multicomponent adsorption study of metal ions onto bagasse fly ash using taguchi's design of...

Multicomponent Adsorption Study of Metal Ions onto Bagasse Fly Ash UsingTaguchi’s Design of Experimental Methodology

Vimal C. Srivastava,* Indra D. Mall, and Indra M. Mishra

Department of Chemical Engineering, Indian Institute of Technology-Roorkee, Roorkee 247667, India

This paper utilizes the Taguchi optimization methodology (L27 orthogonal array) to optimize various parametersfor the simultaneous removal of Cd, Ni, and Zn metal ions from aqueous solutions using bagasse fly ash(BFA) as an adsorbent. The effect of such parameters as the initial metal concentrations (C0,i), temperature,initial pH, adsorbent dosage (m), and contact time on the adsorption of the aforementioned metal ions hasbeen studied at three levels to determine their effect on the selected response characteristic (the total amountof metal adsorbed on BFA, in terms of mg/g of BFA (qtot)). The Pareto analysis of variance shows thatm isthe most significant parameter, with a 53.14% and 31.25% contribution to theqtot and signal-to-noise (S/N)ratio data. The contribution of interactions between theC0,i values is also significant. Confirmation experimentshave been performed to prove the effectiveness of the Taguchi technique after the optimum levels of processparameters are determined.

1. Introduction

Heavy metals are toxic and resistant to biodegradation.Because of their mobility in natural water ecosystems andadsorption onto soils, sediments, thick sludge, etc., they ac-cumulate in aquatic life forms such as fish, vegetation, andhorticultural products via the food chain. When consumed, thesemetals accumulate in tissues, fats, and other organs of humans,causing malfunctions and damage to vital functions of the body’ssystem. In the past four decades, the uncontrolled discharge ofheavy metals from plating/rinsing industries and various othermanufacturing process industries has resulted in serious con-tamination of numerous sites. The heavy-metal ions are oftenencountered at elevated levels, and their exposure is likely topersist for a prolonged time. The Central Pollution ControlBoard, Ministry of Environment and Forests, Government ofIndia (CPCB), has set minimal national standards of 1.0, 3.0,and 5.0 mg/L, respectively, for Cd(II), Ni(II), and Zn(II) forthe safe discharge of the industrial effluents that contain thesemetal ions into surface waters.1

Several methods (e.g., physicochemical, biological, andthermal processes) have been developed for the removal ofheavy metals from waters and wastewaters to decrease theirimpact on the environment. Physicochemical processes applyto all waste types, whereas biological methods are appropriatefor dilute wastewaters that contain metals and thermal techniquesare applicable to organometallics.2 The physicochemical treat-ment processes mainly include adsorption, precipitation, andion exchange. Adsorption technologies for metals wastes includeactivated carbon and ion exchange treatment. For high-strengthand low-volume wastewaters, the removal of heavy metals viaadsorption, using granular/powdered activated carbon, has beenwidely used. Most of the activated carbons available com-mercially are microporous and of high surface area, and exhibithigh efficiency for the adsorption of gaseous molecules.However, the adsorption efficiency of bigger and high-molec-ular-weight molecules on microporous activated carbon is verylow. Also, adsorbent-grade activated carbon is cost-prohibitive

and both regeneration and disposal of the used carbon is oftenvery difficult. Therefore, the search for unconventional and less-expensive adsorbents such as bagasse fly ash (BFA), rice huskash, silica, peat, lignite, bagasse pith, wood, saw dust, etc. forthe removal of various pollutants from industrial effluents hasattracted the attention of several investigators.

BFA, which is a waste that is collected from the particulatecollection equipment attached upstream to the stacks of bagasse-fired boilers, causes disposal problems. It is mainly used forland filling, and it is used in part as a filler in building materialsand paper and wood boards. BFA has good adsorptive propertiesand has been used for the removal of chemical oxygen demand(COD) and color from paper mill effluents.3 Various researchershave utilized it for the adsorptive removal of phenolic com-pounds,4 pyridine,5 dyes,6-8 and metals.9-12 This waste materialis a potent low-cost adsorbent for the removal of heavy-metalions from industrial aqueous effluents.

Much of the work on the adsorption of heavy-metal ions byvarious types of adsorbents has focused on the uptake of singlemetals. Because of the fact that industrial effluents generallycontain several metals, it is necessary to study the simultaneoussorption of two or more metals and also to quantify theinterference of one metal with the sorption of the other. Noinformation is available in the literature for the simultaneousremoval of Cd(II), Ni(II), and Zn(II) ions by BFA. However, ithas also been reported that the adsorption of metal ions fromaqueous solution by any adsorbent is drastically affected byseveral factors, such as the initial concentration of metal ion(C0), temperature (T), initial pH (pH0), adsorbent dosage (m),and contact time (t). Generally, “one-factor-at-a-time” experi-ments have been conducted in most of the previous studies todetermine the operating conditions of optimum metal removal.One-factor-at-a-time designs often overlook interactive effectsof the factors on the sorption process. Fractional factorial designbased on Taguchi’s orthogonal array (OA) can be a veryeffective methodology to investigate the effects of multiplefactors, as well as potential interactions between these factors,in a time- and cost-effective manner.13 To date, no work isavailable in the literature on the optimization of processparameters based on Taguchi’s OA experimental design for thesimultaneous removal of metal ions. The objective of this paperis to apply Taguchi’s fractional factorial experimental design

* To whom correspondence should be addressed. Tel.:+91-1332-285889. Fax:+91-1332-276535, 273560. E-mail address: [email protected].

5697Ind. Eng. Chem. Res.2007,46, 5697-5706

10.1021/ie0609822 CCC: $37.00 © 2007 American Chemical SocietyPublished on Web 07/14/2007

to screen significant factors, which would have a great impacton the multicomponent adsorption efficiency of metal ions fromaqueous solution using BFA as an adsorbent.

The objective of the present study is to maximize the selectedresponse characteristic (the total amount of metal ions adsorbedonto BFA in terms of milligrams per gram of BFA (qtot)) byoptimizing the various parameters that affect the simultaneousremoval of Cd(II), Ni(II), and Zn(II) metal ions from aqueoussolutions by BFA. The effect of individual process parametersand their interactions onqtot will be examined using the standardprocedure suggested by Taguchi. The mean or the averagevalues and S/N ratio of the quality/response characteristics foreach parameter at different levels have been calculated fromexperimental data. For the graphical representation of the changein value of quality characteristic, and that of S/N ratio with thevariation in process parameters, the response curves will beplotted. These response curves are used to examine theparametric effects on the response characteristics. The analysisof variance (ANOVA) will be performed for the raw andS/N data to identify the significant parameters and toquantify their effect on the response characteristics. The most-favorable conditions (optimal settings) of process parameters,in terms of the mean response of characteristics, would beestablished by analyzing the response curves and the ANOVAtables.

2. Materials and Methods

2.1. Adsorbent and Adsorbates.BFA was obtained from anearby sugar mill (Deoband Sugar Mill, U.P., India) and usedas an adsorbent without any pretreatment (except sieving).Detailed physicochemical characteristics of the BFA havealready been presented elsewhere.4,9

All the chemicals used in the study were analytical reagentgrade. Nickel chloride hexahydrate (NiCl2·6H2O) was procuredfrom Qualigens Fine Chemicals, Mumbai, India. Cadmiumsulfate octahydrate (3CdSO4·8H2O), zinc sulfate heptahydrate(ZnSO4·7H2O), NaOH, HCl, HNO3, H2SO4, and CH3COOHwere obtained from s.d. fine chemicals, Mumbai. Stock solutionsof Cd(II), Ni(II), and Zn(II) metal ions (1 g/L strength) wereprepared by dissolving exact amounts of 3CdSO4·8H2O, NiCl2·6H2O, and ZnSO4·7H2O separately in double-distilled water. Thestock solution for each metal salt was diluted to give metal-ionconcentrations in the range of 0-100 mg/L for use in theexperiments.

2.2. Analysis of Metal Ions.The concentration of Cd(II),Ni(II), and Zn(II) in the aqueous samples was determined usinga flame atomic absorption spectrophotometer (GBC Avanta,Australia) with the detection limit of 0.009, 0.040, and 0.008mg/L at wavelengths of 228.8, 232, and 213.9 nm, for Cd(II),Ni(II), and Zn(II), respectively, using an air-acetylene flame.Before the analysis, the sample was diluted, if necessary, withdistilled water to a concentration in the range of 0.2-1.8 mg/Lfor Cd(II), 1.8-8 mg/L for Ni(II) and 0.4-1.5 mg/L for Zn-(II). Metal-ion concentrations were determined with referenceto the appropriate standard metal-ion solutions.

2.3. Taguchi’s Methodology of Experimental Design.Taguchi’s methodology has been used extensively in conductingexperiments and devising a strategy for the quality control ofproducts in the manufacturing industries. Taguchi’s method toimprove the quality of the products is heavily dependent ondesigning and testing a system based on engineer’s judgmentof selected materials and parts, and nominal product/processparameters based on current technology.14 Although system

design helps to identify the working levels of the designparameters, process parameter design seeks to determine theparameter levels that produce the best performance of theproduct/process under study. The optimum condition is selectedso that the influence of uncontrollable factors (noise factors)causes minimum variation to the system performance. The OAs,variance, and S/N analysis are the essential tools of parameterdesign.

Taguchi’s method of experimental design provides the optimalselection of parametric values, based on their intraparametricinteraction, to accomplish a process. This method minimizesthe number of experiments to be conducted based on thestatistical significance of parameters and the interactive influ-ences of these parameters.

Taguchi’s methodology, as adopted in this study, consists offour phases (with various steps), viz., planning, conducting,analysis, and validation. Taguchi’s method of design of experi-ments (DOE) involves the establishment of a large number ofexperimental situations, described as OAs, to reduce experi-mental errors and to enhance their efficiency and the reproduc-ibility of the laboratory experiments. Each phase has separateobjectives that are interconnected sequentially to achieve theoverall optimization process.

2.3.1. Design of Experiment (Phase 1).The first step inPhase 1 is to identify the various factors to be optimized inbatch experiments that have critical effect on the simultaneousadsorptive removal of Cd(II), Ni(II), and Zn(II) metal ions fromaqueous solutions adsorption onto BFA. Factors were selectedand the ranges were further assigned based on the detailedexperiments for metal removal using BFA.9-12 Based on theexperience, seven process parameters that exerted significantinfluence on the metal adsorption have been selected for thepresent experimental design. These process parameters, as wellas their designations and levels, are given in Table 1. The initialconcentration of one metal ion (C0,i) significantly affects theadsorption of other metal ions in the simultaneous adsorptionof metal ions;9,10 therefore, it has been decided to study threetwo-parameter interactions between the initial concentrationsof metal ions, i.e.,C0,Cd × C0,Ni, C0,Cd × C0,Zn, and C0,Ni ×C0,Zn.

2.3.1.1. Selection of Orthogonal Array (OA) and Param-eter Assignment.The next step in phase 1 was to design thematrix experiment and define the data analysis procedure. Theappropriate OAs for the control parameters to fit a specific studywere selected. Taguchi provides many standard OAs andcorresponding linear graphs for this purpose. The OA selectedmust satisfy the following inequality:

Table 1. Process Parameters for Multicomponent Adsorption Studyof Metal Ions onto BFA Using Taguchi’s Orthogonal Arrays (OAs)

parameter level 0 level 1 level 2

A initial concentration of cadmium,C0,Cd (mg/L)

0 50 100

B initial concentration of nickel,C0,Ni (mg/L)

0 50 100

C initial concentration of zinc,C0,Zn (mg/L)

0 50 100

D temperature,T (°C) 20 30 40E initial pH of solution, pH0 4 6 8F BFA dose,m (g/L) 5 10 15G contact time,t (min) 30 60 90

total DOF of the OAgtotal DOF required for the experiment

5698 Ind. Eng. Chem. Res., Vol. 46, No. 17, 2007

where DOF denotes the degrees of freedom. It was decided tostudy each selected process parameter at three levels to accountfor nonlinear behavior (if any) of the parameters of a process.15

With seven parameters each at three levels and three second-order interactions, the total DOF required is 26 [) 7 × (3 -1) + (3 × 4)], because a three-level parameter has DOF) 2(number of levels) 1) and each two-parameter interaction termhas DOF) 4 (2× 2). Hence, an L27 (313) OA (a standard three-level OA) has been selected for this phase of work. The L27

OA, with the assignment of parameters and interactions, isshown in Table 2. The parameters and interactions have beenassigned to specific columns of the OA using the triangulartable.16

2.3.2. Batch Experimental Adsorption Studies (Phase 2).Batch adsorption experiments were performed for simultaneousmetal removal with BFA, using the selected 27 experimentaltrials, in combination with seven process factors at threelevels (Table 1), and the result obtained from each set, interms of the total amount of metal ion adsorbed onto BFA(in mg/g of BFA) (qtot) is shown in Table 2. The resultspresented in the table represent those for three individualdeterminations.

For each experimental run, 150 mL of aqueous solution,having 50 mL of each metal ion solution (viz. Cd(II), Ni(II),and Zn(II)) of known concentration was taken in a 500-mLstoppered conical flask that contained a specific amount of BFA.These flasks were agitated at a constant shaking rate of 150rpm in a temperature-controlled orbital shaker (Remi Instru-ments, Mumbai, India) maintained at 20, 30, or 40°C. Theinitial pH (pH0) of the adsorbate solution was adjusted using 1N (36.5 g/L) HCl or 1 N (40 g/L) NaOH aqueous solutionwithout any further pH adjustment during the sorption process.The samples withdrawn after appropriate contact time werecentrifuged using Research Centrifuge (Remi Instruments,Mumbai, India) at 5000 rpm for 5 min, and then the supernatantliquid was analyzed for the residual concentration of metalions.

The removal of metal ions from the solution and theequilibrium adsorption uptake in the solid phase,qtot (in unitsof mg/g), were calculated using the following relationship:

whereC0,i is the initial metal ion concentration (given in unitsof mg/L),Ce,i the equilibrium metal ion concentration (also givenin mg/L), andm the adsorbent dose (given in units of g/L).

2.3.3. Analysis of Experimental Data and Prediction ofPerformance (Phase 3).The obtained experimental data wasprocessed with “higher-is-better” (HB) quality characteristics(i) to determine the optimum conditions for the adsorption, (ii)to identify the influence of individual factors on adsorption, and(iii) to estimate the performance (qtot) under the optimumconditions. Taguchi defines the loss functionL(y) as a quantityproportional to the deviation from the nominal quality charac-teristic, and he found the following quadratic form to be apractical workable function, viz.,

wherek denotes the proportionality constant,mT is the targetvalue, andy is the experimental value obtained for each trial.

In the case of HB-type quality characteristics, the loss function

can be written asL(y) ) k(1/y)2 and the expected loss functioncan be represented by

whereE(1/y2) can be estimated from a sample ofR as

2.3.3.1. Signal-to-Noise (S/N) Ratio.Taguchi created atransform for the loss function that is called the signal-to-noise(S/N) ratio, which looks at two characteristics of a distributionand combines these characteristics into a single number or figureof merit. The S/N ratio combines the mean level of the qualitycharacteristic and the variance around this mean into a singlemetric.17

A high S/N ratio implies that the signal is much higher thanthe random effects of noise factors. Process operation consistentwith the highest S/N ratio always yields optimum quality withminimum variation.

The S/N ratio consolidates several repetitions (at least twodata points are required) into one value. The equations forcalculating S/N ratios for HB-type characteristics are given asfollows:16

whereyi is the value of the characteristic in an observationiandR is the number of observation or number of repetitions ina trial. From among the methods suggested by Taguchi foranalyzing the data, the following methods have been used inthe present work: (i) plot of average response curves; (ii)ANOVA for raw data; and (iii) ANOVA for S/N data.

The plot of the average response at each level of a parameterindicates the trend. It is a pictorial representation of the effectof a parameter on the response. The change in the responsecharacteristic with the change in levels of a parameter can easilybe visualized from these curves. Typically, ANOVA for OAsis conducted in the same manner as other structured experi-ments.16 The S/N ratio is treated as a response of the experiment,which is a measure of the variation within a trial when noisefactors are present. A standard ANOVA can be conducted onthe S/N ratio which will identify the significant parameters(mean and variation).

2.3.3.2. Prediction of the Mean.After determination of theoptimum condition, the mean of the response (µ) at the optimumcondition is predicted. This mean is estimated only from thesignificant parameters. The ANOVA identifies the significantparameters. Suppose, parameters A and B are significant andA2, B2 (second level of A) A2, second level of B) B2) arethe optimal treatment conditions. The mean under the optimalcondition (optimal value of the response characteristic) then isestimated as

whereTh is the overall mean of the response, andAh2 and Bh2

represent average values of response at the second levels ofparameters A and B, respectively.

qtot ) ∑i)1

3 C0,i - Ce,i

m(1)

L(y) ) k(y - mT)2 (2)

E[L(y)] ) kE( 1

y2) (3)

E(1/y2) )

∑i)1

R

[1/yi2]

R(4)

(S/N)HB ) -10 log(1

R∑i)1

R 1

yi2) (5)

µ ) Th + (Ah2 - Th) + (Bh2 - Th) ) Ah2 + Bh2 - Th (6)

Ind. Eng. Chem. Res., Vol. 46, No. 17, 20075699

Tab

le2.

Col

umn

Ass

ignm

ent

for

the

Var

ious

Fac

tors

and

Thr

eeIn

tera

ctio

nsin

the

Tag

uchi

’sL 27

(313

)O

rtho

gona

lArr

ay(O

A)

and

Exp

erim

enta

lq t

otV

alue

sfo

rM

ultic

ompo

nent

Met

alIo

nsA

dsor

ptio

non

toB

FA

Exp

erim

enta

lq tot

Val

ues

expt

1,A

expt

2,B

expt

3,A×

Bex

pt4,

A×

Bex

pt5,

Cex

pt6,

A×C

expt

7,A×

Cex

pt8,

B×

Cex

pt9,

Dex

pt10

,Eex

pt11

,B×C

expt

12,F

expt

13,G

R1

R2

R3

S/N

ratio

(dB

)1

00

00

00

00

00

00

00.

000.

000.

000.

002

00

00

11

11

11

11

13.

904.

003.

8611

.86

30

00

02

22

22

22

22

4.25

4.21

4.31

12.5

84

01

11

00

01

11

22

22.

482.

362.

477.

735

01

11

11

12

22

00

08.

848.

629.

2518

.98

60

11

12

22

00

01

11

6.99

7.29

6.86

16.9

57

02

22

00

02

22

11

15.

786.

175.

8115

.44

80

22

21

11

00

02

22

4.23

3.83

4.21

12.2

09

02

22

22

21

11

00

013

.41

13.2

013

.38

22.5

010

10

12

01

20

12

01

23.

293.

323.

2410

.33

111

01

21

20

12

01

20

3.11

3.22

3.25

10.0

912

10

12

20

12

01

20

113

.08

12.9

313

.36

22.3

613

11

20

01

21

20

20

110

.00

9.80

10.2

020

.00

141

12

01

20

20

10

12

4.31

4.06

4.17

12.4

115

11

20

20

10

12

12

05.

255.

395.

1314

.41

161

20

10

12

20

11

20

3.52

3.70

3.64

11.1

717

12

01

12

00

12

20

111

.88

10.3

011

.83

21.0

318

12

01

20

11

20

01

27.

306.

507.

1516

.85

192

02

10

21

02

10

21

3.55

3.60

3.48

10.9

820

20

21

10

21

02

10

212

.08

12.7

411

.83

21.7

221

20

21

21

02

10

21

07.

207.

667.

8417

.56

222

10

20

21

10

22

10

4.31

4.10

4.26

12.5

123

21

02

10

22

10

02

14.

164.

054.

2912

.39

242

10

22

10

02

11

02

16.0

014

.02

16.0

823

.68

252

21

00

21

21

01

02

11.0

811

.94

11.4

221

.19

262

21

01

02

02

12

10

7.25

7.36

7.51

17.3

527

22

10

21

01

02

02

15.

506.

065.

3614

.99

tota

l18

2.74

180.

4518

4.18

mea

n6.

76


2.3.3.3. Determination of Confidence Interval.The estima-tion of µ is only a point estimate based on the average of resultsobtained from the experiment. Statistically, this provides a 50%chance of the true average being greater thanµ and a 50%chance of the true average being less thanµ. Therefore, it iscustomary to represent the values of a statistical parameter asa range within which it is likely to fall for a given level ofconfidence. This range is termed as the confidence interval (CI).In other words, the confidence interval is a maximum andminimum value between which the true average should fall atsome stated percentage of confidence.

The following two types of confidence intervals are suggestedby Taguchi, in regard to the estimated mean of the optimaltreatment condition:16

(i) Around the estimated average of a treatment conditionpredicted from the experiment. This type of confidence intervalis designated as CIPOP(confidence interval for the population).

(ii) Around the estimated average of a treatment conditionused in a confirmation experiment to verify predictions. Thistype of confidence interval is designated as CICE (confidenceinterval for a sample group).

The difference between CIPOP and CICE is that CIPOP isassociated with the entire population, i.e., all parts ever madeunder the specified conditions, and CICE is associated withonly a sample group made under the specified conditions.Because of the smaller size (in confirmation experiments),relative to the entire population, CICE must be slightly wider.The expressions for computing the confidence interval are givenas follows:18

whereFR(1, fe) represents theF-ratio at a confidence level of(1 - R) against DOF) 1 and a DOF error offe, Ve is the errorvariance (from ANOVA), andneff is defined as

whereN is the total number of results.R represents the samplesize for the confirmation experiment.

Equation 8 shows that, asRapproaches infinity (i.e., the entirepopulation), the value 1/R approaches zero and CICE ) CIPOP.As R approaches 1, CICE becomes wider.

2.3.4. Confirmation Experiment (Phase 4).The confirma-tion experiment is the final step in verifying the conclusionsdrawn from the previous round of experimentation. Theoptimum conditions are set for the significant parameters (theinsignificant parameters are set at economic levels), and aselected number of tests are conducted under constant specifiedconditions. The average of the results of the confirmationexperiments is compared with the anticipated average, basedon the parameters and levels tested. The confirmation experimentis a crucial step and is highly recommended to verify theexperimental conclusions.

3. Results and Discussion

Experiments were conducted for Cd(II), Ni(II), and Zn(II)ion adsorption onto BFA, according to the test conditionsspecified by L27 OA (see Table 2). Each experiment wasrepeated three times for each trial condition. The average ormean values ofqtot and S/N ratio for each parameter at levels1, 2, and 3 are calculated from Table 2. It is observed that metaladsorption is strongly dependent on the parametric conditions.

3.1. Effect of Process Parameters.The raw data for theaverage value ofqtot and S/N ratio for each parameter at levels1, 2, and 3, along with interactions at the assigned levels, aregiven in Table 3 for metal adsorption onto BFA. Various metalremoval parameters (C0,i, T, pH0, m, andt) significantly affectthe qtot values. The interaction effect of concentration of onemetal ion with respect to (wrt) another metal ion also hassignificant influence on theqtot values. Individually, relative tothe level stage, withqtot as the desired response characteristic,m (parameter F) has the highest influence at level 1, pH0

(parameter E) has the highest influence at level 2, andC0,Zn

(parameter C) has the highest influence at level 3. The differencebetween level 2 and level 1 (L2- L1) of each factor indicatesthe relative influence of the effect. The larger the difference,the stronger the influence. Table 3 shows that no singleparameter has an overriding or predominant influence overothers for the removal of Cd(II), Ni(II), and Zn(II) from aqueoussolution by BFA.C0,i shows a stronger influence onqtot thanthat of other parameters. Theqtot value increased asC0,i

increased, because the resistance to the metal uptake decreasedas the mass-transfer driving force increased.

The response curves for the individual effects of metaladsorption parameters on the average value ofqtot and respectiveS/N ratio for metal adsorption onto BFA are given in Figure 1.An increase in the levels of factors such asC0,i, andT, from 1to 2 and from 2 to 3, has resulted in an increase in theqtot values.

Because sorption is an exothermic process, it would beexpected that an increase inT would result in a decrease in theqtot value. However, if the diffusion process controls theadsorption process, theqtot value will increase asT increases,

Table 3. Average and Main Effects ofqtot Values for BFA: Raw and S/N Data

Raw Data, Average Value Main Effects (Raw Data) S/N Data, Average Value Main Effects (S/N Data)

L1 L2 L3 L2 - L1 L3 - L2 L1 L2 L3 L2 - L1 L3 - L2

A 5.55 6.78 7.95 1.23 1.18 13.14 15.40 16.93 2.27 1.53B 5.68 6.84 7.75 1.16 0.91 13.05 15.45 16.97 2.39 1.52C 4.95 6.60 8.73 1.65 2.13 12.15 15.34 17.99 3.19 2.65D 6.01 6.98 7.28 0.96 0.31 13.81 15.44 16.22 1.63 0.77E 6.06 7.43 6.78 1.37 -0.65 14.14 15.56 15.78 1.42 0.22F 10.64 5.61 4.02 -5.03 -1.59 19.05 14.58 11.84 -4.47 -2.75G 5.94 7.19 7.14 1.25 -0.05 13.84 16.22 15.41 2.38 -0.81A×B 5.89 7.01 7.38 1.12 0.37 13.71 15.72 16.04 2.01 0.32A×C 6.28 6.88 7.10 0.60 0.22 13.95 15.67 15.85 1.72 0.17B×C 5.96 7.22 7.09 1.26 -0.13 13.69 15.82 15.97 2.13 0.15

CIPOP) xFR(1,fe)Ve

neff(7)

CICE ) xFR(1,fe)Ve[ 1neff

+ 1R] (8)

neff )N

1 + [total DOF associated in the estimate of the mean](9)


because of the endothermicity of the diffusion process. Anincrease inT results in increased mobility of the metal ionsand a decrease in the retarding forces acting on the diffusingions. These result in the enhancement in the sorptive capacityof the adsorbents. However, the diffusion of the metal ions intothe pores of the adsorbent is not the only rate-controlling step,12

and the diffusion resistance can be ignored with adequate contacttime. Therefore, the increase in sorptive uptake of the metalions with an increase in temperature may be attributed tochemisorption.

An increase in the level of contact timet, from 1 to 2, resultsin an increase in theqtot value. However, theqtot value remainsconstant with a subsequent increase from level 2 to level 3.Also, the adsorption of metal ions increases ast increases untilequilibrium is attained between the solute molecules in the liquidand the solid phases. During the initial stage of sorption, a largenumber of vacant surface sites are available for adsorption. Asthe sorption process progresses, its intention is to occupy thevacant sites, because of the repulsive forces between the solutemolecules onto the solid surface and the bulk liquid phase.Besides, the metal ions are adsorbed into the mesopores thatget almost saturated with metal ions during the initial stage ofadsorption. Thereafter, the metal ions must traverse farther anddeeper into the pores, encountering much-greater resistance. Thisresults in the slowing of the adsorption during the later periodof adsorption.19

An increase in pH0 has resulted in higher adsorption up tolevel 2 and subsequent increase resulted in the decrease in thedesired characteristic (qtot). The removal of metal ions isdetermined to increase as pH0 increases from 4 to 6. Themaximum uptake of metal ions was obtained at pH0 ∼6, andthe qtot value decreased at pH0 >6. The oxides of aluminum,calcium and silicon present in the BFA develop charges whenin contact with water. Except silica, all other oxides possess apositive charge for the pH range of interest, because the zero-point charge (pHZPC) of SiO2, Fe2O3, Al2O3, and CaO are 2.2,6.7, 8.5, and 11.0, respectively.20 A positive charge developson the surface of the oxides of BFA in an acidic medium,because of the aqua complex formation of the oxides present,as follows:

Metal-ion adsorption at low pH0 ( pH0 e6) is less than thatat higher pH0 (∼6). This is due to the fact that the surface chargethat is developed at low pH0 is not suitable for the adsorptionof these metal ions. For pH0 <6, a significant electrostaticrepulsion exists between the positively charged surface of theBFA and the cationic metal ions. Besides, a higher concentrationof H+ in the solution competes with metal ions for the adsorptionsites, resulting in the reduced uptake of metal ions.

As the pH0 of the system increases, the number of positivelycharged sites decreases and the number of negatively chargedsites increases on the surface of BFA, as shown below:

A negatively charged surface site on the BFA favors theadsorption of cationic metal ions, because of electrostaticattraction.9,10

A pH0 value of 8 is supposed to be more favorable to increasetheqtot value; on the contrary, theqtot value decreases at pH0 8.It is known that metal species [M(II)) Cd(II), Ni(II), and Zn-(II)] are present in deionized water in the forms of M2+,M(OH)+, M(OH)20, M(OH)2(S), etc.21 At pH ∼4.0, the solubility(CT,M) of the M(OH)2(S) is great, so M2+ is the main species.At pH ∼8.0, theCT,M value for M(OH)2(S) is much smaller.With the increase of the pH value, theCT,M value for M(OH)2(S)

decreases further. In the alkaline region, therefore, the mainspecies in the solution is M(OH)2(S), which is electrically neutral.Hence, in the alkaline region, the removal of cationic metal ionsis not due to electrostatic attraction but rather precipitation.Because Cd(II), Ni(II), and Zn(II) ions are simultaneously beingremoved, because of some complex mechanism, theqtot valuedecreases.

In the case ofm, an increase inm from level 1 to level 2 andsubsequently to level 3 led to a decrease in the value ofqtot.Mall et al.6 showed the that unit adsorption decreases asmincreases, although the percentage metal removal increases,because of the availability of a greater surface area and moreadsorption sites.22

Table 3 indicates that the interactions between initial con-centrations of Cd(II), Ni(II), and Zn(II) metal ions [(A× B),

Figure 1. Effect of process parameters onqtot and S/N ratio for multicomponent adsorption of metal ions onto BFA.

-MO + H-OH98H+

M-OH2+ + OH- (10)

-MOH + OH- f -MO- + H2O (11)

-MO- + Cd f s M-O-Cd (12)


(A × C), or (B × C)] are significant in regard to affecting theaverage value ofqtot. These interaction graphs are shown inFigure 2 forqtot values, along with the S/N ratios, for metaladsorption onto BFA.

If the lines are parallel in the interaction graph, then it impliesthat there is no interaction between the factors. Nonparallel linesin the interaction plot indicate the existence of an interactionbetween the factors. According to Taguchi,18 the greater thedifference between the slopes of the lines of two factors in therange tested, the greater the interaction.

Figure 2 shows that the difference in the slopes of the linesis greater when the level of the parameters is lower. Hence, itmay be concluded that the effect of the initial concentration ofmetal ions onqtot is more pronounced when theC0 value ofmetal ions is increased from the lowest level to the middle level,as compared to that whenC0 is increased from the middle levelto the highest level.

Srivastava et al.9,10 have shown that the mutual effect of onemetal ion on the adsorption of another metal ion is antagonisticin nature. The most logical reason for the antagonistic action isclaimed to be the competition between the adsorbate ions tooccupy vacant adsorption sites on the adsorbents. It appears thatCd(II), Ni(II), and Zn(II) metal ions share the binding sites onthe surface of adsorbents, and, therefore, the adsorption of one

metal ion decreases the number of binding sites available forthe adsorption of other metal ions. This ultimately leads to theantagonism behavior. The screening effect may be anotherpossible explanation for the observed antagonism. The increas-ing concentrations of metal ions that are not adsorbed can maskpreferentially adsorbed metal ions.

At lower initial metal concentrations, the antagonistic natureis least observed. Therefore, when the initial concentration isincreased from the lowest level to the next higher level,qtot

values show the highest incremental rate. When the initialconcentration is further increased, this rate of increment ofqtot

decreases for the adsorption of Cd(II), Ni(II), and Zn(II) metalions by BFA.

The contribution of individual factors is the key for the controlto be enforced for the adsorption of various metal ions ontoBFA. ANOVA results for raw and S/N ratio data with desiredresponse characteristics (qtot) are given in Table 4 for multi-component metal ion adsorption onto BFA. From the calculatedratios (F), it can be referred that all factors and interactionsconsidered in the experimental design withqtot as the desiredresponse characteristic are statistically significant at the 95%confidence limit.

Figure 2. Interaction between parameters A, B, and C at three levels onqtot and S/N ratio for multicomponent adsorption of metal ions onto BFA.


By studying the main effects of each of the factors, the generaltrends of the influence of the factors on the adsorptive metalremoval process can be characterized. The characteristics canbe controlled such that a lower or higher value in a particularinfluencing factor produces the preferred result. Thus, the levelsof factors necessary to produce the best results can be predicted.The percentage contribution of each parameter, forqtot as thedesired response characteristic, is shown in Figure 3. Table 4shows that parameter F (m) is the most significant factor with53.14% and 31.25% contribution to the raw and S/N ratiodata for the metal adsorption withqtot as the desired character-istic within the assigned levels for each factor. It can also beobserved from Table 4 and Figure 3 that the interactions betweenthe parameters A, B, and C contribute significantly to both rawand S/N ratio data for simultaneous metal removal onto BFA.

3.2. Selection of Optimal Levels and Estimation ofOptimum Response Characteristics.Becauseqtot is an HB-

type quality characteristic, the greaterqtot value is consideredoptimal. Table 5 summarizes the optimal level of variousparameters obtained after examining the response curves (Figure1) of the average value ofqtot and S/N ratios for metal adsorptiononto BFA. Table 5 indicates that the first level of parameter F(m), the second level of parameters E (pH0) and G (t), and thethird levels of parameters A, B, C (C0,i), and D (temperature)have higher average values ofqtot and S/N ratio. Because theintent of the work is to remove the maximum amount of metalswith the highest possible concentration of Cd(II), Ni(II), andZn(II) metal ions present together, the third levels of parametersA, B, and C (C0,Cd, C0,Ni, andC0,Zn) are suggested for furthercalculations.

Thus, the significant process parameters that affect the metalremoval by BFA and their optimal levels (as already selected)areA3, B3, C3, D3, E2, F1, andG2.

Figure 3. Percent contribution of various parameters forqtot for multicomponent adsorption of metal ions onto BFA: parameter A,C0,Cd (mg/L); parameterB, C0,Ni (mg/L); parameter C,C0,Zn (mg/L); parameter D,T (°C); parameter E, pH0; parameter F,m (g/L); and parameter G,t (min).

Table 4. ANOVA of qtot and S/N Ratio Data for Multicomponent Adsorption of Metal Ions onto BFA

Raw Data S/N Data

parameter sum of squares DOFa mean square % contribution F-value sum of squares DOFa mean square % contribution F-value

A 78.25 2 39.13 6.45 305.47 65.50 2 32.75 8.58 4.58B 58.36 2 29.18 4.81 227.82 70.07 2 35.03 9.18 4.90C 194.44 2 97.22 16.04 759.03 153.74 2 76.87 20.14 10.75D 23.63 2 11.81 1.95 92.23 27.11 2 13.55 3.55 1.90E 25.52 2 12.76 2.10 99.60 (14.30) (2) (7.15) POOLEDF 644.37 2 322.19 53.14 2515.43 238.54 2 119.27 31.25 16.68G 27.01 2 13.51 2.23 105.46 26.38 2 13.19 3.46 1.84A×B 65.85 4 16.46 5.43 128.53 59.96 4 14.99 7.85 2.10A×C 21.36 4 5.34 1.76 41.70 42.21 4 10.55 5.53 1.48B×C 66.87 4 16.72 5.52 130.53 65.57 4 16.39 8.59 2.29residual 6.92 54 0.13 0.57 14.30 2 7.15 1.87model 1205.67 26 564.18 99.43 4404.79 763.39 24 332.61 98.13 46.52cor. total 1212.59 80 564.31 100.00 777.69 26 339.76 100.00

a Degrees of freedom.

Table 5. Predicted Optimal qtot Values, Confidence Intervals, and Results of Confirmation Experiments

adsorbentoptimal levels of

process parameterspredicted optimal

values (mg/g)confidence

intervals (95%)average of confirmation

experiments (mg/g)

BFA A3, B3, C3, D3, E2, F1, G2 16.41 CIPOP: 16.00< µBFA < 16.82CICE: 15.88< µBFA < 16.94

16.01


The averageqtot values (from Table 3) are given as

The overall mean ofqtot (ThBFA) is 6.76 (from Table 2).The predicted optimum value ofqtot for BFA (µBFA) has been

calculated as

The 95% confidence intervals for the mean of the populationand three confirmation experiments (CIPOPand CICE) is calcu-lated by substituting the total number of results (N ) 27× 3 )81), the DOF error (fe ) (80- 26)) 54), and the error variance(Ve ) 0.128) (recalculated from Table 4) into eqs 7-9:

The 95% confidence intervals (CIPOPand CICE) of the predictedranges ofqtot for the simultaneous adsorption of Cd(II), Ni(II),and Zn(II) ions onto BFA are given in Table 5.

3.3. Confirmation Experiments.Three confirmation experi-ments have been conducted for the simultaneous adsorption ofCd(II), Ni(II), and Zn(II) ions onto BFA at selected optimallevels of the process parameters. The average values of thecharacteristics are obtained and compared with the predictedvalues. The results are given in Table 5. The values ofqtot

obtained through the confirmation experiments are within 95%of CICE. Note that these optimal values are valid within thespecified range of process parameters; any extrapolation/interpolation should be confirmed through additional experi-ments.

4. Conclusions

The Taguchi orthogonal array (OA) design of experiments(DOE) methodology was determined to be very economical inthe experimental sorption studies of Cd(II), Ni(II), and Zn(II)metal ions from aqueous solutions by bagasse fly ash (BFA).This approach facilitated understanding of the interaction of a

large number of variables spanned by factors and their settingswith a small number of experiments. Factors such as initialmetal-ion concentration, temperature, pH0, adsorbent dose, andcontact time at three levels with a OA layout of L27 (313) couldbe optimized with the “higher-is-better”-type quality charac-teristics with 27 sets of experiments only. The interactionsbetween initial concentrations of Cd(II), Ni(II), and Zn(II) metalions [(A × B), (A × C) or (B × C)] is significant in affectingthe average values ofqtot. The effect of initial concentration ofone metal ion (C0,Cdor C0,Ni) onqtot values is more pronouncedat the lower concentration of the other metal ions (C0,Ni or C0,Zn)for all possible interactions. All factors and interactionsconsidered in the experimental design withqtot as the desiredresponse characteristic are statistically significant at the 95%confidence limit.

Literature Cited

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(2) Palmer, S. A. K.; Breton, M. A.; Nunno, T. J.; Sullivan, D. M.;Norman, F. Surprenant Technical Resource Document: Treatment Tech-nologies for Metal/Cyanide-Containing Wastes, Volume III. United StatesEnvironmental Protection E Agency. Feb. 1988, EPA/60O/S2-87/106.

(3) Srivastava, V. C.; Mall, I. D.; Mishra, I. M. Treatment of pulp andpaper mill wastewaters with polyaluminium chloride and bagasse flyash.Colloids Surf. A2005, 260, 17.

(4) Srivastava, V. C.; Swamy, M. M.; Mall, I. D.; Prasad, B.; Mishra,I. M. Adsorptive removal of phenol by bagasse fly ash and activatedcarbon: equilibrium, kinetics and thermodynamic study.Colloids Surf. A2006, 272, 89.

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(6) Mall, I. D.; Srivastava, V. C.; Agarwal, N. K.; Mishra, I. M.Adsorptive removal of malachite green dye from aqueous solution bybagasse fly ash and activated carbon-kinetic study and equilibrium isothermanalyses.Colloids Surf. A2005, 264, 17.

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(11) Srivastava, V. C.; Mall, I. D.; Mishra, I. M. Competitive antagonisticadsorption of nickel(II) and zinc(II) metal ions from aqueous solution ontobagasse fly ash. Presented at the 11th APCChE Congress, Asian PacificConfederation of Chemical Engineering, Kuala Lumpur, Malaysia, August27-30, 2006.

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third level of concentration of Cd(II) ion (Ah3) ) 7.95

third level of concentration of Ni(II) ion (Bh3) ) 7.75

third level of concentration of Zn(II) ion (Ch 3) ) 8.73

third level of temperature (Dh 3) ) 7.28

second level of pH0 (Eh2) ) 7.43

first level of adsorbent dosage (Fh1) ) 10.64

second level of contact time (Gh 2) ) 7.19

µBFA ) ThBFA + (Ah3 - ThBFA) + (Bh3 - ThBFA) +(Ch 3 - ThBFA) + (Dh 3 - ThBFA) + (Eh2 - ThBFA) +

(Fh1 - ThBFA) + (Gh 2 - ThBFA)

) 16.41 mg/g

neff )N

1 + [total DOF associated in the estimate of the mean]) 3

F0.05(1, 54)) 4.03 (tabulatedF-value)

CIPOP) xFR(1,fe)Ve

neff) (0.41

CICE ) xFR(1,fe)Ve[ 1neff

+ 1R] ) (0.53


(19) Srivastava, V. C.; Mall, I. D.; Mishra, I. M. Characterization ofmesoporous rice husk ash (RHA) and adsorption kinetics of metal ions fromaqueous solution onto RHA.J. Hazard. Mater.2006, B134, 257-267.

(20) Panday, K. K.; Prasad, G.; Singh, V. N. Mixed adsorbent for Cu-(II) removal from aqueous solutions.EnViron. Technol. Lett.1986, 50 (7),547.

(21) Srivastava, V. C.; Mall, I. D.; Mishra, I. M. Adsorption of toxicmetal ions onto activated carbon. Study of sorption behaviour throughcharacterization and kinetics.Chem. Eng. Process.2007, doi:10.1016/j.cep.2007.04.006.

(22) Ponnusami, V.; Krithika, V.; Madhuram, R.; Srivastava, S. N.;Biosorption of reactive dye using acid-treated rice husk: Factorial designanalysis.J. Hazard. Mater.2007, 142, 397.

ReceiVed for reView July 27, 2006ReVised manuscript receiVed May 17, 2007

AcceptedMay 22, 2007

IE0609822


multicomponent adsorption study of metal ions onto bagasse fly ash using taguchi's design of...

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