Bioprocess Biosyst Eng DOI 10.1007/s00449-014-1181-x
ORIGINAL PAPER
Simulation of wastewater treatment by aerobic granules in a sequencing batch reactor based on cellular automata Hai Benzhai • Liu Lei • Qin Ge • Peng Yuwan Li Ping • Yang Qingxiang • Wang Hailei
•
Received: 3 November 2013 / Accepted: 20 March 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract In the present paper, aerobic granules were developed in a sequencing batch reactor (SBR) using synthetic wastewater, and 81 % of granular rate was obtained after 15-day cultivation. Aerobic granules have a 96 % BOD removal to the wastewater, and the reactor harbors a mount of biomass including bacteria, fungi and protozoa. In view of the complexity of kinetic behaviors of sludge and biological mechanisms of the granular SBR, a cellular automata model was established to simulate the process of wastewater treatment. The results indicate that the model not only visualized the complex adsorption and degradation process of aerobic granules, but also well described the BOD removal of wastewater and microbial growth in the reactor. Thus, CA model is suitable for simulation of synthetic wastewater treatment. This is the first report about dynamical and visual simulation of treatment process of synthetic wastewater in a granular SBR. Keywords Cellular automata Aerobic granule Synthetic wastewater Simulation SBR
Introduction Activated sludge method is a wastewater treatment approach used widely at present [1]. During the wastewater H. Benzhai L. Lei Q. Ge P. Yuwan L. Ping Y. Qingxiang W. Hailei (&) College of Life Sciences, Henan Normal University, Xinxiang 453007, China e-mail: [email protected] H. Benzhai College of Information Engineering, Wuhan University of Technology, Wuhan 430000, China
treatment, the aerated activated sludge is in good contact with wastewaters and reacts with substrates, resulting in the removal of the pollutants. Aerobic granulation is a selfimmobilization result of microbes in activated sludge. Since aerobic granules have many advantages over conventional activated sludge such as compacter microbial structure, better settling ability, higher biomass retention and capability to withstand shock loads [2], in recent years, more and more attention has been paid to their cultivation and application [3]. Aerobic granules appear to have the potential to respond to the challenges of pollutant removal from wastewater, and its capability to treat phenol, organic and toxic wastewaters has already been studied extensively [4, 5]. A good model is important for the operation and control of the activated sludge system for wastewater treatment. Thus, many mathematical models including Andrews model [6], WRC model [7] and ASM model [8] were developed. In addition, data-driven ‘‘black-box’’ modeling approaches such as interval arithmetic simulation [9] and artificial neural networks [10] have been successfully applied to simulate wastewater treatment by activated sludge. Although several different concepts have been employed in activated sludge models, the models are still defective because (1) some descriptions in mathematical models for the process are simplified or not well defined; (2) most of these models simulate activated sludge process from one aspect, not understood the system as a whole [11– 13] and (3) several models such as activated sludge model no. 1 (ASM1) are high dimensional and contain so many kinetic and stoichiometric parameters that the general application of such a complex model to process control and development of operational strategies have been limited [14]. The mathematical models concerning aerobic granule system, including the modeling of the dynamic facets of
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aerobic granulation, mass transfer and detachment in aerobic granules, fate of microbial products in granules and multi-scale modeling of aerobic granule have also been studied extensively [15]. The activated sludge purifies wastewater by microbial metabolism accompanying complex biochemical reactions. Diversity, randomness, indeterminacy and strong nonlinearity of the activated sludge reactor reflect the characteristics of a complex system [16]. Thus, the precise simulation of wastewater treatment by activated sludge is very difficult. However, as an embranchment of intelligent system, cellular automata (CA) offers an ideal model for simulation of the complex systems [17, 18]. Therefore, the modeling of wastewater treatment process with CA has great importance to the practice and theory of wastewater treatment. Hermanowicz [19] has developed a two-dimensional model based on the concept of CA to describe the complicated biofilm morphologies observed experimentally in many real biofilm systems. In addition, a few CA models were also used to visualize wastewater treatment process by the conventional activated sludge [20–22]. However, to the best of our knowledge, there is little literature focused on the simulation of wastewater treatment by aerobic granules based on CA. The aim of this study is to establish a CA model for wastewater treatment in a granular sequencing batch reactor (SBR). In addition, the validity of this model was analyzed by comparing the predicted data and experimental data obtained by the quantitative analysis. This work will be helpful for the development of a novel software for modeling wastewater treatment based on CA.
Materials and methods Chemicals, activated sludge and synthetic wastewater All chemicals used were of analytical grade unless otherwise stated. Seed activated sludge was obtained from Henan Province Key Laboratory for Microbial Resource and Functional Molecules, China. Synthetic wastewater composed of the following: 2.0 g/l glucose, 0.5 g/l NH4CI, 20 mg/l KH2PO4, 50 mg/l CaCl22H20, 20 mg/l MgSO47H2O and 0.7 % trace element solution including 0.5 g/l Glycine, 1.0 g/l NaCl, 0.1 g/l FeSO47H2O, 0.1 g/l CoSO4, 0.1 g/l ZnSO47H2O, 10 mg/l CuSO45H2O, 10 mg/l AlK(SO4)212H2O, 10 mg/l H2BO3 and 10 mg/l Na2MoO2 2H2O was used to cultivate aerobic granules. Experimental setup This test was carried out in a lab-scale SBR system setup including three SBRs as described by Wang et al.
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[23]. The SBR (55 cm in height and 10 cm in diameter; work volume of 3.0 l), under the control of a microcomputer controlled system, was equipped with heater, pH meter, aerator, air diffuser and so forth. Reactor operation Sludge cultivation in SBR was as follows: at 30 °C, the reactor was inoculated with 1 l seed activated sludge (MLVSS 14.5 g/l). During start-up phase (day 1–3), the reactor was fed with 1.0 l synthetic wastewater. After day 3, the influent volume was increased from 1.0 to 2.0 l. Operational phases of reactor were cycle consisted of three phases, i.e., feeding phase (10 min), aerobic phase (7.0 h), and discharge and idle phase (50 min). Aeration was provided by an air bubble diffuser at a superficial air flow velocity of 2.0 cm/s. After 20-day cultivation, the granules were separated from flocculent sludge using a sieve, and a wastewater treatment test was carried out in the completely granular SBR. The practical treatment data were compared with the simulation values obtained from Matlab 7.0 (Mathworks, Natick, MA, USA). Quantification of bacterial and fungal populations Quantification analysis of microbial populations including bacteria and fungi was conducted by a real-time PCR method. Total DNA of the granule samples obtained from reactor was extracted using the cetyltrimethylammonium bromide (CTAB) method [24]. The yield and fragmentation of DNA were determined by agarose gel electrophoresis and UV visualization after staining using ethidium bromide (EB). The primer pairs of 338f (50 CCTACGGGAGGCAGCAG) and 518r (50 -ATTACC GCGGCTGCTGG) for bacteria; FF390 (50 -CGATAAC GAACGAGACCT) and FR1 (50 -AICCATTCAATCGG TAIT) for fungi were used for PCR analysis [25, 26]. The PCR was run at 95 °C for 30 s, 40 cycles of denaturing (5 s at 94 °C), annealing (34 s at 63 °C for bacteria; 60 °C for fungi), and a melting curve analysis from 60 to 95 °C with a plate read every 0.5 °C. The real-time PCR assay was conducted in a volume of 25 ll on an ABI 7500Q-PCR machine (USA) using SYBR green detection system and SYBR premix EX TaqTM kit (Takara, China). The standards were prepared by serially diluted plasmid DNA with 103–108 gene copies/ll. Both agarose gel electrophoresis and melting curve analysis were conducted to confirm the specificity of the amplified products. Three independent real-time PCR assays were performed on each DNA sample.
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CA model Suppositions The following suppositions were made to establish a CA model: (1) Dissolved oxygen is enough to make aerobic granules symmetrically disperse in the reactor; (2) microorganisms in sludge are aerobic, and their species include bacteria, fungi and protozoa; (3) both the temperature and pH value vary in a small range, and their changes have no influence on wastewater treatment; (4) the nutrients in wastewater are suitable for the growth of microorganisms, and no toxicant is produced during the treatment; (5) the relative movements are considered in the bioreactor. That is to say, microorganisms grow, breed at their own lattice and will not leave the lattice, while the substrate molecules are moving forever under aeration; (6) the sludge is granular, and a granule containing a certain quantity of microbes occupies one single lattice. Configuration design of CA A two-dimensional CA was designed in this work. Every cell has various states.All cells change their states unceasingly, which describes the growth of sludge and the removal of substrate. The CA is defined as a quintuplet: CA ¼ fT; Cells; Cell space; Neighborhoods; Rulesg where T is the discrete time; cell is the primary element of CA; cell space is the set of all cells; neighborhoods are the Moore neighborhood [27] of CA cells; and rules are the transition rules of CA regulating the evolution of cell states. In the formal definition of CA, the infinite lattice is usually required in two dimensions. This is reasonable and necessary for considerations of computability and complexity. But, it is impossible to simulate a truly infinite lattice on a computer. Therefore, some boundary conditions [28] are necessary. In this work, we adopted usual periodic boundary conditions because they come closest to simulating an infinite lattice. The two-dimensional version of periodic boundaries, where top and bottom are connected, and the left and right edge are connected, leads to the formation of a torus topology. In computer programming, the lattice is represented by a two-dimensional array and the actual lattice is surrounded by the boundary layer which in this case is a copy of one row/column from the opposite edge. Transition rules of CA All cells change their states depending on the states of their adjacent cells in CA. Thus, the next state of cell is only
relevant to the cells of its neighborhood. In addition, CA is synchronous and the cells change their states simultaneously according to the transition rules. Sij (T) was employed to denote the state of cell (i, j) at time T. According to biological mechanisms and characteristics of aerobic granule-based SBR, Sij (T) takes four values that can be expressed as: Sij ðT Þ 2 f0; 1; 2; 3g
ð1Þ
where Sij(T) = 0 means that cell (i, j) is empty at time t; Sij(T) = 1 means that cell (i, j) adsorbs the small molecule organics such as glucose in this study, which can be directly metabolized by microbes; Sij(T) = 2 means that cell (i, j) is occupied by a certain quantity of bacteria or fungi newly produced; Sij(T) = 3 means that a protozoan occupies the cell. The transition rules of CA was design as follows [18, 20]: Rule 0 if sðtÞ ¼ 1
8 2 ðpk [ p12 and n2 [ 1Þ or ð1\n2 \7 and n0 [ 1Þ > > < then sðt þ DtÞ ¼ 0 ðpk [ p10 and n0 \3Þ or ðpk \p10 and 1\n2 Þ > > : 1 else
if Sij ðTÞ ¼ 0
(
then Sij ðT þ 1Þ ¼
1 pk [ p01 or 2\n2 þ n3 \8 0 else
Rule 1 if Sij ðTÞ ¼ 1
8 2 ðpk [ p12 and n2 1Þ or ð1\n2 \7 and n0 [ 1Þ > > < then Sij ðT þ 1Þ ¼ 0 ðpk [ p10 and n0 \3Þ or ðpk \p10 and 3\n2 Þ > > : 1 else
Rule 2 if Sij ðTÞ ¼ 2
8 0 n2 7 or n2 þ n3 \3 > > < then Sij ðT þ 1Þ ¼ 3 n3 [ 1 > > : 2 else Rule 3 if Sij ðTÞ ¼ 3 then Sij ðT þ 1Þ ¼
(
0 5\n3 8 3 else
where n0, nl, n2 and n3 correspond,respectively, to the number of the state which is 0, 1, 2 and 3 in its neighborhood; pk is the ratio of BOD/MLVSS in the reactor at time T; pab is the probability threshold that cells change
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from state ‘‘a’’ to state ‘‘b’’ (a, b = 0, l, 2). In this work, p01, p10 and p12 are 0.16, 0.10 and 0.15, respectively. The values depend on the specific wastewater treatment parameters. Rule 0 denotes adsorption process of aerobic granules. In this rule, the description of Sij(T ? 1) = l denotes the cell absorbs the small molecule organics. When BOD load is more than p01 or 2 \ n2 ? n3 \ 8, most of the organics are adsorbed, and this reflects the initial high BOD removal which often occurs at the early phase of reactor run. In addition, adsorption is a physical process and the small molecule organics adsorbed will be degraded at last. Rule 1 denotes the process that the small molecule organics are consumed by bacteria or fungi for energy or are used to synthesize for the new bacteria or fungi. In this rule, when pk [ p12 and n2 1 or 1\n2 \7 and n0 [ 1, the description of Sij(T ? 1) = 2 denotes the substrate in this lattice is used to produce the new bacteria or fungi. It should be noted that bacterium and fungus are the domain biomass in the reactor. Thus, the condition n2 C 1 is reasonable in the initial phase of wastewater treatment, and the substrate degraded will be used to produce new bacteria or fungi (pk [ p12). The condition, n0 [ 1, means that some substrates have already been consumed (part of COD of wastewater is removed), and many bacteria or fungi should have already been produced. Thus, when n0 [ 1, n2 B 1 is impossible for a reactor which runs well. In addition, when n0 [ 1, n2 C 7 is also a contradiction considering that the total number of Moore neighborhood cells is 8. The description of Sij(T ? 1) = 0 denotes catabolism. When pk [ p10 and n0 \ 3, the energy or nutrient in the cell is high, but in its cell neighborhood, the energy or nutrient is low. Thus, the small molecule organics in this lattice will be consumed. When pk \ p10 and 3 \ n2, the energy or nutrient in cell neighborhood is low, and the small molecule organics in this lattice are consumed as long as the adjacent live bacteria or fungi exist. Rule 2 denotes the evolvement of microorganisms. The description of Sij(T ? 1) = 0 means bacteria or fungi in this lattice are dead because of overcrowding when n2 C 7 or isolated when n2 ? n3 \ 3 in its neighborhood. The description of Sij(T ? 1) = 3 denotes an adjacent live protozoan captures the dissociative bacteria or fungi and reproduces a new protozoan. Rule 3 denotes the evolvement of protozoan. The description of Sij(T ? 1) = 0 denotes the death of this protozoan because of overcrowding when 5 \ n3 B 8 in its neighborhood. As the above states change, there is a corresponding decrease or increase in BOD and sludge concentration. It should be notified that some cellular residues cannot be biodegraded completely and are still components of sludge. Therefore, when Sij (T) becomes 0 from 2 or 3, there is a
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Fig. 1 Variations of SVI and GR with time during aerobic granule cultivation
Fig. 2 Size distribution of aerobic granules on day 15
little decrease in sludge concentration while a little enhancement in BOD concentration. Analytical methods Five day biochemical oxygen demand (BOD5), mixed liquid volatile suspended solids (MLVSS) and sludge volume index (SVI) were analyzed by standard methods [29]. Morphological observation and enumeration of protozoa were determined using a bio-microscope (Nikon HFX-II A). The microorganisms on the surface and in the interior of aerobic granule were observed by scanning
Bioprocess Biosyst Eng Fig. 3 Microscopy images of aerobic granules. a Overview of aerobic granule; b SEM image of microbes on the surface of granule; c TEM image of spherical and rod-like microbes in the interior of granule; d TEM image of filamentous fungi in the interior of granule
shaking at 200 rpm on a platform shaker, was measured using the method of Ghangrekar et al. [30]. Aerobic granules were separated from flocculent sludge using a sieve (Q200-R20/3, Xinxiang, China). The weight of aerobic granules (W1) and total weight of sludge sample (W2) were obtained, and GR was calculated by the following: GR ð%Þ ¼ W1=W2 100:
ð2Þ
Results and discussion Cultivation of aerobic granules
Fig. 4 MLVSS varied with time during aerobic granule cultivation
electron microscope (SEM, AMRAY-1000B, Japan) and transmission electron microscope (TEM, JEM-1400, Japan), respectively. Number and size distributions of aerobic granules were measured using an image analysis system (Image-Pro Plus, V4.0, Media Cybernetics). Integrality coefficient (%), defined as the ratio of the weight of residual granules vs. total weight of granules after 5 min of
During start-up phase, microbes in sludge propagated rapidly since the reactor was fed with synthetic wastewater with 2.0 g/l of glucose. Color of sludge changed from yellow to white gradually, and sludge settling performance became better with time. On day 6, SVI of sludge reached 68 ml/g (Fig. 1a). Small flocs with the diameter of 0.2–3.1 mm began to shape after day 3, and on day 7, a large quantity of aerobic granules appeared in the reactor. Sludge GR increased with time till the maximum value, 81 %, was obtained on day 15 (Fig. 1b). Size distribution of the granules shows that they have an average diameter of 3.0 mm (Fig. 2), and the granules suspended in reactor are
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Fig. 5 Protozoa in the granular reactor. a Euglena viridis; b Epistylis cambaris; c Podophrya fixa; d Lionotus fasciola; e Rhabdolaimus and f Vorticella microstoma
similar to the cells occupying lattices in CA. Zhang and Cui [22] have constructed a CA model to simulate and analyze the treatment data from an urban sewage plant. However, the conventional activated sludge used in the plant is not granular and symmetrical, which will probably result in interferences to calculation of CA. In addition, the higher integrality coefficient of aerobic granules on day 15, 97.12 %, indicates that the granules have better strength and stability against abrasion and shear, which aerobic granules often undergo during reactor operation [31].
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Microbial biomass in the reactor The milk white aerobic granules obtained in SBR are regular and nearly round in shape. Figure 3a shows that the granules had a compacter, but uneven surface. The microorganisms were observed using SEM and TEM (Fig. 3b), and a large number of microbes including spherical and rob-like bacteria (Fig. 3c) as well as filamentous fungi (Fig. 3d) existed on the surface and in the interior of granules. MLVSS are always used as an
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Fig. 6 The evolution process of cells in CA. a The initial configuration of CA (t = 0 h); b the absorption of aerobic granules (t = 0.5 h); c the lag phase of sludge growth (t = 0.2 h); d the
exponential phase (t = 2.0 h); e the stationary phase (t = 4.0 h); f the decline phase (t = 7.5 h). The blue, cyan, yellow and red lattices represent cells with state 0, 1, 2 and 3, respectively
indication of the amount of biomass in activated sludge reactor [32]. In this work, MLVSS curve (Fig. 4) indicated a sharp decline during the start-up phase because the majority of dispersed sludge with poor settling ability was washed out with the effluent [33]. After day 4, the biomass in the reactor increased with microbial propagation, and MLVSS curve started to have an ascend trend.
Protozoa evolution Protozoa fed with both bacteria and fungi are also the important microbial populations during wastewater treatment [34]. During cultivation of aerobic granules, sludge was examined microscopically. Approximately 30 species of protozoa belonging to 15 genera were found in the
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reactor. Some of them including Euglena viridis, Epistylis cambaris, Podophrya fixa, Lionotus fasciola, Rhabdolaimus and Vorticella microstoma were shown in Fig. 5. In the sludge, the ciliated protozoa were generally dominant, and the flagellated protozoa and amoebae were sometimes observed, but the relative quantity was less. Simulation of wastewater treatment by CA The software Matlab 7.0 was used to realize the rules and built the CA model. In the simulation experiment, the Moore neighborhood was used to build a grid which was constituted by 100 9 100 single lattices. Figure 6, the output result, presents the evolution of CA. The blue, cyan, yellow and red lattices represent cells with state 0, 1, 2 and 3, respectively. Figure 6a is the initial configuration of CA, and the initial parameters given are: state 2 = 361, state 3 = 396, and the left is state 0 [18]. Figure 6b shows the absorption of aerobic granules, and most of the cells absorbed the organics at T = 0.5 h [cycling number/time (h) = 9,500/1]. Figure 6c shows the lag phase of sludge growth. In this phase, some organics were consumed, but only a few microbes were produced [35]. Figure 6d reflects the rapid increase of microbial number in the exponential phase. Microorganisms were growing and dividing at the maximal rate in this phase, and a quantity of microorganisms newly produced (state 2) appeared in CA. After the exponential phase, the microbial growth entered the stationary phase and eventually population growth ceased and the growth curve became horizontal (Fig. 6e). Figure 6f shows the decline phase of microbial growth, and environmental changes like nutrient deprivation led to the decline in the number of viable cells. During the evolution process, the cells went through many life cycles from growth to death. BOD in wastewater was removed by the complicated biochemical reactions, and the wastewater in reactor becomes clearer. From above mentioned, Fig. 6 offers a clear description on the treatment process of wastewater in SBR. Thus, the CA simulation makes wastewater treatment by aerobic granules visual [21]. Analysis on BOD removal After the separation of aerobic granules, a wastewater treatment test was carried out in the completely granular SBR. Through three repeated retrials, BOD removal rate of the practical effluent reached 96 % after 8-h treatment, which is almost equal to the simulation value, 96.7 %. BOD removal curves of the practical and simulated test are shown in Fig. 7. At the same time, there are obvious burr phenomena in the curve of the predicted BOD removal, which depicts complex characteristics of the granular
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Fig. 7 Total removal rate, degradation rate and adsorption rate varied with time during the wastewater treatment
Fig. 8 The predicted and practical values of MLVSS
sludge reactor such as diversity, indeterminacy and strong nonlinearity [16, 22]. During the wastewater treatment, the total BOD removal rate is the amount of absorption and degradation rate. Specifically at the initial phase, a majority of organics were absorbed by aerobic granules. Figure 7 shows the variations of adsorption rate and degradation rate. When the reactor was run, the organics were absorbed and the adsorption rate reached the maximum value, 52.4 % at 0.9 h. After hour 1.0, the adsorption rate started to decrease. The degradation rate of wastewater increased in the whole process. However, the higher slope at hour 1.0–2.0 indicates that the organics were degraded rapidly in this period. At hour 8, 88.2 % of BOD in the wastewater was removed by biodegradation, which is 91.2 % of the
Bioprocess Biosyst Eng Fig. 9 Variations of the number of microbial populations and total cell weight with time. a Enumeration results of the microbial populations including bacteria, fungi and protozoa; b variation of the total cell weight with time
total BOD removal rate. Thus, CA not only can predict the total BOD removal during wastewater treatment, but also can give us more information about the absorption and degradation of organic substances. Analysis on microbial growth As the MLVSS curve (Fig. 8) shown, the biomass in reactor began to propagate after a 0.4 h lag phase, and then, sludge growth came into the exponential phase. In the exponential phase, MLVSS increased rapidly with time. After hour 3.5, the MLVSS curve became stable. The simulated MLVSS curve described the growth trend of biomass in granular SBR, and the result accords with a practical sludge system [36, 37]. The difference between the predicted and practical values is not significant (p \ 0.05), indicating that the effectiveness of CA in analyzing the biomass growth in a granular SBR. Due to the uncultivable characteristics of some microorganisms, a molecular biological method was used to determine microbial populations including bacteria and fungi in sludge samples collected from SBR. Archaea were not considered in view of their low concentration in an aerobic reactor [38, 39]. The real-time PCR results
(Fig. 9a) indicate that aerobic granules harbored a large amount of bacteria and fungi. After start-up of reactor, bacteria in the reactor propagated rapidly, and the quantity increased gradually before hour 4. The fungal number increased in the whole process, and it reached 2.5 9 108 copies/g MLSS at hour 8. Compared with the bacteria and fungi, the number of protozoa was low and their number curve had an undulating change. After the rough estimate according to a general formula (one bacterial copy = 1 cell weight; one fungal copy & 100 cell weights; and one protozoan & 10,000 cell weights), variation of the total microbial cell weight in the reactor was shown in Fig. 9b. In contrast with Fig. 8, the number curve obtained by quantitative analysis had the similar trend with the MLVSS curve simulated by CA. Thus, from both the theoretical analysis and actual quantitative data, we have verified the effectiveness of CA to predict the biomass change in a granular SBR.
Conclusions Aerobic granules were successfully developed in SBR and 81 % of granular rate was obtained after 15-day
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cultivation. The granules have a 96 % BOD removal to glucose wastewater, and the reactor harbors a mount of biomass including bacteria (1011 copies/g MLSS), fungi (107 copies/g MLSS) and protozoa (103 copies/g MLSS). A CA model was established for simulating the treatment of synthetic wastewater, and the results indicate that CA can visualize the complex adsorption and degradation process, and well describe both BOD removal and microbial growth in the granular SBR. The simulation value of BOD removal rate, 96.7 %, is almost equal to the practical removal rate of the wastewater after 8-h treatment. In addition, the simulated MLVSS curve describes the growth trend of biomass in practical granular SBR, and the difference between the predicted and practical MLVSS values is not significant (p \ 0.05). This is the first dynamical and visual simulation of synthetic wastewater treatment by aerobic granules, and in view of the advantages and effectiveness of CA model exhibited, development of the novel software for modeling wastewater treatment based on CA is feasible. The further work will be focused on the effectiveness of CA in the treatment of the real wastewaters. Acknowledgments This work is supported by national science foundation of China (No. 51008119).
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36. Li ZH, Kuba T, Kusuda T (2006) The influence of starvation phase on the properties and the development of aerobic granules. Enzyme Microb Technol 38(5):670–674 37. Jang A, Yoon YH, Kim IS, Kim KS, Bishop PL (2003) Characterization and evaluation of aerobic granules in sequencing batch reactor. J Biotechnol 105:71–82 38. Yang Q, Wang J, Wang H, Chen X, Ren S, Li X, Xu Y, Zhang H, Li X (2012) Evolution of the microbial community in a full-scale printing and dyeing wastewater treatment system. Bioresour Technol 117:155–163 39. Emiliano ED, Alfons JMS, Ricardo A, Sanz JL (2006) Phenotypic properties and microbial diversity of methanogenic granules from a full-scale upflow anaerobic sludge bed reactor treating brewery wastewater. Appl Environ Microbiol 72: 4942–4949
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ORIGINAL PAPER
Simulation of wastewater treatment by aerobic granules in a sequencing batch reactor based on cellular automata Hai Benzhai • Liu Lei • Qin Ge • Peng Yuwan Li Ping • Yang Qingxiang • Wang Hailei
•
Received: 3 November 2013 / Accepted: 20 March 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract In the present paper, aerobic granules were developed in a sequencing batch reactor (SBR) using synthetic wastewater, and 81 % of granular rate was obtained after 15-day cultivation. Aerobic granules have a 96 % BOD removal to the wastewater, and the reactor harbors a mount of biomass including bacteria, fungi and protozoa. In view of the complexity of kinetic behaviors of sludge and biological mechanisms of the granular SBR, a cellular automata model was established to simulate the process of wastewater treatment. The results indicate that the model not only visualized the complex adsorption and degradation process of aerobic granules, but also well described the BOD removal of wastewater and microbial growth in the reactor. Thus, CA model is suitable for simulation of synthetic wastewater treatment. This is the first report about dynamical and visual simulation of treatment process of synthetic wastewater in a granular SBR. Keywords Cellular automata Aerobic granule Synthetic wastewater Simulation SBR
Introduction Activated sludge method is a wastewater treatment approach used widely at present [1]. During the wastewater H. Benzhai L. Lei Q. Ge P. Yuwan L. Ping Y. Qingxiang W. Hailei (&) College of Life Sciences, Henan Normal University, Xinxiang 453007, China e-mail: [email protected] H. Benzhai College of Information Engineering, Wuhan University of Technology, Wuhan 430000, China
treatment, the aerated activated sludge is in good contact with wastewaters and reacts with substrates, resulting in the removal of the pollutants. Aerobic granulation is a selfimmobilization result of microbes in activated sludge. Since aerobic granules have many advantages over conventional activated sludge such as compacter microbial structure, better settling ability, higher biomass retention and capability to withstand shock loads [2], in recent years, more and more attention has been paid to their cultivation and application [3]. Aerobic granules appear to have the potential to respond to the challenges of pollutant removal from wastewater, and its capability to treat phenol, organic and toxic wastewaters has already been studied extensively [4, 5]. A good model is important for the operation and control of the activated sludge system for wastewater treatment. Thus, many mathematical models including Andrews model [6], WRC model [7] and ASM model [8] were developed. In addition, data-driven ‘‘black-box’’ modeling approaches such as interval arithmetic simulation [9] and artificial neural networks [10] have been successfully applied to simulate wastewater treatment by activated sludge. Although several different concepts have been employed in activated sludge models, the models are still defective because (1) some descriptions in mathematical models for the process are simplified or not well defined; (2) most of these models simulate activated sludge process from one aspect, not understood the system as a whole [11– 13] and (3) several models such as activated sludge model no. 1 (ASM1) are high dimensional and contain so many kinetic and stoichiometric parameters that the general application of such a complex model to process control and development of operational strategies have been limited [14]. The mathematical models concerning aerobic granule system, including the modeling of the dynamic facets of
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aerobic granulation, mass transfer and detachment in aerobic granules, fate of microbial products in granules and multi-scale modeling of aerobic granule have also been studied extensively [15]. The activated sludge purifies wastewater by microbial metabolism accompanying complex biochemical reactions. Diversity, randomness, indeterminacy and strong nonlinearity of the activated sludge reactor reflect the characteristics of a complex system [16]. Thus, the precise simulation of wastewater treatment by activated sludge is very difficult. However, as an embranchment of intelligent system, cellular automata (CA) offers an ideal model for simulation of the complex systems [17, 18]. Therefore, the modeling of wastewater treatment process with CA has great importance to the practice and theory of wastewater treatment. Hermanowicz [19] has developed a two-dimensional model based on the concept of CA to describe the complicated biofilm morphologies observed experimentally in many real biofilm systems. In addition, a few CA models were also used to visualize wastewater treatment process by the conventional activated sludge [20–22]. However, to the best of our knowledge, there is little literature focused on the simulation of wastewater treatment by aerobic granules based on CA. The aim of this study is to establish a CA model for wastewater treatment in a granular sequencing batch reactor (SBR). In addition, the validity of this model was analyzed by comparing the predicted data and experimental data obtained by the quantitative analysis. This work will be helpful for the development of a novel software for modeling wastewater treatment based on CA.
Materials and methods Chemicals, activated sludge and synthetic wastewater All chemicals used were of analytical grade unless otherwise stated. Seed activated sludge was obtained from Henan Province Key Laboratory for Microbial Resource and Functional Molecules, China. Synthetic wastewater composed of the following: 2.0 g/l glucose, 0.5 g/l NH4CI, 20 mg/l KH2PO4, 50 mg/l CaCl22H20, 20 mg/l MgSO47H2O and 0.7 % trace element solution including 0.5 g/l Glycine, 1.0 g/l NaCl, 0.1 g/l FeSO47H2O, 0.1 g/l CoSO4, 0.1 g/l ZnSO47H2O, 10 mg/l CuSO45H2O, 10 mg/l AlK(SO4)212H2O, 10 mg/l H2BO3 and 10 mg/l Na2MoO2 2H2O was used to cultivate aerobic granules. Experimental setup This test was carried out in a lab-scale SBR system setup including three SBRs as described by Wang et al.
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[23]. The SBR (55 cm in height and 10 cm in diameter; work volume of 3.0 l), under the control of a microcomputer controlled system, was equipped with heater, pH meter, aerator, air diffuser and so forth. Reactor operation Sludge cultivation in SBR was as follows: at 30 °C, the reactor was inoculated with 1 l seed activated sludge (MLVSS 14.5 g/l). During start-up phase (day 1–3), the reactor was fed with 1.0 l synthetic wastewater. After day 3, the influent volume was increased from 1.0 to 2.0 l. Operational phases of reactor were cycle consisted of three phases, i.e., feeding phase (10 min), aerobic phase (7.0 h), and discharge and idle phase (50 min). Aeration was provided by an air bubble diffuser at a superficial air flow velocity of 2.0 cm/s. After 20-day cultivation, the granules were separated from flocculent sludge using a sieve, and a wastewater treatment test was carried out in the completely granular SBR. The practical treatment data were compared with the simulation values obtained from Matlab 7.0 (Mathworks, Natick, MA, USA). Quantification of bacterial and fungal populations Quantification analysis of microbial populations including bacteria and fungi was conducted by a real-time PCR method. Total DNA of the granule samples obtained from reactor was extracted using the cetyltrimethylammonium bromide (CTAB) method [24]. The yield and fragmentation of DNA were determined by agarose gel electrophoresis and UV visualization after staining using ethidium bromide (EB). The primer pairs of 338f (50 CCTACGGGAGGCAGCAG) and 518r (50 -ATTACC GCGGCTGCTGG) for bacteria; FF390 (50 -CGATAAC GAACGAGACCT) and FR1 (50 -AICCATTCAATCGG TAIT) for fungi were used for PCR analysis [25, 26]. The PCR was run at 95 °C for 30 s, 40 cycles of denaturing (5 s at 94 °C), annealing (34 s at 63 °C for bacteria; 60 °C for fungi), and a melting curve analysis from 60 to 95 °C with a plate read every 0.5 °C. The real-time PCR assay was conducted in a volume of 25 ll on an ABI 7500Q-PCR machine (USA) using SYBR green detection system and SYBR premix EX TaqTM kit (Takara, China). The standards were prepared by serially diluted plasmid DNA with 103–108 gene copies/ll. Both agarose gel electrophoresis and melting curve analysis were conducted to confirm the specificity of the amplified products. Three independent real-time PCR assays were performed on each DNA sample.
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CA model Suppositions The following suppositions were made to establish a CA model: (1) Dissolved oxygen is enough to make aerobic granules symmetrically disperse in the reactor; (2) microorganisms in sludge are aerobic, and their species include bacteria, fungi and protozoa; (3) both the temperature and pH value vary in a small range, and their changes have no influence on wastewater treatment; (4) the nutrients in wastewater are suitable for the growth of microorganisms, and no toxicant is produced during the treatment; (5) the relative movements are considered in the bioreactor. That is to say, microorganisms grow, breed at their own lattice and will not leave the lattice, while the substrate molecules are moving forever under aeration; (6) the sludge is granular, and a granule containing a certain quantity of microbes occupies one single lattice. Configuration design of CA A two-dimensional CA was designed in this work. Every cell has various states.All cells change their states unceasingly, which describes the growth of sludge and the removal of substrate. The CA is defined as a quintuplet: CA ¼ fT; Cells; Cell space; Neighborhoods; Rulesg where T is the discrete time; cell is the primary element of CA; cell space is the set of all cells; neighborhoods are the Moore neighborhood [27] of CA cells; and rules are the transition rules of CA regulating the evolution of cell states. In the formal definition of CA, the infinite lattice is usually required in two dimensions. This is reasonable and necessary for considerations of computability and complexity. But, it is impossible to simulate a truly infinite lattice on a computer. Therefore, some boundary conditions [28] are necessary. In this work, we adopted usual periodic boundary conditions because they come closest to simulating an infinite lattice. The two-dimensional version of periodic boundaries, where top and bottom are connected, and the left and right edge are connected, leads to the formation of a torus topology. In computer programming, the lattice is represented by a two-dimensional array and the actual lattice is surrounded by the boundary layer which in this case is a copy of one row/column from the opposite edge. Transition rules of CA All cells change their states depending on the states of their adjacent cells in CA. Thus, the next state of cell is only
relevant to the cells of its neighborhood. In addition, CA is synchronous and the cells change their states simultaneously according to the transition rules. Sij (T) was employed to denote the state of cell (i, j) at time T. According to biological mechanisms and characteristics of aerobic granule-based SBR, Sij (T) takes four values that can be expressed as: Sij ðT Þ 2 f0; 1; 2; 3g
ð1Þ
where Sij(T) = 0 means that cell (i, j) is empty at time t; Sij(T) = 1 means that cell (i, j) adsorbs the small molecule organics such as glucose in this study, which can be directly metabolized by microbes; Sij(T) = 2 means that cell (i, j) is occupied by a certain quantity of bacteria or fungi newly produced; Sij(T) = 3 means that a protozoan occupies the cell. The transition rules of CA was design as follows [18, 20]: Rule 0 if sðtÞ ¼ 1
8 2 ðpk [ p12 and n2 [ 1Þ or ð1\n2 \7 and n0 [ 1Þ > > < then sðt þ DtÞ ¼ 0 ðpk [ p10 and n0 \3Þ or ðpk \p10 and 1\n2 Þ > > : 1 else
if Sij ðTÞ ¼ 0
(
then Sij ðT þ 1Þ ¼
1 pk [ p01 or 2\n2 þ n3 \8 0 else
Rule 1 if Sij ðTÞ ¼ 1
8 2 ðpk [ p12 and n2 1Þ or ð1\n2 \7 and n0 [ 1Þ > > < then Sij ðT þ 1Þ ¼ 0 ðpk [ p10 and n0 \3Þ or ðpk \p10 and 3\n2 Þ > > : 1 else
Rule 2 if Sij ðTÞ ¼ 2
8 0 n2 7 or n2 þ n3 \3 > > < then Sij ðT þ 1Þ ¼ 3 n3 [ 1 > > : 2 else Rule 3 if Sij ðTÞ ¼ 3 then Sij ðT þ 1Þ ¼
(
0 5\n3 8 3 else
where n0, nl, n2 and n3 correspond,respectively, to the number of the state which is 0, 1, 2 and 3 in its neighborhood; pk is the ratio of BOD/MLVSS in the reactor at time T; pab is the probability threshold that cells change
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from state ‘‘a’’ to state ‘‘b’’ (a, b = 0, l, 2). In this work, p01, p10 and p12 are 0.16, 0.10 and 0.15, respectively. The values depend on the specific wastewater treatment parameters. Rule 0 denotes adsorption process of aerobic granules. In this rule, the description of Sij(T ? 1) = l denotes the cell absorbs the small molecule organics. When BOD load is more than p01 or 2 \ n2 ? n3 \ 8, most of the organics are adsorbed, and this reflects the initial high BOD removal which often occurs at the early phase of reactor run. In addition, adsorption is a physical process and the small molecule organics adsorbed will be degraded at last. Rule 1 denotes the process that the small molecule organics are consumed by bacteria or fungi for energy or are used to synthesize for the new bacteria or fungi. In this rule, when pk [ p12 and n2 1 or 1\n2 \7 and n0 [ 1, the description of Sij(T ? 1) = 2 denotes the substrate in this lattice is used to produce the new bacteria or fungi. It should be noted that bacterium and fungus are the domain biomass in the reactor. Thus, the condition n2 C 1 is reasonable in the initial phase of wastewater treatment, and the substrate degraded will be used to produce new bacteria or fungi (pk [ p12). The condition, n0 [ 1, means that some substrates have already been consumed (part of COD of wastewater is removed), and many bacteria or fungi should have already been produced. Thus, when n0 [ 1, n2 B 1 is impossible for a reactor which runs well. In addition, when n0 [ 1, n2 C 7 is also a contradiction considering that the total number of Moore neighborhood cells is 8. The description of Sij(T ? 1) = 0 denotes catabolism. When pk [ p10 and n0 \ 3, the energy or nutrient in the cell is high, but in its cell neighborhood, the energy or nutrient is low. Thus, the small molecule organics in this lattice will be consumed. When pk \ p10 and 3 \ n2, the energy or nutrient in cell neighborhood is low, and the small molecule organics in this lattice are consumed as long as the adjacent live bacteria or fungi exist. Rule 2 denotes the evolvement of microorganisms. The description of Sij(T ? 1) = 0 means bacteria or fungi in this lattice are dead because of overcrowding when n2 C 7 or isolated when n2 ? n3 \ 3 in its neighborhood. The description of Sij(T ? 1) = 3 denotes an adjacent live protozoan captures the dissociative bacteria or fungi and reproduces a new protozoan. Rule 3 denotes the evolvement of protozoan. The description of Sij(T ? 1) = 0 denotes the death of this protozoan because of overcrowding when 5 \ n3 B 8 in its neighborhood. As the above states change, there is a corresponding decrease or increase in BOD and sludge concentration. It should be notified that some cellular residues cannot be biodegraded completely and are still components of sludge. Therefore, when Sij (T) becomes 0 from 2 or 3, there is a
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Fig. 1 Variations of SVI and GR with time during aerobic granule cultivation
Fig. 2 Size distribution of aerobic granules on day 15
little decrease in sludge concentration while a little enhancement in BOD concentration. Analytical methods Five day biochemical oxygen demand (BOD5), mixed liquid volatile suspended solids (MLVSS) and sludge volume index (SVI) were analyzed by standard methods [29]. Morphological observation and enumeration of protozoa were determined using a bio-microscope (Nikon HFX-II A). The microorganisms on the surface and in the interior of aerobic granule were observed by scanning
Bioprocess Biosyst Eng Fig. 3 Microscopy images of aerobic granules. a Overview of aerobic granule; b SEM image of microbes on the surface of granule; c TEM image of spherical and rod-like microbes in the interior of granule; d TEM image of filamentous fungi in the interior of granule
shaking at 200 rpm on a platform shaker, was measured using the method of Ghangrekar et al. [30]. Aerobic granules were separated from flocculent sludge using a sieve (Q200-R20/3, Xinxiang, China). The weight of aerobic granules (W1) and total weight of sludge sample (W2) were obtained, and GR was calculated by the following: GR ð%Þ ¼ W1=W2 100:
ð2Þ
Results and discussion Cultivation of aerobic granules
Fig. 4 MLVSS varied with time during aerobic granule cultivation
electron microscope (SEM, AMRAY-1000B, Japan) and transmission electron microscope (TEM, JEM-1400, Japan), respectively. Number and size distributions of aerobic granules were measured using an image analysis system (Image-Pro Plus, V4.0, Media Cybernetics). Integrality coefficient (%), defined as the ratio of the weight of residual granules vs. total weight of granules after 5 min of
During start-up phase, microbes in sludge propagated rapidly since the reactor was fed with synthetic wastewater with 2.0 g/l of glucose. Color of sludge changed from yellow to white gradually, and sludge settling performance became better with time. On day 6, SVI of sludge reached 68 ml/g (Fig. 1a). Small flocs with the diameter of 0.2–3.1 mm began to shape after day 3, and on day 7, a large quantity of aerobic granules appeared in the reactor. Sludge GR increased with time till the maximum value, 81 %, was obtained on day 15 (Fig. 1b). Size distribution of the granules shows that they have an average diameter of 3.0 mm (Fig. 2), and the granules suspended in reactor are
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Fig. 5 Protozoa in the granular reactor. a Euglena viridis; b Epistylis cambaris; c Podophrya fixa; d Lionotus fasciola; e Rhabdolaimus and f Vorticella microstoma
similar to the cells occupying lattices in CA. Zhang and Cui [22] have constructed a CA model to simulate and analyze the treatment data from an urban sewage plant. However, the conventional activated sludge used in the plant is not granular and symmetrical, which will probably result in interferences to calculation of CA. In addition, the higher integrality coefficient of aerobic granules on day 15, 97.12 %, indicates that the granules have better strength and stability against abrasion and shear, which aerobic granules often undergo during reactor operation [31].
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Microbial biomass in the reactor The milk white aerobic granules obtained in SBR are regular and nearly round in shape. Figure 3a shows that the granules had a compacter, but uneven surface. The microorganisms were observed using SEM and TEM (Fig. 3b), and a large number of microbes including spherical and rob-like bacteria (Fig. 3c) as well as filamentous fungi (Fig. 3d) existed on the surface and in the interior of granules. MLVSS are always used as an
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Fig. 6 The evolution process of cells in CA. a The initial configuration of CA (t = 0 h); b the absorption of aerobic granules (t = 0.5 h); c the lag phase of sludge growth (t = 0.2 h); d the
exponential phase (t = 2.0 h); e the stationary phase (t = 4.0 h); f the decline phase (t = 7.5 h). The blue, cyan, yellow and red lattices represent cells with state 0, 1, 2 and 3, respectively
indication of the amount of biomass in activated sludge reactor [32]. In this work, MLVSS curve (Fig. 4) indicated a sharp decline during the start-up phase because the majority of dispersed sludge with poor settling ability was washed out with the effluent [33]. After day 4, the biomass in the reactor increased with microbial propagation, and MLVSS curve started to have an ascend trend.
Protozoa evolution Protozoa fed with both bacteria and fungi are also the important microbial populations during wastewater treatment [34]. During cultivation of aerobic granules, sludge was examined microscopically. Approximately 30 species of protozoa belonging to 15 genera were found in the
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reactor. Some of them including Euglena viridis, Epistylis cambaris, Podophrya fixa, Lionotus fasciola, Rhabdolaimus and Vorticella microstoma were shown in Fig. 5. In the sludge, the ciliated protozoa were generally dominant, and the flagellated protozoa and amoebae were sometimes observed, but the relative quantity was less. Simulation of wastewater treatment by CA The software Matlab 7.0 was used to realize the rules and built the CA model. In the simulation experiment, the Moore neighborhood was used to build a grid which was constituted by 100 9 100 single lattices. Figure 6, the output result, presents the evolution of CA. The blue, cyan, yellow and red lattices represent cells with state 0, 1, 2 and 3, respectively. Figure 6a is the initial configuration of CA, and the initial parameters given are: state 2 = 361, state 3 = 396, and the left is state 0 [18]. Figure 6b shows the absorption of aerobic granules, and most of the cells absorbed the organics at T = 0.5 h [cycling number/time (h) = 9,500/1]. Figure 6c shows the lag phase of sludge growth. In this phase, some organics were consumed, but only a few microbes were produced [35]. Figure 6d reflects the rapid increase of microbial number in the exponential phase. Microorganisms were growing and dividing at the maximal rate in this phase, and a quantity of microorganisms newly produced (state 2) appeared in CA. After the exponential phase, the microbial growth entered the stationary phase and eventually population growth ceased and the growth curve became horizontal (Fig. 6e). Figure 6f shows the decline phase of microbial growth, and environmental changes like nutrient deprivation led to the decline in the number of viable cells. During the evolution process, the cells went through many life cycles from growth to death. BOD in wastewater was removed by the complicated biochemical reactions, and the wastewater in reactor becomes clearer. From above mentioned, Fig. 6 offers a clear description on the treatment process of wastewater in SBR. Thus, the CA simulation makes wastewater treatment by aerobic granules visual [21]. Analysis on BOD removal After the separation of aerobic granules, a wastewater treatment test was carried out in the completely granular SBR. Through three repeated retrials, BOD removal rate of the practical effluent reached 96 % after 8-h treatment, which is almost equal to the simulation value, 96.7 %. BOD removal curves of the practical and simulated test are shown in Fig. 7. At the same time, there are obvious burr phenomena in the curve of the predicted BOD removal, which depicts complex characteristics of the granular
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Fig. 7 Total removal rate, degradation rate and adsorption rate varied with time during the wastewater treatment
Fig. 8 The predicted and practical values of MLVSS
sludge reactor such as diversity, indeterminacy and strong nonlinearity [16, 22]. During the wastewater treatment, the total BOD removal rate is the amount of absorption and degradation rate. Specifically at the initial phase, a majority of organics were absorbed by aerobic granules. Figure 7 shows the variations of adsorption rate and degradation rate. When the reactor was run, the organics were absorbed and the adsorption rate reached the maximum value, 52.4 % at 0.9 h. After hour 1.0, the adsorption rate started to decrease. The degradation rate of wastewater increased in the whole process. However, the higher slope at hour 1.0–2.0 indicates that the organics were degraded rapidly in this period. At hour 8, 88.2 % of BOD in the wastewater was removed by biodegradation, which is 91.2 % of the
Bioprocess Biosyst Eng Fig. 9 Variations of the number of microbial populations and total cell weight with time. a Enumeration results of the microbial populations including bacteria, fungi and protozoa; b variation of the total cell weight with time
total BOD removal rate. Thus, CA not only can predict the total BOD removal during wastewater treatment, but also can give us more information about the absorption and degradation of organic substances. Analysis on microbial growth As the MLVSS curve (Fig. 8) shown, the biomass in reactor began to propagate after a 0.4 h lag phase, and then, sludge growth came into the exponential phase. In the exponential phase, MLVSS increased rapidly with time. After hour 3.5, the MLVSS curve became stable. The simulated MLVSS curve described the growth trend of biomass in granular SBR, and the result accords with a practical sludge system [36, 37]. The difference between the predicted and practical values is not significant (p \ 0.05), indicating that the effectiveness of CA in analyzing the biomass growth in a granular SBR. Due to the uncultivable characteristics of some microorganisms, a molecular biological method was used to determine microbial populations including bacteria and fungi in sludge samples collected from SBR. Archaea were not considered in view of their low concentration in an aerobic reactor [38, 39]. The real-time PCR results
(Fig. 9a) indicate that aerobic granules harbored a large amount of bacteria and fungi. After start-up of reactor, bacteria in the reactor propagated rapidly, and the quantity increased gradually before hour 4. The fungal number increased in the whole process, and it reached 2.5 9 108 copies/g MLSS at hour 8. Compared with the bacteria and fungi, the number of protozoa was low and their number curve had an undulating change. After the rough estimate according to a general formula (one bacterial copy = 1 cell weight; one fungal copy & 100 cell weights; and one protozoan & 10,000 cell weights), variation of the total microbial cell weight in the reactor was shown in Fig. 9b. In contrast with Fig. 8, the number curve obtained by quantitative analysis had the similar trend with the MLVSS curve simulated by CA. Thus, from both the theoretical analysis and actual quantitative data, we have verified the effectiveness of CA to predict the biomass change in a granular SBR.
Conclusions Aerobic granules were successfully developed in SBR and 81 % of granular rate was obtained after 15-day
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cultivation. The granules have a 96 % BOD removal to glucose wastewater, and the reactor harbors a mount of biomass including bacteria (1011 copies/g MLSS), fungi (107 copies/g MLSS) and protozoa (103 copies/g MLSS). A CA model was established for simulating the treatment of synthetic wastewater, and the results indicate that CA can visualize the complex adsorption and degradation process, and well describe both BOD removal and microbial growth in the granular SBR. The simulation value of BOD removal rate, 96.7 %, is almost equal to the practical removal rate of the wastewater after 8-h treatment. In addition, the simulated MLVSS curve describes the growth trend of biomass in practical granular SBR, and the difference between the predicted and practical MLVSS values is not significant (p \ 0.05). This is the first dynamical and visual simulation of synthetic wastewater treatment by aerobic granules, and in view of the advantages and effectiveness of CA model exhibited, development of the novel software for modeling wastewater treatment based on CA is feasible. The further work will be focused on the effectiveness of CA in the treatment of the real wastewaters. Acknowledgments This work is supported by national science foundation of China (No. 51008119).
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