Introduction

Due to the rapid development in the textile dying industries and the extensive use of synthetic dyes, wastewater discharges from such industry represent a major environmental pollution issue. Azo dyes are considered the major classes of synthetic dyes since up to 70% of the dyes used for commercial purposes are azo dyes (Hussein 2012). According to the presence of the azo bonds (–N=N–) such dyes are classified to mono-, dia-, tri-azo, etc. In addition, some of those dyes or their precursors are from toxic aromatic amines complexes. Thus, both human and the marine life are seriously affected due to their toxic and highly carcinogenic effect. To develop the environmental performances, different treatment technologies have been applied in order to mange those aquatic effluents (Abu Amr et al. 2014; Kumaravel et al. 2015; Tony et al. 2018). Over the last decades, to overcome the increasing demand of human being for water, several techniques have been developed for industrial wastewater treatment. According to the literature, for instance, some researchers applied non-reagent physical techniques for treatment such as filtration, coagulation and precipitation (Zhao et al. 2009; Kotut et al. 2011; Ashour and Tony 2017). Additionally, some studies reported the application of adsorption methods for wastewater treatment (Parker et al. 2012; Ashour et al. 2014; Torrades and García-Montano 2014; Tony 2019). However, such techniques are not efficient or adequate for treatment since they transfer the pollutant from one phase to another without mineralizing them besides the high cost of maintenance and treatment. Thus, there is an urgent need to improve feasible alternative techniques for treating the industrial wastewater in an economic and viable approach that environmentally friendly and does not produce a second pollution. Recent studies suggested the significant use of the oxidation techniques such as Fenton’s reagent as an attractive alternative method for treating dyed wastewater (Chen et al. 2012; Torrades and García-Montano 2014).

Recent advances in the wastewater treatment oxidation are the innovative nanomaterials application. Nanomaterials revealed a promising improvement in the hazards and toxic contaminant remediation and removal (Babuponnusami 2014; Tony et al. 2016). Nanomaterial is applied previously as an alternative way to enhance the Fenton’s reagent oxidation technique. Nanomaterials as a source of Fenton’s reagent have many applications in wastewater treatment with various pollutants, for instance, humic acids (Nie et al. 2010), chlorophenol (Lu et al. 2002) and phenol (Prucek et al. 2002). However, according to the literature, nanocrystals as a source of Fenton’s reagent have not been applied so far for treating BBD effluents.

Response surface methodology (RSM), which is a combination of mathematical and statistical procedures, is appropriate for modelling and evaluating the several variables influencing the responses even in the existence of involved interactions. To date, the traditional optimization researches of wastewater processes have focused on one-variable-at a time methodology. However, this study does not consider the cross-effects of the considered variables. Thus, this time consuming method with a lack of accurate optimum values (Torrades and García-Montano 2014; Tony et al. 2011; Mourabet et al. 2017). The main objective of RSM application in the treatment can result in improving the Fenton’s reagent process by determining the optimum variables values and maximizing the response with a decrease in the experiments numbers. Also, the extensive literature examination demonstrates none of researchers examined the Fenton’s parameters optimization using CCD for BBD removal. In this respect, the present investigation introduces the application of two-variable central composite design (CCD) for BBD removal using photo Fenton reagent to evaluate the colour (C) removal and COD reduction in the BBD wastewater as dependent variables. The two selected variables applied in this study are nanocrystals dose and hydrogen peroxide reagent as independent variables. Subsequently, the mathematical models correlating C and COD removals of the two variables were established.

Materials

Wastewater

The textile dye Bismarck Brown (BBD), with a molecular formula of (C21H24N8·2HCl), its molecular weight is 461.39 g/mol and water solubility is 11 g/L at 25 °C is the target pollutant in this study. Real textile dying wastewater samples were collected from a service of dying facility of Jeans garments using Bismarck Dye in Menoufia Governorate, Shebin El-Kom City, north of Egypt between the period of February and April 2018. Wastewater was collected subsequent the washing facilities conducted after the re-dying process to remove the extra dyes. The main characteristics of wastewater are BBD load is 13.28 mg/L, and COD 1240 and the Suspended Solids (SS) are 23 mg/L with an effluent pH of 8.0.

Synthesis and Characterization of Fe2O3 nanocrystals

Nano-crystalline Fe2O3 from aqueous solutions of iron chloride precursor was synthesized using a simple sol–gel technique, and the final product is attained after calcination. The synthesized nanocrystals by this method were confirmed using X-ray diffraction (XRD) patterns. IR spectroscopy using FTIR was used for identification of functional groups and confirmed such type of nanocrystals. Also, the surface morphology was performed using transmission electron microscope (TEM), which found the particle size is varied from 6.1 to 18.3 nm as previously found in our preliminary experiments.

Photochemical reactor and procedures

A lab-scale batch mode test of the experimental set-up is shown in Fig. 1. A 12 W UV lamp inside a glass sleeve is immersed in a glass container. Firstly, nanocrystals are added to 100 mL of wastewater solution with pH 3.0 (the optimum pH according to our preliminary investigation). Thereafter, the reaction is initiated by adding H2O2 reagent. The reagents are well mixed in the BBD aqueous solution, and then, the solution is poured to the photo-reactor container. All experiments were conducted at room temperature at stirring speed of 800 rpm and pH 3.0. After 10 min of reaction time treatment, the substrate of the treated wastewater samples is subjected to COD and colour removal analysis.

Fig. 1
figure 1

Scheme methodology of nano-photo-Fenton investigation

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Central composite experimental design (CCD)

Based on response surface methodology (RSM), central composite experimental design (CCD) technique was chosen to conduct the optimization of the experimental conditions for BBD removal from aqueous solution using photo-Fenton reagent. RSM is a valuable statistical tool that maximizes the response surface affected by operating variables based on a few sets of experiments within a chosen range. RSM is applied widely in the optimization of operating variables (Bhatia et al. 2007), experimental conditions of advanced oxidation process, adsorption processes and biological techniques (Raquel et al. 2015), etc. In this study, CCD is applied for optimization of operating parameters, namely nanocrystals Fe2O3 dose and H2O2 reagent concentration for maximizing BBD removal. The leading objectives of BBD reduction are based on colour removal and COD reduction efficiencies of the dyed wastewater. In order to evaluate the process, five levels of the two main factors: nanocrystals (E1) and H2O2 (E2) doses, were chosen as shown in Table 1.

Table 1 Range and levels of natural and corresponded coded variables for CCD
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After conducting the experiments, the actual experimental data aimed at developing the model (shown in Table 2) were analysed through least-square method; thus, the correlation of the independent variables and the responses was estimated by the following second-order polynomial model (Khuri and Cornell 1996):

$$ f_{i} = \beta_{o} + \sum {\beta_{i} e_{i} } + \sum {\beta_{ii} e_{i}^{2} } + \sum {\sum {\beta_{ij} e_{i} e_{j} } } $$
(1)

where fi is the predicted response for C and COD removal, respectively; βo, βi, β2 and βii, are the model coefficient and e1 and e2 are the independent variables.

Table 2 The CCD experimental design with two independent variables
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RSM was applied to the experimental data using statistical analysis software (SAS) to predict the models through regression analysis and analysis of variance (ANOVA) (SAS 1990). Three-dimensional response surface and two-dimensional contour plots were created by applying Mathematica software (V 5.2) to visualize the interactive effects of the independent factors. Optimum region was also located based on the main parameters in the overlay plot.

Dye removal determinations

Concentration of wastewater substrate was monitored by measuring both colour removal using spectrophotometric techniques at a wavelength of 526 nm at the maximum absorbance peak (UV–visible spectrophotometer, model Unico UV-2100, USA). Besides, the measurement of its chemical oxygen demand (COD) following the standard methods (APHA 1998) is conducted. pH of the wastewater samples was measured and adjusted using a digital pH-meter (AD1030, Adwa instrument, Hungary).

Calculation methods

BBD dye removal from wastewater was calculated according to the following equations:

$$ f_{1} = \frac{{C_{{\text{o}}} - C_{{\text{t}}} }}{{C_{{\text{o}}} }} \times 100 $$
(2)
$$ f_{2} = \frac{{{\text{COD}}_{{\text{o}}} - {\text{COD}}_{{\text{t}}} }}{{{\text{COD}}_{{\text{o}}} }} \times 100 $$
(3)

where f1 and f2 were the colour (C) removal and chemical oxygen demand (COD) removal; Co, CODo and Ct, CODt were the initial and finial values of C and COD.

Results and discussion

Mathematical model build-up

In the present study, photo-Fenton reagent based on the nanocrystalline iron oxide was investigated to treat BBD wastewater under different operational conditions: Fe3+ (11.72–68.28 mg/L) and H2O2 (117.2–682.8 mg/L) at the operating pH 3.0. A central composite design (CCD) was applied as it is very commonly used in the RSM technique and the simple linear or quadratic models can be related to the response values (Colour and COD removals in this study) (Tak et al. 2015). The thirteen experiments are necessary for the design matrix of the two factors (Fe3+ and H2O2 doses) with five levels of each factor, which were considered to cover the experimental domain. Values of the experimental responses variable (C and COD reduction) are given in Table 2 along with their predicated responses values. The predicted responses of C and COD removal values were obtained from the second-order polynomial equation fitting using SAS software [Eqs. (4), (5)]. The response values (f1) for %C removal and (f2) for %COD removal are presented by the following equations to attain the interaction between dependent and independent variables:

$$ f_{1} = 75.53 - 4.29E_{1} + 1.88E_{2} - 14.92E_{1}^{2} + 2.22E_{1} E_{2} - 12.74E_{2}^{2} $$
(4)
$$ f_{2} = 72 - 4.72E_{1} + 2.69E_{2} - 12.56E_{1}^{2} - 12.31E_{2}^{2} $$
(5)

where f1 and f2 are %C and %COD removal of BBD from wastewater, E1 and E2 are the codified values of the operating parameters nanocrystals iron oxide and hydrogen peroxide concentrations, respectively. At this point, the experimental responses for %C and %COD removals are presented in Table 2. The corresponding predicted responses obtained from the models [Eqs. (4) and (5)] are very close as shown in Fig. 2, demonstrating that both values were accurate and reliable.

Fig. 2
figure 2

Comparison between actual and predicted values of models af1 model, bf2 model

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In order to test the suitability and the significance of the model, analysis of variance (ANOVA) was conducted using SAS software. The obtained regression equations of the polynomial models from ANOVA indicated that the correlation coefficient (R2) for f1 and f2 are 98.47 and 98.80, respectively. This indicates a good correlation between experimental and predicted models. In fact, the quality of the model is well fitted when R2 is suggested to be at least 0.80 (Arslan-Alaton et al. 2009; Tony and Bedri 2014).

In the ANOVA test of the two models presented in Table 3, the Fisher variance ratio, F value is used as a measure to estimate the way that the factors explains the variation in the mean of data (Montgomery 1991). F value is 89.98 for C removal and 115.73 for COD removal. Simultaneously, the given very low probability (P-value) (0.0001) is considered significant when it is (Pmodel > F) < 0.05 in surface response analysis (Montgomery 1991; Tony et al. 2015). Moreover, the coefficient of variation, CV, recorded low values of 3.39% C removal model and 4.08% for COD removal model that confirms the reliability and good precision of the experimental data as recommended by Kuehl (2000). Therefore, it is concluded that the model fitted well and highly significant and the estimated factors effects are real.

Table 3 ANOVA for the regression model and the respective models terms
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Effect of the investigated variables on colour and COD removal

Subsequently, the verification of significance of the model, the suitability of the variables and interactions is identified following the Student t test (Montgomery 1991).

Based on the F values (Table 3), nanocrystals dose (E1) has significant effect (P < 0.001) and (P < 0.0002) on the responses of C and COD removal (f1, f2), respectively as it had a larger coefficient than hydrogen peroxide concentration (E2). The interaction effects between E1 and E2, were moderate. Positive coefficient of H2O2 (E2) indicated the linear effect of increase in f1 and f2. However, iron nanocrystals dose (E22) had a negative effect along with and quadratic terms (E12 and E22) that decreases f1 and f2 responses. The positive effect of the interaction term (E1E2) increases f1 response. Furthermore, in order to attain a better understanding of the results, 3D surface graph and 2D contour plots were used to attain a better understanding of the results, the dependent (f1 and f2) and independent variables (E1 and E2) using Matlab (7.11.0.584) software. The interaction effects of Fe3+ nanocrystals and H2O2 concentrations on the C removal efficiency presented in the 2D contour plots in Fig. 3b that simulated from Eq. 4. As seen from Fig. 3a, b, the surface and contour plots showed that the %C removal was increased with increasing both the nanocrystals and H2O2 reagent doses. However, further increase in both reagents doses after a certain point, the reaction is retarded. This phenomenon indicates that the optimum operating conditions of Fe3+ and H2O2 according to colour removal are 37.21 and 412.31 mg/L, respectively. Based on the maximum COD reduction, the optimum values are 36.24 and 421.88 mg/L, respectively (as seen in Fig. 4a, b). Notably, this is obviously near to the optimal points with that from colour removal test.

Fig. 3
figure 3

Plots of colour removal response with respect to Fe3+ dose and H2O2 dose of photo-Fenton reaction a 3D surface plot, b 2D contour plot

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Fig. 4
figure 4

Plots of COD removal response with respect to Fe3+ dose and H2O2 dose of photo-Fenton reaction a 3D surface plot, b 2D contour plot

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From Figs. 3 and 4, it can be estimated that the key factor for the colour and COD removals is the nanocrystals Fe3+ concentration. However, even the hydrogen peroxide reagent effect is also important, but its effect is at a lower level. A negative effect in both responses, C and COD reduction, is observed. This effect is related to the scavenging effect of radicals. However, optimal doses are needed for photo-Fenton reaction to occur. This could be illustrated by the increase in H2O2 in the presence of iron crystals resulted in the increase in·OH radicals generation. These radicals are the main responsible of the Fenton’s reagent as those radicals are attaching the organic pollutant and mineralizing them. Thus, C and COD removals are enhanced. However, excessive amounts of both reagents had a determinable effect on the overall removal efficiency because of undesirable reactions that may occur between hydroperoxyl radicals and excessive amounts of H2O2 and Fe3+, thus, reacting with hydroxyl radicals and scavenging them according to the following equations (Tony and Bedri 2014):

$$ {\text{H}}_{2} {\text{O}}_{2} +^{ \cdot } {\text{OH}} \to {\text{HO}}_{2}^{ \cdot } + {\text{H}}_{2} {\text{O}} $$
(6)
$$ {\text{Fe}}^{3 + } + {\text{HO}}_{2}^{ \cdot } \to {\text{O}}^{2} + {\text{Fe}}^{2 + } + {\text{H}}^{ + } $$
(7)
$$ {\text{Fe}}^{2 + } + {\text{HO}}_{2}^{ \cdot } \to {\text{HO}}_{2}^{ - } + {\text{Fe}}^{3 + } $$
(8)
$$ {\text{Fe}}^{2 + } +^{ \cdot } {\text{OH}} \to {\text{HO}}^{ - } + {\text{Fe}}^{3 + } $$
(9)

Parameters optimization within designated constraints and verification

The optimal values for two independent variables, naoncrsyals of iron oxide and hydrogen peroxide reagents for colour and COD removals were attained using the numerical optimization future of Mathematica software (V 5.2). Additionally, a graphical optimization presented in Fig. 5 displays the area (yellow portion zone) of feasible response values in the factors space. All the factors and responses with the particular high limit and low limit experimental region are significant and fit the standards that described the optimum values as stated in Table 4. The optimum values for Fe3+ and H2O2 are 37.21 and 412.31 mg/L with maximum response of 75.9 for C removal. By comparing the optimum values in Fig. 3 with Fig. 4, it can be observed that the location of optimal operating conditions from C test was rational from COD test.

Fig. 5
figure 5

Overlay contour plots for photo-Fenton optimal region of two responses

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Table 4 The preset goal with the constrains for all the independent factors and responses in numerical optimization
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Ultimately, to validate the model adequacy, an additional experiment was performed using those amounts. The experimental colour and COD removals are 76.5% and 73%, compared to 75.9% and 72.6%, respectively, for the predicted values (Table 5). This confirms that the CCD based on RSM was a powerful tool for locating the accurate optimal values of the independent variables.

Table 5 Predicted and experimental values for the responses at optimum conditions
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Conclusion

The performance of photo-Fenton technique for colour removal and COD reduction in BBD wastewater was investigated. Among this study, by observing the effects of interactions among the variables on colour, C, and COD removal efficiencies, the RSM experimental conditions were optimized. The results demonstrated significant effects of the two operating variables (iron oxide nanocrystals and hydrogen peroxide concentrations) as well as their interactive effects on the two responses. CCD was applied to locate the optimum removal rate for both responses. According to ANOVA, the proposed second-order polynomial model is verified and thus the model is accepted. Notably, the obtained high R2 values for the two models are attained (98.47 and 98.80 for C and COD removals, respectively) which confirms the high accuracy of the models. The predicted colour and COD removals are 75.9 and 72.6% at the optimum operating conditions at pH 3.0. Furthermore, the economical experimental values of the two responses maximized to 76.5% and 73% for C and COD models, respectively.