Fullpaper ICRAMST2013 AS Mohruni
Application of response surface methodology on optimizing for PVDF composite
membrane ultrafiltration
A.S. Mohrunia,* E. Yuliwatib,c , A.F. Ismailc,d
a
Department of Mechanical Engineering,Faculty of Engineering, Sriwijaya University, Indralaya 30662,
Indonesia
Tel. +62 (711)580-062; Fax: +62 (711) 580-741
b
Department of Industrial Engineering, Faculty of Engineering, University of Bina Darma, 30251 Palembang,
Indonesia
Tel. +62 (711)515-679; Fax: +62 (711) 515-581
c
Advanced Membrane Technology Research Centre (AMTEC),
d
Faculty of Petroleum and Renewable Energy Engineering,
Universiti Teknologi Malaysia, 81310 UTM, Skudai Johor, Malaysia
Tel. +60 (7) 553-5592; Fax: +60 (7) 558-1463
*Corresponding author: [email protected]
Abstract
Response surface methodology (RSM) is a collection of statistical design and numerical
optimization techniques used to optimize processes and product design. RSM was performed to
optimize in preparing the best composition of polyvinylidene fluoride (PVDF) composite membranes.
Quartic model was used to analyzed the optimized results. Preparation of PVDF composite
membranes was studied by introducing nano particles additives, which include nonsolvents (water and
glycerol) and inorganic salt (LiCl) in varied compositions. Dimethylacetamide (DMAc) was used as a
solvent. Distilled water was used as an internal coagulant, while a mixture of water and glycerol was
used as external coagulant. The prepared PVDF hollow fiber membranes ultrafiltration were
characterized in terms of permeate flux. Average pore size and effective surface porosity were
determined using the water permeation method for the dried membranes. The cross sectional structure
of hollow fiber membranes were examined by field electrostatic scanning electro microscopy. Results
from RSM showed that the highest flux of 145.37 L/m2h was achieved with optimum condition of
.aeration flow rate, hydraulic retention time, mixed liquor suspended solid concentration of 2.25
ml/min, 276.93 min, 4.50 g/L at maintained pH of 6.50.
Keywords: Optimization; response surface methodology; quartic model; composite membrane;
ultrafiltration.
1. Introduction
Membrane can be described as a thin layer of material that is capable of separating
materials as a function of their physical and chemical properties when a driving force is
applied across the membranes. Physically membrane could be solid or liquid. In membrane
separation processes, the feed is separated into a stream that goes through the membrane, i.e.,
permeate and a fraction of feed that does not go through the membrane, i.e., retentate or
concentrate. A membrane process then allows selective and controlled transfer of one species
from one bulk phase to another bulk phase separated by the membrane. The major
breakthrough in the development of membrane technology was recorded in the late of 1950s.
However, industrial application was just started ten years later, by the application of thin
layer asymmetric cellulose acetate reverse osmosis membrane for seawater desalination.
Membrane process can be classified in many ways, i.e., based on its nature, structure,
or driving force. Hydrostatic pressure differences are used in microfiltration (MF), and
nanofiltration (NF), as well as reverse osmosis (RO) and gas separation (GS) as driving force
for the mass transport through the membrane. Ultrafiltration (UF) as the main topic in this
paper is also one of the membrane process based on pressure difference as its driving force.
Ultrafiltration in its ideal definition as mentioned by Cheryan (1986) is a fractionation
technique that can simultaneously concentrate macromolecules or colloidal substances in
process stream. Ultrafiltration can be considered as a method for simultaneously purifying,
concentrating, and fractionating macromolecules or fine colloidal suspensions.
Today, UF technology is being used worldwide for treating various water sources. The
recent global increase in the use of membranes in water application is attributed to several
factors, i.e., increased regulatory pressure to provide better treatment for water, increased
demand for water requiring exploitation of water resources of lower quality than those relied
upon previously, and market forces surrounding the development and commercialization of
the membrane technologies as well as the water industries themselves [1].
Ultrafiltration membranes can be made from both organic (polymer) and inorganic
materials. There are several polymers and other materials used for the manufacture of UF
membrane. The choice of a given polymer as a membrane material is based on very specific
properties such as molecular weight, chain flexibility, chain interaction, etc. Some of these
materials are polysulfone, polyethersulfone, sulfonated polysulfone, polyvinylidene fluoride,
polyacrylonitrile, cellulosics, polyimide, polyetherimide, aliphatic polyamides, and
polyetherketone. Inorganic materials have also been used such as alumina and zirconia [2].
The structure of UF membrane can be symmetric or asymmetric. The thickness of
symmetric membran (porous or nonporous) is range from 10 to 200 μm. The resistance to
mass transfer is determined by the total membrane thickness. A decrease in membrane
thickness results in an increased permeation rate. Ultrafiltration membranes have an
asymmetric structure, which consist of very dense toplayer or skin with thickness of 0.1 to
0.5 μm supported by a porous sublayer with a thickness of about 50 to 150 μm. These
membranes combine the high selectivity of a dense membrane with the high permeation rate
of a very thin membrane. The resistance to mass transfer is determined largely or completely
by thin toplayer. Figure 1 shows the cross-sections of symmetric and asymmetric membrane.
Fig.1 Schematic representation of symmetric and asymmetric membrane cross-section
[3]
Ultrafiltration (UF) is a low-pressure operation at transmembrane pressures of,
typically, 0.5 to 5 bars. This is not only allows nonpositive displacement pumps to be used,
but also the membrane installation can be constructed from synthetic components, which has
cost advantage.
UF membranes can be fabricated essentially in one of two forms: tubular or flat sheet.
Membranes of these designs are normally produced on a porous substrate material. The single
operational unit into which membranes are engineered for use is referred to as a module. This
operational unit consists of the membranes, pressure support structures, feed inlet,
concentrate outlet ports, and permeate draw-off points. Two major types of UF modules can
be found in the market, i.e., hollow fibers (capillary), and spiral wound, as shown in Figure 2.
Fig. 2. Major types of UF modules: (a) spiral wound and (b) hollow fiber [4]
Nowadays, full-scale membrane elements are designed in a number of ways to optimise
membrane area to membrane process condition. The design of experimental apparatus has also been
optimised to increase the flux and rejection. The submerged ultrafiltration membrane is
widely used in water and wastewater treatments due to its high packing density and ease of
module manufacture and operation [5-7]. The removal of organic wastes from wastewater is,
therefore, becoming increasingly important, and submerged ultrafiltration finds its
application in this area. The direct immersion of hollow fiber membranes was assembled in
the feed reservoir with withdrawal of liquid through the fibers by the application of a vacuum
on the outlet of the fiber lumen [8-13]. There has been increasing attention to the application
of refinery effluent in petroleum industry over the last few years because refinery
wastewaters are characterized by presence of several aromatic hydrocarbons and inorganic
substances such as, chemical oxygen demand (COD), total organic carbon (TOC), sulfide,
ammonia nitrogen (NH3-N) , and total suspended solid (TSS) [14-18].
As reported in most of the articles, SMUF have been conducted using one process
variable at a time approach, i.e. the influence of variables is investigated separately. Using
this approach, one requires conducting a large amount of experiments before a conclusion on
the process performance could be drawn. The use of statistical methods such as response
surface methodology (RSM) therefore could overcome the limitations of the one-variable at a
time approach [19-25]. It is generally agreed that RSM is an efficient statistical tool, which
can be used for modelling and optimization of several process variables [26]. Using response
surface plots developed, one could understand
better the relationship between factors
(process variables) and responses (outcomes of experiments).
The main objective of this study is to investigate the effects of four different process
variables, i.e. ABFR, HRT, MLSS and pH on the performances of PVDF composite
ultrafiltration membranes based on the approach of RSM (Design expert® 8.0.5.2 ). The
experimental runs were designed in accordance with the central composite design and carried
out batch-wise. All the refinery wastewater used in this study was synthesizedly prepared
based on ASTM D-1141-90.
2. Experimental
2.1 Materials
The properties of PVDF membranes used in this work have been described in
previous study in detail [21]. The hollow fiber membranes were prepared by dry-jet wet
spinning method with the spinning dope composition of 19 wt.% PVDF in
dimethylacetamide (DMAc), 1.95 wt.% titanium dioxide (TiO2) and 0.98 wt.% lithium
chloride (LiCl).
As a semi-crystalline polymer, PVDF generally exhibits more complicated phase
separation behavior than amorphous polymer. LiCl and TiO2 were added to the spinning dope
to improve thermodynamic/kinetic relations during the phase inversion process in the
preparation of PVDF-based membranes, increase the surface hydrophilicity and thus to
improve membrane water productivity [26]. The porous structure and possible hydrophilicity
of the TiO2 nanoparticles were directly correlated with porosity and might be responsible for
the higher liquid uptake. As can also be seen in Fig. 3, the membranes used to treat refinery
wastewater demonstrated
a microporous surface which is in good agreement with the
properties of ultrafiltration. The details of the membrane fabrication
properties on determination procedure could be found elsewhere [27].
process and its
Fig. 3. FESEM images of the (a) cross section (Mag. 500x) and (b) outer surface (Mag. 40.0k
x) of modified PVDF membrane.
2.2 Refinery wastewater
Synthetic refinery wastewater was prepared to be used as feed solution in submerged
ultrafiltration experiments. Its composition is shown in Table 1 [21].
Table 1. Properties of modified PVDF ultrafiltration membrane
Parameter
Membrane
Membrane configuration
Hollow fiber
Membrane material
PVDF
Hydrophilic additive added
LiCl
Outer diameter (mm)
1.1
Inner diameter (mm)
0.55
Pore size (nm)
34.05
o
Contact angle ( )
54
Zeta potential (mV at pH 6.9)
62
Tensile strength (MPa)
3.37 ± 0.13
Young’s modulus (GPa)
3.81 ± 0.21
Pure water flux (L m-2 h -1)
82.95
mmHg
at
250
2.3 Experimental setup and procedure
The lab-scale experimental set-up shown in Fig. 4 was used in this work. The
submerged membrane separation system consisted of a feed reservoir of 14 L volume, two
hollow fiber bundles, a peristaltic pump, a permeate flowmeter, and an permeate collector.
Fig. 4. Scheme of the submerged membrane system (V1: wastewater valve, T1: biological
treatment tank, V2:feed membrane reservoir valve, S: sparger, M: membrane module, T2:
membrane reservoir, P1: peristaltic pump, P2: air pump, QC: flow control, LC: liquid control,
LI: level indicator, PC: pressure control).
The filtration experiments were conducted at room temperature and under vacuum on
the permeate side (0.5 bar abs) created using a peristaltic pump (Master flex model 7553-79,
Cole Palmer) with the permeate being withdrawn from the open end of fibers. The liquid
level in the feed tank was maintained constant throughout the experiment. Two hollow fiber
bundles with an effective area of 11.23 dm2 for each bundle were immersed in the feed
reservoir and a constant transmembrane pressure (TMP) of 0.5 bar was maintained to let
water permeate from outside to inside of the hollow fiber. The continuous aeration produced
a turbulent flow which could decrease the cake layer thickness and the average particle size
[27]. These air bubbles exerted shear stress to minimize particle deposition on the membrane
surface during filtration process. Moreover, this aeration process would be preferred in terms
of energy consumption in large scale installations.
The volume of the permeated water collected during a certain period (300 min) was
determined using a graduated cylinder. Periodic cleaning was conducted after finishing each
batch of filtration. 1000 mg/L NaOH aqueous solution was used as the alkaline cleaning
agent to wash the membranes for the first 20 min of each cleaning step, followed by 5 min
rinsing with the pure water to remove the particle-packed layer, which might be embedded on
the membrane surface.
2.4 Analytical methods
2.4.1 Morphology observation
Field emission scanning electron microscope (JEOL JSM-6700F) was used to
examine the morphology of the PVDF hollow fiber membrane prepared. Prior to analysis, the
membrane samples were first immersed in liquid nitrogen and fractured carefully. The
samples were then coated with sputtering platinum before testing. The FESEM micrographs
of cross-section and outer surface of the hollow fiber membranes were taken at various
magnifications.
2.4.2 Mechanical test
The breaking strain and strength of the membranes were examined to investigate the
mechanical stability using a tensile tester (LRX2 SKN LLYOD) instrument at room
temperature. Tests were conducted on a cross head speed of 20 mm min-1 at break and gate
length of filament of 25 mm [28-30]. At least five measurements were performed for each
membrane sample and the average values are reported in this study.
2.4.3 Membrane performances
Membrane was tested with a self-made U-shape membrane bundle. Pure water
permeation rate was measured after the steady state was reached, using the following
equation
F=
V
At
(1)
where F is the pure water flux (l/m2 h), V is the permeate volume (l), A is the membrane
surface area (m2), and t is the time (h).
2.5 Response surface methodology (RSM)
Response surface methodology (RSM) is a collection of mathematical and statistical
techniques, commonly used for improving and optimizing processess. It can be used to
evaluate the relative significance of several affecting factors in the presence of complex
interactions. When a combination of several independent variables and their interactions
affects desired responses, RSM is an effective tool for optimizing the process [28]. RSM uses
an experimental design such as the composite central design (CCD) to fit a model by least
squares technique. This methodology optimizes flux in submerged hollow fiber membrane
process.
The Design Expert 8.0.5.2 software (trial version) is used for the statistical design of
experiments and data analysis and performed in duplicate [31]. The four most important
operating variables (factors), air bubble flow rate (x1), hydraulic retention time (x2), mixed
liquor suspended solid concentration (x3), and pH (x4), are optimized. The study ranges
(levels) are chosen as shown in Table 2. In this table, the coded values for x1, x2, x3, x4 are set
at 5 levels -2, -1(minimum), 0 (central), +1(maximum), and +2 [32]. The experimental layout
and results from RSM was showed in Table 3.
Table 2. Process factors and their limit levels in the experimental design
Factors
x1
x2
x3
x4
Note: α =2
Variables
Air bubble flow rate
(ABFR)
Hydraulic retention time
(HRT)
Mixed liquor suspended
solid (MLSS)
pH
Unit
mL/min
-2
0.3
-1
1.2
Levels
0
+1
2.1
3.0
+2
3.9
min
120
180
240
300
360
mg/L
1.5
3.0
4.5
6.0
7.5
pH
3.5
5.0
6.5
8.0
9.5
Table 3 Experimental lay-out designed by Design Expert and its corresponding experimental
and predicted values of responses
Factor variables
Standard
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
ABFR,
ml/min
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
0.30
3.90
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
HRT, min
300.00
300.00
180.00
180.00
300.00
300.00
180.00
180.00
300.00
300.00
180.00
180.00
300.00
300.00
240.00
240.00
120.00
360.00
240.00
240.00
240.00
240.00
240.00
240.00
240.00
240.00
300.00
300.00
Response
MLSSg/L
3.00
3.00
6.00
6.00
6.00
6.00
3.00
3.00
3.00
3.00
6.00
6.00
6.00
6.00
4.50
4.50
4.50
4.50
1.50
7.50
4.50
4.50
4.50
4.50
4.50
4.50
3.00
3.00
pH
5.00
5.00
5.00
5.00
5.00
5.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
6.50
6.50
6.50
6.50
6.50
6.50
3.50
9.50
6.50
6.50
6.50
6.50
5.00
5.00
Flux, L/m2 hr
Experimental
140.09
147.02
138.36
142.78
46.31
87.75
53.59
85.81
61.81
81.63
81.52
92.74
44.05
57.07
61.38
58.95
108.59
106.09
87.41
140.81
174.91
37.97
219.93
41.87
141.37
140.53
138.40
140.10
Predicted
140.10
147.01
138.36
142.79
46.29
87.74
53.60
85.80
61.83
81.67
81.53
92.77
44.07
57.05
61.37
58.97
108.60
106.10
87.41
140.82
174.91
37.97
219.93
41.87
140.10
140.10
140.10
140.10
In RSM, a model with the form of Eq. (2) is fitted to experimental data and, by
optimization methods, the coefficients for the model are calculated. To identify the right
model that can fit the data, it can be started with the simplest model forms like first- and
second-degree Scheffe’s polynomial [33]. After testing these models for adequacy of fit, they
were augmented to simplex centroid and special quartic models by adding the appropriate
terms. In this study, the quartic model used for predicting the optimal point is as follows
�(�) =
4
�
3
�
2 2
�
2
�
∑�
�=1 �� �� + + ∑��(�
membrane ultrafiltration
A.S. Mohrunia,* E. Yuliwatib,c , A.F. Ismailc,d
a
Department of Mechanical Engineering,Faculty of Engineering, Sriwijaya University, Indralaya 30662,
Indonesia
Tel. +62 (711)580-062; Fax: +62 (711) 580-741
b
Department of Industrial Engineering, Faculty of Engineering, University of Bina Darma, 30251 Palembang,
Indonesia
Tel. +62 (711)515-679; Fax: +62 (711) 515-581
c
Advanced Membrane Technology Research Centre (AMTEC),
d
Faculty of Petroleum and Renewable Energy Engineering,
Universiti Teknologi Malaysia, 81310 UTM, Skudai Johor, Malaysia
Tel. +60 (7) 553-5592; Fax: +60 (7) 558-1463
*Corresponding author: [email protected]
Abstract
Response surface methodology (RSM) is a collection of statistical design and numerical
optimization techniques used to optimize processes and product design. RSM was performed to
optimize in preparing the best composition of polyvinylidene fluoride (PVDF) composite membranes.
Quartic model was used to analyzed the optimized results. Preparation of PVDF composite
membranes was studied by introducing nano particles additives, which include nonsolvents (water and
glycerol) and inorganic salt (LiCl) in varied compositions. Dimethylacetamide (DMAc) was used as a
solvent. Distilled water was used as an internal coagulant, while a mixture of water and glycerol was
used as external coagulant. The prepared PVDF hollow fiber membranes ultrafiltration were
characterized in terms of permeate flux. Average pore size and effective surface porosity were
determined using the water permeation method for the dried membranes. The cross sectional structure
of hollow fiber membranes were examined by field electrostatic scanning electro microscopy. Results
from RSM showed that the highest flux of 145.37 L/m2h was achieved with optimum condition of
.aeration flow rate, hydraulic retention time, mixed liquor suspended solid concentration of 2.25
ml/min, 276.93 min, 4.50 g/L at maintained pH of 6.50.
Keywords: Optimization; response surface methodology; quartic model; composite membrane;
ultrafiltration.
1. Introduction
Membrane can be described as a thin layer of material that is capable of separating
materials as a function of their physical and chemical properties when a driving force is
applied across the membranes. Physically membrane could be solid or liquid. In membrane
separation processes, the feed is separated into a stream that goes through the membrane, i.e.,
permeate and a fraction of feed that does not go through the membrane, i.e., retentate or
concentrate. A membrane process then allows selective and controlled transfer of one species
from one bulk phase to another bulk phase separated by the membrane. The major
breakthrough in the development of membrane technology was recorded in the late of 1950s.
However, industrial application was just started ten years later, by the application of thin
layer asymmetric cellulose acetate reverse osmosis membrane for seawater desalination.
Membrane process can be classified in many ways, i.e., based on its nature, structure,
or driving force. Hydrostatic pressure differences are used in microfiltration (MF), and
nanofiltration (NF), as well as reverse osmosis (RO) and gas separation (GS) as driving force
for the mass transport through the membrane. Ultrafiltration (UF) as the main topic in this
paper is also one of the membrane process based on pressure difference as its driving force.
Ultrafiltration in its ideal definition as mentioned by Cheryan (1986) is a fractionation
technique that can simultaneously concentrate macromolecules or colloidal substances in
process stream. Ultrafiltration can be considered as a method for simultaneously purifying,
concentrating, and fractionating macromolecules or fine colloidal suspensions.
Today, UF technology is being used worldwide for treating various water sources. The
recent global increase in the use of membranes in water application is attributed to several
factors, i.e., increased regulatory pressure to provide better treatment for water, increased
demand for water requiring exploitation of water resources of lower quality than those relied
upon previously, and market forces surrounding the development and commercialization of
the membrane technologies as well as the water industries themselves [1].
Ultrafiltration membranes can be made from both organic (polymer) and inorganic
materials. There are several polymers and other materials used for the manufacture of UF
membrane. The choice of a given polymer as a membrane material is based on very specific
properties such as molecular weight, chain flexibility, chain interaction, etc. Some of these
materials are polysulfone, polyethersulfone, sulfonated polysulfone, polyvinylidene fluoride,
polyacrylonitrile, cellulosics, polyimide, polyetherimide, aliphatic polyamides, and
polyetherketone. Inorganic materials have also been used such as alumina and zirconia [2].
The structure of UF membrane can be symmetric or asymmetric. The thickness of
symmetric membran (porous or nonporous) is range from 10 to 200 μm. The resistance to
mass transfer is determined by the total membrane thickness. A decrease in membrane
thickness results in an increased permeation rate. Ultrafiltration membranes have an
asymmetric structure, which consist of very dense toplayer or skin with thickness of 0.1 to
0.5 μm supported by a porous sublayer with a thickness of about 50 to 150 μm. These
membranes combine the high selectivity of a dense membrane with the high permeation rate
of a very thin membrane. The resistance to mass transfer is determined largely or completely
by thin toplayer. Figure 1 shows the cross-sections of symmetric and asymmetric membrane.
Fig.1 Schematic representation of symmetric and asymmetric membrane cross-section
[3]
Ultrafiltration (UF) is a low-pressure operation at transmembrane pressures of,
typically, 0.5 to 5 bars. This is not only allows nonpositive displacement pumps to be used,
but also the membrane installation can be constructed from synthetic components, which has
cost advantage.
UF membranes can be fabricated essentially in one of two forms: tubular or flat sheet.
Membranes of these designs are normally produced on a porous substrate material. The single
operational unit into which membranes are engineered for use is referred to as a module. This
operational unit consists of the membranes, pressure support structures, feed inlet,
concentrate outlet ports, and permeate draw-off points. Two major types of UF modules can
be found in the market, i.e., hollow fibers (capillary), and spiral wound, as shown in Figure 2.
Fig. 2. Major types of UF modules: (a) spiral wound and (b) hollow fiber [4]
Nowadays, full-scale membrane elements are designed in a number of ways to optimise
membrane area to membrane process condition. The design of experimental apparatus has also been
optimised to increase the flux and rejection. The submerged ultrafiltration membrane is
widely used in water and wastewater treatments due to its high packing density and ease of
module manufacture and operation [5-7]. The removal of organic wastes from wastewater is,
therefore, becoming increasingly important, and submerged ultrafiltration finds its
application in this area. The direct immersion of hollow fiber membranes was assembled in
the feed reservoir with withdrawal of liquid through the fibers by the application of a vacuum
on the outlet of the fiber lumen [8-13]. There has been increasing attention to the application
of refinery effluent in petroleum industry over the last few years because refinery
wastewaters are characterized by presence of several aromatic hydrocarbons and inorganic
substances such as, chemical oxygen demand (COD), total organic carbon (TOC), sulfide,
ammonia nitrogen (NH3-N) , and total suspended solid (TSS) [14-18].
As reported in most of the articles, SMUF have been conducted using one process
variable at a time approach, i.e. the influence of variables is investigated separately. Using
this approach, one requires conducting a large amount of experiments before a conclusion on
the process performance could be drawn. The use of statistical methods such as response
surface methodology (RSM) therefore could overcome the limitations of the one-variable at a
time approach [19-25]. It is generally agreed that RSM is an efficient statistical tool, which
can be used for modelling and optimization of several process variables [26]. Using response
surface plots developed, one could understand
better the relationship between factors
(process variables) and responses (outcomes of experiments).
The main objective of this study is to investigate the effects of four different process
variables, i.e. ABFR, HRT, MLSS and pH on the performances of PVDF composite
ultrafiltration membranes based on the approach of RSM (Design expert® 8.0.5.2 ). The
experimental runs were designed in accordance with the central composite design and carried
out batch-wise. All the refinery wastewater used in this study was synthesizedly prepared
based on ASTM D-1141-90.
2. Experimental
2.1 Materials
The properties of PVDF membranes used in this work have been described in
previous study in detail [21]. The hollow fiber membranes were prepared by dry-jet wet
spinning method with the spinning dope composition of 19 wt.% PVDF in
dimethylacetamide (DMAc), 1.95 wt.% titanium dioxide (TiO2) and 0.98 wt.% lithium
chloride (LiCl).
As a semi-crystalline polymer, PVDF generally exhibits more complicated phase
separation behavior than amorphous polymer. LiCl and TiO2 were added to the spinning dope
to improve thermodynamic/kinetic relations during the phase inversion process in the
preparation of PVDF-based membranes, increase the surface hydrophilicity and thus to
improve membrane water productivity [26]. The porous structure and possible hydrophilicity
of the TiO2 nanoparticles were directly correlated with porosity and might be responsible for
the higher liquid uptake. As can also be seen in Fig. 3, the membranes used to treat refinery
wastewater demonstrated
a microporous surface which is in good agreement with the
properties of ultrafiltration. The details of the membrane fabrication
properties on determination procedure could be found elsewhere [27].
process and its
Fig. 3. FESEM images of the (a) cross section (Mag. 500x) and (b) outer surface (Mag. 40.0k
x) of modified PVDF membrane.
2.2 Refinery wastewater
Synthetic refinery wastewater was prepared to be used as feed solution in submerged
ultrafiltration experiments. Its composition is shown in Table 1 [21].
Table 1. Properties of modified PVDF ultrafiltration membrane
Parameter
Membrane
Membrane configuration
Hollow fiber
Membrane material
PVDF
Hydrophilic additive added
LiCl
Outer diameter (mm)
1.1
Inner diameter (mm)
0.55
Pore size (nm)
34.05
o
Contact angle ( )
54
Zeta potential (mV at pH 6.9)
62
Tensile strength (MPa)
3.37 ± 0.13
Young’s modulus (GPa)
3.81 ± 0.21
Pure water flux (L m-2 h -1)
82.95
mmHg
at
250
2.3 Experimental setup and procedure
The lab-scale experimental set-up shown in Fig. 4 was used in this work. The
submerged membrane separation system consisted of a feed reservoir of 14 L volume, two
hollow fiber bundles, a peristaltic pump, a permeate flowmeter, and an permeate collector.
Fig. 4. Scheme of the submerged membrane system (V1: wastewater valve, T1: biological
treatment tank, V2:feed membrane reservoir valve, S: sparger, M: membrane module, T2:
membrane reservoir, P1: peristaltic pump, P2: air pump, QC: flow control, LC: liquid control,
LI: level indicator, PC: pressure control).
The filtration experiments were conducted at room temperature and under vacuum on
the permeate side (0.5 bar abs) created using a peristaltic pump (Master flex model 7553-79,
Cole Palmer) with the permeate being withdrawn from the open end of fibers. The liquid
level in the feed tank was maintained constant throughout the experiment. Two hollow fiber
bundles with an effective area of 11.23 dm2 for each bundle were immersed in the feed
reservoir and a constant transmembrane pressure (TMP) of 0.5 bar was maintained to let
water permeate from outside to inside of the hollow fiber. The continuous aeration produced
a turbulent flow which could decrease the cake layer thickness and the average particle size
[27]. These air bubbles exerted shear stress to minimize particle deposition on the membrane
surface during filtration process. Moreover, this aeration process would be preferred in terms
of energy consumption in large scale installations.
The volume of the permeated water collected during a certain period (300 min) was
determined using a graduated cylinder. Periodic cleaning was conducted after finishing each
batch of filtration. 1000 mg/L NaOH aqueous solution was used as the alkaline cleaning
agent to wash the membranes for the first 20 min of each cleaning step, followed by 5 min
rinsing with the pure water to remove the particle-packed layer, which might be embedded on
the membrane surface.
2.4 Analytical methods
2.4.1 Morphology observation
Field emission scanning electron microscope (JEOL JSM-6700F) was used to
examine the morphology of the PVDF hollow fiber membrane prepared. Prior to analysis, the
membrane samples were first immersed in liquid nitrogen and fractured carefully. The
samples were then coated with sputtering platinum before testing. The FESEM micrographs
of cross-section and outer surface of the hollow fiber membranes were taken at various
magnifications.
2.4.2 Mechanical test
The breaking strain and strength of the membranes were examined to investigate the
mechanical stability using a tensile tester (LRX2 SKN LLYOD) instrument at room
temperature. Tests were conducted on a cross head speed of 20 mm min-1 at break and gate
length of filament of 25 mm [28-30]. At least five measurements were performed for each
membrane sample and the average values are reported in this study.
2.4.3 Membrane performances
Membrane was tested with a self-made U-shape membrane bundle. Pure water
permeation rate was measured after the steady state was reached, using the following
equation
F=
V
At
(1)
where F is the pure water flux (l/m2 h), V is the permeate volume (l), A is the membrane
surface area (m2), and t is the time (h).
2.5 Response surface methodology (RSM)
Response surface methodology (RSM) is a collection of mathematical and statistical
techniques, commonly used for improving and optimizing processess. It can be used to
evaluate the relative significance of several affecting factors in the presence of complex
interactions. When a combination of several independent variables and their interactions
affects desired responses, RSM is an effective tool for optimizing the process [28]. RSM uses
an experimental design such as the composite central design (CCD) to fit a model by least
squares technique. This methodology optimizes flux in submerged hollow fiber membrane
process.
The Design Expert 8.0.5.2 software (trial version) is used for the statistical design of
experiments and data analysis and performed in duplicate [31]. The four most important
operating variables (factors), air bubble flow rate (x1), hydraulic retention time (x2), mixed
liquor suspended solid concentration (x3), and pH (x4), are optimized. The study ranges
(levels) are chosen as shown in Table 2. In this table, the coded values for x1, x2, x3, x4 are set
at 5 levels -2, -1(minimum), 0 (central), +1(maximum), and +2 [32]. The experimental layout
and results from RSM was showed in Table 3.
Table 2. Process factors and their limit levels in the experimental design
Factors
x1
x2
x3
x4
Note: α =2
Variables
Air bubble flow rate
(ABFR)
Hydraulic retention time
(HRT)
Mixed liquor suspended
solid (MLSS)
pH
Unit
mL/min
-2
0.3
-1
1.2
Levels
0
+1
2.1
3.0
+2
3.9
min
120
180
240
300
360
mg/L
1.5
3.0
4.5
6.0
7.5
pH
3.5
5.0
6.5
8.0
9.5
Table 3 Experimental lay-out designed by Design Expert and its corresponding experimental
and predicted values of responses
Factor variables
Standard
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
ABFR,
ml/min
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
1.20
3.00
0.30
3.90
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
HRT, min
300.00
300.00
180.00
180.00
300.00
300.00
180.00
180.00
300.00
300.00
180.00
180.00
300.00
300.00
240.00
240.00
120.00
360.00
240.00
240.00
240.00
240.00
240.00
240.00
240.00
240.00
300.00
300.00
Response
MLSSg/L
3.00
3.00
6.00
6.00
6.00
6.00
3.00
3.00
3.00
3.00
6.00
6.00
6.00
6.00
4.50
4.50
4.50
4.50
1.50
7.50
4.50
4.50
4.50
4.50
4.50
4.50
3.00
3.00
pH
5.00
5.00
5.00
5.00
5.00
5.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
8.00
6.50
6.50
6.50
6.50
6.50
6.50
3.50
9.50
6.50
6.50
6.50
6.50
5.00
5.00
Flux, L/m2 hr
Experimental
140.09
147.02
138.36
142.78
46.31
87.75
53.59
85.81
61.81
81.63
81.52
92.74
44.05
57.07
61.38
58.95
108.59
106.09
87.41
140.81
174.91
37.97
219.93
41.87
141.37
140.53
138.40
140.10
Predicted
140.10
147.01
138.36
142.79
46.29
87.74
53.60
85.80
61.83
81.67
81.53
92.77
44.07
57.05
61.37
58.97
108.60
106.10
87.41
140.82
174.91
37.97
219.93
41.87
140.10
140.10
140.10
140.10
In RSM, a model with the form of Eq. (2) is fitted to experimental data and, by
optimization methods, the coefficients for the model are calculated. To identify the right
model that can fit the data, it can be started with the simplest model forms like first- and
second-degree Scheffe’s polynomial [33]. After testing these models for adequacy of fit, they
were augmented to simplex centroid and special quartic models by adding the appropriate
terms. In this study, the quartic model used for predicting the optimal point is as follows
�(�) =
4
�
3
�
2 2
�
2
�
∑�
�=1 �� �� + + ∑��(�