Advances in Environmental Chemistry

Volume 2014 (2014), Article ID 958134, 8 pages

http://dx.doi.org/10.1155/2014/958134

Research Article

Experimental Design of Photo-Fenton Reactions for the Treatment of Car Wash Wastewater Effluents by Response Surface Methodological Analysis

Maha A. Tony1,2 and Zeinab Bedri³

¹Green Chemistry Centre of Excellence, Department of Chemistry, University of York, York YO10 5DD, UK

²Basic Engineering Science Department, Faculty of Engineering, Minoufiya University, Shebin El Koum, Minoufiya 32511, Egypt

³Centre for Water Resources Research, School of Architecture, Landscape and Civil Engineering, University College Dublin, Newstead, Belfield, Dublin 4, Ireland

Received 2 May 2014; Revised 20 July 2014; Accepted 4 August 2014; Published 25 August 2014

Academic Editor: Huu Hao Ngo

Copyright © 2014 Maha A. Tony and Zeinab Bedri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Establishing a treatment process for practical and economic disposal of car wash wastewater has become an urgent environmental concern. Photo-Fenton’s process as one of the advanced oxidation processes is a potentially useful oxidation process in treating such wastewater. Lab-scale experiments with UV source, coupled with Fenton’s reagent, showed that hydrocarbon oil is degradable through such a process. The feasibility of photo-Fenton’s process to treat wastewater from a car wash is investigated in the present study. A factorial design based on the response surface methodology was applied to optimize the photo-Fenton oxidation process conditions using chemical oxygen demand (COD) reduction as the target parameter to optimize. The reagent (Fe²⁺ and H₂O₂ concentration) and pH are used as the controlling factors to be optimized. Maximal COD reduction (91.7%) was achieved when wastewater samples were treated at pH 3.5 in the presence of hydrogen peroxide and iron in amounts of 403.9 and 48.4 mg/L, respectively.

1. Introduction

Car washing leads to disposal of large amounts of oily polluted water which results in potentially high levels of nutrients, metals, and hydrocarbons flowing into storm drains. The composition of pollutants found in car wash wastewater varies according to the way of washing, mechanical car washing or artificial high-pressure water washing, and the size and type of vehicle (e.g., small car, truck, commercial van, etc.). In some cases, car wash wastewater may also contain heavy metals [1–3].

Considering the large volume of wastewater generated from the car washing process, wastewater treatment coupled with recycling may possibly be an essential water quality measure. For instance, in the US, commercial car wash facilities either recycle or treat their wash water prior to discharge to the sanitary sewer system, so most storm water impacts from car washing are from residential car wash systems that discharge polluted wash water into the storm drain system [1]. Some countries, for example, Switzerland, Germany, and The Netherlands, no longer allow outdoor car washing away from car washing stations [4].

In Egypt, as well as in many countries worldwide, car wash activities within petrol stations and outdoor car washing are among those activities that pose an environmental threat to the main freshwater source, the river Nile, which is already subjected to untreated wastewater [5]. Consequently, there is a growing need for research particularly on the application of innovative technologies in the treatment of such kind of wastewater.

The development of novel treatment methods encompasses investigations of advanced oxidation processes (AOPs), which are characterized by the production of the hydroxyl radical () as a primary oxidant [6]. Examples of AOPs include the use of hydrogen peroxide with ultraviolet light (H₂O₂/UV) to treat hazardous compounds [7], Fenton and photo-Fenton reagent (H₂O₂/Fe²⁺) [8–10], semiconductor photocatalysis [11], and the sonolysis process using ultrasonic irradiation [12]. Among various AOPs, the Fenton reagent is one of the most effective methods for treating various industrial effluents including wastewater [13, 14] and oily wastewater [15]. Previous work by the authors has involved the application of Fenton and the photo-Fenton reagents for the treatment of water polluted with diesel oil emulsion [10, 16]. Although Fenton reagent has been reported extensively in the literature, there is a scarcity of publications focusing on its use for the treatment of car wash wastewater.

The main aim of the present study is to explore the possibility of treating car washing water to an acceptable level that can be recycled and reused for the same application. The study is of both national and international importance as it targets two global water issues: water conservation and water pollution. The study outlines the application of the photo-Fenton process to the mineralization of car wash wastewater. The effect of the reaction operating conditions is investigated and the factors that control the Fenton reaction process (Fe²⁺, H₂O₂, and pH) are optimized. Furthermore, the experimental design of the study applies a well-established [17–19] statistical-based technique, commonly known as RSM (response surface methodology) [20], to explore optimum range of values of Fe²⁺, H₂O₂, and pH for the maximum COD removal.

Factors to control the Fenton reaction process are the amounts of Fe²⁺ and H₂O₂ and the working pH. Optimizing such parameters plays a key role towards the achievement of the Fenton reaction. The experimental design using a statistical-based technique, commonly known as RSM (response surface methodology) [20], has been increasingly applied in many fields including wastewater treatment to study the optimization of the treatment process.

2. Materials and Methods

2.1. Car Wash Wastewater

Wastewater samples were collected from a car washing wastewater tank at a petrol station in the south of Egypt. The principal properties of the wastewater are 82 mg-COD/L, turbidity of 28.1 NTU, pH 8.2, and suspended solids of 55 mg/L.

2.2. Experimental Materials

Fe²⁺ in Fenton’s reagent (Fe²⁺/H₂O₂) is prepared by making a solution from Fe²⁺ salt. H₂O₂ was obtained in liquid (30% of H₂O₂, wt) from a commercial supplier. Sulfuric acid is used for adjusting the pH of the wastewater samples during treatment. Properties of chemicals used in this study are listed in Table 1.

Table 1: Properties of chemicals used in the study¹.

Compound
Molecular weight
Formula
Manufacturer
Purity

Iron chloride tetrahydrate
198.8
FeCl₂·4H₂O
Sigma-Aldrich
98.0%

Hydrogen peroxide
134.01
H₂O₂
Sigma-Aldrich
30 wt%

Sulfuric acid
98.08
H₂SO₄
Sigma-Aldrich
97.0%

¹Hydrogen peroxide solution with a stabilizer (dipicolinic acid (approximately 40 mg/L)).

2.3. Methodology

All photochemical experiments were carried out in a batch mode laboratory scale unit using a 250 mL beaker. Initially, the pH value of 100 mL of the car wash wastewater samples was adjusted at the desired values with sulfuric acid before being subjected to oxidation. Then, ferrous ions solution and hydrogen peroxide were added to produce hydroxyl radicals. Subsequently, the mixture was subjected to magnetic stirring and UV radiation (254 nm wavelength), as illustrated in Figure 1. Samples were taken at regular time intervals in the discontinuous experiments and analyzed.

Figure 1: Schematic diagram of a lab-scale photo-Fenton test.

2.4. Analytical Determinations

The COD measurements were performed using HACH analyser (model HACH DR-2400). Turbidity was undertaken using a HACH 2100N IS Turbidity meter (ISO method 7027). The pH of the wastewater was adjusted using a digital pH-meter (model PHM62 Radiometer).

2.5. Experimental Design

The Fenton oxidation process was optimized by applying the response surface methodology [20]. COD removal, defined by (1), of the effluents was used as the variable to be optimized. The amounts of H₂O₂, Fe²⁺, and pH were chosen as the control factors to be optimized. The initial design involved 15 tests, based on a three-level Box-Behnken factorial design [20]:

where is the percentage of COD removal; CODo measured COD in supernatant before oxidation (mg-O₂/L); and COD is the COD value after the treatment.

The first step in the RSM is to find a suitable approximation for the true functional relationship between the response () and the set of independent variables. The following response function was used to correlate the dependent and independent variables in the response surface:

where is the predicted response; and ; is the constant coefficient (intercept); are the linear coefficients; are the cross product coefficients; and is the input controlling coded variable. In addition, the natural variables of the operating system () were transferred to coded variables () according to (3) [20] to simplify the model calculations. The results of COD removal and the turbidity were analysed through the statistical analysis software package of SAS Institute, Inc., [17] by performing the analysis of variance (ANOVA) and fitted with a second-order polynomial model:

The combined effect of the three independent variables, that is, Fe⁺² concentration, H₂O₂ concentration, and initial pH, is represented as , , and , respectively. The range of the experimental variables investigated in the study and the time of reaction (1 hr) were chosen according to preliminary tests. Therefore, each variable ranged between −1 and 1, as the lower and upper levels, respectively. These ranges and levels are presented in Table 2. Fifteen runs were required for a complete set of the experimental designs.

Table 2: Range and levels of natural and corresponding coded variables for RSM.

Variable
Symbols
Range and levels

Natural
Coded
−1
0
1

Fe²⁺ (mg/L)

30
40
50

H₂O2 (mg/L)

350
400
450

pH

3.5
6
8.5

3. Results and Discussions

3.1. Model Fitting

The three-level experiments were carried out according to the Box-Behnken design and the experimental plan is shown in Table 3 as coded and natural levels. The data shows the results of the photo-Fenton experiments as an average of three duplicate experimental results at each operating condition. The following is the second-order fitting polynomial equation of coded factors:

The values of COD of the car washes wastewater as the responses obtained from the experiments and the predicted values are shown in Table 4 and plotted in Figure 2. A satisfactory agreement between the experimental and predicted data is achieved (Table 4). This is confirmed in Figure 2 which shows a regression coefficient value of 0.97 (the model being rejected if the value is less than 0.8 [20]). Thus, it is reasonable to state that the polynomial model (2) is a reliable tool to describe the Fenton reaction behaviour in car washing wastewater treatment.

Table 3: RSM for the three experimental variables in coded units and corresponding natural values.

Experiment number
Natural variable
Coded variable

Fe²⁺ (mg/L)
H₂O₂ (mg/L)
pH

1
30
350
6

2
30
450
6

3
50
350
6

4
50
450
6

5
40
350
3.5

6
40
350
8.5

7
40
450
3.5

8
40
450
8.5

9
30
400
3.5

10
50
400
3.5

11
30
400
8.5

12
50
400
8.5

13
40
400
6

14
40
400
6

15
40
400
6

Table 4: Experimental and predicted achieved removal responses for RSM.

Experiment number
(%)

Experimental results
Predicted response

1
68
72

2
88
84

3
93
96

4
90
86

5
98
90

6
71
71

7
88
88

8
68
76

9
84
88

10
98
102

11
78
74

12
89
85

13
29
29

14
31
29

15
28
29

Figure 2: Predicted versus experimental data for COD removal (%) ().

3.2. Statistical Analysis

The effect of a certain factor is the change in response produced by the change in the level of that factor. When the effect of a factor depends on the level of another factor, the two factors are said to be interacting. In order to further assess the polynomial model (4) taking into account the interaction of factors, statistical analysis of variance (ANOVA) using SAS software was conducted and the statistical significance of the factors towards the response () of the process was determined by Fisher’s -test (-value is the ratio of mean square of regression to the mean square of the error) [17, 20]. Student’s -test was used to determine the significance of the regression coefficients of the parameters. The probability values ( values) were used as a tool to check the significance of the model. In general, if the significance probability value () is small (below 0.05) and the value is lower than 0.01, the model is acceptable [17]. ANOVA of the tested model (Tables 5 and 6) indicated that the model is significant since the -model is 19.94 and has a low probability value ().

Table 5: ANOVA coefficient of regression and checking¹.

Variable
Standard deviation

Coefficient

2.405956
2.691238
0.043235
6.475

2.405956
0.249381
0.812987
0.6

2.405956
−3.20039
0.023985
−7.7

3.541472
8.704009
0.000331
30.825

3.402536
−1.61644
0.166922
−5.5

3.402536
−0.22042
0.834259
−0.75

3.541472
6.953324
0.000945
24.625

3.402536
0.543712
0.609995
1.85

3.541472
7.673364
0.000599
27.175

: coefficient of determination; values were 0.97 for COD percent removal.

Table 6: Analysis of variance (ANOVA) for the RSM model.

Source
Degree of freedom (df)
Sum of squares (SS)
Mean squares

(MS)
statistics

Model
9
8309.248
923.2498
19.93673
0.002105

Linear
3
812.605
812.605
17.547452
0.880207

Square
3
3631.609
3631.609
78.42124
1.001512

Interaction
3
4979.369
4979.369
107.52485
0.611539

Error
5
231.545
46.309

Total
14
8540.793

The response (COD removal, %) surfaces of two-dimensional contour plots and three-dimensional curves, generated by MATLAB 7.0, notably illustrate the relations between two interacting factors with the response (), while the third factor was kept constant at zero. Figure 3 shows the response under the variable concentrations of Fe²⁺ and H₂O₂. It demonstrates a considerable enhancement of COD removal (%) when the H₂O₂ concentration was increased. However, at higher concentrations of H₂O₂ the reduction rate was negatively affected. This trend (the decline of % COD removal with H₂O₂ concentrations higher than the optimum) is more evident when the iron concentration is low. Thus, an increase in the concentration of this reagent does not grant a continuing improvement to the COD removal efficiency of the treated wastewater. Similarly, the reduction percentage of COD demonstrated an increase with increasing Fe²⁺ concentration to a certain point after which it became slower. This indicates that there is an optimal dosage for both Fe²⁺ and H₂O₂ concentrations. Similarly, the 3D surface and the corresponding contour plot in Figure 4 show that the combination of Fe²⁺ concentration and pH has a significant effect on COD removal. The detrimental effect of higher H₂O₂ concentration is probably due to both autodecomposition of H₂O₂ into oxygen and water and the recombination of OH radicals [21]. If either H₂O₂ or Fe²⁺ is not present in optimal dosage, it will scavenge OH radicals and reduce their available amount in solution [19]. Figure 5 demonstrates that the increase in pH with the increase in the H₂O₂ concentration enhanced the rate of COD removal in a certain zone, beyond which less reduction of COD is observed. Therefore, optimising the sensitive parameters (Fe²⁺, H₂O₂ concentrations, and pH) was conducted to achieve the highest COD removal for the system.

(a)

(b)

Figure 3: 3D surface and contour plot of response surface curve for COD removal showing interaction between (a) Fe²⁺ and (b) H₂O₂.

(a)

(b)

Figure 4: 3D surface and contour plot of response surface curve for COD removal showing interaction between Fe²⁺ and pH.

(a)

(b)

Figure 5: 3D surface and contour plot of response surface curve for COD removal showing interaction between H₂O₂ and pH.

3.3. Optimization Analysis

Using the method of experimental factorial design and response surface analysis, the optimal conditions for COD removal percentage by photo-Fenton’s reagent can be determined. Optimum values of the selected variables can be achieved by solving the regression equation (using MATHEMATICA software (V 5.2)). The optimum values of the test variables in-coded were as follows: Fe⁺² dosage,  mg/L, H₂O₂ dosage,  mg/L, and pH, , while the predicted response was 91.7%. According to the relation between and , the natural values of the test variables are shown in Table 7. This finding is in agreement with the previous observation of Tony et al. [16] and Kositzi et al. [22] for the treatment of wastewater.

Table 7: Optimum values of the process parameters for maximum efficiency.

Parameter
Optimum value

(COD reduction rate, %)
91.7

Fe²⁺ (mg/L)
48.4

H₂O₂ (mg/L)
403.9

pH
3.5

The optimal molar ratio H₂O₂ : Fe⁺² in the present study is 12 : 1; hence, the hydrogen peroxide is in excess. This optimal molar ratio compares well with the molar ratio of 11 : 1 given by Tang and Huang [23] for 2,4-dichlorophenol degradation.

Increasing H₂O₂ concentration results in the generation of additional reaction intermediates () radicals which enhances the degradation process. However, at higher peroxide concentrations, the excess hydrogen peroxide can act as an scavenger, forming , which is also a free radical produced in situ from the H₂O₂ but is a less reactive oxidizing agent and therefore has a longer life time than the and the result is a reduction in the overall reaction rate [24, 25]. Moreover, iron concentrations above the optimal value result in reduced process performance because more species of iron ions are produced rather than the more useful radicals. This finding is in agreement with the previous observation of Kositzi et al. [22].

The recommended pH value in this investigation of pH 3.5 is well in agreement with the suggested value of 3.0 by Fongsatitkul et al. [26] in the treatment of wastewater from textile industry. These findings clearly suggest that the optimal ratio of the reagent concentration and the pH value vary in accordance with the type of the substance to be treated.

3.4. Verification of the Results

In order to validate the efficiency of the model, three additional experiments using these optimum operating conditions were conducted. The duplicate experiments yielded an average COD removal percentage of 97%. The predicted COD reduction efficiencies (%) via (2) are jointly shown in Table 8. A good agreement of the data between the experimental and the predicted can be obtained with regression coefficient value of 0.97 (plotting is shown in Table 2). Thus, it is reasonable to believe that the polynomial model (2) is a reliable model to describe the Fenton reaction behaviour in the wastewater treatment.

Table 8: Predicted and experimental values for the responses at optimum conditions.

Type of value
COD reduction, %

Predicted
91.7

Experimental
93.4

4. Conclusion

Results from the present study have demonstrated the effectiveness of the application of photo-Fenton reagent (Fe²⁺/H₂O₂) in the treatment of wastewater delivered from car wash centres. The response surface methodology for optimising such process parameters was applied. This experimental design methodology was shown to be a valuable tool in optimizing the process, which could be satisfied with the minimum number of experiments. The three statistical variables, Fe²⁺, H₂O₂ concentrations, and pH, showed optimal values, giving maximum percentage COD reduction that reached 97% in treating such car washing water used in the study. The optimal molar ratio of H₂O₂ : Fe²⁺ was found to be 12 : 1 and the optimum pH was 3.5. These findings are comparable to the literature. This demonstrates the usefulness and effectiveness of the Fenton reagent as an advanced technique for the treatment process.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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