ORIGINAL PAPER

Biosorptive removal of cationic dye from aqueous system:a response surface methodological approach

Bikash Sadhukhan • Naba Kumar Mondal •

Soumya Chattoraj

Received: 12 August 2013 / Accepted: 23 November 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract In this study, response surface methodology

(RSM) was employed for the removal of methylene blue

(MB) from aqueous solution using neem (Azadirachta

indica) bark dust (NBD) as a bioadsorbent. The influence

of various process parameters namely initial concentra-

tion (500–1,000 mg L-1), bioadsorbent dose [0.20–1.50 g

(100 mL)-1], pH (5–12), and stirring rate (250–650 rpm)

was taken as an input parameter. A total of 29 biosorption

experimental runs were carried out employing the detailed

conditions designed by RSM based on the Box–Behnken

design (BBD). Response surface plots were studied to

determine the interaction effects of main factors and opti-

mum conditions of process. Regression analysis showed

good fit of the experimental data to the second-order

polynomial model with coefficient of determination (R2)

value of 0.9760 and model F value of 40.68. The predicted

R2 value is 0.8979. In addition, results reported in this

research demonstrated the feasibility of employing NBD as

bioadsorbent for MB removal.

Keywords Methylene blue � Biosorption � Neem

bark dust � Box–Behnken design

Introduction

Dyes are synthetic, having complex aromatic heterocyclic

compound, ionisable in aqueous medium and stable toward

light, heat, chemical oxidant, etc. (Aksu and Tezar 2005).

Various types of chemically synthesized dyes are

extensively used as coloring ingredient in textile, paper,

plastic, paints, varnishes, and cosmetic industries (Zollin-

ger 1991). The colored wastewater from textile, paper, and

plastic industries released into environment causes esthetic

pollution and destroys the aquatic life, clarity of water

(Ong et al. 2007; Daneshvar et al. 2007). Cationic dyes are

widely used in industry such as dyeing of silk, leather,

paper, wool, and cotton. Many dyes and their breakdown

products may be toxic, carcinogenic, and teratogenic for

living organisms (Sariglu and Aatay 2006). The cationic

dyes are more toxic than the anionic dyes (Hao et al. 2000;

Chatterjee et al. 2007), as these interacts easily with neg-

atively charged cells membrane surfaces and enters into

cells then accumulate in cytoplasm. Methylene blue (MB)

is a cationic dye with wide applications, which include

coloring paper, temporary hair colorant, dyeing cottons,

and wood. Although not strongly hazardous, it can cause

some harmful effects, such as heart beat increase, vomiting,

shock, cyanosis, jaundice, and tissue necrosis in humans

(Bhatnagar and Minocha 2010; Garg et al. 2004; Sun and

Yang 2003).

Over the last two decades various techniques like oxida-

tion, biological treatment, coagulation, and adsorption were

explored for the removal of cationic dyes. Amongst the

numerous techniques of dye removal, the adsorption process,

occurring at the solid–liquid and solid–gas interfaces, is one

of the effective processes that have been successfully

employed for color removal, recovery, and recycling of dyes

from the wastewater (Tan et al. 2008a; Chakraborty et al.

2005; Paulino et al. 2006; Dogen et al. 2009).

Commercially available activated carbon is the most

widely used adsorbent for the removal of dyes from the

aqueous solution; however, high cost and considerable loss

of this adsorbent during the regeneration limits its appli-

cations (Kumar et al. 2011). Recently, the usage of

B. Sadhukhan � N. K. Mondal (&) � S. Chattoraj

Department of Environmental Science, The University of

Burdwan, Burdwan 713104, India

e-mail: nkmenvbu@gmail.com

123

Clean Techn Environ Policy

DOI 10.1007/s10098-013-0701-8

agricultural wastes or industrial by-products as a low-cost

alternative adsorbent has received a considerable attention.

Various low-cost bioadsorbent materials are used in the

literature such as cotton waste (Annadurai et al. 2002; Hui

et al. 2011), citrous fruit (orange) peel (Dutta et al. 2011),

rice husk (Vadivelan and Kumar 2005), pre-treated rice

husk (Chowdhury and Das 2011), neem leaf (Bhattacharyya

and Sharma 2005), peat (Fernandes et al. 2007), wood

(McKay and Poots 1980), wood apple shell (Jain and

Jayaram 2010), indian rose wood sawdust (Garg et al.

2004), saw dust composite(Ansari and Mosayebzadeh

2010), jute processing waste (Banerjee and Dastidar 2005),

eggshell and eggshell membrane (Tsai et al. 2006), fly ash

(Wang et al. 2005), hazelnut shell (Dogen et al. 2009),

wheat shells (Bulut and Aydin 2006), coffee husks (Oliveira

et al. 2008), yellow passion fruit waste (Pavan et al. 2008),

fallen phoenix tree’s leaves (Han et al. 2007), spent coffee

grounds (Franca et al. 2009), Posidonia oceanica (L.) fibers

(Ncibi et al. 2007), agricultural waste(Kumar et al.

2011), agricultural waste sugar beet pulp (Aksu and Isoglu

2006), sugarcane bagasse (Raghuvanshi et al. 2004, Filho

et al. 2007), fly ash (Wang et al. 2005; Janos et al. 2003),

activated carbon from biomass (Karagoz et al. 2008), bio-

polymer oak sawdust composite (Abd El-Latif et al. 2010),

tamarind fruit shell (Chowdhury and Das 2010), Partheni-

um waste (Lata et al. 2007), poplar leaf (Xiuli et al. 2012),

spent activated clay (Weng and Pan 2007), kaolin (Tarek

et al. 2011), animal bone meal (Slimani et al. 2011), clay

(Sarma et al. 2011), Lemna minor (Reema et al. 2011),

rejected tea (Nausuha et al. 2010), biosolid (Sariglu and

Aatay 2006), waste news paper (Okada et al. 2003), etc. All

these have been tested for removal of MB from aqueous

systems. The biosorptive removal behavior of Zn(II)

(Bhattacharya et al. 2006; Bhatti 2007; King et al.

2008; Mishra et al. 2011; Naiya et al. 2009), Cd(II) (Naiya

et al. 2009; Tiwari et al. 1999), Cr(VI) (Bhattacharya et al.

2008; Patil and Shrivastava 2009), Pb(II) (Senthil Kumar

et al. 2010), and dyes (Srivastava and Rupainwar 2010;

Senthil kumaar et al. 2006) from aqueous solutions on the

neem bark dust (NBD) had previously been investigated but

no information is available in literature on the improved

removal of MB by NBD. NBD is inexpensive and easily

available; this could make it a viable candidate as an eco-

nomical adsorbent for removing unwanted hazardous

components from contaminated water. In this study the

range of initial concentration is 500–1000 ppm, still now no

researcher have investigated the removal of MB from

aqueous solution within this concentration range.

The aim of the present study is to develop inexpensive

and effective adsorbent from the environment biodiversity

as NBD in order to replace the existing commercial

materials. In this study, the NBD was prepared from bark

of neem plant (Azadirachta indica).

Moreover, conventional and classical method of varying

large number of operating variable creates a major problem

for optimizing the levels which also time-taking process.

These limitations of classical method impact us to use

statistical experimental design such as response surface

methodology (RSM). This RSM model can be effectively

used in adsorption process to improve product yield, reduce

process variability, closer confirmation of the output

response as well as reduce development in time.

Experimental methods

Materials

Preparation of adsorbent

The dried neem bark (A. indica) used in this study was

collected from the university campus, The University of

Burdwan, WB, India. The collected bark was chopped into

small pieces and washed properly with double distilled

water to remove dirty, muddy material and soaked with

0.1 N NaOH followed by 0.1 N H2SO4 (King et al. 2008,

Naiya et al. 2009). The bark was then dried in sunlight for

half a month and grinded to fine powder with kitchen

grinder. The resulting dust was sieved through 250 lm

mesh to use as NBD adsorbent without any further

treatment.

Reagents and apparatus

A.R. grade chemicals were collected from M/S Merck

India Ltd., and used in the present study without further

purification. Double distilled (d.d.) water was used to

prepare all reagents and standards. All glassware was

cleaned by HNO3 and rinsed with d.d. water. For the

adjustment of pH of MB solutions 0.1 N NaOH and 0.1 N

HCl were used.

Adsorbent characterization and instrumentation

Adsorbent characterization was performed by means of

spectroscopic and quantitative analysis. The surface area of

the adsorbent was determined by Quantachrome surface

area analyzer (model—NOVA 2200C). The physico-

chemical characteristics of the adsorbent were determined

using standard procedures (Saha and Sanyal 2010). The

concentrations of sodium and potassium were determined

by Flame Photometer (Model No. SYSTRONICS 126). For

stirring purpose magnetic stirrer (TARSONS, Spinot digi-

tal model MC02, CAT No. 6040, S. No. 173) is used. The

pH of zero-point charge or pHZPC was determined based on

the previous method (Mondal 2009). The Fourier transform

B. Sadhukhan et al.

123

infrared (FTIR) spectra of the adsorbent was recorded with

Fourier transform infrared spectrophotometer (PERKIN-

ELMER, FTIR, Model-RX1 Spectrometer, USA) in the

range of 400–4,000 cm-1 (Fig. 1). In addition, scanning

electron microscopy (SEM) analysis was carried out using

a scanning electron microscope (HITACHI, S-530, Scan-

ning Electron Microscope and ELKO Engineering, B.U.

BURDWAN) at 25 kV to study the surface morphology

of the adsorbent. The SEM images of NBD before

adsorption and after adsorption are shown in Figs. 2 and 3

respectively.

Batch adsorption procedure

The adsorption experiments were performed in triplicate,

and mean values were used in the data analysis (Tables 1,

2). The spectrophotometric determination of MB was done

by the addition of adsorbent to the dye solution of known

strength and stirred for 20 min each time on a magnetic

stirrer on variable rpm basis and kept rest for 5 min and

then the absorbance of the resulting solution was measured

at 665 nm using UV–Vis spectrophotometer (Systronics,

Vis double beam Spectro 1203). The control experiments

were performed without the addition of bioadsorbent under

the same condition each time, which confirmed that the

adsorption of MB on the walls of flasks were negligible.

The influence of pH (5.0–12.0), initial MB concentration

(500–1,000 mg L-1), stirring rate (250–650 rpm), and

bioadsorbent dose [0.20–1.50 g (100 mL)-1] were evalu-

ated during the present study. Samples were collected from

the flasks at predetermined time intervals for analyzing the

residual MB concentration in the solution. The amount of

MB ions adsorbed at equilibrium in milligram per gram

was determined by using the following mass balance

equation:

Fig. 1 FTIR of NBD before adsorption

Fig. 2 SEM image of NBD before adsorption

Fig. 3 SEM image of NBD after adsorption

Biosorptive removal of cationic dye from aqueous system

123

qe ¼ðCi � CeÞV

m; ð1Þ

where Ci and Ce are MB concentrations (mg L-1) before

and after adsorption, respectively, V is the volume of the

dye solution (L), and m is the weight of the adsorbent (g).

The percentage of removal of MB ions was calculated from

the following equation:

Removal ð%Þ ¼ ðCi � CeÞCi

� 100: ð2Þ

Design of experiments

A response surface is a curved surface that represents the

relationship between the design variables xi (i = ‘‘1…n’’)

and the response y. This relationship can be represented by

the following equation:

y ¼ f ð}x1::::::::::xn}Þ þ � ð3Þ

where e is the random error in y. There is no restriction in

the form of function f that approximates the response

surface. For simplicity, a polynomial to express the

function f can be generally used. The experimental

design and statistical significance of investigated factors

and their combinations were carried out using Design

Expert Trial (8.0.1.7, Stat-Ease Inc., Minneapolis, USA). A

multiple regression analysis was conducted based on the

second-order response function with the following design

variables, the model becomes:

Y ¼ b0 þXn

i¼1

bixi þXn

i¼1

Xn

j¼1

bijxixj þXk

i¼1

biix2ii þ e; ð4Þ

where b0, bi, bij are regression coefficients for the intercept,

linear and interactions among factors, respectively, Y is the

response vector for qe and %Removal, whereas Xi and Xj

are the independent factors in coded units, and e is the error

term (Chattoraj et al. 2013a). The fitness of regression

model was evaluated by calculating coefficient of deter-

mination (R2).

Y is the response variable bi, bij are the measures of the

effects of the variables xi and xj, respectively. The known bare obtained by the technique of least squares which min-

imizes the sum of the squares of the residuals and estimated

parameters.

Results and discussion

The final Box–Behnken design obtained for percentage

removal of MB with significant terms was quadratic as

suggested by the software, and is given as:

Y ¼ 90:40� 0:58Aþ 2:96Bþ 3:21Cþ 1:33Dþ 0:50AB

� 1:50ACþ 2:25AD� 0:63BC� 0:50BD� 2:75CD

þ 1:36A2 þ 1:43B2 � 0:45C2 þ 1:99D2:

This equation reveals how the individual variables

(quadratic) or double interaction affected MB removal

from aqueous solution by NBD as an bioadsorbent.

Table 1 Variables and levels considered for the adsorption of MB

onto NBD

Name (factor) Units Low High

Initial concentration (A) ppm 500 1,000

pH (B) 5 12

Adsorbent dose (C) g 0.20 1.50

Stirring rate (D) rpm 250 650

Table 2 Design matrix for four variables together with the actual and

predicted response

Factor 1 Factor 2 Factor 3 Factor 4 Removal of dye (%)

Initial conc.

(mg L-1)

Adsorbent

dose (g)

pH Stirring

rate

(rpm)

Actual Predicted

750 0.85 5 250 84.00 84.65

750 0.85 12 250 96.00 96.06

750 0.85 8.5 450 90.00 90.40

1,000 0.85 8.5 250 89.00 89.58

750 0.85 8.5 450 89.00 90.40

500 0.85 12 450 97.00 96.60

500 0.85 8.5 650 94.00 93.42

1,000 0.85 5 450 89.00 89.02

1,000 0.85 12 450 93.00 92.44

500 0.85 5 450 87.00 87.19

750 0.85 8.5 450 91.00 90.40

750 1.5 8.5 650 97.00 97.60

750 0.2 12 450 92.00 92.25

750 0.85 8.5 450 91.00 90.40

750 0.85 12 650 94.00 93.73

750 1.5 12 450 96.50 96.92

750 0.2 5 450 85.00 84.50

750 0.2 8.5 650 92.00 92.69

750 1.5 8.5 250 97.00 95.94

500 1.5 8.5 450 96.00 96.23

750 0.85 5 650 93.00 92.81

1,000 0.85 8.5 650 97.00 96.75

500 0.2 8.5 450 91.00 91.31

750 0.85 8.5 450 91.00 90.40

750 1.5 5 450 92.00 91.75

1,000 1.5 8.5 450 96.00 96.06

750 0.2 8.5 250 90.00 89.02

1,000 0.2 8.5 450 89.00 89.15

500 0.85 8.5 250 95.00 95.25

B. Sadhukhan et al.

123

The adequacy of the models was justified by the analysis

of variance (ANOVA). The ANOVA of MB adsorption

capacity qe (mg g-1) is given in Table 3. The Model

F value of 40.68 implies that the model is significant. There

is only a 0.01 % chance that a ‘‘Model F value’’ this large

could occur due to noise. The model F value is the ratio of

mean square for the individual term to the mean square for

the residual. The Prob [ F value is the probability of F-

statistics value and is used to test the null hypothesis. The

parameters having an F-statistics probability value less

than 0.05 are said to be significant (Table 4).

In the present study initial concentration (A), bioadsor-

bent dose (B), pH (C), and stirring rate (D) are four

important parameters. Consequently, A, B, C, and D were

chosen as the independent variables while the removal of

MB at equilibrium (Y) was selected as the response

(dependent variable) in the present study. Negative coef-

ficient values indicate that individual or double interactions

factors negatively affect MB adsorption (i.e., adsorption

percentage decreases), whereas positive coefficient values

mean that factors increase MB adsorption in the tested

range.

In this case A, B, C, D, AC, AD, CD, A2, B2, D2 are

significant model terms. The ‘‘Lack of Fit F value’’ of 0.72

implies the Lack of Fit is not significant relative to the pure

error. There is a 69.64 % chance that a ‘‘Lack of Fit

F value’’ this large could occur due to noise (Table 5).

Therefore, it can be concluded that initial concentration

(A), bioadsorbent dose (B), pH (C), and stirring rate

(D) play an important role in case of MB adsorption. The

plot between experimental (actual) and predicted values of

MB adsorption capacity qe (mg g-1) is shown in Fig. 4. It

Table 3 Analysis of variance

(ANOVA) for percentage

removal of MB onto neem bark

dust

Source Sum of squares df Mean square F value p value

Prob [ F

Model 14 40.68 \0.0001 Significant

A—initial conc. 363.28 1 25.95 6.40 0.0240

B—adsorbent dose 4.08 1 4.08 164.66 \0.0001

C—pH 105.02 1 105.02 193.67 \0.0001

D—stirring rate 123.52 1 123.52 33.45 \0.0001

AB 21.33 1 21.33 1.57 0.2310

AC 1.00 1 1.00 14.11 0.0021

AD 9.00 1 9.00 31.75 \0.0001

BC 2.25 1 20.25 2.45 0.1399

BD 1.56 1 1.56 1.57 0.2310

CD 1.00 1 1.00 47.43 \0.0001

A2 30.25 1 30.25 18.88 0.0007

B2 12.04 1 12.04 20.65 0.0005

C2 13.17 1 13.17 2.06 0.1732

D2 1.31 1 1.31 40.17 \0.0001

Residual 25.62 14 25.62

Lack of fit 8.93 10 0.64 0.72 0.6964 Not significant

Pure error 5.73 4 0.57

Cor total 3.20 28 0.80

372.21

Table 4 Sequential model sum

of squaresSource Sum of squares df Mean square F value p value

Prob [ F

Mean vs. total 2.465E?005 1 2.465E?005

Linear vs. mean 253.96 4 63.49 12.89 \0.0001

2FI vs. linear 63.06 6 10.51 3.43 0.0195

Quadratic vs. 2FI 46.26 4 11.56 18.13 \0.0001 Suggested

Cubic vs. quadratic 5.17 8 0.65 1.03 0.4999 Aliased

Residual 3.76 6 0.63

Total 2.468E?005 29 8511.77

Biosorptive removal of cationic dye from aqueous system

123

can be seen from Fig. 4 that both the values were in rea-

sonable agreement with each other. It implied that a good

correlation between input and output variables could be

drawn by the model developed.

By constructing a normal probability plot of the resid-

uals, a check was made for the normality assumption, as

given in Fig. 5. How well the model satisfies the assump-

tions of the analysis of variance (ANOVA) is shown by

residuals and the studentized residuals measure the number

of standard deviations separating the actual and predicted

values (Montgomery 1996; Myers and Montgomery 2002;

Korbahti and Rauf 2008). The normality assumption was

satisfied as the residual plot approximated along a straight

line (Chattoraj et al. 2013b).

A high value of the adjusted determination coefficient

(R2Adj ¼ 0:9520) was estimated. This result means that

95.20 % of the total variation on MB adsorption data can

be described by the selected model. The adequate precision

ratio of 22.670 for the quadratic model indicates an

appropriated signal to noise ratio. Because the adjusted

determination coefficient and adequate precision ratio

exceeded 70 % and 4, respectively, the quadratic model

can be used to explore the design space and to find the

optimal conditions of this process. The hierarchical qua-

dratic model was used to represent the response surface in

three-dimensional plots and to find the optimal conditions

for MB adsorption on NBD. Response surface space was

built taking into account with the operating variables.

Figure 6 shows that the model predicted MB adsorption

efficiency close to 99.51 % onto NBD. A comparison of

the effects of all factors at the optimal conditions of MB

adsorption to the NBD was performed by using a pertur-

bation plot (Fig. 7). The steep curvature of initial concen-

tration, pH, and stirring rate indicate the MB adsorption is

highly affected by those variables which agree with the

results previously discussed.

Optimization using the desirability function

In numerical optimization, we chose the desired goal for

each factor and response. The possible goals were: maxi-

mize, minimize, target, within range, none (for responses

only) and set to an exact value (factors only). A minimum

and a maximum level must be provided for each parameter

included. A weight can be assigned to each goal to adjust

the shape of its particular desirability function. The goals

are combined into an overall desirability function. Desir-

ability is an objective function that ranges from zero out-

side of the limits, to one at the goal. The program seeks to

maximize this function. The goal seeking begins at a

Fig. 4 Comparison between the actual values and the predicted

values of RSM model for adsorption of MB

Fig. 5 Plot of Normal% probability versus residual error

Table 5 Lack of fit tests

Source Sum of

squares

df Mean

square

F value p value

Prob [ F

Linear 115.05 20 5.75 7.19 0.0342

2FI 51.99 14 3.71 4.64 0.0745

Quadratic 5.73 10 0.57 0.72 0.6964 Suggested

Cubic 0.56 2 0.28 0.35 0.7233 Aliased

Pure

error

3.2 4 0.8

B. Sadhukhan et al.

123

random starting point and proceeds up the steepest slope to

a maximum. There may be two or more maximums

because of curvature in the response surfaces and their

combination in the desirability function. Starting from

several points in the design space improve the chances for

finding the ‘‘best’’ local maximum (Saha et al. 2010;

Senthil kumaar et al. 2006). A multiple response method

was applied for optimization of any combination of four

goals, namely initial concentration, bioadsorbent dose, pH,

stirring rate, and percentage removal of MB. The numerical

optimization found a point that maximizes the desirability

function. A maximum level of bioadsorbent dose (1.5 g),

maximum level of stirring rate (650 rpm), and pH within

the range 5.0–12.0, respectively, were set for maximum

desirability. The importance of each goal was changed in

relation to the other goals. Figure 8 shows a ramp desir-

ability that was generated from 10 optimum points via

Fig. 6 Response surface plots

showing the effect of

independent variables on MB

adsorption onto NBD adsorption

Perturbation

Deviation from Reference Point (Coded Units)

rem

oval

of d

ye

-1.000 -0.500 0.000 0.500 1.000

84

87.25

90.5

93.75

97

A

A

B

B

C

C

D

D

Fig. 7 Perturbation plot of removal of dye at the optimal conditions:

(a) initial concentration—674.48 ppm, (b) adsorbent dose—1.37 mg,

(c) pH—11.43, (d) stirring rate 275.10

Biosorptive removal of cationic dye from aqueous system

123

numerical optimization. By seeking from 10 starting points

in the response surface changes, the best local maximum

was found to be at stirring rate 275.10 rpm, initial solution

pH 11.43, bioadsorbent dose of 1.37 g, and MB removal of

99.5122 % and desirability of 1.000. The obtained value of

desirability shows that the estimated function may repre-

sent the experimental model and desired conditions

(Table 6).

Comparison of neem bark dust (NBD) with other

sorbents

A comparative study of the maximum MB uptake

capacity of NBD has been carried out with other reported

sorbents. It is to be noted that the maximum amount of

metal uptake by various sorbents varies as a function of

experimental conditions. Therefore, for a direct and

conc = 674.48

500.00 1000.00

adsorbent dose = 1.37

0.20 1.50

pH = 11.43

5.00 12.00

stirring rate = 275.10

250.00 650.00

removal of dye = 99.5122

84 97

Desirability = 1.000

Fig. 8 Desirability ramp for

numerical optimization of four

selected goals

Table 6 Model summary

statisticsSource SD R2 Adjusted R2 Predicted R2 PRESS

Linear 2.22 0.6823 0.6294 0.5277 175.80

2FI 1.75 0.8517 0.7694 0.6238 140.02

Quadratic 0.800 0.9760 0.9520 0.8979 38.00 Suggested

Cubic 0.79 0.9899 0.9528 0.7689 86.00 Aliased

Table 7 Reviewed results

representing the adsorption

capacity of agriculture and

industrial waste materials for

the adsorption of methylene

blue and their optimized pH

values for maximum adsorption

Adsorbent pH Adsorption capacity References

Rice husk 7.0 312.00 mg g-1 Mckay et al. (1999)

Fly ash 5.0 3.47 mmol kg-1 Woolard et al. (2002)

Activated carbon (newspaper) 7.0 390.00 mg g-1 Okada et al. (2003)

Fly ash CFA 5.0 3.60 9 10-3 mol g-1 Janos et al. (2003)

Activated carbon (pinewood) AC2 8.0 507.00 mg g-1 Tseng et al. (2003)

Sugarcane bagasse 7.0 99.60 mg g-1 Raghuvanshi et al. (2004)

Fe(III)/Cr(III) hydroxide 10.0 10.00 mg g-1 Namasivayam and Sumithra (2005)

Parthenium hysterophorus-swc 7.0 39.70 mg g-1 Lata et al., (2007)

Sugarcane bagasse 5.8 34.20 mg g-1 Filho et al. (2007)

Wheat straw 7.0 312.50 mg g-1 Gong et al. (2009)

Activated carbon (oil palm shell) 6.5 243.90 mg g-1 Tan et al. (2008b)

Sunflower oil cake—AC 6.0 10.21 mg g-1 Karagoz et al. (2008)

Activated carbon fibers 7.0 99.30 mg g-1 Zhi-yuan (2008)

Neem bark dust (NBD) 11.43 49 mg g-1 This study

B. Sadhukhan et al.

123

meaningful comparison, the maximum amount of MB

adsorbed on NBD has been compared to the maximum

MB sorption capacity of other reported sorbents under

different pH and are presented in Table 7. From Table 7

it is observed that the maximum sorption capacity of

NBD for MB is comparable and moderately higher than

that of many corresponding sorbent materials. The easy

availability and cost effectiveness of NBD are some

additional advantages, which make it better bioadsorbent

for treatment of MB.

Desorption experiments

For the desorption study, the MB-adsorbed NBD was

added into 250 mL of ethanol mixture (v/v, ethanol/

water = 10/100). Ethanol and NBD (saturated with MB)

solution samples were taken at specific time intervals (3, 5,

10, 30, 60 min). The desorption percentage of MB des-

orbed from NBD increased from 15 to 92 % (Fig. 9). Thus,

a significant amount of MB is being desorbed, which shows

that the NBD can be effectively reused after desorption,

therefore pollution load is minimum.

Conclusion

In this study, NBD was tested and evaluated as a possible

bioadsorbent for removal of MB, a cationic dye from its

aqueous solution using batch sorption technique. The bio-

sorption studies were carried out as a function initial con-

centration (A), bioadsorbent dose (B), pH (C), and stirring

rate (D). Percentage removal of the dye molecule decreased

with increase in initial dye concentration while it increased

with increase in bioadsorbent dose, pH, and stirring rate up

to a certain level. So removal of MB by NBD is successful

within higher concentration range. The hierarchical qua-

dratic model represents adequately the response surface

space based on the adjusted determination coefficient

(R2Adj ¼ 0:9520) and the adequate precision ratio is 22.670.

At the optimum conditions, the predicted removal efficiency

achieved near to 100 % of MB removal from aqueous

solutions, when using NBD. Finally, the reported results in

this research demonstrate the feasibility of Box–Behnken

model to optimize the experiments for MB removal by

adsorption using NBD as a low-cost bioadsorbent.

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0 10 20 30 40 50 60

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