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Pb(II) and Cu(II) biosorption on Rhizopus arrhizus modeling mono- and multi-component systems
Minerals Engineering 18 (2005) 1325–1330 This article is also available online at: www.elsevier.com/locate/mineng
Pb(II) and Cu(II) biosorption on Rhizopus arrhizus modeling mono- and multi-component systems Mozhgan Alimohamadi
a,b,*
, Giti Abolhamd a, Alireza Keshtkar
b
a
b
Tehran University, Tehran 11365-4563, Iran Iran Atomic Energy Organization, Tehran 14155-1339, Iran Received 30 April 2005; accepted 4 August 2005 Available online 4 October 2005
Abstract The biosorption of Pb(II) and Cu(II) ions, both individually and in mixtures, by Rhizopus arrhizus was investigated in a batch system as a function of single- and dual-metal ion concentrations. The studied initial pH value for single Pb(II) and Cu(II) biosorptions was 5. It was observed that the co-ion effect on the equilibrium uptake became more noticeable as the co-ion concentration in solution increased. Adsorption isotherms were developed for both the single- and dual-metal ion systems at this pH value and expressed by the mono- and multi-component Langmuir and Freundlich adsorption models. Model parameters were estimated by non-linear regression using MATLAB and EXCEL softwares. It was observed that the mono-component adsorption equilibrium data fitted adequately to both the monocomponent adsorption models, for both the components and the pH value studied, while the multi-component Freundlich adsorption model predicted the multi-component adsorption equilibrium data for the ranges of initial mixture concentrations studied, and for the pH value used. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Modelling; Pb(II); Cu(II); Waterwaste
1. Introduction Solving water pollution problems caused by heavy toxic metal contamination resulting from industrial activities has long attracted the attention of scientists. The methods which have been used to remove heavy metal ions, such as chemical precipitation, adsorption, ion-exchange and solvent extraction have been found to be limited, since they often have high capital and operational costs and may also generate secondary wastes which are difficult to treat (Aksu et al., 2002; Hammanini et al., 2003; Muter and Lubinya, 2002; Kapoor et al., 1999). The use of microorganisms as biological adsorbents for removing heavy toxic metals is an alternative and cost effec* Corresponding author. Address: Tehran University, Tehran 113654563, Iran. Tel.: +98 21 666112203. E-mail address: [email protected] (M. Alimohamadi).
0892-6875/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.mineng.2005.08.007
tive technology (biosorption) which has emerged recently, based on the interactions between active sites of biomaterial (carboxylic, phosphate, sulphate, amino, amidic and hydroxyl groups) and metallic ions in the system (Pagnanelli et al., 2002; Yin and Yu, 1999; Veglio and Beolchini, 1997; Hatzikioseyian et al., 2001; Vasudevan et al., 2002). Biosorption can play a significant role in utilisation of industrial by-products or wastes, especially in the case of reusing biomasses emanating from food, pharmaceutical or wastewater treatment (Loukidou and Matis, 2003). Simple sorption isotherm curves are usually constructed by studying the equilibrium batch sorption behavior of different biosorbent materials. These curves allow quantitative evaluation of biosorption performance of these materials for only one metal. However, when more than one metal is present in a sorption system, evaluation, interpretation, and representation of biosorption results becomes much more complicated (Hammanini et al., 2003). This is the reason why much of the work on the biosorption
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M. Alimohamadi et al. / Minerals Engineering 18 (2005) 1325–1330
of heavy metal ions by various kinds of microorganisms has focused on the uptake of single metals, although in practice wastewaters are polluted with multiple metals. The equilibrium modeling of multi-metal biosorption, which is important in the design of treatment systems, has also largely been neglected. The examination of the effects of binary metal ions in various combinations is more representative of the actual environmental problems faced by organisms than are the single-metal studies (Aksu et al., 2002). The present paper investigates the binary sorption of copper and lead ions onto microorganism (Rhizopus arrhizus) using several concentrations of Cu and Pb. Several common models have been used to choose the one which best fits the experimental data. 2. Model considerations Equilibrium sorption studies determine the capacity of the sorbent, which can be described by a sorption isotherm characterized by certain constants whose values express the surface properties and affinity of the sorbent. Sorption equilibrium is established when the concentration of sorbate in the bulk solution is in dynamic balance with that of the interface (Ho and McKay, 2000). Equilibrium relationships between sorbent and sorbate are described by sorption isotherms. The data were determined and analyzed by some of the most frequently used isotherms. The Langmuir sorption isotherm (Langmuir, 1918) is the best known and the most often used isotherm for the sorption of a solute from a liquid solution: q ¼ qmax
bC f 1 þ bC f
q0i bi C i PN 1 þ k¼1 bk C k
qmax1 b1 ðC 1 =g1 Þ 1 þ b1 ðC 1 =g1 Þ þ b2 ðC 2 =g2 Þ qmax2 b2 ðC 2 =g2 Þ q2 ¼ 1 þ b1 ðC 1 =g1 Þ þ b2 ðC 2 =g2 Þ
q1 ¼
ð3Þ ð4Þ
where b1, b2, qmax1 and qmax2 are the individual Langmuir adsorption constants of the first and second metallic ion, and g1 and g2 are correction parameters for the first and the second metallic ion respectively, which are estimated from binary adsorption data. An empirical extension of the Freundlich model (Freundlich, 1907) restricted to binary mixtures (Sag et al., 1998) is given by 1=n þx
q1 ¼
K 1C1 1 1 C x11 þ y 1 C z21
q2 ¼
K 2C2 2 2 C x22 þ y 2 C z12
ð5Þ
1=n þx
ð6Þ
where a0i and b0i (i = 1, 2) are derived from the individual Freundlich isotherms and the other six parameters (b11, a12, b12, b22, a21 and b21) are correction coefficients. One of the other frequently used multi-component adsorption models is the Combination Langmuir and Freundlich model developed by Sips (1948) which gives better agreement with experimental data but uses additional parameters. This equation can be written as 1=ni
ð1Þ
where q is the sorption capacity at the equilibrium solute concentration Cf (mmol/g); Cf is the concentration of sorbate in solution (mmol/l); qmax is the maximum sorption capacity corresponding to complete monolayer coverage (mmol/g); b is a Langmuir constant related to the energy of sorption (l/mmol). The extended predictive Langmuir model can be written as qi ¼
The addition of further correction coefficients into the classical competitive Langmuir isotherm (2) makes this model more flexible and representative of the complexity of multi-metal systems
qi ¼
1þ
ai C i PN
1=ni i¼1 bi C i
ð7Þ
which gives a Freundlich model for mono-component systems with the following formula: q ¼ KC 1=n
ð8Þ
where k and n are Freundlich constants. 3. Materials and methods 3.1. Preparation of the microorganism for biosorption
ð2Þ
where qi is the uptake of the component i in the multi-component system and Ck (k = 1, . . . , N, where N is the total number of metals in the system) is the equilibrium concentration of each component and qmax and bi are obtained from the Langmuir isotherm (1) of each metal ion in a single system. There are several examples in the literature of empirical models, which provide parameters derived from single system data, but also adjustable correction parameters that take into account the interaction between the two metals in solution and that are fitted to binary data (Aksu et al., 1999).
After the growth period, R. arrhizus was washed twice with distilled water, inactivated using 1% formaldehyde and then dried in an oven at 60 °C for 24 h. For biosorption studies, 0.6 g of dried cells were suspended in 60 ml of distilled water and homogenized for 20 min in a homogenizer at 8000 rpm. 3.2. Preparation of biosorption media containing single-metal ions Pb(II) and Cu(II) solutions were prepared by diluting 1.0 g/1 of stock solutions of lead(II) and copper(II), obtained by dissolving Pb(NO3)2 and CuN2O6 Æ 3H2O in
M. Alimohamadi et al. / Minerals Engineering 18 (2005) 1325–1330
distilled water, respectively. The range of concentrations of prepared metal solutions varied between 50 and 500 mg/l. Before mixing with the fungal suspension, the pH of each was adjusted to 5.0, the optimum value for the biosorption of Pb(II) and Cu(II) ions, by adding 1 mol/l of HNO3 (Aksu et al., 1999).
1327
Langmuir Pb(II) Langmuir Cu(II)
20
For the determination of biosorption characteristics of Pb(II), and Cu(II) ions in binary metal mixtures, the initial concentration of the dominant metal was varied between 50 and 500 mg/l while the competing metal ion concentrations in each biosorption medium were held constant in the range 100–500 mg/l. The pH of the biosorption media was adjusted to 5.0 with 1 mol/l of HNO3.
Ceq/qeq (g/l)
15
3.3. Preparation of binary metal mixtures
10 5 0 200
0
400
600
Ceq(mg/l) Fig. 1. Langmuir single-component isotherms at pH = 5 for Cu(II) and Pb(II).
4. Results and discussion 4.1. Effect of initial pH on the sorption of metal cations
Freundlich Cu(II) 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0
4.2. Modeling phase
0
4.2.1. Single-component systems of Cu(II) and Pb(II) at pH = 5.0 The first and simplest step in simulating the binary system data is by using the predictive models such as the Freundlich and Langmuir isotherms (1) and (8). The relative parameters are reported in Table 1 and the corresponding curves are shown in Figs. 1 and 2. Table 1 Langmuir and Freundlich parameters and parameter standard deviation for single-metal systems of Pb and Cu (pH = 5.0) Component
Freundlich Pb(II)
ln qeq
Analysis of the experimental data shows that the influence of antagonistic metals in solution becomes less as the pH decreases for the buffering effect of high H+ concentrations. For equilibrium pH values ranging from 3 to 5, only part of the most acidic group on the cell wall is deprotonated and available for metal interactions. For the lowest tested pH, it is reasonable to suppose that most of the sites are protonated and metallic ions cannot effectively compete with protons for the binding sites (Aksu et al., 2002). It was observed from various experiments that the optimum pH value for gaining the most efficiency in biosorption of Pb(II) and Cu(II) ions was 5.
Parameter q0
B
R2
Langmuir single-component model Pb(II) 56.50 Cu(II) 48.54
0.0089 0.0027
0.997 0.988
Freundlich single-component model K
n
R2
Pb(II) Cu(II)
2.57 1.50
0.989 0.998
4.39 0.48
2
4
6
8
ln Ceq Fig. 2. Freundlich single-component isotherms at pH = 5 for Cu(II) and Pb(II).
In order to compare the adsorptive capacity of the sorbent for different components, adsorption model constants can be used to express its surface properties and affinity. The magnitudes of K and n, the mono-component Freundlich constants, show a selective uptake of Pb(II) at pH = 5 from wastewater with higher adsorptive capacity of biomass. Table 1 also represents n > 1, indicating that both the Pb(II) and Cu(II) ions are favorably adsorbed by this biomass at the studied pH value. While the Freundlich model does not describe the saturation behavior of the biosorbent, q0, the mono-component Langmuir constant is the monolayer saturation at equilibrium. The other mono-component Langmuir constant, b, corresponds to the concentration at which an amount of Cu(II) or Pb(II) ion of q0/2 is bound and indicates the affinity for the binding of Cu(II) or Pb(II) ions. The magnitude
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of q0 indicated that the amount of Pb(II) ions per unit weight of biosorbent required to form a complete monolayer on the surface was higher than that of Cu(II).
C0 Pb(II) = 0 mg/l C0 Pb(II) = 200 mg/l C0 Pb(II) = 400 mg/l
C0 Pb(II) = 100 mg/l C0 Pb(II) = 300 mg/l C0 Pb(II) = 500 mg/l
100
400
4.2.2. Multi-component systems of Cu(II) and Pb(II) at pH = 5.0 To determine the effects of initial Pb(II) and Cu(II) ion concentrations on the equilibrium uptake of Pb(II), the initial Pb(II) concentrations were varied between 100 and 500 mg/l while the initial Cu(II) concentration in each biosorption medium was held constant at 0, 100, 200, 300, 500 at pH = 5.0. The non-linearized adsorption isotherms of Pb(II) in the absence of Cu(II) ions and in the presence of increasing concentrations of Cu(II) ions obtained at this pH value is shown in Fig. 3. In the second part of the biosorption studies, the uptake of Cu(II) in the presence of increasing concentrations of Pb(II) was investigated at the initial pH value of 5. While the initial Cu(II) concentration was changed from 100 to 500 mg/l, initial Pb(II) concentration was held constant between 0 and 500 mg/l for each experimental set. Fig. 4 shows the variations of Cu(II) uptakes at equilibrium with increasing initial Pb(II) concentrations at this pH value. Although none of these predictive models confirms the non-ideal interactions occurring in the biosorption phenomenon, they are able to simulate adequately the binary system behavior starting from the single-metal system characteristic parameters. Good results were obtained using the simple Langmuir competitive model and the multi-component Freundlich model. The relative model parameters are listed in Table 2 and scatter diagrams (qcal vs. qexp) for pH = 5 are reported in Figs. 5 and 6, and show certain data spread around the squared diagonal: the good fitting of a model is represented by points that lie on the square diagonal in a symmetric and tight way (intercept = 0 and slope = 1). In comparison with the multi-component Langmuir model, the multi-component Freundlich model fitted the
C0 Cu(II) = 0 mg/l
C0 Cu(II) = 100 mg/l
C0 Cu(II) = 200 mg/l
C0 Cu(II) = 300 mg/l
C0 Cu(II) = 400 mg/l
C0 Cu(II) = 500 mg/l
qeq Cu(II) (mg/g)
30 25 20 15 10 5 0 0
200
300
500
600
Ceq Cu(II) (mg/l) Fig. 4. The comparison of the non-linearized adsorption isotherms of Cu(II) adsorption with Cu(II) present as the single component and in the presence of increasing concentrations of Pb(II) at pH: 5 and T: 25 °C.
Table 2 Comparison of the multi-component Langmuir and Freundlich adsorption constants for each component evaluated from the competitive modified Langmuir and Freundlich adsorption models for simultaneous biosorption of Pb(II) and Cu(II) to biomass at pH = 5 Component
Parameter
Pb(II) Cu(II) Pb(II) Cu(II)
g1
g2
1.198 – x1
y1
– 0.7 z1
0.54 0.58
1.1 0.36
0.39 0.66
qeq Pb(II) (mg/g)
50 40 30 20 10 0 0
100
200
300
400
500
600
Fig. 5. Scatter diagrams (qcal vs. qexp) obtained for predictive Langmuir model using single-metal system characteristic parameters (Table 2) for the different binary systems at pH = 5.
Ceq Pb(II) (mg/l) Fig. 3. The comparison of the non-linearized adsorption isotherms of Pb(II) adsorption with Pb(II) present as the single component and in the presence of increasing concentrations of Cu(II) at pH: 5 and T: 25 °C.
binary uptake data of Pb(II) and Cu(II) in the studied concentration range and pH value reasonably well, although slight deviations were observed between the experimental
M. Alimohamadi et al. / Minerals Engineering 18 (2005) 1325–1330
Fig. 6. Scatter diagrams (qcal vs. qexp) obtained for multi-component Freundlich model using single-metal system characteristic parameters (Table 2) for the different binary systems at pH = 5.
and calculated results from the model. These results can be attributed to the insensitivity of both models to competitive and interactive effects existing in multi-component systems and the characteristics of the Langmuir model which is not valid for high concentrations assuming limited number of identical sites for sorption. The average percentage errors between the experimental and predicted values (e%) were calculated using the following equation, where the subscripts exp and calc indicate the experimental and calculated values and N the number of measurements: PN i¼1 ðqeq;i;exp qeq;i;cal Þ=qeq;i;exp e% ¼ 100 N The average percentage errors between the experimental and predicted qeq values at pH = 5.0 for the entire data set of Pb(II) and Cu(II) were 4.35%, and 9.27%, respectively, for the multi-component Langmuir model and 2.21% and 3.43%, respectively, for the multi-component Freundlich model. It was concluded that the competitive, multi-component Freundlich model provided a more realistic description of the biosorption process. 5. Conclusion The presence of other metal species in wastewater during single-metal biosorption can have significant impact. Since real wastewaters contain many pollutants, adsorption system design must be based on multi-component effluents. For this reason, it is necessary to make multi-component equilibrium data instead of just single ones. The biosorption of Pb(II), Cu(II) and Pb(II)–Cu(II) binary mixtures on the R. arrhizus was investigated in this study and the mono- and multi-component Langmuir and multi-compo-
1329
nent Freundlich model were used to predict the equilibrium uptake of components, both singly and in combination. The results showed that the R. arrhizus biomass selectively adsorbed Pb(II) ions at pH = 5. Although R. arrhizus had a higher adsorption capacity for Pb(II) and Cu(II) in a single-component situation, due to the initial pH of solution, the equilibrium uptakes of Pb(II) and Cu(II) in the binary mixture decreased because of the levels of Pb(II) and Cu(II) concentrations due to the antagonistic interaction between these components. Application of the mono-component Langmuir and Freundlich models at the studied pH value indicated that the individual biosorption of Pb(II) and Cu(II) ions is suitable and can be characterized as a monolayer, single-site-type phenomenon with no interaction between sorbed components and the microbial surface. The individual Langmuir and multi-component Freundlich constants evaluated from the mono-component isotherms were used to compare the biosorptive capacity of the R. arrhizus for both components and to describe the multi-component adsorption equilibrium. It was concluded that the multi-component Freundlich model agreed reasonably well with the results found experimentally in the studied initial mixture concentration range at the studied pH value. References Aksu, Z., Acikel, U., Kutsal, T., 1999. Investigation of simultaneous biosorption of copper(II) and chromium(VI) on dried Chlorella vulgaris from binary metal mixtures: application of multi-component adsorption isotherms. Sep. Sci. Technol. 34, 501–524. Aksu, Z., Acikel, U., Kabasakal, E., 2002. Equilibrium modelling of individual and simultaneous biosorption of chromium(VI) and nickel(II) onto dried activated sludge. Water Res. 36, 3063–3073. Freundlich, H., 1907. Ueber die Adsorption in Loesungen. Z. Phys. Chem. 57, 385–470. Hammanini, A., Gonzalez, F., Ballester, A., 2003. Simultaneous uptake of metals by activated sludge. Miner. Eng. 16, 723–729. Hatzikioseyian, A., Tsezos, M., Mavituna, F., 2001. Application of simplified rapid equilibrium models in simulating experimental breakthrough curves from fixed bed biosorption reactors. Hydrometallurgy 59, 395–406. Ho, Y.S., McKay, G., 2000. Correlative biosorption equilibria model for a binary batch system. Chem. Eng. Sci. 55, 817–825. Kapoor, A., Viraraghavan, T., Cullimore, D.R., 1999. Removal of heavy metals using the fungus Aspergillus niger. Bioresour. Technol. 70, 95–104. Langmuir, I., 1918. The adsorption of gases on plane surfaces of glass, mica and platinum. J. Am. Chem. Soc. 40, 1361–1403. Loukidou, M.X., Matis, K.A., 2003. Removal of As(V) from wastewaters by chemically modified fungal biomass. Water Res. 37, 4544–4552. Muter, O., Lubinya, I., 2002. Cr(VI) sorption by intact and dehydrated Candida utilis cells in the presence of other metals. Process Biochem. 38, 123–131. Pagnanelli, F., Esposito, A., Veglio, F., 2002. Multi-metallic modeling for biosorption of binary systems. Water Res. 36, 4095–4105. Sag, Y., Kaya, A., Kutsal, T., 1998. The simultaneous biosorption of Cu(II) and Zn on Rhizopus arrhizus: application of the adsorption models. Hydrometallurgy 50, 297–314. Sips, R., 1948. On the structure of a catalyst surface. J. Chem. Phys. 16 (5), 490–495.
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Vasudevan, P., Padmavathy, V., Dhingra, S.C., 2002. Biosorption of monovalent and divalent ions on bakers yeast. Bioresour. Technol. 82, 285–289. Veglio, F., Beolchini, F., 1997. Removal of metals by biosorption: a review. Hydrometallurgy 44, 301–316.
Yin, P., Yu, Q., 1999. Biosorption removal of cadmium from aqueous solution by using pretreated fungal biomass cultured from starch wastewater. Water Res. 33, 1960–1963.
Pb(II) and Cu(II) biosorption on Rhizopus arrhizus modeling mono- and multi-component systems Mozhgan Alimohamadi
a,b,*
, Giti Abolhamd a, Alireza Keshtkar
b
a
b
Tehran University, Tehran 11365-4563, Iran Iran Atomic Energy Organization, Tehran 14155-1339, Iran Received 30 April 2005; accepted 4 August 2005 Available online 4 October 2005
Abstract The biosorption of Pb(II) and Cu(II) ions, both individually and in mixtures, by Rhizopus arrhizus was investigated in a batch system as a function of single- and dual-metal ion concentrations. The studied initial pH value for single Pb(II) and Cu(II) biosorptions was 5. It was observed that the co-ion effect on the equilibrium uptake became more noticeable as the co-ion concentration in solution increased. Adsorption isotherms were developed for both the single- and dual-metal ion systems at this pH value and expressed by the mono- and multi-component Langmuir and Freundlich adsorption models. Model parameters were estimated by non-linear regression using MATLAB and EXCEL softwares. It was observed that the mono-component adsorption equilibrium data fitted adequately to both the monocomponent adsorption models, for both the components and the pH value studied, while the multi-component Freundlich adsorption model predicted the multi-component adsorption equilibrium data for the ranges of initial mixture concentrations studied, and for the pH value used. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Modelling; Pb(II); Cu(II); Waterwaste
1. Introduction Solving water pollution problems caused by heavy toxic metal contamination resulting from industrial activities has long attracted the attention of scientists. The methods which have been used to remove heavy metal ions, such as chemical precipitation, adsorption, ion-exchange and solvent extraction have been found to be limited, since they often have high capital and operational costs and may also generate secondary wastes which are difficult to treat (Aksu et al., 2002; Hammanini et al., 2003; Muter and Lubinya, 2002; Kapoor et al., 1999). The use of microorganisms as biological adsorbents for removing heavy toxic metals is an alternative and cost effec* Corresponding author. Address: Tehran University, Tehran 113654563, Iran. Tel.: +98 21 666112203. E-mail address: [email protected] (M. Alimohamadi).
0892-6875/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.mineng.2005.08.007
tive technology (biosorption) which has emerged recently, based on the interactions between active sites of biomaterial (carboxylic, phosphate, sulphate, amino, amidic and hydroxyl groups) and metallic ions in the system (Pagnanelli et al., 2002; Yin and Yu, 1999; Veglio and Beolchini, 1997; Hatzikioseyian et al., 2001; Vasudevan et al., 2002). Biosorption can play a significant role in utilisation of industrial by-products or wastes, especially in the case of reusing biomasses emanating from food, pharmaceutical or wastewater treatment (Loukidou and Matis, 2003). Simple sorption isotherm curves are usually constructed by studying the equilibrium batch sorption behavior of different biosorbent materials. These curves allow quantitative evaluation of biosorption performance of these materials for only one metal. However, when more than one metal is present in a sorption system, evaluation, interpretation, and representation of biosorption results becomes much more complicated (Hammanini et al., 2003). This is the reason why much of the work on the biosorption
1326
M. Alimohamadi et al. / Minerals Engineering 18 (2005) 1325–1330
of heavy metal ions by various kinds of microorganisms has focused on the uptake of single metals, although in practice wastewaters are polluted with multiple metals. The equilibrium modeling of multi-metal biosorption, which is important in the design of treatment systems, has also largely been neglected. The examination of the effects of binary metal ions in various combinations is more representative of the actual environmental problems faced by organisms than are the single-metal studies (Aksu et al., 2002). The present paper investigates the binary sorption of copper and lead ions onto microorganism (Rhizopus arrhizus) using several concentrations of Cu and Pb. Several common models have been used to choose the one which best fits the experimental data. 2. Model considerations Equilibrium sorption studies determine the capacity of the sorbent, which can be described by a sorption isotherm characterized by certain constants whose values express the surface properties and affinity of the sorbent. Sorption equilibrium is established when the concentration of sorbate in the bulk solution is in dynamic balance with that of the interface (Ho and McKay, 2000). Equilibrium relationships between sorbent and sorbate are described by sorption isotherms. The data were determined and analyzed by some of the most frequently used isotherms. The Langmuir sorption isotherm (Langmuir, 1918) is the best known and the most often used isotherm for the sorption of a solute from a liquid solution: q ¼ qmax
bC f 1 þ bC f
q0i bi C i PN 1 þ k¼1 bk C k
qmax1 b1 ðC 1 =g1 Þ 1 þ b1 ðC 1 =g1 Þ þ b2 ðC 2 =g2 Þ qmax2 b2 ðC 2 =g2 Þ q2 ¼ 1 þ b1 ðC 1 =g1 Þ þ b2 ðC 2 =g2 Þ
q1 ¼
ð3Þ ð4Þ
where b1, b2, qmax1 and qmax2 are the individual Langmuir adsorption constants of the first and second metallic ion, and g1 and g2 are correction parameters for the first and the second metallic ion respectively, which are estimated from binary adsorption data. An empirical extension of the Freundlich model (Freundlich, 1907) restricted to binary mixtures (Sag et al., 1998) is given by 1=n þx
q1 ¼
K 1C1 1 1 C x11 þ y 1 C z21
q2 ¼
K 2C2 2 2 C x22 þ y 2 C z12
ð5Þ
1=n þx
ð6Þ
where a0i and b0i (i = 1, 2) are derived from the individual Freundlich isotherms and the other six parameters (b11, a12, b12, b22, a21 and b21) are correction coefficients. One of the other frequently used multi-component adsorption models is the Combination Langmuir and Freundlich model developed by Sips (1948) which gives better agreement with experimental data but uses additional parameters. This equation can be written as 1=ni
ð1Þ
where q is the sorption capacity at the equilibrium solute concentration Cf (mmol/g); Cf is the concentration of sorbate in solution (mmol/l); qmax is the maximum sorption capacity corresponding to complete monolayer coverage (mmol/g); b is a Langmuir constant related to the energy of sorption (l/mmol). The extended predictive Langmuir model can be written as qi ¼
The addition of further correction coefficients into the classical competitive Langmuir isotherm (2) makes this model more flexible and representative of the complexity of multi-metal systems
qi ¼
1þ
ai C i PN
1=ni i¼1 bi C i
ð7Þ
which gives a Freundlich model for mono-component systems with the following formula: q ¼ KC 1=n
ð8Þ
where k and n are Freundlich constants. 3. Materials and methods 3.1. Preparation of the microorganism for biosorption
ð2Þ
where qi is the uptake of the component i in the multi-component system and Ck (k = 1, . . . , N, where N is the total number of metals in the system) is the equilibrium concentration of each component and qmax and bi are obtained from the Langmuir isotherm (1) of each metal ion in a single system. There are several examples in the literature of empirical models, which provide parameters derived from single system data, but also adjustable correction parameters that take into account the interaction between the two metals in solution and that are fitted to binary data (Aksu et al., 1999).
After the growth period, R. arrhizus was washed twice with distilled water, inactivated using 1% formaldehyde and then dried in an oven at 60 °C for 24 h. For biosorption studies, 0.6 g of dried cells were suspended in 60 ml of distilled water and homogenized for 20 min in a homogenizer at 8000 rpm. 3.2. Preparation of biosorption media containing single-metal ions Pb(II) and Cu(II) solutions were prepared by diluting 1.0 g/1 of stock solutions of lead(II) and copper(II), obtained by dissolving Pb(NO3)2 and CuN2O6 Æ 3H2O in
M. Alimohamadi et al. / Minerals Engineering 18 (2005) 1325–1330
distilled water, respectively. The range of concentrations of prepared metal solutions varied between 50 and 500 mg/l. Before mixing with the fungal suspension, the pH of each was adjusted to 5.0, the optimum value for the biosorption of Pb(II) and Cu(II) ions, by adding 1 mol/l of HNO3 (Aksu et al., 1999).
1327
Langmuir Pb(II) Langmuir Cu(II)
20
For the determination of biosorption characteristics of Pb(II), and Cu(II) ions in binary metal mixtures, the initial concentration of the dominant metal was varied between 50 and 500 mg/l while the competing metal ion concentrations in each biosorption medium were held constant in the range 100–500 mg/l. The pH of the biosorption media was adjusted to 5.0 with 1 mol/l of HNO3.
Ceq/qeq (g/l)
15
3.3. Preparation of binary metal mixtures
10 5 0 200
0
400
600
Ceq(mg/l) Fig. 1. Langmuir single-component isotherms at pH = 5 for Cu(II) and Pb(II).
4. Results and discussion 4.1. Effect of initial pH on the sorption of metal cations
Freundlich Cu(II) 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0
4.2. Modeling phase
0
4.2.1. Single-component systems of Cu(II) and Pb(II) at pH = 5.0 The first and simplest step in simulating the binary system data is by using the predictive models such as the Freundlich and Langmuir isotherms (1) and (8). The relative parameters are reported in Table 1 and the corresponding curves are shown in Figs. 1 and 2. Table 1 Langmuir and Freundlich parameters and parameter standard deviation for single-metal systems of Pb and Cu (pH = 5.0) Component
Freundlich Pb(II)
ln qeq
Analysis of the experimental data shows that the influence of antagonistic metals in solution becomes less as the pH decreases for the buffering effect of high H+ concentrations. For equilibrium pH values ranging from 3 to 5, only part of the most acidic group on the cell wall is deprotonated and available for metal interactions. For the lowest tested pH, it is reasonable to suppose that most of the sites are protonated and metallic ions cannot effectively compete with protons for the binding sites (Aksu et al., 2002). It was observed from various experiments that the optimum pH value for gaining the most efficiency in biosorption of Pb(II) and Cu(II) ions was 5.
Parameter q0
B
R2
Langmuir single-component model Pb(II) 56.50 Cu(II) 48.54
0.0089 0.0027
0.997 0.988
Freundlich single-component model K
n
R2
Pb(II) Cu(II)
2.57 1.50
0.989 0.998
4.39 0.48
2
4
6
8
ln Ceq Fig. 2. Freundlich single-component isotherms at pH = 5 for Cu(II) and Pb(II).
In order to compare the adsorptive capacity of the sorbent for different components, adsorption model constants can be used to express its surface properties and affinity. The magnitudes of K and n, the mono-component Freundlich constants, show a selective uptake of Pb(II) at pH = 5 from wastewater with higher adsorptive capacity of biomass. Table 1 also represents n > 1, indicating that both the Pb(II) and Cu(II) ions are favorably adsorbed by this biomass at the studied pH value. While the Freundlich model does not describe the saturation behavior of the biosorbent, q0, the mono-component Langmuir constant is the monolayer saturation at equilibrium. The other mono-component Langmuir constant, b, corresponds to the concentration at which an amount of Cu(II) or Pb(II) ion of q0/2 is bound and indicates the affinity for the binding of Cu(II) or Pb(II) ions. The magnitude
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of q0 indicated that the amount of Pb(II) ions per unit weight of biosorbent required to form a complete monolayer on the surface was higher than that of Cu(II).
C0 Pb(II) = 0 mg/l C0 Pb(II) = 200 mg/l C0 Pb(II) = 400 mg/l
C0 Pb(II) = 100 mg/l C0 Pb(II) = 300 mg/l C0 Pb(II) = 500 mg/l
100
400
4.2.2. Multi-component systems of Cu(II) and Pb(II) at pH = 5.0 To determine the effects of initial Pb(II) and Cu(II) ion concentrations on the equilibrium uptake of Pb(II), the initial Pb(II) concentrations were varied between 100 and 500 mg/l while the initial Cu(II) concentration in each biosorption medium was held constant at 0, 100, 200, 300, 500 at pH = 5.0. The non-linearized adsorption isotherms of Pb(II) in the absence of Cu(II) ions and in the presence of increasing concentrations of Cu(II) ions obtained at this pH value is shown in Fig. 3. In the second part of the biosorption studies, the uptake of Cu(II) in the presence of increasing concentrations of Pb(II) was investigated at the initial pH value of 5. While the initial Cu(II) concentration was changed from 100 to 500 mg/l, initial Pb(II) concentration was held constant between 0 and 500 mg/l for each experimental set. Fig. 4 shows the variations of Cu(II) uptakes at equilibrium with increasing initial Pb(II) concentrations at this pH value. Although none of these predictive models confirms the non-ideal interactions occurring in the biosorption phenomenon, they are able to simulate adequately the binary system behavior starting from the single-metal system characteristic parameters. Good results were obtained using the simple Langmuir competitive model and the multi-component Freundlich model. The relative model parameters are listed in Table 2 and scatter diagrams (qcal vs. qexp) for pH = 5 are reported in Figs. 5 and 6, and show certain data spread around the squared diagonal: the good fitting of a model is represented by points that lie on the square diagonal in a symmetric and tight way (intercept = 0 and slope = 1). In comparison with the multi-component Langmuir model, the multi-component Freundlich model fitted the
C0 Cu(II) = 0 mg/l
C0 Cu(II) = 100 mg/l
C0 Cu(II) = 200 mg/l
C0 Cu(II) = 300 mg/l
C0 Cu(II) = 400 mg/l
C0 Cu(II) = 500 mg/l
qeq Cu(II) (mg/g)
30 25 20 15 10 5 0 0
200
300
500
600
Ceq Cu(II) (mg/l) Fig. 4. The comparison of the non-linearized adsorption isotherms of Cu(II) adsorption with Cu(II) present as the single component and in the presence of increasing concentrations of Pb(II) at pH: 5 and T: 25 °C.
Table 2 Comparison of the multi-component Langmuir and Freundlich adsorption constants for each component evaluated from the competitive modified Langmuir and Freundlich adsorption models for simultaneous biosorption of Pb(II) and Cu(II) to biomass at pH = 5 Component
Parameter
Pb(II) Cu(II) Pb(II) Cu(II)
g1
g2
1.198 – x1
y1
– 0.7 z1
0.54 0.58
1.1 0.36
0.39 0.66
qeq Pb(II) (mg/g)
50 40 30 20 10 0 0
100
200
300
400
500
600
Fig. 5. Scatter diagrams (qcal vs. qexp) obtained for predictive Langmuir model using single-metal system characteristic parameters (Table 2) for the different binary systems at pH = 5.
Ceq Pb(II) (mg/l) Fig. 3. The comparison of the non-linearized adsorption isotherms of Pb(II) adsorption with Pb(II) present as the single component and in the presence of increasing concentrations of Cu(II) at pH: 5 and T: 25 °C.
binary uptake data of Pb(II) and Cu(II) in the studied concentration range and pH value reasonably well, although slight deviations were observed between the experimental
M. Alimohamadi et al. / Minerals Engineering 18 (2005) 1325–1330
Fig. 6. Scatter diagrams (qcal vs. qexp) obtained for multi-component Freundlich model using single-metal system characteristic parameters (Table 2) for the different binary systems at pH = 5.
and calculated results from the model. These results can be attributed to the insensitivity of both models to competitive and interactive effects existing in multi-component systems and the characteristics of the Langmuir model which is not valid for high concentrations assuming limited number of identical sites for sorption. The average percentage errors between the experimental and predicted values (e%) were calculated using the following equation, where the subscripts exp and calc indicate the experimental and calculated values and N the number of measurements: PN i¼1 ðqeq;i;exp qeq;i;cal Þ=qeq;i;exp e% ¼ 100 N The average percentage errors between the experimental and predicted qeq values at pH = 5.0 for the entire data set of Pb(II) and Cu(II) were 4.35%, and 9.27%, respectively, for the multi-component Langmuir model and 2.21% and 3.43%, respectively, for the multi-component Freundlich model. It was concluded that the competitive, multi-component Freundlich model provided a more realistic description of the biosorption process. 5. Conclusion The presence of other metal species in wastewater during single-metal biosorption can have significant impact. Since real wastewaters contain many pollutants, adsorption system design must be based on multi-component effluents. For this reason, it is necessary to make multi-component equilibrium data instead of just single ones. The biosorption of Pb(II), Cu(II) and Pb(II)–Cu(II) binary mixtures on the R. arrhizus was investigated in this study and the mono- and multi-component Langmuir and multi-compo-
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nent Freundlich model were used to predict the equilibrium uptake of components, both singly and in combination. The results showed that the R. arrhizus biomass selectively adsorbed Pb(II) ions at pH = 5. Although R. arrhizus had a higher adsorption capacity for Pb(II) and Cu(II) in a single-component situation, due to the initial pH of solution, the equilibrium uptakes of Pb(II) and Cu(II) in the binary mixture decreased because of the levels of Pb(II) and Cu(II) concentrations due to the antagonistic interaction between these components. Application of the mono-component Langmuir and Freundlich models at the studied pH value indicated that the individual biosorption of Pb(II) and Cu(II) ions is suitable and can be characterized as a monolayer, single-site-type phenomenon with no interaction between sorbed components and the microbial surface. The individual Langmuir and multi-component Freundlich constants evaluated from the mono-component isotherms were used to compare the biosorptive capacity of the R. arrhizus for both components and to describe the multi-component adsorption equilibrium. It was concluded that the multi-component Freundlich model agreed reasonably well with the results found experimentally in the studied initial mixture concentration range at the studied pH value. References Aksu, Z., Acikel, U., Kutsal, T., 1999. Investigation of simultaneous biosorption of copper(II) and chromium(VI) on dried Chlorella vulgaris from binary metal mixtures: application of multi-component adsorption isotherms. Sep. Sci. Technol. 34, 501–524. Aksu, Z., Acikel, U., Kabasakal, E., 2002. Equilibrium modelling of individual and simultaneous biosorption of chromium(VI) and nickel(II) onto dried activated sludge. Water Res. 36, 3063–3073. Freundlich, H., 1907. Ueber die Adsorption in Loesungen. Z. Phys. Chem. 57, 385–470. Hammanini, A., Gonzalez, F., Ballester, A., 2003. Simultaneous uptake of metals by activated sludge. Miner. Eng. 16, 723–729. Hatzikioseyian, A., Tsezos, M., Mavituna, F., 2001. Application of simplified rapid equilibrium models in simulating experimental breakthrough curves from fixed bed biosorption reactors. Hydrometallurgy 59, 395–406. Ho, Y.S., McKay, G., 2000. Correlative biosorption equilibria model for a binary batch system. Chem. Eng. Sci. 55, 817–825. Kapoor, A., Viraraghavan, T., Cullimore, D.R., 1999. Removal of heavy metals using the fungus Aspergillus niger. Bioresour. Technol. 70, 95–104. Langmuir, I., 1918. The adsorption of gases on plane surfaces of glass, mica and platinum. J. Am. Chem. Soc. 40, 1361–1403. Loukidou, M.X., Matis, K.A., 2003. Removal of As(V) from wastewaters by chemically modified fungal biomass. Water Res. 37, 4544–4552. Muter, O., Lubinya, I., 2002. Cr(VI) sorption by intact and dehydrated Candida utilis cells in the presence of other metals. Process Biochem. 38, 123–131. Pagnanelli, F., Esposito, A., Veglio, F., 2002. Multi-metallic modeling for biosorption of binary systems. Water Res. 36, 4095–4105. Sag, Y., Kaya, A., Kutsal, T., 1998. The simultaneous biosorption of Cu(II) and Zn on Rhizopus arrhizus: application of the adsorption models. Hydrometallurgy 50, 297–314. Sips, R., 1948. On the structure of a catalyst surface. J. Chem. Phys. 16 (5), 490–495.
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Vasudevan, P., Padmavathy, V., Dhingra, S.C., 2002. Biosorption of monovalent and divalent ions on bakers yeast. Bioresour. Technol. 82, 285–289. Veglio, F., Beolchini, F., 1997. Removal of metals by biosorption: a review. Hydrometallurgy 44, 301–316.
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