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Selective sludge discharge as the determining factor in SBR aerobic granulation: Numerical modelling and experimental verification
water research 43 (2009) 3387–3396
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Selective sludge discharge as the determining factor in SBR aerobic granulation: Numerical modelling and experimental verification An-Jie Li, Xiao-Yan Li* Environmental Engineering Research Centre, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
article info
abstract
Article history:
Numerical simulation and laboratory experiments were conducted to investigate the
Received 23 December 2008
determining factor and the underlying mechanism in aerobic sludge granulation in
Received in revised form
a sequencing batch reactor (SBR). In the numerical simulation, a sectional approach was
7 April 2009
used to develop a model to describe the biomass dynamics during the granulation process.
Accepted 5 May 2009
The growth of different classes of the SBR sludge with different substrate uptake rates and
Published online 14 May 2009
different sludge discharge ratios was simulated. The results indicate that the selective discharge of slow-settling sludge flocs is the key determining factor for granulation. In the
Keywords:
laboratory study, experiments were conducted with two identical 2.4-L SBRs, R1 and R2,
Aerobic granulation
using different sludge discharge methods – the selective discharge of slow-settling sludge
Biological wastewater treatment
flocs for R1, and mixed, unselective sludge discharge for R2. The SBRs were fed with
Clone library
glucose-based synthetic wastewater at a chemical oxygen demand (COD) loading rate of
Microbial community
1.5 kg/m3-d. The evolution of the microbial community during the experimental process
PCR-DGGE
was monitored using the molecular techniques of polymerase chain reaction (PCR),
Sequencing batch reactor (SBR)
denaturing gradient gel electrophoresis (DGGE) and clone library analysis. Sludge granulation was achieved in less than three weeks in R1, whereas the sludge in R2 remained in the form of flocs. However, some bacterial species had a significant presence in both the R1 granules and the R2 flocs. The results suggest that aerobic granulation may not require the dominance of any particular species. Small and loose sludge flocs were found to have an advantage over larger and dense granules in substrate uptake. Thus, discharge of loose flocs would remove these competitors from the system and makes the substrate more available for uptake and utilisation by biomass in the attached-growth form, resulting in sludge granulation. ª 2009 Elsevier Ltd. All rights reserved.
1.
Introduction
Aerobic granulation, a process in which loose sludge flocs are transformed into dense granules, is an attractive new technology for biological wastewater treatment. As a form
of microbial self-immobilisation in a bioreactor, sludge granulation may completely eliminate the biomass–effluent separation problems that are frequently encountered in conventional treatment processes. Due to attributes such as a compact structure and fast settling velocity (Morgenroth
* Corresponding author. Tel.: þ852 2859 2659; fax: þ852 2559 5337. E-mail address: [email protected] (X.-Y. Li). URL: http://web.hku.hk/~xlia 0043-1354/$ – see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2009.05.004
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et al., 1997; Beun et al., 1999; Arrojo et al., 2004; Liu and Tay, 2004), granular sludge allows a high level of biomass retention, a very short phase of sludge sedimentation and a much higher organic loading rate in bioreactors (Liu et al., 2003a; Su and Yu, 2005; de Kreuk and van Loosdrecht, 2006). With its potential role in the development of novel, compact and high-rate treatment systems, aerobic granulation may lead to a fundamental advancement in wastewater treatment practices (Moy et al., 2002; Li et al., 2008). Despite the advantages of aerobic granules and many reports of successful sludge granulation in experimental sequencing batch reactors (SBR), the determining factor and the mechanism underlying granule formation are still subject to discussion and investigation. A number of factors have been found to affect the granulation process, such as the type of organic substrate, loading rate, feast-famine regime, starvation time, aeration intensity, hydraulic washout rate and production of extracellular polymeric substances (EPS) (Beun et al., 2002; Liu and Tay, 2002, 2004; Liu et al., 2003a; McSwain et al., 2004; Wang et al., 2006; Yang et al., 2008). However, there is no consensus on the determining mechanism of granule formation. It has been assumed that the sludge washout in an SBR creates a selection pressure that leads to the formation of granules (Liu and Tay, 2004; Li et al., 2006). However, the ecological concept of selection pressure may not be applicable to the more dynamic bioreactors. Aerobic granular sludge has been considered as a special case of biofilm growth without the addition of foreign carrier materials (Beun et al., 2002; Liu and Tay, 2002; Yang et al., 2004). The growth of aerobic granules after the initial cellto-cell attachment is similar to the growth of biofilms. The hydrophobic binding between bacteria may play a crucial role in the initiation of aerobic granulation (Liu et al., 2003b). Upon granulation, the stability of the granules is affected by the sludge removal method (Li et al., 2006). However, the controlling factor in SBR operation that shifts the biomass from the conventional suspended-growth mode to the attached-growth mode in the form of granules is yet to be identified, and the underlying mechanism also needs to be investigated. Change in the microbial community of biomass sludge during granule formation also is an essential issue for the granulation study (Xavier et al., 2007). There is little information about the bacterial population dynamics during the granulation process. DNA-based molecular techniques offer a valuable tool for describing the microbial community in biological wastewater treatment systems (Eichner et al., 1999; Stamper et al., 2003; Shen et al., 2006). However, although the bacterial diversity of aerobic granules has been studied using DNA-based molecular techniques (Zhuang et al., 2005; Xavier et al., 2007), the dynamics of microbial evolution during sludge granulation in terms of species diversity and abundance remain to be characterised. In this study, a mathematical model was formulated for the biomass dynamics during the early phase of granule formation in an SBR. The results of the numerical simulation indicate that the selective discharge of small and loose sludge flocs is the key determining factor in granulation. To validate the modelling result, laboratory experiments were conducted with two SBRs subjected to different sludge discharge
methods. Changes in the microbial community in the course of the experiment were analysed using several molecular tools, including polymerase chain reaction (PCR), denaturing gradient gel electrophoresis (DGGE) and clone library analysis. The aims of the study were (i) to demonstrate the determining factor in granule formation and growth, (ii) to identify the mechanism of selective sludge discharge in granulation and (iii) to reveal the evolution of the microbial community during granulation compared with that of suspended activate sludge.
2.
Materials and methods
2.1. Theoretical modelling of the biomass dynamics during granule formation Biological aggregates in activated sludge reactors span a wide range of sizes, densities and settling velocities (Li and Ganczarczyk, 1991; Li et al., 2003; Sears et al., 2006). Conceptually, sludge flocs can be divided into a number of sections, or classes, such as small and loose flocs, regular flocs, dense flocs and compact granule precursors. Whereas loose flocs are formed by bacteria in a suspended-growth mode, compact granule precursors are thought to be formed by bacteria in an attached-growth mode (Beun et al., 2002; Liu and Tay, 2002; Yang et al., 2004). The total sludge concentration of the mixture in a bioreactor is the sum of the sludge in individual P sections, or X ¼ si¼1 Xi , where Xi is the biomass concentration in section i of the sludge flocs and s is the total number of sludge classes. Following this sectional approach, the change in the biomass concentration of a sludge class in the SBR suspension can be written as dXi ¼ Yi DSi fi Xi ; dt
(1)
where DSi is the amount of organic substrate degraded by the biomass in section i, Yi is the corresponding observed yield coefficient, and fi is the ratio, or proportion, of sludge discharge in the section. Different types of sludge flocs have different substrate uptake capabilities. For example, small and loose flocs are expected to have an advantage over large and dense flocs or granules in substrate uptake and utilisation. More specifically, we have DSi ¼ pi ðDS=XÞXi , where DS is the overall organic removal and pi is the actual substrate uptake factor of the biomass in section i. In SBR operation, the sludge discharge ratios may differ between sludge classes due to their different settling velocities. Hence, fi ¼ qi f , where f is the overall sludge discharge ratio and qi is the effective sludge discharge factor for section i. Assuming the same observed yield coefficient for all sludge sections, the biomass dynamics for the sludge classes in an SBR can be written as dXi DS ¼ Yobs pi Xi qi fXi : dt X
(2)
It should be noted that pi is a variable related to the substrate uptake capability and qi is an operational variable related to the sludge discharge method. In addition, the
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following mass balances in terms of biomass growth and sludge discharge should be observed in the simulation. s X
Yobs pi
i¼1
s X
DS Xi ¼ Yobs DS: X
qi fXi ¼ fX:
(3)
(4)
i¼1
Using the section-based model, the biomass dynamics during the SBR start-up process can be simulated. It is assumed that healthy growth conditions are provided for the SBR sludge, including a sufficient substrate feeding load and aeration intensity. The dominance of different types of biomass sludge can therefore be achieved by adjusting the qi factors.
2.2.
Experimental set-up and SBR operation
Two identical columns (6 cm in diameter and 80 cm in height) with a working volume of 2.4 L each were used as sequencing batch reactors for the sludge granulation study. The two reactors, R1 and R2, were operated in a fixed sequential mode for a 4-h cycle with 4 min of feeding, 172 min of aeration, 60 min of sludge settling and 4 min of effluent withdrawal from the middle ports of the columns. Aeration was conducted at an air flow rate of 4.0 L/min during the aeration phase. The reactors were both fed with synthetic wastewater that consisted of glucose and other nutrients according to the chemical composition given by Tay et al. (2002). The influent had an organic concentration in terms of the chemical oxygen demand (COD) of 500 mg/L, resulting in a COD loading rate of 1.5 kg/m3-d in the two SBRs. Activated sludge from a full-scale sewage treatment plant (Stanley Sewage Treatment Works, Hong Kong) was used as the seed sludge with an initial concentration of 3800 mg/L in MLSS (mixed liquor suspended solids). The sludge had been acclimated in the two SBRs for one month with the glucose-based synthetic wastewater. Sludge was discharged at a ratio of around 10% every day during the aeration phase from the sampling ports near the bottom of the SBR columns. The acclimated sludge was well mixed before loading into the two SBRs for the granulation study. The experiments were performed at room temperature, and the water temperature was 20–22 C. NaHCO3 was dosed into the feed solution to maintain the pH of the reactors in the neutral range between 7.0 and 7.5, and the dissolved oxygen (DO) concentration in the sludge suspension was in a range of 2–5 mg/L. Sludge was discharged once a day from the two SBRs at a predetermined rate. Sludge loss in the effluent was minimised by allowing a sludge settling time of 60 min in each SBR cycle. Sludge was discharged from the middle ports of the SBR columns by different methods – selective discharge and unselective discharge – for the two reactors. For the first SBR, R1, the sludge was removed during the settling phase without aeration after a few minutes of initial settling. This initial settling period varied from 1 to 5 min based on the targeted ratio of sludge removal. More slow-settling sludge flocs were expected in the discharged sludge than in the sludge retained in the reactor. For the second SBR, R2, the sludge mixture was
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discharged while the aeration to mix the sludge was still being conducted. Hence, the discharged sludge was largely the same as the sludge in the reactor. The volume of the sludge suspension and its suspension solid (SS) concentration were measured for each sludge discharge, and the ratio of the actual daily sludge discharge was determined for the sludge in each reactor. The sludge discharge ratio increased from 10% to 33% in the first week after the start-up of the SBRs and then decreased to 10% in the second week and thereafter.
2.3.
Analytical methods
The COD concentration, sludge MLSS concentration and effluent suspended solids (ESS) were measured regularly according to Standard Methods (APHA–AWWA–WEF, 1998). The total organic carbon (TOC) concentration was measured with a TOC analyser (IL550, HACH-Lachat, Milwaukee, WI, USA). The morphology of the sludge flocs and granules was observed under a stereomicroscope (S8 APO, Leica, Cambridge, UK) equipped with a digital camera (EC3, Leica, Cambridge, UK). The particle size distributions of the sludge samples were determined using a computer-based image analysis system (analySIS 3.1, Olympus Soft Imaging Solutions, Germany). On a projected image of the sludge particles, the size of an individual particle or floc can be determined according to its projected area, A, and expressed by the pffiffiffiffiffiffiffiffiffiffiffiffi equivalent diameter of d ¼ 4A=p (Li et al., 2003).
2.4. DNA extraction and denaturing gradient gel electrophoresis (DGGE) analysis DGGE band profiles are used to reveal the most abundant DNA types among the species in a microbial community, and quantitative DGGE analysis has been used as a tool to monitor changes in the structure of a microbial community in a bioreactor (Eichner et al., 1999). In this experiment, the genomic DNA of the sludge samples was extracted following the protocol described by Zhuang et al. (2005) using a beadbeater (Mini-beadbeater, Biospec, Bartlesville, OK, USA) and a microcentrifuge (MiniSpin plus, Eppendorf, Hamburg, Germany). Subsequently, the variable V3 region of the bacterial 16S rDNA gene sequence was amplified by polymerase chain reaction (PCR) (Muyzer et al., 1993) with a DNA Engine Peltier Thermal Cycler (PTC-200, MJ Research, Waltham, MA, USA). A touchdown thermal profile technique was used for the PCR procedure (Watanabe et al., 1998). The PCR-amplified DNA products were separated by DGGE, and the DGGE images were acquired using the ChemiDoc (Bio-Rad, Hercules, CA, USA) gel documentation system following the procedures described previously (Li et al., 2008). The scanned DGGE gels were then analysed quantitatively using the Quantity One 1-D analysis software (Bio-Rad) based on the peak value of the band brightness.
2.5. Identification of the phylogeny of the DGGE bands combined with clone library analysis A 16S rRNA gene sequence clone library was constructed to identify the phylogeny of the DGGE bands of the sludge samples. PCR was performed to obtain most of the full length
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et al., 2006). Thus, the substrate uptake factors for the five sludge classes were assumed to follow the ratio p1:p2:p3:p4:p5 ¼ 1.2:1.05:1.01:0.95:0.9. Loose flocs normally have slower settling velocities than denser flocs, and thus the sludge discharge factor ratio q1:q2:q3:q4:q5 ¼ 2.0:1.5:1.0:0.5:0.1 was assumed for sludge selectively discharged from the SBR during the settling phase. In contrast, when unselective, or mixed, sludge discharge was used, q1–q5 can be assumed to be equal. The changes in the sludge concentrations over time in the five classes were calculated using the section-based model (Eq. (2)) for the two SBR sludge discharge methods. With unselective sludge discharge at a fixed ratio of 10%, the loose sludge flocs X1 and X2 became more dominant and there was no increase in the dense flocs and granules X4 and X5 (Fig. 1a). Thus, sludge granulation could not be achieved in the SBR with mixed sludge discharge, regardless of the biomass
MLSS (mg/L)
4000
a
X1 X2 X3 X4 X5
3000
2000
1000
0
b
4000
MLSS (mg/L)
of the 16S rRNA gene using universal bacterial primers that were designed based on the conserved bacterial regions at the 50 and 30 ends of the 16S rRNA gene (positions 27f and 1495r, respectively, in the Escherichia coli sequence). PCR amplification was performed according to the programme used by Liu et al. (2006). The PCR products, which were approximately 1400 bp long, were purified using a DNA gel extraction kit (MEGA-spin, iNtRON Biotechnology, Korea). The purified PCR products were cloned into E. coli TOP10 using the pCRIITOPO vector system (Invitrogen, Carlsbad, CA, USA). A total of 98 recombinant clones were selected randomly for plasmid recovery and analysis. The extraction and sequencing of the plasmids were conducted by a commercial laboratory (Tech. Dragon, http://www.techdragon.com.hk/index.htm). In brief, the DNA of the clones was sequenced with a single primer set of T7 to generate sequences of about 600 bp. All of the sequences obtained were compared with the 16S rDNA sequences in the GenBank using a BLAST search (National Centre for Biotechnology Information, U.S. National Library of Medicine) to identify the species. The results were grouped into a clone library with a 95% similarity to provisional operational taxonomic units (OTUs). Representative clones of the OTUs were subjected to DGGE analysis under the same conditions used for the biomass PCR products, and the migration positions of the library clones were compared with the DGGE profile of the sludge samples. Based on these comparisons, an OTU in the clone library was assigned to a particular DGGE band. As described previously, the relative abundance of species in a sludge sample was determined using the Bio-Rad DGGE analysis software based on the peak value of the band brightness. For a small number of DGGE bands for which no matching clones were found in the library, the PCR products were excised from the gels and then purified by Spin-X Cartridge (Microspin S-400HR columns, Amersham Pharmacia Biotech, UK), and the DNA was re-amplified following the PCR procedures described previously (Watanabe et al., 1998). The purified PCR products were then sequenced and analysed by a commercial laboratory (Tech. Dragon) for direct OTU identification.
3000
2000
1000
0
Results and discussion
3.1.
Simulation of the aerobic granulation process
The seed sludge was assumed to consist of five biomass classes with particular structural features: looser flocs X1, loose flocs X2, dense flocs X3, denser flocs X4 and compact granule precursors X5. The total initial sludge MLSS concentration X was 4000 mg/L, and it is reasonable to believe that there were more looser flocs than denser flocs in the seed activated sludge. For the five sludge groups, with loss of generality, the initial MLSS concentrations were assumed to be 1600, 1200, 600, 400 and 200 mg/L. In relation to the experimental condition, the influent substrate concentration was 500 mg COD/L, the HRT was 8 h and the organic removal efficiency was around 95%. A typical Yobs value of 0.3 mg SS/ mg COD was assumed for the SBR system. It is reasonable to believe that loose flocs have a better substrate uptake capability than dense flocs and granules (Yang et al., 2004; Sears
4000
MLSS (mg/L)
3.
c
3000
2000
1000
0 0
10
20
30
40
Time (d) Fig. 1 – Numerical simulation of the sludge growth and granulation dynamics: (a) mixed, unselective sludge discharge at an overall ratio of 10% every day, (b) selective discharge of loose sludge flocs at an overall ratio of 10% every day, and (c) selective discharge of loose sludge flocs at a variable overall ratio from 10% to 33% and then to 10%.
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growth condition. This means that if denser and heavy sludge flocs are discharged from the bottom of a bioreactor after sludge sedimentation, which is the conventional method of SBR sludge discharge, then aerobic granulation is impossible. In contrast, with the selective discharge of small and loose sludge flocs, the concentrations of X1 and X2 became lower and the granule precursors and granules X5 accumulated in the reactor (Fig. 1b). The result suggests that the selective discharge of loose sludge flocs is the crucial operating measure for an SBR to achieve sludge granulation. If the overall sludge discharge ratio was increased from 10% to 33% during the early start-up phase, then the formation of granules was considerably accelerated (Fig. 1c). Thus, a variable sludge discharge ratio can be an effective approach to aerobic granulation in SBRs.
3.2. Formation of aerobic granules in the SBR experiment Due to the selective sludge discharge, aerobic sludge granulation was well achieved in R1. Small granules became visible about 7 days after the beginning of the SBR experiment, and became dominant in the sludge after about 14 days. Granulation was nearly complete in R1 after 20 days, which is in general agreement with the results of the model simulation (Fig. 1c). In contrast, in R2, which was subjected to mixed sludge discharge, granules could not be produced at the same COD loading rate of 1.5 kg/m3-d, and the sludge remained in the form of aggregate flocs after 40 days of SBR operation. This experimental result also agrees with the model prediction (Fig. 1a). A comparison of the two SBRs proves that the selective discharge of slow-settling sludge flocs is the key condition for the formation and growth of aerobic granules. The aerobic granules formed in R1 were round with a clear boundary (Fig. 2a), which were completely different from the loose and irregular sludge flocs in R2 (Fig. 2b). After 30 days, R1 was dominated by mature granules that were around 5 mm in average diameter according to the image analysis. Upon completion of the aerobic granulation the sludge settleability had improved greatly. In R2, the sludge flocs were still small and loose (Fig. 2b), and had a similar morphology and settling behaviour as the seed sludge. The sludge was discharged once a day at a predetermined discharge ratio from both SBRs (Fig. 3a), and the amount of sludge loss in the SBR effluent was also recorded (Fig. 3b). The sludge retention time (SRT) and sludge F/M loading rate were then calculated for both R1 and R2 (Fig. 3c and d). To accelerate the granulation process, the technique of variable sludge loading was applied to R1. In the early start-up phase, the sludge discharge rate was increased rapidly from 10% to 33%, which shortened the SRT from 10 days to 3 days and increased the sludge loading from 0.38 to 1.1 g COD/g SS-d. When small granules began to form in R1 after about 7 days, the sludge discharge rate was gradually reduced to 10% to ensure the retention and healthy growth of the granules. At the same time, the SRT was extended to 10 days and the sludge loading reduced to 0.32 g COD/g SS-d. During the controlled discharge of the slow-settling sludge flocs, the sludge concentration first decreased from about 3800 mg/L to less than 1400 mg/L and
Fig. 2 – Photographs of the sludge in two SBRs: (a) large aerobic granules in R1 and (b) small sludge flocs in R2 after 30 days of the SBR operation.
then increased consistently to reach a level of about 4800 mg/L when full granulation was achieved (Fig. 4a). R2 was operated in a similar mode to R1 in terms of the HRT, aeration intensity, sludge discharge ratio, SRT and sludge loading rate (Fig. 3). As has been described, the only difference between R1 and R2 was that the mixed, unselective sludge discharge method was used for R2 whereas the selective discharge of slow-settling sludge flocs was adopted for R1. As a result of the unselective sludge discharge method used, sludge granulation was not achieved in R2. Due probably to the high sludge loading applied, the settleability of the sludge flocs in R2 deteriorated and a high SS concentration of more than 150 mg/L was found in the effluent (Fig. 3b). Hence, as would be expected for a normal SBR, it was difficult to maintain a sludge concentration of higher than 3000 mg/L in R2 (Fig. 4a). Sludge granulation is proven to be an effective technique for biomass enrichment in a wastewater treatment bioreactor. For the same organic loading of 1.5 kg COD/m3-d, both SBRs performed well in organic removal (Fig. 4b). Compared with the influent TOC of around 190 mg/L, the TOC was below
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350
a
300
R1 R2
30
250 200
20 150 100
10
50 0
c
12
0
d
1.2
10
1.0
8
0.8
6
0.6
4
0.4
2
0.2
0
F/M (g COD/g SS.d)
SRT (d)
b
ESS mg/L
Sludge discharge ratio (%)
40
0.0 0
10
20
30
40
0
10
20
Time (d)
30
40
50
Time (d)
Fig. 3 – Process parameters of the two SBRs: (a) sludge discharge ratio, (b) effluent SS concentration, (c) SRT and (d) sludge loading rate.
10 mg/L in the filtered effluent from both R1 and R2 during the experimental period. Nonetheless, due to the increase in the biomass concentration, R1 became more capable than R2 of
5.0
MLSS (g/L)
a
R1 R2
4.0
3.0
2.0
receiving a higher organic load and producing a better quality effluent. The comparison between R1 and R2 demonstrated the advantage of sludge granulation for maintaining a high biomass concentration in a reactor for biological wastewater treatment. The slow-settling sludge flocs discharged from R1 were considerably smaller than those retained in the reactor. The difference in size between the two portions of sludge became more significant after 10 days as the granulation progressed (Fig. 5). With the selective discharge method, relatively small and slow-settling sludge was discharged, which allowed the rapid growth and dominance of dense granules in the SBR. The mean size of the granules increased to more than 2 mm
1.0
0.0
R1-RS R2-RS/DS R1-DS
6000
Mean size (um)
b TOC removal (%)
95
90
85
5000 4000 3000 2000 1000 0 0
80 0
10
20
30
40
Time (d) Fig. 4 – Performance of the two SBRs: (a) MLSS and (b) organic removal efficiency.
50
10
20
30
40
50
Time (d) Fig. 5 – The mean sizes of the sludge particles in R1 and R2 (DS – discharged sludge from the reactors, RS – retained sludge in the reactors).
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160
Average organic uptake rate (mg TOC/g SS.h)
after 20 days and to about 7 mm after 45 days. In comparison, with non-selective sludge discharge, the size of the sludge flocs in R2 increased only slightly under the same loading condition as R1 (Fig. 5).
3.3. Comparison in substrate uptake capability between sludge flocs and granules Although the granular sludge in R1 performed well in sludgewater separation, the sludge flocs in R2 were found to have a faster substrate uptake capability than the granules in R1 (Fig. 6). When there was around 3800 mg/L of seed sludge in the SBRs, the TOC concentration decreased rapidly from 90 mg/L to below 10 mg/L within 30 min of a substrate feed. Ten days after the sludge inoculation, the sludge discharge process resulted in much lower biomass concentrations in both reactors, with small granules being formed in R1 and the sludge flocs retaining largely the same form in R2. Following a substrate feed, the TOC concentration dropped from 90 to 16 mg/L within 60 min in R1 and within 30 min in R2. After a total of 20 days, at which point granulation was largely complete in R1, the TOC decreased from 90 to 9 mg/L within 60 min in R1 and within 30 min in R2 following a substrate feed. After 35 days the substrate uptake rates were comparable between the two SBRs, but R1 had a much higher sludge concentration of 3920 mg/L compared with 2410 mg/L in R2. Based on the TOC uptake testing results, the average specific organic uptake rates can be estimated for the sludge in R1 and R2 in different stages of the experimental study (Fig. 7). The comparison of the substrate uptake dynamics between the two SBRs showed that loose sludge flocs have a clear advantage over dense sludge granules in the uptake of substrates and nutrients. Compared with tightly-packed large aggregates and granules, small and loose flocs can obtain
TOC concentration (mg/L)
100
120 100 80
R1 R2
60 40
0
10
20
30
Fig. 7 – The average specific organic uptake rates of the sludge in R1 and R2.
substrates more easily from the suspension, which allows them to grow faster (Yang et al., 2004). In other words, loose activated sludge flocs can readily out-compete small granules for substrate. Therefore, the selective discharge of small and slow-settling flocs eliminates competitors from the system and allows more granule growth. Conversely, without selective sludge discharge, there is less substrate available for uptake by dense flocs and granules due to competition from loose sludge flocs, which means that it is not possible for granules to grow and become dominant, as was observed in R2.
3.4.
Microbial population dynamics in two SBRs
Well-resolved DGGE bands were obtained to examine the evolution of the bacterial population in the sludge samples
day 0
b
R1 (size: 413 um, MLSS: 3.75 g/L) R2 (size: 421 um, MLSS: 3.81 g/L)
60
day 10
R1 (size: 1163 um, MLSS: 1.59 g/L) R2 (size: 735 um, MLSS: 1.67 g/L)
40 20
c
day 20
d
day 35
80 60
R1 (size: 2538 um, MLSS: 2.02 g/L) R2 (size: 893 um, MLSS: 2.15 g/L)
R1 (size: 7680 um, MLSS: 3.92 g/L) R2 (size: 931 um, MLSS: 2.41 g/L)
40 20 0 0.0
0.5
1.0
Time (h)
1.5
40
Time (d)
80
0
TOC concentration (mg/L)
a
140
2.0 0.0
0.5
1.0
1.5
2.0
Time (h)
Fig. 6 – Comparison of the organic substrate uptake rate between the sludge in the two SBRs – R1 and R2.
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from R1 and R2. To determine the identity of the bands in the DGGE profiles, 24 OTUs from 98 clones in the library were compared with the DGGE patterns. Five bands that did not match any clones in the library were excised and sequenced for identification. Of the 25 bands that appeared in the DGGE profiles, 20 dominant bands were identified (Table 1), which accounted for 85% or more of the microbial abundance represented by the DGGE banding profiles. The majority of the bacterial 16S rDNA sequences grouped with members of Proteobacteria, with three in the a subdivision and nine in the b subdivision. The next three groups clustered with Sphingobacteria, one clustered with Flavobacteria and three clustered with Actinobacteria. Other less dominant bands clustered with Planctomycetes, Actinobacteria and Firmicutes. Bacteria from the a- and b-subclasses of Proteobacteria have been commonly found in conventional activated sludge (Bond et al., 1995; Snaidr et al., 1997; Vigeant et al., 2002). Bacteria from Flavobacteria are commensal bacteria and opportunistic pathogens. They are gram-negative aerobic rods 2–5 mm long and 0.3–0.5 mm wide, and are widely distributed in soil and freshwater habitats (Boone and Castenholz, 2001). Flavobacteria have also been reported to be dominant in aerobic granules (Li et al., 2008). The class Sphingobacteria is composed of a single order of environmental bacteria capable of producing sphingolipids (Boone and Castenholz, 2001). Sphingolipids are believed to protect the surface of cells against harmful environmental factors by forming a mechanically stable and chemically resistant outer leaflet of the plasma lipid bi-layer. Certain complex glycosphingolipids have been found to be involved in specific functions, such as cell recognition and signalling (Spiegel and Milstien, 2002). The abundance of the dominant microbial species in the sludge samples in different phases of the experiment can be
determined based on the quantitative DGGE analysis (Fig. 8). Pedobacter (S14) clustered with Sphingobacteria increased rapidly in the early start-up phase in R1 when the sludge flocs were discharged selectively. Runella (S8) and Riemerella anatipestifer (S13) became dominant in R1 during granule formation. Runella (S8), Ideonella (S10) and R. anatipestifer (S13) clustered with Sphingobacteria, b-Proteobacteria and Flavobacteria, respectively, were more abundant in the sludge in R1 after granulation. Rhodobacter (S7), Ideonella (S10) and Burkholderiales (S12) clustered with a-Proteobacteria, b-Proteobacteria and b-Proteobacteria, respectively, were more abundant in the R2 sludge. It is rather difficult to identify a trend in the species evolution of the microbial community for the granulation process in R1, whilst fluctuations in species diversity and abundance were also found in the sludge in R2. A few of the same species, such as Runella (S8) and R. anatipestifer (S13), showed a significant presence in both R1 and R2 by the end of the experiment. The former granular reactor had the sludge in the attached-growth form of granules, and the latter reactor had the sludge in the suspended-growth form of flocs. It is apparent that the dominance of particular bacterial species may not be required for aerobic granulation. Rather, granules can be formed from the bacteria ordinarily present in biological wastewater treatment systems.
4.
Conclusions
A model has been developed using the sectional approach to describe the biomass dynamics during aerobic granulation in an SBR. The simulation results compare well with the experimental results obtained from two SBRs using different sludge discharge methods.
Table 1 – Phylogenetic analysis of the dominant DGGE bands in comparison to the clone library. Band/species no. 2 4 5 7 8 9 10 12 13 14 16
19 21 23 24 3 11 17 22 25 1, 6, 15, 18, 20
Closest relatives (accession no.)
Identity (%)
Phylogenetic division
Diaphorobacter sp. R-25011 (AM084025) Uncultured Rhodocyclaceae bacterium (AM268350) Uncultured Variovorax sp. clone HKT603 (DQ098969) Rhodobacter sp. TUT3732 (AB251408) Runella sp. EMB111 (DQ372985) Acidovorax sp. BSB421 (Y18617) Ideonella sp. 0-0013 (AB211233) Burkholderiales bacterium YT0099 (AB362826) Riemerella anatipestifer strain RAf68 (EU016551) Pedobacter sp. DS-57 (DQ889723) Kaistomonas ginsengisoli (AB245370) Leucobacter aridicollis (AJ781047) Brevundimonas sp. (AJ227800) Zoogloea ramigera (D14257) Paracoccus sp. BBTR62 (DQ337586) Thauera sp. R-28312 (AM084110) Microsphaera sp. G-96 (EF600014) Streptococcus suis 98HAH33 (CP000408) Planctomyces sp. (X81956) Leptothrix sp. S1.1 (DQ241397) Mycobacterium sp. HXN-1500 (AJ783967) Actinomadura macra (AB364594) Unknown
96 93 99 96 98 99 97 98 98 89 98 95 94 99 98 98 100 98 91 94 97 99
b-Proteobacteria b-Proteobacteria b-Proteobacteria a-Proteobacteria Sphingobacteria b-Proteobacteria b-Proteobacteria b-Proteobacteria Flavobacteria Sphingobacteria Sphingobacteria Actinobacteria a-Proteobacteria b-Proteobacteria a-Proteobacteria b-Proteobacteria Actinobacteria Firmicutes Planctomycetia b-Proteobacteria Actinobacteria Actinobacteria
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Fig. 8 – The evolution of the microbial community in terms of the abundance of the dominant species obtained from the analysis of the clone library and DGGE banding profiles for the sludge samples collected during the SBR experiment.
The results of both the model simulation and the laboratory study indicate that the selective discharge of relatively loose sludge flocs is the crucial operating factor for an SBR to achieve granulation. Nonetheless, according the microbial species analysis, granules can be formed from the bacteria ordinarily present in biological wastewater treatment systems. The main mechanism of the selective sludge discharge for aerobic granulation is the enhanced feeding of substrates to the biomass of attached-growth. Discharge of loose sludge flocs removes these competitors in suspended-growth mode and makes the substrates more available for uptake by the attached-growth biomass, leading to granulation.
Acknowledgements This research was supported by grants N-HKU737/04 and HKU7144/E07 from the Research Grants Council (RGC) of the Hong Kong SAR Government and grant 50828802 from the Natural Science Foundation of China. The technical assistance of Mr Keith C.H. Wong is highly appreciated.
references
APHA–AWWA–WEF, 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Health Association/American Water Works Association/Water Environment Federation, Washington, D.C. Arrojo, B., Mosquera-Corral, A., Garrido, J.M., Mendez, R., 2004. Aerobic granulation with industrial wastewater in sequencing batch reactors. Water Res. 38, 3389–3399. Beun, J.J., Hendriks, A., van Loosdrecht, M.C.M., Morgenroth, E., Wilderer, P.A., Heijnen, J.J., 1999. Aerobic granulation in a sequencing batch reactor. Water Res. 33, 2283–2290.
Beun, J.J., van Loosdrecht, M.C.M., Heijnen, J.J., 2002. Aerobic granulation in a sequencing batch airlift reactor. Water Res. 36, 702–712. Bond, P.L., Hugenholtz, P., Keller, J., Blackall, L.L., 1995. Bacterial community structures of phosphate-removing and nonphosphate-removing activated sludges from sequencing batch reactors. Appl. Environ. Microbiol. 61, 1910–1916. Boone, D.R., Castenholz, R.W., 2001. In: Garrity, G.M. (Ed.)Bergey’s Manual of Systematic Bacteriology, second ed., vol. 1. Springer, New York. de Kreuk, M.K., van Loosdrecht, M.C.M., 2006. Formation of aerobic granules with domestic sewage. J. Environ. Eng. – ASCE 132, 694–697. Eichner, C.A., Erb, R.W., Timmis, K.N., Wagner-Dobler, I., 1999. Thermal gradient gel electrophoresis analysis of bioprotection from pollutant shocks in the activated sludge microbial community. Appl. Environ. Microbiol. 65, 102–109. Li, A.J., Yang, S.F., Li, X.Y., Gu, J.D., 2008. Microbial population dynamics during aerobic sludge granulation at different organic loading rates. Water Res. 42, 3552–3560. Li, D.H., Ganczarczyk, J., 1991. Size distribution of activated sludge flocs. Res. J. Water Pollut. Control Fed. 63, 806–814. Li, X.Y., Yuan, Y., Wang, H.W., 2003. The hydrodynamics of biological aggregates of different sludge ages: an insight into the mass transport mechanisms of bio-aggregates. Environ. Sci. Technol. 37, 292–299. Li, Z.H., Kuba, T., Kusuda, T., 2006. Selective force and mature phase affect the stability of aerobic granule: an experimental study by applying different removal methods of sludge. Enzyme Microb. Technol. 39, 976–981. Liu, B., Zhang, F., Feng, X., Liu, Y., Yan, X., Zhang, X., et al., 2006. Thauera and Azoarcus as functionally important genera in a denitrifying quinoline-removal bioreactor as revealed by microbial community structure comparison. FEMS Microbiol. Ecol. 55, 274–286. Liu, Y., Tay, J.H., 2002. The essential role of hydrodynamic shear force in the formation of biofilm and granular sludge. Water Res. 36, 1653–1665. Liu, Q.S., Tay, J.H., Liu, Y., 2003a. Substrate concentrationindependent aerobic granulation in sequential aerobic sludge blanket reactor. Environ. Technol. 24, 1235–1242. Liu, Y., Yang, S.F., Liu, Q.S., Tay, J.H., 2003b. The role of cell hydrophobicity in the formation of aerobic granules. Curr. Microbiol. 46, 270–274.
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Liu, Y., Tay, J.H., 2004. State of the art of biogranulation technology for wastewater treatment. Biotechnol. Adv. 22, 533–563. McSwain, B.S., Irvine, R.L., Wilderer, P.A., 2004. The effect of intermittent feeding on aerobic granule structure. Water Sci. Technol 49, 19–25. Morgenroth, E., Sherden, T., van Loosdrecht, M.C.M., Heijnen, J.J., Wilderer, P.A., 1997. Aerobic granular sludge in a sequencing batch reactor. Water Res. 31, 3191–3194. Moy, B.Y.P., Tay, J.H., Toh, S.K., Liu, Y., Tay, S.T.L., 2002. High organic loading influences the physical characteristics of aerobic sludge granules. Lett. Appl. Microbiol. 34, 407–412. Muyzer, G., de Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reactionamplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700. Sears, K., Alleman, J.E., Barnard, J.L., Oleszkiewicz, J.A., 2006. Density and activity characterization of activated sludge flocs. J. Environ. Eng. – ASCE 132, 1235–1242. Shen, J., Zhang, B., Wei, G., Pang, X., Wei, H., Li, M., et al., 2006. Molecular profiling of the Clostridium leptum subgroup in human fecal microflora by PCR-denaturing gradient gel electrophoresis and clone library analysis. Appl. Environ. Microbiol. 72, 5232–5238. Snaidr, J., Amann, R., Huber, I., Ludwig, W., Schleifer, K.H., 1997. Phylogenetic analysis and in-situ identification of bacteria in activated sludge. Appl. Environ. Microbiol. 63, 2884–2896. Spiegel, S., Milstien, S., 2002. Sphingosine 1-phosphate, a key cell signaling molecule. J. Biol. Chem. 277, 25851–25854. Stamper, D.M., Walch, M., Jacobs, R.N., 2003. Bacterial population changes in a membrane bioreactor for graywater treatment monitored by denaturing gradient gel electrophoretic analysis
of 16S rRNA gene fragments. Appl. Environ. Microbiol. 69, 852–860. Su, K.Z., Yu, H.Q., 2005. Formation and characterization of aerobic granules in a sequencing batch reactor treating soybeanprocessing wastewater. Environ. Sci. Technol. 39, 2818–2827. Tay, J.H., Liu, Q.S., Liu, Y., 2002. Characteristics of aerobic granules grown on glucose and acetate in sequential aerobic sludge blanket reactors. Environ. Technol. 23, 931–936. Vigeant, M.A.S., Ford, R.M., Wagner, M., Tamm, L.K., 2002. Reversible and irreversible adhesion of motile Escherichia coli cells analyzed by total internal reflection aqueous fluorescence microscopy. Appl. Environ. Microbiol. 68, 2794–2801. Wang, Z.W., Li, Y., Zhou, J.Q., Liu, Y., 2006. The influence of shortterm starvation on aerobic granules. Process Biochem. 41, 2373–2378. Watanabe, K., Teramoto, M., Futamata, H., Harayama, S., 1998. Molecular detection, isolation, and physiological characterization of functionally dominant phenol-degrading bacteria in activated sludge. Appl. Environ. Microbiol. 64, 4396–4402. Xavier, J.B., de Kreuk, M.K., Picioreanu, C., van Loosdrecht, M.C.M., 2007. Multi-scale individual-based model of microbial and bioconversion dynamics in aerobic granular sludge. Environ. Sci. Technol. 41, 6410–6417. Yang, S.F., Liu, Q.S., Tay, J.H., Liu, Y., 2004. Growth kinetics of aerobic granules developed in sequencing batch reactors. Lett. Appl. Microbiol. 38, 106–112. Yang, S.F., Li, X.Y., Yu, H.Q., 2008. Formation and characterisation of fungal and bacterial granules under different feeding alkalinity and pH conditions. Process Biochem. 43, 8–14. Zhuang, W.Q., Tay, J.H., Yi, S., Tay, S.T.L., 2005. Microbial adaptation to biodegradation of tert-butyl alcohol in a sequencing batch reactor. J. Biotechnol. 118, 45–53.
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Selective sludge discharge as the determining factor in SBR aerobic granulation: Numerical modelling and experimental verification An-Jie Li, Xiao-Yan Li* Environmental Engineering Research Centre, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
article info
abstract
Article history:
Numerical simulation and laboratory experiments were conducted to investigate the
Received 23 December 2008
determining factor and the underlying mechanism in aerobic sludge granulation in
Received in revised form
a sequencing batch reactor (SBR). In the numerical simulation, a sectional approach was
7 April 2009
used to develop a model to describe the biomass dynamics during the granulation process.
Accepted 5 May 2009
The growth of different classes of the SBR sludge with different substrate uptake rates and
Published online 14 May 2009
different sludge discharge ratios was simulated. The results indicate that the selective discharge of slow-settling sludge flocs is the key determining factor for granulation. In the
Keywords:
laboratory study, experiments were conducted with two identical 2.4-L SBRs, R1 and R2,
Aerobic granulation
using different sludge discharge methods – the selective discharge of slow-settling sludge
Biological wastewater treatment
flocs for R1, and mixed, unselective sludge discharge for R2. The SBRs were fed with
Clone library
glucose-based synthetic wastewater at a chemical oxygen demand (COD) loading rate of
Microbial community
1.5 kg/m3-d. The evolution of the microbial community during the experimental process
PCR-DGGE
was monitored using the molecular techniques of polymerase chain reaction (PCR),
Sequencing batch reactor (SBR)
denaturing gradient gel electrophoresis (DGGE) and clone library analysis. Sludge granulation was achieved in less than three weeks in R1, whereas the sludge in R2 remained in the form of flocs. However, some bacterial species had a significant presence in both the R1 granules and the R2 flocs. The results suggest that aerobic granulation may not require the dominance of any particular species. Small and loose sludge flocs were found to have an advantage over larger and dense granules in substrate uptake. Thus, discharge of loose flocs would remove these competitors from the system and makes the substrate more available for uptake and utilisation by biomass in the attached-growth form, resulting in sludge granulation. ª 2009 Elsevier Ltd. All rights reserved.
1.
Introduction
Aerobic granulation, a process in which loose sludge flocs are transformed into dense granules, is an attractive new technology for biological wastewater treatment. As a form
of microbial self-immobilisation in a bioreactor, sludge granulation may completely eliminate the biomass–effluent separation problems that are frequently encountered in conventional treatment processes. Due to attributes such as a compact structure and fast settling velocity (Morgenroth
* Corresponding author. Tel.: þ852 2859 2659; fax: þ852 2559 5337. E-mail address: [email protected] (X.-Y. Li). URL: http://web.hku.hk/~xlia 0043-1354/$ – see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2009.05.004
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et al., 1997; Beun et al., 1999; Arrojo et al., 2004; Liu and Tay, 2004), granular sludge allows a high level of biomass retention, a very short phase of sludge sedimentation and a much higher organic loading rate in bioreactors (Liu et al., 2003a; Su and Yu, 2005; de Kreuk and van Loosdrecht, 2006). With its potential role in the development of novel, compact and high-rate treatment systems, aerobic granulation may lead to a fundamental advancement in wastewater treatment practices (Moy et al., 2002; Li et al., 2008). Despite the advantages of aerobic granules and many reports of successful sludge granulation in experimental sequencing batch reactors (SBR), the determining factor and the mechanism underlying granule formation are still subject to discussion and investigation. A number of factors have been found to affect the granulation process, such as the type of organic substrate, loading rate, feast-famine regime, starvation time, aeration intensity, hydraulic washout rate and production of extracellular polymeric substances (EPS) (Beun et al., 2002; Liu and Tay, 2002, 2004; Liu et al., 2003a; McSwain et al., 2004; Wang et al., 2006; Yang et al., 2008). However, there is no consensus on the determining mechanism of granule formation. It has been assumed that the sludge washout in an SBR creates a selection pressure that leads to the formation of granules (Liu and Tay, 2004; Li et al., 2006). However, the ecological concept of selection pressure may not be applicable to the more dynamic bioreactors. Aerobic granular sludge has been considered as a special case of biofilm growth without the addition of foreign carrier materials (Beun et al., 2002; Liu and Tay, 2002; Yang et al., 2004). The growth of aerobic granules after the initial cellto-cell attachment is similar to the growth of biofilms. The hydrophobic binding between bacteria may play a crucial role in the initiation of aerobic granulation (Liu et al., 2003b). Upon granulation, the stability of the granules is affected by the sludge removal method (Li et al., 2006). However, the controlling factor in SBR operation that shifts the biomass from the conventional suspended-growth mode to the attached-growth mode in the form of granules is yet to be identified, and the underlying mechanism also needs to be investigated. Change in the microbial community of biomass sludge during granule formation also is an essential issue for the granulation study (Xavier et al., 2007). There is little information about the bacterial population dynamics during the granulation process. DNA-based molecular techniques offer a valuable tool for describing the microbial community in biological wastewater treatment systems (Eichner et al., 1999; Stamper et al., 2003; Shen et al., 2006). However, although the bacterial diversity of aerobic granules has been studied using DNA-based molecular techniques (Zhuang et al., 2005; Xavier et al., 2007), the dynamics of microbial evolution during sludge granulation in terms of species diversity and abundance remain to be characterised. In this study, a mathematical model was formulated for the biomass dynamics during the early phase of granule formation in an SBR. The results of the numerical simulation indicate that the selective discharge of small and loose sludge flocs is the key determining factor in granulation. To validate the modelling result, laboratory experiments were conducted with two SBRs subjected to different sludge discharge
methods. Changes in the microbial community in the course of the experiment were analysed using several molecular tools, including polymerase chain reaction (PCR), denaturing gradient gel electrophoresis (DGGE) and clone library analysis. The aims of the study were (i) to demonstrate the determining factor in granule formation and growth, (ii) to identify the mechanism of selective sludge discharge in granulation and (iii) to reveal the evolution of the microbial community during granulation compared with that of suspended activate sludge.
2.
Materials and methods
2.1. Theoretical modelling of the biomass dynamics during granule formation Biological aggregates in activated sludge reactors span a wide range of sizes, densities and settling velocities (Li and Ganczarczyk, 1991; Li et al., 2003; Sears et al., 2006). Conceptually, sludge flocs can be divided into a number of sections, or classes, such as small and loose flocs, regular flocs, dense flocs and compact granule precursors. Whereas loose flocs are formed by bacteria in a suspended-growth mode, compact granule precursors are thought to be formed by bacteria in an attached-growth mode (Beun et al., 2002; Liu and Tay, 2002; Yang et al., 2004). The total sludge concentration of the mixture in a bioreactor is the sum of the sludge in individual P sections, or X ¼ si¼1 Xi , where Xi is the biomass concentration in section i of the sludge flocs and s is the total number of sludge classes. Following this sectional approach, the change in the biomass concentration of a sludge class in the SBR suspension can be written as dXi ¼ Yi DSi fi Xi ; dt
(1)
where DSi is the amount of organic substrate degraded by the biomass in section i, Yi is the corresponding observed yield coefficient, and fi is the ratio, or proportion, of sludge discharge in the section. Different types of sludge flocs have different substrate uptake capabilities. For example, small and loose flocs are expected to have an advantage over large and dense flocs or granules in substrate uptake and utilisation. More specifically, we have DSi ¼ pi ðDS=XÞXi , where DS is the overall organic removal and pi is the actual substrate uptake factor of the biomass in section i. In SBR operation, the sludge discharge ratios may differ between sludge classes due to their different settling velocities. Hence, fi ¼ qi f , where f is the overall sludge discharge ratio and qi is the effective sludge discharge factor for section i. Assuming the same observed yield coefficient for all sludge sections, the biomass dynamics for the sludge classes in an SBR can be written as dXi DS ¼ Yobs pi Xi qi fXi : dt X
(2)
It should be noted that pi is a variable related to the substrate uptake capability and qi is an operational variable related to the sludge discharge method. In addition, the
water research 43 (2009) 3387–3396
following mass balances in terms of biomass growth and sludge discharge should be observed in the simulation. s X
Yobs pi
i¼1
s X
DS Xi ¼ Yobs DS: X
qi fXi ¼ fX:
(3)
(4)
i¼1
Using the section-based model, the biomass dynamics during the SBR start-up process can be simulated. It is assumed that healthy growth conditions are provided for the SBR sludge, including a sufficient substrate feeding load and aeration intensity. The dominance of different types of biomass sludge can therefore be achieved by adjusting the qi factors.
2.2.
Experimental set-up and SBR operation
Two identical columns (6 cm in diameter and 80 cm in height) with a working volume of 2.4 L each were used as sequencing batch reactors for the sludge granulation study. The two reactors, R1 and R2, were operated in a fixed sequential mode for a 4-h cycle with 4 min of feeding, 172 min of aeration, 60 min of sludge settling and 4 min of effluent withdrawal from the middle ports of the columns. Aeration was conducted at an air flow rate of 4.0 L/min during the aeration phase. The reactors were both fed with synthetic wastewater that consisted of glucose and other nutrients according to the chemical composition given by Tay et al. (2002). The influent had an organic concentration in terms of the chemical oxygen demand (COD) of 500 mg/L, resulting in a COD loading rate of 1.5 kg/m3-d in the two SBRs. Activated sludge from a full-scale sewage treatment plant (Stanley Sewage Treatment Works, Hong Kong) was used as the seed sludge with an initial concentration of 3800 mg/L in MLSS (mixed liquor suspended solids). The sludge had been acclimated in the two SBRs for one month with the glucose-based synthetic wastewater. Sludge was discharged at a ratio of around 10% every day during the aeration phase from the sampling ports near the bottom of the SBR columns. The acclimated sludge was well mixed before loading into the two SBRs for the granulation study. The experiments were performed at room temperature, and the water temperature was 20–22 C. NaHCO3 was dosed into the feed solution to maintain the pH of the reactors in the neutral range between 7.0 and 7.5, and the dissolved oxygen (DO) concentration in the sludge suspension was in a range of 2–5 mg/L. Sludge was discharged once a day from the two SBRs at a predetermined rate. Sludge loss in the effluent was minimised by allowing a sludge settling time of 60 min in each SBR cycle. Sludge was discharged from the middle ports of the SBR columns by different methods – selective discharge and unselective discharge – for the two reactors. For the first SBR, R1, the sludge was removed during the settling phase without aeration after a few minutes of initial settling. This initial settling period varied from 1 to 5 min based on the targeted ratio of sludge removal. More slow-settling sludge flocs were expected in the discharged sludge than in the sludge retained in the reactor. For the second SBR, R2, the sludge mixture was
3389
discharged while the aeration to mix the sludge was still being conducted. Hence, the discharged sludge was largely the same as the sludge in the reactor. The volume of the sludge suspension and its suspension solid (SS) concentration were measured for each sludge discharge, and the ratio of the actual daily sludge discharge was determined for the sludge in each reactor. The sludge discharge ratio increased from 10% to 33% in the first week after the start-up of the SBRs and then decreased to 10% in the second week and thereafter.
2.3.
Analytical methods
The COD concentration, sludge MLSS concentration and effluent suspended solids (ESS) were measured regularly according to Standard Methods (APHA–AWWA–WEF, 1998). The total organic carbon (TOC) concentration was measured with a TOC analyser (IL550, HACH-Lachat, Milwaukee, WI, USA). The morphology of the sludge flocs and granules was observed under a stereomicroscope (S8 APO, Leica, Cambridge, UK) equipped with a digital camera (EC3, Leica, Cambridge, UK). The particle size distributions of the sludge samples were determined using a computer-based image analysis system (analySIS 3.1, Olympus Soft Imaging Solutions, Germany). On a projected image of the sludge particles, the size of an individual particle or floc can be determined according to its projected area, A, and expressed by the pffiffiffiffiffiffiffiffiffiffiffiffi equivalent diameter of d ¼ 4A=p (Li et al., 2003).
2.4. DNA extraction and denaturing gradient gel electrophoresis (DGGE) analysis DGGE band profiles are used to reveal the most abundant DNA types among the species in a microbial community, and quantitative DGGE analysis has been used as a tool to monitor changes in the structure of a microbial community in a bioreactor (Eichner et al., 1999). In this experiment, the genomic DNA of the sludge samples was extracted following the protocol described by Zhuang et al. (2005) using a beadbeater (Mini-beadbeater, Biospec, Bartlesville, OK, USA) and a microcentrifuge (MiniSpin plus, Eppendorf, Hamburg, Germany). Subsequently, the variable V3 region of the bacterial 16S rDNA gene sequence was amplified by polymerase chain reaction (PCR) (Muyzer et al., 1993) with a DNA Engine Peltier Thermal Cycler (PTC-200, MJ Research, Waltham, MA, USA). A touchdown thermal profile technique was used for the PCR procedure (Watanabe et al., 1998). The PCR-amplified DNA products were separated by DGGE, and the DGGE images were acquired using the ChemiDoc (Bio-Rad, Hercules, CA, USA) gel documentation system following the procedures described previously (Li et al., 2008). The scanned DGGE gels were then analysed quantitatively using the Quantity One 1-D analysis software (Bio-Rad) based on the peak value of the band brightness.
2.5. Identification of the phylogeny of the DGGE bands combined with clone library analysis A 16S rRNA gene sequence clone library was constructed to identify the phylogeny of the DGGE bands of the sludge samples. PCR was performed to obtain most of the full length
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et al., 2006). Thus, the substrate uptake factors for the five sludge classes were assumed to follow the ratio p1:p2:p3:p4:p5 ¼ 1.2:1.05:1.01:0.95:0.9. Loose flocs normally have slower settling velocities than denser flocs, and thus the sludge discharge factor ratio q1:q2:q3:q4:q5 ¼ 2.0:1.5:1.0:0.5:0.1 was assumed for sludge selectively discharged from the SBR during the settling phase. In contrast, when unselective, or mixed, sludge discharge was used, q1–q5 can be assumed to be equal. The changes in the sludge concentrations over time in the five classes were calculated using the section-based model (Eq. (2)) for the two SBR sludge discharge methods. With unselective sludge discharge at a fixed ratio of 10%, the loose sludge flocs X1 and X2 became more dominant and there was no increase in the dense flocs and granules X4 and X5 (Fig. 1a). Thus, sludge granulation could not be achieved in the SBR with mixed sludge discharge, regardless of the biomass
MLSS (mg/L)
4000
a
X1 X2 X3 X4 X5
3000
2000
1000
0
b
4000
MLSS (mg/L)
of the 16S rRNA gene using universal bacterial primers that were designed based on the conserved bacterial regions at the 50 and 30 ends of the 16S rRNA gene (positions 27f and 1495r, respectively, in the Escherichia coli sequence). PCR amplification was performed according to the programme used by Liu et al. (2006). The PCR products, which were approximately 1400 bp long, were purified using a DNA gel extraction kit (MEGA-spin, iNtRON Biotechnology, Korea). The purified PCR products were cloned into E. coli TOP10 using the pCRIITOPO vector system (Invitrogen, Carlsbad, CA, USA). A total of 98 recombinant clones were selected randomly for plasmid recovery and analysis. The extraction and sequencing of the plasmids were conducted by a commercial laboratory (Tech. Dragon, http://www.techdragon.com.hk/index.htm). In brief, the DNA of the clones was sequenced with a single primer set of T7 to generate sequences of about 600 bp. All of the sequences obtained were compared with the 16S rDNA sequences in the GenBank using a BLAST search (National Centre for Biotechnology Information, U.S. National Library of Medicine) to identify the species. The results were grouped into a clone library with a 95% similarity to provisional operational taxonomic units (OTUs). Representative clones of the OTUs were subjected to DGGE analysis under the same conditions used for the biomass PCR products, and the migration positions of the library clones were compared with the DGGE profile of the sludge samples. Based on these comparisons, an OTU in the clone library was assigned to a particular DGGE band. As described previously, the relative abundance of species in a sludge sample was determined using the Bio-Rad DGGE analysis software based on the peak value of the band brightness. For a small number of DGGE bands for which no matching clones were found in the library, the PCR products were excised from the gels and then purified by Spin-X Cartridge (Microspin S-400HR columns, Amersham Pharmacia Biotech, UK), and the DNA was re-amplified following the PCR procedures described previously (Watanabe et al., 1998). The purified PCR products were then sequenced and analysed by a commercial laboratory (Tech. Dragon) for direct OTU identification.
3000
2000
1000
0
Results and discussion
3.1.
Simulation of the aerobic granulation process
The seed sludge was assumed to consist of five biomass classes with particular structural features: looser flocs X1, loose flocs X2, dense flocs X3, denser flocs X4 and compact granule precursors X5. The total initial sludge MLSS concentration X was 4000 mg/L, and it is reasonable to believe that there were more looser flocs than denser flocs in the seed activated sludge. For the five sludge groups, with loss of generality, the initial MLSS concentrations were assumed to be 1600, 1200, 600, 400 and 200 mg/L. In relation to the experimental condition, the influent substrate concentration was 500 mg COD/L, the HRT was 8 h and the organic removal efficiency was around 95%. A typical Yobs value of 0.3 mg SS/ mg COD was assumed for the SBR system. It is reasonable to believe that loose flocs have a better substrate uptake capability than dense flocs and granules (Yang et al., 2004; Sears
4000
MLSS (mg/L)
3.
c
3000
2000
1000
0 0
10
20
30
40
Time (d) Fig. 1 – Numerical simulation of the sludge growth and granulation dynamics: (a) mixed, unselective sludge discharge at an overall ratio of 10% every day, (b) selective discharge of loose sludge flocs at an overall ratio of 10% every day, and (c) selective discharge of loose sludge flocs at a variable overall ratio from 10% to 33% and then to 10%.
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growth condition. This means that if denser and heavy sludge flocs are discharged from the bottom of a bioreactor after sludge sedimentation, which is the conventional method of SBR sludge discharge, then aerobic granulation is impossible. In contrast, with the selective discharge of small and loose sludge flocs, the concentrations of X1 and X2 became lower and the granule precursors and granules X5 accumulated in the reactor (Fig. 1b). The result suggests that the selective discharge of loose sludge flocs is the crucial operating measure for an SBR to achieve sludge granulation. If the overall sludge discharge ratio was increased from 10% to 33% during the early start-up phase, then the formation of granules was considerably accelerated (Fig. 1c). Thus, a variable sludge discharge ratio can be an effective approach to aerobic granulation in SBRs.
3.2. Formation of aerobic granules in the SBR experiment Due to the selective sludge discharge, aerobic sludge granulation was well achieved in R1. Small granules became visible about 7 days after the beginning of the SBR experiment, and became dominant in the sludge after about 14 days. Granulation was nearly complete in R1 after 20 days, which is in general agreement with the results of the model simulation (Fig. 1c). In contrast, in R2, which was subjected to mixed sludge discharge, granules could not be produced at the same COD loading rate of 1.5 kg/m3-d, and the sludge remained in the form of aggregate flocs after 40 days of SBR operation. This experimental result also agrees with the model prediction (Fig. 1a). A comparison of the two SBRs proves that the selective discharge of slow-settling sludge flocs is the key condition for the formation and growth of aerobic granules. The aerobic granules formed in R1 were round with a clear boundary (Fig. 2a), which were completely different from the loose and irregular sludge flocs in R2 (Fig. 2b). After 30 days, R1 was dominated by mature granules that were around 5 mm in average diameter according to the image analysis. Upon completion of the aerobic granulation the sludge settleability had improved greatly. In R2, the sludge flocs were still small and loose (Fig. 2b), and had a similar morphology and settling behaviour as the seed sludge. The sludge was discharged once a day at a predetermined discharge ratio from both SBRs (Fig. 3a), and the amount of sludge loss in the SBR effluent was also recorded (Fig. 3b). The sludge retention time (SRT) and sludge F/M loading rate were then calculated for both R1 and R2 (Fig. 3c and d). To accelerate the granulation process, the technique of variable sludge loading was applied to R1. In the early start-up phase, the sludge discharge rate was increased rapidly from 10% to 33%, which shortened the SRT from 10 days to 3 days and increased the sludge loading from 0.38 to 1.1 g COD/g SS-d. When small granules began to form in R1 after about 7 days, the sludge discharge rate was gradually reduced to 10% to ensure the retention and healthy growth of the granules. At the same time, the SRT was extended to 10 days and the sludge loading reduced to 0.32 g COD/g SS-d. During the controlled discharge of the slow-settling sludge flocs, the sludge concentration first decreased from about 3800 mg/L to less than 1400 mg/L and
Fig. 2 – Photographs of the sludge in two SBRs: (a) large aerobic granules in R1 and (b) small sludge flocs in R2 after 30 days of the SBR operation.
then increased consistently to reach a level of about 4800 mg/L when full granulation was achieved (Fig. 4a). R2 was operated in a similar mode to R1 in terms of the HRT, aeration intensity, sludge discharge ratio, SRT and sludge loading rate (Fig. 3). As has been described, the only difference between R1 and R2 was that the mixed, unselective sludge discharge method was used for R2 whereas the selective discharge of slow-settling sludge flocs was adopted for R1. As a result of the unselective sludge discharge method used, sludge granulation was not achieved in R2. Due probably to the high sludge loading applied, the settleability of the sludge flocs in R2 deteriorated and a high SS concentration of more than 150 mg/L was found in the effluent (Fig. 3b). Hence, as would be expected for a normal SBR, it was difficult to maintain a sludge concentration of higher than 3000 mg/L in R2 (Fig. 4a). Sludge granulation is proven to be an effective technique for biomass enrichment in a wastewater treatment bioreactor. For the same organic loading of 1.5 kg COD/m3-d, both SBRs performed well in organic removal (Fig. 4b). Compared with the influent TOC of around 190 mg/L, the TOC was below
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a
300
R1 R2
30
250 200
20 150 100
10
50 0
c
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8
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F/M (g COD/g SS.d)
SRT (d)
b
ESS mg/L
Sludge discharge ratio (%)
40
0.0 0
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20
30
40
0
10
20
Time (d)
30
40
50
Time (d)
Fig. 3 – Process parameters of the two SBRs: (a) sludge discharge ratio, (b) effluent SS concentration, (c) SRT and (d) sludge loading rate.
10 mg/L in the filtered effluent from both R1 and R2 during the experimental period. Nonetheless, due to the increase in the biomass concentration, R1 became more capable than R2 of
5.0
MLSS (g/L)
a
R1 R2
4.0
3.0
2.0
receiving a higher organic load and producing a better quality effluent. The comparison between R1 and R2 demonstrated the advantage of sludge granulation for maintaining a high biomass concentration in a reactor for biological wastewater treatment. The slow-settling sludge flocs discharged from R1 were considerably smaller than those retained in the reactor. The difference in size between the two portions of sludge became more significant after 10 days as the granulation progressed (Fig. 5). With the selective discharge method, relatively small and slow-settling sludge was discharged, which allowed the rapid growth and dominance of dense granules in the SBR. The mean size of the granules increased to more than 2 mm
1.0
0.0
R1-RS R2-RS/DS R1-DS
6000
Mean size (um)
b TOC removal (%)
95
90
85
5000 4000 3000 2000 1000 0 0
80 0
10
20
30
40
Time (d) Fig. 4 – Performance of the two SBRs: (a) MLSS and (b) organic removal efficiency.
50
10
20
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50
Time (d) Fig. 5 – The mean sizes of the sludge particles in R1 and R2 (DS – discharged sludge from the reactors, RS – retained sludge in the reactors).
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160
Average organic uptake rate (mg TOC/g SS.h)
after 20 days and to about 7 mm after 45 days. In comparison, with non-selective sludge discharge, the size of the sludge flocs in R2 increased only slightly under the same loading condition as R1 (Fig. 5).
3.3. Comparison in substrate uptake capability between sludge flocs and granules Although the granular sludge in R1 performed well in sludgewater separation, the sludge flocs in R2 were found to have a faster substrate uptake capability than the granules in R1 (Fig. 6). When there was around 3800 mg/L of seed sludge in the SBRs, the TOC concentration decreased rapidly from 90 mg/L to below 10 mg/L within 30 min of a substrate feed. Ten days after the sludge inoculation, the sludge discharge process resulted in much lower biomass concentrations in both reactors, with small granules being formed in R1 and the sludge flocs retaining largely the same form in R2. Following a substrate feed, the TOC concentration dropped from 90 to 16 mg/L within 60 min in R1 and within 30 min in R2. After a total of 20 days, at which point granulation was largely complete in R1, the TOC decreased from 90 to 9 mg/L within 60 min in R1 and within 30 min in R2 following a substrate feed. After 35 days the substrate uptake rates were comparable between the two SBRs, but R1 had a much higher sludge concentration of 3920 mg/L compared with 2410 mg/L in R2. Based on the TOC uptake testing results, the average specific organic uptake rates can be estimated for the sludge in R1 and R2 in different stages of the experimental study (Fig. 7). The comparison of the substrate uptake dynamics between the two SBRs showed that loose sludge flocs have a clear advantage over dense sludge granules in the uptake of substrates and nutrients. Compared with tightly-packed large aggregates and granules, small and loose flocs can obtain
TOC concentration (mg/L)
100
120 100 80
R1 R2
60 40
0
10
20
30
Fig. 7 – The average specific organic uptake rates of the sludge in R1 and R2.
substrates more easily from the suspension, which allows them to grow faster (Yang et al., 2004). In other words, loose activated sludge flocs can readily out-compete small granules for substrate. Therefore, the selective discharge of small and slow-settling flocs eliminates competitors from the system and allows more granule growth. Conversely, without selective sludge discharge, there is less substrate available for uptake by dense flocs and granules due to competition from loose sludge flocs, which means that it is not possible for granules to grow and become dominant, as was observed in R2.
3.4.
Microbial population dynamics in two SBRs
Well-resolved DGGE bands were obtained to examine the evolution of the bacterial population in the sludge samples
day 0
b
R1 (size: 413 um, MLSS: 3.75 g/L) R2 (size: 421 um, MLSS: 3.81 g/L)
60
day 10
R1 (size: 1163 um, MLSS: 1.59 g/L) R2 (size: 735 um, MLSS: 1.67 g/L)
40 20
c
day 20
d
day 35
80 60
R1 (size: 2538 um, MLSS: 2.02 g/L) R2 (size: 893 um, MLSS: 2.15 g/L)
R1 (size: 7680 um, MLSS: 3.92 g/L) R2 (size: 931 um, MLSS: 2.41 g/L)
40 20 0 0.0
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80
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TOC concentration (mg/L)
a
140
2.0 0.0
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2.0
Time (h)
Fig. 6 – Comparison of the organic substrate uptake rate between the sludge in the two SBRs – R1 and R2.
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from R1 and R2. To determine the identity of the bands in the DGGE profiles, 24 OTUs from 98 clones in the library were compared with the DGGE patterns. Five bands that did not match any clones in the library were excised and sequenced for identification. Of the 25 bands that appeared in the DGGE profiles, 20 dominant bands were identified (Table 1), which accounted for 85% or more of the microbial abundance represented by the DGGE banding profiles. The majority of the bacterial 16S rDNA sequences grouped with members of Proteobacteria, with three in the a subdivision and nine in the b subdivision. The next three groups clustered with Sphingobacteria, one clustered with Flavobacteria and three clustered with Actinobacteria. Other less dominant bands clustered with Planctomycetes, Actinobacteria and Firmicutes. Bacteria from the a- and b-subclasses of Proteobacteria have been commonly found in conventional activated sludge (Bond et al., 1995; Snaidr et al., 1997; Vigeant et al., 2002). Bacteria from Flavobacteria are commensal bacteria and opportunistic pathogens. They are gram-negative aerobic rods 2–5 mm long and 0.3–0.5 mm wide, and are widely distributed in soil and freshwater habitats (Boone and Castenholz, 2001). Flavobacteria have also been reported to be dominant in aerobic granules (Li et al., 2008). The class Sphingobacteria is composed of a single order of environmental bacteria capable of producing sphingolipids (Boone and Castenholz, 2001). Sphingolipids are believed to protect the surface of cells against harmful environmental factors by forming a mechanically stable and chemically resistant outer leaflet of the plasma lipid bi-layer. Certain complex glycosphingolipids have been found to be involved in specific functions, such as cell recognition and signalling (Spiegel and Milstien, 2002). The abundance of the dominant microbial species in the sludge samples in different phases of the experiment can be
determined based on the quantitative DGGE analysis (Fig. 8). Pedobacter (S14) clustered with Sphingobacteria increased rapidly in the early start-up phase in R1 when the sludge flocs were discharged selectively. Runella (S8) and Riemerella anatipestifer (S13) became dominant in R1 during granule formation. Runella (S8), Ideonella (S10) and R. anatipestifer (S13) clustered with Sphingobacteria, b-Proteobacteria and Flavobacteria, respectively, were more abundant in the sludge in R1 after granulation. Rhodobacter (S7), Ideonella (S10) and Burkholderiales (S12) clustered with a-Proteobacteria, b-Proteobacteria and b-Proteobacteria, respectively, were more abundant in the R2 sludge. It is rather difficult to identify a trend in the species evolution of the microbial community for the granulation process in R1, whilst fluctuations in species diversity and abundance were also found in the sludge in R2. A few of the same species, such as Runella (S8) and R. anatipestifer (S13), showed a significant presence in both R1 and R2 by the end of the experiment. The former granular reactor had the sludge in the attached-growth form of granules, and the latter reactor had the sludge in the suspended-growth form of flocs. It is apparent that the dominance of particular bacterial species may not be required for aerobic granulation. Rather, granules can be formed from the bacteria ordinarily present in biological wastewater treatment systems.
4.
Conclusions
A model has been developed using the sectional approach to describe the biomass dynamics during aerobic granulation in an SBR. The simulation results compare well with the experimental results obtained from two SBRs using different sludge discharge methods.
Table 1 – Phylogenetic analysis of the dominant DGGE bands in comparison to the clone library. Band/species no. 2 4 5 7 8 9 10 12 13 14 16
19 21 23 24 3 11 17 22 25 1, 6, 15, 18, 20
Closest relatives (accession no.)
Identity (%)
Phylogenetic division
Diaphorobacter sp. R-25011 (AM084025) Uncultured Rhodocyclaceae bacterium (AM268350) Uncultured Variovorax sp. clone HKT603 (DQ098969) Rhodobacter sp. TUT3732 (AB251408) Runella sp. EMB111 (DQ372985) Acidovorax sp. BSB421 (Y18617) Ideonella sp. 0-0013 (AB211233) Burkholderiales bacterium YT0099 (AB362826) Riemerella anatipestifer strain RAf68 (EU016551) Pedobacter sp. DS-57 (DQ889723) Kaistomonas ginsengisoli (AB245370) Leucobacter aridicollis (AJ781047) Brevundimonas sp. (AJ227800) Zoogloea ramigera (D14257) Paracoccus sp. BBTR62 (DQ337586) Thauera sp. R-28312 (AM084110) Microsphaera sp. G-96 (EF600014) Streptococcus suis 98HAH33 (CP000408) Planctomyces sp. (X81956) Leptothrix sp. S1.1 (DQ241397) Mycobacterium sp. HXN-1500 (AJ783967) Actinomadura macra (AB364594) Unknown
96 93 99 96 98 99 97 98 98 89 98 95 94 99 98 98 100 98 91 94 97 99
b-Proteobacteria b-Proteobacteria b-Proteobacteria a-Proteobacteria Sphingobacteria b-Proteobacteria b-Proteobacteria b-Proteobacteria Flavobacteria Sphingobacteria Sphingobacteria Actinobacteria a-Proteobacteria b-Proteobacteria a-Proteobacteria b-Proteobacteria Actinobacteria Firmicutes Planctomycetia b-Proteobacteria Actinobacteria Actinobacteria
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Fig. 8 – The evolution of the microbial community in terms of the abundance of the dominant species obtained from the analysis of the clone library and DGGE banding profiles for the sludge samples collected during the SBR experiment.
The results of both the model simulation and the laboratory study indicate that the selective discharge of relatively loose sludge flocs is the crucial operating factor for an SBR to achieve granulation. Nonetheless, according the microbial species analysis, granules can be formed from the bacteria ordinarily present in biological wastewater treatment systems. The main mechanism of the selective sludge discharge for aerobic granulation is the enhanced feeding of substrates to the biomass of attached-growth. Discharge of loose sludge flocs removes these competitors in suspended-growth mode and makes the substrates more available for uptake by the attached-growth biomass, leading to granulation.
Acknowledgements This research was supported by grants N-HKU737/04 and HKU7144/E07 from the Research Grants Council (RGC) of the Hong Kong SAR Government and grant 50828802 from the Natural Science Foundation of China. The technical assistance of Mr Keith C.H. Wong is highly appreciated.
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