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Optimization of Ni(II) & Co(II) removal from wastewater and statistical studies on the results of experimental designs Aghil Igder, Ali Fazlavi, Ebrahim Allahkarami & Ali Dehghanipour To cite this article: Aghil Igder, Ali Fazlavi, Ebrahim Allahkarami & Ali Dehghanipour (2018): Optimization of Ni(II) & Co(II) removal from wastewater and statistical studies on the results of experimental designs, Geosystem Engineering, DOI: 10.1080/12269328.2018.1477072 To link to this article: https://doi.org/10.1080/12269328.2018.1477072
Published online: 22 May 2018.
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GEOSYSTEM ENGINEERING https://doi.org/10.1080/12269328.2018.1477072
Optimization of Ni(II) & Co(II) removal from wastewater and statistical studies on the results of experimental designs Aghil Igdera, Ali Fazlavia, Ebrahim Allahkaramib and Ali Dehghanipourc a
Department of Mining, Faculty of Engineering, Imam Khomeini International University (IKIU), Iran; bMining and Metallurgical Engineering Department, Amirkabir University of Technology, Tehran, Iran; cDepartment of Mining, Faculty of Engineering, University of Tehran, Iran ABSTRACT
In this study, mono-dispersed carboxymethyl chitosan (CCS)-bounded Fe3O4 (OCMCS/ Fe3O4) nanoparticles were used as a novel magnetic nano-adsorbent for the removal of Ni (II) and Co(II) ions from wastewater. Chitosan (CS) was ﬁrst carboxymethylated and then covalently bounded on the surface of Fe3O4 nanoparticles. The micrographs of the scanning electron microscopy analysis showed that the nanoparticles were mono-dispersed and had spherical morphology with mean diameter of 33 nm. X-ray diﬀraction patterns indicated that the magnetic nanoparticles were pure Fe3O4 with a spinel structure, and the binding of O-CCS did not change the phase of Fe3O4. In this study, the most signiﬁcant factors aﬀecting adsorption process, i.e., pH, adsorbent dosage, contact time and concentration of Ni(II) and Co(II) ions were studied. Box–Behnken design and analysis of variance were used to determine the main eﬀects and their interactions. The optimization study revealed that pH, adsorbent dosage and metal concentration had a signiﬁcant eﬀect on metal removal. In addition, results indicated that contact time parameter had no signiﬁcant eﬀect on the removal of Ni(II) and Co(II) ions (p-value > 0.01).
Received 28 November 2017 Accepted 13 May 2018
1. Introduction The treatment of wastewater has been a major concern to the environmentalists. In our wastewaters, main sources of environmental contamination are the metal pollutants. Most of these pollutants enter the aquatic systems through discharging of concentrated industrial eﬄuents, electroplating, textile, industries, etc. (Reddad, Gerente, Andres, & Cloirec, 2002). Heavy metals have both beneﬁcial and detrimental properties on all forms of life (Kortenkamp et al., 1996; Park & Jung, 2001). Two of the heavy metals in wastewater that are toxic to humans are nickel (Ni) and cobalt (Co). Although Ni (50–80 mg/g of diet) and Co (0.012 mg/day in diet) are essential trace elements for human, they can be toxic with acute physiological eﬀects at higher concentrations (Carson, Ellis, & McCann, 2018; Justin & Vivek, 2010). High concentrations of heavy metals can cause high blood pressure, severe mucosal irritation, capillary damage, cancer, hepatic damage, bones, necrotic changes in the liver and kidney (Jianlong, 1986; Singh, Barick, & Bahadur, 2011). Hence, the removal of heavy metals from contaminated aquatic systems has been an important subject for the protection of life.
CONTACT Aghil Igder
© The Korean Society of Mineral and Energy Resources Engineers (KSMER)
Magnetic nanoparticles; carboxymethyl chitosan; wastewater treatment; experimental design
Techniques used for removal of heavy metals such as chemical precipitation, coagulation, ion exchange, chemical reduction, membrane separation, biological treatment and solid-phase extraction are expensive and non-environmental friendly, as compared to adsorption (Chen et al., 2009; p. 9, Ivanov, Tay, Tay, & Jiang, 2004). Adsorption as an attractive and eﬀective process for treatment of wastewater containing heavy metals has gained considerable importance. In the last recent years, numerous adsorbents have been reported for the removal of Co(II) and Ni(II) ions. However, traditional adsorbents show poor recovery of the metal ions from large volumes of solution due to low binding capacity, diﬀusion limitations and the lack of active surface sites. Hence, it would be of great interest to search for novel adsorbents with a large adsorptive surface area, low diﬀusion resistance, fast separation and high adsorption capacity for large volumes of solution. Recently, super paramagnetic particles (Fe3O4) have attracted lots of attentions due to the speciﬁc characteristics. Super paramagnetic iron oxide (Fe3O4) nanoparticles have attracted researchers in various ﬁelds such as catalysis (Banerjee and Chen, 2007), pigment colour (Donadel et al., 2008), clinic diagnosis and therapy
A. IGDER ET AL.
(Gould, 2006), ferroﬂuid (Li, Jiang, Huang, Ding, & Chen, 2008), magnetic storage media (Donadel et al., 2008), environment protection (Monier, Ayad, Wei, & Sarhan, 2010) and cell separation (Li et al., 2008). The Fe3O4-adsorbing target metal ions can be quickly removed from a matrix using a magnetic ﬁeld and be reused without losing active sites. It is reported that chitosan (CS) has increasingly become popular for adsorption of metals (Singh et al., 2011; Yuwei & Jianlong, 2011). The hydroxyl and amine groups on the CS chain act as chelating sites for metal ions. Recent research has been focused on the modiﬁcation of CS for enhanced adsorption performance. The coating of CS with magnetic ﬂuids is a typical modiﬁcation method, which improves the surface area of adsorbent for metal ion adsorption, reduces the required dosage for the adsorption and avoids the agglomeration of Fe3O4 and CS magnetic nanoparticles. O-Carboxymethyl chitosan (OCMCS) has a backbone structure similar to CS, only the O-hydroxyl group of each monomer is substituted by a carboxymethyl group. Although some of synthesized polymers (e.g., polyethylene glycol, polyamides and poly(acrylic acid)) were used as a coating agent of iron oxide particles (Singh et al., 2011; Hu, Lo, & Chen, 2007, p. 18, Latha, George, Kannan, & Ninan, 1991), the polymeric and bioactive properties of OCMCS lead our interest to explore the feasibility of synthesizing a well-dispersed aqueous dispersion of super paramagnetic Fe3O4 nanoparticles stabilized by CS eﬀectively. Some scholars investigated the preparation strategies of CS and its applications for the removal of heavy metal ions from wastewater (Zhang, Zeng, & Cheng, 2016). Recently, many researches have reported about magnetic CS/Fe3O4 composites and their applications in the removal of metal ions (Penga et al., 2010; Zhou, Wang, Liu, & Huang, 2009). However, to our knowledge, there have been a few reports on preparation, characterization and adsorption properties of magnetic nano-sized Fe3O4/CS adsorbent and application on the removal of heavy metal ions. Although the individual eﬀect of diﬀerent variables during adsorption of heavy metal ions using various adsorbents has been widely studied, it is important to know the optimum point of each variable as well as their interactive eﬀects in order to understand the complexity of the adsorption process for maximizing the metal uptake potential values. Response surface methodology (RSM) is a useful method to study the eﬀects of diﬀerent parameters aﬀecting the responses by varying these parameters simultaneously (Evans, 2003). RSM consists of several groups such as Box–Behnken design (BBD), central composite (CC) and D-optimal. It can determine the relationship between dependent and
independent parameters as well as optimize the related processes (Evans, 2003). However, no report is available relating to the application of this method on adsorption of Co(II) and Ni(II) ions using nano-adsorbents. Therefore, BBD was applied to evaluate the interactive eﬀects of adsorption variables and optimize the adsorption process of Co(II) and Ni(II) ions from aqeous solutions onto mono-dispersed carboxymethyl chitosan (CCS)-bounded Fe3O4 (OCMCS/Fe3O4) nanoparticles. In the previous study, authors investigated the adsorption of Cd(II) onto CCS magnetic nanoparticles that studied aﬀecting factors on removal process of Cd (II) from wastewater (Igder, Rahmani, Fazlavi, Ahmadi Azqhandi, & Omidi, 2012). In the present study, the adsorbent (magnetic CCS nanoparticles) was used to investigate the removal of nickel and cobalt ions from wastewater samples. The main aim of this research was to obtain the optimum conditions for the adsorption of Ni(II) and Co(II) onto CCS magnetic nanoparticles. To attain the optimum conditions, the authors used a four-factor, three-level Box-Behnken experimental design. For this purpose, the eﬀects of four important factors, namely, pH, adsorbent dosage, metal concentration and contact time on Co(II) and Ni(II) adsorption onto nano-adsorbent, were investigated. In fact, the eﬀects of four important factors, namely, pH, adsorbent dosage, metal concentration and contact time on Co(II) and Ni(II) adsorption process onto nano-adsorbent, were studied using three-level BBD which provided a mathematical model showing the inﬂuence of each variable and their interactions.
Experimental Materials Polymer of CS was obtained from Chito Tech Company from Tehran, Iran. In this study, monochloroacetic acid (Merck, Germany) and cyanamide (Aldrich, U.S.A) were used as a solvent for the CS polymers and the cross-linker, respectively. Furthermore, all chemical reagents were the guaranteed or analytic grade and used without further puriﬁcation. The deionized water was used in all experiments. Preparation of mangnetic nanoparticles The novel magnetic nano-adsorbents were prepared for the adsorption experiments by the procedure presented by Kang, Rishud, Rabolt, and Stroeve (1996). Brieﬂy, the reagents of FeCl2.4H2O and FeCl3.6H2O [Fe2+:Fe3+ = 1:2] were dissolved in about 10 ml HCl 0.1 M and stirred under strong ultrasonic agitation. Following this, iron-containing
solution was added to the NaOH solution dropwise at a temperature of 70°C for 30 min while nitrogen gas purged into the system. Black Fe3O4 nanoparticles were decanted by permanent magnet and cleaned by water several times. Preparation of CCS To prepare CCS, 10 g CS and 10 g sodium hydroxide were added into 100 ml mixture of isopropanol/water (50/50) to swell and alkalize for 1 h. Following this, 20 ml solution of chloroacetic acid (0.75 gml−1) was added to this mixture dropwise. After 4 h, for stopping the reaction, the reagent of ethyl alcohol was added. To get Na salt CC, the solid was ﬁltered, rinsed with ethyl alcohol (80%) and vacuum-dried. Then, 1 g Na-CC was suspended in 100 ml ethyl alcohol aqueous solution (80%) and stirred for 30 min. Finally, 10 ml hydrochloric acid (37%) was added and stirred to desalt (Zhu, 2008). The coating of magnetic nanoparticles with CCS The binding of Fe3O4 nanoparticles with CCS was done by the method presented by Chang and Chen (2005). Brieﬂy, Fe3O4 nanoparticles and also 0.5 ml carbodiimide solution were added to 2 ml buﬀer solution (0.003 M phosphate, pH 6, 0.1 M NaCl) and were sonicated for 10 min. Then, 2.5 ml CCS was added to the reaction cell and was sonicated for 60 min. The CCS-bounded magnetic nanoparticles were recovered from the reaction cell by magnetic bar and rinsed with water. Characterization methods The particle size and morphology of magnetic CS nanoparticles were determined using a scanning electron microscopy (SEM) (XL30, Philips, Netherlands). In order to determine the crystal structure of the products, they were analysed using an X-ray diﬀractometer (XRD) model X′Pert MPD, Philips, Holland, using Co kα radiation. Also, the elemental analysis present at the magnetic nanoparticles surface before and after adsorption was measured using Energy-dispersive X-ray spectroscopy (EDX) analysis.
adsorption process, several sets of experiments were done in diﬀerent conditions of pH, adsorbent dosage, contact time and concentration of Ni(II) and Co(II) ions. All these experiments were done in laboratory temperature of 25°C. About 25 ml of wastewater containing Ni(II) and Co(II) ions was added to magnetic nanoparticles. The reaction mixture was agitated at a constant speed for all tests. Percent removal of metal ions was calculated from the following equation: Percentage removal = 100 (C0 – Ct)/C0%
where C0 and Ct are the concentrations of metal ions before and after extraction in the solution, respectively. Box–Behnken design BBDs require a lower numbers of actual tests to be performed, which facilitate probing into interactions between the parameters and their eﬀects on the response variable. The advantages of BBDs include the fact that they are all spherical designs and require factors to be run at only three levels. The designs are also rotatable or nearly rotatable. The optimum conditions for maximizing the removal of Ni(II) and Co(II) ions by magnetic nano-adsorbents were determined by means of a four-factor, three-level Box-Behnken experimental design. The signiﬁcant variables like pH, adsorbent dosage, contact time and concentration of Ni and Co were chosen as the critical variables. Each of the independent variables was consecutively coded as X1, X2, X3 and X4 at three levels: −1, 0 and 1. The experimental range and levels of independent variables considered in this study are presented in Table 1. The central values chosen for the experimental design were pH0 = 6, ads0 = 75 (mg), tC = 40 (min) and Cm = 100 (mgl−1).
3. Results and discussions 3.1. Characterization of the magnetic nanoparticles Details about the characterization of the magnetic nanoparticles (Fourier transform infrared spectroscopy Table 1. Level of variables chosen. Range and levels
Adsorption experiments Variable X1 = pH X2 = adsorbent dosage (mg) X3 = Contact time (min) X4 = Metal Concentrations (ppm)
Batch experimental programme To study the capability of magnetic CS nanoparticles for the removal of Ni(II) and Co(II) ions from the wastewater and to ﬁnd optimum conditions of the
Step change values.
High Index level (1) pH 8 ads 120 tC Cm
Centre level (0) 6 75
Low level (−1) 4 30
Δxia 2 45
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and nuclear magnetic resonance spectroscopy) can be found in the literature (Allahkarami, Igder, Fazlavi, & Rezai, 2017). For further characterization of the adsorbent, SEM analysis, XRD and EDX analysis were performed. 3.1.1. SEM analysis SEM images shown in Figure 1 revealed that the Fe3O4 and Fe3O4-CCs nanoparticles were mono-dispersed and quite ﬁne with a mean diameter of 25 and 33 nm, respectively, i.e., smaller than the nanoparticles produced using conventional techniques. As CCS was used to prepare the composites, the obtained CCS-bound Fe3O4 nanoparticles were almost ellipsoidal or spherical. 3.1.2. XRD analysis The electron diﬀraction pattern for the pure and Fe3O4 nanoparticles coated with CS indicated that nanoparticles of Fe3O4 and CC-Fe3O4 were highly crystalline. X-ray diﬀraction pattern is shown in Figure 2. Six characteristic peaks of Fe3O4 for both samples revealed that the nanoparticles were pure Fe3O4 with a spinel structure.
Figure 2. XRD patterns for (a) Fe3O4 and (b) Fe3O4-CCs.
It is also described that the coating process of nanoparticles did not change the phase of Fe3O4. 3.1.3. EDX analysis Figure 3 shows the elemental analysis present at the magnetic nanoparticles surface before and after adsorption. It can be seen in Figure 3(a) that there were no metal ions at the nanoparticle surface and the only composition at the nanoparticle surface was iron-containing compound that was related to the nature of principal nanoparticle. However, the compositions of Ni(II) and Co(II) ions were also present at the nanoparticle surface (Figure 3(b)). Both chemical and physical adsorption mechanisms have been reported for the heavy metal removal using Fe3O4 nanoparticles. To reveal the adsorption mechanism, further spectroscopic studies such as X-ray photoelectron spectroscopy and some other characterization studies before and after the adsorption and desorption are of most importance, which in this case are under investigation and will be reported in the future works on the thermodynamic and kinetic studies of this process. But from what has been discussed in the other relevant literatures, some evidences of low adsorption at high pH were reported as indications of chemical adsorption of Co ions, while the ability of desorption showed the physical adsorption as the dominant mechanism (Samuel & Irene, 2013). In another work, chemical adsorption of Ni(II) ions was referred to the exchange of the electrons between the mentioned ions and Fe3O4 nanoparticles, which might have been considered as a rate-controlling step (Ravindra et al., 2015), being said, the adsorption mechanism in modiﬁed magnetic nanoparticles by polymeric coats (chitosan) can be completely diﬀerent and more complicated, which will be discussed in future works. 3.2. Model building and statistical signiﬁcance test
Figure 1. SEM micrographs of (a) Fe3O4 and (b) Fe3O4CCs nanoparticles.
A Box–Behnken experimental design with four factors and three levels (27-run), including three repeats at the
Figure 3. EDX spectrum for the elemental analysis of Fe3O4-CCs nanoparticles (a) before adsorption and (b) after adsorption.
centre point, was applied for ﬁtting a second-order response surface. Table 2 represents a 27-trial experimental design, where each variable was tested in three diﬀerent coded levels: low (−1), middle (0) and high (+1). The whole set of experiments was performed in triplicate and mean response was used for analysis. A second-order polynomial equation was then ﬁtted to the data using the Minitab (ver. 16) software.
The signiﬁcance of independent variables and their interactions was tested by means of the analysis of variance (ANOVA). In this study, p-value less than 0.01 indicates that the model could explain 99% of the variability. ANOVA for the four variables (adsorbent dosage, pH, contact time and metal ion concentration) indicates that the removal of metal ions can be well explained by a polynomial equation
Table 2. Experimental and predicted values of Co2+ and Ni2+ adsorption (Y %) on the surfaces of nanoparticles. Ypre*
Yexp Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
pH 8 8 6 6 6 4 6 6 6 6 6 8 6 6 6 4 4 6 8 4 8 4 6 4 6 8 6
Adso. conc. (mg/l) 75 75 120 30 120 75 120 30 75 30 75 75 75 120 75 75 30 75 120 75 75 75 75 120 75 30 30
Time (min) 40 20 40 40 20 40 60 40 60 20 20 60 20 40 40 20 40 40 40 40 40 60 60 40 40 40 60
Ion conc. (ppm) 157 100 43 157 100 43 100 43 43 100 43 100 157 157 100 100 100 100 100 157 43 100 157 100 100 100 100
Ni2+ 89.74471 92.87192 76.01772 61.24052 77.30167 62.12836 63.26199 67.29727 60.12215 68.96401 75.18082 88.71106 69.82726 64.32717 74.27691 70.67910 68.11814 72.49880 91.67902 66.11303 90.72665 65.23602 57.63514 71.57493 72.32314 86.98762 58.97068
Co2+ 89.93552 87.77051 67.25004 59.22872 70.76705 60.94027 63.14765 73.53494 69.77194 64.80644 72.69073 88.94482 56.67830 60.16682 66.41121 68.04712 65.60823 63.93544 85.18653 55.77535 84.20653 58.33002 57.47051 71.87641 68.88234 86.39045 61.89605
Ni2+ 86.00612 94.93327 72.62549 63.43137 76.47133 66.95845 64.29946 64.37835 61.83912 69.01743 73.42029 85.42638 68.22218 66.04420 73.36631 72.76229 65.83317 73.36624 94.07615 65.67720 92.25341 61.97348 59.50829 70.64739 73.36627 88.02819 60.89240
E Co2+ 84.24821 86.24713 67.52037 58.17546 69.75929 68.79510 63.87141 69.97739 64.86229 66.24919 70.25139 88.15920 60.20510 62.94249 66.40928 68.05039 62.30745 66.40944 87.10535 55.15825 86.99040 59.07112 58.52744 67.19823 66.40931 89.68641 65.07129
Ni2+ 3.73859 −2.06135 3.39223 −2.19085 0.83034 −4.83009 −1.03747 2.91892 −1.71697 −0.05342 1.76053 3.28468 1.60508 −1.71703 0.9106 −2.08319 2.28497 −0.86744 −2.39713 0.43583 −1.52676 3.26254 −1.87315 0.92754 −1.04313 −1.04057 −1.92172
Co2+ 5.68731 1.52338 −0.27033 1.05326 1.00776 −7.85483 −0.72376 3.55755 4.90965 −1.44275 2.43934 0.78562 −3.5268 −2.77567 0.00193 −0.00327 3.30078 −2.474 −1.91882 0.6171 −2.78387 −0.7411 −1.05693 4.67818 2.47303 −3.29596 −3.17524
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with a high coeﬃcient of determination (R2 = 0.95). The ANOVA for the removal of Co(II) and Ni(II) ions is given in Table 3 that adsorbent dosage, pH, contact time and metal ion concentration have signiﬁcant eﬀect on the removal of Ni(II) ions, but the probability value of the coeﬃcient of linear eﬀect of contact time for the removal of Co(II) ions was 0.2, which shows the insigniﬁcance of this parameter on Co(II) ions removal. In addition, the interaction coeﬃcients of this variable with other variables for Co(II) adsorption were relatively high, which indicates the insigniﬁcance of these factors on Co(II) ion adsorption. A second-order polynomial model where interaction terms have been ﬁtted to the experimental data was obtained from the Box-Behnken model experiment which can be stated in the form of the following equation: Y ¼ a0 þ
ai x i þ
aii xii2 þ
aij xi x
where Y is the eﬃciency of Ni(II) and Co(II) ions removed, a0 is oﬀset term, ai is ﬁrst-order main eﬀect, aii is second-order main eﬀect and aij is the interaction eﬀect. So, the reduced second-order polynomial equation by multiple regressions for Ni(II) and Co(II) adsorption has shown by YNi and YCo, respectively, and the signiﬁcant terms (p < 0.01) are obtained as follows: YNi ¼ 73:3662 þ 11:9729X1 þ 2:7153X2 5:0740X3 1:8821X4 þ 8:6917X1 2 3:2824X3 2 4:3345X4 2 þ 0:3205X1 X3 1:2417X1 X4 (3)
YCo ¼ 66:4093 þ 11:8214X1 þ 0:5775X2 4:0949X4 þ 9:6543X1 2 þ 2:7235X1 X4 (4) where X1, X2, X3 and X4 represent codiﬁed values for pH, adsorbent dosage, contact time and metal ion concentration, respectively. Three repeated centre points in the BBD made it possible to evaluate the pure error of the tests and enabled lack of ﬁt of the model to be checked. The current model was tested for lack of ﬁt for the responses. For p-values of lack of ﬁt, we obtained 0.06 and 0.227 for the removal of Ni(II) and Co(II) ions, respectively. Thus, this model provided a reasonable ﬁt to the data (p > 0.01) and had no lack of ﬁt for Ni(II) and Co(II) ions removal. Figure 4 shows a normal probability plot of the residuals for Ni(II) and Co(II) ions. The data points have a normal distribution because these points follow a straight line, lending support to the conclusion that X1, X2, X3, X4, X12, X32, X42, X1X3 and X1X4 are the signiﬁcant eﬀects in regression equation of Ni(II) ion removal, as it had shown the eﬀective factors has limited to X1, X2, X4, X12 and X1X4 for Co(II) ion removal. Figure 5 shows the relationship between the measured and predicted values for adsorption of Ni(II) and Co(II) onto CCs-Fe3O4. Since the residuals tend to be close to the diagonal line, it is seen in Figure 5 that the developed models are adequate because the residuals for the prediction of each response are minimum. The signiﬁcant interactions of this study are presented in counter plots, which represent relationship between the dependent and independent variables.
Table 3. ANOVA table for YNi and YCo. p-Value
Sum of squares Source Regression Linear pH Adsorb Time Metal con Square pH × pH Adsorb ×Adsorb Time × Time Metal con ×Metal conc Interaction pH × Abso pH × Time pH × Metal con Abso × Time Abso × Metal con Time × Metal con Lack-of-ﬁt pure error Total
Ni2+ 2907.24 2001.08 1778.84 9.35 1.11 211.78 885.12 879.94 1.76 3.38 0.04 21.04 3.17 0.75 15.21 0.002 1.018 0.89 17.321 0.006 2924.567
Co2+ 2738.02 1919.63 1708.93 8.84 0.93 200.93 723.08 719.34 0.75 2.98 0.01 95.31 10.43 0.41 73.94 0.01 5.75 4.77 13.124 0.004 2751.148
DF 14 4 1 1 1 1 4 1 1 1 1 6 1 1 1 1 1 1 10 2 26
Ni2+ 0.000 0.000 0.000 0.014 0.000 0.041 0.000 0.000 0.116 0.040 0.010 0.912 0.854 0.049 0.046 0.550 0.408 0.671 0.060 . . .. . ... . . .. . ..
Remark Co2+ 0.000 0.000 0.000 0.016 0.200 0.008 0.001 0.000 0.798 0.733 0.268 0.601 0.424 0.251 0.045 0.611 0.439 0.688 0.227 . . .. . .. . . .. . ..
Ni2+ Signiﬁcant Signiﬁcant Signiﬁcant Signiﬁcant Signiﬁcant Signiﬁcant Signiﬁcant Signiﬁcant . . .. . .. . .. . .. Signiﬁcant Signiﬁcant . . .. . .. . ... . . .. . .. . .. . .. Signiﬁcant Signiﬁcant . . .. . .. . .. . ... . . .. . .. . .. . ... . . .. . .. . .. . ... . . .. . .. . .. . ... . . .. . .. . .. . ... . . .. . .. . .. . ...
Co2+ Signiﬁcant Signiﬁcant Signiﬁcant Signiﬁcant . . .. . .. . .. . . Signiﬁcant Signiﬁcant Signiﬁcant . . .. . .. . .. . .. . . .. . .. . .. . .. . . .. . .. . .. . .. . . .. . .. . .. . ... . . .. . .. . .. . .. . . . . .. . .. . .. . .. . . Signiﬁcant . . .. . .. . .. . .. . . . . .. . .. . .. . ... . . .. . .. . .. . .. . . . . .. . .. . .. . ... . . .. . .. . .. . .. . . . . .. . .. . .. . ...
Figure 4. Normal % probability plot of the residuals of (a) Ni2+ and (b) Co2+ recovery.
Figure 5. Scatter plots of predicted response versus actual response for the (a) Ni(II) and (b) Co(II)/nanomagnetic chitosan adsorption system.
Figure 6. Contour plots of (a) pH versus metal concentration at a holding adsorbent of 75 mg and contact time of 40 min, (b) pH versus contact time at a holding metal concentration of 100 ppm and adsorbent of 75 mg for the Ni(II)/nanomagnetic chitosan adsorption system and (c) pH versus metal concentration at a holding adsorbent of 75 mg in 40 min for Co(II) adsorption process.
The eﬀects of pH (X1) and concentration of metal ions (X4) with their interaction at a ﬁxed level of contact time and concentration of metal ions (medium
level) are shown in Figure 6(a). Also the interaction of pH (X1) and contact time (X3) is shown in Figure 6 (b). Both plots were found to be non-linear up to 70%
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removal, but above this value, the plots were found to be linear indicating a linear relationship between X1, X4 and also X1, X3. It was determined from the contour plot that a higher value of removal (≥70%) could be obtained with an X1 level range from – 6 to 8.0, an X4 level range from 50 to 150 ppm and X3 level range from 20 to 60 min. It is evident from the contour that the high percentage of Ni(II) ions removal from solution is present in a higher level of X1. The coeﬃcient value for X1 (β1 = 11.9729) is higher than that of X4. It can be found that pH had the most eﬀect on the removal of Ni(II). Figure 6(c) shows the contour plot drawn at a medium level of X2 and X3. The contours were found to be non-linear up to 67% and indicated that a high value of Co(II) removal can be obtained for a combination of the two independent variables, the X1 level in the range of 6–8 and the X4 level in the range of 50–150 ppm. Figure 7 shows the surface plots of these interactions, which represents the expected removal response and correlation between variables in three-dimensional plots and also conﬁrms the contour plot’s results.
3.3. Validation of optimum point The most desirable experimental conditions obtained from the experimental design are listed in Table 4 that were selected to be veriﬁed. Table 4 shows the veriﬁcation
results of the software. The experimental results showed good agreement with the value obtained from the model.
4. Conclusion In this research, a CCS-bound Fe3O4 (OCMCS/ Fe3O4) nanoparticle was prepared, characterized by XRD, EDX and SEM, and ﬁnally was used for the removal of Ni(II) and Co(II) ions from aqueous solution. Ni(II) and Co(II) removal from wastewater using nanomagnetic adsorbent requires a proper process parametric study to determine its optimal performance characteristics. RSM is one of the advanced statistical analysis techniques for parametric studies involving a minimum number of trials. The Box–Behnken surface statistical design of experiments was carried out with four factors, i.e., pH, adsorbent dose, contact time and metal concentration in Ni(II) and Co(II) removal. The statistical analysis of the model showed that pH, adsorbent dosage and metal concentration had a signiﬁcant eﬀect on metal removal. In addition, from the data given it is clear that contact time did not have a prominent eﬀect on the results of the removal processes and a fast adsorption of metal ions on the surfaces of nanoparticles may have occurred. RSM based on four-level Box–Behnken experimental design was used to optimize diﬀerent parameters for Ni(II) and Co(II) adsorption onto nanoparticles. This
Figure 7. Three-dimensional response surface plots of (a) pH versus metal concentration at a holding adsorbent of 75 mg and contact time of 40 min, (b) pH contact time at a holding metal concentration of 100 ppm and adsorbent dosage of 75 mg for the Ni (II)/nano-magnetic chitosan adsorption system and (c) pH versus metal concentration at a holding adsorbent of 75 mg and contact time of 40 min on Co(II) adsorption process.
Table 4. Optimum and conﬁrmative value of the process parameters for maximum removal eﬃciency. Optimized values (predicted values) Process parameters pH Adsorbent dosage (g/l) Contact time (min) Initial metal concentration (ppm) Secondary metal concentration (ppm) % Removal (Y)
Conﬁrmation values (actual values)
Ni2+ 7.6 4.8 20 43
Co2+ 6.4 1.2 20 43
Ni2+ 7.6 4.8 20 43
Co2+ 6.4 1.2 20 43
2.2089 3.7595 2.0033 94.863 91.257 95.341
12.5878 13.9346 13.2238 70.726 67.594 69.247
experimental design helped in locating the levels of parameters which contributed to the maximum removal of 97.47% for Ni(II) and 72.22% for Co(II).
Acknowledgments The authors are indebted to the Iranian National Science Foundation for their assistance and supports for making this research under the grant no. 90003545, to be carried out.
Disclosure statement No potential conﬂict of interest was reported by the authors.
Funding This work was supported by the Iranian National Science Foundation .
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