Supplementary Appendix
Modeling and optimization of granulation process of activated sludge in
sequencing batch reactors
Kui-Zu Su1,2, Bing-Jie Ni1, Han-Qing Yu1,*
1
Department of Chemistry, University of Science & Technology of China, Hefei,
230026 China
2
School of Civil Engineering, Hefei University of Technology, Hefei, 230092 China
*Corresponding author:
Fax: +86 551 3601592; E-mail: hqyu@ustc.edu.cn
This supplementary appendix contains 16-page document, 1 table and 6 figures,
including this cover page.
1
Materials and Methods
Routine analysis
The reactor performance was monitored by determining the COD and volatile
suspended solids (VSS) according to Standard Methods (APHA, 1995). Additionally,
image was conducted to determine the size distribution and number of bioparticles in
the granulation process (Image-pro Express 4.0, Media Cybernetics Inc., USA) with
an CX41 microscope (Olympus Co., Japan).
Sludge sampling and processing
The maintenance of granular integrity in sludge sampling and processing was a
crucial step in this work. A sampling instrument equipped with a wide-bore tube was
employed to avoid mechanical stress. Sludge samples for image analysis were taken
twice each week. For all the samples, the VSS content was determined. Sludge
samples were diluted for image analysis using an optimized dilution factor, which was
determined experimentally for the different operating periods following the methods
proposed by Araya-Kroff et al. (2004). The optimal dilution ratio was defined as the
lowest dilution that enabled the maximum percentage of object recognition
(Araya-Kroff et al., 2004).
Image acquisition and analysis
Images for the bioparticles larger than 0.2 mm in equivalent diameter were
2
acquired through visualization using an Olympus SZ 40 Stereo microscope (Olympus,
Japan), whereas images for the bioparticles smaller than 0.2 mm in equivalent
diameter were acquired using an Olympus CX41 microscope (Olympus Co., Japan).
The granule size was measured using an image analysis system (Image-pro Express
4.0, Media Cybernetics Inc., USA) and a Nikon 4500 digital camera (Nikon Co.,
Japan). For bioparticles with different sizes, diluted samples with a volume varied
from 0.1 to 10 mL were dispensed on a slide and covered with a slip of different areas
(1.2-466.7 mm2) for visualization and image acquisition. Fifty images were acquired
for each sample.
Determination of the parameters for sedimentation and detachment
Parameters involved in the sedimentation and detachment process were
determined in batch tests. After aeration and mixing, sludge samples with a known
size distribution and SS concentration were left to settle. During the settling process,
the SS concentrations and size distributions of sludge samples at different heights and
different time were measured. The settling equipment has a volume of 2 L and 10
flashboards, which can divide the whole column into 10 sections. This could ensure
the simultaneous sampling for all sampling pots. 20 mL of mixed liquor was taken as
sample each time. Fresh mixed liquor was added to 2 L after sampling for one settling
time. The settling parameters (rh and rp) for bioparticles with different sizes were
estimated by a nonlinear fit of the experimental results with the theoretical expression.
In a similar way, batch tests were conducted to determine the parameter kd
3
involved in the detachment process. The superficial upflow velocity was the same as
that applied in reactor operation. The biomass detached (VSSdet) was measured using
the method of sieving. A high-speed camera (Speedcam Pro Ltd., Weinberger, UK)
was used to measure the mean velocity of bioparticles in the reactor. Then, the shear
stress τ could be calculated. The parameter kd was estimated by a linear fit of the
calculated τ and experimental results of VSSdet.
Model development
Biomass growth model
As described in our previous paper (Su and Yu, 2006b), in this work the
Activated Sludge Model No.1 (ASM1) was used to model the biological reaction
processes. Substrate degradation, nitrification, denitrification, and hydrolysis were
taken into account with seven components and seven processes. Moreover,
considering the difference between the predicted and measured results, processes of
polymer storage and heterotrophic biomass growth on the storage polymers under
both aerobic and anoxic conditions were included in this model. To simplify the
model structure, storage polymers (XSTO) were incorporated into the heterotrophic
biomass (XH) and considered as a part of the biomass, instead of an individual
component in the subsequent calculations. Both the maximum storage rate and growth
rate were considered to be changeable with operating time (indicated by COD
concentration), and experiential coefficients with exponential form were proposed as
4
follows:
 max, H S (t )  (1  e P *(S
2
S
(t )  S S ( 0))
S
) max, H SS
k STO (t )  e P2 *(S S (t )  S S ( 0)) k STO
(S1)
(S2)
Based on the reaction-diffusion model of components in the granules, the mass
balance of component i for a slice of granules in the mth size fraction could be written
as:
S mi
 2 S mi 2 S mi
k mi




t
r r
r 2
Dei
(S3)
with boundary conditions:
i
S mi  S sur
, at r  Rm
S mi
 0 , at r   mi
r
where  mi is the penetration depth of component i into the mth size fraction of
granules; r is the distance of the slice from the granule center.
More details of the modeling of bioreactions and parameters can be found in our
previous paper (Su and Yu, 2006b).
Results and Discussion
Effect of substrate concentration on granulation process
The effect of substrate concentration on the aerobic sludge granulation process
was investigated when the other parameters were kept unchanged. At a substrate
concentration as low as 0.2 g COD/L, aerobic granulation was not achieved in the
5
SBR (Fig. S4). At the given degressive settling time, sludge was washout on the 25
day before granulation (results not shown). Thus, the settling time was fixed at 30 min
to ensure sufficient sludge in the reactor. As shown in Fig. S4, the mean radius of
bioparticles was stabilized at 0.1 mm, almost not different from that of the seed sludge.
In the cultivation process, sludge growth was equal to that decayed and washed out,
and VSS changed little. Number of bioparticles per gram VSS remained high due to
the high settling time applied.
Aerobic granulation processes were different at higher substrate concentrations
of 0.4, 1.0, 2.0 and 4.0 g COD/L, equivalent to organic loading rates of 0.8, 2.0, 4.0
and 8.0 kg COD/(m3 d). The mean radius of bioparticles increased, but their number
decreased significantly with the operating time, and the steady state was reached
within about 55 days. The sizes of bioparticles cultivated at different substrate
concentrations were of similar levels, mainly attributed to the same parameter values
of detachment and breakage for calculations. At a higher substrate concentration the
growth rate by size was greater, and the number and VSS reached a higher steady
value. This shows that aerobic granules could be formed in a very wide range of
organic loading rate. Similar experimental results were reported by Moy et al. (2002)
and Liu et al. (2003), who found that the effect of organic loading rate on the
formation of aerobic granules was not significant within the range of 2.5-15.0 kg
COD/(m3 d).
Effect of settling time on granulation process
6
Settling time was found to be one of the most important parameters in the aerobic
granulation process (Wang et al., 2004; Yang et al., 2004; Liu et al., 2005). Fig. S5
shows the effect of settling time on the granulation process with model calculation.
For each calculation, the settling time was initiated at 30 min to avoid severe washing
out of sludge, and was then decreased gradually to the given value. At settling times
of 15.0 and 30.0 min, no granulation occurred and the VSS increased significantly,
attributable to the low sludge discharge ratio (Fig. S5). This resulted in the minimum
settling velocities of 1.7-3.4 cm/min (Wang et al., 2006). Settling times of 2-5 min,
equivalent to the minimum settling velocities of 10-25 cm/min, resulted in a
significant sludge granulation. The model prediction results are consistent with the
experimental results of Wang et al. (2006). With an increasing settling time, the
sludge discharge ratio decreased and accordingly the VSS concentration and number
of bioparticles at steady state increased. The decrease in settling time strengthened the
selection pressure by sedimentation and resulted in an increase in mean radius of
bioparticles. However, when the settling time was as short as 1 min, the granulation
process could not be accomplished because of the sludge washout. In the domain of
this work, the settling times of 2-5 min favored the aerobic sludge granulation. This is
consistent with the experimental results of Wang et al. (2004) and Qin et al. (2004).
Effect of effluent discharge ratio on granulation process
Effluent discharge ratio governed the quantity and size distribution of sludge
discharged in the effluent. At a discharge ratio of 0.3, only about 0.1% of sludge was
7
discharged in one operating cycle, resulting in a high VSS concentration in the reactor
but small-sized bioparticles at steady state (Fig. S6). Attributed to the significant
difference in settling velocity of bioparticles with different sizes, larger bioparticles
all reached the compressing layer and were retained in the reactor at a discharge ratio
greater than 0.5. The granulation processes at discharge ratios of 0.5 and 0.8 were
slightly different, resulted from the small difference in sludge discharged.
References
APHA. 1995. Standard Methods for the Examination of Water and Wastewater. 19th
ed. American Public Health Association, Washington, DC.
Araya-Kroff P, Amaral AL, Neves L, Ferreira EC, Pons MN, Mota M, Alves MM.
2004. Development of image analysis techniques as a tool to detect and quantify
morphological changes in anaerobic sludge: I. Application to a granulation
process. Biotechnol Bioeng 87:184-193.
Liu Y, Wang ZW, Qin L, Liu YQ, Tay JH. 2005. Selection pressure-driven aerobic
granulation in a sequencing batch reactor. Appl Microbiol Biotechnol 67: 26-32.
Liu Y, Yang SF, Tay JH. 2003. Elemental compositions and characteristics of aerobic
granules cultivated at different substrate N/C ratios. Appl Microbiol Biotechnol
61:556-561.
Moy BYP, Tay JH, Toh SK, Liu Y, Tay STL. 2002. High organic loading influences
the physical characteristics of aerobic sludge granules. Lett Appl Microbiol
8
34:407-412.
Qin L, Tay JH, Liu Y. 2004. Selection pressure is a driving force of aerobic
granulation in sequencing batch reactors. Process Biochem 39:579-584.
Su KZ, Yu HQ. 2006a. Gas holdup and oxygen transfer in an aerobic granule-based
sequencing batch reactor. Biochem Eng J 25:201-207.
Su KZ, Yu HQ. 2006b. A generalized model of aerobic granule-based sequencing
batch reactor I. Model development. Environ Sci Technol 40:4703-4708.
Wang ZW, Liu Y, Tay JH. 2006. The role of SBR mixed liquor volume exchange ratio
in aerobic granulation. Chemosphere 62: 767-771.
Yang SF, Liu QS, Tay JH, Liu Y. 2004. Growth kinetics of aerobic granules developed
in sequencing batch reactors. Lett Appl Microbiol 38:106-112.
9
Table S1. Parameter values for model evaluation
Parameter
Unit
Values for evaluation
Substrate concentration
g (COD)/L
0.2; 0.4; 1.0*; 2.0; 4.0
Settling time
min
2.0; 3.0*; 5.0; 15.0; 30.0
Effluent discharge ratio
0.3; 0.5*; 0.8
* Bold values are for benchmark.
10
Parameter value initiation
Cycle number k = k +1
Size fraction number i = i +1
Slice number j = j + 1
Growth of microorganisms ΔX (k, i, j)
Density (k, i, j) = density (k, i, j)
+ ΔX (k, i, j) / Volume (k, i, j)
N
Density (k, i, j) > denmax?
Y
Density (k, i, j) = denmax
N
j = jmax ?
Y
Number (k, i) = 0, Number (k, i+1) =
number (k, i+1) + number (k, i)
N
i = imax ?
Y
Microorganisms detachment
Increase in small particles and decrease in density of outer
layers
Particles in effluent after sedimentation
Decrease in particle number
Density (k+1, i, j) , number (k+1, i) , k = k+1
k = kmax ?
Y
Parameter value output
Figure S1. Calculation procedures for the model
11
N
Mean radius (mm)
0.7
0.7
max=80
max=120
0.6
0.5
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
-10
0
10
20
30
40
50
60
70
80
Time (d)
0.7
Mean radius (mm)
0.5
max=180
0.4
De=1.0
De=1.5
De=2.0
0.6
-10
0
10
20
0.6
Rb=1.0
0.6
0.5
Rb=3.0
0.5
nb=2
nb=4
0.4
Rb=5.0
0.4
nb=8
0.3
0.2
0.2
0.1
0.1
0.0
40
50
60
70
80
50
60
70
80
Time (d)
0.7
0.3
30
0.0
-10
0
10
20
30
40
50
60
70
Time (d)
80
-10
0
10
20
30
40
Time (d)
Figure S2. Sensitivity analysis of the maximum bioparticle density (ρmax), the
effective diffusivity of oxygen (De), parameter of breakage probability (Rb) and
number of pieces a bioparticle is broken into (nb) on the mean radius variation with
time
12
Mean radius (mm)
0.8
0.6
Simulated
Measured
(A)
0.4
0.2
0.0
Settling velocity
-1
(m h )
6
Number ( 10 )
-10 0
10 20 30 40 50 60 70 80
20
Simulated
Measured
16
12
(B)
8
4
0
-10 0
12
10
8
6
4
2
0
10 20 30 40 50 60 70 80
Measured
Simulated
(C)
-10 0 10 20 30 40 50 60 70 80
Operating time (days)
Figure S3. Measured (dot) and simulated (line) mean radius, bioparticle number and
mean settling velocity: (A) Mean radius; (B) Number; and (C) Settling velocity
13
Mean radius (mm)
-1
12
Number (10 g )
16
6
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
-10
0.2
0.4
1.0
2.0
4.0
0
10
(A)
20
30
40
50
60
8
70
80
(B)
4
0
-10
0
10
20
30
40
50
60
70
80
-1
VSS (g l )
10
8
6
(C)
4
2
0
-10
0
10
20
30
40
50
60
70
80
Time (d)
Figure S4. Aerobic granulation processes at various influent substrate concentrations
of 0.2 (); 0.4 (); 1.0 (); 2.0 (); 4.0 () g COD/L: (A) Mean radius; (B)
Bioparticle number per gram VSS; and (C) VSS.
14
0.5
0.4
6
0.6
-1
2.0
3.0
5.0
15.0
30.0
(A)
0.3
0.2
0.1
0.0
-10
7
0
10 20 30 40 50 60 70 80
5
-1
VSS (g l )
6
(C)
4
3
2
1
-10
0
Sludge discharge ratio
Mean radius (mm)
0.7
18
16
14
12
10
8
6
4
2
0
-2
-10
0.15
Number (10 g )
0.8
(B)
0
10 20 30 40 50 60 70 80
0.10
(D)
0.05
0.00
10 20 30 40 50 60 70 80
-10
Time (d)
0
10 20 30 40 50 60 70 80
Time (d)
Figure S5. Aerobic granulation processes at various settling times of 2.0 (); 3.0 ();
5.0 (); 15.0 (); 30.0 () min: (A) Mean radius; (B) Bioparticle number per gram
VSS; (C) VSS; and (D) Sludge discharge ratio.
15
20
6
-1
Number (10 g )
0.3
0.5
0.8
(A)
0
10 20 30 40 50 60 70 80
8
-1
VSS (g l )
10
6
(C)
4
2
0
-10
0
15
(B)
10
10 20 30 40 50 60 70 80
Sludge discharge ratio
Mean radius (mm)
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
-10
5
0
-10
0
10 20 30 40 50 60 70 80
0.1
(D)
0.0
-10
0
10 20 30 40 50 60 70 80
Time (d)
Time (d)
Figure S6. Aerobic granulation processes at various effluent discharge ratios of 0.3
(); 0.5 (); 0.8 (): (A) Mean radius; (B) Bioparticle number per gram VSS; (C)
VSS; and (D) Sludge discharge ratio.
16