Performance of a compartmentalized activated sludge (CAS) system treating a synthetic antibiotics industrial wastewater (SAW)

Journal of Water Process Engineering 3 (2014) 26–33

Contents lists available at ScienceDirect

Journal of Water Process Engineering journal homepage: www.elsevier.com/locate/jwpe

Performance of a compartmentalized activated sludge (CAS) system treating a synthetic antibiotics industrial wastewater (SAW) Z. Shaykhi Mehrabadi, A.A.L. Zinatizadeh ∗ Water and Wastewater Research Center (WWRC), Department of Applied Chemistry, Faculty of Chemistry, Razi University, Kermanshah, Iran

a r t i c l e

i n f o

Article history: Received 23 March 2014 Received in revised form 11 July 2014 Accepted 13 August 2014 Available online 3 September 2014 Keywords: Compartmentalized activated sludge system Biological treatment Amoxicillin Co-amoxiclav and ciprofloxacin

a b s t r a c t In this study, a 3-L compartmentalized activated sludge (CAS) system with 6 compartments was designed and fabricated. In the start-up stage, pre-acclimatized inoculums were added into the CAS and the system was operated under certain operating conditions until the system performance remained at a steady state. The effects of two process variables (mixed liquor volatile suspended solids (MLVSS) and influent chemical oxygen demand (CODin )) on the reactor performance were investigated and the process was modeled and analyzed using response surface methodology (RSM). From the results, the ratio of food to microorganism (F/M) was found to be the most important factor for the process control. Maximum removal efficiency (89%) was determined at a MLVSS and CODin of 4800 and 2000 mg/L, respectively. Kinetic coefficients (Y and Kd ) were determined as 0.0815 g VSSproduced /g CODrem and 0.009 d−1 , respectively. Biodegradation of Co-amoxiclav and ciprofloxacin were also investigated in the CAS system. The COD removal dropped from 29 to −18% after five turnovers (two days) for Co-amoxiclav. The process instability was also observed by a remarkable increase in effluent turbidity (>370 NTU) as a result of intensive biomass decomposition. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction The escalating problem of the emergence of antibiotic resistant bacteria and their resistant genes is becoming a major global health issue [1,2]. The pharmaceutical industries, large academic institutions or the government are not investing the essential resources to produce the next generation of newer safe and effective antimicrobial medicines. In many cases, due to economic reasons, large pharmaceutical establishments have ended their anti-infective research programs altogether. Antibiotic decomposition or transformation occurs when the bacteria produces enzymes that chemically degrade or modify the antimicrobial, inactivating them against the bacteria. This is a common mechanism of resistance and probably one of the oldest ones affecting several antibiotics but especially ␤-lactam antibiotics, via the bacterial production of ␤-lactamases [3]. Although ␤-lactam antibiotics have been reported to dominate the overall antibiotic concentration in some sewage influents, they tend to be considerably reduced in concentrations during biological processes [4]. One of the most widely used antibiotics is amoxicillin ((2S, 5R, 6R)-6-{[(2R)-2-amino-2-(4-hydroxyphenyl)-acetyl] amino}-3,

∗ Corresponding author. Tel.: +98 8314274559; fax: +98 8314274559. E-mail addresses: [email protected], [email protected] (A.A.L. Zinatizadeh). http://dx.doi.org/10.1016/j.jwpe.2014.08.003 2214-7144/© 2014 Elsevier Ltd. All rights reserved.

3-dimethyl-7-oxo-4-thia-1-azabicyclo [3.2.0] heptane-2carboxylic acid), a moderate-spectrum bacteriolytic ␤-lactam antibiotic [5]. Furthermore, the presence of antibiotics in wastewaters has also increased and their abatement will be a challenge in the near future. Two antibiotics-polluted streams may be discharged into the environment; municipal wastewater as a low antibiotic concentration stream (<10 mg/L) and pharmaceutical industrial wastewater as a high concentration stream. Several research works have been carried out for removal of amoxicillin (AMX) from wastewaters with low concentrations. For these wastewaters, the effect of biological treatments, membrane filtration, activated carbon adsorption, advanced oxidation processes (AOPs), and disinfection on different classes of antibiotics has been widely investigated in the last years [6,7]. The technologies examined are activated sludge system, TiO2 photocatalytic process [8], adsorption process [9], photo chemical process using UV light [10], a combination of advanced oxidation and biological methods [11], and so on. The biological processes, filtration, and coagulation/flocculation/sedimentation are the most widely used in conventional wastewater treatment plants [12–16]. For industrial wastewater with a high concentration of AMX (300–600 mg/L), combined treatment technologies including an advanced oxidation process and biological system are proposed [8,17–19]. Extensive research works have been performed on the effect of different physical and chemical treatment methods in

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33 Table 1 Characteristics of synthetic amoxicillin wastewater used. Parameter

Unit

Range

AMX 500 TN TP COD Dissolved oxygen pH MLVSS

mg/L mg/L mg/L mg/L mg O2 /L – mg/L

200–1000 80–400 20–100 400–2000 4.7–5.2 7.5 1600–4800

antibiotics removal from the industrial antibiotic wastewaters [10–12]. The results showed that the individual processes have not been able to treat the compounds completely. Thus combined systems were introduced, including chemical and biological oxidation processes. On this basis, several combined treatment processes, e.g. photo-Fenton+ sequence batch reactor (SBR) [17], Fenton+ SBR [18,19], and TiO2 photocatalysis+SBR [8] were examined for treatment of mixed antibiotic wastewaters with relatively high concentrations (400–670 mg/L). From the studies, the combination of Fenton processes with SBR showed high performance with removal efficiencies higher than 89%, while the photocatalysis used has not been efficient [20]. Elimination and transformation of antibiotics during the biological treatment is the result of different processes. These processes can be biotic (biodegradation, mainly by bacteria and fungi) and non-biotic or abiotic (e.g. sorption, hydrolysis, photolysis). The removal of antibiotics mainly depends on their sorption on the sewage sludge and their degradation or transformation during the biological treatment [21]. Most investigations performed on the biological treatment of antibiotics in the activated sludge system is reported for wastewater with very low concentrations of the antibiotics [21]. In biological systems, activated sludge technology is widely used, especially in industrial effluent treatments. However, the presence of highly toxic compounds, e.g. antibiotics in the industrial effluents with high concentrations prevents the application of such processes [22]. The presence of vestigial levels of some common antibiotics like the ␤-lactams group may cause resistance in bacterial populations. Therefore, the biological treatment process can be suggested as an alternative for removing such compounds from wastewater. Providing a mixing regime close to plug flow by the reactor compartmentalization would be an economic and competent strategy to reduce the required reactor volume (especially for the wastewaters with high slowly biodegradable COD) [23]. Hence, an efficient biological process with a structure of CSTR in-series, (compartmentalized activated sludge (CAS)), was selected to treat a synthetic AMX wastewater in this study. The effect of two influential process factors (biomass concentration and influent COD concentration) on the CAS process performance for high AMX concentration wastewater was investigated [24]. The performance of the CAS system was also assessed for treatment of two other new generation antibiotics (Co-amoxiclav and ciprofloxacin). 2. Materials and methods 2.1. Wastewater and seed sludge preparation The synthetic antibiotic wastewater (SAW) was prepared by dissolving two capsules of amoxicillin (AMX 500 mg) in tap water in the laboratory scale. Equivalent COD of the SAW stock solution was about 2000 mg/L. Other concentrations of the SAW were prepared by dilution of the main solution. Furthermore, the actual COD contents for the samples were verified each time before initiation of experimental work. Supplementary nutrients such as nitrogen (NH4 Cl) and phosphorous (KH2 PO4 ) were added to provide a ratio of COD:N:P as 100:20:5 (Table 1). The seeding source of the

27

Table 2 Physical specifications of the compartmentalized activated sludge (CAS) system applied. Parameter

Value

Unit

Aeration chamber in CAS set-up Length Width Height Freeboard Total volume Working volume

30 6 30 9 5400 3024

cm cm cm cm cm3 cm3

Settling tank in CAS set-up Height Sectional diameter Freeboard Conical volume Total volume Working volume

38 15 20 605 5770 1500

cm cm cm cm3 cm3 cm3

bioreactor was the activated sludge taken from an aerobic sludge digester, Faraman industrial estate, Kermanshah – Iran. Mixed liquor suspended solids (MLSS) concentration of the seed was initially measured as 5000–8000 mg/L. The acclimatization process with the SAW was carried out before use. 2.2. Bioreactor set-up and operation An integrated system including an aeration chamber and a settling tank were designed and fabricated with plexiglass. The schematic diagram of the experimental setup is shown in Fig. 1a and b. Table 2 presents physical dimensions of aeration tank and sedimentation unit used in this study. As can be seen in the Figs., the aeration basin was compartmentalized as 6 identical compartments. Thus, the wastewater stream passes through the unit as up and down flow. An air compressor was applied for aerating the wastewater in the aeration tank. The dissolved oxygen (DO) concentration affects the process performance, so that at low amounts of DO (≤2 mg/L), the COD removal efficiency is considerably decreased. It was maintained approximately at about 5 mg/L. In all the experiments, the reactor was fed by a peristaltic pump (PD5201, Heidolph, Germany). Also, a centrifuge pump was used for returning sludge from the settling unit into the aeration tank. It must be noted that the rate of washout did not have a remarkable effect on the F/M ratio. However, the biomass washed out was monitored daily and returned back to the system manually. As feed flow rate (Q) and the reactor volume (V) were constant throughout the experiments, by applying the CODin and MLVSS at each defined operating condition, a specific F/M ratio was obtained for the applied condition. So, as long as the CODin and MLVSS are controlled, the F/M ratio, [(Q·CODin )/(V·MLVSS)], is also maintained without change. The aerobic consortium used as seed culture for the bioreactor was a sample of the sedimentation sludge from a full-scale industrial activated sludge plant treating industrial estate wastewater (Kermanshah, Iran). In order to start up the system, the bioreactor was fed with an influent COD of 1200 mg/L for two weeks with a constant MLVSS and retention time of 4000 mg/L and 24 h, respectively. Initially, the bioreactor was inoculated with 4500 mL sludge mixture (MLVSS of 7000 mg/L). In order to acclimatize the sludge with SAW, the reactor was daily batch-fed with SAW (1000–2000 mg COD/L) for 14 days. During batch experiments, the liquid content of the reactor was continuously re-circulated for 1 day (until the next feed). Continuous feeding of the reactor was started with an initial organic loading rate (OLR) of 120 mg COD/L h and a hydraulic retention time (HRT) of 10 h. The HRT was maintained constant throughout the start-up period. HRT indicates time duration of the feed retention in the aeration tank. Since the bioreactor volume

28

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33

Fig. 1. (a) The schematic drawing of the laboratory-scale CAS set up and (b) experimental set-up used in this study.

(V) is constant, HRT is thus determined by adjusting the feed flow rate (Q) according to HRT = V/Q. The influent COD concentration was 1200 mg/L for the first 7 days, and it was then increased stepwise to 2000 mg/L from 7 to 14 days. During the experiments, COD removal efficiency, MLVSS, effluent TSS, effluent turbidity, sludge volume index (SVI) and DO were monitored. Steady state was assumed after four turnovers. The MLSS was one of the variables studied. The effect of this variable was investigated at three levels; 2000, 400 and 6000 mg/L. In order to control the level of MLSS in the reactor at the desired amount, the MLSS concentration was daily monitored in each operating condition and its concentration was continuously adjusted by the recycled activated sludge (RAS) from the settling tank to the aeration tank using a recycle pump and a timer. The required amount for the RAS was calculated based on the MLSS mass balance at the entrance of the bioreactor. The sludge concentration settled at the bottom of the clarifiers was also measured during the experiments.

2.3. Experimental design The response surface method (RSM) used in the present study was a central composite face-centered design (CCFD) involving two independent factors, CODin and MLSS concentrations. The region of exploration for bioreactor performance was decided as the area enclosed by CODin (400–2000 mg/L) and MLSS (2000–6000 mg/L) boundaries (Table 3). Selection of the range of both factors was based on the results obtained from preliminary studies. Table 3 The range and levels of the variables studied. Variable

CODin , mg/L MLSS, mg/L

Range and Levels −1

0

+1

400 2000

1200 4000

2000 6000

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33

29

Table 4 Experimental conditions and results of central composite design. Run

1 2 3 4 5 6 7 8 9 10 11 12 13

Variable

Response

Factor 1 A: CODin (mg/L)

Factor 2 B: MLVSS (mg/L)

COD removal (%)

U, (gCODrem /g VSS d)

Effluent TSS (mg/L)

Turbidity (NTU)

SVI (mL/g)

2000 1200 400 2000 2000 1200 400 1200 1200 1200 400 1200 1200

4800 4800 1600 3200 1600 3200 3200 3200 1600 3200 4800 3200 3200

89.3 73.9 53.0 75.0 66.0 86.8 56.9 81.4 72.7 69.3 40.5 80.0 78.0

0.13 0.50 0.82 0.07 0.28 0.47 0.03 0.17 0.37 0.30 0.24 0.27 0.27

98.07 78.40 26.69 42.72 43.30 69.19 36.00 65.93 21.03 47.70 78.40 57.78 62.57

100 79.5 25.6 42.3 42.9 69.9 35.3 66.5 19.7 47.5 79.5 58 63

141.6 83.3 65 155 80 90 117.5 85 65 97.5 100 87 92

The biological treatment process was evaluated based on the full face-centered CCD experimental plan. Accordingly, for 3 levels of each factor, 13 experiments were conducted. The system was operated under different MLSS and influent COD concentrations. The experimental conditions and results of central composite design are presented in Table 4. With the purpose of achieve a comprehensive analysis of the biological process, 4 dependent parameters were either directly measured or calculated as the process response. These parameters were COD removal efficiency, effluent TSS, effluent turbidity, and SVI. The results were completely analyzed using analysis of variance (ANOVA), which was automatically performed by Design Expert software (Stat-Ease Inc., version 8). 2.4. Analytical methods The concentrations of chemical oxygen demand (COD), biological oxygen demand (BOD), MLVSS, MLSS and SVI of the system were determined by using standard methods for the examination of water and wastewater [25]. For COD, a colorimetric method with closed reflux procedure was developed. Spectrophotometer (6320D, Jenway, USA) at 600 nm was used to measure the absorbance of COD samples. Biological oxygen demand (BOD) was measured with BOD meter model (OxiTop IS 6). The DO concentration in wastewater was determined using a DO probe. DO meter was supplied by WTW DO Cell OX 330, electro DO probe, Germany. Turbidity was measured by a turbidity meter model 2100 P (Hach Co., USA). 3. Result and discussion 3.1. Performance of CAS system treating SAW As various responses were investigated in this study, different degree polynomial models were used for data fitting (Table 5). ANOVA results for the response surface models are illustrated in Table 5. In order to quantify the curvature effects, the data from the experimental results were fitted to higher degree polynomial equations i.e. two factor interaction (2FI), quadratic and so on. In the Design-Expert software, the response data were analyzed by default. Some raw data might not be fitted and transformations which apply a mathematical function to all the response data might be needed to meet the assumptions that make the analysis of variance (ANOVA) valid. 3.1.1. COD removal The relationship between the variables studied and COD removal was described by a reduced quadratic model (Eq. (1)). 2

COD removal, % = 79.15 + 13.31A + 8.95AB−12.36A −5.00B

2

(1)

Fig. 2a represents the predicted versus actual values for the response. It showed that the predicted values are in good agreement with the experimental data (R2 = 0.9356). From Table 5, A, AB, A2 , and B2 are selected as the effective terms with confident level less than 0.05. As observed in Eq. (1), first-order effect of A showed an increasing impact on the response while the second-order effect of the variables causes a decrease trend in the response. Fig. 2b depicts the variation of COD removal efficiency as a function of CODin and MLVSS. As can be seen in the Fig., MLVSS showed an inverse impact, decreasing effect at CODin less than 1600 mg/L and increasing influence at COD values higher than 1600 mg/L which was attributed to the balance between microbial growth and death rates. Simultaneous increase in the variables (conditions with constant food to microorganism ratio (F/M)) improved COD removal efficiency, implying the positive effect of COD concentration on the reaction rate as the biochemical reactions mostly follow first-order kinetics [26]. Maximum efficiency of COD removal was modeled as 84% (actual value 89.3%) at the highest level of CODin and MLVSS (2000 and 4800 mg/L, respectively). Minimum COD removal efficiency (40.5%) was found at CODin and MLVSS of 4800 and 400, respectively, which was attributed to microbial soluble products resulted from endogenous respiration. Low amoxicillin removal (45%) from urban wastewater in a constructed wetland (CW) has been reported [27]. Combination of an advanced oxidation process (AOP) (Fenton process) and sequence batch reactor (SBR) has been examined removing amoxicillin compounds [19]. 89% sCOD removal was obtained at a H2 O2 /Fe molar ratio of 150 and reaction time of 12 h in the SBR. It is noted that the biological systems have not shown to be efficient at removing antibiotics from sewage wastewaters which contain low antibiotic concentrations, as the biomass prefers to utilize easily digestible substrates.

3.1.2. Specific substrate utilization rate (U) Fig. 3a depicts good agreement between the experimental data and simulated values of the U. From Table 5, A, B, AB and B2 are selected as the effective terms with confident level less than 0.05. The regression equation describing dependency of the response to the variables is given as Eq. (2). As observed in Eq. (2), first-order effect of B and interactive effect of the variables (AB) showed a decreasing impact on the response while the second-order effect of B and first order effect of A caused an increase trend in the response. The quantity of the coefficient implies the effects intensity. U, mg CODrem /g VSS · d = 0.27 + 0.24A − 0.15B − 0.089AB + 0.067B2

(2)

30

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33

Table 5 ANOVA results for the response surface models applied. Response

Model type

COD removal (%)

ANOVA

Reduced quadratic

Source

Sum of squares

Degrees of freedom (DF)

Mean square

F value

Prob > F

Model A AB A2 B2 Residual Lack of fit

110.53 1063.54 320.41 421.97 69.08 145.19 88.60

4 1 1 1 1 8 4

527.63 1063.54 320.41 421.97 69.08 18.15 22.15

29.07 58.60 17.66 23.25 3.81

<0.0001 <0.0001 0.0030 0.0013 0.0869

1.57

0.3373

2

2

(R = 0.9356, Adj. R = 0.9035, Adeq. Precision = 16.854, Std. Dev. = 4.26, RESS = 575.54, C.V. % = 5.99) Model A B AB B2 Residual Lack of fit

Reduced quadratic

U (gCODremoval /g VSS d)

0.52 0.34 0.13 0.031 0.015 7.6E−3 5.68E−3

4 1 1 1 1 8 4

0.13 0.34 0.13 0.031 0.015 9.5E−4 1.42E−3

135.75 357.96 136.56 33.01 15.45

<0.0001 <0.0001 <0.0001 0.0004 0.0044

2.97

0.1587

(R2 = 0.9855, Adj. R2 = 0.9872, Adeq. Precision = 16.854, Std. Dev. = 0.031, PRESS = 0.041, C.V. % = 10.19) Effluent TSS

Reduced linear

Model B Residual Lack of fit

4474.62 4474.62 1506.10 1225.67

1 1 11 7

4474.62 4474.62 136.92 175.10

32.68 32.68

0.0001 0.0001

2.50

0.1969

(R2 = 0.7482, Adj. R2 = 0.7253, Adeq. Precision = 11.900, Std. Dev. = 11.70, PRESS = 2078.82, C.V. % = 20.90) Turbidity (NTU)

Reduced linear

Model B Residual Lack of fit

4862.11 4862.11 1636.52 1331.81

1 1 11 7

4862.11 4862.11 148.77 190.26

32.68 32.68

0.0001 0.0001

2.50

0.1969

(R2 = R2 = 0.7482, Adj. R2 = 0.7253, Adeq. Precision = 11.900, Std. Dev. = 12.20, PRESS = 2258.84, C.V. % = 21.73) SVI (mL/g)

Model A B A2 B2 Residual Lack of fit

Reduced quadratic

7646.86 1475.80 2200.33 3312.05 2084.24 978.15 884.35

4 1 1 1 1 8 4

1911.72 1475.80 2200.33 3312.05 2084.24 122.27 221.09

15.64 12.07 18.00 27.09 17.05

0.0008 0.0084 0.0028 0.0008 0.0033

9.43

0.0258

(R2 = 0.8866, Adj. R2 = 0.8299, Adeq. Precision = 14.135, Std. Dev. = 11.06, PRESS = 3755.00, C.V. % = 11.42)

90.00

90

COD removal, %

Predicted

77.25

64.50

51.75

77.25 64.5 51.75 39

4800.0

39.00

2000.0 4000.0

39.52

51.97

64.41

76.86

89.30

1600.0 3200.0

B: MLVSS

1200.0 2400.0

800.0 1600.0

400.0

A: CODin

Actual

(a)

(b)

Fig. 2. (a) Actual vs. predicted values for COD removal and (b) response surface plot for COD removal.

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33

31

U, g CODrem/gVSS.d

0.83

Predicted

0.63

0.43

0.23

0.830 0.628 0.425 0.223 0.020

4800.0

0.03

2000.0 4000.0

0.03

0.23

0.43

0.63

1600.0 3200.0

0.82

B: MLVSS

1200.0 2400.0

800.0 1600.0 400.0

A: CODin

Actual

(a)

(b)

Fig. 3. (a) Actual vs. predicted values for U and (b) response surface plot for U.

Fig. 3b shows the simultaneous effect of CODin and MLVSS on U (Specific substrate utilization rate). From Fig. 3, CODin was more effective relative to MLVSS. The maximum value of U was found at CODin and MLVSS of 2000 and 1600 mg/L, respectively. On this basis, the treatment capacity of the CAS for removing antibiotic compounds has been obtained at the condition with the highest CODin and the lowest MLVSS corresponding to the highest OLR (200 mg COD/L h) which can be used for the reactor design. Comparison of the maximum substrate utilization rate obtained in this study (4.3 g CODrem /L d) with others reported in the literature emphasizes the relatively high resistance of the substrate to biological removal (Table 6). In order to estimate biomass yield (Y) and decay coefficient (Kd ), 1/SRT versus U was drawn according Eq. (3) [25] as shown in Fig. 4. Slope and intercept of the equation indicate Y and Kd , respectively. The kinetic coefficients, Y and Kd , were computed as 0.0815 g VSSproduced /g CODremoved and 0.0088 1/d, respectively, implying a slow microbial growth rate. 1 rsu = −Y − Kd SRT X

(3)

Table 6 presents a summary of kinetic results from other studies reported in the literature. Table 6 shows that the Y value (0.082) of SAW treated in the CAS system was the lowest among others.

0.07 0.06

1/SRT, 1/d

0.05 y = 0.0815x - 0.0088 R² = 0.9977

0.04 0.03

Experimental data

0.02

Linear (Experimental data) 0.01 0 0

0.2

0.4

0.6

0.8

U, g CODrem/g VSS.d Fig. 4. 1/SRT versus specific substrate utilization rate (U).

1

This means the SAW is a low biodegradable wastewater and its type of substrate has a significant effect on quantity of the kinetic coefficients. It was also found that the Kd value was rather low compared to others. Kd value is used to find out the net amount of sludge to be handled [28]. Besides, the values of Y and Kd for SAW prove that the MLVSS/MLSS ratio in the SAW is high and this subsequently provides higher performance in a volume unit. This implies that carbon digestion was preferred relative to carbon assimilation [29]. 3.1.3. Biomass washout As the reactor feed was prepared from a completely dissolved antibiotic solution, any effluent suspended solids are considered as biomass washout. In order to control the process stability, biomass washout monitoring is of vital importance. Therefore, effluent TSS concentration and turbidity were measured throughout the experiments as the process responses. As presented in Eqs. (4) and (5), B was the only factor affecting the responses in the design space. Thus, a probable adverse impact of CODin on the responses could be anticipated at lower and higher levels than that examined in this study. Effluent TSS, mg/L = 55.98 + 27.31B

(4)

Effluent turbidity, NTU = 56.13 + 28.47B

(5)

Fig. 5a and b illustrates the effluent TSS and turbidity variations in the design space studied, respectively. Both figures show similar trends, increasing by an increase in MLVSS. The responses changed between 19.7–100 mg/L and 21–98 NTU, respectively for effluent TSS and turbidity. The range obtained shows that the flocs are being disintegrated at high MLVSS due to unfavored conditions. In other words, by increasing MLVSS from 1600 to 4800 mg/L, the effluent turbidity and TSS were increased. It might be attributed to poor conditions in terms of F/M as well as low biodegradability of the feed [35]. It should also be noticed that the COD concentration (A) could be effective for the response in the case of COD being varied at values out of the range studied. It is noted that the experimental results obtained in this study are at steady state conditions. Relatively high biomass washout (in the form of fine disintegrated biomass) from the system, especially at high levels of MLVSS and CODin , causes remarkable biomass loss and consequently made the process operation difficult. So, in order

32

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33

Table 6 Comparison of kinetic constants based on mass balance equation for COD removal reported in the literature. Y, mg VSS/mg COD

Kd , 1/d

0.6112 0.125 0.38–0.67 0.23 0.712 0.161 0.0815

0.0047 0.0065 0.01–0.14 0.14 0.005 0.039 0.0088

Umax , g COD/L d – 7.501 – – 15.2 – 4.3

KS , mg/L – 8.211 – – 14.78 – –

Type of wastewater

Ref.

3600 4214 – 1700–4000 1100 800–1200 400–2000

Municipal wastewater Simulated cotton textile wastewater Simulated textile wastewater Dairy wastewater Synthetic wastewater Faraman Industrial Wastewater Antibiotic wastewater

[30] [31] [32] [33] [26] [34] Present study

Effluent Turbidity, NTU

Effluent TSS, mg/l

99.00

COD, mg/L

79.50 60.00 40.50 21.00

2000.00 1600.00 1200.00

A: CODin

4800 4000 3200 800.00

2400 400.00 1600

B: MLVSS

100.0 79.8 59.5 39.3 19.0

2000.00 1600.00 1200.00

A: CODin

4800 4000 3200 800.00

2400 400.00 1600

B: MLVSS

(b)

(a)

Fig. 5. Response surface plots for (a) effluent TSS, and (b) effluent turbidity.

3.1.4. Sludge volume index (SVI) Sludge volume index (SVI) indicates the process stability. The following equation describes the relationship between SVI and the variables. SVI = 93.53 + 15.68A + 19.15B + 34.63A2 − 27.47B2 A2

(6)

B2

and are selected as the effective terms From Table 5, A, B, with confident level less than 0.05. As observed in Eq. (6), both firstorder and second-order effects of A (A and A2 ) showed an increasing impact on the response. First-order effect of B showed an increasing impact while the second-order effect of B caused a decrease trend in the SVI. Fig. 6 depicts the variation of SVI as a function of CODin and MLVSS. As can be seen in Fig. 6, both variables had an inverse effect on the SVI with opposite inclination. Range of SVI variation was determined between 65 and 155 mL/g. The best sludge settle ability was found at low levels of MLVSS and CODin . 3.2. Performance of the CAS system treating other new antibiotics

acclimatization phase, the CAS system was fed by Co-amoxiclav and ciprofloxacin separately under optimum operating conditions determined in the earlier part (CODin 2000 mg/L, HRT 12 h, and MLVSS 6000 mg/L). Fig. 7a and b shows the performance of the CAS in terms of COD removal efficiency for the Co-amoxiclav and ciprofloxacin, respectively. As can be seen in the Figs., the COD removal was dropped from 29 to −18% after five turnovers (two days) for Co-amoxiclav. The process instability was also observed by a remarkable increase in effluent turbidity (>370 NTU) as a result of intensive biomass decomposition. It showed different results in comparison with those obtained with AMX. For ciprofloxacin, as is shown in Fig. 7b, a lower toxicity was found compared to that of Co-amoxiclav.

160 130

SVI, ml/g

to maintain the steady state, the F/M had to be adjusted over time through provision of the required biomass. As a result, the system is being destabilized as time progresses at a certain condition at high OLR.

100 70 40

In any biological process for treatment of antibiotics, acclimatization of the biomass with the antibiotic is of vital importance. So, adding another antibiotic into the process will definitely inhibit the process unless it has similar effect. In order to evaluate the performance of the CAS bioreactor in the treatment of some of the new generation antibiotics, two new antibiotics (Co-amoxiclav and ciprofloxacin) were selected for study. After a long-term

4800.00 4000.00 3200.00 B: MLVSS 2400.001600.00 400

2000 1600 1200 800

A: CODin

Fig. 6. Response surface plot for SVI.

Z.S. Mehrabadi, A.A.L. Zinatizadeh / Journal of Water Process Engineering 3 (2014) 26–33

(a) 40

Co- amoxiclav , CODin 1500 mg/l, HRT 12 h

COD removal, %

30 20 10 0 -10

0

10

20

30

40

50

60

70

-20

Time, h

-30

(b)

Ciprofloxacin, CODin 1600mg/l, HRT 12 h

60

COD removal, %

50 40 30 20 10 0 0

20

40

60

80

Time, min

100

120

140

160

Fig. 7. Performance of CAS system treating co-amoxiclav and ciprofloxacin.

4. Conclusions Compartmentalized activated sludge (CAS) system was an efficient process to treat amoxicillin wastewater. However, its reliability in long-term performance is not guaranteed owing to biomass washout rate, which might make the process unstable. The significant finding in this study could be relatively good performance in the treatment of amoxicillin while the system did not experience stability in the degradation of Co-amoxiclav; this might be due to long time use of amoxicillin and the environmental adaptation to this antibiotic. The ratio of food to microorganism (F/M) was found to be the most important factor for process control. A decrease in the F/M ratio to values less than 0.4 g CODin /g VSS d decreased the COD removal efficiency. A maximum removal efficiency (89%) was determined at a MLVSS and a CODin of 4800 and 2000 mg/L, respectively. As a conclusion, compartmentalization is an economic and effective solution to reduce the required reactor volume (especially for the wastewaters with high and slowly biodegradable COD). References [1] S.B. Levy, Factors impacting on the problem of antibiotic resistance, J. Antimicrob. Chemother. 49 (2002) 25–30. [2] J.C. Chee-Sanford, N. Garrigues-Jeanjean, R.I. Aminov, I.J. Krapac, R.I. Mackie, Occurrence and diversity of tetracycline resistance genes in lagoons and groundwater underlying two swine production facilities, Appl. Environ. Microbiol. 67 (2001) 1494–1502. [3] G.A. Jacoby, L.S. Munoz-Price, The new ␤-lactamases, N. Engl. J. Med. 352 (2005) 380–391. [4] A.J. Watkinson, E.J. Murbyc, S.D. Costanzo, Removal of antibiotics in conventional and advanced wastewater treatment: implications for environmental discharge and wastewater recycling, Water Res. 41 (18) (2007) 4164–4176. [5] A.N. Morse, Fate and effect of amoxicillin in space and terrestrial water reclamation systems, PhD Thesis, in: Texas University of Technology, 2003, pp. 11–17. [6] H. Yuan, F. Hu, C. Hu, X. Wie, D. Chen, Y. Qu, Photodegradation and toxicity changes of antibiotics in UV and UV/H2 O2 process, J. Hazard. Mater. 185 (2011) 1256–1263.

33

[7] J. Kosutic, K. Dolar, D. Asperger, D. Kunst, Removal of antibiotics from a model wastewater by RO/NF membranes, Sep. Purif. Technol. 53 (2007) 244–249. [8] E.S. Elmolla, M. Chaudhuri, The feasibility of using combined TiO2 photocatalysis-SBR process for antibiotic wastewater treatment, Desalination 272 (2011) 218–224. [9] E.K. Putra, R. Pranowo, J. Sunarso, N. Indraswati, S. Ismadji, Performance of activated carbon and bentonite for adsorption of amoxicillin from wastewater: mechanisms, isotherms and kinetics, Water Res. 43 (2009) 2419–2430. [10] E. Zuccato, S. Castiglioni, R. Bagnati, M. Melis, R. Fanelli, Source, occurrence and fate of antibiotics in the Italian aquatic environment, J. Hazard. Mater. 179 (2010) 1042–1048. [11] G. Zhang, S. Ji, B. Xi, Feasibility study of treatment of amoxicillin wastewater with a combination of extraction, Fenton oxidation and reverse osmosis, Desalination 196 (2006) 32–42. [12] C. Adams, M. Asce, Y. Wang, K. Loftin, M. Meyer, Removal of antibiotics from surface and distilled water in conventional water treatment processes, J. Environ. Eng. 128 (2002) 253–260. [13] A. Gobel, C.S. McArdell, A. Joss, H. Siegrist, W. Giger, Fate of sulfonamides, macrolides, and trimethoprim in different wastewater treatment technologies, Sci. Total Environ. 372 (2007) 361–371. [14] P.E. Stackelberg, J. Gibs, E.T. Furlong, M.T. Meyer, S.D. Zaugg, R.L. Lippincott, Efficiency of conventional drinking-water-treatment processes in removal of pharmaceuticals and other organic compounds, Sci. Total Environ. 377 (2007) 255–272. [15] N.M. Vieno, H. Hrkki, T. Tuhkanen, L. Kronberg, Occurrence of pharmaceuticals in river water and their elimination in a pilot-scale drinking water treatment plant, Environ. Sci. Technol. 41 (2007) 5077–5084. [16] O.A. Arikan, Degradation and metabolization of chlortetracycline during the anaerobic digestion of manure from medicated calves, J. Hazard. Mater. 158 (2008) 485–490. [17] E.S. Elmolla, M. Chaudhuri, Combined photo-Fenton-SBR process for antibiotic wastewater treatment, J. Hazard. Mater. 192 (2011) 1418–1426. [18] S. Behera, H. Kim, J. Oh, H. Park, Occurrence and removal of antibiotics, hormones and several other pharmaceuticals in wastewater treatment plants of the largest industrial city of Korea, Sci. Total Environ. 409 (2011) 4351–4360. [19] E.S. Elmolla, M. Chaudhuri, The feasibility of using combined Fenton-SBR for antibiotic wastewater treatment, Desalination 285 (2012) 203–212. [20] E.S. Elmolla, M. Chaudhuri, Photocatalytic degradation of amoxicillin, ampicillin and cloxacillin antibiotics in aqueous solution using UV/TiO2 and UV/H2 O2 /TiO2 photocatalysis, Desalination 252 (2010) 46–52. [21] I. Michael, L. Rizzo, C.S. McArdell, C.M. Manaia, C. Merlin, T. Schwartz, C. Dagot, D. Fatta-Kassinos, Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review, Water Res. 47 (2012) 957–995. [22] J.M. Britto, M.C. Rangel, Processes advanced de oxidation de composite fenolicosem effluents industrials, Quim. Nova 31 (2008) 114–122. [23] A.A.L. Zinatizadeh, H. Bashiri, A. Asadi, H. Bonakdari, Comparison of different fluid dynamics in activated sludge system for the treatment of a stimulated milk processing wastewater: process analysis and optimization, Korean J. Chem. Eng. 29 (10) (2012) 1352–1361. [24] O. Levenspiel, Chemical Reaction Engineering, John Wiley & Sons, New York, 2003. [25] W.W. Eckenfelder Jr., Industrial Water Control, third ed., McGraw-Hill, Boston, 2000. [26] A. Akhbari, A.A.L. Zinatizadeh, P. Mohammadi, Y. Mansouri, M. Irandoust, M.H. Isa, Kinetic modeling of carbon and nutrients removal in an integrated rotating biological contactor-activated sludge system, Int. J. Environ. Sci. Technol. 9 (2012) 371–378. [27] M. Hijosa-Valsero, G. Fink, M. Schlusener, R. Sidrach-Cardona, J. MartinVillacorta, E. ThomasBecares, Removal of antibiotics from urban wastewater by constructed wetland optimization, Chemosphere 83 (2011) 713–719. [28] G. Tchobanoglous, F. Burton, H.D. Stensel, Wastewater Engineering: Treatment and Reuse, McGraw-Hill, New York, 2003. [29] H.Q. Yu, F. Wilson, J.H. Tay, Kinetic analysis of an anaerobic filter treating soybean wastewater, Water Res. 32 (1998) 3341–3352. [30] A. Pala, O. Bolukba, Evaluation of kinetic parameters for biological CNP removal from a municipal wastewater through batch tests, Process Biochem. 40 (2005) 629–635. [31] M. Pirsaheb, A.R. Mesdaghin, S.J. Shahtaheri, A.A.L. Zinatizadeh, Kinetic evaluation and process performance of a fixed film bioreactor removing phthalic acid and dimethyl phthalate, J. Hazard. Mater. 167 (2009) 500–506. [32] M. Isik, T. Sponza, Substrate removal kinetics in an upflow anaerobic sludge blanket reactor decolorising simulated textile wastewater, Process Biochem. 40 (2005) 1189–1198. [33] S.G. Pavlostathis, E. Giraldo-Gomez, Kinetics of anaerobic treatment, Water Sci. Technol. 8 (1991) 35–59. [34] A. Asadi, High-rate biological treatment of Faraman estate industrial wastewater in an up-flow aerobic/anoxic sludge bed bioreactor (master thesis), Razi, Kermanshah, Iran, 2012. [35] A. Akhbari, A.A.L. Zinatizadeh, P. Mohammadi, Y. Mansouri, M. Irandoust, Process modeling and analysis of biological nutrients removal in an integrated RBC-AS system using response surface methodology, Chem. Eng. J. 168 (2011) 203–212.