Optimization of baker's yeast wastewater using response surface methodology by electrocoagulation

Desalination 286 (2012) 200–209

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Optimization of baker's yeast wastewater using response surface methodology by electrocoagulation Erhan Gengec a, Mehmet Kobya b,⁎, Erhan Demirbas c, Abdurrahman Akyol b, Kadriye Oktor a a b c

University of Kocaeli, Department of Environmental Protection, 41275, Izmit, Kocaeli, Turkey Gebze Institute of Technology, Department of Environmental Engineering, 41400, Gebze, Turkey Gebze Institute of Technology, Department of Chemistry, 41400, Gebze, Turkey

a r t i c l e

i n f o

Article history: Received 20 July 2011 Received in revised form 5 November 2011 Accepted 7 November 2011 Available online 2 December 2011 Keywords: Electrocoagulation Baker's yeast wastewater Response surface methodology Optimization Operating cost

a b s t r a c t In the present paper, electrocoagulation (EC) was employed for removals of color, chemical oxygen demand (COD) and total organic carbon (TOC) from baker' s yeast effluents (BYEs) in a batch EC reactor using aluminium electrodes. Optimizations of anaerobic (AE) and anaerobic–aerobic effluents (AAE) were carried out by response surface methodology to describe interactive effects of the three main process independent parameters (initial pHi, current density and operating time) on removal efficiencies of color, COD and TOC. The responses were related to maximize color, COD and TOC removal efficiencies and to minimize operating cost in the EC process. The quadratic model fitted very well with the experimental data. R2 correlation coefficients (> 95%) for the removal efficiencies showed a high significance of the model. The maximum color, COD and TOC were 88%, 48% and 49% at 80 A/m 2, pHi 4 and 30 min for AE and 86%, 49% and 43% at 12.5 A/m 2, pHi 5 and 30 min for AAE, respectively. The operating costs for AE and AAE at the optimized conditions were 0.418 €/m 3 and 0.076 €/m 3. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The industrial production of baker's yeast by fermentation that generally uses molasses as the raw materials includes operations and processes such as molasses preparation, fermentation, and separation and drying of yeast and produces a large quantity of wastewater [1,2]. There are basically two types of wastewaters; one is high strength process wastewater that originates from yeast separators and processes such as centrifuges and rotary vacuum filters, and the other one is low-medium strength process wastewater that originates from floor washing and equipment cleaning [3]. The baker's yeast wastewaters (BYW) are characterized by extremely high chemical oxygen demand (COD, 80,000–100,000 mg/L) and biochemical oxygen demand (BOD, 40,000–50,000 mg/L), apart from low pH 4–5, strong odor and a large amount of dark brown color [1,4]. The dark brown color in these effluents is imparted by the pigment called melanoidins which is refractory in nature to the biological treatment and resistance to biodegradation, not disappear and can even increase due to re-polymerization of colorants [5,6]. The dark brown color from baker's yeast effluents (BYEs) interferes with the absorption of sunlight which reduces the natural process of photochemical reactions for self-purification of the surface waters.

⁎ Corresponding author. Tel.: + 90 262 6053214; fax: + 90 262 6538490. E-mail address: [email protected] (M. Kobya). 0011-9164/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2011.11.023

Various treatment methods like biological process, viz. anaerobic, aerobic, physico-chemical treatment, viz. adsorption, membrane process, reverse osmosis, coagulation/flocculation and oxidation processes, viz. ozone, Fenton have been attempted for the treatment of BYEs. Current biological treatment of BYEs involves combinations of anaerobic digestion and aerobic systems that successfully reduce BOD to acceptable limits, but does not deal effectively with either the dark color or the associated COD that remains and limits the reuse/recycling of the process water [6–8]. Moreover, anaerobic treatment is a slow process and typically requires long start-up periods. Treatment by oxidation technologies is generally effective on the color but not COD, membrane filtration processes are prone to fouling [2,9], and reverse osmosis generates a high salinity retentate that presents disposal difficulties [10]. Chemical coagulation and adsorption remove color and COD relatively effectively, but has a number of drawbacks such as requirements for high amount of inorganic coagulants, regeneration of adsorbent, cost limitation on a large scale, etc. [11]. Decolorization through chemical treatment with ozone [12,13], Fenton's reagent [14] and H2O2/UV [15] leads to temporary color reduction because of transformation of the chromospheres groups therefore, these are not preferred solutions. As a result, the above treatment processes are disadvantaged for instance, high equipment and operational cost, less effective decolorization rate and simultaneous generation of hazardous secondary pollutants in treated effluents. For these reasons, the BYEs require an alternative treatment technologies in terms of relatively simple and effective in removals of color, COD and total organic carbon (TOC) before its safe disposal into the environment [7].

E. Gengec et al. / Desalination 286 (2012) 200–209

In recent years, electrocoagulation (EC) has the potential to extensively eliminate the disadvantages of the classical treatment techniques and to achieve a sustainable and economic treatment of agro industrial wastewater [16–24]. The EC process is characterized by simple and easy operated equipment, short operation time, no addition of chemicals and low sludge production. Moreover, advantages of the EC compared to conventional chemical coagulation include reduced wastewater acidification and salinity, low dosage of coagulant, superior coagulant dispersion and intrinsic electroflotation separation capability. Iron (Fe) or aluminium (Al) is generally employed as a sacrificial electrode material in the EC process. In an EC process, the coagulating ions are produced in situ involving three successive stages: (i) formation of coagulants by electrolytic oxidation of the sacrificial electrode such as Fe or Al, (ii) destabilization of the contaminants, particulate suspension and breaking of emulsions and (iii) aggregation of the destabilized phases to form flocs. Fe/Al gets dissolved from the anode generating corresponding metal ions, which almost immediately hydrolyze to polymeric iron or aluminium oxyhydroxides. These polymeric oxyhydroxides are excellent coagulating agents. When aluminium electrodes in the EC process are used as an anode and a cathode, the main reactions are at the anode as follows: Al→Al

3þ

þ 3e

−

ð1Þ

Also, oxygen evolution can compete with aluminium dissolution at the anode via following reaction: þ

−

2H2 O→O2ðgÞ þ 4H þ 4e

ð2Þ

At cathode, hydrogen evolution takes place via the following reaction. It helps in floatation of the flocculated particles out of the water [19]. −

3

3H 2 O þ 3e → =2 H 2 þ 3OH

−

ð3Þ

At high pH values, OH − generated at the cathode during hydrogen evolution may attack the cathode by the following reaction [26]. −

−

2Al þ 6H2 O þ 2OH →2AlðOHÞ4 þ 3H2ðgÞ

ð4Þ

Al3+ and hydroxyl ions generated by electrode reactions: Eqs. (1) and (4) to form various monomoric-polymeric species such as Al (OH) 2+, Al(OH)+2, Al(OH) −4, Al2(OH)4+2, Al6(OH)3+15, Al7(OH)4+17, Al8(OH)4+20, Al13O4(OH)7+24, and Al13(OH)5+34 transformed initially into Al(OH)3(s) and finally polymerized to Aln(OH)3n (Eqs. (5) and (6)) in the solution [16,19,25]: nAlðOHÞ3 →Aln ðOHÞ3n

ð5Þ

Table 1 Characterizations of effluents from the treatment plant of the BYW.

Table 2 Process factors and their levels for AE. Factor

3þ

Range of actual and coded variables

initial pH current density (A/m2) operating time (min)

x1 x2 x3

Al

Variables

þ 3H2 O→AlðOHÞ3ðsÞ þ 3H

−2

−1

0

+1

+2

3.32 39.77 3.18

4.00 50.00 10.00

5.00 65.00 20.00

6.00 80.00 30.00

6.68 90.23 36.82

þ

ð6Þ

The concentration of hydrolyzed aluminium species depends on the aluminium concentration and the solution pH. The hydrolysis constants for aluminium cover a very narrower range, and all of the aluminium deprotonations are ‘squeezed’ into an interval of less than 2 pH unit. Therefore, apart from a narrow pH region approximately 5–6, the dominant soluble species are Al 3+ and Al(OH) −4 at low pH and high pH, respectively [25]. COD and TOC successfully reduced with anaerobic and aerobic treatment processes from BYW, but there are no considerable removal efficiencies of color from these effluents. The novelty of this study is to use the EC process using Al electrodes for the first time to both treat anaerobic (AE) and anaerobic–aerobic (AAE) effluents to achieve the higher removal percentage of color as well as COD and TOC to acceptable discharge limits and optimize using response surface methodology (RSM). Optimizations of AE and AAE were carried out by the RSM which was used to develop a mathematical model to describe the effects and relationships of the main process independent variables (initial pHi, current density and operating time), to maximize color, COD and TOC removal efficiencies and to minimize operating cost in relation to energy and electrode consumptions. 2. Materials and methods 2.1. Characterizations of AE and AAE A manufacturing factory producing of baker's yeast located in Kocaeli, Turkey has a full scale two-stage anaerobic and aerobic biological treatment plant. The inlet flow rate of the plant is 1050 m 3/ day. The high strength wastewaters are treated initially in the anaerobic stage and then are combined with the low strength wastewaters to be treated in the aerobic stage. The total COD removals of the twostage treatment plant are in the range 80–90%, but the color removal efficiency is very low. Two wastewater samples collected from AE and AAE were characterized (Table 1) and kept in refrigerators at 4 °C before use. 2.2. Experimental set-up and procedure

Parameters

Anaerobic Anaerobic treated Aerobic treated treated influent effluent (AE) effluent (AAE)

pH Temperature (°C) Conductivity (mS/cm) TS (g/L) TVS (g/L) TSS (g/L) Alkalinity (mg/L) COD (mg/L) TOC (mg/L) Color (Abs475nm/cm) Absorbance (Abs254nm/cm) TKN (mg/L) NH3-N (mg/L) TP (mg/L) PO4-P (mg/L)

5.6 24 17.24 22.2 10.3 1.01 750 27920 6090 2.50 2.92 648 560 17.5 9.75

7.2 30 17.42 15.2 4.5 5.08 4130 2160 919 1.91 2.78 421 297 12.8 9.72

201

7.7 25 7.46 5.1 0.72 0.04 1050 544 184 0.66 2.72 39 19 2.05 1.79

The EC treatment was carried out in a batch electrolytic reactor made from Plexiglas material with dimensions of 100 mm × 100 mm × 130 mm. Four aluminium electrodes in the reactor were used as cathodes and anodes with effective dimensions of

Table 3 Process factors and their levels for AAE. Factor

x1 x2 x3

Variables

initial pH current density (A/m2) operating time (min)

Range of actual and coded variables −2

−1

0

+1

+2

4.32 2.44 3.18

5.00 5.00 10.00

6.00 8.75 20.00

7.00 12.5 30.00

7.68 15.06 36.82

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E. Gengec et al. / Desalination 286 (2012) 200–209

Table 4 CCD of the experimental variables for AE. Variables x1: pHi

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Removal efficiency x2: CD 2

x3: tEC

Re,colour

Re,COD

Consumptions and costs Re,TOC

ENC

ELC 3

OC 3

Wsludge 3

pHf

3

(−)

(A/m )

(min)

(%)

(%)

(%)

(kWh/m )

(kg/m )

(€/m )

(kg/m )

6.00 4.00 5.00 4.00 5.00 6.00 5.00 5.00 5.00 5.00 4.00 5.00 5.00 6.68 3.32 6.00 5.00 5.00 6.00 4.00

50.0 50.0 90.2 80.0 39.8 80.0 65.0 65.0 65.0 65.0 50.0 65.0 65.0 65.0 65.0 80.0 65.0 65.0 50.0 80.0

10.00 30.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 36.82 20.00 20.00 30.00 3.18 20.00 30.00 30.00

42 85 85 78 72 61 80 80 80 79 66 80 82 61 80 78 41 79 68 88

22 44 43 40 35 25 41 38 36 35 34 36 40 24 43 32 20 36 28 48

22 40 43 41 34 22 41 40 37 37 33 37 38 26 43 36 16 35 27 49

0.725 1.665 2.414 1.028 0.788 0.952 1.294 1.372 1.528 1.534 0.677 1.432 3.255 1.326 1.456 3.180 0.297 1.432 1.800 3.600

0.099 0.289 0.359 0.161 0.146 0.152 0.251 0.256 0.256 0.261 0.099 0.256 0.480 0.253 0.258 0.459 0.041 0.256 0.319 0.474

0.216 0.595 0.766 0.339 0.297 0.319 0.508 0.520 0.532 0.542 0.212 0.526 1.026 0.512 0.531 0.987 0.090 0.526 0.644 1.040

1.052 1.602 1.865 1.844 1.194 1.067 1.608 1.394 1.675 1.641 1.027 1.550 2.082 1.445 1.574 2.121 1.123 1.550 1.657 2.359

6.24 5.18 5.84 4.91 5.52 6.31 5.70 5.73 5.70 5.68 4.86 5.72 5.99 7.02 4.89 6.64 5.25 5.71 6.54 5.55

remove any solid residues on the surfaces, dried and reweighed. All experiments were performed at 25 °C.

80 mm × 50 mm × 3 mm. Total effective areas of electrodes were 240 cm 2. The electrodes were situated 5 mm apart from each other and connected to monopolar parallel connection mode. Before each run, the electrodes were dipped into solutions of HCl (35%) and hexamethylenetetramine (2.8%) to remove the oxide and/or passivation layers from the electrodes [16]. The electrodes were placed in the reactor and solutions were mixed at 300 rpm in the EC reactor. In each run, 0.8 L sample was placed into the EC reactor. The current density (CD) was adjusted by a digital DC power supply (TDK-Lambda Genesys model; 50V-30A) operated at galvanostatic mode and the experiment was started. At the end of the run, the electrodes were washed thoroughly with water to

2.3. Analytical method The samples taken from the EC reactor at different operating times were filtered using a 1.6 μm Whatman glass microfiber filter. Total COD and TOC were measured according to standard methods [26]. COD was measured by closed reflux titrimetric method and TOC levels were determined through combustion of the samples at 680 °C using a non-dispersive IR source (Tekmar Dohrmann Apollo 9000). pH of sample was adjusted with H2SO4 or NaOH and measured

Table 5 CCD of the experimental variables for AAE. Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Removal efficiency

Consumptions and costs

x1: pHi

x2: CD

x3: tEC

Re,color

Re,COD

Re,TOC

ENC

ELC

OC

Wsludge

(−)

(A/m2)

(min)

(%)

(%)

(%)

(kWh/m3)

(kg/m3)

(€/m3)

(kg/m3)

4.32 6.00 5.00 7.00 6.00 6.00 7.00 7.00 7.68 6.00 6.00 6.00 6.00 6.00 7.00 5.00 5.00 6.00 6.00 5.00

8.8 2.4 12.5 12.5 8.8 8.8 5.0 12.5 8.8 15.1 8.8 8.8 8.8 8.8 5.0 5.0 12.5 8.0 8.8 5.0

20.00 20.00 30.00 30.00 3.18 20.00 30.00 10.00 20.00 20.00 20.00 36.82 20.00 20.00 10.00 10.00 10.00 20.00 20.00 30.00

75 19 86 55 18 55 22 22 25 75 57 72 61 56 8 50 71 57 53 60

38 4 49 27 1 24 10 4 10 30 21 30 18 21 4 12 29 25 23 28

30 21 43 27 10 23 12 12 10 36 21 26 22 21 5 12 29 25 23 31

0.145 0.030 0.301 0.337 0.026 0.142 0.095 0.095 0.121 0.303 0.137 0.222 0.132 0.137 0.046 0.039 0.103 0.130 0,142 0,096

0.030 0.009 0.068 0.069 0.010 0.030 0.030 0.038 0.030 0.064 0.033 0.056 0.033 0.033 0.010 0.011 0.032 0.034 0,034 0,028

0.060 0.017 0.134 0.138 0.018 0.060 0.056 0.069 0.057 0.127 0.064 0.109 0.064 0.064 0.021 0.021 0.059 0.065 0,066 0,053

0.219 0.144 0.257 0.332 0.066 0.188 0.283 0.145 0.201 0.263 0.214 0.346 0.230 0.214 0.052 0.161 0.215 0.195 0,243 0,273

pHf

5.02 6.12 5.46 7.30 6.14 6.18 7.39 7.24 7.90 6.21 6.17 6.27 6.16 6.18 7.15 5.27 5.22 6.18 6.17 5.40

E. Gengec et al. / Desalination 286 (2012) 200–209

203

Table 6 Observed (actual) and predicted values of responses for AE and AAE. Anaerobic effluent

Anaerobic–aerobic effluent

Run

Re,color(%) Pre.

Act.

Pre.

Act.

Pre.

Act.

Pre.

Act.

Pre.

Act.

Pre.

Act.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

42.00 85.00 85.00 78.00 72.00 61.00 80.00 80.00 80.00 79.00 66.00 80.00 82.00 61.00 80.00 78.00 41.00 79.00 68.00 88.00

42.07 85.35 87.27 74.36 71.04 59.72 79.63 79.63 79.63 79.63 63.71 79.63 79.51 58.85 83.46 79.36 44.80 79.63 70.72 87.00

22.00 44.00 43.00 40.00 35.00 25.00 41.00 38.00 36.00 35.00 34.00 36.00 40.00 24.00 43.00 32.00 20.00 36.00 28.00 48.00

21.33 43.61 42.82 38.1 35.23 25.29 37.00 37.00 37.00 37.00 32.65 37.00 38.11 22.37 44.77 33.25 22.11 37.00 29.79 48.57

22.00 40.00 43.00 41.00 34.00 22.00 41.00 40.00 37.00 37.00 33.00 37.00 38.00 26.00 43.00 36.00 16.00 35.00 27.00 49.00

20.82 39.11 43.96 37.81 33.82 22.34 37.81 37.81 37.81 37.81 32.21 37.81 36.13 24.47 45.31 36.24 18.65 37.81 29.72 49.63

75.00 19.00 86.00 55.00 18.00 55.00 22.00 22.00 25.00 75.00 57.00 72.00 61.00 56.00 8.00 50.00 71.00 57.00 53.00 60.00

80.04 23.81 90.59 60.34 24.93 56.50 26.78 24.99 19.93 70.16 56.50 65.04 56.50 56.50 3.44 44.68 66.24 56.50 56.05 57.03

38.00 4.00 49.00 27.00 1.00 24.00 10.00 4.00 10.00 30.00 21.00 30.00 18.00 21.00 4.00 12.00 29.00 25.00 23.00 28.00

39.79 5.52 50.16 25.57 2.47 21.94 11.12 5.41 10.19 30.14 21.94 30.49 21.94 21.94 1.46 12.04 26.50 21.94 21.94 25.20

30.00 21.00 43.00 27.00 10.00 23.00 12.00 12.00 10.00 36.00 21.00 26.00 22.00 21.00 5.00 12.00 29.00 25.00 23.00 31.00

30.97 18.68 42.67 24.60 7.48 22.53 14.51 14.61 8.16 37.45 22.53 27.65 22.53 22.53 5.95 15.01 27.18 22.53 22.53 29.00

Re,COD(%)

Re,TOC(%)

Re,colour(%)

Re,COD(%)

Re,TOC(%)

by pH meter (WTW Inolab pH 720). Color contents in the AE and AAE were measured using a UV–vis spectrophotometer at 475 nm (Perkin-Elmer 550 SE) [27]. Amount of sludge produced after the EC process was determined after the sludge was placed in an oven at 105 °C for 24 h.

model by least squares technique. RSM makes it possible to represent independent process parameters in quantitative form as:

2.4. Experimental design and data analysis

where y is the response (yield), f is the response function, ε is the experimental error and x1, x2, x3,….,xn are independent parameters. By plotting the expected response of y, a surface known as the response surface is obtained. A higher order polynomial such as the quadratic model (Eq. (8)) was used in this study. Analysis of variance (ANOVA) was used to obtain the interaction between the process variables and the responses. The quality of the fit polynomial model was expressed by R 2, and its statistical significance was checked by the Fisher F-test in the same program. Model terms were evaluated by the P value (probability) with 95% confidence level.

The Design Expert 8.0.4 software (trial version) was used for the statistical design of experiments and data analysis. The three most important operating variables: initial wastewater pH (x1), current density (x2) and operating time (x3) were optimized for both wastewaters. Their range and levels were shown in Tables 2 and 3 for AE and AAE, respectively. The independent variables' ranges and levels were determined from preliminary experiments [28–30]. RSM is a collection of mathematical and statistical techniques, commonly used for improving and optimizing processes. It can be used to evaluate the relative significance of several affecting factors in the presence of complex interactions [29–33]. RSM uses an experimental design such as the Central Composite Design (CCD) to fit a

y ¼ f ðx1 ; x2 ; x3 ;…; xn Þ  ε

ð7Þ

2

2

2

y ¼ β0 þ β1 x1 þ β2 x2 þ β3 x3 þ β11 x1 þ β22 x2 þ β33 x3 þ β12 x1 x2 þ β13 x1 x3 þ β23 x2 x3

ð8Þ

Table 7 Coefficients of the response functions. Responses

β0

β1

Anaerobic effluent (AE) Re,Colour (%) 79.63 − 7.32 Re,COD (%) 37.00 − 6.66 Re,TOC (%) 37.81 − 6.19 ENC (kWh/m3) 1.43 − 0.039 ELC (kg/m3) 0.26 − 5.86 × 10− 4 3 OC (€/m ) 0.53 − 3.74 × 10− 3 Wsludge (kg/m3) 1.57 − 0.085 Anaerobic–aerobic effluent (AAE) Re,Colour (%) 56.50 − 17.87 Re,COD (%) 21.94 − 8.79 Re,TOC (%) 22.52 − 6.78 3 ENC (kWh/m ) 0.14 − 6.19 × 10− 4 ELC (kg/m3) 0.033 5.09 × 10− 4 OC (€/m3) 0.064 7.9 × 10− 4 Wsludge (kg/m3) 0.21 − 9.1 × 10− 3

β2

β3

β11

β22

β33

β12

β13

β23

4.82 2.23 3.01 0.49 0.059 0.13 0.23

10.32 4.73 5.20 0.87 0.13 0.27 0.32

− 3.00 − 1.21 − 1.03 0.0077 – − 8 × 10− 5 –

− 0.17 0.73 0.38 0.082 – 3.48 × 10− 3 –

− 6.18 − 2.45 − 3.68 0.14 – 0.013 –

1.75 − 0.37 − 1.00 − 0.085 − 6.10− 3 − 0.016 − 0.14

1.75 − 0.62 0.50 − 0.032 0.0023 1.46 × 10− 3 0.071

− 2.25 − 0.12 1.25 0.34 0.027 0.07 0.049

13.78 7.23 5.58 0.075 0.016 0.032 0.028

11.92 8.33 6 0.064 0.013 0.027 0.076

− 2.30 1.07 − 1.05 − 0.0018 − 5.49 × 10− 4 − 1.04 × 10− 3 –

− 3.36 − 1.40 1.96 0.01 1.79 × 10− 3 3.68 × 10− 3 –

− 4.07 − 1.93 − 1.75 0.0049 6.6 × 10− 4 7.5 × 10− 4 –

0.001 − 2.62 − 0.87 2.78 × 10− 3 6.72 × 10− 4 1.3 × 10− 3 0.013

2.75 − 0.87 − 1.37 4.44 × 10− 3 − 3.28 × 10− 4 − 2.2 × 10− 4 0.033

3.00 2.63 0.38 0.042 3.9 × 10− 3 9.4 × 10− 3 − 0.014

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E. Gengec et al. / Desalination 286 (2012) 200–209

Table 8 ANOVA results for the EC process applied to AE. Response

R2

Adj. R2

SD

CV

PRESS

F-value

Prob > F

AP

Re,color (%) Re,COD (%) Re,TOC (%) ENC (kWh/m3) ELC (kg/m3) Wsludge (kg/m3)

0.98 0.96 0.95 0.99 0.99 0.95

0.96 0.92 0.91 0.99 0.99 0.92

2.74 2.22 2.56 0.10 b 0.01 0.10

3.74 6.36 7.34 6.37 2.28 6.64

562 230 356 0.55 b 0.10 0.55

47.81 25 21 162 1369 37

b0.0001 b0.0001 b0.0001 b0.0001 b0.0001 b0.0001

23.3 17.3 17.1 43.9 126.2 20.9

SD: Standard Deviation, CV: Coefficient of Variance, PRESS: Predicted Residual Error sum of squares, AP: Adequate Precision, P: Probability of Error.

The predicted response (y) is therefore correlated to the set of regression coefficients (β): the intercept (β0), linear β1, β2, β3), interaction (β12, β13, β23) and quadratic coefficients (β11, β22, β33). 2.5. Operating cost The operating cost (OC) of the EC process includes material, mainly electrodes and electrical energy costs, as well as labour, maintenance, sludge dewatering and disposal, and fixed costs. The latter cost items are largely independent of the type of the electrode material [34,35]. In this study, energy and electrode material costs were taken into account as major cost items in the calculation of the OC (€/m 3): OC ¼ aENC þ bELC þ cCC

ð9Þ

where ENC is energy consumption (kWh/m 3) and ELC is electrode consumption (kg/m 3). Prices provided in the Turkish market in June 2011 for a and b were 0.072 €/kWh for electrical energy and 1.65 €/ kg for Al electrode material. c stands for chemical consumption (CC, kg/m 3) such as NaOH (0.73 €/kg) and H2SO4 (0.29 €/kg) for adjustment of a desired pH. Costs for electrical energy (kWh/m 3) in Eq. (10) and electrode consumptions (kg/m 3) were calculated from Faraday's Law in Eq. (11): ENC ¼

U  i  t EC v

ð10Þ

ELC ¼

i  t EC  Mw zF v

ð11Þ

where U is cell voltage (V), i is current (A), tEC is operating time (s) and v is volume (m 3) of the wastewater, Mw is molecular mass of aluminium (26.98 g/mol), zAl is number of electron transferred (zAl = 3,) and F is Faraday's constant (96487 C/mol). 3. Results and discussion 3.1. Statistical analysis The actual designs of this work for AE and AAE were presented in Tables 4 and 5. The CCD in the form of a 2 3 full factorial design with six additional experimental trials as replicates of the central point

Table 9 ANOVA results for the EC process applied to AAE. Response

R2

Adj. R2

SD

CV

PRESS

F-value

Prob > F

AP

Re,color (%) Re,COD (%) Re,TOC (%) ENC (kWh/m3) ELC (kg/m3) Wsludge (kg/m3)

0.96 0.98 0.96 0.99 0.99 0.97

0.92 0.96 0.93 0.98 0.99 0.95

6.26 2.62 2.61 0.01 b 0.01 0.02

12.56 12.79 11.91 9.19 6.08 7.96

2796 352 481 0.01 b0.01 b0.01

27 48 28 101 159 60

b 0.0001 b 0.0001 b 0.0001 b 0.0001 b 0.0001 b 0.0001

19.7 26.4 19.9 34.6 40.9 30.7

showed for both of wastewaters in these tables. The effects of x1, x2 and x3 investigated on color, COD, TOC removal efficiencies and amounts of ENC, ELC, OC and Wsludge were determined using approximating functions. Observed (actual) and predicted values of responses, and coefficients of the response functions for AE and AA were shown in Tables 6 and 7. The R 2 coefficient gave the proportion of the total variation in the response variable accounted for the predictors (x's) included in the model. A high R 2 value, close to 1, was desirable and a reasonable agreement with the quadratic model to the experimental data (Tables 4 and 5). The RSM was used for analyzing the relationship of three variables (initial pH, current density and operating time) and process responses such as color, COD, and TOC removal efficiencies, amounts of electrode (ELC) and energy (ENC) consumptions, and amount of sludge (Wsludge). Adequacy of the RSM was justified through ANOVA and the results were shown in Tables 8 and 9. The F-statistics values were higher and its values for AAE and AE were 27–48 for colour, 48–25 for COD and 28–21 for TOC removals, respectively. The large F-values indicated that most of the variation in the response could be explained by the regression model. The Prob > F values for the AAE and the AE from the ANOVA was less than 0.05 showed that the model was considered to be statistically significant. In this study, values of Prob > F were b0.0001 for both of the models. The coefficient of variance (CV) as the ratio of the standard error of estimate to the mean value of the observed response (as a percentage) was a measure of reproducibility of the model. CV was considered to be reproducible when it is not greater than 10%. As shown in Tables 8 and 9, values of CV for AE and AAE were 3.4– 12.6% for color, 6.4–12.8% for COD and 7.3–11.9% for TOC removals. The adequate precision (AP) measures the signal to noise ratio and AP was compared for the range of the predicted values at the design points to the average prediction error. The AP values greater than 4 indicated an adequate signal for all responses of AAE and AE (Tables 8 and 9). Therefore, the quadratic model could be used to navigate the design space. A higher value of R 2 (>0.95 for all responses in AE and AAE) showed that the model could explain the response successfully (Fig. 1). Actual values were the measured response data for a particular run, and the predicted values were evaluated from the model and generated by using the approximating functions. It was seen in Fig. 1 that the data points lay close to the diagonal line and the developed model was adequate for the prediction of each response. 3.2. Effect of variables on the color, COD and TOC removals The effects of x1, x2 and x3 were investigated on color, COD, TOC removal efficiencies and amounts of OC were determined using approximating functions. A quadratic model (Eq. (8)) fitted very well with the experimental data and estimated coefficients of functions are given in Table 7 for AE and for AAE. 3D surface plots for AE and AAE in Figs. 2 and 3 were shown to visualize the effects of experimental factors on removal efficiencies of color and COD responses. As seen from Figs. 2 and 3 and Tables 4 and 5, color, COD and TOC removal efficiencies showed the similar removal trends with experiments results. The maximum removal of colour, COD and TOC were presented in Tables 4 and 5 as 88%, 48% and 49% at 80 A/m 2, pHi 4 and 30 min for AE and 86%, 49% and 43% at 12.5 A/m 2, pHi 5 and 30 min for AAE, respectively. It is wellknown that current density and operation time not only determine the coagulant rate but also the bubble production rate, size and the flocs growth which can influence the treatment efficiency and operating cost of the EC process. The effect of current density shown in Figs. 2–4 was studied in the range 39.8–90.2 A/m 2 for AE and 2.4– 15.1 A/m 2 for AAE, respectively. The maximum color, COD and TOC removal efficiencies were obtained at 80 A/m 2 for AE (run 20) and 12.5 A/m 2 for AAE (run 3). On the other hand, increasing of the

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205

Fig. 1. Comparisons of predicted and experimental values for AE (a–c) and AAE (d–f).

operation time showed also the same trend. When all factors held constant (runs 7, 13 and 17) in Table 4 expect for the operating time, the color removal efficiencies for AE were 41%, 80% and 82% for 3.2, 20.0 and 36.8 min of the operation time and runs 5, 6 and 12 in Table 5 showed the removal efficiencies of color for AAE as 18%, 55% and 72% for 3.18, 20 and 36.82 min of the operation time, respectively. The high current density and operation time led to increase in color, COD and TOC removal efficiencies because the high current density and the operating time in the EC process increased the amount of metal species formed by dissolution of the anode calculated from Faraday's law (Eq. (11)). Higher removal efficiency was realized by a greater amount of dissolved Al3 + during the EC process. ENC was closely related to over potential and time as described in Eq. (10). Perturbation plots of ELC and ENC showed that there was no significant effect of initial pH on energy and electrode consumptions (Fig. 4). When compared to traditional chemical treatments, the EC has some advantages since less coagulant is required and less sludge is formed. Nevertheless amount of sludge produced during the EC process is an environmental problem of solid waste generation and an economical problem due to the high disposal costs. In the EC process, amount of sludge production depended on removal efficiency of pollutants and metal ions dissolution at electrode and its species in solution. Generally sludge was separated by gas bubbles formed at electrode (sludge flotation) and by sedimentation. Amount of sludge production was affected by initial pH, current density and operation time for AE and AAE. As seen in Table 4, amount of sludge produced during the EC process was determined in runs 3, 5 and 7 and runs 17, 12 and 13 as 1.865, 1.608 and 1.194 kg/m 3 for current densities of 90.2, 65.0 and 39.8, and 1.123, 1550 and 2.082 kg/m 3 for operating time of 3.2, 20.0 and 36.8 min, respectively. As mentioned the above,

a greater amount of coagulant and removal efficiency of pollutants resulted in a greater amount of sludge with respect to high current density, operation time and low pH. Removal of dissolved organic compounds with different functional groups in the AE and AAE can occur at initial pHi 4–6 where the maximum removals were obtained for color, COD and TOC (final pH was 5–6 after the treatment). Initial pHi and final pHf are the most important parameter due to solution affected for production of polymeric Al-species and hydroxyl ions by the EC reactions and chemical structure of wastewaters. Final pHf affected the removal rate of pollutants in the BYEs. Dissolved organic molecules such as melanoidins in the BYEs are acidic, polymeric and highly dispersed colloids which are negatively charged due to the dissociation of carboxylic, hydroxyl and phenolic groups [36–40]. The most active functional groups are carboxyl and phenolic hydroxyl groups, and dissociation of H + relates to the pH of the solution. When enough amount of coagulant electrochemically produced at anode and increased the final pHf in the solution, coagulant was consequently interacted with negatively charge fragment(s) of melanoidins. This pH dependent phenomenon was related to deprotonation (―COO) of acidic organic functional groups such as carboxylic acid (―COOH) in melanoidins [36–40]. The main components of dissolved organic carbon in the BYEs were hydrophobic acids, hydrophilic bases and hydrophilic acids which contributed to 31.2%, 26.3% and 26.1% of the dissolved organic materials, respectively. Color from BYEs was mainly caused by hydrophobic acids (85.7%) which were long-chain fatty acids and esters, and not soluble in water, leading to their hydrophobic characteristics [37]. Liang et al. [38–40] proposed the insoluble organic fraction with larger molecular size and hence larger molecular weight was more readily removed by chemical coagulation. Our results also confirmed these observations.

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Fig. 2. Effects of the variables on color, COD and TOC removal efficiencies for AE.

Thus, color removal efficiency was relatively higher than COD and TOC removal efficiencies as compared to each other. Co-precipitation, adsorption and sweep flocculation were three major interaction mechanisms considered during the EC process. Each of the mechanism was applicable in different pH ranges. At low pH values, metal species like Al 3 + generated at anode bind to the anionic species, thus neutralizing their charge and reducing their solubility. This process of removal was termed as precipitation. Adsorption mechanism operates at a higher pH range (>6.0) and involves adsorption of organic substances on amorphous metal hydroxide precipitates. Under the optimum conditions, at final pH 4–6, the initially formed colloidal precipitate was positively charged and colloidally stable which suggested that co-precipitation played a vital role in the EC process for AE and AAE. At pH higher than 6, formed

amorphous Al(OH)3(s) (sweep flocs) flocs with the minimum solubility within the pH range 6.5–7.8 had a large specific surface area that can absorb some soluble organic compounds such as melanoidins onto its surface. The EC in the pH range 4–6 was explained as coprecipitation. Therefore, an effective color removal of BYW was realized between these pH ranges as the case in our study. This was in a good agreement with the optimum pH 5–6 reported in the literature for BYEs in the EC and chemical coagulation process using Al electrode and Al salts [16–18,38–40]. 3.3. Optimization results When operating variables were in range, the removal efficiencies of color, COD and TOC was maximized and the operating cost was

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207

Fig. 3. Effects of the variables on color, COD and TOC removal efficiencies for AAE.

minimized in the RSM model with desirability of 0.757 and 0.623 for AE and AAE, respectively (Table 10). Optimization results for the maximum removal efficiencies of color, COD and TOC as 82%, 41% and 39% at 50 A/m 2, pH 4 and 22 min for AE and 77%, 36% and 32% at 11 A/m 2, pH 5 and 18 min for AAE in the EC process were obtained for the system. It is clear that 1128 mg/L of COD for AE and 266 mg/L of COD for AAE satisfied the national legislation's constrain which was 1200 mg/L of COD. The operating costs of the model at the optimised conditions were 0.418 €/m 3 for AE and 0.076 €/m 3 for AAE. Effluents from the EC process can satisfy effluent limits for color, COD and TOC removals. Based on the above results, the EC process may be applied to AAE. There were a couple of studies related to costs of COD and color removals from BYEs reported in the literature [41,42]. 86% of COD and 95% of color removal efficiencies with a cost of 1.58 €/m 3 and 71%

of COD and 96% of color removal efficiencies with a cost of 1.33 €/ m 3 were obtained for treated BYW with ozone and Fenton processes, respectively when properties of BYEs were in range of 529–2006 mg/ L of COD, 0.25–2.4 of Abs/cm and pH 7.9–8.2 [41]. When lime was used for the treatment of BYEs contained 10,000–30,000 mg/L of COD and pH 6.0–6.5, 84.3% of color removal efficiency and 0.84 €/ m 3 of operational cost were obtained [42]. 4. Conclusions In this study, the EC process was applied to removals of color, COD and TOC from the anaerobic and the anaerobic–aerobic effluents of BYW. The process variables were initial pH, current density and operation time which was designed and analyzed by the RSM. Quadratic

208

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Fig. 4. Perturbation plots of ELC, ENC and OC for AE (a–c) and AAE (d–f).

models were developed for the responses to obtain removal efficiencies of color, COD and TOC for AE and AAE. In the results of runs, the maximum color, COD and TOC removal efficiencies and minimized the operating cost were determined from the model. The proposed quadratic model fitted very well with the experimental data with R 2 > 0.95 for all responses in anaerobic and anaerobic–aerobic effluents. The optimum process conditions for removal efficiencies of color, COD and TOC were 82%, 41% and 39% at pH 4, 50 A/m 2, 22 min for AE and 77%, 36% and 32% at pH 5, 11 A/m 2, 18 min for AAE, respectively. In addition, ENC, ELC, OC and Wsludge at the optimum conditions were 1.082 kWh/m 3, 0.208 kgAl/m 3, 0.418 €/m 3 and 1.319 kg/m 3 for AE and 0.169 kWh/m 3, 0.039 kg/m 3, 0.076 €/

m 3 and 0.225 kg/m 3 for AAE. Under the optimum conditions, coprecipitation was the predominant EC mechanism since the maximum decolorization was obtained in pH 4–6. These results showed that RSM was a suitable method to optimize the operating conditions and maximize the color, COD and TOC removal efficiencies for Al electrode while keeping the operating costs to minimal and the EC process was an appropriate process to treat of BYEs. This study provided for removal of higher percentage of colors (>88%) from both AE and AAE in the EC process, but the biological treatment process did not give the same or a better removal rate. Therefore, the EC has more advantages for both removals of color from these wastewaters and COD and TOC as well.

Table 10 Optimization's constraints and results. Name

x1:pH x2:CD (A/m2) x3:time (min) Re,color (%) Re,COD (%) Re,TOC (%) ENC (kWh/m3) ELC (kgAl/m3) OC (€/m3) Wsludge (kg/m3) Desirability

Goal

in range in range in range max. max. max. in range in range min. min. max.

Anaerobic effluent

Anaerobic–aerobic effluent

Lower limit

Upper limit

Optimization results

Lower limit

Upper limit

Optimization results

4 50 10.0 41 20 16 0.297 0.041 0.090 1.027 0

6 80 30.0 88 48 49 3.600 0.480 1.040 2.359 1

4 50 22 82 41 39 1.082 0.208 0.418 1.319 0.757

5 5.0 10.0 8 1 5 0.026 0.009 0.017 0.052 0

7 12.5 30.0 86 49 43 0.337 0.069 0.138 0.346 1

5 11.0 18 77 36 32 0.169 0.039 0.076 0.225 0.623

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