Enhancement of Reverse Osmosis Membranes Performance with .... taleb.pdf · Enhancement of Reverse Osmosis Membranes Performance with Air Sparging Technique . A Thesis . Submitted

Ministry of Higher Education

& Scientific Research

University of Technology

Chemical Engineering Department

Enhancement of Reverse Osmosis Membranes

Performance with Air Sparging Technique

A Thesis

Submitted to the Chemical Engineering Department

Of The University of Technology

In Partial Fulfillment of the Requirements for

The Degree of Doctor of Philosophy in

Chemical Engineering.

By Talib Mohammad Naief

(M. Sc. Chem. Eng.2001)

May - 2009

الرحيم الرحمن هللا بسم

الماء نسوق أنا يروا أولم

فنخرج الجرز األرض إلى

مهم آع أن منه تأكل زرعا به

يبصرون أفال وأنفسهم

العظيم هللا صدق السجدة سورة

)27( االية

Certification

I certify that this thesis entitled (Enhancement of Reverse

Osmosis Membranes Performance with Air Sparging

Technique) was prepared under my linguistic supervision. It was

amended to meet the style of English Language.

Signature

Name: Asst. Prof. Dr. Ahmad AL-Beiruti.

Date: / / 2009

Certification of Supervisor We certify that the thesis entitled (Enhancement of Reverse

Osmosis Membranes Performance with Air Sparging

Technique) was prepared under our supervision as a partial

fulfillment of the requirements of the degree of Philosophy of

Doctorate in Chemical Engineering at the Chemical Engineering

Department, University of Technology.

Signature: Signature: Name: Prof. Dr. Mumtaz A. Yousif Name: Asst. prof. Dr. Qusay Fadhel Date: / / 2009 Date: / / 2009

In view of the available recommendations, I forward this

thesis for debate by the examination committee.

Signature

Asst.Prof. Dr. Kahlid A.Sukkar

Head of post graduate Committee

Department of Chemical Engineering.

Date: / / 2009

Dedication

Especially Dedicated To…. The memory of my Father To my mother with love My brothers and my sisters My Wife and Children Ibrahim and Masarra.

Talib

I

Acknowledgment

I would like to express my sincere thanks, gratitude and

appreciation to my supervisors Prof. Dr. Mumtaz A. Zabluk and

Asst. Prof. Dr. Qusay Fadhel Abdul Hameed for their kind

supervision, advice, reading and Criticizing the proofs of this

study.

First of all, I thanks god who offered me patience, power and

faith in a way that words cannot express.

My respectful regards to head of Chemical Engineering

Department at the University of Technology Prof. Dr. Mumtaz A.

Zabluk for his kind help in providing facilities.

My respectful regards to all staff of Physical Science Faculty

at the University of Complutense – Madrid - Spain. For their kind

help in providing facilities.

My grateful thanks to the staff of AL-Mansour Company for

their help in the experimental work.

My respectful regards to head of Material Engineering

Department at the University of Technology Prof. Dr. Ali H.Ataiwi

for his kind help in providing facilities.

My grateful thanks to Miss Nisreen, the chief of computer

laboratory.

My deepest gratitude and sincere appreciation goes to my

beloved family for their patience and encouragement that gave me

so much hopes and support that I feel short of thanks.

Talib

II

Abstract

In the present work, the central composite design (CCD) technique was

used to study the effects of various operating conditions such as: NaCl

concentration (15-45) gm/l, temperature (10-50) °C, flow rates (100-250) l/hr

and operating pressure (5-15) bar on the performance of the reverse osmosis

membrane (RO) type (Cellulose acetate, Sc-6200, spiral-wound model) were

studied by using the experimental design (Box Wilson) method. The objective

function (Response) was the flux of permeate and salt rejection. The coefficients

of the proposed model (second order polynomial model) were found, and then

the significant and non-significant parameters for the proposed model were

checked by the (F-test) method. In order to ensure a good model the (F-test) for

significance of the regression model was performed by applying the analysis of

variance (ANOVA). The maximum conditions for the proposed model by using

optimization program (Hook and Jeeves) were applied for the permeate flux and

salt rejection, where permeate flux was equal to (3.406 kg/m

2

An application of the gas-liquid two-phase flow for the permeate flux

enhancement during the (RO) membrane has been studied, through sparging air

in the system at different velocities by fixing the four variables at the maximum

conditions and then different velocities of liquid at fixed velocity of air. The

results of the experiments showed a positive effect of the constant gas-liquid

two-phase flow on the permeate flux and salt rejection, where the permeate flux

.hr) and salt

rejection was equal to (85%). In addition, it was found that the flux and the

rejection of NaCl solution throughout the reverse osmosis (RO) dependent on

feed concentration, feed temperature, feed flow rate and operating pressure in

the following sequence: feed concentration > operating pressure > feed

temperature > feed flow rate. A mathematical model was developed by (Jamal et

al, 2004) for prediction the permeate flux during reverse osmosis (RO)

membrane process has been applied and the results showed a good agreement

between the experimental data and the proposed model.

III

was increased from (3.406 kg/m2.hr) to (5.676 kg/m2

.hr) and salt rejection

increased from (85%) to (91%). It might be concluded from the analysis of the

experimental results based on the spiral wound filtration model that a two-phase

flow seemed to enhance the permeate flux and rejection with a slug flow pattern.

Thus allowing higher fluxes which led to an increase in permeate flux by a

factor (1.66).

IV

LLiisstt ooff CCoonntteennttss

Subject Page

Acknowledgments ........................................................... I

Abstract ............................................................................ II

List of Contents ......................................................... IV

Nomenclature ........................................................... VII

Chapter One – Introduction 1

1.1 Types of Membrane .............................................. 2

1.2 Membrane Modules .............................................. 3

1.3 Application of Membrane Filtration ....................... 4

1.4 Transport Phenomena in Membrane ....................... 5

1.5 Two - Phase Flow ................................................... 6

1.6 Aim of the Present Work ........................................ 8

Chapter Two - Theoretical Concepts and Literature Survey 9

2.1 Reverse Osmosis (RO) Process Description and

Terminology..................................................................

11

2.2 Theory ..................................................................... 13

2.3 Factors Affecting Flux ……………………………

2.3.1Operating Parameter ...............................

2.3.2 PH of Feed …………………….

17

17

18

2.4 Flux Decline in Membranes and Strategies to Reduce

Fouling ..........................................................................

19

2.5 Two - Phase Flow ................................................... 24

2.5.1 Flow Pattern .................................................. 24

2.5.2 Air Flow Rate ............................................... 27

2.6 Spiral Wound Modules ........................................... 28

2.7 Previous Studies for Sparging Air in Membrane. ... 28

V

Subject Page

Chapter Three – Mathematical Model 36

3.1 Mathematical Modeling of Reverse Osmosis ........ 37

3.1.1 Models for Solvent and Solute Transport in Reverse

Osmosis……………………………………………………

37

Chapter Four – Experimental work 46

4.1 The Experimental System ...................................... 47

4.1.1 Measuring Devices .............................................. 49

4.1.2. Experimental Procedures .................................... 50

4.2 Experimental Design ……………………………… 51

4.2.1 Fitting the Second Order Model…………………. 51

4.2.2 Central Composite Rotatable Design…………….. 52

4.3 Experimental Work for Injection Air Process……… 55

4.3.1 Equipment and Apparatus………………………… 56

Chapter Five - Results and Discussion 59

5.1 Analysis of Experimental Result ........................... 59

5.2 Postulating the polynomial Model .......................... 60

5.2.1 The second Order Curve Fitting .......................

5.2.2 Effect of Concern Variables ………………

62

64

5.3 Estimating the Maximum Condition ...................... 64

5.4 Examination of The Effective Variables (F-test) .... 64

5.5 The Analysis of Variance (ANOVA) ..................... 64

5.6 Effect of operating conditions on performance of reverse

osmosis membrane ........................................................

5.6.1Effect of Maximum Conditions on Reverse Osmosis

Membrane Permeate Flux ……………………………………

69

69

5.6.2 Effect of optimum conditions on Reverse Osmosis

Membrane Rejection flux ............................................

73

5.7 Simulation Results of Reverse Osmosis Membrane 76

VI

Subject Page

5.8 The Effect of Sparging Air on Reverse Osmosis

Membrane Performance ..............................................

77

5.8.1 Effect of Different Superficial Air Velocities on

Permeate Flux ............................................

77

5.8.2 Effect of Different Superficial Liquid Velocities on

Permeate Flux ............................................

81

5.8.3 Effect of Different Superficial Air Velocities on

Rejection .............................................

84

5.8.4 Effect of Different Superficial Liquid Velocities on

Rejection .............................................

85

Chapter Six – Conclusions and Recommendations For

Further Work

86

6.1 Conclusions ............................................................. 86

6.2 Recommandations .................................................. 87

References 88

Appendix

Appendix A: Optimization Program (Hook and Jeeves)

Appendix B: Examination of the Effective Variables

Appendix B-1: Table (F-1) Analysis of variances of variables

Appendix C: Solution steps of ANOVA analysis

Appendix D: Figures (D1) to (D24)

Appendix E: Computer Program

VII

Nomenclature

Symbols Units

a1-a Constants in Model Equation 5

A Solvent Permeability Constant w m/h

B Solute Permeability Constant s m/h

C Concentration kg/m3

C Solute Bulk Concentration Flow b kg/m3

C Solute Feed Concentration f kg/m3

C Initial Solute Feed Concentration fo kg/m3

C Solute Concentration In The Feed Tank ft kg/m3

C Solute Permeate Concentration p kg/m3

Cp Concentration polarization * dimensionless

C Average Permeate Concentration pav kg/m3

C Solute Retentate Concentration r kg/m3

C Solute Wall Concentration w kg/m3

Cw Solute Concentration at Membrane Wall

with Concentration polarization

* kg/m3

C Solvent Permeate Concentration (water) wp kg/m3

D Diffusion Coefficient m2/h

D Salt Diffusivity Coefficient s m2/h

F Volumetric Flux Rate w m3/m2.h

G Mass Flow Rate kg/m2.h

g Gravitation Acceleration 9.81m/sec2

j Gas Flux gas kg/m2.h

j Liquid Flux l kg/m2.h

J Flux With Bubbling gas kg/m2.h

J Flux Without Bubbling nogas kg/m2.h

J Solute Flux s kg/m2.h

VIII

J Solute (Water Flux) w kg/m2.h

Κ Darcy’s Law permeability coefficient dimensionless

KDa Kilo Dalton molecular weight

Ks Salt Partition Coefficient

NA Mass flux of component A kg/m2.h

NAw Water Flux kg/m2.h

NS Salt Flux kg/m2.h

ni Number of Moles of Species i

p Transmembrane Pressure bar

Pi Inlet Pressure to Compressor bar

Po Outlet Pressure From the Compressor bar

Qf Volumetric Feed Flow Rate m3/h

Qp Volumetric Permeate Flow Rate m3/h

Qr Volumetric Retentate Flow Rate m3/h

R Ideal Gas Constant 8.3J/mol.K

Rj Solute Rejection %

Sa Membrane Surface Area m2

Sc Schmidt Number (Sc = ν/D) Sc

t Time h

T Absolute Temperature K

UGs Gas Superficial Velocity m/sec

ULs Liquid Superficial Velocity m/sec

Um Sum of Superficial Liquid and Gas Velocity m/sec

Us Slug Velocity m/sec

Vf Feed Volume m3

Vfo Initial Feed Volume m3

Vft Feed Tank Volume m3

Vp Permeate Volume m3

vg Gas Volume Flow Rate m3/h

IX

vl Liquid Volume Flow Rate m3/h

W Channel Width m

x Salinity ppm

Greek Letters Units δ , l Membrane Thickness m

∆ Fractional Solvent Recovery

P∆ Membrane Pressure Gradient kg/m.h2

-∆µw Solute potential gradient

ε Porosity and injection factor

θ Dimensional Solute Permeability

μl Liquid Viscosity kg/m.s

ν Kinematic Viscosity m2/sec

ρ Density kg/m3

ρg Gas Density kg/m3

ρl Liquid Density kg/m3

σ Staver man reflection coefficient

σ Surface Tension Dyne/cm

π Osmotic Pressure bar

τ Shear Stress kg.m/s2

τ Tortuousity

Φ Flux Enhancement (Φ = Jgas/Jnogas)

Ψ Selectivity %

X

Abbreviations

EC Electrical- Conductivity

ED Electrodialysis

IE Ion Exchange

IS Ionic Strength

LMTD Logmean Températures Différence

MED Multi- Effect Distillation Process

MF Microfiltration

MSF Multi- Stage Flash Distillation Process

SEM Scanning Electron Microscope

NF Nanofiltration

TFC Thin Film Composite; the Most Popular Type of

Reverse Osmosis Membrane

ppm Parts per Million

PR Performance Ratio

RO Reverse Osmosis Desalting Process

SDI Silt Density Index for Reverse Osmosis

TDS Total Dissolved Solid in Parts per Millions

TVC Thermal Vapour Compression

UF Ultrafiltration

VC Vapour Compression

VCD Vapour Compression Distillation [

Table (B-1) Analysis of variances of variable

Effect X1 X2 X3 X4 X1X2 X1X3 X1X4 X2X3 X2X4 X3X4 X12

X22

X32

X42

∑ 2X

24

24 24 24 16 16 16 16 16

16

24

24

24

24

Sb

2 1.834

1.834 1.834 1.834 2.752 2.752 2.752 2.752 2.752 2.752 1.834 1.834 1.834 1.834

(Coeff)

2 191.24

21.571 0.1518 75.592 1.0916 0.5607 34.557 0.0178 3.6135

0.0093

65.407

0.7035

0.1967

0.0053

Z

104.24

11.761 0.0827 41.217 0.3966 0.2038 12.557 0.0064 1.313

0.0034

35.663

0.3835

0.1073

0.0029

F=4.96

S

S NS S NS NS S NS NS

NS

S

NS

NS

NS

Chapter One Introduction

1

Chapter One

Introduction

Desalination through the use of membranes was introduced in 1960s as an

alternative to distillation. A reverse osmosis membrane process is a physical

separation process, where salt is separated from seawater or brackish water to

produce drinking water.

Reverse osmosis (RO) is relatively new as compared to the distillation

processes. The first commercial unit was installed in Florida in 1971. The

reverse osmosis membrane separation process separates freshwater from

saltwater under high pressure where the freshwater passes through the

membrane layer while the salt content remains outside the membrane. The

amount of freshwater produced varies from 30 to 80% depending on the salt

content of the water, pressure and type of membranes used. Brackish water

membrane systems typically have higher recoveries and operate under lower

pressures, ranging from 225 psi to 375 psi. Seawater reverse osmosis systems

typically have lowers recoveries due to the higher salt content and their

operating range is typically 800 to 1200 psi (Beck, 2002).

Reverse osmosis is a process that transforms an unusable water supply

into a usable resource. It is capable of renovating a broad spectrum of feed

waters from municipal water supplies that need refinement for industrial

purposes to seawater that is refined into a potable water supply. Table 1.1 shows

the different types of feed water being processed by reverse osmosis units

(Mark, 1990). Seawater is considered to have nominal total dissolved solids

(TDS) content of 35,000 mg/l.


2

Table 1.1 Source of Reverse Osmosis Feed Water

Feed Water Capacity,MGD Percent of Total Sea Water 67.9 13.0

Waste Water 26.5 5.0 Brackish Water 429.6 82.0

Total 524.0 100.0 MGD = One million gallons per day

As of the end of 1984, the desalination of brackish water accounted for

82% of capacity. This is due to the fact that early reverse osmosis membranes

were incapable of single stage seawater desalination and, thus, they were limited

to brackish water desalination. Significant advances have been made in both the

flux and rejection capability of membranes and reverse osmosis is technically

able to desalt seawater in a single stage. In the recent past, it has been an

effective competitor to the distillation process in seawater desalination. In fact,

reverse osmosis is now beginning to replace existing distillation capacity in the

Middle East (Smith, 1985). Although reverse osmosis is a relatively new

technology, there is sufficient operating capacity in a number of varied

applications to warrant confidence in the process. From a technical and

economic point of view the process is capable of desalting a broad range of feed

waters from municipal water supplies to seawater. It has economic viability in a

large number of industrial applications.

1.1 Types of Membranes

Water treatment processes employ several types of membranes as shown

in Figure 1.1. They include microfiltration (MF), ultrafiltration (UF), reverse

osmosis (RO), and nanofiltration (NF) membranes. MF membranes have the

largest pore size and typically reject large particles and various microorganisms.

UF membranes have smaller pores than MF membranes and, therefore, in

addition to large particles and microorganisms, they can reject bacteria and

soluble macromolecules such as proteins. RO membranes are effectively non-


3

porous and, therefore, exclude particles and even many low molar mass species

such as salt ions, organics, and etc. NF membranes are relatively new and are

sometimes called “loose” RO membranes. They are porous membranes, but

since the pores are on the order of ten angstroms or less, they exhibit

performance between that of RO and UF membranes (Amjad, 1993), (Perry,

1997) and (Baker, 2004).

Figure 1.1 Range of nominal membrane pore sizes (Perry, 1997) 1.2 Membrane Modules

There are four main types of modules: plate-and-frame, tubular, spiral

wound, and hollow fiber shown in Figure 1.2, (Baker, 2004). The plate-and-

frame module is the simplest configuration, consisting of two end plates, the flat

sheet membrane, and spacers. In tubular modules, the membrane is often on the

inside of a tube, and the feed solution is pumped through the tube. The most

popular module in industry for nanofiltration or reverse osmosis membranes is

the spiral wound module. This module has a flat sheet membrane wrapped

around a perforated permeate collection tube (Baker, 2004). The feed flows on

one side of the membrane. Permeate is collected on the other side of the

membrane and spirals in towards the center collection tube. Hollow fiber

modules used for seawater desalination consist of bundles of hollow fibers in a

pressure vessel. They can have a shell-side feed configuration where the feed

passes along the outside of the fibers and exits the fiber ends. Hollow fiber


4

modules can also be used in a bore-side feed configuration where the feed is

circulated through the fibers. Hollow fibers employed for wastewater treatment

and in membrane bioreactors are not always used in pressure vessels. Bundles of

fibers can be suspended in the feed solution, and permeate is collected from one

end of the fibers (Baker, 2004).

Figure 1.2 Schematic of (a) plate and frame, (b) tubular, (c) spiral wound and (d) hollow fiber modules (Pelligrino and sikdar, 2004)

1.3 Application of Membrane Filtration

Applications of membrane filtration in water treatment can be divided into

two groups: (1) micro- and ultra filtration for the removal of particulate material

and micro organisms and (2) nanofiltration and reverse osmosis for the removal

of dissolved material and micro pollutants.

Although the type and the geometry of the membranes and modules are

different, the principle of membrane filtration is the same. The permeation rate

(flux) ranges from roughly 40 - 300 (l·mP

-2P·hP

-1P·barP

-1P) for microfiltration to 0.08 -

40 (l·mP

-2P·h P

-1P·barP

-1P) for reverse osmosis. At capacities up to several hundreds of

thousands cubic meters of drinking water per day, large membrane areas are

needed. Although careful selection of suitable membrane material (hydrophilic

or hydrophobic) is a necessity for successful application, other phenomena, like

mass transfer, back transport, diffusion and maldistribution are also important.


5

All these phenomena have a clear relation to the hydrodynamics in the

installation. In the design of membrane installations, these hydrodynamics play

an important role in the membrane (module design) and module arrangement

(plant design) to provide successful applications and limited energy

consumption and investment costs (Verberk, 2005).

1.4 Transport Phenomena in Membranes

The driving force for membrane filtration in water treatment is the

pressure gradient across the membrane. As a result of this driving force a

convective transport of material from the bulk to the membrane surface is

obtained. Solvent (water) permeates through the membrane and solutes

(dissolved and particulate material) are partly or completely retained by the

membrane. The retained dissolved solutes and particulate material accumulate in

a boundary layer at the membrane surface and a concentration build-up (in

time), the so-called concentration polarization, is observed see Figure 1.3. As a

result of the build-up of retained solutes at the membrane surface, the

permeation rate will decrease. The convective transport to the membrane surface

is balanced by the back transport from the membrane surface to the bulk. This

back transport is governed by diffusion or turbulence. When the convective

transport is equal to the back transport, a steady state situation is reached and the

permeate flux is constant in time. The back transport is influenced by the flow

conditions inside the membrane. Increase in back transport of rejected solutes

and particles by more turbulent flow conditions results in improvements in

permeation and selectivity. Concentration polarization can result in fouling.

Fouling is defined as: the process resulting in loss of performance of a

membrane due to deposition of suspended or dissolved substances on its

external surfaces, at its pore openings, or within it pores (Koros et al., 1996).

Fouling will always occur when particulate material is present in water.


6

Especially in micro- and ultra filtration the particulate fouling is a major point of

attention because rapid undesired flux decreases occur.

Figure 1.3 Concentration profiles of dissolved or particulate material and the main transport mechanisms in a membrane filtration process (Verberk, 2005)

1.5 Two-Phase Flow

Two-phase flow is the area of fluid mechanics that describes the flow of

mixtures consisting of two or more immiscible phases. Two-phase flow is the

simplest case of multi-phase flow. The different phases of multi-phase flow are

liquid, gas and solid. Two-phase flow is constantly met in our daily practice. For

example sandstorm, fog, snow and rain are natural examples of two-phase flow.

Two-phase flow is a well-known phenomenon in many industrial applications

(Wallis, 1969) and (Bachelor, 1989). Depending on the superficial velocities and

the pipe geometry different two-phase flow patterns occur, like bubble flow,

slug flow and annular flow. The segmented flow pattern slug flow is reported to

be very effective in small diameter tubes to increase heat and mass transfer rates

compared to single-phase flow. Slug flow was found to augment radial mass

transfer in reactors with catalytically active walls (Horvath, 1973). These results

suggest that slug flow could be a useful means to improve the efficiency of


7

many devices, which employ small diameter tubes and laminar flow by

enhancing radial mass transport or reducing axial dispersion. Such devices

include tubes with an absorbing wall for liquid chromatography or for selective

removal of solutes, reverse osmosis, or ultrafiltration systems having a semi-

permeable wall (Wallis, 1969). Water and air two-phase flow is already used in

water treatment processes. Well known examples are the water-air backwashing

of rapid sand filters and the water-air scouring of pipelines in the distribution

network.

From literature on heat and mass transfer, it is known that Taylor flow is a

specific two-phase flow pattern, results in an increased liquid-to-solid mass

transfer rate from bulk to wall compared to single phase liquid flow. This

increased mass transfer is caused by secondary rotating flows in the liquid slugs.

The increased mass transfer takes place at even lower pressure drops compared

to single phase flow (Kreutzer, 2003). In the automotive exhaust gas cleaning

Taylor flow is used to enhance mass transfer in monolith reactors. Monolith

reactors are ceramic structures of many parallel straight channels with a

diameter in the order of one millimeter. Based on structural configuration

membrane modules can be well compared with monoliths and the question

arises whether water-air two phase flow is also applicable in membrane filtration

processes to enhance the mass transfer. A major difference between monoliths

and membrane processes is the operational mode. In monoliths, the superficial

velocities are low compared to the velocities in membranes, so the extrapolation

of existing pressure loss equations, mass transfer relations and scale-up guide

lines are not directly possible.


8

1.6 Aim of the Present Work

This study focuses on investigating gas sparging as a technique to reduce

external fouling. In industrial membrane applications, membranes are typically

operated for several weeks before chemical cleaning. The main focus was on

monitoring the flux development with and without air sparging. Very rare work

was carried out in order to quantify the enhancement of permeates flux in

reverse osmosis membrane using sparging air. Slug flow is the most efficient

flow to enhance significantly the mass transfer in reverse osmosis membranes

when it is limited by particle deposit (Mercier et al; 1995). As a consequence,

this flow pattern has been chosen for the following study.

The aim of the present work can be summarized as follows:

1. Studying the effect of various operating conditions such as: concentration

(15-45) gm/l, temperature (10-50) °C, flow rates (100-250) l/hr and operating

pressure (5-15) bar ; on the performance of the reverse osmosis membrane ( type

(Sc-6200, spiral-wound model) by using NaCl as a feed solution. Flux of

permeate and salt rejection will be the main objective of this work.

2. Using experimental design (Box Wilson) methods in order to obtain the

proposed model (second order polynomial model) and its coefficient.

3. Obtaining maximum conditions for the proposed model by using optimization

program (Hook and Jeeves).

4. Sparging air in the system at different velocities of air after fixing the four

variables at the optimum conditions and different velocities of liquid at a fixed

velocity of air.

Chapter Two Theoretical Concepts and Literature Survey

9

Chapter Two

Theoretical Concepts and Literature Survey

Osmosis is a natural phenomenon in which a solvent (usually water)

passes through a semi permeable barrier from the side with lower solute

concentration to the higher solute concentration side. As shown in Figure 2.1a,

water flow continues until chemical potential equilibrium of the solvent is

established. At equilibrium, the pressure difference between the two sides of the

membrane is equal to the osmotic pressure of the solution. To reverse the flow

of water (solvent), a pressure difference greater than the osmotic pressure

difference is applied see Figure 2.1b; as a result, separation of water from the

solution occurs as pure water flows from the high concentration side to the low

concentration side. This phenomenon is termed reverse osmosis (it has also been

referred to as hyper filtration). A reverse osmosis membrane acts as the semi

permeable barrier to flow in the RO process, allowing selective passage of a

particular species (solvent, usually water) while partially or completely retaining

other species (solutes). Chemical potential gradients across the membrane

provide the driving forces for solute and solvent transport across the membrane:

- Δ μ Rs R , the solute chemical potential gradient, is usually expressed in terms of

concentration; and - Δ μ RwR , the water (solvent) chemical potential gradient, is

usually expressed in terms of pressure difference across the membrane

(Bhattacharyya and Williams, 1992b).


10

Figure 2. 1 Schematic of Osmosis (a) and Reverse Osmosis (b) Phenomena (Bhattacharyya and Williams, 1992b)


11

2.1 Reverse Osmosis Process Description and Terminology

The reverse osmosis process is relatively simple in design. It consists of a

feed water source, feed pretreatment, high pressure pump, reverse osmosis

membrane modules, and, in some cases, post treatment steps. A schematic of the

reverse osmosis process is shown in Figure 2.2a.

The three streams (and associated variables) of the reverse osmosis

membrane process are shown in Figure 2.2b the feed; the product stream called

permeate; and the concentrated feed stream, called the concentrate or retentate.

The water flow through the membrane is reported in terms of water flux, JRwR,

where:-

Solute passage is defined in terms of solute flux, Js:

(2.2)

Solute separation is measured in terms of rejection, defined as:

(2.3)

The quantity of feed water that passes through the membrane (the permeate) is

measured in terms of water recovery, r, defined for a batch RO system as

R R(2.4)

Where is permeate volume (mP

3P) and is feed volume (mP

3P).

And for a continuous system as

(2.5)

Where is permeate flow rate and is feed flow rate

In a batch membrane system, water is recovered from the system as the

concentrate is recycled to the feed tank; as a result, if the solute is rejected the


12

feed concentration (cRfR) continuously increases over time. For a continuous

membrane system, fresh feed is continuously supplied to the membrane. Water

flux is sometimes normalized relative to the initial or pure water flux (JRwoR) as

JRwR/JRwoR or as flux drop, defined by:

(2.6)

The pressure difference between the high and low pressure sides of the

membrane is denoted as ΔP while the osmotic pressure difference across the

membrane is defined as Δπ; the net driving force for water transport across the

membrane is (ΔP - σΔπ), where σ is the Staverman reflection coefficient.

(Gekas, 1988) reviewed the standardized terminology recommended for use to

be used for describing pressure-driven membrane processes, including reverse

osmosis.

Figure 2.2 Schematic of (a) RO Membrane Process and (b) RO Process Streams (Gekas, 1988)


13

2.2 Theory

The theory governing fluid transport through membranes is often

expressed as follows (Bird et al; 2002):

Where NR

A R

is the mass flux of component A through the membrane (mass per

time per area), ρR

A R

is the mass density of component A, v is the mass average

velocity of the fluid through the membrane, DR

AB R

is the effective diffusion

coefficient of component A in the membrane, and ∇ ρRAR is the mass density

gradient. In membranes where pore flow contributes significantly to flux,

Darcy’s Law is often used to characterize the mass average velocity (Bird et al;

2002):

Where κ is the Darcy Law permeability of the medium, μ is the fluid viscosity,

∇p is the pressure gradient (i.e., the rate of pressure change with respect to

position), ρ is the solution density and g is the gravity vector. Introducing

equation (2.8) into equation (2.7) and restricting transport to only the x-

direction, which will typically be the direction perpendicular to the membrane

surface, and by neglecting gravity, yields:

The first term in equation (2.9) represents mass flux due to pressure-driven

convection through pores, and the second term represents flux due to diffusion.

Diffusion through porous membranes is typically negligible relative to

convection. In this case, the flux is directly proportional to the pressure gradient

(2.7)

(2.8)

(2.9)


14

across the membrane. The applied pressure difference across the membrane

which often called the transmembrane pressure difference is the driving force

governing transport of liquid through a porous membrane.

In applying the convective term of equation (2.9) to transport through UF

and MF membranes, the permeability, κ, depends often in a complex way, on

factors such as the porosity and the tortuosity of the membrane. Tortuosity, τ, is

the ratio of the average length of the “tortuous” path that the fluid must travel to

pass through the membrane to the membrane thickness. For example, a

cylindrical pore perpendicular to the surface has a tortuousity of one. Most

phase inversion membranes have tortuousities from 1.5 to 2.5 (Baker, 2004).

Porosity, ε, is the void fraction of the membrane. UF and MF membrane

porosity typically ranges from 0.3 to 0.7 (Baker, 2004).

Since RO membranes are effectively non-porous, the transport of a

molecule across the membrane is diffusion controlled. This means that the

second term of equation (2.9) controls the flux across the membrane. Water

molecules desorb into the upstream face of the membrane, diffuse down the

chemical potential gradient across the membrane, and then desorbed from the

downstream face of the membrane. The second step, diffusion through the

membrane, is the rate-determining step in water transport across the membrane.

This mechanism of mass transport across membranes is commonly referred to as

the “solution- diffusion” modelP

P(Bird et al; 2002).

Beginning with the more general model of mass transport being driven by

chemical potential gradients rather than concentration gradients, the solution-

diffusion transport equation for reverse osmosis can be derived P

P(Bird et al;

2002), (Baker and Wijmans,1995) :

Where NR

Aw R

is the water flux through the membrane, Δp is the transmembrane

pressure difference, Δπ is the difference in osmotic pressure between the feed

(2.10)


15

and the permeate, and L is a constant describing the physical characteristics of

the membrane itself. Within the context of the solution-diffusion model used to

describe transport in nonporous films, L is given byP

P(Baker and Wijmans, 1995):

Where D is the water diffusivity in the membrane, S is the water solubility in the

membrane, V is the molar volume of water, R is the ideal gas constant, T is the

ambient temperature, and l is the membrane thickness. A complete derivation

can be found in the Baker and Wijmans review of the solution-diffusion model

(Baker and Wijmans, 1995) and in Paul’s recent re-examination of the solution-

diffusion model for reverse osmosisP

P(Paul, 2004).

As can be seen from equation (2.10), osmotic pressure of the feed and

permeate solutions plays a role in the separation. Osmotic pressure is the

pressure needed to cause a solvent (water) to leave a solution (seawater, waste

water, etc.) and permeate through the membrane. For an ideal solution, with

complete dissociation of salt ions, osmotic pressure is defined as P

P(Freeman,

1995):

Where π is the osmotic pressure, C is the salt ion concentration, R is the ideal

gas constant, and T is the solution temperature. The salt ion concentration, C, is

given by the number of ions in solution per gram of water divided by the

specific volume of water. Table 2.1 presents the osmotic pressure for several

solutions pertinent to water treatment applications. Table 2.1 Typical osmotic pressure values for solutions at 25°CP

P(Freeman, 1995).

Solute Concentration

(mg/l) Osmotic Pressure

(psi) NaCl NaCl

Brackish water Sea Water

2,000 35,000

2,000-5,000 32,000

23 397

15-39 339


16

In reverse osmosis, salt transport across a membrane is as important as

water transport. However, unlike water flux, which is driven by both applied

transmembrane pressure and osmotic pressure, the salt flux is only a function of

salt concentrationP


Where NR

s R

is the salt flux through the membrane, B is the salt permeability

constant describing the physical characteristics of the membrane, R R

is the salt

concentration in the feed solution, and R R

is the salt concentration in the

permeate solution. Analogous to L in the solution-diffusion equation, B is given

byP


Where DR

s R

is the salt diffusivity in the membrane, KR

s R

is the salt partition

coefficient, and l is the membrane thickness. However, instead of reporting salt

flux values, most membrane performance specifications provide salt rejection

values.

Furthermore, water flux and salt flux depend on each other. Equation (2.15)

relates the water flux, NR

AwR

, to the salt flux, NR

sRP

P(Riley et al; 1967):

Where CR

w R

is the water concentration in permeate and R R

is the salt concentration

in permeate. By substituting equation (2.10) and equation (2.13) into equation

(2.15) and rearranging terms, the following expression for rejection may be

derivedP

P(Riley et al; 1967):


17

Equation (2.16) relates salt rejection to the physical properties of the

membrane (which influence L and B), the applied transmembrane pressure

difference, and the osmotic pressure difference between permeate and the feed.

Equation (2.16) allows one to predict the salt rejection of the membrane based

on the experimental conditions and the membrane properties.

2.3 Factors Affecting Flux

2.3.1 Operating Parameter

There are four major operating parameters that affect the flux: (1)

pressure, (2) feed concentration, (3) temperature, and (4) turbulence in the feed

channel (flow rate).

1. Pressure

The major parameter that directly influences the energy consumption of

the RO plant is the feed pressure. The higher feed pressure is the higher energy

consumption of the plant. The permeate production strongly depends on the feed

pressure. Whereas the feed pressure influences the two primary operating

parameters, productivity and product water conductivity. The pressure drop

affects the mechanical stability of the RO equipment. In a spiral-wound system,

the pressure drop translates into a force directly on the membrane element and

also on the product tube (Herold ., 2001).

2. Feed Concentration

The film theory model states that the flux will decrease exponentially with

increasing feed concentration. This relationship should hold true regardless of

the type of flow or degree of turbulence or the temperature (Munir, 1998).

3. Temperature

In General, higher temperatures will lead to higher flux in both the

pressure controlled region and in the mass transfer-controlled region, this

assumes there are no other unusual effects occurring simultaneously, such as

fouling of the membrane due to precipitation of insoluble salts at higher


18

temperatures or denaturation of proteins or gelatinization of starch at higher

temperatures. In the pressure controlled region, the effect of temperature on flux

is due to its effect on fluid density and viscosity. Activation energies for both

flux and viscosity are similar in the region of 20-50°C, about 3400 kcal/mole. In

practical terms,it will take a temperature rise of 30-45°C to double the flux

(Munir, 1998).

4. Flow Rate and Turbulence

Turbulence, whether produced by stirring, pumping the fluid, or vibrating

the membrane, has a large effect on flux in the mass transfer-controlled region.

Agitation and mixing of the fluid near the membrane surface "sweep" away the

accumulated solute, reducing the hydraulic resistance of the "cake" and reducing

thickness of the boundary layer. There is also a belief that extremely high shear,

such as that obtained with thin - channel and rotary device, actually reduce the

thickness of the "gel" layer. In any case, this is one of the simplest and most

effective methods of controlling the effects of concentration polarization (Munir,

1998).

2.3.2 PH of Feed

The pH of the feed water must be measured and controlled in reverse

osmosis desalination of water for several reasons. The first is to prevent CaCOR3R

precipitation. The second reason is to maximize the life of membrane of the

cellulose acetate type. Cellulose acetate is an ester which reacts slowly with

water to form an alcohol and an acid. The rate of this reaction, which is called

hydrolysis, is dependent on both pH and temperature.

The minimum hydrolysis rate at a particular temperature occurs at a pH of

(4.5-5) as the hydrolysis continues, the passage through the membrane of both

water and salt increases. The salt passage increases the product water

conductivity. In the operation of cellulose acetate membrane, the pH is reduced

to pH 6 or less in order to slow the hydrolysis rate to a value which permits long

term operation (Mindler & Epstein, 1986).


19

2.4 Flux Decline in Membranes and Strategies to Reduce Fouling

The main problem in membranes, where very high permeation fluxes and

complicated feeds containing a broad particle size distribution are present, is

concentration polarization and subsequent fouling.

Concentration polarization is the build-up of rejected solutes at the liquid

boundary layer near the membrane. If there is a certain degree of mixing,

diffusion and inertial lift of the rejected components can result in a backtransport

to the bulk. Convection of particles towards the module exit due to the crossflow

will then limit their accumulation on the membrane. If the transport of the

rejected components back to the bulk solution is not fast enough, deposition of

material on or in the membrane occurs. This process is known as fouling as

shown in Figure 2.4.

Figure 2.4 Concentration polarizations in a reverse osmosis membrane system. (a) Before membrane is fouled and (b) after membrane is fouled.

Particle deposition is a process that is governed mainly by the

hydrodynamic forces acting on the particle near the membrane surface (Zeman

and Zydney, 1996). The important forces on the particle include the viscous drag

force performed by the flowing fluid, the hydrodynamic lift force arising from

the inertial interactions between particle and solid boundary and diffusion forces

due to Brownian motion. The dominant force for small particles is Brownian


20

motion, which is responsible for the equilibrium state in macrosolute-membrane

interactions. As the particle size increases, the importance of Brownian diffusion

decreases since it becomes too slow. Deposition of bigger particles will occur

when the forces towards the membrane surface are greater than the repulsive

interactions between particles, inertial lift forces and shear-induced diffusion.

The analysis of the flux decline due to particle deposition is of special

importance since it can provide some insight to the phenomena that take place

during microfiltration. Depending on the solute and the process conditions,

different blocking mechanisms that explain the flux decline during membrane

filtration has been developed (Hermia, 1982), (Bowen et al, 1995) and

(Wessling, 2001):

– Complete blocking (pore blocking)

– Standard blocking (pore narrowing)

– Intermediate blocking (long term deposition)

– Cake formation (gel/cake layer)

These mechanisms are schematically shown in Figure 2.5. Pore blocking (a) is

caused by rejected particles bigger than the membrane pores. This mechanism

assumes that each particle arriving at the membrane contributes in the complete

inactivation of one or more pores, causing a dramatic flux decline. Pore

narrowing (c) is mostly caused by smaller components that can adhere to the

internal pore wall, accumulate or bridge and finally clog the pore. Intermediate

blocking (b) is the stage preceding cake layer formation (d). A cake layer is

formed when each particle arriving to the surface accumulates on each other,

thus completely blocking the membrane surface. The flux decline due to

particles can be governed by one mechanism but it can also be a combination of

more than one. Although these mechanisms are developed for the filtration of

proteins, they are also valid for different types of solutes.


21

Figure 2.5 Schematically view of particle deposition mechanisms: complete blocking (a),

intermediate blocking (b), standard blocking or pore narrowing (c) and cake formation (d)

Particle deposition is often a reversible type of fouling, which can be

prevented or reduced by a variety of methods. For instance, coagulants can be

added to the feed so that the particles aggregate and can subsequently be swept

off the membrane. Process parameters like cross flow velocity or shear can also

be increased. Shear flow is a major strategy to control mass transfer near the

membrane wall. Some of the techniques shown to be effective for surface shear

enhancement include (Al-Bastaki and Abbas, 2001) and (Cui et al., 2003):

– Turbulence promoters

– Pulsate flow and vortex generation

– Two-phase flow (gas bubbling)

– Corrugated membrane surfaces

– Forward flushing/ pulsing

Turbulence can be promoted by using baffles, like helical relief

geometries (Broussous et al, 2001). Broussous et al. reported that helical stamps

used inside a tubular ceramic membrane resulted in a 6-fold increase of the


22

permeate flux compared to using smooth surfaces (Broussous et al, 1998). Dean

vortices, which are centrifugal instabilities produced in curved channels when

the critical Dean number is exceeded, have also been a mean to improve the flux

of dairy whey and Baker’s yeast (Winzeler and Belfort, 1993), (Luque et al,

1999).

The injection of air bubbles or gas sparging is a resource to enhance

mass transfer. The secondary flows and bubbles promote mixing and reduce the

thickness of the concentration polarization layer. When the bubble diameter

exceeds the channel diameter (from the module, in flat sheets, or from the

hollow fibers) slugs are formed, which can displace the boundary layer and

cause the local pressure to fluctuate. Another flow regime commonly observed

is bubble flow, which occurs when the gas bubbles are significantly smaller than

the fiber or channel size (Cui et al., 2003). Air sparging in combination with

hollow-fiber or flat-sheet UF membranes is very useful to enhance the flux of

dextrans and proteins ( Bellara et al.,1996) enzymes or microparticles ( Laborie

et al.,1998) or more specifically, to fractionate protein mixtures ( Li et al.,1998).

In microfiltration, the main uses are related to enhance yeast filtration (Sur and

Cui, 2001), (Mercier et al., 1998). Other applications include air sparging in

membrane bed reactors for wastewater treatment (Chang and Judd, 2002) and

nanofiltration (Ducom and Cabassud, 2002), (Ducom and Cabassud, 2003) the

enhancement of permeate flux by gas bubbling is clearly demonstrated in all

these studies.

Flow instabilities can also be induced by pulses, with approaches like

backflushing, forward flushing or backpulsing. These strategies are generally

considered as cleaning methods because they remove deposited matter from the

surface. Backflushing and backpulsing are based on temporary permeate flow

reversal; while crossflushing is the stoppage of permeate flow while crossflow is

maintained. The main difference between a backpulse and a backflush is the

force and time used to lift accumulated deposits off the membrane. Generally, in


23

backflushing flow reversal occurs for a few seconds once every several minutes,

while backpulsing occurs at a higher frequency and the pulses are applied for a

short time (< 1s) (Sondhi and Bhave, 2001) , ( Kuberkar and Davis, 1998). The

efficiency of both techniques depends strongly on the frequency, pulse duration,

pressure, etc. (Levesley and Hoare, 1999) used high frequency backflushing (1s

pulse at 1 Hz frequency) for the microfiltration of yeast homogenate suspensions

using a ceramic tubular membrane. Backflushing resulted in a 5.4 times

increased solute flux compared to the non-backflushing situation. (Kuberkar

and Davis, 1998) used high-frequency short backpulses (0.1-1 s) to increase the

permeate flux of washed bacterial suspensions and bacterial fermentation broths.

Washed bacterial suspensions were easier to backpulse, in comparison to

bacterial fermentation broths, due to lower concentration of suspended

components. (Parnham and Davis, 1996) reported higher flux of proteins from

bacterial cell debris by applying high-frequency backpulsing. Beolchini and

coworkers also emphasized the importance of backpulsing with skimmed bovine

milk filtration using ceramic tubular membranes with a pore diameter around

1.4µm (Beolchini et al, 2004). Backpulsing was required in order to reduce

membrane fouling and achieve milk permeation.

For adherent foulants and irreversible fouling other approaches than the

ones discussed above are generally used. Irreversible fouling is triggered by

hydrophobic interactions, hydrogen bonding, van der Waals attractions and

other effects. Some of the methods to eliminate this kind of fouling are based on

modifying the membrane surface by moieties that repel certain components or

change the surface charge of the material. Physically coating the surface with

water-soluble polymers or surfactants for a temporary effect, or grafting

monomers by UV or electron beam irradiation are also frequently reported

techniques.


24

2.5 Two - Phase Flow

Part of the definition of the flow regime is a description of the

morphological arrangement of the components, or flow pattern. The flow pattern

is often obvious from visual or photographic observations but is not adequate to

define the regime completely because of additional distinguishing criteria, such

as the difference between laminar and turbulent flow and the relative importance

of various forces. In order to keep the terminology manageable, the numerous

imaginative expressions which have been used throughout the literatures to

describe flow patterns will not be quoted. It is far simpler to restrict

classification to the morphological flow patterns (for example, bubbly, slug,

annular, and drop flow in gas-liquid system) and create further subdivision into

distinct regimes within each of these classifications. Hybrid flow patterns,

usually representing a region of transition from one pattern to another, are

denoted by hyphenated expression (thus, slug-annular and annular-drop flows).

Some synonyms (e.g., “fog” or “mist” instead of “drop”) may be used when

perfunctory repetition of a single word becomes monotonous (Wallis, 1969).

2.5.1 Flow Patterns

In gas–liquid two-phase flow systems, the mixture can adopt various

dynamic structures known as flow patterns or flow regimes (Whalley, 1987). In

Figures 2.6 & 2.7 common flow patterns in vertical and horizontal tubes are

shown graphically. In membrane systems using two-phase flow within modules

to overcome concentration polarization and membrane fouling, the most likely

flow patterns are bubble flow and slug–flow due to the relatively low gas flow

rates applied.

Numerous authors have presented flow pattern and flow regime maps in

which various areas are indicated on gragh for which there are two independent

coordinates. For a given appratus and specified components this is readily done

in term of flow rates, as shown in Figure 2.8 and 2.9 (Wallis, 1969).


25

Figure 2.6 Flow regimes in vertical upward two-phase flow in large diameter tubes at

constant superficial liquid velocity

Figure 2.7 Flow regimes in horizontal two-phase flow in large diameter tubes (Hewitt and

Hall-Taylor, 1970)


26

Figure (2.8) Flow pattern boundaries for horizontal flow of air and water in (1) inch pipe

(Wallis, 1969)

Figure (2.9) Various “regimes” or subdivision of the annular flow pattern for co- current

upward flow of air and water in a 1 1/4 – in-diameter pipe at 15 psia.(Hall-Tylor and Hewitt.)

2.5.2 Air Flow Rate

By injecting air the cross-flow velocity and thus turbulence at the

membrane surface is increased. In order to identify the two-phase flow pattern in


27

the membranes the injection factor ε is sometimes used. This factor is defined

as:

In large diameter pipes, the two-phase flow depends on the injection

factor defined as shown in Figure 2.9 (Taitel et al., 1980). In equation (2.18),

URGSR and URLSR are the superficial gas and liquid slugs defined as if each phase was

circulating alone in the pipe. The flow pattern influences the hydrodynamic

conditions near the membrane wall and it can, thus, have an influence on the

filtration process efficiency.

Figure 2.9 Two-phase flows inside pipes

At values of ε < 0.2 bubble flow is found, while at values of ε > 0.9

annular flow exists. Slug flow is found when 0.2 < ε < 0.9 (Cabassud et al.,

2001). Because of slug flow is reported to result in optimal process conditions

Therefore; the injection factor is used to determine the range for the superficial

air velocities (Cabassud et al., 1997; Mercier et al., 1997).

2.6 Spiral Wound Modules

It would not be surprising if gas bubbles can enhance permeate flux in

spiral wound membranes. However, there are very few reported data. Some

(2.18)


28

preliminary data (Cui, 1993) were obtained using an l00 kDa spiral wound UF

membrane for solutions of dextrans of different molecular weights. Injecting

bubbles had a positive effect on permeate flux, particularly when concentration

polarization was severe, for the largest MW dextran. The flux enhancement

ranged up to about 25%. The results were obtained with a vertically aligned

spiral module to avoid trapping gas within the module, and with a relative low

flow rate gas injection. The need for vertical alignment, and distribution of

bubbles in the system could limit the use of bubbling in spiral wound systems

(Cui et al., 2003).

2.7 Previous Studies for Sparging Air in Membrane

Membranes are widely used in the chemical, pharmaceutical, food and

water industries. Practical difficulties arise in designing and operating the

process due to concentration polarization and membrane fouling. In membrane

processes, fouling remains the main drawback and the toughest challenge at

present and in the foreseeable future. Enhancement of membrane is highly

desirable to achieve a higher permeate flux at a fixed energy input, or a reduced

energy input whilst maintaining the level of permeate flux, or an improved

selectivity of the membrane. One effective, simple, and economic technique

used to enhance membrane is the use of gas bubbles, i.e. injecting gas into the

feed stream to create a gas– liquid two-phase cross-flow operation. In this

regard, in the last years some studies have pointed out the value of air sparging

to enhance the flux in ultrafiltration, microfiltration and nanofiltration for

different applications — drinking water production(Cui and Taha,2003).

A novel membrane filtration system was proposed by Takuo et al 1993

with the aim of saving energy. That is, a gas-liquid two-phase crossflow

filtration was combined in the anaerobic bioreactor. It was confirmed that the

membrane filtration not only offered very stable and large permeate flux, but

enhanced the processing efficiency by retaining the microorganisms in the


29

bioreactor. Furthermore, the power consumption per unit permeates volume in

the membrane system.

Cui and Wright; 1994 proposed a method for reducing concentration

polarisation and membrane fouling by injecting air into the feed stream, creating

a gas-liquid two-phase flow across the membrane surface. The injected air

promotes turbulence, increasing the superficial cross-flow velocity of the

process fluid, suppressing the polarisation layer and enhancing the ultrafiltration

process. On the addition of air to the liquid stream, permeate flux was observed

to increase by up to 60% for dextran, 113% for dyed dextran and 91% for BSA.

Mercier et al; 1995 used an easy technique, consisting in injecting air into

the liquid stream, is proposed to enhance the permeate flux in cross flow

filtration of a model fluid (i.e. a bentonite suspension). The injected air promotes

turbulence and increases the superficial cross flow velocity that leads to a

regular disturbance of the boundary layer. A systematic study of different two-

phase configurations points up that the slug flow seems the most appropriate

regime. The resulting permeate rate is increased up to 140%, in comparison with

the usual filtration processes.

Gas-liquid two-phase crossflow ultrafiltration was studied in downwards

flow condition by Cui and Wright; 1996. Flux increases up to 320% were

achieved with gas sparging in the experimental study compared to single liquid

phase crossflow ultrafiltration.

Bellara et al; 1996 focused on the use of gas-liquid two-phase crossflow to

overcome concentration polarization in the ultrafiltration of macromolecular

solutions as applied to hollow fibre membrane systems. The results were

encouraging, with flux enhancements of 20-50% obtained for dextran and 10-

60% for albumin, when air was injected into the system over the range of

process variables examined.

The use of a gas-liquid two-phase flow by injecting air directly into the

feed stream was studied by Mercier et al; 1997. The experimental study was


30

carried out by filtering suspensions (bentonite and yeast) through an

ultrafiltration to reduce tubular mineral membrane fouling. Results related to the

permeate flux showed an enhancement by a factor of 3, with a slug flow-

structure for the two kinds of suspension (200% of flux increase).

A new process is proposed to reduce particulate membrane fouling by

injecting air into the feed stream, creating a gas/liquid two-phase flow on the

membrane surface by Laborie et al; 1997. The air injection process led to an

increase in the permeate flux, depending on the liquid velocity and

transmembrane pressure, for all the various feed concentrations. For specific

conditions, the flux can be increased by 155% using a critical gas velocity.

Above this critical value, the flux is no longer enhanced.

The poor selectivity of membranes has been regarded as one of the critical

factors limiting the application of membrane systems to protein fractionation. Li

et al ; 1997 demonstrated that ultrafiltration enhanced by gas sparging, together

with proper adjustment of solution conditions, can dramatically improve the

selectivity of a commercially available tubular membrane, as well as

significantly increase permeate flux. Besides, gas sparged ultrafiltration

experiment are performed using a tubular module with solution of dextran and

human serum albumin (HAS) as the test media. It was found that the permeate

flux increases with the bubbling frequency in the examined range also by Li et

al; 1997. In additing gas sparged ultrafiltration has been applied to a flat sheet

membrane module and the enhancing effect from the injected bubbles was

examined experimentally by Li et al; 1998. Experimental results showed that gas

sparging can increase permeate flux and improve the efficiency of protein

fractionation.

Mercier et al; 1998 studied the use of an upward gas/liquid slug flow to

reduce tubular mineral membrane fouling. Experimental study was carried out

by filtering a biological suspension (yeast). Flux enhancements of a factor of


31

three could be achieved with gas sparging compared with single liquid phase

crossflow filtration.

The effects of gas flow rate, liquid flow rate and feed concentration on the

selectivity of fractionation were examined by Ghosh and Cui; 1998. Gas

sparging enhances protein fractionation; under suitable solution conditions,

nearly complete separation of BSA and lysozyme was achieved with gas

sparged ultrafiltration. The permeate flux was also increased by gas sparing. The

mechanism of flux enhancement in ultrafiltration processes by gas sparging, in

the special case of upward slug flow in tubular membrane module was also

discussed by Ghosh et al ; 1999.The results suggested that gas sparging is more

effective at higher transmembrane pressure, and increasing the liquid flow rate

has opposite effects in single phase flow and gas-sparged ultrafiltration.

Based on air sparging inside hollow fibres throughout the filtering period

a process was proposed by Laborie et al; 1998. The generated gas/liquid two-

phase flow inside fibres showed a high efficiency to enhance the stabilised

permeates flux, by preventing particle deposition. The use of air in backwash of

hollow-fiber modules was investigated experimentally from bench to full scale

by Christophe et al; 1999. Results indicated that the cake layer is

instantaneously lifted off by the reversed permeate flux and is concentrated in

the free volume of the module.

Flux enhancements by gas slugs for dextran T500 solutions ultrafiltrated

in a ZrOR2R/carbon tubular membrane module were measured and discussed for

various resistances of the concentration boundary layer by Cheng et al;1998. It

was concluded that the same permeate flux obtained in single liquid-phase

ultrafiltration with a higher crossflow velocity can also be achieved with a lower

liquid velocity by introducing gas slugs of moderate velocity, and lead to

reduced energy consumption. The permeate fluxes of an inclined gas-slugs

ultrafiltration system were measured and discussed under various gas-liquid

flow ratios and inclination angles by Cheng et al; 1999. The enhancement in flux


32

is due to the combination effects of natural convection and forced convection

induced by the slug flow in the inclined tubular membrane.

Vera et al; 2000, was observed the steady-state flux, for gas-sparged

microfiltration or ultrafiltration through inorganic composite membranes. Also

experimental study was carried out with a ferric hydroxide suspension and a

biologically treated wastewater, both of them filtered through a tubular

inorganic microfiltration membrane. The sparging led to an increase of the

permeate flux with a slug flow structure for the two kinds of suspension and

preventing the membrane fouling and enhancing the microfiltration mass

transfer.

Sheng and Fane; 2000, studied the effect of bubbling on particle deposition

on hollow fiber membranes. The results show that bubbling is effective in

enhancement of the filtration performance but the enhancement is not sensitive

to changes in two-phase flow mixture velocity when the operation is controlled

by the deposition in the falling film zone induce by slugs. Also proved that

injecting air into hollow fibers and tubular membranes to be effective in order to

control flux decline which caused by concentration polarization and particle

deposition. In addition, Sheng and Fane; 2001 examines the effect of fiber

diameter on filtration and flux distribution with inter-fiber two-phase flow for

conditions relevant to submerged bioreactors (SMBR). The experimental results

showed that the effect of the fiber diameter on filtration increased with the

increase in turbulence around the fibers. For filtration with two-phase flow, the

performance was sensitive to changes in fiber diameter and significantly lower

flux declines were obtained with smaller fibers.

The effect of membrane inclination on the flux of single-phase or gas–

liquid two-phase ultrafiltration in a tubular membrane has been investigated by

Cheng; 2002. Experimental result shows that membrane inclination has a

significant enhancement on the flux of two-phase ultrafiltration operated at slug

flow pattern.


33

Petr MikulBSek et al; 2002 studied an application of the gas-liquid two-

phase flow for the flux enhancement during the tubular membrane

microfiltration of aqueous titanium dioxide. The results of experiments showed

a positive effect of the constant gas-liquid two-phase flow on the flux. A

mathematical model for the flux prediction during two-phase gas-liquid

microfiltration has been developed. The results showed a good agreement

between experimental data and model prediction.

Computational fluid dynamics (CFD) was employed to predict the flow

behaviour inside capillaries by Stanton et al; 2002. The CFD model and

experimental results compared well. The CFD model yielded detailed

information of the flow parameters and the flow patterns inside the capillaries

and this allowed for better understanding of the hydrodynamics of the capillary

tube slug-flow process.

Ducom et al ; 2002 studied the enhancement of the flux during

nanofiltration of droplet suspensions in water, using air sparging, which consists

of injecting air directly into the feed stream during filtration. It was first shown

that injecting air, even at high gas velocities, does not modify the permeability

to pure water. In both cases (for stabilised and non-stabilised oil-in-water

emulsions), a significant flux enhancement was observed with air sparging, due

to the ability of air bubbles for disrupting the oil layer over the membrane

surface.

Lev; 2003, studied the hydrodynamic and statistics of naturally occurring

continuous slug flow in pipes, as well as the results of experiments with

controlled injection of elongated bubbles are reviewed. It is demonstrated how

the information obtained in the controlled experiments can be applied to

improve the performance of slug flow and slug tracking models.

Cui and Taha; 2003, made an attempt to compare the effect of ‘bubbling’

on the ultrafiltration performance, using different membrane modules (in

particular, tubular and hollow fibre membrane modules). The difference in


34

performance can be related to the feature of two-phase flow hydrodynamics and

its respective effect on mass transfer. Kaichang et al; 2003 tested a method for

enhancing the critical fluxes by injecting air into a shell-side feed organic

hollow fiber membrane module. It has been found that air sparging promoted

turbulence, resulting significant enhancements of critical flux.

Posp´ıšil et al; 2004, studied the influence of gas flow velocity on flux for

cross flow microfiltration. The results of experiments show positive effects of

constant gas–liquid two-phase flow on the flux.

Psoch and Schiewer; 2006, focused the study on permeate flux

enhancement by air sparging. The results showed that air sparging over several

weeks significantly increased permeate flux. Psoch and Schiewer; 2006,

combined anti-fouling strategies. In a membrane bioreactor (MBR) fed with

synthetic wastewater with mixed liquor suspended solids concentrations

between 3 and 10 g/l, the solid/liquid separation was achieved by a tubular

membrane in side stream. For longer sustainable flux, air sparging was supplied

to fight external fouling with the scouring effect of slug flow. Additional to that,

backflushing was provided as a technique against internal fouling. The

combination of both techniques showed very promising results and was superior

to the operation of only one flux enhancement technique, yielding about three

times higher fluxes compared to the non-enhanced application after continuous

filtration for 8 days. Backflushing accomplished significant flux increases with

minimal product loss.

The effect of air sparging to limit concentration polarization is also

investigated by Verberk and Dijk; 2006. As expected, air sparging decreases

concentration polarization, resulting in an increase in permeate flux and

retention. Computational fluid dynamics (CFD) modeling of gas–liquid two-

phase cross-flow ultrafiltraion in horizontal and inclined tubular membranes is

studied by Taha et al; 2006. Experiments showed that flux enhancement, as a


35

consequence of gas sparging, is profoundly augmented. The wall shear rate and

flux are highest when the membrane was inclined at 45 P

°P from the horizontal.

Gas sparging and back-flushing treatments were compared as a means to

tackle the problem of fouling in yeast microfiltration by Fadaei et al; 2007. In

this condition gas sparging showed greater efficiency in flux enhancement. On

the other hand at lower feed concentration the relative importance of internal

fouling due to pore blockage, increased. In this case back-flushing was more

effective. The flux enhancement in cross-flow microfiltration of submicron

particles by sparged air-bubble is studied by Hwang and Wu; 2007. The results

show that the pseudo-steady filtration flux increases as the air-bubble velocity

and filtration pressure increase. The sparged air-bubble can significantly

improve filtration flux, but the flux enhancement is more remarkable in the

lower air-bubble velocity region. A gas–liquid two-phase flow model is adopted

for estimating the shear stress acting on the membrane surface under various

operating conditions.

Bérubé et al; 2008, characterized the effect of operating a submerged air

sparged membrane system over a wide range of operating conditions (i.e. sub-

and super-critical flux conditions) on the extent and mechanisms of membrane

fouling during drinking water treatment. The overall fouling coefficient, as well

as the evolution of the trans-membrane pressure in a submerged air sparged

hollow fiber membrane system, could be effectively modeled for all operating

zones using a relatively simple semi-empirical relationship which considered the

back-transport of particles (i.e. foulants) from the membrane surface. It is

expected that this simple relationship can be used in parallel with pilot-scale

testing and reduce the extent of testing needed to identify the parameters that

minimize fouling.

Chapter Three Mathematical Model

36

Chapter Three

Mathematical Model

Membrane separation systems are gaining popularity in the food and

bioprocessing industries due to their less energy requirements, negligible

denaturation of food product and retention of aroma and flavours. This technique

has also got numerous applications in processing industries such as chemical,

nuclear, biotechnology, petroleum and petrochemical industries. Reverse osmosis is

the most popular technology for seawater desalination. During the last two decades

hundreds of reverse osmosis seawater desalination plants have been built

worldwide. Each year the plant sizes and cost-effectiveness have increased.

Recently the reverse osmosis has achieved growing acceptance as an economical

and viable alternative to multistage flash distillation (MSF) process for desalting

seawater (Al-Mudaiheem and Miyamura;1985), (Aly;1986) and (Brandt;1985).

A number of investigators carried out the work on different aspects of

reverse osmosis seawater desalination. Few models for solvent and solute fluxes

through membranes have been developed and analyzed neglecting the effect of

mass transfer inhibition. Concentration polarization and fouling of the membrane

are the two serious problems that would prevent the use of RO into many of the

processes. Concentration polarization may be defined as the presence of a higher

concentration of rejected species, at the surface of a membrane than in the bulk

solution, due to the convective transport of both solute and solvent (Ohya, 1976)

and (Jamal, 1996).


37

3.1 Mathematical Modeling of Reverse Osmosis

This model has been developed by (Jamal et al, 2004) and used

as a tool for calculation in the present work. The formulated model

deduced a non-linear differential equation representing the feed

concentration as a function of the operating time. The solution of the

differential equations was obtained by using fourth order Range-

Kutta method due to self starting and stability. The model was

verified using the data of the experimental work which was carried

out at University of Complutense Faculty of Physics, Department of

Applied Physics in Spain-Madrid.

3.1.1 Models for Solvent and Solute Transport in Reverse

Osmosis

The flow of solvent through the membrane is defined in terms of flux (Slater

et al., 1985) & (Jamal. 2004)

wpapw CSQJ )/(= .................................................................... (3.1)

The solvent flux of permeate depends on the hydraulic pressure applied across the

membrane, minus the difference in the osmotic pressure of the solutions of the feed

and permeate side of the membrane (Aly, 1986).

)( π∆−∆= PAJ ww ............................................................... (3.2)

While the solute flux depends on the concentration gradient CBJ ss ∆= ........................................................................ (3.3)

Pf CCC −=∆ ..................................................................... (3.4)

The membrane rejection is defined as the difference between the feed

concentrations and permeates concentration

[ ]fPfPf CCCCCRj /(1/)( −=−= ................................................ (3.5)


38

From the solvent and solute flux equations (3.2) and (3.3) it can be seen that

the rejection is the function of pressure and concentrations. Thus when combining

flux models and then relating it with rejection, one can seen that the permeate

concentration is equal to material balance around the membrane

)J/J(CC wswPP = .............................................................. (3.6)

So that the rejection R is given by

fWwps CJCJRj /1−= ............................................................. (3.7)

Substituting the expressions for the fluxes in the expression of rejection to get:

[ ] 1)(/)(1 −∆−∆+= πPACBRj wwpS .............................................. (3.8)

From this expression, it appears that if pressure drop is increased to large

value then rejection approaches towards unity. However this cannot be achieved

due to the limitation of membrane. Nevertheless one can reach almost up to the

desired level. The model presented above is the model for ideal mass transfer

which does not give the exact picture of the reverse osmosis system.

The simple process case of continuous mode of operation as shown in Figure

(3.1) is run most easily. Under this type of operation, feed characteristics remain

the same and the retentate or concentrate is collected separately, as is permeate.

If an initial feed volume is used, feed is run to exhaustion. In the absence of mass

transfer inhibition, rejection, flux and stream concentrations ideally remain the

same with time. The single pass recovery for this type of operation relates permeate

production to feed rate


39

Figure (3.1) Modes of Reverse Osmosis System Operation

Source: (Jamal, et. al., 2004)

Recovery (Z) = QP/Qf

In a semi-batch, unsteady state mode of operation, as was the basis for

simulation, retentate is recycled to the feed tank and permeates is collected

separately. This process is essentially a closed loop concentrating system. As the

operation time increases, the volume of permeate collected increases. Permeates

produced at any instant of time is called the instantaneous permeate. Permeate or

product collected in the product tank over a span of time is called the average

product. Since permeate is removed continuously from the feed, the volume of the

............................................... (3.9)


40

feed decreases, the feed becomes more and more concentrated with time. The feed

in this type of process can also be referred to as the concentrate. As feed volume diminishes and concentration increases, the system will operate as

if it were running in sequential increments of increasing concentration, in a semi-

batch, steady state mode. Recovery is defined in terms of an overall system

recovery as the total quality of product generated up to a given time divided by the

initial feed volume:

fP V/V)X(Recovery = ............................................................ (3.10)

As in some point in the operation the system must be stopped as the feed

becomes so concentrated that the flux drops significantly, due to a large increase in

the osmotic pressure of the feed. If the permeate flows in a semi-batch, unsteady-

state system is returned to the feed tank, the mode of operation is termed “semi-

batch, steady state”.

The system material balances, together with these mass transfer models,

were used to simulate system operation. Correlation of flux, solute concentrations

and rejection with operating time and overall system recovery are functions of the

model. This model also predicts operational performance characteristics of the

system at various times and recoveries. The effects of pressure, feed concentration,

and volume and membrane characteristics on separation efficiency can also be

described.

A material balance made on the product tank yields to

dt/)CV(dCQ PavPpp = ............................................................ (3.11)

pPavPavppp V)dt/Cd(C)dt/dV(CQ += ....................................... (3.12)

Boundary conditions: at t PPavP CC,0V,0 ===


41

The change in the volume of permeate with time is the production rate of the

membrane.

PP Qdt/dV = ........................................................................ (3.13)

Substitution in equation (3.12) we get

PPavPavPPP VdtCdCQCQ )/(+=

or

PpavPPPav VCCQdtCd /)(/ −= .............................................. … (3.14)

The material balance around the membrane module is:

rrffPP CQCQCQ −= .............................................................. (3.15)

In this balance an assumption is made that in this system the concentration within

the membrane does not change greatly with spatial distribution. A mean permeate

concentration from the membrane module is used.

Similarly the balance around the feed tank becomes

dtCVdCQCQ ftftffrr /)(=− ....................................................... ..(3.16)

In the model it was assumed that the feed tank was well mixed. Therefore, at any

instant in time, t, Cft = Cf.

ftC

Combination of equations (3.15) and (3.16) with

substitution of as fC gives.

ftffftPP VdtdCCdtdVCQ )/()/( +=− .............................................. .. (3.17)

Pft QdtdV =− / ..................................................... …………... (3.18)

Integrating with boundary condition at t= 0, foft V V =

tQVV pfoft −= ........................................................................ .. (3.19)

Substituting this value into equation (3.17) we get

)/()( dtdCtQVCQCQ fpfofPPP −+−=− .................................... (3.20)


42

Rearranging of equation (4.20) gives. )tQV(/)CC(Qdt/dC pfoPfPf −−= ............................................ (3.21)

In order to get the solution of equation (3.21) the relationship between Qp and Cf,

with the expression for Cp in terms of Cf

RTvnQ )/(=π

is needed to achieve this we have to get

the relationship between osmotic pressure and feed concentration, expressed by

Van’t Hoff as:

...................................................................... (3.22)

Where π is the osmotic pressure, T temperature in Kelvin, R ideal gas constant,

)/( vn number of moles and Q is the number of species ions. For

convenience, this model assumed to be at constant temperature and incorporated

the other constant Ψ which simplifies osmotic pressure to solute concentration

coefficient. Cψπ =

C∆=∆ ψπ .......................................................................... (3.23)

The value of Ψ was assumed to be constant over the operating range of the solute

concentration. Incorporation of equation (3.23) into the expression for the solute

flux equation (3.2) yields: [ ])( PfWW CCPAJ −−∆= ψ .......................................................... (3.24)

)( PfSS CCBJ −= ................................................................... (3.3)

)/( WPPWS CCJJ = .................................................................... (3.6)

Combining the above equations to get:

PWWPS CJCJ =

[ ] pPfWWPPfS CCCPACCCB )()( −−∆=− ψ .................................. (3.25)

[ ]PWPSPwWPSPfwWPSPwf CCBCACBCCACBCPAC ++−∆= )/()/()/( 2ψψ


43

Approximating the equation through

PfSWSW CCandBABpA >>>>∆ // ψ For high rejection,

[ ]fWPSWWPSWPf CCBACBPACC )/()/(1 ψ−∆+= ........................... (3.26)

[ ] 1)/()/(1 −−∆+= fWPSWWPSWfP CCBACBPACC ψ ......................... (3.27)

By substituting the expression for CP in equation (3.24) to get the

expression for flux in terms of C [ ])(/)( 43 fffWW CaaCCPAJ −+−∆= ψψ

f

...................................... (4.28)

Substituting of equation (3.28) into equation (3.1)

( ) ( ) [ ]fffP CaaCaCaaQ 43221 / −+−=

Putting the expression for CP and QP

[ ] [ ]/)Caa/(C(C)Caa/()Ca()Ca(adt/dC f43fff43f2f21f −−−+−=

, Equations (3.27) and (3.28)

into the expression for the concentration change with time into

Equation (3.21), we get:

[ ])Caa/(tCatCataa f43f2f215 −−+− .................................. (3.30)

Where the model constants are

WPWa CPASa /)(1 ∆=

WPWa CASa /)(2 ψ=

)/(13 WPSW CBPAa ∆+=

)(/)(4 WPSW CBAa ψ=

foVa =5 Equation (3.30) is the non-linear differential equation, which can be solved

numerically. The solution of this equation gives the relationship between the

operating time and concentration of feed. Concentration of feed is a function of

operating time. In this mode of operation used, the system is essentially closed; that


44

is the mass of the solute in the initial feed must equal the total of the various

process streams and tanks at any instant of time. The overall mass balance is

avpPfPfofofo CVC)VV(CV +−= ............................................ (3.31)

][ pavfoPfoPffo CVVVVCC )/()/(1 +−= ...................................... (3.32)

The overall recovery is expressed into terms of foC , fC and pavC :

)(/)(/ pavffoffoP CCCCVV −−= ................................................ (3.33)

The equation for total dissolved solid (TDS) concentration in the

product tank can be obtained by substituting equation (3.27) into

equation (3.14).

][ Ppavppav VCCQpdtdC /)(/ −=

Or [ ])Caa/(CaCaadt/dC f43f2f21pav −+−=

[ ] [ ])CC(/)CC(V/C)Caa(/C avPffoffpavf43f −−−− .................... (3.34)

Equation (3.30) and then (3.34) can be solved with the help of fourth order Runge-

Kutta technique.

For the determination of model constant the six model constants and two

initial conditions were used in the simulation program. The initial conditions are

feed concentration Cfo and feed volume Vfo. Membrane surface area Sa and

operating pressure gradient ΔP are two model constants that represent design

variables, the solvent (water) concentration is Cwp

Experimental data for aqueous salt (NaCl) solution taken at different concentrations of the feed water is used to verify the model.

.

The constants and initial conditions for model simulation are shown in Table (3.1).


45

Table (3.1) Parameter Values for Model Simulation

Parameter Value

Initial solute feed concentration,

Cfo, kg/m

15 kg/m3

3

Initial feed Volume, Vfo ,m 1.5 m3 3

Solvent permeate concentration,

Cwp , kg/m

1000 kg/m3

3

Membrane surface area, Sa, m

(Sc-2600, spiral wound model

2 35.2 m2

Membrane pressure gradient, ∆p, kg/m.h

3.22×10 P

13

Solvent permeability constant ARwR, h/m

4.88×10 P

-13

Solute permeability constant,

BRsR, m/h

1.13×10 P

-4P

Osmotic pressure to solute

concentration ratio, ψ , m P

2P/h P

2

1.3608×10 P

12

Chapter Four Experimental Work

46

Chapter Four

Experimental work

The present study includes the achievement of experimental work through

central composite rotatable design method to create samples of different artificial

flux and rejection. The experimental work of desalination sample water was carried

out in two stages. The first stage treatment of salt water by laboratory scale of

reverse osmosis membrane was carried out in Spain - Madrid - University of

Complutense – Faculty of Physics, Department of Applied Physics. In this stage

the removing of TDS was within the allowable requirement range and the result

was analyzed theoretically.

The operating conditions using reverse osmosis membrane process were

commenced with the following ranges:

A. Concentration of Feed (15-45) g/l.

B. Temperature of Feed (10-50) °C.

C. Flow rate of Feed (100-250) l/hr.

D. Operating Pressure (5-15) bar.

The second stage includes injection air with salted water for enhancement

permeates flux and rejection after obtaining the maximum conditions from the first

stage. This stage was carried out by laboratory scale of reverse osmosis membrane

system which located at in AL-Mansuor Company - Ministry of Industry and

Minerals - Baghdad - Iraq.

This chapter explains and views in details the experimental part of this work.

It includes the description of the experimental rig in order to study the behavior of

the process and measuring the experimental data.


47

4.1 The Experimental System

Figure (4.1) and Figure (4.2) show the schematic diagram and photograph of

the Reverse Osmosis membrane system used in this study. The Reverse Osmosis

membrane system consists of the following items:-

1. Electrical Pump

It’s a horizontal rotodynamic high pressure pump type (GE motors &

industrial system). The electrical pump is provided to aid up the makeup NaCl

solution into the reverse osmosis membrane module and maintain it at the required

level from the pressure and flow rate. The specifications of this type were (HP ½ ,

Hz 60/50 , V 100-120/200-240 , PH 1, RPM 11725/1425 , A 7.1-7.2/3.4-3.6 , gage

of pressure read 300 Psi as a maximum) assembled in Mexico.

2. Membrane Module

Type of membrane was osmonics (Cellous acetate, Sc-6200, spiral-wound

model, DESAL™ Membrane Products, made in USA) the surface area of membrane

1.12 m2

3. Feed Tank

. This reverse osmosis membrane module can be used to extract fresh water

from salt water but it requires a lot of pressure.

The feed solution was prepared by dissolving NaCl salt in distilled water

according the concentration of each experiment and poured in the feed tank which

was cylindrical glass vessel with total capacity 5 liter (made in Germany).

4. Thermostat

To maintain the temperature for each experiment constant, a rectangular

container of bathwater was filled with water and by circulating this water inside the

jacket of the feed tank while the temperature remains constant during the time of

the experiment.


48

Figure (4.1) the schematic diagram of the experimental rig

Rotameters

Concentrated Manometers

Permeate

Conductivity Monitor

Pressure Controller

Thermometer

Filter

Feed Tank

Thermostat

High pressure pump

Low pressure Pump

Membrane


49

Figure (4.2) the general view of the experimental rig

4.1.1 Measuring Devices

Different measuring devices were used through the experimental

investigation of this study, these devices are as follows:-

1. Pressure Gauge

This device was used to measure the transmembrane pressure across the

reverse osmosis membrane module and by circulating two circular valves to the

right or left direction in order to obtain the exact pressure and flow rate at the same

time.

2. Temperature Measurement

In order to measure the temperature of the feed solution during the running

time of the experiment, sensible device type (Temp.-MeBgreät Pt100, PHYWE.

11759.93, and Nr 000719) to be inserted inside the feed tank, was used.


50

3. Conductivity Meter

Metrohm Ω 712 Digital conduct meter types 1.712.0010 and Nr.10191 was

used to measure the conductivity of feed solution and permeate water for each

sample (made in Switzerland).

4. Rota meter

Is an instrument used for measuring the flow rate of the feed solution.

5. Electronic Balance

Eventually, digital electronic balance from AD instrument LTD. Type GF-

1200-EC, max.1210g, e = 0.01, min.0.02g, d = 0.001g and has Ac adapter DC 12v

with digital means was employed to measure weight of permeate water during flux

calculation (made in Japan).

4.1.2 Experimental Procedures

1. Preparation of salt solution as a test media was achieved to the desired

concentration according to the number of experiments in a table of experimental

design and the checking of the desired concentration was performed by

conductivity meter. Hereinafter, the prepared solution is poured in the glass feed

tank.

2. The electrical current was switched on to operate the experimental rig and the

calibration for the pump was done to obtain the desired pressure and flow rate

while the temperature of salted solution was controlled by thermostat.

3. After adjusting the operating conditions, predetermined a condition that has

been already designed was applied according to the central composite rotatable

design of Box-Wilson. Accordingly, the flow rate measurement, water bath

temperature, concentration of the feed solution and eventually the pressure were

justified.


51

4. Each experiment that was already carried out according to the previous pre

designed conditions, by measuring the volumetric flow rate of permeate water was

followed in order to calculate the flux and rejection. After measuring the

volumetric flow rate of permeates water, the samples were weighted by electronic

digital balance type GF-1200-EC.

4.2 Experimental Design

The experimental work of the present study involves the investigation of the

four variables such as; concentration, temperature, flow rate and pressure of feed

solution. Experimental planning was applied as recommended by Cochran

(Cochran, 1957) to reduce the number of experiments that would give sufficient

information in order to conclude the extent of the effect of each variable on the

membrane efficiency. The application of the experimental design for planning the

required experiment to examine the system, will extract the information from pre-

existing data by using a statistical method in order to interpret the results in a

regular form with the minimum number of observation. (Cochran and Cox, 1957).

The experimental design technique consists of two parts:-

1. Planning the experiments according to a specified plan, taking into account

the description of the variables value in the plan by a coded form.

2. Achieving the regression analysis for the specified set of runs in the plan,

also taking into account the coded form of the objective function regarding

each experiment in the set.

4.2.1 Fitting the Second Order Model

An experimental design for fitting the second-order model must have at least

five levels for each factor so that the model parameters can be estimated (i.e.

variables are usually called factor and the particular value of the variable is called


52

the level). There are many techniques for the application of experimental planning,

such as factorial design, fractional design and box-Wilson. The proper technique

for planning a system of more than three variables "central composite rotatable

design" the total number of treatment combination is equal to (2K +2K +1), where

(K) is the number of variables, plus one additional further treatment that takes the

lack of fit and experimental error into account.

4.2.2 Central Composite Rotatable Design

This design consist of a 2K fractional (i.e. coded to the usual ± 1 notation) augmented by 2K axial points [i.e. (± ,0,0,…..,0),(0, ±, 0,…..,0), (0,0, ±,…..,0), …., (0,0,…., ±,) and center points (0,0,0,…..,0)].

A preliminary step is to set up the relationships between the coded levels and

the corresponding real variables, these relationships are as follows (Box and

George., 1978):

( )1.4][.min

−

=−

KXXXXX

center

centeractualCoded

The operating conditions of Reverse Osmosis membrane system are as follows:-

1. Concentration of Feed (15-45) g/l.

2. Temperature of Feed (10-50) °C.

3. Flow rate of Feed (100-250) l/hr.

4. Operating Pressure (5-15) bar.

The central composite rotatable design of four variables is used. The coded

levels are related to the real process values of these variables as follows:


53

(4.5)2.5

10PX

(4.4)37.5

175FX

(4.3)10

30TX

(4.2)7.5

30CX

4

3

2

1

−=

−=

−=

−=

Where: [C] is the concentration of feed in (g/l), [T] is the operating temperature in

(°C), [F] is the flow rate of feed in (l/hr) and [p] is the operating pressure in (bar).

The working range of the coded and corresponding real variables is listed in

Table 3.1. Thirty one experiments were carried out in a sequence shown in Table

3.2 where the coded values +2, -2, 0 present the maximum, minimum and average

values respectively.

Table (4.1): Working range of coded and corresponding real variables Coded level

Concentration (g/l) Temperature (C⁰) Flow rate(L/hr) Pressure(bar)

-2 15 10 100 5 -1 22.5 20 137.5 7.5 0 30 30 175 10 1 37.5 40 212.5 12.5 2 45 50 250 15

Table (4.2): Sequence of experiments according to central composite design

EXP.

NO.

Coded variable Real variable

X1 X2 X3 X4 Concentration

(g/l)

Temperature

(оC)

Flow rate

(l/hr)

Pressure

(bar)

1 - 1 -1 -1 -1 22.5 20 137.5 7.5

2 1 -1 -1 -1 37.5 20 137.5 7.5

3 - 1 1 -1 -1 22.5 40 137.5 7.5

4 1 1 -1 -1 37.5 40 137.5 7.5


54

5 - 1 -1 1 -1 22.5 20 212.5 7.5

6 1 -1 1 -1 37.5 20 212.5 7.5

7 - 1 1 1 -1 22.5 40 212.5 7.5

8 1 1 1 -1 37.5 40 212.5 7.5

9 -1 -1 -1 1 22.5 20 137.5 12.5

10 1 -1 -1 1 37.5 20 137.5 12.5

11 - 1 1 -1 1 22.5 40 137.5 12.5

12 1 1 -1 1 37.5 40 137.5 12.5

13 - 1 -1 1 1 22.5 20 212.5 12.5

14 1 -1 1 1 37.5 20 212.5 12.5

15 - 1 1 1 1 22.5 40 212.5 12.5

16 1 1 1 1 37.5 40 212.5 12.5

17 - 2 0 0 0 15 30 175 10

18 2 0 0 0 45 30 175 10

19 0 -2 0 0 30 10 175 10

20 0 2 0 0 30 50 175 10

21 0 0 -2 0 30 30 100 10

22 0 0 2 0 30 30 250 10

23 0 0 0 0 30 30 175 5

24 0 0 0 -2 30 30 175 15

25 0 0 0 2 30 30 175 10

26 0 0 0 0 30 30 175 10

27 0 0 0 0 30 30 175 10

28 0 0 0 0 30 30 175 10


55

29 0 0 0 0 30 30 175 10

30 0 0 0 0 30 30 175 10

31 0 0 0 0 30 30 175 10

The polynomial for a system of four variables can be represented as follows:

Y=B0+B1X1+B2X2+B3X3+B4X4+B11X12+B22X²2+B33X²3+B44X²4+B12X1X2+B13X1X3

+B14X1X4+ B23X2X3+B24X2X4+B34X3X4. (4.6)

Where Y is the objective function and the corresponding coefficients of the above

polynomials are called “Regression Coefficients".

4.3 Experimental Work for Injection Air Process

After obtaining the maximum conditions for flux and rejection from the first

stage for the proposed model by using optimization program (Hook and Jives). The

experimental work for the second stage which includes injecting air with salted

water for flux enhancement is started. This stage was performed by fixing the four

general variables at the maximum conditions, and the only change occurred just at

the flow rate of the injecting air in order to study the effect of different injection air

velocities. After that, different velocities of feed water were achieved at a fixed

velocity of air. The special experimental part of this work is explained in Figure

4.3. It includes the description of the experimental rig in order to study the behavior

of the process and the measurement of the experimental data and Figure (4.4)

includes the schematic diagram for injection air process.


56

4.3.1 Equipment and Apparatus

Figure 4.3 shows the reverse osmosis module and other equipment, used in

the experiment and Figure (4.4) show the schematic diagram of air sparging

process. The equipment and apparatus consist of the following parts:

- Horizontally membrane module type (Cellulose acetate, Sc-6200, Spiral

wound model), (width = 0.78 m, length = 0.94 m, number of membrane = 24

and total surface area = 0.78*0.94*24*2= 35.193m2).

- Air Compressor (AAC-WD2, 220V-50Hz, 1500W, 2850rpm, 50L, 208

L/min, serial No. 08093001 Made in China).

- Feed Tank (capacity 1.5 m3, height 100 cm, diameter 140 cm, Material

GFRP/PVC).

- A calibrated rotameters to measure the volumetric flow rate of feed water

with range (1-10) m3/h.

- Two calibrated rotameters are used to measure the recovery and volumetric

flow rate, one on the product line (FI- permeate) and the other on the

rejection stream (FI-rejection) with range (0.6-6.3) m3/hr.

- Three cartridge filters (cartridge Element 5 micron) are fitted at the feed

water inlet of the unit in order to protect the pump and the permeated from

particulate matter.

- Pressure gauges for pressure drop determination across the cartridge filters.

- High Pressure Pump (centrifugal pump, type Radials split casing) is used to

pump feed water into a module separated by a semi-permeable two volumes,

under high pressure.

- A regulating pressure valve (PRV) is used to change the pressure of the feed

water from (1-60) bars.

- A throttling valve on the concentrate (rejection) outlet to control

conversions.


57

- Two Pressure Gauges, one is used in the feed line to indicate the feed

pressure and the other is used in the reject (brine), with a range of (1-60)

bars.

- Two Conductivity Sensors are used to measure the conductivity in the feed

stream and in the product (permeate) stream.

Figure (4.3) Experimental unit for injection air process


58

Concentrated

One way Valve

ReRRr Air

Rota meters Rota meters

Permeate Concentrated

Rota meters Conductivity

Temp. Measurement Meters

High pressure pump

Figure (4.4) the schematic diagram of air sparging process.

Feed

Tank Air Compressor

Reverse Osmosis Membrane

Water Bath

Chapter Five Results and Discussion

59

Chapter Five

Results and Discussion

The experimental results of the effect of operating variables on the reverse

osmosis performance and the maximum operating conditions of the process were

studied.

A series of samples that conducting sodium chloride solution were prepared

to study the effect of the most affective variables (i.e. concentration, temperature,

flow rate and operating pressure for the system). These variables had been

correlated with the flux and rejection of the specimen by a second order polynomial

model after estimation of the coefficients would carry over according to one of the

methods of optimization. In addition, specimens at the predicted maximum values

of the concerned variables were further prepared to prepare a final study by

measuring the samples to calculate the flux and rejection.

Finally the mathematical model of reverse osmosis membrane system was

simulated in the present work to predict the theoretical transient response with aid

of computer program using FORTRAN program. The theoretical results were

compared with the experimental result of reverse osmosis membrane system.

5.1 Analysis of Experimental Result

The response of experiments conducted according to Box-Wilson method,

which represented by flux and rejection are fitted to a second order polynomial

model and the maximum conditions are calculated from this model. The effect of

each variable on the response is also determined by using F-test and ANOVA

analysis of variance.


60

5.2 Postulating the polynomial Model

A second order polynomial model would correlate the four variables (i.e.

concentration, temperature, flow rate and operating pressure of the feed solution)

with the flux and rejection of the specimens. To postulate the best formal of the

proposed model, the coded variables in Tables 5.1 and 5.2 will be first fitted

through nonlinear regression analysis to estimate the coefficient of the proposed

model. Table (5.1) Results of experimental planned for flux according to central composite rotatable design

Exp.

No.

Coded variable Real variable

Experiment permeate

Flux

Predicted permeate

Flux

X1 X2 X3 X4 Conc. (g/l) Temp. (Cº) Flow rate (L/hr)

Pressure

(bar)

Y

(Kg/mP

2P.s)*10P

-5

Y

(Kg/mP

2P.s)*10P

-5

1 -1 -1 -1 -1 22.5 20 137.5 7.5 15.296 20.323

2 1 -1 -1 -1 37.5 20 137.5 7.5 6.7407 8.014

3 -1 1 -1 -1 22.5 40 137.5 7.5 23.023 28.172

4 1 1 -1 -1 37.5 40 137.5 7.5 12.659 11.678

5 -1 -1 1 -1 22.5 20 212.5 7.5 17.819 22.679

6 1 -1 1 -1 37.5 20 212.5 7.5 7.5712 7.370

7 -1 1 1 -1 22.5 40 212.5 7.5 21.8898 29.988

8 1 1 1 -1 37.5 40 212.5 7.5 13.1767 10.500

9 -1 -1 -1 1 22.5 20 137.5 12.5 40.6827 45.479

10 1 -1 -1 1 37.5 20 137.5 12.5 13.554 9.651

11 -1 1 -1 1 22.5 40 137.5 12.5 56.7963 60.927

12 1 1 -1 1 37.5 40 137.5 12.5 23.6584 20.919

13 -1 -1 1 1 22.5 20 212.5 12.5 43.224 48.216

14 1 -1 1 1 37.5 20 212.5 12.5 12.687 9.392

15 -1 1 1 1 22.5 40 212.5 12.5 62.282 63.129

16 1 1 1 1 37.5 40 212.5 12.5 21.229 20.126


61

17 -2 0 0 0 15 30 175 10 95.241 79.311

18 2 0 0 0 45 30 175 10 14.11806 23.994

19 0 -2 0 0 30 10 175 10 10.413 6.659

20 0 2 0 0 30 50 175 10 27.536 25.237

21 0 0 -2 0 30 30 100 10 21.065 16.750

22 0 0 2 0 30 30 250 10 20.046 18.308

23 0 0 0 0 30 30 175 5 8.8373 1.624

24 0 0 0 -2 30 30 175 15 35.2411 36.401

25 0 0 0 2 30 30 175 10 19.427 19.303

26 0 0 0 0 30 30 175 10 20.617 19.303

27 0 0 0 0 30 30 175 10 19.9768 19.303

28 0 0 0 0 30 30 175 10 21.073 19.303

29 0 0 0 0 30 30 175 10 17.368 19.303

30 0 0 0 0 30 30 175 10 18.383 19.303

31 0 0 0 0 30 30 175 10 18.275 19.303

Table (5.2) Sequence of experiments for rejection according to central composite design

Exp.

No.

Coded variable Real variable Experiment

Rejection

Predicted

Rejection

XR1 XR2 XR3 XR4 Conc. (g/l) Temp. (Cº) Flow rate (L/hr)

Pressure

(bar)

Y

(%)

Y

(%)

1 -1 -1 -1 -1 22.5 20 137.5 7.5 41.998 37.652

2 1 -1 -1 -1 37.5 20 137.5 7.5 22.681 24.121

3 -1 1 -1 -1 22.5 40 137.5 7.5 36.4720 33.067

4 1 1 -1 -1 37.5 40 137.5 7.5 14.463 15.295

5 -1 -1 1 -1 22.5 20 212.5 7.5 43.393 37.530

6 1 -1 1 -1 37.5 20 212.5 7.5 22.694 22.943

7 -1 1 1 -1 22.5 40 212.5 7.5 36.8007 33.038

8 1 1 1 -1 37.5 40 212.5 7.5 13.421 14.211

9 -1 -1 -1 1 22.5 20 137.5 12.5 60.217 55.832


62

10 1 -1 -1 1 37.5 20 137.5 12.5 33.748 34.096

11 -1 1 -1 1 22.5 40 137.5 12.5 55.515 51.851

12 1 1 -1 1 37.5 40 137.5 12.5 23.610 25.875

13 -1 -1 1 1 22.5 20 212.5 12.5 59.842 55.598

14 1 -1 1 1 37.5 20 212.5 12.5 32.999 32.807

15 -1 1 1 1 22.5 40 212.5 12.5 56.746 51.7107

16 1 1 1 1 37.5 40 212.5 12.5 23.739 24.6790

17 -2 0 0 0 15 30 175 10 31.174 45.0183

18 2 0 0 0 45 30 175 10 11.3002 4.455

19 0 -2 0 0 30 10 175 10 38.696 43.681

20 0 2 0 0 30 50 175 10 28.955 30.968

21 0 0 -2 0 30 30 100 10 37.889 39.835

22 0 0 2 0 30 30 250 10 33.463 38.516

23 0 0 0 0 30 30 175 5 18.492 22.016

24 0 0 0 -2 30 30 175 15 47.190 50.664

25 0 0 0 2 30 30 175 10 31.354 33.621

26 0 0 0 0 30 30 175 10 33.5611 33.621

27 0 0 0 0 30 30 175 10 32.810 33.621

28 0 0 0 0 30 30 175 10 35.468 33.621

29 0 0 0 0 30 30 175 10 36.817 33.621

30 0 0 0 0 30 30 175 10 31.242 33.621

31 0 0 0 0 30 30 175 10 34.099 33.621

5.2.1 The Second Order Curve Fitting

The statistical software was used for estimating the coefficients of the

proposed model and statistical analysis of the model. By using the coded data of the

central composite rotatable design, Tables 5.1 and 5.2 shows the coefficients of the

2P

ndP order polynomial were estimated by implementing nonlinear regression

estimation technique via the statistical software. The number of iterations was


63

terminated when the proportion of variance accounted for flux was equal to 0.9314

and the correlation coefficient (R) was equal to 0.9651 while for rejection the

proportion of variance accounted was equal to 0.9012 and the correlation

coefficient (R) was equal to 0.9452. Tables 5.3 and 5.4 summarized coefficients of

the proposed model for flux and rejection.

The maximum values of the studied variables were determined by

maximization the predict correlation utilizing Hooks and Jeeves optimization

technique. More details about the program for determining maximum values shown

in appendix A. Table (5.3) Coefficient of the proposed polynomial for flux

Coeff. B0 B1 B2 B3 B4 B11 B22 B33 B44 B12 B13 B14 B23 B24 B34

Value 19.3 -13.8 4.6 0.38 8.69 8.08 -0.8 -0.4 -0.07 -1.0 -0.7 -5.8 -0.1 1.9 0.09

Table (5.4) Coefficients of the proposed polynomial for rejection

Coeff. B0 B1 B2 B3 B4 B11 B22 B33 B44 B12 B13 B14 B23 B24 B34

Value 33.6 -10.1 -3.17 -0.32 7.1 -2.2 0.9 1.3 0.67 -1.06 -0.2 -2.0 0.02 0.15 -0.02

Equations (5.1) and (5.2) are the final form of the proposed flux and rejection models, respectively:

Y = 19.3 -13.8X1 +4.6X2 + 0.38X3 + 8.69X4 +8.08X1

2 - 0.8X²2 - 0.4X²3 - 0.07X²4 – 1.0X1X2 – 0.7 X1 X3 -5.8X1X4 – 0.1X2X3 +1.9X2X4 + 0.09X3X4

Y = 33.6 -10.1X

(5.1)

1 – 3.175X2 - 0.32X3 + 7.1X4 – 2.2X12 + 0.9X²2 +1.3X²3 +0.67X²4 -1.06X1X2 -

0.2 X1 X3 - 2.0X1X4 + 0.02X2X3 + 0.15X2X4 - 0.02X3X4

Equations (5.1) and (5.2) were applied to estimate the flux and rejection as listed in Tables (5.1) and (5.2).

(5.2)


64

5.2.2 Effect of Concern Variable

From equations (5.1) and (5.2), it can be seen that the flux and rejection

dependant on feed concentration (X1) , feed temperature (X2) , feed flow rate (X3)

operating pressure (X4) in the following sequence: (X1>X4>X2>X3

5.3 Estimating the Maximum Conditions

).

Table 5.5 shows the maximum values of the studied variables in coded and

real form using Hooks and Jeeves method. Table (5.5) Coded and real maximum value of variables

Variables Maximum

coded values for Flux

Maximum real value for Flux

Maximum coded value

for Rejection

Maximum real value for

Rejection

Feed Concentration (g/l) X1 X=-2 1 X=15 g/l 1 X=-2 1=15 g/l

Feed Temperature (o XC) 2 X=2 2 X=50 °C 2 X=-2 2=10 °C

Feed flow rate (l/hr) X3 X=2 3 X=250 L/hr 3 X=2 3=250 L/hr

Operating pressure (bar) X4 X=2 4 X=15 bar 4 X=2 4=15 bar

5.4 Examination of the Effective Variables (F-test) The analysis of (F-test) for flux and rejection equations is shown in appendix

(B). The second order response model can be written for flux equation (5.1) as

follows:

Y = 19.303 - 13.829X1 + 4.644X2 + 8.694X4 + 8.087X12

- 5.878X1X4

While for rejection equation (5.2) can be written as follows:

(5.3)

Y = 33.6216 - 10.14o8X1 – 3.175X2 + 7.1620X4 – 2.22X12

(5.4)

5.5 The Analysis of Variance (ANOVA)

In order to ensure a good model the test for significance of the regression

model was performed applying the analysis of variances (ANOVA). Tables 5.6 and

5.7 show the ANOVA tables for the rejection coefficient. The relationships used


65

for calculation of the ANOVA estimators (i.e., Fvalue, R2, and R2adj

According to ANOVA table F

) (Carley et al;

2004), (Liteanu and Rica; 1985).

value > Ftab and the R2 value for permeate flux

is 0.9657, which is desirable. The predicted R2 is in agreement with the adjusted

coefficient of determination R2adj

Table (5.6) ANOVA table for rejection coefficient (Response is Flux)

. All these statistical estimators reveal that

response model is accepted from statistical point of view for the prediction of the

response in the considered range of factors (valid region). The sample of

calculation is shown in appendix C.

Source of variance

Sum of Squares

Degree of

Freedom

Mean Square

F value

F tab

R2

R2

adj

Regression (Model)

SS

9568

R

4 2392 85 3.03 0.9657 0.9356

Error (Residual)

SS

704

E

26 28

Total S 10272.7 yy 30 Table (5.7) ANOVA table for rejection coefficient (Response is Rejection) Source of variance

Sum of Squares

Degree of

Freedom

Mean Square

F value

F tab

R2

R2 adj

Regression (Model)

SS

5828.014

R

4 1457 74.14 2.5 0.9193 0.9069

Error (Residual)

SS

511.099

E

26 19.65

Total S 6339.113 yy 30

The parity plot of predicted and experimental values of the response for

permeates flux and rejection is shown in Figures 5.1 and 5.2, respectively.

According to Figures 5.1 and 5.2 the response model shows a goodness of fit to the


66

experimental data in the range of confidence 0.95. Therefore, the model is

considered adequate for the prediction (simulation) and optimization.

0 10 20 30 40 50 60 70 80 90 100

Experimental

0

20

40

60

80

100Pr

edic

ted

Figure (5.1) Permeate flux, predicted values by response model, against the experimental data

0 10 20 30 40 50 60

Experimental

0

10

20

30

40

50

60

Pred

icte

d

Figure (5.2) Rejection, predicted values by response model, against the experimental data


67

Figure (5.3) shows the effects of two factors, pressure and concentration of

NaCl salt solution in combinations when the temperature and flow rate are holds at

the central level. As it could be observed from Figure 5.3 the increasing of pressure

up to 15 bars led to increase of permeate flux to a maximum level. The

concentration factor exhibits the diminished effect upon the response and the value

of the permeate flux does change significantly with concentration solution in this

range of NaCl concentration. Also the response surface shown in Figure 5.3 reveals

that the interaction affects between pressure and concentration. This corroborates

the affirmation of the authors (Corneliu and Gra˙zyna, 2007).

Figure (5.3) Response surface plot indicating the effect of pressure and concentration upon

permeate flux


68

Figure 5.4 represents the surface plots indicating the effect of pressure and

concentration of NaCl salt solution upon the rejection. As one can see the highest

values of rejection are also observed at the pressure of 15 bars. In this figure the

interaction effect is also observed between concentration and pressure. For instance

at the pressure < 15bar the increasing of concentration from 15gm/l up to 45gm/l

led to decreasing of response function while at the pressure higher than 5bars the

increasing of concentration in the same interval contributes to increase of response

(rejection). That is agreement with the results reported by (Corneliu and Gra˙zyna,

2007).

Figure (5.4) Response surface plot indicating the effect of pressure and concentration upon

rejection


69

5.6 Effect of Operating Conditions on Performance of Reverse Osmosis

Membrane

In this effort, effect of different operating conditions, such as: NaCl

concentration (15-45) gm/l, feed temperature (10-50) °C, feed flow rates (100-250)

l/hr and operating pressure (5-15) bar; on the performance of the reverse osmosis

membrane (RO) were studied and all the results shown in Figures D1 to D24 in

appendix D.

Hereinafter, figures indicate that flux is increased with pressure, temperature

and flow rate and decreased with concentration as for as rejection increased with

pressure and flow rate and decreased with temperature and concentration.

5.6.1 Effect of Maximum Conditions on Reverse Osmosis Membrane Permeate

Flux

Figure 5.5 demonstrates the effect of NaCl feed concentration on permeate flux

at maximum conditions such as; temperature 50°C, flow rate 250 l/hr and pressure

15 bar. With an increase of NaCl concentration from 15 up to 45 gm/l the permeate

flux decreased, and this is due to the effect of concentration polarization

phenomenon. Concentration polarization may be defined as the presence of a

higher concentration of rejected species, at the surface of a membrane than in the

bulk solution, due to the convective transport of both solute and solvent (Jamal,

2004). In addition, higher NaCl concentrations in the feed solution increase the

NaCl flux as indicated in equation (3.3) and increase the driving potential of the

NaCl concentration difference across the membrane, this leads to increase the

osmotic pressure and reducing water flux according to equation (3.2).

The effect of solution temperature on permeates flux of the reverse osmosis

membrane at maximum conditions shown in Figure 5.6. It can be seen that, the

temperature change from 10 to 50°C modified the permeate flux of the membrane at

constant other maximum conditions such as; NaCl concentration 15 gm/l, flow rate


70

250 l/hr, and pressure 15 bar. Higher temperatures reduce the viscosity of the feed

solution, and then the solution will be easier to transfer through the membrane

(Munir, 1998). Thus, temperature is expected to have a fairly significant effect on

permeate flux.

10 15 20 25 30 35 40 45 50Concentration ( gm / l )

0

20

40

60

80

100

120

140

160

Max

imum

Per

mea

t Flu

x (k

g/m2

.s)*

10-5

Figure (5.5) the effect of feed concentration on permeates flux at maximum conditions

(Temperature = 50 °C, Flow rate = 250 l/hr and Pressure = 15 bar)

5 10 15 20 25 30 35 40 45 50 55

Temperature ( 0C)

0

20

40

60

80

100

120

140

160

Max

imum

Per

mea

t Flu

x (K

g/m2 .

s)*1

0-5

Figure (5.6) the effect of feed temperature on permeates flux at maximum conditions

(Concentration = 15 gm/l, Flow rate = 250 l/hr and Pressure = 15 bar)


71

Besides, the effect of feed flow rate on permeate flux of the reverse osmosis

is illustrated in Figure 5.7. With an increase of the feed flow rate the permeate flux

increased. Munir, 1998, reported that the flow rate or turbulence; whether produced

by stirring, pumping the fluid, or moving the membrane, has a noticeable effect on

permeate flux. In addition, agitation and mixing of the fluid near the membrane

surface “sweeps’’ away the accumulated solute, reducing the hydraulic resistance

of the “cake’’ and reducing thickness of the boundary layer. There is also a belief

that extremely high shear, such as that obtained with thin channel and rotary

devices, actually reduces the thickness of the “gel’’ layer. In any case, this is one of

the simplest methods of controlling the effect of concentration polarization.

Figure 5.8 illustrate the effect of operating pressure on permeate flux at

maximum conditions. Equation (3.2) shows that the water flux is directly

proportional to the pressure drop across the membrane. However, there is an indirect

effect of pressure on the product concentration. If the pressure is reduced, NaCl will

tend to accumulate on the feed side of the membrane; it increases the osmotic

pressure, reduces the driving potential )( π−P and hence, reduces the water flux

(Jamal et al, 2004).


72

80 100 120 140 160 180 200 220 240 260 Flow Rate ( L / hr )

0

20

40

60

80

100

120

140

160M

axim

um P

erm

eat F

lux

(Kg/

m2 .s

)*1

0-5

Figure (5.7) the effects of feed flow rate on permeate flux at maximum conditions

(Concentration = 15 gm/l, Temperature = 50 °C and Pressure = 15 bar)

4 6 8 10 12 14 16Pressure ( bar )

0

20

40

60

80

100

120

140

160

Max

imum

Per

mea

t Flu

x (K

g/m

2 .s)

*10-5

Figure (5.8) the effects of operating pressure on permeates flux at maximum conditions

(Concentration = 15 gm/l, Temperature = 50 °C and Flow rate = 250 l/hr)


73

5.6.2 Effect of Maximum Conditions on Reverse Osmosis Membrane Rejection

Figure 5.9 shows the effect of feed concentration on reverse osmosis

membrane rejection with temperature, flow rate, and pressure at maximum

conditions. It can be seen that NaCl rejection is decreased with increase of the feed

concentration. Because of the increase in feed concentration causes an increase in

the osmotic pressure at the membrane surface after that the permeate water flux and

dissolved solids rejection decreases. In this case concentration polarization

appeared and builds up of a boundary layer of more highly concentrated solute on

the membrane surface than in the bulk solution. If the concentration of rejected

species is high enough, the secondary membrane formed on the membrane may

impede the passage of lower molecular solutes. In addition, higher concentrations

lead to a decrease in the apparent MWCO (Cherkasov et al, 1995) and (Jamal et al,

2004).

Figure 5.10 shows the effect of feed temperature on reverse osmosis

membrane rejection with NaCl concentration, flow rate, and pressure at maximum

conditions. The NaCl rejection is inversely proportional with feed temperature, due

to reduce the viscosity of the feed and then, easier to transfer the NaCl particles

through the reverse osmosis membrane. In addition, NaCl rejection depends on the

type of membrane and the salt concentration gradient as reported by (Mindler and

Epstein, 1986). Equation (2.16) shows dependence of NaCl rejection on the

physical properties of the membrane and this will affected by the solution

temperature (Rily et al; 1967).


74

10 15 20 25 30 35 40 45 50Concentration (gm/l)

0

20

40

60

80

100M

axim

um R

ejec

tion

(%)

Figure (5.9) the effect of feed concentration on rejection at maximum conditions

(Temperature = 50 °C, Flow rate = 250 l/hr and Pressure = 15 bar)

5 10 15 20 25 30 35 40 45 50 55

Temperature ( oC)

0

20

40

60

80

100

Max

imum

Rej

ectio

n (%

)

Figure (5.10) the effect of feed temperature on rejection at maximum conditions

(Concentration = 15 gm/l, Flow rate = 250 l/hr and Pressure = 15 bar)


75

The effect of feed flow rate on the NaCl rejection with concentration,

temperature, and pressure at maximum conditions is illustrated in Figure 5.11. With

an increase of feed flow rate, the hydraulic resistance of the “cake’’ and thickness

of the boundary layer will reduce and then, reduce the concentration polarization at

the membrane surface, therefore, both rejection and product rate increase with the

increase of the feed flow rate, as discussed in the previous section (Munir, 1998).

Figure 5.12 shows the effect of operating pressure on rejection with

concentration, temperature, and feed flow rate at maximum conditions.

Concentration polarization is more pronounced at higher pressures which bring

solute to the membrane surface very rapidly. It means, high pressures may

aggravate polarization effects, which will increase the rejection as shown in Figure

5.12 (Kim et al, 1994).

80 100 120 140 160 180 200 220 240 260Flow Rate (L/hr)

0

20

40

60

80

100

Max

imum

Rej

ectio

n (%

)

Figure (5.11) the effect of feed flow rate on rejection at maximum conditions

(Concentration = 15 gm/l, Temperature = 50 °C and Pressure = 15 bar)


76

4 6 8 10 12 14 16Pressure (bar)

0

20

40

60

80

100M

axim

um R

ejec

tion

(%)

Figure (5.12) the effect of operating pressure on rejection at maximum conditions

(Concentration = 15 gm/l, Temperature = 50 °C and Flow rate = 250 l/hr)

5.7 Simulation Results of Reverse Osmosis membrane

Figure 5.13 shows the comparison between the experimental and

theoretical results of the permeate flux behavior of the reverse osmosis (RO)

membrane with time. The results of this figure were obtained by applied

mathematical model Equation (4.30) in chapter (4), using the experimental data in

the present work. Computer program FORTRAN was used to solve this equation as

shown in appendix (L). In Figure 5.13, as operating time increases, the permeate

flux decreases sharply. Also the simulation results suggest that the permeate flux

decreases sharply with time. It can be conclude that, there is a good agreement

between the experimental data and model prediction. The decrease of the permeate

flux with time attributed to the concentration polarization phenomenon. The

objective of this effort is to solve or reduce this phenomenon by using sparging air

technique.


77

Figure (5.13) Simulation Results of Permeate Flux vs. Time

5.8 The Effect of Sparging Air on Reverse Osmosis Membrane Performance

Disturbing the mass transfer boundary layer near the membrane wall is the key

factor for enhancing the performance of membrane processes. The effect of air

sparging on permeate flux and rejection were investigated at different air and liquid

flow rates for maximum conditions such as, NaCl concentration (15 gm/l),

temperature (50°C), flow rate (250 l/hr) and pressure (15 bar). Maximum

conditions performed experimentally in AL-Mansour Company and induced that

the value of permeate flux (3.406 kg/m2

.hr) and the rejection value (85%).

5.8.1 Effect of Different Superficial Air Velocities on Permeate Flux

Tables 5.6 summarized the flux enhancement and rejection with air sparging

of the reverse osmosis membrane at different gas velocities. In Table 5.6 and

Figure 5.14 it can be seen that with increase of superficial air velocity UGS from


78

0.2652 to 1.5923 m s-1 at 0.221 m s-1 constant liquid velocity, the permeate flux

increased from 3.649 to 5.676 kg/m2.hr. This is due to influence of slug two-phase

flow (0.2 < ε < 0.9) on the hydrodynamic conditions at the membrane surface and

then on the filtration process efficiency. The interface for this gas–liquid two-phase

flow in membranes follows a variety of flow patterns. The predominant factor

determining flow regime is the void fraction (gas volume/total volume) in the pipe,

which depends directly on the gas and liquid phase velocities (flow rates). With

increasing void fraction, the flow pattern changes from bubble flow (0 < ε < 0.2)

over slug flow (0.2 < ε < 0.9) to annular and churn flow (0.9 < ε < 1.0) (Levy,

2006), (Zhang et al; 2003), (Soleimani and Hanratty, 2003), (Vera et al; 2000),

(Verberk et al; 2001) and (Mercier et al; 1995). Slug flow is the most effective flow

pattern for reducing cake layer builds up; this is due to high shear stress induced by

water and air slugs, according to studies of (Cabassud et al; 1997) ),( Vera et

al;2000) and (Li et al;1997). In general, horizontally slug flow was used due to the

impact of slugs on pipe bends, fittings and membrane surfaces. Besides, Figures

5.15 and 5.16 show the relationship between flux ratio (Φ = Jgas / Jno gas), and

superficial air velocity (UGS) and injection factor (ε = UGS / UGS+ULS

),

respectively. It can be seen that increasing of superficial air velocity and injection

factor causes increasing of flux ratio. Psoch and schiewer, 2006, reported that, with

increase of slug pattern value the advantages of the air sparging were most

pronounced. High air injection ratios within the slug flow regime give the best flux

ratios if all other parameters remain approximately constant.


79

Table (5.6) Experimental Results for Several Air Velocities

Superficial air

velocity UGS

(m/sec)

Superficial liquid

velocity ULS

(m/sec)

Injection factor(ε)=

UGS / UGS+ULS

Flow rate of the air

(L/min)

Flux ratio(Ф) = J gas / J no gas

Flux (kg/m2.hr)

Rejection (%)

0.2652 0.221 0.545 (slug)

5 1.0713 3.649 87

0.5308 0.221 0.705 (slug)

10 1.1538 3.930 87.55

0.7961 0.221 0.782 (slug)

15 1.249 4.257 88

1.0509 0.221 0.826 (slug)

20 1.363 4.644 89

1.3057 0.221 0.855 (slug)

25 1.5 5.109 90.12

1.5923 0.221 0.878 (slug)

30 1.66 5.676 91

0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8

Superficial Air Velocity ( U GS ) (m/sec)

3,43,63,84,04,24,44,64,85,05,25,45,65,8

Flux

Enh

ance

men

t (K

g/m

2 .hr)

Figure (5.14) Flux enhancement verses superficial air velocity at maximum condition for Cf = 15

gm/l, Temp. =50 °C, ULS = 0.345 m.sec-1, TMP=15 bar


80

0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8


1,0

1,1

1,2

1,3

1,4

1,5

1,6

1,7Fl

ux R

atio

(-)=J

gas

/J n

o ga

s

Figure (5.15) Flux Ratio verses superficial air velocity at maximum condition for Cf = 15 gm/l, Temp. =50 °C, ULS = 0.345 m.sec-1

, TMP=15 bar

0,50 0,55 0,60 0,65 0,70 0,75 0,80 0,85 0,90

Injection Factor(-)

1,0

1,1

1,2

1,3

1,4

1,5

1,6

1,7

Flux

Rat

io(-)

=J g

as/J

no

gas

Figure (5.16) Flux Ratio verses Injection factor at maximum condition for Cf = 15 gm/l, Temp. =50 °C, ULS = 0.345 m.sec-1, TMP=15 bar


81

5.8.2 Effect of Different Superficial Liquid Velocities on Permeate Flux

Table 5.7 summarize the flux enhancement and rejection with air sparging of

the reverse osmosis (RO) membrane at different liquid velocities with constant

superficial gas velocity (1.5923 m s-1). The experimental results show that, the

permeate flux is not modified by air sparging with increasing of the superficial

liquid velocity (ULS) from 0.221 to 0.444 m s-1

In addition, Figure 5.19 shows that the flux ratio (Φ = J

as shown in Table 5.7 and Figure

5.17. That means air does not modify the membrane properties (there is no decrease

of the membrane area in contact with the liquid flow) and has no influence on mass

transfer (Ducom et al; 2002). Besides Figure 5.18 represent the relationship

between superficial liquid velocity and flux ratio (Φ) which inversely proportional

because of the concentration polarization layer. Where flux ratio (Φ) is higher when

concentration polarization is more severe; for example, at a high transmembrane

pressure, a low liquid cross flow velocity and a high feed concentration. This

clearly indicates that the mechanism of the observed flux enhancement is related to

the disruption of the concentration polarization layer and improved mass transfer

(Cheng et al. 1998).

gas/Jno gas) reduced from

1.666 to 1.247 with decreasing of the injection factor (ε = UGS / UGS+ULS

) from

0.87 to 0.78. It means that the effect of slug two-phase flow on the permeate flux is

reduced in comparison with the effect of superficial air velocity (Levy, 2006),

(Cabassud et al, 1997), (Vera et al, 2000).


82

Table (5.7) Experimental Results for Several Liquid Velocities

Superficial liquid

velocity ULS

(m/sec)

Superficial air

velocity UGS

(m/sec)

Injection factor(ε)=

UGS / UGS+ULS

Flow rate of

the liquid (L/hr)

Flux ratio(Ф) = J gas / J no gas

Flux (kg/m2.hr)

Rejection (%)

0.221 1.5923 0.87 (slug)

250 1.666 5.676 90.98

0.265 1.5923 0.857 (slug)

300 1.522 5.185 90.25

0.308 1.5923 0.837 (slug)

350 1.420 4.837 89.85

0.358 1.5923 0.816 (slug)

400 1.330 4.532 89.20

0.400 1.5923 0.799 (slug)

450 1.290 4.395 88.90

0.444 1.5923 0.78 (slug)

500 1.247 4.250 88.35

0,20 0,24 0,28 0,32 0,36 0,40 0,44

Superficial Liquid Velocity ( U Ls ) (m/sec)

4,0

4,2

4,4

4,6

4,8

5,0

5,2

5,4

5,6

5,8

Flux

Enh

ance

men

t (K

g/m2 .h

r)

Figure (5.17) Flux enhancement verses superficial liquid velocity at maximum condition for

Cf = 15 gm / l, Temp. =50 °C, UGS = 1.592 m.sec-1

, TMP=15 bar


83

0,20 0,24 0,28 0,32 0,36 0,40 0,44

Superficial Liquid Velocity ( U Ls ) (m/sec)

1,20

1,25

1,30

1,35

1,40

1,45

1,50

1,55

1,60

1,65

1,70Fl

ux R

atio

(-)=

J ga

s/J

no g

as

Figure (5.18) Flux Ratio verses superficial liquid velocity at maximum condition for Cf = 15 gm/l, Temp. =50 °C, UGS = 1.592 m.sec-1

, TMP=15 bar

0,77 0,78 0,79 0,80 0,81 0,82 0,83 0,84 0,85 0,86 0,87 0,88

Injection Factor (-)

1,20

1,25

1,30

1,35

1,40

1,45

1,50

1,55

1,60

1,65

1,70

Flux

Rat

io (-

)=J

gas/

J no

gas

Figure (5.19) Flux Ratio verses Injection factor at maximum condition for Cf = 15 gm / l, Temp. =50 °C, UGS = 1.592 m.sec-1

, TMP=15 bar


84

5.8.3 Effect of Different Superficial Air Velocities on Rejection

Table 5.6 and Figure 5.20 represent the effect of gas sparging on rejection of

the salt solution (NaCl) by reverse osmosis membrane with different superficial air

velocity. It can be observed that with increasing of superficial air velocity from

0.2652 up to 1.5923 m s-1

the NaCl rejection improved from 87 to 91 %. Because

the permeate flux and the observed membrane rejection are increased for partially

retentive membranes when gas bubbles are introduced (Cui, 1993), (Cui and

Wright, 1994). Besides, Cui et al., 2003, reported that using gas–liquid two-phase

flow could potentially increase the recovery of high value products, or improve the

quality of permeate when a high rejection is desired. The observed effects agree

with the anticipated trends for improved control of concentration polarization,

where a lower value of wall concentration, results in the observed rejection

increasing towards the intrinsic membrane rejection.

0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8


86,5

87,0

87,5

88,0

88,5

89,0

89,5

90,0

90,5

91,0

91,5

Rej

ectio

n (%

)

Figure (5.20) Rejection (%) verses superficial air velocity at maximum condition for Cf = 15 gm/l, Temp. =50 °C, ULS = 0.345 m.sec-1

, TMP=15 bar


85

5.8.4 Effect of Different Superficial Liquid Velocities on Rejection

Table 5.7 and Figure 5.21 illustrate the effect of superficial liquid velocity at

constant air velocity (1.5923 m s-1) on NaCl rejection of the reverse osmosis

membrane. With an increase of the superficial liquid velocity from 0.221 up to

0.444 m s-1

the rejection decreased from 90.98 to 88.35 %. This is attributed to the

high ratio of liquid flow rate to gas flow rate which lead to reduce the contact area

from the surface of the membrane with gas flow. In addition, the value of injection

factor (ε) decrease gradually from 0.87 to 0.78; with increasing of liquid flow rate

(superficial liquid velocity) it means that the effect of slug two-phase flow is

reduced. It is noteworthy that the flow rate of the injected air was usually much

lower than the liquid flow rate as reported by Cui et al, 2003.

0,20 0,24 0,28 0,32 0,36 0,40 0,44

Superficial Liquid Velocity ( U LS ) (m/sec)

88,35

88,90

89,20

89,85

90,25

90,98

Rej

ectio

n(%

)

Figure (5.21) Rejection (%) verses superficial liquid velocity at maximum condition for Cf = 15 gm/l, Temp. =50 °C, UGS = 1.592 m.sec-1

, TMP=15 bar

Chapter Six Conclusions and Recommendations

86

Chapter Six

Conclusions and Recommendations

6.1 Conclusions

The concept of bubbling or gas sparging is to enhance surface mass transfer

can be very effectively applied to membrane processes, such as MF and UF, and

may be useful in NF and RO. The flux enhancement is most significant where the

non-sparged operation is most polarization layer controlled (or in another words

where high boundary layer resistance occurs), such as at low liquid flow rates,

higher solute/particle concentrations and low operating pressures. The following

conclusions are induced from the experimental work:

1. The permeate flux increases with increasing operating pressure, feed

temperature and feed flow rate.

2. The salt rejection of membrane and the quantity of product water increase

with increasing operating pressure and feed flow rate when feed solution was

equal to (15 g/l).

3. The model of reverse osmosis membrane developed by (Jamal et al, 2004)

without concentration polarization is effectively used for the prediction of

flux as a function of operating time.

4. The experiments showed that gas–liquid two-phase flow enhances reverse

osmosis membrane flux by a factor of (1.66) that means permeate flux

increases by (166%) and rejection is also increased up to (91%). This effect

is due to the high and transient wall shear stress induced by the sparging.

5. The hydrodynamic regime inducing the largest enhancement in filtration flux

is slug flow in the case of NaCl solution where a permeate flux plateau was

reached at the beginning of the slug flow regime.

Chapter Six Conclusions and Recommendations

87

6. In the experimental range explored, corresponding to slug flow, the most

significant effects occurred at a moderate liquid flow velocity (0.221 – 0.444

m s−1), gas velocity (0.256 – 1.592 m s−1

6.2 Recommendations

) and high proportion of injected gas

(ε > 0.2).

The following recommendations are presented for future studies:

1. Development of membranes can be studies in order to achieve better

performance at reduced permeation pressures with two- phase flow in

reverse osmosis (RO) membrane.

2. Developing an improved methodology in order to achieve optimal

hydrodynamic conditions of two-phase flow.

3. Using different hydrodynamic conditions, different geometry of modules and

different injection system process.

4. Improving the membrane salt rejection with two-phase flow in reverse

osmosis (RO) membrane.

5. In the continuation of the study, it will be possible to evaluate the effect of

air sparging on permeates flux for complex solution, such as emulsions.

6. Enhancement surface shear with the following technique which include :

a) Turbulence promoters.

b) Pulsate flow and vortex generation.

c) Corrugated membrane surfaces.

d) Forward flushing/ pulsing.

References

88

References

Al-Bastaki N., Abbas A., "Use of fluid instabilities to enhance membrane

performance": a review, Desalination 136 (2001) 255.

Al-Mudaiheem A. M. and Miyamura H., Construction and commissioning of Al

Jobail Phase II desalination plant. Desalination, 55 (1985) 1–11.

Aly S.E., Combined RO/VC desalination system, Desalination, 58 (1986) 85–97.

Amjad Z., Ed. Reverse Osmosis: Membrane Technology, Water Chemistry, and

Industrial Applications; Van Nostrand Reinhold: New York, 1993.

Bachelor G. K, A brief guide to two-phase flow, in: P. Germain, M. Piau, D.

Caillerie (Eds.), Theoretical and Applied Mechanics, Elsevier Science Publishers,

Amsterdam, 1989, pp. 27–40.

Bérubé P.R. , HLin H., Watai Y. "Fouling in air sparged submerged hollow fiber

membranes at sub- and super-critical flux conditions", Journal of Membrane

Science 307 (2008) 169–180.

Baker R. W. Membrane Technology and Applications, 2nd ed.; John Wiley &

Sons, Ltd.: Chichester, 2004.

Baker R.W.; Wijmans J.G. "The solution-diffusion model: A review", Journal of

Membrane Science, 107 (1995) 1-21.

References

89

Beck R.W., “Demineralization Treatment Technologies for the Seawater

Demineralization Feasibility Investigation”. Inc. Special Publication SJ 2004SP7

(December 31, 2002).

Bellara S.R.,Cui Z.F. and Pepper D.S., "Gas sparging to enhance permeate flux in

ultrafiltration using hollow fibre membranes", Journal of Membrane Science 121

(1996) 175 – 184.

Beolchini F., Veglio F., Barba D., Microfiltration of bovine and ovine milk for the

reduction of microbial content in a tubular membrane: a preliminary investigation,

Desalination 161 (2004) 251.

Bhattacharyya D. and Williams M., "Introduction and Definitions - Reverse

Osmosis", in Membrane Handbook, W. Ho and K. Sirkar, eds., pp. 265-268, Van

Nostrand Reinhold, New York(1992b).

Broussous L. Schmitz P. Prouzet E. Becque L. Larbot A. New ceramic membranes

designed for crossflow filtration enhancement, Sep. Purif. Technol. 25 (2001) 333.

Broussous, L., Ruiz J.C., Larbot A. and Cot L., Stamped ceramic porous tubes for

tangential filtration, Sep. Purif. Technol. 14 (1998) 53. Bird R. B., Stewart W. E., Lightfoot E. N. "Transport Phenomena", 2nd ed.; John

Wiley & Sons, Inc.: New York, 2002.

Bowen W. R., Calvo. J.I., Hernndez. A., "Steps of membrane blocking in flux

decline during protein microfiltration", J. Membr. Sci. 101 (1995) 153.

References

90

Box and George E.P., “statistics experimenters”, New York (1978).

Brandt D.C., Seawater reverse osmosis: an economic alternative to distillation.

Desalination, 52 (1985) 177–186.

Cabassud C., Ducom G. and Laborie S. "Measurement and comparison of wall

shear stresses in a gas/ liquid two-phase flow for two module configurations",

Conf. proc. IMSTEC, Sydney, paper 163, 2003, 6 pp.

Cabassud C., Laborie S. and Laîné .J.M.," How slug flow can improve

ultrafiltration flux in organic hollow fibres", J. Membr. Sci. 128 (1997) 93–101.

Cabassud C., Laborie S., Durand-Bourlier L., Lainé J.M. , Air sparging in

ultrafiltration hollow fibers: relationship between flux enhancement, cake

characteristics and hydrodynamic parameters. Journal of Membrane Science 181

(2001) 57–69.

Carley K.M., Kamneva N.Y., Reminga. J.,"Response Surface, Methodology,

CASOS Technical Report", Carnegie Mellon University, CMU-ISRI-04- 136,

October 2004.

Chang I. and Judd I., "Air sparging of a submerged MBR for municipal

wastewater treatment", Process Biochemistry 37 (2002) 915.

Cheng T. W., Yeh H.M., and Gau C.T.," Enhancement of Permeate Flux by Gas

Slugs for Crossflow Ultrafiltration in Tubular Membrane Module", Separation and

Science Technology, 33(15), (1998) pp. 2295-2309.

References

91

Cheng T.W, "Influence of inclination on gas-sparged cross-flow ultrafiltration

through an inorganic tubular membrane", Journal of Membrane Science 196 (2002)

103–110.

Cheng T.W., Yeh H.M., and Wu J.H.,"Effects of gas slugs and inclination angle on

the ultrafiltration flux in tubular membrane module", Journal of Membrane Science

158 (1999) 223-234.

Cherkasov A.N, Tsareva S.V. and Polotsky A.E. Selictive properties of

ultrafiltration membranes from the standpoint of concentration polarization and

adsorption phenomena. J.Membrane Sci. Vol.104: (1995).157-164.

Christophe S., Durand-Bourlier L., Michael J. C., Philippe M., Jean-Christophe

R., Philippe A.," Use of air sparging to improve backwash efficiency in hollow-

fiber modules", Journal of Membrane Science 161 (1999) 95-113.

Cochran W. G.and Cox. G.M.,"experimental design", 2nd ed., john Wiley and sons

Inc, 1957.

Corneliu C. and Gra˙zyna Z., Response surface modeling and optimization of

copper removal from aqua solutions using polymer assisted ultrafiltration, Journal

of Membrane Science 298 (2007) 56–70.

Cui Z. F, Chang. S., Fane .A.G., the use of gas bubbles to enhance membrane

processes", J. Membr. Sci. 221 (2003) 1-35.

References

92

Cui Z.F, Wright K.I.T., "Gas-liquid two phase flow ultrafiltration of BSA and

dextran solutions", J. Membr. Sci. 90 (1994) 183–189.

Cui Z.F, Wright K.I.T.," Flux enhancements with gas sparging in downwards

crossflow ultrafiltration: performance and mechanism", Journal of Membrane

Science 117 (1996) 109-116.

Cui Z.F. , Chang S., Fane A.G.," The use of gas bubbling to enhance membrane

processes",Journal of Membrane Science 221 (2003) 1–35.

Cui Z.F. and Taha T., "Enhancement of ultrafiltration using gas sparging: a

comparison of different membrane Modules", Journal of Chemical Technology and

Biotechnology 78: (2003) 249–253.

Cui Z.F., Bellara S.R., Homewood P., "Airlift crossflow membrane filtration—a

feasibility study with dextran ultrafiltration", J. Membr. Sci. 128 (1997) 83–91.

Cui Z.F.," Experimental investigation on enhancement of cross flow ultra filtration

with air sparging, in: Effective Membrane Processes New Perspectives", Mech.

Eng. London, (1993) pp. 237–245.

Ducom G., Matamoros H., Cabassud C., "Air sparging for flux enhancement in

nanofiltration membranes: application to O/W stabilised and non-stabilised

emulsions", Journal of Membrane Science 204 (2002) 221–236.

Ducom.G and Cabassud C., "Possible effects of air sparging for nanofiltration of

salted solutions", Desalination, 156 (2003) 267–274.

References

93

Fadaei H, Tabaei S.R., Roostaazad R.," Comparative assessment of the

efficiencies of gas sparging and back-flushing to improve yeast microfiltration

using tubular ceramic membranes", Desalination 217 (2007) 93–99.

Feki M,M .Chaabouni H.F.Ayedi,J.C.Heughebaert,M.Vaillant,“Statistical analysis

of the removal of aluminum and magnesium impurities from wet process

phosphoric acid” ,The Canadian Journal of Chemical Engineering,vol.65.1987.

Freeman B. D. “Osmosis”, In Encyclopedia of Applied Polymer Physics, 1995;

Vol. 13, pp 59-71.

Gekas V., "Terminology for Pressure-Driven Membrane Operations", Desalination,

68, 77 (1988).

Ghosh R., Cui Z.F., "Mass transfer in gas-sparged ultrafiltration: upward slug flow

in tubular membranes", Journal of Membrane Science 162 (1999) 91-102.

Ghosh R., Li Q., and Cui Z.F. "Fractionation of BSA and Lysozyme Using

Ultrafiltration: Effect of Gas Sparging", AIChE Journal. Vol. 44, No. 1. (1998) 61-

67.

Hermia J.," Constant pressure blocking filtration laws – Application to power-law

non-Newtonian fluids", Trans. IChemE 60 (1982) 183.

Herold D.,''A small Pv-Driven Reverse Osmosis Desalination Plant''. Desalination,

137, 2001. pp (285-292).

References

94

Hewitt G.F., Hall-Taylor N.S., "Annular two-phase flow", Pergamon: Oxford

Press. (1970).

Horvath C, Solomon B. A., Engasser J. M.,Measurement of radial transport in slug

flow using enzyme tubes, Ind. Eng. Chem. Fund. 12 (1973) 431– 439.

Hwang K.J., Wu Y.J. "Flux enhancement and cake formation in air-sparged cross-

flow microfiltration", Chemical Engineering Journal (2007).5462; No. of Pages 8.

Jamal K., Khan M.A. and Kamil M. “Mathematical modeling of reverse osmosis

systems’’, Desalination 160 (2004) 29–42.

Jamal K. Modeling of reverse osmosis membrane system, M. Tech. dissertation,

AMU Aligarh, 1996.

Kaichang Y., Xianghua W., QingJie B. Huang X.,"Critical flux enhancements with

air sparging in axial hollow fibers cross-flow microfiltration of biologically treated

wastewater". Journal of Membrane Science 224 (2003) 69–79.

Kim k. J., Fane A. G., Ben Aim R., Liu M G., Jonsson G., Tessaro I. C., Broek A.

P. and Bargeman D., 1994, a comparative study of techniques used for porous

membrane characterization: pore chrectraization. Journal of Membrane science

Vol.87. (1994): 35-46.

Koros W.J., Ma Y.H., Shimizu T. Terminology for membrane processes - IUPAC

recommendations, Journal of Membrane Science, 120, (1996), 149-159.

Kreutzer M., Hydrodynamics of Taylor flow in capillaries and monolith reactors.

Delft: Ph.D. Thesis, Delft University of Technology (2003).

References

95

Kuberkar V., Davis R., Flux enhancement for membrane filtration of bacterial

suspensions using high frequency backpulsing, Biotechnol. Bioeng. 60 (1998) 77.

Laborie S., Cabassud C. , Durand-Bourli L., Laine J.M., "Fouling control by air

sparging inside hollow fibre membranes effects on energy consumption",

Desalination 118 (1998) 189-196.

Laborie S. and Cabassud C., Durand-Bourlier. L. and Laine J.M., Flux

Enhancement by a Continuous Tangential Gas Flow in Ultrafiltration Hollow

Fibres for Drinking Water Production: Effects of Slug Flow on Cake Structure,

Filtration and separation,(1997) Page 887-891.

Lee C.K, Chang W.G, Ju Y.H., "Air slugs entrapped cross flow filtration of

bacterial suspensions", Biotechnol. Bioeng. 41 (1993) 525–530.

Lev S., "Hydrodynamic and statistical parameters of slug flow", International

Journal of Heat and Fluid Flow 24 (2003) 334–344.

Levy S., "Two-Phase Flow in Complex Systems", Desalination 197 (2006) 9–22.

Li Q.Y., Cui Z.F., Pepper D.S., "Fractionation of HSA and IgG by gas sparged

ultrafiltration", Journal of Membrane Science 136 (1997) 181-190.

Li Q.Y., Cui Z.F., Pepper D.S.," Effect of bubble size and frequency on the

permeate flux of gas sparged ultrafiltration with tubular membrane", Chemical

Engineering Journal 67 (1997) 71–75.

References

96

Li Q.Y., Ghosh R., Bellara S.R, Cui Z.F. and Pepper D.S., Enhancement of

ultrafiltration by gas sparging with flat sheet membrane modules, Separation and

Purification Technology 14 (1998) 79–83.

Liteanu C., Rica I., "Optimization of Analytical Processes", Ed. Acad., Bucharest,

1985.

Luque S., Maluubhotla H., Gehlert G., Kuriyel R., Dzengeleski S., Pearl S., Belfort

G., A new coiled hollowfiber module design for enhanced microfiltration

performance in biotechnology, Biotechnol. Bioeng. 65 (1999) 247.

Levesley J.A., Hoare M., The effect of high frequency backflushing on the

microfiltration of yeast homogenate suspensions for the recovery of soluble

proteins, J. Membr. Sci. 158 (1999)169.

Majewska-Nowak. K., Kabsch-Korbutowicz M. and Winnicki T., The effect of

gas bubble flow on ultrafiltration efficiency, Desalination, 126 (1999) 187–192.

Mark C. Porter, "Handbook of Industrial membrane Technology", (1990), by

Noyes Publications,United States.

Mercier M., Fonade C., Lafforgue-Delorme C., "How slug flow can enhance the

ultrafiltration flux in mineral tubular membranes", Journal of Membrane Science

128 (1997) 103-113.

Mercier M, Fonade C., and Lafforgue-Delorme C.," Yeast suspension filtration:

enhancement using an upward gas/liquid slug flow-Application to continuous

alcoholic fermentation with cell recycles", Biotechnol. Bioeng. 58 (1998) 47.

References

97

Mercier M., Fonade C. and Lafforgue-Delorme C.," Influence of the flow regime

on the efficiency of a gas liquid two-phase medium filtration", Biotechnology

Techniques. Vol. 9 No.12 (Dec.1995) p.853-858.

Mindler A. B. and Epstein A.C., “Measurements And Control In Reverse Osmosis

Desalination”. Desalination, No. 59, New York, 1986. P (343-379).

Munir C., "Ultrafiltration and Microfiltration Handbook", Technomic Publishing

Copany, Inc.1998.

Ohya H. Reverse Osmosis Method, Ultrafiltration Method, I. Theory. Sachu

Publisher, Japan, 1976.

Paul D.R., "Reformulation of the solution-diffusion theory of reverse osmosis",

Journal of Membrane Science, 241 (2004) 371-386. Parnham C. S., Davis R. H., Protein recovery from bacterial cell debris using

crossflow microfiltration with backpulsing, J. Membr. Sci. 118 (1996) 259. Perry R. H.,Green D. W., Eds. Perry's Chemical Engineers' Handbook, 7th ed.;

McGraw-Hill: New York, 1997.

Pelligrino, J.; Sikdar S.K. Membrane Technology Fundamentals for

Bioremediation. http://www.membranes.nist.gov, (2004).

Petr MikulBSek, Petr PospiSil, Petr DoleCek, Jifi Cakl,"Gas-liquid two-phase flow

in microfiltration mineral tubular membranes: relationship between flux

enhancement and hydrodynamic parameters", Desalination 146 (2002) 103-109.

References

98

Posp´ıšil P., Wakeman R.J., Hodgsonb I.O.A, Mikulášek P. , "Shear stress-based

modelling of steady state permeate flux in microfiltration enhanced by two-phase

flows", Chemical Engineering Journal 97 (2004) 257–263.

Psoch C, Schiewer S.," Dimensionless numbers for the analysis of air sparging

aimed to reduce fouling in tubular membranes of a membrane bioreactor",

Desalination 197 (2006) 9–22.

Psoch C. and Schiewer S.," Anti-fouling application of air sparging and back

flushing for MBR", Journal of Membrane Science 283 (2006) 273–280.

Psoch C. and S. Schiewer, Anti-fouling application of air sparging and

backflushing for MBR, Journal of Membrane Science 283 (2006) 273–280.

Raymond H. M., Douglas. C. Montgomery “Response Surface Methodology".

1995 by John wiley & Sons.

Riley R. J., Lonsdale H. K., Lyons C. R., and Merten U., "Preparation of Ultrathin

Reverse Osmosis Membranes and the Attainment of Theoretical Salt Rejection",

Journal of Applied Polymer Science, 11 (1967) 2143-2158.

Sheng C., Fane A.G.,"The effect of fibre diameter on filtration and flux

distribution—relevance to submerged hollow fibre modules", Journal of Membrane

Science 184 (2001) 221–231.

References

99

Sheng C., Fane A.G., "Characteristics of microfiltration of suspensions with inter-

fiber two-phase flow", Journal of Chemical Technology and Biotechnology75:533-

540 (2000).

Sheng C., Fane A.G., "Filtration of biomass with axial inter-fibre upward slug

flow: performance and mechanisms", Journal of Membrane Science 180 (2000)

57–68.

Slater C. S., Zielinski J.M., Wendel. R.G. and Uchrin C.G.," Modeling of a small

scale reverse osmosis systems". Desalination, 52 (1985) 267–284.

Smith M. C., Ed., Water Desalination Report, Vol. XXI, No. 37 (September 19,

1985).

Soleimani A. and Hanratty T., "Critical liquid flows for the transition from the

pseudo-slug and stratified patterns to slug flow", Internl. J. Multiphase Flow, 29

(2003) 51–67.

Sondhi R., Bhave R., Role of backpulsing in fouling minimization in crossflow

filtration with ceramic membranes, J. Membr. Sci. 186 (2001) 41.

Stanton S., Taha T., Cui Z.F., "Enhancing hollow fibre ultrafiltration using slug-

flow - a hydrodynamic study", Desalination 146 (2002) 69-74.

Sur H. W., Cui .Z.F," Experimental study on the enhancement of yeast

microfiltration with gas sparging", J. Chem. Technol. Biotechnol. 76 (2001) 477.

References

100

Taha T., Cheong W.L., Field R.W., Cui Z.F.," Gas-sparged ultrafiltration using

horizontal and inclined tubular membranes—A CFD study", Journal of Membrane

Science 279 (2006) 487–494.

Taha T. And Cui Z., "CFD modeling of gas-sparged ultrafiltration in tubular

membranes", Journal of Membrane Science, 210 (2002) 13–27.

Taitel Y., Barnéa D., Duckler A.E., "Modeling flow pattern transitions for steady

upward gas–liquid flow in vertical tubes", AICHE J. 3 (1980) 345–354.

Takuo I., Hiroyuki S., Kohnosuke M., Tomoya F., and Nobuhiko K., Application

of gas –liquid two-phase cross-flow filtration to pilot-scale methane fermentation,

Drying technology, 11 (4), (1993) 769-785.

Vera L., Delgado S. and Elmaleh D.,"Gas sparged cross-flow microfiltration of

biologically treated wastewater", Wat. Sci. Technol., 41(10) (2000) 173–180.

Vera L., Delgado S., and Elmaleh S.," Dimensionless numbers for the steady-state

flux of cross-flow microfiltration and ultrafiltration with gas sparging". Chemical

Engineering Science 55 (2000) 3419-3428.

Vera L., Villarroel R., Delgado S. and Elmaleh S., "Enhancing microfiltration

through an inorganic tubular membrane by gas sparging", Journal of Membrane

Science, 165 (2000) 47–57.

References

101

Vera L., Villarroel R., Delgado S., Elmaleh S., “Enhancing microfiltration through

an inorganic tubular membrane by gas sparging". Journal of Membrane Science

165 (2000) 47–57.

Verberk J., Worm G., Futselaar H. and van Dijk J., combined air-water flush in

dead-end ultrafiltration, Wat. Sci. Technol., Wat. Supply, 1(5/6) (2001) 393–402.

Verberk J.Q., "Application of air in membrane filtration". Copyright

by J.Q.J.C.Verberk (2005).

Verberk J.Q., Van Dijk J.C., "Air sparging in capillary nanofiltration", Journal of

Membrane Science 284 (2006) 339–351.

Wallis G.B., One-dimensional two-phase flow. New York: McGraw-Hill. (1969).

Wessling M., Two-dimensional stochastic modeling of membrane fouling, Sep.

Purif. Technol. 24 (2001) 375.

Whalley P.B," Boiling Condensation and Gas–Liquid Flow", Clarendon Press,

Oxford, 1987.

Winzeler H. B., Belfort G., Enhanced performance for pressure-driven membrane

processes: The argument for fluid instabilities, J. Membr. Sci. 80 (1993) 35.

Xu J.H., Luo G.S., Chen G.G., Tan B., "Mass transfer performance and two-phase

flow characteristic in membrane dispersion mini-extractor", Journal of Membrane

Science 249 (2005) 75–81.

References

102

Zeman L.J., Zydney A.L.," Microfiltration and ultrafiltration: principles and

applications", Marcel Dekker, New York, 1996.

Zhang H.Q., Wang Q., Sarica C. and Brill J., "A unified mechanistic model for

slug liquid holdup and transition between slug and dispersed bubbleflows", Internl.

J. Multiphase Flow, 29 (2003) 97–107.

Appendices Appendix (A)

I

Appendix (A) 10 REM********************* OPTIMIZATION TECHNIQUE ************************** 20 REM***************CONSTRAINED HOOK & JEEVES METHOD************************ 30 REM COEFFICIENTS : 40 BO = 33.6216: B1 = -10.1408: B2 = -3.1783: B3 = -.3297: B4 = 7.162: B11 = -2.2212 41 B22 = .9259: B33 = 1.3885: B44 = .6798: B12 = -1.06: B13 = -.2639 42 B14 = -2.0511: B23 = .0233: B24 = .151: B34 = -.0278 50 PRINT " HOOKE & JEEVES " 150 REM Z=F(X1,X2,X3,----------Xn) AT 820 160 PRINT " NUMBER OF VARIABLES ": INPUT N 170 DIM X(N), B(N), Y(N), P(N) 180 PRINT " INITIAL POINT X1,X2,---------,Xn " 190 FOR I = 1 TO N: INPUT X(I): NEXT I 200 PRINT "STEP LENGTH": INPUT H 210 K = H: FE = 0 220 FOR I = 1 TO N 230 Y(I) = X(I): P(I) = X(I): B(I) = X(I): NEXT I 240 GOSUB 760: FI = Z 250 PRINT "INITIAL VALUE"; Z 260 FOR I = 1 TO N: PRINT X(I); " "; : NEXT I: PRINT "" 270 PS = 0: BS = 1 280 REM"EXPLORE ABOUT BASE POINT" 290 J = 1: FB = FI 300 X(J) = Y(J) + K 310 GOSUB 760 320 IF Z > FI GOTO 380 330 X(J) = Y(J) - K 340 GOSUB 760 350 IF Z > FI THEN GOTO 380 360 X(J) = Y(J) 370 GOTO 390 380 Y(J) = X(J) 390 GOSUB 760 400 FI = Z 410 PRINT "EXPLORATION STEP"; Z 420 FOR I = 1 TO N: PRINT X(I); " "; : NEXT I: PRINT "" 430 IF J = N THEN GOTO 460 440 J = J + 1 450 GOTO 300 460 IF FI > FB + 1E-08 THEN GOTO 610 470 IF PS = 1 AND BS = 0 THEN GOTO 490 480 GOTO 560 490 FOR I = 1 TO N: P(I) = B(I): Y(I) = B(I): X(I) = B(I): NEXT I 500 GOSUB 760: BS = 1: PS = 0 510 FI = Z: FB = Z 520 PRINT "BASE CHANGE"; Z 530 FOR I = 1 TO N: PRINT X(I); " "; : NEXT I: PRINT " "

Appendices Appendix (A)

II

540 REM (FOLLOW ON FROM 395) AND EXPLORE ABOUT NEW BASE POINT 550 J = 1: GOTO 300 560 K = K / 10 570 PRINT "CONTRACT STEP TENGTH" 580 IF K < 1E-08 THEN GOTO 690 590 J = 1: GOTO 300 600 REM"PATREN MOVE" 610 FOR I = 1 TO N: P(I) = 2 * Y(I) - B(I) 620 B(I) = Y(I): X(I) = P(I): Y(I) = X(I) 630 NEXT I 640 GOSUB 760: FB = FI: PS = 1: BS = 0: FI = Z 650 PRINT "PATREN MOVE"; Z 660 FOR I = 1 TO N: PRINT X(I); " "; : NEXT I: PRINT "" 670 REM"THEN EXPLORE ABOUT LATEST PATREN POINT" 680 J = 1: GOTO 300 690 PRINT "MAXIMUM FOUND" 700 FOR I = 1 TO N: PRINT "X"; I; "="; P(I): NEXT I: PRINT "" 710 PRINT "FUNCTION MAXIMUM="; FE 730 END 740 REM CONSTRAINTS 750 REM 760 IF X(1) < -2 OR X(1) > 2 THEN Z = 1E-30: GOTO 860 770 IF X(2) < -2 OR X(2) > 2 THEN Z = 1E-30: GOTO 860 780 IF X(3) < -2 OR X(3) > 2 THEN Z = 1E-30: GOTO 860 790 IF X(4) < -2 OR X(4) > 2 THEN Z = 1E-30: GOTO 860 800 QM = 33 * X(4) - 26.1 * X(3) - 8.3 * X(1) * X(3) + X(3) + 8.3 * X(1) - 26.1 + 1089 810 IF QM < 0 THEN Z = 1E-30: GOTO 860 820 Z1 = BO + B1 * X(1) + B2 * X(2) + B3 * X(3) + B4 * X(4) 830 Z2 = B11 * X(1) ^ 2 + B22 * X(2) ^ 2 + B33 * X(3) ^ 2 + B44 * X(4) ^ 2 840 Z3 = B12 * X(1) * X(2) + B13 * X(1) * X(3) + B14 * X(1) * X(4) + B23 * X(2) * X(3) + B24 * X(2) * X(4) + B34 * X(3) * X(4) 850 Z = Z1 + Z2 + Z3 860 FE = FE + 1 870 RETURN

Appendices Appendix (B)

I

Appendix (B) Examination of the effective variables

The effect of (F-test) is examined and a sample of calculation is given below:

1. The variance of coefficients is calculated from equations (F-1 and F-2). [Feki, 1987]:

γ/eS 2i

2r ∑= …… (F-1)

∑= 22r

2b X/SS …... (F-2)

γ = number of experiments-number of coefficients = 31 – 15 = 16 Then from equations (F-1) and (F-2)

0322.4416515489.704S 2

r ==

834675.1240322.44S 2

b ==

2. The coefficient of feed concentration (X1) from table (5.3) is equal to

(B1=-13.8292) then (Coeff) 2

3. Let

= 191.2467

2

2)(

bSCoeff = Z, then Z=104.24013

4. The F-value is calculated for 95% level of confidence with (1,10) degree

of freedom from tables, then: F-value = 3 [Montgomery, 1976]

5. The significant of effects may be estimated by comparing the value of the ratio (Z) to the critical value of F 0.95. Since Z > 3 then X1

6. The results of the examination of the effective variables are given in Table (F-1).

is significant variable.

Appendices Appendix (B)

ΙΙ

Appendices Appendix (C)

I

Appendix(C)

Solution steps of ANOVA analysis

1. SSRER =

2. MRES R = SSRER / n-p = 704.515 / (31-6) = 28.1806

3. ýy =

4. SSRER = ýy - bxy

bxy = ýy - SSRER = 28904.32033 - 704.515 = 28199.805

SSRER = 28904.32033 - 28199.805 = 704.515

5. SSRR R= bxy - (

SSRRR = 28199.805 - (759.987)P

2 P/ 31 = 9568.185

6. SRyy R= ýy - (

SRyy R = 28904.32033 - (759.987)P

2 P/31 = 10272.70033

7. SSRE R= SyyR -R SSRR =R 10272.70033 - 9568.185 = 704.51533

8. RP

2P = SSRRR / SRyyR = 9568.185 / 10272.70033 =0.931418

9. R P

2P RadjR =P

P1-SSE / (n-p) / Syy / (n-1) =1-[(n-1) / n-p) *(1-RP

2P)]

RP

2P Radj R= 1- [(31-1) / (31-6) (1- 0.931418)]

RP

2P adj. = 0.917

Where: MRESR is mean square: SSRR isR Regression: SSRER is Error or residual

SRyyR is Total (SSRRR + SSRER): RP

2P is the coefficient of multiple determinations.

RP

2P Radj Ris adjusted RP

2P statistic: n is number of experiment: p is number of

parameter in equation.

Appendices Appendix (C)

II

Appendices Appendix (D)

I

Appendix (D)

10 15 20 25 30 35 40 45 50Concentration ( gm / L )

0

20

40

60

80

100

Perm

eat F

lux

( Kg

/ m2 .s

)*10

-5 Temperature(10 0C) Temperature(20 0C) Temperature(30 0C) Temperature(40 0C) Temperature(50 0C)

Figure (D1) the effect of feed concentration on permeates flux at different feed temperatures. Feed flow rate and operating pressure being constant

10 15 20 25 30 35 40 45 50Concentration ( gm / L)

0

10

20

30

40

50

60

70

80

90

Perm

eat F

lux

( Kg

/ m2 .s

)*10

-5

Flow Rate(100L/hr) Flow Rate(137.5L/hr) Flow Rate(175L/hr) Flow Rate(212.5L/hr) Flow Rate(250L/hr)

Figure (D2) the effect of feed concentration on permeates flux at different feed flow rates. Feed temperature and operating pressure being constant


II

10 15 20 25 30 35 40 45 50Concentration ( gm / L )

0

20

40

60

80

100

120

140

Perm

eat F

lux

( Kg

/ m2 .s

)*10

-5 Pressure(5bar) Pressure(7.5bar) Pressure(10bar) Pressure(12.5bar) Pressure(15bar)

Figure (D3) the effect of feed concentration on permeates flux at different operating pressures. Feed temperature and feed flow rates being constant

10 15 20 25 30 35 40 45 50Concentration (gm/L)

0

10

20

30

40

50

60

Rej

ectio

n (%

)

Temperature(10 C 0) Temperature(20 C 0) Temperature(30 C 0) Temperature(40 C 0) Temperature(50 C 0)

Figure (D4) the effect of feed concentration on rejection at different feed temperatures. Feed flow rate and operating pressure being constant


III


0

10

20

30

40

50

60

Rej

ectio

n (%

) Flow Rate (100L/hr) Flow Rate (137.5L/hr) Flow Rate (175L/hr) Flow Rate (212.5L/hr) Flow Rate (250L/hr)

Figure (D5) the effect of feed concentration on rejection at different feed flow rates. Feed temperature and operating pressure being constant


0

10

20

30

40

50

60

70

80

Rej

ectio

n (%

)

Pressure(5bar) Pressure(7.5bar) Pressure(10bar) Pressure(12.5bar) Pressure(15bar)

Figure (D6) the effect of feed concentration on rejection at different operating pressures. Feed temperature and feed flow rate being constant


IV

5 10 15 20 25 30 35 40 45 50 55

Temperature ( C0)

0

20

40

60

80

100

120

140

160

Perm

eat F

lux

( Kg

/ m2 .s

)*10

-5

Concentration(15gm/L) Concentration(22.5gm/L) Concentration(30gm/L) Concentration(37.5gm/L) Concentration(45gm/L)

Figure (D7) the effect of feed temperature on permeates flux at different feed concentrations. Feed flow rate and operating pressure being constant

5 10 15 20 25 30 35 40 45 50 55

Temperature ( C0)

02468

10121416182022242628

Perm

eat F

lux

( kg/

m2 .s)*

10-5


Figure (D8) the effect of feed temperature on permeates flux at different feed flow rates. Feed temperature and operating pressure being constant


V

5 10 15 20 25 30 35 40 45 50 55

Temperature ( C0)

0

10

20

30

40

50

60

Perm

eat F

lux

(kg/

m2 .s)*

10-5


Figure (D9) the effect of feed temperature on permeates flux at different operating pressures. Feed temperature and feed flow rate being constant

5 10 15 20 25 30 35 40 45 50 55

Temperature ( C0)

0

10

20

30

40

50

60

Rej

ectio

n (%

)

Concentration (15gm/L) Concentration (22.5gm/L) Concentration (30gm/L) Concentration (37.5gm/L) Concentration (45gm/L)

Figure (D10) the effect of feed temperature on rejection at different feed concentration. Feed flow rate and operating pressure being constant


VI

5 10 15 20 25 30 35 40 45 50 55

Temperature ( C0)

0

10

20

30

40

50

60

Rej

ectio

n (%

)

Flow Rate (100L/hr) Flow Rate (137.5L/hr) Flow Rate (175L/hr) Flow Rate (212.5L/hr) Flow Rate (250L/hr)

Figure (D11) the effect of feed temperature on rejection at different feed flow rates. Feed concentration and operating pressure being constant

5 10 15 20 25 30 35 40 45 50 55

Temperature ( C0)

0

10

20

30

40

50

60

Rej

ectio

n (%

)

Pressure (5bar) Pressure (7.5bar) Pressure (10bar) Pressure (12.5bar) Pressure (15bar)

Figure (D12) the effect of feed temperature on rejection at different operating pressure. Feed concentration and feed flow rates being constant


VII

80 100 120 140 160 180 200 220 240 260Flow Rate ( L / hr )

0

10

20

30

40

50

60

70

80

90

Perm

eat F

lux

( Kg

/ m2 .s

)*10

-5


Figure (D13) the effect of feed flow rate on permeates flux at different feed concentrations. Feed temperature and feed flow rate being constant

80 100 120 140 160 180 200 220 240 260Flow Rate ( L / hr )

0

5

10

15

20

25

30

Perm

eat f

lux

(kg/

m2 .s)*

10-5

Temperature(10 0C) Temperature(20 0C) Temperature(30 0C) Temperature(40 0C) Temperature(50 0C)

Figure (D14) the effect of feed flow rate on permeates flux at different feed temperatures. Feed concentration and feed flow rate being constant


VIII

80 100 120 140 160 180 200 220 240 260Flow Rate(L/hr)

0

10

20

30

40

50

Perm

eat F

lux

(kg/

m2 .s

)*10

-5


Figure (D15) the effect of feed flow rate on permeates flux at different operating pressures. Feed temperature and feed flow rate being constant

80 100 120 140 160 180 200 220 240 260Flow Rate (L/hr)

0

10

20

30

40

50

60

Rej

ectio

n (%

)

Concentration (15gm/L) Concentration (22.5gm/L) Concentration (30gm/L) Concentration (37.5gm/L) Concentration (45gm/L)

Figure (D16) the effect of feed flow rate on rejection at different feed concentrations.

Feed temperature and feed flow rate being constant


IX

80 100 120 140 160 180 200 220 240 260Flow Rate (L/hr)

0

10

20

30

40

50

60

Rej

ectio

n (%

)


Figure (D17) the effect of feed flow rate on rejection at different feed temperatures. Feed

concentration and feed flow rate being constant

80 100 120 140 160 180 200 220 240 260Flow Rate (L/hr)

0

10

20

30

40

50

60

Rej

ectio

n (%

)

Pressure (5bar) Pressure (7.5bar) Pressure (10bar) Pressure (12.5bar) Pressure (15bar)

Figure (D18) the effect of feed flow rate on rejection at different operating pressures. Feed temperature and feed flow rate being constant


X

4 6 8 10 12 14 16Pressure (bar)

0

20

40

60

80

100

120

140Pe

rmea

t flu

x (K

g/m

2 .s)*

10-5


Figure (D19) the effect of operating pressure on permeates flux at different feed

concentrations. Feed temperature and feed flow rate being constant

4 6 8 10 12 14 16Pressure (bar)

0

10

20

30

40

50

60

Perm

eat F

lux

(kg/

m2 .s)*

10-5 Temperature(10 0C)

Temperature(20 0C) Temperature(30 0C) Temperature(40 0C) Temperature(50 0C)

Figure (D20) the effect of operating pressure on permeates flux at different feed

temperatures. Feed concentration and feed flow rate being constant


XI

4 6 8 10 12 14 16Pressure (bar)

0

5

10

15

20

25

30

35

40

Perm

eat F

lux

(Kg/

m2 .s

)*10

-5 Flow Rate(100L/hr) Flow Rate(137.5L/hr) Flow Rate(175L/hr) Flow Rate(212.5L/hr) Flow Rate(250L/hr)

Figure (D21) the effect of operating pressure on permeates flux at different feed flow rate.

Feed concentration and feed temperature being constant

4 6 8 10 12 14 16Pressure (bar)

0

10

20

30

40

50

60

70

80

Rej

ectio

n (%

)


Figure (D22) the effect of operating pressure on rejection at different feed concentrations.

Feed temperature and feed flow rate being constant


XII

4 6 8 10 12 14 16Pressure (bar)

0

10

20

30

40

50

60R

ejec

tion

(%)


Figure (D23) the effect of operating pressure on rejection at different feed temperature. Feed

concentration and feed flow rate being constant

4 6 8 10 12 14 16Pressure (bar)

0

10

20

30

40

50

60

Rej

ectio

n (%

)


Figure (D24) the effect of operating pressure on rejection at different feed flow rate. Feed

concentration and feed temperature being constant

Appendices Appendix (E)

I

Appendix (E) !************************************* !*Fourth Order Runge-kutta method * !************************************* program Runge_kutta_method implicit none real,dimension(1:100)::t,Cf,Cpav,Cp,Jw integer::n,i real::fnf,fmf real::dt,k1,k2,k3,k4,L1,L2,L3,L4 real::init,endt,iniCf,iniCpav real::a1,a2,a3,a4,a5 real::Sa,Aw,dP,Cwp,IPSI,Bs,Vfo common/pro/a1,a2,a3,a4,a5 init=0.0 endt=3. iniCf=15. iniCpav=0.01 n=20 !n number of stage open(2,file='out.dat') Aw=4.2e-13;dP=4.02e13;Cwp=1.0e3 IPSI=1.02e12;Bs=1.12e-4;Vfo=1.5 Sa=35.2 !0.181 a1=Sa*Aw*dP/Cwp a2=Sa*Aw*IPSI/Cwp a3=1+(Aw*dP/(Bs*Cwp)) a4=(Aw*IPSI)/(Bs*Cwp) a5=Vfo print*,a1,a2,a3,a4,a5 dt=(endt-init)/real(n) t(1)=init;Cf(1)=iniCf;Cpav(1)=iniCpav Cp(1)=Cf(1)/(a3-a4*Cf(1)) Jw(1)=Aw*( dP-IPSI*Cf(1)+IPSI*Cf(1)/(a3-a4*Cf(1)) ) write(2,"(5f20.10)")t(1),Cf(1),Cpav(1),Cp(1),Jw(1) write(6,*)t(1),Cf(1),Cpav(1) do i=1,n k1=dt*fnf(t(i),Cf(i),Cpav(i)) L1=dt*fmf(t(i),Cf(i),Cpav(i))


II

k2=dt*fnf(t(i)+dt/2.,Cf(i)+k1/2.,Cpav(i)+L1/2.) L2=dt*fmf(t(i)+dt/2.,Cf(i)+k1/2.,Cpav(i)+L1/2.) k3=dt*fnf(t(i)+dt/2.,Cf(i)+k2/2.,Cpav(i)+L2/2.) L3=dt*fmf(t(i)+dt/2.,Cf(i)+k2/2.,Cpav(i)+L2/2.) k4=dt*fnf(t(i)+dt,Cf(i)+k3,Cpav(i)+L3) L4=dt*fmf(t(i)+dt,Cf(i)+k3,Cpav(i)+L3) Cf(i+1)=Cf(i)+(k1+ 2*k2 + 2*k3 +k4)/6. Cpav(i+1)=Cpav(i)+(L1+ 2*L2 + 2*L3 +L4)/6. t(i+1)=t(i)+dt !*i Cp(i+1)=Cf(i+1)/(a3-a4*Cf(i+1)) Jw(i+1)=Aw*( dP-IPSI*Cf(i+1)+IPSI*Cf(i+1)/(a3-a4*Cf(i+1)) ) write(2,"(5f20.10)")t(i+1),Cf(i+1),Cpav(i+1),Cp(i+1),Jw(i+1) write(6,*)t(i+1),Cf(i+1),Cpav(i+1) end do end program !--------------------------------- ! Functions !--------------------------------- function fnf(t,Cf,Cpav) implicit none real::a1,a2,a3,a4,a5 real::t,Cf,Cpav,fnf,Cft real::var1,var2,var3,var4,var5 common/pro/a1,a2,a3,a4,a5 Cft=Cf var1=a1-a2*Cf var2=a2*Cf/(a3-a4*Cf) var3=Cf-Cf/(a3-a4*Cf) var4=a5-a1*t+a2*Cft var5=a2*Cft/(a3-a4*Cf) fnf=(var1+var2)*var3/(var4-var5) return end !--------------------------------- function fmf(t,Cf,Cpav) implicit none real::a1,a2,a3,a4,a5 real::t,Cf,Cpav,fmf,Vfo,Cfo


III

real::var1,var2,var3,var4 common/pro/a1,a2,a3,a4,a5 Vfo=1.5;Cfo=2. var1=a1-a2*Cf var2=a2*Cf/(a3-a4*Cf) var3=Cf/(a3-a4*Cf)-Cpav var4=Vfo*(Cf-Cfo)/(Cf-Cpav) fmf=(var1+var2)*var3/var4 return end !----------------------------------

ألخالصة

حالة التشغيل المختلفة ظروفتأثير ة سادر حيث تم : إن أهداف العمل الحالي ملخصة كالتالي

– Spiral) نوع (RO)العكسي فذيالغشاء التنا اداء بإستعمال التصميم التجريبي طريقة بوكس ولسن على

Wound Model, Cellulose acetate, SC-6500) ام محلول ملحي من كلوريد الصوديومباستخد

. (RO)من قبل منظومة الغشاء التنافذي العكسي )الراجع( الرفضنسبة التنافذي و التيار جريان معدل لقياس

معدل , )مئوي 50 -10(درجة الحرارة , )لتر/ غرم 45 -15( تركيز المحلول : وهذه الظروف هي كالتالي

.)بار15-10( والضغط التشغيلي )ساعة/ لتر 250 -100(الجريان تدفق

ودققت الحدود المؤثرة وغير المؤثرة للنموذج تم ايجاد معامالت الموديل المتعدد الحدود

بطريقة لتباينليل اوتح (F-test) استخدام طريقةيث تم ح المقترح

(ANOVA analysis of variance ) .جريان على معدل المؤثرة قصوىوكذالك تم ايجاد الظروف ال

Hook) لعكسي باستخدام طريقة تنافذي امن قبل منظومة الغشاء ال) الراجع(التيار التنافذي ونسبة الرفض

& Jeeves ) 3.406 الجريان التنافذي معدل حيث وجد ان kg / mP

2P.hr 85نسبة الرفض هي وان%

التنافذ العكسي الغشاء منظومة رفض فيالو التنافذي الجريان مقدار معدل بانوجد ذلك حيث الى باإلضافة

)RO (تسلسلال حسب ومعدل تدفق الجريان المحلولودرجة حرارة لمحلولتركيز االضغط و على انمعتمد

ن النموذج أ. الجريانمعدل تدفق > المحلول درجة حرارة > الضغط التشغيلي > المحلول تركيز : التالي

.النموذجي بين البيانات التجريبية والتنبؤ جيدا اظهر تطابقا أثناء عملية التنافذ التنافذي الرياضي لتنبؤ الجريان

(RO) على منظومة الغشاء التنافذي العكسي) غاز وسائل(تم تطبيق جريان ذات الطورين

ة حيث تم حقن خالل عملية عمل المنظوم )الراجع(جريان التيار التنافذي ونسبة الرفض معدل لتحسين وزيادة

لقد اظهرت النتائج العملية لهذه الخطوة تاثيرايجابي على .الهواء داخل المنظومة بسرع مختلفة للغاز والسائل

ستندة على نموذج الميستنتج من تحليل النتائج التجريبية .)الراجع(جريان التيار التنافذي ونسبة الرفض معدل

,Spiral – Wound Model, Cellulose acetate) ع نافذي العكسي نوشاء التالغ في منظومة ترشيح

SC-6200) نوع المستخدم في هذه الدراسة جريان ذات الطورين بأن (Slug Flow) ساهم في زيادة

kg / mP 5.676 بمقدار التنافذي التيار جريانمعدل

2P.hr بسبب كسره لطبقة الكعكة %91 بنسبة والرفض

بمقدار الخلفي وزيادة الجريان التنافذيتشجيع االنتقال سطح الغشاء التنافذي مما ادى الىالمترسبة على

1.66.

وزارة التعليم العالي والبحث العلمي

ةالجامعة التكنولوجي قسم الهندسة الكيمياوية

حقن تقنية التناضح العكسي باستخدام أغشيةأداء تحسين الهواء

رسالة قسم الهندسة الكيمياوية إلىمقدمة

في الجامعة التكنولوجية وهي جزء من متطلبات نيل فلسفة شهادة دكتوراه

في الهندسة الكيمياوية

من قبل طالب محمد نايف البياتي

) 2001هندسة كيمياويةماجستير (

هـ1430 م 2009

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