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This article was downloaded by: [Universiti Sains Malaysia]On: 25 July 2013, At: 02:22Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
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Preparation of Activated Carbon From Olive StoneWaste: Optimization Study on the Removal of Cu2+,Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ From Aqueous SolutionUsing Response Surface MethodologyTamer M. Alslaibi a , Ismail Abustan b , Mohd Azmier Ahmad c & Ahmad Abu Foul da School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia , Nibong Tebal ,Pulau Pinang , Malaysiab School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus , Nibong Tebal ,Pulau Pinang , Malaysiac School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia , NibongTebal , Pulau Pinang , Malaysiad Environmental Engineering, Islamic University of Gaza , PalestineAccepted author version posted online: 12 Jul 2013.
To cite this article: Journal of Dispersion Science and Technology (2013): Preparation of Activated Carbon From Olive StoneWaste: Optimization Study on the Removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ From Aqueous Solution Using ResponseSurface Methodology, Journal of Dispersion Science and Technology, DOI: 10.1080/01932691.2013.809506
To link to this article: http://dx.doi.org/10.1080/01932691.2013.809506
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Preparation of Activated Carbon From Olive Stone Waste: Optimization Study on the Removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ from Aqueous Solution Using
Response Surface Methodology
Tamer M. Alslaibi1, Ismail Abustan2, Mohd Azmier Ahmad3, Ahmad Abu Foul4
1School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia, 2School of Civil Engineering, Universiti Sains Malaysia,
Engineering Campus, Nibong Tebal, Pulau Pinang, Malaysia, 3School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Pulau
Pinang, Malaysia, 4Environmental Engineering, Islamic University of Gaza, Palestine
Received 13 May 2013; accepted 25 May 2013.
Address correspondence to Ismail Abustan, School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia E-
mail: ceismail@eng.usm.my
Abstract
The removal efficiencies of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ from aqueous solution
with olive stone activated carbon (OSAC) were investigated in this paper. Central
Composite Design (CCD) method was used to optimize the preparation of OSAC by
chemical activation using potassium hydroxide (KOH) as chemical agent. The optimum
conditions obtained were 715 °Cactivation temperature, 2 h activation time, and 1.53
impregnation ratio. This resulted in removal of 99.25% Cu2+, 94.98% Cd2+, 99.08% Ni2+,
99.33% Pb2+, 99.41% Fe2+, and 99.17% Zn2+, as well as 73.94% OSAC yield. The
surface characteristics of the AC prepared under optimized condition were examined by
pore structure analysis, scanning electron microscopy (SEM) and Fourier transform
infrared spectroscopy (FT-IR). The BET surface area, total pore volume and average pore
diameter of the prepared activated carbon were 886.72 m2/g, 0.507 cm3/g and 4.22 nm,
respectively. The equilibrium data of the adsorption was well fitted to the Langmuir and
the highest value of adsorption capacity (Q) on the OSAC was found for Fe2+ (57.47
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mg/g), followed by Pb2+ (22.37 mg/g), Cu2+ (17.83 mg/g), Zn2+ (11.14 mg/g), Ni2+ (8.42
mg/g), and Cd2+ (7.80 mg/g). The prepared OSAC can be used for efficient removal of
metals from contaminated wastewater.
KEYWORDS: Activated carbon, olive stone, adsorption, heavy metals, response surface
methodology (RSM)
1. INTRODUCTION
Wastewater pollution is one of the many important issues in environmental conservation.
Heavy metals, such as Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+, are toxic to human beings
and other living organisms if their concentrations exceed acceptable limits. These heavy
metals appear in wastewater discharged from hospitals [1] and different industries such as
smelting, metal plating, Cd–Ni battery production, phosphate fertilizer manufacture,
pigment mining, stabilizer production, and alloy manufacturing [2]. Regarding
environmental compartments, heavy metals constitute an ecological and human health
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issue, considering heavy metals do not undergo biological degradation, compared with
certain organic pollutants [3]. Cancer, anemia, liver, and kidney damage are among the
major human health issues that are caused by long-term exposure to heavy metals. Thus,
these toxic heavy metals should be removed from wastewater to protect the people and
the environment[4].
Activated carbons were used as adsorbent materials because of their large surface area,
microporous structure, high degree of surface reactivity, and high adsorption capacity[5].
In addition, the presence of different surface functional groups on activated carbon,
especially oxygen groups, leads to the adsorption of heavy metal ions [2]. Nevertheless,
the application fields of commercially available activated carbon are still limited due to
its high cost, given the use of a non-renewable and relatively expensive starting material
such as coal. The use of low-cost wastes and agriculture byproducts as alternative
solutions to producing activated carbon have been investigated in recent years and
continue to receive renewed attention. These alternatives include tobacco stems [6], rice
husks [7], almond shells [8], mango kernel [9], waste apricot [10], sawdust [11], and cuttlefish
bones [12].
Olive stone waste residue can be considered one of the best candidates among the
agricultural wastes as raw material for the production of activated carbon because it is
cheap and quite abundant, especially in Mediterranean countries. According to
International Olive Council [13], the world annual production of olive oil in 2012 was
more than 3 million tons, translating to approximately 15 million tons of olive cakes as
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the by-products. Middle East is one of the top olive- and olive oil-producing regions in
the world. Furthermore, about 95% of the world’s olive trees are in the Mediterranean
region[14].
The most important characteristic of activated carbon is its adsorption capacity, which is
highly affected by the preparation conditions of activated carbon, including activation
temperature, activation time, and chemical impregnation ratio[15] These conditions
influence the pore development and surface characteristics of the activated carbon
produced. Therefore, the challenge in activated carbon production is to produce very
specific carbons that are suitable for specific applications. Thus, the optimization of
preparation factors (activation temperature, activation time, and impregnation ratio) in the
performance of olive stone activated carbon (OSAC) for removing a group of heavy
metals were not investigated. In assessing the effect of treatments on quality attributes,
the use of an adequate experimental design is particularly important. Response surface
methodology (RSM) has been found to be a suitable tool for investigating the interactions
of two or more variables [16]. The optimization of experimental conditions using RSM is
widely applied in various processes. Some of the previous studies that applied RSM in
the preparation of activated carbons for dye removal used precursors such as bamboo
waste [17], waste tea [18], and rice husk[7].
The main objective of this research is to optimize the preparation conditions of OSAC for
the optimal removal of a group of metals, namely, Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+,
from aqueous solution. A central composite design (CCD) was selected to study
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simultaneously the effects of three activated carbon preparation variables (activation
temperature, activation time, and chemical impregnation ratio) on these responses.
Empirical models correlating the OSAC yield and removal of Cu2+, Cd2+, Ni2+, Pb2+,
Fe2+, and Zn2+ to the three variables were then developed. The characteristics and the
adsorption ability of the OSAC prepared under optimized conditions were investigated,
as well.
2. MATERIAL AND METHODS
2.1. Aqueous Solution
Metal solutions were prepared by dissolving appropriate amounts of NiCl2.6H2O(s),
CdCl2.H2O(s), Pb(NO3)2(s), CuCl2.2H2O(s), FeSO4.7H2O(s) and Zn(NO3)2.6H2O in
deionized water. Metal standard solutions of 1000 mg L−1 also purchased from Merck
were used for inductively coupled plasma optical emission spectroscopy (ICP-Optical
Emission Spectrometer; VARIAN 715-ES) calibration.
2.2. Preparation And Characterization Of Activated Carbon
OS waste was obtained from Gaza, Palestine. The OS waste was rinsed thrice with hot
water, thrice with cold water, and dried in an oven at 105 °C for 24 h to remove moisture
content. Once dried, they were ground and sieved for a particle size of 2.0 mm to 4.75
mm. Carbonization step was carried out at 600 °C for 1 h under purified nitrogen
(99.99%). Chemical activation method using potassium hydroxide (KOH) was used to
activate the char. Char (30 g) was impregnated by certain amount of KOH. The amount
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of KOH used was adjusted to give a certain impregnation ratio (weight of activating
agent: weight of char) of 0.5:1, 1.25:1 and 2:1. The impregnation ratio is given by:
( ) ( ) ( )Impregnation ratio IR dry weight of KOHpellets : dry weight of char=
Deionized water was then added to dissolve all the KOH pellets. Impregnation was
carried out for 24 h at room temperature, thus incorporating all the chemicals in the
interior of the particles. The activation of KOH-impregnated char was carried out at
different temperatures ranging from 400 °C to 800 °C and time 1 h to 3 h under a
nitrogen flow of 150 cm3 g−1 and at a heating rate of 10 °C min−1 in a vertical muffle
furnace. After activation, the samples were cooled down under the nitrogen flow and
were washed sequentially several times with hot deionized water (70 °C) and HCl
(0.1 M) until the pH of the washed solution was within the range 6.5 to 7. Finally,
samples were dried in an oven (LARGE 122AK3002) at 110 °C for 24 h and then stored
in containers.
The surface area, pore volume and average pore diameter of the samples were determined
by using Micromeritics ASAP 2020 volumetric adsorption analyzer. The BET surface
area was measured from the adsorption isotherm using Brunauer-Emmett-Teller equation.
The total pore volume was estimated to be the liquid volume of nitrogen at a relative
pressure of 0.98. The surface morphology of the samples was examined using a scanning
electron microscope (Quanta 450 FEG, Netherland). Chemical characteristics of surface
functional group of the activated carbon was detected by diluting in K-Br pellets were
recorded with FTIR spectroscope (IR Prestige 21 Shimadzu, Japan) in the 400-4000 cm-1
wave number range.
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2.3. Experimental Design
The parameters used in preparing the activated carbon from OS waste were studied using
the standard RSM design called the central composite design (CCD). The CCD was
chosen as the experimental design. This method is suitable for fitting a quadratic surface
and helps optimize the effective parameters based on the higher responses obtained with
a minimum and statistically significant number of experiments, in addition to analyzing
the interaction between parameters (17).The OSAC was prepared using the chemical
activation method. The variables considered were the activation temperature (X1),
activation time (X2), and chemical impregnation ratio (X3). These three variables,
together with their respective ranges, were chosen based on the literature and our
preliminary studies. The ranges and the levels -1, 0, and 1 of the variables investigated
include 400, 600, and 800 °C for activation temperature; 1, 2, and 3 h for activation time;
and 0.5, 1.25 and 2 for impregnation ratio.
The most important parameters affecting the characteristics of activated carbon include
activation temperature, activation time, and chemical impregnation ratio [19]. Generally,
the CCD consists of 2k factorial runs with 2k axial runs and kc center runs (six
replicates). For this case, a 23 full factorial CCD for the three variables, consisting of 8
factorial points, 6 axial points, and 6 replicates at the center points, were employed,
indicating that, altogether, 20 experiments were required (2k+ 2k + 6), where k is the
number of independent variables. The center points were used to determine the
experimental error and the reproducibility of the data. The independent variables were
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coded to the (−1, 1) interval where the low and high levels are coded as −1 and +1,
respectively. The six axial points are located at (± α, 0, 0), (0, ±α, 0) and (0, 0, ±α), and
the six replicates points located at the center (0, 0, 0) were run. Alpha (α) is the distance
of the axial point from the center and makes the design face-centered. The responses
were the carbon yield (Y1) and percentage removals of Cu (Y2), Cd (Y3), Ni (Y4), Pb
(Y5), Fe (Y6), and Zn (Y7).
The complete design matrixes of the experiments carried out in additionof the results
obtained are shown in Table 1. Each response was used to develop an empirical model
that correlated the response to the three activated carbon preparation variables using a
second-degree polynomial equation as given by Eq. (1):
12
1 1 1 1
k k k k
o i i ii i ij i j ii i i j i
Y b b x b x b x x e−
= = = = +
= + + + +∑ ∑ ∑∑ (1)
where Y is the predicted response, b0 the constant coefficient, bi the linear coefficients, bij
the interaction coefficients, bii the quadratic coefficients, and xi and xj the coded values of
the activated carbon preparation variables.
Design-Expert software (version 6.0.7, Stat-Ease, Inc., Minneapolis, USA) was used for
regression analysis of the experimental data to fit the second-degree polynomial equation,
as well as for analyses of variance (ANOVA) and response surface contours.
2.4. Activated Carbon Yield
The activated carbon yield was calculated based on the following equation:
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( ) % 100c
o
WYieldW
= × (2)
where Wc(g) is the dry weight of the final activated carbon, and Wo(g) is the dry weight
of the char.
2.5 Batch Equilibrium Studies
Batch adsorption was performed in 20 flasks of 250 ml Erlenmeyer flasks where 100 ml
of aqueous solution with initial Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ concentrations of
20 mg L-1 was placed in each flask. Prepared activated carbon (0.3 g) with a particle size
range of 2 mm to 4.75 mm was added to each flask and kept in an isothermal shaker of
200 rpm at 30 °C until equilibrium was reached. After agitation, the solid was removed
by filtration through a 0.45 µm pore size Whatman membrane filter paper. The final
metal concentration in the filtrates, as well as in the initial solution was determined by
inductively coupled plasma optical emission spectroscopy (ICP-Optical Emission
Spectrometer; VARIAN 715-ES) calibration. The sorbed metal concentrations were
obtained from the differences between the initial and final metal concentrations in
solution. The percentage removal at equilibrium was calculated as follows:
( ) 0
0
% 100eRemoval −= ×
(3)
where Co and Ce are the liquid-phase concentrations at the initial state and at equilibrium
(mg l-1), respectively.
The amount of metals adsorbed per unit mass of adsorbent at equilibrium conditions, qe
(mg/g), was calculated by:
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( ) o eC C VeW
q −=
(4)
Where, qe (mg/g) is the amount of solute adsorbed per unit weight of adsorbent; Co and
Ce (mg/L) are the liquid-phase concentrations of adsorbate at initial and equilibrium
conditions, respectively; V (L) is the volume of the solution; and W (g) is the mass of
adsorbent used.
The effects of pH on metals removal were tested respectively by varying the pH from 2 to
6, with initial metals concentration of 20 mg/L and adsorption temperature of 30 °C. The
initial pH of the metals solution was adjusted by addition of 0.10 M HCl or NaOH.
2.6 Adsorption Isotherm
Different models, Langmuir and Freundlich were used to investigate the equilibrium
behavior of metals adsorption on the prepared OSAC. Langmuir adsorption isotherm
assumes monolayer adsorption, with no lateral interaction and steric hindrance between
the adsorbed molecules, even on the adjacent sites. The form of the Langmuir isotherm
equation is given as:
1
ee
e
QbCqbC
=+
(5)
where Ce (mg/L) is the equilibrium liquid-phase concentration of metals, qe (mg/g) is the
equilibrium uptake capacity, Q (mg/g) is the Langmuir constant related to adsorption
capacity, and b (L/mg) is the Langmuir constant related to the energy of sorption, which
quantitatively reflects the affinity between the sorbent and the sorbate.
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Another characteristic parameter of the Langmuir isotherm is the dimensionless factor
RL, released to the shap of the isotherm. Its value indicates either unfavorable (RL> 1),
linear (RL =1), favorable (0 <RL< 1), or irreversible (RL = 0) adsorption and it is evaluated
as [20]:
1 (1 )L
o
RbC
=+
(6)
where b is the Langmuir constant and Co is the initial pollutant concentration (mg/L).
Freundlich isotherm is an empirical model describing the multilayer adsorption, with
non-uniform distribution of adsorption heat and affinities over the heterogeneous
surface.The Freundlich model is based on sorption on a heterogeneous surface of varied
affinities. The form of Freundlich model is given as:
1/ne f eq K C= (7)
where qe (mg/g) is the amount of metals adsorbed at equilibrium, Ce (mg/L) is the
adsorbate concentration, Kf (m/g)(L/mg)1/n is the Freundlich constant related to
adsorption capacity, and 1/n is the Freundlich constant related to sorption intensity of the
sorbent.
3. RESULTS AND DISCUSSION
3.1. Model Development
The whole design matrix together with the values of the responses gained from the
experimental works is given in Table 1. For the responses of the OSAC yield and
removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+, the quadratic model was selected, as
suggested by the software. The responses were correlated with the three variables studied
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by multiple regression analysis using the second-order polynomial presented in Eq. (1).
The coefficients of the model equation and their statistical significance were evaluated
using Design-Expert 6.0.7 software. In this study, insignificant model terms, which have
limited influence, were excluded from the study to improve the model. Based on the
results, the quadratic regression models for removal of Cu2+ (Y1), Cd2+ (Y2), Ni2+ (Y3),
Pb2+ (Y4), Fe2+ (Y5), and Zn2+ (Y6), and the OSAC yield (Y7) in terms of coded factors
are expressed, as follows:
Cu2+ removal (%)
=
+98.92 +5.53 X1 +5.90 X3 -6.71 X12-4.53 X1 X3
Cd2+ removal (%)
=
+92.30 +10.10 X1 +23.20 X3 -35.61 X32 -7.89 X1 X3
Ni2+ removal (%)
=
+98.09 +10.02 X1 -2.16 X2 +25.20 X3 -7.94 X12 -27.87 X3
2
-11.58 X1 X3
Pb2+ removal (%)
=
+98.97 +3.34 X1 +3.31 X3 -3.62 X12-3.01 X1 X3
Fe2+ removal (%)
=
+99.11 +1.87 X1 +1.87 X3 -1.81 X12 +0.44 X1 X2 -1.79 X1
X3
Zn2+ removal (%)
=
+97.57 +16.75 X1 -2.68 X2 +22.81 X3 -10.56 X12 -16.21
X32 -15.16 X1 X3
Yield (%) = +79.20 -5.08 X1 -2.90 X2 -8.47 X3 -3.60 X22
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where, X1, X2, and X3 are the coded values of the activation temperature, activation time,
and chemical impregnation ratio, respectively. The quality of the model developed was
evaluated based on the coefficient of determination R-squared (R2). Standard deviation
values are presented in Table 2, and its statistical significance was checked by the F-test
in the same program. In fact, the models developed seems to be the best at low standard
deviation and high R2 statistics, which is closer to unity, considering it will yield
predicted values closer to the actual values of the responses [7]. In this experiment, the
adjusted R2 values of the quadratic model to the experimental data forthe OSAC yield
and removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ranged between 0.8997 and 0.9815.
These high R2 values indicate that the predicted values for the OSAC yield and removal
of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+would be more accurate and closer to its actual
value.
3.2 Statistical Analysis
ANOVA was further carried out to justify the adequacy of the models. The results of the
second-order response surface model fitting in the form of ANOVA are given in Table 2
for removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+, as well asOSAC yield, respectively.
Data given in Table 2 demonstrate that all the models were significant at the 5%
confidence level, given that P values were less than 0.05. The values of the correlation
coefficient (R2 = 0.9421, 0.9741, 0.9874, 0.9210, 0.9594, 0.9836 and 0.9453) obtained in
the present study for removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+ and Zn2+, as well asOSAC
yield were higher than 0.80. For a good fit of model, the correlation coefficient should be
at a minimum of 0.80[21]. A high R2 value close to 1 demonstrates good agreement
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between the calculated and observed results within the range of the experiment and
shows that a desirable and reasonable agreement with adjusted R2 is necessary[22].
Diagnostic plots, such as the predicted versus actual values in Fig. 1 (a–f), help determine
the model satisfactoriness. The predicted versus actual values plots of the parameters of
removal are presented in Fig. 1 (a–f). These plots show an adequate agreement between
real data and the ones gained from the models. Hence, all predictive models can be used
to navigate the design space defined by the CCD.
The coefficient of variance (CV), as the ratio of the standard error of the estimate to the
mean value of the observed response (as a percentage), identifies the reproducibility of
the model. A model typically can be considered reproducible if its CV is not more than
10% [23]. According to Table 2, the CV values obtained for all responses studied are
relatively small with none of them exceeding 6.50%. The statistical results obtained,
show that the above models adequately predicted removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+,
and Zn2+, as well as OSAC yield within the range of variables studied.
3.3. Response Surface Contours
The use of three-dimensional plots of the regression model is highly recommended for
the graphical interpretation of the interactions[24]. Hence, the three-dimensional response
surface curves were plotted using a statistically significant model to understand the
interaction of the medium components. Three-dimensional surface graphs and contour
plots between the factors are presented in Figs. 2(a–g). The representations of the models
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simplify an investigation of the effects of the experimental factors on the responses. The
response plots display clear peaks, indicating the maximum values of the responses are
attributed to temperature (X1) and the impregnation ratio (X3) in the design space. Based
on the F-values (Table 2), the chemical impregnation ratio (X3) presented the largest
ranges of F-values (53.39-461.18), followed by activation temperature (X1) ranges
(43.46-171.13), while the activation time (X2) presented the least ranges of (3.39-19.39).
The chemical impregnation ratio and activation temperature significantly affected the
removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ removal for the prepared activated
carbon, compared with activation time (X2).
Figure 2 (a–g) displays the three-dimensional response surfaces constructed to show the
interaction effects of the activated carbon preparation variables (activation temperature
and chemical impregnation ratio) on the removal of Cu2+ (Y1), Cd2+ (Y2), Ni2+ (Y3), Pb2+
(Y4), Fe2+ (Y5), and Zn2+ (Y6), as well as the yield (Y7). For these plots, the activation
time was fixed at optimum level (t = 2 h). As shown in Fig. 2 (a–g), the removal
generally increases with increasing activation temperature and chemical impregnation
ratio.
The results obtained agreed with the findings of [17], who reported that the activation time
did not significantly affect the pore structure of activated carbon produced from bamboo
waste. Moreover, the pore characteristics changed significantly with the activation
temperature and with the KOH impregnation ratio. Similarly, [25] reported that the
activation time did not significantly affect the pore structure of activated carbon produced
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from cassava peel, whereas the activation temperature and the KOH impregnation ratio
significantly changed the pore characteristics. [26] also found that activation time did not
significantly affect the surface area obtained for activated carbons prepared from apricot
stones using steam activation. [27] similarly reported that activation time showed the least
significant effect on the Remazol Brilliant Blue R (RBBR) removal of mangosteen peel
activated carbon, whereas both the activation temperature and impregnation ratio were
found to significantly affect this response.
In this research, the increase in activation temperature increases the removal of Cu2+,
Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+Fig. 2 (a–f) and decreases the carbon yield Fig. 2 (g). The
maximum removal occurred at temperatures higher than 600 °C because the increase in
activation temperature enhanced the existing pores and created new pores in the material
by increasing the reaction rate between the precursor and the chemical impregnate [28]. At
higher activation temperatures, the activated carbon yield was lesser due to increasing
volatilization rate from the sample. Shevkoplyas and Saranchuk, [29] reported that the
impregnation of coal with KOH causes the breaking of C–O–C and C–C bonds, thereby
facilitating coal decomposition during pyrolysis, hence decreasing the carbon yield.
Ahmad and Alrozi [27] also observed the similar trend where less mangosteen peel
activated carbon yield and higher removal of RBBR obtained at higher activation
temperature. Chowdhury et al.,[30] reported that the temperature and impregnation ratio is
proportional to the removal percentages of Cu2+ and Pb2+ of activated carbon produced
from kenaf fiber up to a certain limit. Increased temperature and impregnation ratio
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enhances the reaction between KOH and the char in the presence of CO2, resulting in a
more porous structure that is suitable for adsorption.
Similarly, an increase in the KOH impregnation ratio increases the removal of Cu2+,
Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+ and decreases carbon yield, as shown in Fig. 2 (a–g). The
maximum removal occurred at an impregnation ratio between 1.25 and 2.0. Accordingly,
at a higher impregnation ratio, heavy metal removal increased as the impregnation ratio
increased from 1.25 to 2 because the impregnation ratio plays an important role in
creating and widening the pores in the activated carbon, thus contributing to the increase
in surface area and adsorption capacity [31]. The intercalation of chemicals used for the
impregnation ratio appeared to be responsible for the drastic expansion of the carbon
material and, hence, the creation of a large surface area and high pore volume. Tan et al.,
[16] mentioned that the KOH:char impregnation ratio played a decisive role in pore
formation. At a high KOH impregnation ratio, the pore development was mostly due to
the intercalation of potassium metal in the carbon structure. As the KOH:char IR
increased, the catalytic oxidation also caused the widening of pores, therefore increasing
the carbon uptake, as well [31]. The increase in KOH might accelerate the reaction rate,
thus increasing the quantity of pores. Nevertheless, a maximum point was attained for the
KOH:char impregnation ratio, beyond which, further impregnation would reduce the
carbon uptake because an excessive amount of KOH could cause further the reaction
between KOH and carbon, thereby destroying the pore structure formed in the previous
stage. Consequently, a reduction in surface area would occur [32]. In addition, at higher
impregnation ratios, the chemical will form insulating layers that cover and block the
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pores [33]. Foo and Hameed, [34] also observed similar results where the excess of K2CO3
and metallic potassium left on the carbon surface blocked the pores, thereby dramatically
decreasing the accessible area. Furthermore, the pores would be widened and burnt off.
Solution pH also affects adsorption by regulating the adsorbent surface charge as well as
degree of ionization of the adsorbate molecules. The percentages of metals removals
using OSAC were found to increase significantly with the increase in solution pH from
pH 3 to pH 6 and the highest metals removals were achieved at pH 5. According to Božić
et al. [35] at low pH<3 the minimal removal may be an effect of the higher concentration
and high mobility of the H+, which competes with metal ions on the active sites on the
sorbent surface, resulting in its preferential adsorption rather than the metal ions.
Therefore, H+ ions react with anionic functional groups on the surface of OSAC and
results in the reduction of the number of binding sites available for the adsorption of
Cu2+, Cd2+, Ni2+, Pb2+, Fe2+ and Zn2+. This increase may have been an effect of the
presence of negative charge on the surface of the adsorbent that may have been
responsible for the metal binding because solution pH can affect the charge of OSAC
surfaces [36]. In addition, at higher pH values, the lower number of H+ and greater number
of ligands with negatives charges result in greater metal adsorption. The same trend was
observed by several researchers who studied metal sorption by different biomaterials,
namely, copper by sawdust [37], and zinc, lead, and cadmium by jute fibers [38], lead and
cadmium by [39], cadmium by orange wastes [40].
3.4. Verification Of The Model And Optimum Condition
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RSM has been used successfully to optimize the parameters affecting the removal of
Cu2+, Cd2+, Ni2+, Pb2+, Fe2+,and Zn2+, as well as OSAC yield. Nevertheless, optimizing
these responses under the same conditions is difficult, considering the interest regions of
factors are different. Numerical optimization was selected as the desired goal for each
factor and response from the menu. According to the software optimization step, the
desired goal for each operational condition (activation temperature, activation time, and
impregnation ratio) was chosen “within” the range. The responses (removal of Cu2+,
Cd2+, Ni2+, Pb2+, Fe2+, and Zn2+, as well as OSAC yield) were defined as the maximum
for achieving the highest performance. The value of desirability (0.99) obtained shows
that the estimated function may represent the experimental model and desired conditions.
The predicted and experimental results for the removal of Cu2+, Cd2+, Ni2+, Pb2+, Fe2+,
and Zn2+, as well as the OSAC yield obtained at optimum conditions are listed in Table 3.
The optimum activated carbon was obtained at 715 °C activation temperature, 2 h
activation time, and 1.53 impregnation ratio resulted in 73.94% OSAC yield and removal
of 99.25% Cu2+, 94.98% Cd2+, 99.08% Ni2+, 99.33% Pb2+, 99.41% Fe2+,and 99.17% of
Zn2+. The experimental values obtained were in good agreement with the values
predicted from the models, with relatively small errors between the predicted and the
actual values as shown in Table 3. This result agrees with the work done by [30].
3.5. Characterization Of OSAC Prepared Under Optimum Conditions
The BET surface area, mesopore surface area, total pore volume and average pore
diameter of the prepared activated carbon were 886.72 m2 g−1, 740.66 m2 g−1, 0.507 cm3
g−1 and 4.22 nm, respectively. The maximum value of activated carbon yield was found
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to be 38.07%. Besides, the average pore diameter of the activated carbon was found to be
4.92 nm, indicated that the activated carbon prepared was in the mesopores region
according to the International Union of Pure and Applied Chemistry (IUPAC), pores are
classified as micropores (<2 nm diameter), mesopores (2–50 nm diameter) and
macropores (>50 nm diameter)[41]. The activated carbon resulting from OS waste
contained relatively large surface area and total pore volume compared to various
adsorbents Table 4 and also commercially available activated carbons such as F100 and
BPL from Calgon Corporation with BET surface area of 957 and 972 m2 g−1in addition to
totalpore volume of 0.526 and 0.525 cm3 g−1, respectively. According to the activation
process using KOH as chemical activating agent, the high BET surface area, total pore
volume and pore developments of the prepared activated carbon were found. The
chemical agent is dehydrating agent that penetrates deep into the structure of the carbon
causing pores to develop [42].
Figures 3(a), 3(b), 3(c) and 3(d) show the SEM images of the precursor, char, OSAC and
the exhausted OSAC, respectively. The surface texture of raw and char OS were uneven,
rough, and undulating, with very little pores available on the surface. However, after
activation treatment, large and well-developed pores were clearly found on the surface of
the OSAC, compared with the original precursor and char. The KOH and activation
process were effective in creating well-developed pores on the surfaces of the OSAC,
hence leading to activated carbon with a large surface area and good porous structure
(mesopores). Similar observations were reported by other researchers in their studies on
preparing activated carbons from coconut husk [16], mangosteen peel[27], and oil palm
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fiber [43].Therefore, after heavy metals adsorption, pores were exhausted with
contaminants.
4. CONCLUSION
In the present study, CCD method was used to optimize the preparation of activated
carbon from olive stone with KOH as activator. The impregnation ratio and activation
temperature were more highly significant factors on metals removal for the prepared AC,
compared with activation time. The impregnation ratio was the greatest impact factor on
the OSAC yield followed by activation temperature and activation time. The optimum
conditions were activation temperature of 715 °C, activation time of 2 h, and
impregnation ratio of 1.53. N2 adsorption showed BET surface area of the prepared
activated carbon was 886.72 m2 g−1. Scanning electron microscopy (SEM) and Fourier
transform infrared spectroscopy (FT-IR) investigation evidenced that the presence of
opened-pore structure and different functionalities on the carbon surfaces compared with
those of olive stone. Langmuir isotherms better fit the experimental equilibrium data of
metals adsorption on the prepared OSAC. The maximum adsorption capacity (Q) on the
OSAC was found for Fe2+ (57.47 mg/g), followed by Pb2+ (22.37 mg/g), Cu2+ (17.83
mg/g), Zn2+ (11.14 mg/g), Ni2+ (8.42 mg/g), and Cd2+ (7.80 mg/g). The prepared OSAC
can be used for efficient removal of metals from contaminated wastewater. The
optimization results obtained by RSM can be used for preparing activated carbon to be
used for heavy metals removal at large scale columns in treatment plants.
ACKNOWLEDGMENTS
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The authors wish to acknowledge the Universiti Sains Malaysia (USM) for its financial
support under the USM and TWAS Fellowship scheme and RU-PRGS grant scheme (No.
8045048) and acknowledge Ministry of Higher Education, Malaysia for providing LRGS
grant No. (203/PKT/670006) and (03-01-05-SF0502) to conduct this study.
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Table 1. Experimental factors and experimental responses
Factors Responses
Ru
n
no.
Type X1:
tem
p
(°C
)
X2:
tim
e
(h)
X3:
IR
Cu
remov
al Y1
(%)
Cd
remov
al Y2
(%)
Ni
remov
al Y3
(%)
Pb
remov
al Y4
(%)
Fe
remov
al Y5
(%)
Zn
remov
al Y6
(%)
Yiel
d Y7
(%)
1 Cent
er
600 2 1.2
5
99.60 95.45 99.06 99.37 99.27 99.51 79.1
0
2 Cent
er
600 2 1.2
5
98.33 95.24 98.54 98.34 99.24 97.82 78.1
3
3 Cent
er
600 2 1.2
5
98.69 93.99 98.48 98.18 99.21 98.76 78.5
3
4 Cent
er
600 2 1.2
5
99.81 94.47 99.06 99.62 99.27 98.87 79.0
3
5 Cent
er
600 2 1.2
5
99.02 94.52 98.92 98.94 99.29 99.34 77.8
1
6 Cent
er
600 2 1.2
5
99.80 95.58 99.43 99.59 99.27 99.70 78.8
1
7 Axia
l
400 2 1.2
5
87.71 82.94 83.76 91.73 95.86 69.72 87.3
0
8 Axia
l
800 2 1.2
5
99.59 94.58 98.61 99.71 98.94 99.36 74.7
4
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9 Axia
l
600 1 1.2
5
99.75 86.66 93.03 99.19 99.27 92.88 80.6
8
10 Axia
l
600 3 1.2
5
98.95 89.62 96.12 98.42 99.23 98.61 67.7
3
11 Axia
l
600 2 0.5
0
95.91 37.43 47.36 98.81 97.75 58.30 86.2
6
12 Axia
l
600 2 2.0
0
99.34 80.47 95.13 99.22 99.27 99.48 72.3
2
13 Fact 400 1 0.5
0
73.89 15.62 14.97 85.80 92.03 17.28 89.8
8
14 Fact 800 1 0.5
0
96.52 60.25 61.24 98.15 98.27 87.71 82.8
7
15 Fact 400 3 0.5
0
76.01 11.26 13.50 84.17 90.73 14.04 87.9
8
16 Fact 800 3 0.5
0
93.19 42.90 56.22 96.55 99.48 73.17 78.4
2
17 Fact 400 1 2.0
0
97.47 79.57 97.17 99.55 99.75 99.26 75.7
1
18 Fact 800 1 2.0
0
99.61 82.29 87.04 99.35 99.59 97.67 63.3
5
19 Fact 400 3 2.0
0
98.32 73.36 79.76 98.78 98.76 86.18 69.3
1
20 Fact 800 3 2.0 99.78 83.75 86.23 99.67 99.59 96.05 60.0
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0 5
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Table 2. ANOVA for analysis of variance and adequacy of the quadratic model for Cu2+,
Cd2+, Ni2+, Pb2+, Fe2+, Zn2+ and OSAC yield
Response Source
of data
Sum of
squares
Degree
of
freedom
Mean
square
F-
value
Prob.
>F
Comment
Cu
removal
(%)
Model 1043.13 5 260.76 61.01 <
0.0001
SD= 2.07,
CV= 2.16,
R2=
0.9421,
Adj R2 =
0.9266.
1X 305.75 1 305.75 71.53 <
0.0001
3X 348.08 1 348.08 81.44 <
0.0001
21X 225.25 1 255.25 52.70 <
0.0001
1 3X X 163.95 1 163.95 38.36 <
0.0001
Residual 64.11 15 4.27 - -
Cd
removal
(%)
Model 13297.24 5 3310.49 141.02 <
0.0001
SD= 4.85,
CV= 6.50,
R2=
0.9741,
Adj R 2 =
0.9672.
1X 1020.35 1 1020.35 43.46 <
0.0001
3X 5381.05 1 5381.05 229.22 <
0.0001
23X 6341.96 1 6341.96 270.15 <
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0.0001
1 3X X 498.60 1 498.60 21.24 0.0003
Residual 352.13 15 23.48 - -
Ni
removal
(%)
Model 14003.78 6 2333.96 169.44 <
0.0001
SD = 3.71,
CV= 4.63,
R2=
0.9874,
Adj R 2 =
0.9815.
1X 1003.74 1 1003.74 72.87 <
0.0001
2X 46.75 1 46.75 3.39 0.0884
3X 6352.57 1 6352.57 461.18 <
0.0001
21X 201.64 1 201.64 14.64 0.0021
23X 2486.34 1 2486.34 180.50 <
0.0001
1 3X X 1073.14 1 1073.14 77.91 <
0.0001
Residual 179.07 13 13.77 - -
Pb
removal
(%)
Model 361.02 5 89.76 43.73 <
0.0001
SD= 1.43,
CV= 1.47,
R2=
0.9210,
Adj R 2 =
0.9000.
1X 111.61 1 111.61 54.38 <
0.0001
3X 109.58 1 109.58 53.39 <
0.0001
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21X 65.59 1 65.59 31.96 <
0.0001
1 3X X 72.26 1 72.26 35.21 <
0.0001
Residual 30.79 15 2.05 - -
Fe
removal
(%)
Model 113.72 6 22.72 66.16 <
0.0001
SD= 0.59,
CV= 0.60,
R2=
0.9594,
Adj R 2 =
0.9449.
1X 35.15 1 35.15 102.35 <
0.0001
3X 34.97 1 34.97 101.83 <
0.0001
21X 16.35 1 16.35 47.62 <
0.0001
1 2X X 1.53 1 1.53 4.45
0.0600
1 3X X 25.60 1 25.60 74.54 <
0.0001
Residual 4.81 14 0.34 - -
Zn
removal
(%)
Model 12818.63 6 2136.44 130.32 <
0.0001
SD= 4.05,
CV= 4.81,
R2=
0.9836,
Adj R 2 =
1X 2805.37 1 2805.37 171.13 <
0.0001
2X 71.64 1 71.64 4.37 0.0568
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3X 5205.13 1 5205.13 317.51 <
0.0001
0.9761.
21X 356.60 1 356.60 21.75 0.0004
23X 840.92 1 840.92 51.30 <
0.0001
1 3X X 1838.60 1 1838.60 112.15 <
0.0001
Residual 213.12 13 26.35 - -
Model 1123.81 4 280.95 64.81 <
0.0001
SD= 2.08,
CV= 2.69,
R2=
0.9453,
Adj R 2 =
0.9307.
OSAC
yield (%)
1X 257.65 1 257.65 59.43 <
0.0001
2X 84.08 1 84.08 19.39 0.0005
3X 717.11 1 717.11 165.41 <
0.0001
22X 64.97 1 64.97 14.99 0.0015
Residual 65.03 15 4.34 - -
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Table 3. Verification of experimental and predicted values of prepared activated carbon
under the optimum conditions (715 oC, 2 h, 1.53 IR) predicted by RSM
Resbonse Experamintal Predicted Error (%) Desirability
Cu2+ removal (%) 99.25 99.99 0.74 0.99
Cd2+ removal (%) 94.98 97.61 1.05
Ni2+ removal (%) 99.08 99.99 0.91
Pb2+ removal (%) 99.33 99.99 0.66
Fe2+ removal (%) 99.41 99.96 0.55
Zn2+ removal (%) 99.17 99.99 0.82
OSAC yield (%) 73.94 74.79 1.14
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Table 4. Comparison of the BET surface area onto various adsorbents
Material Surface Area (m2
g−1)
References
Waste tea 820 (40)
Bamboo waste 988.24 (15)
Date Stone 730 (41)
Rice husk 750 (42)
Bagasse 674 (42)
Rice bran 652 (43)
Rice husk 604 (6)
Oil palm fibre 521 (44)
Olive Stone 886.72 Present work
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Table 5. Langmuir and Freundlich isotherm parameters for the adsorption of Fe2+, Pb2+,
Cu2+, Zn2+, Ni2+, and Cd2+ onto CHOS.
Paramete
r
Langmuir Isotherm Freundlich Isotherm
Q (mg/g) b (L/mg) R2 RL K (mg/g) (L/mg)1/n 1/n R2
Fe2+ 57.47 3.702 0.992 0.013 34.04 0.391 0.919
Pb2+ 22.37 2.847 0.993 0.017 12.90 0.259 0.941
Cu2+ 17.83 3.134 0.990 0.016 11.03 0.222 0.947
Zn2+ 11.14 7.184 0.982 0.007 8.83 0.141 0.947
Ni2+ 8.42 5.094 0.990 0.010 6.41 0.137 0.905
Cd2+ 7.80 3.793 0.992 0.013 6.09 0.099 0.931
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Figure 1. Design-expert plot; predicted vs. actual values plot for (a) Cu2+ removal, (b)
Cd2+ removal, (c) Ni2+ removal, (d) Fe2+ removal, (e) Pb2+ removal, and (f) Zn2+ removal
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Figure 2. Three-dimensional response surface plot: (a) Cu2+ removal, (b) Cd2+ removal,
(c) Ni2+ removal, (d) Fe2+ removal, (e) Pb2+ removal, and (f) Zn2+ removal (effect of
activation temperature and chemical impregnation ratio, t = 2 h)
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Figure 3. Scanning electron micrograph: (a) OS raw (b) OS char (c) OSAC (d) OSAC
exhausted (magnifications: 2000x)
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