Magnetite (Chitosan-Fe3O4.

) nanocomposite for removal of heavy metals

from aqueous solutions

Objective :

To optimize (RSM) removal of lanthanum metal from waste water using Chitosan-Fe3O4.

RSM

Response surface modeling (RSM) is an empirical statistical technique that uses quantitative data obtained from

appropriately designed experiments to determine regression model and operating conditions (Alam et al., 2007;

Ricou-Hoeffer et al., 2001; Tan et al., 2008).

Optimization studies : Four variables

Adsorbent dose

Temperature

pH of the solution

Reaction time

Box–Behnken design

Due to its Suitability to fit quadratic surface

• 28 experiments were formulated

• The optimum values of the selected variables were obtained

by solving the regression equation

• Each of the parameters was coded at Maximum and minimum

The chosen independent variables used in this study were coded

according to Equation

• xi is the dimensionless coded value

• X0 is the value of Xi at the center point and ∆X is the step change value

The behavior of the system is explained by the following empirical second-order polynomial

model Eq

Variables

Adsorbent dose – 3mg to 10mg

Temperature - 205 C to 605 C

pH of the solution- 3 to 11

Reaction time -10min to 250min

Calculating the experimental points

Design-Expert?SoftwareFactor Coding: ActualStd Error of DesignStd Error Shading

1.500

0.500

X1 = A: TempX2 = B: pH

Actual FactorsC: Reaction time = 214.865D: Concentration = 8.67568

20 30 40 50 60

3

5

7

9

11

Std Error of Design

A: Temp (cel)

B: p

H

0.5

0.6

0.7

0.8 0.8

0.8 0.8

0.9 0.9

0.9 0.9

1 1

Design-Expert?Softwareconcentration

Color points by value ofconcentration:

99.88

74.73

Run Number

Ext

erna

lly S

tude

ntiz

ed R

esid

uals

Residuals vs. Run

-6.00

-4.00

-2.00

0.00

2.00

4.00

1 5 9 13 17 21 25 29

Effects Half-Normal Probability Plot

• Large effects (absolute values) appear in the upper-right section of the plot.

• The lower-left portion of the plot contains effects caused by noise rather than a true effect

Design-Expert?Softwareconcentration

Color points by value ofconcentration:

99.88

74.73

Actual

Pre

dict

ed

Predicted vs. Actual

70

80

90

100

110

70 80 90 100 110

PerturbationIt comprises mathematical methods for finding an approximate solution to a problem

It helps to compare the effect of all the factors at a particular point in the design space

Design-Expert?SoftwareFactor Coding: ActualStd Error of Design

Actual FactorsA: Temp = 30.2703B: pH = 3.64865C: Reaction time = 214.865D: Concentration = 8.67568

-2.000 -1.000 0.000 1.000 2.000

0.400

0.600

0.800

1.000

1.200

1.400

1.600

A A

B B

C CD D

Perturbation

Deviation from Reference Point (Coded Units)

Std

Error of D

esig

n

ANOVA for Response Surface Quadratic model

Analysis of variance table [Partial sum of squares - Type III]

Sum of Mean F p-value

Source Squares df Square Value Prob > F

Model 1201.79 14 85.84 6.58 0.0006 significant

A-Temp 2.00 1 2.00 0.15 0.7011

B-pH 544.86 1 544.86 41.79 < 0.0001

C-Reaction time 60.84 1 60.84 4.67 0.0486

D-Concentration 6.37 1 6.37 0.49 0.4962

AB 2.48 1 2.48 0.19 0.6693

AC 24.21 1 24.21 1.86 0.1945

AD 0.18 1 0.18 0.014 0.9080

BC 25.96 1 25.96 1.99 0.1801

BD 0.096 1 0.096 7.371E-003 0.9328

CD 22.71 1 22.71 1.74 0.2081

A^2 0.20 1 0.20 0.015 0.9039

B^2 488.78 1 488.78 37.49 < 0.0001

C^2 6.27 1 6.27 0.48 0.4994

D^2 8.55 1 8.55 0.66 0.4316

Residual 182.53 14 13.04

Lack of Fit 166.03 10 16.60 4.03 0.0958 not significant

Pure Error 16.49 4 4.12

Cor Total 1384.32 28

The Model F-value of 6.58 implies the model is significant. There is onlya 0.06% chance that an F-value this large could occur due to noise.

Design-Expert?SoftwareFactor Coding: ActualDesirabilityX1 = A: TempX2 = B: pHX3 = C: Reaction time

Actual FactorD: Concentration = 10

CubeDesirability

A: Temp (cel)

B: p

H

C: Reaction time (min)

A-: 20 A+: 60B-: 3

B+: 11

C-: 50

C+: 250

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000Prediction 1

A:Temp = 40

20 60

B:pH = 7

3 11

C:Reaction time = 50

50 250

D:Concentration = 3

3 10

Desirability = 1.000

The 3D Surface plot is a projection of the contour plot

Design-Expert?SoftwareFactor Coding: Actualconcentration (ppm)

Design points above predicted value25.27

0.12

X1 = B: pHX2 = C: Reaction time

Actual FactorsA: Temp = 20D: Concentration = 6.5

50

100

150

200

250

3

5

7

9

11

-10

0

10

20

30

conc

entra

tion

(ppm

)

B: pHC: Reaction time (min)

Design-Expert?SoftwareFactor Coding: Actualconcentration (ppm)

Design points above predicted valueDesign points below predicted value25.27

0.12

X1 = A: TempX2 = B: pH

Actual FactorsC: Reaction time = 250D: Concentration = 6.5

3

5

7

9

11

20

30

40

50

60

0

5

10

15

20

25

30

conc

entra

tion

(ppm

)

A: Temp (cel)B: pH

Design-Expert?SoftwareFactor Coding: Actualconcentration (ppm)

Design points above predicted valueDesign points below predicted value25.27

0.12

X1 = A: TempX2 = C: Reaction time

Actual FactorsB: pH = 7D: Concentration = 6.5

50

100

150

200

250

20

30

40

50

60

0

5

10

15

20

25

30

conc

entra

tion

(ppm

)

A: Temp (cel)C: Reaction time (min)

Optimization design

Temp 25.41

pH 10.24

Reaction time 150.00

Concentration 6.50

Mean91.8215

The equilibrium adsorption capacity was calculated from the relationship

=((10-0.13mg/lt *1lt)/3mg=9.7mg/3mg

Optimization of chitosan –MgO ( For Lanthanam)pH

L pH 3

Lanthanum Nitrate(La : 100 mg/L)

0.09 99.91

100

L pH 5 1.64 98.36 L pH 7 24.79 75.21 L pH 9 0.14 99.86 L pH 11 0.45 99.55

L pH 3 L pH 5 L pH 7 L pH 9 L pH 1170.00

80.00

90.00

100.00

110.00

120.00

99.91 98.36

75.21

99.86 99.55

Series1

S pH 3

Strontium Nitrate(Sr : 100 mg/L)

26.1

S pH 5 15.0

S pH 7 24.4

S pH 9 11.0

S pH 11 2.4

Optimization of chitosan –MgO ( For strontium)pH

S pH 3 S pH 5 S pH 7 S pH 9 S pH 1170.00

80.00

90.00

100.00

110.00

120.00

73.91

84.99

75.55

89.04

97.58

Series1