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Adsorptionofmethylenebluebyphoenix
treeleafpowderinafixed-bedcolumn:
experimentsandpredictionof
breakthroughcurves
ARTICLEinDESALINATION·SEPTEMBER2009
ImpactFactor:3.96·DOI:10.1016/j.desal.2008.07.013
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Desalination 245 (2009) 284–297
Adsorption of methylene blue by phoenix tree leaf powder in a
fixed-bed column: experiments and prediction
of breakthrough curves
Runping Hana*,b, Yu Wanga, Xin Zhaoc, Yuanfeng Wanga, Fuling Xieb,
Junmei Chengb, Mingsheng Tanga
a
Department of Chemistry, Zhengzhou University, No. 75 Daxue North Road, Zhengzhou, 450052 PR China
Tel. +86 (371) 6778 1757; Fax: +86 (371) 6778 1556; email: [email protected]
b
China Petroleum and Chemical Corporation, Luoyang Company, Luoyang, 471012 PR China
c
School of Chemistry and Chemical Engineering, Sun Yat-Sen University,
No. 135 Xingang West Road, Guangzhou, 510275 PR China
Received 24 November 2007; Accepted 9 July 2008
Abstract
A continuous adsorption study in a fixed-bed column was carried out by using phoenix tree leaf powder as an
adsorbent for the removal of methylene blue (MB) from aqueous solution. The effect of flow rate, influent MB
concentration and bed depth on the adsorption characteristics of adsorbent was investigated at pH 7.4. Data
confirmed that the breakthrough curves were dependent on flow rate, initial concentration of dye and bed depth. Four
kinetic models, Thomas, Adams–Bohart, Yoon–Nelson and Clark, were applied to experimental data to predict the
breakthrough curves using nonlinear regression and to determine the characteristic parameters of the column that are
useful for process design, while a bed-depth service time analysis (BDST) model was used to express the effect of
bed depth on breakthrough curves and to predict the time needed for breakthrough at other conditions. The Thomas
and Clark models were found suitable for the description of whole breakthrough curve, while the Adams–Bohart
model was only used to predict the initial part of the dynamic process. The data were in good agreement with the
BDST model. It was concluded that the leaf powder column can be used in wastewater treatment.
Keywords: Leaf powder; Methylene blue; Adsorption; Fixed-bed bioreactors; Modeling; Wastewater treatment
*Corresponding author.
0011-9164/09/$– See front matter © 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.desal.2008.07.013
R. Han et al. / Desalination 245 (2009) 284–297
1. Introduction
Dyes are present in different concentrations in
wastewaters of industries such as plastic, textile,
dye, dyestuffs, etc. [1]. Discharge of such colored
effluents imparts color to the receiving water
bodies and interferes with their beneficial use.
Color impedes light penetration, retards photosynthetic activity, inhibits the growth of biota,
etc. The removal of color from dye-bearing
effluents is a major problem due to the difficulty
in treating such wastewaters by conventional
treatment methods. Furthermore, these processes
are costly and cannot effectively be used to treat
the wide range of dye wastewater [2].
The adsorption technique is quite popular due
to its simplicity as well as the availability of a
wide range of adsorbents and it is proved to be an
effective and attractive process for removal of
non-biodegradable pollutants (including dyes)
from wastewater [3]. Activated carbon is the most
effective and widely used as adsorbent because it
has excellent adsorption ability [4]. However, its
high cost has prevented its application, at least in
developing countries. So it is preferable to use
low-cost adsorbents such as an industrial waste,
natural material, or agricultural by-products.
These materials do not require any expensive
additional pretreatment step and could be used as
adsorbents for removal of dyes from solution.
Such low-cost adsorbents have satisfactory performance at laboratory scale for treatment of
colored effluents [5–14].
Many cities in China have planted phoenix
trees in main roads, parks and schools. So a lot of
phoenix tree leaves fall in autumn and often are
collected as waste by cleaners. Several papers
reported plant leaves used to adsorb heavy metals
and dyes from solution in batch mode [15–18],
but no research was reported about dye adsorption to fallen leaves in column mode.
In the present work, methylene blue (MB) was
selected as a model compound for evaluating the
potential of leaves to remove dye from waste-
285
waters. MB is a thiazine (cationic) dye, which is
most commonly used for coloring paper, hair
(temporary colorant), dyeing cottons, wools, etc.
Previously, several researchers had proved
that several low-cost materials such as cereal
chaff [8], rice husk [9,19], giant duckweed [20],
sewage sludge [21], sawdust [22], wheat shell
[23], diatomaceous silica [24], clay [25], fly ash
[26] and natural zeolite [27] for the removal of
MB from its aqueous solutions. Compared to
other adsorbents, the capacity of MB adsorption
onto fallen phoenix tree leaves is higher [17]. As
waste, it is also very cheap, so the leaf can be
used to remove MB from solution.
The aim of the present work is to explore the
possibility of utilizing leaf powder for the adsorptive removal of MB from wastewater. The effect
of flow rate, influent concentration and bed depth
on MB adsorption by leaf powder column was
investigated. The Thomas, Adams–Bohart,
Yoon–Nelson, Clark and BDST models were
used to predict the performance. Error analysis
was carried out to test the adequacy and accuracy
of the model equations.
2. Mathematical description
The performance of a fixed-bed column is
described through the concept of the breakthrough curve. The time for breakthrough
appearance and the shape of the breakthrough
curve are very important characteristics for
determining the operation and the dynamic
response of an adsorption column. The loading
behavior of MB to be adsorbed from solution in
a fixed-bed is usually expressed in term of Ct /C0
as a function of time or volume of the effluent for
a given bed height, giving a breakthrough curve
[28]. The value of qtotal for a given feed concentration and flow rate is equal to the area under the
plot of the adsorbed MB concentration Cad (Cad=
C0–Ct) (mg l!1) vs. t (min) and can be calculated
from Eq. (1):
286
qtotal 
R. Han et al. / Desalination 245 (2009) 284–297
Q
1000

t  ttotal
t 0
Cad d t
(1)
The value of qe(exp) is calculated as the following:
qe (exp) = qtotal/x
(2)
Wtotal is calculated from Eq. (3):
Wtotal = C0Qttotal/1000
(3)
Y is the ratio of the maximum capacity of the
column (qtotal) to the total amount of MB sent to
column(Wtotal).
(4)
Y = (qtotal /Wtotal) ×100
Successful design of a column adsorption process
requires prediction of the concentration-time
profile or breakthrough curve for the effluent.
Kinetic models were used to express the dynamic
process of the column mode.
a plot of Ct/C0 against t for a given flow rate
using nonlinear regression analysis.
2.2. Adams–Bohart model
The Adams–Bohart model assumes that the
adsorption rate is proportional to both the residual
capacity of the adsorbent and the concentration of
the adsorbing species. The Adams–Bohart model
is used for the description of the initial part of the
breakthrough curve, expressed as [30]:
Ct
Z

 exp  kABC0t  k AB N 0 
C0
F

(6)
From this equation, values describing the characteristic operational parameters of the column
can be determined from a plot of Ct/C0 against t at
a given bed height and flow rate using the nonlinear regressive method.
2.3. Yoon–Nelson model
2.1. Thomas model
The maximum adsorption capacity of an
adsorbent is also needed in design. Traditionally,
the Thomas model is used to fulfill the purpose.
The data obtained from a column in continuous
mode studies were used to calculate the maximum solid phase concentration of MB on adsorbent and the adsorption rate constant using the
kinetic model developed by Thomas [29]. The
Thomas solution is one of the most general and
widely used methods in column performance
theory. The expression by Thomas for an adsorption column is given below:
Ct
1

C0 1  exp  kTh qe x / Q  kTh C0t 
(5)
The kinetic coefficient kTh and the adsorption
capacity of the column qe can be determined from
The Yoon–Nelson is based on the assumption
that the rate of decrease in the probability of
adsorption for each adsorbate molecule is proportional to the probability of adsorbate adsorption
and the probability of adsorbate breakthrough on
the adsorbent. The Yoon–Nelson model not only
is less complicated than other models, but also
requires no detailed data concerning the characteristics of adsorbate, the type of adsorbent, and
the physical properties of the adsorption bed [28].
The Yoon–Nelson equation for a single component system is expressed as [31]:
Ct
 exp  k YN t  k YN 
C0  Ct
(7)
The approach involves a plot of Ct/(C0–Ct) vs.
sampling time (t) according to Eq. (7). The
parameters of kYN and τ can be obtained using the
nonlinear regressive method.
R. Han et al. / Desalination 245 (2009) 284–297
2.4. Clark model
The Clark defined a new simulation of breakthrough curves [32]. The model developed by
Clark was based on the use of a mass-transfer
concept in combination with the Freundlich
isotherm [32]:
1/( n 1)
Ct 
1


 rt 
C0  1  Ae 
(8)
From a plot of Ct/C0 against t at a given bed
height and flow rate using nonlinear regressive
analysis, the values of A and r can be obtained.
2.5. Bed-depth/service time analysis (BDST)
model
BDST is a simple model for predicting the
relationship between bed depth, Z, and service
time, t, in terms of process concentrations and
adsorption parameters. The BDST model is based
on the assumption that the rate of adsorption is
controlled by the surface reaction between adsorbate and the unused capacity of the adsorbent
[33].
The values of breakthrough time obtained for
various bed heights used in this study were
introduced into the BDST model. A linear
relationship between bed depth and service time
is given by Eq. (9) [33]:
C

N'
1
t 0 Z
ln  0  1
C0 F
K a C0  Ct

(9)
A plot of t vs. Z should yield a straight line where
N0 and K, the adsorption capacity and rate
constant, respectively, can be evaluated.
A simplified form of the BDST model is
t=aZ!b
where
(10)
287
a
N0 '
C0 F
(11)
b
C

1
ln  0  1
K a C0  Ct

(12)
The slope constant for a different flow rate can be
directly calculated by Eq. (13) [33]:
a'  a
F
Q
a
F'
Q'
(13)
where a and F are the old slope and influent
linear velocity and aN and FN are the new slope
and influent linear velocity, respectively. As the
column used in the experiment has the same
diameter, the ratio of the original (F) and the new
influent linear velocity (FN) and original flow rate
(Q) and the new flow rate (QN) are equal.
For other influent concentrations, the desired
equation is given by a new slope, and a new
intercept is given by the following expressions:
a'  a
C0
C0 '
(14)
C0 ln  C0 '/ Cf ' 1

C0 ' ln  C0 / Cf  1
(15)
b'  b
where bN, b are the new and old intercepts,
respectively and C0N and C0 are the new and old
influent concentrations, respectively. Cf is the
effluent concentration at influent concentration
C0N and Cf is the effluent concentration at influent
concentration C0.
2.6. Error analysis
As different formulate used to calculate R2
values would affect the accuracy more significantly during the linear regressive analysis, the
288
R. Han et al. / Desalination 245 (2009) 284–297
nonlinear regressive analysis can be a better
option in avoiding such errors [27,34]. So the
parameters of different kinetic models were
obtained using nonlinear analysis according to
least square of errors.
In order to confirm which model was better,
error analysis was performed. The relative mathematical formula of SS is:
 C / C 
SS 
t
0 c
  Ct / C0 e 
N
2
(16)
where (Ct/C0)c is the ratio of effluent and influent
MB concentrations obtained from calculation
according to dynamic models, and (Ct/C0)e is the
ratio of effluent and influent MB concentrations
obtained from experiment, respectively; N is the
number of the experimental point. In order to
confirm the best fit isotherm for the adsorption
system, it is necessary to analyze the data using
SS, combined with the values of the determined
coefficient (R2).
[35]. Like other plant materials, the phoenix tree
leaves contain abundant floristic fiber, protein
and some functional groups such as carboxyl,
hydroxyl, etc., which make adsorption processes
possible. The characteristics of leaf powder are
also studied by thermogravimetry analysis
(TGA), differential thermal analysis (DTA)
(figures not shown). Heated up to 210EC, a mass
loss of 11% is observed. From 210–300EC, a
mass loss of 40% is observed. Up to 510EC, the
mass remaining is only 11%. The max peaks at
325EC, 415EC and 510EC in DTA were observed
by an exothermic decomposed reaction.
3.2. Methylene blue solution
MB (CI no. 52015) has a molecular weight of
373.9 g mol!1, which corresponds to methylene
blue hydrochloride with three groups of water.
The structure of MB is as follows:
3. Experimental
3.1. Materials
Phoenix tree leaf powder used in the present
investigation was obtained from Zhengzhou City
in autumn. The collected fallen leaves were
washed with distilled water several times to
remove all the dirt particles. The washed leaves
were dried in an oven at 373 K for a period of
24 h, and then ground and screened through a set
of sieves to get different geometrical sizes 40–60
mesh. This produced uniform material which was
stored in a air-tight plastic container for all the
adsorption tests.
The results of elemental analysis of leaf
powder are N 0.984%, C 45.76%, H 5.39%, O
36.41%, S 0.1%, others 6.4%. The IR spectroscopy of the leaves is mainly composed by the
adsorption of carbohydrates, lignin, cellulose etc.
The stock solutions of MB (500 mg l!1) were
prepared in a 0.01 mol l!1 sodium chloride
solution. All working solutions were prepared by
diluting the stock solution with 0.01 mol l–1 NaCl
solution to the required concentration. Fresh
dilutions were used for each adsorption study.
The initial pH of solution is 7.40. Within the
range of pH 4–10, the effect of pH on MB
adsorption by leaf powder was insig-nificant from
a batch adsorption study [17]; thus the pH of the
MB solution in this study was not adjusted.
3.3. Methods of adsorption studies
Continuous flow adsorption experiments were
conducted in a glass column (1.2 cm internal
R. Han et al. / Desalination 245 (2009) 284–297
diameter and 50 cm height). A series of experiments was conducted with various influent water
and leaf powder columns. The temperature of all
experiments was 293 K.
Leaf powder was packed into a glass column.
Except the bed depth, the mass of adsorbent in
the column was 2.0 g (15 cm). The MB solution
of known concentration was pumped to the
column in a down-flow direction by a peristaltic
pump at 5, 8 or 12 ml min!1, respectively.
Samples were collected at regular intervals in all
the adsorption. The concentration of MB in the
effluent was analyzed using a Uv/Vis-3000
spectrophotometer (Shimadzu Brand UV-3000)
by monitoring the absorbance changes at a wavelength of maximum absorbance (668 nm). Calibration curves were plotted between absorbance
and concentration of the dye solution.
Also, the experiments of three different bed
depths, 10 cm (1.6 g), 15 cm (2.0 g), 30 cm
(4.0 g), were operated at the same influent MB
concentration (50 mg l!1) and flow rate (8 ml
min!1), respectively.
289
Fig. 1. Breakthrough curves: the effect of flow rate on
MB adsorption (C0 = 50 mg l!1, Z = 15 cm).
4. Results
4.1. Effect of flow rate on breakthrough curve
The breakthrough curves at various flow rates
are shown in Fig. 1 where it can been seen that
the breakthrough generally occurred faster with a
higher flow rate. Breakthrough time reaching
saturation was increased significantly with a
decrease in the flow rate. At a low rate of influent, MB had more time to be in contact with
adsorbent, which resulted in a greater removal of
MB molecules in column.
4.2. Effect of influent MB concentration on
breakthrough curve
The effect of influent MB concentration on the
shape of the breakthrough curves is shown in
Fig. 2. It is illustrated that the breakthrough time
Fig. 2. Breakthrough curve: the effect of influent concentration on MB adsorption (Q = 8 ml min!1, Z = 15 cm).
decreased with increasing influent MB concentration. At lower influent MB concentrations,
breakthrough curves were dispersed and breakthrough occurred slowly. As influent concentration increased, sharper breakthrough curves
were obtained. These results demonstrate that the
change of concentration gradient affects the
saturation rate and breakthrough time [33]. This
can be explained by the fact that more adsorption
sites were being covered as the MB concentration
increases.
290
R. Han et al. / Desalination 245 (2009) 284–297
5. Discussion
Successful design of a column adsorption process requires prediction of the concentration–time
profile or breakthrough curve for the effluent. In
order to describe the fixed-bed column behavior
and to scale up it for industrial applications, four
models, Adams–Bohart, Yoon–Nelson, Clark,
and Thomas were used to obtain the kinetic
model in column and to estimate breakthrough
curves. The BDST model is capable of predicting
the breakthrough curves for other experimental
conditions as well.
Fig. 3. Breakthrough curves: the effect of different bed
depths on MB adsorption (C0 = 50 mg ml!1, v = 8 ml
min!1).
4.3. Effect of different bed depths on breakthrough curve
The breakthrough curves at different bed
depths are shown in Fig. 3 where it is seen that as
the bed height (adsorbent mass) increases, MB
had more time to contact leaf powder that
resulted in higher removal efficiency of MB
molecules in column. So the higher bed column
resulted in a decrease in the effluent concentration at the same service time. The slope of the
breakthrough curve decreased with increasing bed
height, which resulted in a broadened mass
transfer zone. High uptake was observed at the
highest bed height. This was due to an increase in
the surface area of adsorbent, which provided
more binding sites for adsorption [36].
The adsorption data were evaluated and the
MB uptakes and removal percents with respect to
flow rate, influent MB concentration and bed
depth are presented in Table 1. From Table 1, the
value of Y decreased with increasing flow rate.
Although the value of qe increased with increasing influent MB concentration, the value of Y
showed an opposite trend. But with increasing
bed depth, both qe and Y increased.
5.1. Thomas model
The column data were fitted to the Thomas
model to determine the Thomas rate constant (kTh)
and maximum solid-phase concentration (qe). The
determined coefficients (R2), relative constants
were obtained using nonlinear regression. The
results and values of SS (less than 0.004) are also
listed in Table 1 where values of R2 range from
0.964 to 0.991. So the correlation of Ct/C0 and t
according to Eq. (5) is significant.
It is shown in Table 1 that as the influent
concentration increased, the value of qe increased
but the value of kTh decreased. The reason was
that the driving force for adsorption is the
concentration difference between the dye on the
adsorbent and the dye in the solution [28,37].
Thus the high driving force due to the higher MB
concentration resulted in better column performance. With flow rate increasing, the value of qe
decreased but the value of kTh increased. As the
bed depth increased, the value of qe increased
significantly while the value of kTh decreased
significantly. So lower flow rate and higher
influent concentration would increase the adsorption of MB on the leaf powder column. A
comparison of values of qe obtained from calculation and experiment showed that they were
close for given experimental conditions.
291
R. Han et al. / Desalination 245 (2009) 284–297
Table 1
Parameters of Thomas model using nonlinear regression analysis and the equilibrium MB uptake (qe) and total removal
percentage (Y) for MB adsorption to leaf powder under different conditions
C0,
mg l!1
Q,
(ml min!1
Z,
cm
kTh(×103)
(ml min!1 mg!1)
qe
(mg g!1)
R2
SS
(×103)
Y, %
qe(exp),
(mg g!1)
30
50
100
50
50
50
50
8
8
8
5
12
8
8
15
15
15
15
15
10
30
75.7±2.3
69.4±3.0
63.0±4.0
52.0±1.7
90.8±5.3
114.0±5.0
65.0±2.0
135±4.17
141±6.04
149±10.7
143±4.50
132±7.93
125±5.48
146±4.68
0.986
0.976
0.964
0.993
0.968
0.983
0.991
1.42
2.26
2.86
0.668
3.47
1.84
1.10
53.2
50.6
50.1
58.7
46.4
44.9
60.7
130
145
152
134
136
131
152
The predicted curves at various experimental
conditions according to the Thomas model are
shown in Figs. 1–3, respectively. It was clear
from the figures that there was a good agreement
between the experimental points and predicted
normalized concentration. The Thomas model is
suitable for adsorption processes where the
external and internal diffusions will not be the
limiting step [28].
5.2. Adams–Bohart model
The Adams–Bohart adsorption model was
applied to experimental data for the description of
the initial part of the breakthrough curve. This
approach focused on the estimation of characteristic parameters such as maximum adsorption
capacity (N0) and kinetic constant (kAB) from the
Adams–Bohart model. For all breakthrough
curves, respective values of N0, kAB were calculated and are presented in Table 2, together with
the correlation coefficients (R2 >0.910) and
smaller SS (less than 0.015). From Table 2, the
values of N0 at all conditions have no significant
difference.
The values of kAB increased with both initial
MB concentration and flow rate increase, but it
decreased with and bed depth increase. This
showed that the overall system kinetics was
dominated by external mass transfer in the initial
part of adsorption in the column [28].
The predicted curves from the Adams–Bohart
model were compared with the corresponding
experimental data at different experimental
conditions (shown in Figs. 1–3). There is a good
agreement between the experimental data and
predicted values. This suggests that the Adams–
Bohart model is valid for the relative concentration region up to 0.5. Large discrepancies can
be found between the experimental and predicted
curves above this value.
5.3. Yoon–Nelson model
A simple theoretical model developed by
Yoon–Nelson was applied to investigate the
breakthrough behavior of MB on leaf powder. So
the values of kYN (a rate constant) and τ (the time
required for 50% MB breakthrough) could be
obtained. The values of kYN and τ are listed in
Table 3. As seen in the table, the rate constant kYN
increased and the 50% breakthrough time τ
decreased with both increasing flow rate and MB
influent concentration. With the bed volume
increasing, the values of τ increased while the
values of kYN decreased. The data in Table 3 also
indicate that values of τ are similar to the experimental results, except the initial dye concen-
292
R. Han et al. / Desalination 245 (2009) 284–297
Table 2
Adams–Bohart parameters at different conditions using nonlinear regression analysis (up to 50%)
C0, mg l!1
Q, ml min!1
Z, cm
kAB (×105), l mg!1 min!1
N0 (×10!3), mg l!1
R2
SS (×103)
30
50
100
50
50
50
50
8
8
8
5
12
8
8
15
15
15
15
15
10
30
5.13±0.40
6.78±0.38
6.92±0.84
4.64±0.24
10.20±0.94
12.18±0.58
5.70±0.20
22.1±1.0
19.4±0.7
19.7±1.6
19.5±0.6
17.2±1.0
19.4±0.6
19.4±0.5
0.915
0.956
0.925
0.969
0.914
0.980
0.988
2.12
0. 87
2.43
1.20
2.50
0.45
0.29
Table 3
Yoon–Nelson parameters at different conditions using nonlinear regression analysis
C0, mg l!1
Q, ml min!1 Z, cm
kYN (×105), l min!1
τ, min
R2
SS (×103)
τexp, min
30
50
100
50
50
50
50
8
8
8
5
12
8
8
429±10
176±10
439±20
212±7
334±9
395±10
207±7
1318±16
492±51
326±12
1038±23
396±12
456±12
1344±28
0.994
0.932
0.988
0.980
0.988
0.991
0.975
21.4
27.6
9.74
4.44
7.91
8.17
10.1
1180
648
335
1060
385
450
1410
15
15
15
15
15
10
30
trations of 30 mg l!1 and 50 mg l!1 at Q = 8 ml
min!1 and Z = 15 cm.
The comparison of the experimental points
and predicted curves according to the Yoon–
Nelson model are also shown in Figs. 1–3 at
different experimental conditions. The experimental breakthrough curves were not close to
those predicted by the Yoon–Nelson model. So
the correlation between the experimental and
predicted values using the Yoon–Nelson model
deviated significantly.
5.4. Clark model
In a previous study, it was found that the
Freund-lich model was approximately valid for
the adsorption of dye on leaf powder in batch
adsorption [17], so the Freundlich constant 1/n
(0.427) obtained in a batch experiment was used
to calculate the parameters in the Clark model.
The values of A and r in the Clark model were
determined using Eq. (8) by nonlinear regression
analysis and are shown in Table 4.
As seen in Table 4, as both flow rate and
influent dye concentration increased, the values
of r increased. Plotting Ct/C0 against t according
to Eq. (8) also gave the breakthrough curves
predicted by the Clark model (also shown in Figs.
1–3). From the experimental results and data
regression, the model proposed by the Clark
model provided good correlation on the effects of
influent MB concentration, flow rate and bed
depth.
5.5. BDST model
The adsorption capacity (N0N) and rate
constant (Ka) can be obtained through the BDST
293
R. Han et al. / Desalination 245 (2009) 284–297
Table 4
Parameters of the Clark model at different conditions using nonlinear regression analysis
C0, mg l!1
Q, ml min!1
Z, cm
A
r (×105), min!1
R2
SS (×103)
30
50
100
50
50
50
50
8
8
8
5
12
8
8
15
15
15
15
15
10
30
31.72±3.56
24.68±3.39
22.52±4.72
50.12±5.47
15.09±2.29
38.85±6.48
332.5±70.6
270±10
388±19
715±52
316±10
524±33
640±32
367±14
0.984
0.969
0.959
0.986
0.945
0.977
0.986
1.52
2.74
3.51
1.09
4.48
2.63
1.52
Table 5
Calculated constants of the BDST model for the adsorption of MB (C0 = 50 mg l!1, Q = 8 ml min!1)
Ct /C0
a, min cm!1
b, min
Ka (×104), l mg!1 min!1
N0N (×10!3), mg l!1
R2
0.2
0.4
0.6
40.5±7.0
47.8±4.6
51.8±0.6
196.0±141
119.0±94
!15.2±11.8
1.41
6.80
5.33
14.2±2.4
16.9±1.6
18.3±0.2
0.990
0.996
0.999
Table 6
Predicted breakthrough time based on the BDST constants for a new flow rate or new influent concentration (Z=15 cm)
Ct /C0
QN = 5 ml min!1, C0 = 50 mg ml l!1
0.2
0.4
0.6
QN = 12 ml min!1, C0 = 50 mg ml l!1
0.2
0.4
0.6
Q = 8 ml min!1, C0N = 30 mg ml l!1
0.2
0.4
0.6
Q = 8 ml min!1, C0N = 100 mg ml l!1
0.2
0.4
0.6
Note: E 
tc  te
 100%
te
aN, min cm!1
bN, min
tc, min
te, min
E, %
64.8
76.5
82.8
196
119
!11
776
1028
1254
605
920
1230
28.3
11.7
1.95
27.0
31.9
34.5
196
119
!11
209
359
530
150
300
505
39.3
19.7
4.95
67.5
79.7
86.3
326.7
198.0
!25.4
685
997
1320
435
915
1342
57.5
8.96
!1.6
20.3
23.9
25.9
98.0
59.5
!7.6
206
299
396
160
275
395
28.8
8.73
0.25
294
R. Han et al. / Desalination 245 (2009) 284–297
model. From the lines of t–Z at values of Ct/C0
0.2, 0.4 and 0.6 (figure not shown), the related
constants of BDST according to the slopes and
intercepts of lines are listed in Table 5,
respectively.
From Table 5, as the value of Ct/C0 increased,
the adsorption capacity of the bed per unit bed
volume, N0, increased. From the values of R2, the
validity of the BDST model for the present system is demonstrated. The BDST model constants
can be helpful to scale-up the process for other
flow rates and concentration without further
experimental runs.
The BDST equation obtained at a flow rate of
8 ml min!1 and influent concentration 50 mg l!1
was used to predict the adsorbent performance at
other flow rates (5 ml min!1 and 12 ml min!1) and
influent concentration (30 mg l!1 and 100 mg l!1),
respectively. The predicted time (tc) and experimental time (te) are shown in Table 6. The percent
values of error (E) between theory (tc) and
experiment (te) were also listed in Table 6. From
Table 6, values of E were lower and good
prediction has been found for the case of changed
feed concentration and flow rate at Ct/C0 = 0.4
and 0.6. Thus, model and the constants evaluated
can be used to design columns over a range of
feasible flow rates and concentrations at Ct/C0 =
0.4, 0.6, respectively. These results indicate that
the equation can be used to predict adsorption
performance at other operation conditions for
adsorption of MB onto leaf powder.
5.6. Comparison of Thomas, Adams–Bohart,
Yoon–Nelson and Clark models
Among the Thomas, Yoon–Nelson and Clark
models, the value of error (SS) for Thomas was
lowest for a given experimental condition, while
it was the largest for Yoon–Nelson. Thus, it was
concluded that the Thomas model was better in
describing the process of MB adsorption in a leaf
powder column. In a comparison of values of SS
and the predicted curves and experimental data,
both the Thomas and Clark models can be used to
describe the behavior of the adsorption process,
but the Yoon–Nelson model did not give better
results.
Regarding the Adams–Bohart model, it is only
used to predict the initial region of breakthrough
curve (Ct /C0 less than 0.15). But in this paper, the
predicted range is up to 0.5, so the value of R2
was slightly lower than that from Thomas under
the same experimental conditions. Comparing the
values of SS from Thomas and Adams–Bohart
models in Tables 1 and 2, the values of SS from
the Thomas model were lower at the conditions
of Q = 8 ml min!1, C0 = 30 mg l!1, Z=15 cm, Q =
5 ml min!1, C0 = 50 mg l!1, Z = 15 cm, and Q =
12 ml min!1, C0 = 50 mg l!1, Z=15 cm, respectively. Under other conditions, they were larger
than those from the Adams–Bohart model.
6. Conclusions
On the basis of the experimental results of this
investigation, the following conclusions can be
drawn:
C Leaf powder as an adsorbent can be used in
wastewater treatment to remove MB from
solution.
C The adsorption of MB was dependent on the
flow rate, influent MB concentration and bed
depth.
C The initial region of breakthrough curve was
described by the Adams–Bohart model well at
all experimental conditions studied while the
transient stage or working stage of the breakthrough curve was described well by the
Thomas and Clark models.
C The BDST model adequately described the
adsorption of MB onto leaf powder in column
mode.
7. Symbols
A
— Clark constants
R. Han et al. / Desalination 245 (2009) 284–297
C0
Cad
Ct
F
Ka
—
—
—
—
—
kAB
—
kTh
—
kYN
—
n
N0
—
—
N0N
—
qe
—
qe(exp)
—
qtotal
—
Q
r
t
ttotal
Veff
Wtotal
—
—
—
—
—
—
x
—
Y
Z
—
—
Influent MB concentration, mg l!1
Adsorbed MB concentration, mg l!1
Effluent MB concentration, mg l!1
Linear velocity, cm min!1
Rate constant in BDST model,
l mg!1 min!1
Kinetic constant of Adams–Bohart
model, l mg!1 min!1
Rate constant of Thomas model,
ml min!1 mg!1
Rate constant of Yoon–Nelson
model, min!1
Freundlich parameter
Saturation concentration from
Adams–Bohart model, mg l!1
Adsorption capacity from BDST
model, mg l!1
Equilibrium MB uptake per g of
adsorbent from Thomas model,
mg g!1
Weight of MB adsorbed per g of
adsorbent from experiment, mg g!1
Total weight of MB adsorbed by
adsorbent in column, mg
Volumetric flow rate, ml min!1
Clark model constants
Effluent time, min
Total flow time, min
Effluent volume, ml
Total amount of MB sent to column,
mg
Total dry weight of leaf powder in
column, g
Total removal percent of MB, %
Bed depth of column, cm
Greek
τ
τexp
— Time required for 50% adsorbate
breakthrough from Yoon–Nelson
model, min
— Time required for 50% adsorbate
breakthrough from experiments, min
295
Acknowledgement
The authors express their sincere gratitude to
Henan Science and Technology Department and
the Education Department of Henan Province in
China for the financial support of this study. This
work was also supported by forty-second Post-doctor
Foundation of China (No. 20070420811).
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