MEDRC

MEDRC Series of R & D Reports MEDRC Project: 00-AS-001

STUDY OF THE INTERACTIVE EFFECTS OF INORGANIC AND BIOLOGICAL FOULING IN

RO DESALINATION UNITS

PHASE - A

Principal Investigator

R. Sheikholeslami University of New South Wales

Sydney, Australia

The Middle East Desalination Research Center Muscat, Sultanate of Oman

February 2007

MEDRC Series of R & D Reports Project: 00-AS-001

This report was prepared as an account of work co-funded by the Middle East

Desalination Research Center. Neither the Middle East Desalination Research Center, nor

any of their employees, or funding contributors makes any warranty, express or implied,

or assumes any legal liability or responsibility for the accuracy, completeness, or

usefulness of any information, apparatus, product, or process disclosed, or represents that

its use would not infringe privately owned rights. References herein to any specific

commercial product, process, or service trade name, trademark, manufacturer, or

otherwise do not necessarily constitute or imply its endorsement, recommendation, or

favouring by the Middle East Desalination Research Center. The views and opinions of

authors expressed herein do not necessarily state or reflect those of the Middle East

Desalination Research Center or third party funding contributors.

Edited and typeset by Dr P M Williams, UK

Email: p.m.williams@ntlworld.com

Hard copies and CD of this report are available from:

Middle East Desalination Research Center

P.O. Box 21,

Al Khuwair / Muscat

Postal Code 133

Sultanate of Oman

Tel: (968) 695 351

Fax: (968) 697 107

E-mail: info@medrc.org.om

Web site: www.medrc.org

© 2004 Middle East Desalination Research Center

All rights reserved. No part of this report may be reproduced, stored in a retrieval system

or transmitted in any form or by any means, electronic, mechanical, photocopying,

recording or otherwise, without prior written permission from the Middle East

Desalination Research Center.

Project participants

Principal Investigator

Dr Roya Sheikholeslami

School of Chemical Engineering and Industrial Chemistry

University of New South Wales

Sydney, 2052

Australia

Tel: +61 (2) 9385 - 4343

Fax: + 61 (2) 9385 - 5966

E-mail: r.sheikholeslami@unsw.edu.au

Other Investigators

The experimental work in this project was conducted by research students under the

supervision of Roya Sheikholeslami. The students also participated in setting up

experiments, data analysis, preparation of reports and conference papers. The research

students are T. Chong, H. Yu, K. Ong, G. Soh, C. Ramalie, F. Tannudin and S. Yuen.

Other students in the research group of Dr. Sheikholeslami might also have contributed

marginally to various aspects of the project through discussion of results and

communications in seminars and presentations.

Partners Mr. Abdulfattah Jaljuli ( Jordanian National)

Metito Overseas Ltd

Sharjah

UAE

THE MIDDLE EAST DESALINATION RESEARCH CENTER

An International Institution

Established on December 22, 1996

Hosted by the Sultanate of Oman in Muscat

Mission Objectives of the Center:

1. to conduct, facilitate, promote, co-ordinate and support basic and applied research

in the field of water desalination and related technical areas with the aim of

discovering and developing methods of water desalination, which are financially

and technically feasible

2. to conduct, facilitate, promote, co-ordinate and support training programs so as to

develop technical and scientific skills and expertise throughout the region and

internationally in the field of water desalination and its applications and related

technical areas

3. to conduct, facilitate, promote, co-ordinate and support information exchange,

including, but not limited to, electronic networking technology, so as to ensure the

dissemination and sharing throughout the region and internationally of technical

information concerning water desalination methods and research and related

technical areas, and to establish with other states, domestic and other

organizations such relations as will foster progress in the development,

improvement and use of water desalination and related technical areas in the

region and elsewhere

Further information about the Center’s activities is available on the web site

(www.medrc.org). The following documents are available from the Center and can be

sent on request in paper format or diskette. Alternatively, they can be downloaded from

the Center’s web site.

1. Guidelines for the Preparation of Research Proposals

2. Guidelines for the Preparation of Project Reports

3. Annual Requests for Proposals

4. Abstracts of On-going Projects and Summaries of Completed Projects

5. Research Reports

6. MEDRC Program Framework and Profile

7. MENA Universities and Research Institutes Directory

8. Partnership-in-Research Information for the Middle East

9. MEDRC Annual Reports

TABLE OF CONTENTS

TABLE OF CONTENTS iv

List of figures Vii

List of tables Viii

Nomenclature ix

Acknowledgements xiii

1. IntROduction 1

2. Literature review 3

2.1 Introduction 3

2.2 Fundamentals of Reverse Osmosis 4

2.2.1 Types of Membranes ...................................................................................... 4

2.2.2 Membrane Performance ................................................................................. 5

2.2.3 Chemical Potential and Osmotic Pressure Relationships............................... 6

2.2.4 Transport Properties of Membrane................................................................. 7

2.2.5 Mechanism of Salt Rejection ....................................................................... 10

2.2.5.1 Sorption-Diffusion Mechanism .......................................................... 10

2.2.5.2 Wetted Surface or Hydrogen Bonding Mechanism............................ 10

2.2.5.3 Preferential Sorption-Capillary Flow Mechanism.............................. 11

2.2.5.4 Frictional Coefficient Mechanism ...................................................... 11

2.2.5.5 Sieve Mechanism................................................................................ 11

2.2.6 Transport Models ......................................................................................... 12

2.2.6.1 Phenomenological Transport Models ................................................. 12

2.2.6.2 Nonporous Transport Models ............................................................. 14

2.2.6.3 Porous Transport Model ..................................................................... 15

2.2.7 Concentration Polarization ........................................................................... 15

2.2.7.1 Turbulent Flow.................................................................................... 16

2.2.7.2 Laminar Flow...................................................................................... 17

2.2.7.3 Mass Transfer Coefficient................................................................... 18

2.3 Properties of Seawater 18

2.3.1 Physical Properties of Seawater ................................................................... 18

2.3.2 Chemistry of Seawater ................................................................................. 19

2. 3.2.1 Alkalinity and Acidity........................................................................ 20

2.3.2.2 Sodium Chloride in Seawater ............................................................. 20

2.3.2.3 Calcium Carbonate in Seawater.......................................................... 21

2.3.2.4 Calcium Sulfate in Seawater ............................................................... 22

2.3.3 Biological Properties of Seawater ................................................................ 24

2.3.4 Scaling Potential of Seawater....................................................................... 26

2.3.4.1 Fouling Indices.................................................................................... 26

2.4 The State of the Art of Fouling 28

2.4.1 Classification of Fouling .............................................................................. 28

2.4.2 Sequential Events in Fouling........................................................................ 28

2.4.2.1 Initiation.............................................................................................. 29

2.4.2.2 Transport ............................................................................................. 29

2.4.2.3 Deposition ........................................................................................... 30

2.4.2.4 Removal .............................................................................................. 31

2.4.2.5 Aging................................................................................................... 32

2.4.3 General Models of Fouling........................................................................... 32

2.5. Inorganic Fouling 33

2.5.1 Crystallisation / Precipitation Fouling.......................................................... 33

2.5.1.1 Principles of Crystallisation Solubility and Supersaturation .............. 34

2.5.1.2 Previous Research on Crystallisation Fouling .................................... 40

2.5.2 Particulate Fouling ....................................................................................... 49

2.5.2.1 Particle Deposition in Laminar Flow.................................................. 50

2.5.2.2 Particle Deposition in Turbulent Flow................................................ 50

2.6 Microbial Fouling 51

2.6.1 Biological Fouling As Biofilm Development .............................................. 51

2.6.2 Successive Events in Microbial Fouling ...................................................... 51

2.6.2.1 Conditioning ....................................................................................... 52

2.6.2.2 Bacteria Transport............................................................................... 52

2.6.2.3 Adhesion ............................................................................................. 53

2.6.2.4 Biofilm Formation .............................................................................. 57

2.6.2.5 Detachment ......................................................................................... 59

2.6.3 Structure and Compositions of Biofilm........................................................ 59

2.6.4 Physico-Chemical Properties of Biofilm...................................................... 61

2.6.5 Microbial Fouling Model ............................................................................. 62

2.7 Interactive Effects 63

2.8 Conclusion 65

3. Thermodynamics And Kinetics Of Calcium SulFATE And Calcium Carbonate

Precipitation 66

3.1 Experimental Conditions................................................................................. 67

3.2 Experimental Method ...................................................................................... 68

3.2.1 pH Measurement.................................................................................... 68

3.2.2 Total Alkalinity Measurement ............................................................... 68

3.2.3 ICP-AES Analysis ................................................................................. 69

3.2.4 SEM Image Analysis ............................................................................. 69

3.3 Results and Discussions .................................................................................. 69

3.3.1 Scale Morphology.................................................................................. 69

3.3.2 Thermodynamics Analysis..................................................................... 72

3.3.3 Kinetics Analysis ................................................................................... 76

3.4 Concluding Remarks ....................................................................................... 87

4.1 Scientific Reason and Value Added to the Design of Experimental Set-Up .. 93

5. Review of method for biological content analysis 95

References 98

LIST OF FIGURES

Figure 2.1: Schematic representation of transport through an asymmetric membrane

[Soltanieh&Gill, 1981] ............................................................................................... 9

Figure 2.2: Transport of solute through membrane .......................................................... 17

Figure 2.3: The effect of pH on the distribution of carbonate ions [Bott, 1995] .............. 22

Figure 2.4: Distribution of sulfate ions with pH [Sudmalis&Sheikholeslami, 2000]....... 23

Figure 2.5: Structure of (a) bacteria cell (b) fungal cell [Maier et al., 2000] ................... 24

Figure 2.6: Comparison of (a) gram-negative and (b) gram-positive bacterial cell walls

[Maier et al., 2000].................................................................................................... 25

Figure 2.7: Transport of solute in cross-flow reverse osmosis system............................. 29

Figure 2.8: Sketch of an electrostatic double layer and a solid particle [Bott, 1995]....... 30

Figure 2.9: Energy distance profile [Bott, 1995] .............................................................. 31

Figure 2.10: Flow disturbances near a solid surface [Cleaver&Yates, 1973] .................. 32

Figure 2.11: Fouling curves [Krause, 1993] ..................................................................... 33

Figure 2.12: Growth of porous crystal-layer under concentration polarization effect

[Okazaki&Kimura, 1984] ......................................................................................... 47

Figure 2.13: Radial growth of non-porous CaSO4 layer in reverse osmosis [Brusilovsky et

al., 1992] ................................................................................................................... 49

Figure 2.14: Formation of biofilm (adapted from [Maier et al., 2000]) ........................... 52

Figure 2.15: Schematic presentation of co-adhesion between two microorganisms 1 and 2

................................................................................................................................... 56

Figure 2.16: An ideal growth curve for bacteria [Characklis, 1990] ................................ 58

Figure 2.17: Heterogeneous mosaic biofilm model [Walker et al., 1995] ....................... 61

Figure 2.18: Schematic illustration of mushroom model of biofilm as revealed by CSLM

[Wimpenny&Colasanti, 1997] .................................................................................. 61

Figure 2.19: Illustration of difference between previous research and proposed research65

Figure 3.1: SEM image of pure CaCO3 ............................................................................ 70

Figure 3.2: SEM image of pure CaSO4............................................................................. 70

Figure 3.3: SEM image of the mixed salts........................................................................ 71

Figure 3.4: Change of ionic activity product (IAP) for pure CaCO3 at various salinities 77

Figure 3.5: Change of ionic activity product (IAP) for pure CaSO4 at various salinities. 78

Figure 3.6: Change of IAP(CaCO3) in mixed system at various salinities....................... 79

Figure 3.7: Change of IAP(CaSO4) in mixed system at various salinities....................... 80

Figure 3.8: Comparison of Ksp(mixture)/Ksp(pure) for (a) CaCO3 and (b) CaSO4........... 82

Figure 3.9: Crystal growth of pure CaCO3 ....................................................................... 82

Figure 3.10: Best fitted curve for experimental data ........................................................ 85

Figure 3.11: Kinetic analysis for pure salts at various salinities....................................... 86

Figure 3.12: Rate constant and reaction order for pure salts precipitation at various

salinities .................................................................................................................... 87

Figure 3.13: Change in [Ca2+

] for pure CaSO4 and mixed salt at various salinities......... 88

Figure 3.14: Comparison of decay coefficients for mixed salt and pure salt (initial total

[Ca2+

] = 0.15M) at various salinities......................................................................... 89

Figure 4.1: Reverse osmosis cell ...................................................................................... 91

Figure 4.2: Dynamic setup of seawater reverse osmosis desalination.............................. 92

LIST OF TABLES

Table 2.1: Qualitative comparison of membrane modules [Porter, 1990].......................... 5

Table 2.2: The particles size spectrum [Osmonics Inc., 1996]......................................... 12

Table 2.3: Mass transfer correlations for Newtonian fluids flowing turbulently in pipes or

flat ducts (Adapted from Gekas and Hallstrom [Gekas&Hallstrom, 1987]) ............ 19

Table 2.4: Compositions of seawater................................................................................ 19

Table 2.5: Physical properties of seawater [Sekino, 1994]............................................... 20

Table 2.6: Medium composition for Pseudomonas fluorescens culture [Bott, 1995] ...... 25

Table 2.7: Mechanisms of Fouling ................................................................................... 28

Table 2.8: Individual ion values of B+ , B− , ε+ , and ε− in aqueous solution at 25oC

[Bromley, 1973] ........................................................................................................ 35

Table 2.9 (a): The attachment of adhesion mutant to hydrophobic polystyrene (PS) and to

more hydrophilic, tissue culture dish polystyrene (TCD) [Fletcher et al., 1983] ..... 56

Table 2.9 (b): Distribution of adhesion mutants in culturing apparatus, determined by

sampling from culturing vessel walls and bulk liquid [Fletcher et al., 1983]........... 56

Table 3.1: NaCl, Ca2+

, SO42-

and T.A. concentrations at various recovery levels............ 67

Table 3.2: Experimental conditions .................................................................................. 67

Table 3.3: Ion interaction parameters of Pitzer model at 25oC [Harvie et al., 1984] ....... 75

Table 3.4: Thermodynamics of CaCO3, CaSO4 and mixed salts..................................... 81

Table 3.5: Decay coefficient of best-fitted [Ca2+

] vs time curve for pure CaSO4 and

mixed salts with initial total [Ca2+

] = 0.15M ............................................................ 89

Table 5.1: Method of analysis of biological content in seawater ..................................... 96

NOMENCLATURE

Symbol Dimension

Group

Description

ABL L2 Contact area between bacterium and liquid

ASB L2 Contact area between solid and bacterium

ASL L2 Contact area between solid and liquid

Aφ M-0.5

L-1.5

Debye-Huckle constant = 0.509 (mol/L)-0.5

at 25oC

Acyc ML-3

Acidity of water, defined in Equation (3-7)

Alkc ML-3

Alkalinity of water, defined in Equation (3-6)

Alkt ML-3

Total alkalinity of seawater, defined in Equation (3-8)

Am L2 Total membrane area

Amf L2 Free membrane area available for permeate flow

a L2t-2

Constant in Equation (4-3)

a” Dimensionless Constant in Equation (2-35)

ai ML-3

Activity of species i

B M-1

L3 Defined in Equation (5-13)

B M-1

L3 Defined in Equation (5-12)

b” Dimensionless Constant in Equation (2-35)

Cb ML-3

Concentration of solute in the bulk solution

Cbio ML-3

Concentration of microorganisms in the bulk

Ccryt-s ML-3

Concentration at the surface of crystal

Ceq ML-3

Concentration of salts at equilibrium

Ci ML-3

Concentration of species I

Cp ML-3

Concentration of solute in the permeate

Cs ML-3

Concentration of solute

(Cs)ln ML-3

Log mean solute concentration in membrane

Csubs ML-3

Concentration of substrate

CT ML-3

Total concentration of carbon in water

CTDS ML-3

Concentration of total dissolved solids in seawater

Cw ML-3

Concentration of solute at membrane surface

(Cw)m ML-3

Concentration of solute in the membrane at the surface

D L2t-1

Diffusivity

Dsea L2t-1

Diffusivity of seawater

Dsm L2t-1

Diffusivity of solute in membrane

Dwm L2t-1

Diffusivity of water in membrane

dh L Hydraulic diameter of channel

dp L Diameter of particle

Ea-part L2t-2

Activation energy for particle attachment

F+ Dimensionless Defined in Equation (5-10)

F- Dimensionless Defined in Equation (5-11)

Fi MLt-2

Conjugate force in Equation (2-15)

Fj MLt-2

Non-conjugate force in Equation (2-15)

Fij” MLt

-2 Frictional forces due interactions between two components

F(v) Dimensionless Fraction of microorganisms deposited and remain on

membrane surface

G ML2t-2

Gibbs free energy

GA ML2t-2

Energy due to attractive forces between two components

adhG∆ ML2t-2

Excess Gibbs energy of bacteria adhesion to the membrane

GB ML2t-2

Energy due to Brownian motion

critG∆ ML2t-2

Critical value of change in Gibbs free energy of nucleation

f Gφ∆ ML

2t-2

Standard molar Gibbs energy of formation

nuclG∆ ML2t-2

Change in Gibbs free energy in nucleation process

rG∆ ML2t-2

Gibbs free energy of reaction

rGφ∆ ML

2t-2

Standard molar Gibbs free energy of reaction

Gp ML2t-2

Energy due to polar interactions

GR ML2t-2

Energy due to repulsion between two components

GS ML2t-2

Surface free energy

GTOT ML2t-2

Total energy of interactions

GV ML2t-2

Volume free energy

h L Half height of channel

Is ML-3

Ionic strength of solution

JD ML-2

t-1

Diffusion flux

Jnucl ML-2

t-1

Rate of nucleation

Js ML-2

t-1

Flux of solute

Jv Lt-1

Flux of solvent

K1 ML-3

Thermodynamic first dissociation constant of H2CO3

K2 ML-3

Thermodynamic second dissociation constant of H2CO3

K’1

ML

-3 Concentration first dissociation constant of H2CO3

K’2 ML-3

Concentration second dissociation constant of H2CO3

KH ML-3

Hydrolysis constant of CO2

Ks Dimensionless Partition coefficient defined in Equation (2-24)

Ks ML-3

Saturation coefficient

Ksp ML-3

Thermodynamic solubility product of salt

K’sp ML-3

Concentration solubility product of salt

ka Lt-1

Particle attachment rate coefficient

kB ML2t-2

T-1

Boltzman constant = 1.38x10-3

JK-1

kbio t-1

Biofilm formation rate constant

kD Lt-1

Crystal growth rate constant due to diffusion control

kd Lt-1

Particle deposition coefficient

kdiss Lt-1

Dissolution rate constant

kG M1-n

L3n-2

t-1

Overall crystal growth rate constant with n reaction order

kG1 M-1

L4t-1

Crystal growth rate constant in Equation (2-107)

kG2 M-1

L4t-1

Crystal growth rate constant in Equation (2-110)

kG3 ML-2

t-1

Crystal growth rate constant in Equation (2-111)

km Lt-1

Mass transfer coefficient

km-bio Lt-1

Mass transport coefficient for micro-organisms

kppt M-1

L4t-1

Precipitation rate constant

kr M1-n

L3n-2

t-1

Surface reaction rate constant with n order

L L Length of channel

m ML-3

T-1

Defined in Equation (2-38)

m t-1

Slope of fully developed, rectangular channel velocity

distribution

mbio ML-2

Mass of biofilm formed per unit membrane area

mbio-F ML-2

t-1

Biofilm formation rate per unit membrane area

mbio-R ML-2

t-1

Biofilm removal rate per unit membrane area

mD ML-2

t-1

Deposition flux

mf ML-2

Mass of deposit per membrane unit area

mf*

ML-2

Asymptotic value of mass of deposit per unit membrane

area

mi ML-3

Molality of species i

mR ML-2

t-1

Removal flux

Nnucl L-2

Number of nucleation sites per unit membrane area

NRe Dimensionless Reynolds number

NSc Dimensionless Schmidt number

NSh Dimensionless Sherwood number

n Dimensionless Order of reaction

ns M Number of moles of solute

nw M Number of moles of water

P ML-1

t-2

Pressure

Ps ML-2

t-1

Solute permeation coefficient

Ps” ML-1

t-1

Localised solute permeation coefficient

Qv L3t-1

Total permeate flow rate

R L2t-2

T-1

Universal gas constant = 8.314 Jmol-1

K-1

Rbio ML-3

t-1

Microbial growth rate

Rcryt ML-2

t-1

Overall crystal growth rate

Rdiss ML-2

t-1

Rate of dissolution

Rf ML-2

t-1

Hydraulic resistance of fouling layer

Rm ML-2

t-1

Hydraulic resistance of membrane

Rppt ML-2

t-1

Rate of precipitation

RR Dimensionless Membrane separation coefficient

RR-real Dimensionless True membrane separation coefficient

rcrit L Critical radius of nucleus

rcryt L Radius of crystal

rnucl L Radius of nucleus

S Dimensionless Supersaturation

SP Dimensionless Particle sticking probability

T T Temperature

tf t Test time required to collect 500 ml water after total test

time in SDI test

tg t Time required for nucleus to grow to a detectable size

ti t Time required to collect 500 ml of water in SDI test

tind t Induction period during crystallisation

tn t Time required for nucleus to form during nucleation

tr t Relaxation time during induction period

tt t Total test time in SDI test

ub Lt-1

Bulk fluid axial velocity

ui Lt-1

Mean linear velocity of component i

uj Lt-1

Mean linear velocity of component j

wV M-1

L3 Molar volume of water

vnucl L3

Volume of nucleus

vv Lt-1

Permeation velocity

w L Half width of channel

xm L Membrane thickness

xm1 L Effective thickness of membrane layer responsible for

solute rejection

xs Dimensionless Mole fraction of solute

xw Dimensionless Mole fraction of water

Zi Dimensionless Valency of ions

z L Distance downstream of channel where deposition begins

pφ ML-2

t-1

Particle deposition flux

θ Dimensionless Fractional free membrane area

β ” Constant defined in Equation (2-164)

α ” Constant defined in Equation (2-163)

fα Dimensionless Resistance factor of fouling layer

Φ Dimensionless Ratio of critical change in Gibbs energy of heterogeneous

nucleation to homogeneous nucleation

Λ ML-2

t-1

Nucleation rate constant

BLσ Mt-2

Interfacial tension between bacterium/liquid

nuclσ Mt-2

Interfacial tension between nucleus/liquid

SBσ Mt-2

Interfacial tension between solid/bacterium

SLσ Mt-2

Interfacial tension between solid/liquid

Ω Dimensionless Relative supersaturation

J Dimensionless Fractional drop in solvent flux

ε+ M-0.5

L1.5

Constant in Equation (2-80)

ε− M-0.5

L1.5

Constant in Equation (2-80)

iγ Dimensionless Activity coefficient of species i

γ± Dimensionless Mean activity coefficient

bioγ M-1

Lt Biofilm removal coefficient

wτ ML-1

t-2

Wall shear stress

f Dimensionless Friction factor

fij” ML-2

t-1

Interaction frictional coefficient between two components

pl M-1

L2t Membrane permeability

"

pl M-1

L3t Localised membrane permeability

η ML-1

t-1

Viscosity of solution

seaη ML-1

t-1

Viscosity of seawater

δ L Boundary layer thickness

ρsea ML-3

Density of seawater

ρcryt ML-3

Density of crystal φ Dimensionless Reflection coefficient

v Dimensionless Number of ions formed in the dissociation of 1 mole of salt ς Dimensionless Dimensionless permeation group defined in Equation (2-

33)

µi ML2t-2

Chemical potential of species i

nuclµ∆ ML2t-2

Chemical potential difference between nucleus and solution

µ t-1

Exponential specific growth rate

maxµ t-1

Maximum specific growth rate

Π ML-1

t-2

Osmotic pressure of solution

Πb ML-1

t-2

Osmotic pressure of bulk solution

Πsea ML-1

t-2

Osmotic pressure of seawater

Πw ML-1

t-2

Osmotic pressure of solution at membrane wall

ACKNOWLEDGEMENTS

University of New South Wales thanks the Middle East Desalination Research Center for

its financial support for the successful completion of this project.

1. INTRODUCTION

Water scarcity is becoming an imminent problem for more and more countries

especially in the arid regions of the world such as Southern Europe, Middle East,

North Africa, many states in United States (California, Florida, New Mexico, etc.),

even in Australia. In Asia Pacific, country such as Singapore that lacks of the natural

resources has to buy water from neighbouring countries. The challenge to meet the

water demand remains a daunting task for the desalination community. One of the

many ways to produce potable drinking water is through desalination of seawater

using reverse osmosis technology. Typically, these waters contain high amounts of

total dissolved solids and biological matter, which have to be removed before it is safe

for human consumption. The removed materials will accumulate at the membrane

surface and are potential fouling agents. Fouling phenomena deteriorates the

performance of reverse osmosis membrane and is costing the desalination industry

billions of dollars annually. Due to complexity, fouling is usually studied in isolation

while this is not practical as industrial water systems contain various constituents and

impurities.

This project is aimed at better understanding the composite biological and inorganic

fouling encountered in seawater desalination, their interactive effects and

development of a composite fouling model. Due to complexity, fouling problems are

usually studied in isolation but this is not practical as in the actual desalination

process, most of the foulants identified are a combination of particulate matters,

crystalline and biological matters. Hence the aim of this project is to understand the

interactive effects of inorganic and biological fouling when present simultaneously.

This will include the 1) investigations of operating conditions on composite fouling;

2) the applicability fouling models, which are for inorganic and biological fouling in

isolation, to composite fouling; 3) develop a mathematical model that accounts for

composite inorganic and biological fouling model. The outcomes of this project will

help to understand the relative extent of each type of fouling in composite fouling,

and present a predictive model for composite fouling. Also, it would indicate which

species might be a precursor for the other and so one knows to remove that first for

mitigation. This will then contribute to the development of guidelines for best

operating conditions and hence to minimise the problem of composite inorganic and

biological fouling.

The phase A of this project was pre-closed due change in the work place of the

principal investigator and unfinished work in phase A is included in the phase B of

the project, which is on-going. The following tasks are carried out in phase A.

A detailed literature review is conducted on fouling and its mechanisms and the

available models, It includes an overview of fouling phenomena, the fundamentals of

reverse osmosis, properties of seawater, fundamental of fouling, inorganic fouling,

biological fouling, interactive effects, and conclusion. The summary of seawater

characteristics expected in the MENA region is included. The regional partner

provided the data on seawater analysis of their water desalination plants in Dubai.

The other tasks carried out in phase A are

1. Thermodynamics and kinetics of calcium sulfate and calcium carbonate at

different seawater recovery levels in batch system were studied.

2. A dynamic system in order to simulate the actual composite fouling

encountered in seawater desalination was set up.

3. Review on the current methods available for analysis of biological matter in

seawater was provided.

2. LITERATURE REVIEW

2.1 Introduction

Fouling is defined as the accumulation of unwanted material at the surfaces of process

equipment hence deteriorating the performance of these equipments such as heat

exchangers, evaporators, condensers, and reverse osmosis membranes. The presence

of foulants increases the resistance to heat and mass transfer and reduces the heat and

mass flux. The fouling problem is costing the industries billions of dollars annually

e.g.

1. Increase capitol cost – careful design must be taken when building the process

equipment to include the allowance for fouling potential. This will mean

building the equipment that has higher capacity than required e.g. higher

surface is required to transfer the same amount of heat during fouling

compared to clean condition in heat exchanger operations. Also, usually

standby unit is installed in case the operating unit is shutdown for cleaning

process. Very often, the feed stream is pre-treated before entering the process

equipment; hence the addition of the pre-treatment units contributes to a

higher capitol cost in the pre-treatment steps.

2. Additional operating cost – when fouling process occurs, the efficiency of

process equipment will be reduced; in order to compensate for the reduced

heat transfer in heat exchangers or flux decline in reverse osmosis, extra

energy has to be supplied to maintain the temperature of the process streams in

heat exchangers or the applied pressure has to be increased to achieve the

same recovery in reverse osmosis. Moreover, the process equipment is

frequently cleaned to avoid serious fouling problem, and the use of cleaner

means a higher operating cost.

3. Loss of production – process equipment has to be periodically shutdown for

cleaning process to take place; that means the loss of production time. Besides,

fouling can also affect the quality of the product, e.g. some foulants may

penetrate the membranes and increase the concentration of solute in the

permeate, thus reduce the quality of the product.

4. Cost of research and development – a large amount of money has been spent

to study the fouling mechanisms, fouling rate and to develop an effective

antifouling agent in order to tackle the fouling problem.

In order to combat the fouling problem, a good understanding of the fouling process is

necessary. The interest of this project is to concentrate on the composite inorganic and

biological fouling in seawater reverse osmosis membranes. Seawater Reverse

Osmosis (SWRO) desalination is the production of water suitable for human

consumption by removing the impurities in seawater using reverse osmosis

technology.Typically, seawater contains high amounts of total dissolved solids (TDS),

averaged about 35,000 to 50,000 ppm [Zidouri, 2000; Al-Ahmad&Aleem, 1993;

Dalvi et al., 2000; Hanra, 2000; Al-Shammiri&Al-Dawas, 1997] while the acceptable

figure for human consumption is below 1000 ppm [WHO, 1993]. The removed

dissolved solids will accumulate at the membrane surface and are potential fouling

agents. The deposits commonly found at the surfaces are particulate matters,

crystalline and biological material. Due to the complexity of fouling problems,

different types of fouling are usually studied in isolation while this is not practical as

in industrial systems, more than one type of fouling is always present. Besides, the

predictive fouling models developed are usually for a single type of foulant (except

the combined crystallisation and particulate fouling model [Sheikholeslami, 2000]

developed for calcium sulfate); and that is the reason that their applications to

practical cases do not result in satisfactory prediction.

This literature review will cover the fundamentals of reverse osmosis, properties of

seawater, fundamental of fouling, inorganic fouling, biological fouling, interactive

effects and follows by concluding remarks.

2.2 Fundamentals of Reverse Osmosis

When an ideal semipermeable membrane is placed between two compartments, one

containing pure solvent and the other containing a mixture of solvent and solute, the

solvent will pass through the membrane from the pure solvent side to the mixture side

due to the chemical potential driving force caused by the solute. This phenomenon is

called osmosis. The exact pressure that must be supplied to stop the solvent flux is

called the osmotic pressure. In reverse osmosis, a pressure greater than the osmotic

pressure is applied to the solution to reverse the flow and drive the solvent from the

solution side to the pure solvent side; hence the name ‘reverse osmosis’.

2.2.1 Types of Membranes

The ideal membrane for desalination by reverse osmosis would consist of an ultra-thin

imperfection-free film of a polymeric material. The transport properties of the

material would be such that water could pass through with little hindrance, while

presenting a virtually impermeable barrier to salts. Most polymers approximate this

ideal behaviour by exhibiting different flow rates, or different permeabilities, to water

and salts [Pusch, 1990]. In order for water and simple salts to be transported across

the barrier, they must first dissolve and penetrate into polymer material. Once there,

passage through the polymeric barrier is controlled by diffusion, with different ions

having different rates of diffusion. To provide high water flux, a real membrane must

be extremely thin. Conversely, the membrane must be extremely strong in order to

withstand the driving pressure of the incoming stream, which is around 800 psi. These

requirements are incompatible and led to the development of various support

methods. Most commercially available reverse osmosis membranes exhibit an

asymmetric or a composite organization where a porous matrix (support) is topped by

a more or less dense layer (active layer, thin film). Asymmetric membranes usually

consists of a skin that is cast from cellulose acetate (CA), polyamides (PA) and

polyimides (PI) while the skin of a composite membrane is cast from a large variety

of film forming polymers e.g. CA, PA, PI, polyurea, poly(ether/amide). The major

advantage of thin film composite membranes is that each layer can be optimised

independently. The support layer can be optimised for maximum strength and

compression resistance, and the ultra thin layer can be optimised for the maximum

solvent flux and solute rejection [Petersen, 1993]. However, this could increase the

cost of membranes, thus asymmetric membranes, which only cast from linear

polymers, are in favour when considering the cost. There is another type of membrane

which has homogeneous structure, but its usage in reverse osmosis is very limited.

There are four types of membrane modules, namely plate and frame, tubular, spiral

wound, and hollow fibre. Spiral wound and hollow fibre configurations are most

common in desalination, whereas plate-frame and tubular modules are used in

specialty separation application. Table 2.1 below summarises the distinguishing

features of various modules.

Table 2.1: Qualitative comparison of membrane modules [Porter, 1990]

Tubular Hollow

Fibre

Plate & Frame Spiral Wound

Cost/Area High Low High Low

Membrane

Replacement Cost

High Moderate Low Moderate/Low

Flux Good Fair/Poor Excellent/Good Good

Packing Density Poor Excellent Good/Fair Good

Hold-up Volume High Low Medium Medium

Energy Usage High Low Medium Medium

Anti-fouling & Ease

of Cleaning

Excellent Poor Good/Fair Good/Fair

2.2.2 Membrane Performance

Typically membrane performance is represented in terms of flux and selectivity.

Membrane or permeate flux, Jv, is the rate of material transported per unit membrane

area; and has the common unit of L/m2hr or gallon/ft

2day. Membrane selectivity is

characterised by the fraction of solute rejected by the membrane or the fraction of

solute passing through the membrane. Hence, it is the relative change in concentration

of the solute from the feed stream to the permeate stream. Membrane rejection

fraction or equivalently called retention fraction, RR, is defined in terms of the feed

and permeate concentrations of solute, Cb and Cp, respectively:

b p

R

b

C CR =

C

−

(2-1)

Due to the effect of concentration polarization (CP), which will be explained in detail

in section below, the concentration of solute at the wall surface of the membrane is

always higher than in the bulk solution, hence the true separation of membrane can be

defined in terms of the concentration at the membrane surface, Cw. Thus RR-real is:

w p

R-real

w

C CR =

C

−

(2-2)

The separation calculated in this manner represents the separation that would be

measured with perfect mixing on the high pressure side of the membrane. The

advantage of using RR-real for modelling purpose is that RR-real is the function of the

concentrations that are adjacent to the membrane surface. The RR and RR-real values

can be related by considering the concentration polarization phenomenon.

2.2.3 Chemical Potential and Osmotic Pressure Relationships

To model the flux through a membrane, the influence of the osmotic pressure driving

force must be considered. Osmotic pressure of a solution is associated with the

chemical potential of the species in that solution. The chemical potential, iµ , of

component i in a solution is defined in terms of the Gibbs free energy, G, by the

relation [Sourirajan, 1970]:

i i

i

dG = SdT + VdP + µ dN− ∑

(2-3)

where S is the entropy, T is the absolute temperature, V is the volume, P is the

pressure, and Ni is the number of moles of component i. The activity ai of component

i is related to iµ by the relation:

0

i i iµ = µ + RT ln a (2-4)

where 0

iµ is the standard chemical potential of pure i which at a given pressure is

dependent on temperature only. In a water-solute binary system, the chemical

potential of water, wµ , is

0

w w wµ = µ + RT ln a (2-5)

The thermodynamic requirement for osmotic equilibrium is that the chemical

potential of water in the solution phases be the same on both sides of the membrane.

If there is just pure water at pressure P1 on both sides of the membrane, the two

phases will be in equilibrium (the chemical potential of water in both phases being 0

wµ ) and there will be no net transfer of water through the membrane. If the pure

water on one side of the membrane is replaced by an aqueous solution (both sides still

being at pressure P1), the chemical potential of water in the solution is less than that of

pure water and water moves from the pure side to the solution. The equilibrium can be

restored by increasing the pressure on the solution side to P2 such that the change in

Gibbs free energy becomes nil; so the chemical potential of water in the solution is

raised to that of pure water, namely 0

wµ ; and can be written as [Sourirajan, 1970]

2

1

P 0w w w

PV dP µ µ= −∫

(2-6)

where wV is the molar volume of water. Integrating and substituting Equation (2-5),

we get

( )w 2 1 wV P - P = RT ln a− (2-7)

The pressure difference (P2 - P1) is by definition the osmotic pressure,Π , of the

solution. Thus

w wV Π = RT ln a−

(2-8)

ww

RTΠ = ln a

V−

(2-9)

Osmotic pressure is a thermodynamic property of a solution and as such values can be

found in various reference books [Sourirajan, 1970; Weast, 1983]. For a dilute

system, water activity, aw, can be replaced by water mole fraction, xw; hence

ww

RTΠ = ln x

V−

(2-10)

Equation (2-10) can be rewritten to relate osmotic pressure with the mole fraction of

solute in the solution, xs,

( )s sw w

RT RTΠ = ln 1 x x

V V− − ≈

(2-11)

Since

s ss

w s w

n nx =

n + n n≈

(2-12)

where ns and nw is the number of moles of solute and water in the solution,

respectively. Then Equation (2-11) can be simplified into van’t Hoff’s equation

ss

w w

nRTΠ = = C RT

nVv

(2-13)

where v is the number of ions formed in the dissociation of one mole of salt (e.g. v =

2 for the fully dissociated NaCl), and Cs is the total molar concentration of ions in

solution. For a real membrane, some solute passes to the permeate side, and therefore

the osmotic pressure of the solution on each side of the membrane must be

considered; hence the osmotic pressure difference across the membrane,∆Π , is

proportional to the concentration difference across the membrane, (Cw – Cp). In

reverse osmosis, a pressure difference, ∆P , higher than that of the osmotic pressure

difference across the membrane, ∆Π , must be applied in order to achieve separation

of water from the seawater.

2.2.4 Transport Properties of Membrane

The transport properties of membranes are commonly defined as the diffusivities and

permeabilities of solute and solvent through the membrane. The equilibrium

solubilities of solutes and solvents in the membrane phase play an important role in

the mechanism of separation by reverse osmosis. Permeation refers to mass transfer

through a medium caused by variety of transport mechanisms under various driving

forces. In general, there are several driving forces that are possible in membrane

transport. The main driving forces are pressure, concentration, electrical potential, and

temperature, each of which primarily influences the flux of solvent, solute, electrical

current and thermal energy, respectively. In reverse osmosis systems, the only driving

forces of interest are pressure and concentration, which lead to flux of solvent and

solute, respectively. In addition to the primary effects, each of the driving forces has a

cross-influence on the other fluxes. The cross-influence of solute concentration

driving force on solvent flux is represented by the osmotic pressure term in the

solvent flux equation. The cross-influence of pressure driving force on solute flux is

often small, especially for high separation membranes, and is therefore neglected;

when it is included, this effect is described by the Staverman (or reflection)

coefficient [Staverman, 1951]. The term permeability, which relates the conditions of

bulk solution, is used to describe the transport ability of membrane regardless of the

actual mechanism of mass transport in the boundary layer of membrane surface or

within the membrane. In contrast, the diffusivity of membrane requires the knowledge

of the actual concentration of different species in the membrane. Further, since the

membrane structure is microporous, with pore size of the same order of magnitude as

that of the solute or solvent molecules, the process of molecular diffusion is hindered

by the pore wall effects. For this reason, an ‘effective diffusivity’ or ‘apparent

diffusivity’ is introduced for diffusion through the membrane; the value of which are

usually several orders of magnitude smaller than the molecular diffusivity and close to

the values of the diffusivities in solids [Soltanieh&Gill, 1981]. Here, the transport of

solutes through the membrane is explained in detail and illustrated in Figure 2.1.

Assume the solution concentration and pressure are higher on the left side of the

membrane; hence, the flow direction is from left to right.

Bulk Region 1

In this region, the concentration, Cb, is uniform under steady state condition.

Membrane Surface Region 1

Near the membrane surface on the high pressure side, there is a region known as the

‘boundary layer’ where the undesirable concentration polarization phenomenon takes

place. The solute, rejected at the membrane surface, builds up in a concentrated layer

near the surface. Thus, the concentration at the membrane surface, Cw, is higher than

the bulk concentration. Due to the great difference between the solubility and

diffusivities of the solutes and solvent in the membrane, therefore, the concentration

of solutes inside the membrane but at the surface region, (Cw)m, is much smaller than

the concentration of the solutes in the solution side but at the surface, Cw. The ratio of

the two is usually defined as the ‘partition coefficient’ or ‘solubility coefficient’ or

‘distribution coefficient’ [Soltanieh&Gill, 1981]. The partition coefficient of the

solute must be much smaller than that of the solvent for an effective separation.

Skin Layer Region

This layer is highly dense, asymmetric and has a micro-porous structure. This skin

layer is made as thin as possible in order to reduce the resistance to flow and thus

increase the permeability of the membrane. The solute transport in this layer is by

diffusion and convection in the fine pores of membrane which refers to the void

spaces or holes with the dimensions of the same order of magnitude as the diffusing

molecules [Soltanieh&Gill, 1981]. Thus free molecular diffusion in the pores is

hindered, as described by Merten [Merten, 1966]. The concentration of the solutes

decreases exponentially in this region.

Figure 2.1: Schematic representation of transport through an asymmetric

membrane [Soltanieh&Gill, 1981]

Porous Support Region

This highly porous region serves as the support for the skin layer. Because of large

pores and open structure, this region does not reject the solutes, so the concentration

profile is almost flat in this region. Although this layer does not affect the salt

rejection property of the membrane, it certainly adds some resistance to the hydraulic

permeability of the membrane, which in turn requires a higher pressure difference

across the membrane in order to maintain a certain flux. In other words, at a given

pressure difference the water flux will be decreased by the presence of this region due

to the added resistance to hydraulic permeability [Soltanieh&Gill, 1981].

Membrane Surface Region 2

In this region, solute is desorbed out of the membrane. This porous layer is non-

selective as such the solute concentration inside the membrane in the product side is

almost equal to the solute concentration in the stream leaving the membrane. The

boundary layer exists at the solution-interface is not that severe as compared to the

high pressure side. Usually, it is assumed the concentration is same as the permeate

concentration, Cp.

Bulk Region 2

This region is similar to Bulk Region 1 where there is a constant bulk concentration of

the product, Cp, at steady state conditions. Therefore, we can see that the membrane

permeability to solute or solvent depends on the conditions of the solution as well as

the physical and chemical properties of the membrane itself. The overall resistance to

mass transfer will be the sum of the boundary layer resistances and the membrane

High Pressure

(Feed)

Low Pressure

(Permeate)

Porous Support LayerDense

Skin

Layer

Cb

Cw

(Cw

)m

Cp

layers resistances. The equations that describe the transport of solutes and solvents

through the membrane itself are normally called ‘Membrane Transport Models’ as

will be presented in detail in the following section.

2.2.5 Mechanism of Salt Rejection

The mechanism of salt rejection can be classified into two groups. The first method is

the ‘structural’ approach where the semipermeability of membranes is correlated with

known, or assumed structural configurations or chemical properties of the membrane

material [Punzi&Muldowney, 1987]. Solute rejection is explained in terms of some

interactions which occur to a different extent between membrane and solute than

between membrane and solvent. The principle theories which have evolved under this

category are the sorption-diffusion model, the hydrogen bonding model, and the

preferential sorption-capillary flow model. The second approach, which may be

termed ‘phenomenological’, describes the system in terms of the thermodynamics of

irreversible process. An example of such treatment is the frictional coefficient model.

The fifth principle, the sieve mechanism, is a less widely accepted theory. The five

principle theories of transport in reverse osmosis membranes are presented in the

following section.

2.2.5.1 Sorption-Diffusion Mechanism

It is assumed that both solvent and solute dissolve in homogeneous nonporous surface

layer of the membrane and then they are transported by a diffusion mechanism in an

uncoupled manner [Merten, 1966; Lonsdale et al., 1965].According to this

mechanism, it is desirable to have membranes with a completely nonporous surface

layer (perfect membrane), which has high solubility and diffusivity for the solvent as

compared with those of the solute. The fundamental assumptions made in sorption-

diffusion mechanism are as follow:

• The flux of each component is a monotonic function of its chemical potential

gradient across the membrane;

• The fluxes are uncoupled;

• The chemical potential of each component is continuous across both

solution-membrane interfaces;

• Henry’s Law is applicable; that is, the solvent activity is proportional to the

concentration at all points.

Of these conditions, the first three are reasonably valid but the assumption that the

activity of water in the membrane is proportional to its concentration is found to be

inexact by an examination of the sorption isotherm [Punzi&Muldowney, 1987]. Also,

incorrect is the assumed pressure independence of the concentration of the dissolved

water in the membrane. The justification of these assumptions is that the deviation

from Henry’s Law behaviour can be to some extent corrected by the use of activity

coefficients.

2.2.5.2 Wetted Surface or Hydrogen Bonding Mechanism

In this mechanism, it is assumed that the membrane surface is wettable, where the

solvent or water tends to cling to it by means of hydrogen bonding as an absorbed

film. Then solvent will progress through the membrane by passing from one wetted

site to another within the membrane structure. On the other hand, ions and molecules

that are incapable of forming the hydrogen bonds are transported through the

membrane by hole-type diffusion [Punzi&Muldowney, 1987]. Hence the permeation

of such species, which depends entirely on the porosity of the membranes, was

hindered when the membrane was filled tightly with solvent. So this model also

incorporates the sieve mechanism where the separation of solute also depends on the

size of gaps that exist in the membrane.

2.2.5.3 Preferential Sorption-Capillary Flow Mechanism

In preferential sorption-capillary flow model [Sourirajan, 1970], the surface layer of

membrane is assumed to be microporous and heterogenous at all levels of solute

separation. The mechanism of reverse osmosis separation, according to this model, is

partly governed by surface phenomena and partly by fluid transport under pressure

through capillary pores. Thus the size and number of pores and the chemical nature of

the layer constitute the separation mechanism. The membrane is assumed to be

preferential sorption to water or preferential repulsion to solute, where a layer of pure

water is formed at the membrane-solution interface. The water is removed by means

of membrane capillary flow when pressure is applied. This model will then give rise

to the concept of critical pore diameter, which is twice the thickness of the interfacial

pure water layer for maximum separation and permeability [Sourirajan, 1970].

However, it is very hard or impossible to accurately measure the thickness of the

interfacial pure water.

2.2.5.4 Frictional Coefficient Mechanism

In this mechanism, both solute and solvent pass through the membrane due to the

frictional forces exerted on each molecule [Punzi&Muldowney, 1987]. These forces

arise from the interaction of one kind of molecule with molecules of the other kind as

well as with the membrane materials. These frictional forces, Fij, are proportional to

the mean relative velocities of the components [Soltanieh&Gill, 1981]:

( )" "

ij ij i jF u uf= − (2-14)

where fij” is the friction coefficient between components i and j; and ui and uj are the

mean linear velocities of the respective components.

2.2.5.5 Sieve Mechanism

In sieve filtration, separation is based on the difference between the molecular sizes of

solutes and solvent; with the pore size of the membrane in the intermediate range

between the two. Particle sizes of common species are shown in Table 2.2.

In reverse osmosis, this mechanism is ruled out since in solution such as sodium

chloride – water, the sizes of salt and water molecules are almost the same; there is

too small a difference in steric characteristics between the ions and water for a sieving

effect to occur.

Table 2.2: The particles size spectrum [Osmonics Inc., 1996]

Ionic/low molecular weight (MW) range (1 to 20 A&)

Water MW = 18 2 A&

Metal ions MW = 50 – 150 2 – 8 A&

Pesticides, Herbicides MW = 150 – 1000 6 – 20 A&

Macromolecular range (10 to 100 A&)

Enzymes MW = 104 – 10

5

20 – 50 A&

Protein, polysaccharides MW = 104 – 10

6 20 – 100 A&

Colloidal range (0.01 to 1 µm)

Viruses

0.01 – 0.3 µm

Colloidal suspensions 0.1 – 1 µm

Micro-particles (suspended solids, supracolloids) range (0.1 to 10 µm)

Oil emulsions

0.1 – 10 µm

Bacteria 0.3 – 10 µm

Yeast cells 1 – 10 µm

Red blood cells 5 – 10 µm

Fine particles range (10 to 100 µm)

Human hair

30 – 100 µm

Coal dust 1 – 100 µm

Milled flour 1 – 100 µm

2.2.6 Transport Models

The general purpose of a membrane mass transfer model is to relate the performance

(usually expressed in terms of flux of solvents and solute) to the operating conditions

(usually expressed in terms of pressure and concentration driving forces). In the

model, some coefficients emerge that must be determined based on some

experimental data.

2.2.6.1 Phenomenological Transport Models

In this section, models which are independent of the mechanism of transport are

discussed. These models are called phenomenological transport models and are based

on the theory of irreversible thermodynamics.

Irreversible Thermodynamics – Phenomenological Transport Relationship

In the absence of any knowledge of the mechanism of transport or the nature of the

membrane structure, it is possible to apply the theory of irreversible thermodynamics

to membrane systems [Kedem&Katchalsky, 1958]. In this model, the membrane is

treated as a ‘black box’. Models stating the relationship between forces acting on the

system and the flux of material through the membrane are formulated by using

Onsager’s phenomenological irreversible thermodynamics equation [Onsager, 1931].

Onsager [Onsager, 1931] suggested that in which, n, simultaneous flows take place,

any of the flows, Ji, depends in a direct linear manner not only on its own conjugate

force, Fi, but also on all other non-conjugate forces, Fj. The fluxes and forces could be

expressed by the following linear equations:

i ii i ij j

i j

J = L F L F for i = 1,.....,n≠

+∑

(2-15)

where the fluxes, Ji, are related to the forces, Fj, by the phenomenological coefficients,

Lij. This equation can be simplified by assuming that ‘coupling coefficients’ are equal

for the system that is close to equilibrium, as proposed by Onsager [Onsager, 1931]:

ij jiL = L for i j≠ (2-16)

For membrane systems, the driving forces can be related to the pressure difference,

( )P Π∆ − ∆ , and concentration differences across the membrane, (Cw – Cp); and the

fluxes are solvent and solute fluxes, Jv and Js, respectively; to derive what are known

as the phenomenological transport equations [Kedem&Katchalsky, 1958]:

v pJ = ( P φ∆Π)∆ −l (2-17)

( )s s w pJ P C C= − (2-18)

where pl is the permeability of the membrane to solution flow; Ps is the solute

permeation coefficient; φ is the reflection coefficient acts to describe the degree of

interaction between solute and membrane. For a high separation membrane, this effect

is small, and φ approaches 1 so that

v pJ = ( P ∆Π)∆ −l (2-19)

For low separation membrane, the solute is significantly carried through the

membrane by solvent flux and φ approaches 0 so that the osmotic driving force

becomes insignificant and negligible in Equation (2-17). Thus the reflection

represents the relative permeability of membrane to the solute. For reverse osmosis

systems, the phenomenological transport equations have only been used to a limited

extent for describing membrane transport for two reasons. First, the concentration

differences across the membrane are often large enough that the linear laws are not

valid. As a result, the Lij coefficients are concentration dependent [Onsager, 1931].

Second, by considering the membrane as a black box, the resulting analysis does not

give any insight into the transport mechanism.

Irreversible Thermodynamics – Spiegler-Kedem Relationship

As mentioned above, phenomenological approach assumes the applicability of linear

laws over the whole thickness of the membrane is not really a valid assumption.

Spiegler and Kedem [Spiegler&Kedem, 1966] resolved the problem by rewriting the

original linear irreversible thermodynamic equations in differential form, assuming

the linear laws are applied on local portions of the membrane rather than to the

membrane as a whole and then integrating them over the thickness of membrane. By

applying the boundary conditions: x = 0, C = Cw and x = xm, C = Cp; the solvent and

solute flux, Jv and Js, respectively, are:

( ) ( )"

p

v p

m

J = ∆P φ∆π ∆P φ∆πx

− = −l

l

(2-20)

( )

( )s v p

v v"

s m s s v w

J 1 φ J C1 φ 1 φJ = J = ln

P x P J 1 φ J C

− − − −

− −

(2-21)

where "

pl is the localised permeability of membrane to solution flow; "

sP is the

localised solute permeation coefficient; and xm is the thickness of membrane. Again,

this approach lends no insight into the mechanism of membrane transport.

2.2.6.2 Nonporous Transport Models

In this section, models in which it is specifically assumed that the membrane is

nonporous are described. The solution-diffusion model is presented below.

Solution-Diffusion Relationship

This model was originally proposed by Merten and co-workers [Merten, 1966;

Lonsdale et al., 1965], which treats the membrane surface layer as homogeneous and

nonporous. Transport of both solvent and solute occurs by the molecules dissolving in

the membrane phase and then diffusing through the membrane. Solute is assumed to

have a lower solubility and diffusivity as compared to water, thus enabling solute-

solvent separation. It is assumed that the transport of solute and solvent across the

membrane is completely uncoupled. The water flux is proportional to the solvent

chemical potential difference (usually expressed as the effective pressure difference

across the membrane), and the solute flux is proportional to the solute chemical

potential difference (usually given as the solute concentration difference across the

membrane). The solvent and solute flux, Jv and Js, respectively are [Dickson, 1988]:

( ) ( )wwm

v p

m1

D VJ = ∆P ∆Π P Π

R T x− = ∆ − ∆l

(2-22)

( ) ( )sm ss w p s w p

m1

D KJ = C C P C C

x− = −

(2-23)

where Dwm and Dsm is the diffusivities of water and solute in the membrane,

respectively; wV is the molar volume of water; xm1 is the thickness of active skin

layer of membrane; and Ks is the partition coefficient defined as follows:

3

s 3

kg solute/m membraneK =

kg solute/m solution

(2-24)

where Ks is a measure of the relative solute affinity between solute-membrane

material and solute-solution affinity (for Ks > 1.0) and repulsion (for Ks < 1.0).

Equation (2-22) and Equation (2-23) are identical to the solvent and solute flux given

by the phenomenological transport model for the case of φ 1= . Since

s v pJ = J C (2-25)

Substituting Equation (2-22) and (2-23) into Equation (2-25) gives

( ) ( )w p p pP C C P Π Cs − = ∆ − ∆l (2-26)

Recalling the expression of salt rejection, RR-real, given in Equation (2-2), combining

both Equation (2-2) and (2-26) gives:

( )w s

R-real w p p

C P1 1 = = 1 +

R C C P Π− ∆ − ∆l

(2-27)

One restriction of the solution-diffusion model is that the separation obtained at

infinite flux or very high pressure is always equal to 1.0. However, this limit is not

reached for many solutes. For this reason, the solution-diffusion model is only

appropriate for solute-solvent-membrane systems where the separation is close to 1.0.

Notwithstanding this restriction, the solution-diffusion model has been applied to

many different inorganic and organic solute systems with different types of

membranes [Spiegler&Kedem, 1966; Lonsdale et al., 1965; Merten, 1966; Pusch et

al., 1976]. The primary advantage of this model is that it is simple and as such has

only two adjustable parameters.

2.2.6.3 Porous Transport Model

Transport model developed in this section is based on the assumption that membrane

is microporous. The porous flow models are primarily developed to describe transport

in ultrafiltration membranes, and for application to reverse osmosis membrane needs

further assumptions.

Preferential Sorption Capillary Flow Model

This model was developed by Sourirajan [Sourirajan, 1970], which treats the tight

skin of reverse osmosis membrane as a microporous surface. Surface phenomena are

invoked to explain how a microporous layer is able to separate between molecules

such as H2O and NaCl, which have the same size. The final equation for water flux

and salt flux are identical to those derived from the solution-diffusion model, e.g.

Equation (2-22) for water flux and Equation (2-23) for solute flux. However, the

interpretation of the parameters is different:

• Dsm is the salt diffusivity through the micropores rather than in a non-porous

material.

• Ks is a partition coefficient based on the material in the pores rather than the

membrane.

• xm1 is an effective micropore length rather than thickness of the active skin

layer.

2.2.7 Concentration Polarization

When solute is rejected by the membrane, the solute concentration near the membrane

surface increases due to convective transport of both solute and solvent. RO

membranes achieve a net rejection of solute because the flux of solvent through the

membrane is much higher than the flux of rejected solute. At steady state, solute is

assumed not to accumulate on the membrane, so that solute transport by back-

diffusion away from the membrane surface must occur simultaneously with

convective transport of solute towards the membrane. For the back-diffusion to occur,

a negative concentration gradient must exist, with a higher solute concentration at the

surface than in the bulk. The build-up in concentration in this boundary layer region is

referred to as concentration polarization (CP).

Concentration polarization is unwanted for three reasons. First, the osmotic pressure

near the membrane surface is increased due to the higher salt concentration.

According to the most common solution-diffusion model for RO transport, solvent

flux is directly proportional to the net pressure gradient across the

membrane, ( )∆P - ∆Π . Therefore, flux will decline as ∆Π is increased unless

additional pressure is applied. Secondly, as more solutes are present at the membrane

wall, the solute passage across the membrane is increased since the driving force for

solute transport across the membrane is the concentration gradient, (Cw – Cp). The

third effect is the promotion of fouling. As the concentration of sparingly soluble salts

near the membrane increases and as its solubility limit is exceeded, the salt will

precipitate onto the membrane surface.

2.2.7.1 Turbulent Flow

In most analyses of concentration polarization, bulk solution is considered well mixed

and in turbulent flow, as such the boundary layer at the surface is best described by

the Nernst-type film theory [Bird et al., 1960]. According to this model, the bulk

solution flowing parallel to the membrane surface is well mixed, and concentration

and velocity gradients are restricted to the boundary layer. The water transport is one-

dimensional and a very thin laminar film exists at the membrane surface e.g. there is

no turbulence within the film [Sherwood et al., 1965]. At steady state, the flux of

solute to the membrane, JvC, the flux of solute through the membrane, Js, and the

solute back diffusion, ( )D C x− ∂ ∂ , are balanced as illustrated in Figure 2.2.

It can be written mathematically as [Dickson, 1988]:

s v

CJ = J C D

x

∂−

∂

(2-28)

Solving this equation with appropriate boundary conditions at the solution-film

interface: x = 0; C = Cb, at the membrane wall surface: x = δ; C = Cw gives

w p v v

b p m

C - C J J = exp exp

C - C D/δ k

=

(2-29)

where δ is the thickness of boundary layer; and km is the mass transfer coefficient,

the ratio of diffusion coefficient to film thickness. For low solute concentration in the

permeate, Equation (2-29) becomes:

w v

b m

C Jexp

C k

=

(2-30)

Figure 2.2: Transport of solute through membrane

The ratio of (Cw/Cb) is known as the concentration polarization modulus, CP. From

Equation (2-30), it is shown that the extent of CP is a function of permeate flux and

mass transfer coefficient; the CP level increases exponentially with either an increase

in Jv or a decrease in km, which means CP is favoured at a high permeation rates and

low flow velocities.

2.2.7.2 Laminar Flow

The film model assumes a fully developed boundary layer. The entrance lengths in

turbulent flow are relatively short but can extend considerably in laminar flow.

Development of the boundary layer in laminar flow between parallel plates has been

studied by Dresner [Dresner, 1968], who made use of the Leveque simplification of

the velocity profile to obtain an approximate concentration polarization parameter.

For the entrance region:

1 3w

b

C 1 1.536 0.02

Cς ς= + ≤

(2-31)

( )1 2w

b

C 1 + + 5 1 exp /3 0.02

Cς ς ς = − − >

(2-32)

The dimensionless permeation group, ς , is

3

v h

2

b

J d L =

3u Dς

(2-33)

where dh is the hydraulic diameter of the channel; L is the length of the channel; ub is

the axial flow velocity. For the downstream region:

Feed Boundary Layer Permeate

x = 0 x δ=

vJ C

sJ sJ

vJ

CD

x

d

d−

bC

wC

pC

xdSolution Bulk

Flow

Mem

bran

e

2 2

w v h

2

b

C J d 1 +

C 3D=

(2-34)

2.2.7.3 Mass Transfer Coefficient

The mass transfer coefficient is a function of feed flow rate, cell geometry and solute

system. Generalised correlations of mass transfer, which have been used by several

authors [Sourirajan, 1970; Matsuura et al., 1974], suggest that the Sherwood number,

NSh is related to the Reynolds, NRe, and Schmidt, NSc, numbers as:

b" 1/3

Sh Re ScN = a" N N (2-35)

where a” and b” are parameters that can be determined experimentally. Gekas and

Hallstrom [Gekas&Hallstrom, 1987] had performed a critical review on the mass

transfer correlations used to describe the transport in membrane operations under

turbulent condition and the correlations are summarised in Table 2.3.

2.3 Properties of Seawater

Various cations, anions, particulate matter and living organisms are present as

impurities in seawater. These impurities are the main fouling agents to the process

equipment. A good understanding of seawater composition is essential in studying the

fouling behavior as well as treating the fouling problems. The composition of

seawater was collected from published data and the desalination industry and is

presented in Table 2.4.

As seen from Table 2.4, seawater usually contains total dissolved solids in the range

of 30,000 to 45,000 ppm (salinity of about 3.5 wt%). Seawater is slightly alkaline

with pH around 8.0 and the major contributing species to the alkalinity is the

bicarbonate ions. The amount of calcium ions is in the range of 400 to 600 ppm;

HCO3- around 150 ppm; sulfate ions around 3000 ppm; except the Ajman plant which

has a higher Ca2+

and HCO3- contents (1611 ppm and 750 mg/L CaCO3, respectively)

and SO42-

concentration of 1620 ppm. The Jeddah plant also has a lower HCO3-

content (only 43 mg/L CaCO3) as compared to seawater intake at other plants.

Generally, although the total organic content of seawater is low (about 2 to 4 ppm) it

contains a high concentration of biological matters.

2.3.1 Physical Properties of Seawater

The most important physical properties of seawater in terms of reverse osmosis

operations are the osmotic pressure, density, viscosity and diffusivity. All of these

properties are important when determining the flux across the membranes. The

osmotic pressure of seawater as a function of concentration of dissolved solids and

temperature is [Miyake, 1939]:

( ) 8 TDSsea

sea

CΠ 0.6955 0.0025 T 10

ρ= + ×

(2-36)

where CTDS is the concentration of total dissolved solids in the seawater in mg/L.

Seawater density, viscosity and diffusivity are tabulated in Table 2.5 below as a

function of CTDS and temperature, T:

Table 2.3: Mass transfer correlations for Newtonian fluids flowing turbulently in

pipes or flat ducts (Adapted from Gekas and Hallstrom [Gekas&Hallstrom,

1987])

A) Based on momentum, mass, heat transfer analogies

Correlations Conditions

of validity

References

NSh = 0.023NRe0.8

NSc0.33

(2-36) NRe>105;

NSc>0.5

Chilton-Colburn

[Bennett&Myers,

1982]

NSh = 0.34NRe0.75

NSc0.33

(2-37) 104<NRe<10

5;

NSc> 0.5

Chilton-Colburn

[Bennett&Myers,

1982]

( )( )Re Sc

Sh

Sc

/ 2 N NN

1 5 / 2 N 1

f

f=

+ −

where f is friction factor

(2-38) Prandtl-Taylor

[Bennett&Myers,

1982]

( )( )

Re Sc

Sh

Sc Sc

/ 2 N NN

1 5 / 2 N 1 ln 1 5N / 6

f

f=

+ − + −

(2-39) Von Karman

[Bennett&Myers,

1982]

B) Based on eddy diffusivity models

NSh = 0.023NRe0.875

NSc0.25

(2-40) 300<NSc<700 Deissler [Deissler,

1961]

NSh = 0.0149NRe0.88

NSc0.33

(2-41) NSc>100 Notter-Sleicher

[Notter&Sleicher,

1971]

Table 2.4: Compositions of seawater

Reference [Dana Beach Resort,

2001]

[Tawfiq, 2001] [Zidouri, 2000] [Al-Shammiri&Al-

Dawas, 1997]

[Bou-Hamad et al.

1997]

Location Ajman Laayonne, Morocco Doha, Kuwait Doha, Kuwait

Description Shallow beach intake Gulf sea Beach well intake,

Atlantic Ocean

Beach well intake

Composition

TDS 31340 42770 40077 45300

pH 7.5 8.1 8.0

HCO3- 750 155 162 152

SO42- 1620 3250 3075 3150

Cl- 17172 23480 22109 23300

Na+ 9552 13225 12255 15800

Ca2+ 1611 520 464 600

TOC

Bacteria 8.61x105

Note: All concentrations are expressed in ppm except pH is dimensionless; HCO3- in mg/L CaCO3; and

bacteria counts in cfu/mL

2.3.2 Chemistry of Seawater

Seawater contains a large amount of inorganic species hence various chemical

reactions may take place. It is impossible to cover the whole range of species present

in the seawater, so only the most predominant fouling species such as calcium

carbonate and calcium sulfate will be discussed in detail. Calcium carbonate and

calcium sulfate are chosen as the model to study the fouling problems, as they are the

most common inorganic foulants identified from the seawater reverse osmosis

autopsy [Butt et al., 1995]. It is also important to understand the species that are

contributing to the alkalinity and acidity of the seawater.

Table 2.5: Physical properties of seawater [Sekino, 1994]

Density 2

sea TDSρ 498.4 248, 400 752.4 Cm m m= + +

where 41.0069 2.757 10 Tm−= − ×

(2-37)

(2-38)

Viscosity 6

sea TDS

1.965η 1.234 10 exp 0.00212 C

273.15 T

− = × +

+

(2-39)

Diffusivity 6 3

sea TDS

2.513D 6.725 10 exp 0.1546 10 C

273.15 T

− − = × × −

+

(2-40)

2. 3.2.1 Alkalinity and Acidity

By definitions, alkalinity is the number of H ions reacted over a given pH range

during an acid titration while on the other hand, acidity is the number of OH ions

reacted over a given pH during a base titration [Kramer, 1982]. For natural water, the

contributing species to the alkalinity and acidity are H+, OH

-, H2CO3, HCO3

-, and

CO32-

. Hence, the alkalinity and acidity is defined as follow [Kramer, 1982]:

2

c 3 3Alkalinity Alk HCO 2 CO OH H− − − + = = + + − (2-41)

[ ]c 2 3 3Acidity Acy 2 H CO HCO H OH− + − = = + + − (2-42)

However, for actual seawater, the alkalinity and acidity are not just based on the

carbonate ion system. There are some inorganic soluble species which may contribute

to alkalinity and acidity such as H4SiO4 and NH4. So for the alkalinity of seawater is

better represented by [Edmond, 1970]:

( )

( )

2 2

t 3 3 44

3 4

Alkalinity Alk HCO 2 CO B OH HPO

H SiO Mg OH OH H

− − − −

−− − +

= = + + +

+ + + −

(2-43)

2.3.2.2 Sodium Chloride in Seawater

Sodium chloride or NaCl is the major contributor to the salinity of seawater. Seawater

usually has a salinity of about 3.5 wt%. NaCl fully dissociates in water and does not

take part in the fouling process. However, due to the high quantity, NaCl is the major

component that contributes to the osmotic pressure of the seawater and also

significantly affects the solubility of other salts (sparingly soluble salts) present in the

seawater.

2.3.2.3 Calcium Carbonate in Seawater

Physical Properties of Calcium Carbonate

Calcium carbonate, CaCO3, has a molecular weight of 100.09 g/mol. It occurs

naturally in three crystal structures, calcite, aragonite and vaterite. Calcite is the most

stable form of calcium carbonate. The aragonite polymorph is metastable and

irreversibly changes to calcite when heated in dry air to about 400 °C. Vaterite is

metastable and least prevalent and transforms to calcite and aragonite under

geological conditions [Reeder, 1990]. The crystal forms of calcite are in the

hexagonal system. There are more than 600 reported crystal habits for calcite in

contrast to 10-15 for other isostructural carbonates. Aragonite is in the orthorhombic

system. The usual crystal habits are circular or elongated prismatic. In the commercial

forms of precipitated calcium carbonate where aragonite predominates, crystals have

parallel sides and large length-to-width ratios.

Chemistry of Calcium Carbonate

The formation of calcium carbonate is a reversible reaction as follow:

( ) ( ) ( )2 2

3 3Ca aq CO aq CaCO s + − →+ ← (2-44)

Dissolved CO2 forms weak carbonic acid, H2CO3:

( ) ( ) ( )2 2 2 3H O aq CO aq H CO aq→+ ← (2-45)

H2CO3 will dissociate according to the following reactions:

( ) ( ) ( )2 3 3H CO aq H aq HCO aq+ −→ +←

(2-46)

( ) ( ) ( )2

3 3HCO aq H aq CO aq− + −→ +← (2-47)

The first and second dissociation constants of H2CO3, K’1 and K’2 respectively, are:

[ ]3

1

2 3

H HCOK'

H CO

−+ =

(2-48)

2

3

2

3

H COK '

HCO

−+

−

=

(2-49)

The presence of CO2 in the solution further influences the CO32-

equilibria and the

saturation solubility of Ca2+

. The equilibrium is sensitive to pH as well. The quantity

of CO2 in solution will also be dependent on the CO2 partial pressure in the air in

contact with the water. The total carbon concentration CT in the solution is given by

[ ] 2

T 3 2 3 3C HCO H CO CO− − = + + (2-50)

By combining Equation (2-41), (2-48), (2-49), and (2-50) the concentration of each

carbon species can be obtained:

[ ]c2

3

2

Alk H OHCO

H2 1

2K'

+ −

−

+

+ − = +

(2-51)

[ ]c

3

2

Alk H OHHCO

2K'1

H

+ −

−

+

+ − = +

(2-52)

[ ][ ]c

2

1 2

Alk H OHCO

K' 2K'1

H H

+ −

+ +

+ − = +

(2-53)

The distribution of CO2 related ions and CO2 gas in solution as a function of pH is

shown in Figure 2.3.

Figure 2.3: The effect of pH on the distribution of carbonate ions [Bott, 1995]

2.3.2.4 Calcium Sulfate in Seawater

Physical Properties of Calcium Sulfate

The CaSO4 – H2O system is characterised by five different solid phases. Four exist at

room temperature: calcium sulfate dihydrate or commonly known as gypsum, calcium

sulfate hemihydrate, anhydrite III, and anhydrite II. The fifth phase, anhydrite I, only

exists above 1180 °C, and it has not been possible to produce a stable form of

anhydrite I below that temperature [Wirsching&Gipswerke, 1985]. Calcium sulfate

Fra

ctio

n o

f T

ota

l C

arb

on

Dio

xid

e

pH

1. CO32-

2. HCO3-

3. H2CO

3

dihydrate CaSO4.2H2O is both the starting material before dehydration and the final

product after rehydration. Calcium sulfate hemihydrate CaSO4.1/2H2O occurs in α

and β forms. They differ in their heat of hydration. The α-hemihydrate consists of

compact, well-formed, transparent, large particles. The β-hemihydrate forms flaky,

rugged secondary particles made up of extremely small crystal. Anhydrite III is

soluble while anhydrite II is insoluble in water. Gypsum, hemihydrate and anhydrite

crystallises in well-defined latices known to be monoclinic, hexagonal and

orthorhombic, respectively [Glater et al., 1980]. Much research has been performed

in an attempt to analyse the mechanism and determine the form of calcium sulfate at

different temperatures. Partridge and White [Partridge&White, 1929] found that

gypsum is the usual precipitating phase in the range of 0 – 98oC while anhydrite and

hemihydrate are the species likely to precipitate above 98oC. Some other authors

[Blount&Dickson, 1973; Hardie, 1967] indicated the transition temperature between

anhydrite and gypsum to be in the range of 56 – 58oC. This is incongruent with the

results of Partridge and White [Partridge&White, 1929]; however, all the works

[Partridge&White, 1929; Blount&Dickson, 1973; Hardie, 1967] indicated that

gypsum is the dominant phase in the range of operation of majority of heat transfer

equipment (temperature < 100oC). In addition, SEM (Scanning Electron Microscope)

and chemical analyses of samples confirmed that the scale formed on the reverse

osmosis membranes was gypsum [Hasson et al., 2001; Gilron&Hasson, 1987;

Brusilovsky et al., 1992]; so it is justified to conclude that gypsum is the dominant

phase in reverse osmosis system.

Chemistry of Calcium Sulfate

CaSO4 forms according to the reaction

( ) ( ) ( )2 2

4 4Ca aq SO aq CaSO s + − →+ ← (2-54)

Sulfuric acid dissociates according to the following reactions:

( ) ( ) ( )2 4 4H SO aq HSO aq H aq − +→ +←

(2-55)

( ) ( ) ( )2

4 4HSO aq SO aq H aq− − +→ +← (2-56)

But sulphuric acid is a strong acid compared to carbonic acid. For pH levels above 3,

the major ionic species are SO42-

and Ca2+

, compared to HSO4- and Ca

2+ at lower pH

levels as shown in Figure 2.4:

Figure 2.4: Distribution of sulfate ions with pH [Sudmalis&Sheikholeslami,

2000]

Fra

ctio

n

pH

SO4

2-

HSO4

-

0 1 2 3 4 5 6 7 8 9 10 11 12

0.2

0.40.6

0.81.0

2.3.3 Biological Properties of Seawater

The most common organisms found in seawater that are capable of adhering to and

colonizing RO membrane surfaces are Fusarium, Penicillium, Trichoderma,

Acinetobacter, Arthrobacter, Bacillus, Flavobacterium, Lactobacillus, Micrococcus,

and Pseudomonas [Ho et al., 1983; Baker&Dudley, 1998]. Two groups of

microorganisms can be identified, eucaryotic and prokaryotic microorganisms.

Microalgae, fungi, and protozoa are eucaryotic where they have a well-defined

nuclear membrane and chromosomes, and they exhibit mitotic cell division

[Characklis et al., 1990]. On the other hand, bacterial cells are prokaryotic, where

they do not possess a true nucleus. In these organisms, the DNA is present as a single

circular molecule that is neither complexed with histones nor surrounded by a nuclear

membrane. Cell division is preceded by a simple replication of DNA molecule

[Characklis et al., 1990]. Figure 2.5 shows the difference between a bacterial cell and

a fungal cell.

(a) (b)

Figure 2.5: Structure of (a) bacteria cell (b) fungal cell [Maier et al., 2000]

In view of such a diversified groups of microorganisms which could be present in the

water system, it is impossible to study biofouling caused by actual colony in seawater

as the situation is further complicated by the interactions between species of

organisms such as synergism, mutualism, competition and autoganism [Maier et al.,

2000]. So, only Pseudomonas fluorescens is considered, as it is the most studied

bacteria in biofouling. P. fluorescens is a prokaryotic bacterial cells, and has a rod

shaped with average size of 0.7-0.8 by 2.3-2.8 µm during exponential growth and able

to produce diffusible fluorescent pigments particularly in iron deficient media

[Buchanan et al., 1974]. There are two basic cell wall types are associated with

bacteria, namely gram-positive and gram-negative [Maier et al., 2000] as shown in

Figure 2.6.

In both cell wall types, the rigid protective nature of the wall is due to a

macromolecule known as peptidoglycan. The gram-positive cell wall consists mostly

of peptidoglycan plus acidic polysaccharides including teichoic acids. The teichoic

acids are negatively charged and are partially responsible for the negatively charge of

the cell surface as a whole. Beneath the surface of the gram-positive cell wall is the

cell membrane. The gram-negative cell wall is more complex than the gram-positive

cell wall and contains two membranes, an outer membrane as well as an interior cell

membrane. Both membranes are composed of phospholipids, however, the interior

membrane is uniform in structure while the outer leaflet of the outer membrane also

contains lipopolysaccharide (LPS). Gram-negative cell walls contain a narrow layer

of peptidoplycan compared to the thick layer found in gram-positive walls.

(a) (b)

Figure 2.6: Comparison of (a) gram-negative and (b) gram-positive bacterial cell

walls [Maier et al., 2000]

Bacteria require nutrients, a source of energy and a terminal electron acceptor for

growth and metabolism. Nutrients required in large amount (> 100ppm) are known as

macronutrients (i.e. Nitrogen, Phosphorus, Sulfur, Potassium, Magnesium, Calcium,

Sodium and Iron) while micronutrients or trace elements are required in relatively

small amounts, less than 10ppm (i.e. Zinc, Cobalt, Copper, Molybdenum, Manganese,

Nickel, Tungsten and Selenium) [Brock&Madigan, 1991]. Macronutrients are

generally involved in cell structure and metabolism while micronutrients are needed

as catalyst for enzymes. Table 2.6 shows the compositions of medium recommended

for the growth of P. fluorescens.

Table 2.6: Medium composition for Pseudomonas fluorescens culture [Bott, 1995]

Mineral salts (g/L) Trace elements (g/L) Glucose

(carbon source)

(g/L)

NaH2PO4.2H2O 1.01 MnSO4.4H2O 13.3x10-3

C6H12O6 5.0

Na2HPO4 5.50 H3BO3 3.0x10-3

K2SO4 1.75 ZnSO4.7H2O 2.0x10-3

MgSO4.7H2O 0.10 Na2MoO4.2H2O 0.24x10-3

Na2EDTA.2H2O 0.83 CuSO4.5H2O 0.025x10-

3

NH4Cl 3.82 CoCl2.6H2O 0.024x10-

3

Bacteria can be divided into autotrophic and heterotrophic depending where they

obtain the carbon source [Maier et al., 2000]. Autotrophic bacteria obtain carbon

source via carbon dioxide and energy source from sunlight (known as

photoautotrophs) and by oxidation of inorganic substances (known as

chemoautotrophs). Heterotrophic bacteria derive carbon from preformed organic

compounds that are broken down enzymatically. For chemoheterotrophs, energy is

derived through the oxidation of organic compounds via respiration while

photoheterotrophs derive energy from light. Generation of energy through chemical

oxidation is referred to as respiration regardless of whether the substrate is inorganic

or organic [Maier et al., 2000]. During the oxidative process, electrons are removed

via the electron transport chain to a terminal electron acceptor (TEA). For aerobic

organisms the TEA is oxygen while for anaerobic organisms the TEA is a combined

form of oxygen such as an organic metabolite CO2, NO3- or SO4

2- or an oxidized

metal, e.g. Fe3+

. P. fluorescens is a type of aerobic-chemoheterotrophic bacteria that

breaks down complex organic molecules in the presence of oxygen [Buchanan et al.,

1974].

2.3.4 Scaling Potential of Seawater

A large amount of work has been carried out to try to quantify the scaling potential of

seawater. This enables the engineers to predict how likely the membranes will foul

when exposed to the seawater. This will also provide guidelines to pre-treat the

seawater before being fed to the reverse osmosis system.

2.3.4.1 Fouling Indices

There are several fouling indices originally developed to characterize the fouling

potential of the industrial water systems especially in the heat exchanger operations.

These indices are also adopted to describe the scaling tendency of seawater in

membrane desalination process.

Silt Density Index (SDI)

SDI is a measure of the tendency of water-borne particles to foul the RO membrane. It

is often the choice of method to predict the colloidal fouling potential of RO feed

water. It is not a direct measure of the particles concentration, which is more properly

measured by turbidity [Hooley et al., 1993]. The SDI value is derived from the time

required to filter a standard volume of water through a membrane at a constant

pressure. It is a measure of the rate at which the membrane becomes plugged with the

feed-water source under standard conditions. The SDI value is calculated using the

following formula:

( )i f

t

1 t tSDI 100

t

−= ×

(2-57)

where ti is the time required to collect 500 ml; tt is the total test time, usually 15

minutes; tf is the time required to collect 500 ml after the total test time. Generally a

maximum SDI value of 4 is recommended [Hydranautics Inc., 2001] to minimize

problems of fouling caused by suspended solids. There are three other fouling indices,

which are related to the carbonate species in solution, namely the Saturation Index,

the Langelier Saturation Index, and the Ryznar Stability Index.

The Saturation Index

The Saturation Index, S.I., is defined as follow [Kemmer, 1988]:

22

3

sp

Ca COS.I.

K'

−+ =

(2-58)

where K’sp is the concentration solubility product. This index uses the degree of

saturation to predict the scaling potential of CaCO3 solution. If S.I. > 1, then scaling

may occur. As can be seen from the plot of carbonic species distribution in Figure 2.3,

the S.I. is very small (<< 1) for pH < 7 and increases sharply at pH > 7.

The Langelier Saturation Index

The Langelier Saturation Index, L.S.I., was developed [Langelier, 1939; Langelier,

1946] by combining the pH, total alkalinity, hardness and temperature of solution to

predict the scaling potential of CaCO3 in the solution. The equations are as follow:

sL.S.I. pH pH= − (2-59)

where pHs is the pH at saturation and can be calculated from

2+

s 2 sp cpH pK' pK' pCa pAlk= − + + (2-60)

If L.S.I. > 0, then scaling will occur. Both L.S.I. and S.I. only indicate the driving

force for scaling but do not indicate what degree of supersaturation is required to

initiate the fouling process as well as how much deposit is formed if there is

precipitation.

The Ryznar Stability Index

Ryznar [Ryznar, 1944] suggested an index to determine the amount of calcium

carbonate scale formed in a solution. The Ryznar Stability Index, R.S.I., is defined as

follows:

sR.S.I. 2pH pH= − (2-61)

where [Larson&Buswell, 1942]

s2

s 2 sp c

s

2.5 IpH pK' pK' pCa pAlk 9.3

1 5.3 I 5.5

+= − + + + ++ +

(2-62)

2

s c 3 3I 0.5 6Ca 2Alk HCO OH H 4CO− −+ − + = + + + + + (2-63)

where Is is the ionic strength of the solution. The R.S.I. < 6 denotes a scaling

tendency.

2.4 The State of the Art of Fouling

Fouling is defined as the process of build-up of unwanted materials at interfaces e.g.

heat exchangers or the reverse osmosis membranes.

2.4.1 Classification of Fouling

According to Epstein [Epstein, 1983], the mechanisms of fouling process can be

categorised into five major groups as explained in Table 2.7.

In most operating environments, more than one type of fouling occurs simultaneously.

For example, biological fouling is not just limited to the adhesion of organisms to the

surface or the growth of organisms, very often, biological fouling will induce the

deposition of particulate matter onto the surface and hence cause particulate fouling.

The interactions between the various types of fouling are poorly understood, and

consequently, provide a challenge to the engineer when treating the water.

Table 2.7: Mechanisms of Fouling

No Category Description

1 Crystallisation

Crystallisation can be subdivided into:

Precipitation – crystallisation of dissolved substances onto the

surface. This occurs when the solubility limit of the sparingly

soluble salt has been exceeded.

Freezing - Solidification of pure liquid or higher melting

constituents of a multi-component solution onto a subcooled

surface. This event is not encountered in seawater reverse osmosis

desalination as the process is maintained at ambient temperature.

2 Particulate Accumulation of finely divided solids suspended in the process

fluid onto the surface.

3 Biological Attachment and metabolism of biological matter. This includes

the macroorganisms and microorganisms.

4 Corrosion The surface itself reacts to produce corrosion products, which foul

the surface and may foster the attachment of other potential

fouling materials.

5 Chemical

Reaction

Deposit formation by chemical reaction at the surface in which

the surface material itself is not a reactant. This problem is most

commonly encountered in the petroleum refining.

2.4.2 Sequential Events in Fouling

For all the above categories of fouling, the successive events that commonly occur in

most situations are up to five in numbers [Epstein, 1983]:

1. Initiation (delay, nucleation, induction, incubation, surface conditioning)

2. Transport (mass transfer)

3. Attachment (surface integration, sticking, adhesion, bonding)

4. Removal (release, re-entrainment, detachment, scouring, erosion, spalling,

sloughing off, shedding)

5. Aging

The words in parentheses are alternative terms often used to designated the given step

in the fouling sequence, in some cases for particular categories of fouling (e.g.

nucleation and surface integration are unique to crystallisation).

2.4.2.1 Initiation

Initiation refers to the delay period where no fouling was observed. This delay period

decreases as the surface roughness increases. The roughness projections provide

additional sites for nucleation, adsorption, and chemical surface activity, while the

grooves provide regions for deposition that are sheltered from the mainstream

velocity. Besides, surface roughness also decreases the thickness of the viscous

sublayer and hence increases eddy transport to the wall. For crystallisation the delay

period is closely related to the nucleation process.

2.4.2.2 Transport

In a cross-flow reverse osmosis system, the fouling species will be transported to the

membrane surface via convection, diffusion and drag due to permeation as illustrated

in Figure 2.7.

Figure 2.7: Transport of solute in cross-flow reverse osmosis system

Mathematically, it can be written as follows:

2

b v 2

C C Cu v D

x y y

∂ ∂ ∂+ =

∂ ∂ ∂

(2-64)

where ub is the axial velocity; vv is the permeation velocity; D is the diffusivity of

solute; and C is the concentration of solute. For submicron particles, the Brownian

diffusivity of particles, D, is given by Stokes-Einstein equation:

Reverse Osmosis Membrane

Drag Due to Permeation

Back-Diffusion

Axial Transport

y

x

Feed

Permeate

b

Cu

x

∂

∂

v

Cv

y

∂

∂

2

2

CD

y

∂

∂

B

p

k TD

3πηd=

(2-65)

where kB is the Boltzman constant; T is the temperature; η is the viscosity of solution;

and dp is the particle diameter.

2.4.2.3 Deposition

Deposition process is the adherence of solid particles to the surface. When a particle

arrives within the vicinity of the wall, several surface forces arise which can

profoundly influence whether or not it will remain at the wall. The attachment of

particle is best described by the DLVO theory (Derjaguin-Landau-Verwey-Overbeek

theory) [Derjaguin&Landau, 1941; Vervey&Overbeek, 1948]. The net interaction is

the sum of two additive forces, van der Waals forces, which are always attractive; and

the electrical double layer interaction forces (shown in Figure 2-8), which are

attractive if the particle and the wall have zeta potentials of opposite sign, and

repulsive if these charges are of the same sign. The two forces are illustrated in Figure

2-9, as a plot of interaction energy vs. separation distance, for the case of zeta

potentials of like signs. It is seen that the summation of the interaction energies

involved results in a large energy barrier which must be overcome before a particle

can actually get to the wall.

Figure 2.8: Sketch of an electrostatic double layer and a solid particle [Bott,

1995]

When particles are transported to the surface, some fraction will remain there, while

the rest remain suspended, either to deposit later or to be carried back to the bulk

fluid. For particulate fouling, particle attachment is often simplified and represented

with a dimensionless quantity called sticking probability, Sp. Watkinson and Epstein

[Watkinson&Epstein, 1970] developed a relationship between the sticking

+

+

+

+

+

+

+

+

+

+

+

+

+

+

-

+

+

+

+

+

+

-

+

+

+ ++

+

+

++

+

+

+

++

+

++

+

-++

+

+

-

-

-

+

+

+ -

-

-

-

-

-

-+

+

-

- +

++

-

+- - -

-

--

--

-

-

-

-

-

+

-

-

-

-

-

-

-

-

+

Diffuse Layer

So

lid

Su

rfac

e

probability, Sp, and the fluid velocity, ub, as follows:

( )a-part

P 2

b

a exp E / RTS

u f

−=

(2-66)

where Ea-part is the activation energy for particle attachment; a is a constant; and f is

the friction factor.

Figure 2.9: Energy distance profile [Bott, 1995]

For crystallisation process, the attachment refers to the surface integration of cations

and anions into the crystalline structure. The detail of this process will be covered in

Section 2.5 under crystal growth.

2.4.2.4 Removal

Removal is the dislodgement of attached particles from the heat transfer surface to the

bulk fluid due to shear forces and level of tenacity of deposit. The process may or

may not occur immediately after the deposition process has started. Re-entrainment

does occur at shear stress (or drag force) above a critical value, which acts parallel to

a solid surface and provide a lifting force acting perpendicular to the surface sufficient

to transfer the material to the bulk fluid. Cleaver and Yates [Cleaver&Yates, 1973]

suggested that the removal might be affected by instabilities in the laminar sub-layer

of a fluid flowing across a surface. Amongst the complex instabilities that can occur

in the regime close to the wall are the ‘turbulent bursts’. Figure 2.10 is an idealised

sketch of the situation. A ‘tornado’ of fluid is ejected outwards towards the faster

moving bulk fluid, to be replaced by ‘downsweeps’ of fluid towards the surface. The

Po

ten

tial

en

erg

y o

f in

tera

ctio

n G

T

Gmax

Distance

Primary minimum

Energy barrier

Secondary

minimum

GA

= f(x 2)

GR

= f(exp(x) )

former could provide lift force for particle removal, and the latter could transport the

material towards the surface being fouled.

Figure 2.10: Flow disturbances near a solid surface [Cleaver&Yates, 1973]

2.4.2.5 Aging

Aging is the process that begins straight after the deposition process has started.

Commonly involves alteration of physical, chemical or biological structure of fouling

layers. Aging usually strengthens the deposits. No sufficient study has been conducted

to understand the aging process.

2.4.3 General Models of Fouling

In mathematical terms the net rate of deposit growth may be regarded as the

difference between the rate of deposition and rate of removal [Bott, 1995]

fD R

mm m

t

d

d= −

(2-67)

where mf is the mass of deposit per unit area; mD and mR are the rate of deposition

and removal per unit area, respectively. Plotting the mass rate of solid deposition per

unit area mf versus time t, the fouling curves are obtained. Figure 2.11 shows different

fouling curves, wherein four different fouling curves may be observed:

1) Linear rate of growth of scale mass per unit area. This behaviour is observed

for very hard scales, characterised by a great degree of adhesion. Example of

this will be the calcium carbonate scale.

2) The removal rate becomes equal to that of gross deposition rate and hence the

net rate of deposition approaches zero. Here the mass per unit area of the

fouling deposit approaches the asymptotic values of *

fm . The thickness of the

deposit remains virtually constant.

3) A decreasing rate of growth of scale with increasing scale thickness is found

for deposits of small mechanical strength. The shearing forces of flow act on

Flow direction

Turbulent flow

Particle

Stagnation point

Turbulent burst

Essentially laminar flow

developing with time

the deposit and cause removal of solids. The rate of removal is however less

than the gross deposition rate.

4) A saw-tooth-shaped asymptotic fouling curve could be observed when the

deposited material is removed in significant quantities during the process. As

the time proceeds the fouling layer grows and again becomes subject to

massive removal as it thickens.

Figure 2.11: Fouling curves [Krause, 1993]

2.5. Inorganic Fouling

Inorganic fouling in reverse osmosis membranes refers to crystallisation of sparingly

soluble salts directly at the membrane surface (crystallisation) or in the bulk and

subsequent deposition of particulate material on the membrane surface (particulate

fouling). This section will cover the principles of crystallisation and particulate

fouling.

2.5.1 Crystallisation / Precipitation Fouling

Crystallisation or precipitation fouling in reverse osmosis system is referred to the

precipitation of sparingly soluble salts at the surface of membrane when the

solubilities of the salts near the surface are exceeded due to the concentration

polarization effect. Since the experimental work will involve fouling due to

crystallisation, it is necessary to understand the basic principles related to

crystallisation process.

2.5.1.1 Principles of Crystallisation

Crystallisation process involves the formation of a new heterogeneous phase (solid

phase in a liquid). The concentration of the solution at the growing crystal surface is

higher than the saturation concentration, although in the interfacial region equilibrium

(1)

(2)

(3)

(4)

mf o

r R

f

Time

Induction

is generally assumed to exist. Consequently, crystallisation process can only be

understood by considering its thermodynamic aspects, diffusion theory, and particle-

liquid fluid dynamics. Those particular physical phenomena associated with

crystallisation which require knowledge and understanding are supersaturation,

nucleation and crystal growth.

Solubility and Supersaturation

Solubility Product: If X Yν ν+ − is a sparingly soluble salt such that

z z

+X (aq) + Y (aq) X Y (s)v v

v v+ −

+ −

−→← (2-68)

where +v and v− is the number of moles of cation and anion in 1 mole of electrolyte,

respective; Z+ and Z- is the valency of cation and anion, respectively. The

thermodynamic solubility product expression is defined by the equation

( ) ( )( )

z z

sp

X YK =

X Y

a a

a

ν ν

ν ν+ −

+ −+ −

(2-69)

where a and is the activity for the species. Activity for species i is defined as

i i i = γ .ma (2-70)

where im is its molality, and iγ is a dimensionless quantity called the activity

coefficient. Activity of solid is assumed to be unity, therefore by substituting Equation

(2-70) into Equation (2-69), we get

z z z zsp X X Y YK = γ m .γ m

ν ν ν ν+ + − −

+ + − − (2-71)

For a dilute solution, the molality can be approximated by the molar concentration of

the solute. Hence

z z z z z zsp spX X Y Y X YK = γ C .γ C γ γ K'

ν ν ν ν ν ν+ + − − + −

+ + − − + −= (2-72)

where K’sp is the concentration solubility product. In an electrolyte solution, it is hard

to determine the activity coefficient of the individual species, so the mean activity

coefficient, γ± , is used instead. So Equation (2-72) can be rewritten as

( )( ) ( )sp spK = γ K'

ν ν+ + −

± (2-73)

The activity coefficient and thus the activity of the species vary with the ionic strength

of the solution, SI which is defined as follow

2

12S i iI = m Z∑

(2-74)

where Zi is the valency of ionic species i. The mean activity coefficient is related to

the ionic strength for up to about 0.2 M by the modified Debye-Huckel limiting law

[Davies, 1962]

0.5

S+ S0.5

S

Iln (γ ) A Z Z 0.3 I

1 + Iφ± −

= − −

(2-75)

where Aφ = 0.509 (mol/L)-0.5

is the Debye-Huckel parameter for aqueous solutions at

25oC [Chen et al., 1982]. For a concentrated mixed electrolyte solution, more

complex relationships [Bromley, 1973] have to be employed (valid up to 6 M):

s

s

A Z Z I Z Z F Flog γ

Z Z Z Z1 I

φ + − + − + −±

+ − + −

= − + +

++

(2-76)

where 2

i

Z ZF B m

2

+ −

+ +

+=

∑

(2-77)

2

i

Z ZF B m

2

+ −

− −

+=

∑

(2-78)

( )2

s

0.06 0.6B Z ZB B

1.5I1

Z Z

+ −

+ −

+= +

+

(2-79)

B B B ε ε+ − + −= + + (2-80)

Values of B+ , B− , ε+ , and ε− given by Bromley [Bromley, 1973] for ions in aqueous

solutions at 25oC are tabulated in Table 2.8.

Table 2.8: Individual ion values of B+ , B− , ε+ , and ε− in aqueous solution at

25oC [Bromley, 1973]

Cation B+ ε+ Anion B− ε−

H+ 0.0875 0.103 OH

- 0.0760 -1.000

Na+ 0.0000 0.028 Cl

- 0.0643 -0.067

Ca2+

0.0374 0.119 SO42-

0.0000 -0.400

CO32-

0.0280 -0.670

Supersaturation of Solution: A solution that is in equilibrium with the solid phase is

said to be saturated with respect to that solid. Supersaturation is defined as the

concentration in excess of saturated concentration, and is expressed by the difference

from the saturation concentration or the ratio to the saturation or relative

supersaturation [Mullin, 1972; Mullin, 1993]

Supersaturation Ratio, S = C / Ceq

(2-81)

Relative Supersaturation, Ω = ∆C / Ceq = S – 1 (2-82)

where C is the solution concentration and Ceq is the equilibrium concentration at given

temperature. The supersaturated solution is composed of two zones, the metastable

and the unstable or labile zone. Metastable zone is the region where if there are

existing crystals, they will grow without nucleating; in the unstable zone, new crystals

appear as nucleation occurs. The most important single factor determining the

intensity of scaling is the supersaturation level of the deposit forming species

[Hasson, 2001]. Supersaturation can be achieved by:

1. Cooling of a normal solubility salt below it’s solubility temperature or heating an

inverse solubility salt above it’s solubility temperature.

2. Evaporating the salt solution beyond the solubility limit of the dissolved

substance.

3. Mixing of different solutions. The addition of a soluble salt to a saturated aqueous

solution of another salt will usually result in precipitation if the two salts have a

common ion, referred to as the “common ion effect”.

4. The mixing of saturated or near saturated solutions.

Nucleation

Classical Theory of Nucleation: The condition of supersaturation alone is not

sufficient for a system to begin to crystallise, there must exist in the solution a number

of minute solid bodies known as centre of crystallisation before crystals can grow

[Mullin, 1972; Mullin, 1993]. The fundamental nature of the nucleation process is not

fully understood. Initially, in an under-saturated solution the solute is generally

dispersed without association. When the solution become supersaturated, the solutes

begin to associate with one another. The population density and the size of these

associated clusters increase with the concentration of the solution. These clusters are

called embryos. An embryo is defined as a particle whose size is not yet large enough

for it to grow in the existent supersaturation. Though the embryo is presumed to be

born mainly by collisions in the crystallising solute, it is too small to survive. Some

embryos grow to nuclei by additional collisions, but others disappear by decreasing

their size or contributing to the growth of crystals in suspension. There are two

different types of nucleation: primary and secondary nucleation. Primary nucleation

refers to the generation of new crystals without effective seed crystals. Primary

nucleation may be subdivided into homogeneous and heterogeneous nucleation.

Homogeneous nucleation is a spontaneous reaction while heterogeneous nucleation is

induced by the presence of foreign particles in the solution. On the other hand, nuclei

are often generated in the vicinity of crystals present in a supersaturated system and

this establishes the macro-crystallisation process, known as secondary nucleation.

Gibbs Free Energy of Nucleation: The formation of crystal nuclei and integration of

ions into the crystal lattice is controlled by the tendency of the Gibbs free energy to

reach a minimum value. When nuclei are formed in the homogeneous nucleation,

there is a change in the Gibbs free energy of the system. The overall excess free

energy, nucl∆G , between a small solid particle of solute and the solute in solution is

equal to the sum of the surface excess free energy, S∆G , e.g. the excess free energy

between the surface of the particle and the bulk of the particle, and the volume free

energy, V∆G , e.g. the excess free energy between a very large particle (r = ) and the

solute in solution [Mullin, 1972; Mullin, 1993]. S∆G is a positive quantity, the

magnitude of which is proportional to r2. In a supersaturated solution, V∆G is a

negative quantity proportional to r3. Thus, if a spherical nucleus of radius, rnucl, and

volume, vnucl, is formed, then

nucl S V∆G ∆G ∆G= +

(2-83)

32 nucl

nucl nucl nucl nucl

nucl

πr4∆G 4πr σ ∆µ

3 v= −

(2-84)

where nuclσ is the interfacial tension between the nucleus and solution, and nuclµ∆ is

the difference between the chemical potential of the nucleus and that of the saturated

solution. Change in Gibbs free energy of the system is a function of the radius of the

nucleus; ∆Gnucl = f(rnucl). At the maximum value, crit∆G , such that nucl nuclG r 0∂∆ ∂ = ,

the critical r value becomes:

nucl nuclcrit

nucl

2 σ vr

∆µ=

(2-85)

From Equation (2-84) and Equation (2-85),

2

nucl critcrit

4πσ r∆G

3=

(2-86)

The behaviour of a newly created crystalline lattice structure in a supersaturated

solution depends on its size; it can either grow or re-dissolve, but the process which it

undergoes should result in the decrease in the free energy of the particle. The critical

size, rcrit, therefore represents the minimum size of a stable nucleus. Particles smaller

than rcrit will dissolve in order to achieve a reduction in its free energy while particle

larger than rcrit will continue to grow. If the ratio of the activity coefficient of the

saturated solution and that of the supersaturated solution is of the order of unity, then

the difference of the chemical potential of the nucleus and the saturated solution could

be written as

( )nucl∆µ RT ln Sv= (2-87)

where v is the number of ions per dissociated molecules in the solution. So

( )nucl nucl

crit

2 σ vr

RT ln Sv=

(2-88)

The higher the degree of supersaturation and the temperature, the smaller would be

the critical radius of the nucleus. Now, Equation (2-86) could be rewritten by

replacing for rcrit for the work of nucleus formation in homogeneous nucleation:

( )( )

3 2

nucl nuclcrit 2

σ v16πG

3 RT ln Sv∆ =

(2-89)

The rate of nucleation, Jnucl, e.g. the number of nuclei formed per unit time per unit

area, can be expressed in the form of Arrhenius equation

nuclnucl

B

GJ exp

k T

−∆= Λ

(2-90)

where Λ is a constant and kB is the Boltzmann constant. Combining Equation (2-89)

and Equation (2-90),

( )

3 2

nucl nuclnucl 23 3

B

16 π σ vJ exp

3 k T ln S

= Λ −

(2-91)

This equation indicates that three main variables govern the rate of nucleation: the

temperature, the degree of supersaturation and the interfacial tension.

The rate of nucleation of a solution can be affected by the presence of impurities in

the systems. An impurity may either act as an inhibitor that retards the crystallisation

process or act as an accelerator that increases the rate of crystallisation. Very often,

crystallisation is induced by inoculate or seed a supersaturated solution with small

particles of the material to be crystallised. This will have a better effect in controlling

the product size and size distribution of crystals. In either of the above two cases

mentioned (heterogeneous and secondary nucleation), the necessary work of nucleus

formation is less than for homogeneous nucleation. For heterogeneous nucleation one

can write [Mullin, 1972; Mullin, 1993]:

crit, heterogeneous crit, homogeneousG = G∆ Φ ∆

(2-92)

where Φ < 1.

Induction Period: A period of time usually elapses between the achievement of

supersaturation and the appearance of crystals. This time lag, generally referred to as

an ‘induction period’ is considerably influenced by the level of supersaturation, state

of agitation, presence of impurities, viscosity, etc. [Mullin, 1993]. The existence of an

induction period in a supersaturated system is contrary to expectations from the

classical theory of homogeneous nucleation, which assumes ideal steady-state

conditions and predicts immediate nucleation and growth once supersaturation is

achieved. So, induction period may consist of [Sohnel&Mullin, 1988; Mullin, 1993]:

1) time required to achieve a quasi-steady-state distribution of molecular clusters,

known as the relaxation time, tr;

2) time required for the formation of a stable nucleus, tn;

3) time for the nucleus to grow to a detectable size, tg.

So the induction period, tind, may be written as

ind r n gt t t t= + + (2-93)

It is difficult to actually isolate these separate quantities as each step is influenced by

various factors such as viscosity and diffusivity. Very often, induction period is

defined as the point at which the crystals are first visibly detected in a system or the

onset of a change in solution property. Despite its complexity, the induction period

has frequently been used as a measure of the nucleation event, making the simplifying

assumption that it can be considered inversely proportional to the rate of nucleation,

Jnucl,

( )1

ind nuclt J−

∝ (2-94)

The classical nucleation relationship may therefore be written [Mullin, 1972]:

( )( )

3

nuclind 23

σlog t

T log S

∝

(2-95)

which suggests that, for a given temperature, a plot of log(tind) vs. (T3log

2S)

-1 should

yield a straight line, the slope of which should allow a value of the interfacial tension,

nuclσ , to be calculated.

Crystal Growth

After induction period, once stable nuclei have been formed, they begin to grow into

crystals of finite size. The step-by-step building up of lattice layers may be subdivided

into two major steps that may occur at different rates, depending on the type of

substances present and on the existing operating conditions. The following steps

control the rate of growth:

1) Transport of ions at the membrane wall into the immediate vicinity of the

crystal surface by convection or diffusion. The crystallising component

diffuses from the membrane wall, Cw, to the crystal surface, Ccryt-s.

2) Integration of the ions into the crystal lattice. This is a surface reaction process

where the molecules arrange themselves into a crystal lattice. The difference

between the surface concentration, Ccryt-s, and the saturation concentration,

Ceq, e.g. (Ccryt-s - Ceq) is the driving force for integration process.

The crystal growth rate, Rcryt, can be mathematically represented by the diffusion

model and the surface reaction model [Mullin, 1972; Mullin, 1993]:

( )cryt D w cryt-sR k C C= − (diffusion)

(2-96)

( )n

cryt r cryt-s eqR k C C= − (reaction) (2-97)

where kD and kr are mass transfer coefficient by diffusion and surface reaction rate

constant, respectively; and n is the order of reaction. Combining both equations gives

( )

( )

w eq

cryt

n-1

D r cryt-s eq

C CR

1 1 +

k k C C

−=

−

(2-98)

When mass transfer controls, e.g. crystal growth at sufficiently low fluid velocity, the

first term in the denominator of equation dominates over the second and therefore

( )cryt G w eqR k C C= − (diffusion control) (2-99)

where kG is the overall crystal growth rate constant. When the surface attachment

controls, e.g. crystal growth at sufficiently high fluid velocities, the second term

dominates, assuming Cw ≈ Ccryt-s and therefore

( )n

cryt G w eqR k C C= − (surface reaction control) (2-100)

As shown in Equation (2-99) and Equation (2-100), the knowledge of equilibrium

concentration or solubility limit of the crystallising species is important for

determining the growth rate of the crystal.

2.5.1.2 Previous Research on Crystallisation Fouling

Only crystallisation of CaCO3 and CaSO4 will be covered here. Generally, two groups

of work can be identified; one is in the batch system where thermodynamic and

kinetic data are obtained through spontaneous precipitation or seeded growth of

crystal. While in the dynamic system, precipitation fouling is studied under various

operating parameters such as velocity, pH, temperature, and surface roughness in

different process systems in order to model the fouling process. Below provides a

summary of those research.

CaCO3 Crystallisation

Plummer and Busenberg [Plummer&Busenberg, 1982] determined the solubilities of

calcite, aragonite and vaterite in CO2-H2O solutions between 0 and 90oC for the

reaction ( ) 22

3 3CaCO s Ca CO−+= + as:

( )calcitelog K 171.9065 0.077993 T 2839.319 T 71.595 log T= − − + +

(2-101)

( )aragonitelog K 171.9773 0.077993 T 2903.293 T 71.595 log T= − − + +

(2-102)

( )vateritelog K 172.1295 0.077993 T 3074.688 T 71.595 log T= − − + + (2-103)

The CO2-H2O equilibria has also been evaluated and expressed as

( ) 2

Hlog K 108.39 0.0199 T 6919.53 T 40.451 log T 669365 T= + − − +

(2-104)

( ) 2

1log K 356.31 0.0609 T 21834.37 T 126.83 log T 1684915 T= − − + + −

(2-105)

( ) 2

2log K 107.89 0.033 T 5151.79 T 38.93 log T 563713.9 T= − − + + − (2-106)

which may be used up to 250oC. KH is the hydrolysis constant of CO2.

There are several rate expressions developed to describe the precipitation of

CaCO3. Inskeep and Bloom [Inskeep&Bloom, 1985] adopted the adsorption layer

model originally proposed [Davies&Jones, 1955] for precipitation of silver chloride to

evaluate the rate equations for calcite precipitation kinetics at pressure of CO2 less

than 0.01 atm and pH greater than 8. In this model, it is assumed that 1) there is a

monolayer of hydrated ions covering crystal in aqueous solutions and 2) hydration or

dehydration of lattice ions at surfaces sites precedes dissolution or precipitation. The

expression for calcite crystal growth takes the form:

( )( )2 22 2

cryt G1 3 3eq eqR k Ca Ca CO CO

− −+ + = − −

(2-107)

where kG1 is the overall growth constant and the subscript, eq, denotes the

concentration at equilibrium. Reddy and Nancollas [Reddy&Nancollas, 1971;

Nancollas&Reddy, 1971] presented a mechanistic model for calcite precipitation in

which the precipitation, Rppt, and dissolution rate, Rdiss, are given by:

22

ppt ppt 3R k Ca CO−+ =

(2-108)

( )diss diss 3R k CaCO s= (2-109)

where kppt and kdiss refers to the rate constant of precipitation and dissolution,

respectively; and [CaCO3(s)] = 1 for solid. At equilibrium, the net rate of crystal

growth, Rcryt, can be described by Rppt – Rdiss:

( )2 22 2

cryt G2 3 3eq eqR k Ca CO Ca CO

− −+ + = −

(2-110)

where kG2 is the overall growth constant. Another expression that is widely accepted

[Dawe&Zhang, 1997; Inskeep&Bloom, 1985; Nilsson&Sternbeck, 1999;

Spanos&Koutsoukos, 1998; Pokrovsky, 1998; Turner&Smith, 1998; Zhang&Dawe,

1998; Zuddas&Mucci, 1998] to describe the rate of crystal growth of CaCO3 based on

the supersaturation of CaCO3 in the solution is as follow:

( )n

1 n

cryt G3R k S 1= −

(2-111)

where kG3 is the overall growth constant and 1 < n < 2. The supersaturation ratio, S,

is:

2 23Ca CO

sp

SK

a a+ −

= (2-112)

where a is the activity of component i and Ksp is the thermodynamic solubility

constant. It should be noted that all of these rate expressions would end up with a

different growth constants (e.g. kG1, kG2, kG3) as pointed out by Inskeep and Bloom

[Inskeep&Bloom, 1985].

It was revealed in the study of spontaneous and seeded growth of CaCO3 at pH 9.0 to

10.0 at constant supersaturation (1.88–3.39) by Sapnos and Koutsoukos

[Spanos&Koutsoukos, 1998] that the induction times were shorter in the seeded

precipitation compared to those in the unseeded precipitation and displayed a marked

inverse dependence on the solution supersaturation. This was expected, as

crystallisation is greatly dependent on the surface available for growth e.g. the seed

provides an effective nucleation sites for crystal growth. Besides, it was also found

that high pH and high supersaturation favours the precipitation of vaterite and not the

stable form of calcium carbonate (calcite). This outcome agreed with the results

reported by Turner and Smith [Turner&Smith, 1998], which indicated that the

crystalline phase of calcium carbonate precipitated on a heat transfer surface depends

on the degree of supersaturation, with aragonite at higher supersaturation (9 < S < 22)

and calcite at lower supersaturation (8 < S < 10).

Kim and Cho [Kim&Cho, 2000] have studied the crystal growth of CaCO3 fouling

using a microscope. The study provided crystal growth data as a function of time,

which includes crystals creation, crystal growth, the number of crystals, and removal

process. It was found that the rate and location of crystal growth were dictated by

small seed crystals formed in the early stage of fouling e.g. a crystal formed earlier

grew faster than one formed later, since the former retarded the growth of the latter.

However, the crystal formed later tended eventually to grow to a size equivalent to

that one formed earlier. Also, the removal rate was found to be negligible compared to

the deposition rate.

Surface roughness was found to increase the tenacity and porosity of calcite layer

formed on the heat transfer surface [Keysar et al., 1994] e.g. the tensile stress required

to disbond a calcite deposit adhering to a rough metal surface was 30 times higher

than those from a smooth surface. This increase is the consequence of enhancement

of surface nucleation sites induced by surface roughness.

CaSO4 Crystallisation

Marshall and coworkers [Marshall&Slusher, 1966; Marshall&Slusher, 1968; Marshall

et al., 1964] provided the thermodynamic solubility product for three different forms

of calcium sulfate; they are

splog K (anhydrite) 215.509 6075.2/T 85.685 log T 0.0707 T= − + + −

(2-113)

splog K (gypsum) 390.9619 12545.62/T 152.6246 log T 0.08185 T= − − +

(2-114)

splog K (hemihydrate) 154.527 54.958 log T 6640.0/T= − − (2-115)

Nancollas and co-workers [Liu&Nancollas, 1970; Liu&Nancollas, 1971; Nancollas et

al., 1973; Liu&Nancollas, 1975; Nancollas et al., 1978; Zhang&Nancollas, 1992]

presented a large amount of work on the crystallisation of calcium sulfate and

developed a second-order expression for the seeded growth of calcium sulfate

dihydrate as follow:

( ) ( ) 11

1 1222 2

22 22 2

cryt G 4 4eq eqR k Ca SO Ca SO

− −+ + = −

(2-116)

The rate of CaSO4 crystal growth was found to be surface-controlled. Unlike the

precipitation of CaCO3, the growth of calcium sulfate was not sensitive to the change

in pH e.g. from 3.2 to 9.2 [Nancollas et al., 1978]. The growth kinetics of gypsum had

been investigated using constant composition method over a range of calcium/sulfate

molar ratios in supersaturated solutions [Zhang&Nancollas, 1992]. In spite of

constancy of ion activity product, the rate increased with decreasing Ca+/SO4

2- molar

ratio, indicating that the rate of crystal growth is not merely a function of the

thermodynamic driving forces but also depends upon the relative concentrations and

characteristics of individual lattice ions.

Edinger [Edinger, 1972] investigated the factors that affect the size of gypsum. The

results indicated that a smaller degree of supersaturation, in general, resulted in larger

and equidimensional crystals. The presence of electrolytes such as Na+, Sr

2+, NO3

- had

as well changed the size of crystals by influencing the supersaturation of solutions.

Prisciandaro [Prisciandaro et al., 2001] had reported that the chloride salts influenced

the gypsum nucleation by increasing the induction period, thus retarding the

nucleation kinetics. The experimental results were interpreted by means of the

behaviour of gypsum solubility in NaCl solutions where CaSO4 solubility increased as

the concentration of NaCl increased.

Budz and co-workers [Budz et al., 1986] had studied the effect of cationic and anionic

impurities (e.g. Al3+

at 10 – 200 ppm, maleate ions at 120 – 800 ppm and fumarate

ions at 120 – 300 ppm) on the continuous precipitation of gypsum. The SEM showed

that gypsum crystallises in twinned and agglomerated forms. For pure crystallisation,

only the smallest crystals, up to about 5 µm, remained discrete. The effect of Al3+

was

a marked increase in the degree of agglomeration and the agglomerates exhibited a

more open structure in comparison with those of pure crystals whereas maleate ions

appeared to induce the opposite effect. The crystals in the presence of fumarate were

more elongated and included some star-like agglomerates.

Mixed Crystallisation

The study in this area is very limited. Sheikholeslami and co-workers

[Sudmalis&Sheikholeslami,2000;Chong&Sheikholeslami, 2001; Sheikholeslami&Ng,

2001] have conducted the study on coprecipitation by CaCO3 and CaSO4. In the case

of CaCO3 as the dominance salt [Chong&Sheikholeslami, 2001], the presence of

CaSO4 had weakened the tenacity as well as the adherence of CaCO3. The rod-shaped

CaSO4 crystals were seemed to grow in the hexagonal shaped CaCO3 crystals. On the

other hand, when CaSO4 is the predominant salt [Sheikholeslami&Ng, 2001], CaCO3

have made the crystals more compact. The findings were consistent with the results at

different proportions of CO32-

and SO42-

[Sudmalis&Sheikholeslami, 2000]. From the

thermodynamic and kinetic data, it was shown that the single salt data could not be

extendable to coprecipitation; e.g. the thermodynamic solubility constant differ by

about 1000% from those of pure CaCO3 [Chong&Sheikholeslami, 2001]. However,

this effect depends on the type of species that is in dominance or acts as impurity e.g.

the presence of CaCO3 only changed the Ksp of CaSO4 in mixed crystallisation by

about 70% compared to those of pure CaSO4 crystallisation [Sheikholeslami&Ng,

2001]. A hypothesis based on thermodynamics of coprecipitation was proposed

[Chong&Sheikholeslami, 2001] to explain this phenomenon. The formation of pure

CaCO3 from Ca2+

and CO32-

proceeds according to the reaction below

2 2

3 3Ca CO CaCO (s)+ − →+ ← (2-117)

At equilibrium, the Gibbs free energy of reaction, ∆rG, is zero. Therefore based on

Equation (2-118), at equilibrium the thermodynamic solubility constant will be related

to the standard molar Gibbs free energy of reaction, ∆rGφ, according to Equation (2-

119)

2+ 2-3

3

Ca CO

r r

CaCO

. G G RT ln

a a

a

φ

∆ = ∆ +

(2-118)

sp rRT ln(K ) = Gφ− ∆ (2-119)

By definition, ∆rGφ is the difference between the total standard Gibbs free energy of

formation of products ∆fGφ(products) and the reactants ∆fG

φ(reactants). For the

formation of CaCO3, the following equation can be written

2-2+

r f 3 f f 3G G (CaCO ) G (Ca ) G (CO )φ φ φ φ∆ = ∆ − ∆ + ∆ (2-120)

In the coprecipitation process, the product differs from that of single salt precipitation

as supported by the scale morphology of salt formed from mixture where the needle-

shaped CaSO4 grows in the hexagonal-shaped CaCO3. Since the standard Gibbs free

energy of formation of that mixed salt is different from that of the pure salt, hence the

thermodynamic equilibrium constant of the coprecipitation is different from that of

single salt precipitation. In addition, the 2nd

order crystal growth expression also failed

to fit the kinetic data in both cases [Chong&Sheikholeslami,2001;

Sheikholeslami&Ng, 2001]. Sheikholeslami and Ng [Sheikholeslami&Ng, 2001] has

provided an extensive analysis into the kinetic of coprecipitation and explain the

limitation of modelling coprecipitation using single salt model as discussed in the

following. For single salt precipitation, the following expression can be written:

A B s ns A + s B s S + s N→← (2-121)

where A and B are the reactants, S is the solid product, N is the non-solid byproduct,

and s represents the stoichiometric number for each component. The rate of

precipitation, Rppt, can be shown by Equation (2-122)

3 51 2 4

ppt ppt A B S N OR k C .C .C .C .Ca aa a a= (2-122)

where kppt is temperature dependent precipitation constant, C represents the

concentration of each substance and the exponents are the reaction partial orders. In

the above equation, C0 represents the concentration of other species present and if

they are catalytic to the reaction, 5a is positive and if inhibitory, then 5a is negative.

If 3a and 4a are negative, the reaction is auto inhibitory, if they are positive, the

reaction is auto catalysing and if they are equal to zero the reaction is neither auto

inhibited nor it is autocatalytic. For the last case, Equation (2-122) can be further

simplified to:

1 2

ppt ppt A BR k .C .Ca a= (2-123)

The net rate of crystallization, Rcryt, is the rate of reaction for solid formation less of

dissolution and therefore is shown by Equation (2-124).

1 2

cryt G A B spR = k [C .C K' ]a a − (2-124)

where kG is the overall growth constant and K’sp is the concentration product at

equilibrium. In case of coprecipitating salts, Equation (2-122) can be written for each

salt but it cannot be simplified to Equation (2-123) because the presence of one salt

might have a catalytic or inhibitory effect on the other. Therefore, Equation (2-124)

that usually is used for precipitation of a single sparingly soluble salt cannot be

employed in case of co-precipitating salts. When there is a common-ion in co-

precipitating salts, the reaction can be described by the following set of relationships:

A B1 S1 1 N1 1s A + s B s S + s N→←

(2-125)

C B2 S2 2 N2 2s C + s B s S + s N→← (2-126)

where S1 and S2 represent solids coprecipitating with each other. Then the reaction

can be written as:

3 51 2 4

ppt-1 ppt-1 A B S1 N1 S2R k .C .C .C .C .a aa a a

C=

(2-127)

3 51 2 4

ppt-2 ppt-2 C B S2 N2 S1R k .C .C .C .C .b bb b b

C= (2-128)

It can safely be considered that neither of the reactions is auto-inhibitory or auto-

catalytic but the effect of the second solid phase cannot be ignored. Therefore, the rate

equations can only be simplified partially:

51 2

ppt-1 ppt-1 A B S2R k .C .C .aa a

C=

(2-129)

51 2

ppt-2 ppt-2 C B S1R k .C .C .bb b

C= (2-130)

Then the dissolution rates can be incorporated and subtracted from the above crystal

formation rates in order to obtain the net rate of crystallization. Therefore, to obtain

the rate of crystallization and the partial order with respect to all the components, one

should know the instantaneous concentrations for Ca2+

, SO42-

, CO32-

, CaCO3, and

CaSO4 in the solution as well as the solubility constants of CaCO3 and CaSO4 when

they co-exist. This is a very difficult experimental task considering that the accurate

determination of solid concentrations is not possible as it requires homogeneous

samples representing the concentrations in the whole reactor and that cannot be

possible as there is precipitation on the surfaces as well. This is not withstanding the

difficulty in determining each individual solid concentration as a function of the time.

Crystallisation Fouling Model in Reverse Osmosis

Okazaki and Kimura Model for CaSO4 Crystallisation: In this model

[Okazaki&Kimura, 1984], it is assumed that the growth of porous crystal-layer takes

place at the membrane surface and creates resistance to permeation. The flux

decreases with time by the increase of scale-layer thickness. But this decrease in flux

reduces the extent of concentration polarization, and thus the rate of crystallization

also decreases. Finally, crystallisation ceases and the flux become constant when the

concentration of salts at the membrane surface, Cw, becomes equal to that of the

saturated solution, Ceq. The crystal growth-concentration polarization is illustrated in

Figure 2.12.

The crystal growth is assumed to follow the widely adopted equation for CaSO4

crystallisation:

( )2

fG w eq

mk C C

t

∂= −

∂

(2-131)

The permeate flux, Jv, (assuming total salt rejection) is determined by flow resistance

due to membrane and scale layer as follow:

( )v

m f

P ΠJ

R R

∆ − ∆=

+

(2-132)

where Rm and Rf are the resistance of membrane and scale layer (taken into account of

the concentration polarization effect), respectively. Equation (2-132) is identical to

Equation (2-19) except where the permeability of membrane, pl , is replaced with

1/(Rm+Rf). The value of Rf is defined as:

f f fR α m= (2-133)

where fα is the resistance factor of scale; mf is the weight of scale formed per unit

membrane area as a function of time. Combining Equation (2-131), (2-132), (2-133),

and

v v f

f

J J R.

t R t

∂ ∂ ∂=

∂ ∂ ∂

(2-134)

the following equation is obtained:

( ) ( )v G f

22

v w eq

J k αt

P ΠJ C C

∂− = ∂

∆ − ∆−

(2-135)

Figure 2.12: Growth of porous crystal-layer under concentration polarization

effect [Okazaki&Kimura, 1984]

Integrating Equation (2-135) and assuming the term ( )P Π∆ − ∆ is constant, which is

true when P∆ >> Π∆ as in the actual desalination process; the following expression is

obtained:

( )

( )

( )

v

v

J t 0

v G f

22J v w eq

J k αt

P ΠJ C C

=∂

=∆ − ∆−

∫

(2-136)

The integral form of Equation (2-136) is calculated by Simpson’s formula and is

plotted against time, t, from which the plot is linear and the value of ( )G fk α P Π∆ − ∆

is estimated from the slope.

Flux Decline due to Surface Blockage: The flux decline due to surface blockage

model was developed for CaSO4 fouling [Gilron&Hasson, 1987; Brusilovsky et al.,

Permeate

δ

vJ

bC

wC

pC

Solution Bulk

Flow

Mem

bran

e

Boundary Layer

Scale L

ayer

1992]. It is assumed that the membrane is rapidly inoculated with Nnucl nucleation

sites per unit of total membrane area and that each nucleation site has an identical

hemispherical geometry of radius rcryt. The radial growth of crystals will block the

membrane and prevent the solution flow across the membrane. The crystallisation

fouling in reverse osmosis membrane is illustrated in Figure 2.13. The permeate flow

at time t, Qv(t), is given by:

( ) ( )v mf vQ t A v t= (2-137)

where Amf represents the crystal free membrane area available for permeate flow (so

at t = 0, Amf = Am where Am is the total membrane area); and vv(t) is the permeation

velocity through the crystal free membrane area at time t, which is given by

(assuming total salt rejection):

( ) ( ) b wv p p

b

Π Cv t ∆P ∆Π P 1

∆P C

= − ≅ ∆ −

l l

(2-138)

where Cw is the solute concentration at the wall surface at time t; Cb is the bulk salt

concentration which remains constant throughout the experiment; and bΠ is the

osmotic pressure corresponding to solution bulk concentration, which is also constant

throughout the experiment. It should be noted the desalination process is carried out at

constant pressure, so only the osmotic pressure term (e.g. osmotic pressure

corresponding to the solute at the membrane wall, wΠ ) varies with time as solute at

the membrane surface builds up. Amf is related to Am by:

2mfnucl cryt

m

Aθ 1 N πr

A= = −

(2-139)

where θ represents the fractional membrane area free from crystals available for

permeate flow; and the term 2

nucl crytN πr represents the total area projected by the

hemispherical crystals that cover the membrane area. The permeate flux at time t,

Jv(t), based on the total membrane area, Am, is defined as:

( )( )v

v

m

Q tJ t

A=

(2-140)

By combining Equation (2-137), (2-138), (2-139) and (2-140), the fractional flux

decline (DJ) is given by:

( )( )

( ) ( ) ( )( )

v vv v

m mv v

J t v tQ t Q t 0θ

A AJ t 0 v t 0DJ

== = =

= =

(2-141)

Figure 2.13: Radial growth of non-porous CaSO4 layer in reverse osmosis

[Brusilovsky et al., 1992]

By assuming P Π∆ ≈ ∆ , which is true for the actual desalination process where the

applied pressure is always much higher than the osmotic pressure of the solution,

then:

( ) ( )v vv t 0 v t constant= = = (2-142)

and hence the fractional flux decline is identical to the surface blockage by crystals

growth:

( )( )v

v

J tθ

J t 0DJ = =

=

(2-143)

By combining Equation (2-138) and the kinetic expression for radial growth of the

crystals:

( )ncryt

cryt G w eq

rρ k C C

t

∂= −

∂

(2-144)

where crytρ is the crystal density; kG is the crystal growth constant; and n is the

reaction order; so the rate of surface blockage (which is equivalent to the rate of flux

decline) is given by:

( )nG nucl

w eq

cryt

2k πNθC C 1 θ

t t ρ

DJ∂ ∂− = − = − −

∂ ∂

(2-145)

2.5.2 Particulate Fouling

Particulate fouling in reverse osmosis is caused by the deposition of undissolved

materials onto the membrane surface; which includes colloidal particles, organic

Permeate

δ

vJ

bC

wC

pC

Solution Bulk

Flow

Mem

bran

e

Boundary Layer

rcryt

Scale

no

permeation

molecules, biological matters (as most microorganisms are in the range of 1 µm),

products from crystallisation that are being sloughed off by the shear force, or crystals

formed in the bulk solution due to high supersaturation. Particles are transported from

the bulk fluid, some will attach to the surface while others will be re-entrained. These

three steps usually are inseparable, so in particulate fouling, the term particle

deposition is used to refer to the overall process. Particle depositions can be strongly

affected by fluid turbulence, physical and chemical interactions between particles and

wall, particle shape, and wall roughness.

2.5.2.1 Particle Deposition in Laminar Flow

For the case where there is an electrostatic attraction between the particles and the

channel wall and for the low shear rates encountered in laminar flow, it is expected

that particle deposition will be controlled by diffusive mass transfer from the bulk

suspension.By assuming the developing concentration boundary layer to be

sufficiently thin that the fully developed laminar velocity profile can be replaced by

its tangent line at the wall, Bowen et al. [Bowen et al., 1976] obtained simple

analytical expressions for the mass-transfer controlled deposition rates for

suspensions flowing in parallel-plate and cylindrical channels. A straightforward

modification of their theory yields the following equation for the local particle

deposition flux, pφ , in a rectangular channel with a cross-sectional dimension of 2w ×

2h [Vasak et al., 1995]:

( )

1 32

p

d

b

1 Dk

C 4 3 9z

mφ = =

Γ

(2-146)

where Cb is the bulk concentration; kd is the deposition coefficient; D is the

unhindered (i.e. Stokes-Einstein) particle diffusion coefficient, z is the distance

downstream of the point where deposition begins; ( )4 3Γ = 0.893; and m , the slope

of the fully developed, rectangular channel velocity distribution, is given by:

( ) ( )

( ) ( )

22n 1

b

55n 1

sech n 1 2 πw h21π n 1 23u

h tanh n 1 2 πw h6 h1π w n 1 2

m

∞

=

∞

=

−−

− = −

− −

∑

∑

(2-147)

where ub is the average fluid velocity in the rectangular channel.

2.5.2.2 Particle Deposition in Turbulent Flow

In turbulent flow, the wall shear stress as well as the particle lift force and fluid

drainage have to be taken into account when considering the deposition. Epstein

[Epstein, 1993] assumed that the initial deposition process for Brownian particles

takes place via two first order steps in series such that

1

d

m a

1 1k

k k

−

= +

(2-148)

where km and ka are the mass transfer coefficient and attachment rate coefficient,

respectively. ka, is related to the activation energy for attachment, Ea-part, by

[Sheikholeslami, 2000]:

a-part

a

Ek exp

RT

− ∝

(2-149)

2.6 Microbial Fouling

The microbial fouling of reverse osmosis can only be understood from the biofilm

development concept. This section will discuss the structure and properties of a

biofilm as well as the successive events in the biofilm formation process.

2.6.1 Biological Fouling As Biofilm Development

Biofilm is defined as a surface accumulation, which is not necessarily uniform in time

or space, that comprises cells immobilized at a substratum and frequently embedded

in an organic polymer matrix of microbial origin [Characklis&Marshall, 1990]. In an

aqueous environment, dissolved organic molecules and macromolecules are

immediately adsorbed from the liquid medium to a support, termed a substratum.

Bacterial cells present in the fluid are transported to the substratum by a variety of

mechanisms. Just prior to or upon arriving at a substratum, bacterial cells alter certain

gene expression patterns; enzymes and pathways are altered (induced or repressed) to

create a phenotypically adherent cell. Once at the substratum, the cells can adsorb

either reversibly or irreversibly. The cells will secrete extracellular polymers that

attach the cells tenaciously to the substratum. Attached cells metabolise prevailing

energy and carbon sources, which come from the surrounding fluid or adsorbed to the

substratum surface, to grow, replicate, and produce insoluble extracellular

polysaccharides, thus accumulating more living organisms and incorporated into the

biofilm community. As a result of hydrodynamic forces and stresses exerted by

replication, there can be a continual erosion of cells and extracellular material from

the biofilm back to the bulk fluid. A more random stochastic process known as

sloughing can occur where either large sections or the entire biofilm become

displaced from the substratum and enter the liquid. Thus, biofouling is a result of the

complex interaction between the membrane material, fluid parameters (such as

dissolved substances, flow velocity, pressure, etc.), and microorganisms. Biofouling

is basically a problem of biofilm growth. It can really be understood only if the

implications of the biofilm mode of bacterial growth and its dynamics are considered.

2.6.2 Successive Events in Microbial Fouling

The biofouling process can be divided into five stages; 1) the formation of

conditioning film, 2) bacteria transport, 3) reversible and irreversible adhesion, 4)

biofilm development and accumulation, and 5) biofilm detachment [Characklis,

1990]. The formation of biofilm can be illustrated in Figure 2.14.

Figure 2.14: Formation of biofilm (adapted from [Maier et al., 2000])

2.6.2.1 Conditioning

The first step in biofilm formation, prior to microbial adhesion, is the adsorption of

macromolecules (e.g. humic substances, lipopolysaccharides, and other products of

microbial turnover) on the membrane surface, which is known as the conditioning

film. This phase is completed within seconds to minutes after immersion of a surface

into an aqueous system. Little and Zsolnay [Little&Zsolnay, 1985] measured as much

as 0.8 mg/m2 organic matter had adsorbed onto the stainless steel surface after 15

minutes exposure in seawater. These particles can mask the original surface properties

and cause a slightly negative surface charge. A macromolecular conditioning film on

a solid substratum presents a new set of surface characteristics to the bulk liquid

phase. Thus, the electrostatic charge and the critical surface tension (an indication of

surface free energy) may change with the accumulation of a conditioning layer.

Accordingly, a variety of protein conditioning layers, on surfaces such as polystyrene

and glass, may both increase and inhibit the attachment of various bacterial species.

2.6.2.2 Bacteria Transport

Before bacteria adhere to the surface, bacteria have to be transported from the bulk

liquid to the solid-water interface. Bacteria are transported to the membrane surface

by the following mechanisms: 1) chemotaxis; 2) Brownian motion and diffusion; and

3) hydrodynamic forces (flow, turbulence, flux).

Quiescent Conditions

Under quiescent conditions, bacteria are transported by gravitational forces, Brownian

diffusion and motility from the bulk liquid phase to a substratum. Since most bacteria

only have an average size of about 1 µm, the sedimentation rate due to gravity is very

Clean

Surface

Conditioning Reversible

Attachment

Irreversible

Attachment

Biofilm

Formation

Surface

microorganism organic substances

slow as well as a small Brownian diffusivity. Therefore, motility is more important

mode of transport for bacteria in quiescent system [Bryers, 2000]. The motility

movement of bacteria is influenced by the chemotaxis effect whereby a cell will move

toward the source of chemical attractant and move away from a negative chemotaxis.

Laminar flow

For laminar flow, the mechanism for mass transport of cells is by molecular diffusion

and is described by Fick’s law of diffusion. Bacteria cells are treated as particle where

the diffusive flux, JD, of a particle in the y-direction is proportional to the

concentration gradient in that direction [Bryers, 2000]:

D

CJ D

y

∂= −

∂

(2-150)

where D is the diffusivity of particle.

Turbulent Flow

Bacteria cells are transported by fluid dynamic forces in a turbulent flow. Like

particle transport, the cells transport is dependent on the size, shape, density,

concentration and is influenced by various forces near the attachment surface. From

theory of mass transfer, we know that there exists a layer termed the viscous sublayer

where the bacteria in the bulk liquid must penetrate this layer in order to be deposited

at the solid surface. The thickness of this layer is dependent on the flow velocity,

viscosity and wall roughness. Hydrodynamic forces are considered to be responsible

for transporting the cells to the boundary layer, whereas Brownian motion, motility,

and/or diffusion seem to be the mechanisms to cross the boundary layer [Marshall,

1985].

Flux

In the RO process, the flux as a transport vector vertical to the membrane must be

considered to be a strong force assisting the cell to penetrate the viscous sublayer.

However, there has not yet been established a clear relationship between the flow

velocity, the flux, and the deposition of bacteria [Flemming et al., 1993]. Probstein et

al. [Probstein et al., 1981] found that the initial rate of colloid (or microbial)

deposition on the RO membranes (hence the flux decline kinetics) was largely

independent of fluid shear (e.g. brine flow velocity) within the range tested, although

it can significantly limit the overall thickness of the fouling layer.

2.6.2.3 Adhesion

Upon arriving at the substratum, microorganisms will attach to the surface. The

factors affecting the adhesion rate is dependent on:

• The microorganisms (semi-solid phase), governed by factors such as species

composition of microflora, cell number in the bulk, viability, nutrient status,

growth phases, hydrophobicity, surface charge, and extracellular polymeric

substances (EPS).

• The fluid (liquid phase), with factors such as temperature, pH, dissolved

organic and inorganic substances, viscosity, surface tension, and

hydrodynamic parameters (shear forces, flux, turbulence).

• The membrane surface (solid phase), influencing adhesion via chemical

composition, hydrophobicity, surface charge, conditioning film, and

‘biological affinity’.

Very often the adhesion of microorganisms is divided into reversible adhesion and

irreversible adhesion. Reversible adhesion refers to that association of a bacteria to a

surface where the bacterial cells continues to exhibit a two dimensional Brownian

motion and can be removed from the surface by relatively weak forces including the

bacterium’s own motility while in irreversible adhesion bacteria no longer exhibit

Brownian motion and cannot be removed by moderate shear forces [Bryers, 2000].

Irreversible adsorption is generally considered a permanent bonding to the

substratum, frequently mediated by extracellular polymers. It involves dipole-dipole

interactions, dipole-induced dipole interactions, ion-dipole interactions, hydrogen

bonds, or significant polymeric bridging [Characklis, 1990; Marshall, 1985;

Marshall&Blainey,1991]. Very often, bacteria adhesion is treated as a physiochemical

process where the deposition of bacteria is equivalent to particle deposition on a

surface and can be explained in terms of Gibbs energy involved in the destruction and

creation of interfaces [Absolom et al., 1983; Busscher et al., 1984; Korber et al.,

1995] as well as the DLVO theory for colloid stability [van Loosdrecht et al., 1989;

Hermansson, 1999; Azeredo et al., 1999]. However the use of these two approaches

has its weakness as pointed out by Bryers and Marshall et al. [Bryers, 2000;

Marshall&Blainey, 1991] bacterial cells are not ‘ideal’ particles where they have no

simple geometry, definitive boundary, or uniform exterior molecular composition;

they are living organisms and capable of metabolism and growth. The internal

chemical reactions can lead to changes in molecular composition both in the interior

and at the cell surface, with molecules and ions constantly crossing the

bacterium/water interface. Although altered, these chemical processes also continue

after adhesion, therefore the adhered cells are rarely in complete chemical equilibrium

with their environment.

Thermodynamics of Bacteria Adhesion

Bacteria adhesion to the substratum process is favoured by a decrease in the free

energy of system and this process can be expressed in terms of surface free energy. In

the thermodynamic approach, the change in the interfacial Gibbs free energy upon

adhesion, adhG∆ , is given as [Absolom et al., 1983; Busscher et al., 1984]:

adh SB SL BLG G G G∆ = − − (2-151)

where GSB, GSL, GBL is the Gibbs free energy associated with the interfaces between

substratum/bacterium, substratum/liquid and bacterium/liquid, respectively. If the

molecular composition of the interface, the pressure, and the temperature do not

change, then Equation (2-151) may be written as a balance of interfacial tensions

[Bryers, 2000]:

adh SB SB SL SL BL BLG A σ A σ A σ∆ = − − (2-152)

where SBσ , SLσ , BLσ is the interfacial tension associated with the interfaces between

substratum/bacterium, substratum/liquid and bacterium/liquid, respectively; ASB, ASL

and ABL are the area of contact between substratum/bacterium, substratum/liquid and

bacterium/liquid, respectively. Adhesion is favoured and will proceed spontaneously

if the free energy, adhG∆ , is negative as a result of adhesion, that is, if SBσ is smaller

than the sum of SLσ and BLσ . However, Equation (2-151) and (2-152) only applied if

both interacting surface make direct contact, as how much of the cell is actually in

contact with the substratum in adhesion process is questionable. The cell may make

contact through an energy barrier via surface polymers that may represent a small

fraction of the cell surface so that it may contribute very little to the cell surface free

energy as measured by contact angle measurement [Hermansson, 1999].

Bacterial Adhesion as Colloidal Deposition

Bacteria can be considered as living colloidal particles and exhibit a net negative

surface charge. Then the DLVO theory can be applied to describe the interacting

forces at the cell/substratum surface. The DLVO theory assumes that the total long-

range interaction, GTOT, between a colloidal particle and a surface is the balance

between two additive factors, GA resulting from van der Waals interactions (generally

attractive) and repulsive interactions, GR, from the overlap between the electrical

double layer of the cell and the substratum (generally repulsive, due to the negative

charge of cells and substratum) [Hermansson, 1999]:

TOT A RG G G= + (2-153)

The theory predicts two possible positions for attraction, 1) the primary minium at

small separations, 2) the secondary minimum at larger distances of separation. At

some point between these two minima, repulsive forces are maximal. Problems with

this approach reside in the values used for the charges on the surfaces, the different

geometry at the attachment site and the varying dielectric constant of the liquid as the

two surfaces approach [Characklis, 1990; Bryers, 2000]. Hence an extended DLVO

theory was developed to include the short-range interactions that are also important

for adhesion, mainly Brownian movement forces, GB, and polar interactions, GP, into

the total free energy of interaction, GTOT, as:

TOT A R B PG G G G G= + + + (2-154)

Co-adhesion Between Microbial Pairs

Like microbial adhesion to inert substratum surface, microbial co-adhesion to other

microbial cell surfaces can also be described in terms of van der Waals forces,

electrostatic forces, and acid-base interactions [Bos et al., 1999]. Figure 2.15 shows a

schematic presentation of co-adhesion between two microorganisms.

Previous Research on Bacteria Adhesion

According to Fletcher et al. [Fletcher et al., 1983], bacterial attachment to surface

comprises of three components, the bacterial surface, the liquid medium and the

substratum surface. The bacteria surface is the most important factor in the attachment

mechanism, but it is the most difficult to characterise. This is due to the fact that

different bacterial strains had shown a great variation in the attachment ability. There

are bacteria which attach very readily to certain surface, so that monolayer coverage is

reached within 1 to 2 hours, whereas some bacteria have very little ability to attach.

The variation in attachment ability is mainly due to the differences in composition of

cell-surface polymers which play a significant role in the adhesion process. A

Pseudomonas fluorescens strain (a freshwater isolate) attached to surfaces in

moderately high numbers. However, two mutants of the strain were subsequently

selected for and obtained, one which attached to surfaces more readily than the

original wild type (crenated colony mutant) and one which had little ability to attach

(mucoid colony mutant). The results are shown in Table 2.9 (a) and (b):

Figure 2.15: Schematic presentation of co-adhesion between two microorganisms

1 and 2

Table 2.9 (a): The attachment of adhesion mutant to hydrophobic polystyrene

(PS) and to more hydrophilic, tissue culture dish polystyrene (TCD) [Fletcher et

al., 1983]

Attachment measured on A590 of crystal violet-stained

attached cell

Bacteria strain

PS TCD

Wild type 0.190 0.067

Mucoid mutant 0.025 0.011

Crenated mutant 0.216 0.094

Table 2.9 (b): Distribution of adhesion mutants in culturing apparatus,

determined by sampling from culturing vessel walls and bulk liquid [Fletcher et

al., 1983]

Bacteria numbers (viable counts) Bacteria strain

On vessel wall (10 cm-2

) In liquid (mL-1

)

Wild type 2.3 x 108

< 1.0 x 105

Mucoid mutant 3.0 x 106

4.8 x 108

Crenated mutant 8.6 x 108 2.0 x 10

6

These differences in attachment ability were shown in measurement of attachment to

selected test surfaces and in the ability of the different type to colonise and persist in

various walls or liquid medium of a laboratory continuous culture apparatus. The

M1

M2

M1

L : liquid phase L

1M Lσ

2M Lσ

1M Lσ

2M Lσ1 2M Mσ

M2

mucoid mutant with little attachment ability produced a negatively charged

extracellular polymer which apparently inhibited adhesion.By contrast, the wild type

with moderate attachment ability produced no extracellular polymer and had a typical

Gram-negative bacterium lipopolysaccharide (LPS) exposed at it surface, whereas the

mutant with increased attachment ability had a LPS with reduce polysaccharide chain

length, thus possibly exposing the hydrophobic lipid component of the LPS.

The number of cells in suspension influences the number of adhering cells. Bryers and

Characklis [Bryers&Characklis, 1981] described the rate of initial biofilm formation

in flowing systems using a first order expression. The rate constant is shown to be a

linear function of the biomass concentration, the Reynolds number, and the biomass

growth rate. According to this theory, particle flux from the bulk fluid is expected to

increase with increasing Reynolds number, but experimental results show that the rate

of biofilm accumulation decreases as turbulence increases, suggesting that particle

flux from the bulk solution is only one of the mechanisms contributing to biofilm

accumulation. Flemming and Schaule [Flemming&Schaule, 1988] found a linear

correlation between the number of Pseudomonas diminuta cells in suspension and that

on a polysulfone membrane surface (until the surface was completely covered).

However, this was true only in a concentration range above 106 cells/ml. Below this

limit, the adsorption rate was significantly lower.

2.6.2.4 Biofilm Formation

Once irreversibly attached, cells may grow and proliferate into microcolonies,

excreting EPS, colonizing free surface areas, and forming a biofilm. The formation of

biofilm can be considered in three stages [Marshall, 1989]:

1. Biofilm formation begins with a progressive coverage of the surface by

multiplication of the primary colonizing bacteria and further colonization by

bacteria from the aqueous phase. This stage is characterised by low

population densities, a low level of competition between the colonizing

organisms, and organisms with relatively high growth rates.

2. A transition stage with multilayers of cells becoming embedded in their own

polymer material. This results in increasing population density, increasing

competition, and a progressive development of populations with higher

competitive abilities.

3. The development of a mature biofilm, in which the population density is high,

with high levels of competition.

During the growth of bacteria, nutrients (here consider glucose) will be oxidised to

produce CO2 and H2O in aerobic respiration according to

6 12 6 2 2 2C H O 6O 6CO 6H O+ → + (2-155)

The growth cycle of bacteria can be divided into four different phases: 1) the lag

phase, 2) the exponential growth phase, 3) the stationary phase, 4) the phase of

decline or death phase [Characklis, 1990]. Figure 2.16 shows a typical growth curve

of bacteria population.

The Lag Phase

When bacteria are inoculated into a new environment, the population remains

temporarily unchanged and this period is known as the lag phase. During this period,

bacteria cells will increase in size; synthesise enzymes and coenzymes required for

metabolisms. At the end of lag phase, cell division commences.

Figure 2.16: An ideal growth curve for bacteria [Characklis, 1990]

The Exponential Phase

During this period the cells divide steadily at a rate determined by their specific

physiological capabilities in that growth environment. Under optimal conditions, the

growth rate is maximum and the entire population is most nearly uniform in terms of

chemical compositions of cells, metabolic activity, and other physiological

characteristics [Characklis, 1990]. The exponential specific growth rate expression,

µ , that relates to the substrate (or nutrient) concentration is given by [Maier et al.,

2000]:

max subs

subs

C

CsK

µµ =

+

(2-156)

where maxµ is the maximum specific growth rate; Ks is the saturation coefficient; Csubs

is the substrate concentration. The overall rate of microbial growth, Rbio, is

proportional to the concentration of microorganisms, Cbio, hence

max subsbio bio bio

subs

CR C C

CsK

µµ= =

+

(2-157)

The Stationary Phase

In stationary phase, there is no net increase or decrease in cell number. The growth is

limited by the exhaustion of nutrients or the production of waste from organism that

builds up in the medium to an inhibitory level.

Lag Exponential Growth Stationary Death

Time

Cel

l N

um

ber

The Phase of Decline or Death

In the death phase, the net production of cells is negative as the rate of death is faster

than the production of cells. This occurs when the nutrients are completely consumed

by the microorganisms.

2.6.2.5 Detachment

Detachment is an interfacial transfer process which transfers cells from the biofilm

back to the bulk liquid. The detachment process is dependent on the fluid dynamic

conditions. Trulear and Characklis [Trulear&Characklis, 1982] have observed that

biofilm detachment increases with both fluid shear stress and biofilm mass. As the

fluid velocity increases, the viscous sublayer thickness decreases. Consequently, the

region near the surface subject to relatively low shear forces is reduced. However,

biological factor also plays an important role in biofilm detachment. First, release of

bacteria from a biofilm may be due to production of unattached daughter cells through

attached cells replication as suggested by Characklis [Characklis, 1990]. The

availability of nutrients or oxygen also can also determine the detachment of biofilms.

As the nutrients in the biofilm are consumed and amount of dissolved oxygen are

depleted, these situations will unfavour the growth of microorganisms. Hence,

microbes will then swim to a high nutrients or oxygen environment. More

importantly, when there are more than one species present in the biofilm matrix, this

could either create competition for nutrients, which means the weaker species will be

rejected from the biofilm or one species produce toxin waste that kill another species

and causes detachment [Brading et al., 1995].

2.6.3 Structure and Compositions of Biofilm

In the early years, the biofilm is said to have a rather homogeneous structure,

which can range from monolayer of scattered single cells to thick, mucous structures

of macroscopic dimensions while with the advanced microscopes, physico-chemical,

molecular and biological techniques developed in the recent years, biofilms are

modelled as a complex 3D structure [Wimpenny et al., 2000]. As a general rule, there

are three types of biofilm model: 1) dense confluent, 2) heterogenous mosaic, and 3)

penetrated water channel [Sutherland, 2001; Wimpenny&Colasanti, 1997].

According to the dense confluent model [Characklis&Marshall,1990],

microorganisms are distributed in a continuous matrix of extracellular polymer.

Generally, there are two different compartments can be identified in a biofilm 1) the

base film and 2) the surface film. The base film consists of a rather structured

accumulation, having relatively well-defined boundaries. Molecular diffusion is the

transport mode that dominates in the base film. The surface film provides a transition

between the bulk liquid compartment and the base film. Gradients in biofilm

properties in the direction away from the substratum (e.g. a decrease in biofilm

density with distance from the substratum) are most important in the surface film. In

some cases, the surface film may extend from the bulk liquid compartment all the way

to the substratum, especially if preponderance of filamentous or sheathed

microorganisms is present. In other cases, the surface film may not exist at all, as in

certain monoculture biofilm systems. On the other hand, the surface film may be the

dominant compartment of the biofilm. Advective transport dominates the surface film.

The biofilm compartment contains at least two phases:

1. A continuous liquid phase which fills a connected fraction of the biofilm volume

and contains different dissolved and suspended particulate materials. The

suspended material consists of particles which can move in space independently

of one another.

A series of solid compartments each composed of specific particulate material, such

as a species of microorganisms, extracellular material, or inorganic particles. The

solids cannot move freely in space, because they are attached to each other.

Movement of attached particles within one solid compartment causes displacement of

neighbouring particles. Thus, each type of attached solid constitutes a different solid

phase, which in addition may contain other compartments (e.g. sorbed components,

components stored within microbial cells).

A heterogeneous mosaic biofilm model, as shown in Figure 2.17, has been suggested

by Keevil at al. [Keevil&Walker, 1992; Walker et al., 1995], who examined the

structure of natural biofilms forming in water distribution system. A modified

differential interference contrast (DIC) microscope (Nomarski optics) was used to

generate an image with wide in-focus range, allowing the structure of the biofilm as a

whole to be examined. It was pointed out that in a biofilm matrix, microcolonies

forming stacks attached to the substratum at the base but generally well separated

from their neighbours. In addition to the stacks, a background of individual cells

attached to the surface forming a very thin ‘film’ ≈ 5 µm thick. With the new

technologies such as the confocal laser scanning microscopy (CLSM), the biofilm is

described as having the mushroom or tulip model [Wimpenny&Colasanti, 1997;

Wimpenny et al., 2000] as shown in Figure 2.18. CSLM coupled with the use of

fluorescent markers has revealed that biofilm forming as mushroom shaped objects

with stalks narrower than the upper surface parts, the whole penetrated by channels

through which a liquid phase is free to move with the prevailing flow. The difference

between the water channel model and the mosaic model is that the sparseness of the

observed stacks which form as unconnected towers surrounded by the water phase. It

means there are no channels run through the film in the mosaic model as compared to

the mushroom model. The importance of the water channels has been stressed as

representing a primitive circulatory system analogous to that of higher organism.

Because of this remarkable biofilm architecture, bacterial cells within a microcolony

have a degree of homeostasis, optimal spatial relationships with cooperative

organisms, and an effective means of exchanging nutrients and metabolites with the

bulk fluid phase [Costerton et al., 1995].

From the study of biofilm [Baker&Dudley, 1998; Sutherland, 2001], the biofilm

consists mainly of

- > 90% moisture content

- of dried deposit, > 50% total organic matter

- up to 40% humic substances of total organic matter

- high microbiological counts (> 106 cfu/cm

2) including bacteria, fungi and

yeasts

- inorganic compounds

Figure 2.17: Heterogeneous mosaic biofilm model [Walker et al., 1995]

Figure 2.18: Schematic illustration of mushroom model of biofilm as revealed by

CSLM [Wimpenny&Colasanti, 1997]

Due to high content of water, when extracting the biofilm from membrane surface,

one must be very careful to avoid causing the shrinkage of biofilm and hence give rise

to wrong analysis. Among the dry content of biofilm, most are EPS. EPS are mainly

responsible for the structural and functional integrity of biofilms and are considered as

the key components that determine the physicochemical and biological properties of

biofilms. EPS consists mainly of polysaccharides (40–90%) and some proteins (< 1–

60%), nuclei acids (< 1–10%) and lipids (< 1–40%) [Flemming&Wingender, 2001].

As expected, a high number of microorganisms are present in the biofilm, which

provides a shelter as well as nutrient source to the microorganisms. Some inorganic

compounds are also being extracted from the biofilm.

2.6.4 Physico-Chemical Properties of Biofilm

Water flow

Biofilms display physico-chemical properties including mechanical stability, binding

of water and making surfaces hydrophilic; which are of fundamental importance for

their function in nature and their effects on fouling process. Biofilms are held together

by EPS, which are the key components that determine the physical and physico-

chemical properties of the biofilm. EPS form a three-dimensional, gel like, highly

hydrated and locally charged biofilm matrix, in which microorganisms are embedded

and more or less immobilized. The structure of the EPS matrix is composed mainly of

polysaccharides and proteins as well as by many different macromolecules such as

DNA, lipids and humic substances [Flemming&Wingender, 2001]. It offers the gel

matrix in which the organisms can be fixed with a long retention time next to each

other. As a result, formation of a stable microconsortia is achieved with the lowest

expense of energy and nutrients. The forces that are responsible for keeping the

matrix together are the London (dispersion) forces, electrostatic interactions and

hydrogen bond [Flemming et al., 1999]. Biofilms can also sorb water, inorganic and

organic solutes and particles due to the different sorption sites by EPS, cell walls, cell

membranes and cell cytoplasm [Flemming, 1995]. The different regions that serve as

sorption sites are

1) EPS

• cationic groups in amino sugars and proteins (e.g. NH+− − )

• anionic groups in uronic acids and proteins (e.g. COO−− − , 4HPO−

− − )

• apolar groups from proteins (e.g. aromatic amino acids)

• groups with high hydrogen bonding potential (e.g. polysaccharides)

2) Outer membrane and lipopolysaccharides of gram negative cells with their lipid

membrane, the lipoteichoic acids in gram positive cells

3) Cell wall consisting of N-acetylglucosamine and N-acetylmuramic acid, offering

cationic and anionic sites

4) Cytoplasmic membrane offering a lipophilic region

5) Cytoplasm as a water phase separated from the surrounding water

2.6.5 Microbial Fouling Model

Characklis [Characklis, 1990] described the fouling process consists of 1) an

induction period, 2) a exponential growth and 3) a plateau period, which will result in

a asymptotic biofilm growth curve and the biofouling process can be described by the

net effects of growth and removal of biofilm as follow:

biobio-F bio-R

mm m

t

∂= −

∂

(2-158)

where mbio-F is the biofilm formation rate; and mbio-R is the biofilm removal rate. The

biofilm formation rate can be written as:

bio-F bio biom k m= (2-159)

where kbio is the biofilm formation rate constant; and mbio is the mass of the biofilm

formed per unit membrane area. The removal of the biofilm is proportional to the wall

shear stress, wτ , and can be written as

bio-R bio w biom γ τ m= (2-160)

where bioγ is the biofilm removal coefficient. Equation (2-158) is developed without

considering the deposition of microorganisms, hence Panchal et al. [Panchal et al.,

1997] proposed the following expression to describe the microbial fouling process:

( )biom-bio bio bio bio bio w bio

mk F v C k m γ τ m

t

∂= + −

∂

(2-161)

where km-bio is the transport coefficient for microorganisms; F(v) is the fraction of

microorganisms deposited and remain on the surface as a function of velocity; Cbio is

the concentration of the microorganisms in the bulk solution. The first term represents

the net deposition of microorganisms, which is a function of the fraction of the

microorganisms remaining on the surface. The growth rate is assumed to be first order

as shown by the second term. The removal is proportional to the wall shear stress. For

constant velocity and the interface temperature during a test, Equation (2-160) can be

simplified by grouping coefficients as [Panchal et al., 1997]:

biobio bio

mα C β m

t

∂= +

∂

(2-162)

where

( )"

m-bioα k F v=

(2-163)

"

bio bio wβ k γ τ= − (2-164)

Equation (2-162) can be integrated to get mbio as a function of time, t, as follows:

( )" " "

bio biom α C β exp β t 1 = − (2-165)

2.7 Interactive Effects

Previous sections have covered the basic principle of reverse osmosis and discussed

various aspects in inorganic and biological fouling. Due to complexity, most research

only studied the inorganic and biological fouling in isolation, not many studies have

been conducted on understanding the actual mechanisms and interactive effects of

composite inorganic and biological fouling. Two review papers by Sheikholeslami

[Sheikholeslami, 1999; Sheikholeslami, 2000] have pointed out the lack of knowledge

in this area. Various possible interactive and synergistic effects such as those

encountered by changes in pH, velocity, concentration, CO2 and O2 content, nutrition,

etc. have been discussed but no actual experiment was done to confirm the

hypotheses. One example of the interactive effects is the induction of precipitation by

biological fouling. When in isolation, both inorganic (due to calcium carbonate) and

biological fouling are strongly affected by the presence of CO2. In aerobic respiration,

nutrients and oxygen is consumed by biological matters to produce new cells and

discharge CO2 as the product, hence increases the dissolved CO2 in water. This will

then changes the pH as well as the carbonate content of solution through the

dissociation of carbonic acid as discussed in Section 2.3. As the carbonate ion

increases and exceeds the supersaturation, CaCO3 fouling will be enhanced. In a study

of microbiologically precipitation of CaCO3 by Bacillus pasteurii [Stocks-Fischer et

al., 1999], the examination by SEM identified bacteria in the middle of calcite

crystals, which acted as nucleation sites. Besides, the urea discharged by the bacteria

also takes part indirectly in the precipitation process. The enzymatic hydrolysis of

urea will produce ammonia which then creates an alkaline micro-environment around

the cell. The high pH of these localised areas, commence the growth of CaCO3

crystals around the cell. The biochemical reaction of precipitation of CaCO3 is

summarized as follow;

2 2Ca Cell Cell Ca+ ++ → −

(2-166)

2

3 3 4 3HCO NH NH CO− + −

+ → +

(2-167)

22

3 3Cell Ca CO Cell CaCO−+− + → − (2-168)

Another possibility is the induction of inorganic fouling by biological fouling through

the enrichment of concentration polarization effect by the extracellular polymeric

substances in biofilms. The EPS excreted by organisms usually are elastic and

negatively charged in nature and this could trap the dissolved ions, especially cations

transported from the bulk solution to the membrane surface during filtration. This is

one of the reason biofilm has been used to study the removal of heavy metals (Cu, Cd,

Cr, Co, Pb, Zn and Ni) from wastewater [Jang et al., 2001; Liu et al., 2001]. It was

shown that under initial metal ion concentration of 0.25 mM, the removal of copper

and lead has reached equilibrium in 18 and 20 hours with removal efficiency of 80%

and 68%, respectively [Jang et al., 2001]. Another interesting aspect to look at is what

effects imposed by inorganic fouling upon biological fouling. In the recent

investigation by Korstgens et al. [Korstgens et al., 2001] to determine the mechanical

stability of biofilms of mucoid Pseudomonas aeruginosa under the influence of

calcium concentration, it was shown that the Young’s modulus representing the

stiffness of biofilm, increases strongly up to a critical concentration of calcium and

subsequently remains constant for higher calcium concentrations. This behaviour is

explained by the presence of calcium ions cross-linking alginate, which is the major

component of the EPS produced by the P. aeruginosa. This will mean calcium has

increased the strength of biofilm and could prevent it from being removed. Last,

inorganic and biological fouling when present in isolation, both mechanisms have

different characteristics of fouling curve and the corresponding decline in flux. When

both fouling mechanisms are present simultaneously as in actual RO operation,

whether the resulting fouling curve and flux decline plot is simply the summation of

the two in isolation, or represented by a more complex mathematical model is still a

mystery. Of particular interest is the effect upon the induction period, growth curve

and the steady state. In the preliminary study by Li et al. [Li et al., 2001], the mixed

growth curves of microbial (Pseudomonas fluorescens) and CaCO3 fouling on

different solid surfaces under different CaCO3 saturation levels (0, 0.64, 1 and 100)

and bulk fluid velocities (0.8, 1.0 and 1.2 m/s), showed that the induction period is

greatly reduced for mixed fouling. It has also been found that the average mass of

mixed fouling is less than that of biofouling in isolation. It should be noted that this

experiment is not carried out in a reverse osmosis system, so there is no concentration

polarization effect.

2.8 Conclusion

In view of all the research that has been done, there is no relationship established

between the composite inorganic and biological fouling and the flux decline of RO

desalination. This is one of the most important issues to engineers when designing

the RO plant. So the proposed study will look at the interactive effects of composite

inorganic and biological fouling and the performance of RO under the mixed fouling.

Simulated model seawater feed solution will be used to study the mixed fouling by

crystallisation, particulate and biological mechanisms on thin film composite reverse

osmosis membranes under operating conditions of those similar to the actual RO

desalination process. This is illustrated in Figure 2.19.

Figure 2.19: Illustration of difference between previous research and proposed

research

The outcome of the research is to understanding the interactive effects as well as

development of a mathematical model to describe the composite fouling and relate to

the performance of membranes. It is significant, as it will result in a better

understanding of the fouling process encountered in more real industrial condition and

provide a better guideline for operation of RO desalination units.

Surface

Crystallisation Particulate Biological

Feed

Operating

Conditions

Product

Reverse Osmosis Membranes

Crystallisation + Particulate + Biological

(CaCO3 + CaSO

4 + Pseudomonas fluorescens)

Simulated Model

Seawater

Operating

Conditions

Drinking Water

a) Previous Research - different feed is used, under various operating

conditions and surfaces, inorganic and biological fouling in isolationb) Proposed Research - simulated seawater, actual desalination operating

conditions, reverse osmosis membrane surface, composite inorganic and

biological fouling

3. THERMODYNAMICS AND KINETICS OF CALCIUM SULFATE AND CALCIUM CARBONATE PRECIPITATION

Crystallization has been studied for many years as shown in the two monographs by

Mullin [Mullin, 1972; 1993]. An immense body of information is available on

thermodynamics and kinetics of crystallization of calcium carbonate [Augustin and

Bohnet, 1995; Nancollas and Reddy, 1971; Reddy and Nancollas, 1971; Plummer and

Busenberg, 1982] and calcium sulfate [Liu and Nancollas, 1971; Liu and Nancollas,

1970; 1975; Nancollas et al., 1978; Nancollas et al., 1973; Marshall and Slusher,

1966]. The research in the area of crystallization fouling, including the dynamic

effects, has also been extensive as covered in two comprehensive reviews [Hasson,

1981; 1999]. Most of the studies employed the seeded growth technique where seed

crystals of the precipitating species with known surface area are added to the solution.

Although this method can ensure reproducible results, but the data obtained is not

applicable to the actual water system where there is no seed for crystals to initiate the

fouling process. Also due to the complexity of fouling process the research in this

area usually involves fouling by a single precipitant. The area to which not much

attention has been paid to is the interactive effects of co-precipitating salts with or

without common ions. These include solubility effects, rate data, crystal structure and

strength, inhibitor effects and also dynamic effects. This is important for two reasons.

One is that the mechanism of fouling might be different for different salts as it was

shown to be different for CaCO3 and CaSO4 [Bansal et al., 1997]. The second is the

fact that co-existence of salts affects the thermodynamic and kinetic behaviour of each

salt and therefore, the single salt data might not be applicable to the condition when

the salts co-exist. The research [Bramson et al., 1995; Hasson and Karmon, 1984] on

the strength and tenacity of co-precipitated calcium carbonate and sulfate shown that a

major factor affecting scale tenacity was the purity of the deposit. For calcium sulfate,

the higher the impurities, the greater the strength of the scale; however, with calcium

carbonate, adhesive strength was seen to decrease with increasing impurities. The

most difficult deposit to remove from heat transfer surfaces was calcium carbonate

scale with impurities measuring less than 5% by mass; pure calcium sulfate deposits

were found to be far less adherent than deposits containing co-precipitated calcium

carbonate. The co-precipitated calcium carbonate seems to act as bonding cement,

enhancing considerably the strength of calcium sulfate scale layer. In the recent work

[Sudmalis and Sheikholeslami, 2000; Chong and Sheikholeslami, 2001;

Sheikholeslami and Ng, 2001] on co-precipitation of CaCO3 and CaSO4 when they

exist in comparable ratios with constant total Ca2+

, it has been qualitatively and

quantitatively shown that co-precipitation affects thermodynamics and kinetics of

precipitation as well as the scale structure. The thermodynamic and kinetic data for

the pure salts are no longer applicable to the mixed system. The objective of this study

is to investigate the co-precipitation process of CaSO4 and CaCO3 at different

seawater recovery levels. In reverse osmosis desalination, as the feed water is being

concentrated, the concentration of sparingly soluble salts as well as the salinity

increases. Accompanying by the concentration polarization phenomena, the

concentration observed by the membrane surface is even much higher than that in the

bulk. So it is important to investigate the effect of high ionic strength and

supersaturation upon the co-precipitating salts, which was not covered in previous

studies by Sheikholeslami and co-worker [Chong and Sheikholeslami, 2001;

Sheikholeslami and Ng, 2001].

3.1 Experimental Conditions

Seawater inlet feed (listed below as 0% recovery) to RO desalination plants typically

contains 0.5 M of NaCl, 0.010 – 0.015 M of Ca2+

, 0.0300 - 0.0375 M of SO42-

and

0.003 - 0.005 M of total alkalinity. Normal commercial seawater-RO plants can

operate up to 50% recovery. However, to take into account concentration polarization

effect (approximately ≈2) and simulate the concentration observed by the membrane

surface, recoveries of up to 75% is covered. Therefore, 65% and 75% recoveries

though above the industrial operating range are intended to take into account the

effect of concentration polarization (CP = 2) and refer to actual plant recoveries of

30% and 50% respectively. The predicted salt concentration levels for these salts are

summarized in Table 3.1.

Table 3.1: NaCl, Ca2+

, SO42-

and T.A. concentrations at various recovery levels

Recovery 0% 30% (CP = 1) 50% (CP = 1)

65% (Recovery =

30%, CP = 2)

75% (Recovery =

50%, CP = 2)

NaCl (M) 0.5 0.7 1.0 1.5 2.0

Ca2+

(M) 0.0100 – 0.0150 0.0143 – 0.0214 0.0200 – 0.0300 0.0286 – 0.0429 0.0400 – 0.0600

SO42-

(M) 0.0300 – 0.0375 0.0425 – 0.0535 0.0600 – 0.0750 0.0850 – 0.1070 0.1200 – 0.1500

T.A. (M) 0.0030 - 0.0050 0.0043 – 0.0071 0.0060 – 0.0100 0.0085 – 0.0143 0.0120 – 0.0200

In view of such a great variations of the concentration values, the initial study would

only cover those at the low and high end. Special focus would be for recovery level of

50% and 75%. In the mixed system, total calcium was kept constant at 0.15M while

altering the SO4/CO3 ratio (7.5, 6.5, 5 and 3). Experiments were carried out to study

the precipitation of CaSO4 and CaCO3 in isolation and the results obtained for pure

salts were compared with published data. Then the pure salts data will be used as a

baseline for comparison with the data for co-precipitation of mixed salts. The

experimental conditions are summarised in Table 3.2.

Table 3.2: Experimental conditions

No NaCl

(M)

CaCO3

(M)

CaSO4

(M)

Total

Ca2+

(M)

Molar

Ratio

SO42-

/CO32-

No NaCl

(M)

CaCO3

(M)

CaSO4

(M)

Total

Ca2+

(M)

Molar

Ratio

SO42-

/CO32-

01 0.5 0.0070 - 0.0070 - 19 1.0 - 0.1200 0.1200 -

02 0.5 0.0100 - 0.0100 - 20 1.0 - 0.1500 0.1500 -

03 0.5 0.0150 - 0.0150 - 21 2.0 - 0.0600 0.0600 -

04 0.5 0.0200 - 0.0200 - 22 2.0 - 0.0850 0.0850 -

05 1.0 0.0070 - 0.0070 - 23 2.0 - 0.1200 0.1200 -

06 1.0 0.0100 - 0.0100 - 24 2.0 - 0.1500 0.1500 -

07 1.0 0.0150 - 0.0150 - 25 0.5 0.0175 0.1325 0.1500 7.5

08 1.0 0.0200 - 0.0200 - 26 0.5 0.0200 0.1300 0.1500 6.5

09 2.0 0.0070 - 0.0070 - 27 0.5 0.0250 0.1250 0.1500 5.0

10 2.0 0.0100 - 0.0100 - 28 0.5 0.0375 0.1125 0.1500 3.0

11 2.0 0.0150 - 0.0150 - 29 1.0 0.0175 0.1325 0.1500 7.5

12 2.0 0.0200 - 0.0200 - 30 1.0 0.0200 0.1300 0.1500 6.5

13 0.5 - 0.0600 0.0600 - 31 1.0 0.0250 0.1250 0.1500 5.0

14 0.5 - 0.0850 0.0850 - 32 1.0 0.0375 0.1125 0.1500 3.0

15 0.5 - 0.1200 0.1200 - 33 2.0 0.0175 0.1325 0.1500 7.5

16 0.5 - 0.1500 0.1500 - 34 2.0 0.0200 0.1300 0.1500 6.5

17 1.0 - 0.0600 0.0600 - 35 2.0 0.0250 0.1250 0.1500 5.0

18 1.0 - 0.0850 0.0850 - 36 2.0 0.0375 0.1125 0.1500 3.0

3.2 Experimental Method

The supersaturated solutions of calcium sulfate (CaSO4) and calcium carbonate

(CaCO3) in sodium chloride (NaCl) were prepared by mixing solutions of CaCl2,

Na2SO4, NaHCO3 and NaCl. These solutions were prepared from analytical grade

chemicals and with micro-filtered (with 0.22 µm Millipore filter) distilled water. The

solutions prepared were micro-filtered with 0.22 micron Millipore filter to remove

any undissolved salts. Solutions concentration and pH was measured before mixing in

a 5.0 L beaker; and right after mixing the individual solutions, 5 mL of mixed solution

was withdrawn, filtered with 0.22 µm filter and was analysed for water quality. Water

quality was determined by measurement of pH, total alkalinity, calcium, sulfate and

sodium content (by ICP-AES) according the standard methods of water analysis. Then

the mixture was carefully transferred to a series of 30ml plastic test tubes and the test

tubes were placed in the temperature baths at 30°C. All test tubes had been scrubbed,

rinsed with concentrated hydrochloric acid and washed thoroughly with distilled

water to remove any impurities. Also, it is important to avoid air bubbles formation

during the transfer process, as the air bubbles will affect the equilibrium of CO2 hence

the concentration of CO32-

in the solution. Water quality was monitored for each

sample taken during the run. The water quality tests were carried out until equilibrium

had been achieved. The precipitates formed were then removed for SEM analysis by

filtering out the solution. For those experiments with highly supersaturated CaSO4

(e.g. 0.12 M and 0.15 M), due to extremely fast reaction rate, it is impossible to

transfer the solutions to the 30ml plastic test tubes, so the individual solutions were

mixed and the mixture was left in the beaker which was placed in the temperature

bath. Each time only a small amount of sample is withdrawn from the beaker for

water analysis so that the final volume remains at least 95% of the initial volume in

order to ensure constant volume reactor for the kinetic data analysis.

3.2.1 pH Measurement

pH was measured using the Orion pH electrode and meter. pH 4.00 ± 0.01 (at 25 °C)

and pH 7.00 ± 0.01 (at 25 °C) buffer solutions were used to calibrate the pH meter

giving a slope of more than 97%.

3.2.2 Total Alkalinity Measurement

Total alkalinity (T.A.) was measured by titration using bromocresol green-methyl red

mixed indicator. 0.1 M Na2S2O3 was used as an inhibitor for the removal of residue

chlorine that would otherwise impair the colour changes. 0.02 N HCl was used as the

titrant. One drop of Na2S2O3 and 3 drops of mixed indicator were added to 25 ml of

sample. The blue sample was titrated with the 0.02 N HCl until the appearance of a

light pink which occurs at pH 4.5. Total alkalinity was then calculated using the

following formula:

3

B N 50000T.A. (mg CaCO /L)

V

× ×=

(3-1)

where B = volume of titrant (ml)

N = normality of titrant (N)

V = volume of sample (ml)

3.2.3 ICP-AES Analysis

Ca2+

, Na+ and SO4

2- in the diluted solution were measured using Inductive Coupled

Plasma Atomic Emission Spectroscopy (ICP-AES) by Varian. 5-points calibration

curve (with a slope of greater than 99.5%) was used with Ca2+

and SO42-

standards

being 20, 50, 100, 150 and 200 ppm while Na+ standards being 100, 200, 300, 500

and 1000 ppm. All these standards were prepared from the 1000 ppm Standards

purchased from Sigma-Aldrich. Ca2+

and Na+ were measured at the wavelength of

315.880 nm and 330.298 nm respectively. Since IAP-AES only measures cations, S2+

was measured at 181.972 nm instead of SO42-

; though there is no need to do

conversion when calculating the concentration since SO42-

standards were used in the

calibration.

3.2.4 SEM Image Analysis

At the end of the run, precipitates were collected by filtering out the solution using

0.22 micron Millipore filter. The samples were left dry at room temperature, mounted

on the stub and coated with a 20 nm layer of gold before SEM analysis. The

instrument used is Hitachi S-4500II Field Emission Scanning Electron Microscope

specially, for high resolution imaging and analysis.

3.3 Results and Discussions

3.3.1 Scale Morphology

Figure 3.1, 3.2 and 3.3 show the SEM image of the pure CaCO3, pure CaSO4 and

mixed salts precipitation, respectively.

In general, for single CaCO3 precipitation, the crystals formed exhibited a hexagonal

structure whereas needle shape crystals were observed for the pure CaSO4

precipitation. As the NaCl is increased from 0.5M to 1.0M (e.g. higher ionic strength),

the size of the calcium carbonate crystal increased from approximately 30 micron (at

0.010M CaCO3) to 60 micron (at 0.007M CaCO3) as shown in Figure 3.1a & b,

respectively. Similar trend is expected for pure CaSO4. In the mixed system, the

CaSO4 crystals no longer exhibited the sharp needle shape structure; instead one can

only see the flat edges of sulfate precipitates as shown in Figure 3.3a for 0.1300M

CaSO4 + 0.0200M CaCO3 in 0.5M NaCl. The presence of CaCO3 has altered the

structure and retarded the growth of CaSO4. Besides, the CaSO4 crystals seem to be

clustered together hence increasing the tenacity as compared to the pure CaSO4 where

the crystals are loosely held within each other. As the concentration of NaCl increased

to 2.0M, at lowest CaSO4 concentration of 0.1125M and the highest CaCO3

concentration of 0.0375M (in Figure 3.3d), the calcium sulfate scales formed not only

increase in size but also seems to be held strongly by the calcium carbonate crystals

that act as the bonding cement; thus reducing the porosity between the CaSO4

crystals. This phenomenon occurred probably due to longer induction period of

calcium sulfate precipitation resulted from higher ionic strength of solution, which

allowed enough time for the adsorption of calcium carbonate into the calcium sulfate

crystals during the formation; or the calcium sulfate crystals simply grow from the

calcium carbonate crystals. Moreover, there seems to be a change of crystal shape

when the salinity is increased; spherical shape particles can be seen to grow from the

CaSO4 crystals as shown in Figure 3.3d.

(a) 0.010M CaCO3 in 0.5M NaCl (i) x100 (ii) x500

(i)

(ii)

(b) 0.007M CaCO3 in 1.0M NaCl (i) x100 (ii) x500

(i)

(ii)

Figure 3.1: SEM image of pure CaCO3

0.060M CaSO4 in 0.5M NaCl (i) x100 (ii) x500

(i)

(ii)

Figure 3.2: SEM image of pure CaSO4

(a) 0.1300M CaSO4 + 0.0200M CaCO3 in 0.5M NaCl (i) x100 (ii) x500

(i)

(ii)

(b) 0.1250M CaSO4 + 0.0250M CaCO3 in 0.5M NaCl (i) x100 (ii) x500

(i)

(ii)

(c) 0.1125M CaSO4 + 0.0375M CaCO3 in 0.5M NaCl (i) x100 (ii) x500

(i)

(ii)

(d) 0.1125M CaSO4 + 0.0375M CaCO3 in 2.0M NaCl (i) x100 (ii) x500

(i)

(ii)

Figure 3.3: SEM image of the mixed salts

At this stage, it is not known what is the spherical-shape particle; additional analysis

such as EDS (Energy Dispersive Spectroscopy) and XRD (X-Ray Diffraction) are

needed to confirm the composition and nature of the crystals. Also, if the there exists

chemical bond between CaSO4 and the spherical particles, then one can say a new

crystal is born and therefore will change the kinetics of co-precipitation as discussed

in Section 3.3.3; otherwise, if they are held together by physical forces only, growth

kinetics of these individual crystals are not affected.

3.3.2 Thermodynamics Analysis

Thermodynamics solubility product, Ksp, for CaSO4 and CaCO3 are defined as follow:

( ) 2 23

2 2

sp 3 3Ca COeq eqK CaCO γ Ca γ CO+ −

+ − =

(3-2)

( ) 2 24

2 2

sp 4 4Ca SOeq eqK CaSO γ Ca γ SO+ −

+ − = (3-3)

iγ is the activity coefficient of the respective ionic species and [ ]eq denotes the

concentration of the species at equilibrium. Total alkalinity measurements were used

for determination of carbonic species in the solutions according to the following

equations that relate total alkalinity to pH and the concentration of various carbonic

species in the solution:

+ 23

+2-

3 +

H CO

2

[T.A.] [H ] [OH ][CO ]

γ γ [H ]2 1

2K

−

−+ −=

+

(3-4)

+ 23

3

2

H CO

[T.A.] [H ] [OH ][HCO ]

2K1γ γ [H ]−

+ −−

+

+ −=

+

(3-5)

where K1 and K2 are the first and second dissociation constants of carbonic acid

[Plummer and Busenberg, 1982]:

( ) 2

1log K 356.31 0.0609 T 21834.37 T 126.83 log T 1684915 T= − − + + −

(3-6)

( ) 2

2log K 107.89 0.033 T 5151.79 T 38.93 log T 563713.9 T= − − + + − (3-7)

To calculate the activity coefficient, iγ , Pitzer model was used. The activity

coefficient given by Pitzer (valid up to 6M) [Pitzer, 1979] for the salt dissociates

according to Eq (3-8), which takes into account the ion interaction due to short range

and electrostatic forces is:

z z

X Y X Yv v

v v+ −

+ − + −→ +← (3-8)

( )( )s "

s c a ca2c as

X

" "

c c" cc" a a" aa"

c c" a a"

a Xa c c a a Xa

a c a

c Xc a Xca a a" aa"X

c a a"

I 2ln 1 1.2 I m m

1.21 1.2 Iln γ z

m m m m

1 m 2 m z m z

2

m 2 m ψ m m ψ

Aφ

+

− + + + + = + Φ + Φ

+ + +

+ Φ + +

∑∑

∑∑ ∑∑

∑ ∑ ∑

∑ ∑

B

B C

c a ca

a c a

z m m

++∑∑ ∑∑ C

(3-9)

( )( )s "

s c a ca2c as

Y

" "

c c" cc" a a" aa"

c c" a a"

c cY c c a a cY

c c a

a Ya c Yac c c" cc"Y

a c c"

I 2ln 1 1.2 I m m

1.21 1.2 Iln γ z

m m m m

1 m 2 m z m z

2

m 2 m ψ m m ψ

Aφ

−

− + + + + = + Φ + Φ

+ + +

+ Φ + +

∑∑

∑∑ ∑∑

∑ ∑ ∑

∑ ∑

B

B C

c a ca

c c a

z m m

−+∑∑ ∑∑ C

(3-10)

where subscripts X, c, and "c are cationic species and Y, a, and "a are anionic

species; X and Y are cation and anion of interest with charge z+ and z− , v+ and v−

moles dissociated in one mole of salt, respectively. The summation index, c or a,

denotes the sum over all cations or anions in the system while the double summation

index, c and "c or a and "a , denotes the sum over all distinguishable pairs of

dissimilar cations or anions; m is the molality of the species; the ionic strength of the

solution, SI , which is defined as follow:

2

12S i iI = m z∑ (3-11)

Aφ is the Debye-Huckel parameter as a function of absolute temperature, T, as follow:

( ) ( )24 60.3770 4.684 10 T 273.15 3.74 10 T 273.15Aφ

− −= + × − + × − (3-12)

and for 1-1 (e.g. NaCl), 1-2 (e.g. Na2SO4) or 2-1 (e.g. CaCl2) electrolytes:

( ) ( )

( )( ) ( )0 1

ij ij ij s s2

s

21 1 2 I exp 2 I

2 I

= + − + −

B B B

(3-13)

( )

( )( )

( )2

1sij"

ij s s2

ss

2 I21 1 2 I exp 2 I

I 22 I

− = − + + −

BB

(3-14)

For 2-2 (e.g. CaSO4) electrolytes:

( ) ( )

( )( ) ( )

( )

( )( ) ( )

0 1

ij ij ij s s2

s

2

ij s s2

s

21 1 1.4 I exp 1.4 I

1.4 I

2 1 1 12 I exp 12 I

12 I

= + − + −

+ − + −

B B B

B

(3-15)

( )

( )( )

( )

( )

( )( )

( )

21

sij"

ij s s2

ss

22

sij

s s2

ss

1.4 I21 1 1.4 I exp 1.4 I

I 21.4 I

12 I2 1 1 12 I exp 12 I

I 212 I

− = − + + −

− + − + + −

BB

B

(3-16)

and

( )ij

ij 0.5

i j2 z z

φ

=C

C

(3-17)

The mixed electrolyte term Φ and Ψ account for interactions between ions of like

sign. The definitions for second virial coefficients ijΦ are:

E

ij ij ijθ θΦ = +

(3-18)

" E "

ij ijθΦ = (3-19)

where ijθ is a single parameter for each pair of anions or cations respectively. The

terms E

ijθ and E "

ijθ , which are zero when the ions i and j are of the same charge,

account for electrostatic un-symmetric mixing effects and are given as follow:

( ) ( ) ( ) ( )E

ij i j s ij ii jjθ z z 4I 0.5 0.5J x J x J x = − −

(3-20)

( ) ( ) ( ) ( ) ( )E " E 2 " " "

ij ij s i j s ij ij ii ii jj jjθ θ I z z 8I 0.5 0.5x J x x J x x J x = − + − − (3-21)

ij i j s6 z z Ix Aφ=

(3-22)

( ) ( )1

0.7237 0.5284 4.581 exp 0.0120J x x x x−

− = + −

(3-23)

( )( )

( )

0.7237 0.528 0.528

"

20.7237 0.528

4 4.581 exp 0.0120 0.006336 1.7237

4 4.581 exp 0.0120

x x xJ x

x x

−

−

+ − + = + −

(3-24)

The third virial coefficient mixing parameters ijkψ , used when i and j are different

anions and k is a cation or when i and j are different cations and k is an anion, are

assumed to be independent of the concentration. The values of ion interaction

parameters of Pitzer model e.g. ( )0

ijB , ( )1

ijB , ( )2

ijB , ij

φC , ijθ , and ijkψ , which will be used

in this study are tabulated in Table 3.3.

Table 3.3: Ion interaction parameters of Pitzer model at 25oC[Harvie et al., 1984]

i j ( )0

ijB ( )1

ijB ( )2

ijB ij

φC

Na Cl 0.0765 0.2644 0 0.00127

Na SO4 0.01958 1.113 0 0.00497

Na OH 0.0864 0.253 0 0.0044

Na HCO3 0.0277 0.0411 0 0

Na CO3 0.0399 1.389 0 0.0044

Ca Cl 0.3159 1.614 0 -0.00034

Ca SO4 0.20 3.1973 -54.24 0

Ca OH -0.1747 -0.2303 -5.72 0

Ca HCO3 0.40 2.977 0 0

i j ( )0

ijB ( )1

ijB ( )2

ijB ij

φC

Ca CO3 0 0 0 0

H Cl 0.1775 0.2945 0 0.0008

H SO4 0.0298 0 0 0.0438

H OH 0 0 0 0

H HCO3 0 0 0 0

H CO3 0 0 0 0

i j ijθ ijClψ

4ijSOψ ijOHψ 3ijHCOψ

3ijCOψ

Na Ca 0.07 -0.007 -0.055 0 0 0

Na H 0.036 -0.004 0 0 0 0

Ca H 0.092 -0.015 0 0 0 0

i j ijθ ijNaψ ijCaψ ijHψ

Cl SO4 0.02 0.0014 -0.018 0

Cl OH -0.050 -0.006 -0.025 0

Cl HCO3 0.03 -0.015 0 0

Cl CO3 -0.02 0.0085 0 0

SO4 OH -0.013 -0.009 0 0

SO4 HCO3 0.01 -0.005 0 0

SO4 CO3 0.02 -0.005 0 0

OH HCO3 0 0 0 0

OH CO3 0.10 -0.017 -0.01 0

HCO3 CO3 -0.04 0.002 0 0

From the experimental data and calculation, the instantaneous ionic activity product,

IAP, of Ca2+

& SO42-

, and Ca2+

& CO32-

is plotted against time; Ksp of the salt is the

IAP value at equilibrium. Figure 3.4, 3.5, 3.6 and 3.7 shows the IAP vs time for pure

CaCO3, pure CaSO4 and mixed system respectively. The induction period and Ksp

values are summarised in Table 3.4. The calculated Ksp for pure CaCO3 and pure

CaSO4 is 3.7 ± 0.4 ×10-9

(mol/L)2 and 2.7 ± 0.3 ×10

-5 (mol/L)

2 respectively and are in

good agreement with the published data by Plummer & Busenberg [1982] for CaCO3

(3.1×10-9

(mol/L)2 at 30°C) and Liu & Nancollas [1971] for CaSO4 (2.6 ×10

-5

(mol/L)2 at 25°C). From Figure 3.8, it seems that the deviation of Ksp value for the

mixed salts systems from those of pure salts is enhanced by increasing the ionic

strength of solution. For the mixed salts systems at 0.5M and 1.0M NaCl, the Ksp

value for CaCO3 and CaSO4 at 0.15M total Ca2+

does not differ much (only about

15% on average for both salts) from those of pure salts. However, when the NaCl was

increased to 2.0M, deviation from the pure salts is more obvious. The Ksp value for

CaCO3 shows an increase of 70 % (from 3.7 ×10-9

(mol/L)2

to 6.3 ×10-9

(mol/L)2)

whereas the difference between the Ksp values for pure and mixed CaSO4 is about

22%. One explanation is that at this high supersaturation, the precipitation of CaSO4

at low NaCl content is very rapid, therefore did not allow sufficient time for the

interaction between the two precipitating species. As a result, the thermodynamics is

not affected. However with increasing NaCl, the induction period is much longer, so

the CaCO3 crystals that formed first may be adsorbed into the CaSO4 systems or vice

versa; therefore a new crystal is born and according to the hypothesis provided by

Chong and Sheikholeslami [Chong and Sheikholeslami, 2001] which relates the Ksp to

the Gibbs energy of formation of crystals, the new crystals would have a different

Gibbs energy of formation and hence different Ksp value. Form this result, it would

mean that the interactive effect of co-precipitation is higher at high salinity or low

level of supersaturation and this result is supported by previous finding where the Ksp

for the CaCO3 in mixed system can differ by up to 1000% than those pure salt at low

supersaturation [Chong and Sheikholeslami, 2001]. Though, there is no apparent

effect of SO42-

/CO32-

ratio upon the Ksp value in the mixed system. Probably this is

because the ratio used in this study is not high enough to observe their effect on

precipitation thermodynamics.

3.3.3 Kinetics Analysis

For the precipitation of pure CaCO3, during the crystal growth, solutes will diffuse to

the crystals surface, and then integrate into the crystal lattice. The driving force for the

surface integration process is the difference between the concentration at the crystals

interface and the equilibrium concentration. The crystal growth process is represented

schematically in Figure 3.9.

The following mathematical expressions can be written to describe the diffusion of

solutes (Eq (3-25) and Eq (3-26)) and surface integration (Eq (3-27)) during the

formation of CaCO3 assuming negligible dissolution process:

( ) 23

2

2 2b

diff,Ca CaCO b i

Cak Ca Ca

t+

+

+ + ∂ = − ∂

(3-25)

(a) 0.5M NaCl

0.0E+00

1.0E-07

2.0E-07

3.0E-07

4.0E-07

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

CO

3)

0.007M CaCO3 0.010M CaCO3 0.015M CaCO3 0.020M CaCO3

(b) 1.0M NaCl

0.0E+00

1.0E-07

2.0E-07

3.0E-07

4.0E-07

0.01 0.1 1 10 100 1000

Time (h)

IAP

(CaC

O3)

0.007M CaCO3 0.010M CaCO3 0.015M CaCO3 0.020M CaCO3

(c) 2.0M NaCl

0.0E+00

1.0E-07

2.0E-07

3.0E-07

4.0E-07

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

CO

3)

0.007M CaCO3 0.010M CaCO3 0.015M CaCO3 0.020M CaCO3

Figure 3.4: Change of ionic activity product (IAP) for pure CaCO3 at various

salinities

(a) 0.5M NaCl

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

0.01 0.1 1 10 100 1000

Time (h)

IAP

(C

aS

O4)

0.060M CaSO4 0.085M CaSO4 0.120M CaSO4 0.150M CaSO4

(b) 1.0M NaCl

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

SO

4)

0.060M CaSO4 0.085M CaSO4 0.120M CaSO4 0.150M CaSO4

(c) 2.0M NaCl

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

SO

4)

0.060M CaSO4 0.085M CaSO4 0.120M CaSO4 0.150M CaSO4

Figure 3.5: Change of ionic activity product (IAP) for pure CaSO4 at various

salinities

(a) 0.5M NaCl

0.0E+00

2.0E-07

4.0E-07

6.0E-07

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

CO

3)

0.1325M CaSO4 + 0.0175M CaCO3 0.1300M CaSO4 + 0.0200M CaCO3

0.1250M CaSO4 + 0.0250M CaCO3 0.1125M CaSO4 + 0.0375M CaCO3

(b) 1.0M NaCl

0.0E+00

2.0E-07

4.0E-07

6.0E-07

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

CO

3)

0.1325M CaSO4 + 0.0175M CaCO3 0.1300M CaSO4 + 0.0200M CaCO3

0.1250M CaSO4 + 0.0250M CaCO3 0.1125M CaSO4 + 0.0375M CaCO3

(c) 2.0M NaCl

0.0E+00

2.0E-07

4.0E-07

6.0E-07

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

CO

3)

0.1325M CaSO4 + 0.0175M CaCO3 0.1300M CaSO4 + 0.0200M CaCO3

0.1250M CaSO4 + 0.0250M CaCO3 0.1125M CaSO4 + 0.0375M CaCO3

Figure 3.6: Change of IAP(CaCO3) in mixed system at various salinities

(a) 0.5M NaCl

0.0E+00

1.0E-04

2.0E-04

3.0E-04

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

SO

4)

0.1500M CaSO4 + 0.0000M CaCO3 0.1325M CaSO4 + 0.0175M CaCO3

0.1300M CaSO4 + 0.0200M CaCO3 0.1250M CaSO4 + 0.0250M CaCO3

0.1125M CaSO4 + 0.0375M CaCO3

(b) 1.0M NaCl

0.0E+00

1.0E-04

2.0E-04

3.0E-04

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

SO

4)

0.1500M CaSO4 + 0.0000M CaCO3 0.1325M CaSO4 + 0.0175M CaCO3

0.1300M CaSO4 + 0.0200M CaCO3 0.1250M CaSO4 + 0.0250M CaCO3

0.1125M CaSO4 + 0.0375M CaCO3

(c) 2.0M NaCl

0.0E+00

1.0E-04

2.0E-04

3.0E-04

0.01 0.1 1 10 100 1000

Time (h)

IAP

(Ca

SO

4)

0.1500M CaSO4 + 0.0000M CaCO3 0.1325M CaSO4 + 0.0175M CaCO3

0.1300M CaSO4 + 0.0200M CaCO3 0.1250M CaSO4 + 0.0250M CaCO3

0.1125M CaSO4 + 0.0375M CaCO3

Figure 3.7: Change of IAP(CaSO4) in mixed system at various salinities

Table 3.4: Thermodynamics of CaCO3, CaSO4 and mixed salts

No NaCl(M) CaSO4(M) CaCO3(M)

Total

Calcium (M)

Ksp(CaSO4) x105

(mol/L)2

Ksp(CaCO3)x109

(mol/L)2

Induction

Time (h)

01 0.5 - 0.0070 0.0070 - 4.1 0

02 0.5 - 0.0100 0.0100 - 3.3 0

03 0.5 - 0.0150 0.0150 - 3.7 0

04 0.5 - 0.0200 0.0200 - 3.3 0

05 1.0 - 0.0070 0.0070 - 3.8 0

06 1.0 - 0.0100 0.0100 - 3.6 0

07 1.0 - 0.0150 0.0150 - 3.9 0

08 1.0 - 0.0200 0.0200 - 3.4 0

09 2.0 - 0.0070 0.0070 - 4.1 0

10 2.0 - 0.0100 0.0100 - 3.9 0

11 2.0 - 0.0150 0.0150 - 3.0 0

12 2.0 - 0.0200 0.0200 - 4.5 0

13 0.5 0.0600 - 0.0600 2.6 - 1.50

14 0.5 0.0850 - 0.0850 2.8 - 0.13

15 0.5 0.1200 - 0.1200 2.8 - 0.07

16 0.5 0.1500 - 0.1500 2.7 - 0

17 1.0 0.0600 - 0.0600 2.8 - 15.0

18 1.0 0.0850 - 0.0850 3.2 - 0.92

19 1.0 0.1200 - 0.1200 2.7 - 0.05

20 1.0 0.1500 - 0.1500 2.5 - 0

21 2.0 0.0600 - 0.0600 No Reaction

22 2.0 0.0850 - 0.0850 2.2 - 0.08

23 2.0 0.1200 - 0.1200 2.6 - 0

24 2.0 0.1500 - 0.1500 2.3 - 0

25 0.5 0.1325 0.0175 0.1500 3.0 4.3 0

26 0.5 0.1300 0.0200 0.1500 2.9 4.4 0

27 0.5 0.1250 0.0250 0.1500 2.9 4.2 0

28 0.5 0.1125 0.0375 0.1500 2.7 4.4 0

29 1.0 0.1325 0.0175 0.1500 3.1 4.5 0

30 1.0 0.1300 0.0200 0.1500 3.3 4.2 0.30

31 1.0 0.1250 0.0250 0.1500 3.2 4.7 0.35

32 1.0 0.1125 0.0375 0.1500 2.7 3.7 0.35

33 2.0 0.1325 0.0175 0.1500 3.1 6.2 0.10

34 2.0 0.1300 0.0200 0.1500 3.4 6.1 0

35 2.0 0.1250 0.0250 0.1500 3.5 6.5 0.42

36 2.0 0.1125 0.0375 0.1500 3.2 6.3 0.40

(b) CaSO4

0.5

1.0

1.5

2.0

2.0 4.0 6.0 8.0

SO42-

/CO32-

Ksp(m

ixe

d)/

Ksp(p

ure

)

0.5M NaCl 1.0M NaCl

2.0M NaCl

(a) CaCO3

0.5

1.0

1.5

2.0

2.0 4.0 6.0 8.0

SO42-

/CO32-

Ksp(m

ixe

d)/

Ksp(p

ure

)

0.5M NaCl 1.0M NaCl

2.0M NaCl

Figure 3.8: Comparison of Ksp(mixture)/Ksp(pure) for (a) CaCO3 and (b) CaSO4

( ) ( ) ( )

( )

( ) ( ) ( )

2diff ,Ca CaCO3

2diff ,CO CaCO33

k2 2

b i

3

k2 2

3 3b i

Ca Ca

CaCO s

CO CO

+

−

+ +

− −

→

→+ ←

→

Figure 3.9: Crystal growth of pure CaCO3

( ) 23 3

2

3 2 2b3 3diff,CO CaCO b i

COk CO CO

t−

−

− − ∂ = − ∂

(3-26)

[ ] 3

3 2 2 2 2

f,CaCO pure 3 3i eq i eq

CaCOk Ca Ca CO CO

t

a b+ + − −

−

∂ = − − ∂

(3-27)

where ( )2

3diff,Ca CaCOk + and

( )23 3diff,CO CaCO

k − is the diffusion coefficient of Ca2+

and CO32-

respectively in the pure system; 3f,CaCO purek − is the formation constant and

3diss,CaCO purek −

is the dissolution constant of calcium carbonate in the pure system. [ ]b, [ ]i, and [ ]eq

denote the concentration of the bulk solution, concentration of solutes at the crystals

interface and concentration at equilibrium, respectively. The exponents a and b

represent the reaction order with respect to Ca2+

and CO32-

, respectively. Similar

expressions could be written for pure CaSO4 formation by replacing CO32-

with SO42-

.

( ) 24

2

2 2b

diff,Ca CaSO b i

Cak Ca Ca

t+

+

+ + ∂ = − ∂

(3-28)

( ) 24 4

2

4 2 2b4 4diff,SO CaSO b i

SOk SO SO

t−

−

− − ∂ = − ∂

(3-29)

[ ] 4

4 2 2 2 2

f,CaSO pure 4 4i eq i eq

CaSOk Ca Ca SO SO

t

c d+ + − −

−

∂ = − − ∂

(3-30)

The exponents c and d represent the reaction order with respect to Ca2+

and SO42-

,

respectively. For mixed system, the formation expression becomes very complex.

First, if individual crystals of CaCO3 and CaSO4 are formed, i.e. no new species is

formed, then Eq (3-31) to Eq (3-35) is sufficient to describe the rate of formation of

these two crystals by replacing the diffk and fk of the species in the pure system with

that of the mixed system.

( ) 2

2

2 2b

diff,Ca mixed b i

Cak Ca Ca

t+

+

+ + ∂ = − ∂

(3-31)

( ) 23

2

3 2 2b3 3diff,CO mixed b i

COk CO CO

t−

−

− − ∂ = − ∂

(3-32)

( ) 24

2

4 2 2b4 4diff,SO mixed b i

SOk SO SO

t−

−

− − ∂ = − ∂

(3-33)

[ ] 3

3 2 2 2 2

f,CaCO mixed 3 3i eq i eq

CaCOk Ca Ca CO CO

t

e f+ + − −

−

∂ = − − ∂

(3-34)

[ ] 4

4 2 2 2 2

f,CaSO mixed 4 4i eq i eq

CaSOk Ca Ca SO SO

t

g h+ + − −

−

∂ = − − ∂

(3-35)

In this case, the presence of another solute is said to only have an effect on the

diffusion step in the crystallisation i.e. the mass transfer coefficient of solutes through

the boundary layer in the pure system differ from the mixed system. The principle of

multi-component diffusion on crystal growth in electrolyte solutions based on

Maxwell-Stefan multi-component diffusion and Pitzer theory of electrolytes solution

has been studied in detail by Louhi-Kultanen and co-workers [Louhi-Kultanen et al.,

2001]. It is expected that the rate of formation of either CaCO3 or CaSO4 remain

unchanged i.e. 3 3f ,CaCO mixed f ,CaCO purek k− −= ;

4 4f ,CaSO mixed f ,CaSO purek k− −= ; e = a, f = b, g = c,

h = d. As mentioned previously, XRD is planned to be used to check the existence of

new crystals bond for the mixed salts system; if the crystal formed is a new species

(here indicated as 3 4Ca CO SO− − ) rather than the individual CaCO3 and CaSO4 as

proposed in the previous work [Chong and Sheikholeslami, 2001], the diffusion step

is similar to the previous case but the surface reaction has to be rewritten as follow:

[ ]

3 4

3 4 2 2

f,Ca CO SO i eq

2 2 2 2

3 3 4 4i eq i eq

Ca CO SOk Ca Ca

t

CO CO SO SO

i

j k

+ +− −

− − − −

∂ − − = − ∂

− −

(3-36)

where 3 4f ,Ca CO SOk − − is the formation constant of the mixed salt; the exponents i, j, and

k are the reaction order corresponding to the solutes in the mixed system. So to

determine, from Eq (3-25) to Eq (3-36), the net rate of formation of pure CaCO3, pure

CaSO4 or the mixed salt, it is necessary to know the instantaneous concentration of

the reacting species as well as the product. However, it is impossible to determine the

amount of solid formed since the crystals are sticked to the wall. Moreover, the above

equations for surface reaction i.e. Eq (3-27), (3-30), (3-34), (3-35) and (3-36) only

predicts the dependency of crystals formation on concentration of the solutes. It

should be noted that for surface integration process, the surface of the crystals present,

plays an important role in the overall formation rate. Especially for spontaneous

precipitation where there are no surfaces available for crystal growth in the initial

stage, the reaction is expected to be slow; whereas when crystals start to form, the

subsequent solutes will be easily integrated into the first layer of surface, therefore the

rate of formation will be enhanced; the crystal is said to be playing an autocatalytic

role. In view of such complexity in kinetic analysis, a simple method based on the

driving force for crystallisation is applied here to calculate the kinetics of precipitation

for pure salts. The rate of formation of pure CaCO3 or CaSO4 is fitted with the

following expression:

rxn rxn

eq

k 1 k S 1t

n

nq q

q

∂ − = − = − ∂

(3-37)

where ( )1

22 2

3Ca COq+ − = for pure CaCO3 precipitation or ( )

122 2

4Ca SOq+ − =

for pure CaSO4 precipitation. The subscript “eq” denotes the value at equilibrium; and

the ratio q/qeq is the supersaturation ratio, S; n is the order of reaction and krxn (with

unit of molL-1

hr-1

) is the reaction rate constant. To calculate krxn and n, method of

initial rates [Levenspiel, 1972] is used, where the initial rate of reactions after the

induction period, ( )0

tq− ∂ ∂ , are determined based on a series of initial q values

(represented by q0); such that Eq (3-37) becomes:

rxn 0

0

k S 1t

nq∂ − = −

∂

(3-38)

The plot of ( ) 0log tq− ∂ ∂ vs 0log S 1− will give the slope of n and intercept of

logkrxn. In order to find ( )0

tq− ∂ ∂ , the experimental data for q vs time (refer to

time after induction period, tind) is fitted using the Least Square Non-Linear

Regression (lsqnonlin) Optimization Toolbox in Matlab. The best-fit curve for

0.007M CaCO3 in 0.5M NaCl and 0.060M CaSO4 in 0.5M NaCl is shown in

Figure3.10 (a) and (b) respectively. The equation for the best-fit curve has the form

( ) ( )0 eq eqexp tq q q qλ= − − + (3-39)

where λ is the decay coefficient (in hr-1

). Differentiation of Eq (3-39) at t = 0, will

give the initial rate of reaction, ( )0

tq− ∂ ∂ .

(a) 0.007M CaCO3 in 0.5M

NaCl

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 200 400 600

t - tind (hr)

q (

mo

l/L

) x

10

3

Exp Fitted

(b) 0.060M CaSO4 in 0.5M

NaCl

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 50 100

t - tind (hr)

q (

mo

l/L

) x

10

2

Exp Fitted

Figure 3.10: Best fitted curve for experimental data

The kinetic analysis for pure CaCO3 and pure CaSO4 precipitation is shown in Figure

3.11 (a) and (b) respectively. The rate constant, krxn, and order of reaction, n, for pure

CaCO3 and pure CaSO4 at various salinities are summarized in Figure 3.12 (a) and (b)

respectively.

For pure calcium carbonate precipitation, an initial surge stage could be identified

where the amount of Ca2+

in the solution depleted rapidly. Similar observation of a

surge stage in pure CaCO3 precipitation was reported by previous researchers as well

[Chong and Sheikholeslami, 2001; Nancollas and Reddy, 1971]. When including the

surge stage, the n value was found to be varied from 4.69 to 5.15 when the NaCl

concentration increased from 0.5M to 2.0M. However when excluding the surge

stage, the n value varied from 1.16 to 1.61 and by extrapolating the graph to 0.0M

NaCl, the n value is closed to 1 and this is in agreement with the results in literature

[Nancollas and Reddy, 1971; Gutjahr et al., 1996]. It should be noted that the change

in salinity of the solution does not significantly affect the kinetic of CaCO3

precipitation; no significant change in n value. Nancollas and Reddy [1971] obtained

a value of krxn = 156 Lmol-1

hr-1

for seeded crystal growth using seed crystals with area

of 0.3 m2/g at 25

oC. Our reaction rate constant extrapolated for 0.0M NaCl is krxn =

1.0x10-5

molL-1

hr-1

. However, it is not possible to compare our value with Nancollas

result either directly (krxn = 1.0x10-5

molL-1

hr-1

) or after converting through division

by (qeq)2 which results in krxn = 111 Lmol

-1hr

-1; because the number of nucleus as well

as the growth area to initiate the spontaneous precipitation, which is in our case, is not

known.

For pure CaSO4 precipitation, the order of reaction is greatly affected by the presence

of NaCl; the n values were 3, 4 and 6 for 0.5M, 1.0M and 2.0M NaCl respectively.

This result suggesting a polynuclear kinetic reaction for CaSO4 precipitation in

sodium chloride solution. When extrapolating the curve for reaction order to 0.0M

NaCl (n ≅ 2) and when compared with the literature value [Liu and Nancollas, 1970;

Nancollas et al., 1973], they are in good agreement. The predicted value of krxn at

0.0M NaCl for pure CaSO4 precipitation from Figure 3.12 (b) is 0.16 molL-1

hr-1

,

which is higher than the value (krxn = 0.02 molL-1

hr-1

at 30oC) in literature for

spontaneous precipitation [Klepetsanis et al., 1999]. The reason for this is that for

spontaneous precipitation the number of nucleus formed during the induction period

is depending on the supersaturation of the solution; the effective growth sites

available to initiate crystal growth is higher for higher supersaturation. In this study,

the supersaturation varied between 2 to 4, which are higher than the values

(supersaturation < 2) used in the previous study [Klepetsanis et al., 1999]; therefore

resulting in a higher reaction rate constant. The effect of growth sites on

crystallization rate was shown by the work of He et al. [1994] for seeded growth of

pure CaSO4 in 0.1M at 25oC where the rate constant increased from 5.5x10

-3 to 0.15

molL-1

hr-1

when the seeds concentration was increased from 0.2 to 4.0 g/L.

(a)(ii) pure CaCO3 (excluding

surge period)

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

0.0 1.0 2.0 3.0

NaCl (M)

log(k

rxn)

0.0

0.5

1.0

1.5

2.0

n

log(k) n

(a)(i) pure CaCO3 (including surge

period)

-6.1

-6.0

-5.9

-5.8

-5.7

-5.6

-5.5

-5.4

0.0 1.0 2.0 3.0

NaCl (M)

log(k

rxn)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

n

log(k) n

(b) pure CaSO4

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.0 0.5 1.0 1.5 2.0 2.5

NaCl (M)

log(k

rxn)

0

1

2

3

4

5

6

7

n

log(k) n

Figure 3.11: Kinetic analysis for pure salts at various salinities

For the mixed salt system, since only runs with one initial [Ca2+

] = 0.15M were

conducted, it is not possible to carry out detailed kinetic analysis as was done for pure

salts. For proper kinetic analysis, more than one initial total calcium concentration is

needed in order to apply the method of initial rates [Levenspiel, 1972]. So only the

change in total Ca2+

for the mixed salt at various salinities are compared with the pure

0.15M CaSO4 precipitation as shown in Figure 3.13. The decay coefficient of the

best-fitted curve for the mixed salt, ( )mixedλ and pure salt ( )pureλ is tabulated in

Table 3.5 and the comparison of the decay coefficients is shown in Figure 3.14. The

presence of 0.01175M carbonate (SO42-

/CO32-

= 7.5) in 0.5M NaCl has slowed down

the reaction rate i.e. λ changed from 25.71 to 1.28 hr-1

; when the ratio of SO42-

/CO32-

is further reduced to 3, the decay coefficient is 0.33 hr-1

, which is only 0.013 times the

value for pure CaSO4; indicating the kinetic for pure salt is not applicable to mixed

salt system. Further tests with different initial total Ca2+

are necessary to assess the

kinetics for mixed system.

(a)(ii) pure CaCO3 (excluding

surge period)

y = 1.1592x - 4.7699

R2 = 0.8393

y = 1.6092x - 4.4707

R2 = 0.8748y = 1.6104x - 3.8427

R2 = 0.9987

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

-1.0 -0.5 0.0 0.5

log(S0 - 1)lo

g(R

0)

0.5M NaCl 1.0M NaCl 2.0M NaCl

(a)(i) pure CaCO3 (including surge

period)

y = 4.6938x - 5.9873

R2 = 0.9610

y = 5.1544x - 5.8640

R2 = 0.9915

y = 5.0930x - 5.4751

R2 = 0.9153

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

0.0 0.2 0.4 0.6 0.8 1.0

log(S0 - 1)

log

(R0)

0.5M NaCl 1.0M NaCl 2.0M NaCl

(b) pure CaSO4

y = 3.0202x - 1.007

R2 = 0.9959

y = 4.0556x - 1.1724

R2 = 0.9478 y = 6.0314x - 1.2713

R2 = 0.9584

-4.0

-3.0

-2.0

-1.0

0.0

1.0

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

log(S0 - 1)

log

(R0)

0.5M NaCl 1.0M NaCl 2.0M NaCl

Figure 3.12: Rate constant and reaction order for pure salts precipitation at

various salinities

3.4 Concluding Remarks

Experimental work has been carried out to study the precipitation of pure calcium

carbonate and pure calcium sulfate as well as the mixed salts of CaCO3 and CaSO4

with sulfate as the predominant species in different levels of salinity ranging from

0.5M to 2.0M NaCl. As expected, the thermodynamic equilibrium constant of pure

salts are not affected by the level of salinities but it was found that the kinetics of pure

CaSO4 was strongly affected by the level of salinity i.e. reaction order increases from

3 to 6 when NaCl concentration increases from 0.5M to 2.0M; meaning polynuclear

reaction. However for pure CaCO3, the order of reaction as well as the rate constant

was only slightly affected by different salinity levels.

(a) 0.5M NaCl

0.00

0.05

0.10

0.15

0.20

0.0 0.1 1.0 10.0 100.0 1000.0

Time (hr)

[Ca

2+]

(mol/L

)

pure CaSO4 SO4/CO3 = 7.5 SO4/CO3 = 6.5 SO4/CO3 = 5.0 SO4/CO3 = 3.0

(b) 1.0M NaCl

0.00

0.05

0.10

0.15

0.20

0.0 0.1 1.0 10.0 100.0 1000.0

Time (hr)

[Ca

2+]

(mol/L

)

pure CaSO4 SO4/CO3 = 7.5 SO4/CO3 = 6.5 SO4/CO3 = 5.0 SO4/CO3 = 3.0

(c) 2.0M NaCl

0.00

0.05

0.10

0.15

0.20

0.0 0.1 1.0 10.0 100.0 1000.0

Time (hr)

[Ca

2+]

(mo

l/L

)

pure CaSO4 SO4/CO3 = 7.5 SO4/CO3 = 6.5 SO4/CO3 = 5.0 SO4/CO3 = 3.0

Figure 3.13: Change in [Ca2+

] for pure CaSO4 and mixed salt at various salinities

Table 3.5: Decay coefficient of best-fitted [Ca2+

] vs time curve for pure CaSO4

and mixed salts with initial total [Ca2+

] = 0.15M

Decay Coefficient λ (hr-1

)

0.5M NaCl 1.0M NaCl 2.0M NaCl

Pure CaSO4 25.71 8.54 14.07

SO42-

/CO32-

= 7.5 1.28 0.53 2.82

SO42-

/CO32-

= 6.5 1.04 0.77 0.13

SO42-

/CO32-

= 5.0 0.46 0.56 0.13

SO42-

/CO32-

= 3.0 0.33 0.35 0.20

0.00

0.05

0.10

0.15

0.20

0.25

2.0 3.0 4.0 5.0 6.0 7.0 8.0

SO42-

/CO32-

λλ λλ(m

ixed)

/ λλ λλ

(pure

)

0.5M NaCl 1.0M NaCl 2.0M NaCl

Figure 3.14: Comparison of decay coefficients for mixed salt and pure salt (initial

total [Ca2+

] = 0.15M) at various salinities

The interactive effects of co-precipitation have changed the crystals structures of

CaSO4 (e.g. the tenacity and shape), the thermodynamics (e.g. higher Ksp value as

compared to the pure salts) and kinetics (e.g. decay coefficients of total calcium

change in the mixed salt system are of order of magnitude lower than those of the

pure CaSO4) and the effects are greatest at high salinity or in other word low level of

supersaturation. From the data obtained for mixed solution, it can be concluded that

the thermodynamics and kinetics data for pure salts are no longer applicable to predict

the composite fouling. This information is critical as in general practice to avoid the

fouling of CaSO4, low level of CaSO4 supersaturation is maintained in seawater

desalination and this will result in the severe interactive effects in terms of co-

precipitation. Further work will be carried out at 30% recovery and CP of 2

(equivalent to 65% recovery) as well as higher molar ratio of SO42-

/CO32-

to

investigate the role of co-precipitating species in the mixed system. Also, chemical

analysis such as XRD will be carried out to assess the existence of new crystal

bonding in the mixed salt precipitation.

4. SET UP OF THE NOVEL DYNAMIC SYSTEM

In this project, a novel technique, known as the Direct Observation Over the

Membrane (DOOM) is applied to visually examine the sequential events occurring

during surface fouling. Flat sheet RO membrane with active area of 6.0 cm x 48.7 cm

in a parallel plate geometry stainless steel cell with transparent viewing windows,

together with a microscopic-video system is designed and will be used in this project.

The flow channel is 0.2 cm high. There are eight permeate collection ports on the

bottom plate of the RO cell along the length of the flow channel in order to measure

the differential flux along the membrane. Each port is equally sized and covers an

area of 6.0 cm x 6.0 cm. The top and bottom plates of the RO cell are held together by

bolts and nuts. Figure 4.1 shows a photo of the reverse osmosis cell and Figure 4.2

shows the schematics of the experimental set-up. Simulated seawater test solution is

prepared from chemicals and bacteria and put in the plastic feed tank with a capacity

of 20 L. During the experiment, concentrate, permeate and bypass will be recycled

back to the feed tank. In order to ensure well-mixing, solution in the feed tank is

circulated using a centrifugal pump. Conductivity, pH, and dissolved oxygen (in

biofouling experiment) of the solution are monitored with the respective electrode.

Test solution (or after passing through the cartridge filter in those experiments that

involve determining the effect of particulate fouling) will be pressurized with a

variable speed high-pressure pump. Pressure of the stream is monitored and controlled

by the electrical actuator control valve located at the downstream of RO cell.

Temperature of the solution is kept constant with cooling water before entering the

RO cell. Six On/Off direct-lift solenoid valves are installed at the permeate outlet

which operate at 5 minutes interval such that only permeate from one channel is

allowed to pass through the volumetric flow meter (Flow Meter 1) at one time. Flow

rates of the permeate on the entrance and exit ports are not monitored due to

fluctuation. After measurement, the permeate will then be recycled back to the feed

tank together with permeate from other channels. The total permeate flow rate is

measured with a volumetric flow meter (Flow Meter 2). After passing through the

cartridge filter, the concentrate is recycled back to the feed tank. Events that occur

throughout the experiment will be captured with the microscopic and imaging system

installed.

The concentration of the feed is monitored and maintained at a constant value. This is

important as in the actual seawater desalination; fresh seawater is always supplied to

the reverse osmosis membranes. To do so, the permeate and the concentrate are both

recycled back to the feed tank. Also, the concentration of feed is monitored

throughout the experiment; when there is drop in concentration due to fouling, extra

salts will be added. As for biofouling study, when there is an increase in biomass in

the bulk solution due to growth, water and if required make-up chemicals will be

added to maintain constant concentration.

Figure 4.1: Reverse osmosis cell

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Figure 4.2: Dynamic setup of seawater reverse osmosis desalination

94

Scientific Reason and Value Added to the Design of Experimental Set-Up

Two mathematical fouling models, Okazaki & Kimura’s model [1984] and Gilron &

Hasson’s model [1987], have been developed to predict the calcium sulfate fouling in

the reverse osmosis membranes.The first model, assumes that calcium sulfate

precipitation occurs due to supersaturation and the precipitates form a porous cake

layer that covers the whole membrane surface hence reducing the flux. According to

this model, the first layer of deposit is uniformly distributed on the membrane surface.

Subsequent layers of deposit will build up on the first layer of deposit, thus increasing

the thickness of cake layer accompanying by the reduction of porosity of the cake and

permitting less water to cross the membrane. On the other hand, according to the

surface blockage model of Gilron and Hasson [1987], calcium and sulfate ions

initially present as soluble form will be transported to the membrane surface. Due to

concentration polarization effect, the solubility of calcium sulfate is exceeded hence

nucleation follow by crystal growth will occur at the membrane surface. Distinctive

nuclei will form on certain area of the membrane and as time goes by, the crystals will

grow only from these nucleation sites. This means there will be no increase in the

population of crystals but rather the size of the individual crystal. Similar observation

that the existing crystals continue to grow rather that the creation of new seed crystals

has been reported by several authors who studied the precipitation of calcium

carbonate in heat exchanger systems with a microscopic technique [Kim et al., 2002;

Dawe and Zhang, 1997]. The crystals formed are considered as non-porous, therefore

blocking the passage of water across the membrane and other area free of crystals are

available for filtration. As a result, the membrane area covered by the growth of

crystal corresponds to the decline in flux. At a glance, the Okazaki & Kimura’s model

assumes particulate fouling for CaSO4 while the Gilron & Hasson’s model assumes

crystallization fouling. These two mathematical models are developed based on the

above assumptions to predict the flux decline and experimental work has been carried

out to verify these models. However the flux measurement alone is not sufficient and

conclusive enough to support the predicted events actually occurring during fouling

process. This is for two reasons. First, the crystals initially deposited as layer of

particles may be removed by hydrodynamic force, leaving some portion of the

membrane area uninhabited. This would give a wrong impression that Gilron &

Hasson’s model more suitably describes the CaSO4 fouling. Also there could be a

combined mechanisms of fouling, particulate and crystallization, as proposed by

Sheikholeslami [Sheikholeslami, 2000]. In this scenario, crystallization may occur

first as described in Gilron & Hasson’s model. However, some of the crystals may be

removed and induced bulk precipitation hence causing particulate CaSO4 to deposit

on the initially formed crystal. The scale may then appear to be layers by layers.

Applying the DOOM technique will enable one to monitor the instantaneous events

occurring during the fouling process e.g. whether fouling is initiated by the generation

of nuclei on the membrane surface or the deposition of particulate CaSO4 follow by

the build up of scale layer.

Based on the studies by Gilron and Hasson [1987] and Brusilovsky et al. [1992], it is

shown that flux decline due to gypsum precipitation on RO membranes is caused by

the blockage of surface by lateral growth of the deposit. The fouling model is based

on a single adjustable parameter km(N)1/2

embodying a crystal growth rate coefficient

(km) and a nucleation sites density magnitude (N). By applying the above model and

95

measuring the differential flux along the membrane; it was found that the degree of

flux decline at a particular time is lowest for the permeate collected from the

collection port nearest to the entrance and highest for the collection port near the exit.

This divergence in permeate flow rate collected from the different ports increased

with time. The tendency for a more rapid flux decline along the downstream direction

of the membrane represents an increase in the density of nucleation sites in the flow

direction. Hence by separating the membrane into different sections; one can

determine the effect of km(N)1/2

upon the local permeate flux. The flux decline data

were also successfully correlated by this model for calcium carbonate precipitation

[Brusilovsky et al., 1992].

Calcium sulfate fouling mechanism has been suggested to be a combination of

particulate and crystallization [Sheikholeslami, 2000]. So, to determine the extent of

particulate fouling by CaSO4, a cartridge filter has to be installed in-line. The second

cartridge filter installed on the concentrate return line is to ensure all particulate

matter formed or the scale sloughed-off from the membrane surface being removed

before recycled back to the feed tank. This is to avoid the bulk precipitation in the

feed tank.

96

5. REVIEW OF METHODS FOR BIOLOGICAL CONTENT ANALYSIS

The objective of this chapter is to study the methods available for analysis of

biological content of seawater. Unlike the measurement of the inorganic salts, the

biological analysis usually takes longer time (some up to 7 days) and also the result

usually does not give the direct reading of the biomass. This is important as time

consuming method is considered non feasible for study of the dynamic systems as

there is a need to monitor and to maintain constant concentration in the feed side.

Table 5.1 provides a summary of the methods and their advantages and disadvantages.

In view of all the methods available, the ATP determination method is more suitable

to be used for this study, as it is the fastest and direct method to determine the amount

of viable cells in water sample. To reduce the contamination from external sources,

proper hand gloves and sterilized equipment will be used when handling the sample in

order to achieve high accuracy.

97

Table 5.1: Methods of analysis of biological content in seawater

Method Direct count

Description A small quantity of sample (usually 1.0 mL depending on the concentration) is taken and distributed evenly on a microscope

slide and stained with a dye (e.g. acridine orange). Counting of number of cells is performed under fluorescens microscope.

Results are expressed in (cells/mL).

Advantages Inexpensive method

Disadvantages Counting of cells is very tedious and time consuming. Also, it is impossible to count the cells when they appear in cluster

form.

Method Heterotrophic plate count

Description A sample of water with known volume is filtered through a 0.45 microns filter at constant pressure. Bacteria are incubated

at 30oC for 5 to 7 days in growth medium (e.g. commercially available R2A agar). After the incubation period, plate counts

are carried out and the result is expressed as colony forming units (cfu/mL).

Advantages Inexpensive method.

Disadvantages Long test time. Incubation of bacteria takes about 5 to 7 days. Also, it is very tedious to count the colony formed.

Method Live/Dead Baclight Bacteria Viability Kit from Molecular Probe [Molecular Probes, 2001]

Description Utilize a mixture of SYTO 9 green-fluorescent nuclei acid stain and the red-fluorescent nuclei acid stain, propidium iodide.

These stains differ both in their spectral characteristics and in their ability to penetrate healthy bacterial cells. When used

alone, the STYO 9 stain generally labels all bacteria in a population - those with intact membranes and those with damaged

membranes. In contrast, propidium iodide penetrates only bacteria with damaged membranes, causing a reduction in the

SYTO 9 stain fluorescens when both dyes are present. Thus, with an appropriate mixture of the SYTO 9 and propidium

iodide stains, bacteria with intact cell membranes stain fluorescent green, whereas bacteria with damaged membranes stain

fluorescent red. The excitation/emission maxima for these dyes are 480/500 nm for SYTO 9 stain and 490/635 nm for

propidium iodide. The background remains virtually non-fluorescent.

Advantages Staining of cells and the analysis only takes about 20 mins. Amount of the fluorescent-stained cells can be calculated

directly under fluorescent microscope. Alternatively, the intensity of the stain can be measured with a pre-calibrated

fluorescence spectrophotometer, which will then gives the amount of cells presence.

Disadvantages Those bacteria with compromised membranes may recover and reproduce – such bacteria may be scored as ‘dead’ in this

assay. On the other hand, some bacteria with intact membranes may be unable to reproduce in nutrient medium, and yet

these may be scored as ‘alive’ in this assay.

Method Adenosine 5-Triphosphate (ATP) Determination [Molecular Probes, 2001]

Description ATP is present in all living cells and is rapidly destroyed upon the death of organisms. This bioluminescence assay is a

98

quantitative method for determination of ATP with recombinant firefly luciferase and its substrate luciferin. The assay is

based on luciferase’s requirement for ATP in producing light (emission maximum at 560 nm at pH 7.8) from the reaction:

( )2Mg

2 2luciferaseluciferin ATP O oxyluciferin AMP Adedosine Monophosphate pyrophosphate CO light

+

+ + → + + + +

Advantages Light emitted can be measured with a pre-calibrated luminometers. Most commercially available luminometers are able to

detect as little as 0.1 picomole of pre-existing ATP, or ATP as it is being formed in kinetic systems; making this assay

extremely sensitive.

Disadvantages This method only measures the viable cells. Also, due to the high sensitivity of the luciferin-luciferase reaction, extra care

has to be taken to avoid contamination with ATP from exogenous biological sources, such as bacteria in the air or

fingerprints.

Method Measurement of Total Dissolved Organic Carbon (TDOC) and Biodegradable Dissolved Organic Carbon (BDOC) as an

indication of bacterial growth potential of water

Description Total dissolved organic carbon (TDOC) refers to the total amount of organic carbon present in the water sample as soluble

form while biodegradable dissolved organic carbon (BDOC) refers to the portion of TDOC that can be consumed by living

organisms in the water for metabolism and reproduction. TDOC can be determined by high temperature catalytic oxidation

(HTCO) or photocatalytic oxidation method. In HTCO, combustion of water sample is carried out at temperatures around

680oC and all the DOC is converted to CO2 which is then measured by non-dispersive infra red (NDIR) whereas DOC is

oxidized by UV to CO2 in the photocatalytic method. To determine BDOC, water sample with known amount of TDOC is

used to grow single species of bacteria (e.g. Pseudomonas fluorescens for this research purpose); BDOC is the difference

between the initial TDOC and the minimum TDOC during the incubation period. A correlation between the total amount of

TDOC consumed and the amount of biomass increased can be developed and used to calculate the bacteria growing

potential of a water sample with unknown concentration.

Advantages Commercially available TDOC determination instruments have high accuracy and the measurement can be completed in

less then 15 mins.

Disadvantages Both methods require the prior removal of inorganic carbon by acidification and purging of CO2. Purging of sample can

remove some dissolved organic carbon in the solution, therefore resulting in a lower value detected. Also, the TDOC and

BDOC method can only be used to give an indication of the biomass gain but could not tell whether there is a drop in

biomass in the bulk solution if the value of TDOC stays constant even though fouling has occurred at the membrane surface.

99

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