Process optimization studies of lead (Pb(II)) biosorption onto immobilized cells of Pycnoporus sanguineus using response surface methodology

Bioresource Technology 99 (2008) 8549–8552

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Process optimization studies of lead (Pb(II)) biosorption onto immobilized cells of Pycnoporus sanguineus using response surface methodology Y. Yus Azila, M.D. Mashitah *, S. Bhatia School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, 14300 Nibong Tebal, Seberang Prai Selatan, Penang, Malaysia

a r t i c l e

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Article history: Received 9 December 2007 Received in revised form 12 March 2008 Accepted 12 March 2008 Available online 2 July 2008 Keywords: Pb(II) removal Biosorption Pycnoporus sanguineus Response surface methodology Optimization

a b s t r a c t A central composite design (CCD) was employed to optimize the biosorption of Pb(II) ions onto immobilized cells of Pycnoporus sanguineus. The independent variables were initial Pb(II) concentration, pH and biomass loading. The combined effects of these variables were analyzed by response surface methodology (RSM) using quadratic model for predicting the optimum point. Under these conditions the model predicted a maximum of 97.7% of Pb(II) ions removal at pH 4, 200 mg/L of initial Pb(II) concentration with 10 g/L of biosorbent. The experimental values are in good agreement with predicted values within +0.10 to +0.81% error. Crown Copyright Ó 2008 Published by Elsevier Ltd. All rights reserved.

1. Introduction Industrial effluents containing heavy metals when release to the environment without a proper treatment could harm the aquatic life including human beings (Tunali et al., 2006). According to agency of toxic substance and disease registry (ATSDR, 2005), lead is a naturally bluish-gray metal with its solubility is highest in soft, acidic water. Lead pollution may come from various sources e.g. battery manufacturing, textiles, mining wastes, automobiles and metal finishing (Lai et al., 2006; Tunali et al., 2006). The exposure to lead over the permissible limit could cause anemia, nephritic syndrome and attack on gastrointestinal track or nervous system (Lo et al., 1999; Ho and Ofomaja, 2005; Zulkali et al., 2006). Conventional methods used for heavy metals removal include activated carbon adsorption, evaporation, chemical precipitation and ion-exchange. These technologies have limitations such as high operating cost, incomplete metal ions removal and generation of toxic sludge (Zulkali et al., 2006; Cabuk et al., 2007; Malkoc and Nuhoglu, 2005). Biosorption process has received great attention as an alternative method to remove toxic metals from wastewaters (Ahalya et al., 2003). Biosorption process offers several advantages such as low cost biosorbent, efficient and regeneration of biosorbent (Cruz et al., 2004; Naddafi et al., 2007). Microorganisms such as al-

* Corresponding author. Tel.: +604 5996403; fax: +604 5941013. E-mail addresses: [email protected], [email protected] (M.D. Mashitah).

gae, yeast, bacteria and fungi have been widely evaluated as a biosorbent in order to remove metal ions from aqueous solution because of its low cost and abundant supply (Tunali et al., 2006). As reported in literature, biosorption of metal ions onto microorganisms is affected by several factors such as pH, metals concentration, biomass loading, temperature and surface of the microorganisms’ cell wall (Gadd, 1993; Saglam et al., 2002; Ahalya et al., 2003; Arica et al., 2003). Fungi have been investigated as a biosorbent and capable to sequester metal ions from aqueous solutions (Aloysius et al., 1999). Fungi could be chosen as an economical biosorbent for metals ion removal because it is available and could be easily grown in a low cost growth media (Tunali et al., 2006). Metal ions that adsorbed by fungal can be classified as: extracellular accumulation, cell surface sorption and intracellular accumulation (Ahalya et al., 2003; Cruz et al., 2004). Fungal immobilization onto natural polymers such as sodium alginate, chitin and chitosan can improve cell productivity and stability, regeneration in continuous operation and easy separation of the cells from the reaction system (Baklashova et al., 1984; Li et al., 1984; Federici et al., 1987, 1990; Federici, 1993; Annadurai et al., 2007). The present study is focused to determine the optimum condition for Pb(II) removal by live immobilized cells of Pycnoporus sanguineus from aqueous solution using central composite design (CCD) combined with response surface methodology (RSM). In this study, the CCD was selected to determine effect of parameters and their interactions over removal of Pb(II) ions. The interactions between the factors that influence the percent of Pb(II) removal were established. The optimum value of the parameters was determined

0960-8524/$ - see front matter Crown Copyright Ó 2008 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2008.03.056

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Y. Yus Azila et al. / Bioresource Technology 99 (2008) 8549–8552

for removal of Pb(II) ions from the aqueous solution using response surface methodology. 2. Methods 2.1. Microorganism, production medium and immobilized cells preparation P. sanguineus capable of adsorbing heavy metals was obtained from the Forest Research Institute of Malaysia (FRIM), Kepong, Selangor (Mashitah et al., 1999). It was maintained by weekly transfer on malt extract agar slants incubated at 30 oC for 6 days, after which the slants were stored at 4 oC until required. The composition of the production medium was (g/L): glucose 20, yeast extract 10 and malt extract 10. The pH of the medium was adjusted to 9.0 prior autoclaving at 121 oC (150 kN/m2) for 15 min. 2.2. Immobilized cell preparation A cell suspension was prepared by inoculating a stock culture of P. sanguineus onto malt extract agar plates and incubating them at 30 °C for 6 days. The mycelial mat formed was scraped off by using a sterile blade and mixed with 10 ml sterile Tween 20 (Sigma) solution prior putting it into a sterile sampling bottle (100 ml). The sampling bottle was vortexed for 3 min so that the mycelium was evenly distributed in the liquid. Fifteen ml of the cell suspension was inoculated into an Erlenmeyer flask containing 135 ml of the production medium. The flask was incubated on a rotary shaker at 30 °C, 150 rpm for 66 h. The harvested sample was centrifuged at 3500 rpm for 4 min at 25 °C. P. sanguineus beads were prepared by dropping a mixture of 1.5% (w/v) sodium alginate solution and P. sanguineus mycelial mat into a 2% (w/v) CaCl2 solution stirring slowly at room temperature (25–30 °C). The beads were stirred slowly for 30 min, then collected by filtration, washed three times with sterile deionized water and stored in Tris–HCl buffer (pH 7) at 4 oC until used. 2.3. Preparation of metal stock solution Metal solutions were prepared by diluting 1000 mg/L of Pb (NO3)2 (Mallinckrodt) solutions with deionized water to obtain concentration of 50–350 mg/L. For each solution, the initial lead concentration and the concentration in the samples after biosorption treatment were determined using an Atomic Absorption Spectrometer (Model Shimadzu AA 6650). 2.4. Experimental design for biosorption studies In order to obtain the optimum condition for percentage of Pb(II) removal, three independent parameters were selected for the study and are presented in Table 1. The range of study for initial Pb(II) concentration (A), pH (B) and biomass loading (C) were chosen based on preliminary experiments. The relationship between the parameters and response were determined using central composite design (CCD) under Response Surface Methodology of Design Expert Software (version 6.0.6) Stat Ease Inc. USA. The CCD design was chosen in this study as it is efficient, flexible and Table 1 Experimental independent variables Factor

Initial Pb(II) concentration pH Biomass loading

Units

mg/L g/L

Factor code

A B C

Levels and range (coded) 1

0

1

50 2 2

200 4 6

350 6 10

robust. The parameters presented in Table 1 with 3 levels are coded as 1, 0 and +1, respectively. Twenty experiments were conducted with six star points (a = 1) and six replicates a centre points according to CCD. The percent of Pb(II) removal was taken as a response (Y) of the experimental design and calculated as   Ci  Cf %PbðIIÞremoval ¼  100% ð1Þ Ci where Ci (mg/L) is the initial concentration and Cf (mg/L) is the final or equilibrium concentration. Each experiment was repeated three times and the results reported are the average values. Samples taken after the desired incubation period were analyzed with an Atomic Absorption Spectrophotometer (Model Shimadzu AA 6650). The regression analyses, graphical analyses and analyses of variance (ANOVA) were done using the Design Expert Software (version 6.0.6), Stat Ease Inc, USA. The statistical significance of the coefficient was determined by Student’s t-test and p-values (Zulkali et al., 2006). The proportion of variance obtained by the model was explained by the multiple coefficient of determination, R2. 3. Results and discussion 3.1. Statistical analysis In order to determine an optimum condition for Pb(II) ions removal, the parameters that have greatest influence over the response need to be identified. In the present study, the relationship between three independent variables and percent of Pb(II) ions removal fitted well with the quadratic model. The quadratic regression model for percent of Pb(II) removal (after biosorption process) obtained from CCD design in terms of coded factors is presented as PbðIIÞremovalðYÞ ¼ þ93:27  2:88A þ 0:89B þ 7:25C  6:93B2  2:84C 2  1:57AB þ 2:72AC  1:82BC

ð2Þ

where A, B and C were the coded values of tested variables such as initial Pb(II) concentration, pH and biomass loading, respectively. Table 2 presents the variations in the corresponding coded values of three parameters and response based on experimental runs Table 2 Comparison of experimental and predicted values on Pb(II) ions removal (%) Standard order

Coded values

Response (Y)

A

B

C

Experimental value

Predicted value

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1 1 1 1 1 1 1 1 1 0 0 0 0

1 1 1 1 1 1 1 1 0 0 1 1 0 0

1 1 1 1 1 1 1 1 0 0 0 0 1 1

76.7 68.8 87.5 71.2 90.5 91.4 92.0 88.7 93.5 91.2 88.4 85.4 84.0 98.0

77.6 69.5 86.1 71.8 90.3 93.1 91.6 88.1 96.2 90.4 85.5 87.2 83.2 97.7

0.9 0.7 1.4 0.6 0.3 1.7 0.4 0.6 2.7 0.8 3.0 1.8 0.8 0.3

Repeated runs 15 16 17 18 19 20 Meana

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

92.3 94.7 92.5 92.0 94.7 94.0 93.4a

93.3 93.3 93.3 93.3 93.3 93.3 93.3

0.9 1.4 0.7 1.3 1.5 0.8

a

Calculated statistic for repeated experiments.

Residual

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Y. Yus Azila et al. / Bioresource Technology 99 (2008) 8549–8552 Table 3 Analysis of variance (ANOVA) for the regression model for percentage of Pb(II) ions removal Sum of squares

DF

Mean square

F-Value

Prob > F

Model A B C B2 C2 AB AC BC Residual Lack of fit

1120.6 82.9 8.0 525.4 153.6 25.9 19.8 59.3 26.5 35.73 27.84

8 1 1 1 1 1 1 1 1 11 6

140.1 82.9 8.0 525.4 153.6 25.9 19.8 59.3 26.5 3.25 4.64

43.1 25.5 2.5 161.8 47.3 8.0 6.1 18.3 8.2

<0.0001a 0.0004 0.1458 <0.0001 <0.0001 0.0166 0.0313 0.0013 0.0156

2.94

0.1284b

97.7 91.7

Pb (II) removal

Source

85.6 79.6 73.5 10

8

R2 = 0.9691, Predicted R2 = 0.8162. a Significant under 95% level of confidence. b Not significant relative to the pure error due to noise.

6 2 3 4

and predicted values proposed by CCD design. The percentage of Pb(II) removal from aqueous solution response value was in the range between 68.8% and 98.0%. The ANOVA of this model (Eq. (2)) presented in Table 3 was significant (P < 0.0001) with a model F-value of 43.1. In this study, the determination coefficient obtained at 95% of confidence level was 0.9691 while the rest (3.09%) is explained as residues. The lack of fit obtained for this model was 0.1284. The non-significance lack of fit explained that the quadratic model was valid for the present work when it was more than 0.05 (Hamsaveni et al., 2001; Zulkali et al., 2006). Table 3 also shows that the first order effects of initial Pb(II) concentration (A) and biomass loading (C) were highly significant compared to the first order effect of pH (B). Even the first order effect of pH (B) was not the significant term, it cannot be eliminated in order to support the hierarchy model. However, only the second-order effect of pH (B2) was much significant compared to (A2) and (C2) as proposed by the model. The coefficient of variance (CV) is the ratio of the standard error of estimate to the mean value of observed response (as a percentage) and considered reproducible once it is not greater than 10% (Beg et al., 2003). In this work, the CV obtained was 2.04%. The adequate precision value is the ‘‘signal to noise ratio”. A ratio greater than 4 is desirable. A ratio of 23.3 obtained indicated an adequate signal. Therefore, this model can be used to navigate the design space. The optimum condition with higher percentage of Pb(II) ions removal was chosen from a predicted condition proposed by CCD design with a real model presented as

C: Biomass loading

4 5

B: pH

6

2

Fig. 1. Effect of pH and biomass loading on Pb(II) ions removal.

sults showed that the percentage of Pb(II) ions removal increased with the increase in solution pH from 2.0 to 4.0. At solution pH lower than 3, less than 80% removal was achieved due to competition between hydrogen and metal ions on the binding sites of biosorbent (Nasir et al., 2007). The maximum Pb(II) ions removal onto immobilized cells of P. sanguineus was observed at pH 4.0. As the solution pH increased more than pH 4.0, which resulted in precipitation of Pb(II) ions thus lower the biosorption efficiency (Vilar et al., 2005). Fig. 2 shows the interaction between initial metals concentration and solution pH. It was observed that more than 90% removal was achieved with the initial Pb(II) ions concentration increased from 50 to 350 mg/L at pH 4. The interaction between initial lead concentration and biomass loading presented in Fig. 3. The interaction shows that for a higher biomass loading more than 9 g/L, the percentage of Pb(II) ions removal was more than 97% with initial Pb(II) ions concentration in the range of 50–350 mg/L. This phenomenon shows that increasing in initial Pb(II) ions concentration

PbðIIÞremoval ¼ þ46:14063  0:025471  Initial concentration þ 16:71544  pH 97.8

þ 3:94691  Biomass loading  1:73209 2

95.4

 5:24043  103  Initial concentration

93.0

 pH þ 4:5387  103  Initial concentration  Biomass loading  0:22743  pH  Biomass loading

ð3Þ

Pb (II) removal

 pH2  0:17773  Biomass loading

90.5 88.1 6

Table 2 shows that the predicted optimum condition with 97.7% of Pb(II) ions removal is at pH 4, 200 mg/L of Pb(II) ions and 10.0 g/L of biosorbent, respectively.

5 4

3.2. Effect of pH, biomass loading and initial concentration on Pb(II) ions removal

B: pH

50 125

3

200 275

Solution pH and biomass loading play an important role in heavy metals biosorption as the combination of both effects indicates a significant influence in Pb(II) ions removal as shown in Fig. 1. Re-

A: Initial concentration

350

2

Fig. 2. Effect of initial Pb(II) ions concentration and pH on Pb(II) ions removal.

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10.0 g/L of biosorbent. The experimental data fitted well the model predicted values within +0.10–0.81% error. 98.0

Acknowledgement

92.9

Pb (II) removal

87.8

The present work was supported by a short term Grant from the Universiti Sains Malaysia (6035132).

82.7 77.6

References

10 8 6

350

C: Biomass loading 275

4

200 125 2

50

A: Initial concentration

Fig. 3. Effect of initial Pb(II) ions concentration and biomass loading on Pb(II) ions removal.

Table 4 The optimum conditions by the CCD design on Pb(II) ions removal (%) Run

1 2 3 4 5

A

200 200 200 200 200

B

4 4 4 4 4

C

10.0 10.0 10.0 10.0 10.0

Pb(II) removal (%) Observed

Predicted

98.5 97.5 98.5 97.6 97.2

97.7 97.7 97.7 97.7 97.7

% Error (e)

0.81 0.21 0.81 0.10 0.51

from 50 to 350 mg/L did not significantly influence provided that the lead solution was at optimum pH and higher biomass loading. Similar results were reported in the literature that increase in biomass loading will increase the removal of lead ions due to more binding sites available for the biosorption of lead ions onto the biosorbent (Gong et al, 2005; Tewari et al, 2005; Cabuk et al, 2007). 3.3. Verification studies In order to confirm the model adequacy, five set of experiments were repeated randomly at optimum condition to obtain a maximum Pb(II) ions removal experimentally. Table 4 presents the experimental results under the optimum condition compared with the simulated values from the proposed model (Eq. (3)). The percentage error difference between the experimental and predicted value was in the range of 0.10–0.81%. The experimental error less than 1.0% indicates that the proposed model is adequate for obtaining optimum value in the range of studied parameters. 4. Conclusions The response surface methodology (RSM) was used for obtaining optimum process condition for Pb(II) removal using immobilized cells of P. sanguineus as a biosorbent. A significant interaction was observed in Pb(II) ions removal with the combination effects of pH, biomass loading and initial Pb(II) ions concentration. The optimum condition for 97.7% of 200 mg/L of Pb(II) ions removal onto immobilized cells of P. sanguineus was at pH 4 and

Agency of Toxic Substances and Disease Registry, 2005. Toxicological profile for lead (Draft for Public Comment). Atlanta, GA: US Department of Health and Human Services, Public Health Services. Ahalya, N., Ramachandra, T.V., Kanamadi, R.D., 2003. Biosorption of heavy metals. Res. J. Chem. Environ. 7, 71–79. Aloysius, R., Karim, M.I.A., Arif, A.B., 1999. The mechanism of cadmium removal from aqueous solution by nonmetabolizing free and immobilized live biomass of Rhizopus oligosporus. World J. Microbiol. Biotechnol. 15, 571–578. Annadurai, G., Ling, L.Y., Lee, J.F., 2007. Biodegradation of phenol by Pseudomonas pictorum on immobilized with chitin. Afr. J. Biotechnol. 6, 296–303. Arica, M.Y., Arpa, C., Ergene, A., Bayramoglu, G., Genc, O., 2003. Ca-alginate as a support for Pb(II) and Zn(II) biosorption with immobilized Phanerochaete chrysosporium. Carbohydr. Polym. 52, 167–174. Baklashova, T.G., Koshcheenko, K.A., Skryabin, G.K., 1984. Hydroxylation of indolyl3-acetic acid by immobilized medium of A. niger. Appl. Microbiol. Biotechnol. 19, 217–223. Beg, Q., Sahai, V., Gupta, R., 2003. Statistical media optimization and alkine protease production from Bacillus mojavensis in bioreactor. Process. Biochem. 39, 203–209. Cabuk, A., Akar, T., Tunali, S., Gedikli, S., 2007. Biosorption of Pb(II) by industrial strain of Saccharomyces crevisiae immobilized on the biomatrix of cone biomass of Pinus nigra: equilibrium and mechanism analysis. Chem. Eng. J. 131, 293–300. Cruz, C.C.V., Da Costa, A.C.A., Henriques, C.A., Luna, A.S., 2004. Kinetic modeling and equilibrium studies during cadmium biosorption by dead Sargassum sp. biomass. Bioresour. Technol. 91, 249–257. Federici, F., Miller, M.W., Petruccioli, M., 1987. Glucoamylase production. Ann. Microbiol. Enzimol. 37, 17–24. Federici, F., Petruccioli, M., Millar, M.W., 1990. Enhancement and stabilization of the production of Glucoamylase by immobilized cells of Aureobasidium pullulans in a fluidized bed reactor. Appl. Microbiol. Biotechnol. 33, 407–409. Federici, F., 1993. Potential applications of viable, immobilized fungal cell systems. World J. Microbiol. Biotechnol. 9, 495–502. Gadd, G.M., 1993. Interaction of fungi with toxic metals. New Phytol. 124, 25–60. Gong, R., Ding, Y., Liu, H., Chen, Q., Liu, Z., 2005. Lead biosorption and desorption by intact and pretreated Spirulina maxima biomass. Chemosphere 58, 125–130. Hamsaveni, D.R., Prapulla, S.G., Divakar, S., 2001. Response surface methodological approach for the synthesis of isobutyl isobutyrate. Process. Biochem. 36, 1103– 1109. Ho, Y.S., Ofomaja, A.E., 2005. Kinetics and thermodynamics of lead ion sorption on palm kernel fibre from aqueous solution. Process. Biochem. 40, 3455–3461. Lai, C.H., Gao, C.K., Lin, T.T., Yeh, S.H., Chen, C.Y., Wang, M.K., 2006. Simulation of lead ions adsorption onto the iron-coated médium: dependent on temperature and pH. Pract. Period. Hazard. Toxic Radioactive Waste Manage. 10, 28–32. Li, G.X., Linko, Y.Y., Linko, P., 1984. Glucoamylase and a-amylase production by immobilized A. niger. Biotechnol. Lett. 6, 645–650. Lo, W., Chua, H., Lam, K.H., Bi, S.H., 1999. A comparative investigation on the biosorption of lead by filamentous fungal biomass. Chemosphere 39, 2723– 2736. Malkoc, E., Nuhoglu, Y., 2005. Investigations of nickel(II) removal from aqueous solutions using tea factory waste. J. Hazard. Mater. B127, 120–128. Mashitah, M.D., Zulfadhly, Z., Bhatia, S., 1999. Ability of Pycnoporus sanguineus to remove copper ions from aqueous solution. J. Artif. Cells, Blood Subst. Immob. Biotechnol. 27 (5/6), 429–433. Naddafi, K., Nabizadeh, R., Saeedi, R., Mahvi, A.H., Vaezi, F., Yaghmaeian, K., Ghasri, A., Nazmara, S., 2007. Biosorption of lead(II) and cadmium(II) by protonated Sargassum glaucescens biomass in a continuous packed bed column. J. Hazard. Mater. 147, 785–791. Nasir, M.H., Nadeem, R., Akhtar, K., Hanif, M.A., Khalid, A.M., 2007. Efficacy of modified distillation sludge of rose (Rosa centifolia) petals for lead(II) and zinc(II) removal from aqueous solution. J. Hazard. Mater. 147, 1006–1014. Saglam, A., Yacinkaya, Y., Denizli, A., Arica, M.Y., Genc, O., Bektas, S., 2002. Biosorption of mercury by carboxycellulose and immobilized Phanerochaete chrysosporium. Microchem. J. 71, 73–81. Tewari, N., Vasudevan, P., Guha, B.K., 2005. Study on biosorption of Cr(VI) by Mucor hiemalis. Biochem. Eng. J. 23, 185–192. Tunali, S., Akar, T., Ozcan, A.S., Kiran, I., Ozcan, A., 2006. Equilibrium and kinetics of biosorption of lead(II) from aqueous solutions by Cephalosporium aphidicola. Sep. Purif. Technol. 47, 105–112. Vilar, V.J.P., Botelho, C.M.S., Boaventure, R.A.R., 2005. Influence of pH, ionic strength and temperature on lead biosorption by Gelidium and agar extraction algal waste. Process. Biochem. 40, 3267–3275. Zulkali, M.M.D., Ahmad, A.L., Norulakmal, N.H., 2006. Oryza sativa L. husk as heavy metal adsorbent: optimization with lead as model solution. Bioresour. Technol. 97, 21–25.