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Biofilm and biomass characteristics in high-performance fluidized-bed biofilm reactors
ARTICLE IN PRESS
Water Research 38 (2004) 4262–4270 www.elsevier.com/locate/watres
Biofilm and biomass characteristics in high-performance fluidized-bed biofilm reactors Fahid K.J. Rabah, Mohamed F. Dahab Department of Civil Engineering, University of Nebraska-Lincoln, W348 Nebraska Hall, Lincoln, NE 68588-0531, USA Received 2 April 2004
Abstract Two laboratory-scale fluidized-bed biofilm reactors (FBBRs) were used to investigate the biomass concentration and the biofilm characteristics in a high-performance FBBR used for the denitrification of exceptionally high-nitrate wastewater (1000 mg N/L). Reported correlations by other workers for predicting the biomass concentration in FBBR were examined for their validity in comparison with the experimental results of this study and the best set of applicable correlations was recommended. The effects of the two main operational parameters, the superficial velocity and nitrogen loading rate on the biomass concentration in the FBBR were also studied. Correlations for the drag coefficient and the expansion index from the literature, together with the biofilm dry density correlation produced from this study were found to produce the best prediction of the FBBR biomass concentration compared to other reported correlations. The average biomass concentration in the FBBR decreased with the increase of the superficial velocity in the range of 45–65 m/h at all applied nitrogen loadings (i.e. 6, 8, 12 and 16 kg N/m3bed d). r 2004 Elsevier Ltd. All rights reserved. Keywords: Biofilm characteristics; Biomass concentration; Denitrification; Fluidized beds; Nutrient removal
1. Introduction Fluidized-bed biofilm reactors (FBBR) for treating industrial and municipal wastewater have been known for more than 20 years (Rabah, 2003). The results from laboratory and field pilot-scale studies have consistently illustrated the technical advantage of the fluidized bed over most other suspended and attached growth biological systems. Typically, the efficiency of the FBBR can be as much as 10 times that of the activated sludge system and it typically occupies about 10% of the space required by stirred tank reactors of similar capacities (Rabah, 2003). This is attributed to the high biomass Corresponding author. Tel.: +1-402-472-5020; fax: +1402-472-8934. E-mail address: [email protected] (M.F. Dahab).
concentration that can be maintained in FBBRs compared to that in the activated sludge system; 40,000 mg/L vs. 3000 mg/L (Shieh et al., 1981). Furthermore, unlike the suspended growth processes, there is no need to incorporate special measures in the FBBR process (e.g., sedimentation and biomass recirculation) to retain the biomass in culture due to the attachment of the biomass to the fluidized media (Cattaneo et al., 2003). The main advantages of an FBBR over other suspended and attached growth treatment systems are 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 (Mulcahy and Shieh, 1987). The prediction of biomass concentration in FBBRs for a given set of
0043-1354/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2004.08.012
ARTICLE IN PRESS F.K.J. Rabah, M.F. Dahab / Water Research 38 (2004) 4262–4270
operating conditions depends mainly on the fluidization mechanics within the reactor, the biofilm characteristics and the carrier media characteristics. A mathematical expression correlating the biomass concentration in FBBRs to these factors was developed by Sheih et al. (1981) based on mass balance principles as follows: " 3 # dm X ¼ X f ð1 Þ 1 ; (1) dp where X is the biomass concentration per unit volume of the FBBR (M/L3), Xf is the dry density of the biofilm(M/L3), e is the fluidized-bed porosity, dm is the carrier media diameter (L), dp is the bioparticle diameter (L). In this correlation, the fluidization mechanics are characterized by the bed porosity while the biofilm characteristics are represented by the biofilm dry density and the bioparticle diameter. The effect of the carrier material is represented by the media diameter. Many experimental and theoretical correlations have been proposed to predict the bed porosity and to define the relation between the biofilm dry density and the biofilm thickness. The Richardson and Zaki (1954) correlation relating the bed porosity to the superficial velocity and the particle terminal settling velocity is generally used to estimate the bed porosity: 1=n Vs ¼ ; (2) Vt where Vs is the superficial velocity (L/T), Vt is the bioparticle terminal settling velocity (L/T), and n is the expansion index. The terminal velocity is defined as 4gd p ðrp rw Þ 1=2 Vt ¼ ; 3C d rw
(3) 2
where g is the gravitational acceleration (L /T), rp is the bioparticle density (M/L3), rw is the water density (M/ L3), and C d is the drag coefficient. Many mathematical expressions have been developed for the drag coefficient (Table 1) and for the expansion index (Table 2). Unfortunately, these correlations result in different predictions of bed porosity under the same operational conditions and consequently result in considerable Table 1 Reported equations for the drag coefficient for bioparticles in FBBRs in relation to the Reynolds Number (Grady et al., 1999) Equation
Source
(a) Cd=17.1Re0.47 t
Hermanowicz and Ganczarczyk (1983) Mulcahy and Shieh (1987) Ro and Neethling (1990) Chang et al. (1991)
(b) Cd=36.66Re0.67 t (c) Cd=24Re0.1 +21.55Re0.518 t t 0.33 (d) Cd=24Re1 t +3.6Ret
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Table 2 Reported equations for the expansion index for FBBRs Equation
Source 0.2576
(e) n=47.36Ga
, 1000oGao 15000
(f) n=4.45Re0.1 t (g) n=10.35Re0.18 t (h) n=8.733Re0.341 t (i) n=4.26–0.73 log Ret a
a
Mulcahy and LaMotta (1978) Richardson and Zaki (1954) Mulcahy and Shieh (1987) Harada et al. (1987) Nieuwstad (1984)
Ga=Galileo number.
Table 3 Reported equations for the relationship between the biofilm thickness (Lf) and the biofilm dry density (Xf) in FBBRs Equation
Source
Xf=65 mg/cm for 0oLf p300 mm Xf=96.8–0.106Lf mg/cm3 for 300oLfp630 mm Xf=30 mg/cm3 for Lf4630 mm 3
Xf=104.3–0.125Lf mg/cm3 for Lfo622 mm Xf=30 mg/cm3 for Lf4622 mm
Mulcahy and LaMotta (1978b)
Boaventura and Rodrigues (1988)
Xf=120(Lf/180)3.7 mg/cm3 for Lfo180 mm Xf=120(Lf/180)1.8 mg/cm3 for Lf4180 mm
Hermanowicz and Cheng (1990)
Xf=191.4–0.224Lf mg/cm3 for Lfo593 mm Xf=58.6 mg/cm3 for Lf4593 mm
Coelhoso et al. (1992)
difference in biomass concentration prediction in the FBBR when applying Eq. (1). On the other hand, many experimental correlations relating the biofilm dry density and the biofilm thickness were proposed by several researchers (Table 3). The great variation in the prediction of these relations is obvious. As a result, the prediction of biomass concentration will vary considerably. The work described herein was designed to investigate the biomass concentration and the biofilm characteristics in a high-performance FBBR used for the denitrification of exceptionally high-nitrate wastewater (1000 mg N/L). High-nitrate wastewater is typically generated from industries such as cellophane, petrochemical, explosives, fertilizers, pectin and many metal-
ARTICLE IN PRESS F.K.J. Rabah, M.F. Dahab / Water Research 38 (2004) 4262–4270
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finishing industries (Chen and Chen, 2000; Glass and Silverstein, 1999). The main objectives of this study are: 1. To test the validity of the proposed expressions for the prediction of bed porosity and for the estimation of the biofilm dry density in correlation with the biofilm thickness in high-performance FBBR used for denitrification. 2. To propose a suitable set of correlations for Cd, n and Xf to be used for predicting the biomass concentration in FBBRs based on the experimental results. 3. To determine the effect of Vs and the nitrogen loading rate on the biomass concentration in the FBBR. 4. To estimate the biomass concentration, biofilm thickness and bed porosity variations along the height of the FBBR.
2.2. Synthetic wastewater The synthetic wastewater was prepared using deionized water in addition to other chemicals. Potassium nitrate was added as the nitrogen source at a concentration of 1000 mg NO 3 –N/L. Methanol was added as the carbon source at a concentration of 3000 mg CH3OH/L. Trace mineral constituents essential to the bacterial growth added per liter were: 0.85 mg FeSO4 7H2O, 0.25 mg Na2MoO4 2H2O, 0.157 mg MnSO4 7H2O and 33 mg NaHCO3. Sodium sulfite and cobalt chloride were added at concentrations of 20 and 0.55 mg/L, respectively, to reduce the oxygen concentration to below 0.5 mg/L to ensure anoxic conditions in the reactors. Monobasic and dibasic potassium phosphates were added as a buffer system. The two reactors were seeded with an inoculum from the anoxic zone of the activated sludge tank at the Theresa Street Wastewater Treatment Plant in Lincoln, NE.
2. Materials and methods 2.3. Operating conditions 2.1. Laboratory-scale fluidized-bed reactors The experimental set-up used in this study consisted mainly of two identical upflow columns. A schematic diagram of one fluidized-bed column installation is shown in Fig. 1. Each column consisted of a 2 m Plexiglas tube with a 76 mm inner diameter. The lower end of the column was packed up with a 100 mm layer of 4 mm diameter gravel to obtain uniform fluid flow in the bed and to avoid backflow of the media. The reactor was loaded with 2 kg of uniform sand as a biofilm carrier to a settled depth of 0.30 m. The sand had a mean diameter of 0.84 mm, a specific gravity of 2.65, a porosity of 0.42, and a specific surface area of 4200 m2/m3. Synthetic wastewater was pumped through a perforated flange to the bottom of the reactor from a 200 L sealed plastic tank using a peristaltic tubing pump.
1.Fluidized-bed reactor 2. Feed tank 3. Recycle tank 4.Acidic solution 5. Pump 6. pH controller 7. Mixer 8. Influent line 9. Effluentline 10. Sampling port 11. Recycle line 12. Valve
Fig. 1. Schematic diagram of the FBBR.
The pH of the reactors was maintained at 7.570.1 using an automatic pH control device, which was connected to a peristaltic tubing pump that automatically pumped 38% hydrochloric acid solution into the recycle tank. The experiments were operated at room temperature (2372 1C). Three superficial velocities were applied to investigate the effect of Vs on the biomass concentration in the FBBR. Superficial velocities of 45 and 65 m/h were used in the first fluidized bed (R1) and a superficial velocity of 55 m/h was used in the second fluidized bed (R2). A total of four nitrogen loading rates (6, 8, 12 and 16 kg N/m3 d) were applied under each superficial velocity to investigate the effect of nitrogen loading rate on the biomass concentration in the FBBR. The reactor beds reached the target height of 1.40 m above gravel layer at the bottom of the reactors in 4 weeks of operation. The bed height was maintained at 1.40 m by daily withdrawal of extra bioparticles from the sampling port at that level. The withdrawn bioparticles were mixed vigorously in a rapid mixer to separate the biofilm from sand particles and the clean sand was returned to the reactor. 2.4. Analytical methods Slurry samples (i.e. biofilm coated particles plus water) were collected from different locations in the FBBR reactors using a specially designed sampler described elsewhere (Rabah, 2003) to protect the biofilm from being sheared off. These samples were tested for biofilm thickness, biofilm dry density, bed porosity, biomass concentration and volatile suspended solids per unit volume of the reactor. The biofilm dry density (Xf) and the biofilm thickness (Lf) were measured using the
ARTICLE IN PRESS F.K.J. Rabah, M.F. Dahab / Water Research 38 (2004) 4262–4270
method of Schreyer and Coughlin (1999) according to the following procedure. A slurry sample of known volume was gently washed to remove the suspended solids and filtered. The wet bioparticles were carefully removed from the filter into a ceramic dish to find its wet weight. After that it was oven dried for 24 h at 105 1C, cooled in a desiccator and weighed. The dried sample was ignited in a 550 1C furnace for 30 min, cooled in a desiccator and weighed. The dry weight of biomass (mb), also called volatile solids, was computed as the difference in weight measured before and after ignition of the dried sample. The sand weight (ms) is the weight of the fixed solids remaining after ignition. The ash weight remaining with the fixed solids after ignition was neglected (compared to the sand weight). The wet weight of biomass (mw) was computed as the difference between the wet weight (i.e. weight of the bioparticles sample before drying) and the sand weight (ms). After the determination of mb, mw and ms, the following equations were used to estimate the average biofilm thickness (Schreyer and Coughlin, 1999): 4 3 1 mw pðro r3s Þ ¼ ; (4) 3 np rbw where np ¼
ms ; rs vs
(5)
rs is the sand specific density (2.65 g/cm3), vs is the volume of one clean sand particle computed from the average diameter of the sand (0.84 mm) assuming spherical geometry, np is the number of sand particles in the sample and rb is the biofilm wet density taken as 1.03 g/cm3 (Zhang and Bishop, 1994b). The biofilm on a single particle was assumed to form a uniform spherical shell of outer radius ro and inner radius rs (equal to the radius of the average clean sand particle). The mean biofilm thickness (Lf) was calculated as the difference between ro and rs. The biofilm thickness was also measured using a high-resolution microscope equipped with a micrometer to double check on the results obtained from the Schreyer and Coughlin (1999) method. (Note: when comparing the results of the two measurements, the relative error was always less than 10%.) The biofilm dry density (Xf) was estimated by dividing the dry volatile solids weight of the biomass (mb) by the total biofilm volume in the sample, as follows: Xf ¼
mb ; np V biofilm
(6)
where Vbiofilm is the volume of biofilm (solids plus moisture content) on one bioparticle calculated as the difference between the volume of the bioparticle and the
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volume of the clean sand particle as follows: 4 V biofilm ¼ pðr3o r3s Þ: (7) 3 A slurry sample of known volume was taken from the FBBR to measure the biomass concentration per unit volume of the FBBR. The sample was filtered, oven dried for 24 h at 105 1C, cooled in a desiccator and weighed. The dried sample was then ignited in a furnace for 30 min at 550 1C, cooled in a desiccator and weighed. The dry weight of biomass (mb) (i.e. volatile solids) was the difference in weight before and after ignition of the dried sample. The concentration per unit volume was estimated by dividing mb by the sample volume. The bed porosity of the FBBRs was determined gravimetrically. Slurry samples of known volume in the fluidized state were collected from different locations in the FBBR using a specially designed sampler described elsewhere (Rabah, 2003). Each sample (water and bioparticles) was weighed and then vacuum filtered on a 25 mm glass filter. The weight of the solids retained on the filter was determined. The weight of the water in the sample was calculated as the difference in the weight of the slurry sample and the weight of the solids retained on the filter. The volume of water was estimated using its weight and a water specific gravity of 1.0. The bed porosity was then estimated by dividing the volume of water by the volume of the slurry sample. Volatile suspended solids (VSS) were performed in accordance with Standard Methods for the Examination of Water and Wastewater following Method 2540D, E (APHA, 1998).
3. Results and discussion 3.1. Predicting the bed porosity of the FBBR All possible combinations of the proposed formulas for Cd and n as shown in Tables 1 and 2 were used to estimate the bed porosity as a function of the biofilm thickness using Eqs. (2) and (3). The results of these estimations for a superficial velocity of 55 m/h are presented graphically in Fig. 2 together with the experimental data. It is noted from this figure that there is a great variation between the predictions of the bed porosity using the different reported formulas. The best prediction of the bed porosity, compared to the experimental data, was achieved when using the Cd correlation of Mulcahy and Shieh (1987) together with the n correlation of Mulcahy and LaMotta (1978) (i.e. combination b+e). The average percent relative error (APRE) of the predicted porosity using this combination compared to the experimental values was the least (1%); N 100 X ðexperimental Þi ðpredicted Þi APRE ¼ ; N i¼1 ðexperimental Þi
ARTICLE IN PRESS 0.85 0.80
Porosity (ε)
0.75 0.70 0.65
Best Prediction (b+e)
Data
0.60 0.55 0.50 0.45 100
150
200
250
300
350
400
450
Biofilm Thickness ( Lf), µm a+f c+g Experimental Data
b+e d+f b+g
Biofilm dry density (Xf ), g/cm3
F.K.J. Rabah, M.F. Dahab / Water Research 38 (2004) 4262–4270
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0.18 0.16
This Study: Xf = 0.44Lf
0.14
-0.26
2
R = 0.7848
0.12 0.1 0.08 0.06 0.04 0.02 0 0
100
200
300
400
500
600
700
Biofilm Thickness ( Lf ), µm b+f d+g a+e
Fig. 2. Comparison between experimental results of bed porosity with the predictions of different combinations of the correlations of Cd and n shown in Tables 1 and 2 (Vs=55 m/h).
N=no. of measurements. This observation was true for the superficial velocities of 45 and 65 m/h as well. However, there are two other combinations of Cd and n that resulted in close predictions. The first one is to use the Cd and the n correlations of Mulcahy and Shieh (1987) (i.e. combination a+e; APRE=2%). The second combination is to use the Cd correlation of Hermanowicz and Ganczarczyk (1983) together with the n correlation of Mulcahy and LaMotta (1978) (i.e. combination b+g; APRE=2.5%). The use of the Cd correlation of Chang et al. (1991) together with the n correlation of Richardson and Zaki (1954) (i.e. combination d+f) resulted in the highest APRE (37%). 3.2. Correlation between the biofilm dry density and the biofilm thickness The correlations between the biofilm dry density and the biofilm thickness mentioned in Table 3 are graphically presented in Fig. 3 together with the experimental data from this study and from the work of Nieuwstad (1984) and Eramo et al. (1994). It was observed that the correlations of Mulcahy and LaMotta (1978b) and Boaventura and Rodrigues (1988) underestimated the biofilm dry density through the range of biofilm thickness of the experimental data (130–500 mm) (APRE=38% and 31%, respectively). The correlation of Hermanowicz and Cheng (1990) exhibited a good match with the experimental results from this study in the range of biofilm thickness of 170–220 mm (APRE=10%), but it underestimated the biofilm dry density at biofilm thicknesses lower and higher than this range (APRE=44%). The correlation of Coelhoso et al. (1992) exhibited a good match with the experimental data from this study in the range of biofilm thickness of 300–500 mm (APRE=11%), but it overestimated the biofilm dry density at biofilm thickness lower than
Experimental Data (This study) Hermanow icz and Cheng (1990) Eramo et al. (1994) Coelhoso et al. (1992) Mulcahy and LaMotta (1978b) Boaventura and Rodrigues (1988) Nieuw stad (1984)
Fig. 3. Comparison between experimental results of biofilm dry density as a function of biofilm thickness with reported literature results.
300 mm (APRE=32%). The data from the work of Nieuwstad (1984) and Eramo et al. (1994) were in evident agreement with the experimental results of this study (APRE=11% and 13%, respectively). Using the results of this study, a regression correlation between the biofilm dry density and the biofilm thickness was produced and expressed as follows: X f ¼ 0:44 Lf0:26 ðR2 ¼ 0:7848Þ for ð130 mmpLf p500 mmÞ:
ð8Þ
This correlation is supported by the results of Nieuwstad (1984) and Eramo et al. (1994) and partially, (i.e. in Lf range of 300–500 mm), by the results of Coelhoso et al. (1992). The variations in the biomass dry density are attributed to the variations of the physical conditions and the characteristics of the organisms in the FBBR. One of the most effective physical conditions is the high mechanical stress on the biofilm which tends to increase its density (Rittmann and McCarty, 2001). Moreover, higher shear stress exerted on the biofilm (due to higher Vs) leads to higher rate of biomass detachment and a thinner biofilm and consequently, leads to a denser biofilm (Rittmann and McCarty, 2001). Other factors such as biofilm age and mineralization can be important sources of variation of the biofilm dry density. 3.3. Predicting the biomass concentration per unit volume of the FBBR As discussed above, Eq. (1) is used to predict the biomass concentration in FBBRs together with an
ARTICLE IN PRESS F.K.J. Rabah, M.F. Dahab / Water Research 38 (2004) 4262–4270
appropriate correlation to estimate the bed porosity and the biomass dry density. Since the Cd correlation of Mulcahy and Shieh (1987) together with the n correlation of Mulcahy and LaMotta (1978) resulted in the best predictions of the bed porosity, they were used to predict the biomass concentration. Eq. (4) proposed in this study was used to estimate the biofilm dry density. Table 4 shows the predicted biomass concentrations in comparison with the experimental results at a superficial velocity of 55 m/h. It is observed that the predictions are adequately close to the experimental results. The APRE between the predicted and the experimental values of the biomass concentration were 4.6%, 4.9% and 5.8% at superficial velocities of 45, 55 and 65 m/h, respectively. The main parameters that affect the biomass concentration predictions based on Eq. (1) are Lf, Xf and : To show the effect of changes in these parameters on biomass concentration predictions, a sensitivity analysis was used. The value of each parameter was changed in the range of 725% from its original estimated value (while keeping the other two constant) and the corresponding change in the predicted biomass concentration was estimated using Eq. (1). According to this analysis, the biomass concentration changed in the range of 740%, 720% and 75% from its original predicted value when ; Xf and Lf were changed in the range of 725% from their original estimated values, respectively. As noted, e has the largest effect on the biomass predictions followed by Xf while Lf has the least effect. Thus, care should be taken when estimating and Xf before using them in the prediction of biomass concentrations in FBBRs. 3.4. Effect of the superficial velocity and nitrogen loading rate on the biomass concentration in the FBBR The effect of the superficial velocity on the average biomass concentration in the FBBR is illustrated by
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Fig. 4. It was observed that the average biomass concentration per unit volume of the FBBR decreased with the increase of the superficial velocity at all of the applied nitrogen loadings (i.e. 6, 8, 12 and 16 kg N/ m3bed d). For example, at a nitrogen loading rate of 6 kg N/m3bed d, the average biomass concentration decreased from 33 to 21 g VSS/Lbed when Vs was increased from 45 to 65 m/h. The decrease in the average biomass concentration due to the increase in Vs is attributed to two main factors. When Vs was increased, the bed porosity increased leading to a lower concentration of bioparticles per unit volume of the FBBR and consequently a lower biomass concentration. Moreover, when Vs was increased, the shear stress exerted on the biofilm increased. This resulted in a higher rate of biomass detachment and a thinner biofilm and consequently resulted in a lower biomass concentration. The effect of the nitrogen loading rate on the biomass concentration in the FBBR is illustrated in Fig. 5. It was observed that the average biomass concentration in the FBBR generally decreased with increases in the nitrogen loading rate up to some loading rate, where the change of biomass concentration as a function of the nitrogen rate became negligible. For example, at Vs of 55 m/h, the average biomass concentration decreased from 25 to 23 g VSS/Lbed when the nitrogen loading rate was increased from 6 to 8 kg N/m3bed d, respectively. Then, when the loading rate was increased from 8 to 16 kg N/ m3bed d, the average biomass concentration reached an approximately constant value of 23 g VSS/Lbed. The same pattern was observed at superficial velocities of 45 and 65 m/h, however, at Vs of 45 m/h, the rate of change in the average biomass concentration started to decrease at a higher loading rate (12 kg N/m3bed d) than that of the other two superficial velocities. The biomass concentration decreases when the nitrogen loading rate increase occurs due to the increase in the biofilm thickness as a result of the increase in the substrate concentration in
Table 4 Comparison between predicted and measured values of biomass concentration (Vs=55 m/h) Lf (mm)
222 255 162 253 283 194 279 308 207 305 363
Predicted values
Experimental values
Relative error (%)
X (g VSS/Lbed)
X (g VSS/Lbed)
Based on
Based on X
0.71 0.72 0.70 0.72 0.73 0.71 0.72 0.73 0.71 0.73 0.74
22.66 22.24 22.08 22.22 21.68 22.17 22.40 21.71 22.42 21.71 20.96
0.66 0.69 0.65 0.71 0.72 0.68 0.70 0.73 0.69 0.72 0.74
25.16 24.13 24.95 23.39 21.61 24.44 23.56 21.47 23.85 22.44 21.88
7.0 4.2 7.1 1.4 1.4 4.2 2.8 0.0 2.8 1.4 0.0
9.9 7.8 11.5 5.0 0.3 9.3 4.9 1.1 6.0 3.3 4.2
ARTICLE IN PRESS Biomass concentration, g-VSS/L bed
40 3
Rapplied in kg-N/m ·d
35
8 16
6 12
30 25 20 15 40
45
50
55
60
65
70
Biomass concentration, g-VSS/L bed
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35
Vs(m/h) 45 55 65
30
25
20
15 4
6
3.5. Biomass concentration, biofilm thickness and bed porosity profiles along the FBBR Figs. 6–8 show typical profiles of biomass concentration, biofilm thickness and bed porosity along the FBBR, respectively. As shown in Fig. 6, the highest biomass concentration was observed at the bottom part of the reactor, then it decreased gradually towards the top. This was attributed to the increase in the bed porosity along the reactor from the bottom to the top leading to a fewer bioparticles per unit volume of the FBBR and consequently a lower biomass concentration. Fig. 7 shows the typical pattern of biofilm thickness along the FBBR. It was observed that the biofilm thickness increased from the bottom to the top of the reactor indicating a stratification of the media in the FBBR. Stratification is a result of the variability of the bioparticle densities in the reactor. Assuming equal size of the sand particles, bioparticles with thinner biofilms are denser than those with thicker biofilms, so they occupy the lower part of the reactor while the lighter bioparticles occupy the upper part. The biofilm densities vary with depth within the biofilm because the tops of biofilm are more porous as reported by Zhang and Bishop (1994). The densities in the top layers are usually 5–10 times higher than those in the top layers, and the porosities in the top layers are in the range of 84–93%, while it is in the range of 58–67% in the bottom layers (Zhang and Bishop, 1994). Fig. 8 shows the typical pattern of bed porosity along the FBBR. It was observed that the bed porosity increased from the bottom to the top of the reactor. The porosity is a function of the bioparticles density and the superficial velocity. Since the superficial velocity is constant along the FBBR, the main factor that controls the porosity is
12
14 bed
16
18
.d
Fig. 5. Effects of nitrogen loading rates on the biomass concentration in the FBBR at different superficial velocities.
140
Height along the reactor, cm
the bulk liquid. As the biofilm thickness increases, the porosity of the FBBR increases, and consequently the average biomass concentration per unit volume of the bed decreases.
10
Nitrogen loading rate, kg-N/m
120 100 80
Vs(m/h)
60
45 55 65
40 20 18
20
22
24
26
28
30
32
34
Biomass concentration (X), g-VSS/L bed
Fig. 6. Typical biomass concentration profiles along the FBBR at different superficial velocities.
140
Height along the reactor , cm
Fig. 4. Effects of the superficial velocity on the biomass concentration in the FBBR at different nitrogen loading rates.
8
3
Vs, m/h
120 100 80 60
Vs(m/h)
40
45 55 65
20 150
175
200
225
250
275
300
Biofilm thickness (Lf ), µm
Fig. 7. Typical biofilm thickness profiles along the FBBR at different superficial velocities.
the bioparticle density. The bed porosity in this case was inversely proportional to the bioparticle density. The lower part of the reactor had denser bioparticles and consequently had lower bed porosity.
ARTICLE IN PRESS F.K.J. Rabah, M.F. Dahab / Water Research 38 (2004) 4262–4270
gradually towards the top. The biofilm thickness increased from the bottom to the top of the reactor indicating a stratification of the media in the FBBR. The bed porosity increased from the bottom to the top of the reactor.
Height along the reactor, cm
140 120 100 80 60
Vs(m/h) 45 55 65
40 20 0.55
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0.6
0.65
0.7
0.75
Acknowledgments and disclaimer 0.8
Porosity ()
Fig. 8. Typical bed porosity profiles along the FBBR at different superficial velocities.
4. Conclusions Two laboratory-scale fluidized-bed reactors were used to investigate the biomass concentration and the biofilm characteristics in a high-performance FBBR used for the denitrification of exceptionally high-nitrate wastewater (1000 mg N/L). Based on the results obtained during this study, the following conclusions are made: 1. The drag coefficient (Cd) correlation of Mulcahy and Shieh (1987) together with expansion coefficient (n) correlation of Mulcahy and Shieh (1987) were found to produce the best prediction of the FBBR bed porosity (). 2. A correlation between the biofilm dry density and the biofilm thickness was produced from the results of this study. The correlations of the biofilm dry density suggested by Mulcahy and LaMotta (1978b) and Boaventura and Rodrigues (1988) failed to predict the biofilm dry density throughout the range of biofilm thickness of the experimental data (130–500 mm). The correlation of Hermanowicz and Cheng (1990) gave good prediction only in the range of biofilm thickness of 170–200 mm. 3. A good prediction of the biomass concentration was produced using the correlations of Mulcahy and Shieh (1987) and Mulcahy and LaMotta (1978) to predict the bed porosity and using the biofilm dry density correlation produced in this study. 4. The average biomass concentration per unit volume of the FBBR decreased with increase in the superficial velocity at all the applied nitrogen loadings (i.e. 6, 8, 12 and 16 kg N/m3bed d). 5. The biomass concentration decreased with increases in the nitrogen loading rate up to some loading rate, where the change of biomass concentration as a function of the nitrogen rate became negligible. 6. The highest biomass concentration was observed at the bottom part of the reactor, then it decreased
The work reported herein was supported, in part, by the Department of Civil Engineering at the University of Nebraska-Lincoln and, in part, by the US Environmental Protection Agency under Cooperative Agreement No. R-82834101-1. Views or opinions expressed in this article are those of the authors and should not be construed as views or opinions of the US Environmental Protection Agency.
References APHA, 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Health Association/American Water Works Association/Water Environment Federation, Washington, DC, USA. Boaventura, R., Rodrigues, A.E., 1988. Consecutive reactions in fluidized-bed biological reactors: modeling and experimental study of wastewater denitrification. Chem. Eng. Sci. 43 (10), 2715–2728. Cattaneo, C., Nicolella, C., Rovatti, M., 2003. Denitrification performance of Pseudomonas denitricanc in a fluidized-bed biofilm reactor and in a stirred tank reactor. Eng. Life Sci. 3 (4), 187–192. Chang, H.T., Rittmann, B.E., Amar, D., Heim, R., Ehlinger, O., Lesty, Y., 1991. Biofilm detachment mechanisms in a liquid-fluidized bed. Biotechnol. Bioeng. 38, 499–506. Chen, C.Y., Chen, S.D., 2000. Biofilm characteristics in biological denitrification biofilm reactors. Water Sci. Technol. 41 (4), 147–154. Coelhoso, I., Boaventura, R., Rodriques, A., 1992. Biofilm reactors: an experimental and modeling study of wastewater denitrification in fluidized-bed reactors of activated carbon. Biotechnol. Bioeng. 40, 625–633. Eramo, B., Gavasci, R., Misiti, A., Viotti, P., 1994. Validation of a multi substrate mathematical model for the simulation of the denitrification process in fluidized bed biofilm reactors. Water Sci. Technol. 29 (10–11), 401–408. Glass, C., Silverstein, J., 1999. Denitrification of high-nitrate, high-salinity wastewater. Water Res. 33 (1), 223–229. Grady, C.P., Daigger, G.T., Lim, H.C., 1999. Biological Wastewater Treatment, second ed. Marcel Dekker Inc., New York. Harada, H., Ando, H., Momonoi, K., 1987. Process analysis of fluidized bed biofilm reactor for denitrification. Water Sci. Technol. 19, 151–162. Hermanowicz, S.W., Cheng, Y.W., 1990. Biological fluidized bed reactor: hydrodynamics, biomass distribution and performance. Water Sci. Technol. 22 (1–2), 193–202.
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Hermanowicz, S.W., Ganczarczyk, J.J., 1983. Some fluidization characteristics of biological beds. Biotechnol. Bioeng. 25, 1321–1330. Mulcahy, L.T., LaMotta, E.J., 1978a. Mathematical model of the fluidized bed biofilm reator. Report No. 58-78-2, Department of Civil Engineering, University of Massachusetts, Amherst, MA, USA. Mulcahy, L.T., LaMotta, E.J., 1978b. Mathematical model of the fluidized bed biofilm reactor. Proceedings of the 51st Annual Conference of the Water Pollution Control Federation, Anaheim, CA, USA. Mulcahy, L.T., Shieh, W.K., 1987. Fluidization and reactor biomass characteristics of denitrification fluidized bed biofilm reactor (FBBR). Water Res. 21, 451–458. Nieuwstad, T.J., 1984. Modeling, optimization and design of fluidized beds for biological denitrification. Water Sci. Technol. 22 (2–3), 367–383. Rabah, F.K., 2003. Denitrification of high-strength nitrate wastewater using fluidized-bed biofilm reactors. Doctoral Dissertation, University of Nebraska-Lincoln Libraries, Lincoln, NE, USA.
Richardson, J.F., Zaki, W.N., 1954. Sedimentation and fluidization: part I. Inst. Chem. Eng. Trans. 32, 35–53. Rittmann, B.E., McCarty, P.L., 2001. Environmental Biotechnology: Principles and Application. McGraw-Hill, New York, USA, pp. 220–221. Ro, K.S., Neethling, J.B., 1990. Terminal settling characteristics of bioparticles. J. Water Pollut. Control Fed. 62, 901–906. Schreyer, H.B., Coughlin, R.W., 1999. Effect of stratifaction in a fluidized bed bio-reactor during treatment of metalworking wastewater. Biotechnol. Bioeng. 63 (2), 129–140. Shieh, W.K., Sutton, P.M., Kos, P., 1981. Predicting reactor biomass concentration in a fluidized-bed system. J. Water Pollut. Control Fed. 53 (11), 1574–1578. Zhang, T.C., Bishop, P.L., 1994a. Density, porosity, and pore structure of biofilms. Water Res. 28 (11), 2267–2277. Zhang, T.C., Bishop, P.L., 1994b. Structure, activity and composition of biofilms. Water Sci. Technol 29 (11), 335–344.
Water Research 38 (2004) 4262–4270 www.elsevier.com/locate/watres
Biofilm and biomass characteristics in high-performance fluidized-bed biofilm reactors Fahid K.J. Rabah, Mohamed F. Dahab Department of Civil Engineering, University of Nebraska-Lincoln, W348 Nebraska Hall, Lincoln, NE 68588-0531, USA Received 2 April 2004
Abstract Two laboratory-scale fluidized-bed biofilm reactors (FBBRs) were used to investigate the biomass concentration and the biofilm characteristics in a high-performance FBBR used for the denitrification of exceptionally high-nitrate wastewater (1000 mg N/L). Reported correlations by other workers for predicting the biomass concentration in FBBR were examined for their validity in comparison with the experimental results of this study and the best set of applicable correlations was recommended. The effects of the two main operational parameters, the superficial velocity and nitrogen loading rate on the biomass concentration in the FBBR were also studied. Correlations for the drag coefficient and the expansion index from the literature, together with the biofilm dry density correlation produced from this study were found to produce the best prediction of the FBBR biomass concentration compared to other reported correlations. The average biomass concentration in the FBBR decreased with the increase of the superficial velocity in the range of 45–65 m/h at all applied nitrogen loadings (i.e. 6, 8, 12 and 16 kg N/m3bed d). r 2004 Elsevier Ltd. All rights reserved. Keywords: Biofilm characteristics; Biomass concentration; Denitrification; Fluidized beds; Nutrient removal
1. Introduction Fluidized-bed biofilm reactors (FBBR) for treating industrial and municipal wastewater have been known for more than 20 years (Rabah, 2003). The results from laboratory and field pilot-scale studies have consistently illustrated the technical advantage of the fluidized bed over most other suspended and attached growth biological systems. Typically, the efficiency of the FBBR can be as much as 10 times that of the activated sludge system and it typically occupies about 10% of the space required by stirred tank reactors of similar capacities (Rabah, 2003). This is attributed to the high biomass Corresponding author. Tel.: +1-402-472-5020; fax: +1402-472-8934. E-mail address: [email protected] (M.F. Dahab).
concentration that can be maintained in FBBRs compared to that in the activated sludge system; 40,000 mg/L vs. 3000 mg/L (Shieh et al., 1981). Furthermore, unlike the suspended growth processes, there is no need to incorporate special measures in the FBBR process (e.g., sedimentation and biomass recirculation) to retain the biomass in culture due to the attachment of the biomass to the fluidized media (Cattaneo et al., 2003). The main advantages of an FBBR over other suspended and attached growth treatment systems are 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 (Mulcahy and Shieh, 1987). The prediction of biomass concentration in FBBRs for a given set of
0043-1354/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2004.08.012
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operating conditions depends mainly on the fluidization mechanics within the reactor, the biofilm characteristics and the carrier media characteristics. A mathematical expression correlating the biomass concentration in FBBRs to these factors was developed by Sheih et al. (1981) based on mass balance principles as follows: " 3 # dm X ¼ X f ð1 Þ 1 ; (1) dp where X is the biomass concentration per unit volume of the FBBR (M/L3), Xf is the dry density of the biofilm(M/L3), e is the fluidized-bed porosity, dm is the carrier media diameter (L), dp is the bioparticle diameter (L). In this correlation, the fluidization mechanics are characterized by the bed porosity while the biofilm characteristics are represented by the biofilm dry density and the bioparticle diameter. The effect of the carrier material is represented by the media diameter. Many experimental and theoretical correlations have been proposed to predict the bed porosity and to define the relation between the biofilm dry density and the biofilm thickness. The Richardson and Zaki (1954) correlation relating the bed porosity to the superficial velocity and the particle terminal settling velocity is generally used to estimate the bed porosity: 1=n Vs ¼ ; (2) Vt where Vs is the superficial velocity (L/T), Vt is the bioparticle terminal settling velocity (L/T), and n is the expansion index. The terminal velocity is defined as 4gd p ðrp rw Þ 1=2 Vt ¼ ; 3C d rw
(3) 2
where g is the gravitational acceleration (L /T), rp is the bioparticle density (M/L3), rw is the water density (M/ L3), and C d is the drag coefficient. Many mathematical expressions have been developed for the drag coefficient (Table 1) and for the expansion index (Table 2). Unfortunately, these correlations result in different predictions of bed porosity under the same operational conditions and consequently result in considerable Table 1 Reported equations for the drag coefficient for bioparticles in FBBRs in relation to the Reynolds Number (Grady et al., 1999) Equation
Source
(a) Cd=17.1Re0.47 t
Hermanowicz and Ganczarczyk (1983) Mulcahy and Shieh (1987) Ro and Neethling (1990) Chang et al. (1991)
(b) Cd=36.66Re0.67 t (c) Cd=24Re0.1 +21.55Re0.518 t t 0.33 (d) Cd=24Re1 t +3.6Ret
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Table 2 Reported equations for the expansion index for FBBRs Equation
Source 0.2576
(e) n=47.36Ga
, 1000oGao 15000
(f) n=4.45Re0.1 t (g) n=10.35Re0.18 t (h) n=8.733Re0.341 t (i) n=4.26–0.73 log Ret a
a
Mulcahy and LaMotta (1978) Richardson and Zaki (1954) Mulcahy and Shieh (1987) Harada et al. (1987) Nieuwstad (1984)
Ga=Galileo number.
Table 3 Reported equations for the relationship between the biofilm thickness (Lf) and the biofilm dry density (Xf) in FBBRs Equation
Source
Xf=65 mg/cm for 0oLf p300 mm Xf=96.8–0.106Lf mg/cm3 for 300oLfp630 mm Xf=30 mg/cm3 for Lf4630 mm 3
Xf=104.3–0.125Lf mg/cm3 for Lfo622 mm Xf=30 mg/cm3 for Lf4622 mm
Mulcahy and LaMotta (1978b)
Boaventura and Rodrigues (1988)
Xf=120(Lf/180)3.7 mg/cm3 for Lfo180 mm Xf=120(Lf/180)1.8 mg/cm3 for Lf4180 mm
Hermanowicz and Cheng (1990)
Xf=191.4–0.224Lf mg/cm3 for Lfo593 mm Xf=58.6 mg/cm3 for Lf4593 mm
Coelhoso et al. (1992)
difference in biomass concentration prediction in the FBBR when applying Eq. (1). On the other hand, many experimental correlations relating the biofilm dry density and the biofilm thickness were proposed by several researchers (Table 3). The great variation in the prediction of these relations is obvious. As a result, the prediction of biomass concentration will vary considerably. The work described herein was designed to investigate the biomass concentration and the biofilm characteristics in a high-performance FBBR used for the denitrification of exceptionally high-nitrate wastewater (1000 mg N/L). High-nitrate wastewater is typically generated from industries such as cellophane, petrochemical, explosives, fertilizers, pectin and many metal-
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finishing industries (Chen and Chen, 2000; Glass and Silverstein, 1999). The main objectives of this study are: 1. To test the validity of the proposed expressions for the prediction of bed porosity and for the estimation of the biofilm dry density in correlation with the biofilm thickness in high-performance FBBR used for denitrification. 2. To propose a suitable set of correlations for Cd, n and Xf to be used for predicting the biomass concentration in FBBRs based on the experimental results. 3. To determine the effect of Vs and the nitrogen loading rate on the biomass concentration in the FBBR. 4. To estimate the biomass concentration, biofilm thickness and bed porosity variations along the height of the FBBR.
2.2. Synthetic wastewater The synthetic wastewater was prepared using deionized water in addition to other chemicals. Potassium nitrate was added as the nitrogen source at a concentration of 1000 mg NO 3 –N/L. Methanol was added as the carbon source at a concentration of 3000 mg CH3OH/L. Trace mineral constituents essential to the bacterial growth added per liter were: 0.85 mg FeSO4 7H2O, 0.25 mg Na2MoO4 2H2O, 0.157 mg MnSO4 7H2O and 33 mg NaHCO3. Sodium sulfite and cobalt chloride were added at concentrations of 20 and 0.55 mg/L, respectively, to reduce the oxygen concentration to below 0.5 mg/L to ensure anoxic conditions in the reactors. Monobasic and dibasic potassium phosphates were added as a buffer system. The two reactors were seeded with an inoculum from the anoxic zone of the activated sludge tank at the Theresa Street Wastewater Treatment Plant in Lincoln, NE.
2. Materials and methods 2.3. Operating conditions 2.1. Laboratory-scale fluidized-bed reactors The experimental set-up used in this study consisted mainly of two identical upflow columns. A schematic diagram of one fluidized-bed column installation is shown in Fig. 1. Each column consisted of a 2 m Plexiglas tube with a 76 mm inner diameter. The lower end of the column was packed up with a 100 mm layer of 4 mm diameter gravel to obtain uniform fluid flow in the bed and to avoid backflow of the media. The reactor was loaded with 2 kg of uniform sand as a biofilm carrier to a settled depth of 0.30 m. The sand had a mean diameter of 0.84 mm, a specific gravity of 2.65, a porosity of 0.42, and a specific surface area of 4200 m2/m3. Synthetic wastewater was pumped through a perforated flange to the bottom of the reactor from a 200 L sealed plastic tank using a peristaltic tubing pump.
1.Fluidized-bed reactor 2. Feed tank 3. Recycle tank 4.Acidic solution 5. Pump 6. pH controller 7. Mixer 8. Influent line 9. Effluentline 10. Sampling port 11. Recycle line 12. Valve
Fig. 1. Schematic diagram of the FBBR.
The pH of the reactors was maintained at 7.570.1 using an automatic pH control device, which was connected to a peristaltic tubing pump that automatically pumped 38% hydrochloric acid solution into the recycle tank. The experiments were operated at room temperature (2372 1C). Three superficial velocities were applied to investigate the effect of Vs on the biomass concentration in the FBBR. Superficial velocities of 45 and 65 m/h were used in the first fluidized bed (R1) and a superficial velocity of 55 m/h was used in the second fluidized bed (R2). A total of four nitrogen loading rates (6, 8, 12 and 16 kg N/m3 d) were applied under each superficial velocity to investigate the effect of nitrogen loading rate on the biomass concentration in the FBBR. The reactor beds reached the target height of 1.40 m above gravel layer at the bottom of the reactors in 4 weeks of operation. The bed height was maintained at 1.40 m by daily withdrawal of extra bioparticles from the sampling port at that level. The withdrawn bioparticles were mixed vigorously in a rapid mixer to separate the biofilm from sand particles and the clean sand was returned to the reactor. 2.4. Analytical methods Slurry samples (i.e. biofilm coated particles plus water) were collected from different locations in the FBBR reactors using a specially designed sampler described elsewhere (Rabah, 2003) to protect the biofilm from being sheared off. These samples were tested for biofilm thickness, biofilm dry density, bed porosity, biomass concentration and volatile suspended solids per unit volume of the reactor. The biofilm dry density (Xf) and the biofilm thickness (Lf) were measured using the
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method of Schreyer and Coughlin (1999) according to the following procedure. A slurry sample of known volume was gently washed to remove the suspended solids and filtered. The wet bioparticles were carefully removed from the filter into a ceramic dish to find its wet weight. After that it was oven dried for 24 h at 105 1C, cooled in a desiccator and weighed. The dried sample was ignited in a 550 1C furnace for 30 min, cooled in a desiccator and weighed. The dry weight of biomass (mb), also called volatile solids, was computed as the difference in weight measured before and after ignition of the dried sample. The sand weight (ms) is the weight of the fixed solids remaining after ignition. The ash weight remaining with the fixed solids after ignition was neglected (compared to the sand weight). The wet weight of biomass (mw) was computed as the difference between the wet weight (i.e. weight of the bioparticles sample before drying) and the sand weight (ms). After the determination of mb, mw and ms, the following equations were used to estimate the average biofilm thickness (Schreyer and Coughlin, 1999): 4 3 1 mw pðro r3s Þ ¼ ; (4) 3 np rbw where np ¼
ms ; rs vs
(5)
rs is the sand specific density (2.65 g/cm3), vs is the volume of one clean sand particle computed from the average diameter of the sand (0.84 mm) assuming spherical geometry, np is the number of sand particles in the sample and rb is the biofilm wet density taken as 1.03 g/cm3 (Zhang and Bishop, 1994b). The biofilm on a single particle was assumed to form a uniform spherical shell of outer radius ro and inner radius rs (equal to the radius of the average clean sand particle). The mean biofilm thickness (Lf) was calculated as the difference between ro and rs. The biofilm thickness was also measured using a high-resolution microscope equipped with a micrometer to double check on the results obtained from the Schreyer and Coughlin (1999) method. (Note: when comparing the results of the two measurements, the relative error was always less than 10%.) The biofilm dry density (Xf) was estimated by dividing the dry volatile solids weight of the biomass (mb) by the total biofilm volume in the sample, as follows: Xf ¼
mb ; np V biofilm
(6)
where Vbiofilm is the volume of biofilm (solids plus moisture content) on one bioparticle calculated as the difference between the volume of the bioparticle and the
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volume of the clean sand particle as follows: 4 V biofilm ¼ pðr3o r3s Þ: (7) 3 A slurry sample of known volume was taken from the FBBR to measure the biomass concentration per unit volume of the FBBR. The sample was filtered, oven dried for 24 h at 105 1C, cooled in a desiccator and weighed. The dried sample was then ignited in a furnace for 30 min at 550 1C, cooled in a desiccator and weighed. The dry weight of biomass (mb) (i.e. volatile solids) was the difference in weight before and after ignition of the dried sample. The concentration per unit volume was estimated by dividing mb by the sample volume. The bed porosity of the FBBRs was determined gravimetrically. Slurry samples of known volume in the fluidized state were collected from different locations in the FBBR using a specially designed sampler described elsewhere (Rabah, 2003). Each sample (water and bioparticles) was weighed and then vacuum filtered on a 25 mm glass filter. The weight of the solids retained on the filter was determined. The weight of the water in the sample was calculated as the difference in the weight of the slurry sample and the weight of the solids retained on the filter. The volume of water was estimated using its weight and a water specific gravity of 1.0. The bed porosity was then estimated by dividing the volume of water by the volume of the slurry sample. Volatile suspended solids (VSS) were performed in accordance with Standard Methods for the Examination of Water and Wastewater following Method 2540D, E (APHA, 1998).
3. Results and discussion 3.1. Predicting the bed porosity of the FBBR All possible combinations of the proposed formulas for Cd and n as shown in Tables 1 and 2 were used to estimate the bed porosity as a function of the biofilm thickness using Eqs. (2) and (3). The results of these estimations for a superficial velocity of 55 m/h are presented graphically in Fig. 2 together with the experimental data. It is noted from this figure that there is a great variation between the predictions of the bed porosity using the different reported formulas. The best prediction of the bed porosity, compared to the experimental data, was achieved when using the Cd correlation of Mulcahy and Shieh (1987) together with the n correlation of Mulcahy and LaMotta (1978) (i.e. combination b+e). The average percent relative error (APRE) of the predicted porosity using this combination compared to the experimental values was the least (1%); N 100 X ðexperimental Þi ðpredicted Þi APRE ¼ ; N i¼1 ðexperimental Þi
ARTICLE IN PRESS 0.85 0.80
Porosity (ε)
0.75 0.70 0.65
Best Prediction (b+e)
Data
0.60 0.55 0.50 0.45 100
150
200
250
300
350
400
450
Biofilm Thickness ( Lf), µm a+f c+g Experimental Data
b+e d+f b+g
Biofilm dry density (Xf ), g/cm3
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0.18 0.16
This Study: Xf = 0.44Lf
0.14
-0.26
2
R = 0.7848
0.12 0.1 0.08 0.06 0.04 0.02 0 0
100
200
300
400
500
600
700
Biofilm Thickness ( Lf ), µm b+f d+g a+e
Fig. 2. Comparison between experimental results of bed porosity with the predictions of different combinations of the correlations of Cd and n shown in Tables 1 and 2 (Vs=55 m/h).
N=no. of measurements. This observation was true for the superficial velocities of 45 and 65 m/h as well. However, there are two other combinations of Cd and n that resulted in close predictions. The first one is to use the Cd and the n correlations of Mulcahy and Shieh (1987) (i.e. combination a+e; APRE=2%). The second combination is to use the Cd correlation of Hermanowicz and Ganczarczyk (1983) together with the n correlation of Mulcahy and LaMotta (1978) (i.e. combination b+g; APRE=2.5%). The use of the Cd correlation of Chang et al. (1991) together with the n correlation of Richardson and Zaki (1954) (i.e. combination d+f) resulted in the highest APRE (37%). 3.2. Correlation between the biofilm dry density and the biofilm thickness The correlations between the biofilm dry density and the biofilm thickness mentioned in Table 3 are graphically presented in Fig. 3 together with the experimental data from this study and from the work of Nieuwstad (1984) and Eramo et al. (1994). It was observed that the correlations of Mulcahy and LaMotta (1978b) and Boaventura and Rodrigues (1988) underestimated the biofilm dry density through the range of biofilm thickness of the experimental data (130–500 mm) (APRE=38% and 31%, respectively). The correlation of Hermanowicz and Cheng (1990) exhibited a good match with the experimental results from this study in the range of biofilm thickness of 170–220 mm (APRE=10%), but it underestimated the biofilm dry density at biofilm thicknesses lower and higher than this range (APRE=44%). The correlation of Coelhoso et al. (1992) exhibited a good match with the experimental data from this study in the range of biofilm thickness of 300–500 mm (APRE=11%), but it overestimated the biofilm dry density at biofilm thickness lower than
Experimental Data (This study) Hermanow icz and Cheng (1990) Eramo et al. (1994) Coelhoso et al. (1992) Mulcahy and LaMotta (1978b) Boaventura and Rodrigues (1988) Nieuw stad (1984)
Fig. 3. Comparison between experimental results of biofilm dry density as a function of biofilm thickness with reported literature results.
300 mm (APRE=32%). The data from the work of Nieuwstad (1984) and Eramo et al. (1994) were in evident agreement with the experimental results of this study (APRE=11% and 13%, respectively). Using the results of this study, a regression correlation between the biofilm dry density and the biofilm thickness was produced and expressed as follows: X f ¼ 0:44 Lf0:26 ðR2 ¼ 0:7848Þ for ð130 mmpLf p500 mmÞ:
ð8Þ
This correlation is supported by the results of Nieuwstad (1984) and Eramo et al. (1994) and partially, (i.e. in Lf range of 300–500 mm), by the results of Coelhoso et al. (1992). The variations in the biomass dry density are attributed to the variations of the physical conditions and the characteristics of the organisms in the FBBR. One of the most effective physical conditions is the high mechanical stress on the biofilm which tends to increase its density (Rittmann and McCarty, 2001). Moreover, higher shear stress exerted on the biofilm (due to higher Vs) leads to higher rate of biomass detachment and a thinner biofilm and consequently, leads to a denser biofilm (Rittmann and McCarty, 2001). Other factors such as biofilm age and mineralization can be important sources of variation of the biofilm dry density. 3.3. Predicting the biomass concentration per unit volume of the FBBR As discussed above, Eq. (1) is used to predict the biomass concentration in FBBRs together with an
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appropriate correlation to estimate the bed porosity and the biomass dry density. Since the Cd correlation of Mulcahy and Shieh (1987) together with the n correlation of Mulcahy and LaMotta (1978) resulted in the best predictions of the bed porosity, they were used to predict the biomass concentration. Eq. (4) proposed in this study was used to estimate the biofilm dry density. Table 4 shows the predicted biomass concentrations in comparison with the experimental results at a superficial velocity of 55 m/h. It is observed that the predictions are adequately close to the experimental results. The APRE between the predicted and the experimental values of the biomass concentration were 4.6%, 4.9% and 5.8% at superficial velocities of 45, 55 and 65 m/h, respectively. The main parameters that affect the biomass concentration predictions based on Eq. (1) are Lf, Xf and : To show the effect of changes in these parameters on biomass concentration predictions, a sensitivity analysis was used. The value of each parameter was changed in the range of 725% from its original estimated value (while keeping the other two constant) and the corresponding change in the predicted biomass concentration was estimated using Eq. (1). According to this analysis, the biomass concentration changed in the range of 740%, 720% and 75% from its original predicted value when ; Xf and Lf were changed in the range of 725% from their original estimated values, respectively. As noted, e has the largest effect on the biomass predictions followed by Xf while Lf has the least effect. Thus, care should be taken when estimating and Xf before using them in the prediction of biomass concentrations in FBBRs. 3.4. Effect of the superficial velocity and nitrogen loading rate on the biomass concentration in the FBBR The effect of the superficial velocity on the average biomass concentration in the FBBR is illustrated by
4267
Fig. 4. It was observed that the average biomass concentration per unit volume of the FBBR decreased with the increase of the superficial velocity at all of the applied nitrogen loadings (i.e. 6, 8, 12 and 16 kg N/ m3bed d). For example, at a nitrogen loading rate of 6 kg N/m3bed d, the average biomass concentration decreased from 33 to 21 g VSS/Lbed when Vs was increased from 45 to 65 m/h. The decrease in the average biomass concentration due to the increase in Vs is attributed to two main factors. When Vs was increased, the bed porosity increased leading to a lower concentration of bioparticles per unit volume of the FBBR and consequently a lower biomass concentration. Moreover, when Vs was increased, the shear stress exerted on the biofilm increased. This resulted in a higher rate of biomass detachment and a thinner biofilm and consequently resulted in a lower biomass concentration. The effect of the nitrogen loading rate on the biomass concentration in the FBBR is illustrated in Fig. 5. It was observed that the average biomass concentration in the FBBR generally decreased with increases in the nitrogen loading rate up to some loading rate, where the change of biomass concentration as a function of the nitrogen rate became negligible. For example, at Vs of 55 m/h, the average biomass concentration decreased from 25 to 23 g VSS/Lbed when the nitrogen loading rate was increased from 6 to 8 kg N/m3bed d, respectively. Then, when the loading rate was increased from 8 to 16 kg N/ m3bed d, the average biomass concentration reached an approximately constant value of 23 g VSS/Lbed. The same pattern was observed at superficial velocities of 45 and 65 m/h, however, at Vs of 45 m/h, the rate of change in the average biomass concentration started to decrease at a higher loading rate (12 kg N/m3bed d) than that of the other two superficial velocities. The biomass concentration decreases when the nitrogen loading rate increase occurs due to the increase in the biofilm thickness as a result of the increase in the substrate concentration in
Table 4 Comparison between predicted and measured values of biomass concentration (Vs=55 m/h) Lf (mm)
222 255 162 253 283 194 279 308 207 305 363
Predicted values
Experimental values
Relative error (%)
X (g VSS/Lbed)
X (g VSS/Lbed)
Based on
Based on X
0.71 0.72 0.70 0.72 0.73 0.71 0.72 0.73 0.71 0.73 0.74
22.66 22.24 22.08 22.22 21.68 22.17 22.40 21.71 22.42 21.71 20.96
0.66 0.69 0.65 0.71 0.72 0.68 0.70 0.73 0.69 0.72 0.74
25.16 24.13 24.95 23.39 21.61 24.44 23.56 21.47 23.85 22.44 21.88
7.0 4.2 7.1 1.4 1.4 4.2 2.8 0.0 2.8 1.4 0.0
9.9 7.8 11.5 5.0 0.3 9.3 4.9 1.1 6.0 3.3 4.2
ARTICLE IN PRESS Biomass concentration, g-VSS/L bed
40 3
Rapplied in kg-N/m ·d
35
8 16
6 12
30 25 20 15 40
45
50
55
60
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3.5. Biomass concentration, biofilm thickness and bed porosity profiles along the FBBR Figs. 6–8 show typical profiles of biomass concentration, biofilm thickness and bed porosity along the FBBR, respectively. As shown in Fig. 6, the highest biomass concentration was observed at the bottom part of the reactor, then it decreased gradually towards the top. This was attributed to the increase in the bed porosity along the reactor from the bottom to the top leading to a fewer bioparticles per unit volume of the FBBR and consequently a lower biomass concentration. Fig. 7 shows the typical pattern of biofilm thickness along the FBBR. It was observed that the biofilm thickness increased from the bottom to the top of the reactor indicating a stratification of the media in the FBBR. Stratification is a result of the variability of the bioparticle densities in the reactor. Assuming equal size of the sand particles, bioparticles with thinner biofilms are denser than those with thicker biofilms, so they occupy the lower part of the reactor while the lighter bioparticles occupy the upper part. The biofilm densities vary with depth within the biofilm because the tops of biofilm are more porous as reported by Zhang and Bishop (1994). The densities in the top layers are usually 5–10 times higher than those in the top layers, and the porosities in the top layers are in the range of 84–93%, while it is in the range of 58–67% in the bottom layers (Zhang and Bishop, 1994). Fig. 8 shows the typical pattern of bed porosity along the FBBR. It was observed that the bed porosity increased from the bottom to the top of the reactor. The porosity is a function of the bioparticles density and the superficial velocity. Since the superficial velocity is constant along the FBBR, the main factor that controls the porosity is
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Fig. 5. Effects of nitrogen loading rates on the biomass concentration in the FBBR at different superficial velocities.
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the bulk liquid. As the biofilm thickness increases, the porosity of the FBBR increases, and consequently the average biomass concentration per unit volume of the bed decreases.
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Fig. 4. Effects of the superficial velocity on the biomass concentration in the FBBR at different nitrogen loading rates.
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Fig. 7. Typical biofilm thickness profiles along the FBBR at different superficial velocities.
the bioparticle density. The bed porosity in this case was inversely proportional to the bioparticle density. The lower part of the reactor had denser bioparticles and consequently had lower bed porosity.
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gradually towards the top. The biofilm thickness increased from the bottom to the top of the reactor indicating a stratification of the media in the FBBR. The bed porosity increased from the bottom to the top of the reactor.
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Fig. 8. Typical bed porosity profiles along the FBBR at different superficial velocities.
4. Conclusions Two laboratory-scale fluidized-bed reactors were used to investigate the biomass concentration and the biofilm characteristics in a high-performance FBBR used for the denitrification of exceptionally high-nitrate wastewater (1000 mg N/L). Based on the results obtained during this study, the following conclusions are made: 1. The drag coefficient (Cd) correlation of Mulcahy and Shieh (1987) together with expansion coefficient (n) correlation of Mulcahy and Shieh (1987) were found to produce the best prediction of the FBBR bed porosity (). 2. A correlation between the biofilm dry density and the biofilm thickness was produced from the results of this study. The correlations of the biofilm dry density suggested by Mulcahy and LaMotta (1978b) and Boaventura and Rodrigues (1988) failed to predict the biofilm dry density throughout the range of biofilm thickness of the experimental data (130–500 mm). The correlation of Hermanowicz and Cheng (1990) gave good prediction only in the range of biofilm thickness of 170–200 mm. 3. A good prediction of the biomass concentration was produced using the correlations of Mulcahy and Shieh (1987) and Mulcahy and LaMotta (1978) to predict the bed porosity and using the biofilm dry density correlation produced in this study. 4. The average biomass concentration per unit volume of the FBBR decreased with increase in the superficial velocity at all the applied nitrogen loadings (i.e. 6, 8, 12 and 16 kg N/m3bed d). 5. The biomass concentration decreased with increases in the nitrogen loading rate up to some loading rate, where the change of biomass concentration as a function of the nitrogen rate became negligible. 6. The highest biomass concentration was observed at the bottom part of the reactor, then it decreased
The work reported herein was supported, in part, by the Department of Civil Engineering at the University of Nebraska-Lincoln and, in part, by the US Environmental Protection Agency under Cooperative Agreement No. R-82834101-1. Views or opinions expressed in this article are those of the authors and should not be construed as views or opinions of the US Environmental Protection Agency.
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