2016-09-15T00:15:23+05:302016-09-15T00:14:59+05:302016-09-15T00:15:23+05:30Acrobat PDFMaker 11 for Worduuid:fed18e8f-23ad-4917-9529-92020ec0ab94uuid:f1acf6ea-acc1-427b-922c-8ebc585bd6d22xmlRemoval of Methylene blue from aqueous solution by Anthacephalous Cadamba based activated carbon: Process Optimization using Response Surface Methodology (RSM)saiAdobe PDF Library 11.0D:20160912082312<egyptian hak>

International Journal of ChemTech Research CODEN (USA): IJCRGG, ISSN: 0974-4290, ISSN(Online):2455-9555 Vol.9, No.08 pp 236-245, 2016

Removal of Methylene blue from aqueous solution by Anthacephalous cadamba based activated carbon: Process Optimization using Response Surface Methodology (RSM) Kalpana P1*, Bhagya lakshmi K2, Rakesh N3 1Department of Chemical Engineering, GMR Institute of Tehnology, Rajam,Andhra Pradesh532127, India. 2Department of Environmental Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh532127, India. 3Department of Chemical Engineering, Salalah College of Technology, Salalah, Sultanate of Oman. Abstract: The unlimited discharge of dyes into the natural water bodies is a global environmental concern due to their toxic effects. Increasing environmental awareness is forcing waste creators to consider new options such as adsorption for the disposal of colored wastewaters. Due to prohibitive costs of commercially available activated carbon, low-cost adsorbents with high adsorption capacities have gained increasing attention. The present investigation deals with the adsorption of methylene blue on Anthacephalous cadamba leaf powder as activated carbon. The parameters pH, adsorbent dose and initial dye concentration considered in this investigation play an important role in the adsorption studies of MB dye removal. The optimum values of pH, adsorbent dose and initial dye concentration were found to be 10, 1g/L and 30 mg/L, for removal of MB dye, respectively. The experimental values were in good agreement with predicted values. Keywords: Activated carbon, Adsorption, Methylene blue, Response surface methodology, Anthacephalous Cadamba leaf. 1. Introduction India’s dye industry manufactures different types of dyes, from which 50 % are reactive dyes. Large amounts of dyes are annually produced and used in textile, paper, pharmaceutical, cosmetics, food, leather and other industries1,2. Removal of these dyes from effluents in a cost- effective way remains as a major problem for textile industries3. The most commonly used methods for color removal are electro coagulation, ion exchange, irradiation, ozonation and advanced oxidation4. However, these processes are economic and effective only in cases where solute concentrations are relatively high. Many physicochemical methods have been tested, but adsorption is by far the most versatile and widely used method because of its ease of operation and low cost. Therefore, there is the need to look for low cost alternatives in easily available bio-materials such as peat5, chitin6, silica7, Fugas saw dust 8, hardwood sawdust9, hardwood10, bagasse pith11, mixture of flyash and coal12, fly ash13, chitosan fibre14, rice husk15, acid treated spent bleaching earth16, slag17, chitosan fibre18-19, palm fruit bunch20 , Bauhinia Purpuria leaves 21 and Chitosan modified Watermelon rind Composite22 which can adsorb dyes from waste waters23. In this paper, we attempt to use activated carbon developed from Anthacephalous cadamba (AC) leaves, as an adsorbent for the removal of dyes from water. Since the AC leaves are available in GMR institute of technology, Rajam for free of cost, only the carbonization of its involved in the wastewater treatment. Therefore, it is a good adsorbent for the possibility of using dried AC leaves to develop a new low-cost activated carbon. The conventional method, changing one variable by keeping the other variables at a constant level does not give the effect of all the factors involved. Moreover, this method is time-consuming and also requires a large number of experiments to determine optimum levels, which may or may not be reliable. These problems can be eliminated by varying all the affecting parameters by using a statistical experimental design such as the response surface methodology (RSM)24. The present investigation is the utilization of the low-cost activated carbon, and the determination of the optimum conditions for Methylene blue (MB) dye removal from an aqueous solution using response surface central composite design methodology. The effect of experimental and their relations for the removal of MB were carried out by using CCD. The interactions between factors that influence the percentage of MB removal were established. The optimum value of the parameters was also determined for removal of MB from the aqueous solution using RSM.

2. Materials and methods

AC leaves as activated carbon, is used as adsorbent for dye removal. To get large surface area and to increase the porous capacity the adsorbent was completely activated. 2.1 Preparation of activated carbon from leaf powder The objective of an activation process is to increase the volume and diameter of the pores. The leaves were washed, dried and powdered. The powder was carefully weighed and put in a beaker containing of chemicals such as ZnCl2 and HCl. The materials were mixed continuously to make it paste and heated to 6000C for 2 hrs in a furnace and cooled to room temperature. Then the product was washed to make it to pH of 6 to7 and dried for 24 hrs at 900C.The powder was crushed and screened. The powder is taken for the experimental analysis. 2.2. Dye solution A Stock solution of MB, concentration 1000 mg/L was prepared by dissolving 1gm of MB in 1000 ml of distilled water 25. The range of concentration of prepared solutions varied between 10 and 50 ppm. 3. Experimental design A standard response surface methodology (RSM) design known as central composite design (CCD) was used to study the influence of process parameters such as pH(X1), dosage(X2) and concentration(X3) on the percentage removal of MB. Based on the ranges and the levels given, a complete design matrix of the experiments was employed as shown in Table 1. There are 8factorial points, 6 axial points and 6 replicates at the center points, indicated by a total of 20 experiments were employed in this study. The center points were used to verify the reproducibility of the data and the experimental error. The variables were coded to the (-1, 1) interval where low and high level were coded as -1 and 1, respectively. The axial points are located at (±a, 0, 0) (0, ±a, 0) and (0, 0, ±a) where a is the distance of the axial point from the center and makes the design rotatable. In this study, a value was fixed at 1.682 (rotatable). The response is MB removal efficiency (Y1). It was used to develop an empirical model which correlated the response to the variables using a second degree polynomial equation. The optimum values of the test variables were obtained using the numerical point prediction tool in MINITAB (Version 16, PA, USA). The ‘‘prob [F’’ value of less than 0.05] indicates that the models significant26-27. It is desirable to indicate the influence of particular model terms that have significant effects on the response. Experimental conditions with the highest desirability were selected to be verified. 4. Results and discussion 4.1 Statistical analysis The ultimate objective of CCD method used in this study was to find out the significant effects of the parameters viz. pH, initial concentration and adsorbent dosage and then to evaluate the optimum condition for MB removal from aqueous solution. Levels of selected variables are presented in Table 1. For statistical calculations, the variables Xi (the real value of an independent variable) were coded as Xi (dimensionless value of an independent variable) according to equation (1):Xi = (Xi–X0) / ΔX, Where X0 is the value of Xi at the center point and ΔX represents the step change. Table1. Experimental variables and levels investigated by central composite design

Variables

Coded values

-1.682

-1

0

1

1.682

pH (X1)

4

8.81

10

11.18

12

Dosage (X2, g/l)

0.06

0.076

0.1

0.12

0.14

Concentration (X3, mg/l)

10

18.1

30

41.89

50

The experimental design matrix, the experimental results and the predicted dye removal efficiency are presented in Table 2. Table 2: Experimental design matrix and results for adsorption of MB

Run

Actual level of factors

Coded level of factors

% removal of MB

X1

X2

X3

X1

X2

X3

%Removal

prediction

1

8.81

0.07

18.1

-1

-1

-1

86.2

85.15808

2

11.18

0.07

18.1

1

-1

-1

82

81.16808

3

8.81

0.12

18.1

-1

1

-1

91.8

91.12362

4

11.18

0.12

18.1

1

1

-1

86

86.11862

5

8.81

0.07

41.89

-1

-1

1

86.4

85.53384

6

11.18

0.07

41.89

1

-1

1

85.3

85.22884

7

8.81

0.12

41.89

-1

1

1

87.2

87.28438

8

11.18

0.12

41.89

1

1

1

85.67

85.96438

9

8.0

0.095

29.995

-1.68

0

0

86.5

87.62607

10

11.98

0.095

29.995

1.68

0

0

83.23

83.16091

11

9.995

0.052

29.995

0

-1.68

0

82

83.31105

12

9.995

0.137

29.995

0

1.68

0

89.2

88.94597

13

9.995

0.095

9.9900

0

0

-1.68

86.1

87.18535

14

9.995

0.095

49.999

0

0

1.68

87.4

87.37163

15

9.995

0.095

29.995

0

0

0

90.84

91.5314

16

9.995

0.095

29.995

0

0

0

91.75

91.5314

17

9.995

0.095

29.995

0

0

0

91.81

91.5314

18

9.995

0.095

29.995

0

0

0

92.32

91.5314

19

9.995

0.095

29.995

0

0

0

90.11

91.5314

20

9.995

0.095

29.995

0

0

0

92.54

91.5314

The behavior of the adsorption process is explained by the following empirical second-order polynomial model equation28 (2):
jiijiiiiixxxxY∑∑∑+++=ββββ20Where Y is the predicted response (dye removal efficiency), β0 the constant coefficient, βi the linear coefficients, βii the quadratic coefficients, βij the interaction coefficients and xi, xj are the coded values of the variables. MiniTab was used for the regression and graphical analyses of the data obtained. The reliability of the fitted model was justified through ANOVA and the coefficient of R2. Y=91.5314-X1(1.32750)+X2(1.67527)+X3(0.0554)-X12(2.17008)-X22(1.91021)-X32(1.50363)-X1*X2(0.253750)+X1*X3(0.921250)-X2*X3(1.05375) Statistical regression coefficients for MB dye removal efficiency (%) are provided in Table 3. An amount of P (P < 0.05) for all independent parameters confirms that three selected factors are significant. However, it was found that all square and interaction terms except X12 and X22 (P values of 0.000) were insignificant to the response. The significance of each coefficient was evaluated by t test and p values, which are given in Table 3. The larger the magnitude of the t value and smaller the magnitude of the P value, the more significant is the corresponding coefficient 29-30. Values of P less than 0.05 indicate the model term is significant. From the Table 3, the P values for solution pH, adsorbent dosage and initial dye concentration were found to be 0.001, 0 and 0.853. This implies that the linear effects of solution pH, adsorbent dosage and initial dye concentration are highly significant and influence the percentage of adsorption of the dye. Table 3: Statistical regression coefficients for MB removal efficiency (%) in coded units

Term

Coef

SEcoef

T

P

Constant

91.5314

0.4392

208.407

0

X1

-1.3275

0.2914

-4.556

0.001

X2

1.6753

0.2914

5.749

0

X3

0.0554

0.2914

0.19

0.853

X12

-2.1701

0.2837

-7.65

0

X22

-1.9102

0.2837

-6.734

0

X32

-1.5036

0.2837

-5.301

0

X1*X2

-0.2537

0.3807

-0.666

0.52

X1*X3

0.9212

0.3807

2.42

0.036

X2*X3

-1.0537

0.3807

-2.768

0.02

ANOVA for the selected dye removal is also listed in Table 4. In this case, the P-value of 0.000 (P < 0.05) for regression model equation implies that the second-order polynomial model fitted to the experimental results as well. The lack-of-fit was also calculated from the experimental error (pure error) and residuals. “F-value of Lack-of-fit” of 1.72 implies the significance of model correlation between the variables and process response for dye removal. Additionally, the value of R2 =0.9282 confirm the accuracy of the model. Furthermore, the parity plot for the experimental and predicted value of MB removal efficiency (%) is demonstrated in below Fig.1. Fig.1 Parity plot for MB adsorption The adequacy and significance of second order response surface models were further tested by the analysis of variance (ANOVA). The ANOVA summary is given in Table 4. ANOVA gives the information about quadratic and interaction effects along with the normal linearized effects of the independent variables. The statistical significance of model equations was determined by the F-test. The significance of each coefficient was evaluated by F-values and P-values. The larger fisher F- value with a low probability value (<0.05) demonstrate that the developed mathematical models were fitted well to the experimental data. From the ANOVA results, it is evident that the main and square effects were highly significant in comparison with interaction effects. Table 4: ANOVA results for MB removal efficiency (%)

Source

DF

SeqSS

AdjMS

F

P

Regression

9

207.201

23.0223

19.85

0

Linear

3

62.437

20.8124

17.95

0

X1

1

24.067

24.0668

20.75

0.001

X2

1

38.328

38.3283

33.05

0

X3

1

0.042

0.0419

0.04

0.853

Square

3

128.576

42.8587

36.96

0

X12

1

50.862

67.8661

58.52

0

X22

1

45.132

52.5857

45.35

0

X32

1

32.582

32.5825

28.1

0

Interaction

3

16.188

5.3959

4.65

0.028

X1*X2

1

0.515

0.5151

0.44

0.52

X1*X3

1

6.79

6.7896

5.85

0.036

X2*X3

1

8.883

8.8831

7.66

0.02

Residual Error

10

11.596

1.1596

Lack-of-fit

5

7.339

7.339

1.72

0.282

Pure Error

5

4.257

0.8515

Total

19

218.797

The response surface plots and contour plots estimate the % RE of MB onto activated carbon over independent variables of initial solution pH, initial concentration and the adsorbent dosage. The response surface plots as a function of two variables at a time, maintaining all other variables at fixed levels is more useful in understanding both the main and the interaction effects of these two variables. Fig. 2 shows the interactive effect of concentration and dosage by keeping the pH at their optimum values. Similarly, remaining surface plots explain the interaction effects of other independent variables on percentage of adsorption of dyes. It is evident from the Fig. 2 to 7 that the percentage of adsorption was increased with an increase in adsorbent dosage and initial dye concentration of the dye31. 4.2. Response surface plots and contour plots Surface and contour plots demonstrate the effects of different process parameters (two parameters varied at a time while the third parameter is maintained at the middle level) on the % RE of MB (shown in Fig. 2 to 7). The stationary points were examined by analyzing these plots. In general, circular contour plots indicate that the interactions between parameters are almost negligible while the elliptical ones indicate the evidence of the interactions. Figure.2. Surface plot showing the interactive effect of dosage and concentration on MB Figure.3. Contour plot showing the interactive effect of dosage and concentration on MB Figure.4. Surface plot showing the interactive effect of dosage and pH on methylene blue Figure.5. Contour plot showing the interactive effect of dosage and pH on MB Figure.6. Surface plot showing the interactive effect of pH and concentration on MB Fig.7. Contour plot showing the interactive effect of concentration and pH on MB The Figures 4 and 6 showed the effect of pH in the % RE of MB. The maximum removal is obtained at pH of 10. It was found that at low pH, the dyes become protonated, the electrostatic repulsion between the protonated dyes and positively charged adsorbent sites results in decreased adsorption. Higher adsorption at increased pH may be due to increased protonation by the neutralization of the negative charges on the surface of the adsorbent; which facilitates the diffusion process and provides more active sites for the adsorbent32-33. It can be observed from the Figures 2 and 4, the % removal of MB on ACLAC was increased with the increasing of adsorbent dosage from 0.06g until 0.1 g (92.54%). The increase of the removal of MB is due to the increase of the adsorbent surface area and availability. From the Fig. 2 and 6 the adsorption percentage decreases and the extent of adsorption increase with increasing initial dye concentration. This is obvious from the fact that the initial MB concentration provides an important driving force to overcome all of mass transfer resistance. Furthermore, the increase of loading capacity of ACLAC with increasing initial MB concentration may be due to higher interaction between MB and adsorbent. For constant dosage of adsorbent, at higher initial concentrations, the available adsorption sites of adsorbent became fewer and hence the removal of MB depends upon the initial concentration33. According to the results it was observed that there was an increase in the dye removal efficiency, with an increase in the initial pH and adsorbent dose. And with an increase in the initial dye concentration, there was a decrease in the dye removal efficiency. The effect of the three selected independent parameters and interactions among the RSM were analyzed which showed that some interactions like (X12X22 and X32) effected the adsorption performance as well as all the selected parameters. The ANOVA showed a high R2 (0.9282) value of the regressions model equation, showing a satisfactory adjustment of the second-order regression model with the experimental data34. The optimum MB removal efficiency was found at an initial pH of 10, adsorbent dose of 0.1g, and initial dye concentration of 30 mg/l. An experiment was accomplished in optimum conditions which confirmed that the model and experimental results are in close agreement (92.54% compared to 90.54% for the model). 6. Conclusions The biomass of AC leaves as activated carbon demonstrates a good capacity of biosorption of MB, highlighting its potential as an effective biosorbent for the treatment of industrial effluent. This study clearly shows that the response surface methodology is one of the suitable methods for optimization of process parameters to maximize the dye removal. The statistical analysis results proved the significance of the model developed from experimental data to optimize the parameters. The optimum values of pH, adsorbent dose and initial dye concentration were found to be 10, 0.1 g and 30 mg/L, for complete removal of MB dye, respectively. The experimental values were in good agreement with the 2nd order polynomial model predicted values. From the results it can be concluded that AC leaves can be effectively utilized for the treatment of industrial wastewater. References

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