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Cr(VI) adsorption from aqueous solution by an
agricultural waste based carbon
Taimur Khan,
a
Mohamed Hasnain Isa,*
ab
Muhammad Raza Ul Mustafa,
ab
Ho Yeek-
Chia,
ab
Lavania Baloo,
ab
Teh Sabariah Binti Abd Manan
a
and Mohamed Osman Saeed
b
The study examined the adsorption of hexavalent chromium [Cr(VI)] from aqueous solution by acidically
prepared rice husk carbon (APRHC). APRHC was characterized in terms of surface area, micropore area,
micropore volume, average pore diameter and surface morphology. The effects of pH, contact time,
initial Cr(VI) concentration and adsorbent dose on the adsorption of Cr(VI) from aqueous solution were
investigated. Batch adsorption tests showed that Cr(VI) adsorption depends on initial concentration,
contact time and pH. Equilibrium adsorption was achieved in 120 min, while maximum Cr(VI) adsorption
occurred at pH 2. An artificial neural network (ANN) was used to model Cr(VI) adsorption. The
Levenberg–Marquardt (LM) training algorithm was found to be the best among the 11 backpropagation
(BP) algorithms tested, with a lowest mean square error (MSE) of 8.8876 and highest coefficient of
determination (R
2
) of 0.987. Adsorption of Cr(VI) by APRHC followed pseudo-second order kinetics.
Langmuir and Freundlich isotherm equations were fitted to the equilibrium adsorption data; the former
isotherm yielded a better fit. The thermodynamic results indicate that the process of Cr(VI) adsorption by
APRHC was endothermic in nature. Desorption of Cr(VI) was very low, i.e. in the range from 0.1 to 9%.
Cr(VI) adsorption capacity by APRHC was compared with that of various adsorbents. APRHC showed
a high capacity for adsorption of Cr(VI). APRHC can be employed as an effective adsorbent and substitute
for commercially available activated carbon for the removal of Cr(VI) from aqueous solutions and
wastewater systems.
1. Introduction
The discharge of heavy metals to the aquatic environment
caused by rapid industrialization, urbanization and techno-
logical advancement has gained signicant attention from
many researchers due to their toxicity and carcinogenic effects.
Continuous exposure to chromium beyond permissible limits
causes severe health risks such as digestive tract and lung
cancers, nausea and hemorrhages.
1
Large amounts of chro-
mium bearing wastewater are generated by a variety of indus-
trial processes such as paint and pigment manufacturing,
stainless steel production, corrosion control, textile dyeing,
leather tanning, chrome electroplating, wood preservation and
photography.
2
A small-sized electroplating industry (i.e. volume
of wastewater < 10 000 gal per day) can produce effluents
contaminated with chromate in concentrations ranging from 10
to 500 mg L
1
.
3
The most common forms of chromium are
Cr(0), Cr(III), and Cr(VI). In industrial wastewater, chromium is
primarily present in the form of hexavalent Cr(VI) as chromate
(CrO
42
) and dichromate (Cr
2
O
72
). According to the Agency for
Toxic Substances and Disease Registry,
4
Cr(VI) shows higher
mobility than Cr(III) and hence it is considered more toxic to
humans.
5,6
The US EPA allowable concentrations of Cr(VI)in
drinking water and inland surface water are 0.05 and 0.1 mg
L
1
, respectively.
7
The Malaysian limit for discharge of Cr(VI)
into inland water is 0.05 mg L
1
.
8
It is therefore necessary to
reduce Cr(VI) concentrations in water and wastewater to an
acceptable level, prior to discharge into receiving waters.
Several conventional methods have been applied in the
removal of Cr(VI) from wastewater. These include chemical
precipitation, electrochemical treatment, ion exchange and
evaporative recovery. These methods may be inefficient or
expensive when initial heavy metal concentration is in the range
from 10–100 mg L
1
. Another disadvantage associated with
these methods is incomplete removal of Cr(VI). Some of these
methods produce sludge containing high concentrations of
chromium, hence nal disposal is still a problem.
9,10
It is
observed that in several states of Malaysia, there are no desig-
nated landll sites for hazardous sludges/chemicals and most
of the landlls are not designed with a proper leachate collec-
tion system. As a result, industries either store the hazardous
sludge on their premises or dispose it in open areas, which may
contaminate soil, surface water, groundwater or other
a
Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS,
32610 Bandar Seri Iskandar, Perak, Malaysia. E-mail: hasnain_isa@petronas.com.
my; hasnain_isa@yahoo.co.uk
b
Sustainable Resource Mission Oriented Research (SUREMOR) Centre, Universiti
Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia
Cite this: RSC Adv.,2016,6, 56365
Received 3rd March 2016
Accepted 22nd May 2016
DOI: 10.1039/c6ra05618k
www.rsc.org/advances
This journal is © The Royal Society of Chemistry 2016 RSC Adv.,2016,6, 56365–56374 | 56365
RSC Advances
PAPER
components of the environment. Proper management of
hazardous sludges/chemicals is still inadequate. Hence, there is
a need to develop treatment methods, which are simple,
economical and address local resources and constraints.
11,12
Adsorption is one of the most effective methods due to its
simplicity of design, sludge free operation, ease of handling,
ease of regeneration and ability to remove heavy metals at low
concentration levels.
13
Activated carbon is the most widely used adsorbent due to its
high surface area, microporous structure, high adsorption
capacity and high degree of surface reactivity. However, the high
price of commercially available activated carbon limits its use
on a large scale and has led to cheaper substitutes being
explored. Consequently, a number of low-cost adsorbents have
been prepared for Cr(VI) removal.
14
Such adsorbents include
palm bers,
15
coconut coir,
16
wheat straw and barley straw,
14
sawdust,
17
hazelnut shells
7
and apricot stones.
18
However, since
the adsorption capacities of these adsorbents are not high or
their preparation is not easy, a new low-cost and effective
adsorbent is still needed.
Rice husk is an abundantly available agricultural waste. It
consists of cellulose (32.23%), hemicelluloses (21.34%), lignin
(21.44%) and mineral ash (15.05%);
19
with a high percentage of
silica (96.34%) in the mineral ash.
20
It is expected that rice husk-
based adsorbents would be effective in adsorbing heavy metals
from aqueous solution. However, the rice husk needs to be
modied or treated before being applied in the adsorption of
heavy metals.
21
Chemical or thermal treatment reduces cellu-
lose, hemicellulose and lignin crystallinity and results in an
increase in surface area for maximum adsorption.
22
Recently, articial neural networks (ANNs) have drawn the
attention of researchers as an alternative tool to determine
complex relationships between variables. Based on the poten-
tial of ANNs to precisely describe non-linear relationships, the
technique has been applied in many areas of environmental
engineering.
23
However, only a limited amount of work is found
to have been devoted to the use of ANNs for the modelling of
heavy metal adsorption from aqueous solution. The present
study includes this aspect of ANN applications. The main
objective of this study was to develop a low-cost carbon adsor-
bent for inexpensive removal of Cr(VI). Acidically prepared rice
husk carbon (APRHC) was developed from a low-cost agricul-
tural waste (i.e. rice husk) by treatment with sulfuric acid
without any activation process. The efficiency of the APRHC in
removing Cr(VI) from aqueous solution was tested and the
adsorption mechanism was investigated. The chromium(VI)
adsorption capacity of the APRHC was compared with that of
commercial activated carbon and other low-cost adsorbents.
2. Materials and methods
2.1 Preparation of APRHC
Rice husk was collected from a local rice mill. It was cleaned and
washed several times with tap water followed by distilled water
in order to remove dust and dried in an oven at 105 C for 24 h.
APRHC was prepared following the procedure described by El-
Shafey.
24
The washed and dried rice husk (20 g) was weighed in
a clean dry 500 mL beaker. 100 mL of 13 M sulfuric acid was
added to the rice husk and the mixture was heated at 175–180
C for 20 min with occasional stirring. The resulting mixture
was then cooled and ltered through a B¨
uchner funnel by
vacuum suction. The residue was washed several times with
distilled water, dried in an oven at 120 C to a constant weight
and cooled in a desiccator. The resulting acidically prepared
rice husk carbon was ground to a ner size of 212–500 mm and
used in adsorption tests.
2.2 Adsorption experiments
Batch adsorption experiments were conducted by stirring 100
mL of Cr(VI) solutions of desired concentrations with a known
amount of adsorbent (APRHC) in conical asks at room
temperature (22 C), using an orbital shaker at 150 rpm. Aer
pre-decided contact times, the asks were removed from the
orbital shaker and the supernatant was ltered through 0.45 mm
membrane lter paper. The residual concentrations of Cr(VI)in
the supernatant were determined using the Hexavalent Chro-
mium method 8023.
25
Batch experiments were carried out at
various pHs (2–6), contact times (5–180 min), initial Cr(VI)
concentrations (20–80 mg L
1
) and adsorbent doses (1–10 g
L
1
), followed by the determination of the adsorption capacity
for Cr(VI). The pH of the solutions was adjusted using 0.1 N
NaOH or 0.1 N HCl. The adsorption isotherms, kinetics and
thermodynamics were also studied.
2.3 Desorption test
Desorption was carried out by treating 0.1 g of the Cr(VI)-loaded
APRHC with 50 mL of 1 M H
2
SO
4
, NaOH, HCl and KOH. The
mixtures were then shaken on an orbital shaker at 150 rpm for 2
h. Thereaer, the asks were removed from the orbital shaker
and the supernatant was decanted, ltered through 0.45 mm
membrane lter paper and measured for residual Cr(VI)
concentration.
2.4 Articial neural networks
Articial Neural Networks (ANNs) are a recognized modelling
technique due to their accurate, fast and salient characteristics
in capturing the non-linear relationships existing between
variables (multi-input/output) in complex systems. Many
applications of ANNs are found in various elds of environ-
mental engineering
26
including some involving the prediction
of adsorption system performance. The inspiration of using
ANNs came from the primary concept of simulating the
processes of the human brain and nervous system.
27,28
An ANN
is a data processing network which consists of several units
known as neurons or nodes. Neurons are arranged in layers and
between the layers; these neurons are interconnected by weights
and biases. The rst layer is called the input layer, while the last
layer is the output layer. Between these two layers is another
layer, which is known as the hidden layer. The number of input
neurons (l) in ANN architecture is equal to the number of input
parameters, whereas the number of output neurons (n) are the
desired output variables from the network. There is no specic
rule to determine the number of neurons (m) in the hidden
56366 |RSC Adv.,2016,6, 56365–56374 This journal is © The Royal Society of Chemistry 2016
RSC Advances Paper
layer. It depends on the complexity of the problem being
studied. Three stages need to be considered during ANN
applications, namely (1) training, (2) validation and (3) testing.
In training and validation stages both the input and target data
are introduced to the model, while only input data is considered
by the model in the testing stage.
29
The network architecture
used in this study is shown in Fig. 1, which consists of three
layers (input, hidden and output).
In this study, a three-layered backpropagation neural
network with a tangent sigmoid transfer function (tansig) at the
hidden layer and a linear transfer function (purelin) at the
output layer was used. The neural network toolbox of MATLAB
R2013a soware was used to develop the ANN model to describe
Cr(VI) adsorption. Forty four experimental sets were used to
develop the ANN model. The data obtained from the batch
experiments were divided into input matrix [p] and target
matrix [t]. The input variables were initial pH, contact time,
initial Cr(VI) concentration and adsorbent dose. The corre-
sponding adsorption capacity was used as the target. The data
sets were divided for training (68%), validation (16%) and
testing (16%). These groups contained 30, 7 and 7 samples,
respectively.
3. Results and discussion
3.1 Characterization of APRHC
The FTIR spectra of APRHC before and aer the adsorption of
Cr(IV) were used to determine changes in the vibrational
frequency of the functional groups in the adsorbent. The FTIR
spectra of APRHC and Cr(VI) loaded APRHC are shown in
Fig. 2(a) and (b), respectively. The FTIR spectra of APRHC show
a number of absorption peaks, indicating the complex nature of
APRHC. The peaks at 3703 and 3780 cm
1
indicate the presence
of Si–OH bonds in APRHC.
30
These peaks disappear aer
adsorption of Cr(VI), indicating the involvement of these func-
tional groups in the adsorption process (Fig. 2(b)). The two
peaks at 1720 and 1174 cm
1
in the APRHC spectra are due to
the presence of the C]O bonds of carboxyl ester or amide
groups.
31
The C]O absorption peaks were observed to shito
1618 and 1105 cm
1
when APRHC was loaded with Cr(VI). This
shows that this functional group participates in metal binding.
The weak bands of C^C and –C^N triple bonds or accumu-
lated –C]C]C–and –N]C]O double bonds at 2000–2500
cm
1
also disappear aer the adsorption of Cr(VI), indicating
their involvement in the adsorption mechanism. New peaks at
3411 and 802 cm
1
were seen in the spectra of APRHC loaded
with Cr(VI), which did not exist before. This is probably due to
OH groups from CrOH that would be formed aer Cr(VI) binds
to the APRHC.
32
The functional groups whose peaks either
decrease in intensity or disappear aer adsorption reveal that
these functional groups are involved in the adsorption of Cr(VI)
by APRHC.
The BET surface area, micropore area, micropore volume
and average pore diameter of APRHC are 58.54 m
2
g
1
, 14.53 m
2
Fig. 1 Artificial neural network architecture.
Fig. 2 FTIR spectra of (a) APRHC and (b) Cr(VI) loaded APRHC.
This journal is © The Royal Society of Chemistry 2016 RSC Adv.,2016,6, 56365–56374 | 56367
Paper RSC Advances
g
1
, 0.007209 mL g
1
, and 45.46 ˚
A, respectively. The surface
morphology of APRHC and Cr(VI) loaded APRHC was investi-
gated using an SEM technique; the respective SEM images are
presented in Fig. 3(a) and (b). It can be noticed that APRHC has
an uneven surface with macro- and micropores which would
inuence Cr(VI) adsorption. In the SEM image of Cr(VI) loaded
APRHC (Fig. 3(b)), it is found that the APRHC surface
morphology changed, and that Cr(VI) is adhered in the form of
crystals on its surface. The SEM images show the adsorption of
Cr(VI) on the macro- and micropores of APRHC. Elemental
analysis of APRHC was obtained via a CHNS analyzer. The
elemental analysis results show that APRHC is composed of
39.23% carbon, 5.87% hydrogen, 1.027% nitrogen and 0.92%
sulphur. Sulphur groups are sobases and have chemical
affinity towards metal ions; the presence of sulphur in APRHC
qualies it as an effective adsorbent.
33
In order to investigate the thermal behavior of APRHC,
thermogravimetric analysis (TGA) was carried out using a ther-
mogravimetric analyzer. APRHC was heated from 25 C to 600
C in the presence of a nitrogen ow at a rate of 10 C min
1
.
Fig. 4 shows the TGA plot for APRHC; the weight loss can be
categorized in three stages. In the rst phase, a weight loss of
about 5% occurred when the temperature ranged from 25 Cto
200 C. This weight loss of APRHC may be attributed due to the
elimination of moisture content. In the second phase, i.e.
between a temperature of 200 C to 350 C, the weight loss was
Fig. 3 Scanning electron micrographs of (a) APRHC and (b) Cr(VI)
loaded APRHC.
Fig. 4 TGA plot of APRHC.
Table 1 Comparison of 11 BP algorithms with the optimum number of neurons in their hidden layer
a
Backpropagation (BP) algorithm Function
Optimum number of
neurons MSE IN R
2
Resilient backpropagation (Rprop) Trainrp 12 30.6247 25 0.956
Fletcher–Reeves conjugate gradient
backpropagation
Traincgf 8 21.6459 49 0.971
Polak–Ribi´
ere conjugate gradient
backpropagation
Traincgp 12 21.8585 23 0.966
Powell–Beale conjugate gradient
backpropagation
Traincgb 12 23.0215 22 0.964
Levenberg–Marquardt backpropagation Trainlm 5 8.8876 14 0.987
Scaled conjugate gradient
backpropagation
Trainscg 12 22.5002 25 0.965
BFGS quasi-Newton backpropagation Trainbfg 4 18.4783 410 0.973
One step secant backpropagation Trainoss 10 29.5912 35 0.957
Batch gradient descent Traingd NA NA NA NA
Variable learning rate backpropagation Traingdx 4 19.8886 130 0.971
Batch gradient descent with momentum Traingdm NA NA NA NA
a
MSE, mean square error; IN, iteration number; R
2
, correlation coefficient; NA, not applicable.
Fig. 5 Effect of pH on the adsorption of Cr(VI) by APRHC.
56368 |RSC Adv.,2016,6, 56365–56374 This journal is © The Royal Society of Chemistry 2016
RSC Advances Paper
14.8% and is due to the pyrolysis of lignin.
34
In the third stage,
starting from 350 C to 600 C, the weight loss occurred due to
the decomposition of carbonaceous materials.
35
3.2 Selection of backpropagation (BP) training algorithm
In this study, eleven BP training algorithms were studied to
determine the best training algorithm. For all BP algorithms,
a tangent sigmoid transfer function (tansig) at the hidden layer
and a linear transfer function (purelin) at the output layer were
used. The optimization for all BP algorithms was done by
varying the number of neurons in the range from 4 to 30. Table
1 shows the optimum number of neurons based on low mean
square error (MSE), the maximum R-squared values for each BP
algorithm and the performance of the ANN models for the
entire training BP algorithm. It can be seen from Table 1 that
the Levenberg–Marquardt backpropagation algorithm (LMA)
has the lowest mean square error (MSE) of 8.8876 and the
highest R
2
of 0.987 among the eleven BP algorithms tested.
Thus, the LMA was selected as the most suitable training
algorithm for the prediction of Cr(VI) adsorption in this study.
3.3 Effect of pH on Cr(VI) adsorption
Fig. 5 shows the effect of pH on Cr(VI) adsorption by APRHC
from a 60 mg L
1
solution. Maximum adsorption occurred at
pH 2. Similar observations have been reported for adsorption of
Fig. 6 Effect of contact time and initial Cr(VI) concentration on the adsorption of Cr(VI) by APRHC and comparison between experimental and
predicted data.
Fig. 7 Effect of APRHC dose on the adsorption of Cr(VI) by APRHC and
comparison between experimental and predicted data.
Fig. 8 Pseudo-first order kinetic plot of Cr(VI) adsorption by APRHC.
This journal is © The Royal Society of Chemistry 2016 RSC Adv.,2016,6, 56365–56374 | 56369
Paper RSC Advances
Cr(VI) by activated carbons prepared from coconut coir,
16
used
tyres, tea waste and coconut husk,
36
treated oil palm bers,
15
and hazelnut shells.
37
In the present study, the dichromate anion (Cr
2
O
72
)of
potassium dichromate was the source of Cr(VI) in aqueous
solution. In an aqueous solution, chromate (CrO
42
) and
dichromate (Cr
2
O
72
) exist in chemical equilibrium as follows:
2CrO
42
+2H
+
#Cr
2
O
72
+H
2
O. The position of the equilib-
rium depends on both pH and analytical concentration of
chromium. The dichromate anion is the predominant ion in
acidic solution and the chromate ion is the predominant ion in
alkaline solution. The chromate ion is a weak base; HCrO
4
#
CrO
42
+H
+
has a pK
a
¼ca. 5.9, but the hydrogenchromate ion
Fig. 9 Pseudo-second order kinetic plot of Cr(VI) adsorption by
APRHC.
Table 2 Pseudo-first order and pseudo-second order reaction rate
constants for Cr(VI) adsorption by APRHC
Cr(VI) concentration
Pseudo-rst
order Pseudo-second order
k
1
(m
1
)R
2
k
2
(g mg
1
min
1
)R
2
60 mg L
1
0.01 0.93 0.0028 0.95
80 mg L
1
0.02 0.89 0.0023 0.97
100 mg L
1
0.02 0.99 0.0037 0.99
120 mg L
1
0.01 0.97 0.0044 0.99
Fig. 10 Langmuir isotherm for Cr(VI) adsorption by APRHC.
Fig. 11 Freundlich isotherm for Cr(VI) adsorption by APRHC.
Table 3 Values of Langmuir and Freundlich constants
Langmuir constants Freundlich constants
Q
(mg g
1
)b(L g
1
)K
f
(mg g
1
)1/n
47.62 0.018 4.23 0.42
Fig. 12 Plot of ln K
C
vs. 1/Tfor the estimation of thermodynamic
parameters for adsorption of Cr(VI) by APRHC.
Table 4 Thermodynamic parameters calculated for adsorption of
Cr(VI) by APRHC
T
(C) K
C
DG
DH
(kJ mol
1
)
DS
(J mol
1
)
25 0.42 0.64 33.56 115.23
35 2.30 2.31
45 3.35 3.20
60 5.49 4.72
56370 |RSC Adv.,2016,6, 56365–56374 This journal is © The Royal Society of Chemistry 2016
RSC Advances Paper
(HCrO
4
) is also in equilibrium with the dichromate ion;
2HCrO
4
#Cr
2
O
72
+H
2
O. The maximum adsorption of Cr(VI)
by APRHC at low pH, i.e. 2, may be attributed to the presence of
a large amount of hydrogen ions (H
+
) at such a pH level, which
neutralize the negatively charged adsorbent surface and thus
reduce hindrance to the diffusion of HCrO
4
anions. On the
other hand, at higher pH values up to pH 5.9, the abundance of
hydroxyl ions (OH
) increased hindrance to the diffusion of
HCrO
4
anions and at pH 5.9–6.0 to the diffusion of CrO
42
anions, thereby reducing the adsorption capacity.
14
As a conse-
quence, pH 2 was selected as the optimum pH for adsorption of
Cr(VI) by APRHC and all the subsequent adsorption tests were
conducted at pH 2. A comparison of the ANN model predictions
and the experimental data as a function of pH is depicted in
Fig. 5. The predicted values generated by the ANN model are in
good agreement with the experimental data.
3.4 Effect of contact time and initial Cr(VI) concentration
The adsorbate and adsorbent should be in contact for an
adequate length of time in order to allow adsorption to reach
equilibrium. For this purpose, the initial contact time was xed
in the range of 5–180 min for four different Cr(VI) concentra-
tions of 60, 80, 100 and 120 mg L
1
, an APRHC dose of 2 g L
1
and pH ¼2. It can be seen from Fig. 6 that adsorption of Cr(VI)
depends on initial concentration and contact time. The
percentage adsorption of Cr(VI) increased with an increase in
contact time and decreased as the initial concentration
increases. Equilibrium adsorption was achieved in 120 min
with a percentage removal of 62.7, 56.3, 46.7 and 43.2% for 60,
80, 100 and 120 mg L
1
Cr(VI) concentrations, respectively. All
subsequent adsorption tests were conducted using a contact
time of 120 min. In terms of the relationship between the
experimental results and predicted values of Cr(VI) adsorption
by the model, Fig. 6 shows that the predicted values are very
close to the experimental results.
3.5 Effect of APRHC dose
In order to assess the effect of APRHC dose on Cr(VI) adsorption,
a test was conducted under the optimum conditions (contact
time 120 min and pH 2). The effects of APRHC dose (1–10 g L
1
)
on the percentage adsorption of Cr(VI) and uptake of Cr(VI) per
unit weight of adsorbent are shown in Fig. 7. The adsorption
was found to increase with APRHC dose and the maximum
adsorption of 99.9% for a 80 mg L
1
Cr(VI) concentration
occurred at 8 g L
1
. As the adsorbent dose was increased, more
area became available for Cr(VI) adsorption, while the amount of
Cr(VI) adsorbed (mg g
1
adsorbent) by APRHC decreased with
an increasing amount of adsorbent (g L
1
). Similar results have
been reported by Isa et al.
15
for the adsorption of Cr(VI)by
treated oil palm bers, which suggested that the relationship
between the amounts of adsorbent and Cr(VI) adsorbed was
close to a hyperbolic curve. Therefore, an optimum adsorbent
dosage, which limits the amount of Cr adsorbed, is a signicant
parameter to be considered in batch adsorption. Fig. 7 shows
that the predicted values are in good agreement with the
experimental results. Thus, an ANN model can predict the
adsorption of Cr(VI) signicantly.
3.6 Adsorption kinetics study
Two kinetic models viz., pseudo-rst order and pseudo-second
order were applied to t the experimental data.
The linear form of the pseudo-rst order model may be
written as in eqn (1),
logðqeqtÞ¼log qek1t
2:303 (1)
and the linear form of the pseudo-second order model as in eqn
(2),
t
qt
¼1
k2qe2þt
qe(2)
where, q
e
and q
t
are the amounts of Cr(VI) adsorbed (mg g
1
)at
equilibrium and at any time t, respectively, k
1
is the reaction
rate constant for pseudo-rst order kinetics (min
1
) and k
2
is
the reaction rate constant for pseudo-second order kinetics [g
(mg min)
1
].
The plots of pseudo-rst order and pseudo-second order
kinetics for the adsorption of Cr(VI) are shown in Fig. 8 and 9,
Table 5 Comparison of the Cr(VI) adsorption capacities of various adsorbents
Adsorbent
Adsorption capacity
(mg g
1
) pH Reference
Coconut coir activated carbon 38.5 1.5–216
Commercial activated carbon (F-400) 27.8 1.5–216
Treated oil palm bers 22.73 1.5–215
Boiled rice husk 8.5 2 43
Boiled saw dust 10.34 2 43
Palm pressed bers 14.0 1.5–344
Hazelnut shells 17.7 2.0 37
Palm shells 12.6 3.0–4.0 5
Sugar beet pulp 17.2 2.0 45
Maize cob 13.8 1.5 45
Sugarcane bagasse 13.4 2.0 45
Lignocellulosic substrate 35.0 2.1 46
Acidically prepared rice husk carbon (APRHC) 47.61 2 This study
This journal is © The Royal Society of Chemistry 2016 RSC Adv.,2016,6, 56365–56374 | 56371
Paper RSC Advances
respectively. The reaction rate constants for both models,
calculated from eqn (1) and (2), are shown in Table 2. The R
2
values as well as Fig. 8 and 9 indicate that the adsorption of
Cr(VI) by APRHC follows a pseudo-second order kinetic model,
strongly suggesting chemisorption or chemical adsorption.
3.7 Adsorption isotherms
In a solid–liquid system during adsorption, the distribution
ratio of the solute between the solid and the liquid phases is
a measure of the position of equilibrium. The preferential form
of representing this distribution is to express the quantity q
e
as
a function of C
e
at a xed temperature; the quantity q
e
being the
amount of solute adsorbed per unit weight of the solid adsor-
bent, and C
e
the concentration of solute remaining in the
solution at equilibrium. An expression of this type is known as
an adsorption isotherm.
38
The Langmuir adsorption isotherm is
represented as in eqn (3),
qe¼QbCe
1þbCe
(3)
where, Qis the amount of solute adsorbed per unit weight of
adsorbent in forming a monolayer on the surface (monolayer
adsorption capacity) and bis a constant related to the energy of
adsorption.
The Freundlich adsorption isotherm is represented as in eqn
(4),
q
e
¼K
f
C
e1/n
(4)
where, K
f
is the Freundlich constant (adsorption capacity) and
1/nrepresents the adsorption intensity or surface heterogeneity.
Isotherms for Cr(VI) adsorption by APRHC were developed by
varying the initial Cr(VI) concentration, while other parameters
were kept constant.
Linear forms of the Langmuir isotherm (C
e
/q
e
¼1/(bQ)+C
e
/
Q) (Fig. 10) and the Freundlich isotherm (log q
e
¼log K
f
+ (1/n)
log C
e
) (Fig. 11) were tted to the batch adsorption data. The
values of Langmuir constants Qand b, and Freundlich
constants K
f
and 1/nfor Cr(VI) adsorption by APRHC are shown
in Table 3. Both the Langmuir and Freundlich isotherms tted
well to the experimental data, with the former isotherm yielding
a better t as indicated by the high R
2
value. The applicability of
this isotherm suggests a monolayer coverage of Cr(VI) on APRHC
surface. The characteristics of the Langmuir isotherm can be
dened by a dimensionless constant, the equilibrium param-
eter R
L
,
39
which is expressed as:
RL¼1
1þbCo
(5)
where bis the Langmuir constant and C
o
is the initial Cr(VI)
concentration. The values of R
L
indicate whether the isotherm is
unfavorable (R
L
> 1), linear (R
L
¼1), favorable (0 < R
L
<1)or
irreversible (R
L
¼0).
40
From the bvalues (Table 3) and the range
of Cr(VI) concentrations (60–200 mg L
1
) tested, it follows that
R
L
lies between 0 and 1 and thus, adsorption is favorable.
3.8 Thermodynamic studies
In order to conclude whether adsorption is spontaneous or not,
knowledge of thermodynamic considerations of the adsorption
process is required. The values of thermodynamic parameters
are actual indicators for the practical application of the
adsorption process.
41
To calculate the thermodynamic param-
eters, the amount of Cr(VI) adsorbed by APRHC at the equilib-
rium time at four different temperatures (25, 35, 45 and 60 C)
was investigated. Various thermodynamic parameters such as
change in free energy (DG), enthalpy (DH) and entropy (DS)
were determined using the following equations:
KC¼CA
CS
(6)
DG¼RT ln K
C
(7)
ln KC¼DS
RDH
RT (8)
where K
C
is the equilibrium constant, C
A
is the amount of Cr(VI)
adsorbed by APRHC at equilibrium (mg L
1
), C
S
is the equi-
librium concentration of Cr(VI) in the solution (mg L
1
), Ris the
universal gas constant and is equal to 8.314 J mol
1
K
1
and Tis
the operating temperature (K). The values of DHand DSwere
determined from the slope and the intercept of the plot of
log K
C
versus 1/T(Fig. 12). DGvalues were calculated from the
experimental values using eqn (7). The values of all thermody-
namic parameters are shown in Table 4.
The values of Gibbs free energy change are negative, indi-
cating that the adsorption of Cr(VI) on APRHC is spontaneous
and a feasible process. The positive value of DH(33.56 kJ
mol
1
) indicates that the adsorption is endothermic. The
positive value of DS(115.23 J mol
1
) indicates the affinity of
APRHC for Cr(VI) adsorption and an increase in randomness of
solid–liquid interactions, most probably due to the release of
water molecules from the APRHC surface.
42
3.9 Desorption of Cr(VI)
In wastewater treatment, regeneration and recovery of adsor-
bents are important characteristics that need to be considered.
For this purpose, attempts were made to desorb Cr(VI) with
various desorbing agents. Cr(VI)-loaded APRHC was treated with
1 M HCl, H
2
SO
4
, KOH and NaOH. The amount of Cr(VI) des-
orbed was 0.23, 0.1, 4 and 9% by HCl, H
2
SO
4
, KOH and NaOH,
respectively. It was found that the desorption of Cr(VI) was very
low, i.e., 0.1 to 9%. Isa et al.
15
also reported only 9% Cr(VI)
desorption from treated oil palm bres using 1 N NaOH solu-
tion. Based on these results, it can be assumed that the reason
behind low desorption is the strong bonds between Cr(VI)
particles and the carbon surface; chemical (chemisorption).
3.10 Cr(VI) adsorption capacity of different adsorbents
The Cr(VI) adsorption capacity of APRHC was compared with
that of various adsorbents and commercial activated carbons
(Table 5). APRHC shows good capacity for Cr(VI) adsorption
compared with other adsorbents. Therefore, APRHC is
56372 |RSC Adv.,2016,6, 56365–56374 This journal is © The Royal Society of Chemistry 2016
RSC Advances Paper
a suitable substitute for commercially available activated
carbon for removal of Cr(VI) from water and wastewater.
4. Conclusions
Acidically prepared rice husk carbon (APRHC) was found to be
effective in the adsorption of Cr(VI) from aqueous solution and
maximum adsorption occurred at 120 min and pH 2. Complete
removal of 80 mg L
1
Cr(VI) was achieved at 8 g L
1
of APRHC
dose. A pseudo-second order kinetic model shows a better tto
the experimental data, indicating chemisorption. Langmuir
constants Qand bwere 47.62 and 0.018, and Freundlich
constants K
f
and 1/nwere 4.23 and 0.42, respectively. The
Langmuir isotherm model tted better to the experimental data
than the Freundlich isotherm model with a high R
2
value. A
thermodynamic study revealed that the adsorption process was
endothermic in nature. APRHC showed a high capacity for the
adsorption of Cr(VI) compared to commercial activated carbon
and various adsorbents reported in the literature. However,
once adsorbed, it is difficult to desorb Cr(VI) ions from the
adsorbent. This suggests the suitability of APRHC as a single
use adsorbent. APRHC can be employed as an effective adsor-
bent and substitute for commercial activated carbon for the
removal of Cr(VI) from aqueous solution and wastewater systems
in developing countries as a low-cost treatment material.
A three layer backpropagation neural network with a tangent
sigmoid transfer function (tansig) at the hidden layer and
a linear transfer function (purelin) at the output layer was used
to predict Cr(VI) adsorption by APRHC. The Levenberg–Mar-
quardt backpropagation algorithm (LMA) produced the lowest
mean square error (MSE) of 8.8876 and highest R
2
of 0.987
among the other 11 BP algorithms tested. The ANN results show
that neural network modelling could effectively simulate and
predict the adsorption of Cr(VI) from aqueous solution.
Acknowledgements
The authors are thankful to the management authorities of the
Universiti Teknologi PETRONAS (UTP), Malaysia and the
Department of Civil and Environmental Engineering, UTP for
providing facilities and technical support for this research.
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