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Biosorption Equilibria of Binary Cd(II) and Ni(II)Systems onto Saccharomyces cerevisiae andRalstonia eutropha Cells: Application of

Response Surface Methodology

ARTICLE in JOURNAL OF HAZARDOUS MATERIALS APRIL 2009

Impact Factor: 4.53 DOI: 10.1016/j.jhazmat.2009.03.041 Source: PubMed

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3 AUTHORS:

Mohammad Fereidouni

Birjand University of Medical Sciences

24PUBLICATIONS 115CITATIONS

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Ali Daneshi

9PUBLICATIONS 341CITATIONS

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Habibollah Younesi

Tarbiat Modares University

173PUBLICATIONS 2,194CITATIONS

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Available from: Habibollah Younesi

Retrieved on: 09 March 2016
https://www.researchgate.net/profile/Habibollah_Younesi?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_7https://www.researchgate.net/profile/Habibollah_Younesi?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_4https://www.researchgate.net/profile/Habibollah_Younesi?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_5https://www.researchgate.net/profile/Mohammad_Fereidouni2?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_4https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/publication/24428190_Biosorption_Equilibria_of_Binary_CdII_and_NiII_Systems_onto_Saccharomyces_cerevisiae_and_Ralstonia_eutropha_Cells_Application_of_Response_Surface_Methodology?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_3https://www.researchgate.net/?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDIzMjE1&el=1_x_1https://www.researchgate.net/profile/Habibollah_Younesi?enrichId=rgreq-9f0c07b4-c871-4b2d-9eb2-c85eb2b92c7a&enrichSource=Y292ZXJQYWdlOzI0NDI4MTkwO0FTOjEwMjgyMTc0Nzc1NzA1N0AxNDAxNTI2MDI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Journal of Hazardous Materials 168 (2009) 14371448

Contents lists available atScienceDirect

Journal of Hazardous Materials

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j h a z m a t

Biosorption equilibria of binary Cd(II) and Ni(II) systems ontoSaccharomyces cerevisiaeandRalstonia eutrophacells: Application ofresponse surface methodology

Mohammad Fereidouni, Ali Daneshi, Habibollah Younesi

Department of Environmental Science, Faculty of Natural Resources & Marine Sciences, Tarbiat Modares University, P.O. Box 46414-356, Imam Reza Street, Noor, Iran

a r t i c l e i n f o

Article history:Received 9 September 2008Received in revised form 9 March 2009Accepted 10 March 2009Available online 18 March 2009

Keywords:

BiosorptionRalstonia eutropha

Saccharomyces cerevisiae

RSMNi(II)Cd(II)

a b s t r a c t

Present study investigated the biosorption of Cd(II) and Ni(II) from aqueous solution onto Saccharomycescerevisiaeand Ralstonia eutropha non-living biomass. Biomass inactivated by heat and pretreated byethanol was used in determination of optimum conditions. The important process parameters, such asinitialsolutionpH (28), initial Ni(II)concentration (1142mg/l), initial Cd(II)concentration (1142mg/l),and biomass dosage(0.24.7g/l) were optimizedusing design of experiments (DOE). A central compositedesign (CCD) under response surface methodology (RSM) was applied to evaluate and optimize the effi-ciency of removing each adsorbent. Moreover, the two responses were simultaneously studied by usinga numerical optimization methodology. The optimum removal efficiency of Cd(II) and Ni(II) ontoS. cere-visiaewas determined as 43.4 and 65.5% at 7.1 initial solution pH, 4.07 g/l biomass dosage, 16 mg/l initialNi(II) concentration and 37mg/l initial Cd(II) concentration. The optimum removal efficiency of Cd(II)and Ni(II) ontoR. eutrophawas ascertained as 52.7 and 50.1% at 5.0 initial solution pH, 2.32 g/l biomassdosage, 28 mg/l initial Ni(II) concentration and 37 mg/l initial Cd(II) concentration. The present analysissuggests that the predicted values are in good agreement with experimental data. The characteristics ofthe possible interactions between biosorbents and metal ions were also evaluated by scanning electronmicroscope (SEM) and Fourier transform infrared (FT-IR) spectroscopy analysis.

2009 Published by Elsevier B.V.

1. Introduction

The intensification of industrial activity during recent years isgreatly contributing to the increase of heavy metals in the envi-ronment, mainly in the aquatic systems[1].The main sources ofheavy-metal pollution are mining, milling and surface finishingindustries, discharging a variety of toxic metals such as Cd, Cu, Ni,Co, Zn and Pb into the environment[2,3]. It is well known thatheavy metals can be extremely toxic as they damage nerves, liverand bones, and also block functional groups of vital enzymes[2].

Ni(II) is one such heavy metal frequently encountered in raw

wastewater streams from industries such as non-ferrous metal,mineral processing, paint formulation, electroplating, porcelainenameling, copper sulphate manufacture and steam-electric powerplants[4,5].Nickel is also listed as a possible human carcinogen(group 2B) and associated with reproductive problems and birthdefects. Besides, a rangeof detrimental effects on faunaand flora arealso well documented[2].Cadmium has a half-life of 1030 years[6]and its accumulation in human body affects kidney, bone and

Corresponding author. Tel.: +98 122 625 31013; fax: +98 122 625 3499.E-mail addresses:[email protected],[email protected](H. Younesi).

also causescancer andits useis increasing in industrial applicationssuch as electroplating and making pigments and batteries[7].

Since these heavy metals are a valuable resource for differentindustrial applications, their recovery and recycling assumes evengreater significance. Further, strictenvironmental regulations com-pel industries to shift to cleaner production methods, demandingthe development of environmentallyfriendly, low-costand efficienttreatment techniques for metal rich effluents[2,8].Biosorption isan emerging and attractive technology which involves sorption ofdissolved substances by a biomaterial. It is a potential techniquefor the removal of heavy metals from solutions and recovery of

precious metals[9,10].Even though there are many methods forthe removal of metal ions from solutions, such as chemical pre-cipitation[11,12],solvent extraction[1315],membrane processes[16] and adsorption on activated carbon [17], biosorption processesshow many advantages over these methods. It is selective, effectiveandcheapandisabletoremoveverylowlevelsofheavymetalsfromsolutions. While the conventional methods have several disadvan-tages, which include incomplete metal removal, and toxic sludgegeneration[18,19].In present years, different kinds of non-livingbiomass including bacteria, fungi, [20] algae, mosses, macrophytes,higher plants,[21]plant material[22,23]and waste products fromindustrial or agricultural operations[24]have been examined as

0304-3894/$ see front matter 2009 Published by Elsevier B.V.

doi:10.1016/j.jhazmat.2009.03.041
http://www.sciencedirect.com/science/journal/03043894http://www.elsevier.com/locate/jhazmathttp://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-mailto:[email protected]:[email protected]://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://localhost/var/www/apps/conversion/tmp/scratch_3/dx.doi.org/10.1016/j.jhazmat.2009.03.041http://localhost/var/www/apps/conversion/tmp/scratch_3/dx.doi.org/10.1016/j.jhazmat.2009.03.041http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-mailto:[email protected]:[email protected]://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://www.elsevier.com/locate/jhazmathttp://www.sciencedirect.com/science/journal/03043894


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1438 M. Fereidouni et al. / Journal of Hazardous Materials 168 (2009) 14371448

potential biosorbent for heavy metals. Non-living biomass appearsto present specific advantages in comparison to the use of liv-ing microorganisms. Non-living cells may be stored or used forextended periods at room temperature, they are not subject tometal toxicity and nutrient supply is not necessary[1].

Response surface methodology (RSM) is a collection of mathe-matical and statistical techniques useful for analyzing the effectsof several independent variables on the response[25,26].RSM hasan important application in the process design and optimizationas well as for the improvement of existing design. This method-ology has become much more practical with the development ofinteractive computer programs compared to traditional method asit includes interactive effects among the variables while buildingon the experimenters prior knowledge and, eventually, it depictsthe overall effects of the parameters on the process[27].

The objective of the present research was to study the effectof initial Cd(II) and Ni(II) concentrations, pH and biomass con-centration on biosorption of Cd(II) and Ni(II) using Saccharomycescerevisiae and Ralstonia eutropha non-living biomass. The main aimof this work was to compare the biosorption characteristics ofS.cerevisiaeand R. eutrophafor the removal of Cd(II) and Ni(II) in anaqueous solution. Furthermore, optimum conditions for elimina-tion of Cd(II) and Ni(II) were determined with RSM under design of

experiment (DOE).

2. Materials and methods

2.1. Microorganism and its preparation for biosorption

The yeastS. cerevisiae(PTCC 5010) was provided from Researchand Technology Department of Ministry of Science, Iran (PersianType Culture Collection) in the form of freeze-dried, and then cul-tured in sterilized medium. The composition of growth mediumwas (grams per liter): 9; (NH4)2SO4, 40; sugarcane molasses, 2.5;MgSO4, 1.00; yeast extract, 1.00; KH2PO4, 0.2; K2HPO4. The bac-terium R. eutropha (DSM 534)was provided by Deutsche Sammlung

von Mikroorganismen und Zellkulturen (DSMZ) in the form offreeze dry. The composition of growth medium was (grams perliter): 2.30; KH2PO4, 2.90; Na2HPO4, 1.00; NH4Cl, 0.50; MgSO4,0.50; NaHCO3, 0.01; CaCl2, 0.05; Fe(NH4) citrate, 40; sugarcanemolasses andcon syrup,and 5 ml trace element solution (gramsperliter): MgSO4, 2.2 g; FeSO4,0.1 g; MnSO4,0.1g;K2SO4,2.2g; H3BO3,0.02g; CuSO4, 0.08 g.

The medium was sterilized by autoclaving at a pressure of1.5atm and temperature of 121C for 20 min. Temperature and pHof growth condition were at ambient temperature (25 C) and 6.8,respectively, with shaking at 200 rpm. The yeasts and bacteria cellswere collected at the end of the exponential phase, and then cen-trifuged at 3000 rpm for 15 min, followed thrice by re-suspensionin deionized water and re-centrifugation. Collected cells were ovendried at 70 C for 20h. The yeasts and bacteria were ground andscreened through a set of sieve with 230 meshes. The pretreat-ment of the biosorbent was carried out non-living of cells into 70%ethanol for 20 min at room temperature. Then, the cells were cen-trifuged, washed, dried and powdered as mentioned above. Thusa monotonous powder was produced which was stocked in therefrigerator at 4 C for future use.

2.2. Scanning electron microscopy (SEM) experiments

Scanning electron microscope (SEM,Phillips XL30, Holland) wasused for the observation of S. cerevisiae and R. eutropha beforeand after treatment by 70% ethanol. The morphology of non-livingbiomass ofS. cerevisiae [28]and R. eutrophawas assessed before

and after treatment.

2.3. Fourier transform infrared (FT-IR) spectroscopy analysis

FT-IR spectroscopy was used to detect changes in vibration fre-quency in the S. cerevisiae and R. eutropha biomass. The spectrawere collected by FTS-135 (Bio-Rad) spectrometerwithin the range4004000cm1 using a KBr window. The background obtainedfrom the scan of pure KBr was automatically subtracted from thesample spectra. Spectra were plotted using the same scale on theabsorbance axis.

2.4. Preparation of metal solutions

Cd(II) solution (1000 mg/l) was preparedby dissolving 2.282 g ofCd(II) sulphate (CdSO48/3 H2O) (Merck) in deionized water. StockNi(II) solution was prepared by dissolving 4.050g of NiCl26H2O(Merck) in deionized water. All solutions, their dilutions andstandards were prepared using deionized water (EYELA STILLACESA-2100E1). Any pH adjustments were made using 0.1 M H2SO4 and0.1 M NaOH.

2.5. Metal biosorption studies

In order to study the effect of initial solution pH, initial Cd(II)and Ni(II) concentrations and biomass dosage on removal effi-ciency of each ion, thirty batch biosorption experiments designedby response surface methodology were conducted at the equilib-rium time of 240min, agitation speed of 200 rpm and temperatureof 25 C. Each experiment was carried out in 250 ml Erlenmeyerflasks containing 100 ml Cd(II) and Ni(II) solution by shaking theflasks at 120rpm for period contact time of 240 min. Samples werewithdrawn at pre-determined timeintervals (2, 5, 10, 15, 20, 30, 40,60, 120 and 240 min) and filtered through 0.25m filters. Filteredsamples were analyzed for residual Cd(II) and Ni(II) concentration.Metal removal by both yeast and bacterium were determined asaccording to Eq.(1):

R=

C0 Ce

C0 100 (1)

where R is the percentage of metal adsorbed by biomass in percent-age, C0is the initial concentration of metal ion in mg/l and Ceis theequilibrium concentration of metal ion in mg/l[1,29].

2.6. Experimental and optimization of biosorption

Optimum conditions for the biosorption of Cd(II) and Ni(II) byS. cerevisiae and R. eutropha were determined by means of cen-tral composite design (CCD) under response surface methodology.The RSM consists of a group of empirical techniques devoted tothe evaluation of relationship existing between a cluster of con-trolled experimental factors and measured responses according to

oneor more selected criteria.Optimization studies were carried outby studying the effect of four variables including S. cerevisiaeandR. eutropha doses, initial Cd(II) and Ni(II) concentrations and pHof solutions[3032].The independent variables used in this studywere coded according to Eq.(2):

xcoded =XActual (XHi + XLow) /2

(XHi XLow)/2 (2)

wherex isthecodedvariables, Xis the actual variables.The behaviorof the system is explained by the following empirical second-orderpolynomial model Eq.(3):

y = 0 +

k

i=1ixi +

k

i=1iix

2i +

k1

i=1k

j=2ijxixj + (3)
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M. Fereidouni et al. / Journal of Hazardous Materials 168 (2009) 14371448 1439

Table 1

Experimental ranges and levels of the independent variables.

Independent variables Range and level

(1.414) 1 0 +1 +(1.414)

pH (X1) 2.1 3.0 5.1 7.1 8.0Biomass (X2) 0.20 0.86 2.45 4.04 4.70Initial cadmium concentration (X3) 11 16 27 37 42Initial nickel concentration (X4) 11 16 27 37 42

wherey isthepredictedresponse, xi,xj . . .xk are the input variables,which affect the responsey,x2

i,x2

j, . . .,x2

kare the square effects,xixj,

xixkandxjxkare the interaction effects,0is the intercept term, i(i =1, 2, . . .,k) is the linear effect,ii (i =1, 2,. . .,k) is the squaredeffect,ij(i = 1, 2,. . .,k;j = 1, 2,. . .,k) is the interaction effect andis a random error[33,34].

The Design-Expert 7.0.0 (Stat-Ease, Inc., Minneapolis, MN, USA)software was used for regression and graphical analysis of theobtained data. The most popular response surface methodologydesignis thecentralcomposite designwhichis designed toestimatethe coefficients of quadratic Eq.(3). A design of 30 experimentsfor the four factor case, i.e. sixteen factorial points, eight axial(star) points and six replicate points at the central points, wereemployed to the quadratic model. The optimum values of theselected variables were obtained by solving the regression equa-tion at desired values of the process responses as optimizationcriteria. Each of the parameters was coded at five levels of eachfactor: , 1, 0, +1 and +. The range and level of the variablein coded values from RSM studies are given inTable 1.The codedand actual values of the test variables as well as the experimen-

tal and predicted values of removal efficiency of Cd(II) and Ni(II) asresponses for optimization of the process variables arealso given inTable 2.

3. Results and discussion

3.1. Fitting the process models

The results of the statistical analysis for removal efficiency ofCd(II) and Ni(II) onto S. cerevisiae and R. eutrophaaccording toanalysis of variance (ANOVA) are given in Table 3. Model ade-quacy was tested through lack-of-fit (LOF), P-values and F-values

[35]. The lack-of-fit term was insignificant as desired (Table 3).The insignificant value of lack-of-fit (more than 0.05) showed thatthe models were valid for the present study[36].The lack-of-fitF-values of 3.60, 0.37, 4.46 and 3.04 for Cd(II) and Ni(II) onto S.cerevisiaeand R. eutropha, respectively, were not statistically sig-nificant as theP-values were greater than 0.05 (Table 3).Adequateprecision is a measure of the range in predicted response rela-tive to its associated error. Its desired value is 4 or more [37].

Table 2

Full factorial central composite design matrix of orthogonal and real values along with observed responses for removal of cadmium(II) and nickel(II).

Run order Real (coded) values Removal efficiency, %

X1 X2 X3 X4 Saccharomyces cerevisiae Ralstonia eutropha

Cadmium(II) Nickel(II) Cadmium(II) Nickel(II)Experimental Predicted

valueExperimental Predicted



value

1 2.1() 2.5(0) 26.5(0) 26.5(0) 31.6 32.1 10.9 11.2 23.3 23.5 20.2 20.92 3.0(1) 4.0(+1) 37.5(+1) 37.5(+1) 26.7 26.8 12.2 11.9 14.3 13.8 22.0 22.03 3.0(1) 4.0(+1) 15.5(1) 37.5(+1) 25.6 25.6 10.1 10.1 11.3 12.0 14.3 14.44 3.0(1) 4.0(+1) 37.5(+1) 15.5(1) 46.4 46.1 34.1 34.2 10.8 11.0 22.9 22.05 3.0(1) 0.9(1) 15.5(1) 37.5(+1) 42.0 41.7 36.9 36.9 10.4 10.5 10.3 10.06 3.0(1) 0.9(1) 37.5(+1) 37.5(+1) 53.0 52.7 17.1 17.1 21.2 20.5 23.5 22.87 3.0(1) 0.9(1) 37.5(+1) 15.5(1) 46.3 46.4 28.6 28.7 19.1 19.8 20.2 20.98 3.0(1) 4.0(+1) 15.5(1) 15.5(1) 47.0 46.7 23.5 23.7 22.6 22.2 36.1 36.89 3.0(1) 0.9(1) 15.5(1) 15.5(1) 45.9 46.0 34.9 34.4 23.0 22.7 30.9 30.4

10 5.1(0) 0.2() 26.5(0) 26.5(0) 51.5 51.9 10.8 10.9 36.2 36.3 19.0 19.611 5.1(0) 2.5(0) 26.5(0) 42.0(+) 50.3 50.2 26.6 26.7 18.2 18.4 34.1 34.612 5.1(0) 2.5(0) 26.5(0) 26.5(0) 50.5 50.2 27.3 26.7 46.2 46.2 42.1 42.913 5.1(0) 2.5(0) 42.0(+) 26.5(0) 50.3 50.2 26.4 26.7 54.4 54.5 54.9 55.414 5.1(0) 2.5(0) 26.5(0) 11.0() 62.3 62.6 37.7 37.9 30.7 30.1 37.3 36.815 5.1(0) 2.5(0) 26.5(0) 26.5(0) 50.4 50.2 26.4 26.7 46.6 46.2 43.6 42.916 5.1(0) 2.5(0) 26.5(0) 26.5(0) 50.3 50.2 27.4 26.7 46.2 46.2 43.1 42.917 5.1(0) 2.5(0) 11.0() 26.5(0) 34.4 34.7 49.7 50.0 45.7 45.3 50.0 49.518 5.1(0) 4.7(+) 26.5(0) 26.5(0) 50.9 50.2 26.6 26.7 32.9 32.4 25.0 24.419 5.1(0) 2.5(0) 26.5(0) 26.5(0) 29.7 30.1 33.3 33.3 45.8 46.2 42.9 42.920 5.1(0) 2.5(0) 26.5(0) 26.5(0) 61.7 62.1 35.9 36.0 45.7 46.2 42.9 42.921 5.1(0) 2.5(0) 26.5(0) 26.5(0) 68.6 68.9 21.0 21.2 46.0 46.2 42.8 42.922 7.1(+1) 4.0(+1) 37.5(+1) 37.5(+1) 44.2 43.9 27.5 27.9 39.7 40.1 37.2 37.723 7.1(+1) 4.0(+1) 15.5(1) 37.5(+1) 43.7 43.5 19.7 19.6 24.7 24.1 20.5 19.924 7.1(+1) 0.9(1) 37.5(+1) 37.5(+1) 56.1 56.3 31.7 31.6 44.8 45.2 37.9 37.325 7.1(+1) 0.9(1) 15.5(1) 15.5(1) 50.8 51.0 41.0 40.8 39.7 40.2 17.4 17.326 7.1(+1) 0.9(1) 37.5(+1) 15.5(1) 62.5 62.7 42.5 42.4 52.1 51.5 18.1 18.027 7.1(+1) 0.9(1) 15.5(1) 37.5(+1) 67.8 67.9 42.5 42.3 21.1 21.0 13.3 14.228 7.1(+1) 4.0(+1) 37.5(+1) 15.5(1) 54.0 53.8 42.0 41.9 44.4 44.4 20.0 20.429 7.1(+1) 4.0(+1) 15.5(1) 15.5(1) 54.6 54.3 40.8 41.0 40.4 41.3 24.2 24.930 8.0(+) 2.5(0) 26.5(0) 26.5(0) 49.7 49.9 27.2 27.3 55.0 54.5 23.3 22.7

X1, pH;X2, biomass concentration;X3, initial cadmium concentration;X4 , initial nickel concentration.
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Table 3

Analysis of variance (ANOVA) for the response surface quadratic model.

Non-living cells Metal ions Source of variation Sum of squares DF Mean square F-value P-value (Prob > F)

S. cerevisiae Cadmium(II) Model 3458.54 14 247.04 1579.95


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Table

4

ANOVAresultsforresponseparameters.

Response

Modifiedequationswithsignific

antterms

R2

Adjusted

R2

Predicted

R2

Adequate

precision

SD

CV

PRESS

S.cerevisiae

YCd=

50.2

3+

6.3

2X

1+

5.9

8X

2+

0.2

0X

3+

1.6

1X

4

4.6

2X2 1+

5.0

8X2 2+

6.0

5X2 3

8.9

2X2 4

0.4

7X

1X

2

0.2

1X

1X

3

1.9

3X

1X

4+

1.2

0X

2X

3

5.2

9X

2X4

1.4

2X

3X

4

0.9

993

0.9

987

0.9

969

154.7

63

0.4

0

0.8

1

10.7

3

YNi=

26.6

7+

5.6

7X

1+

3.6

6X

2+

065X

3+

5.9

0X

4

3.7

1X2 1

5.3

1X2 2+

5.5

1X2 37.48

X2 4+

2.3

1X

1X

2+

1.6

4X

1X

3

3.0

2X1

X4

3.3

4X2

X3+

0.2

3X2X4

2.1

3X3

X4

0.9

994

0.9

988

0.9

976

154.9

69

0.3

6

1.2

5

7.5

0

R.eutropha

YCd=

46.1

9+

10.9

6X1

1.3

9X2

+

3.2

6X3

4.1

2X4

3.6

0X2 1

5.9

3X2 2+

1.8

4X2 3

10.9

8X2 4+

0.4

0X1

X2+

3.5

6X1

X3

1.7

5X1

X4

2.0

5X2

X3+

0.5

2X2X4

3.2

4X3

X4

0.9

990

0.9

980

0.9

937

98.2

02

0.6

3

1.8

8

36.5

0

YNi=

42.9

0+

0.6

4X1+

1.7

0X2+

2.0

8X3

0.7

7X4

10.5

6X2 1

10.4

5X2 2+

4.7

7X2 3

3.6

1X2 4+

0.3

1X1

X2+

2.5

6X1

X3+

4.3

4X1

X4

1.3

0X2

X3

0.4

8X2X4+

5.6

0X3

X4

0.9

979

0.9

960

0.9

883

83.7

58

0.7

7

2.5

9

49.2

1

X1,p

H;X2,

biomassconcentration;X3,

initialcadm

iumconcentration;X4,i

nitialnickelconcentration;SD,

standarddeviation;DF,

degreeoffreedom

;CV,c

oefficientofvariation;PRESS,p

redictedresidualerrorsumofsquares.

efficiency of both metals was sensitive to changes in the solu-tion pH.Fig. 1(a) shows the effect of removal efficiency of Ni(II)ontoS. cerevisiaedosage and initial solution pH in 3-dimentionalresponse surface of the RSM when the other two variables wereheld at zero level. Interestingly, the Ni(II) removal efficiency ofS.cerevisiae was significantly increased with an increase in initialsolution pH at 37 when biomass dosage was decreased from 4.04to 0.86g/l. In this case, a maximum removal efficiency of 30.3%was obtained at initial solution pH of 7 and biomass dosage of3.32 g/l.Fig. 1(b) shows the effect ofS. cerevisiae dosage and ini-tial solution pH in 3-dimentional response surface plot for removalefficiency of Cd(II) when the other two variables are held at zerolevel. The Cd(II) removal efficiency ofS. cerevisiaeincreased mod-erately when biomass dosage and initial pH of the solution wasincreased. FromFig. 1(b), a maximum Cd(II) removal efficiencyof 63% was achieved at initial solution pH of 6.3 and biomassdosage of 4.4 g/l.Fig. 1(c) shows the changes in the removal effi-ciency of Ni(II) using R. eutropha with varying initial pH solutionand biomass dosage when other two variables were held at zerolevel. A maximum removal efficiency of Ni(II) at 43% was reachedwhen initial solution pH and biomass dosage were 5 and 2.58 g/l,respectively. Fig. 1(d) shows results of the contribution of the inter-action between initial pH and biomass dosage for Cd(II) removal

efficiency using R. eutropha. It can be seen from the surface thatthe removal efficiency of Cd(II) onto R. eutropha increased withan increase in initial solution pH at 37 when biomass dosagewas decreased from 4.04 to 0.86g/l. A maximum removal effi-ciency of Cd(II) at 62.9% was achieved when initial solution pH andbiomass dosage were 6.7 and 4.04 g/l, respectively. On the otherhand, increases in pH from 2 to 7 caused more Cd(II) removal by R.eutropha.ThemosteffectiveandoptimumpHforCd(II)removalwasobserved at 57 for both organisms. At pH less than 3, the amountof Cd(II) and Ni(II) removal efficiency onto both dried biomass wassmall. This indicates that initial solution pH influences not onlymetal binding sites on cell surface ofS. cerevisiaeand R. eutropha,but also the solution chemistry of the heavy metals in water [40].Optimum pH by Marques et al. [41]for biosorption of three dif-

ferent cations including Cd(II) was obtained between 4.5 and 5.5.Vasudevan et al.[42]observed that Cd(II) adsorption capacity ofsorbent increased with increasing pH, and was maximum at a pHof6.5. Zhaiet al. [43] reported pH 6.0 as optimum pH for Cd(II) andNi(II) removal. The result showed that maximum removal of Cd(II)ions was achieved at pH 5.1 and 8.0 with 68.56 and 55.03% for S.cerevisiaeandR. eutropha, respectively.

Fig. 2(ad) shows the simultaneous effect of initial solution pHand initial concentration of Cd(II) and Ni(II) on Cd(II) and Ni(II)removal efficiency in an aqueous solution. The contour plot inFig. 2(a and b) shows the removal efficiency of both metals tobe sensitive to initial pH solution and ions concentration. It canbe seen that Cd(II) and Ni(II) removal efficiencies of S. cerevisiaewere increased with an increase in initial solution pH at 27 when

concentration of both ions was at the lowest level. At maximumconcentration of both ions (37 mg/l), the Cd(II) and Ni(II) removalefficiencies ofS. cerevisiaewere 58.4% at initial pH of 5 and 38% atinitial pH of 6.4, respectively, as presented inFig. 2(a and b). Thecontour plot inFig. 2(c and d) shows results of the contributionof the interaction between initial pH and initial ions concentrationfor Cd(II) and Ni(II) removal efficiency usingR. eutropha.Fig. 2(c)shows that a maximum removal efficiency of Ni(II) of 43% wasachieved when initial solution pH and Ni(II) concentration were5 and 25 mg/l. This result is due to the influence of adsorptionmedium pH on the sorption capacity. FromFig. 2(d), it can be seenfrom the contour plot that the removal efficiency of Cd(II) onto R.eutrophaincreased with an increase in initial solution pH at 37when Cd(II) was increased from 16 to 37 mg/l. In this case, a maxi-

mum removal efficiency of Cd(II) at 62% was achieved when initial
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Table 5

Regresion analysis using 24 factorial central composite design for S. cerevisiaeandR. eutropha.

Biomass Model term Coefficient estimate Standard error F-value P-value

Cd(II) Ni(II) Cd(II) Ni(II) Cd(II) Ni(II) Cd(II) Ni(II)

S. cerevisiae Intercept 50.2 26.7 0.14 0.13 1579.95 1712.24


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Fig. 1. The combined effects of initial biomass dosage and solution pH on removal efficiency when other variables held at zero level; (a and b) S. cerevisiaeand (c and d) R.eutropha.

3.3. Optimization using the desirability functions

Myers and Montgomery [35] describe a multiple responsemethod called desirability. The method makes use of an objectivefunction,D, called the desirability function. It reflects the desirableranges for each response (di). The desirable ranges are from zeroto one (from least to most desirable, respectively). The simulta-neous objective function is a geometric mean of all transformedresponses:

D = (d1 d2 . . . dn)1/n

=

ni=1

di

1/n

(4)

wheren is the number of responses in the measure. If any of theresponses or factors fall outside their desirability range, the overallfunction becomes zero.

In the numerical optimization, we choose the desired goal foreach factor and response from the menu. The possible goals are:maximize, minimize, target, withinrange,none (forresponsesonly)andsettoanexactvalue(factorsonly).Aminimumandamaximumlevel must be provided for each parameter included. A weight canbe assigned for each goal to adjust the shape of its particular desir-ability function. The goals are combined into an overall desirability

function. Desirability is an objective function that ranges from zero

outside of the limits to one at the goal. The program seeks to max-imize this function. The goal seeking begins at a random startingpoint and proceeds up the steepest slope to a maximum. There maybe two or more maximums because of curvature in the responsesurfaces and their combinationinto an overall desirability function.For finding the best local maximum, changes improve by start-ing from several points in the design space. A multiple responsemethod was applied for optimizing any combination of four goals,namely the initial solution pH, biomass dosage, initial Cd(II) and

Ni(II) concentrations, and removal of each metal ion. The numeri-cal optimization found points out how to maximize the desirabilityfunction. The optima of removal efficiency for Cd(II) and Ni(II) ontoS. cerevisiae and R. eutropha are shown in Table 6. The obtained val-uesofdesirability(above0.861)showedthattheestimatedfunctionmay represent the experimental model under desired conditions.The results showed that pH was an important parameter affectingthe biosorption of heavy metals. From a set of solutions gener-ated by the Design-Expert software, optima of removal efficiencyof 67.9% for Cd(II) and 42.3% for Ni(II) ontoS. cerevisiaeand 51.8%for Cd(II) and 50.4% for Ni(II) onto R. eutrophawere achieved at theoptimum values of the process variables: pH, 7.1; biomass dosage,4.04g/l; Cd(II) concentration, 37mg/l; Ni(II) concentration, 16 mg/land pH,5.0; biomass dosage, 2.32g/l; Cd(II) concentration, 37mg/l;

Ni(II) concentration, 28 mg/l, respectively.
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Fig. 2. The combined effects of initial ions concentration and solution pH on removal efficiency when other variables held at zero level; (a and b)S. cerevisiaeand (c and d)R. eutropha.

In order to verify the model developed, four more experimentswere conducted for Cd(II) and Ni(II) removal efficiency onto S.cerevisiaeand R. eutropha as presented in Table 6 using numer-ical optimization of the RSM. The experiments were conductedunder these conditions and comparison between the experimentalresults with the predicted results from the model was made. Theresults demonstrated that the model prediction for both responsesinTable 4agreed reasonably well with the experimental data.

3.4. Characteristics of the biosorbent and mechanism of Cd(II)

and Ni(II) biosorption

Previous studies showed that ethanol treated S. cerevisiaehadincreased Cd(II) biosorption capacity and the Cd(II) removal was

two times greater than original S. cerevisiae [1,28]. In order to

evaluate the textual structure of cell surface, SEM micrographs ofnon-living biomass ofS. cerevisiaeandR. eutrophabefore and aftertreatment are shown inFig. 4(ad). For the treated biomass ofS.cerevisiae (Fig. 4(b)) and R. eutropha (Fig. 4(d)), the cell surface mor-phology is quite clean surface and generates more accessible spacewithin -glucan-chitin skeleton with new micro-porous structurewhich were absent on the irregular surface of both biomass beforetreatment.There were also many thinsheetson the cell surface andsome rudimentary pores were present due to therelease of thecellwall materials containing COOH, phosphates groups, amorphouspolysaccharides and other impurities [52,53]. The irregular surfacemorphology ofS. cerevisiaeand R. eutrophaexhibited microstruc-ture porosity for pretreated biomass, and this may be attributedto the fact that the microstructure plays a role in Cd(II) and Ni(II)

biosorption [54]. Actuallychemical treatment of thecell wallsusing
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Fig. 3. The combined effects of initial biomass dosage and Ni(II) and Cd(II) ions concentration on removal efficiency when other variables held at zero level; (a and b) S.cerevisiaeand (c and d)R. eutropha.

esterification ofethanol with carboxyl groups( COOH)has notpro-duced any pore structure on the biosorbent surface but increase

the specific surface area for reaction. Microporous active sites dis-tinguished on the surface morphology of non-viable S. cerevisiaemay precede faster action of biosorption, hence allowing moreheavy metal ions chelation at the surface. These S. cerevisiae par-

ticles with clean surface and high porosity may have applicationas biosorbent for heavy metal removal from wastewater effluents

[28].The FT-IR Spectroscopy is an important analytical technique

which detects the vibration characteristics of chemical functionalgroups in a molecule. Upon interaction of an infrared light with

Table 6

Desirability option for Cd(II) and Ni(II) removal byS. cerevisiaeandR. eutropha.

Microorganism pH Biomass dosage, g/l Cd(II) concentration,mg/l

Ni(II) concentration,mg/l

Cd(II) removal, % Ni(II) removal, % Desirability

Experimental Predicted Experimental Predicted

S. cerevisiae 7.1 4.04 37 16 66.5 67.9 43.4 42.3 0.9297.1 3.87 37 26 67.3 68.6 35.1 35.3 0.861

R. eutropha 5.0 2.32 37 28 52.7 51.8 50.1 50.4 0.9145.0 2.01 37 30 50.8 51.2 51.8 50.2 0.905
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Fig. 4. Micrographs of scanning electron microscope (SEM) of (a) surface ofS. cerevisiaebefore treatment, (b) surface ofS. cerevisiaeafter treatment, (c) surface ofR. eutrophabefore treatment, and (d) surface ofR. eutrophaafter treatment.

matter, chemical bonds will stretch, contract and bend. As a result,chemical functional groups tend to absorb infrared radiation ina specific wavelength range regardless of the structure of therest of the molecule [55].Fig. 5displays a number of absorptionpeaks, indicating the complex nature of the examined biomass ofS. cerevisiae and R. eutropha. The broad absorption peak around32303400cm1 and 32003520cm1 are indicative of the exis-tence of the OH groups and the NH groups on S. cerevisiaeandR. eutropha, respectively[56].Treated S. cerevisiaebiomass ofspectrum (b) in comparison with untreated biomass of spectrum

(a) showed more clear peaks as depicted in Fig. 5. Two peakswere added in the bands 14501510cm1 and 13801406cm1

after treatment in spectrum (b), which was taken as a sign ofbiomass enrichment. There was a clear disappearance of thebands 540 cm1 and 1406 cm1 after metal loaded on S. cerevisiae

in spectrum (c). There was also shifting of the bands 822cm1

(J), 1252cm1 (H), 1464 cm1 (G), 2879 (C) and 2931cm1 (B).The metal loaded of the R. eutropha biomass in spectrum (f) ofFig. 5shows that clear disappearance of the bands 700 cm1 and1464cm1 and shifting of the bands 550 cm1 (K), 1103cm1 (I),1258cm1 (H), 1562cm1 (E), 1688 cm1 (D) and 2962cm1 (B)were observed. These observations could indicate the involvementof these functional groups in the biosorption process. The FT-IRwavelengths of each peak and the corresponding functional groupsare depicted in Table 7, which shows that several functional groups

on thesurface of the S. cerevisiae and R. eutropha are responsible forbinding of Cd(II) and Ni(II) ions. The FT-IR analysis demonstratesactive functional groups of O H stretching (36003200cm1),alkane C H stretching (28502956cm1), N O stretch-ing (15001600cm1), amine bending (14501550 cm1),

Table 7

FT-IR peaks forS. cerevisiaeandR. eutropha.

Biomass Label Treatedbiomass

Metal loadedbiomass

Change in peak behaviorafter metal loaded

IR peak range (cm1) Functional group reported corresponding tothe observed peak behavior[55,56]

S. cerevisiae A 3327.1 36003200 O H stretching vibrationB 2931.8 2966.5 Shift 28502956 Alkanes CH stretching vibrationC 2879.7 2862.3 Shift 28502956 Alkanes CH stretching vibrationD 1659.1 1659.1 Reduction 17301625 C O stretching vibrationE 1551.2 1551.1 Reduction 16001500 N O stretching vibration

F 1464.4 1491.4 Shift 15501450 N H bending vibrationG 1406.5 Not present Omit 14001300 N O stretching vibrationH 1252.3 1267.7 Shift 12601000 CO stretching vibration, carboxylic acidI 1082.6 1082.6 Reduction 13001000 C O stretching vibrationJ 822.3 708.5 Shift 7501750 Phenyl ring substitution band

R. eutropha A 3330.9 Not present Omit 32003600 O H stretching vibrationB 2962.6 2933.6 Shift 28502956 Alkanes CH stretching vibrationC 2875.8 2875.8 No change 28502956 Alkanes CH stretching vibrationD 1688.1 1742.0 Shift 17301625 C O stretching vibrationE 1657.2 1657.2 No change F 1562.7 1551.2 Shift G 1464.4 Not present Omit H 1258. 1 1385.1 Shift 10001260 C O stretching vibration, carboxylic acidI 1103.8 1071.0 Shift J Not present 841.6 Additional peak K 700.8 Not present Omit 7501750 Phenyl ring substitution bandL 552.3 610.2 Shift
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Fig. 5. FT-IR spectra of (a) untreated biomass ofS. cerevisiae, (b) ethanol pretreatedbiomassofS.cerevisiae, (c)Ni(II)and Cd(II)loaded S.cerevisiaebiomass,(d) untreatedbiomass ofR. eutropha, (e) ethanol pretreated biomass ofR. eutropha, and (f) Ni(II)and Cd(II) loadedR. eutrophabiomass.

N O stretching (13001400cm1), C O stretching(16251730 cm1), C O stretching (10001300cm1), carboxylicacid (10001260cm1), vibrations, and phenyl ring substitutionband (7501750 cm1), as shown in Table 7.The above evidencesuggests that the mechanism of the Cd(II) and Ni(II) biosorption

involves hydroxyl ( O H), amine ( NH) and carboxylate ions( COO) groups of the polysaccharides on the peptidoglycan layer,and for the interaction of the metal-biomass at the cell surface andmetal ions can be assumed to be the same.

4. Conclusion

The use of an experimental design allowed the rapid screen-ing of a large experimental domain for optimization of the Cd(II)and Ni(II) removal efficiency of S. cerevisiae and R. eutropha. Themodel adequacy, tested through lack-of-fit (LOF), P-values and F-values, was verified successfully by the validation of experimentaldata. A maximum removal efficiency of Cd(II) onto S. cerevisiae wasobtained at 68.5% at biomass dosage of 4.04 g/l and Cd(II) concen-

tration of 37 mg/l. However, a maximum Cd(II) removal efficiency

onto R. eutropha (52%) was observed at 1.95g/l andCd(II)concentra-tion of 37 mg/l, while a maximum Ni(II) removal efficiency ontoR.eutropha (43%) was obtainedat Ni(II)concentration of37 mg/l wheninitial solution pH and biomass dosage were 5 and 2.58 g/l, respec-tively. When calculating optimum removal efficiency of Cd(II) andNi(II) ontoS. cerevisiaethe points were found to be 43.4 and 65.5%,respectively, at initial solution pH of 7.1, biomass dosage of 4.07 g/l,initial Ni(II) concentration of 16 mg/l and initial Cd(II) concentra-tion of 37 mg/l. The points giving optimum removal efficiency ofCd(II) and Ni(II) ontoR. eutrophawere found to be 52.7 and 50.1%,respectively, at initial solution pH of 5.0, biomass dosage of 2.32g/l,initial Ni(II) concentration of 28 mg/l and initial Cd(II) concentra-tion of 37mg/l.It was observed that modelpredictions of Cd(II) andNi(II)removalefficienciesareingoodagreementwithexperimentalobservations.

Acknowledgements

The present research was made possible through a universitygrant, sponsored by Ministry of Science, Iran. The authors wish tothank Mrs. Haghdoust (Technical assistant of Environmental Labo-ratory) for her assistance,Ellen Vuosalo Tavakoli for English editing,Tarbiat Modares University and Ministry of Science for their finan-cial support.

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