Experimental-investigation-of-bioethanol-liquid-phase-_2016_Journal-of-Advan

Journal of Advanced Research (2016) 7, 435–444

Cairo University

Journal of Advanced Research

ORIGINAL ARTICLE

Experimental investigation of bioethanol liquid
phase dehydration using natural clinoptilolite
Samira Karimi a, Barat Ghobadian a,*, Mohammad-Reza Omidkhah b,
Jafar Towfighi b, Mohammad Tavakkoli Yaraki c,d
a

Biosystem Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran
Chemical Engineering Department, Tarbiat Modares University, Tehran 14115-143, Iran
c
Department of Chemical and Biomolecular Engineering, National University of Singapore (NUS), Singapore 117585, Singapore1
d
Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15875-4413, Iran
b

G R A P H I C A L A B S T R A C T

A R T I C L E

I N F O

Article history:
Received 18 January 2016
Received in revised form 25 February

2016

A B S T R A C T
An experimental study of bioethanol adsorption on natural Iranian clinoptilolite was carried
out. Dynamic breakthrough curves were used to investigate the best adsorption conditions in
bioethanol liquid phase. A laboratory setup was designed and fabricated for this purpose. In
order to find the best operating conditions, the effect of liquid pressure, temperature and flow

* Corresponding author. Tel.: +98 2148292308.
E-mail address: (B. Ghobadian).
1
Current affiliation.
Peer review under responsibility of Cairo University.

Production and hosting by Elsevier
/>2090-1232 Ó 2016 Production and hosting by Elsevier B.V. on behalf of Cairo University.


436
Accepted 29 February 2016
Available online 5 March 2016
Keywords:
Adsorption
Clinoptilolite
Dehydration
Bioethanol
Isotherm
Box–Behnken model

S. Karimi et al.

rate on breakthrough curves and consequently, maximum ethanol uptake by adsorbent were
studied. The effects of different variables on final bioethanol concentration were investigated
using Response Surface Methodology (RSM). The results showed that by working at optimum
condition, feed with 96% (v/v) initial ethanol concentration could be purified up to 99.9% (v/v).
In addition, the process was modeled using Box–Behnken model and optimum operational conditions to reach 99.9% for final ethanol concentration were found equal to 10.7 °C, 4.9 bar and
8 mL/min for liquid temperature, pressure and flow rate, respectively. Therefore, the selected
natural Iranian clinoptilolite was found to be a promising adsorbent material for bioethanol
dehydration process.
Ó 2016 Production and hosting by Elsevier B.V. on behalf of Cairo University.

Introduction
Fuel grade bioethanol is one of the widely used alternative for
fossil fuels or gasoline additive [1,2]. In bioethanol–gasoline
mixture, the presence of even a very small amount of water
in bioethanol is unfavorable and leads to a two phase mixture
[3–5]. The bioethanol dehydration is a process to eliminate
water from bioethanol–water mixture up to 99.6% (V/V).
There are several methods including azeotropic distillation
[6–8], extractive distillation [9,10], pervaporation with membranes [11–13] and adsorption using adsorbents [3,5,14–18],
that are being used for water elimination to overcome the

Fig. 1 (a) Adsorption/desorption of N2 gas on Iranian Clinoptilolite sample at T = 77 K. (b) Pore size distribution based on
BJH method.

ethanol–gasoline mixing problem. The azeotropic distillation
and extractive distillation are too expensive process [9,19]. Literatures show that extractive distillation is more complex due
to the design and process application and articles on energy
consumption and cost, during recent years confirm that this
method has high performance but needs further studies on
energy consumption [20]. Conventional extractive distillation

is energy consumption process because of using reboilers and
condensors. Different refine processes were used to improve
conventional extractive distillation such as heat-pumpassisted extractive distillation for bioethanol purification [21],
Ethanol dehydration via azeotropic distillation with gasoline
fraction mixtures as entrainers [20] and Control comparison
of conventional and thermally coupled ternary extractive distillation processes [22]. In addition, although the pervaporation is a new generation in separation technology, it has
industrial installation limitations. The adsorption by selective
porous adsorbents is a common high performance method in
bioethanol dehydration. Many studies have focused on different types of water adsorbents including biobased adsorbents
namely natural corncobs, natural and activated palm stone
and oak [3,23,24], Calcium Carbide [25], calcium chloride
and lime [26], silica gel [27], cellulose and lignocellulose based
(bleached wood pulp, oak sawdust and kenaf core) [14,28],
Aluminas and c-alumina [29], Starch-Based Adsorbents
[5,24,30] and different types of molecular sieves especially the
zeolites [16,31–34]. Finding appropriate, effective and cheap
adsorbent material is a way to reduce the final bioethanol production costs. The zeolites with porous structures and selectivity properties can let water molecules to penetrate inside pore
volumes of hydrophilic adsorbents and separate ethanol–water
mixture. The natural zeolites and clays such as clinoptilolite
[17,35–37], chabazite and phillipsite [38] are plentiful material
in nature with hydrophilic properties suitable for ethanol–water separation. For instance, it has been shown that the clinoptilolite water adsorption capacity is more than 50% of water
adsorption capacity of 3A zeolite [39]. In various previous
studies, the parameters influencing ethanol–water separation
such as temperature [39], system pressure [40], adsorption heat
[41] and particle size [39] have been investigated. As a lot of
industrial separation processes based on adsorption mechanism are carried out in liquid phase [42], using mesoporous
adsorbents such as clinoptilolite is highly recommended to
adsorb the big molecules in liquid phase [43]. Although there
are some studies on using clinoptilolite as a adsorbent for
purification of ethanol in liquid phase [35,37], the effect of

operational conditions has not well understood. So, we aim
to use of Iranian clinoptilolite in both batch and continuous


Investigation of bioethanol dehydration using clinoptilolite
Table 1

437

Porosity characterization of Iranian clinoptilolite.
BET method

BJH method

aBET (m2/g)

Vm (cm3/g)

Vt (cm3/g)

Dmean (nm)

aBET (m2/g)

Vm (cm3/g)

Dmean (nm)

14.394

3.3071

0.094766

26.334

15.4

0.094327

26.334

adsorption processes to separate the water contents from
water/ethanol mixture which is usual product of biofuel production. In this research work, the Iranian natural clinoptilolite is presented as a cheap water adsorbent media to
separate the water from hydrous bioethanol in a fixed bed
setup. Furthermore, the optimum operational conditions have
been found both experimentally and theoretically.
Material and methods
The deionised water and ethanol were purchased from Bidestan Co. (Qazvin-Iran). The natural clinoptilolite used in this
research work was purchased from Afrazand Co. (East
Semnan–Iran). The chemical analysis showed the high content
of K+ and Na+. The zeolite was approximately 65 wt.% pure
in clinoptilolite. The composition of the material based on
X-ray fluorescence (XRF) (Model: Philips PW 2404) analysis
was 71.159 wt.% SiO2, 11.335 wt.% Al2O3, 0.936 wt.% Fe2O3,
0.807 wt.% CaO, 0.478 wt.% MgO, 3.064 wt.% Na2O,
4.48 wt.% K2O, 0.164 wt.% TiO2 and 0.847 wt.% SO3. Loss
of ignition (LOI) is 6.23. The bulk density was calculated and
it was found 820 kg mÀ3 (1–2 mm particle size). The silica modulus (molar ratio) of the sample was g = SiO2/Al2O3 = 6.26.
The pore structure and surface area of Iranian clinoptilolite

were characterized by N2 adsorption–desorption isotherm at
77 K which has been illustrated in Fig. 1a. Nitrogen adsorption was carried out using Belsorp mini II (Bel Japan). Before

the experiments, the sample was dried to be degassed at 25 °C
for 5 h and vacuum. The adsorption isotherm has hysteresis
loop along with a relative pressure from 0.4 to 0.99. This isotherm is type I, which is typical property for mesoporous materials [44]. As it has been presented in Table 1, the BET surface
area (aBET), total pore volume (Vt), (from the last point of isotherm at a relative pressure of 0.99), micropore volume (Vm)
and mean pore size have been calculated using Brunauer–
Emmett–Teller (BET) method. The Barrett–Joyner–Halenda
(BJH) pore size distribution of the Iranian clinoptilolite sample
was calculated based on the adsorption data. As it can be seen
in Fig. 1b, the majority of pores have the radius size of less
than 10 nm with mean pore diameter of 26.47 nm based on
BJH method which has good agreement with what has been
calculated from BET method [45] (See Table 1).
A stainless steel column with 4 cm diameter and 55 cm
height was designed and fabricated to regenerate samples using
high temperature and vacuum pressure. Regeneration column
consists of three heating elements with a heating rate of
approximately 20 °C/min and indicators. Three thermocouples
provide the required feedback for an on/off temperature controlling system. Vacuum gage is used for indicating the column
vacuum pressure. A cooling setup – condensers and cooling
water circulator – collects regeneration liquid. Afterward,
regenerated zeolite is cooled to ambient temperature in desiccator. Regeneration operation is completed in 0.6 bar vacuum
pressure and 300 °C for 50 min.
Static adsorption isotherms (batch)
Water removal from water/ethanol mixture by natural clinoptilolite in batch condition was examined for different initial
concentrations of water. The experiments were carried out at
ambient temperature (20 °C) and static conditions in a
thermo-stated laboratory scale adsorption vessel, with an

Scheme 1 Schematic of experimental apparatus used for
bioethanol dehydration. (1. Initial Container, 2. Liquid Pump, 3.
Adsorption Column, 4. Cooling Circulator, 5. Temperature
Sensor, 6. Pressure Sensor, 7. Flowmeter and 8. Final Container.)

Fig. 2 Raman spectrum of natural clinoptilolite before and after
water adsorption.


438

S. Karimi et al.

Table 2

Langmuir and Freundlich parameters in different temperatures.

Temperature (K)

283
298
313
328
343

Uptake (kmol/kg)

0.00746
0.00701

0.00689
0.00662
0.00628

Fig. 3 The
temperatures.

Extended

Langmuir

Langmuir

Freundlich

kl (m3/kg)

qm (kmol/kg)

R2

kf (kmol/kg)

1/n

R2

0.0179
0.0156
0.0125

0.0113
0.0106

0.00746
0.00701
0.00689
0.00662
0.00629

0.986
0.986
0.991
0.971
0.974

0.00066
0.00051
0.00041
0.00033
0.000264

0.412
0.442
0.47
0.498
0.529

0.973
0.984
0.987

0.979
0.992

isotherms

in

different

initial liquid weight of 100 g. The ethanol–water mixture at different concentrations was applied as adsorptive and 60 g zeolite and the contact time of 24 h was selected for
experiments. The water concentration in feed was varied
between 50 and 363 kg mÀ3 (kg of water in feed to feed volume). The Langmuir and Freundlich isotherm models were
used for description of the adsorption process (Eqs. (1) and
(2)):
qe ¼

kl Á Ce Á qm
1 þ kl Á Ce

qe ¼ kf C1=n
e

ð1Þ
ð2Þ

where qe is the amount of solute adsorbed per unit weight of
solid (kmol/kg), Ce is equilibrium concentration of water
remaining in solution (kg/m3), qm is maximum adsorption
capacity (kmol/kg) and kf and kl are Freundlich and Langmuir
constants (m3/kg), respectively. 1/n is a measure of intensity of

adsorption. The higher the 1/n value, the more favorable is the
adsorption. qe is calculated from equation as follows (Eq. (3)):
qeðkmol=kgÞ ¼

C0À Ce
Czeo

ð3Þ

Because of using Temperature Swing Adsorption process
(TSA) for adsorbents regeneration (high temperature and
low pressure), it was necessary to find temperature dependent
isotherm. An Extended Langmuir isotherm was used to find
adsorption dependence with temperature. For this, qe and Ce
values in different temperature between 10 and 70 °C were
found and temperature dependent equation was expressed as
follows:

Fig. 4 Experimental data (a) and Extended Langmuir isotherms
(b) in different temperatures.

qe ¼

Àk3 Á

Ce
ÀT Á
1 þ k2 exp kT3 Ce
k1 k2 exp

ð4Þ

where k1 is qm (kmol/kg), k2 is kel (m3/kg) and k3 is DH/R (°K).
DH and R are enthalpy changes and gas constant, respectively.
Dynamic adsorption (continuous)
An apparatus with packed bed adsorption column was
designed for dynamic standard experiments. The schematic
of the designed apparatus is shown in Scheme 1. The column
was designed based on the Yamamoto’s set-up dimensions
for liquid phase adsorption [16]. The retention time in the


Investigation of bioethanol dehydration using clinoptilolite

Fig. 5 Breakthrough curves at (a) different flow rates and
constant T = 288 K and P = 3 bar, (b) different pressures and
constant F = 14 mL/min T = 293 K, and (c) different temperatures and constant F = 10 mL/min and P = 1 bar.

439
size and its length was at least 100 times as much as the particle
size [40]. A jacket of cooling water with 4 cm thickness was
connected to circulator and surrounded the main column to
fulfill the isotherm conditions. Two pressure and temperature
sensors were located on both inlet and outlet of the column
for monitoring pressure drop and changes in system temperature. A copper coil was used in initial bioethanol container to
control the initial bioethanol temperature. Scheme 1 illustrates
the experimental setup.
Two pressure sensors in bottom and top of the column
show the pressure and pressure drop. Using circulator and
temperature sensors, initial and final bioethanol temperatures

were controlled. The bioethanol concentration in initial container was constant and it was 96%. The final bioethanol concentration leaves the top valve and is shown by the Portable
Density Meter of Anton DMA 35. A water flow meter (calibrated for ethanol v/v concentration) was used for adjustment
of bioethanol flow rate and it is one of the main experimental
parameters. Before carrying out the experiments, the adsorbent samples were treated by thermal regeneration for elimination of water from the adsorbent pores. The procedure was
completed by putting samples in furnace for 2 h at 300 °C
and then it was cooled to ambient temperature in desiccator.
Natural zeolites with the HEU (Heulandite) framework are
divided into two distinct classes based on Si/Al ratio. Those
with Si/Al of less than 4 are known as heulandite and those
with Si/Al greater than 4 are known as clinoptilolite or
silica-rich heulandite. The key difference in these materials is
those with Si/Al of less than 4 are not thermally stable to calcination above 350 °C [46] and High silica Clinoptilolite is
thermally stable to temperatures in excess of 500 °C [47].
The initial tank was filled with 96% (v/v) ethanol–water
mixture. The bioethanol entered from the bottom of the column which was packed with a mass of natural zeolite. When
the mixture leaved the column, the pressure, temperature and
flow rate were controlled. The data collected and concentration were obtained every minute by Density Meter. All of
the experiments were organized by RSM to find the optimal
operational conditions. This was done by Design-Expert 7
software.
Results and discussion
Static isotherm models

set-up was determined to be 21.2 min and for this research
work this value was assumed as 25 min. According to the maximum flow rate of 14 mL/min, a stainless steel column with
4 cm diameter and 40 cm height was designed and fabricated.
Its dimension ensured good flow distribution since the bed
internal diameter was at least 10 times as much as the particle

Table 3

The three level factors used in the Box–Behnken design.

Coded factors

A
B
C

To determine the model to be used to describe the adsorption
for an adsorbent–adsorbate isotherm experiments were carried
out. Initial concentration was varied from 50 to 363 kg mÀ3 for
all the experiments at different temperature. Raman spectroscopy test was used to determine water–ethanol adsorption

Corresponding parameters

Pressure (bar)
Flow rate (mL/min)
Temperature (°C)

Coded levels
À1
Corresponding values

0

1

1
6

10

3
10
15

5
14
20


440

S. Karimi et al.
Table 4

The experimental design used in this research work.

Pattern

A (pressure)

B (flow)

C (temperature)

Final concentration

000
000

000
++0
+À0
À0+
À+0
0++
000
ÀÀ0
+0À
0ÀÀ
À0À
0À+
000
+0+
0+À

3
3
3
5
5
1
1
3
3
1
5
3
1
3

3
5
3

10
10
10
14
6
10
14
14
10
6
10
6
10
6
10
10
14

15
15
15
15
15
20
15
20

15
15
10
10
10
20
15
20
10

99.3
99.2
99.3
99.3
99.7
98.4
98.7
99.1
99.4
98.9
99.9
99.6
98.9
99.0
99.3
99.5
99.3

Fig. 6 The different variables to response diagrams in linear model, (a) effects of A and C on response, (b) effects of B and C on
response, and (c) effects of B and A on response.

on natural clinoptilolite. The Raman spectra were collected at
the Spectroscopy Laboratory, Atomic and Molecular Group,
Physics Department, Tarbiat Modares University by using a
Thermo Nicolet Almega dispersive micro-Raman scattering
spectrometer. Results showed that the main peak in water

Raman spectrum is for stretching O–H bond around 3000–
3400 cmÀ1 and it is obvious in Fig. 2 that there is a strong peak
in this area after the treatment of zeolite by mixture of water
and ethanol; hence, it could be concluded that only water is
adsorbed and the amount of adsorbed ethanol is negligible.


Investigation of bioethanol dehydration using clinoptilolite
Table 5

441

Analysis of concentration variance for ANOVA table by linear model.

Source

Sum of squares

df

Mean square

F value

P-value Prob > F

Model
A-pressure
B-flow rate
C-temperature
Residual
Lack of fit
Error

1.84
1.45
0.080
0.32
0.22
0.20
0.020

3
1
1
1
13
9
4

0.61
1.45
0.080

0.32
0.017
0.022
5.0EÀ3

36.39
85.50
4.73
18.93

<0.0001
<0.0001
0.0486
0.0008

4.44

0.0826

Hence, Extended Langmuir isotherm was selected as a temperature dependent isotherm equation to describe the temperature
behavior in adsorption process that could also be used for any
future process simulation or scale-up and design the industrial
plant. Eq. (5) shows the Extended Langmuir model that
describes the static data. Fig. 3 shows the experimental data
and Extended Langmuir model at different temperatures. Furthermore, Fig. 4a and b shows the amount of uptake as a function of temperature and initial concentration for both
experimental data and Extended Langmuir model, respectively
that are well matched together.
À
Á
Ce

0:0076 Â 0:0001244 exp 1386:5
À1386:5ÁT
qe ¼
ð5Þ
1 þ 0:0001244 exp T Ce
Breakthrough curves based on dynamic study (Continuous)
Fig. 7

The predicted vs. actual data in linear model.

By applying the linearization of both Langmuir and Freundlich models, it has been indicated that data could be well
described by both models (Figs. S1–S5 in Supplementary information data). Linear form of experimental data for Langmuir
and Freundlich isotherms at different temperatures was shown
in Supplementary details (Figs. S6–S15). Table 2 shows Langmuir and Freundlich isotherm parameters in different temperatures varied between 283 and 343 K. It is obvious that
Langmuir isotherm has a better correlation than Freundlich
at low temperatures near ambient. Fortunately, working at
low temperature is desirable for industrial adsorption process
and based on data presented in Table 2, it is obvious that
decreasing in temperature causes increase in final uptake.

In order to understand the effect of parameters including flow
rate, pressure and temperature on the breakthrough curves
and finding the optimum operating condition, the variation
of each parameter was studied. The breakthrough curves in
Fig. 5 show the effects of pressure, temperature and flow rate
on breakthrough point. Fig. 5a shows that most of the mass
transfer and adsorption takes place at the moment that the
fluid first comes in contact with the inlet of the bed and by
increasing the flow rate, there is a decrease in time required
for saturation of adsorbent and breakthrough point shifts to

the right side by increasing the flow rate. Increasing pressure
from 1 to 5 bar sat defined temperature (T = 293 K) also
enhances the saturation time of adsorbent while there is a
slight increase in adsorption capacity (Fig. 5b). Reducing the
temperature changes breakthrough point and increases

Table 6

ANOVA in quadratic model for concentration.

Source

Sum of squares

df

Mean square

F value

P-value Prob > F

Model
A-pressure
B-flow rate
C-temperature
AB
AC
BC
A2

B2
C2
Residual
Lack of fit
Error

1.98
1.45
0.080
0.32
0.010
0.010
0.040
0.066
2.632EÀ3
2.632EÀ3
0.085
0.065
0.020

9
1
1
1
1
1
1
1
1
1

7
3
4

0.22
1.45
0.080
0.32
0.010
0.010
0.040
0.066
2.632EÀ3
2.632EÀ3
0.012
0.022
5.0EÀ3

18.11
119.00
6.59
26.35
0.82
0.82
3.29
5.42
0.22
0.22

0.0005

<0.0001
0.0372
0.0013
0.3943
0.3943
0.1124
0.0528
0.6557
0.6557

4.33

0.0953


442
adsorption, the breakthrough point shifts to the right side and
the maximum adsorption is obtained.
To find the total adsorption capacity (q) from breakthrough curve, the area above the curve divided into the total
area (above and under the curve) results in adsorption percentage [16]. Hence, according to Eq. (6), total adsorption capacity
could be calculated from feed loading and adsorption
percentage.

Z tb 
Fqf
Ct
q¼
C0
dt
ð6Þ

1À
w
C0
0
The total adsorption capacity q in Fig. 5a for flow rates 6,
10 and 14 mL/min (in 3 bar pressure and 288 K) was obtained
1.75, 1.52 and 1.26 mol/kgzeo, respectively. Also in Fig. 5b, for
column pressures 1, 3 and 5 bar (14 mL/min and 293 K), q values were calculated 1.5, 1.74 and 1.97 mol/kgzeo respectively.
According to Fig. 5c, for temperatures 283, 288 and 293 K
and 10 mL/min flow rate and 1 bar pressure, q values obtained
were 1.13, 1.31 and 1.44 mol/kgzeo, respectively.
The response surface methodology
The response surface methodology (RSM) is a collection of
statistical and mathematical techniques for obtaining empirical
models. Understanding the effect of parameters and optimization is the common advantage of the RSM application. Table 3

S. Karimi et al.
shows the coded level for each parameter. For the three
parameters, the Box–Behnken Method was used. In this
method, each factor or independent variable is placed at one
of the three equally spaced values, usually coded as À1, 0
and +1. In this research work, the temperature, pressure
and flow rate are independent variables and the final bioethanol concentration is the responses. Table 4 shows the experimental design used in this study.
The two linear and Quadratic models were used for the concentration modeling in Box–Behnken method. Fig. 6 illustrates
the different variables to response diagrams in linear model for
concentration response. An analysis of variance for linear
model is shown in Table 5. The Model F-value of 36.39 implies
the model significant. The values of Prob > F less than 0.05
indicate that the model terms are significant and the parameter
has a significant effect on the response. In this case, A (pressure), B (flowrate) and C (temperature) are significant model

terms. The values greater than 0.10 indicate that the model
terms are not significant. The prediction expression is given
as follows:
Concentration ¼ 99:43 þ 0:2125 Â P À 0:025 Â F À 0:04 Â T
ð7Þ
According to Fig. 6 increasing the pressure causes an
increase in maximum final bioethanol concentration. For the
low temperatures, the effect of pressure on maximum

Fig. 8 The independent variables to response diagrams in quadratic model, (a) effects of A and C on response, (b) effects of B and C on
response and (c) effects of B and A on response.


Investigation of bioethanol dehydration using clinoptilolite
bioethanol final concentration was intensified. It can be seen
that there is a positive relation between the pressure and maximum bioethanol final concentration for a given temperature.
Decreasing the temperature causes the maximum bioethanol
concentration to increase. Reducing the temperature in liquid
affects the adsorbent surface and makes water molecules enter
the adsorbent pores. The temperature control in liquid phase
adsorption process is more effective and it is simpler than
the other two operational parameters and the obtained results
show that the temperature has more influence on bioethanol
final concentration. The flow rate decrease causes an increase
in maximum bioethanol concentration. Reducing the flow rate
makes more retention time and hence creates a better contact
between the adsorbent and solute.
Fig. 7 shows the relationship between the predicted and
actual data line. The R2 value of 0.89 indicated that the actual
and the predicted data had a relatively good correlation in linear model.

Analysis of variance for quadratic model is shown in
Table 6. The Model F-value of 18.11 implies that the model
is significant. The values of Prob > F less than 0.05 indicate
that the model terms are significant and have a significant
effect on the response. In this case, A (temperature), B (flowrate) and C (Temperature) are significant model terms. The
values greater than 0.1 indicate that the model terms are not
significant. The prediction expression in quadratic model is
given as follows (Eq. (8)):
Conc ¼ 99:637 þ 0:3875 Â P À 0:050 Â F À 0:0750 Â T
À 0:00625 Â PF þ 0:005 Â PT þ 0:005 Â FT
À 0:03125P2 À 0:00015F2 À 0:001T2

ð8Þ

Fig. 8 shows the independent variables to response diagrams in quadratic model. Fig. S16 illustrates the relationship
between the predicted and actual data line. The R2 value of
0.92 shows a relatively good correlation between predicted
and actual data. The optimization as a point of view of concentration shows that at 10.7 °C liquid temperature, 4.9 bar
pressure and 8 mL/min liquid flow rate, the best response
was 99.9% bioethanol final concentrations.
Conclusions
In this research work, Iranian natural clinoptilolite was used to
dehydrate hydrous ethanol. Results showed that in optimum
operating conditions, bioethanol final concentration can reach
to 99.9% and above. Static and dynamic studies were done and
adsorption isotherms were obtained and experimental data
were well described by Extended-Langmuir isotherm. The
effects of operating parameters such as temperature, pressure
and flow rate were investigated on final ethanol concentration
and the adsorption process was optimized. In Box–Behnken

analysis, the linear and quadratic models could be successfully
applied for description of dynamic process. Results showed
that the selected natural clinoptilolite could be used as a favorable adsorbent in bioethanol drying without any pretreatment
processes.
Conflict of Interests
The authors have declared no conflict of interests.

443
Compliance with Ethics Requirements
This article does not contain any studies with human or animal
subjects.

Acknowledgment
The authors express their sincere thanks to Iranian Fuel Conservation Company (IFCO) for the support during the course
of this research work.
Appendix A. Supplementary material
Supplementary data associated with this article can be found,
in the online version, at />02.009.
References
[1] Costa RC, Sodre´ JR. Hydrous ethanol vs. gasoline–ethanol
blend: engine performance and emissions. Fuel 2010;89
(2):287–93.
[2] Yu¨ksel F, Yu¨ksel B. The use of ethanol–gasoline blend as a fuel
in an SI engine. Renewable Energy 2004;29(7):1181–91.
[3] Al-Asheh S, Banat F, Al-Lagtah N. Separation of ethanol–water
mixtures using molecular sieves and biobased adsorbents. Chem
Eng Res Des 2004;82(7):855–64.
[4] Teo WK, Ti HC. Liquid phase dehydration of ethanol solutions
in a fixed bed of molecular sieves. Appl Biochem Biotechnol
1990;24(1):521–32.

[5] Wang Y, Gong C, Sun J, Gao H, Zheng S, Xu S. Separation of
ethanol/water azeotrope using compound starch-based
adsorbents. Bioresour Technol 2010;101(15):6170–6.
[6] Knapp JP, Doherty MF. Thermal integration of homogeneous
azeotropic distillation sequences. AIChE J 2004;36(7):969–84.
[7] Chianese A, Zinnamosca F. Ethanol dehydration by azeotropic
distillation with a mixed-solvent entrainer. Chem Eng J 1990;43
(2):59–65.
[8] Gomis V, Pedraza R, Frances O, Font A, Asensi JC.
Dehydration of ethanol using azeotropic distillation with
isooctane. Ind Eng Chem Res 2007;46(13):4572–6.
[9] Meirelles A, Weiss S, Herfurth H. Ethanol dehydration by
extractive distillation. J Chem Technol Biotechnol 1992;53
(2):181–8.
[10] Pinto R, Wolf-Maciel M, Lintomen L. Saline extractive
distillation process for ethanol purification. Comput Chem
Eng 2000;24(2):1689–94.
[11] Sano T, Yanagishita H, Kiyozumi Y, Mizukami F, Haraya K.
Separation of ethanol/water mixture by silicalite membrane on
pervaporation. J Membr Sci 1994;95(3):221–8.
[12] Sander U, Soukup P. Design and operation of a pervaporation
plant for ethanol dehydration. J Membr Sci 1988;36:463–75.
[13] Kanti P, Srigowri K, Madhuri J, Smitha B, Sridhar S.
Dehydration of ethanol through blend membranes of chitosan
and sodium alginate by pervaporation. Sep Purif Technol
2004;40(3):259–66.
[14] Benson TJ, George CE. Cellulose based adsorbent materials for
the dehydration of ethanol using thermal swing adsorption.
Adsorption 2005;11(1):697–701.
[15] Jeong J-S, Jeon H, Ko K-M, Chung B, Choi G-W. Production

of anhydrous ethanol using various PSA (Pressure Swing
Adsorption) processes in pilot plant. Renewable Energy
2012;42:41–5.


444
[16] Yamamoto T, Kim YH, Kim BC, Endo A, Thongprachan N,
Ohmori T. Adsorption characteristics of zeolites for dehydration
of ethanol: evaluation of diffusivity of water in porous structure.
Chem Eng J 2012;181:443–8.
[17] Ivanova E, Karsheva M. Ethanol vapours adsorption by natural
clynoptilolite. J Univ Chem Technol Metallurgy 2007;42
(4):391–8.
[18] Simo M, Brown CJ, Hlavacek V. Simulation of pressure swing
adsorption in fuel ethanol production process. Comput Chem
Eng 2008;32(7):1635–49.
[19] Van Hoof V, Van den Abeele L, Buekenhoudt A, Dotremont C,
Leysen R. Economic comparison between azeotropic distillation
and different hybrid systems combining distillation with
pervaporation for the dehydration of isopropanol. Sep Purif
Technol 2004;37(1):33–49.
[20] Gomis V, Pedraza R, Saquete MD, Font A, Garcı´ a-Cano J.
Ethanol dehydration via azeotropic distillation with gasoline
fractions as entrainers: a pilot-scale study of the manufacture of
an ethanol–hydrocarbon fuel blend. Fuel 2015;139:568–74.
[21] Luo H, Bildea CS, Kiss AA. Novel heat-pump-assisted
extractive distillation for bioethanol purification. Ind Eng
Chem Res 2015;54(7):2208–13.
[22] Luyben WL. Control comparison of conventional and thermally
coupled ternary extractive distillation processes. Chem Eng Res

Des 2016;106:253–62.
[23] Ranjbar Z, Tajallipour M, Niu CH, Dalai AK. Water removal
from ethanol vapor by adsorption on canola meal after protein
extraction. Ind Eng Chem Research 2013;52(40):14429–40.
[24] Beery KE, Ladisch MR. Adsorption of water from liquid-phase
ethanol–water mixtures at room temperature using starch-based
adsorbents. Ind Eng Chem Res 2001;40(9):2112–5.
[25] Ismael AA, Mohammad RR, Younis HA. Utilizing calcium
carbide for aqueous ethanol dehydration. Tikrit J Pure Sci
2008;13(1):176–8.
[26] Noyes WA. Preparation of absolute alcohol with calcium
chloride and lime. J Am Chem Soc 1923;45(3):857–62.
[27] Davis PJ, Jeffrey Brinker C, Smith DM, Assink RA. Pore
structure evolution in silica gel during aging/drying II. Effect of
pore fluids. J Non-Cryst Solids 1992;142:197–207.
[28] Sun N, Okoye C, Niu CH, Wang H. Adsorption of water and
ethanol by biomaterials. Int J Green Energy 2007;4(6):623–34.
[29] Golay S, Doepper R, Renken A. In-situ characterisation of the
surface intermediates for the ethanol dehydration reaction over
c-alumina under dynamic conditions. Appl Catal A 1998;172
(1):97–106.
[30] Lee JY, Westgate PJ, Ladisch MR. Water and ethanol sorption
phenomena on starch. AIChE J 1991;37(8):1187–95.
[31] Aguayo AT, Gayubo AG, Tarrı´ o AM, Atutxa A, Bilbao J.
Study of operating variables in the transformation of aqueous
ethanol into hydrocarbons on an HZSM-5 zeolite. J Chem
Technol Biotechnol 2002;77(2):211–6.

S. Karimi et al.
[32] De las Pozas C, Lopez-Cordero R, Gonzalez-Morales J,

Travieso N, Roque-Malherbe R. Effect of pore diameter and
acid strength in ethanol dehydration on molecular sieves. J Mol
Catal 1993;83(1):145–56.
[33] Lu L, Shao Q, Huang L, Lu X. Simulation of adsorption and
separation of ethanol–water mixture with zeolite and carbon
nanotube. Fluid Phase Equilib 2007;261(1):191–8.
[34] Zhang K, Lively RP, Noel JD, Dose ME, McCool BA, Chance
RR, et al. Adsorption of water and ethanol in MFI-type zeolites.
Langmuir 2012;28(23):8664–73.
[35] Pourreza K, Sharifnia S, Khatamian M, Rezaei B. Effect of
cation exchange on the performance of clinoptilolite mineral in
the ethanol dewatering process. Adsorpt Sci Technol 2011;29
(9):861–70.
[36] Ivanova E, Karsheva M. Ethanol vapours adsorption on natural
clinoptilolite – equilibrium experiments and modelling. Sep Purif
Technol 2010;73(3):429–31.
[37] Ivanova E, Damgaliev D, Kostova M. Adsorption separation of
ethanol–water liquid mixtures by natural clinoptilolite. J Univ
Chem Technol Metall 2009;44(3):267–74.
[38] Caputo D, Iucolano F, Pepe F, Colella C. Modeling of water
and ethanol adsorption data on a commercial zeolite-rich tuff
and prediction of the relevant binary isotherms. Microporous
Mesoporous Mater 2007;105(3):260–7.
[39] Tihmillioglu F, Ulku S. Use of clinoptilolite in ethanol
dehydration. Sep Sci Technol 1996;31(20):2855–65.
[40] Carmo MJ, Gubulin JC. Ethanol–water separation in the PSA
process. Adsorption 2002;8(3):235–48.
[41] Ben-Shebil SM. Effect of heat of adsorption on the adsorptive
drying of solvents at equilibrium in a packed bed of zeolite.
Chem Eng J 1999;74(3):197–204.

[42] Torres A, Neves S, Abreu J, Cavalcante Jr C, Ruthven D.
Single-and
multi-component
liquid
phase
adsorption
measurements by headspace chromatography. Braz J Chem
Eng 2001;18(1):121–5.
[43] Wu K-T, Wu P-H, Wu F-C, Jreng R-L, Juang R-S. A novel
approach to characterizing liquid-phase adsorption on highly
porous activated carbons using the Toth equation. Chem Eng J
2013;221:373–81.
[44] Zhang F, Li P, Dai G, He S. Fabrication and properties of threedimensional nanoporous graphene foams with magnesium
binder. Scripta Mater 2016;111:89–93.
[45] Seth S. Increase in Surface Energy by Drainage of Sandstone
and Carbonate. ProQuest; 2006.
[46] Schmidt JE, Xie D, Davis ME. High-silica, heulandite-type
zeolites prepared by direct synthesis and topotactic
condensation. J Mater Chem A 2015;3(24):12890–7.
[47] Zhao D, Cleare K, Oliver C, Ingram C, Cook D, Szostak R,
et al. Characteristics of the synthetic heulandite–clinoptilolite
family of zeolites. Microporous Mesoporous Mater 1998;21
(4):371–9.