1 Introduction

Nowadays, dyes are widely produced in various industrial fields including the textile industry, food industry, leather industry, and pharmaceutical industry [1, 2]. Annually, about 700,000 tons of dyes are produced all over the world, of which 15% of that amount has entered the environment without any treatment process and about 70% of all of them are azo group dyes [3,4,5]. Meanwhile, acid blue 113 (AB 113) dye is considered one of the most widely used azo dyes in the textile industry due to its high photolytic stability, resistance to microbial degradation, and high strength on fabrics [6, 7]. AB 113 dye has difficultly dissociated in natural environment and has a high stability due to its aromatic rings and azo bond (-N = N-). On the other hand, the dyes of the azo group are known to be carcinogenic and mutagenic agents with toxic metabolites. In recent years, various methods have been proposed and used for the treatment of colored wastewaters. Existing methods for dye removal include aerobic and anaerobic digestion, coagulation, advanced oxidation, combined chemical and biochemical processes, adsorption, and membrane treatment. Each of these methods has different removal efficiencies, advantages, disadvantages, investment, and operating costs. The oxidation power for removing contaminants in the advanced oxidation process based on the production of hydroxyl free radicals (OH*) has been high that convert many organic chemical compounds into minerals. These radicals are unstable and highly active, which are produced through chemical or photochemical reactions at the site. Free radicals are strong oxidants that rapidly attack organic matter molecules and separate a hydrogen atom from the structure of organic matter [8,9,10,11,12,13,14].

Nowadays, the usage of persulfate as an oxidizing agent is expanding. Persulfate [15] is a non-selective anion, soluble and relatively stable at room temperature, and is the strongest oxidant of the peroxygen family. Its oxidation–reduction potential is 2.1 V and it is stronger than hydrogen peroxide (1.8 V) and permanganate (1.7 V) but is slightly weaker than ozone (2.2 V). In addition, the sulfate radical has more chance for reacting with organic compounds than the hydroxyl radical (20 ns) due to its longer durability (30–40 μs). PS and its resulting sulfate radicals have special and unique properties such as high kinetic velocity, greater stability compared with hydroxyl radicals, and less dependence on natural organic matter, which itself has a greater impact on organic matter [16, 17]. Under atmospheric conditions, the oxidation of PS does not have much effect on organic pollutants, but if heat, light, or certain metal ions are used as catalysts, the reaction of PS will significantly increase. The processes that occurred in the case of the use of PS are given in Eqs. 1 and 2 [18].

$${\mathrm S}_2\mathrm O_8^{2-}+\mathrm{heat}\;\mathrm{or}\;\mathrm{UV}\rightarrow{2\mathrm{SO}}_4^{-\bullet}$$
(1)
$${\mathrm S}_2{\mathrm O}_8^{2-}+\mathrm{Metal}\;\mathrm{ions}\rightarrow\mathrm{Me}^++{\mathrm{SO}}_4^{2-}+{2\mathrm{SO}}_{4^-}$$
(2)

The advantages of using this oxidizer include high solubility in aqueous environments, non-selective reactivity, relative stability at ambient temperature, reaction with most organic-based contaminants, and chemical stability in aqueous systems [19]. Hydroxyl radical and sulfate radical are both strong oxidants. The produced sulfate radical from PS can destroy a number of radical chain reactions in which organic matter presents [20]. As mentioned, one of the ways of activating PS is using metal ions such as iron and cobalt. Iron is considered to be atoxic, inexpensive, and effective. Reactions that occur in the presence of iron ion include the following reaction [21]. The following reaction shows the chemical activation of PS by an intermediate metal (Eq. 3).

$${{\mathrm S}_2{\mathrm O}_8}^{2-}+\mathrm{Me}^{2+}\rightarrow{\mathrm{SO}}_{4^{\bullet -}}+\mathrm{Me}^{3+}+{\mathrm{SO}}_{4^{2-}}$$
(3)

Among metals, the most application is related to divalent iron (Fe2+), in which the large amounts of requirement, high production of sludge, and consumption of SO4●− radicals in high concentrations are the main problems of this activator [22]. Due to the problems associated with the usage of this activator, in this study, zero-valent iron nanoparticle (nZVI) was applied, which is able to act as a permanent source of Fe2+ and continuously enters iron into the reaction. The reaction of Fe৹ with PS is shown in the form of the following reactions (Eqs. 4 and 5) [23, 24].

$$\mathrm{Fe}^\circ +{{\mathrm{ S}}_{2}{\mathrm{O}}_{8}}^{2-}\to {\mathrm{Fe}}^{2+}+{{ 2\mathrm{SO}}_{4}}^{2-}$$
(4)
$${\mathrm S}_2{\mathrm O}_8^{2-}+\mathrm{Fe}^{2+}\rightarrow{\mathrm{SO}}_4^{\bullet-}+\mathrm{Fe}^{3+}+{\mathrm{SO}}_4^{2-}$$
(5)

In 2021, Soubh et al. used the optimization process for the removal of methylene blue from aqueous solutions using activated persulfate by nano-scale zero-valent iron (nZVI) which is supported by reduced expanded graphene oxide (rEGO). The highest removal efficiency obtained with pH = 3, activating dose = 1.2 g/L, persulfate concentration of 0.576 g/L, and reaction time of 20 min was 96% [25]. The response surface method (RSM) is a powerful tool for statistical modeling that is performed by applying the least number of experimental experiments according to the experiment design [26]. RSM is based on a nonlinear multivariate model that consists of a design of experiment for providing sufficient and reliable response values and subsequently a mathematical model that has the best fits with the information obtained from the experimental design and determines the optimal value of the independent variables that produce the most or the least response [27,28,29]. Central points are a method for estimating and evaluating experiments’ error and measuring fitting weakness [30]. RSM itself has different types and this statistical method can be used in various ways. One of its types is the Box-Behnken (BBD) method, which is a second-order design based on three-level incomplete factorial designs [31, 32]. This method can estimate the value of the parameters in a second-order model, make the required designs, and calculate the amount of the non-conformance of parameter [15]. The aim of this study was the optimization of AB 113 dye removal in the zero-valent iron nanoparticle/persulfate process (nZVI @PS) by using a statistical model of the response procedure from aqueous environments.

2 Method

2.1 Required chemicals

AB 113 dye was provided as a precursor of colored pollutant from Alvan Sabet Hamedan Company and nZVI nanoparticle was prepared by synthesis in the laboratory. The general characteristics of AB 113 dye are presented in Table 1 [6, 33]. Also, H2SO4 and NaOH were prepared from Germany’s Merck Company with laboratorial degree of purity to adjust the pH of the solution containing dye. It should be noted that double-distilled water was used in all stages of the experiments [12].

Table 1 Physicochemical characteristics of AB 113dye
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2.2 Preparation of nZVI nanoparticles by green synthesis method

Grape leaf was used to synthesize nanoparticles by green method. For this purpose, the first 30 g of grape leaf was added to 500 mL of distilled water and then placed on a magnetic stirrer-heater at a speed of 300 rpm for 60 min at 80 °C till the mixing is done. After elapsing mentioned the time, the extraction process was performed from the solution and after cooling, the vacuum pump was filtered by using paper filters. The extract made by iron chloride was mixed with a specific normality in a ratio of 2 to 3. In the next step, the synthesized nanoparticles were dried for 1–2 days at ambient temperature. After this step, the produced nanoparticles were stored for later uses [16, 34, 35].

2.3 Experiment method

Dye stock solution was prepared by dissolving the powder of AB 113 dye in double-distilled water at a concentration of 1000 mg/L. Then, the desired concentrations in mg/L were prepared in a volume of 100 mL of stock solution. The most important effective variables include solution pH, catalyst value (nZVI), PS value, contact time, and initial concentration of dye (Table 2). In this study, the effect of these variables on the performance of dye dissociation by nZVI@PS nanoparticle was investigated. The pH of the solution was adjusted by using 0.1 N sulfuric acid and sodium hydroxide solutions. At all stages, the mixing rate was 250 rpm and all experiments were performed at ambient temperature. At the end of the determined reaction time, the solution was centrifuged by a centrifuge at 3000 rpm for 5 min and prepared for measuring the concentration of residual dye. For ensuring of the results’ repetition, each step of the experiment was repeated three times and the average results were reported. The residual concentration of AB 113 dye was determined by the spectrophotometer DR5000 manufactured by the American HACH company at a wavelength of 570 nm [22]. AB 113 dye removal efficiency was determined after the process through Eq. 6 [36,37,38].

Table 2 Information about the levels and range of studied variables
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$$\mathrm{Removal}\;\mathrm{efficiency}(\%)=\frac{{\mathrm C}_{\mathrm o}-{\mathrm C}_{\mathrm t}}{{\mathrm C}_{\mathrm o}}\times100$$
(6)

where Co and Ct are respectively the initial and final concentrations of AB 113 dye in the solution in mg/L.

2.4 Experiment design based on BBD

Response surface methodology evaluates the existing relationships between clusters of experimental agents by using experimental techniques and then analyzes and presents graphs by investigating the responses based on one or more selected criteria [39, 40]. In this study, solution pH, catalyst dose, amount of persulfate, contact time, and contaminant concentration were selected as independent variables affecting the removal efficiency of AB113. Design Expert 10 software was applied to survey the effect of independent variables on response performance (AB 113 removal efficiency). The design was done by using the Box-Benken Design method. The number of test steps was determined by using Eq. 7 [41,42,43].

$$\mathrm{N}=2\mathrm{K}\left(\mathrm{K}-1\right)+{\mathrm{C}}_{\mathrm{O}}$$
(7)

where K is the number of factors examined and Co is the number of repetition steps of the test. After selecting the design, the model equation and its predicted coefficients were determined by the second-order equation (Eq. 8):

$$Y={C}_{k0}+\sum_{i=1}^{4}{C}_{ki}{x}_{i}+\sum_{i=1}^{4}{C}_{kii}{x}_{i}^{2}+\sum_{i<j=2}^{4}{C}_{kij}{x}_{i}{x}_{j}$$
(8)

The values of Ck0, Cki, Ckii, and Ckij are constant, linear, second-order, and regression interaction coefficients, respectively. Xi and Xj are independent coded variables [40, 44]. Statistical analysis was performed to evaluate the accuracy and adequacy of the models by using the analysis of variance (ANOVA) with probability values of Prob ≤ F < 0.05. The adequacy and predictability of the model were tested using the lack of fit criterion, the determination of linear regression coefficient R2, R2Adjust, R2Predicted, adequate precision, and residual detection.

2.5 Presentation of second-order polynomial model and ANOVA analysis

According to the Box Behnken scheme, the empirical relationship between input variables and experimental results is represented by a second-order polynomial equation. The resulting equation based on coded factors is:

$$\begin{array}{l}\mathrm{Y}=+87.15-32.49\mathrm{A}+9.6\mathrm{B}+6.4\mathrm{C}-15.3\mathrm{D}+18.32\mathrm{E}-2.47\mathrm{AB}-3.7\mathrm{AC}+6.56\mathrm{AD}+11.46\mathrm{BC}-\\ 1.72\mathrm{BD}+1.35\mathrm{BE}+0.4375\mathrm{CD}-2.01\mathrm{CE}-2.93\mathrm{DE}-{21.9\mathrm{A}}^{2}-{17.08\mathrm{B}}^{2}-{16.38\mathrm{C}}^{2}-{15.93\mathrm{D}}^{2}-{18.44\mathrm{E}}^{2}\end{array}$$
(9)

which in this equation, Y represents the removal rate (%) and A, B, C, D, and E represent pH, nZVI, PS, initial concentration, and reaction time, respectively. The results of the ANOVA analysis in Table 3 show that the proposed model has statistically a significant relationship with linear conditions according to one-way analysis of variance with value of p ≤ 0.001. Also, A, B, C, D, and E parameters are quite significant interaction of B2 (p ≤ 0.001). The value of F for this model has been 48.69, which means that the variance of each variable is significant compared with the error variance, and all the main parameters play an important role as the answer.

Table 3 Analysis of variance of operating parameters in AB 113 oxidation
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With F = 434.28 is the most effective factor in the oxidation process of AB 113. In addition, the adjusted correlation coefficient (R2 (adj)) is equal to 0.9724 that indicates the high accuracy of the statistical model.

2.6 Analysis tools

For determining the functional groups at the nZVI nanoparticle surface, FTIR analysis was performed by PerkinElmer, Spectrum Two model, in the range of 450–4000 cm−1. X-ray diffraction for the prepared nZVI nanoparticle in the angle range of 2θ = 10–80° was determined by XRD (Philips PNA-analytical diffractometer device). FE-SEM scanning electron microscope at an accelerated voltage of 10 keV was used to determine the surface and morphological characteristics.

3 Results and discussion

3.1 Surveying the structural nature of the nanoparticles

3.1.1 Investigation of nZVI factor groups by using FTIR analysis

One of the common methods used for identifying and analyzing materials is infrared spectroscopy. The results of FTIR nZVI nanoparticle at a frequency of 450–4000 cm−1 as illustrated in Fig. 1. The largest peak in the range of 3364 has been shown that is related to the O–H tensile vibrations of the hydrogen bond of polyphenols [45]. Peaks of 1649, 1153, and 1018 are respectively related to the tensile vibrations of C = C alkenes, the tensile vibrations of C–C aromatics, the tensile vibrations of C–O–C carbonyls, and the flexural vibrations of C–H aromatics [45,46,47,48]. Peaks at 830 cm−1 indicate H-C groups [49, 50]. Peaks at 604 and 535 cm−1 indicate the presence of Fe–O groups [51]. Polyphenols act as the main stabilizing agent for nanoparticles, which are found in the range of 3200–3500 cm−1 [52, 53]. Bonds of functional groups such as CO–C, C–O, and C = C are derived from heterocyclic compounds and amide bonds are derived from proteins in plant extracts, and the ligands are nanoparticle coating. In addition, the available proteins prevent the formation of clots and help to stabilize the nanoparticles by forming a membrane and cover the metal nanoparticles [54].

Fig. 1
figure 1

FTIR spectrum for nZVI

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3.1.2 Investigation of the structure and nature of nZVI using XRD analysis

The X-ray diffraction pattern (XRD) can be seen in Fig. 2 for nanoparticle in the angle range of 2θ. The XRD diffraction pattern was used to characterize the crystalline phase of nanoparticles and to measure their structural properties. As shown in the figure, the peak of Fe˚ was determined at angle of 2θ = 44.67 [55]. There are some other weak peaks with less intensity at angles of 2θ = 32–47-59, which indicate the presence of KCl. Also, the peak at points of 2Ɵ = 31–36-53–63 indicates the presence of NaCl. In the study of Hosseinzadeh et al., impurities such as copper, cadmium, lead, and magnesium were recognized in very small amounts in the composition of nanoparticles used in their study [56]. Also, Lily et al. reported NaCl peaks in XRD test on iron nanoparticles synthesized from plant extract [52].

Fig. 2
figure 2

XRD spectrum for nZVI

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3.1.3 Morphology surveying of nZVI using FE-SEM analysis

Scanning electron microscopy (FE-SEM) was used to determine the surface and morphological characteristics. The results of nZVI nanoparticle analysis are illustrated in Fig. 3. As shown in the figure, nZVI particles have a regular morphology and are uniformly, spherically, and homogeneously placed next to each other to form a chain-like structure [57]. The high magnification FE-SEM image shows that the sizes of these nZVI particles range from 15 to 40 nm. Some of nZVI nanoparticles agglomerate together and precipitate during the heating process and indicate the morphology of nZVI particles with a spherical nanosphere size [58].

Fig. 3
figure 3

FE-SEM spectrum for nZVI

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3.2 The effect of pH on the oxidation efficiency of AB 113 dye

The results of the effect of pH on the rate of dye removal are shown in Fig. 4a. The surveyed pHs in this process were 3, 7, and 11. As it is known, the highest efficiency of the process in the dye removal was related to pH = 3 and was 100%. By increasing the pH of the studied samples, the removal efficiency significantly decreased in which the minimum amount of dye was removed at pH 11. pH and optimal time were respectively determined to be 3 and 50 min. The pH of the solution is one of the most effective and important factors in performing chemical reactions that affect the path and synthetics, the studied contaminant structure, the mechanism of hydroxyl radical production, and the reaction of reactants [59]. In oxidation processes, including electrochemical methods, pH is one of the important items [60]. At alkaline pH due to the sedimentation of iron hydroxide at the surface of nZVI, which leads to occupy the reaction sites and to slow down the reaction by releasing iron ions and electrons. At pH = 3, nZVI dissolves rapidly in the solution, leading to insufficient levels [61]. Also at pHs above 3, the solubility of the residual iron in the solution decreases and the iron becomes colloidal in form. This phenomenon reduces efficiency in turn [62, 63], which is consistent with the results of Qayateri et al. [64].

Fig. 4
figure 4

Overlap diagram of the independent variable effects on AB 113 removal efficiency. a PS and nZVI, b pH and nZVI; c initial dye concentration and reaction time

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3.3 Investigation of the effect of persulfate concentration on the oxidation efficiency of AB 113 dyes

The results of the effect of persulfate concentration on the removal of AB 113 dye are shown in Fig. 4b. At this stage of the study, the effect of persulfate anion concentration in the range of 0.01 to 0.2 g/L was evaluated and the optimal dose was obtained 0.14 g/L. The reason of increasing the reaction efficiency by rising PS concentration is the boost in the production of active radicals [65]. Increasing the concentration of PS as the main source of hydroxyl radical production will enhance efficiency. However, in relation to the consumption concentration of PS, increasing the concentration of this substance to a certain extent not only does not increase the efficiency of the desired contaminant removal, but also has been turned into a factor for abduction and consumption of hydroxyl radicals in aqueous solution, which will reduce the process efficiency [66, 67]. The results of this study agree with the results of the study of Shokouhi et al. [68].

3.4 Surveying the effect of nZVI concentration on the oxidation efficiency of AB 113 dye

To study the effect of nZVI concentration on the degradation of AB 113 dye, nZVI concentration was selected in the range of 0.01–0.1 g/L. As shown in Fig. 4b, the degradation efficiency increased by rising nZVI concentration from 0.01 to 0.1 mg/L and in this study, the optimal concentration of nZVI was 0.08 mg/L. As the amount of nZVI increased, more active surface sites were created to accelerate the initial reaction, leading to more nZVI surface collisions with azo dye molecules to increase AB 113 degradation [61]. nZVI augments the oxidation process due to its large surface area and thus increases the amount of acid blue dye degradation [69].

3.5 The effect of contact time on the oxidation efficiency of AB 113 dye

Figure 4c shows the effect of contact time on the removal efficiency of AB 113 dye. In order to evaluate the optimal contact time, the experiments were performed in the range of 10–60 min. The stirrer speed was 250 rpm. Contact time is one of the most important factors influencing the removal processes. In this process, the highest percentage of AB113 dye removal is related to the reaction time of 50 min, which is describable because of having enough opportunity to produce more free radicals and their contact with dye molecules [70]. Since the addition of electrolysis time leads to the rise in amount of produced hydroxyl radical, therefore, degradation efficiency of AB 113 increases by this process [71]. By adding the contact time, the amount of AB113 dye removal increases, which is consistent with the results of Samarghandi et al. [72].

3.6 The effect of initial concentration of AB 113 dye at equilibrium time on process efficiency

The results of this part of the study about the effect of changes on initial concentration of dye are illustrated in Fig. 4c. Increasing the concentration of AB 113 dye encountered the efficiency of the process with reduction. In order to evaluate the optimal concentration of AB 113 dye, the experiments were performed in the range of 25–200 mg/L. The optimal concentration of AB 113 dye in the process was 46 mg/L. Due to the fact that the number of produced radicals in different concentrations are different, so the removal efficiency is different too [73]. In most of the studies related to dye oxidation, increasing the concentration of studied contaminants has been associated with decreasing process efficiency. The cause can be attributed to the decrease in the ratio of the produced radical to the concentration of the pollutant and the other cause can be attributed to the increase in the concentration of intermediate substances resulting from the oxidation of the pollutant that tend to consume the radical [74]. The results of this study agree with the results of studies by Seid Mohammadi et al. [59].

3.7 Investigation the accuracy and validity of the proposed model

In order to validate the proposed model, various analyses were performed. The graph of experimental data versus the predicted data by the model of Fig. 5 shows that the values are placed uniformly and compatible with each other along a straight line and have a high correlation [75]. In statistical analysis of the experimental data, checking that the data has a normal distribution is necessary. In the normal distribution, the points related to the data are very close together and are the follower of a straight line that is descending. To determine whether the data is normally distributed or not, the normal probability diagram is shown in Fig. 6. According to the relevant diagram, it is quite obvious that the data related to oxidation reasonably have a normal distribution. Based on the results of Pareto diagram in Fig. 7, it was observed that in the negative effect, pH had the highest efficiency in the removal of AB 113 and the most positive effect on the removal of AB 113 was related to the reaction time. Figure 8 indicates the considered effective variables for oxidation AB 113 including (reaction time, pH, catalyst value (nZVI), PS value, and initial concentration of dye). The selective range of variables, the degree of impact, and the optimal points of each variable can also be seen in this figure.

Fig. 5
figure 5

Fit of experimental data against predicted data

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Fig. 6
figure 6

Normal probability

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Fig.7
figure 7

Pareto diagram showing the effect of factors on the removal of AB 113

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Fig. 8
figure 8

The effect of the considered initial parameters and their optimal points

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3.8 Investigation of process kinetics

In all AOP and EOP studies, determining the reaction kinetics is one of the main steps of the study. The study of reaction kinetics is performed to find the oxidation mechanisms of pollutants and the reaction of the model and the better execution of the process. For investigating the reaction kinetics, the first-order reaction model was evaluated under optimal conditions and at different times [76, 77].

$$\frac{\mathrm{lnc}}{{\mathrm{c}}_{0}}=-{k}_{1}\mathrm{t }{r}_{c}=\frac{dc}{dt}={k}_{1}\mathrm{c}$$
(10)

Based on the indicated kinetic coefficient, the removal rate follows the first-order reaction in this process, in which the results of study are shown in Fig. 9. Reaction kinetics used to study the rate of chemical reactions. The reaction rate can be indicated by decreasing the concentration of a reactant per unit time or increasing the concentration of a product per unit time [78]. The results of the study are consistent with the results of the study of Dargahi et al. [79].

Fig. 9
figure 9

Reaction kinetic for oxidation of AB 113

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3.9 TOC analysis

At this stage, all variables were selected and adjusted for the optimal case. The optimal conditions including values of pH = 3, nZVI = 0.08, PS = 0.14, and the concentration of AB 113 = 46 mg/L and reaction time of 50 min were used for this step and the results showed that after 50 min in optimal conditions, TOC efficiency reached to 79.37%. The initial TOC value is equivalent to 640 mg/L, which has reached a concentration of 132 mg/L with a removal efficiency of 79.37% at the end of the reaction time. The reduction of mineralization efficiency compared with dye removal can be observed in the most oxidation processes. This cause can be a result of the production of carbon generator intermediate compounds that are not fully mineralized [80, 81].

3.10 Stability and repeatability of nZVI

An important feature of a heterogeneous catalyst is its reusability and high stability for real-scale applications. In this study, the reusability of the nZVI catalyst was evaluated in four consecutive oxidation cycles under optimal conditions (see Fig. 10). Here, the reusability of the nZVI was evaluated by repeated regeneration via a washing catalyst using 0.1 mol/L NaOH solution and deionized water. As can be seen, under the optimum conditions, the efficiency for AB113 removal was 100% and reached 95.43% after four cycles. These results confirm that the nZVI catalyst has high reusability. However, the slight decrease in the efficiency can be attributed to the change in the physicochemical properties of the catalyst, which will eventually reduce the number of active sites for fe.2+ release [82,83,84]

Fig. 10
figure 10

Recycle efficiency of nZVI

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3.11 Application of nZVI @PS using real samples

Further investigation of the removal of AB113 onto the nZVI @PS was performed by means of the real wastewater samples. The samples were obtained from the Sivan Textile Company in Ardabil. The treatment was subjected to the nZVI @PS under optimized conditions. It should be noted that the samples contained a large variety of other contaminants which could compete with the target contaminants for the adsorption sites. However, the results showed that the nZVI @PS still had significant efficiency of dye removal (81.43%). This observation suggests that the nZVI @PS has a good potential for the removal of dyes in real samples.

4 Conclusion

This study was performed to optimize the removal of AB 113 dye in the process of zero valent iron nanoparticle/persulfate (nZVI @PS) by using the statistical model of response procedure (RSM) with BBD test design. The results indicated that by increasing the pH and concentration of AB 113, the oxidation efficiency declines so that the pH and the optimal concentration are 3 and 46 mg/L, respectively. Also, the catalyst dose in the values of nZVI = 0.08 and PS = 0.14 g/L has the highest efficiency and by increasing and decreasing the dose of that amount, the efficiency decreases. Time is one of the main parameters in this process, in which the optimal value of oxidation time is 50 min. Under optimal conditions, the process efficiency is 100%. Due to the acceptable performance and proper removal of AB 113 dye, this process can be used as a suitable method and with high efficiency in the treatment of wastewater containing AB 113 dye. Also, according to the Pareto diagram, the greatest effect on the removal of AB 113 was related to the pH variable.