B

Ca

b

a

ARRAA

KBAHR

1

racact

irCscdmitrelt

0d

Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188

Contents lists available at ScienceDirect

Colloids and Surfaces A: Physicochemical andEngineering Aspects

journa l homepage: www.e lsev ier .com/ locate /co lsur fa

iosorption of copper(II) ions from aqua solutions using dried yeast biomass

orneliu Cojocarua, Mariana Diaconua, Igor Cretescua,∗∗, Jasmina Savic b,∗, Vesna Vasic b

Department of Environmental Engineering and Management, Technical University of Iasi, Bvd. D. Mangeron 67, 700050 Iasi, RomaniaDepartment of Physical Chemistry, Vinca Institute of Nuclear Sciences, P.O. Box 522, 11001 Belgrade, Serbia

r t i c l e i n f o

rticle history:eceived 18 April 2008eceived in revised form 5 October 2008ccepted 7 November 2008vailable online 14 November 2008

a b s t r a c t

The ability of dried yeast Saccharomyces biomass to remove Cu(II) ions from aqueous solutions was inves-tigated by using of batch techniques. The influence of different parameters on copper uptake by driedyeast, such as initial Cu(II) concentration, initial pH of solution and temperature, was studied. The Fre-undlich, Langmuir, Redlich–Peterson and Sips isotherms were applied to the obtained experimental data.According to Langmuir isotherm the maximum adsorption capacity of investigated non-living biomass

eywords:iosorptiondsorption isothermeavy metalsesponse surface methodology (RSM)

was found to be 2.59 mg/g. The thermodynamic parameters (e.g. free energy and enthalpy) were cal-culated and discussed. The adsorption of Cu(II) onto the dried cells of Saccharomyces cerevisiae is anendothermic process and become more favorable with the increasing of temperature in pH range from 3to 4. Optimization studies by means of response surface methodology were carried out, which resultedin improvement of the efficiency of sorption removal by using of biomass. The removal efficiency ofreal wastewater originating from electroplating industry which contains Sn(II) ions was determined and

wast

compared with synthetic

. Introduction

Wastewater contamined with heavy metals is a serious envi-onmental problem because they do not undergo biodegradationnd are accumulated into the organism entering into the foodhains. During recent years, the intensive industrial activities, suchs electroplating, microelectronics, battery manufacture, dyestuff,hemical, metallurgical, pharmaceutical and other, greatly con-ribute to the increase of heavy metals in the environment [1].

The stringent limits of different pollutant concentrations inndustrial and municipal wastewaters, imposed by the envi-onmental legislation, make the treatment to be imperative.onventional methods for removing of metal ions from aqueousolutions, like chemical precipitation, ion exchange, electrochemi-al treatment, and adsorption on activated carbon, have significantisadvantages. Chemical precipitation and electrochemical treat-ent become ineffective particularly when metal ion concentration

n the solution is low (in the range from 10 to 100 mg/L), becausehey produce large quantity of sludge to be treated and which

equire disposal. Ion exchange and activated carbon adsorption arextremely expensive processes, especially for the treatment of aarge amount of wastewater containing low heavy metal concen-rations [2,3].

∗ Corresponding author. Tel.: +381 11 2453 967; fax: +381 11 2447 207.∗∗ Corresponding author. Tel.: +40 741 914342; fax: +40 232 271311.

E-mail addresses: icre@ch.tuiasi.ro (I. Cretescu), jasnas@vinca.rs (J. Savic).

927-7757/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.colsurfa.2008.11.003

ewater obtained in laboratory.© 2008 Elsevier B.V. All rights reserved.

These disadvantages of conventional methods together with theneed of more effective and low-cost methods for the metal ionsremoval from wastewater resulted in the development of new sep-aration technologies. The biosorption has attracted the attention asa low-cost treatment technology for the removal of heavy metalsfrom wastewaters [1–15]. The biosorbents are prepared from nat-urally abundant materials and from by-products or waste biomassfrom other industries [16]. Among biosorbents, those of microbio-logical origin (e.g. bacteria, fungi, yeast and algae biomass) are ofespecial interest and were studied as potential heavy metal sor-bents in various environments [4,6,7,11,12,14,17–23].

Although many effects on biosorption have been widely stud-ied, such as initial metal concentration, pH, temperature, andsorbent dose, the mechanism of metal biosorption is not com-pletely understood because of its complexity. The metal uptake bybiosorption can occur due to physico-chemical interactions suchas complexation, coordination, chelation, ion exchange, physicaladsorption or microprecipitation. The biosorption (passive sorp-tion) by biomass occurs through interactions between metal ionsand functional groups at the cell surface like amino, phosphoryl,carboxyl, sulphydril and hydroxyl groups [3]. In spite of great num-ber of papers reported for biosorption, this process is still at thestage of laboratory-scale study [3]. Therefore complex research is

necessary in order to select the best sorbent for certain pollutant[12].

This work is focused on Cu(II) ions removal from aqueoussolutions using the dried non-living yeast biomass (Saccharomycescerevisiae) as biosorbent. An effort to discuss the equilibrium

1 Physicochem. Eng. Aspects 335 (2009) 181–188

aRm(oea

2

2

ci1gwaaaa

2

iaaimTTp±

2

wt5T(tstw

Q

wcttr

ocetaw

rs

Table 1pH dependence of the isotherm parameters for copper biosorption onto Saccha-romyces from aqueous solutions at T = 293 K.

Type of isotherm pH 3 pH 4 pH 5

Freundlich

n = 2.234 n = 2.226 n = 2.814KF = 0.260 KF = 0.252 KF = 0.287R2 = 0.623 R2 = 0.673 R2 = 0.686ARE = 24.230 ARE = 21.889 ARE = 16.653

Langmuir

Qmax = 2.533 Qmax = 2.489 Qmax = 1.794KL = 4.241 × 10−2 KL = 3.980 × 10−2 KL = 4.872 × 10−2

R2 = 0.958 R2 = 0.940 R2 = 0.975ARE = 18.832 ARE = 16.256 ARE = 11.422

Redlich–Peterson

A = 5.858 × 10−2 A = 6.551 × 10−2 A = 4.698 × 10−2

B = 9.407 × 10−4 B = 2.313 × 10−3 B = 1.583 × 10−3

g = 1.637 g = 1.482 g = 1.550ARE = 10.883 ARE = 10.423 ARE = 5.342

82 C. Cojocaru et al. / Colloids and Surfaces A:

dsorption isotherms on basis of Freundlich, Langmuir,edlich–Peterson (R–P) and Sips models as well as to opti-ize the sorption process using response surface methodology

RSM) is also included in this work. In order to check the RSMptimization of sorption process, the real sample obtained fromlectroplating industry was compared with synthetic wastewaterttained in laboratory conditions.

. Experimental

.1. Reagents

Commercial yeast biomass S. cerevisiae, available from localommercial company, was prepared as non-living biomass by dry-ng in a hot air oven at 105 ◦C for 24 h. Copper stock solution of000 mg/L was prepared using CuSO4·5H2O of analytical reagentrade. This salt was chosen since sulphate ions exist in mostastewater and mine drainage and thus simulating the potential

pplication of biosorption for copper ions removal. All solutionsnd their dilutions were prepared by using of bidistilled water. Thecidity of the solutions was adjusted by addition of 0.1 N sulphuriccid or 0.1 N sodium hydroxide solutions.

.2. Instrumentation

The concentrations of copper solutions were determinedn aqueous solutions spectrophotometrically by measuring thebsorbance of complex formed between rubeanic acid and Cu(II)t 390 nm. The absorbance was recorded using the direct read-ng spectrophotometer (HELIOS, USA). For pH measurements a pH

eter BOECO PT-370, with a combined glass electrode, was used.he batch experiments were carried out using an orbital shaker.he temperature adjustment, in the range from 20 to 50 ◦C, waserformed by using of BMT Ecocell incubator, with the precision of0.5 ◦C.

.3. Sorption studies

In order to obtain the adsorption isotherms, batch experimentsere conducted with copper aqueous solutions of initial concen-

ration varying from 25 to 200 mg/L and in pH range from 3 to. Samples were mixed and left for 24 h to reach the equilibrium.he resulting filtrate was analyzed for copper. The biosorbent doseBSD) in these experiments was maintained constant, i.e. 1.5% (w/v)hat corresponds to 1.5 g of biosorbent added to 100 mL of aqueousolution. The adsorption isotherms were determined as the func-ion of pH and temperature. The equilibrium adsorption capacityas determined by the following expression:

e = (Co − Ce) V

m 1000(1)

here Qe is the equilibrium adsorption capacity, i.e. the amount ofopper ions adsorbed onto the biosorbent (in mg/g); Co and Ce arehe initial and final metal concentrations in the solution, respec-ively (in mg/L); V is the volume of the solution (in mL) and mepresents the weight of the dried biosorbent (in g).

In addition, another set of the experiments was performed inrder to optimize the process efficiency. The experiments con-erning optimization were conducted in batch mode. In thesexperiments, the initial copper concentration was varied from 20 upo 80 mg/L. After reaching the equilibrium the resulting filtrate was

nalyzed for final copper concentration and the removal efficiencyas determined.

In this respect the experimental design was applied and theemoval efficiency of copper ions from aqueous solution was cho-en as the response for optimization. The removal efficiency Y% was

Sips

QSM = 2.060 QSM = 2.019 QSM = 1.524b = 1.274 × 10−5 b = 4.114 × 10−5 b = 1.977 × 10−4

nS = 0.271 nS = 0.303 nS = 0.348ARE = 2.743 ARE = 1.823 ARE = 1.852

determined by means of subsequent equation:

Y =(

1 − Ce

C0

)100 (2)

3. Results and discussion

3.1. Adsorption isotherms

In the design of sorption systems, the equilibrium sorptionisotherms are very important from fundamental point of view. Theequation parameters and the underlying thermodynamic assump-tions of these equilibrium models often provide some insight intoboth the sorption mechanism and the surface properties and affin-ity of the sorbent [1]. In order to investigate the sorption isothermFreundlich, Langmuir, Redlich–Peterson and Sips equilibrium mod-els were applied.

The well-known expressions of the Freundlich and Langmuirmodels with two parameters are given by the following Eqs. (3)and (4).

The Freundlich equation is an empirical relationship establishedupon sorption onto a heterogeneous surface supposing that differ-ent sites with several adsorption energies are involved [5].

Qe = KF C1/ne (3)

where KF denotes the relative adsorption capacity and n the inten-sity of adsorption.

The Langmuir isotherm:

Qe = QmaxKLCe

1 + KLCe(4)

where Qmax and KL are Langmuir isotherm constants. Thus, Qmax

denotes the maximum adsorption capacity (mg/g) while KL is theequilibrium constant connected to the energy of sorption that quan-titatively reflects the affinity between the biosorbent and sorbate[5].

The simplest method to determine isotherm constants for twoparameter isotherms is to transform the corresponding equationinto the linear form and then to apply linear regression [1]. Thus, theapplicability of Freundlich and Langmuir sorption isotherms havebeen checked by plotting log(Qe) versus log(Ce) and (Ce/Qe) versus

(Ce), respectively. The values of parameters in Freundlich (KF and n)and Langmuir (Qmax and KL) were determined and are reported inTables 1 and 2.

The Redlich–Peterson and Sips isotherm equation with threeparameters are given by Eqs. (5) and (6).

C. Cojocaru et al. / Colloids and Surfaces A: Physi

Table 2Temperature dependence of the isotherm parameters for copper biosorption ontoSaccharomyces from aqueous solutions at pH 4.

Type of isotherm T = 308 K T = 323 K

Freundlich

n = 2.444 n = 3.176KF = 0.323 KF = 0.546R2 = 0.707 R2 = 0.890ARE = 19.075 ARE = 8.409

Langmuir

Qmax = 2.402 Qmax = 2.595KL = 5.619 × 10−2 KL = 7.466 × 10−2

R2 = 0.982 R2 = 0.994ARE = 13.652 ARE = 5.524

Redlich–Peterson

A = 6.326 × 10−2 A = 0.118B = 1.021 × 10−3 B = 2.007 × 10−2

g = 1.630 g = 1.153ARE = 6.345 ARE = 4.073

S

QSM = 2.154 QSM = 2.592−4 −2

Q

wAshc

z Qexpi

ipsb = 3.16518 × 10 b = 4.110 × 10nS = 0.369 nS = 0.861ARE = 2.649 ARE = 4.498

The Redlich–Peterson isotherm:

e = A Ce

1 + B Cge

(5)

here Qe and Ce have the same definitions as in Eqs. (3) and (4),

(L/g) and B (L mmol−1)g are the Redlich–Peterson isotherm con-

tants, while g is the Redlich–Peterson isotherm exponent whichas values between 0 and 1. For g = 1 the Redlich–Peterson modelonverts to the Langmuir model.

Fig. 1. Adsorption isotherms obtained a

Fig. 2. Adsorption isotherms obtained at p

cochem. Eng. Aspects 335 (2009) 181–188 183

The Sips isotherm:

Qe = QSM b C1/nSe

1 + b C1/nSe(6)

where QSM is the Sips maximum adsorption capacity (mg/g) andb is the Sips constant related to affinity between solute andsorbent.

It is noticeable that, at low solute concentrations the Sips equa-tion is reduced to a Freundlich isotherm, while at high soluteconcentration it predicts a monolayer adsorption capacity charac-teristic of Langmuir equilibrium equation [24].

The parameters of Redlich–Peterson (A, B, g) and Sips (QSM, b,nS) isotherms, listed in Tables 1 and 2, were determined by meansof non-linear regression method using a Gauss–Newton technique.The non-linear regression method provides a mathematically rig-orous method for determining isotherm parameters by using ofthe original form of the isotherm equation. This is an advantagecomparing to linear regression approach where the deviation biasappears as a result of linearization.

In order to reveal the agreement of the equilibrium models withexperimental results, the average relative error (ARE) was computedas follows [20]:

ARE (%) = 100z∑(

|Qexp − Qcalc |)

(7)

i=1

where z is the number of data points; Qexp and Qcalc are theexperimental sorption capacity and calculated sorption capacity bytheoretical models, respectively.

t 293 K for pH 4 (a) and pH 5 (b).

H 4 for T = 308 K (a) and T = 323 K (b).

1 Physicochem. Eng. Aspects 335 (2009) 181–188

ae

ptmSpAfctvLt2FaRt

pd

TteLpcu

ITtshCofb

attmre

ta

R

wttaato0Ct

84 C. Cojocaru et al. / Colloids and Surfaces A:

The average relative errors, represented in Tables 1 and 2,pprove the effectiveness of the applied models in fitting of thexperimental data.

According to results represented in Tables 1 and 2, the cop-er uptake was in the range from 2.40 to 2.59 mg/g at pH 4.0 andhe values of the Langmuir isotherm parameters indicate that the

onolayer saturation capacity of Cu(II) ions, Qmax, is 2.59 mg/g for. cerevisiae dried cells. This value is comparable with the cop-er ions uptake by yeast biomass reported in the literature [2,25].s the illustration, the copper uptake by S. cerevisiae was ranged

rom 2.04 to 9.05 mg/g for adapted and growing cells, while forommercially available dry cells it was in the range from 2.98o 12.03 mg/g [2,25,26]. The Sips model provided slightly loweralues of maximum adsorption capacity than those achieved byangmuir equation. According to Sips model, the maximum adsorp-ion capacity for dry yeast cell was in the range from 2.02 to.59 mg/g for pH 4.0 and different temperature conditions. Thereundlich isotherm constant n indicates that the intensity ofdsorption is increasing with the increment of temperature. Theedlich–Peterson isotherm constants indicate that this model tendsoward a Langmuir isotherm.

Using the isotherm parameters and mass balance equations, theredicted amount of copper adsorbed per gram of sorbent wasetermined and compared with experimental data (Figs. 1 and 2).

As can be seen from Figs. 1 and 2 and ARE values listed inables 1 and 2, the Sips isotherm give the best fit with experimen-al results. The other models, in order of decreasing agreement withxperimentally obtained results, are as follows: Redlich–Peterson,angmuir and Freundlich. Thus, the equilibrium models with threearameters (Sips and R–P) fit better to the experimental resultsomparing to the two parameters models (Langmuir and Fre-ndlich).

Likewise, Fig. 1 shows the effect of pH on the adsorption capacity.ncreasing pH from 4 to 5 lead to a decrease of adsorption capacity.his could be attributed to the strong effect of initial concentra-ion of solute which is very high comparing with the relatively loworption capacity of biosorbent. Also, parallel precipitation of Cu(II)ydroxide is possible which means that the initial concentration ofu(II) ion is lower than it should be. Besides, increase in densityf the negative charge on the cell surface, causing proton removalrom the solution, thereby decreasing biosorption capacity of Cu(II)ecause of competitively adsorption of protons.

The adsorption capacities, reported for different temperatures,re represented in Fig. 2. As can be seen, a slight increase of adsorp-ion capacity was induced with temperature growth. According tohe obtained results, the isotherm obtained by using of Langmuir

odel at 323 K fit the experimental data better than the isothermseported for lower temperatures and also better than three param-ters models.

On the basis of additional analysis of the Langmuir equation,he dimensionless parameter of the equilibrium (RL—known alsos separation factor) may be calculated as [27]

L = 11 + KLCo

(8)

here Co (in mg/L) is the initial concentration of solute. Accordingo the value of separation factor RL, following types of adsorp-ion exists [27]: (1) favorable adsorption 0 < RL < 1; (2) unfavorabledsorption RL > 1; (3) linear adsorption RL = 1; (4) irreversibledsorption RL = 0. In our experiments, the initial solute concentra-

ion was in the range of 25–200 mg/L and corresponding valuesf the separation factor RL has been found to be in the range of.063–0.501. Hence, the results underlines that the adsorption ofu(II) onto the dried cells of biomass is favorable under all condi-ions considered in this work.

Fig. 3. Dependence of ln(KL) vs. 1/T for Cu(II) adsorption on dried cells of Saccha-romyces cerevisiae.

3.2. Thermodynamics of adsorption

The thermodynamics of sorption of the copper onto the driedcells of S. cerevisiae biosorbent was evaluated using the followingequations:

�G = −RgT ln KL (9)

ln KL = −�H

RgT+ const (10)

�G = �H − T �S (11)

where �H, �S, �G and T are the enthalpy, entropy, Gibbs freeenergy, and absolute temperature, respectively; Rg is the gas con-stant and KL the equilibrium constant (L/mol). The computationof thermodynamic parameters gives the following numerical val-ues of �G = (−20.98 ± 1.72) kJ/mol, �H = (14.33 ± 0.51) kJ/mol and�S = (114.67 ± 0.11) J/mol K. The negative value of �G indicates thespontaneous nature of adsorption process. Positive value of �H(computed from the slope of linear dependence of ln(KL) versus 1/T(R2 = 0.997) as shown in Fig. 3) indicates the endothermic enthalpyof adsorption, favored by increased temperatures, while the pos-itive value of �S shows the affinity of the adsorption for copperions.

As we noticed, according to data shown in Fig. 3, the adsorptionof Cu(II) onto the dried cells of S. cerevisiae is an endothermic pro-cess which becomes more favorable with the increasing of solutiontemperature.

3.3. Response surface modeling

The response surface methodology deals with experimentaldesign strategy, statistical modeling and process optimization. TheRSM applications concerns the particular situations where severalinput variables (factors), which can be set by experimenter, influ-ence the result of measurement (response). Also, RSM includes theempirical statistical modeling in order to develop an appropriateapproximating relationship between the response and factors to beused for process optimization. In this work the response surfacemodeling was applied to develop an appropriate regression model

to find out the optimal values of experimental factors in order toenhance removal efficiency. The most significant factors that affectsorption removal efficiency (response) are the initial concentrationof copper ions in aqueous solutions Co (mg/L), initial pH of solutionand the biosorbent dose (%, w/v). The real values of independent

C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188 185

Table 3Central composite orthogonal design and experimental response.

Run number (N) Factors (input variables) Response

Initial concentration of Cu(II) pH of solution Biosorbent dose (%, w/v) Removal efficiency,b Y%

Co (mg/L) Levela x1 pH Levela x2 BSD Levela x3

1 75 1 5 1 2.25 1 32.732 25 −1 5 1 2.25 1 37.963 75 1 3 −1 2.25 1 36.984 25 −1 3 −1 2.25 1 41.225 75 1 5 1 1.50 −1 29.476 25 −1 5 1 1.50 −1 34.697 75 1 3 −1 1.50 −1 34.208 25 −1 3 −1 1.50 −1 38.949 80 ˛ 4 0 1.88 0 20.64

10 20 −˛ 4 0 1.88 0 37.4011 50 0 5.2 ˛ 1.88 0 33.9612 50 0 2.8 −˛ 1.88 0 32.9813 50 0 4 0 2.33 ˛ 39.9214 50 0 4 0 1.42 −˛ 35.0215 50 0 4 0 1.88 0 36.33

0

vp

m(c

Y

s

wtv

oFemoat

Y

16 50 0 4

a 1 = low value, 0 = center value, +1 = high value, ±˛ = star point value.b The removal efficiency Y% was determined experimentally by using Eq. (2).

ariables as well as their coded limits are listed in the central com-ositional experimental design given in Table 3.

As can be seen from Table 3, the coefficients of the empiricalodel have been calculated by means of multiple linear regression

MLR) method [28] and according to MLR empirical model (withoded variables) may be written as follows:

ˆ = 33.87 − 3.633x1 − 1.397x2 + 1.602x3 − 2.205x21 + 3.523x2

3

ubjected to : − ˛ ≤ xj ≤ +˛; j = 1, 2, 3; (12)

here x1, x2 and x3 are the coded levels of factors and ˛ = 1.215 ishe star point in experimental design that gives the limits of thealid region (region of experimentation).

The regression coefficients were tested for significance by meansf Student’s t-test [28] retaining in Eq. (12) only the significant ones.or the graphical representation and analysis of the factors’ influ-nce upon response it is worth converting the response surfaceodel in terms of coded variables to an empirical model in terms

f actual variables. For this purpose the substitution technique waspplied and the empirical coefficients were computed. Thus, inerms of actual variables the empirical model may be written as

ˆ = 117.969 + 0.207 C0 − 1.397 pH − 89.675 BSD

Fig. 4. Removal efficiency, experimental data plotted against the

1.88 0 36.06

−3.528 × 10−3C20 + 25.052 BSD2

subjected to : 20 ≤ C0 ≤ 80 (mg/L); 2.8 ≤ pH ≤ 5.2;

1.42 ≤ BSD ≤ 2.33 (%, w/v) (13)

The goodness-of-fit between empirical model and experimen-tal data was verified using FC-ratio test [28] for a confidence levelp = 0.05 and degrees of freedom f1 = 9 and f2 = 1. The FC-ratio wascomputed as the ratio between the variance of residual and thevariance of experimental error (replication). The FC-ratio was foundto be FC = 234.1 that is smaller than tabulated value Ftab (p, f1,f2) = 240.5. This means that the mathematical model is validatedfrom statistical standpoint. Fig. 4 compares the experimental andpredicted data of removal efficiency. Likewise, the model ade-quacy has been investigated by the examination of the residuals(e = Yexp − Ypredic). The residual analysis is also shown in Fig. 4 out-lining a good concordance between experimental and predicted

responses.

As one can see the response model shows a goodness-of-fit toexperimental data (Fig. 3). Therefore, the model has been consid-ered adequate for the prediction and optimization. Having a validmodel the graphical representations of the response surface were

predicted ones and residual analysis of empirical model.

186 C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188

o and

pF

irtriCta

Fig. 5. Response surface plot and contour-line map depending on C

lotted. The most relevant response surface plots are reported inigs. 5–7.

The resulted response surface plots and contour maps shownn Figs. 5–7 indicate the influence of the experimental factors onemoval efficiency. Thus, the first variable Co (initial concentra-ion of copper ions in aqua solutions) has the biggest influence on

esponse (removal efficiency) being ca. 2.4-fold greater than thenfluence of other variables. The increasing of initial concentrationo leads to obviously diminishing of the removal efficiency whilehe increment of pH contributes to slightly decreasing of responsend the rise of sorbent amount, BSD, results in increasing of the

Fig. 6. Response surface plot and contour-line map depending on C

Fig. 7. Response surface plot and contour-line map depending on pH an

pH, holding the other variable at its center level BSD = 1.88% (w/v).

removal efficiency especially for the values of BSD > 1.88% (w/v).For the opposite interval (i.e. BSD < 1.88%, w/v) the trend is slightlyreversed and the effect of this variable on response is diminished inthis interval. This could be attributed to small interval of variationof BSD variable.

3.4. Response surface optimization

The goal of the optimization task was to find the optimal valuesof the factors into the region of experimentation in order to ensurethe maximal value of the removal efficiency. For this purpose, the

o and BSD, holding the other variable at its center levels pH 4.

d BSD, holding the other variable at its center levels Co = 50 mg/L.

C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188 187

Table 4Optimal biosorption parameters for maximum removal efficiency.

Initial Cu(II) concentration pH of solution ˆ

x1 Co (mg/L) x2 pH

−0.973 25.7 −1.080 3

Table 5The main characteristics of wastewater stream from electroplating industry.

Wastewater characteristics Values

Particles in suspension 55.3 mg/LTurbidity 19 FTUpH 6.87Fixed residue 1.08 g/LSO4

2− 356.4 mg/LCOD 219.2 mg/LBCS

dt

m

tT

xnoitsaomctpt

toeTa

fistrocwcpFa

4

ri

[

[

OD 108.3 mg/Lu(II) 23.2 mg/Ln(II) 1.8 mg/L

eveloped regression Eq. (12) has been used as the objective func-ion for prediction and optimization that may be written as

axx

{Y(x1, x2, x3, )} subject to : xj ∈ [−˛, +˛], j = 1, 3 (14)

First of all, the canonical analysis was employed to determinehe stationary point of the objective function and the nature of this.he canonical analysis has lead to the following stationary point

¯S =(

−0.824 1 −0.227)T

. According to canonical analysis, theature of stationary point reveals the existence of a minimax pointr point of inflexion. In order to determine the optimal point locatednside of the valid region the stochastic simulation-optimizationechnique was employed (i.e. Monte–Carlo method) [29]. A set oftochastic simulation was carried out using multistage zooming inpproach to localise the optimal point more accurately. The foundedptimal point by stochastic simulations corresponds to the maxi-al value of removal efficiency computed into the valid region. The

onfirmation run was carried out in order to verify experimentallyhe computed optimal point. The results are shown in Table 4 byresenting both coded levels as well as the actual optimal values ofhe factors.

As can be observed from Table 4 the optimal point is located intohe region of experimentation (valid region). The removal efficiencybtained experimentally under these conditions represent the bestxperimental point comparing with the results reported in Table 3.he discrepancy between the response given by empirical modelnd experiment is in agreement with the model adequacy.

In addition, the yeast biomass was applied for copper removalrom local wastewater stream originating from electroplatingndustry. The main characteristics of wastewater employed arehown in Table 5. In the sorption tests with the real wastewa-er a biosorbent dosage of 2.32% (w/v) was used. In this case, theemoval efficiency of cooper was 35.88%. The removal percentagebtained for real wastewater is lower than maximal removal effi-iency of 43.08% determined in optimal conditions for syntheticastewater (aqua solutions). This can be attributed to complex

omposition of real wastewater and possible competitive sorptionrocesses on binding sites of different species from wastewaters.or instance, the final concentration of Sn(II) after biosorption wasbout 0.72 mg/L.

. Conclusions

The possibility of use of dried yeast biomass of Saccharomyces toemove Cu(II) ions from aqueous solution was investigated. Exper-mentally obtained results were fitted by using of two and three

[

[

Biosorbent dose Y% exp. Y% model

x3 BSD (%, w/v)

1.191 2.32 43.08 43.73

parameters equilibrium isotherm equations. We found out that theSips model showed the best agreement with experimental data.According to Langmuir isotherm, the maximum adsorption capac-ity of investigated non-living biomass was found to be 2.59 mg/g.The negative value of free energy �G indicates the spontaneousnature of sorption. The adsorption of Cu(II) onto the dried cells ofS. cerevisiae is an endothermic process and become more favorablewith the increasing of temperature in pH range from 3 to 4.

Likewise, the response surface methodology and central com-posite experimental design (CCD) have been employed foroptimization the removal efficiency of Cu(II) ions from aqueoussolutions by sorption onto the used biomass. The optimal condi-tions for Cu(II) sorption according to RSM are: Co = 25.7 mg/L, pH3, and BSD = 2.32% (w/v); under these conditions the removal effi-ciency determined by experiment was of 43.08% being the bestexperimental point determined into the region of experimentation.

The removal percentage obtained for real wastewater is lowerthan maximal removal efficiency determined in optimal conditionsfor synthetic wastewater because of complex composition of realwastewater and possible competitive sorption processes on bindingsites of different species from wastewaters.

Acknowledgements

C.C., M.D. and I.C. greatly acknowledge the Ministry of Scienceof Romania; J.S. and V.V. would like to acknowledge the Ministry ofScience of the Republic of Serbia (Grant no. 142051).

References

[1] Y.S. Ho, J.F. Porter, G. McKay, Equilibrium isotherm studies for the sorptionof divalent metal ions onto peat: copper, nickel and lead single componentsystems, Water, Air Soil Pollut. 141 (2002) 1–33.

[2] J. Wang, C. Chen, Biosorption of heavy metals by Saccharomyces cerevisiae: areview, Biotechnol. Adv. 24 (2006) 427–451.

[3] A.I. Zouboulis, K.A. Matis, M. Loukidou, F. Sebesta, Metal biosorption by PAN-immobilized fungal biomass in simulated wastewaters, Colloids Surf. A 212(2003) 185–195.

[4] K. Parvathi, R. Nagendran, R. Nareshkumar, Lead biosorption onto waste beeryeast by-product, a means to decontaminate effluent generated by batterymanufacturing industry, Electronic J. Biotechnol. 10 (2007) 92–105.

[5] E.L. Cochrane, S. Lu, S.W. Gibb, I. Villaescusa, A comparison of low-cost biosor-bents and commercial sorbents for the removal of copper from aqueous media,J. Hazard. Mater. B137 (2006) 198–206.

[6] J.C. Igwe, A.A. Abia, A bioseparation process for removing heavy metals fromwaste water using biosorbents, Afr. J. Biotechnol. 5 (2006) 1167–1179.

[7] M. Mapolelo, N. Torto, Trace enrichment of metal ions in aquatic environmentsby Saccharomyces cerevisiae, Talanta 64 (2004) 39–47.

[8] P. Vasudevan, V. Padmavathy, S.C. Dhingra, Kinetics of biosorption of cadmiumon Baker’s yeast, Bioresour. Technol. 89 (2003) 281–287.

[9] V. Padmavathy, P. Vasudevan, S.C. Dhingra, Thermal and spectroscopic stud-ies on sorption of nickel(II) ion on protonated baker’s yeast, Chemosphere 52(2003) 1807–1817.

10] V. Padmavathy, P. Vasudevan, S.C. Dhingra, Biosorption of nickel ions on baker’syeast, Process Biochem. 38 (2003) 1389–1395.

[11] L.N.L. Vianna, M.C. Andrade, J.R. Nicoli, Screening of waste biomass from Sac-charomyces cerevisiae, Aspergillus oryzae and Bacillus lentus fermentations forremoval of Cu, Zn and Cd by biosorption, World J. Microbiol. Biotechnol. 16(2000) 437–440.

12] B. Volesky, Advances in biosorption of metals: selection of biomass types, FEMSMicrobiol. Rev. 14 (1994) 291–302.

13] R. Ashkenazy, S. Yannai, R. Rahman, E. Rabinovitz, L. Gottlieb, Fixation of spentSaccharomyces cerevisiae biomass for lead sorption, Appl. Microbiol. Biotechnol.52 (1999) 608–611.

14] M. Mapolelo, N. Torto, B. Prior, Evaluation of yeast strains as possible agentsfor trace enrichment of metal ions in aquatic environments, Talanta 65 (2005)930–937.

1 Physi

[

[

[

[

[

[

[

[

[

[

[

[

[adsorption on Celtek clay, J. Serb. Chem. Soc. 72 (2007) 485–494.

[28] S. Akhnazarova, V. Kafarov, Experiment Optimization in Chemistry and Chem-

88 C. Cojocaru et al. / Colloids and Surfaces A:

15] Y. Göksungur, S. Üren, U. Güvenc, Biosorption of cadmium and lead ions byethanol treated waste baker’s yeast biomass, Bioresour. Technol. 96 (2005)103–109.

16] K.S. Low, C.K. Lee, S.C. Liew, Sorption of cadmium and lead from aqueous solu-tions by spent grain, Process Biochem. 36 (2000) 59–64.

17] V.K. Gupta, A. Rastogi, V.K. Saini, N. Jain, Biosorption of copper(II) from aqueoussolutions by algae spirogyra species, J. Colloid Interface Sci. 296 (2006) 59–63.

18] W. Jianlong, Biosorption of copper(II) by chemically modified biomass of Sac-charomyces cerevisiae, Process Biochem. 37 (2002) 847–850.

19] R. Han, H. Li, Y. Li, J. Zhang, H. Xiao, J. Shi, Biosorption of copper and lead ionsby waste beer yeast, J. Hazard. Mater. 137 (2006) 1569–1576.

20] A.I. Ferraz, T. Tavares, J.A. Teixeira, Cr(III) removal and recovery from Saccha-romyces cerevisiae, Chem. Eng. J. 105 (2004) 11–20.

21] P. Dostalek, M. Patzak, P. Matejka, Influence of specific growth limitation onbiosorption of heavy metals by Saccharomyces cerevisiae, Int. Biodeterior. Biode-grad. 54 (2004) 203–207.

22] A. Özer, D. Özer, Comparative study of the biosorption of Pb(II), Ni(II) and Cr(VI)ions onto S. cerevisiae: determination of biosorption heats, J. Hazard. Mater.B100 (2003) 219–229.

[

cochem. Eng. Aspects 335 (2009) 181–188

23] B. Mattuschka, G. Straube, Biosorption of metals by waste biomass, J. Chem.Technol. Biotechnol. 58 (1993) 57–63.

24] A.B. Perez-Marin, V. Meseguer Zapata, J.F. Ortuno, M. Aquilar, J. Saez, M. Lorens,Removal of cadmium from aqueous solutions by adsorption onto orange waste,J. Hazard. Mater. B139 (2007) 122–131.

25] J.R. Zhu, L. Xu, Simulated experiment of Cu2+ treatment using Saccharomycescerevisiae, Chin. J. Appl. Environ. Biol. 4 (1998) 400–404.

26] G. Dönmez, Z. Aksu, The effect of copper(II) ions on the growth and bioac-cumulation properties of some yeasts, Process Biochem. 35 (1999) 135–142.

27] A. Sari, M. Soylak, Equilibrium and thermodynamic studies of stearic acid

ical Engineering, Mir Publishers, Moscow, 1982, p. 151.29] M. Redhe, M. Giger, L. Nilsson, An investigation of structural optimization in

crashworthiness design using a stochastic approach, Struct. Multidisc. Optim.27 (2004) 446–459.