assisted Fenton Oxidation Process: A Statistical Model for MDF ...

Clean 2009, 37 (8), 629 – 637

Maedeh Galehdar1 Habibollah Younesi1 Mojtaba Hadavifar1 Ali Akbar Zinatizadeh2 1

Department of Environmental Science, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran.

2

Department of Applied Chemistry, Faculty of Chemistry, Razi University, Kermanshah, Iran.

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Research Article Optimization of a Photo-assisted Fenton Oxidation Process: A Statistical Model for MDF Effluent Treatment In the present study, the effects of initial COD (chemical oxygen demand), initial pH, Fe2+/H2O2 molar ratio and UV contact time on COD removal from medium density fiberboard (MDF) wastewater using photo-assisted Fenton oxidation treatment were investigated. In order to optimize the removal efficiency, batch operations were carried out. The influence of the aforementioned parameters on COD removal efficiency was studied using response surface methodology (RSM). The optimal conditions for maximum COD removal efficiency from MDF wastewater under experimental conditions were obtained at initial COD of 4000 mg/L, Fe2+/H2O2 molar ratio of 0.11, initial solution pH of 6.5 and UV contact time of 70 min. The obtained results for maximum COD removal efficiency of 96% revealed that photo-assisted Fenton oxidation is very effective for treating MDF wastewater. Keywords: Fenton reagent; Medium density fiberboard (MDF) wastewater; Response surface methodology (RSM); UV light; Received: March 8, 2009; revised: May 26, 2009; accepted: July 6, 2009 DOI: 10.1002/clen.200900052

1 Introduction The industrial processing of medium density fiberboard (MDF or MDFB) consumes large amounts of water. For example, large quantities of water are used for resin make up, in chip washing/refining, softening and to raise steam, generating considerable amount of wastewater. In the manufacture of MDF in particular, wood chips may also be washed before downstream processing, primarily to remove soil residues which cause premature wear of machine equipment [1]. This wash water contains high quantities of sediments and leachate from wood chips. The effluents of the MDF industry are considered to be very high strength wastewater containing large amounts of suspended solids (SS), with high chemical oxygen demand (COD) and biological oxygen demand (BOD), generally with fluctuating pH values [2]. The organic components of the effluent include celluloses, lignin and resin acids. The high COD and suspended solids content of MDF effluents mean that they cannot be discharged into sewers without treatment as this would have serious consequences to the aquatic environment as the recalcitrant pollutants could be toxic to marine life, and the organic material in the MDF wastewater would begin to decompose depleting the water body of oxygen [3]. For these reasons the environmental techniques Correspondence: Dr. H. Younesi, Department of Environmental Science, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, P.O. Box: 64414-356 Iran. E-mail: [email protected] Abbreviations: AOP, Advanced oxidation process; BOD, Biological oxygen demand; CCD, Central composite design; COD, Chemical oxygen demand; MDF, Medium density fiberboard; RSM, Response surface methodology; SS, Suspended solids; SVI, Sludge volume index; TSS, Total suspended solids; VSS, Volatile suspended solids

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2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

used by the MDF industry are still a relatively new topic and mainly involve the treatment of wastewater similarly to thermal mechanical pulping wastewater (TMP) from pulp and paper industries [3]. At present, numerous methods have been developed to treat MDF effluents conventionally including evaporation, biological oxidation, coagulation, electro-oxidation, separation, sedimentation, dewatering, etc. Evaporating the effluent in dryers has been attempted, but has proved too expensive in terms of energy and maintenance requirements; besides this the capacity was inadequate. Conventional biological processes are not effective for treating MDF wastewater because many recalcitrant organic compounds are toxic to the organisms being used and this results in sludge bulking, rising of sludge and pinpoint floc [4 – 6]. Small, compact, weak, roughly spherical flocs are formed, the largest of which settle rapidly, while smaller aggregates settle slowly (pinpoint floc) due to a low SVI (sludge volume index) and a cloudy and turbid effluent [7]. Several different physicochemical treatments like coagulation and flocculation, ozonation, and Fenton's oxidation have been proposed for COD removal, reduction of color and suspended solids in the effluents of MDF. Advanced oxidation processes (AOPs) are used for pollutant abatement owing to the high oxidative power of the hydroxyl radical (9OH). One of the most important AOPs used to generate 9OH radicals employs the Fe2+-H2O2 system, where the catalyst (ferrous ions) is dissolved in water; this is known as the Fenton process [8 – 11]. In conventional experimentation, the experiments are conducted by keeping all the variables constant except for the parameter whose influence is being studied. This type of experiment reveals the effect of the chosen parameters under set conditions, assuming that variables are independent and that the effect will be the same at other values of the remaining variables. However, it does not

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show what would happen if other variables are changed. Experimental design is an effective and efficient optimization strategy to overcome this drawback, which has gained wide application in chemical engineering optimization [12]. The combined effect of variables can be predicted and optimization can be achieved with the help of the experimental design tool, which in practice is difficult in conventional experimentation. Response surface methodology (RSM) is a combination of mathematical and statistical techniques used for developing, improving and optimizing the processes and it is used to evaluate the relative significance of several factors even in the presence of complex interactions. RSM usually consists of three steps: (i) designing an experiment which is the determination of the independent variables and how their levels are carried out; (ii) response surface modeling (RSM) through regression; (iii) determining the response surface or contour plots of the response as the function of the independent variables and of the maximum and minimum points; (iv) optimization of the experimental variables and determination of the optimal points through numerical methods. The main objective of RSM is to determine the optimum operational conditions of the process, or to determine a region that satisfies the operating specifications. This methodology is widely used in chemical engineering, notably to optimize process variables [13]. In the present study, a two stage process was applied for the treatment of the MDF effluent. In the first stage, the Fenton process was performed by using FeSO4 N 7 H2O as coagulant coupled with H2O2 which produces 9OH radicals. In the second stage, UV light was applied to the wastewater to complete the oxidation of the remaining organic matter. The objective of the research work was to study the effect of four influential factors (initial COD, Fe2+/H2O2 molar ratio, initial pH and contact time of UV light) on the process in order to achieve significant COD removal in the MDF effluent. The process optimization was performed using response surface methodology (RSM).

Clean 2009, 37 (8), 629 – 637

Table 1. Characteristics of the MDF effluents wastewater.

Characteristics

Values

pH COD (mg/L) BOD (mg/L) TSS (g/L) TS (g/L) SS (mL)

4 – 4.5 50 000 – 55 000 2 500 – 2750 3.83 – 4.62 35 – 40 48 – 52

man, Germany). After the filtering process, a 10 mL supernatant sample was withdrawn and the excess hydrogen peroxide removed by addition of 0.2 g MnO2 (Merck) powder and left for a period of 30 min. The sample was then centrifuged at 3000 rpm for 10 min to remove the additional MnO2. The COD was monitored by a closed reflux colorimetric method in accordance with standard methods [14] using a Plaintest system, Photometer 8000 (UK). The sample digestion was performed by an ECO16 Thermoreactor (Velp Scientifica, UK) at 1508C for 120 min. The amounts of TSS (total suspended solids) and VSS (volatile suspended solids) on the filter paper were measured by drying the remaining filtrate in an oven (Memmert, Germany) to a constant weight at 1058C for 2 h, followed by burning the ashless filter paper in an electrical furnace set (Nabertherm, Germany) at 5508C for 15 min. The irradiation was carried out using two parallel adjustable F8T5 UV-B, 302 nm fluorescent black tubes (Japan), (5300 lw/cm2 UV-B; 1150 lw/cm2 UV-A and 3.9 lw/cm2 UV-C) and radiation values in lw/cm2 were obtained by the use of a UVX radiometer (UVP, USA) using three sensors (UVX-25, UVX-31 and VUX-36). The distance between the UV lamp and reaction vessel surface was fixed at 50 mm.

2.3 Experimental Design for the Process Optimization

2 Materials and Method 2.1 Characteristics of MDF Wastewater The effluent was collected from a MDF manufacturer located in an Industrial Estate in Amol, Iran, and it was stored at 48C. The chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids and turbidity, were determined according to standard methods for the examination of water and wastewater [14], and are given in Tab. 1.

2.2 Analytical Methods Ferrous chloride (FeCl2 N 4 H2O) and hydrogen peroxide (H2O2) (Merck) were utilized for the coagulation procedure. The solution pH of the MDF effluent was adjusted by 1 M NaOH (Merck) and 1 M H2SO4 (Merck) and measured using a Cyber Scan pH meter set (Cyber Scan, Singapore). Stock solutions of FeCl2 N 4 H2O, alkaline (NaOH) and acid (HCl) solutions were prepared with tap water. The MDF effluents were diluted with tap water. The experiments were conducted after adjusting the solution pH and addition of the coagulant by rapid mixing of the effluent at 50 rpm for 15 min on a magnetic stirrer. Thereafter, the mixing was stopped and the flocs formed were allowed to settle for a period of 30 min for SS measurement. Then the sample was filtered using ashless filter paper (What-

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The optimization of COD removal was carried out by four chosen independent process variables, i. e., effluent COD (x1), initial solution pH (x2), Fe2+/H2O2 molar ratio (x3), and UV contact time (x4). A mathematical model produced by RSM can be used to predict the response. In most case, a quadratic response surface model for predicting the optimal point was expressed in accordance with Eq. (1) [13]:

yCOD ¼ b0 þ

k X i¼1

bi xi þ

k X

bii x2i þ

i¼1

k1 X k X

bij xi xj þ e

ð1Þ

i¼1 j¼2

where y is the predicted response, xi, xj, …, xk are the input variables, which affect the response y, x2i , x2j , …, x2k are the square effects, xi xj, xi xk and xj xk are the interaction effects, b0, bi (i = 1, 2, …, k), bii (i = 1, 2, …, k), bij (i = 1, 2, …, k; j = 1, 2, …, k) are the regression coefficients for the intercept, linear, quadratic, and interaction terms, respectively, e is the interaction effect and e is the random error [15]. The actual variables are converted to coded variables using Eq. (2) [16]:

xcoded ¼

XActual ðXHi þ XLow Þ=2 ðXHi XLow Þ=2

ð2Þ

where, x is the coded variable, and X is the actual variables. In the present study, Design-Expert 7.0 (Stat-Ease Inc., Minneapolis, MN, USA) software was used for the regression and graphical analysis of the obtained data. A design of 30 experiments was formulated for four factorial points with six replicates at centre points. www.clean-journal.com

Clean 2009, 37 (8), 629 – 637

Photo-assisted Fenton Oxidation Treatment of MDF Effluents

Table 2. Experimental ranges and levels of the independent variables. Independent Variables

Initial pH Initial COD, mg/L Fe2+/H2O2 ratio, mol/mol UV contact time, min

standard RSM design called central composite design (CCD), which has been the most frequently used for optimization studies in the recent years. Optimum values of the selected variables were obtained by solving the regression equation and by analyzing the response surface and contour plots [13]. The variability in dependent variables was explained by the multiple coefficient of determination (R2, adj. – R2, and pred. – R2) and the model equation was used to predict the optimum value and subsequently to elucidate the interaction between the factors within the specified range [13] The experiments with five levels of initial solution pH, i. e., 0.5, 2.5, 4.5, 6.5, and 8.5, five levels of initial COD, i. e., 2000, 4000, 6000, 8000, and 10 000 mg/L and five levels Fe2+/H2O2 molar ratio, i. e., 0.05, 0.11, 0.17, 0.24 and 0.3, (H2O2 concentration of 3 mL/L was used in the molar ratio and coupled and varied simultaneously to each other in order to cover the combinations of variables in the central composite design), and with five levels of UV contact time, i. e., 50, 70, 90, 110 and 130 min (H2O2 concentration was constant at 20 mL/L). The response surface methodology (RSM) was a robust design technology based on central composite design (CCD) and could be applied to modeling and analysis of multiple parameters. The range and level of the variables in coded units from RSM studies in the experiments for the removal of COD are given in Tab. 2. The sum of 30 experimental designs along with the observed and predicted responses and residuals given in Tab. 3 were conducted in duplicate.

Range and Level –a (1.682)

–1

0

+1

+a (1.682)

0.5 2000 0.05 50

2.5 4000 0.11 70

4.5 6000 0.18 90

6.5 8000 0.24 110

8.5 10000 0.3 130

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Central composite design (CCD) was employed to estimate the coefficients of the second order polynomial model. All point descriptions were in terms of the coded values of the factors. The optimum values of the selected variables were obtained by solving the regression equation at desired values of the process responses as optimization criteria. The low and high values of the factors that are entered in this experiment can be assigned to – a, – 1, 0, + 1 and + a in terms of coded factors. The regression analysis was performed to fit the response function to the experimental data. The significance of each coefficient was determined by F-values and P-values. The larger the magnitude of the F-value and the smaller the P-value, the more significant the corresponding coefficient is [15]. The amount of COD removal (y) was taken as the response of the design experiments. The percentage removal was studied with

Table 3. Experimental design based on central composite design (CCD).

Run No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

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Independent values Real Values (Coded Values) x3

x1

x2

6000 (0) 6000 (0) 8000 (+ 1) 2000 (+ 2) 4000 (+ 1) 8000 (+ 1) 4000 ( – 1) 8000 (+ 1) 8000 (+ 1) 10000 (+ 2) 4000 ( – 1) 8000 (+ 1) 6000 (0) 4000 ( – 1) 6000 (0) 6000 (0) 8000 (+ 1) 8000 (+ 1) 4000 ( – 1) 6000 (0) 6000 (0) 8000 (+ 1) 4000 ( – 1) 6000 (0) 6000 (0) 6000 (0) 4000 ( – 1) 4000 ( – 1) 6000 (0) 6000 (0)

0.17 ( – 2) 0.17 ( – 2) 0.24 ( – 2) 0.17 ( – 2) 0.24 ( – 2) 0.11 ( – 2) 0.24 ( – 2) 0.11 ( – 2) 0.11 ( – 2) 0.17 ( – 2) 0.24 ( – 2) 0.24 ( – 2) 0.17 ( – 2) 0.11 ( – 2) 0.3 ( – 2) 0.05 ( – 2) 0.24 ( – 2) 0.11 ( – 2) 0.11 ( – 2) 0.17 ( – 2) 0.17 ( – 2) 0.24 (+ 2) 0.11 ( – 2) 0.17 ( – 2) 0.17 ( – 2) 0.17 ( – 2) 0.11 ( – 2) 0.24 ( – 2) 0.17 ( – 2) 0.17 ( – 2)

130 (+ 2) 50 ( – 2) 70 ( – 1) 90 (0) 70 ( – 1) 70 ( – 1) 110 (+ 1) 110 (+ 1) 110 (+ 1) 90 (0) 70 ( – 1) 110 (+ 1) 90 (0) 110 (+ 1) 90 (0) 90 (0) 70 ( – 1) 70 (+ 1) 110 (1) 90 (0) 90 (0) 110 (+ 1) 70 ( – 1) 90 (0) 90 (0) 90 (0) 70 ( – 1) 110 (+ 1) 90 (0) 90 (0)


Response

Residual

x4

Observed Values Predicted Value Yobs. Ypred.

Yobs.

4.5 (0) 4.5 (0) 2.5 ( – 1) 4.5 (0) 6.5 (+ 1) 2.5 – 1) 2.5 ( – 1) 6.5 (+ 1) 2.5 ( – 1) 4.5(0) 2.5 ( – 1) 2.5 ( – 1) 4.5 (0) 6.5 (+ 1) 4.5 (0) 4.5 (0) 6.5 (+ 1) 6.5 (+ 1) 2.5 ( – 1) 4.5 (0) 4.5 (0) 6.5 (+ 1) 6.5 (+ 1) 0.05 ( – 2) 4.5 (0) 4.5 (0) 2.5 ( – 1) 6.5 (+ 1) 4.5 (0) 8.5 (+ 2)

Ignore 81.40 67.06 87.00 77.51 73.74 70.77 88.60 Ignore 66.33 79.19 64.09 82.86 94.36 73.04 86.28 62.60 85.40 Ignore 82.43 83.47 77.46 96.04 Ignore 82.00 85.55 85.03 86.77 85.03 87.12

– – 0.25 0.34 0.51 0.32 – 0.047 – 0.46 0.63 – – 0.76 – 0.66 0.46 – 0.69 – 0.061 0.33 – 0.59 – 0.091 0.80 – – 1.13 – 0.085 – 0.34 – 0.54 – – 1.55 2.00 0.62 0.014 1.48 – 0.25

– 81.65 66.72 86.49 77.19 73.79 71.23 87.97 – 67.09 79.86 63.62 83.55 94.42 72.71 86.87 62.69 84.60 – 83.55 83.55 77.79 96.58 – 83.55 83.55 84.41 86.76 83.55 87.37

– Ypred.


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2.4 Experimental Procedure

Table 4. Analysis of variance (ANOVA) for the response surface quadratic modela).

The COD removal from MDF wastewater was performed in a static mode. Experiments were performed according to the central composite design (CCD) matrix in accordance with the Design-Expert software. The process analysis and optimization was investigated by RSM considering the effects of initial COD, initial solution pH, dose of Fenton reagent and UV contact time on the COD removal. The MDF wastewater was characterized with a relatively high amount of TSS, two physicochemical processes, coagulation and oxidation processes were thereby integrated, applying Fenton's reagent induced by UV radiation. Therefore, TSS and SCOD (soluble chemical oxygen demand?) contents are respectively removed and partially oxidized in the first stage. The oxidation will then be enhanced by applying UV light in the subsequent stage. The coagulation/flocculation experiments were performed in 200 mL beakers which contained 100 mL MDF wastewater with different COD and pH values. Then, various concentrations of the coagulant were added progressively to each beaker. The samples were stirred for 5 min at 120 rpm (magnetic stirrer, Velp Scientifica, UK) in order to complete dissolving of the coagulant, and then hydrogen peroxide in various concentrations was added to solution and stirred for 15 min at 30 rpm. All the experiments were carried out at room temperature. Afterwards, the solution was replaced into a 100 mL graduated cylinder and allowed to precipitate. After 1 h the sludge volume was measured and all the solutions were filtered using ashless filter paper with 0.45 lm pore size in order to determine VSS and TSS. All the filtered solutions were applied for the second stage treatment. A filtered solution of 5 mL was taken to measure the COD for the first stage. The sample containing H2O2, which interferes with the COD measurements, was eliminated by addition of MnO2 powder [6, 17]. The MnO2 decomposes the reminder of the H2O2 in accordance with Eq. (3):

2 MnO2 + 4 H2O2 fi 2 Mn(OH)2 + 3 O2 + 2 H2O

(3)

In the UV light stage, a constant concentration of hydrogen peroxide (20 mL/L) was added to the filtered solution and then it was contacted with 6400 lux intensity radiation (by UV-B lamp F8T5) at a distance of 5 cm, at varied contact intervals. After the UV contact time the sludge volume was measured and all the solutions were filtered using ashless filter paper with 0.45 lm pore size in order to determine the VSS and TSS. A sample of 5 mL of the filtered solution was taken to measure COD for the second stage. MnO2 was also added for the elimination of any remaining H2O2 (see Eq. (3)) and COD removal was calculated in accordance with Eq. (4):

R¼

Initial soluble COD Final soluble COD 6100 Initial soluble COD

ð4Þ

where R is the soluble COD removed by the treatment in each run, given as a percentage.

3 Results and Discussion In the present study, the RSM was used to optimize operating parameters involved in the experimental design, i. e., initial COD (in the range of 2000 – 10 000 mg/L, x1), initial solution pH (in the range of 0.5 – 8.5, x2), the molar ratio of FeCl2 to H2O2 (in the range of 0.05 – 0.3 mol/mol, x3) and UV contact time (in the range of 50 – 130 min, x4) to find the relationship between the response function and varia-

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Source

Degrees of Sum of Freedom Squares

Mean Square

F-value

Prob. A F

Model Residual Lack of fit Pure error Cor. total

14 11 6 5 25

138.72 1.34 0.73 2.07 –

103.69 – 0.35 – –

a 0.0001 – 0.8832 – –

a)

1942.13 14.72 4.35 10.36 1956.85

R2 = 0.9925; adjusted R2 = 0.9829; predicted R2 = 0.9688; C.V. = 1.44.

bles. Finally, predicted runs delivered by the software. A sum of 30 experimental runs offered by Design-Expert were performed, the responses including COD removal percentage, TSS, VSS/TSS, SVI and initial and final pH were analyzed. The analyses focused on how COD reduction is influenced by the aforementioned variables. Analysis of variation (ANOVA) done on inputs and optimization of the parameters and their interactions was demonstrated.

3.1 Statistical Analysis of the Model Obtained from the RSM Approach for COD Removal The RSM is an effective sequential and stepwise procedure and defines the effect of independent variables, alone or in combination, on the process [17]. The COD removal under different experimental conditions designed by a 30 full factorial central composite design (CCD) with eight star points and six replicates at the central points. Table 4 shows the results of CCD experiments for ANOVA to determine the significant effects of four independent variables. The application of RSM after ANOVA gave the level of COD removal as a function of the initial COD (x1), Fe2+/H2O2 molar ratio (x2), UV contact time (x3) and initial solution pH (x4), as expressed by Eq. (5):

yCOD ¼ 21998:15 þ 99:96 x1 15534:03 x2 þ 442:07 x3 553:93 x4 33:86 x1 x2 þ 1:38 x1 x3 0:34 x1 x4 þ157:95 x2 x3 199:68 x2 x4 þ 4:55 x3 x4 1:69 x21 2731:56 x22 1:25 x23 2:48 x24

ð5Þ

where x1, x2, x3 and x4 are the independent variables. The large Fvalue indicates that most of the variation in the response can be explained by the regression quadratic model. The corresponding pvalue is shown to estimate whether the F-value is large enough to indicate statistical significance. The low p-value (p a 0.0001) indicates that the model can be considered to be statistically significant (Tab. 4). The lack of fit of F-values of 0.43 implies that there is a 87.02% (p = 0.8702) chance that this small lack of fit of F-values could occur due to the variation of the data around the fitted model. The insignificant value of lack of fit (more than 0.05) indicated that the quadratic model was valid for removal of COD by photo-assisted Fenton oxidation treatment. This means that if the model fitted the data well, the lack of fit will be insignificant. The examination of the fit summary output revealed that the quadratic model was statistically significant for the response, and therefore it will be used for further analysis [1]. www.clean-journal.com

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Table 5. Regression analysis using the 24 factorial central composite designa).

Intercept x1 x2 x3 x4 x21 x22 x23 x24 x1 x2 x1 x3 x1 x4 x2 x3 x2 x4 x3 x4 a)

Coefficient Estimate

Degrees of Freedom

Standard Error

Sum of Squares

Mean Square

F-value

P-value

– 21998.15 – 99.96 – 15534.03 442.07 – 553.93 – 1.69 – 2731.56 – 1.25 – 2.48 – 33.86 1.38 – 0.34 157.95 – 199.68 4.55

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

5546.78 48.12 3952.43 57.80 57.80 0.23 704.07 0.33 0.33 17.15 0.32 0.32 20.65 20.65 0.38

–

–

–

–

5.77 20.67 78.25 122.86 75.08 20.14 19.40 76.92 5.21 24.90 1.50 78.28 125.10 188.19

5.77 20.67 78.25 122.86 75.08 20.14 19.40 76.92 5.21 24.90 1.50 78.28 125.10 188.19

4.31 15.45 58.49 91.83 56.12 15.05 14.50 57.49 3.90 18.61 1.12 58.51 93.50 140.66

0.0620 0.0024 a 0.0001 a 0.0001 a 0.0001 0.0026 0.0029 a 0.0001 0.0740 0.0012 0.3129 a 0.0001 a 0.0001 a 0.0001

x1, x2, x3 and x4 are the main effects; x21 ; x22 ; x23 and x24 are the square effects; x1 x2, x1 x3, x1 x4, x2 x3, x2 x4 and x3 x4 are the interaction effects.

The significance of each coefficient presented in Eq. (5) was determined by the F-values and values of probability A F. The value of probability A F of less than 0.05 indicates that the model terms are considered to be statistically significant. In general, the larger the magnitude of F-values and the smaller the p-values, the greater the significance of the corresponding coefficient term. The results of the quadratic model in the form of ANOVA are given in Tab. 5, which shows that there is only one insignificant model term (p = 0.1972) for the coefficient of the interaction effect of the initial COD and pH. It can be seen from Tab. 5 that the coefficients of the main effect of initial COD (p = 0.0268) and Fe2+/H2O2 molar ratio (p = 0.0013) are less significant compared to the other linear effects, i. e., the UV contact time (p a 0.0001) and the pH (p a 0.0001). The coefficient of the interaction effect of the initial COD and Fe2+/H2O2 molar ratio (p = 0.0332) is less significant compared to the other interaction effects, i. e., the initial COD and UV contact time (p a 0.0002), the Fe2+/H2O2 molar ratio and UV contact time (p a 0.0001), Fe2+/H2O2 molar ratio and pH (p a 0.0001), and UV contact time and pH (p = 0.0001). The modeling results emphasized the noteworthy effect of UV as first order, interaction and second order effects on the process with high confidence levels. While the coefficients in the quadratic term for Fe2+/H2O2 molar ratio (p = 0.0014) and UV contact time (p = 0.0005) are more significant compared to coefficients in the quadratic term for initial COD (p = 0.0001) and pH (p = 0.0001). Finally, the lowest p-values of the coefficients in the quadratic term were more significant compared to coefficients in the main and interaction terms that are an indication for the rejection of the null hypothesis. The value of R-squared (R2 = 0.9925) and the adjusted Rsquared (Adj. R2 = 0.9848) are close to 1, indicating a high correlation between the observed values and the predicted values [17]. The predicted R-squared (Pred. R2) of 0.9730 is in reasonable agreement with the adjusted R-squared. Adequate precision measures the signal to noise ratio. It compares the range of the predicted values at the design points to the average prediction error. A ratio greater than 4 is desirable. The ratio of 41.392 indicates an adequate signal. The prediction of COD removal using Eq. (5) was compared with the experimental values given in Tab. 3 and shown in Fig. 1. It can be seen from Fig. 1 that the quadratic model equation predictions are statistically a satisfactory match with the experimental values. Therefore, this model can be used to navigate the design space.

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Figure 1. Figure 1. Comparison of predicted values versus actual values for the response in photo-assisted Fenton oxidation process.

3.2 Optimization of Process Parameters The analysis of the combined effect of initial COD and Fe2+/H2O2 molar ratio on the percentage COD removal is demonstrated in a contour plot in Fig. 2. It can be seen from Fig. 2 that the reduction of COD increased with decreasing initial COD and Fe2+/H2O2 molar ratio. In the range of initial COD of 4000 – 4500 mg/L and Fe2+/H2O2 molar ratio of 0.05 – 0.08, a maximum percentage COD removal of 89% was achieved. The contour plot Fig. 2 also shows that the percentage COD removal decreased with increasing initial COD and Fe2+/H2O2 molar ratio. On the other hand, an increase in Fe2+ and H2O2 molar concentration did not seem to have a significant effect on reducing the COD. This behavior could be attributed to a photowww.clean-journal.com

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Figure 2. Contour plot of the combined effect of effluent COD and Fe2+/ H2O2 molar ratio within the design space on COD removal efficiency when other two variables at its middle level.

Figure 3. Contour plot of the combined effect of UV contact time and Fe2+/H2O2 mole ratio within the design space on COD removal efficiency when other two variables at its middle level.

catalytic effect [9]. In the present study, it was observed that a lesser Fe2+/H2O2 molar ratio was more effective for the reduction of MDF effluent COD. A comparison of the results of the COD reduction shown in Fig. 2 suggested that the principal mechanism of the reaction of the hydroxyl radicals with the organic compounds of MDF wastewater seemed to be less operative, owing to the recalcitrance of MDF [11]. The combined effect of UV contact time and the Fe2+/H2O2 molar ratio on the percentage of COD removal is shown in Fig. 3. It can be seen from Fig. 3 that the percentage COD removal increased with decreasing Fe2+/H2O2 molar ratio and initial UV contact time. This shows that a lesser contact time (70 min) had more influence on COD removal. A decrease in percentage COD removal was observed at higher UV contact time and it did not have any considerable influence on the removal percentage, as shown in Fig. 2. This is because UV light affects the decomposition of all the H2O2 and the production of hydroxide radicals leads to more reduction of the COD [18]. The decreased COD removal percentage at higher Fe2+/ H2O2 molar ratios is due to the existence of a lower amount of H2O2, or an excess amount of Fe2+ ions in the reaction vessel. This leads to a low production of hydroxide radicals and they in turn produced a reduction in the decomposition of organic matter. However, superoxide radical anions (O29 – ) and hydroperoxide anions (HO2– ) can also be formed (shown in reaction Eqs. (6) – (8)), which are the substances responsible for the removal of organic pollutants [19].

according to the equations was less efficient. But UV light decomposes organic matter directly. Then, more UV radiation contact time has no effect on COD removal; adversely, there may be some complicated organic molecules in insoluble form which may be converted to soluble ones. However, this can increase COD value. Regarding the appearance, the color of the solution had a further decreasing trend with long UV contact times, but COD removal did not follow this trend. The effect of Fe2+/H2O2 molar ratio on UV exposure time shows how by increasing this ratio, the effect on UV contact time was that it reduced removal equilibrium. But such duration times (50 – 110 min) were more agreeable than other times. It is also seen from Fig. 3 that an increase in removal efficiency to ca. 91% occurs with a moderate Fe2+/H2O2 ratio of 0.18 mol/mol and UV contact time of 90 min. An increase in initial pH was very efficient in terms of percentage removal. Reduction of Fe2+/H2O2 ratio (i. e., higher concentrations of hydrogen peroxide) exhibited a reversed relationship, indicating a decrease in COD removal as UV contact time increased (see Fig. 3). However, a higher concentration of hydrogen peroxide was expected as the optimization of Fenton reagent in the second stage had to be determined experimentally. In the degradation mechanism of aromatic and alkyl groups of organic pollutants, aliphatic radicals R9 are formed by extracting hydrogen atoms, yielding water molecule via the reaction shown in Eq. (9) [20]:

HO29 fi O29 – + H+

(6)

HO9 + RH fi H2O + R9

Fe2+ + O29 – + H+ fi Fe3+ + HO2–

(7)

HO29 O29 – fi HO2– + O2

(8)

However, a better effect was observed when there was 20 mL/L of H2O2 in the wastewater. The combined effect of initial COD and UV contact time on percentage COD removal is shown in Fig. 4. High COD removal efficiency occurred at low initial COD values. Runs were performed with 5 different COD values. With a COD of 2000 mg/L (as a low limit

Therefore, under the conditions of our experiment (at a maximum Fe2+/H2O2 ratio of 0.3) the formation of these substances

i


(9)


Clean 2009, 37 (8), 629 – 637


635

Fe2+ + H2O2 fi Fe3+ + OH – + HO9

(10)

Fe3+ + H2O2 fi Fe2+ + H+ + HO29

(11)

Under UV-visible irradiation, the following photochemical reaction occurs [12]: Fe(OH)2 + hm fi Fe2+ + 2 HO9

Figure 4. Contour plot of the combined effect of effluent COD and UV contact time within the design space on COD removal efficiency when other two variables at its middle level.

COD value introduced to the software) we achieved 87% reduction in COD, while the optimum value of initial COD was 4000 mg/L (96% removal). When the COD rises from 4000 to 10 000 mg/L, the removal percent varies from 96 to 66%. The results show that COD values in the range of 4000 – 6000 mg/L caused more elimination than other concentrations. As mentioned earlier, MDF wastewater contains pollutants with a high value of TSS and COD.

3.3 Effect of Initial Solution pH on COD Removal Efficiency In the first stage, as the pH value of the effluent was basic, the pH was adjusted. Figure 5 shows that the optimum pH value for the Fenton-reaction is in the range of 3 – 4. For rapid mineralization of organic pollutants, both Fenton (Eqs. (10) and (11)) and the photoFenton reactions (Eq. (12)) are expected to be produced within these optimum pH ranges [8, 20, 21].

(12)

By adding Fenton reagents into the solution and contacting it with UV light, the pH values differ at a limited range. The COD removal increased with an increase in the initial solution pH and initial COD. The initial COD plays a role as an important factor in COD removal when the initial pH and UV contact time were set at 4.5 and 90 min, respectively. Besides this, an increase in the initial solution pH caused an increasing Fe2+/H2O2 ratio, meaning that fixed conditions of Fe2+/H2O2 molar ratio and an increase in the initial pH may enhance COD removal efficiency. An increase in UV contact time caused a decrease in Fe2+/H2O2 molar ratio and a shorter UV contact time also resulted in more effective COD removal efficiency (see Fig. 3), indicating a decreasing influence of UV contact time on COD removal at conditions of H2O2 molar concentrations ca. five times that of ferrous concentration. It may be attributed to many other possible reactions at such conditions which include radical-radical reactions [22, 23]: HO9 + HO9 fi H2O2

(13)

HO29 + HO9 fi H2O2 + O2

(14)

At the same time, the initial pH decrease was negatively affected by UV contact time which showed a decrease in COD removal efficiency. It is well known that solution pH is the most important factor that affects the hydroxyl radical generation capacity [9] and the formation of mono- and polynuclear complexes [24]. This is due to the fact that ferric iron undergoes hydrolysis in water to form Fe(OH)2+, Fe(OH)2+, Fe(OH)24+, Fe(OH)3 and higher oligomers and insoluble polymers of iron oxide [25]. It seems that hexaaquo and hydroxopentaaquo ions are in the majority of compounds in the pH region around 3 [19]. In the Fenton reaction, the generation of protons (see Eqs. (15) – (17)) cause a drop of the solution pH to the acidic region, as H2O2 reacts with the organic matter of MDF wastewater [20].

Figure 5. Comparison of initial and final solution pH in photo-assisted Fenton oxidation treatment.

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Clean 2009, 37 (8), 629 – 637

Figure 6. The effects of operating process variables on TSS, VSS and SVI after 30 min settling.

Fe3+ + H2O2 fi Fe2+ + H+ + HO29

(15)

HO29 fi O2– + H+

(16)

Fe2+ + O29 – + H+ fi Fe3+ + HO2–

(17)

all the solution was filtered using ashless filter paper with 0.45 lm pore size. Subsequently, the sludge volume index (SVI) for each run was calculated by the following equation: SVI

However, catalyzed H2O2 propagation reactions (see Eqs. (18) – (20)) introduced at an acidic pH promote solubility of the Fe2+ [26]. HO9 + RH fi H2O + R9

(18)

R9 + H2O2 fi ROH + HO9

(19)

HO9 + H2O2 fi H2O + H2O9

(20)

It was stated that if Fe2+/H2O2 molar ratio is affected by the initial pH, the initial pH effect could be decreased by augmenting this ratio. In this study, the increase in Fe2+/H2O2 molar ratio reversed the influence of pH on COD removal efficiency. However, the COD removal efficiency was increased by increasing the initial pH and the initial COD (see Fig. 5), while with decreasing Fe2+/H2O2 molar ratio and augmentation of the initial pH, a considerable increase in COD removal efficiency was observed (see Fig. 5). The result of the present study is in agreement with a previous study on the integrated Fenton-coagulation/flocculation for COD removal and depuration of wine distillery wastewater [8]. Here, MDF wastewater was treated by the Fenton process (H2O2/Fe2+) with an optimal concentration ratio [H2O2]:[Fe2+] = 15 mol/mol, whereby the COD removal was 74% [8]. Furthermore, biological treatment of MDF wastewater after coagulation/flocculation (CF) and electrochemical oxidation (EO) processes has been also investigated [27]. Their experimental results showed that the CF process with a coagulant concentration of 20 g/L and pH 8.4 removed a significant amount of COD (84%) and at EO stage it was ca. 95%.

3.4 VSS, TSS and SVI Measurements In each run, volatile suspended solids (VSS) and total suspended solids (TSS) were determined. The settled volume of sludge was determined by placing a mixed liquor sample in a 100 mL cylinder after 30 min of settling. These two factors are important in designing a wastewater treatment plan. The sludge volume was measured and

i


mL g

mL Settled volume of sludge 1000 mg L mg 6 ¼ g Suspended solids L

ð21Þ

Figure 6 shows the effect of different operating process variables on SVI, TSS and VSS after 30 min settling. Sludge volume index measures the compaction of the sediment. In the present study, a series of experiments using photo-Fenton process were conducted in order to determine the optimal conditions of Fe2+/H2O2 molar ratio, initial COD, initial solution pH and UV contact time for good sedimentation that will produce the lowest sludge volume index. On the other hand, the different operating variables should influence the sedimentation process [24]. The lowest and highest SVI were obtained at 0.0 and 103.0 L/g, respectively. As can be seen from Fig. 6, the lowest initial COD gave the lowest TSS (38.0 mg/L), VSS (18.7 mg/L) and 0.0 mL/g SVI. The greatest TSS (2763 mg/L) was obtained for run number 8, whereas this value for number 10 was ca. 1900 mg/L. Taking into account the removal of COD by photoassisted Fenton oxidation treatment, it can be considered that the best settleability parameters were obtained for high initial COD (8000 – 10 000 mg/L). Furthermore, the addition of a higher molar ratio of Fe2+/H2O2 and a pH between 3 and 4 favored the higher formation of SVI, increasing the COD removal efficiency of MDF wastewater. As a result, the SVI was not followed, only the quantity of suspended solids as a high SVI was obtained at a low TSS (Run 3) and less SVI was measured at high TSS (Runs 13, 15, 17) which was because of the nature of the sludge produced at different conditions.

4 Conclusions Photo-assisted Fenton oxidation treatment resulted in high COD removal efficiency of highly polluted MDF wastewater, due to the high removal capacity of the applied treatment. A maximum COD removal efficiency of 96% was obtained under the following experimental conditions: initial COD of 4000 mg/L, Fe2+/H2O2 molar ratio of 0.11, initial solution pH of 6.5 and a UV contact time of 70 min. First, the removal capacity of COD decreased with an increase in initial COD, because the H2O2 strength decreased with the higher www.clean-journal.com

Clean 2009, 37 (8), 629 – 637

organic matter content of the effluent, and this fact has the effect of reducing the Fenton oxidation efficiency. Second, the lesser Fe2+/ H2O2 molar ratio was more efficient. A decrease in the Fe2+/H2O2 molar ratio and the initial UV contact time resulted in an increase in the COD removal efficiency. A third factor was the initial pH, as the measured final pH of the solution remained constant after photo-assisted Fenton oxidation either at high or low initial pH adjustment. This result was consistent for the total initial CODs studied.

Acknowledgements The study was funded through a research grant and supported by Ministry of Science, Iran, Tarbiat Modares University (TMU). The authors wish to thank Mrs. Haghdoust for her assistance (Technical Assistant of Environmental Laboratory), Ellen Vuosalo Tavakoli for editing the English text, and the Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University for their financial support.

References [1] Environmental, Health, and Safety Guidelines: Board and Particle-Based Products, World Bank Group, International Finance Corporation, Washington, DC 2007. [2] P. Widsten, A. Kandelbauer, Laccase Applications in the Forest Products Industry: A Review, Enzyme Microb. Technol. 2008, 42, 293 – 307. [3] K. Portenkirchner, J. Draxler, W. Somitsch, O. Stassen, Combined Waste Air and Wastewater Treatment Plant for the Wood Panel Industry, Eng. Life Sci. 2003, 3, 465 – 468. [4] A. M. Amat, A. Arques, F. Lpez, M. A. Miranda, Solar Photo-catalysis to Remove Paper Mill Wastewater Pollutants, Sol. Energy 2005, 79, 393 – 401. [5] J. L. Tambosi et al., Treatment of Paper and Pulp Wastewater and Removal of Odorous Compounds by a Fenton-like Process at the Pilot Scale, J. Chem. Technol. Biotechnol. 2006, 81, 1426 – 1432. [6] N. Azbar, T. Yonar, K. Kestioglu, Comparison of Various Advanced Oxidation Processes and Chemical Treatment Methods for COD and Color Removal from a Polyester and Acetate Fiber Dyeing Effluent, Chemosphere 2004, 55, 35 – 43. [7] N. F. Gray, Activated Sludge, Water Technology, 2nd ed., Butterworth-Heinemann, Oxford 2005, pp. 444 – 481. [8] J. Beltran de Heredia, J. R. Dominguez, E. Partido, Physicochemical Treatment for the Depuration of Wine Distillery Wastewaters (Vinasses), Water Sci. Technol. 2005, 51, 159 – 166. [9] P. Bouras, P. Lianos, Synergy Effect in the Combined Photodegradation of an Azo Dye by Titanium Dioxide Photocatalysis and PhotoFenton Oxidation, Catal. Lett. 2008, 123, 220 – 225.

i



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[10] P. Caizares et al., Treatment of Fenton-refractory Olive Oil Mill Wastes by Electrochemical Oxidation with Boron-doped Diamond Anodes, J. Chem. Technol. Biotechnol. 2006, 81, 1331 – 1337. [11] A. F. Martins, C. D. S. Frank, M. L. Wilde, Treatment of a Trifluraline Effluent by Means of Oxidation-coagulation with Fe(VI) and Combined Fenton Processes, Clean: Soil, Air, Water 2007, 35, 88 – 99. [12] A. C. S. C. Teixeira et al., Photo-Fenton Remediation of Wastewaters Containing Agrochemicals, Braz. Arch. Biol. Technol. 2005, 48, 207 – 218. [13] U. K. Garg, M. P. Kaur, V. K. Garg, D. Sud, Removal of Nickel (II) from Aqueous Solution by Adsorption on Agricultural Waste Biomass using a Response Surface Methodological Approach, Bioresour. Technol. 2008, 99, 1325 – 1331. [14] Standard Methods for the Examination of Water and Waste Water, 20th ed., American Public Health Association, Washington, DC 1998. [15] C. M. Pey et al., Optimization of Nano-emulsions Prepared by Lowenergy Emulsification Methods at Constant Temperature Using a Factorial Design Study, Colloids Surf., A 2006, 288, 144 – 150. [16] C. Fan et al., Efficient Photo-assisted Fenton Oxidation Treatment of Multi-walled Carbon Nanotubes, Chin. Sci. Bull. 2007, 52, 2054 – 2062. [17] T. Yonar, K. Kestioglu, N. Azbar, Treatability Studies on Domestic Wastewater Using UV/H2O2 Process, Appl. Catal., B 2006, 67, 223 – 228. [18] J. Perkowski, L. Kos, Treatment of Textile Dyeing Wastewater by Hydrogen Peroxide and Ferrous Ions, Fibres Text. East. Eur. 2002, 38, 78 – 81. [19] E. Lipczynska-Kochany, J. Kochany, Effect of Humic Substances on the Fenton Treatment of Wastewater at Acidic and Neutral pH, Chemosphere 2008, 73, 745 – 750. [20] A. Altin, An Alternative Type of Photoelectro-Fenton Process for the Treatment of Landfill Leachate, Sep. Purif. Technol. 2008, 61, 391 – 397. [21] J. A. Peres, J. Beltrn de Heredia, J. R. Domnguez, Integrated Fenton's Reagent-coagulation/Flocculation Process for the Treatment of Cork Processing Wastewaters, J. Hazard. Mater. 2004, 107, 115 – 121. [22] I. Gulkaya, G. A. Surucu, F. B. Dilek, Importance of H2O2/Fe2+ Ratio in Fenton's Treatment of a Carpet Dyeing Wastewater, J. Hazard. Mater. 2006, 136, 763 – 769. [23] S. S. Ashraf, M. A. Rauf, S. Alhadrami, Degradation of Methyl Red Using Fenton's Reagent and the Effect of Various Salts, Dyes Pigm. 2006, 69, 74 – 78. [24] T. Gonzlez et al., Aluminum Sulfate as Coagulant for Highly Polluted Cork Processing Wastewater: Evaluation of Settleability Parameters and Design of a Clarifier-thickener Unit, J. Hazard. Mater. 2007, 148, 6 – 14. [25] J. J. Pignatello, D. Liu, P. Huston, Evidence for an Additional Oxidant in the Photoassisted Fenton Reaction, Environ. Sci. Technol. 1999, 33, 1832 – 1839. [26] J. Sahl, J. Munakata-Marr, The Effects of In Situ Chemical Oxidation on Microbiological Processes: A Review, Remed. J. 2000, 16, 57 – 70. [27] T. Zayas et al., Applicability of Coagulation/Flocculation and Electrochemical Processes to the Purification of Biologically Treated Vinasse Effluent, Sep. Purif. Technol. 2007, 52, 270 – 276.
