Application of response surface methodology for optimization of lead removal from an aqueous solution by a novel superparamagnetic nanocomposite

Abstract

The present study focuses on the response surface methodology (RSM) for the optimization of lead removal from an aqueous solution by a novel superparamagnetic nanocomposite. A rotatable central composite design and the response surface methodology were used to conduct and to analyze the experiments, respectively. The adsorption process was investigated as a function of the four factors consisting of pH (4.0–6.0), temperature (20℃–60℃), initial lead concentration (10–90 mg/L) and adsorbent dosage (0.2–1.0 g/L). The maximum lead adsorption capacity was obtained to be 124.955 mg/g under the optimal conditions of 5.49, 60℃, 89.08 mg/L, and 0.48 g/L for the solution pH, temperature, initial lead ion concentration, and the adsorbent dosage, respectively. The desirability function was used to find an optimum point where the desired conditions could be obtained. The superparamagnetic nanocomposite could be used as an adsorbent for the removal of toxic heavy metals from water and wastewater.

Keywords

Lead removal central composite design nanocomposite optimization adsorption

Introduction

Environmental contamination of heavy metals has become a worldwide problem and a special attention has been given to them because of their high toxicity, nonbiodegradability, and accumulation in the food chain (Javanbakht et al., 2011; Kumar et al., 2014; Kumari et al., 2015; Kumari and Tripathi, 2015; Mendoza-Castillo et al., 2015; Moftakhar et al.; Singh and Prasad, 2015; Song et al., 2011; Xu et al., 2012a). The heavy metal sources from industry include fertilizers, pigments, batteries, and so on (Javanbakht et al., 2014; Mahdavi et al., 2012; Nędzarek et al., 2015; Sarioglu et al., 2009; Şen et al., 2015; Tan et al., 2012). Heavy metals, even at trace level, are very harmful for human beings. Thus, the removal of heavy metals from water systems is a very critical and essential topic and has attracted a considerable attention (Ahmed et al., 2013; Lata et al., 2015).

Different methods have been proposed for the efficient heavy metal removal from water systems, including chemical precipitation, ion exchange, adsorption, membrane filtration, and electrochemical technologies (Salam, 2013; Shen et al., 2012). Among these techniques, adsorption as a cost-effective technology offers flexibility in design and operation, and in many cases it will generate high-quality treated effluent (Gong et al., 2012; Komkiene and Baltrenaite, 2016). In addition, a lot of adsorbents can be regenerated by desorption processes of ease of operation, for multiple use. Thus, the adsorption process has come as one of the major techniques for the removal of heavy metals from water and wastewater effluents (Xu et al., 2012a).

Various natural materials such as clay, seaweed, and adsorbent, as well as synthetic adsorbents such as activated carbon, resin, and mesoporous silica, have been used to remove heavy metals (Pang et al., 2011). Among minerals that possess adsorbent properties, natural zeolite appears as one of the most promising minerals for metal purification (Kurniawan et al., 2006; Monroy-Figueroa et al., 2015). The special structural, physical, and chemical properties of zeolites make them valuable for many industrial, agricultural, and environmental applications (Liu et al., 2013). Natural zeolites are low cost and available materials functioning as excellent cation exchangers (Oliveira et al., 2004). Clinoptilolite (CPL), a hydrated alumina–silicate member of the heulandite group, is one of the most common natural zeolites, being widespread in the world (Dinu and Dragan, 2010).

Recently, magnetic nanocomposites have attracted much attention because of their widespread application in different fields such as adsorption, mineral separation, catalysis, drug delivery, and magnetic resonance imaging (Faraji et al., 2010; Huang and Chen, 2009; Mahdavian and Mirrahimi, 2010; Mahmoud et al., 2013; Panneerselvam et al., 2011; Unsoy et al., 2012). Magnetic adsorbents can be easily separated from the solution by an external magnetic field and therefore, adsorbents combining magnetic separation technique and nanotechnology would remove heavy metals in perfect performance (Chang et al., 2012; Gao et al., 2013; Gong et al., 2009; Xu et al., 2012b). Magnetic composite particles can be designed to have specific physical, chemical, and surface properties that allow the selective attachment of ions (Hakami et al., 2012). Several magnetic nanomaterials, including magnetite and maghemite nanoparticles, functionalized or modified with some compounds, have been explored for the removal of heavy metals ions (Faghihian et al., 2014; Hao et al., 2010).

Chitosan (CS), a nontoxic, biocompatible, and biodegradable material has been investigated as adsorbent for the heavy metal removal (Ngah et al., 2011). It is one of the cheap and abundant biopolymers with the ability to form complexes with metals (Babel and Kurniawan, 2003; Kyzas and Deliyanni, 2013). Numerous studies have been done to the modification of the CS surface by the cross-linking with different functional agents, such as epichlorhydrin, glutaraldehyde sodium, and tripolyphosphate, to prevent CS dissolution in an acidic medium (Dinu and Dragan, 2010; Guibal, 2004).

The objective of this work was to study the effect of pH, temperature, initial lead concentration, and adsorbent dosage on the lead adsorption as a toxic heavy metal using a novel superparamagnetic CS/CPL nanocomposite. We synthesized the nanocomposite (adsorbent) by embedding a magnetite in a matrix of the zeolite microparticles, cross-linking them with glutaraldehyde and coating the product with the chitosan to improve the stability and adsorption capacity of the nanocomposite. Optimization of the lead adsorption by the classical method involves changing one independent variable (i.e., pH, temperature, lead ion concentration, and adsorbent dosages) while maintaining the others at a fixed level. The experimental factorial design and response surface methodology (RSM) can be employed to optimize the lead adsorption onto the magnetic CS/CPL nanocomposite in an aqueous solution. For a better understanding of different stages of the adsorption at a varying pH, temperature, lead concentration, and adsorbent dosages, the RSM (under DESIGN EXPERT software) was used to optimize the lead adsorption.

Materials and methods

Materials

The CPL sample (KNa₂Ca₂(Al₇Si₂₉)O₇₂·24H₂O) was collected from Semnan region in the central Alborz Mountains, Iran. The CS as a powder (with a medium molecular weight and a deacetylation degree of 75–85%) was obtained from the Sigma–Aldrich. Pb(NO₃)₂, NaOH (99%), HCl (99%), HNO₃ (99%), and glutaraldehyde solution (25%) were supplied by the Merck company (Germany). FeCl₃.6H₂O and FeCl₂.4H₂O were supplied by the UNI-CHEM, Germany. All the solutions were prepared with distillated water. Also, all the chemical reagents were of an analytical reagent grade.

Synthesis of the modified CPL (CPL/Fe₃O₄)

A known amount of the CPL powder was added to a 30 mL of distilled water. The mixture was homogenized using a vigorously mechanical stirring (350 r/min) for 10 min. The solutions of FeCl₂·4H₂O and FeCl₃·6H₂O in water with a molar ratio of 2:1 were prepared and were mixed together and then were deoxygenated by bubbling of Argon (Ar) gas. The initial pH of the solutions was adjusted to approximately 2 with 2 M HCl. About a 9 mL of 1 M NaOH solution was added dropwise to the suspension while the mixture was vigorously mechanical stirred under an ultrasonic irradiation and in Ar atmosphere for 1 h. The prepared composite (zeolite powder, iron oxide with a 1:1 mass ratio) was separated using a magnet. The product was washed with distilled water for four times and then was dried at 80℃ in a vacuum oven for 24 h.

Preparation of the magnetic CS/CPL nanocomposite

In this research, the CPL/Fe₃O₄ nanocomposite was synthesized by embedding magnetite nanoparticles in a matrix of the CPL microparticles with precipitation method and then, crosslinked the CS around the CPL/Fe₃O₄ nanocomposite with glutaraldehyde crosslinker to improve the stability and adsorption capacity of the nanocomposite. As the prepared CPL/Fe₃O₄ nanocomposite (50 mg) was completely washed and dispersed into distillated water, the initial pH of the suspension was adjusted to about 11 with 1 M NaOH. Glutaraldehyde solution (0.200 mL) as a crosslinker was dropped into the suspension with a continual stirring of 200 r/min under the ultrasonic irradiation for 30 min. Next, a solution of chitosan (CS/CPL with a 1:5 mass ratio) in an acetic acid with 1 wt% was added dropwise to the suspension while the mixture was vigorously mechanical stirred under the ultrasonic irradiation for 1 h. As a novel way to prevent deterioration of synthesized magnetite when adding acidic chitosan solution, an alkaline solution was added dropwise to maintain the suspension pH of about 11. After the addition was completed, the stirring was continued for 60 min, then the solid–liquid separation was performed using a magnetic ﬁeld. The product was washed and then was dried at 70℃ for 44 h. Finally, it was grinded to obtain the nanocomposite (adsorbent).

Analytical methods

The scanning electron microscopy (SEM) (Cam-Scan MV 2300) was used to study the surface of the adsorbent. Prior to the SEM studies, the samples were coated with a thin layer of gold. The crystal structure of nanocomposite was examined by a JEOL JDX-8030 diffractometer using a Cu Kα radiation (λ = 0.1541 nm) in the range of 10–90° (2θ) (X-ray powder diffraction (XRD)). The Fourier transform infrared (FTIR) spectra of the nanocomposite were analyzed using a Bruker Tensor 27 spectrometer. The magnetization was measured at room temperature with a vibrating sample magnetometer (VSM, MPMS-5 SQUID). The concentration of lead solution was determined using a Buck 210VGP atomic absorption spectrophotometer. A stock lead ion solution of 1000 mg/L was prepared by dissolving 1.5980 g of lead nitrate into a 1000 mL of deionized water. The solution was diluted for different considered ion concentrations. The pH of the solutions was adjusted by adding 1 N NaOH and 1 N HNO₃ solutions.

Lead adsorption process

By means of the RSM, 30 batch adsorption experiments were designed and the effect of the pH, temperature, initial lead concentration, and the adsorbent dosage on the lead removal was studied. Each experiment was carried out in 50 mL of the lead solution under a mechanical stirring at 100 r/min for 120 min to reach the equilibrium and finally, the adsorbent was separated from the solution using a magnetic ﬁeld. The separated solutions were analyzed for the residual lead ion concentration. The removal capacity of lead (Y) was given by

Y = \frac{(C_{0} - C_{e})}{m} \times V

(1)

where m, V, C₀, and C_e are the mass of adsorbent, the volume of the solution and the initial and residual lead concentrations in the solution (mg/L), respectively.

Design of experiments

The RSM consists of several experimental techniques dedicated to the evaluation of relationship between a group of controlled experimental factors and measured responses based on one or more criteria (Amini et al., 2008; Ghorbani et al., 2008; Radaei et al., 2014). RSM can be used to evaluate the effects of individual parameters, the interaction of variables, and the optimum conditions for responses (Ghoreishi et al., 2012; Roosta et al., 2014). A five-level, four-factor central composite design (CCD) was used to investigate the effect of the selected parameters on the lead removal capacity by the adsorbent particles. The pH (X₁), temperature (X₂), initial lead concentration (X₃), and the adsorbent dosage (X₄), were selected as the independent variables, and the lead removal capacity was considered as the response (dependent variable). The variables were coded according to the following equation

x_{i} = \frac{(X_{i} - X_{0})}{Δ X}

(2)

where x_i, X_i, and X₀ are the coded value, the real value, and the real value at the center point of an independent variable, respectively, and ΔX is the step change value.

A CCD is made rotatable by the choice of the rotatability factor α which depends on the number of the points in the factorial portion of the design as expresses

α = (2^{n})^{1 / 4}

(3)

where n is the number of factors which is 4 and 2 ⁿ is the number of the points used in the factorial portion of design witch gives α = 2. The uncoded levels of each parameter were defined as follows

Centerpoint = \frac{minimumlevel + maximumlevel}{2}

(4)

Levelcodedas (+ 1) = \frac{(α + 1) maximumlevel + (α - 1) minimumlevel}{2 α}

(5)

Levelcodedas (- 1) = \frac{(α - 1) minimumlevel + (α + 1) maximumlevel}{2 α}

(6)

Table 1 represents the experimental ranges and levels of the independent variables for the lead ions removal. Tables 2 and 3 give the response variables and the experimental design points consisting of the 2⁴ factorial points, the 8 axial points, and the 6 central points. The center points are used to determine the reproducibility of the data and the experimental error.

Table 1.

The levels and ranges of the independent variables.

Independent variables	Range and level
Independent variables	−α	−1	0	+1	+α
pH (X₁)	4	4.5	5	5.5	6
Temperature, ℃ (X₂)	20	30	40	50	60
Lead ion concentration, mg/L (X₃)	10	30	50	70	90
Adsorbent dosage, g/L (X₄)	0.2	0.4	0.6	0.8	1.0

Table 2.

Experimental conditions and values obtained through the CCD.

Runs no.	X₁	X₂	X₃	X₄	Lead removal capacity	Categorical factor levels
1	4.50	30.00	30.00	0.02	71.9	Fractional factorial points (16 points)
2	5.50	30.00	30.00	0.02	74.4
3	4.50	50.00	30.00	0.02	66.9
4	5.50	50.00	30.00	0.02	74.6
5	4.50	30.00	70.00	0.02	115.4
6	5.50	30.00	70.00	0.02	124.7
7	4.50	50.00	70.00	0.02	108.4
8	5.50	50.00	70.00	0.02	122
9	4.50	30.00	30.00	0.04	37.1
10	5.50	30.00	30.00	0.04	37.2
11	4.50	50.00	30.00	0.04	37.2
12	5.50	50.00	30.00	0.04	36.9
13	4.50	30.00	70.00	0.04	84.2
14	5.50	30.00	70.00	0.04	86
15	4.50	50.00	70.00	0.04	86
16	5.50	50.00	70.00	0.04	87.1
17	4.00	40.00	50.00	0.03	81.2	Axial points (8 points)
18	6.00	40.00	50.00	0.03	82.5
19	5.00	20.00	50.00	0.03	79.8
20	5.00	60.00	50.00	0.03	83.1
21	5.00	40.00	10.00	0.03	16.2
22	5.00	40.00	90.00	0.03	115.9
23	5.00	40.00	50.00	0.01	127.6
24	5.00	40.00	50.00	0.05	49.7
25	5.00	40.00	50.00	0.03	80.6	Central points (6 points)
26	5.00	40.00	50.00	0.03	81.2
27	5.00	40.00	50.00	0.03	82.4
28	5.00	40.00	50.00	0.03	82.7
29	5.00	40.00	50.00	0.03	82.3
30	5.00	40.00	50.00	0.03	82.5

Table 3.

Observed and predicted values.

Run no.	Actual value	Predicted value	Residual
1	71.90	72.06	−0.16
2	74.40	76.08	−1.68
3	66.92	68.72	−1.79
4	74.65	74.80	−0.15
5	115.38	116.90	−1.53
6	124.75	124.87	−0.12
7	108.40	113.05	−4.65
8	122.00	123.08	−1.08
9	37.15	37.05	0.097
10	37.25	33.43	3.82
11	37.24	37.96	−0.72
12	36.95	36.41	0.54
13	84.25	84.94	−0.69
14	86.08	85.27	0.81
15	86.04	85.34	0.69
16	87.06	87.74	−0.68
17	81.20	77.73	3.47
18	82.50	84.14	−1.64
19	79.82	81.00	−1.18
20	83.13	80.13	3.01
21	16.20	17.09	−0.89
22	115.97	113.26	2.71
23	127.60	122.93	4.67
24	49.73	52.58	−2.85
25	80.62	81.98	−1.36
26	81.17	81.98	−0.81
27	82.43	81.98	0.46
28	82.72	81.98	0.74
29	82.38	81.98	0.41
30	82.55	81.98	0.57

The independent variables vary between the lowest level of −1 and the highest level of +1. The axial points are located at the distance α from the center and make the design rotatable. The experimental results were analyzed using the Design Expert 7.0.0 (Stat-Ease, Inc., Minneapolis, MN, USA) software and the mathematical relationship between the four independent variables were approximated by the second-order polynomial model (Amini et al., 2008; Ghorbani et al., 2008; Ghoreishi et al., 2012; Radaei et al., 2014; Roosta et al., 2014).

y = β_{0} + \sum_{i = 1}^{4} β_{i} x_{i} + \sum_{i = 1}^{4} \sum_{j = 1}^{4} β_{ij} x_{i} x_{j} + \sum_{i = 1}^{4} β_{ii} x_{i}^{2} + ɛ

(7)

where y is the predicted response (lead removal capacity), x_i and x_j are the independent variables (pH, temperature, initial lead concentration, and adsorbent dosage), β₀, β_i, β_ii and β_ij are the model constant, the linear coefficients, the quadratic coefficients, and the cross-product coefficients, respectively, and ɛ represents the random error.

Results and discussion

Characterization of the adsorbent

Figure 1(a) and (b) show the SEM image and the surface morphologies of the modified CPL and the nanocomposite. The white spots in Figure 1(b) show the presence of the chitosan covering the CPL/Fe₃O₄ nanocomposite surface.

Figure 1.

SEM micrograph of (a) the modified CPL and (b) the nanocomposite.

In the FTIR result of the nanocomposite (Figure 2), the band at around 3430 cm⁻¹ can be assigned to the stretching modes of the OH groups on the surface of the magnetite particles or to the N–H extension vibration on the surface of the Cs (Batista et al., 2011). The C–H stretching vibration of the Cs is manifested through a strong peak of 2923 cm⁻¹ while the characteristic band centered at 1634 cm⁻¹ can be attributed to the N–H bending of the NH₂ group (Unsoy et al., 2012). The Fe–O–Fe band observed at 575 cm⁻¹ corresponds to the intrinsic stretching vibrations of the metal (Ozkaya et al., 2009; Petcharoen and Sirivat, 2012). The absorption appeared in the regions of around 450 and 800 cm⁻¹ is resulted from the stretching and bending modes of Si–O or Al–O in the CPL framework (Faghihian et al., 2014; Mansouri et al., 2013). The bands at 1417 cm⁻¹ can be attributed to the CH₃ bending vibration (Kyzas and Deliyanni, 2013).

Figure 2.

FTIR results of the nanocomposite.

The XRD results of the nanocomposite represent the characteristic peaks of the crystalline magnetite at 2θ = 30.4, 35.5, 43.1, 53.4, 57.0, and 62.9° marked by their indices (2 2 0), (3 1 1), (4 0 0), (4 2 2), (5 1 1), and (4 4 0), respectively, of the crystalline CPL at 2θ = 22.5, 26.0, and 42.9°, and of the chitosan at 2θ = 20.2° (Figure 3). There is no significant shift in the characteristic peaks of the magnetite and zeolite in the nanocomposite particles. This reveals that the crystallinity of the magnetite and zeolite is still retained in the nanocomposite. Indeed, for a relative change in the peak intensities ca. the reaction has not altered the crystalline structures of the magnetite and zeolite. It is also suggested that the Cs modification does not change the phase of nanocomposite compositions.

Figure 3.

XRD results of (a) magnetite, (b) CPL, (c) Cs, and (d) nanocomposite.

The nanocomposite revealed good magnetic properties in the magnetic field. After the preparation, a test with a magnet showed that the particles were superparamagnetic and were completely attracted to the magnet. The saturation magnetization of the nanocomposite is 9.50 emu g⁻¹ (Figure 4).

Figure 4.

The hysteresis loops of the nanocomposite.

Lead adsorption process

In this section, the effects of process parameters on the removal response are presented. The removal of lead as a function of the pH and the adsorbent dosage is given in Figure 5(a). The Figure 5(a) shows that the lead removal decreased with increasing the adsorbent dosage from 0.2 to 1.0 g/L. Although increasing the adsorbent dosage can be attributed to the increased surface area and adsorption sites of adsorbent, the values of lead removal decreased with increasing the adsorbent dosage (Ghorbani et al., 2008). The primary reason is that the adsorption sites remain unsaturated during the adsorption reaction whereas the number of sites available for the adsorption increases by increasing the adsorbent dosage. The agglomeration of the adsorbent can also be a reason for the decrease in the lead removal capacity at the high concentrations of the adsorbent. This means that the higher values of lead adsorption are obtained by simultaneously, the decrease in the adsorbent dosage and the increase in the pH. This may be explained by the increase in availability of the adsorption sites at a higher pH which improved the access of lead to the adsorption sites of the adsorbent. In fact, the protons will combine with the lead ions for the ligands and consequently, will decrease the interaction of lead ions with the adsorbent (Tan et al., 2012).

Figure 5.

Response surface plots for the effect of (a) the pH and the adsorbent dosage (g/L); (b) the pH and the temperature (℃); (c) the pH and the initial lead concentration (mg/L); (d) the temperature (℃) and the adsorbent dosage (g/L); (e) the initial lead concentration (mg/L) and temperature (℃); (f) the adsorbent dosage (g/L) and the initial lead concentration (mg/L); on the lead removal.

The effect of temperature and pH on the lead removal is shown in Figure 5(b). Increasing the temperature in a lower pH had a positive effect on the lead removal. The increased lead removal with increasing the temperature may be a result of the faster chemical precipitation rate of the lead hydroxide at higher temperatures (Ahmadi et al., 2014). The effect of the initial pH and the initial lead ion concentrations on the lead removal is shown in Figure 5(c). The lead removal increased with an increase in the initial lead ion concentration varying from 10 to 90 mg/L and with an increase in the initial solution pH varying from 4 to 6 in the higher lead ion concentrations.The solution pH affects both the adsorbent surface adsorption sites and the lead chemistry in solution. The functional groups such as amino groups and hydroxyl of Cs were found to be responsible in the adsorption of lead ions (Ngah et al., 2011). This proofs that the lead adsorption occurs through the ion exchange mechanism in witch, the lead ion binds to adsorption sites by replacing two acidic H⁺ at a low pH. However, the amount of the lead adsorbed is influenced by the properties of the adsorbent surface. An increase of the lead removal by increasing the initial lead concentration is a result of the increase in the driving force of the lead concentration gradient (Javanbakht et al., 2011). The removal of lead as a function of the adsorbent dosage and the temperature is shown in Figure 5(d). The results showed that the lead removal decreased with increasing the adsorbent dosage from 0.2 to 1.0 g/L. Thus, the effect of the adsorbent dosage is more significant than that of the temperature. Figure 5(e) shows the effects of the temperature and the initial lead concentration on the lead removal. The effect of the initial lead concentration is found to be more significant than that of the temperature. Figure 5(f) depicts the lead removal as a function of both the adsorbent dosage and the initial lead concentration.

Fitting the model

The analysis of variance (ANOVA) was used to evaluate the statistical significance of the quadratic model. It was found that the regression was statistically significant at the F-value of 204.42 and the values of prob > F (<0.0001). The determination coefficient (R²) was used to check the accuracy of the model. The determination coefficient (R²= 0.9948) indicated that the model cannot explain only 0.52% of the total variable. The adjusted determination coefficient (R²_adj = 0.9899) is also high, indicating a high significance of the model. The predicted R² (R²_pred) is also high enough to indicate the high significance of the model. The R²_pred is 0.9707, revealed that it is in a reasonable agreement with the R²_adj and showing that the model does not explain only 2.93% of the total variations. “Adeq Precision” gives the ratio of the signal to the noise. A ratio greater than 4 is desirable. Thus, the Adeq precision of 54.891 indicates an adequate signal. This model can be used to navigate the design space. A low value of the variation coefficient (CV = 3.50) shows reliability of the experiments.

The multiple regression analysis to the experimental data results a second-order polynomial model. The regression equation between the removal of lead (y) and the coded variables can be expressed as

y = 81.98 + 1.60 x_{1} - 0 . {22 x}_{2} + 24.0 {4 x}_{3} - 17 . {59 x}_{4} + 0 . {52 x}_{1} x_{2} + 0 . {99 x}_{1} x_{3} - 1 . {91 x}_{1} x_{4} - 0 . {13 x}_{2} x_{3} + 1.0 {6 x}_{2} x_{4} + 0 . {76 x}_{3} x_{4} - 0 . {26 x}_{1}^{2} - 0 . {35 x}_{2}^{2} - 4.20 x_{3}^{2} + 1 . {44 x}_{4}^{2}

(8)

where y is the lead removal response, x₁, x₂, x₃, and x₄ are the coded values of the variables including the initial solution pH (x₁), temperature (x₂), initial lead ion concentration (x₃), and the adsorbent dosage (x₄), respectively. The ANOVA has been performed for the second-order response surface model and the results are listed in Tables 4 and 5. The significance of each coefficient was determined by the P-values and the F-values. The smaller the P-values and the larger the F-value, the more significant is the corresponding coefficients. The values of “prob > F” < 0.05 indicate a significant regression at a 95% confidence level. Therefore, the significant model terms were the following: (a) the first order main effects of the initial solution pH, the initial lead concentration, and the adsorbent dosage; (b) the square effects of the initial lead concentration and the adsorbent dosage; and (c) the interaction effects of the initial solution pH and the adsorbent dosage.

Table 4.

ANOVA for the response surface quadratic model.

Source of variations	Degrees of freedom	Sum of squares	Mean square	F-value	P-value
Regression	14	22067.64	1576.26	204.42	<0.0001
X₁	1	61.64	61.64	7.99	0.0127
X₂	1	1.15	1.15	0.15	0.7047
X₃	1	13873.05	13873.05	1799.15	<0.0001
X₄	1	7424.65	7424.65	962.88	<0.0001
X₁²	1	1.85	1.85	0.24	0.6312
X₂²	1	3.43	3.43	0.44	0.5150
X₃²	1	484.19	484.19	62.79	<0.0001
X₄²	1	57.18	57.18	7.42	0.0157
X₁X₂	1	4.27	4.27	0.55	0.4684
X₁X₃	1	15.58	15.58	2.02	0.1757
X₁X₄	1	58.28	58.28	7.56	0.0149
X₂X₃	1	0.25	0.25	0.033	0.8586
X₂X₄	1	18.09	18.09	2.35	0.1464
X₃X₄	1	9.28	9.28	1.20	0.2898
Residual	15	115.66	7.71
Total	29	22183.31

Note: R²= 0.9948; R²_adj = 0.9899; R²_pred = 0.9707; CV = 3.50.

Table 5.

Regression analysis using the 2⁴ factorial CCD.

Model term	Coefficient estimate	Standard error	F-value	P-value
Intercept	81.98	1.13
X₁	1.60	0.57	7.99	0.0127
X₂	−0.22	0.57	0.15	0.7047
X₃	24.04	0.57	1799.15	<0.0001
X₄	−17.59	0.57	962.88	<0.0001
X₁²	0.52	0.69	0.24	0.6312
X₂²	0.99	0.69	0.44	0.5150
X₃²	−1.91	0.69	62.79	<0.0001
X₄²	−0.13	0.69	7.42	0.0157
X₁X₂	1.06	0.69	0.55	0.4684
X₁X₃	0.76	0.69	2.02	0.1757
X₁X₄	−0.26	0.53	7.56	0.0149
X₂X₃	−0.35	0.53	0.033	0.8586
X₂X₄	−4.20	0.53	2.35	0.1464
X₃X₄	1.44	0.53	1.20	0.2898

Note: X₁, X₂ X₃, and X₄ are the main effects; X²₁, X²₂, X²₃, and X²₄ are the square effects; X₁X₂, X₁X₃, X₁X₄, X₂X₃, and X₂X₄ are the interaction effects.

In order to estimate the adequacy of the regression model, the diagnostic plots are given in Figure 6. The actual and the predicted removal capacity values are given in Figure 6(a). As can be seen, the model adequately explains the experimental studied range, and there are tendencies in the linear regression fit. The normal percentage probability plot is presented in Figure 6(b). The data points show that neither there is any apparent problem with normality nor the response transformation is required. The plot of the residuals versus the fits was used to examine the reliability of the model, as illustrated in Figure 6(c). As a result, no series of the increasing or decreasing points, of the patterns such as the increasing residuals with increasing fits and of a predominance of the positive or negative residuals should be found which is fortunately met in our work.

Figure 6.

(a) The actual and the predicted lead removal capacity, (b) the studentized and the normal percentage probability plot of the lead removal capacity, and (c) the predicted removal and the studentized residual plot.

Optimization using the desirability function

For the numerical optimization, we consider the desired goal for each factor and the corresponding response (Batista et al., 2011). A weight can be assigned to each goal to adjust the shape of its particular desirability function. Our goals are the pH, temperature, initial lead concentration, adsorbent dosage, and the removal of lead. The overall desirability function, made of a combination of the goals, is an objective function ranging from zero outside of the limits to one at the goal. The function can be maximized by using a random starting point and then proceeding along the steepest slope up to a maximum. Starting from several points in the design space increases the chance of achieving the “best” maximum among all possible maxima of the function. A multiple response method was applied for optimizing any combination of our goals. The optimization resulted in a point that maximizes the desirability function. The maximum level of the initial lead concentration (90 mg/L), the minimum level of the adsorbent dosage (0.2 g/L), the level of the pH within the range of 4.0–6.0, the level of temperature within the range of 20.0–60.0℃, and the level of the removal capacity within the range of 16.2–127.6 (mg/g) were set for the maximum desirability. Figure 7 shows a ramp desirability made from 30 optimum points via the numerical optimization. By seeking 30 starting points in the response surface changes, the best local maximum was found to be at the pH of 5.49, the temperature of 60.0℃, the initial lead concentration of 89.08 mg/L, the adsorbent dosage of 0.48 g/L, the lead removal of 124.955 mg/g, and the desirability of 0.864. The desirability of 0.864 indicates that the estimated function may express the desired conditions and the experimental model.

Figure 7.

Desirability ramp for the optimization of the response and the variables.

The maximum capacity of the adsorbent indicates a large adsorption capacity for the lead. The Table 6 indicates the comparison of lead adsorption capacity of the adsorbent with various adsorbents reported in literature including its components. The results show a proper adsorption capacity for the lead in comparison with its components and other adsorbents. In addition, one of the important advantages of the synthesized adsorbent in this study is that it could be regenerated and recycled using simple magnetic separation which is a great advantage.

Table 6.

Comparison of adsorption capacity of various adsorbents for lead ions.

Adsorbent	Adsorption capacity (mg/g)	Ref.
Clinoptilolite particles	80.933	Günay et al. (2007)
Magnetite nanoparticles	19–53	Kumari et al. (2015); Rajput et al. (2015)
Chitosan	7.5–101.7	Rangel-Mendez et al. (2009); Suc and Ly (2013)
Chitosan/magnetite composite	63.33	Tran et al. (2010)
Different chitosan–zeolite composites	14.75–51.32	Wang and Chen (2014)
Clinoptilolite/magnetite/chitosan nanocomposite	124.955	This work

Conclusion

The results of this work show that the nanocomposite adsorbent is an efficient adsorbent of the lead in dilute solutions and has high adsorption yields for the treatment of wastewater containing lead ions. Adsorption processes were performed as a function of the pH, temperature, initial lead concentration, and the adsorbent dosage. The optimization of the lead adsorption by the RSM resulted in 124.955 mg/g. The levels of the following four variables were found to be optimal for the maximum lead removal: the pH of 5.49, the temperature of 60.0℃, the initial ion concentration of 89.08 mg/L, and the adsorbent dosage of 0.48 g/L. The scanning electron microscope and the energy dispersive X-rays results of the adsorbent show the presence of the chitosan covering the CPL/Fe₃O₄ nanocomposite surface. According to these observations, the nanocomposite revealed good magnetic properties in the magnetic field so that after the adsorption process is completed, the nanocomposite was completely attracted to the magnet.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by Isfahan University of Technology (IUT).

References

Ahmadi

Heidarzadeh

Mokhtari

(2014) Optimization of heavy metal removal from aqueous solutions by maghemite (γ-Fe₂O₃) nanoparticles using response surface methodology. Journal of Geochemical Exploration 147: 151–158.

Ahmed

Ali

El-Dek

(2013) Magnetite–hematite nanoparticles prepared by green methods for heavy metal ions removal from water. Materials Science and Engineering: B 178: 744–751.

Amini

Younesi

Bahramifar

(2008) Application of response surface methodology for optimization of lead biosorption in an aqueous solution by Aspergillus niger. Journal of Hazardous Materials 154: 694–702.

Babel

Kurniawan

(2003) Low-cost adsorbents for heavy metals uptake from contaminated water: A review. Journal of Hazardous Materials 97: 219–243.

Batista

Villanueva

Amorim

RVS

(2011) Chromium (VI) ion adsorption features of chitosan film and its chitosan/zeolite conjugate 13X film. Molecules 16: 3569–3579.

Chang

Chen

(2012) Synthesis and properties of core-shell magnetic molecular imprinted polymers. Applied Surface Science 258: 6660–6664.

Dinu

Dragan

(2010) Evaluation of Cu²⁺, Co²⁺ and Ni²⁺ ions removal from aqueous solution using a novel chitosan/clinoptilolite composite: Kinetics and isotherms. Chemical Engineering Journal 160: 157–163.

Faghihian

Moayed

Firooz

(2014) Evaluation of a new magnetic zeolite composite for removal of Cs⁺ and Sr²⁺ from aqueous solutions: Kinetic, equilibrium and thermodynamic studies. Comptes Rendus Chimie 17: 108–117.

Faraji

Yamini

Rezaee

(2010) Magnetic nanoparticles: Synthesis, stabilization, functionalization, characterization, and applications. Journal of the Iranian Chemical Society 7: 1–37.

10.

Gao

Zhao

Cheng

(2013) Removal of anionic azo dyes from aqueous solution using magnetic polymer multi-wall carbon nanotube nanocomposite as adsorbent. Chemical Engineering Journal 223: 84–90.

11.

Ghorbani

Younesi

Ghasempouri

(2008) Application of response surface methodology for optimization of cadmium biosorption in an aqueous solution by Saccharomyces cerevisiae. Chemical Engineering Journal 145: 267–275.

12.

Ghoreishi

Bataghva

Dadkhah

(2012) Response surface optimization of essential oil and diosgenin extraction from Tribulus terrestris via supercritical fluid technology. Chemical Engineering & Technology 35: 133–141.

13.

Gong

J-L

Wang

Zeng

G-M

(2009) Removal of cationic dyes from aqueous solution using magnetic multi-wall carbon nanotube nanocomposite as adsorbent. Journal of Hazardous Materials 164: 1517–1522.

14.

Gong

J-L

Wang

X-Y

Zeng

G-M

(2012) Copper (II) removal by pectin–iron oxide magnetic nanocomposite adsorbent. Chemical Engineering Journal 185: 100–107.

15.

Guibal

(2004) Interactions of metal ions with chitosan-based sorbents: A review. Separation and Purification Technology 38: 43–74.

16.

Günay

Arslankaya

Tosun

(2007) Lead removal from aqueous solution by natural and pretreated clinoptilolite: Adsorption equilibrium and kinetics. Journal of Hazardous Materials 146: 362–371.

17.

Hakami

Zhang

Banks

(2012) Thiol-functionalised mesoporous silica-coated magnetite nanoparticles for high efficiency removal and recovery of Hg from water. Water Research 46: 3913–3922.

18.

Hao

Y-M

Man

Z-B

(2010) Effective removal of Cu (II) ions from aqueous solution by amino-functionalized magnetic nanoparticles. Journal of Hazardous Materials 184: 392–399.

19.

Huang

S-H

Chen

D-H

(2009) Rapid removal of heavy metal cations and anions from aqueous solutions by an amino-functionalized magnetic nano-adsorbent. Journal of Hazardous Materials 163: 174–179.

20.

Javanbakht

Alavi

Zilouei

(2014) Mechanisms of heavy metal removal using microorganisms as biosorbent. Water Science & Technology 69(9): 1775–1787.

21.

Javanbakht

Zilouei

Karimi

(2011) Lead biosorption by different morphologies of fungus Mucor indicus. International Biodeterioration & Biodegradation 65: 294–300.

22.

Komkiene

Baltrenaite

(2016) Biochar as adsorbent for removal of heavy metal ions [Cadmium (II), Copper (II), Lead (II), Zinc (II)] from aqueous phase. International Journal of Environmental Science and Technology 13: 471–482.

23.

Kumar

Isloor

Ismail

(2014) Preparation and evaluation of heavy metal rejection properties of polysulfone/chitosan, polysulfone/N-succinyl chitosan and polysulfone/N-propylphosphonyl chitosan blend ultrafiltration membranes. Desalination 350: 102–108.

24.

Kumari

Pittman

Mohan

(2015) Heavy metals [chromium (VI) and lead (II)] removal from water using mesoporous magnetite (Fe₃O₄) nanospheres. Journal of Colloid and Interface Science 442: 120–132.

25.

Kumari

Tripathi

(2015) Effect of Phragmites australis and Typha latifolia on biofiltration of heavy metals from secondary treated effluent. International Journal of Environmental Science and Technology 12: 1029–1038.

26.

Kurniawan

Chan

W-H

(2006) Comparisons of low-cost adsorbents for treating wastewaters laden with heavy metals. Science of the Total Environment 366: 409–426.

27.

Kyzas

Deliyanni

(2013) Mercury (II) removal with modified magnetic chitosan adsorbents. Molecules 18: 6193–6214.

28.

Lata

Singh

Samadder

(2015) Regeneration of adsorbents and recovery of heavy metals: A review. International Journal of Environmental Science and Technology 12: 1461–1478.

29.

Liu

B-D

Hou

L-A

(2013) Magnetic multi-functional nano-fly ash-derived zeolite composites for environmental applications. Journal of Materials Chemistry A 1: 12617–12626.

30.

Mahdavi

Jalali

Afkhami

(2012) Removal of heavy metals from aqueous solutions using Fe3O4, ZnO, and CuO nanoparticles. Journal of Nanoparticle Research 14: 1–18.

31.

Mahdavian

Mirrahimi

MA-S

(2010) Efficient separation of heavy metal cations by anchoring polyacrylic acid on superparamagnetic magnetite nanoparticles through surface modification. Chemical Engineering Journal 159: 264–271.

32.

Mahmoud

Abdelwahab

Fathallah

(2013) Design of novel nano-sorbents based on nano-magnetic iron oxide–bound-nano-silicon oxide–immobilized-triethylenetetramine for implementation in water treatment of heavy metals. Chemical Engineering Journal 223: 318–327.

33.

Mansouri

Rikhtegar

Panahi

(2013) Porosity, characterization and structural properties of natural zeolite-clinoptilolite-as a sorbent. Environment Protection Engineering 39: 139–152.

34.

Mendoza-Castillo

Rojas-Mayorga

García-Martínez

(2015) Removal of heavy metals and arsenic from aqueous solution using textile wastes from denim industry. International Journal of Environmental Science and Technology 12: 1657–1668.

35.

Moftakhar

Yaftian

Ghorbanloo

(2016) Adsorption efficiency, thermodynamics and kinetics of Schiff base-modified nanoparticles for removal of heavy metals. International Journal of Environmental Science and Technology 13: 1707–1722.

36.

Monroy-Figueroa

Mendoza-Castillo

Bonilla-Petriciolet

(2015) Chemical modification of Byrsonima crassifolia with citric acid for the competitive sorption of heavy metals from water. International Journal of Environmental Science and Technology 12: 2867–2880.

37.

Nędzarek

Drost

Harasimiuk

(2015) The influence of pH and BSA on the retention of selected heavy metals in the nanofiltration process using ceramic membrane. Desalination 369: 62–67.

38.

Ngah

Teong

Hanafiah

(2011) Adsorption of dyes and heavy metal ions by chitosan composites: A review. Carbohydrate Polymers 83: 1446–1456.

39.

Oliveira

Petkowicz

Smaniotto

(2004) Magnetic zeolites: a new adsorbent for removal of metallic contaminants from water. Water Research 38: 3699–3704.

40.

Ozkaya

Toprak

Baykal

(2009) Synthesis of Fe₃O₄ nanoparticles at 100 C and its magnetic characterization. Journal of Alloys and Compounds 472: 18–23.

41.

Pang

Zeng

Tang

(2011) Preparation and application of stability enhanced magnetic nanoparticles for rapid removal of Cr (VI). Chemical Engineering Journal 175: 222–227.

42.

Panneerselvam

Morad

Tan

(2011) Magnetic nanoparticle (Fe₃O₄) impregnated onto tea waste for the removal of nickel (II) from aqueous solution. Journal of Hazardous Materials 186: 160–168.

43.

Petcharoen

Sirivat

(2012) Synthesis and characterization of magnetite nanoparticles via the chemical co-precipitation method. Materials Science and Engineering: B 177: 421–427.

44.

Radaei

Moghaddam

MRA

Arami

(2014) Removal of reactive blue 19 from aqueous solution by pomegranate residual-based activated carbon: optimization by response surface methodology. Journal of Environmental Health Science and Engineering 12: 65.

45.

Rajput

Pittman

Mohan

(2015) Magnetic magnetite (Fe₃O₄) nanoparticle synthesis and applications for lead (Pb ²⁺) and chromium (Cr ⁶⁺) removal from water. Journal of Colloid and Interface Science 468: 334–346. .

46.

Rangel-Mendez

Monroy-Zepeda

Leyva-Ramos

(2009) Chitosan selectivity for removing cadmium (II), copper (II), and lead (II) from aqueous phase: pH and organic matter effect. Journal of Hazardous Materials 162: 503–511.

47.

Roosta

Ghaedi

Daneshfar

(2014) Simultaneous ultrasound-assisted removal of sunset yellow and erythrosine by ZnS: Ni nanoparticles loaded on activated carbon: Optimization by central composite design. Ultrasonics Sonochemistry 21: 1441–1450.

48.

Salam

(2013) Removal of heavy metal ions from aqueous solutions with multi-walled carbon nanotubes: Kinetic and thermodynamic studies. International Journal of Environmental Science and Technology 10: 677–688.

49.

Sarioglu

Güler

UAA

Beyazit

(2009) Removal of copper from aqueous solutions using biosolids. Desalination 239: 167–174.

50.

Şen

Pereira

Olivella

(2015) Heavy metals removal in aqueous environments using bark as a biosorbent. International Journal of Environmental Science and Technology 12: 391–404.

51.

Shen

Pan

Zhang

(2012) A new insight on the adsorption mechanism of amino-functionalized nano-Fe 3 O 4 magnetic polymers in Cu (II), Cr (VI) co-existing water system. Chemical Engineering Journal 183: 180–191.

52.

Singh

Prasad

(2015) Remediation of heavy metal contaminated ecosystem: An overview on technology advancement. International Journal of Environmental Science and Technology 12: 353–366.

53.

Song

Kong

Jang

(2011) Adsorption of heavy metal ions from aqueous solution by polyrhodanine-encapsulated magnetic nanoparticles. Journal of Colloid and Interface Science 359: 505–511.

54.

Suc

HTY

(2013) Lead (II) removal from aqueous solution by chitosan flake modified with citric acid via crosslinking with glutaraldehyde. Journal of Chemical Technology and Biotechnology 88: 1641–1649.

55.

Tan

Chen

Hao

(2012) High efficient removal of Pb (II) by amino-functionalized Fe₃O₄ magnetic nano-particles. Chemical Engineering Journal 191: 104–111.

56.

Tran

Dai Tran

Nguyen

(2010) Preparation of chitosan/magnetite composite beads and their application for removal of Pb (II) and Ni (II) from aqueous solution. Materials Science and Engineering: C 30: 304–310.

57.

Unsoy

Yalcin

Khodadust

(2012) Synthesis optimization and characterization of chitosan-coated iron oxide nanoparticles produced for biomedical applications. Journal of Nanoparticle Research 14: 1–13.

58.

Wang

Chen

(2014) Chitosan-based biosorbents: Modification and application for biosorption of heavy metals and radionuclides. Bioresource Technology 160: 129–141.

59.

Zeng

Huang

(2012a) Adsorption of Pb (II) by iron oxide nanoparticles immobilized Phanerochaete chrysosporium: Equilibrium, kinetic, thermodynamic and mechanisms analysis. Chemical Engineering Journal 203: 423–431.

60.

Zeng

Huang

(2012b) Use of iron oxide nanomaterials in wastewater treatment: A review. Science of the Total Environment 424: 1–10.

Application of response surface methodology for optimization of lead removal from an aqueous solution by a novel superparamagnetic nanocomposite

Abstract

Keywords

Introduction

Materials and methods

Materials

Synthesis of the modified CPL (CPL/Fe3O4)

Preparation of the magnetic CS/CPL nanocomposite

Analytical methods

Lead adsorption process

Design of experiments

Results and discussion

Characterization of the adsorbent

Lead adsorption process

Fitting the model

Optimization using the desirability function

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

References

Synthesis of the modified CPL (CPL/Fe₃O₄)