**1. Introduction**

Some of the effluents produced by industries such as textiles, dyes, tanneries, cosmetics and pulp are colored [1]. In the pulp industry, effluents are colored due to the presence of lignin byproducts and other phenolic compounds formed [2]. These compounds are considered dangerous and recalcitrant because of their low biodegradability and resistant to chemical degradation [1,3]. Besides, recalcitrant compounds with biological activity contained in treated effluent discharges are generating a loss of biodiversity in ecosystems. Even more, some of these compounds with benzyl and phenolic structures are considered endocrine disruptors [4–7]. In addition to the presence of recalcitrant compounds, the bacteria in the effluents of the pulp industry must also be seriously considered. The presence of bacteria in effluents discharged to water bodies are generating humans and animals diseases. In this sense, *Escherichia coli* and other bacteria have been identified in pulp industry effluent [8]. The presence of these bacteria in the effluents of the pulp industry raises an important concern regarding the current technologies (biological treatment) and regulations that govern the discharge of these

effluents [9]. The inactivation of a wide range of pathogens in the cellulose industry effluent is effective by chlorination at a relatively low cost [10,11]. However, despite its effectiveness there is a problem to consider: the formation of organochlorine compounds [12]. Tawabini, et al. [13] states that chlorine has a high reactivity that affects the formation of these byproducts (chlorinated organic compounds) when reacting with organic matter. These byproducts are characterized by high toxicity and mutagenic capacity for the environment.

The low effectiveness of conventional water treatments in the destruction of recalcitrant contaminants and the formation of hazardous byproducts has encouraged the search for treatments with a higher oxidative capacity avoiding the formation of harmful byproducts. In this sense, it has been proposed the use of the so-called advanced oxidation processes (AOP) for the elimination of recalcitrant compounds and disinfection of effluents [3,14,15]. Nevertheless, there are differences between bacterial inactivation and decolorization of recalcitrant organic compounds by AOP [16]. Bacteria with the ability to self-repair and grow again after damage are much more complex than recalcitrant compounds [17].

A common point of the vast majority of AOP is the formation of hydroxyl radicals (·OH). Furthermore, ·OH is considered one of the species with the greatest oxidizing power. For example, chlorinated compounds used in conventional effluent treatments such as Cl<sup>2</sup> and ClO<sup>2</sup> have standard reduction potentials of 1.36 and 1.27 V/SHE respectively, while ·OH has a standard potential of 2.8 V/SHE [18].

Among the AOP, the Fenton technologies has focused a lot of attention for many years [19]. This process involves the reaction of H2O<sup>2</sup> as an oxidant agent with Fe2<sup>+</sup> ions as a metal catalyst to produce the degradation agent of ·OH as illustrated in Equation (1). Fe3<sup>+</sup> produced by the Fenton reaction can also oxidize H2O<sup>2</sup> to produce perhydroxyl radicals (HO2·; Equation (2)), named the Fenton-like reaction. The ·OH and HO2· produced in Fenton and Fenton-like reactions can participate in parallel reactions to produce singlet oxygen (1O2; Equations (3) and (4)).

$$\text{Fe}^{2+} + \text{H}\_2\text{O}\_2 \rightarrow \text{Fe}^{3+} + \text{OH}^- + \cdot\text{OH} \tag{1}$$

$$\rm Fe^{3+} + H\_2O\_2 \rightarrow Fe^{2+} + HO\_2\cdot + H^+ \tag{2}$$

$$\cdot \text{HO}\_2\cdot + \text{HO}\_2\cdot \rightarrow \text{H}\_2\text{O}\_2 + {}^1\text{O}\_2\tag{3}$$

$$\cdot \text{HO}\_{2}\cdot + \cdot \text{OH} \rightarrow \text{H}\_{2}\text{O} + \text{}^{1}\text{O}\_{2} \tag{4}$$

Andreozzi, et al. [20] highlight the reactivity of ·OH, since this species has adequate properties to attack organic compounds, in addition to reacting 106–10<sup>12</sup> times faster than other oxidants. Additionally, the cellular damage produced by ·OH in the disinfection processes takes place on different macromolecules present in the bacterial membrane causing its inactivation [21].

Many of the parameters that can affect any type of chemical reaction could affect the Fenton reaction, among which the effects of pH and reagent concentration stand out. The pH is one of the most important parameters in the Fenton reaction. However, it is possible to find that the optimum pH varies. One of the reasons why it is possible to find this variety at the optimum pH of the Fenton reaction may be associated with the speciation of Fe2<sup>+</sup> and Fe3<sup>+</sup> [22], changes in redox potentials of the main oxidizing species produced [23], or changes in the type of oxidizing species produced depending on the pH [24–26].

Without the presence of Fe2<sup>+</sup> in the Fenton system there is no formation of ·OH, so the presence of Fe2<sup>+</sup> is essential. However, it has been studied that too high Fe2<sup>+</sup> concentrations can cause the Fenton reaction oxidizing capacity to decrease (Equation (5)) [27].

$$\cdot \text{OH} + \text{Fe}^{2+} \rightarrow \text{Fe}^{3+} + \text{OH}^- \tag{5}$$

The H2O<sup>2</sup> concentration, like Fe2+, is also essential in Fenton systems [28]. However, an excess of H2O<sup>2</sup> could act as a scavenger of ·OH [27,28] according to the Equation (6).

$$\cdot\text{OH} + \text{H}\_2\text{O}\_2 \rightarrow \text{HO}\_2\cdot + \text{H}\_2\text{O}\tag{6}$$

Accordingly, to minimize Fe2<sup>+</sup> and H2O<sup>2</sup> acting as scavengers, but maximizing the production of oxidizing species from these reagents, it is very important to know the optimal [H2O2]/[Fe2+] [29].

Therefore, the aim of this work is to evaluate the decolorization of a model recalcitrant compound (methylene blue) and the inactivation of a model bacteria (*E. coli* K12 strain) by Fenton technology considering the operational parameters such as pH, Fe2<sup>+</sup> concentration ([Fe2+]) and the molar ratio between H2O<sup>2</sup> and Fe2<sup>+</sup> ([H2O2]/[Fe2+]), and to reveal the single and combinative effects of the these variables influencing on degree of methylene blue (MB) decolorization (D% MB), rate constant of MB decolorization (kapp MB) and *E. coli* K12 inactivation in uLog units (IuLog EC).

#### **2. Results and Discussion**

#### *2.1. Models and Regression Analysis*

Table 1 lists the factors and levels in the experimental design using the following variable: pH, Fe2<sup>+</sup> concentration ([Fe2+], mol/L) and molar concentration ratio of Fe2<sup>+</sup> and H2O<sup>2</sup> ([H2O2]/[Fe2+]). Also, the experimental degree of MB decolorization (D% MB), the apparent rate constant of MB decolorization (kapp MB) and the inactivation of *E. coli* K12 bacteria in logarithmic units (IuLog EC) are presented.


**Table 1.** Actual values and coded levels (in parentheses) of the variables in the Box–Behnken design and experimental values for each response.

Using Design Expert software (version 10), experimental data in Table 1 were analyzed by a second-order linear polynomial regression model (Equation (7)).

$$\eta = \gamma\_0 + \gamma\_1 \mathbf{A} + \gamma\_2 \mathbf{B} + \gamma\_3 \mathbf{C} + \gamma\_{12} \mathbf{A} \mathbf{B} + \gamma\_{13} \mathbf{A} \mathbf{C} + \gamma\_{23} \mathbf{B} \mathbf{C} + \gamma\_{11} \mathbf{A}^2 + \gamma\_{22} \mathbf{B}^2 + \gamma\_{33} \mathbf{C}^2 \tag{7}$$

in which η is the dependent factor (response), γ<sup>0</sup> is the intercept; A ([H2O2]/[Fe2+]), B ([Fe2+]) and C (pH) are the independent variables; γ1, γ<sup>2</sup> and γ<sup>3</sup> are the coefficients of the linear part of the predicted model; γ12, γ<sup>13</sup> and γ<sup>23</sup> are the interaction coefficients and γ11, γ<sup>22</sup> and γ<sup>33</sup> are the quadratic coefficients. Interaction and quadratic coefficients refer to the effects of the interaction among independent variables.

Analysis of variances (ANOVAs) and significant test results for the quadratic regression equations are shown in Table 2.


**Table 2.** ANOVA of the regression model for the prediction of degree of methylene blue (MB) decolorization, rate constant of MB decolorization and *E. coli* K12 inactivation.

Df: degrees of freedom. Parameter "A" represents the [H2O2]/[Fe2+], "B" represents the [Fe2+] and "C" represent the pH. AC, AC, BC, A<sup>2</sup> , B<sup>2</sup> and C<sup>2</sup> represent the interactions of A, B and C parameters on the responses.

Table 2 listed the results of variance analysis for the MB decolorization and *E. coli* K12 removal using the Fenton process. The values of the sum of squares demonstrate the contribution of independent variables on responses [30]. The mean squares, which are the sums of squares divided by the degree of freedom. Adequacy of the model parameters in the present study for response variables (D% MB , kapp MB and IuLog EC) was determined by the Fisher value (*F*-value), obtained by dividing the mean squares of each effect by the mean squares of error [31]. The probability critical level (*p*-value) of 0.05 was considered to reflect the statistical significance of the parameters of the proposed model. The F-values > 0.001 (975.81, 6.47 and 62.10) and *p*-values < 0.05 obtained for D% MB, kapp MB and IuLog EC responses confirming the qualification of the model to predict the decolorization of MB (D% MB and kapp MB) and the inactivation of *E. coli* K12 (IuLog EC) by the Fenton reaction. In addition, the validity of the model is confirmed by the *p*-value of the lack of fit with values greater than the lowest limit of fit as recommended (>0.05) [32]. As a result, the models developed in this work for predicting the D% MB , kapp MB and IuLog EC by the Fenton process were considered adequate. These models can be described as shown in Table 3 with coded three factors.

**Table 3.** Statistical results of the proposed models in terms of the coded factors.


The ANOVA results of three parameters (D% MB, kapp MB and IuLog EC) showed that the significant (*p* < 0.05) response surface models with high R<sup>2</sup> value (0.9210–0.9994) were obtained as shown in Table 3, ensuring a satisfactory adjustment of the quadratic models to the experimental data. The Radj<sup>2</sup> values (0.7787–0.9984) obtained suggests that the three proposed models had an adequate predictive capacity. Even more, plots comparing the experimental and predicted values for D% MB, kapp MB and IuLog EC indicated a good agreement between experimental and predicted data from the model (Figure 1). Therefore, this finding indicates high correlation and adequacy of the proposed model to predict performance of the Fenton process (D% MB, kapp MB and IuLog EC).

#### *2.2. E*ff*ect of Variables on MB Decolorization (D% MB)*

Figure 2a showed the standardized effects of the components and their contribution to the D% MB in a Pareto chart. The sign of standardized effects in Pareto chart, + (favorable effect) or − (unfavorable effect), along the length of the bars provided the physical meaning of model terms. In this Pareto chart, we saw that A, B, C, AB, BC, AA and CC crossed the reference line (*p* = 0.05). It is evident that the most important model term was A ([H2O2]/[Fe2+]), followed by linear terms B and C ([Fe2+] and pH respectively), quadratic terms corresponding to AA, etc. Thus, e.g., it can be concluded that larger A value, i.e., higher [H2O2]/[Fe2+] values, would result in an increase in the D% MB .

The perturbation plots (Figure 2b) illustrates the effect of all parameters on the D% MB. The positive effect means that if the effect factor level increases then the response value increases. On the other side, the negative effect means that if the effect factor level increases then the response value decreases. In other words, steep slope or curvature in a factor shows that the response is sensitive to that variable, while a relatively flat line indicates a low sensitivity of response to change with that particular variable. It was observed that the [H2O2]/[Fe2+] (A) and [Fe2+] values (B) had significant positive effects on D% MB, while the initial pH (C) had a negative effect on this response. Gulkaya, Surucu and Dilek [29] demonstrated that [H2O2/Fe2+] is a critical parameter for improving the Fenton technology as a treatment of a carpet dyeing wastewater. Otherwise, Babuponnusami and Muthukumar [33] and Chen, et al. [34] demonstrated the positive effect of [Fe2+] on the degradation of phenol and Acridine Orange by Fenton technologies. In both publications it was established that an increase in the [Fe2+] values leads to an increase in the percentage of degradation of phenol and Acridine Orange by the Fenton reaction, in line with what has been demonstrated in the present investigation. Regards to pH in a large part of the experiments carried out by Fenton technologies exhibit an optimal pH close to

3 [24,33,34]. In these research, at pH less or greater than 3 the efficiency of Fenton technology decreases, as observed in this publication. *Catalysts* **2020**, *10*, x FOR PEER REVIEW 6 of 16

**Figure 1.** Correlations between the experimental and predicted values of (**a**) D%MB values, (**b**) kappMB values and (**c**) IuLogEC values. **Figure 1.** Correlations between the experimental and predicted values of (**a**) D% MB values, (**b**) kapp MB values and (**c**) IuLog EC values.

**Figure 2.** (**a**) Pareto chart showing the standardized effects of variables (first order, quadratic and interaction terms) on D% MB (vertical line represents the 95% confidence interval), and (**b**) Perturbation graphs for D% MB (A-[H2O<sup>2</sup> ]/[Fe2+], B-[Fe2+], C-pH).

The 3D surface and contour plots in Figure S1 show the individual effects of the process variables and their interactions on the D% MB. Optimum conditions determination of different variables is the main objective of the response surface methodology (RSM) study, which can affect the D% MB . By considering the predicted response, [H2O2]/[Fe2+] <sup>=</sup> 2.9, [Fe2+] <sup>=</sup> 1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> mol/L and pH <sup>=</sup> 3.2 of Fenton process were the optimum condition for D% MB (94.57%).

#### *2.3. E*ff*ect of Variables on the MB Decolorization Rate Constant (kapp MB)*

Figure 3a shows the standardized effects of the components and their contribution to the kapp MB in a Pareto chart. In this Pareto chart, we saw that B, AA and AB crossed the reference line (*p* = 0.05). It is evident that the most important model terms are AA and B, followed by interaction term AB ([H2O2]/[Fe2+] and [Fe2+] interaction). Thus, e.g., the AA term implies that kapp MB were not influenced by [H2O2]/[Fe2+] in a linear level, but strongly influenced by this parameter in a quadratic level, i.e., a slight variation in [H2O2]/[Fe2+] will result in an increase in the kapp MB .

**Figure 3.** (**a**) Pareto chart showing the standardized effects of variables (first order, quadratic and interaction terms) on kapp MB (vertical line represents the 95% confidence interval), and (**b**) Perturbation graphs for kapp MB (A-[H2O<sup>2</sup> ]/[Fe2+], B-[Fe2+], C-pH).

1

1

Figure 3b shows the perturbation plot of the effect of the parameters on the kapp MB. It was observed that the [Fe2+] values (B) had a significant positive effect on kapp MB, while the [H2O2]/[Fe2+] (B) and pH (C) had an insignificant effect on the response. It has also been reported the main role of [Fe2+] on the rate constants of discoloration of other dyes. For example, Tunç, et al. [35] indicated that the rate constant of acid orange 8 decolorization increased almost 10 times (0.0027–0.0267 min−<sup>1</sup> ) if [Fe2+] values change from 5.0 <sup>×</sup> <sup>10</sup>−<sup>6</sup> to 2.5 <sup>×</sup> <sup>10</sup>−<sup>5</sup> mol/L. In the same publication the rate constant of acid red 44 decolorization increased almost 4 times (0.0085–0.0331 min−<sup>1</sup> ) if [Fe2+] values incremented from 2.5 <sup>×</sup> <sup>10</sup>−<sup>6</sup> to 2.5 <sup>×</sup> <sup>10</sup>−<sup>5</sup> mol/L. Melgoza, et al. [36] reported also that the decolorization rate constant of MB increased 2.5 times (0.0014–0.0035 min−<sup>1</sup> ) if [Fe2+] values change from 1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> to 2.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> mol/L.

Figure S2 show the individual effects of the process variables and their interactions on the kapp MB in the 3D surface and contour plots. By considering the predicted response, [H2O2]/[Fe2+] = 1.7, [Fe2+] <sup>=</sup> 1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> mol/L and pH <sup>=</sup> 3.7 of the Fenton process were the optimum condition providing kapp MB (2.08 min−<sup>1</sup> ).

#### *2.4. E*ff*ect of Variables on E. coli K12 Removal (IuLog EC)*

Figure 4a showed the standardized effects of the components and their contribution to the IuLog EC in a Pareto chart. In this Pareto chart, we saw that B, C, AA, BB, CC, AB and BC crossed the reference line (*p* = 0.05). It is evident that the most important model terms was AB, followed by linear term C (pH) and quadratic term BB ([Fe2+] 2 ). Thus, e.g., it can be concluded that smaller C value, i.e., lower pH values, would result in an increase in the IuLog EC.

**Figure 4.** (**a**) Pareto chart showing the standardized effects of variables (first order, quadratic and interaction terms) on IuLog EC (vertical line represents the 95% confidence interval), and (**b**) Perturbation graphs for IuLog EC (A-[H2O<sup>2</sup> ]/[Fe2+], B-[Fe2+], C-pH).

1 The perturbation plots (Figure 4b) illustrates the effect of all parameters on the IuLog EC. It was observed that the [Fe2+] (B) and pH (A) values had a significant negative effects on IuLog EC, while the [H2O2]/[Fe2+] (C) values did not have a statistically significant effect on this response. Asad, et al. [37] also reported that the inactivation of *E. coli* was mostly affected by Fenton technologies if the [Fe2+] was low. This is evident when considering Equation (5), since at high [Fe2+] values the activity of the ·OH formed could be inhibited.

On the other hand, it is known that the ·OH production by Fenton technology is benefited at acidic pH close to 3. Some examples of Fenton technologies applied to the inactivation of a few bacteria are presented in Table 4. Although the experiments do not have the same conditions of [H2O2]/[Fe2+] and reaction time, it is possible to identify that at lower pH values and at lower values of [Fe2+] the bacterial elimination efficiencies tend to increase.


**Table 4.** Examples of Fenton technologies applied to the bacteria inactivation.

Figure S3 show the individual effects of the process variables and their interactions on the IuLog EC in the 3D surface and contour plots. By considering the predicted response, [H2O2]/[Fe2+] = 2.9, [Fe2+] <sup>=</sup> 7.6 <sup>×</sup> <sup>10</sup>−<sup>4</sup> mol/L and pH <sup>=</sup> 3.2 of the Fenton process were the optimum condition providing IuLog EC (0.89 uLog).

## *2.5. Analysis of Optimization and Model Validation*

The optimal conditions obtained for MB decolorization and *E. coli* K12 inactivation are different for each of the responses studied. These results indicate that although some authors have suggested that it is possible to analyze the bacteria inactivation of AOP by extrapolating from dye decolorization [42], these processes have differences. The results in the present study (Table 1) show that if for example experiments #1 ([H2O2]/[Fe2+] <sup>=</sup> 1:1, [Fe2+] <sup>=</sup> 6.0 <sup>×</sup> <sup>10</sup>−<sup>4</sup> mol/L and pH <sup>=</sup> 4.0) and #10 ([H2O2]/[Fe2+] <sup>=</sup> 2:1, [Fe2+] <sup>=</sup> 1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> mol/L and pH <sup>=</sup> 3.0) are compared, the MB decolorization reaches 72.8% and 96.7% respectively, while in the same experiments the *E. coli* K12 inactivation reaches 61.3% (0.412 uLog) and 33.4% (0.176 uLog), i.e., when the MB decolorization is high the *E. coli* K12 inactivation tends to be low, and vice versa. Similar compartments can be observed when comparing (Table 1), for example, experiments #3 ([H2O2]/[Fe2+] <sup>=</sup> 1:1, [Fe2+] <sup>=</sup> 1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> mol/L and pH <sup>=</sup> 4.0) and #14 ([H2O2]/[Fe2+] <sup>=</sup> 2:1, [Fe2+] <sup>=</sup> 8.0 <sup>×</sup> <sup>10</sup>−<sup>4</sup> mol/L and pH <sup>=</sup> 4.0). These observations support what has been observed in other studies on the complexity involved in the bacteria inactivation compared to the recalcitrant compounds elimination [43].

To validate the model obtained by the Box–Behnken optimization technique, experiments were carried out with the suggested optimum values of independent variables. Table 5 shows the optimal conditions predicted by the models, the predicted response value and the response value obtained experimentally (Table 5).


**Table 5.** Results of validation experiments under optimized conditions.

The result obtained from experiments for all response parameters was in agreement with the model prediction. Low errors showed the model and parameters could accurately reflect on the three responses analyzed.
