Integral technique for evaluation and optimization of Ni (II) ions adsorption onto regenerated cellulose using response surface methodology

Reza Davarnejad; Mostafa Keshavarz Moraveji; Marjan Havaie

doi:10.1016/j.arabjc.2015.05.022

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Original article

11 (

); 370-379

doi:

10.1016/j.arabjc.2015.05.022

Integral technique for evaluation and optimization of Ni (II) ions adsorption onto regenerated cellulose using response surface methodology

Reza Davarnejad^{^a,⁎}, Mostafa Keshavarz Moraveji^{^b}, Marjan Havaie^{^a}

a Chemical Engineering Department, Faculty of Engineering, Arak University, 3945-5-38138 Arak, Iran

b Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, Tehran 15875-4413, Iran

⁎Corresponding author. Tel.: +98 9188621773; fax: +98 8634173450. R-Davarnejad@araku.ac.ir (Reza Davarnejad)

Received: 2015-1-19, Accepted: 2015-5-31, Published: 2015-03-01

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

The removal efficiency of Ni (II) ions from aqueous solution using regenerated cellulose was studied. The effects of solution pH, time, initial metal concentration and adsorbent dosage on metal adsorption efficiency were investigated. Response surface methodology (RSM) was applied to predict the behavior of the system. Based on the developed model, pH was found to be the main factor which had the highest influence on Ni (II) removal efficiency. It was observed that an increase in the pH from 3.75 to 7.25 resulted in a 51.6% increase in Ni (II) removal efficiency. Additionally, the time and adsorbent dosage were found to have positive influence on metal removal efficiency while Ni (II) removal efficiency reduced with initial metal concentration. The optimization of the integral main factors was performed. The suggested optimum values for pH, time, initial metal concentration, adsorbent dosage and Ni (II) removal efficiency were 6.4, 175.27 min, 32.5 ppm, 0.4 g and 98%, respectively.

Keywords

Ni (II) adsorption

Optimization

Response surface methodology

Tissue paper

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1 Introduction

In these past few decades, heavy metals have become one of the major sources of water contamination, and their removal is a necessity, particularly in effluents from industrial plants. The concentration of heavy metals in aquatic environments has risen as a direct effect from contact between water bodies and industrial water which are polluted with heavy metal ions. Metals such as Copper, Nickel, Lead, Cadmium, Chromium, Zinc are called heavy metals as they are heavier than Iron in terms of atomic weight. The specific gravity of the mentioned metals is more than 5.0 (Srivastava and Majumder, 2008). One of the most common toxic heavy metals is Nickel where referring to the US Environmental Protection Agency (EPA) its maximum concentration in drinking water is 0.5 mg l⁻¹ (Gupta et al., 2010). Therefore, nickel removal from wastewater is necessary.

There are several processes to eliminate heavy metals from wastewater namely chemical precipitation, ion exchange, membrane processes, flotation, electrochemical treatment coagulation, flocculation and adsorption (Ku and Jung, 2001; Kang et al., 2004; Landaburu-Aguirre et al., 2009; Lundh et al., 2000; Wang et al., 2007; El Samrani et al., 2008 ). Among various available procedures, adsorption is an economically feasible process. There are different adsorbents used by several previous researches such as activated carbon (AC), carbon nanotubes and biosorbents (Fu and Wang, 2011; Jusoh et al., 2007; Kabbashi et al., 2009; Apiratikul and Pavasant, 2008; Wan Ngah and Hanafiah, 2008 ). The main benefit of biosorbents in relation to other conventional adsorbents is its intensive affinity and significant selectivity against heavy metals due to the excess availability of binding groups in the biosorbents structure (Banerjee et al., 2012). Additionally, biosorbents are popular due to their low cost and can be generated from easily acquired, abundant, agricultural source materials. Moreover, they can be easily processed, used and recovered without inflicting any harmful influence on the environment (Marin-Rangel et al., 2012).

By the knowledge of the authors, there is some information available in the literature regarding the integral optimization of Ni (II) removal process from industrial wastewater. Yavuz et al. (2003) studied the removal of some heavy metals such as Mn (II), Co (II), Ni (II), and Cu (II) from aqueous solution using a raw kaolinite. The sorption of these metals on kaolinite also conformed to linear form of Langmuir adsorption equation. Popuri et al. (2009) developed a new biosorbent by coating chitosan onto polyvinyl chloride (PVC) beads. They successfully investigated that the biosorbent can remove Cu (II) and Ni (II) ions from aqueous medium through adsorption. Ho et al. (2002) considered the sorption of three divalent metal ions -copper, nickel and lead- from aqueous solution onto peat in single component systems. They found the equilibrium isotherms, as well. Integral experimental design, including response surface methodology (RSM) is able to optimize all of the pertinent parameters and omitting restrictions of a single-factor optimization process (Ferreira et al., 2009). Moreover, RSM is advantageous due to its appropriate possibility in making various projections, providing graphical illustrations. Thereby, this method provides visual interpretation of the functional relations between the response and experimental variables (Zinatizadeh et al., 2010; Yang et al., 2009). In recent years RSM has been applied to analyze, optimize and evaluate interactive effects of independent factors in numerous chemical, biochemical and bioenvironmental processes (Ahmadi et al., 2005; Sharma et al., 2009). Central composite design is a useful application in building second-order response surface models (Zinatizadeh et al., 2010; Sharma et al., 2009). The main aim of CCD (Central Composite Design) is to indicate the optimum operational values for a system or an area satisfying the operational considerations. There is no information in the literature regarding the optimization of Ni (II) adsorption using regenerated cellulose as an adsorbent. Therefore, the main objective of this study was to investigate and optimize the influence of pH, time, initial metal concentration and adsorbent dosage on the elimination of Ni (II) ions from aqueous solution by tissue paper. In order to reach this goal, integral optimization for obtaining optimum conditions was carried out using RSM.

2 Materials and method

2.1

2.1 Adsorbent

Regenerated cellulose which is commercially known as tissue paper was used as the adsorbent in this research. It mainly consists of cellulose, hemi-cellulose and lignin which have a considerable amount of hydroxyl, carboxyl groups. Therefore, it is able to attach itself to heavy metals efficiently (Okoro and Okoro, 2011). To prepare adsorbent to conduct the experiments, regenerated cellulose was purchased from Shokoh Company, Tehran, Iran, and submerged in liquefied nitrogen to freeze-dry it. Next, the frozen regenerated cellulose was ground, and was sieved in the size range of 0.5–2 mm to obtain the final applicable adsorbent.

2.2

2.2 Adsorbate solution

A 100 mg/l Ni (NO₃)₂ solution was prepared as a stock solution using distilled water. All the solutions required for conducting the experiments were diluted from the stock solution. The chemicals used in the experiments were analytical grade products from Merck, Germany.

2.3

2.3 Adsorption experiments

Batch adsorption experiments were carried out by 200 ml of adsorbate solution of Nickel (II) Nitrate (Ni (NO₃)₂) at various concentrations. The pH values of the solutions were adjusted by adding 0.01 M HCl and 0.001 M NaOH solutions to the experiments solutions. Then samples were mixed with different adsorbent dosages and were stirred continuously at the rotor speed of 500 rpm at 25 °C in various time periods. After separating adsorbent from adsorbate solutions, residual adsorbate was measured using an atomic adsorption spectrophotometer.

2.4

2.4 Experimental design

In this study experimental design was accomplished using Design –Expert 7.0 Software. A 2⁴ factorial design with six central points and eight axial points was performed. Each factor was defined in five levels according to CCD experimental design. The characteristics of the system are explained by the following third-order polynomial empirical model (Dean and Voss, 1999):

(1)

Y_{i,j,k} = β_{0} + \sum β_{i} xi + \sum β_{ii} x_{i}^{2} + \sum \sum β_{ij} x_{i} xj + \sum β_{iii} x_{i}^{3} + \sum \sum \sum β_{ijk} x_{i} x_{j} x_{k} + ε

where x_i, β₀, β_i, β_ii, β_ij, β_iii, β_ijk and ε represent variables, constant term, coefficients of linear parameters, coefficients of the quadratic parameters, coefficients of the binary interaction parameters, coefficients of the cubic parameters, coefficients of the trio interaction parameters and residual associated with the experiments, respectively. An analysis of variance (ANOVA) was accomplished to reveal the main and interaction effects of factors influencing the Ni (II) removal efficiency. The verification of the fit of the polynomial model was expressed using the coefficient of determination (R²), and its integral significance was tested using the F-test.

3 Results and discussion

3.1

3.1 Scanning electron microscopy (SEM)

The SEM image of the regenerated cellulose is shown in Fig. 1. It was used to study the morphology of the adsorbent surface. According to this figure, the adsorbent is suitable for Ni (II) adsorption from aqua solution, for it holds porous structure which can provide high surface area for an acceptable adsorption.

SEM micrographs of regenerated cellulose.

3.2

3.2 FTIR studies

Fig. 2a shows the FTIR spectra, which illustrates the chemical functional groups in the structure of regenerated cellulose. The strong band at 3443 cm⁻¹ indicates the presence of hydroxyl groups (O—H). The peak at 2901 cm⁻¹ is due to the C—H stretching frequency of methyl, methylene and methoxy groups. The peak at 1655 cm⁻¹ is due to C⚌O band, and the peak observed at 1058 cm⁻¹ can be attributed to the C—O stretching vibration of carboxylic groups (Kurniawan et al., 2011; Feng et al., 2011).

FTIR spectra of: (a) regenerated cellulose and (b) Ni-adsorbed regenerated cellulose.

FTIR spectra of Ni (II)-sorbed regenerated cellulose indicate that the peak expected at 3443, 2901, 1655 and 1058 cm⁻¹ had shifted, respectively, to 3454, 2906, 1651 and 1037 cm⁻¹ due to Ni (II) ion biosorption as shown in Fig. 2b. These results reveal that the biosorption of Ni (II) occurs at carboxyl, hydroxyl and carbonyl functional groups present on the surface of regenerated cellulose (Kurniawan et al., 2011; Feng et al., 2011; Reddy et al., 2011; Senthil Kumar et al., 2011 ).

3.3

3.3 Integral design of experiment

Initial metal concentration, pH, required time and adsorbent dosage for a four-factor-five – level CCD design were performed to achieve the optimum conditions. The levels of the factors are presented in Table 1. According to the defined ranges and levels of the factors, factorial, central and axial points are coded as ±1, 0 and ±α, respectively. The CCD experimental conditions are represented in Table 2. The ANOVA for Ni (II) removal is shown in Table 3.

Table 1 Experimental range and levels of independent test variables.

Variable	Unit	Low axial (−α)	Low factorial (−1)	Center (0)	High factorial (+1)	High axial (+1)
A: pH	Min	2	3.75	0	7.25	9
B: time	Min	30	82.5	135	187.5	240
C: initial metal concentration	ppm	10	32.5	55	77.5	100
D: absorbent dosage	g	0.1	0.2	0.3	0.4	0.5

Table 2 Experimental plan of adsorption tests and obtained results.

Run	pH	Time (min)	Initial metal concentration (ppm)	Adsorbent dosage (g)	Ni (II) removal%
1	7.25	82.5	32.5	0.4	46
2	7.25	187.5	32.5	0.4	79
3	7.25	187.5	77.5	0.4	58
4	7.25	82.5	32.5	0.2	55
5	5.5	135	100	0.3	33
6	7.25	187.5	77.5	0.2	64
7	7.25	82.5	77.5	0.4	50
8	3.75	187.5	32.5	0.4	14
9	3.75	187.5	77.5	0.2	4
10	5.5	30	55	0.3	25
11	7.25	187.5	32.5	0.2	64
12	5.5	135	55	0.3	40
13	3.75	187.5	77.5	0.4	44
14	3.75	187.5	32.5	0.2	10
15	3.75	82.5	77.5	0.4	33
16	3.75	82.5	77.5	0.2	47
17	5.5	135	55	0.1	40
18	5.5	135	55	0.3	36
19	5.5	240	55	0.3	70
20	3.75	82.5	32.5	0.4	33
21	2	135	55	0.3	22
22	5.5	135	10	0.3	61
23	5.5	135	55	0.5	49
24	5.5	135	55	0.3	38
25	5.5	135	55	0.3	39
26	7.25	82.5	77.5	0.2	49
27	9	135	55	0.3	67
28	5.5	135	55	0.3	38
29	5.5	135	55	0.3	43
30	3.75	82.5	32.5	0.2	3

Table 3 The results of ANOVA analysis for the developed response surface model.

Source	Sum of square	df	Mean square	F-value	p-value
Model	8308.61	15	553.91	67.43	<0.0001
A	2363.24	1	2363.24	287.71	<0.0001
B	1878.25	1	1878.25	228.66	<0.0001
C	768.82	1	768.82	93.6	<0.0001
D	1364.22	1	1364.22	166.08	<0.0001
AD	1231.17	1	1231.17	149.89	<0.0001
BC	292.36	1	292.36	35.59	<0.0001
BD	327.49	1	327.49	39.87	<0.0001
CD	189.64	1	189.64	23.09	0.0005
A²	54.08	1	54.08	6.58	0.0262
B²	124.75	1	124.75	15.19	0.0025
C²	110.95	1	110.95	13.51	0.0037
D²	54.08	1	54.08	6.58	0.0262
BCD	418.77	1	418.77	50.98	<0.0001
A³	181.56	1	181.56	22.10	0.0006
D³	564.57	1	564.57	68.73	<0.0001
Residual	90.35	11	8.21
Lack of fit	32.35	6	5.39	0.46	0.8109
Pure error	58.00	5	11.60

3.4

3.4 Nickel removal

The percent of removal efficiency of Ni (II), R%, is defined as the following equation:

(2)

R % = (\frac{C_{i} - C_{f}}{C_{i}}) \times 100

where C_i and C_f represent the initial and final Ni concentration, respectively.

The experimental results are expressed by a cubic model in coded values as shown in the following equation:

(3)

% Ni (II) Removal = 38 + 19.42 A + 11.57 B - 7.4 C + 18.22 D - 10.05 AD - 6.42 BC + 6.8 BD - 5.17 CD + 1.45 A^{2} + 2.20 B^{2} + 2.07 C^{2} + 1.45 D^{2} - 7.35 BCD - 2.04 A^{3} - 3.99 D^{3}

where A is pH, B is time in min, C is initial metal concentration in mg/l and D is adsorbent dosage in g. Fig. 3 represents predicted against actual values for Ni (II) removal efficiency. R² and adjusted R² (R²_adj) were found to be 0.98 and 0.97, respectively. According to the experimental data, a cubic equation with maximum R² was automatically chosen by software. The applied parameters effect and their interactions on the removal [Eq. (3)] can be checked by p-value parameter. For each term, the software user can check the p-value column from the ANOVA table (Table 3) obtained from the software. In fact, a term with p-value <0.05 is significant in the correlated model while the other terms with p-value >0.05 are ignorable.

Predicted vs. actual values of Ni (II) adsorption efficiency.

Additionally, assuming that the data have a normal distribution is necessary to integral analysis of the experimental data. Therefore, the normal probability plot of residual values is shown in Fig. 4. Evidently, the figure demonstrates that the experimental data fall close enough to the straight line suggesting normal distribution of the data.

Normal probability plot of residuals for Ni (II) adsorption efficiency.

Fig. 5 shows the one factor plot of the whole factors. According to Fig. 5 and Eq. (3), the pH of the solution, uptake time and sorbent dosage have positive influence on Ni (II) adsorption while metal initial concentration has a negative effect on the metal removal efficiency. As shown in Fig. 5a Ni (II) removal efficiency increased along with the rise of pH. An increase in the pH from 3.75 to 7.25 resulted in a 22.6% increase in Ni (II) removal. Previous researches have reported that metal adsorption is considerably dependent to the pH growth, and as the pH went up, the metal removal improved intensively. The removal efficiency improvement is probably due to the reduction of hydrogen ions in the solution regarding the competition between hydrogen and metal ions for the adsorption sites of the tissue (Kurniawan et al., 2011; Feng et al., 2011; Reddy et al., 2011; Senthil Kumar et al., 2011; Nadeem Zafar et al., 2007 ). The effectiveness intensity of pH on metal removal is obvious according to the pH coefficient in the Eq. (3). It is visible that the pH coefficient in the reduced cubic model is considerably large in comparison to the other terms’ coefficients in the model expressing the significance of this factor in the model. In addition, the positive sign of the pH coefficient in the model describes the positive effect of this factor on Ni (II) removal efficiency.

The main effects of independent variables on metal adsorption efficiency: (a) pH, (b) time, (c) initial metal concentration and (d) adsorbent dosage.

The other main factor affecting metal removal efficiency after pH was found to be adsorbent dosage, which is shown in Fig. 5d. It is illustrated that Ni (II) removal efficiency improves with the adsorbent dosage increase. This event can be explained regarding the greater existence of the exchangeable sites in higher adsorbent values (Reddy et al., 2011; Fong Lo et al., 2012). A growth in the adsorbent dosage from 0.2 to 0.4 g resulted in a 34.4% increase in Ni (II) removal.

The third factor influencing Ni (II) adsorption was found to be time, which is shown in Fig. 5b. It can be observed that the more the time increased, the more metal ions were exposed to the adsorbent pores, and therefore high metal adsorption was attained (Senthil Kumar et al., 2011; Anirudhan and Sreekumari, 2011). A change in the time from 82.5 to 187.5 min resulted in a 45.3% increase in Ni (II) removal.

The last factor impacting on the adsorption process was metal initial concentration, which is illustrated in Fig. 5c. It is obvious that high metal removal values were obtained in low metal initial concentration levels. This would be probably due to the saturation of sorbent sites over a specific concentration of Ni (II) ions. In other words, in high metal initial concentration values a large number of metal ions accumulate in the solution resulting in weaker adsorption performance (Nadeem Zafar et al., 2007; Saadat and Karimi-Jashni, 2011). A rise in the amount of initial metal concentration from 32.5 to 77.5 mg/L resulted in a 42.1% decrease in Ni (II) removal.

According to Eq. (3) there are positive interactions between time and adsorbent dosage (B × D) on Ni (II) removal efficiency. Additionally, negative interactions were observed between pH and adsorbent dosage (A × D), time and initial concentration (B × C) and initial concentration and adsorbent dosage (C × D) on Ni (II) removal efficiency.

The interaction plots for the whole factors are illustrated in Fig. 6. The interaction plot for A × D (Fig. 6a) represents that decreasing the adsorbent dosage from 0.4 to 0.2 g enhances Ni (II) removal efficiency from 11% (from 86% to 97%) to 45% (from 11% to 56%) by increasing pH values from 3.75 to 7.25. As a result, by raising pH values and lessening the adsorbent dosage simultaneously, a significant improvement was observed in metal removal efficiency, illustrated in Fig. 7a.

The interaction effects of independent variables on metal adsorption efficiency: (a) AD, (b) BC, (c) BD, and (d) CD.

Contour plots of the interactive effect for Ni (II) removal efficiency: (a) effect of pH and adsorbent dosage, (b) effect of time and initial metal concentration, (c) effect of time and adsorbent dosage and (d) effect of initial metal concentration and adsorbent dosage.

Fig. 6b shows that reducing the metal initial concentration from 77.5 to 32.5 mg/L increased the metal removal efficiency from 11% (from 29% to 33%) to 39% (from 32% to 71%) as the time turned from 82.5 into 187.5 min. Therefore, reducing metal initial concentration and increasing time result in strong Ni (II) removal efficiency. This is depicted in Fig. 7b. Similarly, Fig. 6c reveals the interaction plot for B × D indicating that increasing adsorbent dosage from 0.2 to 0.4 results in Ni (II) removal efficiency improvement from 3.5% (from 27.5% to 31%) to 58% (37% to 95%) as the time value goes up from 82.5 to 187.5 min. (Fig. 7c). Finally, as it is shown in Fig. 6d when the metal initial concentration drops from 77.5 to 32.5 mg/l, Ni (II) removal efficiency increases from 2% (from 31% to 33%) to 44% (from 51% to 95%) as the adsorbent dosage grows from 0.2 to 0.4 g. This event is fully presented in Fig. 7d.

3.5

3.5 Process optimization

Based on an experimental response, areas where requirements meet the critical properties, which are called “sweet spots” need to be determined. Overlaying critical response contours on a contour plot results in the best adjustment to be found visually. Graphical optimization provides an overlay plot in order to display the region of feasible response values in the factor segment. The yellow colored zone describes the possible response conditions in the factor space as illustrated in Fig. 8. The optimum region was obtained at the metal initial concentration of 32.5 mg/l and adsorbent dosage of 0.4 g.

Model confirmation was performed at one experimental condition to validate the integral model. The final results of the experiments expressed that the experimental values were in good agreement with the predicted values which is represented in Table 4. The obtained result confirmed the verification of the model, and the experimental values were found to be considerably similar to the predicted values.

Table 4 Optimum process conditions according to numerical optimization.

Case	Target	pH	Time (min)	Initial metal concentration (ppm)	Adsorbent dosage (g)	Predicted Ni (II) removal (%)	Experimental Ni (II) removal (%)
Ni (II)	Maximize	6.4	175.27	32.5	0.4	98	91

3.5.1

3.5.1 Prediction of the responses at the optimum set of conditions

In order to verify the prediction of the results prior to the confirmation tests, point prediction was performed which is represented in Table 5. The 95% confidence level interval (C.I) is explained as the range in which the average of the process is expected to reduce 95% of the time. The 95% prediction interval (P.I) is defined as the range in which any individual value is expected to fall 95% of the time. As it is shown in Table 5, due to the fact that more scatter is expected in individual values in comparison to averages, the P.I. is greater (a broader spread) than C.I. According to the integral definitions, SE mean explains a concept regarding the standard deviation in relation to an average value prediction at the selected component levels. In addition, SE pred. demonstrates an individual observation prediction at the selected factor or component levels.

Table 5 Point prediction of the responses at the optimum conditions.

Response	Prediction	SE	95% Cl	95% Cl	SE	95% Pl	95% Pl
		Mean	Low	High	Pred	Low	High
Ni (II) removal	44.01	7.52	28.33	59.7	13.49	15.88	72.15

4 Conclusions

The effects of various operational parameters such as pH (A), time of adsorption (B), metal initial concentration (C) and adsorbent dosage (D) on Ni (II) removal efficiency using tissue paper as the adsorbent were investigated and optimized in this study. RSM was found to be an appropriate method to optimize the main parameters that control the metal removal process. Reduced cubic model was considered as a suitable integral model provided adequate prediction of Ni (II) removal efficiency. The significant agreement between the model and experimental data was verified by ANOVA results. According to the experimental model, factors A and B and interactions of A × B and A × C were significant integrally. Metal initial concentration was found to have the less impact on Ni (II) removal efficiency and was not significant. Results showed that maximum Ni (II) removal efficiency of 79% was obtained at the values of 7.25 and 187.5 min for pH and time, respectively. Integral optimization was performed using RSM and the optimal value of 91% was obtained after conducting the confirmation test. The final result of the Ni (II) removal efficiency after optimization was found to be so close to the predicted value showing an appropriate agreement between the experimental and predicted values.

Acknowledgments

The authors are very thankful from Professor Sultan Abu-Orabi (Secretary General, Association of Arab Universities, Amman, Jordan), Professor Ahmad Fauzi Ismail (Universiti Teknologi Malaysia, Johor, Malaysia) and Mr. Alireza Majdi Nasab (Tarbiat Modares University, Tehran, Iran) for their kind supports and cooperation. Furthermore, this work was financially supported by Arak University (Grant No. 92/57).

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Introduction

Materials and method

Adsorbent
Adsorbate solution
Adsorption experiments
Experimental design

Results and discussion

Scanning electron microscopy (SEM)
FTIR studies
Integral design of experiment
Nickel removal
Process optimization

Conclusions

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[Google Scholar]

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[Google Scholar]

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[Google Scholar]

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[Google Scholar]

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Integral technique for evaluation and optimization of Ni (II) ions adsorption onto regenerated cellulose using response surface methodology

Abstract

Keywords

Ni (II) adsorption

Optimization

Response surface methodology

Tissue paper

1 Introduction

2 Materials and method

2.1 Adsorbent

2.2 Adsorbate solution

2.3 Adsorption experiments

2.4 Experimental design

3 Results and discussion

3.1 Scanning electron microscopy (SEM)

3.2 FTIR studies

3.3 Integral design of experiment

3.4 Nickel removal

3.5 Process optimization

3.5.1 Prediction of the responses at the optimum set of conditions

4 Conclusions

Acknowledgments

References

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