DETERMINATION OF FOULING MECHANISMS FOR
ULTRAFILTRATION OF OILY WASTEWATER
A thesis submitted to the
Division of Graduate Studies and Research
of the University of Cincinnati
in partial fulfillment of the requirements
of the degree of
MASTER OF SCIENCE (M.S.)
in the Department of Chemical Engineering
of the College of Engineering and Applied Science
2011
By
Leila Safazadeh Haghighi
BS Chemical Engineering, University Of Tehran, Tehran, Iran, 2008
Thesis Advisor and Committee Chair: Professor Rakesh Govind
Abstract
The use of Membrane technology is extensively increasing in water and
wastewater treatment, food processing, chemical, biotechnological, and pharmaceutical
industries because of their versatility, effectiveness, high removal capacity and ability to
meet multiple treatment objectives.
A common problem with using membranes is
fouling, which results in increasing operating costs due to higher operating pressure
losses, membrane downtime needed for cleaning, with associated production loss and
manpower costs. In the literature, four different mechanisms for membrane fouling have
been studied, which are complete pore blocking, internal pore blinding, partial pore
bridging and cake filtration. Mathematical models have been developed for each of these
fouling mechanisms.
The objective of this thesis was to investigate the membrane fouling mechanisms
for one porous and one dense membrane, during ultrafiltration of an emulsified industrial
oily wastewater. An experimental system was designed, assembled and operated at the
Ford Transmission Plant in Sharonville, Ohio, wherein ultrasonic baths were used for
cleaning transmission parts before assembly. The oil wastewater, containing emulsified
oils and cleaning chemicals was collected in a batch vessel and then pumped through a
porous polyethersulfone, monolithic membrane, and through a dense cuproammonium
cellulose membrane unit. For the porous membrane, use of a Dupont’s flurosurfactant
(FS 63) and backwashing with permeate and for the dense membrane the use of both the
flurorosurfactant and sparged air were investigated to reduce membrane fouling.
ii
For the porous membrane study, it was observed that the permeate flux was
strongly dependent on the transmembrane pressure difference, and addition of the
flurosurfactant significantly improved the performance of the membrane.
The
backwashing cleaning efficiency was found to depend on the duration of backwashing
and its frequency.
An integrated fouling model was developed by combining the
individual models for each fouling mechanism, originally published by Hermia [18], and
analysis of the experimental data for ultrafiltration of oily emulsion revealed that the
primary mechanism for fouling of the porous membrane was cake filtration.
With
increasing transmembrane pressure, the role of other mechanisms, such as pore blocking
and partial pore bridging, increases, although the effect of cake filtration dominates.
Hence, for oily emulsions, methods to disrupt the formation of a cake layer at the
membrane surface would have the most impact in increasing the water permeation rates
through the membrane.
For the dense membrane study, permeate flux also increased with increasing
transmembrane pressure difference, as in the porous membrane, and the major
mechanisms for fouling were found to be concentration polarization gel layer formation
on the membrane surface. In this case, the use of both sparged air and fluorosurfactant,
increased the water permeation rates, but the permeation rate improvement with sparged
air alone was significantly higher than with fluorosurfactant only. A mathematical model
was developed to derive the mass transfer coefficients under the various operation
conditions.
iii
Future studies will concentrate on improving membrane performance by reducing
the impact of the dominant fouling mechanisms, found in this study, for both porous and
dense membranes.
iv
v
DEDICATION
THIS THESIS IS DEDICATED TO MY FAMILY
vi
Acknowledgements
I would like to express my sincere gratitude to my adviser, Professor Rakesh
Govind. I am deeply indebted to him for giving me the opportunity to work on this
project. I’m particularly grateful for his enthusiastic guidance, discussion, understanding,
encouragement and numerous hours spent helping me complete this thesis.
I’d also like to express my special thanks to the thesis committee, Professor
Junhang Dong and Professor Joo-Youp Lee for the efforts to provide valuable comments
during my proposal presentation, for the their valuable time to review this thesis and for
offering me an opportunity to defend in front of them.
My appreciation is also extended to Mr. Lyle Carman and Mr. David Ferguson for
helping me with the experimental set-up.
I am really grateful to my dear husband for his company, understanding,
continuous encouragement and support all the way. Without his help, these
accomplishments would not have been possible.
vii
Table of Contents
Abstract ..................................................................................................................…..…...ii
Acknowledgement……………………………………………………………….………vii
Table of Contents ......................................................................................................…..viii
List of Figures ....................................................................................................................xi
List of Tables ............................................................................................................…...xiv
List of Symbols and Abbreviations………………………………………...…………...xvi
Chapter 1: Introduction .......................................................................................................1
1.1 Motivation for Research……........................................................................................1
1.2 Membrane Filtration …………….................................................................................1
1.3 Membrane Fouling and its Mechanisms........................................................................6
1.3.1 Concentration Polarization………………………………………..................7
1.3.2 Cake Formation………………………………………………..................….8
1.3.3 Natural Organic Matter Adsorption……………………………...…..……...8
1.3.4 Calcium, Iron and Manganese Precipitation……………………………..… 9
1.3.5 Fouling Mechanisms………………………..…………………………….... 9
1.4 Factors Affecting Membrane Fouling……………………………….………….……10
1.5 Conventional Membrane Cleaning Methods……………………………………...…11
1.5.1 Backwashing………………………………………………...………….……11
1.5.2 Enhanced Backwashing……………………………………..………….……12
1.5.3 Chemical Cleaning…………………………………………..………………13
1.6 Disadvantages of the Conventional Cleaning Methods………….………..................13
1.7 Prevention and Reduction of Membrane Fouling……………………………………14
1.8 Pretreatment……………………………………………………………………….....14
1.8.1 Physical Disruption of Concentration Polarization………………………….15
1.9 Use of Surfactants……………………………………………………………………16
viii
1.10 Thesis Outline………………………………………………………………………17
Chapter 2: Literature Review ............................................................................................18
2.1 Ultrafiltration of Oily Emulsions.................................................................................19
2.2 Conventional Membrane Cleaning Methods….…………….……………………….22
2.2.1 Backwashing ................................................................................................23
2.2.2 Gas Sparging ................................................................................................24
2.3 Ultrasonic Cleaning of Membranes.............................................................................27
2.3.1. Effect of Sonication on Polymeric Membranes..............................................29
2.4 Fouling Mechanism in Ultrafiltration..........................................................................31
Chapter 3: Thesis Objectives ...........................................................................................34
Chapter 4: Materials and Methods ....................................................................................38
4.1 Selection of Membranes..............................................................................................38
4.2 Experimental Systems .................................................................................................42
4.3 Cleaning Procedure......................................................................................................45
4.4 Theory of Ultrafiltration of Oily Wastewater………….………………………….....48
4.5 Models for Membrane Fouling Mechanism……….………………………………...52
4.5.1. Fouling Mechanisms Involved In UF Using Porous Monolith Polyether
Sulfone Membrane…………………………………………………………52
4.5.1.1 Complete Pore Blocking Model (n=2) …………………………….55
4.5.1.2. Internal Pore Blocking Model (n=3/2)…………..…….…………..56
4.5.1.3. Partial pore bridging model (n=1)…………………………..…….56
4.5.1.4. Cake Layer Formation Model (n=0)………………………………57
Chapter 5: Results and Discussions……………………………………………………...59
5.1 Filtration of Oily Wastewater using Porous Monolith Polyether Sulfone Membrane
……………………………………………………………………………………………59
5.1.1Effect of Transmembrane Pressure……………………………….…..……..59
5.1.2Effect of Feed Concentration……………………………………….…….....64
5.1.3Effect of Backwashing on Permeate Flux Recovery…………………...……66
ix
5.1.4Prediction of Permeate Flux by Hermia’s models……………...……………70
5.1.5. Flux Decay Analysis by using a combination of Hermia’s models…...........73
5.1.6. Mass Balance Analysis……………………………………………………..82
5.2 Filtration of Oily Wastewater using Dense Hollow-Fiber Regenerated Cellulose
Ultrafiltration Membrane……………………………………………...…………...84
5.2.1. Effect of Transmembrane Pressure……………………………………….....84
5.2.2. Effect of Air Injection and Surfactant on Membrane Performance………....90
5.2.3. Analysis of Permeate Flux for Ultrafiltration of oily emulsion in Dense
Hollow-Fiber Regenerated Cellulose Membrane……………………….….92
5.2.4. Mass Balance Analysis………………………………………………...……99
Chapter 6: Conclusions and Recommendations………………………………………..101
Bibliography ...................................................................................................................104
Appendix………………………………………………………………………………..111
Appendix1………………………………………………………………………112
Appendix2………………………………………………………………………122
x
List of Figures
Figure 1.1. Membrane filtration application for solute molecules removal (Page 3)
Figure 1.2. Different fouling mechanisms happening in porous membrane (Page9)
Figure 4.1. Tangential cross-flow filtration (Page 38)
Figure 4.2. Structure of the porous membrane fibers (Page 40)
Figure 4.3. Image of the cuprammonium regenerated cellulose hollow fibers (Page 41)
Figure 4.4. Schematic figure of the experimental set-up (Page 43)
Figure 4.5. Photographs of the experimental system, operated at Ford Motor Company
Plant, Sharonville, Ohio.(Page 44)
Figure 4.6. Schematic figure of backwash system (Page 46)
Figure 4.7. Schematic figure of air injection system (Page 47)
Figure 4.8. Demonstration of the contact angle of a liquid sample (Page 49)
Figure 4.9. Different fouling mechanisms happening in porous membranes (Page 52)
2
Figure 5.1 Variation of permeate flux ( l /m .h ) with time at different transmembrane
pressures (TMP ) with surfactant (Page 60)
2
Figure 5.2 Variation of permeate flux ( l /m .h ) with time at different transmembrane
(Page 60)
pressures (TMP) without surfactant
2
Figure 5.3 Effect of transmembrane pressure (kPa) permeate flux ( l /m .h ) for
61)
ultrafiltration with surfactant (Page
2
Figure 5.4 Effect of transmembrane pressure (kPa) permeate flux ( l /m .h ) for
ultrafiltration without surfactant (Page 62)
2
Figure 5.5 Variation of permeate flux ( l /m .h ) with time at transmembrane pressure of
289.69 kPa & 186.16 kPa , for ultrafiltration with and without surfactant
(Page 64)
Figure 5.6 Comparison of flux variation with time at different feed concentration for
ultrafiltration with and withoutsurfactant (Page 65)
Figure 5.7 Effect of backwashing interval on permeate flux recovery (Page 67)
Figure 5.8. Effect of backwashing duration on permeate flux recovery (Page 69)
Figure 5.9. Error values for different Hermia’s models for ultrafiltration of oily
wastewate with surfactant at different transmembrane pressure (Page 78)
xi
Figure 5.10. Error values for different Hermia’s models for ultrafiltration of oily
wastewate without surfactant at different transmembrane pressure (Page 78)
Figure 5.11. reduction precentages of error values combined model with respect to the
cake filtration model, for ultrafiltration of oily wastewate with surfactant at different
transmembrane pressures (Page 79)
Figure 5.12. reduction precentages of error values combined model with respect to the
cake filtration model, for ultrafiltration of oily wastewate without surfactant at different
transmembrane pressures (Page 80)
Figure 5.13. contribution precentages of cake filtration and pore blocking mechanisms,
for ultrafiltration of oily wastewate with surfactant at different transmembrane pressure
(Page 81)
Figure 5.14. contribution precentages of cake filtration and pore blocking mechanisms,
for ultrafiltration of oily wastewate with surfactant at different transmembrane pressure
(Page 81)
Figure 5.15. Comparision of the contribution precentages of cake filtration and pore
blocking mechanisms, for ultrafiltration of oily wastewate at different transmembrane
pressure with and without surfactant (Page 82)
2
Figure 5.16. Variation of permeate flux ( l /m .h ) with time at different transmembrane
pressures for ultrafiltration of oily wastewate without surfactant and without air injection
(Page 85)
flux ( l /m 2 .h ) with time at different transmembrane
Figure 5.17. Variation of permeate
pressures for ultrafiltration of oily wastewate with surfactant and without air injection
(Page 86)
flux ( l /m 2 .h ) with time at different transmembrane
Figure 5.18. Variation of permeate
pressures for ultrafiltration of oily wastewate with surfactant and with air injection (Page
86)
flux ( l /m 2 .h ) with time at different transmembrane
Figure 5.19 Variation of permeate
pressures for ultrafiltration of oily wastewate without surfactant and with air injection
(Page 87)
Figure 5.20. Effect of transmembrane
pressure on permeate flux for ultrafiltration of oily
wastewate without surfactant and without air injection (Page 88)
Figure 5.21. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate with surfactant and without air injection (Page 88)
Figure 5.22. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate with surfactant and with air injection (Page 89)
xii
Figure 5.23. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate without surfactant and with air injection (Page 89)
Figure 5.24 Effect of air injection and surfactant on permeate flux for transmembrane
pressure (Page 90)
Figure 5.25 Effect of air injection and surfactant on permeate flux variation with time
(Page 91)
Figure 5.26 The comparison of the values of the mass transfer coefficient for
ultrafiltration of oily emulsion using dense membrane under different experimental
conditions (Page 99)
xiii
List of Tables
Table.1.1.Thesis Outline (Page 17)
Table 4.1. Design characteristics of the membrane cartridge (Page 39)
Table 4.2. Characteristics of the porous membrane (Page 40)
Table 4.3. Hollow fiber module and membrane characteristics (Page 41)
Table 5.1. Empirical constants for the linear relation between permeate flux and
transmembrane pressure (Page 62)
Table 5.2. Percentages of flux enhancement after backwashing with intervals of 60
minutes and 90 minutes (Page 67)
Table 5.3. Percentage of flux enhancement after backwashing with durations 100s and
200s (Page 69)
Table 5.4. Hermia’s model relation for different fouling mechanisms and the simplified
equations (Page 71)
Table 5.5. K values of Hermia’s models obtained from experimental data for
ultrafiltration with surfactant (Page 72)
Table 5.6. K values of Hermia’s models obtained from experimental data for
ultrafiltration without surfactant (Page 72)
Table 5.7. K values of Hermia’s models obtained for ultrafiltration of oily emulsion with
surfactant (Page 76)
Table 5.8. K values of Hermia’s models obtained for ultrafiltration of oily emulsion
without surfactant (Page 76)
Table 5.9.Error values measured for Hermia’s models and the combined model, with
Surfactant (Page 77)
Table 5.10.Error values measured for Hermia’s models and the combined model, without
Surfactant (Page 77)
Table 5.11. Calculation of Oil Concentration in the Porous Membrane Reject Flow, with
Surfactant (Page 83)
Table 5.12. Calculation of Oil Concentration in the Porous Membrane Reject Flow,
without Surfactant (Page 83)
Table 5.13. Operating conditions for the experiments conducted using the dense
membrane (Page 84)
xiv
Table 5.14. Experimental data for ultrafiltration oily emulsion using dense membrane
(Page 97 & 98)
Table5.15.The fitting parameter of experimental data (Page 98)
Table 5.16. Calculation of Oil Concentration in the Dense Membrane Reject Flow (Page
100)
xv
List of Symbols & Abbreviations
2
A : Membrane surface ( m )
2
A0 : Membrane porous surface ( m )
2
Ac : Cross- sectional area of the fiber ( m )
2
Am : Total effective area of membrane ( m )
2
At : Total membrane active area ( m )
2
Al : Fiber lateral area ( m )
C1 : Empirical constant
C2 : Empirical constant
Coil : Oil Concentration (wt %)
2
Dwm : Water diffusion coefficient ( m /s )
2
Dw : Water diffusivity (m /s)
d fiber : The fiber internal diameter (m)
2
ErrorCombination : The error value obtained for combined model ( l /m .h )
2
ErrorCake : The error value obtained for cake filtration model ( l /m .h )
FD : Flux declination percentage (%)
2
J 0 : Initial permeate flux ( l /m .h )
2
J p : Permeate flux ( l /m .h )
2
J f : Final permeate flux ( l /m .h )
2
J 2 : The permeate flux passed through the pores that are internally blocked ( l /m .h )
J 3 : The permeate flux passed through the opening of the pores that are partially blocked
2
( l /m .h )
xvi
J 3 : The permeate flux passed through the pores that are blanketed with a formed gel
2
layer ( l /m .h )
2
J Experimental : The flux value ( l /m .h ), achieved from experiment at time t(s) and
transmembrane pressure (kPa).
2
( l /m .h ), predicted by a Hermia model at time t(s) and
J Estimated : The flux value
transmembrane pressure (kPa).
m 3 /m 2 .s )
J stst : The steady-state
flux (
K : Phenomenological coefficient (K unit depend on the parameter n in equation (4.8)
K A : Parameter in equation 4.10 that represents the membrane surface blocked per unit of
1
the total volume permeated through the membrane ( m )
K B : Parameter in equation 4.12 that represents the decrease in the cross-sectional area of
the membrane pores per unit of the total volume permeated through the membrane
( 1/s )
K c : Constant in equation 4.9 and 4.10 that corresponds to the complete pore blocking
1
model ( m )
K D : Parameter in equation 4.16 that represents the cake layer area per unit of the total
1/m 3)
volume permeated through the membrane (
K S : Constant in equation 4.11 and 4.12 that corresponds to the standard pore blocking
3
model ( 1/s )
K i : Constant in equation 4.14 that corresponds to the intermediate pore blocking model
1
( m )
K gl : Constant in equation 4.16 that corresponds to the cake layer formation model
6
( s/m )
k c : The mass transfer coefficient ( m /s)
L : Membrane length with end cup (m)
end cup (m)
L1 : Membrane length without
l fiber : The fiber length (m)
n : General index depending on type of fouling
xvii
N : Number of fibers in dense membrane module
Pc : Pressure of the oil droplet or capillary pressure (KPa)
Pinlet : Pressure at the membrane inlet port (KPa)
Poutlet : Pressure at the membrane outlet port (KPa)
P : Pressure difference across the membrane ( Pa )
Q : Permeate volume flow rate (ml/min-m3/s)
q : volume flow rate (gallons/min)
r : Pore effective radius ( m )
t : Time (unit is s or hr, based on the related equation)
t fiber : Membranethickness ( m )
rfiber : Radius of the membrane fiber ( m )
1
Rt : Total membrane resistance ( m )
1
Rm : Hydraulic membrane resistance ( m )
R f : Fouling resistance ( m1 )
1
Rg : Cake layer resistance ( m )
1
R2 : Fouling resistance due to the internal pore blocking fouling mechanism ( m )
1
R3 : Fouling resistance due to the partial pore bridging mechanism ( m )
1
R4 : Fouling resistance due to the cake filtration mechanism ( m )
3
R : The gas constant ( Pa.m /mol.K )
Re : The Reynolds number
cubic feet per hour
SCFH: Standard
T : Feed temperature ( C )
t fiber : Fiber thickness (m)
pressure (KPa)
TMP : Transmembrane
xviii
2
u : Water flow velocity inside the fiber (m /s)
V0 : Initial feed volume (liter )
3
V : Accumulated permeate volume ( m )
3
: Molar volume of water ( m /gmole)
water : Water viscosity (Pa.s)
(Pa.s)
p is permeate viscosity
water 970(kg / m3 )
o / w : Interfacial tension between water and oil droplets
o / w : Contact angle of the oil droplet on the membrane surface
2
3
: The proportional constant ( m .s/m )
xix
Chapter 1: Introduction
1.1. Motivation for Research
Emulsions of oil in water are encountered in many applications including crude
oil recovery, oil refining, automotive, metal plating, food, and wastewater treatment. Due
to the presence of surfactants and co-surfactants, the oil-in-water emulsions are stable. In
recent years, the use of porous and dense membranes for separating the water from the
emulsified oil has been studied extensively, and the major challenge is minimizing the
deleterious effect of membrane fouling.
Initially, it was thought that the major
mechanism of membrane fouling was concentration polarization, which resulted in
building up a layer of high concentration of solutes and particles that were rejected by the
membrane. Various methods of disrupting concentration polarization were developed,
such as high shear stress near the membrane surface, ultrasonic vibration of the fluid, etc.
However, a detailed literature search, presented here in the following section, revealed
that concentration polarization was only one mechanism that could explain permeate flux
decline through a membrane.
The objectives of this research work were therefore
changed from studying a specific mechanism of disrupting concentration polarization, to
determining the various mechanisms for permeate flux decline when separating water
from emulsified oily waste streams.
1.2. Membrane Filtration
The membrane filtration process is a process based on the application of semipermeable membranes. A membrane is a thin layer of material that is capable of
separating particles as a function of their physical and chemical properties when a driving
1
force such as pressure, is applied across the membrane. This pressure difference is either
applied pressure, or vacuum. Membrane filtration process can be an effective alternative
for the traditional separation techniques such as flocculation, sedimentation, extraction,
and distillation.
Based on the pore size, which basically shows the membrane rejection ability,
membranes are classified into reverse osmosis (RO), nanofiltration (NF), ultrafiltration
(UF), and microfiltration (MF) membranes [1], Figure (1). All these four types of
membranes are in the same category based on their applied driving force, which is the
pressure difference across the membrane.
Reverse Osmosis and Nanofiltration, also known as hyper-filtration, are typically
applied for removing the dissolved contaminants from feed stream. A Reverse Osmosis
membrane has a pore diameter less than 1 nanometer and it’s been designed to retain the
salts and low-molecular-weight solutes. A typical Nanofiltration membrane, has pore
diameters smaller than 5 nanometer, which lies between RO and UF membrane in terms
of selectivity of the membrane, and is designed for removal of the multivalent ions (e.g.
calcium and magnesium), in softening operation [1]. Generally, NF membranes exhibit
much lower rejection of monovalent ions than RO membranes [3].
An Ultrafiltration membrane is a porous membrane, with a pore diameter ranges
from 0.005 to 0.4 µm (nominally 0.01 m). Ultrafiltration membranes are typically
categorized based on their molecular weight cut off (MWCO) rather than by a particular
pore size. In membrane industry, MWCO or the molecular weight of globular protein that
is 90% retained by the membrane, and it’s generally given in Daltons or gram-molecular
weight. Ultrafiltration membranes can be applied in different industries such as water
2
treatment, food, and pharmaceutical industry, and basically wherever the recovery of the
product compensates for the cost of ultrafiltration. These membranes can be used for
treatment of a variety of wastewaters, and also for clarifying juice, beer, and broth [3, 4].
Figure 1.1. Membrane filtration application for solute molecules removal [2]
MF membrane is a step bigger than UF membrane; with a general pore size
ranges from 0.1 to 0.2 m (nominally 0.1 m). The main application of MF is for
clarification of wastewater. MF membranes are often placed prior to other membranes
(UF, NF, RO) to retain small particles, because all other membranes are at risk of getting
fouled by microorganisms and colloids which might be in the feed stream.
Membranes are generally made in four main configurations, Plate and frame,
spiral wound, tubular, and hollow fiber. The plate and frame configuration includes a
number of flat sheet membranes, which are placed in a series or in parallel form,
separated by support plates or filtration spacers. The feed circulates between the
3
membranes of two adjacent plates. The packing density of this configuration is about 100
to 400 m 2 /m 3 , which is not high enough to make the unit efficient. The benefit of this
configuration is that, the units are simply disassembled to have access for manual
cleaning or membrane replacement. Depending on the design, permeate can is collected
separately from different support plates, and this makes the diagnosis of the faulty
membranes easy [1]. Spiral wound configuration is made up of two flat sheet membranes,
called a leaf wound, enclosing a flexible porous sheet, which collects permeate and is
sealed on three of its ends. The diameter of this module can be up to 30 cm, and its length
can be up to 1.5m. The benefit of this configuration is its high packing density (700 to
1000 m 2 /m 3 ), and also its low head loss. However this configuration is more susceptible
to fouling than plate and frame, and can’t be used without a pretreatment of feed [1].
Tubular configuration is the simplest membrane module and consists of a
membrane placed inside the wall of a porous support tube. These tubes with internal
diameter of 6 to 40mm may be placed individually inside a stainless steel or PVC sleeves
or can be potted in bundles of 3 to 151 tubes in a cylindrical housing. The advantage of
this configuration is that it doesn’t require any pre-filtration of feed stream and they are
simple to clean, but the main disadvantage of this module is that they have a low packing
density and therefore a high capital cost. The last configuration that a membrane can be
formed in is hollow fiber. Hollow fiber module is made up of a bundle of thousands, even
millions of fibers. Depending on the position of the feed flow, these modules can be
categorized into (inside-out configuration) for the flow, taking place inside the fibers, or
(outside-in configuration) for flow taking place outside the fibers. One of the advantages
of this configuration is that the packing density inversely depends on the diameter of the
4
module, and therefore these units are very compact, with the packing density of 1000
m 2 /m 3 in UF modules to 10,000 m 2 /m 3 in RO modules. The other benefit of this
configuration, which has made the UF and MF hollow fiber modules, very popular in
water treatment industry, is that the fibers are self supporting, and therefore, if the
membranes get fouled, they can be backwashed without getting damaged.
Other than these four configurations, membranes can also be made in “Rotating
Disc” configuration. This module has the advantage that it promotes secondary flows to
help depolarize the solute and particle formation at the membrane interface with solution,
which causes the improvement of flux in pressure driven processes. The disadvantage of
this module is its high energy consumption, difficulties in maintenance and scaling up the
capacity of the module [5]. In general, RO and NF membranes are typically made in
spiral wound configuration, while MF and UF membranes are mostly made in hollow
fiber configuration [6].
The use of Membrane technology is extensively increasing in water and
wastewater treatment, food processing, chemical, biotechnological, and pharmaceutical
industries because of their versatility, effectiveness, high removal capacity and ability to
meet multiple treatment objectives [3, 7]. In water and wastewater treatment, membrane
processes provide an alternative approach to conventional systems for desalination, ultrapure water production, pathogen removal from water, and solid- liquid separation. With
membranes having high removal thresholds, conventional coagulation / flocculation, and
sedimentation operations can be replaced by a single process [3].
5
However, a common problem encountered during the membrane filtration
processes, is membrane fouling. Membrane fouling leads to increase of operating costs
due to the higher pressures needed to maintain permeate flux, downtime needed for
membrane cleaning and ultimate membrane replacement [7,8,9].
1.3. Membrane Fouling and its Mechanisms
Fouling is the blockage of membrane pores during filtration, which is caused by
the combination of sieving and adsorption of particles and compounds onto the surface of
membrane or within its pores. This blockage is the limiting phenomenon that is
responsible for a flux decline over time while all operation parameters like temperature,
pressure, feed concentration and flow rate are kept constant [10]. Fouling is responsible
for most of the difficulties encountered in the generalization of membrane technology for
filtration processes, as it worsens membrane performance and shortens membrane life [1,
11].
Fouling can be broadly classified into reversible and irreversible. Reversible
fouling is referred to those types, which can be removed with applied cleaning methods
such as; backwashing, flushing, and chemical cleaning, and if they are not removable by
any means, they are considered as irreversible fouling and in these cases the membrane
cannot be restored to its original flow rate [1].
Depending on the foulant, membrane fouling can also be classified into four major
categories of;
1. Inorganic fouling/scaling
2. Particle/colloidal fouling
6
3. Microbial/biological fouling
4. Organic fouling
Among different types of membrane fouling, colloids are of particular concern
[12]. Colloidal particles are small enough to easily pass through most pretreatment
systems, and consequently block the pores of membrane and/or form a compact cake
layer or gel layer on the membrane surface [1]. A wide range of colloids in natural waters
may lead to fouling during water treatment, including natural organic matter (NOM),
silicate, iron oxides, calcite, and clays [10]. When colloids are present in the feed
solution, in low-pressure membrane systems, such as ultra- and micro-filtration,
concentration polarization, cake formation, pore blocking, and adsorptive fouling appear
to be the predominant causes of decreased permeate flux over time [13,14].
1.3.1. Concentration Polarization
Concentration polarization is essentially a consequence of the solute being stopped
by a physical barrier of the membrane rather than a fouling phenomenon. When solutes
accumulate on the membrane surface, the consequent concentration gradient reduces the
permeate flux. Concentration polarization is considered as one of the main limiting
phenomena leading to the permeate flux reduction during the membrane filtration [3].
Since stopping the filtration process, results in the disappearance of the solute
concentration gradient, this phenomenon can be considered reversible, except in the case,
when accumulated solutes on the membrane wall form a gel layer, where a hydraulic or
chemical washing process will be required [15, 16].
7
1.3.2. Cake Formation
Accumulation of the colloidal particles, which are retained due to sieving
mechanism, results in the formation of a cake layer on the membrane surface. The cake
layer may function as a filter and retain smaller particles or it may compact with time.
Cake layer formation along with pore blocking and adsorption of solute particles into the
pore walls, could lead to resistances to permeate flux that can potentially exceed the
membrane resistance. This type of fouling can be considered partially reversible, with
using hydraulic washing techniques such as backwashing or flushing [1].
1.3.3. Natural Organic Matter Adsorption
Natural organic matter (NOM), in water can cause the membrane blockage, either
by adsorption on the formed cake or by adsorption on the membrane pores. All these
phenomena can be considered as fouling due to adsorption, and they mainly depend on
the affinity that these natural organic matters have for the polymeric membrane material.
This type of fouling is irreversible or at least slowly reversible, since it needs desorption
of the organic molecules, to be removed [1]. The reversibility is only possible if the
NOM concentration in feed water drops suddenly. The decrease of concentration shifts
the adsorption equilibrium toward desorption, which leads to cleaning of the membrane.
The use of oxidizing shocks, such as chlorine shock during membrane backwashing or
chemical cleaning, generally improves the possibility of the membrane permeate flux
recovery [8, 14, 17].
8
1.3.4. Calcium, Iron and Manganese Precipitation
Ultrafiltration processes don’t have a high retention for dissolved salts and so these
dissolved salts lead to mineral precipitation on the membrane. The mineral precipitation
is one of the major causes of membrane fouling. This type of fouling is limited to the
calcium carbonate precipitation in unbalanced waters (scaling waters), or dissolved metal
precipitation metals such as iron and manganese that happens due to oxidation and
hydrolysis during the filtration process. This fouling is reversible by using backwashing,
but an increase of the cake thickness leads to an increase in longitudinal head loss of the
module which can increase the energy consumption for concentrate recycling [1,3].
1.3.5. Fouling Mechanisms
Membrane structure has a significant impact on fouling mechanisms. For
instance, if the membrane is porous, and its pores are larger than the size of the solute
macromolecules, these oil/particles droplets could enter the pores resulting in irreversible
fouling. However, if the pores are smaller than the size of particulates droplets present in
the feed, particles could accumulate over the membrane surface causing pore blockage or
formation of a cake layer [18]. Four different type of fouling involved in UF processes
are shown in Figure 1.2.
Figure 1.2.Different fouling mechanisms happening in porous membrane: (a) Complete Pore Blocking; (b)
Internal pore blinding; (c) Partial Pore bridging; and (d) cake filtration[18]
9
For dense membranes, the only fouling mechanism is deposition of material on
the dense surface of the membrane, which is similar to cake filtration. In the case of oily
emulsions, as the concentration of oil increases, free oil tends to blanket the membrane
surface, and since free oil density is less than water, it tends to float in the top section of
the membrane and blanket part of the surface.
1.4. Factors Affecting Membrane Fouling
Membrane fouling significantly depends on the operating conditions such as, feed
concentration, permeability, cross-flow velocity, back wash interval (permeation period),
backwash pressure, and back wash mode [19,20]. One of the most effective ways to
control fouling is by adjusting the cross flow velocity. Increasing the cross flow velocity
decreases the polarization degree, by increasing the mass transfer or improving other
back-transport mechanisms [9]. Generally in a cross flow filtration, the deposition of
particles on a surface, is controlled by two competitive processes: fouling caused by
deposition of particles, transported by permeate flux from the bulk solution on the
membrane surface, and removing and back transport of the particles from the membrane
surface to bulk solution by the cross flow [1,12,17]. At steady state, these two
competitive processes reach a balance in particle transport [3]. According to this theory,
the permeation flux is higher at higher cross flow velocities and lower feed colloid
concentrations [3, 8, 20].
In addition, the feed concentration, also affects the extent of membrane fouling.
With this effect, there are changes in transmembrane pressure (TMP), and permeate
quality, and with higher feed concentration of solute, transmembrane pressure increases
10
rapidly in a short time. Also, due to the role of concentration polarization, the
concentration of the membrane surface solution increases, and the fouling layer is slowly
formed. Therefore, in practical operation, the feed should be pretreated to decrease the
concentration of feed organics and retard membrane fouling resulting in improved
membrane efficiency [3].
Both the backwash interval, and backwash pressure, has effects on transmembrane
pressure, but backwash frequency, is not an important factor to influence the
transmembrane pressure. Generally as the filtration continues, the transmembrane
pressure effect on the permeation flux is such that, its increase improves the permeation
flux [10].
1.5. Conventional Membrane Cleaning Methods
Membrane chemical cleaning process, is an integral part of the operation for
microfiltration and ultrafiltration systems in water treatment industry, and has important
impact on membrane performance. Certain fouling materials are reversible by using
hydraulic means, such as backwashing, and more can be removed by enhanced
backwashing (EBW), cleaning in place (CIP), or off-line chemical cleaning or soaking
[10,21].
1.5.1. Backwashing
Backwashing, is carried out by placing membrane permeates under a pressure,
higher than the feed pressure. Usually a backwash pump, is employed to reverse the
permeate flow from the permeate side to the feed side of the membrane, at an effective
2
backwash pressure ranging from 5 to 50 lb /in , depending on the membrane employed.
11
The permeate fluid, is used to clean the membrane surface (i.e., membrane cake), and
deconcentrate the system piping. The backwash water is then wasted [1, 10]. A periodic
backwash cleans the membrane surface in cross-flow and dead-end UF/MF systems by
disturbing the onset of the mass transfer boundary layer near the surface. However, due to
backwashing, the overall membrane recovery is decreased as permeate is used [7, 22].
Therefore, backwashing is usually combined with a cross-flushing or forward flushing. A
backwash lifts the accumulated material from the membrane surface, while a cross flush
transports material out of membrane module. The efficiency of backwashing is dependant
on the frequency, process duration, flux, applied pressure, and type of fouling [10,23].
1.5.2. Enhanced Backwashing
In this method, a low dose of oxidant disinfectant, is automatically added to the
permeate flow during the backwash, to improve its cleaning efficiency. While cleaning
duration for a normal backwash is short (about 15 -30 s), with frequency of (15-45 min),
an enhance backwash usually takes long (i.e. approx 10-15 min), and the frequency is
usually every 4-6 h [10, 24, 25].
Enhanced backwash includes three steps: first backwashes with permeate to
remove the precipitated particles from the membrane surface, second, a short soak with a
low dose of disinfectant to remove adsorbed particles from the membrane, and, finally
another backwash without oxidant/disinfectant to remove the cleaning chemicals from the
system [10].
12
1.5.3. Chemical Cleaning
When normal backwash and enhanced backwash, are not sufficient to remove the
fouling layer from the membrane surface, chemical cleaning is used. In chemical
cleaning, dose of used chemicals are higher than what is used during the enhanced
backwash, and its frequency is usually lower. Chemical cleaning cannot be done
automatically, and it involves labor [1, 10].
The chemicals that are used depend on the fouling type and resistance of the
membrane to different chemical agents [3]. Once the cause of membrane fouling is
recognized, different chemical agents can be applied to remove the fouling materials and
recover the membrane flux. The common chemicals used in cleaning of MF/UF systems
in the water industry are categorized into five groups of Caustic agents,
Oxidants/disinfectants, Acids, Chelating agents, and surfactants [10, 14, 26]. Since
membrane cleaning efficiency is directly conducted to the chemical reaction between
cleaning chemicals and fouling materials, so all factors, that have an impact on the mass
transfer and chemical reaction, such as temperature, concentration, duration of cleaning
process, and hydrodynamic conditions, also affect the efficiency of the applied cleaning
method [27].
1.6. Disadvantages of the Conventional Cleaning Methods
Conventional cleaning methods, mentioned in the previous section, are ineffective
for reducing the different types of fouling [27]. Membranes exposed to back
flushing/backwashing cycles, typically experiences degradation in membrane flux, and
require a break in operation to be applied, which increases the labor and complexity of
13
the membrane filtration process [7, 15]. Also, that these methods do not completely
remove adherent films or material trapped within the porous substructure of the
membrane. For chemical membrane cleaning, using strong chemicals such as acids,
detergents, etc. for flux recovery sometimes damages the membrane materials and results
in a secondary pollution. Thus, chemical cleaning should be minimized or avoided [22,
28, 29]. In addition, using chemicals for cleaning also increases the operating cost, due to
high price of some chemicals, and also the cost of chemical waste disposal [1, 10].
1.7. Prevention and Reduction of Membrane Fouling
Membrane fouling cannot be completely avoided, but its effect can be limited by
various methods. The prevention of fouling can improve filtration efficiency and make
membrane cleaning simpler. This also reduces the need for a severe cleaning regime, and
has the potential to extend the life of polymeric membranes [3].
1.8. Pretreatment
Membrane filtration requires some measures of upstream feed pretreatment.
However, it is important to realize that the applied pretreatment method depends on the
quality of the feed and also on membrane application. Pretreatment is the first step to
control the fouling and it can be really effective [1, 10]. The simplest form of
pretreatment involves microstraining with no chemical addition. However, when surface
water is treated, various pretreatment procedures are required such as; pH adjustment,
chlorination, addition of coagulants (e.g. alum, polyelectrolytes), sedimentation,
clarification, decholorination (e.g., addition of sodium bisulphate), adsorption onto
14
activated carbon, addition of complexing agents (e.g., EDTA, SHMP), pH adjustment,
and finally polishing [1].
Some of the factors that are important and must be considered are:
Membrane material structure
Module arrangement
Feed-water quality
Recovery ratio
Final water quality
1.8.1. Physical Disruption of Concentration Polarization
Enhancement of the shear stress near the membrane surface is another effective
way to reduce the fouling. This method increases the mass transfer of accumulated
materials back into the bulk feed [30,31], and limits the concentration polarization and
cake formation. Some of the methods that can increase the local shear rate near the
membrane surface are:
•
Rotating membranes
•
Vibrating membrane modules
In both these methods, the whole module is vibrating and this vibration generates a
shear rate at the membrane surface, which decreases the formation of fouling layer or
cake layer and reduces the concentration polarization. It’s been proved that rotating disks,
and specially dose which are equipped with the vanes, show higher permeate flux, than
vibratory shear enhanced process (VSEP) modules, due to the higher maximum shear rate
which they can achieve [32,33]. The disadvantage of these methods is that since the
15
whole module is vibrating, the energy consumption is high, and because the range of the
applied vibration frequency is low, these methods are not effective enough in removing
the small particles that are trapped inside the pores.
Gas sparging or injection of gas into feed stream, has been found effective, in
improving the cross flow UF performance, by disrupting the concentration polarization
layer. Additional force such as magnetic, electric, ultrasonic and centrifugal, can also be
used to enhance the permeate flux [7, 30]. The disadvantages of the continuously use of
applied electrical fields are the corrosion of electrodes and high power consumption, but
the use of pulsed electrical fields has shown great results.
1.9. Use of Surfactants
Surfactants reduce the surface tension between the oil-water phases, enabling oil
to break up into smaller droplets, which are then readily removed from the surface of the
membrane. Surfactants also reduce the ability of the oil to adsorb on the membrane
surface, which results in blanketing the membrane pores that would have otherwise
permeated water. In this study, use of DuPont’s fluorosurfactants, FS63, when added to
the influent feed at a 0.01% concentration reduces the surface tension of water to 32
dynes/cm from its normal value of 72 dynes/cm.
16
1.10 Thesis Outline
The main goal of this thesis was to evaluate and determine the fouling mechanisms
involved in ultrafiltration processes of oily emulsions using porous and dense
membranes. This thesis is organized into six chapters, listed below:
Chapter
Title
Content
1
Introduction
2
Literature Review
3
Research Objectives
4
Materials and Methods
5
Results and Discussion
6
Conclusions and
Recommendations
Discuss the problem of fouling and the
different mechanisms involved.
Present the findings from a detailed
review of the literature on membrane
fouling
Present the objectives of the research
work that was conducted
Experimental studies conducted in this
research work and methods used for
these experiments
Analysis of the experimental data and
development of an integrated model to
understand the major fouling
mechanisms in porous and dense
membranes
Conclusions of this research work and
recommendations for future studies
Table.1.1.Thesis Outline
17
Chapter 2: Literature Review
A significant number of studies have been conducted on membrane technology
that is focused on ultrafiltration membrane performance, and applications. Since every
single membrane is susceptible to fouling, several researchers have spent considerable
time and effort to verify the fouling mechanisms and develop methods to prevent or at
least minimize them.
In the literature, many researchers have investigated the effect of the membrane
operating conditions on the performance yield. A study by Hyeok Choi et al [34], showed
that during microfiltration and ultrafiltration of biological suspension, the permeate flux
had a linear proportionality to cross flow velocity, and that increasing the cross flow
velocity had a greater effect on decreasing the MF membrane fouling than that of UF
membrane. They also found the optimum cross flow velocity for removing the reversible
fouling layer formed on the MF and UF membrane surfaces. In another study by
Seungkwan Hong et al. [35], it’s been revealed that at the transient stages of filtration, the
cross flow velocity had no influence on permeate flux, and also that increasing feed
particle concentration and transmembrane pressure dramatically decreases the flux. In
addition, in a study by Lei Wang et al.[36], the influence of operational conditions on
membrane fouling in UF of a synthetic water was evaluated .The results showed that row
water quality, membrane types, the set of permeability, membrane surface cross-flow
velocity and backwash conditions were significant for keeping the transmembrane
pressure stable and maintaining long-term stable permeability. Johannes de Bruijn et
al.[37] , studied the performance of zirconium oxide membranes for filtration of juice,
18
and it was indicated that the high feed velocity and low TMP across the membrane
resulted high permeate flux.
2.1. Ultrafiltration of Oily Emulsions
Every day, large amount of wastewaters are produced in different industries,
which can’t be discharged into the environment or drained into the sewage systems
unless they pass through different treatment processes. These effluents typically content a
complex combination of different emulsifiers, fatty acids, oil, corrosion inhibitors,
bactericides, and other chemicals [38]. An important part of these wastewaters is oilwater emulsions, which is a major pollutant of environment. In wastewater treatment
plants many traditional filtration techniques such as dissolved air flotation, centrifuge,
coalescence, adsorption, gravity settling (API oil-water separator), skimming, etcetera,
are used for separation of oily wastewaters. In addition to these techniques, for treatment
of unstable emulsions, which contain oil, droplets with diameters higher than 100 m ,
chemical filtration techniques such as coagulation and flocculation are applied. However
there are some limitations in using the above techniques in treatment of emulsions
which
contain oil droplets with diameter lower than 20 m , and also, these techniques no longer
satisfy the international standards be cause of the high concentrations of oil in their
and high operating costs [39,40].
treated waters, and also their low efficiency
The membrane technology developed in the last 30 years, have shown to be a
great effective alternative treatments for separation of Oily waters. Many researchers
have studied the performance of UF, and MF membranes for oily water treatment, and
they have shown that both UF and MF are highly effective in oil rejection, and against
19
other traditional techniques they don’t need chemical additives, and they are more
economical [40]. A comparative study between the performances of UF membrane and
some a biological treatment method, showed that UF was more effective in removing the
oil content, TSS and turbidity, and the oil concentration in its permeate was low enough
that it could be discharged into the environment [39]. A. Salahi et al. [39], studied the
effects of different operating conditions such as transmembrane pressure, cross flow
velocity, temperature and pH on the performance, fouling resistance, permeate flux, and
rejection of a polysulfone (PS), a polyacrylonitrile (PAN), and an API oil-water separator
in treatment of the oily wastewater produced in a refinery. The results showed that the
PAN membrane showed a higher permeate flux, rejection, and less membrane resistance
that PS membrane. This study also showed that the best cleaning method to recover PAN
membrane efficiency was a combination of SDS (as a surfactant) and EDTA (as a
chelating agent). Hong-Jian Li et al. [41], developed a hydrophilic hollow fiber UF
membrane, made of a new dope containing cellulose/monohydrate N-methylmorpholineN-oxide, for oil-water treatment. The membrane performance and its tolerance to
acid/alkali (pH1-14) were studied and fouling resistances were measured by osmotic
pressure-adsorption model. The results showed that the cellulose hollow fiber UF
membrane developed in this experiment not only was resistant to fouling, but also it
tolerated a wide range of pH, and showed to be a feasible and desirable choice for
treatment of oily wastewaters. B. Chakrabarty et al. [42], tested the performances of
different polysulfone UF membrane for treatment of oily wastewaters, and they indicated
that the TMP and feed properties are important factors in permeate flux, and also oil
separation. H. Ohya and J.J. Kim [43] studied the influence of the pore size on separation
20
mechanisms of MF of oil-water emulsion by applying a glass tubular membrane. They
specified the transition of separation mechanism from blocking to cake formation, and
also suggested that the pore sizes of oil concentrations and TMP to be adjusted before the
start of the filtration process. Hesampour et al. [44] tested the influences of different
operating conditions on UF of oil-water emulsion using Taguchi method. The results
showed that among different parameters, Temperature had the most impact on permeate
flux. S. Elmaleh et al. [45] used an M9 Carbosep UF membrane for filtration of a mixed
suspension containing hydrocarbons and biological solids from an active sludge plant.
The results showed that the inorganic membrane has a high retention efficiency for
hydrocarbons and suspended solids, and also that the temperature had no effect on the
particle size distribution of the suspension.
K.Karakulski and A.Kozlowski [46], tested the performance efficiency of three
tubular UF membrane made of three different materials. They showed that all three
membrane with their own certain pore size was able to retain the oil particles to a very
good extent that in fact their filtrate had less than 10 mg/l oil content which is an
acceptable amount to discharge into the natural environment. J. M. Benito et al. [47],
used two UF membranes with different pore sizes, to study the effects of different
operating conditions on permeate flux. Their experiments showed that there’s an
optimum operating condition, such as TMP, Temperature, cross-flow velocity for each
individual membrane, which gave the highest, permeate flux. M. Gryta et al. [48]
investigated the oil rejection efficiency of a combination of a UF and membrane
distillation. The measured oil concentration was reduced to less than 5 ppm after UF, and
passing through the second stage of membrane distillation completely removed the oil
21
contents, as well as remained soluble particles in the feed. N.Ghaffour et al. [38]
compared the oil rejection efficiency of four different UF membranes, two organic, made
up of Alumine(A) and Alumine zircon(AZ), and two inorganic made up of
Carbonzircon(CZ) and Carbon carbon(CC), in treatment of refinery wastewater
containing emulsions of Arabic Aramco crude oil diluted in tap water. The results
showed that the tubular UF membrane made up of CZ was a complete barrier for the oil
at any feed concentration, and had the highest permeate flux. In this research, the effects
of operating conditions were also evaluated, and the results showed that high feed
concentration had a severe impact on permeate flux reduction, and transmembrane
pressure and shear stress had relatively little effect on permeate flux loss. The severity of
the formation of fouling layer on the membrane surface depends mainly on the pressure
and the feed concentration, and is independent of the temperature and cross flow velocity
due to shearing forces. The results showed that the main parameter in controlling the
permeate flux is temperature, since it has a direct influence on the droplet size
distribution which specifies the permeation flux.
2.2. Conventional Membrane Cleaning Methods
As mentioned above, fouling is the limiting phenomenon for the wide application
of membrane, and it’s the result of concentration polarization and particle accumulation
on membrane surface. Many approaches have been studied so far, to minimize membrane
fouling, such as pretreatment of water, backwashing the membrane with or without
applying chemical agents, hydrodynamic cleaning with high cross-flow velocity and
many other mechanical or chemical methods that have been applied individually or in a
combinations to enhance membranes performances.
22
2.2.1. Backwashing
W.J.C. Van, et al [24] invented the concept of partial backwashing as an effective
method for enhancing the performance of the membrane systems where fouling occurs as
the result of either local accumulation of particles at the end of the fiber, or partially
formation of concentration polarization layer. They indicated that this cleaning method
wouldn’t be effective if whole module is affected by fouling. Heng Liang et al. [50]
evaluated the effects of hydraulic and chemical cleaning on membrane performance in
filtration process of algae-rich reservoir water. They showed that backwashing followed
by multiple forward flushing was more effective in improving membrane performance,
than backwashing alone, and that using a combination of NaOH and NaOCl increased the
cleaning efficiency. Keith N. Bourgeous et al. [51] indicated that using rapid
backwashing alleviated rapid fouling of UF membrane. Paul et al. [52] found that a
periodic backwashing could be effective in removing most of the membrane reversible
fouling, and he also developed a new control system to optimize the backwashing which
led to 40 % reduction in the required backwash permeate. J. Paul Chen et
al.[53]developed a statistical factorial design to determine the key elements as well as
their interactions in both physical and chemical cleaning of US and reverse osmosis
membranes in municipal wastewater treatment. The results indicated that physical
cleaning of both modules was highly affected by the filtration duration between
cleanings, as well as applied pressure during forward washing, and also cleanings
durations. They also realized that chemical cleaning was influenced by temperature,
concentration of cleaning solution and backwash after chemical cleaning.
23
Mansoor Kazemimoghadam et al. [54] used chemical cleaning for removing the
precipitated milk components on the polysulfone UF membrane surface. They reached to
an optimum membrane recovery by cleaning the membrane with a combination of
NaOH, EDTA, and sodium dodecyl sulfate. H. Peng et al. [55] used MF- UF hybrid
system for filtration of an oily wastewater. They found that fouling of MF membranes is
the result of aggregation of oil droplets inside the pores. They also found that cleaning
with methods such as hot water heating, stream cleaning and air backflushing is effective
in enhancing membrane performance.
2.2.2. Gas Sparging
Gas sparging is defined as injection of air bubbles into feed stream, and has been
found to be an effective, simple and economical technique in improve ultrafiltration in
both downward and upward cross-flow filtrations [56]. In an experiment by Cui and
Wright [57, 58], gas sparging effect on permeate flux and membrane rejection of a
tubular ultrafiltration membrane was investigated. In this study, the tested feeds were of
dextrans and BSA solution, and it was found that gas injecting into feed stream, even at a
very low flow rate could significantly increase the permeate flux and membrane rejection
ratio. Their results also indicated that gas sparging would have a more significant effect
on permeate flux enhancement, when concentration polarization is more severe, and it’d
most likely happen at higher transmembrane pressures, lower feed cross flow velocities,
and higher feed concentrations. In another study by S. R. Bellara, et al. [59], effect of gas
sparging on enhancing the permeate flux and membrane sieving coefficient was
evaluated at different TMPs, feed concentrations and gas to liquid flow ratios. In their
experimental work with hollow fiber membranes using dextran and albumin as test
24
media, flux enhancement of up to 63% was observed in protein filtration. It was found
that in protein ultrafiltration, flux enhancement was insensitive feed concentration, and
it’s not highly dependent to TMP either, but it was highly sensitive to gas flow rate,
however for dextran ultrafiltration it was seen that gas flow rate didn’t have much impact
on the flux enhancement.
R. Ghosh and Z. F. Cui [60] discussed the mechanism of flux enhancement in the
special case of upward slug flow in tubular ultrafiltration membrane in terms of
enhancement of mass transfer coefficient. In their study, they modeled the gas sparged
ultrafiltration based on dividing the membrane surface area into different zones in respect
to the hydrodynamic regime in those areas. By evaluating the fluid flow in these regions
they found the mass transfer coefficients for each zone and consequently the averaged
permeate flux was predicted. The simulation results showed that gas sparging would be
more effective at higher TMP and higher feed concentration, while it looses its
effectiveness at higher feed flow rate.
It’s known that the mass transfer is related to the wall shear stress, as high shear
stresses inducing higher fluxes. In a study by Cui[57], it was claimed that gas sparging
could increase the turbulence inside the membrane tubes or fibers, and therefore could
increase the shear stress[61]. Experimental results from Cui work showed that the
permeate enhancement is a result of a combination of the concentration polarization layer
disruption and also increased feed fluid cross-flow velocities at the membrane surface.
Inspired by this study, C. Cabassud et. Al [61], verified the mechanism by which a
tangential gas-flow two-phase flow in a hollow fiber ultrafiltration membrane, intended
to filtrate drinking water, could influence the formation of a particle layer, by affecting
25
the wall shear stresses. The experimental results indicated that in filtration with hollow
fiber UF membranes, air slugs effects on flux enhancement is related to their beneficial
effect on enhancing the shear stresses at the wall which could effectively sweep the
particle deposit from the surface. They also found that intermittent use of gas injection is
far less effective than a continuous injection throughout the filtration, and it was the
result of the deposit built up during the air flow interruption.
In another study by Mercier et al [62], using a tubular membrane, they evaluated
the effect of gas sparging on flux enhancement, and its sensitivity to the membrane
geometry. They realized that the difference between the inner diameters of tubular
membranes and hollow fibers results to different hydrodynamic characteristics inside
each membrane. This would cause a discrepancy in the gas sparging performance in these
two different modules, which suggests that the mechanism of flux enhancement is
different between these two membranes. Under the most filtration conditions, the flow
pattern is laminar for the hollow fiber membranes and turbulent for tubular ones, and it’s
been shown that the less turbulent the fluid is, the more effective the gas sparging would
be in enhancing the permeate flux.
The gas sparging method effectiveness mainly corresponds to its effect in
disrupting the concentration polarization layer. It does not seem to be an effective in
membrane systems where the main mechanism of fouling is pore blocking and cake
formation [58].
26
2.3. Ultrasonic Cleaning of Membranes
Ultrasound waves have a frequency range from 16 kHz to 10 MHz. It is above the
human hearing range. Ultrasound waves, as with any sound waves, consist of series of
compression and expansion waves made in the molecules of the medium though, which it
propagates. Compression cycles exert a positive pressure on the liquid – pushing
molecules together. Expansion cycles may exceed the attractive forces of the liquid
molecules, and cavitation bubbles will form. Cavitation can be defined as: the formation,
growth, and implosive collapse of bubbles [63, 64]. And it occurs at frequencies of
roughly 20-1000KHZ. Collapse of these bubbles has a significant effect on the chemical
and physical properties of the medium.each of these bubbles can behave like a hot spot,
that generates temperatures of up to 4000 K, extreme heating or cooling rates of 1010 K/s
and pressures exceeding 100 MPa are observed in the transient bubbles while the bulk
fluid remains at ambient temperature and pressure. The implosion happens with lifetimes
shorter than 0.1 μs [65]. In a heterogeneous liquid-solid system, a collapse near a surface
produces a nonsymmetrical inrush of fluid to fill the void, which leads to the formation of
the liquid jets. These liquid jets may lead to particle release from a fouled surface as a
result of ultrasound irradiation. Along with these microjets, cavitational mechanisms such
as microstreaming and mictostreamer are also important in detaching the particles from
the surface, while turbulence associated with ultrasound, i.e. acoustic streaming plays a
role in the transport of particles away from the surface.
In a study by T.Kobayashi [27] ultrasound effect on UF of Dextran solution with
polyacrylonitrile membrane at different frequencies and power intensities was
investigated. The result showed that at lower frequencies, the cavitation had more power
27
and would lead to decrease of fouling layer and increase of mass transfer through the
membrane. Also they found that increasing the power and decreasing the feed
concentration would increase the permeate flux. Beside these factors, direction of
ultrasound propagation was also affecting the permeate flux, but its affect was negligible
in compare to power intensity and frequency of the ultrasound waves. Xijun Chai et al
[28] studied the effect of ultrasound cleaning on four polymeric membranes made of PS,
PAN8, PAN15, and PVDF in cross-flow filtration of peptone solution. The applied
cleaning procedure included three steps: sonication, water cleaning, and water cleaning
under sonication. They found that water cleaning under sonication had a better affect on
increasing the permeate flux, than either of these two methods separately. Furthermore,
they evaluated the effects of different operating temperature and peptone concentration
on PS membrane performance .The results indicated that increasing the temperature
increases the cleaning efficiency for water filtration under sonication.
The intensity of cavitation can be affected by: feed water properties such as
viscosity, amount of dissolved gas, and vapor pressure, Operating conditions like
Temperature and static pressure, ultrasound frequency and intensity. The most important
factor in controlling the improving the ultrasound cleaning efficiency is its frequency.
The lower the frequency, the larger the bubbles will be and subsequently its implosion
energy release would also be higher.
28
2.3.1. Effect of Sonication on Polymeric Membranes
Although ultrasound is widely used for cleaning of membranes in different areas of
industrial process engineering, and it’s led to significant results in permeate flux
improvement, but it’s been also observed in some studies, especially those with the
polymeric membranes, that ultrasound caused some damage on membrane. Of course the
influence of ultrasound irradiation on the membrane is dependant on the factors such as,
membrane material, power intensity, distance between membrane surface and ultrasound
transducer, but it’s mainly come from the main mechanism behind ultrasound cleaning,
which is collapse of the cavitation bubbles near the surface. Although this collapse leads
to particle detach from the membrane surface, but it gradually damages the structure of
the membrane, and eventually leads to its failure. Verification of the exact cause of
damage has been an area of investigation.
Plesset [67] conjectured the irradiated shock waves from bubbles explosion could
be the reason behind the damage occurrence, but he also noted that this collapse needs to
happen in the vicinity of the substrate and that the bubbles have to be low-gas contained.
Bubble collapse and explosion causes extreme temperature and liquid jet velocities,
which could significantly damage the surface structure. It was indicated in Plesset study
that the liquid jets that are spread out of collapsed bubbles could also cause surface
damage. In a study by Markov and Rosenberg [68], high speed cinematography was used
to detect the causes of surface damage in ultrasonic cleaning. Their results indicated that
the shock waves formed by cavity explosion resulted in disintegration of the surface.
They also noticed that the gradual peeling of pieces of the membrane occurred due to
vapor bubbles penetration between the film and solid surface placed under that.
29
Reviewing literature on membrane cleaning by means of a traditional laboratory
ultrasonic bath, we noticed that the cleaned membrane, presented an unexpected behavior
after sonication. For instance, a study by Isabelle Masselin, et al [69], and effect of
ultrasound on the polymeric membranes was investigated. This study was specifically
important for us, since they have shown the effect of ultrasonic cleaning on the PES
ultrafiltration membrane, which is identical to what we’re using in our system. Their
results showed that over the three polymeric membrane tested in their study; polyether
sulfone(PES), polyvinylidene fluoride(PVDF) and polyacrylonitrile (PAN), PES
membrane got significantly damaged over its surface , while the others didn’t much. A
comparison between the microscopic images of a non-irradiated and irradiated PES100
membrane indicated membrane surface degradation under ultrasonic stress which would
consequently led to an increase in pores size for large pores, an overall increase in pore
density and membrane porosity and to the formation of large cracks formed between the
adjacent pores and at the edges of the membrane.
This result is consistent with what Xiao-li Wang et al. [70] found from their
experiment. They used ultrasound on four different polymeric membranes including PES,
to evaluate the effect of ultrasound on membrane polymeric structure. They noticed that
membrane filtration behavior dramatically changed after irradiation in respect of its
permeate flux and the rejection rate. In addition, it was found out that the membranes are
affected by sonication over their entire surface, and also that the ultrasonic irradiation
leads to formation of large cracks and in some cases large holes on membrane surface.
From all these findings it’s obvious that in spite of their great benefits in
enhancing membrane performance and filtration process, ultrasonic waves have to be
30
used with care. The nature of the polymeric membranes and specifically PES membranes
makes them so sensitive to the ultrasonic waves at the chosen frequency, as well as
irradiation duration and intensity. Since maintaining the membrane integrity is the first
priority in a cleaning process, and the main goal of this project, is applying a cleaning
procedure with the minimum damaging effect on membrane surface structure, we’ve
come to this conclusion that under the operating conditions that we would conduct our
tests, and in spite the fact that our tested membranes are polymeric, the backwashing with
permeate fluid and also warm water would be a suitable choice for enhancing the
membrane performance.
Except the sensitive nature of the PES membrane to the ultrasonic irradiation, our
tested membrane is a porous monolith and like any other porous membrane is likely to
suffer from the pore blockage during the operation. It’s well known that the ultrasonic
cleaning benefits in enhancing the permeate flux is basically the result of the waves effect
on disruption of the cake layer, and it’s not highly beneficial to removing the adsorbed
foulants on the pores walls or on pores openings. As the result, we didn’t find the
ultrasonic cleaning, an effective method for enhancing our tested membrane efficiency.
2.4. Fouling Mechanism in Ultrafiltration
In recent decades many studies have been done on empirical models for the
explanation of permeate flux decline with time during an ultrafiltration process. One of
the most common models to determine the fouling mechanisms involved in membrane
filtration is the Hermia’s modeling. This method of modeling was originally developed
for the dead-end ultrafiltration membranes, but further it was modified by R W. Field et
31
al [71] for cross-flow ultrafiltration membranes. Abdolhamid Salahi et al. [18] used the
Hermia’s models to evaluate the fouling mechanisms occurred in UF of a refinery oily
wastewater. They found that cake layer formation on the membrane surface followed by
partial pore bridging is the best fit to the experimental data and under different tested
experimental conditions. They also described the fouling mechanism in more details by
dividing the filtration curve into different regions to show the partial contribution of each
mechanism. S. T. D. de Barros et al. [72] evaluated the flux behavior of ceramic and
polysulfone membranes during cross-flow UF of depectinized pineapple juice. They
modeled the observed flux decays in their experiments by means of the modified forms of
Hermia’s mechanisms, and by estimation of the models parameters according to a
nonlinear regression optimization method, they found the dominated mechanisms for
hollow fiber PS membrane and ceramic membrane to be cake formation and pore
blocking respectively.
In another study by Abdolhamid Salahi et al [73], Hermias models were applied
for evaluation of fouling mechanisms in cross-flow UF polymeric membranes. The
results from UF and MF membranes showed that the best fit to the experimental curve
was cake filtration model and the worst one was complete pore blocking. T. Mohammadi
et al [74] investigated the fouling mechanisms in reverse osmosis during filtration of an
oily emulsion. Using Hermia’s models they found the partial pore bridging to be the best
fit to their experimental data. They also believed that complete pore blocking model is
oversimplified assuming that molecules block the pores without having superimposition
effect on one another. A.L. Lim and Renbi Bai [75] studied the type of fouling
mechanisms in MF of activated sludge wastewater. They found that the main types of
32
fouling correspond to initial pore blocking followed by cake formation. They indicated
that pore-blocking mechanism is responsible mechanism for initial flux decline and cake
formation is responsible for the slower flux decline over filtration duration. M. Cinta
Vincent Vela et al [76], used Hermia’s models to investigate the fouling mechanisms
involved in the cross flow UF of polyethylene glycol. They found that the best fit to
experimental data corresponds to the cake formation mechanism followed by the partial
pore bridging under all tested experimental conditions. They also conducted a more detail
investigation on determining the fouling mechanisms by dividing the experimental curve
into different regions that attribute to different fouling mechanisms.
33
Chapter 3: Thesis Objectives
The original objectives of the proposed research work were as follows:
1. Conduct a complete survey of current methods to reduce ultrafiltration fouling and
understand their advantages and disadvantages;
2. Study the papers in detail that employ ultrasonic methods to reduce ultrafiltration
fouling;
3. Study the modes of vibration of a rectangular membrane, which can be employed to
reduce ultrafiltration fouling;
4. Conduct experiments in which an ultrasonic flat element will be used to vibrate a flat
membrane at different resonant frequencies;
5. Conduct experiments with various ultrafiltration membrane/solute combinations,
which includes the following:
(a) Regenerated cellulose, 10,000 MWCO and Polyethylene Glycol with average
molecular weight of 10,000
(b) Polysulfone, 100,000 MWCO and Dextran with average 162,000 molecular
weight
(c) Actual laundry water obtained from CINTAS and GE/Osmonics membrane
6. Measure the water flux with and without ultrasonic vibration of the flat membranes,
employing different resonant frequencies of the flat membrane sheet
7. Develop a simple mathematical analysis of the impact of the membrane vibrating
modes on ultrafiltration fouling
8. Write papers and thesis
34
However, when a detailed literature search was conducted, it was found that
ultrasound causes significant physical damage to the structure and surface of polymeric
membranes. Although the factors such as, membrane material, power intensity, distance
between membrane surface and ultrasound transducer, are influential on the efficiency
of ultrasound irradiation, but the vital parameter is the quality of the bubble collapses
near the membrane surface. Despite the fact that this collapse leads to particle
detachment from the membrane surface, but it also gradually damages the structure of
the membrane, and eventually leads to its failure.
Based on what’s been reported in literature about the effects of using ultrasound
for cleaning of polymeric membranes, it’s obvious that in spite of their great benefits in
enhancing membrane performance and filtration process, ultrasonic waves have to be
used with care. The nature of the polymeric membranes and specifically PES membranes
makes them so sensitive to the ultrasonic waves at the chosen frequency, as well as
irradiation duration and intensity. Since maintaining the membrane integrity is the first
priority in a cleaning process, and the main goal of this project, is applying a cleaning
procedure with the minimum damaging effect on membrane surface structure, we’ve
come to this conclusion that under the operating conditions that we would conduct our
tests, and in spite the fact that our tested membranes are polymeric, the backwashing with
permeate fluid and also warm water would be a suitable choice for enhancing the
membrane performance.
Except the sensitive nature of the PES membrane to the ultrasonic irradiation, our
tested membrane is a porous monolith and like any other porous membrane is likely to
suffer from the pore blockage during the operation. It’s well known that the ultrasonic
35
cleaning benefits in enhancing the permeate flux is basically the result of the waves effect
on disruption of the cake layer, and it’s not highly beneficial to removing the adsorbed
foulants on the pores walls or on pores openings. As the result, we didn’t find the
ultrasonic cleaning, an effective method for enhancing our tested membrane efficiency.
Based on these detailed literature searches, the specific objectives of the proposed
research work were changed, to the following:
1. Assemble an experimental apparatus that can be used to test two types of
membranes: (1) Porous membrane; and (2) Dense Membrane;
2. Conduct a detailed literature search to select two commercially available
membranes that can be used for separation of water from an oily-water emulsion,
one which is porous and the second has a dense structure;
3. Collaborate with a local company to conduct experimental testing of the two
membrane types, using a real oily emulsion;
4. Experimentally measure the water permeation rates as a function of inlet feed
pressure, to determine the “optimum” feed pressure that maximizes the water
permeation rates;
5. Operate the membrane unit at the “optimum” feed pressure to determine the
permeate flow rate as a function of time; Take influent and reject flow water
samples to determine the oil concentration and characterize the type of oil in the
oily emulsion; Obtain experimental data with and without use of fluorosurfactant
for the porous membrane and sparged air for the dense membrane;
6. Determine the backwashing procedure and protocol for the porous membrane;
36
7. Develop a model that can quantitate the role of each fouling mechanism (refer to
Figure 1.2) for each type of membrane;
8. Write papers and thesis, presenting the findings of the proposed research work.
37
Chapter 4: Materials and Methods
4.1. Selection of Membranes
As mentioned earlier, two types of membranes were selected for testing in the
proposed research work:
(1) Porous Membranes: Porous membranes are manufactured in various geometries,
including flat sheets, pleated flat sheets, hollow fibers, tubular, monoliths, and spiral
wound. Spiral wound membranes suffer from fouling issues mainly due to lack of
sufficient turbulence near the membrane surface, although in recent years, use of wider
spacers and higher operating liquid velocities have attempted to overcome this problem.
In most membranes, tangential flow is used as opposed to dead-end filtration, to
minimize deposition of particle and rejected solute on the membrane surface, as shown
below in Figure 4.1.
Figure 4.1. Tangential cross-flow filtration [77]
In this research work, a monolith structure, porous, membrane, made of
hydrophilic polyether sulfone material, was selected, since it exhibited the following
advantages: (1) high surface area; (2) high permeation capability for water at low trans38
membrane pressure difference; (3) capability to withstand fouling; (4) permanently
hydrophilic membrane surface; (5) nominal pore size of 0.02 mm, which does not allow
any oil to enter the pores, unless the feed pressure exceeds a high limit; (6) less
probability of fiber breakage due to pressure fluctuations, especially during start-up and
shutdown of the membrane module; (7) able to be backwashed; and (8) low cost,
compared to ceramic membranes.
The cartridge used in our experiments was a cross-flow UF cartridge, a cross flow
UF cartridge, dizzer® XL 0.9 MB 38W manufactured by inge water technologies AG.
The module and membrane specifications are given in Table 4.1 and Table 4.2:
Polyether Sulfone Membrane Module Data
Membrane area
38 m2
Length with end cup (L)
1180 ± 3 mm
Length without end cup (L1)
986 ± 1.5 mm
Outer diameter module (D)
250 mm
Housing material
PVC-U, grey
End cap material
PVC-U, grey
Pressure max.
70 psi
Temperature range
32-104 °F
Table 4.1. Design characteristics of the membrane cartridge
39
Polyether Sulfone Membrane Data
Membrane Material
Polyether sulfone
Capillaries per fiber
7
Membrane Inside Diameter
0.9 mm
Membrane Outside Diameter
4.0 mm
Pore Size
Approx. 0.02 μm
Molecular weight cut-off
100 KD
Table 4.2. Characteristics of the porous membrane
Figure 4.2. Structure of the porous membrane fiber [78]
(2) Dense Membranes: The main advantages of dense membranes is their ability to
produce a higher quality permeate, since oil cannot penetrate the dense layer and
elimination of pore fouling mechanisms, such as pore blocking, internal pore blinding,
and pore bridging. Deposition on the membrane surface is still possible, although with
high liquid operating velocities, this can be minimized considerably. The main
40
disadvantage of dense membranes is their lower water permeation rates, compared to
porous membranes, which increases the needed surface area.
The
selected
dense
membrane
is
hollow-fiber,
manufactured
from
cupraammonium regenerated cellulose. The module and membrane specifications are
shown in Tables 4.3 and 4.4.
Regenerated Cellulose Membrane and Module Data
Total Membrane Active Area
12.9 m2
Module Housing Material
4-inch diameter CPVC
Temperature Range
32 -180 °F
Pressure max.
55 psig
Membrane Material
cupraammonium regenerated cellulose
Membrane Internal Diameter:
420 μm
Membrane Outer Diameter:
500 μm
Table 4.3. Hollow-fiber module and membrane characteristics
a)
b)
Figure 4.3. Image of the cuprammonium regenerated cellulose hollow fibers [79]
41
4.2. Experimental System
Figure 4.4 shows experimental setup used in all the experiments. The oily
wastewater treatment was operated in cross flow batch concentration mode. During
filtration process, the feed was pumped from the tank (Tk-101) by means of a horizontal
centrifugal pump P-101, to the bottom of the membrane module where it was fed to the
membrane channels. The experiments were ran in a complete recycle mode of filtration,
where retentate and permeate flows were continuously recycled back into the feed tank
by using V-09 and V-10. Total recycling of the retentate and permeate helped to keep the
feed concentration almost constant. There was a by-pass prior to the feed inlet V-02 and a
by-pass before the membranes inlets V-04, to recycle the extra feed to the tank. The main
flow rate and the desired transmembrane pressure were adjusted using the valve V-04 in
the bypass flow and valve V-10 in the retentate flow.
The pressures were measured prior to the venturi, and at the inlet and outlet ports
of the membrane, with pressure gauges (PGM, Omega) ranges from 0-100Psi (0-690
KPa). To examine permeate during the runs, it was collected in the permeate tank TK102, with using V-08.
The cleaning method that was applied for porous polyether sulfone UF
membrane, was backwashing using the permeate flow. The backwashing loop included a
backwashing tank (TK-102), a horizontal centrifugal pump (P-102) and four valves (V05, V-06, V-08, V-09). Permeate collected in tank TK-102, was pressurized by the pump
P-102, and derived to the membrane channels from the permeate side. The volume of the
permeate sample collected in tank TK-102, was measured and the permeate flow rate was
calculated by dividing the volume of permeate sample by the sampling time. The tank
42
volume was 130 Gallon or 492 Litter. Experiments were conducted at TMPs lower than
70 Psig for polyether sulfone membrane, to prevent membrane compaction, and below 55
Psig for cuprammonium regenerated cellulose membrane. The experiments were done at
room temperature of 25 °C, and feed temperature of 40°C.
Figure 4.4. Schematic figure of the experimental set-up
43
Figure 4.5. Photographs of the experimental system, operated at Ford Motor Company Plant, Sharonville,
Ohio
44
4.3. Cleaning Procedure
In this project, backwashing with clean warm water of 50 ( C ), and also with the
permeate itself, were used as a clean-in-place (CIP) method to increase the efficiency of
porous monolithic polyether sulfone membrane in filtration of oily wastewater, and for
the hollow fiber dense membrane, air injection was applied as a continuous cleaning
method throughout the filtration duration.
Backwashing process is defined as a periodic mode in which the membrane fibers
are cleaned by pushing pressurized water in the reverse direction of membrane filtration,
Figure 4.6 [1]. The backwashing loop is shown in Figure 4.6 in bold line. As it’s been
indicated backwashing was operated in the opposite direction of ultrafiltration by sending
the permeate flow through the membrane fibers from permeate side. In order to start
backwashing the membrane, the system was shut down completely. The backwash pump
P-102 pushed permeate (or water) flow from the tank Tk-102 to the membrane inlet ports
where permeate forced through the fibers. Pushing the permeate from the opposite side
through the fibers, made precipitated solids to be lifted off and flushed out of the
membrane pores and then out of lumens to the retentate line, where they recycled back to
the feed tank.
45
P
V-12
I-04
Retentate flow
By-pass flow
V-10
Air in
Feed tank
V-02
V-01
I-01
P
Feed
flow
P-103
I-05
FI
Tk-101
I-02
V-04
V-10
PESM UF
Membrane
Module
Cellulose
UF
Membrane
Module
I-03
P
V-06
V-05
V-03
P-101
V-07
V-09
Tk-102
P-102
V-08
Permeate flow
Figure 4.6. Schematic figure of backwash system
For the dense membrane however, the cleaning method that was introduced, was
air injection into feed stream, as it’s shown in Figure 4.7 in bold line. When the valve V03 is completely open, the entire feed stream preferably flows through the valve instead
of the venturi, and by partially closing this valve; a portion of feed flows through the
venturi I-02 and as it passes through the constricted section of venturi, it sucks the air.
The volumetric flow rate of air addition can be monitored by the rotameter I-05. No effort
was done to observe and control the pattern of gas-liquid two-phase flow inside the
membrane fibers. Pressure gauges were mounted before and after the membrane module
to measure the inlet and outlet pressure, where the mean of these two pressure values
gives the transmembrane pressure (TMP). Different TMPs were applied to the membrane
module by adjusting the V-10 valve at the outlet of the module. Permeate flow rate was
measured by the timed collection of permeate. The experiments were conducted at room
temperature around 25°C.
46
Figure 4.7. Schematic figure of air injection system
In order to evaluate the effect of adding surfactant to the feed stream, all the
conducted experiments on two polyether sulfone and cuprammonium regenerated
cellulose membranes, were repeated under comparable related operating conditions, with
adding surfactant to the feed stream. For this purpose, before starting the filtration, the
fluorosurfactant, DuPont, FS-63 Capstone surfactant, was added to the feed tank with
concentration of 0.05 ( l /m 3) and it was mixed well with the feed by recycling the fluid to
the tank for 15 minutes.
47
4.4. Theory of Ultrafiltration of Oily Wastewater
Permeation flux is a critical parameter to assess performance of the membrane,
and it indicates the amount of permeate and the product rate [40]. The permeate flux is
calculated as follow:
Jp
Q
3600
A0
Eq.4.1
where:
J p is the permeate flux ( l /m 2 .h )
Q is the permeate volumetric flow rate (l/s)
A0 is the total membrane active area ( m 2 )
The permeate flux can also
be expressed by Darcy’s law as:
Jp
TMP
3.6 10 9
T Rt
Eq.4.2
where:
TMP is transmembrane pressure ( KPa )
T is feed viscosity ( Pa.s )
Rt is total membrane resistance(
m1 )
As it’s been explained
in [6], the feed viscosity can be expressed as a function of
temperature as follows:
water (1.784 103 ) (5.75 105 T) (1.1106 T 2 ) (108 T 3 )
48
Eq.4.3
where T is water temperature ( C ).
In our experiments, the permeate tank was open and exposed to ambient
condition. Therefore the transmembrane pressure (TMP) was calculated as the average of
the inlet pressure ( Pinlet ) and outlet pressure ( Poutlet ) or:
(P Poutlet )
TMP inlet
2
Eq.4.4
The total membrane resistance can be calculated as:
Rt Rm R f
Eq.4.5
where:
Rm is hydraulic membrane resistance( m1 )
R f is fouling resistance( m1 )
Flux decline percentages
were calculated as follows:
FD(%) (1
Jf
) 100
J0
Eq.4.6
where J i is the initial permeate flux ( l /m 2 .h ) and J f is the final permeate flux ( l /m 2 .h ).
Figure 4.8. Demonstration of the contact angle of a liquid sample [80]
49
Figure 4.8 shows the principle of contact angle. Basically the contact angle is a
quantitative measure of the wettability of a solid surface by a liquid. It is defined
geometrically as the angle formed by a liquid, and the tangent line to the upper surface
where three phase boundaries where a liquid, gas and solid intersect. The contact angle
between any droplet and a solid surface is indicative of a very important characteristic of
that solid surface called “hydrophobicity”. Generally, Surfaces are categorized in to two
major categories of hydrophilic and hydrophobic surfaces. Usually in defining the
hydrophobicity and hydrophilicity, the contact angle range is defined based on the water
droplet on a solid surface, and so when 90 surface is hydrophobic, and when
0 90 surface is hydrophilic. However, in our experiment we are dealing with the
case of oil droplet on the solid surface, or membrane surface in our study, this definition
is reversed.
In our study, if 90 the membrane is hydrophilic and when 0 90 , the
membrane is hydrophobic, which means water can’t be dispersed over the membrane
surface, because the membrane is wet by oil droplets, that have spread out over the
surface like a layer.
Surface chemistry, interactions between solute-solute and solute-membrane
surface and wettability are major parameters in oil-water separation by ultrafiltration, and
can be expressed in terms of some elements, like the interfacial tension between water
and oil droplets ( o / w ), the contact angle of the oil droplet on the membrane surface
( o / w ), the pore effective radius (r), and the pressure of the oil droplet ( Pc ) or also called
capillarypressure.
50
Capillary pressure is expressed by following equation:
Pc
2 o / w cos o / w
r
Eq.4.7
When the system is ran under high operating pressure, after some time the
membrane get fouled and oil droplets spread out over the membrane surface, causing a
change in surface chemistry. And as mentioned above, change of surface chemistry
means a change of critical surface tension, pore size and contact angle of the membrane.
Typically, 90 and the membranes are hydrophilic (leading to high permeate fluxes),
and the capillary pressure of is negative. This keeps the oil droplets from entering the
membrane pores against the operating pressure. A major and critical point in preventing
the membrane fouling is keeping the operating pressure bellow the approximate value of
the capillary pressure of oil droplet, because if the operating pressure exceeds the
capillary pressure value, the oil droplets could be deformed and penetrate through the
membrane pores, pollute the permeate, and further adsorb in pores causing membrane
fouling [40,81].
The UF membranes used in our experiments were hydrophobic. During filtration
with porous membrane, small foulant particles mainly oil droplets existed in feed stream,
can enter the membrane pores, coalesce and potentially block the pores. This pore
clogging leads to membrane fouling and therefore permeation flux reduction. In the case
of filtration with dense membrane, although there’s no pores to be blocked, but deposits
can coalesce on the surface and make a resistant layer in way of water transfer through
the membrane wall.
51
4.5. Models for Membrane Fouling Mechanism
Membrane fouling in cross-flow ultrafiltration is the key factor that challenges the
technological viability of ultrafiltration process. Typically in ultrafiltration with
polymeric membranes permeate flux variation over time is an initial quick decline
followed by a long gradual decrease. Therefore, modeling of flux decline to determine
the fouling mechanism in ultrafiltration of macromolecules is essential from the
technological and economical point of view.
4.5.1. Fouling Mechanisms Involved In UF Using Porous Monolith Polyether
Sulfone Membrane
Membrane structure has a significant impact on fouling mechanisms. For instance,
if the membrane is porous, and its pores are larger than the size of the solute
macromolecules, these oil/particles droplets could enter the pores leading irreversible
fouling. However, if the pores are smaller than the size of particles droplets existed in the
feed, particles could accumulate over the membrane surface causing pore blockage or
formation of a cake layer [18]. Four different type of fouling involved in UF processes
are shown in Figure 4.9:
Figure 4.9. Different fouling mechanisms happening in porous membranes [18]
52
In general fouling mechanisms could be the result of a) complete pore blocking:
when the pores are sealed; b) Internal pore blinding: when particles not rejected by the
pore entrance is adsorbed or trapped on the pore wall or in the membrane support; c) Pore
bridging: where the pore entrance is partially obstructed d) particles droplets deposition
on the membrane surface forming a cake/gel layer [72]. During UF process these
mechanisms may occur simultaneously. As mentioned earlier, the intensity of the
membrane fouling is dependent to three major factors: operating conditions, feed
characteristics and membrane type. Typically operating conditions are important factors
in determining the degree of fouling, in particular transmembrane pressure plays a vital
role as its increasing could increase the formed cake layer density and lead to complete
pore blocking [18,72].
In the past two decades many studies have been done on empirical models for the
explanation of permeate flux decline with time during an ultrafiltration process. In spite
of the precision of these empirical models, they are not able to describe adequately the
fouling mechanisms occurred in membrane filtration. Theoretical models on the other
hand, can explain the fouling mechanism to some extent. However the complete
theoretical models developed in the literature suffer from inaccuracy in prediction of flux
decline with time, if they are applied without experimental data getting fit to them to
estimate at least one of the model constants. As the result, semi-empirical models, those
with model constants having actual physical meanings have shown to be suitable
solutions to achieve an accurate prediction of the flux decline in UF and also to determine
the fouling mechanisms [18].
53
So far, quantification of the impacts of operating parameters such as
transmembrane pressure on membrane fouling in UF is not completely known. For this
purpose, there is not a completely theoretical model that describes UF dynamics with
acceptable accuracy for engineering applications [18]. In this study, the effect of
transmembrane pressure on cross flow UF of oily wastewater and involved fouling
mechanisms are investigated. The applied empirical models to explain permeate flux
behavior and to determine the involved fouling mechanisms are the Hermia’s models
[82]. Hermia developed four empirical models that correlate with four main types of
fouling: complete pore blocking, pore bridging, and internal pore blinding and cake
formation. The models parameters have a physical meaning and correspond to the
comprehension of the fouling mechanisms [76]. Hermia’s models were originally
developed for dead-end filtration and based on the constant pressure filtration laws.
However, it’s worthy to be mentioned that in spite of the different sets of applied mass
and momentum equations for dead-end and cross-flow filtrations, many researchers have
applied the Hermia’s models to cross-flow filtrations. By comparing the model’s
predicted values with the results from conducted experiments, they found that the models
perfectly predict the models under different operating conditions [74, 76, 83, 84, 85]. In
Theory, it’s assumed that in cross-flow filtration system reaches to a steady-state, but it
should be considered that this steady-state is a quasi-steady-state condition not a rigorous
one, and this is due to the fact that although permeate flux is almost constant for a long
time scale, it actually decreases very slowly over time until a permeate flux of zero is
achieved for a very long time scale [18].
54
Hermia’s model is expressed by the following general differential equation:
(
d 2t
dt
) K( ) n
2
dV
dV
Eq.4.8
where:
V is accumulated permeate volume ( m 3 ), t is filtration time (s), and K and n are
phenomenological coefficient and general index, respectively, both depending on type of
fouling depicted in Figure 4.9. (K unit depend on the parameter n in equation 4.8).
4.5.1.1 Complete Pore Blocking Model (n=2)
When particles sizes are larger than membrane pore size, the portion of the
membrane area, which is reached out by particles, is blocked as a consequence of pore
obstruction with pore sealing. Hermia concluded that n was equal to 2 in this case. For
n=2, Eq.4.8 is expressed in terms of permeate flux versus time as [85]:
ln(J p ) ln(J0 ) Kc t
Eq.4.9
where:
J p is the permeate flux ( l /m 2 .h )
J 0 is the initial permeate flux ( l /m 2 .h )
constant
K c is the equation
The parameter K c can be described as a function of the membrane surface
obstructed per unit of the total permeate volume that permeates through the membrane,
K A , and as a function
of the initial permeate flux, J 0 , as it’s shown by Eq.4.10 [86]. As
the result, the active membrane area reduces due to the pores being completely clogged
[18].
55
K c K A J0
Eq.4.10
4.5.1.2. Internal Pore Blocking Model (n=3/2)
When the solute molecule size is smaller than the membrane pore size, pore
blocking occurs inside the pores [74]. This model considers that particles droplets either
adsorb or deposit over the pore walls. Therefore the volume of membrane pores declines
proportionally to the permeate volume permeates through the membrane. As a result, the
cross sectional area of the membrane pore decreases with time, and consequently
membrane resistance increases [73]. It’s been considered that pores lengths and diameters
are constant along the entire membrane. Considering these hypotheses, Hermia [82]
concluded that n was equal to 3/2 in this model.
For the internal pore blocking mechanism, permeate flux is expressed, as a
function of time as follows [80]:
(
1
1
) ( 1/ 2 ) K st
1/ 2
Jp
J0
Eq.4.11
The parameter K s is expressed as:
K
K s 2 B A J1/0 2
A0
Eq.4.12
4.5.1.3. Partial pore bridging model (n=1)
When the particles sizes are similar to the membrane pore size, Partial pore
bridging occurs. As in the complete pore blocking model, this model considers that, solid
particles or macromolecules that at any time reach an open pore might block it.
56
Nevertheless, dynamic situation of blocking/unblocking may occur. Also, particles may
bridge a pore by blocking the opening but not completely seal it [73]. Considering these
hypotheses, Hermia [82] concluded that n was equal to 1 in this model.
Mohammadi et al. [74] linearized Eq.4.8 for n=1, and expressed permeate flux as
a function of time, resulting in:
1
1
Kit
J p J0
Eq.4.13
The parameter K i can be expressed as a function of blocked membrane surface
per unit of the total permeate volume that passes through the membrane, K A , Eq.4.14.
the membrane surface that is not blocked diminishes with time [87]. As
The portion of
pore continuously
the result, the probability of a molecule obstructing a membrane
decreases with time [80].
Ki KA
Eq.4.14
4.5.1.4. Cake Layer Formation Model (n=0)
As in the case of pore blocking model, solute molecules are greater than the
membrane pore size, and they can’t penetrate inside them [87]. In this model, a cake layer
forms on the surface. Nevertheless, the concentration of the solute molecules is
considerable and they can deposit on the surface and also on the already deposited layer
of solute molecules.
57
For cake layer formation model, permeate flux is given as a function of time by linearized
Eq.4.15:
(
1
1
) ( 2 ) K gl t
2
Jp
J0
Eq.4.15
The parameter K gl is defined as:
K gl 2
K D Rg
J0 Rm
Eq.4.16
58
Chapter 5: Results and Discussions
5.1. Filtration of Oily Wastewater using Porous Monolith Polyether Sulfone
Membrane
In this section, the results are obtained for filtration of oily wastewater with
polyether sulfone membrane and the effect of transmembrane pressure on permeate flux
has been studied. In addition, backwashing as a cleaning method, was applied using
permeate flow, with different cleaning duration and different intervals, to obtain the most
efficient cleaning condition.
5.1.1. Effect of Transmembrane Pressure
Figure 5.1 and 5.2 show variation of the permeate flux with time for ultrafiltration
of oily emulsion with and without surfactant respectively, at different transmembrane
pressures (TMP). The graphs show the typical flux decline during ultrafiltration. As oil
droplets and other macromolecules existing in the feed stream accumulate near, on and
within the membrane, they reduce the permeate flux by blocking or constricting pores
and by forming a layer of additional resistance to water permeation through the
membrane.
59
Figure 5.1. Variation of permeate flux ( l /m
with surfactant
2
.h ) with time at different transmembrane pressures (TMP )
Figure 5.2. Variation of permeate flux ( l /m
without surfactant
2
.h ) with time at different transmembrane pressures (TMP)
Figure 5.3 and 5.4 indicate the positive effect of the pressure on the permeate
flux, with and without surfactant respectively. As shown, the permeate flux increases
60
linearly with increasing transmembrane pressure for both cases, until it attains a
condition, where permeate flux is almost independent of the transmembrane pressure.
This condition provides the optimum transmembrane pressure for the membrane. Note
that the maximum transmembrane pressure used was significant less than 70 psi, which
was the maximum pressure recommended by the manufacturer beyond which membrane
compaction begins to occur. The optimum transmembrane pressure was found to be 41
Psig or 282.69 kPa without surfactant addition in the feed and 39 Psig or 268.89 kPa ,
when surfactant was added to the feed. Formation of a cake layer with accumulation of
oil droplets on the membrane surface is the main reason for permeation flux decline. As
the filtration continues, more oil droplets precipitate on the cake layer and therefore its
thickness increases, until a gel layer is formed. The permeate flux cannot be further
increased after gel polarization occurs, and it may eventually decline with further
transmembrane pressure increase due to compaction of the gel layer [38, 88].
Flux Vs Transmembrane Pressure
(Porous Membrane, with Surfactant)
Permeate Flux(l/m2.h)
21
20
19
18
17
16
15
230
240
250
260
270
280
290
Transmembrane Pressure (kPa)
Figure 5.3. Effect of transmembrane pressure (kPa) permeate flux ( l /m
surfactant
61
2
.h ) for ultrafiltration with
Flux Vs Transmembrane Pressure
(Porous Membrane,without Surfactant)
19
Permeate Flux(l/m2.h)
18
17
16
15
14
13
12
230
240
250
260
270
280
290
300
310
Transmembrane Pressure (kPa)
Figure 5.4. Effect of transmembrane pressure (kPa) permeate flux ( l /m
surfactant
2
.h ) for ultrafiltration without
As the results show, the permeate flux can be related to transmembrane pressure
by a linear relation such as J p C1 TMP C2 , while C1 and C2 are two empirical
constants. The constant values obtained from experimental data are presented in Table
5.1:
C1
Empirical Constants of the
Equation J p C1 TMP C2
With surfactant
Without surfactant
0.082
0.074
C2
-2.012
-3.364
Table 5.1. Empirical constants for the linear relation between permeate flux and transmembrane pressure.
62
The total membrane resistance can be measured by applying the Darcy’s law:
Jp
TMP
3.6 10 9
water Rt
Eq.5.1
where:
J p is permeate flux ( l /m 2 .h )
TMP is transmembrane pressure ( KPa )
viscosity ( Pa.s )
water is water
m1 )
Rt is total membrane resistance(
law when water is almost 6.04E-4 at feed temperature of 40
Based on Darcy’s
C (Eq.4.3), total membrane resistance or Rt is 6.55 E+13 m1 with surfactant and
o
7.26E+13 m1 without surfactant addition the feed. Surfactants molecule solubilize the
feed by formingmicelles around them, and assists
solute macromolecules present in the
in dislodging the precipitated particles from surface the membrane which decreases the
total membrane resistance to water permeation through the membrane [39].
Figure 5.5 shows the flux decline during ultrafiltration of oily wastewater with
and without surfactant, and at transmembrane pressures of 41 Psig (282.69 kPa) and 27
(186.16kPa). As the results show at higher transmembrane pressure the permeate flux
decreases faster than at lower transmembrane pressure. The results from experimental
data for ultrafiltration at 282.69 kPa show that permeate flux declined by 36% and 39%
with and without surfactant respectively. The flux decline percentage for ultrafiltration at
186.16 kPa is 34% and 31% with and without surfactant respectively. It indicates that at
higher the transmembrane pressure is, the flux decline rate is higher. At high pressures
63
traces of oil can be seen in permeates, which indicates oil penetration through the pores,
although the pore sizes are smaller than oil droplets. High pressure increases the effect of
concentration polarization which causes the oil droplets to coalesce on the membrane
surface, and when driving pressure exceeds the capillary pressure in some pores, the oil
droplets can be deformed and therefore be pushed though the pores and into the permeate
[38].
Flux Vs Time (Porous Membrane)
25
Permeate Flux(l/m2.h)
TMP=186.16kPa, without surfactant
20
TMP=186.16kPa, with surfactant
TMP=282.7kPa, with surfactant
15
TMP=282.7kPa, without surfactant
10
5
0
0
1
2
3
4
5
Time(hr)
2
Figure 5.5. Variation of permeate flux ( l /m .h ) with time at transmembrane pressure of 289.69 kPa &
186.16 kPa , for ultrafiltration with and without surfactant
5.1.2. Effect of Feed Concentration
Figure 5.6 compares the permeation flux decline with time for ultrafiltration of
oily wastewater with constant and variable feed concentrations of oil. For ultrafiltration
of emulsion with constant oil concentration, the experimental set-up works in a complete
recycle mode, where both retentate and permeate flows were pumped back to the feed
tank and as a result of that the oil concentration was kept constant through the process.
64
However, in ultrafiltration of emulsion with variable oil concentration, the permeate flow
was driven out of the system instead of getting recycled back to the feed tank. Therefore,
the oil concentration increased gradually with time, and because of that, as it’s shown in
Figure 5.6 the flux decreased with a much faster rate than ultrafiltration with constant oil
concentration. This phenomenon can be ascribed to the fact that when concentration of
oil increases, the rate of accumulation of particles on the formed cake layer on membrane
surface also increases, which gradually thickens the cake layer. The thicker the formed
cake layer on the membrane surface, the lower is the permeation flux. At first, most of
the oil particles precipitate on membrane surface and then with greater accumulation of
oil particles at higher concentrations, they gradually adsorbed onto the membrane pores
and plugged them. The higher the oil concentration, the higher is the pore-clogging rate.
After sometime, pore clogging slows down, and the cake layer begins to govern. [42,81].
The results show that with surfactant the flux decline rate is lower as surfactant helps to
dislodge the precipitated particles from surface of the membrane.
Permeate Flux (l/m2.h)
Flux Vs Time ( Porous Membrane )
25
Constant Feed Concentration,
without Surfactant
20
Variable Feed Concentration,
without Surfactant
15
Constant Feed Concentration,
with Surfactant
10
Variable Feed Concentration,
with Surfactant
5
0
0
1
2
3
4
5
Time (hr)
Figure 5.6. Comparison of flux variation with time at different feed concentration for ultrafiltration with
and without surfactant
65
5.1.3 Effect of Backwashing on Permeate Flux Recovery
As mentioned above, the use of ultrafiltration membranes in industries is limited
to some extent. The major reason for this limitation is the flux decline, which occurs
because of fouling. Membrane fouling lowers the treated water production rate, increases
the energy consumption for membrane operation and necessitates a frequent membrane
washing [89]. In this study, backwashing was applied to improve the membrane
performance and also to recover the permeate flux to its initial value. To further
investigate the effects of cleaning conditions on ultrafiltration membrane fouling, several
experiments were carried out on polyether sulfone membrane to examine the influence of
backwash interval and duration on the permeate flux.
In order to evaluate the effect of backwash interval on permeate flux recovery,
two sets of experiments were done on the polyether sulfone membrane. In both
experiments, ultrafiltration was carried out until the permeate flux declined to its constant
value, and then for purpose of comparison, backwashing was applied, once in interval of
60 minutes and once, in interval of 90 minutes. For backwashing the membrane,
permeate which had been collected in tank TK-102 for this purpose, was used as the
backwashing fluid. Backwashing was done with permeate flux of 8.5 ( l /m 2 .h ), and for
duration of 200s.
Figure 5.7, shows the results of backwashing with two different intervals, for ultra
filtration of oily wastewater with and without surfactant. Ultrafiltration was operated for
about 3.5 hours until the permeate flux declined to a constant value. At this point,
66
backwashing was applied with duration of 200s, and in intervals of 60 and 90 minutes.
The flux enhancement percentage for each backwash is presented in Table 5.2:
Backwashing
Interval (min)
With Surfactant
Without Surfactant
Flux Enhancement
Percentage after the
First Backwash (%)
Flux Enhancement
Percentage after the
Second Backwash
(%)
Flux Enhancement
Percentage after the
Third Backwash
(%)
60
55%
18%,
17%
90
57%
26%
24%
60
55%
17%
14%
90
54%
25%
23 %
Table.5.2. Percentages of flux enhancement after backwashing with intervals of 60 minutes and 90
minutes.
25
Flux Vs Time (Porous Membrane)
60min Backwash
Interval,with Surfactant
Permeate Flux (l/m2.h)
20
60min Backwash
Interval,without
Surfactant
90min Backwash
Interval,with Surfactant
15
10
90min Backwash
Interval,without
Surfactant
5
0
0
2
4
Time (hr)
6
8
10
Figure 5.7. Effect of backwashing interval on permeate flux recovery. Smooth lines have been drawn
through the data points to demonstrate the trend of permeate flux versus time
As Figure 5.7 shows, a very significant recovery of membrane flux is clearly
observed immediately after each backwashing step, while a reduced efficiency of
backwashing in recovery of flux over time is also evident. The results show that the
backwashing efficiency is higher when surfactant was added to the feed and this shows
67
that the combination of surfactant and backwashing is more effective in recovery of the
flux as well as maintaining the flux at a higher value than just applying the backwashing.
It’s clear from the graphs that despite the fact that the absolute values of fluxes for
both intervals are quite similar , when backwash interval was 60 min, it could greatly
improve membrane filtration efficiency, and this could be due to the fact that the more
frequent backwash could more effectively peel off the formed cake layer on the
membrane surface, which is also consistent with the results that Lei Wang et al got in
their study [25]. Irrespective of the use of surfacant , when backwash was carried out in
90 min interval, the flux decline percentage within an interval was more significant than
that within 60 min interval.These results also suggest that no matter how often the
backwashing is carried out, a frequent backwashing, could increase the permeate flux to
its initial value.
In next two experiments, the effect of backwash duration on permeate flux
recovery was examined.For this purpose, ultrafiltration of oily wastewater was carried for
several hours until the permeate flux declined to a constant value, and then backwashing
was carried out using the permeate flow. Backwashing was done with flux of 8.5
( l /m 2 .h ), and it was repeated three times in 60 min interval. In first test the backwash
duration was 100s and in second test it was 200s. The same tests were ran on oily
wastewater with and without surfactant, and results are shown in Figure 5.8. In addition,
the flux recovery percentages are presented in Table 5.3:
68
Backwashing
Duration (s)
With Surfactant
Without Surfactant
Flux Enhancement
Percentage after the
First Backwash (%)
Flux Enhancement
Percentage after the
Second Backwash
(%)
Flux Enhancement
Percentage after the
Third Backwash
(%)
100
54%
8%
7%
200
53%
7%
6%
100
51%
10%
8%
200
55%
7%
6%
Table 5.3. Percentage of flux enhancement after backwashing with durations 100s and 200s
The results show that the longer the backwashing duration, the higher is the rate
of flux recovery. In addition, it should also be noticed that the flux decline within an
interval is lower for backwash with higher duration than that for backwash with lower
duration, and this could be due to the fact that the longer the backwash duration is, the
more effictive is the backwash in dislodging and removing the particles which have been
fixed in a form of layer on the membrane surface.
Permeate Flux(l/m2.h)
25
Flux Vs Time ( Porous Membrane )
100s B ackwash Duration, with
Surfa cta nt
20
200s B ackwash Duration, with
Surfa cta nt
100s B ackwash Duration,
without S urf actant
15
200s B ackwash Duration,
without S urf actant
10
5
0
2
4
6
8
10
Time(hr)
Figure 5.8. Effect of backwashing duration on permeate flux recovery. Smooth lines have been drawn
through the data points to demonstrate the trend of permeate flux versus time
69
The results from the experimental data for flux enhancement with backwashing
indicates that, backwashing is an effective cleaning method in recovering the permeate
flux. Furthermore, the results from applying the backwashing, along with surfactant,
show that surfactant can significantly improve the cleaning efficiency of backwashing.
5.1.4. Prediction of Permeate Flux by Hermia’s models
Hermia’s models were comprehensively expressed in section 4.5. In this study, in
order to identify the mechanism of fouling during ultrafiltration of oily wastewater, the
model k parameter was estimated by the linear regression method. The adjusted values
of k for n= 0, 1.0, 1.5 and 2.0 were used to solve the respective Hermia’s equations and
also the obtained prediction models in terms of flux decay which are presented in Table
5.4. At the end, in order to find which Hermia model better predicts the flux variation
behavior, the error parameter was calculated from equation 5.2, and the comparison
between the errors values achieved from different models determined the predominant
fouling mechanism, as the model with the lowest error value, fits better to the
experimental data.
The error parameter was defined as:
Error JExperimental JEstimated
Eq.5.2
where:
JExperimental is the flux value ( l /m 2 .h ), achieved from experiment at time t(s) and
transmembrane pressure (kPa).
JEstimated is the flux value ( l /m 2 .h ), predicted by a Hermia model at time t(s) and
transmembrane pressure (kPa).
70
Hermia’s Model
Simplified Relation
ln(J p ) ln(J0 ) Kc t
J p J0 exp(Kc t)
Model n value Fouling Fouling
Mechanism
2
Complete Pore
Blocking
1.5
Internal Pore
Blocking
1
(
1
1
) ( 1/ 2 ) K st
1/ 2
Jp
J0
1
1
Kit
J p J0
Partial Pore
Bridging
0
Cake Filtration
(
Jp
J0
(1 J K st) 2
1/ 2
0
Jp
1
1
) ( 2 ) K gl t
2
Jp
J0
Jp
J0
(1 J0 K i t)
J0
(1 J K gl t)1/ 2
2
0
Table5.4. Hermia’s model relation for different fouling mechanisms and the simplified equations
Table 5.5 presents the numerical values of Hermia’s models constant (k), as well
as errors value. This analysis is conducted based on the experimental data from
ultrafiltration of oily wastewater with surfactant, at different transmembrane pressures.
As it’s shown in Table 5.5, the lowest values of error for all the transmembrane pressures
tested are for the cake layer formation mechanism, also the partial pore bridging
mechanism can well fit to the experimental data. Therefore, it can be concluded that the
best fitting mechanism to the experimental data is the cake layer formation and the other
suitable mechanism is the partial pore bridging for all the transmembrane pressures
tested.
The same analysis was done on the experimental data from ultrafiltration of oily
wastewater without surfactant to the feed. As the results shown in Table 5.6, the lowest
values of error for all the transmembrane pressures tested are related to the cake layer
formation mechanism followed by the partial pore bridging mechanism. Therefore, it can
be concluded that the best fitting mechanism to the experimental data is the cake layer
71
formation and the other suitable mechanism is the partial pore bridging for all the
transmembrane pressures tested in this study.
Transmembrane Pressure (kPa)
186.16
236.4
241.3
248.2
255.1
262
268.9
275.8
Complete pore
blocking
0.229
0.226
0.226
0.212
0.223
0.24
0.242
0.24
Internal
pore
K*1.00
blocking
0.035
0.031
0.029
0.027
0.0285
0.030 0.029 0.029
E04
Partial pore
bridging
0.02
0.018
0.015
0.014
0.015
0.015 0.015 0.014
Cake filtration
0.004
0.003
0.002
0.002
0.002
0.002 0.002 0.002
Complete pore
blocking
2.959
2.591
3.831
3.733
4.09
5.624 5.664 5.699
Internal pore
blocking
2.779
2.361
3.579
3.493
3.812
5.271 5.348 5.394
Error
Partial pore
bridging
2.606
2.150
3.337
3.277
3.546
4.937 5.048 5.103
Cake filtration
2.275
1.788
2.867
2.861
3.042
4.314 4.482
4.56
Table5.5. K values of Hermia’s models obtained from experimental data for ultrafiltration with surfactant
Transmembrane Pressure
(kPa)
Complete pore
blocking
Internal pore
K*1.00
blocking
E04
Partial pore
bridging
Cake filtration
Error
Complete pore
blocking
Internal pore
blocking
Partial pore
bridging
282.7
0.228
0.028
0.014
0.002
6.029
5.757
5.501
5.029
186.16
236.4
241.3
248.2
255.1
262
268.9
275.8
282.7
0.234
0.258
0.289
0.296
0.297
0.298
0.299
0.271
0.276
0.286
0.039
0.039
0.044
0.044
0.043
0.043
0.042
0.037
0.037
0.039
0.025
0.025
0.027
0.026
0.025
0.025
0.024
0.021
0.020
0.021
0.006
0.005
0.005
0.005
0.004
0.004
0.004
0.003
0.003
0.003
2.780
3.868
4.180
4.136
4.032
4.429
4.359
5.425
5.503
5.503
2.543
3.552
3.761
3.669
3.549
3.929
3.839
4.988
5.005
5.005
2.31
3.247
3.353
3.215
3.076
3.440
3.328
4.558
4.526
4.526
Cake filtration
289.6
1.862 2.656 2.547 2.303 2.119
2.457
2.297 3.741 3.592
3.592
Table5.6. K values of Hermia’s models obtained from experimental data for ultrafiltration without
surfactant
72
5.1.5. Flux Decay Analysis by using a combination of Hermia’s models
In this section, the permeate flux decay is analyzed by applying the combination
of the constant pressure blocking filtration laws (Hermia’s models) with the measurement
of the membrane fouling resistances in series. In order to define the fouling resistances, it
was considered that the membrane fouling is mainly caused by either pore blocking or
cake/gel layer formation. When pore blocking happens, it can occur inside the pores
(internal pore blocking) or outside (partial pore bridging or complete pore blocking).
Analyzing the blocking filtration laws in terms of the resistances of the membrane, allows
a better understanding of the actual phenomena involved in membrane fouling, and the
significance of both internal and external fouling resistances [90].
As it’s been widely discussed in the literature [72], typically, in first couple of
minutes of ultrafiltration and microfiltration, complete pore blocking predominates and
then there’s a transition to the cake filtration model as the dominated phenomenon
involved in fouling. In other words, by the start of filtration a portion of the pores is
immediately blocked because of complete pore blockage, and then the flux declines
gradually as the result of other three phenomena of cake filtration, partial pore bridging,
and internal pore blocking involved in membrane fouling [18, 91]. Therefore the total
membrane resistance can be expressed as:
Rtotal
1
R4
1
1
R2 R3
Eq.5.3
Where: R2,R3,R4 are resistances due to the internal pore blocking, partial pore bridging
and cake filtration, respectively.
73
As mentioned earlier, Darcy’s law, the permeate flux can be expressed in terms of total
membrane resistance as, Eq.5.1:
Jp
TMP
3.6 10 9
p Rt
Eq.5.1
where:
J p is permeate flux ( l /m 2 .h )
TMP is transmembrane pressure ( KPa )
viscosity ( Pa.s )
p is permeate
m1 )
Rt is total membrane resistance(
Substituting the resistances
in Eq. 5.3 by their equivalents from Eq.5.1, we obtain:
Rt
1
TMP
1
1
p J4
TMP TMP
p J2 p J3
Eq.5.4
or more simplified as follows:
Rt
1
p J2 p J3
TMP
TMP
TMP
Rt
p J4
p J2 p J3 p J4
Eq.5.5
TMP
Therefore we obtain Eq. 5.6:
Rt
TMP
p
1
1
)
J2 J3 J4
Eq.5.6
1
1
)
J2 J3 J4
Eq.5.7
(
or
p Rt
TMP
(
74
If we substitute the fluxes term by the suggested form from Darcy’s law we’ll have:
1 p Rt
JP
TMP
Eq.5.8
Therefore, equation 5.4 can be written as:
1
1
1
(
)
Jp
J2 J3 J4
Eq.5.9
According to the Hermia’s models, permeate fluxes of J2,J3 and J 4 are defined as:
1
1
1
K 2 t J2 (
)2
1
J2
J0
K 2t
J0
1
1
1
K 3t J3
1
J3 J0
K 3t
J0
1
1
2 2 K 4 t J4
J0
J4
1
1
K4 t
J 02
By plugging these terms into Eq.5.9 we obtain:
1
JP (
1
1
1
K 2t
J0
)
2
1
1
K 3t
J0
1
1
Eq.5.10
1
K4 t
J 02
Based on equation 5.10, an optimization was performed by using program
MATLAB; for each set of flux versus time experimental data, minimum error was
calculated for different values of Hermia’s models constants ( k2,k3,k4 ). Optimization
space for these coefficients was defined from zero up to 10 times the K values, which
were obtained in the previous section, by fitting each Hermia’s
model to the experimental
75
data. Table 5.7 and 5.8; show the values of minimum error and the corresponding values
of k for ultrafiltration at different pressures, with and without surfactant, respectively.
Transmembrane Pressure
(kPa)
186.16 236.4
241.3
248.2
255.1
262
268.9
275.8
K values obtained for different models fitted to experimental data individually
Complete
pore blocking K1 (1.0E-04) 0.2285 0.2263 0.2261 0.2115 0.223
0.24
0.242
0.24
Internal pore
blocking
K2 (1.0E-04) 0.0354 0.0314 0.0295 0.0273 0.0285 0.0302 0.0298 0.0297
Partial pore
bridging
K3 (1.0E-04) 0.022 0.0175 0.0154 0.0141 0.0146 0.0152 0.0147 0.0147
Cake
filtration
K4 (1.0E-04) 0.0043 0.0027 0.0021 0.0019 0.0019 0.0019 0.0018 0.0018
K values obtained for different models in combination
Complete
pore blocking K1 (1.0E-04) 0.4571 0.4256 0.4506 0.4596 0.5387 0.6264 0.5786 0.5814
Internal pore
blocking
K2 (1.0E-04) 0.2239 0.1146 0.1577 0.1165 0.2851 0.1508 0.2105 0.1877
Partial pore
bridging
K3 (1.0E-04) 0.0023 0.0018 0.0016 0.0014 0.0021 0.0025 0.0026 0.0028
Cake
filtration
K4 (1.0E-04) 0.0035 0.0022 0.0017 0.0016 0.0015 0.0014 0.0013 0.0012
Table5.7. K values of Hermia’s models obtained for ultrafiltration of oily emulsion with surfactant
Transmembrane Pressure
(kPa)
186.16
236.4
241.3
248.2
255.1
262
268.9
275.8
K values obtained for different models fitted to experimental data individually
Complete
K1(1.0Epore blocking 04)
0.2342 0.2575 0.2885 0.2958 0.2965
0.298 0.2994 0.2714
Internal pore K2(1.0Eblocking
04)
0.0385 0.0399 0.0435 0.0435
0.043 0.0426 0.0423 0.0373
Partial pore
K3(1.0Ebridging
04)
0.0253 0.0248 0.0264 0.0257 0.0251 0.0245
0.024 0.0206
Cake
K4(1.0Efiltration
04)
0.0055 0.0048 0.0049 0.0045 0.0043 0.0041 0.0039 0.0032
282.7
0.2277
0.0282
0.014
0.0017
0.6146
0.1995
0.0027
0.0012
282.7
289.6
0.2763
0.2856
0.0373
0.039
0.0202
0.0214
0.003
0.0032
K values obtained for different models in combination
Complete
K1 (1.0Epore blocking 04)
0.48 0.5923 0.6382 0.6065 0.6255 0.6428 0.6316 0.6446 0.6442
Internal pore K2 (1.0Eblocking
04)
0.0727 0.1262 0.1646 0.1539 0.2151 0.1348
0.122 0.2156 0.0906
Partial pore
K3 (1.0Ebridging
04)
0.0025 0.0027 0.0028 0.0027 0.0036
0.004 0.0039 0.0036 0.0035
Cake
K4 (1.0Efiltration
04)
0.0046
0.004
0.004 0.0037 0.0033
0.003 0.0028 0.0023 0.0022
Table5.8. K values of Hermia’s models obtained for ultrafiltration of oily emulsion without surfactant
76
0.6722
0.0919
0.0038
0.0023
In addition, Table 5.9 and 5.10 and also the Figure 5.9 and 5.10, show the error
values obtained for the proposed model, as well as the error values obtained from fitting
each Hermia’s model individually, to the experimental data for ultrafiltration with and
without surfactant, respectively. The results indicate that the lowest error values
correspond to the combination of Hermia’s models, which shows the great consistency of
the proposed model.
Transmembrane Pressure (kPa)
Complete pore
blocking
Internal pore
blocking
Partial pore
Error
bridging
Cake filtration
186.16
236.4
241.3
248.2
255.1
262
268.9
275.8
282.7
2.9595
2.5908
3.8307
3.7334
4.09
5.6236
5.6641
5.6988
6.0294
2.7792
2.3611
3.5797
3.4928
3.8116
5.2707
5.3484
5.3926
5.7567
2.6059
2.1503
3.3367
3.2765
3.5456
4.9366
5.0481
5.1027
5.5008
2.2749
1.7876
2.8671
2.8612
3.0424
4.314
4.4823
4.56
5.0293
Combined model
2.1207 1.6481 2.5448 2.5393 2.517 3.7047 3.7218 3.8714
Table 5.9.Error values measured for Hermia’s models and the combined model(with Surfactant)
4.2311
186.16
236.4
241.3
248.2
255.1
262
268.9
275.8
282.7
289.6
2.7803
3.8683
4.1801
4.1356
4.0318
4.4297
4.3587
5.4245
5.3805
5.5025
2.5434
3.5519
3.7606
3.6699
3.549
3.9295
3.8385
4.9819
4.9021
5.0051
2.313
3.247
3.3531
3.2149
3.0755
3.4402
3.3281
4.558
4.4402
4.5261
1.8621
2.6555
2.5468
2.303
2.1196
2.457
2.2966
3.7405
3.5381
3.5918
1.8429 2.4905 2.5196 2.2797 2.0704 2.3778 2.2313 3.4138 3.2528
Table 5.10.Error values measured for Hermia’s models and the combined model(without Surfactant)
3.3575
Transmembrane Pressure (kPa)
Complete pore
blocking
Internal pore
blocking
Partial pore
Error
bridging
Cake filtration
Combined model
77
Figure 5.9. Error values for different Hermia’s models for ultrafiltration of oily wastewate with
surfactant at different transmembrane pressure
Error (l/m2.h)
Comparison of error for different Fouling Mechanisms
(Porous Membrane, Without Surfactant)
6
Complete Pore
Blocking
Internal Pore Blocking
5
Partial Pore Bridging
Cake Filteration
4
Combination
3
2
1
0
186.16 236.4 241.3 248.2 255.1 262 268.9 275.8 282.7 289.6
Transmembrane Pressure (kPa)
Figure 5.10. Error values for different Hermia’s models for ultrafiltration of oily wastewate without
surfactant at different transmembrane pressure
78
As shown earlier, after the combined model, the best consistency corresponds to
the cake filtration model. Figure 5.11 and 5.12 show the percentage of error reduction
with respect to the error values obtained by fitting cake filtration model . The error
reduction is obtained by:
reduction (%)
( ErrorCombination ErrorCake )
100
ErrorCake
Eq.5.11
where :
ErrorCombination is the error value obtained for combined model
ErrorCake is the error value obtained for cake filtration model
Figure 5.11. Reduction precentages of error values combined model with respect to the cake filtration
model, for ultrafiltration of oily wastewate with surfactant at different transmembrane pressures
79
Figure 5.12. Reduction precentages of error values combined model with respect to the cake filtration
model, for ultrafiltration of oily wastewate without surfactant at different transmembrane pressures
During the ultrafiltration process, as the transmembrane pressure increases, the
gel layer that’s formed on the membrane surface becomes denser, and if the pressure
value exceeds the capillary pressure in some pores, the oil droplets can be deformed and
therefore be pushed though the pores and block them [38] . Looking at this fact, from the
point of the involved fouling mechanisms, it implies that with the increase of
transmembrane pressure, the percentage of the contribution of internal pore blocking and
pore bridging model, and in contrast the percentage of the contribution of the cake
filtration model decreases. This is clearly seen in Figure 5.13 and 5.14 for ultrafiltration
of oily emulsion, with and without surfactant. In order to verify the effect of surfactant on
the involved fouling mechanism, the contribution percentages of cake filtration and pore
blocking models are compared in Figure 5.15. As the results show, the surfactant didn’t
have a sensible impact on contribution of fouling mechanisms.
80
Figure 5.13. contribution precentages of cake filtration and pore blocking mechanisms, for ultrafiltration of
oily wastewate with surfactant at different transmembrane pressure
Figure 5.14. contribution precentages of cake filtration and pore blocking mechanisms, for ultrafiltration of
oily wastewate with surfactant at different transmembrane pressure
81
Figure 5.15. Comparision of the contribution precentages of cake filtration and pore blocking mechanisms,
for ultrafiltration of oily wastewate at different transmembrane pressure with and without surfactant
5.1.6. Mass Balance Analysis
Mass balance for oil gives thef olowing equation:
(Coil q) Feed (Coil q) Permeate (Coil q) reject
Eq.5.12
where:
Coil is the oil concentration (wt%)
q is volumetric feed flow rate (Gallon/min)
The oil concentration of feed and reject measured by the EPA method (refer to Appendix
2), we can calculate the oil concentration of reject flow using equation 5.12.The results
are presented in Table 5.11 and 5.12 for ultrafiltration process with and without
surfactant ,respectively:
82
Mass Balance Analysis for the Porous Membrane ( Ultrafiltration with Surfactant). Transmembrane
Pressure:268.9kPa,Feed Flow Rate:41.86 (gallons/min)
Experimental
Calculated Oil
Experimental
Permeate Flow
Reject Flow
Oil
Concentration
Value of Oil
Rate
Rate
Concentration
in Reject Flow
Concentration
(Gallon/min)
(Gallon/min)
in Feed Flow
(wt%)
in Reject Flow
(wt%)
(wt%)
Start of
3.40
37.88
2.7 0.3
2.943 0.9
2.9 0.3
Filtration
End of
2.40
38.84
2.9 0.3
3.079 0.9
3.4 0.3
Filtration
Table 5.11. Calculation of Oil Concentration in the Porous Membrane Reject Flow (with Surfactant)
Mass Balance Analysis for the Porous Membrane ( Ultrafiltration without Surfactant). Transmembrane
Pressure:282.7kPa, Feed Flow Rate:41.24 (gallons/min)
Experimental
Calculated Oil
Experimental
Permeate Flow
Reject Flow
Oil
Concentration
Value of Oil
Rate
Rate
Concentration
in Reject Flow
Concentration
(Gallon/min)
(Gallon/min)
in Feed Flow
(wt%)
in Reject Flow
(wt%)
(wt%)
Start of
2.98
38.88
3.3 0.3
3.553 1.1
3.7 0.3
Filtration
End of
1.90
39.96
3.9 0.3
4.085 1.2
4.2 0.3
Filtration
Table 5.12. Calculation of Oil Concentration in the Porous Membrane Reject Flow (without Surfactant)
The calculated values for the oil concentration in the membrane reject flow agrees quite
well with the experimental measured values, as obtained by Cardinal Laboratories, Inc.
(Wilder, KY), using EPA Method (Appendix 2).
83
5.2. Filtration of Oily Wastewater using Dense Hollow-Fiber Regenerated Cellulose
Ultrafiltration Membrane
In order to evaluate the performance of the cupraammonium regenerated cellulose
membrane for filtration of oily wastewater, different experiments categorized into four
main cases were conducted on the membrane. As mentioned above, for this membrane,
the applied cleaning technique was gas sparging. Running the same experiments under
comparable operating conditions such as feed concentrations, temperature and feed cross
flow velocity, helped to achieve a better understanding of membrane performance and
also effect of surfactant and air sparging on flow rate enhancement. These four categories
are displayed in Table 5.13:
Experiment Condition
Feed
Surfactant Volume
3
3
Fraction ( m /m )
Air Sparging
Volumetric Flow Rate
(SCFH)
Ultrafiltration without
surfactant/without air
Oily wastewater of
Ford motor Co. Plant
0
0
Ultrafiltration with
surfactant/without air
Oily wastewater of
Ford motor Co. Plant
4.4 105
0
Ultrafiltration
withsurfactant/with
air
Oily wastewater of
Ford motor Co. Plant
4.4 105
0.5
Ultrafiltration without
surfactant/with air
Oily wastewater of
Ford motor Co. Plant
0
0.5
Table 5.13. Operating conditions for the experiments conducted using the dense membrane
5.2.1. Effect of Transmembrane Pressure
Figure 5.16-5.19 show that permeate flow rate versus time at different
transmembrane pressures and under different experimental conditions presented in
Table5.13. As typically is the case in conventional cross-flow ultrafiltration, the permeate
84
flow rate, decreases with time. During the first few minutes of filtration, the permeate
flow rate decline is high, which is mainly due to accumulation of oil droplets at the top of
the membrane module. As oil is rejected at the membrane surface, the emulsion breaks
releasing free, insoluble oil, which rises to the top of the membrane module, due to lower
density, thereby blanketing the surface area at the top of the membrane module, and
reducing the filtration rate.
Once a steady-state amount of free oil has accumulated at the top of the
membrane module, the permeate flow reaches a steady-state, constant value.
The
constant permeate flow rate indicates the fact that the fouling resistance due to the
precipitated particles is constant with time.
Permeate Flow Rate Vs Time (Dense Membrane,without
Surfactant,without Air, Membrane Area=12.9 m2)
Permeate Flow Rate (gallons/min)
0.8
0.7
TMP(137.9kPa)
0.6
TMP(144.8kPa)
0.5
TMP(151.7kPa)
TMP(158.6kPa)
0.4
TMP(165.5kPa)
0.3
TMP(172.4kPa)
0.2
TMP(179.3kPa)
0.1
0
0
0.5
1
1.5
2
Time(hr)
2
2.5
3
3.5
Figure 5.16. Variation of permeate flux ( l /m .h ) with time at different transmembrane pressures for
ultrafiltration of oily wastewate without surfactant and without air injection
85
Permeate Flow Rate(gallons/min)
Permeate Flow Rate Vs Time (Dense Membrane, with
Surfactant,without Air, Membrane Area=12.9m2)
0.8
TMP(131 kPa)
0.7
TMP(137.9kPa)
TMP(144.8kPa)
0.6
TMP(151.7kPa)
0.5
TMP(158.6kPa)
0.4
TMP(165.5kPa)
0.3
TMP(172.4kPa)
0.2
0.1
0
0
0.5
1
1.5
2
Time(hr)
2.5
3
3.5
2
Figure 5.17. Variation of permeate flux ( l /m .h ) with time at different transmembrane pressures for
ultrafiltration of oily wastewate with surfactant and without air injection
Permeate Flow Rate(gallons/min)
Permeate Flow Rate Vs Time (Dense Membrane, with
Surfactant,without Air, Membrane Area=12.9m2)
0.8
TMP(131 kPa)
0.7
TMP(137.9kPa)
0.6
TMP(144.8kPa)
0.5
TMP(151.7kPa)
0.4
TMP(158.6kPa)
TMP(165.5kPa)
0.3
TMP(172.4kPa)
0.2
0.1
0
0
0.5
1
1.5
2
Time(hr)
2
2.5
3
3.5
Figure 5.18. Variation of permeate flux ( l /m .h ) with time at different transmembrane pressures for
ultrafiltration of oily wastewate with surfactant and with air injection
86
Permeate Flow Rate Vs Time ( Dense Membrane, without
Surfactant, with Air, Membrane Area=12.9m2)
Permeate Flow Rate(gallons/min)
0.8
TMP(137.9kPa)
0.7
TMP(144.8kPa)
TMP(151.7kPa)
0.6
TMP(158.6kPa)
0.5
TMP(165.5kPa)
TMP(172.4kPa)
0.4
0.3
0.2
0
0.5
1
1.5
2
2.5
3
3.5
Time(hr)
2
Figure 5.19. Variation of permeate flux ( l /m .h ) with time at different transmembrane pressures for
ultrafiltration of oily wastewate without surfactant and with air injection
Figure 5.20-5.23 represent the permeate flux versus transmembrane pressure, for
different experimental conditions. Results show that high transmembrane pressure leads
to higher driven force but also flux decline due to higher fouling resistance; however the
average flux for all cases showed a significant enhancement with transmembrane
pressure. This implies that flux enhancement rate due to higher transmembrane pressure
(TMP) overcomes the decrease in flux due to fouling resistance.
87
Permeate Flux Vs Transmembrane Pressure (Dense Membrane,
without Surfactant and without Air)
Permeate Flux (l/m2.h)
16
14
12
10
8
6
4
2
0
130
140
150
160
170
180
190
Transmembrane Pressure (kPa)
Figure 5.20. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate without surfactant and without air injection
Permeate FLux Vs Transmembrane Pressure ( Dense
Membrane, with Surfactant, without Air)
16
Permeate Flux (l/m2.h)
14
12
10
8
6
4
2
0
125
135
145
155
Transmembrane Pressure (kPa)
165
175
Figure 5.21. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate with surfactant and without air injection
88
Permeate Flux Vs Transmembrane Pressure (Dense
Membrane, with Surfactant, with Air)
18
Permeate Flux (l/m2.h)
16
14
12
10
8
6
4
135
140
145
150
155
160
165
170
Transmembrane Pressure (kPa)
Figure 5.22. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate with surfactant and with air injection
Permeate Flux Vs Transmembrane Pressure ( Dense
Membrane, without Surfactant, with Air)
16
Permeate FLux (l/m2.h)
14
12
10
8
6
4
2
0
135
140
145
150
155
160
165
170
Transmembrane Pressure (kPa)
Figure 5.23. Effect of transmembrane pressure on permeate flux for ultrafiltration of oily
wastewate without surfactant and with air injection
89
5.2.2. Effect of Air Injection and Surfactant on Membrane Performance
Figure 5.24 shows the permeate flux versus transmembrane pressure for
ultrafiltration of oily wastewater with and without surfactant, and with and without air
sparging. The experiments were conducted under the same operating conditions such as
feed concentration and temperature.
Permeate Flux Vs Transmembrane Pressure (Dense
Membrane)
Permeate Flux (l/m2.h)
16
sparged/with Surfactant
14
unsparged/with Surfactant
12
sparged/without Surfactant
10
unsparged/without
Surfactant
8
6
4
135
140
145
150
155
160
Transmembrane Pressure (kPa)
Figure 5.24. Effect of air injection and surfactant on permeate flux for transmembrane pressure
For conventional unsparged filtration, permeate flux increased gradually with
increasing of transmembrane pressure, and higher permeate flux achieved at higher
transmembrane pressure. Gas injection caused significant flux enhancements, and in
terms of percentage increase in permeate flux, gas sparging can lead to a higher
enhancement where concentration polarization is expected to be severe, which is, at
higher transmembrane pressure [92]. As the figure implies the flux enhancement, is
higher for filtration with surfactant than without surfactant, and it shows the effective role
of surfactant in enhancement of membrane filtration efficiency.
90
Figure 5.25 shows the permeate flow rate versus time for ultrafiltration of oily
wastewater at optimized condition, with and without surfactant, and with and without air
sparging.
Permeate Flow Rate ( Gallon/min)
Permeate Flow Rate Vs Time ( Dense Membrane)
1
sparged/without Surfactant
0.9
sparged/with Surfactant
0.8
unsparged/with Surfactant
0.7
unsparged/without Surfactant
0.6
0.5
0.4
0.3
0
1
2
3
4
Time (hr)
Figure 5.25. Effect of air injection and surfactant on permeate flux variation with time
As the graph shows, the initial permeate flow rate as well as steady state permeate
flow rate is higher for ultrafiltration with surfactant and air sparging. The comparison
between the permeate flow rate values clearly shows that air injection keeps the permeate
flow rate higher throughout the process and this is mainly due to the fact that air sparging
decreases the membrane fouling resistance by disrupting the formed concentration
polarization layer [92]. The results also indicate that combination of surfactant and air
injection is more effective in enhancing the flux than either air sparging or surfactant,
although the effect of air sparging alone is higher than with surfactant only the surfactant
reduces the surface tension between oil and water, and with forming micelles around the
oil droplets, it prevents formation of dense oil aggregates, and makes it easier for the air
91
slugs to get through the deposits layer, disrupting it with creating turbulence, which
significantly increases water mass transfer through the membrane which is seen as higher
permeate flow rate.
5.2.3. Analysis of Permeate Flux for Ultrafiltration of oily emulsion in Dense
Hollow-Fiber Regenerated Cellulose Membrane
The hollow fiber module tested here, is consisted of N fibers of same size, in
which the membrane is formed on the inside of N tiny dense tubes. In ultrafiltration with
a dense membrane, water permeation occurs by diffusion through the membrane in
contrast to diffusion through the pores, which happens during ultrafiltration with a porous
membrane. Therefore, water molecules need to diffuse through the emulsion and then
through the membrane wall, in order to transfer to the permeate side of the membrane.
As mentioned earlier, ultrafiltration is a pressure-driven process, and for a small
applied pressure, it’s been observed that the permeate flux through the membrane is
proportional to the pressure difference across the membrane. It’s like a Darcy’s law
permeability for flow through porous membrane. However, as the transmembrane
pressure increases the permeate flux begins to decline bellow the value which would
result from a linear-pressure behavior. According to Ho-Ming Yeh and Jin-Hong Dong
[93], the water permeation flux in a cross-flow ultrafiltration is related to the
transmembrane pressure by this equation:
92
Jp
P
RT 1 t fiber
(
) P
kc Dwm
1
Eq.5.13
J stst
where:
J p is water (permeate) flux( m 3 /m 2 .s )
kc is the mass transfer coefficient( m /s)
Dwm is water diffusion
coefficient( m /s2)
t fiber is membrane thickness(
m)
P is pressure differenceacross the membrane ( Pa )
is proportional constant
( m 2 .s/m 3 )
Jstst is the steady-state flux( m 3 /m 2 .s )
R =8.314 ( Pa.m3 /mol.K ) & T =40 oC
is molar volume
of water at 40 oC and it’s equal to 1.81E -5 ( m 3 /gmole )
Equation 5.13 in fact is an expression of the resistance-in-series model, where
1/kc denotes the mass transfer resistance, t /Dwm is the membrane intrinsic resistance and
P is the resistance due to the concentration polarization which will be proportional to
resistance of the compressible layer formed on the
the amount and specific hydraulic
membrane and can be assumed to be a linear function of transmembrane pressure with
as a proportional constant.
With the use of the results from the experimental data of permeate flux for pure
water at 379 kPa, a straight line of (1/J p )exp versus (1/P)exp could be constructed by the
93
least-squares method [93,94,95]. Thus the intrinsic resistance of the hollow fiber
membrane module employed in this study can be determined from the experimental data
of permeate flux for pure water and by using the following equation, which can be
modified from Eq.5.13 by setting zero for pure water.
1
RT 1 t fiber 1
(
)
(J p ) exp kc Dwm Pexp
Eq.5.14
The measured value of RT(1/kc t fiber /Dwm ) / for the membrane system employed in
this study was determined graphically as [96]:
RT 1 t fiber
(
) 5 107 (Pa.m 2 .s /m 3 )
v kc Dwm
Since the pure water flow regime inside the fiber is laminar, the mass transfer resistance
of pure water was obtained from this equation:
kc d fiber
d 2fiber u 1/ 3
1.62(
)
Dw
l fiber Dw
Eq.5.15
where:
kc is mass transfer coefficient (m /s)
Dw is water diffusivity ( 3.2 109 m2 /s)
diameter (m)
d fiber is the fiber internal
(m)
l fiber is fiber length
inside the fiber (m 2 /s)
u is the water flow velocity
94
The Reynolds number is defined as:
Re
ud fiber
& n water
n
water
Eq.5.16
Where, water is the pure water viscosity, water is the pure water density. The water flow
velocity inside the fiber u can be obtained by:
u
d fiber
Q/N
& Ac
Ac
4
2
Eq.5.17
Where:
Q is the feed volume flow rate ( m 3 /s)
Ac is the cross- sectional area of the fiber ( m 2 )
and can be expressed as:
N is the number of fibers
N
At
Al
Eq.5.18
where:
At is the total membrane active area ( m 2 )
Al is the fiber lateral area ( m 2 )
l fiber is fiber length ( m )
The value of N is
obtained as follows:
106 (m) 2 rfiber rfiber 210 106 (m)
d fiber 420
Ac (rfiber ) 2 1.4 107 (m 2 )
l fiber 0.986(m)
A l (2rfiber ) l fiber 2 210 106 0.986 1.31103 (m 2 )
N
12.9
9847
(1.31103 )
95
Having the value of Reynolds number, pure water viscosity and density, we found the
feed velocity inside the fiber as:
ud fiber
& n water
n
water
Re 2000
Re
water 670 106 (Pa.s)
water 970(kg/m 3 )
The water flow velocity inside the fiber obtained as:
u=16.8 (m /s)
and furthermore the mass transfer coefficient was determined from equation 5.15 as:
kc 9.16 (m /s)
As mentioned above, the value of the total membrane resistance determined graphically
for pure water was:
RT 1 t fiber
(
) 5 107 (Pa.m 2 .s /m 3 )
v kc Dwm
Therefore, by having the mass transfer coefficient of water we can determine the
diffusivity of water in membrane and further the membrane intrinsic resistance t fiber /Dwm :
96
while :
1
1
0.109( s / m)
kc 9.164
and
t fiber 80 10 6 (m)
R 8.314( Pa.m 3 / gmole.K )
T 313(k )
v 1.81 10 5 (m 3 / gmole)
t fiber
1.81105
(5 10 7 )
0.109 0.239(s /m)
Dwm
8.314 313
and
Dwm 3.35 104 (m 2 /s)
Furthermore, the experimental data obtained in ultrafiltration of emulsion in this study is
also applied to Eq.5.13, so:
1 RT 1 t fiber 1
(
)
Jp
v kc Dwm P
Eq.5.19
Therefore, from a straight line plot of (1/J p )expversus (1/P)exp at constant feed
concentration, the experimental values of (the intersection at the ordinate) and
RT(1/kc t fiber /Dwm ) /v (the slope), as well as 1/kc , were determined graphically from
Table 5.14 as function of surfactant and air. The results are presented in Table 5.15 as
well as Figure 5.26:
97
Ultrafiltration
Operating Conditions
Without Surfactant &
Without Air
Transmembrane
5
Pressure 10
(Pa)
1.379
1.448
1.517
1.586
1.655
Permeate Flux
3
2
( m /m .s )
106
1.563
2.149
2.393
2.638
3.175
With Surfactant &
Without Air
1.379
1.448
1.517
1.586
1.655
2.247
2.345
2.687
3.175
3.615
With Surfactant &
With Air
1.379
1.448
1.517
1.586
1.655
2.491
3.077
3.419
4.005
3.761
Without Surfactant &
With Air
1.379
1.448
1.517
1.586
2.345
2.687
3.126
3.761
1.655
3.566
Table5.14. Experimental data for ultrafiltration oily emulsion using dense membrane
(RT(1/kc t fiber /Dwm ) /v) 1011 1/kc 103
(s/m)
(Pa.m 2 .s/m 3 )
Without Surfactant &
Without Air
With Surfactant &
Without Air
2.465
With Surfactant &
With Air
98
1.71
107
(s/m)
5.83
0.119
1.474
1.02
9.76
0.061
1.170
0.814
12.3
0.047
0.929
10.8
0.055
Without Surfactant &
With Air
1.336
Table5.15.The fitting parameter of experimental data,
kc 104
(m /s)
t fiber /Dwm 0.238(s/m)
As expected, as it’s shown in Table 5.15 and Figure 5.26, the mass transfer
resistance as well as the coefficient of the resistance due to concentration polarization
decreases when ultrafiltration is ran with surfactant and with air injection, and this
flux with
indicates that the present model easily describes the relationships of permeate
the transmembrane pressure.
1.40E-03
Mass Transfer Coefficient Value for Different Ultrafiltration
Experiments using Dense Membrane
Mass T ransfer Coefficient (m/s)
1.20E-03
1.00E-03
8.00E-04
6.00E-04
4.00E-04
2.00E-04
0.00E+00
Without
Surfactant,Without
Air
With
With Surfactant,With
Without
Surfactant,Without
Air
Surfactant,With Air
Air
Experimental Condition for Ultrafiltration with Dense M embrane
Figure 5.26. The comparison of the values of the mass transfer coefficient for ultrafiltration of oily
emulsion using dense membrane under different experimental conditions
5.2.4. Mass Balance Analysis
Recalling from section 5.1.6, and according to the law of mass balance we have:
(Coil q) Feed (Coil q) Permeate (Coil q) reject
Eq.5.12
Having the values of the oil concentration in the feed flow at the start and the end of filtration
process, we calculated the oil concentration in the reject flow with the use of Eq.5.19 and the
results are presented in Table 5.16:
99
Mass Balance Analysis for the Dense Membrane (Ultrafiltration without Surfactant/without
air),Transmembrane Pressure: 172.4 kPa, Feed Flow Rate: 21.33 (gallons/min)
Permeate Flow
Rate
(gallons/min)
Reject Flow Rate
(gallons/min)
Experimental Oil
Concentration in
Feed Flow (wt%)
Calculated Value of
Oil Concentration in
Reject Flow (wt%)
Start of Filtration
0.7
20.63
3.1 0.3
3.21 1.0
End of Filtration
0.39
20.94
3.5 0.3
3.57 1.1
Table 5.16. Calculation of Oil Concentration in the Dense Membrane Reject Flow
The oil concentration in the feed flow is slightly lower than the oil concentration in the
reject flow due to the low permeation rates of water obtained through the dense
membrane.
100
Chapter 6: Conclusions and Recommendations
A comprehensive study has been performed on ultrafiltration system using the oily
wastewater, in order to determine the involved fouling mechanism and predict the permeate
flux behavior. The ultrafiltration membranes tested in this study included a porous monolithic
polyether sulfone membrane and a dense hollow fiber cellulose membrane. The results from
the experimental data for each membrane are presented here:
1) Porous Ultrafiltration Membrane:
It was observed that the permeate flux is a strong function of transmembrane pressure,
as it’s consistent with the empirical correlation cited in Eq.4.2. High transmembrane
pressure resulted in high permeate flux, but high flux declination rate as the result of
gel layer formation and intensive pore blocking.
The results indicated that addition of surfactant to the feed, significantly improved the
membrane performance by enabling oil to break up into smaller droplets, which are
then readily removed from the surface of the membrane.
High transmembrane pressure aggregated fouling, that required membrane cleaning.
Backwashing was found to be an effective way to enhance the permeate flux, but
combination of backwashing and surfactant achieved a higher flux recovery, so the
combination cleaning is a more effective cleaning method.
It was noted that the backwashing duration and interval could significantly affect the
backwashing cleaning efficiency. The results showed that the longer was the
backwashing duration, the higher was the flux recovery percentage. It was also found
that the more frequent backwash could more effectively peel off the formed cake layer
on the membrane surface and consequently enhance the permeate flux.
101
In order to determine the fouling mechanism, flux decay was analyzed by using the
combination of the Hermia’s models with the measurments of membrane resistances
arranged in series and parralel format. It was found that permeate flux was governed
by two major fouling mechanisms: the complete pore blocking that occurrs at the
initial stages of filtration, followed by an external surfaca fouling conforming to the
cake filtration model.It was shown that the predominant fouling resistance arises from
the formation of a fouling layer over the membrane surface.
The results indicated that with the increase of transmembrane pressure the influence of
the pore blocking mechanisms such as partial pore bridging and internal pore blocking
becomes more significant, while the effect of cake filtration mechanism weekens,
however the predominant fouling mechanism is still the cake filtration model.
2) Dense Ultrafiltration Membrane:
The results showed that the permeate flux was highly enhanced by increase in
transmembrane pressure. However, gel layer formation on the membrane surface, as
well as concentration polarization led to high flux declination rate.
The permeate flux enhanced by addition of surfactant to the feed, as it happened for
porous membrane.
Air was injected into the feed stream, to reduce concentration polarization and
membrane fouling. The results from experimental data showed a significant
enhancement in permeate flux by air injection and the enhancement rate was even
higher when a combination of air injection and surfactant was applied; The effect of
air sparging only was higher than with surfactant by itself.
The resistance in series model, proposed by Ho-Ming Yeh and Jin-Hong Dong [92],
102
was applied to describe the relationship of permeate flux with transmembrane pressure
and operating conditions. Further, as the results presented in Table 5.15 indicate, the
mass transfer coefficient is higher when either air is injected or surfactant is added to
the feed stream. This indicates the important role of air and surfactant in decreasing
the fouling resistance and enhancing permeate flux.
Future work recommended includes testing the porous and dense membranes for other
types of wastewaters, such as waters produced in the plating industry, which typically
contains oils, greases, metal precipitates and dirt, or domestic and industrial wastewaters
containing high chemical oxygen demand (COD) contaminants and particulates.
103
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110
Appendix
Appendix 1: Raw Data
Appendix 2: EPA Analytical Methods - Method 1664, Revision A: N-Hexane
Extractable Material (HEM; Oil and Grease) and Silica Gel Treated N-Hexane
Extractable Material (SGT-HEM; Non-polar Material) by Extraction and Gravimetry
111
Appendix1: Raw Data
Transmembrane Pressure (kPa)
186.16
236.40
241.30
Time(hr)
248.20
255.10
262.00
268.90
275.80
282.70
Permeate Flux (l/m2.h)
0.05
12.54
15.64
17.55
17.91
18.63
19.40
20.30
20.12
20.00
0.50
11.64
14.39
16.54
16.42
16.82
17.31
18.51
18.21
17.91
1.00
11.10
13.79
15.52
15.64
15.90
16.20
17.01
16.83
16.66
1.50
10.33
12.95
14.69
15.04
15.06
15.40
16.18
16.00
15.70
2.00
9.67
12.54
13.85
14.27
14.51
14.74
15.34
14.92
15.10
2.50
9.37
11.94
13.31
13.43
13.70
13.91
14.57
14.51
14.39
3.00
9.19
11.64
13.01
13.37
13.55
13.79
14.45
14.39
14.27
3.50
9.13
11.34
12.89
13.25
13.37
13.73
14.39
14.21
14.21
4.00
9.13
11.34
12.89
13.25
13.37
13.73
14.33
14.21
14.21
Experimental Results shown in Figure 5.1
Transmembrane Pressure (kPa)
186.16
236.40
241.30
Time(hr)
248.20
255.10
262.00
268.90
275.80
282.70
289.60
Permeate Flux (l/m2.h)
0.00
11.46
13.31
14.33
15.16
15.64
16.12
16.54
17.25
17.79
17.61
0.50
10.69
12.12
13.07
13.79
14.15
14.57
15.04
15.34
16.12
15.82
1.00
10.03
11.16
12.06
12.72
13.07
13.55
13.85
14.15
14.92
14.45
1.50
9.49
10.39
11.28
11.76
12.24
12.60
12.83
13.07
13.97
13.55
2.00
8.89
9.79
10.51
11.16
11.52
11.82
12.18
12.42
13.07
12.66
2.50
8.48
9.49
9.85
10.51
10.86
11.16
11.46
11.94
12.36
12.00
3.00
8.30
9.19
9.55
10.03
10.39
10.63
10.98
11.58
11.82
11.58
3.50
8.06
8.95
9.25
9.73
10.03
10.21
10.57
11.10
11.64
11.34
4.00
7.94
8.83
9.13
9.55
9.79
10.09
10.33
11.04
11.46
11.16
4.50
7.94
8.66
9.01
9.43
9.67
9.97
10.21
10.98
11.34
10.98
Experimental Results shown in Figure 5.2
112
Transmembrane Pressure (kPa)
premeate flux(l/m2.h)
282.70
19.88
275.80
19.88
268.91
19.90
262.01
19.70
255.12
19.16
248.22
18.51
241.33
17.91
234.43
17.13
Experimental Results shown in Figure 5.3
Transmembrane Pressure (kPa)
Permeate Flux (lmh)
234.43
14.03
241.33
14.69
248.22
15.16
255.12
15.64
262.01
16.12
268.91
16.54
275.80
17.25
282.70
17.70
289.59
17.67
296.49
17.67
303.38
17.67
Experimental Results shown in Figure 5.4
113
With Surfactant
TMP=186.16kPa
Without Surfactant
TMP=282.7kPa
Time(hr)
TMP=186.16kPa
TMP=282.7kPa
Permeate Flux (l/m2.h)
0.05
12.54
20.00
11.46
17.79
0.5
11.64
17.91
10.69
16.12
1
11.10
16.66
10.03
14.92
1.5
10.33
15.52
9.49
13.97
2
9.67
14.98
8.89
13.07
2.5
9.37
14.39
8.48
12.36
3
9.19
14.27
8.30
11.82
3.5
9.13
14.21
8.06
11.64
4
9.13
14.21
7.94
11.46
Experimental Results shown in Figure 5.5
With Surfactant
Constant Feed
Concentration
Time(hr)
Without Surfactant
Variable Feed
Concentration
Constant Feed
Concentration
Variable Feed
Concentration
Permeate Flux (l/m2.h)
0.05
20.30
20.00
17.79
17.79
0.50
18.39
17.91
16.12
15.40
1.00
16.83
16.12
14.92
13.55
1.50
15.82
14.63
13.97
11.76
2.00
15.04
13.61
13.07
10.45
2.50
14.57
12.89
12.36
9.37
3.00
14.21
12.36
11.82
8.66
3.50
13.97
11.88
11.46
8.18
4.00
13.73
11.70
11.10
7.70
Experimental Results shown in Figure 5.6
114
With Surfactant
60min Backwash
Interval
Time(hr)
Without Surfactant
90min Backwash
Interval
60min Backwash
Interval
90min Backwash
Interval
Permeate Flux (l/m2.h)
0
19.94
17.73
20.36
17.61
0.5
18.51
16.18
18.80
15.94
1
16.95
14.86
17.43
14.86
1.5
15.58
13.91
16.30
13.79
2
14.69
13.01
15.16
13.13
2.5
13.73
11.94
14.15
12.24
3
12.83
11.28
13.07
11.58
3.5
12.83
11.10
12.77
11.10
3.55
19.88
17.19
20.00
17.07
4.5
16.71
14.33
17.01
14.45
4.55
19.76
16.71
16.77
14.27
5
17.91
15.22
15.52
13.19
5.05
17.79
15.10
19.52
16.54
5.5
16.42
14.15
17.85
15.28
5.55
19.28
16.12
17.25
14.80
6
17.73
15.28
16.24
13.79
6.5
16.30
13.91
15.46
13.01
Experimental Results shown in Figure 5.7
115
With Surfactant
100s
Backwash
Duration
Time (hr)
Without Surfactant
200s Backwash
Duration
100s Backwash
Duration
200s Backwash
Duration
Permeate Flux (l/m2.h)
0
19.94
20.00
17.73
17.55
0.5
18.51
18.92
16.12
15.82
1
16.95
17.61
14.74
14.98
1.5
15.94
16.42
13.91
13.97
2
14.80
15.16
12.95
13.13
2.5
13.73
14.15
12.24
12.12
3
12.83
13.07
11.58
11.16
3.5
12.83
13.07
11.10
11.04
3.53
19.76
20.06
16.71
17.07
4.5
17.97
18.63
14.92
15.64
4.53
19.34
19.88
16.48
16.71
5.5
17.67
18.27
14.63
15.22
5.53
18.98
19.40
15.82
16.12
6.5
17.37
17.91
14.15
14.74
7
16.36
16.89
13.31
13.85
7.5
15.28
15.82
12.42
12.89
8
14.09
14.74
11.76
12.24
Experimental Results shown in Figure 5.8
With
Surfactant
Error
Reduction
(%)
Transmembrane Pressure (kPa)
186.16
236.40
241.30
248.20
255.10
262.00
268.90
275.80
282.70
6.78
7.81
11.24
11.25
17.27
14.12
16.97
15.10
15.87
Results Obtained from modeling for Porous Membrane, shown in Figure 5.11
116
289.60
Without
Surfactant
Error
Reduction
(%)
Transmembrane Pressure (kPa)
186.16
236.40
241.30
248.20
255.10
262.00
268.90
275.80
282.70
289.60
1.03
6.21
1.07
1.01
2.32
3.22
2.84
8.73
8.06
6.52
Results Obtained from modeling for Porous Membrane, shown in Figure 5.12
Transmembrane Pressure (kPa)
Without
Surfactant
With
Surfactant
Relative
Contribution
Percentage (%)
of Fouling
Mechanism
186.2
236.4
241.3
248.2
255.1
262.0
268.9
275.8
282.7
289.6
90.35
89.44
89.62
89.82
86.00
84.38
84.42
83.20
83.53
82.97
9.65
10.56
10.38
10.18
14.00
15.62
15.58
16.80
16.47
17.03
89.72
90.13
90.01
90.07
85.59
84.04
82.70
81.39
81.23
10.28
9.87
9.99
9.93
14.41
15.96
17.30
18.61
18.77
Results Obtained from modeling for Porous Membrane, shown in Figure 5.15
Transmembrane Pressure (kPa)
137.9
144.8
Time (hr)
151.7
158.60
165.5
172.4
179.3
Permeate Flow Rate (Gallon/min)
0.01
0.38
0.44
0.49
0.54
0.65
0.7
0.67
0.5
0.3
0.36
0.38
0.42
0.48
0.54
0.5
1
0.24
0.28
0.31
0.34
0.38
0.44
0.41
1.5
0.21
0.246
0.28
0.30
0.35
0.4
0.38
2
0.19
0.24
0.27
0.29
0.34
0.39
0.36
2.5
0.19
0.24
0.27
0.29
0.324
0.385
0.35
3
0.19
0.24
0.27
0.29
0.33
0.385
0.35
Experimental Results shown in Figure 5.16
117
Transmembrane Pressure (kPa)
131
137.9
Time (hr)
144.8
151.7
158.6
165.5
172.4
Permeate Flow Rate (Gallon/min)
0.01
0.31
0.4
0.48
0.55
0.64
0.72
0.66
0.5
0.26
0.33
0.4
0.43
0.52
0.55
0.5
1
0.22
0.3
0.35
0.38
0.44
0.47
0.43
1.5
0.2
0.27
0.335
0.36
0.41
0.44
0.4
2
0.197
0.263
0.33
0.35
0.4
0.435
0.39
2.5
0.195
0.26
0.325
0.347
0.398
0.435
0.39
3
0.193
0.259
0.325
0.347
0.398
0.435
0.39
Experimental Results shown in Figure 5.17
Transmembrane Pressure (kPa)
137.9
144.8
Time (hr)
151.7
158.60
165.5
172.4
Permeate Flow Rate (Gallon/min)
0.01
0.51
0.63
0.7
0.82
0.77
0.74
0.5
0.44
0.51
0.56
0.65
0.6
0.58
1
0.4
0.43
0.46
0.54
0.52
0.49
1.5
0.38
0.41
0.438
0.52
0.495
0.47
2
0.37
0.399
0.435
0.51
0.483
0.46
2.5
0.36
0.4
0.43
0.5
0.474
0.45
3
0.364
0.4
0.43
0.5
0.474
0.445
Experimental Results shown in Figure 5.18
118
Transmembrane Pressure (kPa)
137.9
144.8
Time (hr)
151.7
158.60
165.5
172.4
Permeate Flow Rate (Gallon/min)
0.01
0.48
0.57
0.66
0.77
0.75
0.7
0.5
0.41
0.46
0.51
0.62
0.6
0.57
1
0.36
0.41
0.45
0.53
0.51
0.48
1.5
0.33
0.375
0.41
0.49
0.47
0.44
2
0.32
0.36
0.4
0.482
0.463
0.435
2.5
0.31
0.35
0.4
0.47
0.457
0.428
3
0.31
0.35
0.4
0.475
0.45
0.42
Experimental Results shown in Figure 5.19
Transmembrane
Pressure (kPa)
137.90
Permeate Flux
(l/m2.h)
6.68
144.80
7.74
151.70
8.97
158.60
10.20
165.50
11.43
172.40
12.31
179.30
11.78
Experimental Results shown in Figure 5.20
119
Transmembrane
Pressure (kPa)
Permeate Flux
(l/m2.h)
131.00
5.63
137.90
7.03
144.80
8.44
151.70
9.67
158.60
11.25
165.50
12.66
172.40
11.61
Experimental Results shown in Figure 5.21
Transmembrane
Pressure (kPa)
Permeate Flux
(l/m2.h)
137.90
8.79
144.80
11.08
151.70
13.01
158.60
14.42
165.50
13.54
Experimental Results shown in Figure 5.22
Transmembrane
Pressure (kPa)
Permeate Flux
(l/m2.h)
137.90
8.44
144.80
10.02
151.70
11.61
158.60
13.54
165.50
13.19
Experimental Results shown in Figure 5.23
120
With Surfactant
unsparged
Transmembrane
pressure (kPa)
sparged
Without Surfactant
unsparged
sparged
Permeate Flux (l/m2.h)
137.90
7.03
8.97
6.51
8.44
144.80
8.44
11.08
7.74
10.02
151.70
9.67
13.01
8.62
11.61
158.60
11.25
14.95
9.50
13.54
Experimental Results shown in Figure 5.24
Time (hr)
Permeate Flow Rate ( Gallon/min)
0.01
0.7
0.72
0.82
0.77
0.5
0.54
0.56
0.65
0.62
1
0.43
0.47
0.54
0.52
1.5
0.4
0.44
0.52
0.485
2
0.39
0.435
0.518
0.482
2.5
0.385
0.435
0.518
0.48
3
0.385
0.435
0.518
0.48
Experimental Results shown in Figure 5.25
Ultrafiltration with Dense Membrane
Mass Transfer Coefficient (m/s)
Without Surfactant/Without Air
5.83E-04
With Surfactant/Without Air
9.76 E-04
With Surfactant/With Air
12.3 E-04
Without Surfactant/With Air
10.8 E-04
Results shown in Figure 5.26
121
Appendix2: EPA Analytical Methods - Method 1664, Revision A: N-Hexane Extractable
Material (HEM; Oil and Grease) and Silica Gel Treated N-Hexane Extractable Material (SGTHEM; Non-polar Material) by Extraction and Gravimetry
EPA-821-R-98-002; February 1999
A.2.1. Introduction:
Method 1664 is a performance-based method applicable to aqueous matrices that requires the use of n-hexane as
the extraction solvent and gravimetry as the determinative technique. Alternative extraction and concentration
techniques are allowed, provided that all performance specifications are met. In addition, QC procedures
designed to monitor precision and accuracy have been incorporated into Method 1664.
A.2.2. Summary of Method:
A 1-L sample is acidified to pH <2 and serially extracted three times with n-hexane in a
separatory funnel. The extract is dried over sodium sulfate.
The solvent is distilled from the extract and the HEM is desiccated and weighed. If the HEM is to
be used for determination of SGT-HEM, the HEM is redissolved in n-hexane.
For SGT-HEM determination, an amount of silica gel proportionate to the amount of HEM is
added to the solution containing the redissolved HEM to remove polar materials. The solution is
filtered to remove the silica gel, the solvent is distilled, and the SGT-HEM is desiccated and
weighed.
Quality is assured through calibration and testing of the extraction, distillation, and gravimetric
systems.
A.2.3. Definitions:
HEM and SGT-HEM are method-defined analytes; i.e., the definitions of both HEM and SGTHEM are dependent on the procedure used. The nature of the oils and/or greases, and the
presence of extractable non-oily matter in the sample will influence the material measured and
interpretation of results.
Definitions for terms used in this method are given in the glossary at the end of the method.
A.2.4. Procedure:
This method is entirely empirical. Precise and accurate results can be obtained only by strict adherence
to all details.
NOTE: The procedure below is based on the preparation, extraction, and analysis of a 1-L sample. If a smaller
volume is collected for analysis, the laboratory should dilute the sample to 1 L with reagent water so that results
across the IPR, blank, OPR, MS, and, if performed, the MSD, are consistent. It is also important that all
glassware surfaces be rinsed with n-hexane to effect a quantitative transfer of the constituents in the sample and
of the hexadecane/stearic acid in the IPR, OPR, MS, and, if performed, the MSD.
122
A.2.4.1. Preparation of the analytical batch:
Bring the analytical batch of samples, including the sample aliquots for the MS (and MSD), to
room temperature.
Place approximately 1000 mL (950-1050 mL) of reagent water in a clean sample bottle to serve
as the laboratory blank.
Prepare the OPR using the PAR standard
Either mark the sample bottle at the water meniscus or weigh the bottle for later determination of
sample volume. Weighing will be more accurate. Mark or weigh the MS (and MSD).
A.2.4.2. pH verification:
A.2.4.2.1 Verify that the pH of the sample is less than 2 using the following procedure:
Dip a glass stirring rod into the well mixed sample.
Withdraw the stirring rod and allow a drop of the sample to fall on or touch the pH paper.
NOTE: Do not dip the pH paper into the bottle or touch it to the sample on the lid.
Rinse the stirring rod with a small portion of n-hexane that will be used for extraction (to ensure
that no extractable material is lost on the stirring rod). Collect the rinsate in the separatory funnel
to be used for sample extraction.
A.2.4.2.2 If the sample is at neutral pH, add 5-6 mL of HCl or H2SO4 solution to the 1-L sample. If the sample is
at high pH, use a proportionately larger amount of HCl or H 2SO4 solution. If a smaller sample volume was
collected, use a proportionately smaller amount of HCl or H2SO4 solution.
A.2.4.2.3 Replace the cap and shake the bottle to mix thoroughly. Check the pH of the sample using the
procedure in Section 11.2.1. If necessary, add more acid to the sample and retest.
A.2.4.2.4 Add the appropriate amount of HCl or H2SO4 solution to the blank, OPR, MS (and MSD) to adjust the
pH of these solutions to <2.
NOTE: The procedure detailed below is for separatory funnel liquid-liquid extraction. Solid-phase extraction
(SPE) may be used at the discretion of the discharger/generator and its laboratory. However, if SPE is used, it is
the responsibility of the discharger/generator and laboratory to assure that results produced are equivalent to
results produced by the procedure below.
A.2.4.3. Extraction:
A.2.4.3.1 Tare a clean boiling flask containing 3-5 boiling chips as follows:
Place the flask containing the chips in an oven at 105-115 C for a minimum of 2 h to dry the flask
and chips.
Remove from the oven and immediately transfer to a desiccator to cool to room temperature.
When cool, remove from the desiccator with tongs and weigh immediately on a calibrated
balance (Section 10).
A.2.4.3.2 Pour the sample into the separatory funnel.
123
A.2.4.3.3 Add 30 mL of n-hexane to the sample bottle and seal the bottle with the original bottle cap. Shake the
bottle to rinse all interior surfaces of the bottle, including the lid of the bottle cap. Pour the solvent into the
separatory funnel.
A.2.4.3.4 Extract the sample by shaking the separatory funnel vigorously for 2 minutes with periodic venting into
a hood to release excess pressure.
A.2.4.3.5 Allow the organic phase to separate from the aqueous phase for a minimum of 10 minutes. If an
emulsion forms between the phases and the emulsion is greater than one-third the volume of the solvent layer,
the laboratory must employ emulsion-breaking techniques to complete the phase separation. The optimum
technique depends upon the sample, but may include stirring, filtration through glass wool, use of solvent phase
separation paper, centrifugation, use of an ultrasonic bath with ice, addition of NaCl, or other physical methods.
Alternatively, solid-phase extraction (SPE), continuous liquid-liquid extraction, or other extraction techniques
may be used to prevent emulsion formation, provided that the requirements in Section 9.1.2 are met.
A.2.4.3.6 Drain the aqueous layer (lower layer) into the original sample container. Drain a small amount of the
organic layer into the sample container to minimize the amount of water remaining in the separatory funnel.
NOTE: The amount of water remaining with the n-hexane must be minimized to prevent dissolution or clumping
of the sodium sulfate in the solution drying process.
A.2.4.3.7 Place a filter paper in a filter funnel adds approximately 10 g of anhydrous Na 2SO4, and rinse with a
small portion of n-hexane. Discard the rinsate.
NOTE: The specific properties of a sample may necessitate the use of larger amounts of Na 2SO4.
A.2.4.3.8 Drain the n-hexane layer (upper layer) from the separatory funnel through the Na 2SO4 into the preweighed boiling flask containing the boiling chips.
NOTE: It is important that water be removed in this step. Water allowed to filter through the Na 2SO4 will
dissolve some of the Na2SO4 and carry it into the boiling flask compromising the determination.
A.2.4.3.9 Repeat the extraction twice more with fresh 30-mL portions of n-hexane, combining the extracts in the
boiling flask.
A.2.4.3.10 Rinse the tip of the separatory funnel, the filter paper, and the funnel with 2-3 small (3-5 mL) portions
of n-hexane. Collect the rinsings in the flask.
NOTE: For samples that are expected to contain a high concentration of salt (e.g., waters from oil production
facilities), it may be prudent to collect the extract in a 250-mL separatory funnel and back-extract with reagent
water. After back-extraction, the extract should be drained through Na 2SO4 to remove all traces of water.
A.2.4.3.11 A milky extract indicates the presence of water. If the extract is milky, allow the solution to stand for
up to one hour to allow the water to settle. Decant the solvent layer (upper layer) through sodium sulfate to
remove any excess water as in Sections A.2.4.3.7 and A.2.4.3.8. Rinse the glassware and sodium sulfate with
small portions of n-hexane to affect a quantitative transfer.
A.2.4.3.12 If only SGT-HEM is to be determined, proceed to Section 11.5.
124
A.2.4.4. Solvent distillation:
A.2.4.4.1 Connect the boiling flask to the distilling head apparatus and distill the solvent by immersing the lower
half of the flask in a water bath or a steam bath. Adjust the water temperature as required to complete the
concentration in less than 30 minutes. Collect the solvent for reuse.
A.2.4.4.2 When the temperature in the distilling head reaches approximately 70C or the flask appears almost dry,
remove the distilling head. Sweep out the flask for 15 seconds with air to remove solvent vapor by inserting a
glass tube connected to a vacuum source. Using tongs, immediately remove the flask from the heat source and
wipe the outside surface dry to remove moisture and fingerprints.
NOTE: The laboratory should carefully monitor the flask during the final stages of distillation to assure that all
of the solvent is removed and to prevent loss of the more volatile sample constituents.
A.2.4.4.3 Inspect the residue in the boiling flask for crystals. Crystal formation is an indication that sodium
sulfate may have dissolved and passed into the boiling flask. This may happen if the drying capacity of the
sodium sulfate is exceeded or if the sample is not adjusted to low pH. If crystals are observed, redissolve the
extract in n-hexane, quantitatively transfer through a filter into another tared boiling flask, and repeat the
distillation procedure.
A.2.4.4.4 Dry the boiling flask for 30 - 45 minutes in an oven maintained at 70 ± 2 C. Cool to room temperature
in a desiccator and maintain in the desiccator for 30 minutes minimum. Remove with tongs and weigh
immediately. Repeat the cycle of drying, cooling, desiccating, and weighing until the weight loss is less than 4 %
of the previous weight or less than 0.5 mg, whichever is less.
If the extract was from the HEM procedure, determine the HEM (W h) by subtracting the tare weight
from the total weight of the flask.
If the extract was from the SGT-HEM procedure , determine the weight of SGT-HEM (Ws) by
subtracting the tare weight from the total weight of the flask.
A.2.4.4.5 Determine the original sample volume (Vs) in liters by filling the sample bottle to the mark with water
and measuring the volume of water in a 1- to 2-L graduated cylinder. If the sample weight was used , weigh the
empty bottle and cap and determine Vs by difference, assuming a sample density of 1.00.
A.2.4.5. SGT-HEM determination:
A.2.4.5.1 Silica gel capacity--To ensure that the capacity of the silica gel will not be exceeded, the amount of
HEM must be less than 100 mg or, if above 100 mg, must be known.
If it is known that the amount of HEM is less than 100 mg, the laboratory may proceed with the
determination of SGT-HEM per Sections A.2.4.5.3-A.2.4.5.5 without determination of HEM.
If, however, the amount of HEM is not known, HEM must first be determined using the procedure in
Sections A.2.4.3-A.2.4.4.
A.2.4.5.2 Extractable materials in silica gel--Because the capacity of silica gel is not known for all substances, it
is presumed that 3 g will normally adsorb 100 mg of all adsorbable materials. Therefore, for samples containing
1000 mg HEM, 30 g of silica gel will be needed. The amount of silica gel that can be used for adsorption in the
SGT-HEM procedure below has been limited to 30 g because of concerns about possible extractable impurities
in the silica gel. If the amount of HEM in the sample is greater than 1000 mg, split the extract per the following
procedure:
Add 85-90 mL of n-hexane to the boiling flask to redissolve the HEM. If necessary, warm the solution
to completely redissolve the HEM.
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Va
Quantitatively transfer the extract to a 100-mL volumetric flask. Dilute to the mark with n-hexane.
Calculate the extract volume that contains 1000 mg of extractable material according to the following
equation:
1000Vt
Wh
Eq.A.2.1
Vt is the total volume of solvent used in section A.2.5.5 (ml)
W h is the weight of extractable material HEM measurement (mg)
Va is the volume of aliquat to be withdrawn (ml)
Using a calibrated pipet, remove the volume to be withdrawn (V a) and return to the boiling flask. Dilute
to approximately 100 mL with n-hexane.
A.2.4.5.3 Adsorption with silica gel
Add 3.0 ± 0.3 g of anhydrous silica gel to the boiling flask for every 100 mg of HEM, or fraction
thereof, to a maximum of 30 g of silica gel. For example, if the weight of HEM is 735 mg, add 3 x 8 =
24 g of silica gel.
Add a fluoropolymer-coated stirring bar to the flask and stir the solution on a magnetic stirrer for a
minimum of 5 minutes.
A.2.4.5.4 Filter the solution through n-hexane moistened filter paper into a pre-dried, tared boiling flask
containing several boiling chips. Rinse the silica gel and filter paper with several small amounts of n-hexane to
complete the transfer.
A.2.4.5.5 Distill the solution and determine the weight of SGT-HEM per Section A.2.4.4.
A.2.5 Data Analysis and Calculations:
A.2.5.1 n -Hexane extractable material--Calculate the concentration of HEM ("oil and grease") in the sample per
the following equation:
HEM(mg/L)
W h (mg)
Vs (L)
Eq.A.2.2
W h is the weight of extractable material from section A.2.4.4
Vs is sample volume from section A.2.4.5
A.2.5.2 Silica gel treated n-hexane extractable material--Calculate the concentration of SGT-HEM ("non-polar
material") in the sample per the equation above, substituting W s for Wh. If the extract was split to decrease the
total amount of material to 1,000 mg, determine the corrected total weight of SGT-HEM in the un-split extract
(Wc) using the following equation:
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W e (mg)
Vt
W d (mg)
Va
Eq.A.2.3
W e is the weight in the portion of the extract split for adsorption from section A.2.5.2 and A.2.4.4
Vt and Va are defined in Eq.A.2.1
Use the corrected total weight of SGT-HEM in the unsplit extract (Wc) to determine the total SGT-HEM in the
sample by substituting Wc for Wh in Equation 5.
A.2.5.3 Reporting--Report results to three significant figures for HEM and SGT-HEM found at or above 10
mg/L, and report results to two significant figures for HEM and SGT-HEM found below 10 mg/L.
Samples--Report results for HEM and SGT-HEM found below the ML as < 5.0 mg/L, or as required by
the permitting aut hority or permit.
Blanks--Report results for HEM and SGT-HEM found below the MDL as < 1.4 mg/L, or as required by
the permitting authority or permit. Do not report results below the MDL unless required by the
permitting authority or permit.
Results from tests performed with an analytical system that is not in control must not be reported or
otherwise used for permitting or regulatory compliance purposes but do not relieve a discharger or
permittee of timely reporting.
A.2.6. Glossary of Definitions and Purposes:
The definitions and purposes are specific to this method but have been conformed to common usage to the extent
possible.
A.2.6.1 Units of weight and measure and their abbreviations
Symbols
C: degrees Celsius
<: less than
%: percent
±: plus or minus
Alphabetical
g: gram
h: hour
L: liter
mg: milligram
mg/g: milligram per gram
mg/L: milligram per liter
mg/mL: milligram per milliliter
mL: milliliter
No. : Number
rpm: revolutions per minute
Definitions, acronyms, and abbreviations
Analyte: The HEM or SGT-HEM determined by this method.
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Analytical batch: The set of samples started through the extraction process in a 12-hour shift, to a maximum of
20 field samples. Each analytical batch of 20 or fewer samples must be accompanied by a laboratory blank
(Section 9.4), an ongoing precision and recovery sample (OPR, Section 9.6), and a matrix spike, (Section 9.3),
resulting in a minimum of four analyses (1 sample, 1 blank, 1 OPR, and 1 MS) and a maximum of 23 analyses
(20 field samples, 1 blank, 1 OPR, and 1 MS) in the batch. If greater than 20 samples are to be extracted in a 12hour shift, the samples must be separated into analytical batches of 20 or fewer samples.
Discharge (matrix type): A sample medium with common characteristics across a given industrial subcategory
(40 CFR parts 403-500). For example, C-stage effluents from chlorine bleach mills in the Pulp, Paper, and
Paperboard industrial category; effluent from the Continuous Casting subcategory of the Iron and Steel industrial
category; publicly owned treatment work (POTW) sludge; and in-process streams in the Atlantic and Gulf Coast
Hand-shucked Oyster Processing subcategory are each a matrix type.
Field blank: An aliquot of reagent water that is placed in a sample container in the laboratory or in the field and
treated as a sample in all respects, including exposure to sampling site conditions, storage, preservation, and all
analytical procedures. The purpose of the field blank is to determine if the field or sample transporting
procedures and environments have contaminated the sample.
HEM: See n-Hexane extractable material.
n-Hexane extractable material: Material that is extracted from a sample and determined by this method (oil and
grease). This material includes relatively non-volatile hydrocarbons, vegetable oils, animal fats, waxes, soaps,
greases, and related matter.
IPR: See initial precision and recovery.
Initial precision and recovery (IPR): Four aliquots of the diluted PAR analyzed to establish the ability to generate
acceptable precision and accuracy. An IPR is performed the first time this method is used and any time the
method is modified.
Laboratory blank (method blank): An aliquot of reagent water that is treated exactly as a sample including
exposure to all glassware, equipment, solvents, reagents, internal standards, and surrogates that are used with
samples. The laboratory blank is used to determine if analytes or interferences are present in the laboratory
environment, the reagents, or the apparatus.
Laboratory control sample (LCS): See Ongoing precision and recovery standard (OPR).
Matrix spike (MS) and matrix spike duplicate (MSD): Aliquots of an environmental sample to which known
quantities of the analytes are added in the laboratory. The MS and MSD are prepared and/or analyzed exactly
like a field sample. Their purpose is to quantify any additional bias and imprecision caused by the sample matrix.
The background concentrations of the analytes in the sample matrix must be determined in a separate aliquot and
the measured values in the MS and MSD corrected for background concentrations.
May: This action, activity, or procedural step is neither required nor prohibited.
May not: This action, activity, or procedural step is prohibited.
Method Detection Limit: The lowest level at which an analyte can be detected with 99 percent confidence that
the analyte concentration is greater than zero.
Minimum Level (ML): The lowest level at which the entire analytical system gives a recognizable signal and
acceptable calibration point for the analyte. It is equivalent to the concentration of the lowest calibration
standard, assuming that all method-specified sample weights, volumes, and cleanup procedures have been
employed.
Must: This action, activity, or procedural step is required.
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Ongoing precision and recovery standard (OPR, also called a laboratory control sample): A laboratory blank
spiked with known quantities of analytes. The OPR is analyzed exactly like a sample. Its purpose is to assure that
the results produced by the laboratory remain within the limits specified in this method for precision and
accuracy.
OPR: See ongoing precision and recovery standard.
PAR: See precision and recovery standard.
Precision and recovery standard: Secondary standard that is diluted and spiked to form the IPR and OPR.
Quality control sample (QCS): A sample containing analytes of interest at known concentrations. The QCS is
obtained from a source external to the laboratory or is prepared from standards obtained from a different source
than the calibration standards. The purpose is to check laboratory performance using test materials that have been
prepared independently from the normal preparation process.
Quantitative transfer: The process of transferring a solution from one container to another using a pipet in which
as much solution as possible is transferred, followed by rinsing of the walls of the source container with a small
volume of rinsing solution (e.g., n-hexane), followed by transfer of the rinsing solution, followed by a second
and third rinse and transfer.
Reagent water: Water demonstrated to be free from HEM and SGT-HEM and potentially interfering substances
at or above the minimum level of this method.
Regulatory Compliance Limit: A limit on the concentration or amount of a pollutant or contaminant specified in
a nationwide standard, in a permit, or otherwise established by a regulatory authority.
SGT-HEM: See Silica gel treated n-hexane extractable material.
Should: This action, activity, or procedural step is suggested but not required.
Silica gel treated n-hexane extractable material: Components of n-Hexane extractable material (HEM) that are
not adsorbed by silica gel; i.e., non-polar material (NPM).
Stock solution: A solution containing an analyte that is prepared using a reference material traceable to EPA, the
National Institute of Science and Technology (NIST), or a source that will attest to the purity and authenticity of
the reference material.
A.2.7. References:
1.
2.
3.
4.
5.
6.
7.
"Methods for Chemical Analysis of Water and Wastes," 3rd Edition, Environmental Protection Agency,
Environmental Monitoring Systems Laboratory-Cincinnati (EMSL-Ci), Cincinnati, Ohio 45268, EPA600/4-79-020, Method 413.1, (1983).
Ibid., Method 418.1
Guidelines Establishing Test Procedures for the Analysis of Oil and Grease and Non-polar Materials;
Final Rule; Preamble, Responses to Comments, and Docket, as referenced in the Final Rule.
"Carcinogens - Working With Carcinogens," Department of Health, Education, and Welfare, Public
Health Service, Center for Disease Control, National Institute for Occupational Safety and Health,
Publication No. 77-206, August 1977.
"OSHA Safety and Health Standards, General Industry," (29 CFR 1910), Occupational Safety and
Health Administration, OSHA 2206 (Revised, January 1976).
"Safety in Academic Chemistry Laboratories," American Chemical Society, Committee on Chemical
Safety, 3rd Edition, 1979.
"Standard Practices for Sampling Water," ASTM Annual Book of Standards, Part 31, D3370-76,
American Society for Testing and Materials, 1916 Race Street, Philadelphia, PA 19103-1187, 1980.
129
8.
9.
"Handbook of Analytical Quality Control in Water and Wastewater Laboratories," USEPA, EMSL-Ci,
Cincinnati, OH 45268, EPA-600/4-79-019, March 1979.
Report of the Method 1664 Validation Studies, April 1995. Available from the Sample Control Center
(operated by DynCorp I&ET), 6101 Stevenson Avenue, Alexandria, VA 22304, (703) 461-2100.
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