Application of Box–Behnken factorial design for parameters optimization of basic dye removal using nano-hematite photo-Fenton tool
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Abstract
A huge amount of water is consumed in the textile industry, and the result is the production of a large amount of wastewater. The treatment of such wastewater significantly reduces the pollution load. Oxidation by nano-Fenton reactions (Fe^{3+}/H_{2}O_{2}) is a reasonable and cost-efficient process for the remediation of harmful pollutants in wastewater. In the present study, nano-hematite was applied as a source of iron in Fenton’s reagent for methylene blue dye removal from wastewater. The effects of different parameters, presence of nano-hematite, hydrogen peroxide concentrations and pH, were optimized using the response surface methodology technique. A Box–Behnken design was applied, and the response (dye removal) was maximized. A maximal dye removal (81.6%) was attained when wastewater was treated at pH 2.5 in the presence of nano-hematite and hydrogen peroxide in the amounts of 41 and 388 mg/L, respectively. The model is well fitted and described using the second-order polynomial equation. Moreover, the model validation showed a 97% fit between the theoretical and experimental ones.
Keywords
Methylene blue dye Photo-Fenton Nano-hematite Response surface methodology (RSM) Statistical optimizationIntroduction
Recently, one of the major problems facing industrialized societies is the pollution of the environment by hazardous chemicals, especially the aquatic environment. Such industries that cause water pollution are: paint, textile, dyeing, pharmaceutical industries, tannery and paper industry. The residual dyes from different sources contain a wide variety of organic pollutants that discharge into the natural water supplies or conventional wastewater treatment techniques. Textile industry is considered as one of the main causes of pollution problems worldwide that discharges severe dye-containing wastewaters. In general, the effluent wastewater discharged from this dyeing industry is esthetically and environmentally unacceptable as it is a highly persistent of organic pollutants and has strong color and high pH value (Wang et al. 2007; Sathian et al. 2013). The volume of wastewater from the industries has increased and needs to be treated. Such wastewater could be reused in order to reduce the amount of discharged effluents into the environment once it has been treated.
Traditionally, textile wastewater management practices have been concerned with treating the effluents using various systems. Several physical, chemical and biological approaches are available for the treatment of dye effluents wastewater, for example coagulation–flocculation, filtration, biological treatment and chemical adsorption (Singer and Chen 1980; Mohapatra et al. 2010). However, these processes can only convert the contaminants from one phase to another one without destroying them. Moreover, their costs are very high for treating raw textile wastewater (Altinbas et al. 1995; Coloma 1998).
Recently, alternative advanced oxidation processes (AOPs), which include both homogeneous and heterogeneous photocatalysis, have been emerged as promising technologies. The main aim of those technologies is the mineralization of the largest number of dyes into biodegradable and harmless end products besides the color removal from such wastewater (Tang and An 1995; Andreozzi et al. 1990).
AOPs are a set of alternative systems that substitute the conventional methods; AOPs are proceeded along the creation of highly oxidative species specifically hydroxyl radicals (^{·}OH) which are accomplished of oxidizing the organics to such a level of harmless products, CO_{2} and H_{2}O. The application of homogeneous catalysis in oxidizing different organics specially dyes concerning much attention. This is due to their high efficiency in the mineralization and oxidation such as Fe^{n+}/H_{2}O_{2} (Fenton) and Fe^{n+}/H_{2}O_{2}/UV (photo-Fenton) processes (Hsueh et al. 2005; Daneshvar and Khataee 2006). Compared to the other systems, the use of AOPs has the advantages that there is no sludge formation, considerably safe and its ease of operation. Besides, there is the advantage of short reaction time (Marechal et al. 1997).
The application of UV/Fenton has been reported extensively in the literature, though there is a scarcity of the literature published in the case of using the nano-Fenton catalyzed reactions. For example, Rahman et al. (2009) applied photo-Fenton’s reagent for the oxidation of commercial textile dye wastewater named Malachite green. Such reagent was used for treating water containing diesel oil Tony et al. (2009) and synthetic wastewater contaminated by phenol Tony et al. (2017). The decolorization of methylene blue by Fenton’s reagent is achieved by Liu et al. (2011). Furthermore, Tony et al. 2012 investigated the photo-Fenton’s reagent for the mineralization of oil refinery effluents. Abou-Gamra (2014) applied the reagent for the treatment of red dye-contaminated wastewater. El Haddad et al. (2014) treated azo dye-polluted wastewater from textile industry using Fenton’s reagent. In addition, Minella et al. (2014), Srodowiska (2015) and Assadi and Eslami (2010) applied the reagent for the degradation of phenolic wastewater. Moreover, Khan et al. (2016) using magnetite + H_{2}O_{2} + UV process as a source of Fenton’s reagent investigated the photocatalytic degradation of methylene blue in wastewater.
To a very large extent in many studies, Fenton’s reagent optimization parameters are still conducted on a trial-and-error finding, i.e., changing one factor at a time. However, this is an experimentation technique based on the variation of a single factor while fixing the all-remaining parameters at a certain set of conditions. Moreover, single-dimensional (factor) search is not only a time-consuming technique, but also the attained optimum conditions is not accurate due to the neglection of the interaction between the operating variables (Mason et al. 2003). To overcome this experimental problem, the response surface methodology (RSM) has been suggested to define the effects of individual parameters. RSM is referred to a set of mathematical and statistical techniques to establish an experimental design model. Thus, investigating the consequences of various independent parameters on the response is conducted to locate the optimum conditions with a reduced number of experimental trials (Khuri and Cornell 1996). The experimental design used to establish the RSM model is based on the least squares method. The diagnostic checking tests specified by the analysis of variance (ANOVA) is used to check the adequacy of the proposed model. In addition, a graphical representation plots the response surface to locate the optimum and study the surface (Montgomery 1991). As illustrated in the literature, RSM was applied in many areas to optimize the process parameters (Tekindal et al. 2012; Duc 2014; Elboughdiri et al. 2015; Suarez-Escobar et al. 2016; Ramirez et al. 2005).
The objective of this current work is to locate the optimum conditions of Fenton’s reagent amount and pH to attain the highest of methylene blue (MB) removal rate in wastewater. The optimization is carried out via Box–Behnken RSM experimental design. The interaction between Fe^{3+}, H_{2}O_{2} and pH variables that affecting the dye removal rate is studied, and model describing the effect of those variables is explained.
Materials and methods
Materials
Methylene blue (MB) with a chemical formula of (C_{16}H_{18}N_{3}SCl) is selected as a model wastewater pollutant. Nano-powder, ∝-Fe_{2}O_{3}, with spherical-shaped particles and size ranging from 6.1 to 18.3 nm is used as an iron source for the nano-photo-Fenton method. It was previously prepared in our laboratory using sol–gel technique. Moreover, H_{2}O_{2} (30%) supplied by Sigma-Aldrich was used to initiate the Fenton’s reagent. pH values were adjusted at the desired values using sulfuric acid and sodium hydroxide, and both are supplied by Alpha Chemicals.
Experimental methodology
Analytical determinations
Experimental design
Range and levels of natural and corresponded coded variables for RSM
Variable | Symbols | Range and levels | |||
---|---|---|---|---|---|
Natural | Coded | − 1 | 0 | 1 | |
Fe^{3+} (mg/L) | x _{1} | X _{1} | 20 | 40 | 60 |
H_{2}O_{2} (mg/L) | x _{2} | X _{2} | 200 | 400 | 600 |
pH | x _{3} | X _{3} | 2.5 | 3.0 | 3.5 |
RSM for the three experimental variables in coded units and corresponding natural values
Experiment no. | Natural variable | Coded variable | ||||
---|---|---|---|---|---|---|
Fe^{3+}(mg/L) | H_{2}O_{2} (mg/L) | pH | X _{1} | X _{2} | X _{3} | |
1 | 20 | 200 | 3.0 | − 1 | − 1 | 0 |
2 | 20 | 600 | 3.0 | − 1 | 1 | 0 |
3 | 60 | 200 | 3.0 | 1 | − 1 | 0 |
4 | 60 | 600 | 3.0 | 1 | 1 | 0 |
5 | 40 | 200 | 2.5 | 0 | − 1 | − 1 |
6 | 40 | 200 | 3.5 | 0 | − 1 | 1 |
7 | 40 | 600 | 2.5 | 0 | 1 | − 1 |
8 | 40 | 600 | 3.5 | 0 | 1 | 1 |
9 | 20 | 400 | 2.5 | − 1 | 0 | − 1 |
10 | 60 | 400 | 2.5 | 1 | 0 | − 1 |
11 | 20 | 400 | 3.5 | − 1 | 0 | 1 |
12 | 60 | 400 | 3.5 | 1 | 0 | 1 |
13 | 40 | 400 | 3.0 | 0 | 0 | 0 |
14 | 40 | 400 | 3.0 | 0 | 0 | 0 |
15 | 40 | 400 | 3.0 | 0 | 0 | 0 |
The probability (p value) of the given statistical model determines the significance of the model to accepted or rejected. Generally, based on the p value the model is accepted with 95% confidence level. Three surfaces dimensional plot curves (using MATLAB 7.0 software) and their corresponding contour plots were attained based on the effects of the levels of the three variables (Fe^{3+} and H_{2}O_{2} dosage and pH). These 3-D plots the simultaneous interaction of two variables on the response while keeping the third variable constant in the polynomial equation. Furthermore, based on the main variables in the 3-D surface and contour plots, the optimum region is determined. Finally, Mathematica software (V 5.2) was applied to locate the accurate optimum operating parameters.
Results and discussions
Optimization of Fenton’s reagent parameters with RSM
RSM model
Experimental and predicted achieved removal responses for RSM after 30 min of reaction time
Experiment no. | γ (%) | |
---|---|---|
Experimental results | Predicted response | |
1 | 26.13 | 24.18 |
2 | 31.00 | 24.34 |
3 | 27.45 | 34.11 |
4 | 20.22 | 22.17 |
5 | 50.00 | 45.98 |
6 | 28.23 | 27.54 |
7 | 49.60 | 50.33 |
8 | 07.42 | 11.40 |
9 | 67.91 | 73.86 |
10 | 53.00 | 50.34 |
11 | 15.10 | 17.76 |
12 | 55.00 | 49.05 |
13 | 68.12 | 68.08 |
14 | 68.14 | 68.08 |
15 | 68.00 | 68.08 |
Effect of independent variables and their interaction
MATLAB software has been used to build the response surfaces by fixing one variable in the polynomial equation to plot a 3-D representation of two independent variables with their response (represented in MB removal percentage after 90 min of reaction time).
In addition, Figs. 3 and 4 give data of different ∝-Fe_{2}O_{3} initial concentration and percentages of dye reduction. Adding a too high initial ∝-Fe_{2}O_{3} concentration results in lowering the detrimental effect because of a smaller excess of H_{2}O_{2}. In addition, when the iron salt increases, the dye removal rate is increased; however, after a certain limit, the increase in such reagent is unfavorable. The detrimental effect of high H_{2}O_{2} loads could be described by the fact that instead of H_{2}O_{2} producing the reactive species to mineralizing the dye, it reacts with the one of the reactive species (^{·}OH radicals), and therefore, the quantity of OH radicals reduced and the overall reaction rate thus reduced (Tony et al. 2009). Thus, the doses of the hematite and hydroxide should be in an optimal balance.
As shown in Fig. 4, it is possible to change the Fenton reagent ratios (Fe^{3+}:H_{2}O_{2}) while keeping the remaining experimental conditions at constant values. Moreover, it is possible to increase or decrease the dye reduction (Fig. 3) after 90 min of treatment under UV light irradiation.
Figure 5 represents the effects of Fe^{3+} concentration on the color removal after 90 min of reaction. Usually, the effect of both pH and/or iron load may be positive or negative on the rate of MB removal. From Fig. 6, which illustrates the contour plot of the coded Fe^{3+} concentration and pH, the range of optimum value of the dye removal is related to both Fe^{3+} concentration and pH. Indeed, it is the same trend as previously illustrated in the literature; the optimum pH sharply affects the removal rate (Benatti et al. 2006; Torrades and García-Montaño 2014).
Thus, it could be concluded that any variable could affect the reaction rate positively or negatively, depending on the interaction effect of the other parameters. This validates the application of Box–Behnken tools for system optimization.
A similar performance is observed at a constant Fe^{3+} load, while varying the pH and H_{2}O_{2} load (Figs. 7, 8). This could be illustrated by the fact that at some experimental conditions, very high H_{2}O_{2} load causes a decrease in the overall MB dye removal rate, and this is due to the competition between these species for attacking the highly reactive OH radical species. Certainly, rather ^{·}OH radicals are a non-selective particles; thus, it reacts with the organic matter present in wastewater as well as with other species (Freitas et al. 2014; Duc 2014; Elboughdiri et al. 2015).
It could be concluded from Figs. 5, 6, 7 and 8) that at optimum pH value the optimum concentration of both reagents (Fe^{3+} and H_{2}O_{2}) seems to affect positively the final performance.
ANOVA for response surface model
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 (5) taking into account the interaction of factors, statistical analysis of variance (ANOVA) using SAS software was conducted and the statistical significance of the factors toward the response (\(\gamma\)) of the process was determined by Fisher’s F-test (SAS 1990; Montgomery 1991; Torrades et al. 2003; Benatti et al. 2006).
Coefficient of determination, R^{2}, was calculated to measure the degree of fir of the model. The R^{2} value is expressed as the ratio of the explained variation to the total variation. It additionally provides a measurement of the proportion of the variability in the observed response variables and how it explains variables and their interactions (Haber and Runyon 1977). Moreover, the better the empirical model to predict the response accopained by good fitting with the actual data when R^{2} approaches unity. However, a low R^{2} value indicates that there is a less relevance of the dependent parameters in the model to illustrate the performance variation (Little and Hills 1978; Mendenhall 1975; Joglekar and May 1987).
ANOVA coefficient of regression and t checking
Variable | SD | T | p > t | Coefficient |
---|---|---|---|---|
X _{1} | 2.314715 | 0.83844 | 0.440013 | 1.94075 |
X _{2} | 2.314715 | − 1.27300 | 0.258995 | − 2.94663 |
X _{3} | 2.314715 | − 6.19736 | 0.001596 | − 14.3451 |
X _{1} X _{1} | 3.407168 | − 4.10112 | 0.009345 | − 13.9732 |
X _{1} X _{2} | 3.273502 | − 0.92409 | 0.397846 | − 3.02500 |
X _{1} X _{3} | 3.273502 | 4.18619 | 0.008603 | 13.70350 |
X _{2} X _{2} | 3.407168 | − 8.19257 | 0.000441 | − 27.9135 |
X _{2} X _{3} | 3.273502 | − 1.55896 | 0.179748 | − 5.10325 |
X _{3} X _{3} | 3.407168 | − 1.86664 | 0.120934 | − 6.35996 |
Analysis of variance (ANOVA) for the RSM model
Source | Degree of freedom (DF) | Sum of squares (SS) | Mean squares (MS) | F values | p > F |
---|---|---|---|---|---|
Model | 9 | 6076.486 | 675.1651 | 15.75 | 0.004 |
Linear | 3 | 1745.854 | 1745.854 | 40.73 | 0.701 |
Square | 3 | 3747.178 | 3747.179 | 87.42 | 0.130 |
Interaction | 3 | 891.9187 | 0891.919 | 20.81 | 0.586 |
Error | 5 | 214.3162 | 042.8633 | ||
Total | 14 | 6290.802 |
Determination of optimal removal conditions
Optimum values of the process parameters for maximum efficiency
Parameter | Optimum value |
---|---|
\(\gamma\) (dye removal, %) | 81.6 |
Fe^{3+} (mg/l) | 41 |
H_{2}O_{2} (mg/l) | 388 |
pH | 2.5 |
Finally, a model describing photo-Fenton based on nanoparticles reagent for remediation of a synthetic prepared MB dye wastewaters was established based on Fenton reagent loads and pH, as related to the initial organic matter load in the wastewater. The model obtained was revealed to appropriate of the predicting for MB dye removal rate within the ranges of the investigated parameters.
Model validation
Under the optimum conditions given in Table 6, three replicates were conducted and the average dye removal efficiency was 80% that is very close to the predicted value of 81.6% (as shown in Fig. 7) for 30 min of reaction. The validity of the model for the dye removal performance of MB was confirmed by the high correlation concerning the experimental and predicted data.
Predicted and experimental values for the responses at optimum conditions
Type of value | Dye removal (%) |
---|---|
Predicted | 81.6 |
Experimental | 80.0 |
Conclusion
Optimization of nano-photo-Fenton reagent with respect to the dye removal rate for the treatment of MB dye-contaminated wastewater effluents has been examined. Box–Behnken experimental design based on response surface methodology was used to locate the optimum operating variables to maximizing the dye removal efficiency. The Fenton dosages and pH are both important terms to attain higher dye removal rate. The model developed using RSM for dye removal can be applied for predicting MB dye removal rate within the ranges of the variables investigated. The optimum region for the photo-Fenton’s process system is determined using RSM technique. The optimum values of the operating parameters are 41 and 388 mg/L for Fe^{3+} and H_{2}O_{2}, respectively, and pH 2.5, where 80% of dye removal can be obtained.
Notes
References
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