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oxic process
Applied Mathematical Modelling 38 (2014) 278–290
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Applied Mathematical Modelling journal homepage: www.elsevier.com/locate/apm
Experimental investigation and modeling of innovative five-tank anaerobic-anoxic/oxic process Saad Abu-Alhail a,b, Xi Wu Lu a,⇑ a b
Environmental Science and Engineering, Southeast University, Jiangsu Province, Nanjing City 210096, PR China College of Engineering, Civil Engineering, University of Basrah, Basra City, Iraq
a r t i c l e
i n f o
Article history: Received 12 July 2012 Received in revised form 1 June 2013 Accepted 24 June 2013 Available online 4 July 2013 Keywords: Innovative five-tank process Domestic wastewater A2/O ASM2d Modeling Biomass
a b s t r a c t A five-tank anaerobic-anoxic/oxic (A2/O) activated sludge process was investigated as innovative configuration in the activated sludge process technologies which has advantage of save energy power, cost and enhance nitrogen and phosphorus removal whereas it does not need equipment for sludge and mixed liquor recycle and also it required small land for construction. The five-tank process achieved 89.1% ± 1.37%, 87.78% ± 1.15%, 73.62% ± 2.13%, and 83.78% ± 0.92% of chemical oxygen demand, NHþ 4 –N, TN, and total phosphorus (TP) removal efficiencies, respectively, during a 16-month operation with the effluent meeting Chinese sewage discharge standard GB18918-Grade A. A computer program was built based on activated sludge model No. 2d for simulating the performance of each compartment in five-tank process. The difficulty of simulation is coming from the system operation condition where it is operated under unsteadily state condition in all its compartments .The results showed that the growth rate constant of autotrophic organisms was 2.4 day1 and yield coefficient was 0.14. According to simulation results, heterotrophic organism; phosphate accumulating organism, and autotrophic organism are decreased in the anaerobic compartments because of the lysis reaction. Then these organisms are increased in the aerobic compartments due to aerobic growth. The heterotrophic organism; phosphate accumulating organism; and autotrophic organism are increased in quantities by about 56.6%, 36.12% and 74.31% in compartment one due to change the operation condition from anaerobic to aerobic and decreased in quantities by about 20.21%, 44.18%, and 0.142% in the compartment three due to change from aerobic to anoxic. The total nitrifying species to total active biomass was fluctuated between 1–11.89% in the process reactor. Ó 2013 Elsevier Inc. All rights reserved.
1. Introduction Over the last ten years, a number of biological nitrogen and phosphorus removal processes have been used to remove phosphorus with simultaneous nitrification and denitrification process. Most of the enhanced biological nutrient removal processes consist of a sequential anaerobic and aerobic phase for biological phosphorus removal, and recycle mixed liquor suspended solids (MLSS) into anoxic zones to hold up the removal efficiency of total nitrogen. This method is increased energy required for mixed liquid recirculation or addition of additional carbon source for denitrification process in anoxic zones. According to that, the operational cost of these processes will increase. Phosphorus and nitrogen removal was improved via reconfiguring biological nutrient removal processes through canceling internal mixed liquor and sludge recirculation. This method was prepared by configuring the process into anaerobic, aerobic, anoxic, aerobic zones in sequence in ⇑ Corresponding author. Tel.: +86 130572937. E-mail addresses: [email protected] (S. Abu-Alhail), [email protected] (X.W. Lu). 0307-904X/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.apm.2013.06.019
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southeast university of China. A flow was fed into the anaerobic/anoxic zone by changing intake location. Many different kinds of organic compounds are formed in the complex biological transformation processes, such as heterotrophic organisms, phosphate accumulating organisms, and autotrophic organisms. In order to recognize bacterial reactions in biological nutrient removal processes, many different types of mathematical models have been proposed [1–3] and applied in the biological nutrient removal processes [4–7]. Some of them two-stage nitrification models [8–10] and others multi-stage denitrification models [11–13] were proposed in the biological nutrient removal processes. Although these models could predict nitrification or denitrification successfully but the application of these models were merely related to nitrogen removal. That was, the behaviors of heterotrophic organisms, phosphate accumulating organisms, and autotrophic organisms in both nitrogen and phosphorus removal process were not taken into account simultaneously. Activated sludge model No. 2d represents a model for biological phosphorus removal with simultaneous nitrification –denitrification in activated sludge systems. ASM2d is extension of ASM2 model where it expanded to include the denitrifying activity of the phosphorus accumulating organism (XPAOs). Since ASM2d model can be described denitrification and phosphorus removal simultaneously so that the objectives of this study are listed as follows: (1) to establish an model (Activated Sludge model No. 2d) in which denitrification and phosphorus removal were taken into account simultaneously, (2) to validate the model by exploring the consistency between simulated and observed values of different components including soluble COD SS; NHþ 4 –N; NO3 – 3 2 N and PO4 –P in five-tank A /O process, and (3) to analyze the kinetics of different microorganisms including heterotrophic organisms, phosphate accumulating organisms and autotrophic organisms in the process reactor.
2. Materials and methods 2.1. Experimental equipment The main parts of a pilot plant utilized in this study are the main body which are rectangular box 750 630 900 mm, air compressor, pre-static pumps, mechanical agitation mixers, PLC programmable logic control, LCD display screen, inlet wastewater electromagnetic valves, outlet water electromagnetic valves, aeration electromagnetic valves, sludge discharge electromagnetic valves, and PVC pipes and others. The principle diagram of pilot plant with all major components is shown in Fig. 1. The effective water depth in the five-tank continuous flow activated sludge system is 650 mm while the total depth is 900 mm. A plane dimension of compartment two, compartment three, and compartment four are square plane while compartment one and compartment five are rectangular plane whereas the effective volume of compartment two, compartment three, and compartment four are 250 250 650 mm while compartment one and compartment five are 380 290 650 mm which make volume ratio between rectangular and square compartments 1.75. An operation cycle is composed of two half-cycles with same running schemes, in which the raw wastewater flows from compartment one to compartment five during the first half-cycle, and from compartment five to compartment one during the second; the first half-cycle is similar to second half cycle. The scheme of first half cycle is shown in Fig. 2; it is divided into four phases named as phase I–IV, respectively. In this scheme, compartment one, compartment two, compartment three and compartment four operated as reactor, and compartment five as settler. The direction of flow was changed automatically via changing of intake location so that the system achieved automatic recirculation without equipment to return sludge and mixed liquor.
Fig. 1. Configuration of five-tank device with all main parts. 1–5-five compartments, 6-inlet reservoir, 7-outlet reservoir, 8–11-inlet electromagnetic valve, 12-PLC programmable logic controllers, 13-inlet pipe, 14–18,-aeration electromagnetic valves, 19–23-mixer, 24, 25-effluent electromagnetic valve, 26, 27sludge electromagnetic valves, 28-sludge tank, 29-Air compressor, 30-prestatic water pump.
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Fig. 2. Running scheme of five-tank A2/O process.
Therefore, this system is effective for reducing energy consumption. Time and environmental state condition was controlled during each phase to achieve the function of A2/O process in the five-tank process reactor. 2.2. Five-tank model algorithms This study was conducted in Wuxi campus, southeast university in four different runs for calibration and parameter estimation and also four different runs for simulation. The algorithms of five-tank are illustrated in details as shown in Fig. 3. The equations for implementing the calculation of ASM2d were described as follows.
Input Output t þ Reaction ¼ Accumulation:
ð1Þ
Therefore; the summary of the mathematical model equations for the reaction rate of each component could be described as in the following equation:
CðtÞ
dc dt
¼
C oi;j C i;j þ M i;j Ni;j ; V i;j C i;j
ð2Þ
where Q: inlet flow rate; V: volume of each compartment; C2i,j: component concentration in the reaction compartments, i: compartment number; j: component number; C0,ji: influent concentration (MT1); Ci,j: effluent concentration; Mi,j, Ni,j are production and consumption (MT1) terms of the j No. component in the i No. compartment; and ri is obtained by summing the
Fig. 3. Algorithms of five-tank A2/O process.
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S. Abu-Alhail, X.W. Lu / Applied Mathematical Modelling 38 (2014) 278–290 Table 1 Operation condition of five-tank A2/O activated sludge process. Item
Symbol
Values
Item
Hydraulic retention time Solid retention time Temperature Air/water ratio
HRT SRT T A/W
16hr 21 day 19–23C0 35%
Phase Phase Phase Phase
No. No. No. No.
1/Phase 2/Phase 3/Phase 4/Phase
No. No. No. No.
5 6 7 8
Symbol
Values
Phase Phase Phase Phase
0–90 min/240–330 min 90–150 min/330–390 min 150–210 min/390–450 min 210–240 min/450–480 min
I/phase V II/Phase VI III/Phase VII IV/Phase VIII
product of the stoichiometric coefficients mij (Table 2) and the process rate expression qj (Table 3) for the component i being considered in the mass balance.
ri ¼
X
tij qij :
ð3Þ
j
2.3. Analysis of model sensitivity The effects of usually large uncertainties parameters in the five-tank A2/O should be taken into account before starting in the simulation of the system via sensitivity analysis. Interval analysis or stochastic techniques could be applied in steady and transient state condition [14–16]. In this work, the sensitivity of effluent components for some key parameters were analyzed based on a 5% increased rate in the standard values which included four parameters of stoichiometric, forty-two parameters of kinetic parameters of the ASM2d model and sixteen parameters for the component concentrations in the influent. The sensitivity analyses of the above parameters (P) with respect to effluent components (E) were calculated by the sensitivity equation;
sensitivity ¼ ðP dE=E dPÞ;
ð4Þ
where dp is the change in the parameter value p and dE is the change in the output E. The effluent component concentrations (E) have different sensitivities towards different parameters (P) according to sensitivity analysis via above equation. This study showed that the effluent ammonia–nitrogen and nitrate–nitrogen had a sensitivity of more than one towards five parameters, influent NHþ 4 –N, lA, YA, qpp, YPO4 the effluent nitrate–nitrogen had sensitivity of more than one towards four parameters, influent flow-rate, lA, YH, YPO4; The effluent orthophosphate has more sensitivity towards PO3 4 –P, qPHA, qPP, lPAO, YH, KMAX, YPO4.
Table 2 Stoichiometric matrix. NO.
Process
SF
1 2 3
1=Y H
5 6
Aerobic growth on SF Aerobic growth on SA Anoxic growth on SF, denitrification (SNO) Anoxic growth on SA, denitrification (SNO) Fermentation Lysis
7
Aerobic growth of XA
A 4:57Y YA
8
Lysis
9 10 11 12
Aerobic hydrolysis Anoxic hydrolysis Anaerobic hydrolysis Storage of XPHA
13 14
17
Aerobic storage of XPP Anoxic storage of XPP, denitrification (SNO) Aerobic growth of XPAO Anoxic growth of XPAO, denitrification (SNO) Lysis of XPAO
18 19
Lysis of XPP Lysis of XPHA
4
15 16
SA
SNH
1=Y H
V1,NH4 V2,NH4 V3,NH4
1=Y H 1=Y H 1
1-fSI 1-fSI 1-fSI
V4,NH4
SNO
1Y H 2:86Y H 1Y H 2:86Y H
SPO
SALK
XS
XH 1 1 1
V4,PO4
1
V5,NH4 V6,NH4
V5,PO4 V6,PO4
V7,NH4
iPBM
V8,NH4
V8,PO4
V9,NH4 V10,NH4 V11,NH4
1fXI
XPAO
V16,NH4 V17,NH4 V18,NH4
V17,NO3
V9,Alk V10,Alk V11,Alk
1fXI 1 1 1
XI
XTSS
fXI
1
V9,Tss V10,Tss V11,Tss YPO4 1 1 1 1
iPBM iPBM
1
XPHA
fXI
1 1
V18,PO4
XPP
1
YPO4
V15,NO3
XA
1
fSI fSI fSI
1
1
SI
V1,PO4 V2,PO4 V3,PO4
1fXI
1 -YPHA -YPHA 1=Y H 1=Y H fXI
1 1 1
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Table 3 Process rate equations. j
Process
Process rate equation qf ql P 0ðM 1 L1 T 1 Þ
Heterotrophic organisms: XH 1 Aerobic growth on SF
SALK SF SNH SPO F lH ðK O2HSO2þSO2 Þ ðK FHSþS XH F Þ ðSA þSF Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALK þSALK Þ SPO SALK SNH F lH ðK O2HSO2þSO2 Þ ðK AHSAþSA Þ ðSASþS XH F Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALK þSALK Þ K O2H SNO SPO SALK SF SF SNH lH gNO3H ðK O2H þSO2 Þ ðK NO3H þSNO Þ ðK FH þSF Þ ðSA þSF Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALK þSALK Þ X H SA K O2H SNO SNH SPO SA lH gNO3H ðK O2H þSO2 Þ ðK NO3H þSNO Þ ðK AH þSAÞ ðSA þSF Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALKSALK þSALK Þ X H
2
Aerobic growth on SA
3
Anoxic growth on SF, denitrification (SNO)
4
Anoxic growth on SA, denitrification (SNO)
5
Fermentation
K O2H K NO3H SALK SF qfe ðK O2H þSO2 Þ ðK NO3H þSNO Þ ðKfeþSFÞ ðK ALK þSALK Þ X H
6
Lysis
bH X H
Ammonia oxidizers (nitrifying organisms, autotrophic): XA 7 Aerobic growth of XA 8
Lysis
SALK PO lA ðK O2ASO2þSO2 Þ ðK NH4ASNHþSNH Þ ðK PASþS XA PO Þ ðK ALK þSALK Þ
bA X A
Hydrolysis process 9 Aerobic hydrolysis
ðX S =X H Þ K h ðK O2SSO2 þSO2 Þ ðK XS þX S =X H Þ X H
10
Anoxic hydrolysis
ðX S =X H Þ K O2S SNO SO2 K h gNO3S ðK O2S þSO2 Þ ðK NO3S þSNO Þ ðK O2S þSO2 Þ ðK XS þX S =X H Þ X H
11
Anaerobic hydrolysis
ðX S =X H Þ K O2S SNO3s K h gfe ðK O2S þSO2 Þ ðK NO3S þSNO Þ ðK XS þX S =X H Þ X H
Phosphorus accumulating organisms (PAO): XPAO 12 Storage of XPHA
ðX PP =X PAO Þ SALK SA qPHA ðK APAO þSAÞ ðK ALKPAO þSALK Þ ðK PP þX PP =X PAO Þ X PAO
13
Aerobic storage of XPP
ðX PHA =X PAO Þ ðK MAX X PP =X PAO Þ SO2 SPO SALK qPHA ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ ðK IPP þK MAX X PP =X PAO Þ X PAO
14
Anoxic storage of XPP, denitrification (SNO)
ðX PHA =X PAO Þ ðK MAX X PP =X PAO Þ SALK SNO SO2 SPO qpp gNO3PAO K O2PAO SO2 ðK NO3PAO þSNO Þ ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ ðK IPP þK MAX X PP =X PAO Þ X PAO
15
Aerobic growth of XPAO
ðX PHA =X PAO Þ SALK SNH SO2 SPO lPAO ðK NH4PAO þSNH Þ ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ X PAO
16
Anoxic growth of XPAO, denitrification (SNO)
SNO lPAO gNO3PAO K O2PAO SO2 ðK NO3PAO þSNO Þ
17 18 19
Lysis of XPAO Lysis of XPP Lysis of XPHA
bPAO X PAO bPP X PP bPHA X PHA
ðX PHA =X PAO Þ SALK SNH SO2 SPO ðK NH4PAO þSNH Þ ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ X PAO
2.4. Model calibration There are many parameters in ASM2d model. For those parameters that are known to be approximately constant in domestic wastewater, the default values from previous research studies [3,17] were used as shown in Table 3. Model calibration procedure is a process of adjusting coefficient values of the model as shown in Fig. 2, therefore the results simulated by the ASM2d model with these coefficients closely agree with the observed data. The model parameters are highly dependent on environmental state conditions. The parameter values are estimated by minimizing the sum of squares of the deviations between the observed data and the model predictions data with the objective function given in the following equation [18]:
coefficient of determinationðR2 Þ ¼ P
P
2
ðSimulated average observedÞ : P 2 2 ðSimulated average observedÞ þ ðSimulated observedÞ
ð5Þ
The standard deviation for parameter determination was required to be lower than 50% to ensure the validity of the values of the parameters obtained. To make the first move of the calibration procedure, an initial guess of the parameters is essential. Such initial values are obtained with the values in the literatures as shown in Table 4. To make simpler calibration process, it is preferred to change little constants as possible, due to the limited changeability of some parameters. The choice of the parameters for calibration is mostly based on the result of sensitivity analysis. 2.5. Analytical methods The influent and effluent samples were obtained every 2 days to analyze the pollutant removal performance of the fivetank A2/O process. Additionally, samples were taken at appropriate intervals from the compartment one, two, three and four of the reactor, which represented the effluent of the compartment one, two, three and four, respectively. All samples were immediately filtered through 1.2 lm, 0.45, and 0.1 lm fiber filters to analyze COD characterization, ammonia nitrogen 3 (NHþ 4 –N), nitrate, total nitrogen (TN) and phosphate (PO4 -P) according to standard methods (APHA, 1998) [19]. All samples were analyzed for twice times and the results were shown as the average and deviation. 2.5.1. Biomass batch-tests The procedures were fully or partially adopted from Standard Methods [20] or previous studies [21–24]. In order to determine the kinetic parameters of XAOB, we determined oxygen uptake rate of different microbial functional group for calcula-
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20oc
Units
Maximum growth rate on substrate Maximum rate for fermentation Reduction factor for denitrification (SNO) Rate constant for lysis and decay Saturation/inhibition coefficient for oxygen Saturation coefficient for growth on SF Saturation coefficient for fermentation on SA Saturation coefficient for growth on acetate SA Saturation/inhibition coefficient for nitrate Saturation coefficient for ammonium (nutrient) Saturation coefficient for phosphate (nutrient) Saturation coefficient for alkalinity (HCO_3)
6.00 3.3 0.6 0.4 0.2 4 4 4 0.5 0.05 0.01 0.1
g XS g1 XH d1 g XS g1 XH d1 – d1 g O2 m3 g COD m3 g COD m3 g COD m3 g N m3 g N m3 g N m3 mole HCO3 m3
Ammonia-nitrite oxidizers bacteria (nitrifying organisms, autotrophic): XA lA Maximum growth rate of XA Decay rate of XA bA K O2A Saturation/inhibition coefficient for oxygen K NH4A Saturation coefficient for ammonium (nutrient) K ALKA Saturation coefficient for alkalinity (HCO3) K PA Saturation coefficient for phosphorus (nutrient)
2.4 0.14 0.5 1 0.5 0.01
d1 d1 g O2 m3 g N m3 mole HCO3 m3 g P m3
Hydrolysis of particulate substrate: XS Kh Hydrolysis rate constant gNO3S Anoxic hydrolysis reduction factor gfe Anaerobic hydrolysis reduction factor
3 0.6 0.4
d1 – –
0.2 0.5 0.1
g O2 m3 g N m3 g XS g1 XH
3.3 1.5 1.2 0.8 0.2 0.2 0.2 0.2 0.5 4 0.05 0.2 0.01 0.1 0.01 0.34 0.02 0.01
g XPHA g1 XPAO d1 g XPHA g1 XPAO d1 d1 – d1 d1 d1 g O2 m3 g N m3 g COD m3 g N m3 g P m3 g P m3 mole HCO3 m3 g XPP g1 XPAO g XPP g1 XPAO g XPP g1 XPAO g XPHA g1 XPAO
Item Heterotrophic organisms: XH
lH qfe
gNO3H bH K O2H K FH K fe K AH K NO3H K NH4H K PH K ALK
K O2S K NO3S K XS
Saturation/inhibition coefficient for oxygen Saturation/inhibition coefficient for nitrite and nitrate Saturation coefficient for particulate COD
Phosphorus-accumulating organisms: XPAO qPHA Rate constant for storage of XPHA (base XPP) qpp Rate constant for storage of XPP lPAO Maximum growth rate of PAO gNO3PAO Reduction factor for anoxic activity Rate for lysis of XPAO bPAO Rate for lysis of XPP bPP Rate for lysis of XPHA bPHA K O2PAO Saturation/inhibition coefficient for oxygen K NO3PAO Saturation coefficient for nitrate, SNO K APAO Saturation coefficient for acetate SA K NH4PAO Saturation coefficient for ammonium (nutrient) K ps Saturation coefficient for phosphorus in storage of PP K PPAO Saturation coefficient for phosphate (nutrient) K ALKPAO Saturation coefficient for alkalinity (HCO3) K PP Saturation coefficient for poly-phosphate K MAX Maximum ratio of XPP/XPAO K IPP Inhibition coefficient for PP storage K PHA Saturation coefficient for PHA
tion [25,26]. Batch experimental tests were performed for four different runs for determining the kinetic parameters of XA. The oxygen uptake rate measuring system consisted of four airtight, cylindrical chambers, with same height and volume, four magnetic stirrers for stirring and an aeration stone in each chamber. DO was monitored by four oxygen meters of high stability connected to a data acquisition system. Since this oxygen uptake rate measurer was airtight, the actual respiration rate of the tested biomass at any time during the batch-test did not depend on oxygen input. Therefore the dissolved oxygen concentration represented the actual oxygen uptake rate. A certain amount of activated sludge sample was taken from the pilot plants and added into oxygen uptake rate chambers. Distilled water containing organic carbon source and nutrient including glucose, NH4SO4 and KH2PO4 were added resulting in total volume of 800 ml in chamber. The measuring system was periodically aerated, then the difference between the measured OUR and baseline oxygen respiration was calculated and compared. The pH value was maintained at seven during batch-test. In order to evaluate the kinetic parameters and active biomass of XH, and XA, different types of oxygen uptake rate values should be considered: total oxygen uptake rate (OURT), oxygen uptake rate of XH (OURH) and oxygen uptake rate of XA (OURA). The determination of oxygen uptake rates of XH and XA were based on the subsequent addition of allylthiourea (ATU) and NaN3. Allylthiourea (86 lM) was added to the chambers to keep the NHþ 4 –N concentration constant during the incubation by selectively inhibiting XA while Azide (86 lM) was added to the chambers to keep the NO 2 –N concentration constant during the incubation by selectively inhibiting XNOB activity without affecting the activity of XAOB [27].When determining OURT, no inhibitor was added. When determining OURH, both allylthiourea (86 lM) and NaN3 (24 lM) were added and OURA = (OURT OURH).
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2.5.2. PHA test The initial concentration of PHA in each zone of a new system was analyzed according to the method that is described in Ref. [28] for estimating initial concentration of PHA in each zone. In the initial step, Duplicate 20 ml samples of MLSS were obtained and immediately centrifuged at 4 °C. Then, the cold sludge pellet was lyophilized. After that, the pellet was added to the tube closed with a Teflon-lined screw cap for drying. 2 ml of sulfuric acid 3% methanol and 2 ml of chloroform were added to the tube. This was digested for 1200 min in an oven at 104 °C. At the second step, once the sample had cooled at 25 °C, 1 ml of water was added and the tube contents were shaken for 600 s. The chloroform content remained to the bottom of the tube, and this was drawn off for GC examination. The digested product was exposed on a Varian 3400 GC fitted with a 1.8-m Alltech 0.2% Carbowax 1500 on Graphpac-GC 80/100 mesh stainless steel column. The column temperature was 170 °C and the inoculation temperature was 180 °C. PHA was measured by comparison to a standard consisting of a copolymer of the above-described alkanoates. 2.5.3. Characterization of the carbonaceous material Activated sludge models (ASM) distinguishes between the mechanisms acting on different components in the influent wastewater stream. The term wastewater characteristic refers to the partitioning of influent organic material into biodegradable and un-biodegradable (inert) portions, the ammonia portion of the total nitrogen and so on. The influent wastewater is often varied from one municipal wastewater to another. Wastewater characteristics have a very significant impact on system performance, particularly for nutrient removal systems. Characterization of the carbonaceous material in municipal wastewater for modeling purposes usually in terms of the chemical oxygen demand (COD).The division of the total influent COD (CODT) into the various fractions used in nutrient removal system design and modeling is shown in Fig. 4. In ASM2d model, the CODtot of the wastewater is considered consist of inert soluble organic matter (SI), readily and slowly biodegradable substrate (SS and XS respectively) and inert suspended organic matter (XI), whereas biomass in the wastewater is considered to be insignificant. 2.6. Raw wastewater characteristics The raw wastewater used in the experiment was collected from a main manhole of southeast university in Wuxi city and the characteristic of wastewater quality is listed in Table 5. In this study, four testing runs with different operations were implemented in Wuxi campus, southeast university. The raw wastewater is typical where the infiltrated chemical oxygen demand was varied between 157 and 855 mg/l with average of 651 mg/l, of which SS, SI, XS and XI accounted for about 380%, 2.0%, 43.0% and 11.00%, respectively. Mixed liquor suspended solid was varied between 45.0 and 93.00 mg/l with average of 76 mg/l. NHþ 4 –N concentration was varied between 10 and 40 mg/L with average of 22.0 mg/l. Total phosphor was varied between 1.70 and 4.50 mg/L with average of 2.60 mg/l, of which orthophosphate accounted for about 70.0–90.0%. 2.7. Program structure of five-tank process Program structure of innovative process was built using stoichiometric matrix Table 1, process rate Table 2, Algorithms and Five-tank A2/O environmental state conditions during each phase. The algorithms of five-tank is shown in Fig. 2.The
Fig. 4. Diagram depicting method of carbonaceous material characterization of influent COD components [29].
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S. Abu-Alhail, X.W. Lu / Applied Mathematical Modelling 38 (2014) 278–290 Table 5 Raw wastewater characteristics. Item
Symbol
Concentration mg/L
Run No.
No.
1
2
3
4
Total chemical oxygen demand Particulate inert organic material Slowly biodegradable substrate Readily biodegradable substrate Active heterotrophic biomass Active autotrophic biomass Volatile fatty acids Inert soluble organic material Oxygen Nitrate nitrogen Ammonia nitrogen Total nitrogen Orthophosphate Total nitrogen Alkalinity Mixed liquor suspended solid
COD in-filtrated XI XS SS XH XA SA SI SO SNO SNH TN SPO TP SALK MLSS
805 63 340 306 5 0.1 88 7 0 2.23 18 31.7 2.59 3.07 4.97 87
688 61 297 261.5 4 0.7 73 5 0 1.23 27 39.6 3.25 3.71 5.01 73
458 55 201 174 4 0.3 61 4 0 1.26 32 43.2 3.41 4.32 4.99 71
513 59 222 195 1 0.0 67 4 0 2.8 19.4 33.1 1.01 1.79 5.00 67
model structure has taken change in the operation state condition of each compartment in addition to phase time in consideration. The equations that described the transformation of the wastewater quality in the model formed an ordinary differential equations (ODEs) system. The set of equations in this model then was integrated simultaneously by the 4th order range kutta numerical integration method [30]. According to the program structure of five-tank, the entire model was implemented by means of a computer program that was coded with MATLABÒ2010 language. When all the vectors 1/Cij (dC/dt) were nearly equal to zero, a steady state was reached. The integration was most accurate when time step is very small but the computing time increased inversely with the size of time step. Conversely, too large time step would result in large errors and other numerical problem. Thus, one criterion for an upper limit on time step is: 1
Dthh CðtÞ ðdC=dtÞ ;
ð6Þ
where Dt is time step. By combining Eqs. (2) and (6), and neglecting the Mij, Nij terms in the mass balance, resulted the maximum step size as shown below:
DthhV j C ij =ðC 2ij þ Nij Þ ¼ uij :
ð7Þ
The term hij is the mean residence time of component j in reactor component i at steady state. 3. Results and discussions 3.1. Removal efficiency According to investigation results, The five-tank process achieved 89.1% ± 1.37%, 87.78% ± 1.15, 73.62% ± 2.13%, and 83.78% ± 0.92% of chemical oxygen demand, NHþ 4 –N, TN, and total phosphorus (TP) removal efficiencies, respectively, during a 16-month operation with the effluent meeting Chinese sewage discharge standard GB18918-Grade A. The results showed that this system is operated with simultaneous nitrification denitrification phenomena (SND) and denitrifying phosphorus removal (DBP sludge/DNPAOs) which is benefit for enhancing nitrogen and phosphorus removal and also reducing energy power because it is need low dissolved oxygen concentration. 3.2. Model evaluation The model evaluation was carried out through comparison predicated and experimental data of four related runs in Wuxi campus. The raw wastewater characteristics from Wuxi campus were illustrated in Table 3. Fig. 5 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentrations of compartment one under different runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations (R2) of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.95, 0.96, 0.93 and 0.98 respectively of at run one, 0.98, 0.95, 0.91 and 0.97 respectively at run two, 0.99, 0.95, 0.87 and 0.99 respectively at run three and 0.95, 0.97, 0.87 and 0.92 respectively at run four. Fig. 6 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentration of compartment two under different runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations R2 of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.95, 0.73, 0.94 and 0.99 respectively at run one, 0.99, 0.89, 0.95 and0.99 respectively at run two, 0.99, 0.89, 0.97 and 0.98 respectively at run three, and
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1.Compartment one –Run 1
2.Compartment one - Run 2
3.Compartment one - Run 3
4.Compartment one - Run 4
Fig. 5. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment one.
1.Compartment two - Run 1
2.Compartment two - Run 2
3.Compartment two - Run 3
4.Compartment two - Run 4
Fig. 6. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment two.
0.97, 0.86, 0.97 and 0.98 respectively at run four. Fig. 7 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentration of compartment three under different runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations R2 of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.95, 0.66, 0.95 and 0.95 respectively at run one, 0.96, 0.77, 0.95 and 0.95 respectively at run two, 0.98, 0.63, 0.95, and 0.99 respectively at run three, and 0.97, 0.74, 0.95 and 0.997 respectively at run four. Fig. 8 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentration of compartment four under different
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1.Compartment three - Run 1
2.Compartment three - Run 2
3.Compartment three - Run 3
4.Compartment three - Run 4
287
Fig. 7. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment three.
runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations R2 of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.96, 0.56, 0.96, and 0.98 respectively at run one, 0.97, 0.61, 0.93 and 0.96 respectively at run two, 0.99, 0.60, 0.95 and 0.94 respectively at run three, and 0.95, 0.59, 0.96 and 0.99 respectively at run four. 3.3. Model simulation of the biomass The particulate component concentrations could be also calculated from model simulation as shown in Fig. 9 whereas the heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA concentrations in five-
1.Compartment four - Run 1
2.Compartment four - Run 2
3.Compartment four - Run 3
4.Compartment four - Run 4
Fig. 8. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment four.
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(a) Run 1
(b) Run 2
(c) Run 3
(d) Run 4
Fig. 9. Variations of biomass in each compartment of five-tank process reactor.
tank process reactor were 638–1204 mg L1, 58.4–275 mg L1, and 90–200.12 mg L1 at run one, 594.9–1321.4 mg L1, 58.43–337.76 mg L1, and 35–203.88 mg L1 at run two, 514.22–1160 mg L1, 58.42–271.13 mg L1, and 95.5–208.6 mg L1 at run three, 534.26–1174.5 mg L1, 58.42–272.5 mg L1, and 89.72–185.36 mg L1 at run four. As result, The XH; XPAO; and XA decreased in the anaerobic compartment because of the lysis reaction. Then the heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA increased in the aerobic compartments due to aerobic growth. The heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA increased in quantities by about 56.6%, 36.12% and 74.31% in compartment one due to change the environmental state condition from anaerobic to aerobic and decreased in quantities by about 20.21%, 44.18%, and 0.142% in compartment three due to change the environmental state condition from aerobic to anoxic. The ratio of total nitrifying species to total active biomass was fluctuated between 1% and 11.89% in the process reactor. In this study, the disadvantages of the previous BNR processes were improved by reconfiguring the process without mixed liquor and sludge recirculation. This was done by configuring the process into five-tank with variable operation state condition, anaerobic/anoxic, aerobic and settling condition, in each compartment to achieve optimum removal of phosphorus and nitrogen. The heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA decreased in the anoxic compartment due to the dilution effect of the flow. In addition to the dilution effect of the influent, the XA also decreased due to the negative growth rate resulted from lysis reaction in the anoxic compartment. In full-scale wastewater treatment plant, the transient system behavior is of high practical importance since variations of composition, influent flow-rate as well as changes of operation prevents each real-world wastewater treatment plant from reaching the steady state condition. Although the application of this ASM2d under steady state was validated in this study, the application in transient state can be implemented in the future study. In addition, the practical applications of the ASM2d model including plant optimization, controller layout, mathematical verification of the purification performance, and model-based state and parameter estimation should be taken into account in the future study. 4. Conclusions The variation of pollutants ammonia–N, PO3 4 –P, COD and nitrate–N in five-tank e process reactor could be modeled successfully using the ASM2d. The microbial kinetic behaviors of four testing runs were analyzed based on ASM2d model. The results which obtained in this study can be summarized as follows: A. The five-tank process achieved 89.1% ± 1.37%, 87.78% ± 1.15, 73.62%% ± 2.13%, and 83.78% ± 0.92% of chemical oxygen demand, NHþ 4 –N, TN, and total phosphorus (TP) removal efficiencies, respectively, during a 16-month operation with the effluent meeting Chinese sewage discharge standard GB18918-Grade A. B. In this study, the growth rate constant of autotrophic organisms XA and its yield coefficient value were 2.4 day1 and 0.14, respectively.
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C. According to model simulation, the heterotrophic organism; phosphate accumulating organism, and autotrophic organism concentrations in five-tank process were varied between 638–1204 mg L1, 58.4–275 mg L1, and 90– 200.12 mg L1 at run one, 594.9–1321.4 mg L1, 58.43–337.76 mg L1, and 35–203.88 mg L1 at run two, 514.22– 1160 mg L1, 58.42–271.13 mg L1, and 95.5–208.6 mg L1 at run three, 534.26–1174.5 mg L1, 58.42–272.5 mg L1, and 89.72–185.36 mg L1 at run four. D. According to simulation results, heterotrophic organism; phosphate accumulating organism, and autotrophic organism are decreased in the anaerobic compartments because of the lysis reaction. Then these organisms are increased in the aerobic compartments due to aerobic growth. The heterotrophic organism; phosphate accumulating organism; and autotrophic organism are increased in quantities by about 56.6%, 36.12% and 74.31% in compartment one due to change the operation condition from anaerobic to aerobic and decreased in quantities by about 20.21%, 44.18%, and 0.142% in the compartment three due to change from aerobic to anoxic. The total nitrifying species to total active biomass was fluctuated between 1% and 11.89% in the process reactor.
Acknowledgements This work was financially supported by the major science and technology program for water pollution control and treatment (Grant No. 2012ZX07101-005) and key grant project of national natural science foundation of China (51078074). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.apm.2013.06.019. References [1] M. Henze, W. Gujer, T. Mino, M.C.M. van Loosdrecht, Activated sludge models: ASM1, ASM2, ASM2d and ASM3, International Water Association, London, UK, 2000. [2] WEF, Biological and Chemical Systems for Nutrient Removal, Water Environment Federation, Alexandria, Virginia, US, 1999. [3] N. Mario, H. Predrag, Mathematical modelling and optimisation of a waste water treatment plant by combined oxygen electrode and biological waste water treatment model, J. Appl. Math. Model. 36 (2012) 3813–3825. [4] T.Y. Pai, Y.P. Tsai, Y.J. Chou, H.Y. Chang, H.G. Leu, C.F. 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Chinese, Water and Wastewater Monitoring Methods, fourth ed., Chinese Environmental Science Publishing House, Beijing, China, 2000. [20] APHA, AWWA, WEF, Standard Methods for the Examination of Water and Wastewater, 19th ed., American Public Health Association/American Water Works Association/Water Environment Federation, Washington, DC, 1995. [21] H. Brouwer, M. Bloemen, B. Klapwijk, H. Spanjers, Feedforward control of nitrification by manipulating the aerobic volume in activated sludge plants, Water Sci. Technol. 38 (3) (1998) 245–254. [22] H. Brouwer, B. Klapwijk, M. Bloemen, Identification of activated sludge and wastewater characteristics using respirometric batch-experiments, Water Res. 32 (4) (1998) 1240–1254. [23] U. Sollfrank, W. Gujer, Characterisation of domestic wastewater for mathematical modelling of activated sludge process, Water Sci. Technol. (Kyoto) 25 (1991) 1057–1066. [24] G.H. Kristensen, P.E. Jorgensen, M. 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Contents lists available at SciVerse ScienceDirect
Applied Mathematical Modelling journal homepage: www.elsevier.com/locate/apm
Experimental investigation and modeling of innovative five-tank anaerobic-anoxic/oxic process Saad Abu-Alhail a,b, Xi Wu Lu a,⇑ a b
Environmental Science and Engineering, Southeast University, Jiangsu Province, Nanjing City 210096, PR China College of Engineering, Civil Engineering, University of Basrah, Basra City, Iraq
a r t i c l e
i n f o
Article history: Received 12 July 2012 Received in revised form 1 June 2013 Accepted 24 June 2013 Available online 4 July 2013 Keywords: Innovative five-tank process Domestic wastewater A2/O ASM2d Modeling Biomass
a b s t r a c t A five-tank anaerobic-anoxic/oxic (A2/O) activated sludge process was investigated as innovative configuration in the activated sludge process technologies which has advantage of save energy power, cost and enhance nitrogen and phosphorus removal whereas it does not need equipment for sludge and mixed liquor recycle and also it required small land for construction. The five-tank process achieved 89.1% ± 1.37%, 87.78% ± 1.15%, 73.62% ± 2.13%, and 83.78% ± 0.92% of chemical oxygen demand, NHþ 4 –N, TN, and total phosphorus (TP) removal efficiencies, respectively, during a 16-month operation with the effluent meeting Chinese sewage discharge standard GB18918-Grade A. A computer program was built based on activated sludge model No. 2d for simulating the performance of each compartment in five-tank process. The difficulty of simulation is coming from the system operation condition where it is operated under unsteadily state condition in all its compartments .The results showed that the growth rate constant of autotrophic organisms was 2.4 day1 and yield coefficient was 0.14. According to simulation results, heterotrophic organism; phosphate accumulating organism, and autotrophic organism are decreased in the anaerobic compartments because of the lysis reaction. Then these organisms are increased in the aerobic compartments due to aerobic growth. The heterotrophic organism; phosphate accumulating organism; and autotrophic organism are increased in quantities by about 56.6%, 36.12% and 74.31% in compartment one due to change the operation condition from anaerobic to aerobic and decreased in quantities by about 20.21%, 44.18%, and 0.142% in the compartment three due to change from aerobic to anoxic. The total nitrifying species to total active biomass was fluctuated between 1–11.89% in the process reactor. Ó 2013 Elsevier Inc. All rights reserved.
1. Introduction Over the last ten years, a number of biological nitrogen and phosphorus removal processes have been used to remove phosphorus with simultaneous nitrification and denitrification process. Most of the enhanced biological nutrient removal processes consist of a sequential anaerobic and aerobic phase for biological phosphorus removal, and recycle mixed liquor suspended solids (MLSS) into anoxic zones to hold up the removal efficiency of total nitrogen. This method is increased energy required for mixed liquid recirculation or addition of additional carbon source for denitrification process in anoxic zones. According to that, the operational cost of these processes will increase. Phosphorus and nitrogen removal was improved via reconfiguring biological nutrient removal processes through canceling internal mixed liquor and sludge recirculation. This method was prepared by configuring the process into anaerobic, aerobic, anoxic, aerobic zones in sequence in ⇑ Corresponding author. Tel.: +86 130572937. E-mail addresses: [email protected] (S. Abu-Alhail), [email protected] (X.W. Lu). 0307-904X/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.apm.2013.06.019
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southeast university of China. A flow was fed into the anaerobic/anoxic zone by changing intake location. Many different kinds of organic compounds are formed in the complex biological transformation processes, such as heterotrophic organisms, phosphate accumulating organisms, and autotrophic organisms. In order to recognize bacterial reactions in biological nutrient removal processes, many different types of mathematical models have been proposed [1–3] and applied in the biological nutrient removal processes [4–7]. Some of them two-stage nitrification models [8–10] and others multi-stage denitrification models [11–13] were proposed in the biological nutrient removal processes. Although these models could predict nitrification or denitrification successfully but the application of these models were merely related to nitrogen removal. That was, the behaviors of heterotrophic organisms, phosphate accumulating organisms, and autotrophic organisms in both nitrogen and phosphorus removal process were not taken into account simultaneously. Activated sludge model No. 2d represents a model for biological phosphorus removal with simultaneous nitrification –denitrification in activated sludge systems. ASM2d is extension of ASM2 model where it expanded to include the denitrifying activity of the phosphorus accumulating organism (XPAOs). Since ASM2d model can be described denitrification and phosphorus removal simultaneously so that the objectives of this study are listed as follows: (1) to establish an model (Activated Sludge model No. 2d) in which denitrification and phosphorus removal were taken into account simultaneously, (2) to validate the model by exploring the consistency between simulated and observed values of different components including soluble COD SS; NHþ 4 –N; NO3 – 3 2 N and PO4 –P in five-tank A /O process, and (3) to analyze the kinetics of different microorganisms including heterotrophic organisms, phosphate accumulating organisms and autotrophic organisms in the process reactor.
2. Materials and methods 2.1. Experimental equipment The main parts of a pilot plant utilized in this study are the main body which are rectangular box 750 630 900 mm, air compressor, pre-static pumps, mechanical agitation mixers, PLC programmable logic control, LCD display screen, inlet wastewater electromagnetic valves, outlet water electromagnetic valves, aeration electromagnetic valves, sludge discharge electromagnetic valves, and PVC pipes and others. The principle diagram of pilot plant with all major components is shown in Fig. 1. The effective water depth in the five-tank continuous flow activated sludge system is 650 mm while the total depth is 900 mm. A plane dimension of compartment two, compartment three, and compartment four are square plane while compartment one and compartment five are rectangular plane whereas the effective volume of compartment two, compartment three, and compartment four are 250 250 650 mm while compartment one and compartment five are 380 290 650 mm which make volume ratio between rectangular and square compartments 1.75. An operation cycle is composed of two half-cycles with same running schemes, in which the raw wastewater flows from compartment one to compartment five during the first half-cycle, and from compartment five to compartment one during the second; the first half-cycle is similar to second half cycle. The scheme of first half cycle is shown in Fig. 2; it is divided into four phases named as phase I–IV, respectively. In this scheme, compartment one, compartment two, compartment three and compartment four operated as reactor, and compartment five as settler. The direction of flow was changed automatically via changing of intake location so that the system achieved automatic recirculation without equipment to return sludge and mixed liquor.
Fig. 1. Configuration of five-tank device with all main parts. 1–5-five compartments, 6-inlet reservoir, 7-outlet reservoir, 8–11-inlet electromagnetic valve, 12-PLC programmable logic controllers, 13-inlet pipe, 14–18,-aeration electromagnetic valves, 19–23-mixer, 24, 25-effluent electromagnetic valve, 26, 27sludge electromagnetic valves, 28-sludge tank, 29-Air compressor, 30-prestatic water pump.
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Fig. 2. Running scheme of five-tank A2/O process.
Therefore, this system is effective for reducing energy consumption. Time and environmental state condition was controlled during each phase to achieve the function of A2/O process in the five-tank process reactor. 2.2. Five-tank model algorithms This study was conducted in Wuxi campus, southeast university in four different runs for calibration and parameter estimation and also four different runs for simulation. The algorithms of five-tank are illustrated in details as shown in Fig. 3. The equations for implementing the calculation of ASM2d were described as follows.
Input Output t þ Reaction ¼ Accumulation:
ð1Þ
Therefore; the summary of the mathematical model equations for the reaction rate of each component could be described as in the following equation:
CðtÞ
dc dt
¼
C oi;j C i;j þ M i;j Ni;j ; V i;j C i;j
ð2Þ
where Q: inlet flow rate; V: volume of each compartment; C2i,j: component concentration in the reaction compartments, i: compartment number; j: component number; C0,ji: influent concentration (MT1); Ci,j: effluent concentration; Mi,j, Ni,j are production and consumption (MT1) terms of the j No. component in the i No. compartment; and ri is obtained by summing the
Fig. 3. Algorithms of five-tank A2/O process.
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S. Abu-Alhail, X.W. Lu / Applied Mathematical Modelling 38 (2014) 278–290 Table 1 Operation condition of five-tank A2/O activated sludge process. Item
Symbol
Values
Item
Hydraulic retention time Solid retention time Temperature Air/water ratio
HRT SRT T A/W
16hr 21 day 19–23C0 35%
Phase Phase Phase Phase
No. No. No. No.
1/Phase 2/Phase 3/Phase 4/Phase
No. No. No. No.
5 6 7 8
Symbol
Values
Phase Phase Phase Phase
0–90 min/240–330 min 90–150 min/330–390 min 150–210 min/390–450 min 210–240 min/450–480 min
I/phase V II/Phase VI III/Phase VII IV/Phase VIII
product of the stoichiometric coefficients mij (Table 2) and the process rate expression qj (Table 3) for the component i being considered in the mass balance.
ri ¼
X
tij qij :
ð3Þ
j
2.3. Analysis of model sensitivity The effects of usually large uncertainties parameters in the five-tank A2/O should be taken into account before starting in the simulation of the system via sensitivity analysis. Interval analysis or stochastic techniques could be applied in steady and transient state condition [14–16]. In this work, the sensitivity of effluent components for some key parameters were analyzed based on a 5% increased rate in the standard values which included four parameters of stoichiometric, forty-two parameters of kinetic parameters of the ASM2d model and sixteen parameters for the component concentrations in the influent. The sensitivity analyses of the above parameters (P) with respect to effluent components (E) were calculated by the sensitivity equation;
sensitivity ¼ ðP dE=E dPÞ;
ð4Þ
where dp is the change in the parameter value p and dE is the change in the output E. The effluent component concentrations (E) have different sensitivities towards different parameters (P) according to sensitivity analysis via above equation. This study showed that the effluent ammonia–nitrogen and nitrate–nitrogen had a sensitivity of more than one towards five parameters, influent NHþ 4 –N, lA, YA, qpp, YPO4 the effluent nitrate–nitrogen had sensitivity of more than one towards four parameters, influent flow-rate, lA, YH, YPO4; The effluent orthophosphate has more sensitivity towards PO3 4 –P, qPHA, qPP, lPAO, YH, KMAX, YPO4.
Table 2 Stoichiometric matrix. NO.
Process
SF
1 2 3
1=Y H
5 6
Aerobic growth on SF Aerobic growth on SA Anoxic growth on SF, denitrification (SNO) Anoxic growth on SA, denitrification (SNO) Fermentation Lysis
7
Aerobic growth of XA
A 4:57Y YA
8
Lysis
9 10 11 12
Aerobic hydrolysis Anoxic hydrolysis Anaerobic hydrolysis Storage of XPHA
13 14
17
Aerobic storage of XPP Anoxic storage of XPP, denitrification (SNO) Aerobic growth of XPAO Anoxic growth of XPAO, denitrification (SNO) Lysis of XPAO
18 19
Lysis of XPP Lysis of XPHA
4
15 16
SA
SNH
1=Y H
V1,NH4 V2,NH4 V3,NH4
1=Y H 1=Y H 1
1-fSI 1-fSI 1-fSI
V4,NH4
SNO
1Y H 2:86Y H 1Y H 2:86Y H
SPO
SALK
XS
XH 1 1 1
V4,PO4
1
V5,NH4 V6,NH4
V5,PO4 V6,PO4
V7,NH4
iPBM
V8,NH4
V8,PO4
V9,NH4 V10,NH4 V11,NH4
1fXI
XPAO
V16,NH4 V17,NH4 V18,NH4
V17,NO3
V9,Alk V10,Alk V11,Alk
1fXI 1 1 1
XI
XTSS
fXI
1
V9,Tss V10,Tss V11,Tss YPO4 1 1 1 1
iPBM iPBM
1
XPHA
fXI
1 1
V18,PO4
XPP
1
YPO4
V15,NO3
XA
1
fSI fSI fSI
1
1
SI
V1,PO4 V2,PO4 V3,PO4
1fXI
1 -YPHA -YPHA 1=Y H 1=Y H fXI
1 1 1
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Table 3 Process rate equations. j
Process
Process rate equation qf ql P 0ðM 1 L1 T 1 Þ
Heterotrophic organisms: XH 1 Aerobic growth on SF
SALK SF SNH SPO F lH ðK O2HSO2þSO2 Þ ðK FHSþS XH F Þ ðSA þSF Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALK þSALK Þ SPO SALK SNH F lH ðK O2HSO2þSO2 Þ ðK AHSAþSA Þ ðSASþS XH F Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALK þSALK Þ K O2H SNO SPO SALK SF SF SNH lH gNO3H ðK O2H þSO2 Þ ðK NO3H þSNO Þ ðK FH þSF Þ ðSA þSF Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALK þSALK Þ X H SA K O2H SNO SNH SPO SA lH gNO3H ðK O2H þSO2 Þ ðK NO3H þSNO Þ ðK AH þSAÞ ðSA þSF Þ ðK NH4H þSNH Þ ðK PH þSPO Þ ðK ALKSALK þSALK Þ X H
2
Aerobic growth on SA
3
Anoxic growth on SF, denitrification (SNO)
4
Anoxic growth on SA, denitrification (SNO)
5
Fermentation
K O2H K NO3H SALK SF qfe ðK O2H þSO2 Þ ðK NO3H þSNO Þ ðKfeþSFÞ ðK ALK þSALK Þ X H
6
Lysis
bH X H
Ammonia oxidizers (nitrifying organisms, autotrophic): XA 7 Aerobic growth of XA 8
Lysis
SALK PO lA ðK O2ASO2þSO2 Þ ðK NH4ASNHþSNH Þ ðK PASþS XA PO Þ ðK ALK þSALK Þ
bA X A
Hydrolysis process 9 Aerobic hydrolysis
ðX S =X H Þ K h ðK O2SSO2 þSO2 Þ ðK XS þX S =X H Þ X H
10
Anoxic hydrolysis
ðX S =X H Þ K O2S SNO SO2 K h gNO3S ðK O2S þSO2 Þ ðK NO3S þSNO Þ ðK O2S þSO2 Þ ðK XS þX S =X H Þ X H
11
Anaerobic hydrolysis
ðX S =X H Þ K O2S SNO3s K h gfe ðK O2S þSO2 Þ ðK NO3S þSNO Þ ðK XS þX S =X H Þ X H
Phosphorus accumulating organisms (PAO): XPAO 12 Storage of XPHA
ðX PP =X PAO Þ SALK SA qPHA ðK APAO þSAÞ ðK ALKPAO þSALK Þ ðK PP þX PP =X PAO Þ X PAO
13
Aerobic storage of XPP
ðX PHA =X PAO Þ ðK MAX X PP =X PAO Þ SO2 SPO SALK qPHA ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ ðK IPP þK MAX X PP =X PAO Þ X PAO
14
Anoxic storage of XPP, denitrification (SNO)
ðX PHA =X PAO Þ ðK MAX X PP =X PAO Þ SALK SNO SO2 SPO qpp gNO3PAO K O2PAO SO2 ðK NO3PAO þSNO Þ ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ ðK IPP þK MAX X PP =X PAO Þ X PAO
15
Aerobic growth of XPAO
ðX PHA =X PAO Þ SALK SNH SO2 SPO lPAO ðK NH4PAO þSNH Þ ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ X PAO
16
Anoxic growth of XPAO, denitrification (SNO)
SNO lPAO gNO3PAO K O2PAO SO2 ðK NO3PAO þSNO Þ
17 18 19
Lysis of XPAO Lysis of XPP Lysis of XPHA
bPAO X PAO bPP X PP bPHA X PHA
ðX PHA =X PAO Þ SALK SNH SO2 SPO ðK NH4PAO þSNH Þ ðK O2PAO þSO2 Þ ðK PS þSPO Þ ðK ALKPAO þSALK Þ ðK PHA þX PHA =X PAO Þ X PAO
2.4. Model calibration There are many parameters in ASM2d model. For those parameters that are known to be approximately constant in domestic wastewater, the default values from previous research studies [3,17] were used as shown in Table 3. Model calibration procedure is a process of adjusting coefficient values of the model as shown in Fig. 2, therefore the results simulated by the ASM2d model with these coefficients closely agree with the observed data. The model parameters are highly dependent on environmental state conditions. The parameter values are estimated by minimizing the sum of squares of the deviations between the observed data and the model predictions data with the objective function given in the following equation [18]:
coefficient of determinationðR2 Þ ¼ P
P
2
ðSimulated average observedÞ : P 2 2 ðSimulated average observedÞ þ ðSimulated observedÞ
ð5Þ
The standard deviation for parameter determination was required to be lower than 50% to ensure the validity of the values of the parameters obtained. To make the first move of the calibration procedure, an initial guess of the parameters is essential. Such initial values are obtained with the values in the literatures as shown in Table 4. To make simpler calibration process, it is preferred to change little constants as possible, due to the limited changeability of some parameters. The choice of the parameters for calibration is mostly based on the result of sensitivity analysis. 2.5. Analytical methods The influent and effluent samples were obtained every 2 days to analyze the pollutant removal performance of the fivetank A2/O process. Additionally, samples were taken at appropriate intervals from the compartment one, two, three and four of the reactor, which represented the effluent of the compartment one, two, three and four, respectively. All samples were immediately filtered through 1.2 lm, 0.45, and 0.1 lm fiber filters to analyze COD characterization, ammonia nitrogen 3 (NHþ 4 –N), nitrate, total nitrogen (TN) and phosphate (PO4 -P) according to standard methods (APHA, 1998) [19]. All samples were analyzed for twice times and the results were shown as the average and deviation. 2.5.1. Biomass batch-tests The procedures were fully or partially adopted from Standard Methods [20] or previous studies [21–24]. In order to determine the kinetic parameters of XAOB, we determined oxygen uptake rate of different microbial functional group for calcula-
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20oc
Units
Maximum growth rate on substrate Maximum rate for fermentation Reduction factor for denitrification (SNO) Rate constant for lysis and decay Saturation/inhibition coefficient for oxygen Saturation coefficient for growth on SF Saturation coefficient for fermentation on SA Saturation coefficient for growth on acetate SA Saturation/inhibition coefficient for nitrate Saturation coefficient for ammonium (nutrient) Saturation coefficient for phosphate (nutrient) Saturation coefficient for alkalinity (HCO_3)
6.00 3.3 0.6 0.4 0.2 4 4 4 0.5 0.05 0.01 0.1
g XS g1 XH d1 g XS g1 XH d1 – d1 g O2 m3 g COD m3 g COD m3 g COD m3 g N m3 g N m3 g N m3 mole HCO3 m3
Ammonia-nitrite oxidizers bacteria (nitrifying organisms, autotrophic): XA lA Maximum growth rate of XA Decay rate of XA bA K O2A Saturation/inhibition coefficient for oxygen K NH4A Saturation coefficient for ammonium (nutrient) K ALKA Saturation coefficient for alkalinity (HCO3) K PA Saturation coefficient for phosphorus (nutrient)
2.4 0.14 0.5 1 0.5 0.01
d1 d1 g O2 m3 g N m3 mole HCO3 m3 g P m3
Hydrolysis of particulate substrate: XS Kh Hydrolysis rate constant gNO3S Anoxic hydrolysis reduction factor gfe Anaerobic hydrolysis reduction factor
3 0.6 0.4
d1 – –
0.2 0.5 0.1
g O2 m3 g N m3 g XS g1 XH
3.3 1.5 1.2 0.8 0.2 0.2 0.2 0.2 0.5 4 0.05 0.2 0.01 0.1 0.01 0.34 0.02 0.01
g XPHA g1 XPAO d1 g XPHA g1 XPAO d1 d1 – d1 d1 d1 g O2 m3 g N m3 g COD m3 g N m3 g P m3 g P m3 mole HCO3 m3 g XPP g1 XPAO g XPP g1 XPAO g XPP g1 XPAO g XPHA g1 XPAO
Item Heterotrophic organisms: XH
lH qfe
gNO3H bH K O2H K FH K fe K AH K NO3H K NH4H K PH K ALK
K O2S K NO3S K XS
Saturation/inhibition coefficient for oxygen Saturation/inhibition coefficient for nitrite and nitrate Saturation coefficient for particulate COD
Phosphorus-accumulating organisms: XPAO qPHA Rate constant for storage of XPHA (base XPP) qpp Rate constant for storage of XPP lPAO Maximum growth rate of PAO gNO3PAO Reduction factor for anoxic activity Rate for lysis of XPAO bPAO Rate for lysis of XPP bPP Rate for lysis of XPHA bPHA K O2PAO Saturation/inhibition coefficient for oxygen K NO3PAO Saturation coefficient for nitrate, SNO K APAO Saturation coefficient for acetate SA K NH4PAO Saturation coefficient for ammonium (nutrient) K ps Saturation coefficient for phosphorus in storage of PP K PPAO Saturation coefficient for phosphate (nutrient) K ALKPAO Saturation coefficient for alkalinity (HCO3) K PP Saturation coefficient for poly-phosphate K MAX Maximum ratio of XPP/XPAO K IPP Inhibition coefficient for PP storage K PHA Saturation coefficient for PHA
tion [25,26]. Batch experimental tests were performed for four different runs for determining the kinetic parameters of XA. The oxygen uptake rate measuring system consisted of four airtight, cylindrical chambers, with same height and volume, four magnetic stirrers for stirring and an aeration stone in each chamber. DO was monitored by four oxygen meters of high stability connected to a data acquisition system. Since this oxygen uptake rate measurer was airtight, the actual respiration rate of the tested biomass at any time during the batch-test did not depend on oxygen input. Therefore the dissolved oxygen concentration represented the actual oxygen uptake rate. A certain amount of activated sludge sample was taken from the pilot plants and added into oxygen uptake rate chambers. Distilled water containing organic carbon source and nutrient including glucose, NH4SO4 and KH2PO4 were added resulting in total volume of 800 ml in chamber. The measuring system was periodically aerated, then the difference between the measured OUR and baseline oxygen respiration was calculated and compared. The pH value was maintained at seven during batch-test. In order to evaluate the kinetic parameters and active biomass of XH, and XA, different types of oxygen uptake rate values should be considered: total oxygen uptake rate (OURT), oxygen uptake rate of XH (OURH) and oxygen uptake rate of XA (OURA). The determination of oxygen uptake rates of XH and XA were based on the subsequent addition of allylthiourea (ATU) and NaN3. Allylthiourea (86 lM) was added to the chambers to keep the NHþ 4 –N concentration constant during the incubation by selectively inhibiting XA while Azide (86 lM) was added to the chambers to keep the NO 2 –N concentration constant during the incubation by selectively inhibiting XNOB activity without affecting the activity of XAOB [27].When determining OURT, no inhibitor was added. When determining OURH, both allylthiourea (86 lM) and NaN3 (24 lM) were added and OURA = (OURT OURH).
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2.5.2. PHA test The initial concentration of PHA in each zone of a new system was analyzed according to the method that is described in Ref. [28] for estimating initial concentration of PHA in each zone. In the initial step, Duplicate 20 ml samples of MLSS were obtained and immediately centrifuged at 4 °C. Then, the cold sludge pellet was lyophilized. After that, the pellet was added to the tube closed with a Teflon-lined screw cap for drying. 2 ml of sulfuric acid 3% methanol and 2 ml of chloroform were added to the tube. This was digested for 1200 min in an oven at 104 °C. At the second step, once the sample had cooled at 25 °C, 1 ml of water was added and the tube contents were shaken for 600 s. The chloroform content remained to the bottom of the tube, and this was drawn off for GC examination. The digested product was exposed on a Varian 3400 GC fitted with a 1.8-m Alltech 0.2% Carbowax 1500 on Graphpac-GC 80/100 mesh stainless steel column. The column temperature was 170 °C and the inoculation temperature was 180 °C. PHA was measured by comparison to a standard consisting of a copolymer of the above-described alkanoates. 2.5.3. Characterization of the carbonaceous material Activated sludge models (ASM) distinguishes between the mechanisms acting on different components in the influent wastewater stream. The term wastewater characteristic refers to the partitioning of influent organic material into biodegradable and un-biodegradable (inert) portions, the ammonia portion of the total nitrogen and so on. The influent wastewater is often varied from one municipal wastewater to another. Wastewater characteristics have a very significant impact on system performance, particularly for nutrient removal systems. Characterization of the carbonaceous material in municipal wastewater for modeling purposes usually in terms of the chemical oxygen demand (COD).The division of the total influent COD (CODT) into the various fractions used in nutrient removal system design and modeling is shown in Fig. 4. In ASM2d model, the CODtot of the wastewater is considered consist of inert soluble organic matter (SI), readily and slowly biodegradable substrate (SS and XS respectively) and inert suspended organic matter (XI), whereas biomass in the wastewater is considered to be insignificant. 2.6. Raw wastewater characteristics The raw wastewater used in the experiment was collected from a main manhole of southeast university in Wuxi city and the characteristic of wastewater quality is listed in Table 5. In this study, four testing runs with different operations were implemented in Wuxi campus, southeast university. The raw wastewater is typical where the infiltrated chemical oxygen demand was varied between 157 and 855 mg/l with average of 651 mg/l, of which SS, SI, XS and XI accounted for about 380%, 2.0%, 43.0% and 11.00%, respectively. Mixed liquor suspended solid was varied between 45.0 and 93.00 mg/l with average of 76 mg/l. NHþ 4 –N concentration was varied between 10 and 40 mg/L with average of 22.0 mg/l. Total phosphor was varied between 1.70 and 4.50 mg/L with average of 2.60 mg/l, of which orthophosphate accounted for about 70.0–90.0%. 2.7. Program structure of five-tank process Program structure of innovative process was built using stoichiometric matrix Table 1, process rate Table 2, Algorithms and Five-tank A2/O environmental state conditions during each phase. The algorithms of five-tank is shown in Fig. 2.The
Fig. 4. Diagram depicting method of carbonaceous material characterization of influent COD components [29].
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Symbol
Concentration mg/L
Run No.
No.
1
2
3
4
Total chemical oxygen demand Particulate inert organic material Slowly biodegradable substrate Readily biodegradable substrate Active heterotrophic biomass Active autotrophic biomass Volatile fatty acids Inert soluble organic material Oxygen Nitrate nitrogen Ammonia nitrogen Total nitrogen Orthophosphate Total nitrogen Alkalinity Mixed liquor suspended solid
COD in-filtrated XI XS SS XH XA SA SI SO SNO SNH TN SPO TP SALK MLSS
805 63 340 306 5 0.1 88 7 0 2.23 18 31.7 2.59 3.07 4.97 87
688 61 297 261.5 4 0.7 73 5 0 1.23 27 39.6 3.25 3.71 5.01 73
458 55 201 174 4 0.3 61 4 0 1.26 32 43.2 3.41 4.32 4.99 71
513 59 222 195 1 0.0 67 4 0 2.8 19.4 33.1 1.01 1.79 5.00 67
model structure has taken change in the operation state condition of each compartment in addition to phase time in consideration. The equations that described the transformation of the wastewater quality in the model formed an ordinary differential equations (ODEs) system. The set of equations in this model then was integrated simultaneously by the 4th order range kutta numerical integration method [30]. According to the program structure of five-tank, the entire model was implemented by means of a computer program that was coded with MATLABÒ2010 language. When all the vectors 1/Cij (dC/dt) were nearly equal to zero, a steady state was reached. The integration was most accurate when time step is very small but the computing time increased inversely with the size of time step. Conversely, too large time step would result in large errors and other numerical problem. Thus, one criterion for an upper limit on time step is: 1
Dthh CðtÞ ðdC=dtÞ ;
ð6Þ
where Dt is time step. By combining Eqs. (2) and (6), and neglecting the Mij, Nij terms in the mass balance, resulted the maximum step size as shown below:
DthhV j C ij =ðC 2ij þ Nij Þ ¼ uij :
ð7Þ
The term hij is the mean residence time of component j in reactor component i at steady state. 3. Results and discussions 3.1. Removal efficiency According to investigation results, The five-tank process achieved 89.1% ± 1.37%, 87.78% ± 1.15, 73.62% ± 2.13%, and 83.78% ± 0.92% of chemical oxygen demand, NHþ 4 –N, TN, and total phosphorus (TP) removal efficiencies, respectively, during a 16-month operation with the effluent meeting Chinese sewage discharge standard GB18918-Grade A. The results showed that this system is operated with simultaneous nitrification denitrification phenomena (SND) and denitrifying phosphorus removal (DBP sludge/DNPAOs) which is benefit for enhancing nitrogen and phosphorus removal and also reducing energy power because it is need low dissolved oxygen concentration. 3.2. Model evaluation The model evaluation was carried out through comparison predicated and experimental data of four related runs in Wuxi campus. The raw wastewater characteristics from Wuxi campus were illustrated in Table 3. Fig. 5 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentrations of compartment one under different runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations (R2) of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.95, 0.96, 0.93 and 0.98 respectively of at run one, 0.98, 0.95, 0.91 and 0.97 respectively at run two, 0.99, 0.95, 0.87 and 0.99 respectively at run three and 0.95, 0.97, 0.87 and 0.92 respectively at run four. Fig. 6 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentration of compartment two under different runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations R2 of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.95, 0.73, 0.94 and 0.99 respectively at run one, 0.99, 0.89, 0.95 and0.99 respectively at run two, 0.99, 0.89, 0.97 and 0.98 respectively at run three, and
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1.Compartment one –Run 1
2.Compartment one - Run 2
3.Compartment one - Run 3
4.Compartment one - Run 4
Fig. 5. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment one.
1.Compartment two - Run 1
2.Compartment two - Run 2
3.Compartment two - Run 3
4.Compartment two - Run 4
Fig. 6. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment two.
0.97, 0.86, 0.97 and 0.98 respectively at run four. Fig. 7 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentration of compartment three under different runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations R2 of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.95, 0.66, 0.95 and 0.95 respectively at run one, 0.96, 0.77, 0.95 and 0.95 respectively at run two, 0.98, 0.63, 0.95, and 0.99 respectively at run three, and 0.97, 0.74, 0.95 and 0.997 respectively at run four. Fig. 8 depicts the observed and predicated data of ammonia–N, PO3 4 –P, COD and nitrate–N concentration of compartment four under different
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1.Compartment three - Run 1
2.Compartment three - Run 2
3.Compartment three - Run 3
4.Compartment three - Run 4
287
Fig. 7. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment three.
runs. This figure has a good consistency between the observed and predicated data whereas the sum of squares of the deviations R2 of ammonia–N, PO3 4 –P, COD and nitrate–N were 0.96, 0.56, 0.96, and 0.98 respectively at run one, 0.97, 0.61, 0.93 and 0.96 respectively at run two, 0.99, 0.60, 0.95 and 0.94 respectively at run three, and 0.95, 0.59, 0.96 and 0.99 respectively at run four. 3.3. Model simulation of the biomass The particulate component concentrations could be also calculated from model simulation as shown in Fig. 9 whereas the heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA concentrations in five-
1.Compartment four - Run 1
2.Compartment four - Run 2
3.Compartment four - Run 3
4.Compartment four - Run 4
Fig. 8. Simulated and observed ammonia–N, PO3 4 –P, COD and nitrate–N in compartment four.
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(a) Run 1
(b) Run 2
(c) Run 3
(d) Run 4
Fig. 9. Variations of biomass in each compartment of five-tank process reactor.
tank process reactor were 638–1204 mg L1, 58.4–275 mg L1, and 90–200.12 mg L1 at run one, 594.9–1321.4 mg L1, 58.43–337.76 mg L1, and 35–203.88 mg L1 at run two, 514.22–1160 mg L1, 58.42–271.13 mg L1, and 95.5–208.6 mg L1 at run three, 534.26–1174.5 mg L1, 58.42–272.5 mg L1, and 89.72–185.36 mg L1 at run four. As result, The XH; XPAO; and XA decreased in the anaerobic compartment because of the lysis reaction. Then the heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA increased in the aerobic compartments due to aerobic growth. The heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA increased in quantities by about 56.6%, 36.12% and 74.31% in compartment one due to change the environmental state condition from anaerobic to aerobic and decreased in quantities by about 20.21%, 44.18%, and 0.142% in compartment three due to change the environmental state condition from aerobic to anoxic. The ratio of total nitrifying species to total active biomass was fluctuated between 1% and 11.89% in the process reactor. In this study, the disadvantages of the previous BNR processes were improved by reconfiguring the process without mixed liquor and sludge recirculation. This was done by configuring the process into five-tank with variable operation state condition, anaerobic/anoxic, aerobic and settling condition, in each compartment to achieve optimum removal of phosphorus and nitrogen. The heterotrophic organism XH; phosphate accumulating organism XPAO, and autotrophic microorganism XA decreased in the anoxic compartment due to the dilution effect of the flow. In addition to the dilution effect of the influent, the XA also decreased due to the negative growth rate resulted from lysis reaction in the anoxic compartment. In full-scale wastewater treatment plant, the transient system behavior is of high practical importance since variations of composition, influent flow-rate as well as changes of operation prevents each real-world wastewater treatment plant from reaching the steady state condition. Although the application of this ASM2d under steady state was validated in this study, the application in transient state can be implemented in the future study. In addition, the practical applications of the ASM2d model including plant optimization, controller layout, mathematical verification of the purification performance, and model-based state and parameter estimation should be taken into account in the future study. 4. Conclusions The variation of pollutants ammonia–N, PO3 4 –P, COD and nitrate–N in five-tank e process reactor could be modeled successfully using the ASM2d. The microbial kinetic behaviors of four testing runs were analyzed based on ASM2d model. The results which obtained in this study can be summarized as follows: A. The five-tank process achieved 89.1% ± 1.37%, 87.78% ± 1.15, 73.62%% ± 2.13%, and 83.78% ± 0.92% of chemical oxygen demand, NHþ 4 –N, TN, and total phosphorus (TP) removal efficiencies, respectively, during a 16-month operation with the effluent meeting Chinese sewage discharge standard GB18918-Grade A. B. In this study, the growth rate constant of autotrophic organisms XA and its yield coefficient value were 2.4 day1 and 0.14, respectively.
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C. According to model simulation, the heterotrophic organism; phosphate accumulating organism, and autotrophic organism concentrations in five-tank process were varied between 638–1204 mg L1, 58.4–275 mg L1, and 90– 200.12 mg L1 at run one, 594.9–1321.4 mg L1, 58.43–337.76 mg L1, and 35–203.88 mg L1 at run two, 514.22– 1160 mg L1, 58.42–271.13 mg L1, and 95.5–208.6 mg L1 at run three, 534.26–1174.5 mg L1, 58.42–272.5 mg L1, and 89.72–185.36 mg L1 at run four. D. According to simulation results, heterotrophic organism; phosphate accumulating organism, and autotrophic organism are decreased in the anaerobic compartments because of the lysis reaction. Then these organisms are increased in the aerobic compartments due to aerobic growth. The heterotrophic organism; phosphate accumulating organism; and autotrophic organism are increased in quantities by about 56.6%, 36.12% and 74.31% in compartment one due to change the operation condition from anaerobic to aerobic and decreased in quantities by about 20.21%, 44.18%, and 0.142% in the compartment three due to change from aerobic to anoxic. The total nitrifying species to total active biomass was fluctuated between 1% and 11.89% in the process reactor.
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