BOX-BEHNKEN DESIGN FOR SEQUESTRATION OF DYE: …

BOX-BEHNKEN DESIGN FOR SEQUESTRATION OF

DYE: STATISTICAL OPTIMIZATION AND

MECHANISM STUDIES

Sivahankar Raja1, Sathya. A. Bose

2, Kanimozhi Jayaram

3, Sivasubramanian

Velmurugan4*

1Department of Chemical Engineering, Hindustan Institute of Technology and Science, Chennai,

India-603103

2Department of Biotechnology, Aarupadai Veedu Institute of Technology, Paiyanoor, India-

603104

3Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil,

India- 626128.

4Department of Chemical Engineering, National Institute of Technology, Calicut, India-673601.

*Corresponding mail id: [email protected]

Abstract: The wastewater treatment containing dyes are challenging due to their recalcitrant properties.

Sequestration of dyes via adsorption principle seems to be promising. The purpose of this study was to

optimize the operational condition and enhance the adsorption of MY dye using response surface modeling.

Aquatic macrophyte (Salvinia molesta), iron oxide synthesized via chemical co-precipitation, and iron oxide

modified aquatic macrophyte have been successfully exploited for the sequestration of dye from aqueous

solution. For the investigation of effects of major operational variables and optimization conditions, response

surface modeling based three factors, three levels Box-Behnken experimental design associated with

quadratic programming has been utilized for maximizing the sequestration processes. Initial dye

concentration (10 to 100 mg/L), adsorbent dosage concentration (0.5 to 1 g), and initial solution pH (2.0 to

12.0) are the three independent variables were studied from 17 experimental run in a batch system. For

analysis of variation with 95 % confidence limit, the significance of the model terms contained in the

Regression equations have been assessed using the F - and P - test. The adsorbent dosage concentration of

1.0 mg/100 ml, pH of 2, and initial dye concentration of 10 mg/L were found to be the economical operating

conditions for maximum removal efficiency of 96.42, 97.25 and 98.51 using aquatic macrophyte, iron oxide

and iron oxide modified aquatic macrophyte, respectively. As a result, Box-behnken design was suggested as

a virtuous statistical tool for optimal design of dye sequestration that provides appropriate results for the

maximum percentage decolorization.

Keywords: Box-behnken design, optimization, sequestration, dye.

1. INTRODUCTION Dyes are substances that provide color by a process that alters temporarily or

permanently. The chemicals applied for producing dyes are frequently highly toxic,

carcinogenic, or even explosive. About 10,000 different dyes and pigments are

industrially used, with annual global production of more than 7x105 tons of synthetic

dyes [1]. Industries such as textile, cosmetics, paper, pharmaceutical, leather, plastics, and

food are the major users of these synthetic dyes. Azo dyes that consist of one or more (–

N=N–) azo bonds are the most significant class of synthetic organic dyes. These azo dyes

contribute to more than 60% of all used synthetic dyes and are difficult to degrade

because of their complex structure and synthetic nature [2]. Many azo dyes are thought to

be recalcitrant and toxic contaminants prevent photosynthetic activity and harmful to

aquatic organisms. It is a renowned fact that exposure to these dyes results in aesthetic

problems and sometimes mutagenic and carcinogenic effect in human beings. Further,

degradation of these azo dyes gives rise to the formation of toxic amines [3]. Hence,

sequestration of these dyes from aqueous solution is desirable. Adsorption is an easy and

cost-effective wastewater processing technique, among several techniques available for

the removal of dyes from effluent, such as flocculation, oxidation and electrolysis [4].

Generally, it is difficult to separate the used adsorbents after saturation adsorption. In

these circumstances, magnetic separation of used adsorbents would be one of the

successful strategies for wastewater treatment due to its advantages such as zero

flocculants formation and substantial quantities of wastewater can be processed over short

time [5].

In this study, the possibility of aquatic macrophyte (Salvinia molesta), iron oxide, and its

combination (iron oxide modified Salvinia molesta) as an inexpensive eco-friendly

adsorbent for the sequestration of MY dye from aqueous solution have been studied. For

this purpose, the Box–Behnken model (BBD) of experimental design in response surface

modeling (RSM) was identified to be a useful tool for optimizing the experimental

conditions and evaluating the multiple analytical factors and their interactions. In the

event of complex interactions of process variables (initial solution pH, adsorbent dosage

concentration, and initial dye concentration), RSM is a fusion of mathematical and

statistical techniques used to develop, enhance and optimize processes and to determine

the relative value of some of these parameter. Further, RSM offers a mathematical model

for accomplishing maximum percentage yield, minimizing process uncertainty, superior

output response validation to nominal target attainment, and reduced treatment time with

minimum cost. In light of these important characteristics, the objective of this research is

to develop magnetically modified biosorbents as alternatives and environmentally friendly

adsorbents for removal of dyes from aqueous solution.

2. MATERIALS AND METHODS

2.1. Chemicals Metanil Yellow (MY) dye (70% dyes) from Sigma Aldrich, was employed in this study as

an adsorbate. The stock solution was prepared at a concentration of 1g/L. The dilution of

stock solution in distilled water was used to develop dye solutions of the desired

concentration. Diluted HCl and NaOH were used to adjust each dye solution to its pH. An

UV-visible spectrophotometer, which operates in the visible absorbance range, used for

the determination the optical density of the color solution.

2.2. Preparation of adsorbents The adsorbents used for the present experiments are Salvinia molesta (SM), iron oxide

(IO) and iron oxide modified SM (MBC). The dried SM was prepared as reported in the

literature [6]. The collected SM was thoroughly cleaned in warm water and dried. The

dried particles were pulverized for fine powder (approximately 88µm). The iron oxide

particles (γ-Fe2O3) were prepared by chemical co-precipitation method using ferric and

ferrous salts under the presence of N2 gas [7]. Similarly the iron oxide modified SM used

in this study was also synthesized by co-precipitation method with MnCl2.4H2O and

FeCl3.6H2O as reported in the literature [5]. 0.1 M of MnCl2.4H2O and 0.3 M of

FeCl3.6H2O were blended in 100 ml distilled water under constant stirring. To the

mixture, 1.0 g of aquatic weed was added and followed by 10% NaOH until the solution

pH reaches 10. The precipitated particles were separated using magnetic field and dried

for further experimental employment.

2.3. Batch adsorption studies Adsorption of MY dye was investigated in a batch system at different pH values,

temperature, agitation and adsorbent dosage to determine the rate constant and extent of

dye uptake by the adsorbent. These studies were carried out for SM, IO and MBC

separately. Three significant factors such as initial dye concentration, initial solution pH

and adsorbent dosage concentration were selected for batch adsorption processes based

on one variable at a time (OVAT) method (results not shown). The absorption spectra of

the clear supernatant were recorded at λmax (430 nm) using UV-visible

spectrophotometer [8]. The quantity of dye adsorbed per adsorbent unit (mg dye per g

adsorbent) was computed from below Eq. (1).

The quantity of dye adsorbed on to adsorbent at time t,

(1)

where, the solution concentrations at t= 0 and t= t are C0, Ct (mg/l), respectively, V is

referred to be volume of the solution (L) and m is the mass of adsorbent. The dye removal

efficiency was computed from below Eq. (2),

(2)

2.4. Box-Behnken design for process parameter optimization Design of experiments is a technique for systematically applying statistics to experiments.

BBD based RSM has been employed to optimize process parameters that affect dye

sequestration. Optimization was carried out involving three factors such as amount of

adsorbent, initial dye concentration and initial solution pH. All factors were examined at

low and high range of rates. Based on preliminary tests the actual values of the system

parameters and their range have been chosen. Seventeen experimental runs have been

performed to investigate adsorbent & adsorbate interactions and the cumulative effects of

all process parameters. The statistical software employed for the design of 17 experiments

is Design Expert (Version 10.0.3, Stat-Ease, Inc., Minneapolis, U.S.A.) Further, by

solving the regression equations and by analyzing the 3D surface plots, the optimum

conditions for all adsorbents were obtained. The regression analyzes were conducted for

the response functions, i.e. dye decolorization %. The developed regression model is a

response based on adsorbent dosage concentration, initial dye concentration and pH of the

solution. The three independent variables chosen were designated as A (adsorbent

dosage), B (initial dye concentration), and C (initial solution pH). The number of

experimental was computed from the following Eq. (3)

(3)

where k and cp represent the factor number and replicate number of the central point,

respectively.

Similarly, the coded values of process parameters have been accessed from Eq. (4):

(4)

where X0 is the value of Xi at the center point, the real value of an independent variable is

Xi , step change is represented by ΔX and xi represent the dimensionless factor of a

process variable.

In order to compare the dependent and independent variables, the second order equation

response was employed. A higher-order polynomial, such as quadratic model may be

expressed as Eq.(5),

(5)

where Y is the predicted response, the constant coefficient, the linear coefficients, the

quadratic coefficients, the interaction coefficients and Xi, Xj are the coded values of the

independent process variables and e is the residual error. By setting up of constraints of

variables, analysing the surface of counter response surface plot, and solving the

regression equation, the optimum values of the factors were obtained [9]. The extreme

values of the variables were determined from preliminary experiments conducted. In

order to obtain the correlating regression coefficients, linear, two factor interaction (2FI),

cubic and quadratic equations have been used to interpret the experimental results. The

sequential model sum of squares and the model summary statistics have been carried out

to assess the adequacy of models depicting removal of dye by adsorbents. Coefficient of

determination (R2) determined the fitness of the model and its statistical significance was

assessed by an F-test. In the analysis of variance (ANOVA), the significances of the

coded variables of the regression equation have been assessed by the F-test [9].

3. RESULTS AND DISCUSSION 3.1. Reconciliation of process variables and statistical optimization

Low level and high level of all three independent variables such as adsorbent dosage

concentration, initial dye concentration and initial solution pH are shown in Table 1. BBD

matrix for coded value with their experimental values for removal efficiency (%) of MY

dye by SM, IO and MBC are shown in Table 2. From the table, it could be observed that a

maximum dye decolorization (%) was 96.42, 98.25 and 99.51% for SM, IO and MBC

respectively. Percentage decolorization of MY dye was related with the function of

biomass dosage (A), initial dye concentration (B) and initial solution pH (C) in

determination of regression equation during optimization of process variables. BBD

developed a second order solution that matches the surface effectively for all three

variables that were analyzed. In order to determine the collective impact of desired

variables, batch mode experiments were performed for various parameters combinations,

in consonance with BBD. Sequential sum of squares and model summary statistics are

two different tests conducted to evaluate the adequacy of the model. The models were

tested for significances of regression and the sequential model summary statistics are

listed in the Table 3. However, to analyze the experimental data and to obtain an

appropriate regression equation, models like linear, two-factor interaction, cubic and

quadratic equation were employed. It could be observed from table 3 that the quadratic

model was deemed to be most fitting one of the four types.

Table 1: Coding of process variables

Adsorbent Symbol Variable Unit Coded factor level

(-1) (+1)

Salvinia molesta (SM)

A Adsorbent concentration mg/100ml 0.1 2.0

B Initial dye concentration mg/L 10 100

C Initial solution pH pH 2 12

Iron oxide (IO)




Iron oxide modified SM (MBC)




Table 2: Box–Behnken design matrix*

Run

order Factors

Decolorization efficiency (%)

A B C SM IO MBC

Actual Predicted Actual Predicted Actual Predicted

1 0 1 -1 83.65 84.33 79.32 79.89 81.98 82.0 2 -1 -1 0 4.56 5.11 3.72 4.1 2.22 2.12 3 -1 0 -1 78.57 78.03 71.99 72.0 74.98 75.0 4 0 0 0 9.82 10.28 10.41 10.51 16.34 15.88 5 1 1 0 18.63 17.21 16.24 16.89 20.0 20.72 6 -1 0 1 1.98 2.1 3.25 3.56 6.5 6.98 7 0 0 0 10.3 10.68 9.92 10.21 15.99 16.0 8 0 0 0 11.32 10.68 8.29 9.21 11.99 12.0 9 0 0 0 12.32 10.68 9.43 9.50 9.01 9.0

10 -1 1 0 2.31 2.02 1.52 1.82 12.01 12.0 11 1 0 1 14.52 15.01 11.98 12.21 17.98 18.0 12 0 -1 1 13.54 12.48 6.25 6.24 1.6 1.89 13 0 0 0 9.65 10.12 9.35 9.56 12.64 12.54 14 1 0 -1 96.42 95.78 96.2 95.21 98.51 98.56 15 0 1 1 11.65 12.21 16.02 16.0 16.02 15.87 16 1 -1 0 18.21 18.33 19.01 19.0 34.03 33.54 17 0 -1 -1 95.36 95.27 97.25 97.0 98.21 98.74

*A: Adsorbent dosage; B: Initial MY dye concentration; C: Initial solution pH

Table 3: Sequential model sum of squares

Model Sequential

P-value

Lack of

fit P-

value

F-value Adjusted

R2

Predicted

R2

Standard

division

Remarks

Aquatic macrophyte (Salvinia molesta)

Linear 0.0011 0.0005 9.96 0.6269 0.4520 2.87

2FI 0.8405 0.0003 0.28 0.5523 -0.0780 22.86

Quadratic <0.0001 0.5141 194.33 0.9924 0.9755 2.98 Suggested

Cubic 0.5141 - 0.90 0.9921 - 3.04 Aliased

Iron oxide

Linear 0.0024 <0.0001 8.31 0.5780 0.3674 22.31

2FI 0.9319 <0.0001 0.14 0.4740 -0.3353 24.91

Quadratic <0.0001 0.2413 2242.87 0.9992 0.9964 0.96 Suggested

Cubic 0.2413 - 2.11 0.9995 - 0.79 Aliased

Iron oxide modified aquatic macrophyte (MBC)

Linear 0.0021 <0.0001 8.57 0.5866 0.3818 22.20

2FI 0.9968 <0.0001 0.017 0.4653 -0.3641 25.24

Quadratic <0.0001 <0.0715 594.80 0.9970 0.9829 1.89 Suggested

Cubic 0.0715 - 5.25 0.9989 - 1.12 Aliased

The quadratic model‟s F-value of 194.33, 2242.87 and 594.80 shows that the model was

significant for MY dye sequestration using SM, IO and MBC respectively. In addition,

the maximum decolorization percentage for all the three adsorbents was estimated from

the second order quadratic equation. The R2 values (0.9967, 0.9997and 0.9970), adjusted

R2 values (0.9924, 0.9992 and 0.9970) and predicted R2 (0.9755, 0.9964 and 0.9829) are

close to one for SM, IO and MBC respectively (Table 4). This indicates a greater

correlation and acceptable agreement. The adequate precision value of 43.440, 131.528

and 65.570 was estimated for SM, IO and MBC respectively and it is due to the signal to

noise ratio that indicates an adequate signal. Such models will then be used to handle the

RSM model design space. For the second-order model, the differences in the means were

tested by ANOVA and the results are exhibited in Table 5. In general, F-values and P-

values determine the significance of all coefficients. The greater F values and the lower P-

values imply that the corresponding coefficients are more significant. The model is

suggested to be significant when the (Prob>F) is less than 0.1 and not significant if the

values are greater than 0.05 under specified conditions. However, the values of "Prob > F"

less than 0.05 indicates that model terms are significant. From the results of ANOVA, the

model terms such as A, B, C, AC, BC, A^2, B^2, C^2 were found to be significant. In

general, a regression model exhibits lack-of-fit when it fails to adequately describe the

functional relationship between the experimental factors and the response variable.

However, the “lack of fit P-value” was estimated as 0.514, 0.2413 and 0.1715 which is

greater than 0.05 and this suggests that lack-of-fit is not significant for the selected

variables. Further, this may be due to the quadratic model that has a good agreement and

greater correlation amongst selected variable for MY dye adsorption onto the adsorbents.

Similarly, the "lack of Fit F-value" of 0.9, 2.11 5.25 implies that the lack of Fit is not

significant relative to the pure error. There is a 24.13% chance for "lack of Fit F-value"

could be large due to noise. Non-significant lack of fit is good for the model to fit.

Regression analysis was carried out to fit the response functions, i.e. percentage

decolorization of MY dye. An empirical relationship between the response and input

variables were expressed as response surface reduced quadratic polymeric model

equations. The regression equation for the sequestration of MY dye by SM, IO and MBC

were predicted from the model and presented in Eq. (6), (7) and (8) respectively.

(6)

(7)

(8)

It can be noted from a statistically validated model that a randomly constructed series of

experiments has verified the theoretical adsorption potential of the model, apparently

confirming that the model equation fits well with the experimental response at 95 percent

certainty. Table 4 Response surface quadratic model summary statistics

Model R2 Adjusted

R2 Predicted

R2 Adequate precision

SD* CV* PRESS*

Salvinia molesta

0.9967 0.9924 0.9755 43.440 2.98 9.51 458.34

Iron oxide 0.9997 0.9992 0.9964 131.528 0.96 3.46 67.13 Magnetic biocomposite

0.9970 0.9970 0.9829 65.570 1.89 6.51 325.57

*SD: Standard deviation; CV: Coefficient of variation; PRESS: Predicted residual error sum of square

3.2. Effect of process variables and interactions Figures 1, 2 and 3 exhibit the 3D surface plot of sorption process of MY dye on to SM, IO

and MBC, respectively. The adsorbent concentration is one of the significant parameter

that determines the potential of adsorbent in removal of dye from aqueous solution. The

effect of dosage concentration with dye concentration against decolorization efficiency

were studied for all adsorbents and presented in Figures 1a, 2a, and 3a. The decolorization

efficiency has been found to be increased as the adsorbent dosage concentration increased

gradually. More binding site and increased surface area have been offered by increasing

the dosage concentration of adsorbent. Moreover, the figure shows clearly that by

increasing the amount of catalyst, the adsorption rate has increased, and that the amount

adsorbed per unit mass has decreased. Depending on the availability of active sites of

adsorbent in the aqueous solution, the extent of adsorption of MY dye varies. It is well

seen that the number of available adsorption sites increased by increasing the adsorbed

amount but the slump in adsorption capacity is fundamentally due to the sites remaining

unsaturated during the adsorption process. Another main parameter in the adsorption

process is the pH solution. The pH range was varied from 2 to 12 in this study. The effect

of pH with adsorbent dosage concentration against decolorization efficiency was studied

and shown in Figures 1b, 2b, and 3b. Maximum decolorization percentage was obtained

with increase in dosage concentration and decrease in solution pH. In addition to its

adsorbent dosage, it can be perceived that the influence of pH in the process of adsorption

of MY dye played a crucial function. All the prepared adsorbents SM, IO and MBC

showed similar scheme of decolorization for the range of pH. The increase in pH

decreases the decolorization percentage of MY dye for all the prepared adsorbents.

However, the percentage decolorization in the neutral and alkaline pH was significantly

lower than the acidic solution. The influence of pH on solid–liquid equilibrium could be

explained on the basis of ion exchange, i.e, the formation of –NH+3 group occurs at

acidic pH because of the excess availability of protons to protonate the amino group of

adsorbents. Thus increased protonation may contribute to the increased electrostatic

attraction amongst the negatively charged anions of dye molecules and positively charged

active sites of adsorbents which results in the greater percentage decolorization. In

addition, the rise in the solution pH from acidic to neutral or alkaline condition lead to the

engagement of negatively charged OH- ions on the surface of adsorbents and this results

in the decreases in adsorption efficiency due to the formation of repulsive force with

anionic dye at neutral and alkaline condition. Therefore, the dye decolorization decreases

since the external surface of the adsorbent does not produce exchangeable anions at

higher pH. As shown in Figures 1c, 2c, and 3c, decolorization efficiency of MY dye on to

all the prepared adsorbents decreased with increase in initial solution pH. A maximum

decolorization percentage of 96.42, 97.25 and 98.51 were obtained for SM, IO and MBC,

respectively. However, dye decolorization percentage increases with increase in adsorbent

concentration and decrease in solution pH.

Table 5 Analysis of variance (ANOVA)*

Source Sum of Squares

Mean Square

F Value

p-value Prob > F

Remarks

Salvinia molesta

Model 18615.80 2068.42 233.48 < 0.0001 significant A 718.39 718.39 81.09 < 0.0001 B 6.21 6.21 0.70 0.4300 C 12291.55 12291.55 1387.47 < 0.0001 AB 141.85 141.85 16.01 0.0052 AC 42.58 42.58 4.81 0.0645 BC 250.43 250.43 28.27 0.0011 A

2 16.10 16.10 1.82 0.2196

B2 15.45 15.45 1.74 0.2282

C2 5038.63 5038.63 568.76 < 0.0001

Residual 62.01 8.86 Lack of Fit 25.04 8.35 0.90 0.5141 not significant Pure Error 36.98 9.24 Cor Total 18677.81

Iron oxide

Model 18860.39 2095.60 2275.68 < 0.0001 significant A 495.34 495.34 537.90 < 0.0001 B 24.96 24.96 27.10 0.0012 C 11878.03 11878.03 12898.73 < 0.0001 AB 0.081 0.081 0.088 0.7751 AC 59.91 59.91 65.06 < 0.0001 BC 205.92 205.92 223.62 < 0.0001 A

2 12.62 12.62 13.70 0.0076

B2 23.73 23.73 25.76 0.0014

C2 6114.05 6114.05 6639.44 < 0.0001


Magnetic biocomposite

Model 19041.32 2115.70 594.78 < 0.0001 significant A 452.85 452.85 127.31 < 0.0001 B 29.76 29.76 8.37 0.0232 C 12178.92 12178.92 3423.83 < 0.0001 AB 1.78 1.78 0.50 0.5019 AC 6.60 6.60 1.86 0.2152 BC 24.11 24.11 6.78 0.0353 A

2 9.31 9.31 2.62 0.1497

B2 12.64 12.64 3.55 0.1014

C2 6284.98 6284.98 1766.88 < 0.0001


*A: Adsorbent concentration; B: Dye concentration; C: pH

3.3. Effect of independent variables based on superimposed

perturbation plot The perturbation plot had to be used to concurrently evaluate the effect of three variables

during the dye adsorption processes. Perturbation plot illustrates how a function of a

(1a)

Design-Expert® SoftwareFactor Coding: ActualDecolorization %

99.51

1.6

X1 = A: Dosage ConcentrationX2 = B: Dye Concentration

Actual FactorC: pH = -0.675676

-1

-0.5

0

0.5

1

-1

-0.5

0

0.5

1

-20

0

20

40

60

80

100

120

Decolo

rization %

A: Dosage Concentration (g/100L)

B: Dye Concentration (mg/L)

47.2255

55.6669

48.9306

75.5271

(2a)

(1b)


Design points above predicted valueDesign points below predicted value99.51

1.6

X1 = A: Dosage ConcentrationX2 = C: pH

Actual FactorB: Dye Concentration = -1

-1

-0.5

0

0.5

1

-1

-0.5

0

0.5

1

-20

0

20

40

60

80

100

120

Decolo

rization %


C: pH (pH)

45.7645.76

55.5734

-0.789206

(2b)

(1c)



1.6

X1 = B: Dye ConcentrationX2 = C: pH

Actual FactorA: Dosage Concentration = 0

-1

-0.5

0

0.5

1

-1

-0.5

0

0.5

1

-20

0

20

40

60

80

100

120

Decolo

rization %

B: Dye Concentration (mg/L)C: pH (pH)

61.407361.4073

68.4119

5.30401

6.40254

(2c)

Figure 1 Surface plot showing the collective effect of operating variable for sorption of MY dye on to

Salvinia molesta (1a, 1b, 1 c)

Figure2 Surface plot showing the collective effect of operating variable for sorption of MY dye on to iron

oxide (2a, 2b, 2c)

certain factor responded as the level of that factor changes, when the other factors are

fixed at their optimum levels. Further, the perturbation analysis assists in the selection of

surface plots to examine. The interactive variables of all three adsorbents displayed a

superimposed perturbation line in Figure 4.

(3a)

(4a)Design-Expert® SoftwareFactor Coding: ActualDecolorization %

Actual FactorsA: Dosage Concentration = 1B: Dye Concentration = 0C: pH = -1

-2.000 -1.000 0.000 1.000 2.000

-20

0

20

40

60

80

100

120

A

A

B

BC

C

Perturbation

Deviation from Reference Point (Coded Units)

Dec

olo

riza

tion

%

(3b)

(4b)Design-Expert® SoftwareFactor Coding: ActualDecolorization %


-2.000 -1.000 0.000 1.000 2.000

-20

0

20

40

60

80

100

120

A

A

B

BC

C

Perturbation


Deco

loriza

tion %

(3c)



1.98

X1 = A: Dosage ConcentrationX2 = C: pH

Actual FactorB: Dye Concentration = 0

-1

-0.5

0

0.5

1-1

-0.5

0

0.5

1

-20

0

20

40

60

80

100

Deco

lori

zation %


C: pH (pH)

8.169

79.7169

70.7459

-5.12623

17.1299

(4c)Design-Expert® SoftwareFactor Coding: ActualDecolorization %


-1.000 -0.500 0.000 0.500 1.000 1.500 2.000

0

20

40

60

80

100

120

A

AB

BC

C

Perturbation


Deco

loriza

tion %

Figure 3 Contour plot showing the collective effect of

operating variable for sorption of MY dye on to magnetic biocomposite (3a, 3b, 3c)

Figure 4 Superimposed perturbation plot (4a) Salvinia molesta; (4b) iron oxide; (4c) magnetic

biocomposite

The perturbation plot demonstrates that the decolorization efficiency shift from its desired

benchmark while all other variables remains constant at the coded „0‟ level. Therefore, the

perturbation plots exhibit the divergence from the adjusted reference point of factorial

level of all the variables. Further, the adsorbent dosage concentration (A) and pH solution

(C) can be observed as regulatory variabilities for achieving the maximum dye

decolorization efficiency. Consequently, the model was found to be sensitive to these

parameters these parameter (adsorbent concentration and solution pH) by the indication of

sharp curvature. The flat curves of the dye concentration shows that the effect of this

factor was less on the dye adsorption. The effect of adsorbent dosage was showed by

neither sharp curve nor flat line which implies that it has minimal effect compared to

solution pH on the dye removal. The steep curvature of pH (C) for SM, IO and MBC

indicate that MY dye adsorption is highly affected by the operating parameter pH

compared to other two parameters dosage concentration (A) and dye concentration (B).

3.4. Model validation The experiments were conducted to verify the predicted optimal conditions. The obtained

experimental values have been closely associated with the Box-behnken predicted values.

As a result, the experiments have been demonstrated that the optimization of process

variable using response surface methodology significantly enhanced the dye

sequestration. Therefore, with 95 percent accuracy, the predicted regression model is in

line with the test results. The findings ensured that the observed and forecast groups

agreed reasonably.

4. CONCLUSION The purpose of this research is to determine the optimum values of process parameters for

sequestration of dyes from the quadratic model obtained using experimental data. RSM

based BBD was employed to optimize the process parameters such as initial dye

concentration, adsorbent dosage concentration and initial solution pH during adorption of

MY dye using SM, IO and MBC. From the results of ANOVA, the decolorization

percentage is more sensitive to adsorbent dosage and pH of the solution. The R2 values

(0.9967, 0.9997and 0.9970), adjusted R2 values (0.9924, 0.9992 and 0.9970) and

predicted R2 (0.9755, 0.9964 and 0.9829) are close to 1 for SM, IO and MBC

respectively. This indicates a high correlation and acceptable agreement with

experimental data. Finally, RSM based Box-behnken design was identified as a good

statistical tool for optimal design of dye sequestration that provides appropriate results for

the maximum percentage decolorization.

Acknowledgments The authors are thankful to National Institute of Technology Calicut, India.

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