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Original article
12 (
8
); 2524-2532
doi:
10.1016/j.arabjc.2015.04.009

Optimization of profenofos organophosphorus pesticide degradation by zero-valent bimetallic nanoparticles using response surface methodology

, ,
 Department of Chemistry, Islamic Azad University, North Tehran Branch, P.O. Box 1913674711, Tehran, Islamic Republic of Iran

⁎Corresponding author. Tel.: +98 9144115705; fax: +98 41 34264004. nafisehmansoriieh@yahoo.com (Nafiseh Mansouriieh),

Received: , Accepted: ,
© The Author(s) 2015
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
1 Tel.: +98 21 22222151; fax: +98 21 22222512.

Abstract

This study synthesized bimetallic Fe/Ni nanoparticles and used them for catalytic degradation of profenofos, an organophosphorus pesticide. This novel bimetallic catalyst (Fe/Ni) was characterized by scanning electron microscopy (SEM), energy-dispersive X-ray analysis spectroscopy (EDAX) and X-ray diffraction (XRD). The bimetallic nano-catalyst was prepared at diameters of 20–50 nm and was shown to effectively degrade profenofos. A three-factor central composite design combined with response surface methodology was used to maximize profenofos removal using the bimetallic system. A quadratic model was built to predict degradation efficiency. ANOVA was used to determine the significance of the variables and interactions between them. Good correlation between the experimental and predicted values was confirmed by the high F-value (16.38), very low P-value (<0.0001), non-significant lack of fit, an appropriate coefficient of determination (R2 = 0.936) and adequate precision (14.75). The highest removal rate attained was 94.51%.

Keywords

Bimetallic zero-valent nanoparticles
Organophosphorus pesticide
Optimization
Central composite design
Response surface methodology
1

1 Introduction

Pesticides are chemical and biological substances used to control weeds, insects and other pests. Despite their important role in agriculture, pesticides released into the environment cause significant environmental problems (Meyer, 2005). Organophosphates are widely-used as insecticides and acaricides. These compounds are esters of phosphoric acid, thiophosphoric acid and other phosphoric acids (Edwards, 1973). Research has determined that under suitable environmental conditions they can persist long-term in environmental compartments (Ragnarsdottir, 2000). Extensive use of this class of insecticides is most frequently associated with toxicity to domestic animals, wildlife and humans (Gallo and Lawryk, 1988). Profenofos {O-(4-bromo-2-chlorophenyl) O-ethyl S-propyl phosphorothioate} is a highly active organophosphorous pesticide used for efficient control of insect pests.

Long-term exposure to water polluted with profenofos (PFF) is harmful to human health because it produces significant inhibitory effects on acetylcholinesterase activity and instability of the erythrocyte membrane (Shi et al., 2012). Acetylcholinesterase inhibition in the nervous system causes respiratory, myocardial, and neuromuscular transmission impairment, which are acute toxicologic effects of organophosphorus pesticides (OPPs) (Perera et al., 2003). Over the last two decades, environmental concerns associated with the aggregation of OPPs in food products and water supplies have focused efforts on expanding safe, accessible and economically feasible methods for detoxification (Bourquin, 1977; Sunitha et al., 2007). Wastewater containing OPPs cannot be treated efficiently by biological techniques. The toxicity of PFF to microorganisms makes biodegradation impossible (Hincapie et al., 2005).

Low cost zero-valent iron (ZVI) is a powerful reducing agent that is easily accessible, effectively degrades pollutants, and generates very little waste and secondary pollutants (Gillham and O’Hannesin, 1994). These nanoparticles have been used for remediation of contaminants such as halogenated organics (Xu et al., 2014), heavy metals (Zhang et al., 2012), nitrates (Choi et al., 2009) and dyes (Epolito et al., 2008). Nanoscale ZVI (nZVI) tends to react with surrounding media or agglomerate into larger particles in response to its high surface energy and intrinsic magnetic interaction, which results in significant loss of reactivity (Phenrat et al., 2007). nZVI particles easily oxidize or ignite spontaneously when exposed to air (Ponder et al., 2000), which lowers their chemical reactivity from iron oxide formation. Attempts have been made to increase the efficiency of the redox reaction by decreasing the inactivation effects of the passivation layer (Huguet and Marshall, 2009; Powell et al., 1995).

A second metal coating on the surface of the nZVI particle serves as a protective agent against corrosion of the iron surface (Huguet and Marshall, 2009). Moreover, the addition of a noble metal such as Ni lowers the activation energy of the reaction, increasing the interaction between the compounds and increasing the reaction rate. Bimetallic nanoparticles are used to catalyze the dechlorination of compounds that typically have very slow reaction rates with nZVI (Lien and Zhang, 2001; Zhu and Lim, 2007). In bimetallic systems, iron corrosion in water generates hydrogen gas and secondary elements that act as a hydrogenation catalyst in a dehalogenation reaction (Smuleac et al., 2011). Prevention or the reduction of the formation and accumulation of toxic byproducts are advantages of bimetallic nanoparticles (Hosseini et al., 2011).

Optimization of process variables is required to achieve the maximum process efficiency for removal of a contaminant. In customary methods, one variable is used at a time to monitor the influence of operational parameters. In this optimization technique, the studied parameter is changed while the others are kept fixed. In the present study, the method does not examine the interactive effects between variables, which would result in a high number of experiments and would not be economical in terms of cost or time (Bezerra et al., 2008). Multivariate statistical techniques were used to optimize the effective parameters in the minimum number of experiments.

Response surface methodology (RSM) is a powerful statistical technique for investigating the interactive effects between several factors at different levels and has been successfully employed to optimize removal of pollutants (Pang et al., 2011). The actual responses are fitted to a polynomial model in a range of optimal responses. The model then determines the relationship between the responses and the variables and calculates the optimal responses and variables (Abdollahi et al., 2012).

There is no information in the literature about PFF degradation using zero-valent bimetallic nanoparticles. In this study, central composite design (CCD) optimization was used for PFF degradation using a Fe/Ni bimetallic system. The effect of catalyst dosage, pH of the solution and initial concentration of pesticide on the degradation process was optimized.

2

2 Material and methods

2.1

2.1 Materials and instruments

The following materials were employed: sodium borohydride (NaBH4; Merck, Germany), ferric chloride hexahydrate (FeCl3·6H2O; Merck, Germany), and nickel (II) nitrate hexahydrat (Ni(NO3)2·6H2O; Merck, Germany). Profenofos (C11H15BrClO3PS; mol wt: 373.63 g) was purchased from National Farmers’ Chemical (Iran). Sodium hydroxide (NaOH) and hydrochloric acid (HCl) (Merck, Germany) were used to adjust pH.

The reaction was followed by a double-beam UV–VIS spectrometer equipped with a 1 cm quartz cell (Cary 100; Varian). All analyses were run at room temperature.

The nano-catalyst was separated before the concentration was determined using a centrifuge (1000 rpm; Kokusan). Fe/Ni particle efficiency in PFF removal was calculated as follows (Salman, 2013):

(1)
R ( % ) = C 0 - C t C 0 × 100 where R (%) is the PFF removal efficiency; C0 is the initial PFF concentration in solution (mg/L) and Ct is the PFF concentration at t min (mg/L).

2.2

2.2 Bimetallic Fe/Ni nanoparticles preparation

The nZVI particles used in this study were chemically synthesized by liquid-phase reduction, also known as the boron hydride method (Frost et al., 2010). The experiments were carried out under N2 atmosphere and all aqueous solutions were prepared using distilled deionized water (DDW). Sodium borohydride solution (0.3 M) was added dropwise into ferric chloride solution (0.1 M) and the product was mixed vigorously using a mechanical stirrer. The resulting reaction is as follows (Sun et al., 2007):

(2)
4 Fe ( aq ) 3 + + 3 BH 4 ( aq ) - + 9 H 2 O ⟶ 4 Fe ( s ) 0 + 3 H 2 BO 3 ( aq ) - + 12 H ( aq ) + + 6 H 2 ( g )

Black particles of nZVI appeared immediately after introducing the first drop of NaBH4 solution. The generated iron particles were separated and washed with DDW. To prepare the bimetallic Fe/Ni nanoparticles, a specific amount of freshly prepared nZVI and (Ni(NO3)2·6H2O) was stirred in 250 mL DDW for 20 min. Separation and washing were similar to that for nZVI. The process is represented by the following equation:

(3)
Fe ( s ) + Ni 2 + → Fe 2 + + Ni ( s )

2.3

2.3 Bimetallic particle characterization

The morphology, size and surface chemical composition of the Fe/Ni nanoparticles were characterized and identified using scanning electron microscopy (SEM) and energy-dispersive X-ray analysis spectroscopy (EDAX) (ESEM, XL 30; Philips). The high tension was at 25 kV. X-ray powder diffraction (XRD) was performed using a Cu Kα radiation in the 2θ range from 5° to 90° to determine the composition of the newly-synthesized Fe/Ni bimetallic nanoparticles. The accelerating voltage was 40 kV and applied current was 30 mA.

3

3 Experimental design

In this study five level, three factor central composite design was used to the experimental design for degradation of the pesticide using the bimetallic system. A set of 20 experiments was designed to optimize the degradation by the Fe/Ni nano-catalyst. Three independent variables (initial concentration (A; mg/L), catalyst dosage (B; g/L) and pH (C)) were used to evaluate the influence of the operating parameters and each variable in the design at five coded levels (Table 1). The variables were coded according to the following equation (Eremia et al., 2008):

(4)
x i = X i - X 0 Δ X i where xi is the coded value of the independent variable; Xi, X0 are real values of independent variables where X0 is at the center point and; ΔXi is the step-changing value (Eremia et al., 2008). The data were tested using different mathematical models (linear, two factorial, quadratic and cubic) and analysis of variance (ANOVA) showed that PFF degradation was best approximated by a quadratic polynomial model. Table 2 shows that F-values were calculated for each model; the topmost order with significant terms is usually chosen. Significance was determined when the F-value calculated from the data exceeded a theoretical threshold (Muralidhar et al., 2001). The quadratic equation for the variable is as follows (Bezerra et al., 2008):
(5)
Y = β 0 + ∑ i = 1 n β i x i + ∑ i = 1 n β ii x i 2 + ∑ i = 1 n - 1 ∑ j = i + 1 n β ij x i x j + ε where Y is the predicted response; β0 is a constant coefficient; βi is the coefficient of linear parameters; βii and βij are the interaction and second-order coefficients, respectively; xi and xj are coded independent variables (Bezerra et al., 2008; Cho and Zoh, 2007; Eremia et al., 2008; Fu et al., 2007).
Table 1 Independent variables and their levels used for this study.
Variable Unit Limits Step change value, Δxi
−1.68 −1 0 1 1.68
A-Initial concentration mg/L 0.33 0.6 1 1.4 1.67 0.4
B-Catalyst amount g/L 6.95 9 12 15 17 3
C-pH 1.64 3 5 7 8.36 2
Table 2 Statistical parameters for sequential models.
Source Sum of squares DF Mean square F value Prob > F Remarks
Mean 1.582E+005 1 1.582E+005 – – –
Linear 495.71 3 165.24 5.55 0.0083 –
2FI 210.20 3 70.07 3.42 0.0495 –
Quadratic 204.27 3 68.09 11.03 0.0016 Suggested
Cubic 47.83 4 11.96 5.16 0.0381 Aliased
Residual 13.92 6 2.32 – – –
Total 1.591E+005 20 7956.60 – – –

The values of P > F less than 0.0500 indicate model terms are significant, whereas the values greater than 0.1 are not significant.

4

4 Results and discussion

4.1

4.1 Characterization of the bimetallic nanoparticles

Fig. 1(a) shows the morphology and size of the nanoscaled bimetallic particles. The SEM image shows Fe/Ni nanoparticles to be approximately spherical and forms chains resulting from the magnetic interaction between nanoparticles. Results show that the Fe/Ni particles are in the range of 20–50 nm in the diameter and the diameter of the particles was less than 50 nm. In Fig. 1(b), the results of EDAX analysis show Fe, Ni and O present on the nanoparticle surface. Fig. 1(c) shows the XRD pattern of nZVI and Fe/Ni synthesized nanoparticles. An apparent peak at 2θ = 44.9° indicated the presence of nZVI in the samples. However the peak at 2θ = 44.8° corresponded to Fe/Ni nanoparticles. However Fe0 peak was reduced significantly in Fe/Ni sample and iron oxide layers such as γ-Fe2O3 (2θ = 35.68°) and Fe3O4 (2θ = 35.45°) were formed (Weng et al., 2013).

(a) SEM image of Fe/Ni bimetallic nanoparticles. (b) EDAX spectra of the prepared Fe/Ni bimetallic nanoparticles. (c) XRD pattern of Fe0 and Fe/Ni bimetallic nanoparticles.
Figure 1 (a) SEM image of Fe/Ni bimetallic nanoparticles. (b) EDAX spectra of the prepared Fe/Ni bimetallic nanoparticles. (c) XRD pattern of Fe0 and Fe/Ni bimetallic nanoparticles.

4.2

4.2 Regression model and residuals analysis

RSM was used to determine the influence of individual factors and their optimal performances using Design Expert software (version 6.0.6) (Gunawan et al., 2005). The most reliable way to evaluate the quality of the model fitted is to apply ANOVA (Khataee et al., 2011). Fig. 2 shows the statistical relationships between outputs as calculated by ANOVA. As shown, the predicted values were fitted well with the actual values.

Scatter plot of predicted removal % value versus actual removal % value using RSM experimental design.
Figure 2 Scatter plot of predicted removal % value versus actual removal % value using RSM experimental design.

The responses predicted using RSM were compared to the actual responses for verification of the predicted data. The coefficient of determination (R2 = 0.936), the root mean square error (RMSE = 1.75) and the absolute average deviation (AAD = 0.0166) were determined. The RMSE and AAD are calculated using the following equations:

(6)
RMSE = 1 n ∑ i = 1 n y i - y p 2 1 / 2
(7)
ADD = ∑ i n ( | y p - y a | / y a ) n × 100 where n is the number of experiments, yp is the value predicted by RSM and ya is the actual value. The adjusted determination coefficient (adjusted R2 = 0.87) was also ideal.

Fig. 3(a–d) shows residual plots of PFF degradation efficiency using Fe/Ni bimetallic system. When a suitable model cannot illustrate residuals, it envisages a normal distribution for them. Drawing a normal possibility diagram for residuals is a useful way to examine the hypothesis of normality of the observations. If the distribution of residuals is normal, the resulting diagram will be a straight line. Fig. 3(a) shows the predicted normal residual diagram. The overall interpretation of this diagram is that the distribution of the faults is fairly normal. A chronological depiction of the data is useful to identify coordination of residuals. Fig. 3(b) shows the chronological diagram of residuals. If the correct model and hypothesis are fulfilled, the residuals should not produce a structure nor show a relation with other variables that is distinct from that of the predicted response. Fig. 3(c) depicts the diagram of the residuals based on the predicted pesticide degradation efficiency. No distinct process is evident in this diagram.

Residual plots of PFF degradation efficiency using Fe/Ni bimetallic system, (a) residuals normal possibility diagram, (b) chronological diagram of the residuals, (c) diagram of the residuals based on the predicted amounts of degradation percent efficiency, (d) histogram.
Figure 3 Residual plots of PFF degradation efficiency using Fe/Ni bimetallic system, (a) residuals normal possibility diagram, (b) chronological diagram of the residuals, (c) diagram of the residuals based on the predicted amounts of degradation percent efficiency, (d) histogram.

4.3

4.3 ANOVA

F-tests were used for ANOVA and the F-value was calculated for each model. The value of F increases as the value of P decreases. A factor is considered to be significant if p < 0.05. ANOVA shows that the proposed model was significant and had a F-value of 16.38. Generally, the calculated F-value must be higher than the value listed in the table, so that the model to be considered is logically good. The critical F-value is calculated as F0.05, df,(n–df+1). Since F0.05, 9, 10 = 3.02 is lower than the calculated F-value, the model is meaningful (Table 3) (Sen and Swaminathan, 2004; Yetilmezsoy and Saral, 2007). The results of ANOVA indicate that the proposed model’s lack of fit is non-significant. R2 is defined as

(8)
R 2 = SS Model SS Total
Table 3 Analysis of variance (ANOVA) of the response surface quadratic model for prediction of pesticide degradation efficiency.
Source Sum of squares DF Mean square F value Prob > F
Model 910.18 9 101.13 16.38 <0.0001 Significant
A 357.97 1 357.97 57.97 <0.0001
B 17.93 1 17.93 2.90 0.1192
C 119.81 1 119.81 19.40 0.0013
A2 117.74 1 117.74 19.07 0.0014
B2 10.54 1 10.54 1.71 0.2205
C2 104.76 1 104.76 16.97 0.0021
AB 25.40 1 25.40 4.11 0.0700
AC 123.83 1 123.83 20.05 0.0012
BC 60.97 1 60.97 9.87 0.0105
Residual 61.75 10 6.17
Lack of fit 48.09 5 9.62 3.52 0.0968 Not significant
Pure error 13.66 5 2.73
Cor total 971.93 19
Std. Dev 2.48 R-squared 0.9365
Mean 88.93 Adj R-squared 0.8793
C.V 2.79 Pred R-squared 0.5741
PRESS 413.99 Adeq precision 14.752

The values of P > F less than 0.0500 indicate model terms are significant, whereas the values greater than 0.1 are not significant.

This could be considered to be the rate of changes explained by ANOVA. R2 should be 0 ⩽ R2 ⩽ 1 and larger values denote better results. The standard deviation (std. dev) is the square root of the variance and coefficient of variation (CV) is the square root of the coefficient of variation. The CV indicates an unexplained rate of change or the change remaining in the data based on the mean of the response variable. The predicted residual sum of squares (PRESS) is the ability of the model to efficiently predict the responses of a new experiment. Smaller PRESS values are desirable.

In statistics, an adequate prediction is the highest predicted response minus the lowest predicted response divided by the mean criterion deviation of the total predicted response. Larger values for this statistic are desirable and values greater than 4 indicate efficient application of the model for prediction (Beg et al., 2003; Jeirani et al., 2013). The matrix of the 3-variable CCD and the empirical results for PFF degradation by Fe/Ni nanoparticles are depicted in Table 4. The polynomial response quadratic model depicts the relationship of the independent and dependent variables. The results show an empirical relation between independent variables and the response is stated the following second-order polynomial equation:

(9)
Y = 93.3 + 5.12 A - 1.15 B + 2.96 C - 2.86 A 2 - 0.86 B 2 - 2.70 C 2 + 1.78 AB - 3.93 AC + 2.76 BC
Table 4 Response surface central composite design and experimental and predicted responses.
Run Initial concentration (mg/L) Catalyst amount (g/L) PH Actual removal (%) Predicted removal (%)
1 0 0 0 94.57 93.30
2 −1.68 0 0 77.81 76.61
3 1 1 −1 92.064 90.86
4 0 0 −1.68 83.127 80.70
5 −1 1 −1 66.21 69.19
6 0 0 0 90.5 93.30
7 0 −1.68 0 92.86 92.81
8 0 0 0 93.15 93.30
9 −1 −1 −1 80.45 80.57
10 0 0 1.68 87.91 90.66
11 1 1 1 94.78 94.44
12 0 1.68 0 88.59 88.96
13 −1 1 1 90.74 88.50
14 1 −1 −1 93.1 95.11
15 −1 −1 1 87.86 88.84
16 0 0 0 94.039 93.30
17 0 0 0 95.059 93.30
18 0 0 0 92.56 93.30
19 1 −1 1 90.85 87.64
20 1.68 0 0 92.31 93.83

Table 4 shows the removal efficiency of Fe/Ni nanoparticles as predicted using Eq. (9). These results reflect high conformity between the empirical removal efficiency and the predicted one.

4.4

4.4 Effects of model components and their interaction for PFF degradation

ANOVA tests the significance of a model. Both the amount and sign are important for regression coefficients. A positive sign increases the response and the negative sign decreases the response (Celevi et al., 2007). The initial concentration, pH, interaction between catalyst amount and pH and the interaction between the amount of catalyst and initial concentration were positive and all others were negative. The percentage of contribution (PC) of each individual term in the final model is shown in Table 5. The PC of a term is calculated as follows (Singh et al., 2011; Yetilmezsoy and Saral, 2007):

(10)
PC = SS ∑ SS × 100
Table 5 Multiple regression result and significance of the components for the quadratic model.
Factor Coefficient estimate Effect Sum of squares PC
Intercept 93.30
A-Concentration 5.12 33.3 357.97 38.12
B-Catalyst amount −1.15 1.68 17.93 1.90
C-pH 2.96 11.13 119.81 12.75
A2 −2.86 10.39 117.74 12.53
B2 −0.86 0.94 10.54 1.12
C2 −2.70 9.26 104.76 11.15
AB 1.78 4.02 25.40 2.70
AC −3.93 19.61 123.83 13.18
BC 2.76 9.67 60.97 6.49

The values of P > F less than 0.0500 indicate model terms are significant, whereas the values greater than 0.1 are not significant.

The table indicates that the initial concentration of PFF was at the topmost level of importance with a contribution of >38.

Pareto analysis provides more significant information about the results and is calculated as follows (Haaland, 1989; Kesraoui-Abdessalem et al., 2008):

(11)
P i = b i 2 ∑ b i 2 × 100 ( i ≠ 0 )

Fig. 4 shows the Pareto graphic analysis. The initial concentration of PFF (33.3%), the interaction of pH and initial PFF concentration (19.61%), pH (11.13%) and the quadratic effect of initial PFF concentration (10.39%) affected PFF degradation using Fe/Ni.

Pareto graphic analysis.
Figure 4 Pareto graphic analysis.

4.5

4.5 Optimization of reaction and model validation

RSM suggests three solutions with different conditions to predict the optimal parameters for PFF degradation using Fe/Ni bimetallic nanoparticles. Testing was done under controlled conditions and the results were compared to the predicted amounts. The following were the optimal parameters for the reaction: amount of catalyst (13.38 g/L), initial concentration of PFF (1.4 mg/L) and pH (5.12). The highest removal rate (94.51%) was attained under these conditions. The relative deviation coefficient was obtained using RSM. A comparison between actual and predicted values shows a good fit, which indicates that the empirical results well describe the relation between factors and the response for PFF degradation (Jaehoon et al., 2008; Khuwijitjaru et al., 2012).

4.6

4.6 Three-dimensional response surface plots

Three-dimensional (3D) response surface plots were used to assign the interaction between three variables. The effect of two relative variables on the degradation efficiency was tested as the others were held constant (Fig. 5). The 3D response surface plots were organized based on the quadratic model. The optimal relative variables are located at the coordinates of the central point in the highest level in each figure. Fig. 5(a) shows the interactive influence of pH and initial concentration of PFF on degradation efficiency. It is evident that PFF degradation increased as the PFF concentration increased and pH of the solution fell in the range discussed earlier. A decrease in PFF removal with a decrease in pH can be attributed to protonation of the PFF molecules, repulsion forces with similar charges (positive) on the catalyst surface, and PFF molecules with lower pH.

The response surface plot showing the effect of (a) pH and initial concentration of PFF, (b) catalyst dosage and initial concentration of PFF, (c) pH and catalyst dosage on pesticide degradation using Fe/Ni bimetallic nanoparticles. (A: Initial concentration (mg/L), B: Catalyst dosage (g/L), C: pH).
Figure 5 The response surface plot showing the effect of (a) pH and initial concentration of PFF, (b) catalyst dosage and initial concentration of PFF, (c) pH and catalyst dosage on pesticide degradation using Fe/Ni bimetallic nanoparticles. (A: Initial concentration (mg/L), B: Catalyst dosage (g/L), C: pH).

The combined effect of catalyst dosage and initial PFF concentration is shown in Fig. 5(b). PFF degradation increased as initial concentration increased for lower concentrations of PFF. This trend reversed at higher concentrations of PFF. Increasing the catalyst dosage at lower concentrations decreased efficiency; increasing it at higher concentrations increased PFF degradation. This can be attributed to the fact that, at low dosages, increasing the PFF concentration after saturation of the binding sites decreased degradation. At higher doses, degradation was relatively higher because of the increased availability of active binding sites.

The response surface plot for the combined effect of solution pH and catalyst dosage is shown in Fig 5(c). It shows that, at lower pH, PFF degradation decreased as the catalyst dosage increased; this trend was reversed for higher (alkaline) pH. This can be explained by the reversal in the ionic structure of nanoparticles under acidic and basic media. The Fe/Ni surface charge for the pH area at pHzpc (pH area is the pHzpc in spaces where the catalyst surface charge is zero) is positive in response to the increase in H in the solution. Nevertheless, for the pH area above pHzpc (7.3), the Fe/Ni surface charge was negative in the presence of OH in the solution; colonic forces are the main reason for this behavior.

5

5 Conclusion

Synthesized Fe/Ni nanoparticles were used for PFF removal and RSM was used to optimize the removal of PFF by the nanoparticles. A central composite design was applied to provide the experimental conditions for degradation of PFF. The variables included amount of catalyst, pH of the solution and initial concentration of PFF. The quadratic mathematical model was suggested for PFF degradation by Fe/Ni. The predicted values fitted well to the actual values (R2 = 0.936, RMSE = 1.75 and AAD = 0.0166). ANOVA corroborated the accuracy of the model with a high F value (16.38), very low P value (<0.0001), non-significant lack of fit, a coefficient of determination (R2 = 0.936) and adequate precision (14.75).

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