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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61
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)
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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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65
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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67
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)
68 C. Liu et al. / Journal of Water Sustainability 2 (2015)
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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.
C. Liu et al. / Journal of Water Sustainability 2 (2015) 59-73
69
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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71
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
72 C. Liu et al. / Journal of Water Sustainability 2 (2015)
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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.
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