equilibrium and kinetic studies of various heavy metals on
TRANSCRIPT
pp59-73 JWS-A-15-008C. Liu et al. / Journal of Water Sustainability
2 (2015) 59-73 59
Equilibrium and kinetic studies of various heavy metals on sugarcane
bagasse
Cong Liu, Huu Hao Ngo*, Wenshan Guo
Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of
Technology Sydney, Broadway, NSW 2007, Australia.
ABSTRACT Equilibrium and kinetic studies of copper (Cu), zinc (Zn) and lead (Pb) on sugarcane bagasse were investigated in a
batch mode. There is an increase in adsorption with increase in contact time and maximum adsorption takes place at
1 h and again after 1 h contact time there was no further adsorption. Therefore, biosorption equilibrium was reached
within 60 min. It has also been found out that most of the heavy metal removal occurred in the first 10 min.
Equilibrium was reached within 60 min and optimal time for biosorption process was between 1 to 10 minutes from
an economic perspective. Biosorptive capacity of Cu, Zn and Pb was determined to be 10.64 mg/g, 4.05 mg/g and
122.75 mg/g, respectively. Three adsorption isotherms were analyzed and obtained results followed
Redlich-Peterson adsorption isotherm. Non-linear models were also evaluated and exhibited a better fitness.
Furthermore, four kinetic models were applied and pseudo second-order kinetic model was found in satisfactory
accordance with obtained data.
1. INTRODUCTION
With the rapid development of industries such as metal plating, smelting, mining, pigment and metallurgical industries, heavy metal- bearing effluents have been continuously and excessively discharged, intensifying environ- mental pollution issues and deterioration of several aqueous ecosystems (Volesky, 1987). Due to their technological importance in multiple industries, it becomes unrealistic to reduce massive use of heavy metals (Nadeem et al., 2009). In consideration of the extreme toxicity of heavy metals towards aquatic, human and other forms of life, the removal of excess heavy metals from wastewater has become critical important with respect to both environmental and economic considerations.
What’s worse, in nature, heavy metals cannot
be degraded or destroyed (Coral et al., 2005), generating various permanent toxic effects (Hanif et al., 2005), which, subsequently, results in serious ecological and health problems throughout the whole food chain (Kim et al., 2007). To solve this problem, multitudinous conventional physicochemical methods have been used, such as electro- chemical treatment, ion-exchange, precipita- tion, reverse osmosis, evaporation and oxidation/reduction; however, all these methods have its respective limitations, such as expensive, not eco-friendly and inefficient for removing trace level of heavy metals (Vijayaraghavan and Yun, 2008).
In the past few decades, biosorption have received a staggering amount of interests and
Journal of Water Sustainability, Volume 5, Issue 2, June 2015, 59–73
© University of Technology Sydney & Xi’an University of Architecture and Technology
*Corresponding to: [email protected]
DOI: 10.11912/jws.2015.5.2.59-73
60 C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73
extensive researches have been carried out concerning this area. As a process that utilizes inexpensive biomass to sequester toxic heavy metals, biosorption is particularly useful for removing trace level of heavy metals from industrial effluents (Ahalya et al., 2003; Volesky, 1987). Common mechanisms responsible for biosorption of heavy metals are grouped as electrostatic interaction, ion exchange, surface complexation, micro- precipitation and biomass characterization (Ahalya et al., 2003). In comparison with conventional methods, biosorption process exhibits several advantages, including low operating cost, less sludge to be disposed of, high efficiency in detoxifying extremely dilute effluents, and no nutrient requirement (Kawwsarn, 2002).
A number of types of biomass have been investigated for biosorption, including yeast, algae, fungi, bacteria, industrial by-products and agricultural by-products (Liang et al., 2009). Among these various materials, agricultural by-products have been studied most extensively. For one single year, agricultural by-products as a whole exceed 320,000,000 tonnes (Aksu and Isoglu, 2005), offering inexhaustible materials and great selectivity for biosorption investigations. Besides, agricultural by-products possessan abundance of benefits, such as non-hazardous, relatively cheap, high biosorption potential, specifically selectivity and easily disposed by incineration (Aksu and Isoglu, 2005).
As a common agricultural by-product, the utilization of sugarcane bagasse as a bio- sorbent is considered environmentally friendly and economically viable. As the fibrous residue remaining after sugarcane stalks are crushed to extract the juice, sugarcane bagasse contains around 50% cellulose, 27% polyoses and 23% lignin (Aksu and Isoglu, 2005). These various substances, which contain abundant carboxyl functions, can strongly bind metal ions in aqueous solution, enabling
sugarcane bagasse a great potential to become an excellent biosorbent (Aksu and Isoglu, 2005; Kawwsarn, 2002).
2. MATERIAL AND METHODS
2.1 Preparation of biosorbent
Sugarcane bagasse was obtained from a local market. The collected sugarcane bagasse was washed with tap water and then rinsed with distilled water. Subsequently, sugarcane bagasse was dried and grounded into powder before its use in the biosorption experiments. The drying experiments were carried out in a laboratory scale oven. Dried sugarcane bagasse was stocked in desiccator at room temperature (20 °C).
2.2 Experimental conditions
All the chemicals used in this study were of analytical grade. Stock solutions were prepared in miliQ water. During the biosorption experiments, stock solutions were diluted to the specified concentration. Sugarcane bagasse was contacted with each solution at pH 6.48 (the pH of tap water). The reaction mixture was agitated at 125 rpm on a shaker. Agitation contact time was kept for 10 h which was sufficient to reach equilibrium. The whole experiment was conducted at room tempera- ture (20 °C).
2.2.1 Isotherm experiments
The equilibrium isotherms were determined by contacting a constant mass 0.5 g of sugarcane bagasse with 1 L standard solution at a range of different concentrations from 5 to 300 mg/L. A pH value of 6.48 was maintained throughout the experiment by adding 0.1 mol/L NaOH or HNO3. Langmuir, Freundlich, Redlich- Peterson and non-linear adsorption isotherms were applied in order to identify suitable adsorption isotherms.
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2.2.2 Kinetic studies
A kinetic study with different time intervals (1 min, 2 min, 3 min, 4 min, 5 min, 10 min, 15 min, 20 min, 25 min, 30 min, 45 min and 60 min) having fixed metal concentration (10 mg/L), biosorbent amount (0.1 g) and bio- sorbent particle size (< 150 µm) was per- formed. Elovich, Ritchies second-order, Pseudo first-order and Pseudo second-order kinetic model were investigated to determine optimal time for biosorption process.
2.3 Analysis
All the samples from the experiments were filtered through a 0.45 µm nylon membrane filter and the filtrate was kept for analysis. Biosorption experiments were conducted in triplicate and average values were used for discussion. Cu, Zn and Pb concentrations were measured using a contrAA 300 atomic absorption spectrophotometer. Before meas- urement, the solutions containing metals were appropriately diluted with miliQ water to ensure that the concentrations in the sample were linearly dependent on the absorbance detected so as to increase accuracy and avoid unexpected errors.
2.4 Adsorption isotherms
2.4.1 Langmuir and Freundlich adsorption isotherms
In this study, two common adsorption isotherms as Langmuir and Freundlich ad- sorption isotherms were applied to describe equilibrium data. The Langmuir equation has the form:
q =
(1)
where,
qe = amount of metal adsorbed at equilibrium (mg/g); qm = amount of metal per unit weight of bio- sorbent to form a complete monolayer on the
=
+
(2)
In general, the essential characteristics of the Langmuir adsorption isotherm can be ex- pressed as dimension-less constant separation factor or equilibrium constants given by RL:
R =
(3)
where,
KL = Langmuir’s equilibrium constant related to the affinity of binding sites, KL = qm Ka; Co = initial concentration of heavy metals (mg/L). The Freundlich adsorption isotherm has the form:
q = K × C / (4)
where KF and n are empirically constants and can be determined from a linearized form:
logq = + log K (5)
2.4.2 Redlich-Peterson adsorption isotherm
Besides the above two normal adsorption isotherms, there is one more isotherm that is commonly used as Redlich-Peterson isotherm, which contains three constants and involves the features of both the Langmuir and the Freundlich isotherms. It can be described as:
q = !× "
# (6)
− 1* = g × LnC + LnB (7)
Three isotherm constants, A, B and g can be calculated from the pseudo-linear plot using a trial-and-error optimization method. MATLAB program can be developed to determine the
62 C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73
correlation coefficient r2 for a series of values of A for linear regression of Ln(Ce) on Ln[A × (Ce/qe)−1], which, subsequently, yields the best value of A and a maximum optimized value of r2. After that, g and B can also be determined from the linear regression.
2.5 Kinetic models
In this study, four kinetic models known as Elovich kinetic model, Ritchie second-order kinetic model, pseudo first-order kinetic model and pseudo second-order kinetic model were applied to describe the experimental data so as to investigate the optimal time for the biosorption process.
2.5.1 Elovich kinetic model
Elovich kinetic model is a normal kinetic model based on the biosorptive capacity, which usually is in the form as: ,- ,. = ae1- (8)
where,
a = initial adsorption rate; = desorption constant during experiment; qt = amounts of adsorbed metal ions on biosorbent at any time t (mg/g). To simplify Elovich’s equation, assumed that at >>1 and by applying the boundary condi- tions of qt = 0 at t = 0 and qt = qt at t = t, then Eq. (8) becomes:
q. = × lna + ln t (9)
Thus, the constants can be obtained from the slope and the intercept of a straight line plot of qt against ln(t).
2.5.2 Ritchies second-order kinetic model
Assuming that the rate of biosorption depends solely on the fraction of sites unoccupied at any time, Ritchies kinetic model can be determined, which can be expressed as: ,4 ,. = K5 × 1 − θ (10)
where,
θ = fraction of surface sites that are occupied by an adsorbed metal ions, θ= qt/qe; n = number of surface sites occupied by each molecule of adsorbed metal ions and represents the order of the reaction; KR = rate constant (min-1). At time t = 0, it is assumed that no site is occupied. Introducing the term qt and qe, for n = 2, the integrated form of Eq. (10) becomes:
- 1-
= K5 × t (11)
Thus, the rate constant KR can be obtained from the plot of qt/(qe – qt) vs t.
2.5.3 Pseudo first-order kinetic model
The pseudo first-order kinetic model is also known as the Lagergren equation, takes the form: ,- ,. = K × q − q. (12)
where,
K1 = Lagergren rate constant of the first-order biosorption (g/mg min). After integration and applying boundary conditions t = 0 to t = t and qt = 0 to qt = qt, the integrated form of equation becomes:
logq − q. = log q − K t (13)
When the values of log (qe - qt) were linearly correlated with t, the plot of log (qe - qt) versus t will give a linear relationship from which K1 and qe can be determined respectively.
2.5.4 Pseudo second-order kinetic model
The second order kinetic model considered here is given as: ,- ,. = K7 × q − q.7 (14)
where,
K2 = the rate constant of second order bio- sorption (g/mg min).
which has a linear form as:
- =
3. RESULTS AND DISCUSSION
3.1 Equilibrium studies
In general, there are two stages for uptaking of metal ions by biosorbents in batch systems:
·an initial rapid stage with passive uptake.
·a much slower process with active uptake.
The first stage is physical adsorption or ion exchange carried out at the surface of the biosorbent. The biosorption equilibrium occurs at the end of rapid physical adsorption process (Parvathi et al., 2007).
3.1.1 Langmuir adsorption isotherm
Typically, biosorption data were analyzed in accordance with linear form of Langmuir adsorption isotherm (Ho et al., 2005) and linear plots of the specific sorption (Ce/qe) against the equilibrium concentration (Ce) for Cu, Zn and Pb were shown in Fig. 1. Linear
isotherm constants qm, Ka, RL and correlation coefficient r2 were presented in Table 1. The isotherms of Cu and Zn were found to be linear over the entire concentration range studied with extremely high r2 values, suggesting that the Langmuir adsorption isotherm provided a good model for these biosorption processes. While for Pb, due to the excellent biosorptive capacity, the equilibrium concentration became extremely low when the initial concentration was lower than 100 mg/L, which, subsequently, affected the accuracy of both concentration determination and linear regression.
The saturated monolayer biosorptive capacity qm for Pb were significantly higher than those for Cu and Zn, indicating that sugarcane bagasse had a great potential to separate Pb from wastewater or even metal contaminated soil. Based on the study McKay et al. (1982), RL between 0 and 1 indicates favorable biosorption. In the current experiment, RL for Cu, Zn and Pb were all found between 0 and 1 and again the biosorption was believed to be favorable.
Figure 1 Langmuir isotherms of metals sorbed on sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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Table 1 Constants of Langmuri, Freundlich and Redlich-Peterson adsorption isotherms for the biosorption of three metals on sugarcane bagasse
Langmuir qm (cal) Ka r2 RL
Cu 10.64 0.587 0.999 0.016
Zn 4.05 0.266 0.999 0.085
Pb 122.75 0.023 0.297 0.028
Freundlich KF n r2
Cu 8.933 34.48 0.979
Zn 3.155 25.64 0.886
Pb 56.49 6.85 0.819
Redlich-Peterson g A B r2
Cu 0.973 67.14 7.36 0.9999
Zn 0.999 1.65 0.42 0.9999
Pb 0.849 47.80 0.39 0.9997
Figure 2 Freundlich isotherms of metals sorbed on sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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3.1.2 Freundlich adsorption isotherm
The linear Freundlich adsorption isotherm plots were presented in Fig. 2. Examination of the plots suggested that the linear Freundlich adsorption isotherm was also a suitable model. Table 1 showed the linear Freundlich adsorp- tion isotherm constants KF and 1/n, and correlation coefficients r2. Based on the r2 values,the linear form of the Freundlichad- sorption isotherm appeared to produce a reasonable model all three metals, with the Cuisotherm seemingly better fitted of the experimental data than Zn and Pb. Normally, the magnitude of KF and 1/n illustrats the separation of metal ions from water and the biosorptive capacity. In the present study, comparatively higher KF value of Pb biosorption was observed, implying that sug- arcane bagasse had a much higher biosorptive capacity toward Pb and an excellent specificity between Pb ions and sugarcane bagasse was believed to exist. According to Kadirvelu and Namasivayam (Kadirveln and Namasivayam, 2000), n values between 1 and 10 represents beneficial adsorption and therefore again, confirmed that the biosorption of Pb on sugarcane bagasse was beneficial.
3.1.3 Redlich-Peterson adsorption isotherm
The Redlich-Peterson isotherm plots for sorption of three heavy metals on sugarcane bagasse were presented in Fig. 3. Again, examination of the plots showed that Redlich- Peterson isotherm accurately described the biosorption behaviors of Cu, Zn and Pb on sugarcane bagasse over the concentration ranges studied. The Redlich-Peterson isotherm constants, A, B, g, and r2 were given in Table 1. Since the MATLAB program used to derive the isotherm constants maximized the linear correlation coefficient r2, it was unsurprising that in all cases, the Redlich-Peterson isotherms exhibited extremely high r2 values, indicating that it produced a considerably better fit compared to the preceding two-constant isotherms (Langmuir and Freundlich adsorption isotherms). For Cu and Zn biosorption, Redlich-Peterson isotherm was the most- suitable adsorption isotherm for the data followed by the Langmuir and then Freundlich adsorption isotherm. On the other hand, for Pb biosorption, the Redlich-Peterson adsorption isotherm was still the most-suitable isotherm, followed by the Freundlich then the Langmuir adsorption isotherm.
Figure 3 Redlich-Peterson isotherms of metals sorbed on sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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3.1.4 Non-linear models
The Chi-square statistic correlation coeffi- cients x2, were obtained and the comparison between x2 and correlation coefficients of linear models r2 were shown in Table 2. In the non-linear analysis, compared with Langmuir adsorption isotherm, Redlich-Peterson and Freundlich adsorption isotherms exhibited lower x2 values and were considered to be a better fit. Detailed results were shown in following figures (Fig. 4, Fig. 5, Fig. 6) and isotherm plots and experimental data were exhibited, respectively. Drawing conclusions from non-linear Chi-square analysis, for Cu biosorption on sugarcane (Fig. 4), Freundlich adsorption isotherm was determined to be the most suitable one. While for the Zn biosorption on sugarcane bagasse (Fig. 5), the Redlich-Peterson adsorption isotherm was the best-fitting isotherm, followed by the Freundlich model for this sorption system. However, for Pb biosorption on sugarcane bagasse (Fig. 6), all these models were found not suitable to describe obtained results. The main reason was believed to be the excellent biosorptive capacity of sugarcane bagasse
toward Pb. When initial concentration was lower than 100 mg/L, extremely low concentration Pb ions in the solution was left, which, subsequently, affected the construction and the accuracy of the plots. Based on previous studies, unlike the linear analysis, different forms of the equation affected x2 values less significantly (Ho et al., 2005). Therefore, the non-linear Chi-square analysis should be considered as a method to avoide such errors.
Based on the above results, it could be found that linear regression and the non-linear Chi-square analysis gave different best-fitting isotherm for the given data set, implying that a significant difference existed between linear and non-linear isotherms (Ho, 2004). Com- pared to the non-linear Chi-square analysis, due to its simplicity, most of the biosorption equilibrium analysis still mostly relied on linear regression, which might have led to an inaccurate conclusion. Therefore, to ensure better results, it should be suggested that both linear and non-linear regression analyses be evaluated so as to describe obtained data in a more comprehensive way.
Table 2 Comparison of linear regression correlation coefficients r2 and non-linear regression
coefficients x2
Cu
Langmuir 0.297 2082.1
Freundlich 0.819 287.2
Redlich-Peterson 0.9997 39.06
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Figure 4 Comparison of different isotherms for biosorption of Cu onto sugarcane bagasse (pH:
6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
Figure 5 Comparison of different isotherms for biosorption of Zn onto sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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Figure 6 Comparison of different isotherms for biosorption of Zn onto sugarcane bagasse
(pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
3.2 Kinetic modelling
For designing batch biosorption systems, prediction of biosorption rate plays a critical role and information on the kinetics of metal uptake is very important for selecting optimum operating conditions for full-scale batch process and industrial-scale application (Rao et al., 2010). Generally, linear regression is the most commonly used method to obtain the constants involved in the kinetic models and also in predicting the best-fit kinetic model (Pandey et al., 2010). The kinetic constants involved in the Elovich kinetic model (Fig. 7), Ritchie second-order kinetic model (Fig. 8), Pseudo first-order kinetic model (Fig. 9) and Pseudo second-order kinetic model (Fig. 10) were obtained. The calculated kinetic rate constants and their corresponding correlation coefficient r2 were given in Table 3, which also compared the experimental qeq values and calculated qeq values. From Table 3, suggested by the highest r2 values, it could be observed that pseudo second-order kinetic model provided the best fit to the experimental kinetic
data than other kinetic models. When comparing the experimental qeq values with qeq calculated values, it was found that it was inappropriate to use the pseudo first-order kinetic model to predict the qeq values from the kinetic data. The similar calculated qeq values when compared to the experimental qeq also indicated that pseudo second-order kinetic model had an advantage for predicting the qeq value more precisely. The similar r2 values for Elovich, Ritchie second-order and Pseudo second-order kinetic models suggested all these models were in same error distribution structure (Kumarl and Porkodi, 2008). Based all these facts, the uptake of Cu, Zn and Pb were all believed to follow pseudo second- order kinetic model, which relied on the assumption that biosorption might be a rate- limiting step. Thus, sugarcane bagasse should be considered to have a great potential to become a biosorbent for removing heavy metals from wastewater in full batch-scale process and industrial-scale application. Mechanisms involving in this process should also be studied for the better understanding.
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Figure 7 Elovich kinetic model of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L; particle size < 150 µm;
125 rpm; 20 °C)
Figure 8 Ritchie second-order kinetic model of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L;
particle size < 150 µm; 125 rpm; 20 °C)
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Table 3 Constants of four kinetic models for the biosorption of three metals on various sugarcane bagasse
Metal type qeq (exp) Elovich kinetic model
Ritchie second-order kinetic model
Cu 8.721 165.7 0.800 0.787 0.0774 0.914
Zn 2.972 7.09×109 0.102 0.984 0.0359 0.933
Pb 9.151 1.44×1033 0.109 0.985 0.1078 0.941
Metal type
Cu 0.016 5.058 0.843 0.029 8.197 0.968
Zn 0.003 0.796 0.896 0.699 2.445 0.998
Pb 0.003 0.955 0.902 0.560 8.621 0.999
Figure 9 Pseudo first-order kinetics of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L; particle size < 150 µm;
125 rpm; 20 °C)
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Figure 10 Pseudo second-order kinetics of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L; particle size < 150 µm;
125 rpm; 20 °C)
3.3 Further discussion
In spite of its development for several decades, biosorption is still basically at lab scale (Wang and Chen, 2006). The mechanisms involved in biosorption process should be further studied with great ongoing efforts byutilizing various techniques and the combination of them (Kratochvil and Volesky, 1998) as follows:
·Physicochemical characteristics of real wastewater on the basis of reaction equilib- riums and kinetics;
·Screening of biosorbents for high metal- binding capacity and selectivity;
·Optimization of conditions;
·Combining biosorption with physico- chemical treatment technologies forcom- plete wastewater treatment, recovery of metals and reuse of biosorbents.
This present study was a small step towards such an approach in characterizing the bio- sorption process on the basis of equilibrium and kinetic modeling. Studies in the direction
of using really industrial wastewater should be conducted for the further development.
4. CONCLUSION
This study showed that sugarcane bagasse could efficiently remove Cu, Zn and Pb from water. It was found not appropriate to use linear regression method alone for comparing the best-fitting isotherms. It was suggested that both linear and non-linear regression analyses should be applied to ensure better results so as to describe obtained data in a more comprehensive way. The findings of this study indicated that sugarcane bagasse should be a promising biosorbent for removing heavy metals, emphasizing the importance and need for carry out extended testing for the compatibility of biosorption to real industrial wastewater. Further studies on metal- biosorbent specificity, applicability to different types of metal-laden effluents are necessary in developing biosorption of sugarcane bagasse
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for industrial-scale applications.
ACKNOWLEDGEMENT
This work was supported by Centre for Tech- nology in Water and Wastewater (CTWW), School of Civil and Environmental Engineer- ing (CEE), University of Technology, Sydney (UTS) and Australian Postgraduate Award.
REFERENCES
Ahalya, N., Ramachandra, T.V. and Kanamadi, R.D. (2003). Biosorption of Heavy Metals. Journal of Chemistry and Environment, 7(4), 71-79.
Aksu, Z. and Isoglu, A.I. (2005). Removal of cop- per(II) ions from aqueous solution by biosorp- tion onto agricultural waste sugar beet pulp. Process Biochemistry, 40(9), 3031-3044.
Coral, M.N.U., Korkmaz, H., Arkan, B. and Coral, G. (2005). Plasmid mediated heavy metal re- sistance in Enterobacter spp. isolated from Sofulu landfill, in Adana, Turkey. Annals of Mi- crobiol, 55(3), 175-179.
Hanif, M.A., Nadeem, R., Rashid, U. and Zafar, M.N. (2005). Assessing pollution levels in ef- fluents of industries in city zone Faisalabad, Pa- kistan. Journal of Applied Science, 5(10), 1713-1717.
Ho, Y.S. (2004). Selection of optimum sorption isotherm. Carbon, 42(10), 2115-2116.
Ho, Y.S., Chiu, W.T. and Wang, C.C. (2005). Re- gression analysis for the sorption isotherms of basic dyes on sugarcane dust. Bioresource Technology, 96(11), 1285-1291.
Kadirvelu, K. and Namasivayam, C. (2000). Ag- ricultural by-products as metal adsorbent: sorp- tion of lead (II) from aqueous solutions onto coir-pith carbon. Environmental Technology, 21(10), 1091-1097.
Kawwsarn, P. (2002). Biosorption of copper (II) from aqueous solutions by pre-treated biomass of marine algae Padina sp. Chemosphere,
47(10), 1081-1085. Kim, S.U., Cheong, Y.H., Seo, D.C., Hur, J.S.,
Heo, J.S. and Cho, J.S. (2007). Characterisation of heavy metal tolerance and biosorption capacity of bacterium strain CPB4 (Bacillus spp.). Water Science and Technology, 55(1-2), 105-111.
Kratochvil, D. and Volesky, B. (1998). Advances in the biosorption of heavy metals. Trends in Biotechnology, 16(7), 291-300.
Kumar1, K.V. and Porkodi, K. (2008). Pseudo se- cond order kinetics for the sorption of auramine O onto activated carbon. Indian Chemical En- gineer, 50, 227-238.
Liang, S., Guo, X.Y., Feng, N.C. and Tian, Q.H. (2009). Isotherms, kinetics and thermodynamic studies of adsorption of Cu2+ from aqueous so- lutions by Mg2+/K+ type orange peel adsorbents. Journal of Hazardous Materials, 174(1-3), 756-762.
Mckay, G., Blair, H.S. and Gardner, J.R. (1982). Adsorption of dyes on chitin. I. Equilibrium studies. Journal of Applied Polymer Science, 27(8), 3043-3057.
Nadeem, R., Hanif, A.M., Mahmood, A., Jamil, S.M. and Ashraf, M. (2009). Biosorption of Cu (II) ions from aqueous effluents by blackgram bran (BGB). Journal of Hazardous Materials, 168(2-3), 1622-1625.
Pandey, P.K., Sharma, S.K. and Sambi, S.S. (2010). Kinetics and equilibrium study of chromium adsorption on zeoliteNaX. International Journal of Environmental Science and Technology, 7(2), 395-404.
Parvathi, K., Nagendran, R. and Nareshkumar, R. (2007). Lead biosorption onto waste beer yeast by-product, a means to decontaminate effluent generated from battery manufacturing industry. Electronic Journal of Biotechnology, 10(1), 181-188.
Rao, H.J., Kalyanil, G., Rao, V.K., Kumar, T.A., Mariadas, K., Kumar, Y.P., Vijetha, P., Pallavi, P., Sumalatha, B. and Kumaraswamy, K. (2010). Kinetic studies on biosorption of Lead from aqueous solutions using egg shell powder. In-
C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73 73
ternational Journal of Biotechnology and Bio- chemistry, 6(6), 957-968.
Vijayaraghavan, K. and Yun, Y.S. (2008). Bacterial biosorbents and biosorption. Biotechnology Advances, 26(3), 266-291.
Volesky, B. (1987). Biosorbents for metal recovery.
Trends in Biotechnology, 5(4), 96-101. Wang, J.L. and Chen, C. (2006). Biosorption of
heavy metals by Saccharomyces cerevisiae: A review. Biotechnology Advances, 24(5), 427-451.
Equilibrium and kinetic studies of various heavy metals on sugarcane
bagasse
Cong Liu, Huu Hao Ngo*, Wenshan Guo
Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of
Technology Sydney, Broadway, NSW 2007, Australia.
ABSTRACT Equilibrium and kinetic studies of copper (Cu), zinc (Zn) and lead (Pb) on sugarcane bagasse were investigated in a
batch mode. There is an increase in adsorption with increase in contact time and maximum adsorption takes place at
1 h and again after 1 h contact time there was no further adsorption. Therefore, biosorption equilibrium was reached
within 60 min. It has also been found out that most of the heavy metal removal occurred in the first 10 min.
Equilibrium was reached within 60 min and optimal time for biosorption process was between 1 to 10 minutes from
an economic perspective. Biosorptive capacity of Cu, Zn and Pb was determined to be 10.64 mg/g, 4.05 mg/g and
122.75 mg/g, respectively. Three adsorption isotherms were analyzed and obtained results followed
Redlich-Peterson adsorption isotherm. Non-linear models were also evaluated and exhibited a better fitness.
Furthermore, four kinetic models were applied and pseudo second-order kinetic model was found in satisfactory
accordance with obtained data.
1. INTRODUCTION
With the rapid development of industries such as metal plating, smelting, mining, pigment and metallurgical industries, heavy metal- bearing effluents have been continuously and excessively discharged, intensifying environ- mental pollution issues and deterioration of several aqueous ecosystems (Volesky, 1987). Due to their technological importance in multiple industries, it becomes unrealistic to reduce massive use of heavy metals (Nadeem et al., 2009). In consideration of the extreme toxicity of heavy metals towards aquatic, human and other forms of life, the removal of excess heavy metals from wastewater has become critical important with respect to both environmental and economic considerations.
What’s worse, in nature, heavy metals cannot
be degraded or destroyed (Coral et al., 2005), generating various permanent toxic effects (Hanif et al., 2005), which, subsequently, results in serious ecological and health problems throughout the whole food chain (Kim et al., 2007). To solve this problem, multitudinous conventional physicochemical methods have been used, such as electro- chemical treatment, ion-exchange, precipita- tion, reverse osmosis, evaporation and oxidation/reduction; however, all these methods have its respective limitations, such as expensive, not eco-friendly and inefficient for removing trace level of heavy metals (Vijayaraghavan and Yun, 2008).
In the past few decades, biosorption have received a staggering amount of interests and
Journal of Water Sustainability, Volume 5, Issue 2, June 2015, 59–73
© University of Technology Sydney & Xi’an University of Architecture and Technology
*Corresponding to: [email protected]
DOI: 10.11912/jws.2015.5.2.59-73
60 C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73
extensive researches have been carried out concerning this area. As a process that utilizes inexpensive biomass to sequester toxic heavy metals, biosorption is particularly useful for removing trace level of heavy metals from industrial effluents (Ahalya et al., 2003; Volesky, 1987). Common mechanisms responsible for biosorption of heavy metals are grouped as electrostatic interaction, ion exchange, surface complexation, micro- precipitation and biomass characterization (Ahalya et al., 2003). In comparison with conventional methods, biosorption process exhibits several advantages, including low operating cost, less sludge to be disposed of, high efficiency in detoxifying extremely dilute effluents, and no nutrient requirement (Kawwsarn, 2002).
A number of types of biomass have been investigated for biosorption, including yeast, algae, fungi, bacteria, industrial by-products and agricultural by-products (Liang et al., 2009). Among these various materials, agricultural by-products have been studied most extensively. For one single year, agricultural by-products as a whole exceed 320,000,000 tonnes (Aksu and Isoglu, 2005), offering inexhaustible materials and great selectivity for biosorption investigations. Besides, agricultural by-products possessan abundance of benefits, such as non-hazardous, relatively cheap, high biosorption potential, specifically selectivity and easily disposed by incineration (Aksu and Isoglu, 2005).
As a common agricultural by-product, the utilization of sugarcane bagasse as a bio- sorbent is considered environmentally friendly and economically viable. As the fibrous residue remaining after sugarcane stalks are crushed to extract the juice, sugarcane bagasse contains around 50% cellulose, 27% polyoses and 23% lignin (Aksu and Isoglu, 2005). These various substances, which contain abundant carboxyl functions, can strongly bind metal ions in aqueous solution, enabling
sugarcane bagasse a great potential to become an excellent biosorbent (Aksu and Isoglu, 2005; Kawwsarn, 2002).
2. MATERIAL AND METHODS
2.1 Preparation of biosorbent
Sugarcane bagasse was obtained from a local market. The collected sugarcane bagasse was washed with tap water and then rinsed with distilled water. Subsequently, sugarcane bagasse was dried and grounded into powder before its use in the biosorption experiments. The drying experiments were carried out in a laboratory scale oven. Dried sugarcane bagasse was stocked in desiccator at room temperature (20 °C).
2.2 Experimental conditions
All the chemicals used in this study were of analytical grade. Stock solutions were prepared in miliQ water. During the biosorption experiments, stock solutions were diluted to the specified concentration. Sugarcane bagasse was contacted with each solution at pH 6.48 (the pH of tap water). The reaction mixture was agitated at 125 rpm on a shaker. Agitation contact time was kept for 10 h which was sufficient to reach equilibrium. The whole experiment was conducted at room tempera- ture (20 °C).
2.2.1 Isotherm experiments
The equilibrium isotherms were determined by contacting a constant mass 0.5 g of sugarcane bagasse with 1 L standard solution at a range of different concentrations from 5 to 300 mg/L. A pH value of 6.48 was maintained throughout the experiment by adding 0.1 mol/L NaOH or HNO3. Langmuir, Freundlich, Redlich- Peterson and non-linear adsorption isotherms were applied in order to identify suitable adsorption isotherms.
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2.2.2 Kinetic studies
A kinetic study with different time intervals (1 min, 2 min, 3 min, 4 min, 5 min, 10 min, 15 min, 20 min, 25 min, 30 min, 45 min and 60 min) having fixed metal concentration (10 mg/L), biosorbent amount (0.1 g) and bio- sorbent particle size (< 150 µm) was per- formed. Elovich, Ritchies second-order, Pseudo first-order and Pseudo second-order kinetic model were investigated to determine optimal time for biosorption process.
2.3 Analysis
All the samples from the experiments were filtered through a 0.45 µm nylon membrane filter and the filtrate was kept for analysis. Biosorption experiments were conducted in triplicate and average values were used for discussion. Cu, Zn and Pb concentrations were measured using a contrAA 300 atomic absorption spectrophotometer. Before meas- urement, the solutions containing metals were appropriately diluted with miliQ water to ensure that the concentrations in the sample were linearly dependent on the absorbance detected so as to increase accuracy and avoid unexpected errors.
2.4 Adsorption isotherms
2.4.1 Langmuir and Freundlich adsorption isotherms
In this study, two common adsorption isotherms as Langmuir and Freundlich ad- sorption isotherms were applied to describe equilibrium data. The Langmuir equation has the form:
q =
(1)
where,
qe = amount of metal adsorbed at equilibrium (mg/g); qm = amount of metal per unit weight of bio- sorbent to form a complete monolayer on the
=
+
(2)
In general, the essential characteristics of the Langmuir adsorption isotherm can be ex- pressed as dimension-less constant separation factor or equilibrium constants given by RL:
R =
(3)
where,
KL = Langmuir’s equilibrium constant related to the affinity of binding sites, KL = qm Ka; Co = initial concentration of heavy metals (mg/L). The Freundlich adsorption isotherm has the form:
q = K × C / (4)
where KF and n are empirically constants and can be determined from a linearized form:
logq = + log K (5)
2.4.2 Redlich-Peterson adsorption isotherm
Besides the above two normal adsorption isotherms, there is one more isotherm that is commonly used as Redlich-Peterson isotherm, which contains three constants and involves the features of both the Langmuir and the Freundlich isotherms. It can be described as:
q = !× "
# (6)
− 1* = g × LnC + LnB (7)
Three isotherm constants, A, B and g can be calculated from the pseudo-linear plot using a trial-and-error optimization method. MATLAB program can be developed to determine the
62 C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73
correlation coefficient r2 for a series of values of A for linear regression of Ln(Ce) on Ln[A × (Ce/qe)−1], which, subsequently, yields the best value of A and a maximum optimized value of r2. After that, g and B can also be determined from the linear regression.
2.5 Kinetic models
In this study, four kinetic models known as Elovich kinetic model, Ritchie second-order kinetic model, pseudo first-order kinetic model and pseudo second-order kinetic model were applied to describe the experimental data so as to investigate the optimal time for the biosorption process.
2.5.1 Elovich kinetic model
Elovich kinetic model is a normal kinetic model based on the biosorptive capacity, which usually is in the form as: ,- ,. = ae1- (8)
where,
a = initial adsorption rate; = desorption constant during experiment; qt = amounts of adsorbed metal ions on biosorbent at any time t (mg/g). To simplify Elovich’s equation, assumed that at >>1 and by applying the boundary condi- tions of qt = 0 at t = 0 and qt = qt at t = t, then Eq. (8) becomes:
q. = × lna + ln t (9)
Thus, the constants can be obtained from the slope and the intercept of a straight line plot of qt against ln(t).
2.5.2 Ritchies second-order kinetic model
Assuming that the rate of biosorption depends solely on the fraction of sites unoccupied at any time, Ritchies kinetic model can be determined, which can be expressed as: ,4 ,. = K5 × 1 − θ (10)
where,
θ = fraction of surface sites that are occupied by an adsorbed metal ions, θ= qt/qe; n = number of surface sites occupied by each molecule of adsorbed metal ions and represents the order of the reaction; KR = rate constant (min-1). At time t = 0, it is assumed that no site is occupied. Introducing the term qt and qe, for n = 2, the integrated form of Eq. (10) becomes:
- 1-
= K5 × t (11)
Thus, the rate constant KR can be obtained from the plot of qt/(qe – qt) vs t.
2.5.3 Pseudo first-order kinetic model
The pseudo first-order kinetic model is also known as the Lagergren equation, takes the form: ,- ,. = K × q − q. (12)
where,
K1 = Lagergren rate constant of the first-order biosorption (g/mg min). After integration and applying boundary conditions t = 0 to t = t and qt = 0 to qt = qt, the integrated form of equation becomes:
logq − q. = log q − K t (13)
When the values of log (qe - qt) were linearly correlated with t, the plot of log (qe - qt) versus t will give a linear relationship from which K1 and qe can be determined respectively.
2.5.4 Pseudo second-order kinetic model
The second order kinetic model considered here is given as: ,- ,. = K7 × q − q.7 (14)
where,
K2 = the rate constant of second order bio- sorption (g/mg min).
which has a linear form as:
- =
3. RESULTS AND DISCUSSION
3.1 Equilibrium studies
In general, there are two stages for uptaking of metal ions by biosorbents in batch systems:
·an initial rapid stage with passive uptake.
·a much slower process with active uptake.
The first stage is physical adsorption or ion exchange carried out at the surface of the biosorbent. The biosorption equilibrium occurs at the end of rapid physical adsorption process (Parvathi et al., 2007).
3.1.1 Langmuir adsorption isotherm
Typically, biosorption data were analyzed in accordance with linear form of Langmuir adsorption isotherm (Ho et al., 2005) and linear plots of the specific sorption (Ce/qe) against the equilibrium concentration (Ce) for Cu, Zn and Pb were shown in Fig. 1. Linear
isotherm constants qm, Ka, RL and correlation coefficient r2 were presented in Table 1. The isotherms of Cu and Zn were found to be linear over the entire concentration range studied with extremely high r2 values, suggesting that the Langmuir adsorption isotherm provided a good model for these biosorption processes. While for Pb, due to the excellent biosorptive capacity, the equilibrium concentration became extremely low when the initial concentration was lower than 100 mg/L, which, subsequently, affected the accuracy of both concentration determination and linear regression.
The saturated monolayer biosorptive capacity qm for Pb were significantly higher than those for Cu and Zn, indicating that sugarcane bagasse had a great potential to separate Pb from wastewater or even metal contaminated soil. Based on the study McKay et al. (1982), RL between 0 and 1 indicates favorable biosorption. In the current experiment, RL for Cu, Zn and Pb were all found between 0 and 1 and again the biosorption was believed to be favorable.
Figure 1 Langmuir isotherms of metals sorbed on sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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Table 1 Constants of Langmuri, Freundlich and Redlich-Peterson adsorption isotherms for the biosorption of three metals on sugarcane bagasse
Langmuir qm (cal) Ka r2 RL
Cu 10.64 0.587 0.999 0.016
Zn 4.05 0.266 0.999 0.085
Pb 122.75 0.023 0.297 0.028
Freundlich KF n r2
Cu 8.933 34.48 0.979
Zn 3.155 25.64 0.886
Pb 56.49 6.85 0.819
Redlich-Peterson g A B r2
Cu 0.973 67.14 7.36 0.9999
Zn 0.999 1.65 0.42 0.9999
Pb 0.849 47.80 0.39 0.9997
Figure 2 Freundlich isotherms of metals sorbed on sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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3.1.2 Freundlich adsorption isotherm
The linear Freundlich adsorption isotherm plots were presented in Fig. 2. Examination of the plots suggested that the linear Freundlich adsorption isotherm was also a suitable model. Table 1 showed the linear Freundlich adsorp- tion isotherm constants KF and 1/n, and correlation coefficients r2. Based on the r2 values,the linear form of the Freundlichad- sorption isotherm appeared to produce a reasonable model all three metals, with the Cuisotherm seemingly better fitted of the experimental data than Zn and Pb. Normally, the magnitude of KF and 1/n illustrats the separation of metal ions from water and the biosorptive capacity. In the present study, comparatively higher KF value of Pb biosorption was observed, implying that sug- arcane bagasse had a much higher biosorptive capacity toward Pb and an excellent specificity between Pb ions and sugarcane bagasse was believed to exist. According to Kadirvelu and Namasivayam (Kadirveln and Namasivayam, 2000), n values between 1 and 10 represents beneficial adsorption and therefore again, confirmed that the biosorption of Pb on sugarcane bagasse was beneficial.
3.1.3 Redlich-Peterson adsorption isotherm
The Redlich-Peterson isotherm plots for sorption of three heavy metals on sugarcane bagasse were presented in Fig. 3. Again, examination of the plots showed that Redlich- Peterson isotherm accurately described the biosorption behaviors of Cu, Zn and Pb on sugarcane bagasse over the concentration ranges studied. The Redlich-Peterson isotherm constants, A, B, g, and r2 were given in Table 1. Since the MATLAB program used to derive the isotherm constants maximized the linear correlation coefficient r2, it was unsurprising that in all cases, the Redlich-Peterson isotherms exhibited extremely high r2 values, indicating that it produced a considerably better fit compared to the preceding two-constant isotherms (Langmuir and Freundlich adsorption isotherms). For Cu and Zn biosorption, Redlich-Peterson isotherm was the most- suitable adsorption isotherm for the data followed by the Langmuir and then Freundlich adsorption isotherm. On the other hand, for Pb biosorption, the Redlich-Peterson adsorption isotherm was still the most-suitable isotherm, followed by the Freundlich then the Langmuir adsorption isotherm.
Figure 3 Redlich-Peterson isotherms of metals sorbed on sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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3.1.4 Non-linear models
The Chi-square statistic correlation coeffi- cients x2, were obtained and the comparison between x2 and correlation coefficients of linear models r2 were shown in Table 2. In the non-linear analysis, compared with Langmuir adsorption isotherm, Redlich-Peterson and Freundlich adsorption isotherms exhibited lower x2 values and were considered to be a better fit. Detailed results were shown in following figures (Fig. 4, Fig. 5, Fig. 6) and isotherm plots and experimental data were exhibited, respectively. Drawing conclusions from non-linear Chi-square analysis, for Cu biosorption on sugarcane (Fig. 4), Freundlich adsorption isotherm was determined to be the most suitable one. While for the Zn biosorption on sugarcane bagasse (Fig. 5), the Redlich-Peterson adsorption isotherm was the best-fitting isotherm, followed by the Freundlich model for this sorption system. However, for Pb biosorption on sugarcane bagasse (Fig. 6), all these models were found not suitable to describe obtained results. The main reason was believed to be the excellent biosorptive capacity of sugarcane bagasse
toward Pb. When initial concentration was lower than 100 mg/L, extremely low concentration Pb ions in the solution was left, which, subsequently, affected the construction and the accuracy of the plots. Based on previous studies, unlike the linear analysis, different forms of the equation affected x2 values less significantly (Ho et al., 2005). Therefore, the non-linear Chi-square analysis should be considered as a method to avoide such errors.
Based on the above results, it could be found that linear regression and the non-linear Chi-square analysis gave different best-fitting isotherm for the given data set, implying that a significant difference existed between linear and non-linear isotherms (Ho, 2004). Com- pared to the non-linear Chi-square analysis, due to its simplicity, most of the biosorption equilibrium analysis still mostly relied on linear regression, which might have led to an inaccurate conclusion. Therefore, to ensure better results, it should be suggested that both linear and non-linear regression analyses be evaluated so as to describe obtained data in a more comprehensive way.
Table 2 Comparison of linear regression correlation coefficients r2 and non-linear regression
coefficients x2
Cu
Langmuir 0.297 2082.1
Freundlich 0.819 287.2
Redlich-Peterson 0.9997 39.06
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Figure 4 Comparison of different isotherms for biosorption of Cu onto sugarcane bagasse (pH:
6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
Figure 5 Comparison of different isotherms for biosorption of Zn onto sugarcane bagasse (pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
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Figure 6 Comparison of different isotherms for biosorption of Zn onto sugarcane bagasse
(pH: 6.48; dosage: 0.5 g; particle size < 150 µm; contact time 10 h; 125 rpm; 20 °C)
3.2 Kinetic modelling
For designing batch biosorption systems, prediction of biosorption rate plays a critical role and information on the kinetics of metal uptake is very important for selecting optimum operating conditions for full-scale batch process and industrial-scale application (Rao et al., 2010). Generally, linear regression is the most commonly used method to obtain the constants involved in the kinetic models and also in predicting the best-fit kinetic model (Pandey et al., 2010). The kinetic constants involved in the Elovich kinetic model (Fig. 7), Ritchie second-order kinetic model (Fig. 8), Pseudo first-order kinetic model (Fig. 9) and Pseudo second-order kinetic model (Fig. 10) were obtained. The calculated kinetic rate constants and their corresponding correlation coefficient r2 were given in Table 3, which also compared the experimental qeq values and calculated qeq values. From Table 3, suggested by the highest r2 values, it could be observed that pseudo second-order kinetic model provided the best fit to the experimental kinetic
data than other kinetic models. When comparing the experimental qeq values with qeq calculated values, it was found that it was inappropriate to use the pseudo first-order kinetic model to predict the qeq values from the kinetic data. The similar calculated qeq values when compared to the experimental qeq also indicated that pseudo second-order kinetic model had an advantage for predicting the qeq value more precisely. The similar r2 values for Elovich, Ritchie second-order and Pseudo second-order kinetic models suggested all these models were in same error distribution structure (Kumarl and Porkodi, 2008). Based all these facts, the uptake of Cu, Zn and Pb were all believed to follow pseudo second- order kinetic model, which relied on the assumption that biosorption might be a rate- limiting step. Thus, sugarcane bagasse should be considered to have a great potential to become a biosorbent for removing heavy metals from wastewater in full batch-scale process and industrial-scale application. Mechanisms involving in this process should also be studied for the better understanding.
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Figure 7 Elovich kinetic model of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L; particle size < 150 µm;
125 rpm; 20 °C)
Figure 8 Ritchie second-order kinetic model of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L;
particle size < 150 µm; 125 rpm; 20 °C)
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Table 3 Constants of four kinetic models for the biosorption of three metals on various sugarcane bagasse
Metal type qeq (exp) Elovich kinetic model
Ritchie second-order kinetic model
Cu 8.721 165.7 0.800 0.787 0.0774 0.914
Zn 2.972 7.09×109 0.102 0.984 0.0359 0.933
Pb 9.151 1.44×1033 0.109 0.985 0.1078 0.941
Metal type
Cu 0.016 5.058 0.843 0.029 8.197 0.968
Zn 0.003 0.796 0.896 0.699 2.445 0.998
Pb 0.003 0.955 0.902 0.560 8.621 0.999
Figure 9 Pseudo first-order kinetics of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L; particle size < 150 µm;
125 rpm; 20 °C)
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Figure 10 Pseudo second-order kinetics of three metals using sugarcane bagasse as a potential biosorbent (pH: 6.48; dosage: 0.1 g; initial metal concentration: 10 mg/L; particle size < 150 µm;
125 rpm; 20 °C)
3.3 Further discussion
In spite of its development for several decades, biosorption is still basically at lab scale (Wang and Chen, 2006). The mechanisms involved in biosorption process should be further studied with great ongoing efforts byutilizing various techniques and the combination of them (Kratochvil and Volesky, 1998) as follows:
·Physicochemical characteristics of real wastewater on the basis of reaction equilib- riums and kinetics;
·Screening of biosorbents for high metal- binding capacity and selectivity;
·Optimization of conditions;
·Combining biosorption with physico- chemical treatment technologies forcom- plete wastewater treatment, recovery of metals and reuse of biosorbents.
This present study was a small step towards such an approach in characterizing the bio- sorption process on the basis of equilibrium and kinetic modeling. Studies in the direction
of using really industrial wastewater should be conducted for the further development.
4. CONCLUSION
This study showed that sugarcane bagasse could efficiently remove Cu, Zn and Pb from water. It was found not appropriate to use linear regression method alone for comparing the best-fitting isotherms. It was suggested that both linear and non-linear regression analyses should be applied to ensure better results so as to describe obtained data in a more comprehensive way. The findings of this study indicated that sugarcane bagasse should be a promising biosorbent for removing heavy metals, emphasizing the importance and need for carry out extended testing for the compatibility of biosorption to real industrial wastewater. Further studies on metal- biosorbent specificity, applicability to different types of metal-laden effluents are necessary in developing biosorption of sugarcane bagasse
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for industrial-scale applications.
ACKNOWLEDGEMENT
This work was supported by Centre for Tech- nology in Water and Wastewater (CTWW), School of Civil and Environmental Engineer- ing (CEE), University of Technology, Sydney (UTS) and Australian Postgraduate Award.
REFERENCES
Ahalya, N., Ramachandra, T.V. and Kanamadi, R.D. (2003). Biosorption of Heavy Metals. Journal of Chemistry and Environment, 7(4), 71-79.
Aksu, Z. and Isoglu, A.I. (2005). Removal of cop- per(II) ions from aqueous solution by biosorp- tion onto agricultural waste sugar beet pulp. Process Biochemistry, 40(9), 3031-3044.
Coral, M.N.U., Korkmaz, H., Arkan, B. and Coral, G. (2005). Plasmid mediated heavy metal re- sistance in Enterobacter spp. isolated from Sofulu landfill, in Adana, Turkey. Annals of Mi- crobiol, 55(3), 175-179.
Hanif, M.A., Nadeem, R., Rashid, U. and Zafar, M.N. (2005). Assessing pollution levels in ef- fluents of industries in city zone Faisalabad, Pa- kistan. Journal of Applied Science, 5(10), 1713-1717.
Ho, Y.S. (2004). Selection of optimum sorption isotherm. Carbon, 42(10), 2115-2116.
Ho, Y.S., Chiu, W.T. and Wang, C.C. (2005). Re- gression analysis for the sorption isotherms of basic dyes on sugarcane dust. Bioresource Technology, 96(11), 1285-1291.
Kadirvelu, K. and Namasivayam, C. (2000). Ag- ricultural by-products as metal adsorbent: sorp- tion of lead (II) from aqueous solutions onto coir-pith carbon. Environmental Technology, 21(10), 1091-1097.
Kawwsarn, P. (2002). Biosorption of copper (II) from aqueous solutions by pre-treated biomass of marine algae Padina sp. Chemosphere,
47(10), 1081-1085. Kim, S.U., Cheong, Y.H., Seo, D.C., Hur, J.S.,
Heo, J.S. and Cho, J.S. (2007). Characterisation of heavy metal tolerance and biosorption capacity of bacterium strain CPB4 (Bacillus spp.). Water Science and Technology, 55(1-2), 105-111.
Kratochvil, D. and Volesky, B. (1998). Advances in the biosorption of heavy metals. Trends in Biotechnology, 16(7), 291-300.
Kumar1, K.V. and Porkodi, K. (2008). Pseudo se- cond order kinetics for the sorption of auramine O onto activated carbon. Indian Chemical En- gineer, 50, 227-238.
Liang, S., Guo, X.Y., Feng, N.C. and Tian, Q.H. (2009). Isotherms, kinetics and thermodynamic studies of adsorption of Cu2+ from aqueous so- lutions by Mg2+/K+ type orange peel adsorbents. Journal of Hazardous Materials, 174(1-3), 756-762.
Mckay, G., Blair, H.S. and Gardner, J.R. (1982). Adsorption of dyes on chitin. I. Equilibrium studies. Journal of Applied Polymer Science, 27(8), 3043-3057.
Nadeem, R., Hanif, A.M., Mahmood, A., Jamil, S.M. and Ashraf, M. (2009). Biosorption of Cu (II) ions from aqueous effluents by blackgram bran (BGB). Journal of Hazardous Materials, 168(2-3), 1622-1625.
Pandey, P.K., Sharma, S.K. and Sambi, S.S. (2010). Kinetics and equilibrium study of chromium adsorption on zeoliteNaX. International Journal of Environmental Science and Technology, 7(2), 395-404.
Parvathi, K., Nagendran, R. and Nareshkumar, R. (2007). Lead biosorption onto waste beer yeast by-product, a means to decontaminate effluent generated from battery manufacturing industry. Electronic Journal of Biotechnology, 10(1), 181-188.
Rao, H.J., Kalyanil, G., Rao, V.K., Kumar, T.A., Mariadas, K., Kumar, Y.P., Vijetha, P., Pallavi, P., Sumalatha, B. and Kumaraswamy, K. (2010). Kinetic studies on biosorption of Lead from aqueous solutions using egg shell powder. In-
C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73 73
ternational Journal of Biotechnology and Bio- chemistry, 6(6), 957-968.
Vijayaraghavan, K. and Yun, Y.S. (2008). Bacterial biosorbents and biosorption. Biotechnology Advances, 26(3), 266-291.
Volesky, B. (1987). Biosorbents for metal recovery.
Trends in Biotechnology, 5(4), 96-101. Wang, J.L. and Chen, C. (2006). Biosorption of
heavy metals by Saccharomyces cerevisiae: A review. Biotechnology Advances, 24(5), 427-451.