916 0,762
1.300 0.760 1.130 0.740 1.000 0.690 0.881 0.730 1.130 0.759 0.960 0.755 0.990 0.731 1.120 0.749 0.922 0.791 1.010 0.751 0.738 0.688 0.890 0.758 0.861 0.779 0.719 0.710 0.835 0.779 0.839 0.759
1.140 0.714 1.080 0.750
1.460 0.745
1.110 0.770 1.180 0.760 1.370 0.780 1.040 0.805 1.240 0.790 0.850 0.701 0.955 0.762 0.979 0.801 0.846 0.740 0.940 0.795 0.980 0.780
0.938 0.719 1.160 0.800 1.230 0.819 0.975 0,759 1.190 0.839 1.140 0.818
1.270 0.777
1.230 0.800
0.965 0.785 1.050 0.795
*U in cm s ~ (experimentally measured using the stop watch and bucket approach).
mine whether or not the Richardson-Zaki correlation as defined by equation (1) is applicable for characterizing the FBBR fluidization phenomena. For 19 of the 20 sets of data, correaltion coefficients in excess of 0.99 were obtained, indicating that equation (1) provides an excellent description of denitrification Table 3. Data of bioparticle terminal settling velocity experimentation Biofilm thickness (/~m) 40 88 167 207 220 268 281 318 344 420 547 561 590 666 703 812 858 1005 1089 1200
Biofilm dry density (mgml -~) U, (cms ~) 57.6 70,6 66,8 62,1 73.6 32,6 62.0 56,1 30,8
52,9 45,7 37,0 26,6 20,8 36,6 26.8 34.7 31.1 24.7 21.1
6.17 5.15 5.41 4.75 5.02 4.87 4.16 4.50 3.24 4.23 4.12 3.67 3.09 3.45 3.03 2.89 3.50 3.17 2.87 2.95
FBBR fluidization phenomena. Typical examples of log(E)-log(U) plots are illustrated in Fig. 2. The least square analysis of the experimental data summarized in Tables 2 and 3 yields: n = 1 0 . 3 5 N ~ °'18.
(17)
The correlation coefficient for the expression is -0.43.
Performance of denitrification FBBR The denitrification FBBR was observed to be able to operate at significantly higher liquid throughputs while maintaining its excellent efficiency. Nitratenitrogen removal efficiencies in the range of 70-96% were achieved in this investigation when the denitrification FBBR was operated at NO3-N loadings of between I0.1 and 2 2 . 3 k g m - 3 d a y -t, HRTs of between 3 and 6 min, and feed NO3-N concentrations of between 16 and 57 mg 1-l. These denitrification efficiencies were obtained when the methanol supplied was excessive so that it was not a rate-limiting factor in the denitrification process. The equilibrium biomass concentrations observed in the denitrification FBBR ranged from 5 to 16 g 1-1, depending on the biofilm thickness maintained in the reactor.
Characteristics of denitrification FBBRs
describe the fluidization phenomena of the denitrification FBBR and therefore, provides a sound basis for biomass concentration prediction. An empirical correlation was developed which links the expansion index (n) in the Richardson-Zaki correlation to a measurable parameter, the terminal Reynolds number (N~,). Furthermore, an explicit expression, equation (16), was also developed which allows a direct calculation of the bioparticle terminal settling velocity (U,). Therefore, the biomass concentration in the denitrification FBBR under a given set of operating conditions can be independently predicted. These correlations are useful for determination of: (1) the expanded bed height to be maintained; (2) the equilibrium biofilm thickness (or biomass concentration) prevailed in the reactor; (3) the frequency of bioparticle wasting and biofilm separation; and (4) the amount of waste sludge generated.
2.0 8:88~m 1.0
I
0.8
0.6 0.5
i
I
0.3
I
E
o
]
455
]
2.0
8 : 2 0 7 g.m '1.0 0.8 0.6
Prediction of bioparticle terminal settling velocity
0.5
0.3 I 0.2
OO
I 0.4
I 0.6
0.8
I t.0
q[
Fig. 2 DISCUSSION
The fluidization characteristics of the denitrification FBBR has been defined in terms of measurable parameters in order to provide a convenient means for prediction of reactor biomass concentration. Experimental evidence obtained indicates that the Richardson-Zaki correlation, equation (1), is able to Table 4. Comparison of predicted and measured bioparticle terminal settling velocities d= (mm)
6 ~m)
U,p (cm s -t)
U~ (crn s -I)
% Error*
0.651 0.743 0.751 0.754 0.761 0.774 0.781 0.795 0.798 0.799 0.804 0,807 0.822 0.824 0.853 0.861 0.878 0.903 0.905 0.915 0.922 0.954
34 98 0 68 234 47 47 114 13 9 120 65 114 48 66 116 92 91 102 54 70 51
6.3 6.1 8.4 6.9 4.2 7.6 7.7 6.5 8.7 8.8 7.6 7.6 7.0 8.0 8.2 7.3 8.0 7.1 8.2 8.3 9.1 8.8
7.3 6.6 8.9 7.5 5.2 7.2 7.7 6.9 9.2 8.2 8.1 8.7 8.0 7.4 8.3 7.7 7.7 7.6 8.7 8.1 8.8 8.4
14 8 6 8 19 6 0 6 5 7 6 13 13 8 1 5 4 7 6 2 3 5
*O/oError= { I V , , - v . I } × 100/=.. U,p = predicted bioparticle terminal settling velocity. U~ = measured bioparticle terminal settling velocity as reported by Hermanowicz and Ganczarczyk 0983).
The ability of equation (16) for accurate prediction of the bioparticle terminal settling velocity was evaluated by comparing the predicted U, values with those measured in a denitrification FBBR as reported by Hermanowicz and Ganczarczyk (1983). The resuits are summarized in Table 4. A biofilm dry density of 65 mg ml -~ and a temperature of 20°C were used. In general, the relative errors observed between the predicted and measured Ut values are < 8% except for 4 sets of data, indicating that equation (16) is able to provide a good prediction of the bioparticle terminal settling velocity. The experimental approach used to obtain the bioparticle terminal settling velocity is simple and straightforward. The experimentation can be independently performed so that it is unaffected by the operating conditions of the FBBR concerned. However, because equation (16) is developed mainly on the basis of denitrification data, further refinement of this correlation with other types of data is recommended so that an unified correlation is available for a variety of applications.
Prediction of biomass denitrification FBBR
concentration
in
the
The prediction of biomass concentration in the denitrification FBBR using the fluidizaton correlations developed herein can proceed as follows: (1) Calculate p~ by substituting equations (10) and (11) into equation (6); (2) Calculate U, by substituting equation (6) into equation (16); (3) Calculate n by substituting equation (16) into equation (17); (4) Calculate E by substituting n into equation (1); and (5) Calculate X b y substituting ~ and equation (11) into equation (7).
456
LEo T. MuLc^mr and WEN K. SH[Eri o/
16--
(7) Calculate the resultant equilibrium bed porosity (E) at the specified superficial velocity (U) using equation (1); (8) Calculate the resultant trial expanded bed height (Hs) using a volume balance on solids within the reactor:
12--
i=1
s~
4
0 ~ *0' 0
#"-
/ I
t
I
I
4
8
12
16
Fig. 3 These predicted biomass concentrations were compared with those experimentally measured in this investigation as illustrated in Fig. 3. It is seen that the fluidization correlations developed herein provide a good prediction of biomass concentration in the denitrificaton FBBR. The relative errors are < 10%. The use of Sauter mean to account for particle size distribution is adequate because of the close agreement of predicted and measured biomass concentration values. The microscopic technique used for determination of particle size and biofilm thickness is adequate.
ENGINEERINGAPPLICATIONS The essential pieces of information for a specific application using the fluidization and biomass correlations developed herein include: (1) specifications of media; (2) volume of media; (3) characteristics of wastewater; and (4) superficial velocity. A prescribed expanded bed height is used as the design criterion. An iterative procedure has been developed to facilitate the application: (1) Estimate a biofilm thickness based on past experience, oe; (2) Calculate the bioparticle diameter:
(]8)
(3) Calculate the bioparticle density (p,) using equation (6); (4) Calculate the bioparticle terminal settling velocity (Us) using equation (16); (5) Calculate the terminal Reynolds number (NR<,): N~e, =
Utdppl #
(6) Calculate the expansion equation (17);
;
(20)
(9) Compare the calculated expanded bed height (/tn) with the prescribed expanded bed height (HB) and calculate the relative error; (10) If the relative error is unacceptable, return to step (1) and repeat the process with a new g; and (11) If the relative error is acceptable, use equation (7) to calculate the biomass concentration (X).
Xp (g~)
de = d~ + 2oe;
A(1 - ~) La,,j '
(19) index (n) using
The above iterative procedure can be repeated for different HB, U, and Vm values in order to select the design conditions which are most appropriate for a specific application.
Practical applications The fluidization and reactor biomass characteristics of a denitrification FBBR are affected by five parameters which are under direct control of the design engineer. They are: -----expanded bed height (Hn); --cross-sectional area of the reactor perpendicular to the wastewater flow (A); --media density (p,,); --media diameter (arm); and --media volume (V,,). A parameter which is indirectly controllable by the design engineer is the equilibrium biofilm thickness. For a given type of media, the equilibrium biofilm thickness is dependent on the superficial velocity, expanded bed height, and media volume. The biofilm thickness is the single most important parameter affecting the fluidization and reactor biomass concentration of a denitrification FBBR. The effect of biofilm thickness on the biomass concentration in a denitrification FBBR is illustrated in Fig. 4. Note that these curves are specific to the given superficial velocities and media characteristics. To the left of the maximum, the rate of biomass increase due to biofilm accumulation is faster than the rate of biomass decrease due to reduction of bioparticle number per unit fluidized volume caused by bed expansion. To the right, the opposite holds. If the objective was to maximize the biomass concentration for the given media characteristics, the FBBR should be operated so as to control the biofilm thickness at approx. 300 #m. However, thin biofilms in an FBBR are advantageous from the standpoint of bed stability against hydraulic shocks, as suggested in Fig. 5. The effect of media characteristics on the
Characteristics of denitrification FBBRs
15-
457
pm{orn1-1)
t20
t.4 80 io
40 2.4
5 I 1
I 2
I 3
U (cm s-~) I
I
O
200
4.00
I
600
Fig. 6
(p.m)
Fig. 4 fluidization phenomena of a denitrification FBBR can also be assessed, as illustrated in Fig. 6. In general, it appears that the use of larger or denser media in a denitrification FBBR improves its stability against hydraulic shocks. However, any such advantages should be weighed against the higher energy requirements for fluidization of these media.
CONCLUSIONS
An attempt has been made to develop the fluidization and biomass concentration correlations for the denitrification FBBR which are readily available for practical engineering applications. The fluidization
t20
correlation originally developed for rigid solid particles (i.e. Richardson-Zaki correlation) is equally applicable for description of fluidization characteristics of bioparticles. However, correlations for calculation of drag coefficient and expansion index should be specific for FBBR bioparticles. The fluidization and biomass concentration correlations developed herein are capable of predicting the bioparticle terminal settling velocity and biomass concentration in the denitrification FBBR with good accuracy. These correlations can be readily applied for prediction of biomass concentration and bed expansion of a denitrification FBBR operated under a variety of conditions. The experimental approach used in this investigation is simple and straightforward. Furthermore, the experimentation can be performed independent of the FBBR operation. Because the fluidization and biomass concentration correlations reported herein were developed mainly on the basis of denitrification data, further refinement of these correlations with other types of data is recommended. REFERENCES
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Atkinson B., Cunningham J. D. and Pinches A. (1984) Biomass hold-ups and overall rates of substrate (glucose) uptake of support particles containing a mixed microbial culture. Chem. Engng Res. Des. 62, 155-164. Bailey J. E. and OUis D. F. (1977) Biochemical Engineering Fundamentals. McGraw-Hill, New York. Chen P. and Pei D. C. T. (1984) Fluidization characteristics of fine particles. Can. J. chem. Engng 62, 464-468. Hermanowicz S. W. and Ganczarczyk J. J. (1983) Some fluidization characteristics of biological beds. Biotechnol. Bioengng 25, 1321-1330. Lewis E. W. and Bowerman E. W. (1952) Fluidization of solid particles in liquids. Chem. Engng Prog. 48, 503-610. Mulcahy L. T. and LaMotta E. J. (1978) Mathematical model of the fluidized bed biofilm reactor. Report No. Env. E. 59-78-2, Department of Civil Engineering, University of Massachusetts at Amherst.
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