REVERSE OSMOSIS DESALINATION PROCESS: WATER ...pselab.chem.polimi.it/wp-content/uploads/2014/05/Marta-Barello... · REVERSE OSMOSIS DESALINATION PROCESS: WATER PERMEABILITY ... Laura,

Politecnico di Milano

Scuola di Ingegneria Industriale e dell'Informazione

Corso di Laurea in Ingegneria Chimica

REVERSE OSMOSIS DESALINATIONPROCESS: WATER PERMEABILITY

CONSTANT ASSESSMENT

Relatore: Prof. Davide MANCA

Correlatore: Prof. Iqbal Mohammed MUJTABA

Tesi di Laurea di:Marta BARELLOMatricola n. 782769

Anno Accademico 2012-2013

Dedication

This degree is dedicated to my family: my mother Chiara and father Piero,for always being supportive in my pursuit of higher education and personaldevelopment, to my brother Andrea who has been, is, and will always be onmy side, to my grandparents Rita, Cesare, Primina e Peppino, who rose me uppampering me, with all the love that only grandparents can give, to my auntGiuse and uncle Luca, for being awesome just the way they are, and an exampleof strength and tenacity every day, to my baby cousin Vittoria as a wish for abright and happy future, to my dog Happy because she is part of the family.

I also dedicate this dissertation to my dearest friends Martina, Ra�aella,Giusy, Gilda, Rossella, Mariyan, Delia, Ventsislava, Marta, Mara, Giulia, Mar-ica, Laura, Andrea, Michele, Carlo, Walter, for their understanding, encourage-ment, and help for the past years.

i

Dedica

Questa laurea è dedicata alla mia famiglia: mia mamma Chiara e papà Piero, peressere sempre stati di supporto durante il mio percorso educativo e di sviluppopersonale, a mio fratello Andrea che è stato, è e sarà sempre al mio �anco, ai mieinonni Rita, Cesare, Primina e Peppino che mi hanno cresciuta coccolandomi,con tutto l'amore che solo i nonni possono dare, ai miei zii Giuse e Luca, peressere semplicemente speciali ed un esempio di forza e tenacia ogni giorno, allamia cuginetta Vittoria come augurio di un futuro brillante e felice, alla miacagnolina Happy, perché è parte della famiglia.

Dedico inoltre questa tesi ai miei amici più cari Martina, Ra�aella, Giusy,Gilda, Rossella, Mariyan, Delia, Ventsislava, Marta, Mara, Giulia, Marica,Laura, Andrea, Michele, Carlo, Walter, per la loro comprensione, incoraggia-mento e aiuto in tutti questi anni.

iii

Acknowledgments

I would like to thank all those who have helped me in completing my degreein Chemical Engineering at the Politecnico di Milano. I am especially gratefulto my italian supervisor, Professor Davide Manca, for providing me with theprecious opportunity to develop my disseration abroad and for his constanthelp, and Professor Iqbal Mohammed Mujtaba, my supervisor in Bradford, forhis guidance, advice, inspiration, and valuable suggestions during the courseof this dissertation. Sincere thanks also go to the members of the ChemicalEngineering Laboratory at the University of Bradford, Dr R. Patel, Mr D. Steeland Mr M. Palmer for their comments and suggestions. I would also thank myfriend Stefano for his help in developing this dissertation using LATEX.

v

Contents

Contents vii

List of Figures xi

List of Tables xv

Abstract xvii

Introduzione xix

Symbology xxi

I The desalination process 1

1 An overview 31.1 Desalination background . . . . . . . . . . . . . . . . . . . . . . . 31.2 Water resources, supply and demand . . . . . . . . . . . . . . . . 41.3 Composition of the seawater . . . . . . . . . . . . . . . . . . . . . 61.4 History of industrial desalination . . . . . . . . . . . . . . . . . . 71.5 Development of desalination . . . . . . . . . . . . . . . . . . . . . 10

2 Desalination methods 112.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Distillation/evaporation methods . . . . . . . . . . . . . . . . . . 12

2.2.1 Single e�ect evaporation . . . . . . . . . . . . . . . . . . . 132.2.1.1 Single e�ect thermal vapor compression (TCV ) 142.2.1.2 Single E�ect Mechanical Vapor Compression (MVC ) 14

2.2.2 Multiple E�ect Evaporation (MEE ) . . . . . . . . . . . . 152.2.2.1 Forward Feed Multiple E�ect Evaporation . . . 162.2.2.2 Parallel Feed Multiple E�ect Evaporation . . . 16

2.2.3 Multi Stage Flash Distillation (MSF ) . . . . . . . . . . . 172.2.3.1 Flashing Stage . . . . . . . . . . . . . . . . . . . 182.2.3.2 Once through MSF . . . . . . . . . . . . . . . . 192.2.3.3 Brine Circulation MSF . . . . . . . . . . . . . . 21

vii

viii CONTENTS

2.3 Freezing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.3.1 Direct Contact Freezing Method . . . . . . . . . . . . . . 22

2.3.2 Indirect Contact Freezing Method . . . . . . . . . . . . . 23

3 Reverse osmosis methods 25

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2 Historical Background . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 Elements of Membrane Separation . . . . . . . . . . . . . . . . . 27

3.4 Reverse Osmosis System . . . . . . . . . . . . . . . . . . . . . . 29

3.5 RO Membrane Module Con�gurations . . . . . . . . . . . . . . . 32

3.5.1 Hollow Fiber Membrane Module . . . . . . . . . . . . . . 32

3.5.2 Spiral Wound Membrane Module . . . . . . . . . . . . . 32

3.5.3 Tubular Membrane Module . . . . . . . . . . . . . . . . . 34

3.6 Desalination methods review . . . . . . . . . . . . . . . . . . . . 35

II Modeling and Analysis of a Batch Reverse OsmosisDesalination Plant 37

4 Experimental results 39

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2 Experiments setup . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . 44

4.3.1 Permeate �ux . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.3.2 Permeate salinity . . . . . . . . . . . . . . . . . . . . . . . 46

4.3.2.1 E�ect of feed salinity . . . . . . . . . . . . . . . 46

4.4 Constant feed salinity . . . . . . . . . . . . . . . . . . . . . . . . 48

4.4.1 Materials and methods . . . . . . . . . . . . . . . . . . . . 48

4.4.2 Results and discussion . . . . . . . . . . . . . . . . . . . . 49

5 Assessment of RO key parameters 53

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.2 Water permeability calculations . . . . . . . . . . . . . . . . . . . 53

5.2.1 El-Dessouky and Ettouney model . . . . . . . . . . . . . . 54

5.2.2 Meares model . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.3 Concentration polarization . . . . . . . . . . . . . . . . . . . . . . 60

5.4 Salt transport mechanism . . . . . . . . . . . . . . . . . . . . . . 62

6 RO process modeling 67

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

6.2 Equations system . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

6.2.1 Degrees of freedom . . . . . . . . . . . . . . . . . . . . . . 68

6.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . 74

CONTENTS ix

III Water permeability arti�cial neural network basedcorrelation 79

7 Identi�cation of Kw by Arti�cial Neural Networks 817.1 General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 817.2 Membrane fouling . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7.2.1 Description and mechanism . . . . . . . . . . . . . . . . . 827.2.2 Literature fouling correlations . . . . . . . . . . . . . . . 83

7.3 Arti�cial neural network based correlation . . . . . . . . . . . . . 847.3.1 ANN architecture and training . . . . . . . . . . . . . . . 857.3.2 Development of correlations . . . . . . . . . . . . . . . . 85

7.4 Data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

8 ANN results and discussion 918.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 918.2 The correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 918.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . 928.4 Number of hidden layers . . . . . . . . . . . . . . . . . . . . . . . 998.5 Number of neurons . . . . . . . . . . . . . . . . . . . . . . . . . . 1008.6 Transfer functions . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Conclusions and Recommendations 105

Conclusions 105

Recommendations 107

References 111

Journals 111

Books 113

Appendices 117

Appendix A 117

Appendix B 137

Appendix C 147

List of Figures

1.1 De�nition of desalination process. . . . . . . . . . . . . . . . . . . 41.2 The hydrologic Cycle. . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1 Principle of distillation method. . . . . . . . . . . . . . . . . . . . 132.2 Single e�ect evaporation system. . . . . . . . . . . . . . . . . . . 142.3 Single e�ect thermal vapor compression desalination process. . . 152.4 Single e�ect mechanical vapor compression (MVC). . . . . . . . . 162.5 Schematic diagram of MEE-FF desalination process. . . . . . . . 172.6 Schematic diagram of MEE parallel �ow. . . . . . . . . . . . . . . 182.7 MSF Flashing Stage. . . . . . . . . . . . . . . . . . . . . . . . . . 192.8 Once through multistage �ash desalination process. . . . . . . . . 202.9 Brine circulation multistage �ash desalination process. . . . . . . 222.10 Principle of freezing method. . . . . . . . . . . . . . . . . . . . . 232.11 Basic direct contact freezing process. . . . . . . . . . . . . . . . . 242.12 Schematic diagram of indirect freezing method. . . . . . . . . . . 24

3.1 Separation process, applied pressure and size of material. . . . . 283.2 Osmosis and reverse osmosis (RO) processes. . . . . . . . . . . . 283.3 Single stage RO system. . . . . . . . . . . . . . . . . . . . . . . . 293.4 Two stage RO system. . . . . . . . . . . . . . . . . . . . . . . . . 303.5 Two pass RO system. . . . . . . . . . . . . . . . . . . . . . . . . 303.6 Hollow �ber membrane modules. (a) Assemble. (b) Fiber dimen-

sions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.7 Spiral wound membrane module. . . . . . . . . . . . . . . . . . . 333.8 Tubular membrane module. . . . . . . . . . . . . . . . . . . . . . 34

4.1 Reverse osmosis process (Courtesy of Arm�eld). . . . . . . . . . . 404.2 Schematic diagram of Batch RO process . . . . . . . . . . . . . . 414.3 Salinity-Conductivity calibration curve. . . . . . . . . . . . . . . 434.4 Permeate �ux trend with xf0=25 g/L. . . . . . . . . . . . . . . . 454.5 Feed tank salinity trend with xf0=25 g/L. . . . . . . . . . . . . . 454.6 Permeate �ux with P=40 bar. . . . . . . . . . . . . . . . . . . . . 464.7 Permeate salinity trend with xf0=15 g/L. . . . . . . . . . . . . . 474.8 Permeate salinity trend at P=45 bar. . . . . . . . . . . . . . . . 47

xi

xii LIST OF FIGURES

4.9 Permeate salinity variation with the feed salinity at P=40 bar. . 484.10 Permeate �ux variation with the feed salinity at P=40 bar. . . . 494.11 Volume-Height calibration curve. . . . . . . . . . . . . . . . . . . 504.12 Feed tank salinity trend. . . . . . . . . . . . . . . . . . . . . . . . 504.13 Permeate �ux trend for batch and continuous running. . . . . . . 514.14 Permeate salinity trend for batch and continuous running. . . . . 51

5.1 Water permeability trend at xf0=25 g/L (Dessouky and Ettouneymodel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

5.2 Water permeability trend at P=45 bar (Dessouky and Ettouneymodel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.3 Water permeability trend with feed salinity at P=40 bar (Dessoukyand Ettouney model). . . . . . . . . . . . . . . . . . . . . . . . . 56

5.4 Water permeability trend at xf0=15 g/L (Meares model). . . . . 585.5 Water permeability trend at P=45 bar (Meares model) . . . . . 595.6 Water permeability trend with feed salinity at P=40 bar (Meares

model). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.7 Concentration polarization trend with xf0=35 g/L. . . . . . . . . 615.8 Concentration polarization trend with permeate �ux at xf0=35

g/L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.9 Feed salinity in�uence on concentration polarization at P=40 bar. 625.10 Salt permeability coe�cient trend at P=40 bar. . . . . . . . . . . 635.11 Salt permeability coe�cient at xf0=25 g/L. . . . . . . . . . . . . 645.12 Salt permeability trend with feed salinity at P=40 bar. . . . . . 645.13 Salt �ow trend at P=45 bar. . . . . . . . . . . . . . . . . . . . . 655.14 Salt �ow trend with xf0=15 g/L. . . . . . . . . . . . . . . . . . 65

6.1 Permeate salinity trends Matlab calculations (x0f=35 g/L). . . . 75

6.2 Permeate �ux trends Matlab calculations (x0f=35 g/L). . . . . . 76

6.3 Permeate salinity trends Matlab calculations (x0f=35 g/L). . . . 76

6.4 Permeate �ux trends Matlab calculations (x0f=36 g/L). . . . . . 77

7.1 Water permeability trend according to literature correlations fora period of 370 days. . . . . . . . . . . . . . . . . . . . . . . . . . 84

7.2 A typical neural network architecture (adapted from Matlab©User's guide). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

7.3 Back propagation algorithm scheme. . . . . . . . . . . . . . . . . 87

8.1 A three layer arti�cial neural network. . . . . . . . . . . . . . . . 928.2 . Performance evaluation of the ANN training procedure. . . . . 938.3 Linear regression of ANN predicted data with correlations . . . . 948.4 Experimental Al-Bastaki and Abbas Kw evaluation and prediction. 958.5 Experimental Zhu Kw evaluation and prediction. . . . . . . . . . 958.6 Zhu Kw experimental and ANN based correlation extrapolation. 968.7 Water permeability constant at di�erent pressures. [At feed salin-

ity of Al-Bastaki and Abbas correlation] . . . . . . . . . . . . . . 97

LIST OF FIGURES xiii

8.8 Water permeability constant at di�erent pressures [At concentra-tion of Zhu et al. correlation] . . . . . . . . . . . . . . . . . . . . 97

8.9 Water permeability constant at di�erent feed salinity [At pressureof Al-Bastaki and Abbas correlation] . . . . . . . . . . . . . . . . 98

8.10 Water permeability constant at di�erent feed salinity [At pressureof Zhu et al. correlation] . . . . . . . . . . . . . . . . . . . . . . . 98

8.11 E�ect of number of hidden layers on the ANN performance. . . . 998.12 E�ect of number of neurons on the ANN performance. . . . . . . 1008.13 Transfer function in�uence. . . . . . . . . . . . . . . . . . . . . . 102A.1 Permeate �ux trend withxf0=15 g/L. . . . . . . . . . . . . . . . 118A.2 Permeate �ux trend with xf0=35 g/L. . . . . . . . . . . . . . . . 118A.3 Permeate �ux trend with P=45 bar. . . . . . . . . . . . . . . . . 119A.4 Feed tank salinity trend with xf0=35 g/L. . . . . . . . . . . . . . 119A.5 Feed tank salinity trend with xf0=15 g/L. . . . . . . . . . . . . . 120A.6 Permeate salinity trend with xf0=35 g/L. . . . . . . . . . . . . . 120A.7 Permeate salinity trend with xf0=25 g/L . . . . . . . . . . . . . 121A.8 Permeate salinity trend at P=40 bar. . . . . . . . . . . . . . . . 121A.9 Permeate salinity variation with the feed salinity at P=45 bar. . 122A.10 Permeate �ux variation with the feed salinity at P=45 bar. . . . 122A.11 Water permeability trend at xf0=15 g/L (Dessouky and Ettouney

model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123A.12 Water permeability trend at xf0=25 g/L (Dessouky and Ettouney

model). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123A.13 Water permeability trend at P=40 bar (Dessouky and Ettouney

model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124A.14 Water permeability trend with feed salinity at P=45 bar (Dessouky

and Ettouney model). . . . . . . . . . . . . . . . . . . . . . . . . 124A.15 Water permeability trend at xf0=25 g/L (Meares model). . . . . 125A.16 Water permeability trend at xf0=35 g/L (Meares model). . . . . 125A.17 Water permeability trend with P=45 bar (Meares model). . . . . 126A.18 Water permeability trend with feed salinity at P=45 bar (Meares

model). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126A.19 Concentration polarization trend with xf0 =15 g/L. . . . . . . . 127A.20 Concentration polarization trend with xf0 =15 g/L. . . . . . . . 127A.21 Concentration polarization trend with permeate �ux at xf0=25

g/L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128A.22 Concentration polarization trend with permeate �ux at xf0=15

g/L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128A.23 Feed salinity in�uence on concentration polarization at P=45 bar. 129A.24 Salt permeability coe�cient trend at P=45 bar. . . . . . . . . . . 129A.25 Salt permeability coe�cient at xf0=15 g/L. . . . . . . . . . . . . 130A.26 Salt permeability coe�cient at xf0=35 g/L. . . . . . . . . . . . . 130A.27 Salt permeability trend with feed salinity at P=40 bar. . . . . . 131A.28 Salt �ow trend at P=40 bar. . . . . . . . . . . . . . . . . . . . . 131A.29 Salt �ow trend with xf0=25 g/L. . . . . . . . . . . . . . . . . . . 132A.30 Salt �ow trend with xf0=25 g/L. . . . . . . . . . . . . . . . . . . 132

xiv LIST OF FIGURES

A.32 Permeate �ux trends Matlab calculations (xf0=25 g/L). . . . . . 133A.31 Permeate salinity trends Matlab calculations (xf0=25 g/L). . . . 133A.33 Permeate salinity trends with feed salinity dependence on water

and salt permeability (xf0=25 g/L). . . . . . . . . . . . . . . . . 134A.34 Permeate �ux trends with feed salinity dependence on water and

salt permeability (xf0=25 g/L). . . . . . . . . . . . . . . . . . . . 134A.35 Permeate salinity trends Matlab calculations (xf0=15 g/L) . . . 135A.36 Permeate �ux trends Matlab calculations (xf0=15 g/L.) . . . . . 135A.37 Permeate salinity trends with feed salinity dependence on water

and salt permeability (xf0=15 g/L). . . . . . . . . . . . . . . . . 136A.38 Permeate �ux trends with feed salinity dependence on water and

salt permeability (xf0=25 g/L). . . . . . . . . . . . . . . . . . . . 136

List of Tables

1.1 Distribution of water resources across the globe [Adapted fromEl-Dessouky and Ettouney (2002)]. . . . . . . . . . . . . . . . . . 6

1.2 Mayor components of seawater [Adapted from Spiegler and El-Sayed (1994)]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Historical developments in thermal and membrane desalinationprocesses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1 Characteristics of RO con�guration. . . . . . . . . . . . . . . . . 313.2 Qualitative comparison of membrane modules [Porter, 1990]. . . 323.3 Desalination methods pros and cons. . . . . . . . . . . . . . . . . 35

4.1 Reverse osmosis process (Courtesy of Arm�eld). . . . . . . . . . . 424.2 Experimental settings . . . . . . . . . . . . . . . . . . . . . . . . 44

6.1 Degrees of freedom. . . . . . . . . . . . . . . . . . . . . . . . . . . 696.2 Parameters involved in each equation for El-Dessouly and Et-

touney (2002) model (1st part). . . . . . . . . . . . . . . . . . . . 706.3 Parameters involved in each equation for El-Dessouly and Et-

touney (2002) model (2nd part). . . . . . . . . . . . . . . . . . . . 716.4 Parameters involved in each equation for Meares (1976) model

(1st part). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726.5 Parameters involved in each equation for Meares (1976) model

(2nd part). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 736.6 Parameters values. . . . . . . . . . . . . . . . . . . . . . . . . . . 746.7 Water and salt permeability correlations for x0

f =35 g/L(this work). 74

6.8 Water and salt permeability correlations for x0f=25 g/L (this work). 75

6.9 Water and salt permeability correlations for x0f=15 g/L (this work). 75

7.1 Di�erent correlation for estimating Kw. . . . . . . . . . . . . . . 837.2 Weights, biases and transfer functions for 3-layered network. . . . 867.3 ANN data example. . . . . . . . . . . . . . . . . . . . . . . . . . 89

8.1 Calculated weights and biases of the arti�cial neural network. . . 938.2 Number of layers performance evaluations. . . . . . . . . . . . . . 99

xv

xvi LIST OF TABLES

8.3 Number of layers performance evaluations. . . . . . . . . . . . . . 1008.4 Transfer functions list and representation. . . . . . . . . . . . . . 1018.5 Performance of the ANN training procedure as a function of the

adopted transfer functions. . . . . . . . . . . . . . . . . . . . . . 101

Abstract

After an introduction about the desalination process, its methodologies and con-�guration, this work assesses the performance of a reverse osmosis desalinationprocess. The experiments are conducted in a batch system, where the recircu-lation of the brine in the feed tank is considered. The performance is evaluatedin terms of permeate quantity and salinity as a function of other operatingparameters such as pressure and feed salinity. The concentration polarizationmechanism and the salt transport phenomenon across the membrane are alsostudied. Special attention is paid to the water permeability constant, which isan important parameter that a�ects the permeate �ux. One of the most im-portant observation in this section is a strong salinity dependence on the waterpermeability constant, which was always neglected or ignored in the literaturefrom authors that studied this aspect of the process considering only the waterpermeability decay with the time due to the fouling phenomena. In addition, asystem of di�erential-algebraic equations is proposed to model the batch reverseosmosis system. If feed salinity dependencies for water and salt permeabilityconstants are introduced in the model, the simulation results better match theexperimental results, proving that in modeling the desalination process, suchdependence should be considered.

In order to better quantify the batch performance, further experiments wereconducted with a constant feed salinity (to reproduce the operating conditionof continuous systems) and the results were compared with the batch system.

The water permeability constant, (Kw) is one of many important parametersthat a�ect optimal design and operation of RO processes; it should thereforebe evaluated in an appropriate way. For a given membrane type, Kw graduallydecreases with increasing the operating pressure, feed salinity and fouling (dueto build up of salt). This decay results in less �ux through the membrane andshould be taken into account when designing a RO desalination plant. Thereare only two available literature correlation calculating the dynamic Kw values.However, each of them are only applicable for a given membrane type and givenfeed salinity over a certain operating pressure range.

In this work, using the data generated by these two existing correlations,we develop a time dependent arti�cial neural network (ANN ) based correlationto predict Kw in RO desalination processes under fouling conditions. It isfound that the ANN based correlation can predict the Kw values very closelyto those obtained by the existing correlations for the same membrane type,

xvii

xviii ABSTRACT

operating pressure range and feed salinity. However, the novel feature of thiscorrelation is that it is able to predict Kw values for any of the two membranetypes, for any operating pressure and any feed salinity within a wide range. Inaddition, for the �rst time the e�ect of feed salinity onKw values at low pressureoperation is reported. While developing the correlation, a number of di�erentneural network architectures in terms of number of hidden layers and number ofneurons in each layer are simulated, and the in�uence of the transfer functionsis eventually investigated. ANN based correlations can be updated reliably interms of new sets of weights and biases for the same architecture or for a newarchitecture, with new plant data.

Part of this dissertation is reported in the following papers:M. Barello, D. Manca, R. Patel, I.M. Mujtaba, �Study of a Batch Reverse

Osmosis Desalination Laboratory Plant�, European Symposium on ComputerAided Process Engineering, Budapest, 2014;

M. Barello, D. Manca, R. Patel, I.M. Mujtaba, �Neural Network Based Cor-relation for Estimating Water Permeability Constant in RO Desalination Pro-cess Under Fouling�, 2013, submitted to Desalination;

M. Barello, D. Manca, R. Patel, I.M. Mujtaba, �Modeling and Analysis ofa Batch Reverse Osmosis Desalination Plant�, 2014, submitted to ChemicalEngineering Science.

Keywords:

Reverse Osmosis; Batch system; Pilot plant; Performance assessment; Physi-

cal property models; Fouling; Water permeability evaluation; Arti�cial Neural

networks modelling.

Introduzione

Dopo un'introduzione sul processo di dissalazione, le sue metodologie e con-�gurazioni, questo lavoro di tesi investiga le prestazioni di un impianto didissalazione a osmosi inversa. Gli esperimenti sono svolti in un sistema batch,nel quale viene considerato il ricircolo dell'acqua salata di scarto nel bacino dialimentazione. Le prestazioni sono quindi valutate in termini di quantità di ac-qua dissalata uscente e relativa salinità, in funzione di alcuni parametri operativicome per esempio pressione e salinità dell'alimentazione. Inoltre, vengono stu-diati il fenomeno della concentration polarization e il meccanismo di trasporto disale attraverso la membrana. Ulteriore attenzione è stata spesa sullo studio dellacostante di permeabilità dell'acqua, essendo un parametro chiave che riguarda il�usso di acqua dissalata prodotta. Una delle osservazioni più importanti fatte inquesta sezione è una forte dipendenza della permeabilità dell'acqua dalla salinitàdell'alimentazione, che è sempre stata ignorata in letteratura dagli autori chehanno studiato questo aspetto del processo, che si sono concentrati solo sulla suadiminuzione con il tempo dovuta allo sporcamento. In aggiunta, viene propostoun sistema di equazioni algebrico-di�erenziale per modellare il sistema batch aosmosi inversa. Quando vengono introdotte nel modello delle espressioni per lapermeabilità dell'acqua e del sale dipendenti dalla salinità dell'alimentazione, irisultati della simulazione combaciano meglio con quelli sperimentali, mettendoin evidenza il fatto che tale dipendenza dovrebbe di fatto essere consideratodurante la modellazione del processo.

In modo da aver un occhio più critico sulle prestazioni del sistema batch,sono stati condotti ulteriori esperimenti con una salinità dell'alimentazione man-tenuta costante (in modo da riprodurre il caso di un'operazione in continuo) ei risultati sono stati dunque confrontati con il caso batch.

La costante di permeabilità dell'acqua (Kw) è uno dei parametri più impor-tanti che riguarda un progetto e�ciente e l'operabilità di un processo RO ; devequindi essere valutata in modo appropriato. Per un dato tipo di membrana,Kw decresce gradualmente con l'aumento della pressione operativa, della salin-ità dell'alimentazione e dello sporcamento (dovuto all'impaccamento di sale).Questa diminuzione porta a un minor �usso attraverso la membrane e devequindi essere tenuto in considerazione quando si progetta un impianto RO didissalazione. Ci sono solo due correlazioni disponibili in letteratura per calco-lare la Kw dinamica. Comunque, esse sono applicabili solo per un dato tipodi membrana e salinità dell'alimentazione speci�ca su un intervallo di pressione

xix

xx INTRODUZIONE

operativa.In questo lavoro, usando i dati generati dalle due correlazioni esistenti, è

stata sviluppata una correlazione dipendente dal tempo basata su una rete neu-rale arti�ciale (ANN ) per predire Kw in un processo di dissalazione in caso diavvenuto sporcamento. Il risultato è che la correlazione ANN riesce a prediredei valori della Kw molto vicini a quelli predetti dalle correlazioni disponibili inletteratura per lo stesso tipo di membrana, intervallo di pressione operativa esalinità della alimentazione. Comunque, la nuova caratteristica di questa corre-lazione è che essa è in grado di predire la Kw per entrambi i tipi di membrane eper ogni valore di salinità dell'alimentazione e pressione, all'interno di un vastointervallo. Inoltre, per la prima volta, viene messo in evidenza l'e�etto dellasalinità dell'alimentazione sulla Kw. Durante lo sviluppo della correlazione,viene simulato un numero di diverse architetture neurali in termini di numerodi strati nascosti e numero di neuroni in ciascuno strato, e l'in�uenza delle fun-zioni di trasferimento è analizzata. Le correlazioni basate su una ANN possonoessere aggiornate in modo a�dabile tramite un nuovo set di pesi e bias per lastessa architettura neurale, o una nuova basata su un nuovo set di dati di input.

Parte di questo lavoro di tesi è riportato nei seguenti articoli:M. Barello, D. Manca, R. Patel, I.M. Mujtaba, �Study of a Batch Reverse

Osmosis Desalination Laboratory Plant�, European Symposium on ComputerAided Process Engineering, Budapest, 2014;

M. Barello, D. Manca, R. Patel, I.M. Mujtaba, �Neural Network Based Cor-relation for Estimating Water Permeability Constant in RO Desalination Pro-cess Under Fouling�, 2014, inviato alla rivista Desalination;

M. Barello, D. Manca, R. Patel, I.M. Mujtaba, �Modeling and Analysis of aBatch Reverse Osmosis Desalination Plant�, 2014, inviato alla rivista ChemicalEngineering Science.

Parole chiave:

Osmosi inversa; Sistema batch; Impianto pilota; Valutazione di performance;

Modelli di proprietà �siche; Sporcamento; Calcolo della costante di permeabilità

dell'acqua; Modellazione di reti neurali arti�ciali.

Symbology

A, Membrane area [m2]a, Neuron value [�]b, Bias [�]C, Water permeability target value [m/bar/min]C1, Conversion factor (from m3/min to L/min) = 1000C2, Conversion factor (from L/min to m3/h) = 1/60000CP , Concentration polarization factor [�]d, Degrees of freedom, [�]D, Feed di�usivity [m2/s]dh, Hydraulic diameter [m]e, Density temperature factor [°C]f , Transfer function [�]J , Flow rate [m3/min/m2]j, Chilton-Colburn factor [�]Js, Salt �ow through the membrane, [g/min]k, Mass transfer coe�cient [m/s]Ks, Salt permeability coe�cient [L/m2/min]Kw, Water permeability constant [m3/min/m2/bar]L, Membrane length [m]m, Total number of variables [�]M , Membrane type,[�]Mb, Brine �ow rate [L/min]Mf , Feed �ow rate [L/min]Mp, Permeate �ow rate [L/min]n, Total number of equations [�]P , Operating pressure [bar]Qf , Feed quantity [L]Qp, Permeate quantity [L]Qpt, Total permeate quantity [L]R, Gas constant [atm/m3/kmol/K]Re, Reynolds number [�]Rj , Salt rejection [�]Sc, Schmidt number [�]Sh, Sherwood number [�]

xxi

xxii SYMBOLOGY

t, Time [min (Section 5, 6 and 7) and day (Section 8)]T , Operating temperature [°C]u, Brine velocity [m/s]V , Water permeability ANN value [m/bar/min]w, Weight [�]x̄, Average salinity [g/L]x, Concentration [g/L]xb, Brine salinity [g/L]xf , Feed salinity [g/L]xp, Permeate salinity [g/L]xpt, Total permeate salinity [g/L]xw, Wall salinity [g/L]α, Number of ions produced on complete dissociation of one molecule of elec-trolyte [�]∆P , Net hydraulic pressure di�erential across the membrane [bar]∆π, Net osmotic pressure di�erential across the membrane [bar]µ, Feed dynamic viscosity, [kg/s/m]υ, Feed kinematic viscosity [m2/s]π, Average osmotic pressure on the feed side [bar]πb, Brine osmotic pressure [bar]πf , Feed osmotic pressure, [bar]πp, Permeate osmotic pressure [bar]ρ, Feed density [kg/m3]

Subscripts

0, Initial valued, Target valuef , Feedj, Neuronk, i-1mean, Average valuescaleup, Scaled up valuestd, Standard deviationW , Water

Superscripts

i, Layer

Part I

The desalination process

1

Chapter 1

An overview

1.1 Desalination background

Desalination is a water treatment process that removes dissolved salts fromhighly mineralized ground waters, seawater, brackish waters of inland sea andmunicipal wastewaters, where the salts are concentrated in the rejected brinestream, Figure 1.1. Desalination process provides unusable waters accessible forhuman normal life, industrial application, irrigation and other various purposes(Ullmann, 1987). The two common methods of desalination process are the ther-mal and membrane separation method, which are also the main methods beenapplied in industrial practice. The two main thermal separation techniques arethe evaporation process and followed condensation of the formed water vapor,and the freezing process followed by melting the formed water ice crystals. Theevaporation method is the most common in desalination and it is usually cou-pled with power generation units, which may be based on steam or gas turbinesystems, choice based on the more e�cient usage of the required huge amountof energy for desalination process.

3

4 CHAPTER 1. AN OVERVIEW

Figure 1.1: De�nition of desalination process.

1.2 Water resources, supply and demand

The earth contains about 140 million km3 of water, which covers approximately70% of the earth surface area, where the percentage of salt water is 97.5%. Theremaining 2.5% is fresh water with 80% amount of frozen icecaps or combinedas soil moisture. Both forms are not easily accessible for human use. Theremaining quantity around 0.5% of surface water is believed to be adequate tosupport all life in Earth. Unfortunately, this water is not distributed evenlythroughout the planet and it is not available in su�cient quantities in somespeci�c areas. The global daily average of rainfall is 2 billion m3. This amountis extremely poor distributed across the globe. The major driver for formationof fresh water from oceans is the sun. The thermal energy from the sun absorbedby the earth surface could generate su�cient temperature gradients that leadwater evaporate from the large surfaces of ocean water. After the evaporation,the water rises through the air and forms a cloud in the sky. The clouds areformed of �ne water drops with an average diameter of 10µm. The cloudsare transported and gradually grow over the land, where the precipitation willtake place. Air temperature a�ects the form of precipitation. Snow is formedin cold weather and high elevations like mountain; on the other hand, rain isformed in warm weather and low elevations. Mixtures of ice, snow, and rain areformed during springtime of cold weather. In addition, precipitation dependson the wind direction and speed. Moreover, precipitation is also a�ected bygeographical conditions that are presence of mountains, �at land, local ambientconditions like local temperature and humidity. In Figure 1.2 is shown thehydrology cycle on the earth. The major part of water precipitation ends upas ground moisture in the form of deep aquifers. Deep aquifers proved to beusable for human consumption, which is because the natural process throughvarious rock formations, provides the water with natural minerals and keeps itspH at acceptable level. Table 1.1 has shown the distribution of water resources

1.2. WATER RESOURCES, SUPPLY AND DEMAND 5

across the globe. For example, the United Kingdom water supply derives fromthe following sources: one-third comes from high ground, one-third comes fromboreholes in aquifers and the other one third comes from abstraction from rivers.Aquifer abstraction is now at its limits, it is not possible to sustain higherpumping rates. Moreover, new catchment areas on high ground are becomingscarcer, costlier and environmentally. The limits of conventional water suppliesare being reached and rivers are the recipients of liquid wastes, sewage and otherpollution stu�. Sewage e�uents are now a signi�cant proportion of the volumeof many rivers whose water is now used for public water supply as the demandpresses on limited resources. As the demand for water increases, the cost willincrease as well. In the area where there is lack of water, the supply can take theshape of more catchment schemes like aquifer abstraction, river abstraction and,of course, desalination. UK water demands are increasing as the increasing ofpopulation, especially in the capital and major cities. The demands of industryand a�uence in the UK mean that water supply is a critical factor for thegrowth of many areas. The world arid and semi-arid zones are desperate forwater. Some form of desalination can be practiced in these areas.

Figure 1.2: The hydrologic Cycle.


Table 1.1: Distribution of water resources across the globe [Adapted from El-Dessouky and Ettouney (2002)].

Resource Volume (km3) Total percentage

Atmospheric Water 12900 0.001Glaciers 24064000 1.72

Ground Ice 30000 0.021Rivers 2120 0.0002Lakes 176400 0.013

Marshes 11470 0.0008Soils Moisture 16500 0.0012

Aquifers 10530000 0.75Lithosphere 23400000 1.68Oceans 1338000000 95.81Total 139653390

1.3 Composition of the seawater

Seawater is also known as one of the natural salt water which contains Na-,Ca+, K+, Mg+, (SO4)-, Cl- and all other known elements, but most of themare present in only minor concentrations. The exact amount of those minorconstituents is not accurately known. This chemical composition of natural sea-water is a�ected by the local conditions, which could change the total dissolvedamount of dissolved solids. This is because the time required for complete �ll-ing or replenishment is much higher than the time required to obtain completemixing of all seas and oceans. Table 1.2 shows the concentrations of the ionsrather than of the salts because of the salts are almost completely dissociatedin seawater. Obviously, the sodium and chloride ions are present in the largestconcentrations, but seawater is not a solution of pure sodium chloride, which iscommon salt. In most of the desalting process, calcium, magnesium bicarbon-ate and sulphate are the troublemakers. They yield various insoluble deposits,which when unchecked, impair the proper operation of many types of desaltinginstallations. The total salt concentration of sea water is expressed in terms ofsalinity, which is approximately equal to the total amount if dry solids (grams)per kilogram of seawater or parts per thousand (ppm); except of this, chlorinity,which is very nearly the concentration of chloride ions, is also one of the usedterm of total salt concentration of sea water. In addition, salinity and chlorinityvary with depth and geographical position.

1.4. HISTORY OF INDUSTRIAL DESALINATION 7

Table 1.2: Mayor components of seawater [Adapted from Spiegler and El-Sayed(1994)].

Compound Composition PPM

Sodium Na+ 10561Magnesium Mg2+ 1271Calcium Ca2+ 400Potassium K+ 380Chloride Cl- 18980Sulphate SO4

- 2649Bicarbonate HCO3

- 142Bromide Br- 65

Other solutes 34Total dissolved solids 34483Speci�c gravity (20°C) 1.0243

Water (Balance) 965517

1.4 History of industrial desalination

Desalination process has an ancient history. In ancient Greek and India, watertreatment methods were recommended. In 1500 BC, the Egyptians disclosedthe principle of coagulation. This puri�cation technique was found on the wall'spainting of the tomb of Amenophis II and Ramses II (Christman, 1998). Thestart of the desalination industry dates back to the early of the twentieth cen-tury. In 1912, a six-e�ect desalination plant with a capacity 75 m3/d wasinstalled in Egypt. The total production capacity of the desalination increasedduring the period 1929-1937 due to the start of the oil industry. Consequently,exponential growth occurred during the period from 1935 to 1960 at an annualrate of 17%. Table 1.3 has shown the recent history of thermal and membranedesalination processes, which are the main two processes applied in desalinationplants. Historical developments in arti�cial membranes are summarized in thefollowing points (El-Dessouky and Ettouney, 2002):

� In 1823, Dutrochet proposed correct explanation of osmosis, passage ofsolvent across a membrane from low to high concentration, and dialysis,passage of solute across a membrane from high to low concentration.

� In 1867, Traube and Pfe�er performed one of the �rst quantitative studieson performance of arti�cial membranes.

� Moritz Taube, 1867, prepared the �rst synthetic membrane.

� In the late 1800's, Graham discovered that arranging a membrane betweena reservoir of pressurized air and another reservoir of unpressurized aircould produce oxygen-enriched air.


� Early use of membranes was applied to recovery NaOH by dialysis fromwastewater containing hemicelluloses from the viscose-rayon industry.

� Uranium isotopes (235 and 238) are separated in the vapor phase throughporous membranes.

� 1959, Reid and Breton developed cellulose acetate RO membranes in Uni-versity of Florida.

� 1963, Loeb and Sourirajan developed the �rst asymmetric cellulose acetatemembrane which has higher salt rejection and water �ux in University ofCalifornia, Los Angeles.

Commercialization of the RO membranes have been summarized in the followingpoints (El-Dessouky and Ettouney, 2002):

� In the late 1960's, the Loeb-Sourirajan cellulose acetate membranes areused to construct spiral wound modules.

� In 1971, DuPont introduced the Permasep B-9 permeator for brackish wa-ter desalination. The permeator contains millions of asymmetric aromaticpolyamide (aramid) hollow �ne �bers.

� In late 1973, DuPont introduced the Permasep B-10 permeator, also us-ing asymmetric aramid �bers, capable of producing potable water fromseawater in a single pass.

� In the mid-1970s, cellulose triacetate hollow �ber permeators were intro-duced by Dow Chemical Company, followed by Toyobo of Japan.

� During the same period, �uid systems and Filmtec introduced the spiralwound polyamide thin �lm composite membranes.

� Throughout the 1980s, these membranes have been improved to increasesalt rejection and water �ux with both seawater and brackish water.

� Today the predominate membrane materials are still aramids, polyamides,cellulose acetate and triacetate in spiral wound and hollow �ber con�gu-rations.

� Applications of the RO membranes include potable water production,waste recovery, food applications, kidney dialysis, high-purify water forboiler feed, and ultrapure water electronics applications.

� In 2000, this market is expected to continue growing during the �rst halfof the 21st century.

1.4. HISTORY OF INDUSTRIAL DESALINATION 9

Table 1.3: Historical developments in thermal and membrane desalination pro-cesses.

Year Achievement

1957 First industrial scale �ashing unit by Westinghouse in Kuwait.Four stage �ashing system a performance ration of 3.3.

1957 Silver patent for the MSF (Multi-Stage Flash) con�guration.1959 Shuwaikh mix (poly-phosphate based) allowed for increase in the

plant factor to values between 70-90%.1960 First MSF plants commisioned in Shuwaikh, Kuwait and

Guernsey, Channel Island. The MSF unit in Shuwaik had 19stages, a 4550 m3/d capacity, and a performance ratio of 5.7. InGuernsey, the unit had 40 stages, a 2775 m3/d capacity, and aperformance ratio of 10.

1962 Point Lome MSF plant with a capacity of 1 migd.1965 Detraction of feed stream.1966 Reduction in speci�c volume.1967 Acid cleaning.1969 Co-Generation, energy cost reduction by 50%.1969 Increase in load factor to 85%.1970 Development of commercial grade RO membranes.1973 Cladding of partition walls.1980 Design and operation of low temperature mechanical vapor

compression units.1980 Design anf operation of low temperature multiple e�ect

evaporation units combined with thermal vapour compression.1985 Use of polymer antiscalent at top brine temperature of 110°C.1996 Construction of the largest MSF units known today with

capacity of 57735 m3/d in UAE.1999 Construction of the large scale RO plant in Florida, USA.1999 Increase in unit capacity of multiple e�ect evaporation units.2000 Design and construction of high performance MSF system with

43 stages and a performance ratio of 13.


1.5 Development of desalination

In 2000, thermal desalination like multistage �ash process and multiple e�ectevaporation remains to be a front-runner in seawater desalination. Furthermore,the multistage �ash process constitutes more than 54% of the total operatingcapacity from all desalination processes and more than 93% of all thermal pro-cesses. Moreover, the reverse osmosis process represents more than 88% ofall the membrane-based process. The world's desalination capacity expandedquickly during the 1970s and 1980s in the oil-rich countries such as Middle Eastfor improving their standard of living, which greatly increases their usable wa-ter. The desalination industry is experiencing great expansion currently. Thisis due to the increasing of global water demand and higher cost of fresh waterfrom natural resources, as well as increasing of the population (Popkin, 1968).

Chapter 2

Desalination methods

2.1 Introduction

There are some major desalination processes available those are reverse osmo-sis, electro dialysis, freezing and distillation. The range of applicability of oneprocess over the other is determined primarily by the salinity and compositionof the feed water. As previous said, desalination processes can be based onthermal or membrane separation methods and the thermal processes separateto two categories, which are:

� Evaporation followed condensation of the formed water vapor

� Freezing followed by melting of the formed water ice crystals.

In addition, if the evaporation process takes place over a heat transfer areait is called as boiling, or within the liquid bulk, it is called as �ashing. Theevaporation processes include single e�ect vapor compression (SEE ), multiplee�ect evaporation (MEE ), multistage �ash desalination (MSF ), humidi�cation-dehumidi�cation (HDH ) and solar stills. The di�erences between humidi�ca-tion, dehumidi�cation and solar from other evaporation processes are the fol-lowing:

� The main driving force for evaporation is the concentration di�erence ofwater vapor in the air stream.

� The water evaporate temperature is lower than the boiling temperature.

Furthermore, the single e�ect vapor compression includes thermal vapor com-pression (TVC ), mechanical vapor compression (MVC ), adsorption vapor com-pression (ADVC ), absorption vapor compression (ABVC ) and chemical vaporcompression (CVC ). Solar energy can also be used to desalt water directly insolar stills or used as an energy source for other thermal processes for exam-ple hybrid desalination system. Vapor compression is combined with the singleor multiple e�ect desalination units to improve the process thermal e�ciency.

11

12 CHAPTER 2. DESALINATION METHODS

This technique is meant that the low temperature vapor formed in the samee�ect or the last evaporation e�ect is compressed to a higher temperature andis then used to derive or initiate the evaporation process in the �rst or the sameevaporation e�ect. The vapor compression devices include steam jet ejector ifcalled as thermal vapor compression, mechanical compressors, adsorption, des-orption beds and absorption, or desorption columns. On the other hand, themain membrane desalination process is reverse osmosis (RO) which will dis-cuss deeply in later chapters. In a brief description, RO process is meant thatfresh water permeates under high pressure through semi-permeable membranesleaving behind highly concentrated brine solution. There is another membraneprocess called electro dialysis (ED) which is very limited used in industrial.This process separates the electrically charged salt ions through speci�c ion ex-change membranes leaving behind low salinity permeate water. Consequently,a highly concentrated brine stream is formed on another side of the membrane.(Porteous, 1975, El-Dessouky and Ettouney, 2002)

2.2 Distillation/evaporation methods

Distillation is the best developed and the front leader of desalination methods.More than 50% of the fresh water produced from seawater in the world is ob-tained by some variant of a distillation method. All distillation methods forsaline water are based on the principle that only water and the gases dissolvedin it are volatile, whereas the salts are not. In the process of continuous heatingof salt water, only the water evaporates and the salts stay behind. The watervapor is condensed and thus pure liquid water is formed. The principle of dis-tillation is illustrated in Figure 2.1. Seawater is boiled in the round bottomedbottle that has a side tube at its neck, which inclines downwards. The side tubeof a distillation bottle is attached to a condenser. When the seawater in the bot-tle is boiled, the solvent (permeate water) alone vaporizes. When the hot vaporpasses through the inner tube, it is cooled by the water circulating in the jacketand it is condensed. After that, the liquid has been obtained which is called asdistillate and it is collected in a clean container. The temperature of the boilingseawater is a very important process variable. The temperature of the condens-ing steam must be higher than the boiling point of seawater. Otherwise, thelarge amount of heat released in the condensation could not be transmitted tothe boiling water because heat always �ows from points of higher temperatureto those of lower temperature. Considering these high heat requirements forevaporation, the economic necessity for using the vapor latent heat rising fromthe boiling seawater for the distillation of additional amounts of salt water, isobvious like multiple e�ect and multistage �ash (Spiegler and El-Sayed, 1994)

2.2. DISTILLATION/EVAPORATION METHODS 13

Figure 2.1: Principle of distillation method.

2.2.1 Single e�ect evaporation

The single e�ect evaporation desalination system has very limited industrial ap-plication. Normally this system is used in marine vessels. This is because thesystem has a thermal performance ratio less than one, that means the amountof water produced is less than the amount of heating steam used to operate thesystem. Figure 2.2 has shown the schematic diagram for the single e�ect evap-oration system. The main components of the system are the evaporator andthe feed preheater, which is also called as condenser. The evaporator consistsof an evaporator/condenser heat exchange tubes, a vapor space, un-evaporatedwater pool, and a line for removal of non-condensable gases, a mist elimina-tor and a water distribution system. The feed preheater has a shell and tubecon�guration and operates in a counter current mode, where the latent heat ofcondensed vapor is transferred to the intake seawater, which includes the feed,and the cooling seawater. The condenser has three functions: improves theprocess performance ratio, adjusts the boiling temperature inside the evapora-tor, and removes the excess heat from the system. The function of the coolingwater in the condenser is the removal of the excess heat added to the systemin the evaporator by the heating steam. This causes that the evaporator doesnot consume all the supplied heat, that means, degrades the quality. The vaporcondensation temperature and, consequently, the pressure in the vapor spacefor both the evaporator and the condenser, are controlled by: the feed watertemperature, the cooling water �ow rate, the available heat transfer area in thecondenser, the overall heat transfer coe�cient between the condensing vaporand the circulating seawater. The feed seawater is chemically treated beforebeing pumped into the evaporator. The chemical treatment is essential to pre-vent the tendency and the foaming for scale formation in the evaporator. Bothfactors seriously impair unit operation. The feed water is sprayed at the topwithin the evaporator where it falls in the form of thin �lm down the succeeding


Figure 2.2: Single e�ect evaporation system.

rows of tubes arranged horizontally. The saturated heating steam and its latentheat provides the required sensible and latent heat for water evaporation formthe feed seawater, so that the feed water temperature is raised to the boilingtemperature (El-Dessouky and Ettouney, 2002).

2.2.1.1 Single e�ect thermal vapor compression (TCV )

The simple form of single e�ect thermal vapor compression (TVC ) seawater de-salination process has been illustrated in Figure 2.3. The main components ofthe unit are the jet ejector, the evaporator and the condenser. The evaporatorconsists of evaporator/condenser heat exchanger, a vapor space, a mist elimi-nator and a water distribution system. The steam jet ejector is composed of asuction chamber, a steam nozzle, a di�user and a mixing nozzle. This bringsthe cooling of the non-condensable gases to the minimum possible temperature,therefore, minimizing the amount of vapor that may escape with the gases anddecreases the volume of pumped gases. The single e�ect thermal vapor com-pression desalination process is not widely used in industrial scale. Normallythermal vapor compression is used with the multiple e�ect evaporation (MEE )which is known asMEE-TVC. The thermal vapor compression method has beenused due to its simple operation, simple geometry, inexpensive maintenance andabsence of moving parts (Ettounet, 2009).

2.2.1.2 Single E�ect Mechanical Vapor Compression (MVC )

The schematic diagram of the mechanical vapor compression (MVC ) has beenshown in Figure 2.4. The MVC system includes �ve main elements, whichare evaporator, mechanical vapor compressor, preheaters for the intake seawa-ter, venting system and brine and product pumps. In addition, the evaporatorcontains spray nozzles, falling �lm horizontal tubes, wire-mesh mist eliminator


Figure 2.3: Single e�ect thermal vapor compression desalination process.

and suction vapor tube. The MVC desalination process is the most fascinatingprocess among various single stage desalination processes. The MVC systemis con�ned, does not require external heating source, this is unlike to thermal,absorption or adsorption vapor compression. This system is driven by electricpower; therefore, it is suitable for remote population areas with access to powergrid lines. Not only that, theMVC system does not contain the down condenserand the cooling water requirement. This is due to the compressor operating onthe entire vapor formed within the system. Other advantages of the system in-clude, (Ettounet, 2009): proven industrial reliability to long lifetime operation,simple seawater intake and pre-treatment, moderate investment cost comparedto other systems. Also, the low temperature operations allow reduced scaling,heat losses, a minimum requirement of thermal insulation and high product pu-rity. The system is modular type and it is possible to enlarge production volumeby adopting additional modules and a horizontal falling �lm tube con�guration,which allows high heat transfer coe�cient.

2.2.2 Multiple E�ect Evaporation (MEE)

The multiple e�ect evaporation system is formed of a sequence of single e�ectevaporators and the vapor formed in one e�ect is used in the next one. Thevapor reuse in the multiple e�ect system allows reduction of the brine and thetemperature to low values and prevents rejection of large amount of energyto the surrounding. Furthermore, the multiple e�ect evaporation processes is


Figure 2.4: Single e�ect mechanical vapor compression (MVC).

applied widely in the petroleum, food, pulp, paper and petrochemical industries.The multiple e�ect evaporation process can be con�gured in forward, backwardand parallel feed. The three con�gurations di�er in the �ow direction of theheating steam and the evaporating brine. Variation of the salt solubility is thefactor of selection among these three con�gurations as a function of the topbrine temperature and the maximum brine concentration.

2.2.2.1 Forward Feed Multiple E�ect Evaporation

Forward feed multiple e�ect evaporation is widely used in the sugar and paperindustries but seldom found on industrial scale for the desalination process.The forward feed multiple evaporation system was not used in the desalinationindustry because it has a more complex layout than the parallel feed multiplee�ect evaporation one. Moreover, the �rst multiple e�ects that were designedand constructed were parallel con�guration. Some results of the parallel e�ectunits had proved their reliability. Figure 2.5 has shown the schematic diagramof the forward feed multiple e�ect evaporation (MEE-FF ) seawater desalinationsystem. The system includes a series of feed water preheaters, a train of �ashingboxes, evaporators, a down condenser and a venting system. The direction ofheat �ow and the �ow direction of the brine and vapor is from left to right. Inaddition, the pressure in the e�ect decreases in the �ow direction.

2.2.2.2 Parallel Feed Multiple E�ect Evaporation

The number of parallel feed multiple e�ect evaporation system applied in de-salination industry is approximate 3% of the total desalination market, fromthe data of International Desalination Association (IDA, 2000). The process isfound in the stand-alone mode or combined with mechanical or thermal vaporcompression. The process has evolved from small production units, which ca-


Figure 2.5: Schematic diagram of MEE-FF desalination process.

pacities can be less than 5000 m3/d to four times the original, with capacitiesnearly of 20000 m3/d. The schematic diagram of the parallel feed multiple e�ectevaporation has been shown in Figure 2.6. The e�ect are from left to right in the�gure and it is same for the direction of heat �ow. Each e�ect constitutes a heattransfer area, mist eliminator, vapor space and other accessories. In this system,the vapor �ows from left to right in the direction of falling pressure, while thefeed seawater �ows in a perpendicular direction. In addition, the brine streamleaving the �rst stage �ows to the second stage where it �ashes and mixes withthe feed seawater. Each system consists of a number of evaporators, a downcondenser, a venting system and a train of �ashing boxes.

2.2.3 Multi Stage Flash Distillation (MSF)

The multi stage �ash distillation process is an innovative concept. This is be-cause the vapor formation takes place within the liquid bulk instead of thesurface of hot tubes. The hot brine is allowed to �ow freely and �ashes in aseries of chambers, this will keep the hot and concentrated brine from the insideor outside surfaces of heating tubes. This is the major advantage over the orig-inal and simple concept of thermal evaporation. The MSF process is the leaderof the desalination industry in the Gulf States as well as several other countriesacross the world. The �ash desalination process was introduced in the 1950s.The �rst installed unit was constructed by Westinghouse and it included four�ashing stage, although this system was not a true �ash desalination con�gura-tion. In addition, the patent of the multistage �ash desalination con�gurationwas made by Silver in 1957. The main feature of this patent is the optimizationof the number of stages versus the heat transfer area; it was found that largernumber of stages, like above 20 stages, results in better cost for the MSF pro-cess. There are two layouts for theMSF process, which are the brine circulationMSF, and once through MSF. Both processes include �ashing stages, pumps,brine heater, chemical addition system, ejector for removal of non-condensablegases and feed treatment.


Figure 2.6: Schematic diagram of MEE parallel �ow.

2.2.3.1 Flashing Stage

The schematic diagram of the MSF �ashing stage has been shown in Figure2.7. The �ashing stage includes the demister, condenser tubes, distillate tray,venting line, inlet/outlet brine ori�ces and air ba�e. The processes run insidethe �ashing stage are given below (El-Dessouky and Ettouney, 2002):

� The inlet brine �ashes o� as it enters the �ashing stage. This happensbecause the saturation temperature/pressure of the brine is higher thanthe stage temperature / pressure, �ashing implied that the formed vaporextracts its latent heat from the brine stream. As a result, the brinetemperature decreases as the brine �ows across each stage.

� The brine ori�ce is designed to control the brine �ow. Besides that, itmaintains su�cient brine heat within the stages to prevent vapor escapebetween stages.

� The �ashed o� brine �ows through the demister where most of the en-trained brine droplets are captured by the demister wires. Continued re-moval of the droplets would result in the increase of the captured dropletsize. In the end, this would result in detachment of the large droplets andsettling back to the brine pool.

� When the vapor �ows to the condenser tubes, the condensed vapor ac-cumulates in the distillate tray and �ows to the next stage. The inletdistillate stream has a higher temperature and it �ashes o� as it �owsbetween stages.


Figure 2.7: MSF Flashing Stage.

� Vapor condensation results in the increase of the temperature if the brinestream �ows inside the tubes. This is a very important design feature,where most of the thermal energy provided by the heating steam is recov-ered by the brine stream before entering the brine heater.

� The air ba�e shown within the stage is placed to control vapor �owthrough the vent line and to prevent condensate splashing in the ventline.

2.2.3.2 Once through MSF

The schematic diagram for once throughMSF process has been shown in Figure2.8. This system includes the vacuum ejector, brine heater, chemical additionpumps, the �ashing stages and the freed screens. The processes run in thissystem are given below:

� The feed is pumped through a large tube, which contains rough screens.The screens remove large suspended solids.

� The feed is de-aerated to remove dissolved gases like oxygen, nitrogen,which will reduce the heat transfer rate. De-aeration is taking by heatingsteam, which results in increasing the feed temperature and reduces thegas solubility in the feed water.

� Other treatments are added to the feed water. The chlorine is added tothe feed water to prevent biofouling inside the condenser tubes.


Figure 2.8: Once through multistage �ash desalination process.

� After that, the de-aerated feed water �ows through the condenser tubesstarting from the cold end or the last stage. The feed seawater is heatedto the desired top brine temperature in the brine heater.

� The feed seawater �ows on the tube side of the brine heater.

� The heating steam �ows on the shell side. In addition, the steam conden-sate is collected in a small well at the bottom of the heater.

� After that, the hot feed seawater then �ows through the stages wherevapor �ashing takes place.

� The distillate product �ows in the distillate trays among the �ashingstages. The distillate product �ashes o� generating a small amount ofvapor as it �ows from one stage to another. This �ashing process alsoregards as for further heating of the feed seawater.

� The brine blow down and distillate are collected in the last stage. Thebrine blow down is rejected to the sea and the distillate is treated in furtherprocess like chlorination and adjustment of pH value.

� The vacuum steam ejector's function is to remove the non-condensablegases from the �ashing stages.

� Hydraulic heads should be made between the outlet distillate and brineblow down and the pumping units. This is to prevent vapor �ashing fromthese warm streams.

2.3. FREEZING METHODS 21

2.2.3.3 Brine Circulation MSF

The schematic diagram of brine circulation MSF has been shown in Figure 2.9.From the diagram, the �ashing stages are divided in two sections, which are theheat recovery section and rejection sections. The heat recovery section is theidentical of the �ashing stages of the once through system. Normally the heatrejection section contains three stages and it is designed to reject the excessheat added in the brine heater and to control the temperature of the intakeseawater. Part of the rejected cooling seawater is recycled and is mixed withthe intake seawater during winter due to the reason of control the intake seawatertemperature. This principle is common in most of the brine circulation MSF

plants, especially the country has the long winter season like United Kingdom.Control the feed seawater temperature can prevent reduction of the last stagesystem temperature. Another main feature of the brine circulationMSF systemis that expensive chemicals are only added to the feed seawater. The �ow rateof the brine recycled stream is about two to three times that of the distillateproduct, as the brine recycle stream �ow rate in once through system is closeto ten times of the distillate product. In addition, another feature of the brinecirculation MSF system is that the temperature of feed and cooling seawaterleaving the condenser tubes from the heat rejection section is similar to the brineblow down temperature form the last �ashing stage. This is to prevent thermalshock upon mixing of the feed seawater in the brine pool of the last stage.The calcium bicarbonate would decompose and result in calcium carbonateprecipitate if the temperature of the feed stream and the brine below down aredi�erent. This might scale the condenser tubes in the heat recovery section(Porteous, 1975).

2.3 Freezing Methods

Freezing process consists in the separation of fresh water from salt solutions byfreezing and forms ice crystals which should be free of salts. In this respect, thefreezing method is similar to distillation, which leads to salt free vapor althoughthe liquid may hold a high concentration of salts. However, these two processesare di�erent; distillation is carried out well above ambient temperature and allthe equipments of distillation process must be designed for minimal heat losses.Moreover, only liquid and vapor have to be moved and puri�ed. On the otherhand, freezing methods must be protected against heat gains or cold losses,in addition, ice is transported and puri�ed in this method, and this is morecomplex than �uids handling. Furthermore, the low operating temperature offreezing processes greatly reduces corrosion and scale problems. The freezingmethod contains two major stages, which are stage 1, ice crystallization andstage 2, separation and melting. This has been shown in Figure 2.10. In stage1, ice crystallization, nucleation occurs at a suitable super cooling temperature,after that, the nuclei in solution grow to become large ice crystals in a crystal-lizing unit. In stage 2, separation and melting, crystals are separated from the


Figure 2.9: Brine circulation multistage �ash desalination process.

concentrate by a mechanical separator and then become pure water by melt-ing process. Several freezing approaches had been developed until now. Themost common freezing processes are direct contact freezing and indirect contactfreezing. These di�er in whether the refrigerant is in direct contact with thesalt water or it is separated from water by a heat transfer surface. When therefrigerant is allowed to mix with the salt water, like butane, or the refrigerantis the water itself, we called this process as direct contact freezing. If the refrig-erant is separated from water by a heat transfer surface, the process is called asindirect contact freezing.

2.3.1 Direct Contact Freezing Method

Direct contact freezing method means that the feed water is cooled by mixingit with refrigerant, for example Freon and Butane, and then with the productto be frozen. The refrigerant expands through a nozzle into the product liquidwhere it vaporizes at a lower pressure. The vaporization provides a refrigeratione�ect and causes the formation of ice and solutes crystals within the product.A simple direct contact freezing system has been shown in Figure 2.11. The�rst step comprises an ice nucleation crystallizer, which allows the growth ofthese nuclei up to a suitable size for separation, ice crystal separator, washingunit and melting unit. (Hartel, 1992). The advantage of this system is that thecompressor works across a temperature di�erence much closer to the freezingpoint depression, so that less work is required per unit of freshwater product.This means higher production rate per unit volume at lower driving force. Other

2.3. FREEZING METHODS 23

Figure 2.10: Principle of freezing method.

advantages are small system power consumption and absence of moving parts.

2.3.2 Indirect Contact Freezing Method

The schematic diagram of a simple indirect freezing method has been shownin Figure 2.12. The feed water �rst passes through a pre-cooler and enters thefreezing chamber. Inside the freezing chamber, the pre-cooled feed water �owsdownwards inside the tubes and it is cooled to the freezing point by a refrigerantevaporating on the outside of the tubes. Crystallization is taked in the body ofthe �owing solution and slurry comprising ice crystals and concentrated brineleaves the freezer and is pumped to a separator. In the separator, the ice isseparated from the brine and is washed. The brine is recycled to the freezingchamber and in the end is rejected by the pre-cooler. The washed ice is meltedin the melter by heat, which is rejected by the condensing refrigerant and isdischarged through the pre-cooler. In addition, the washing water to separatoris provided after melter and normally it is �ve percent of melted ice. Further-more, an additional heat rejection cooler is required in the refrigerant circuit.This is because the melting ice does not absorb su�cient heat to condense theentire refrigerant, that means extra heat without the heat rejection cooler. Inthis indirect system, no vacuum problems like are encountered, but the workrequirement per unit of product tends to be high. This disadvantage makes thisprocess non-competitive to industrial scale. Indirect freezing system can also bedriven by a heat source of temperature above environment if the refrigerationcycle used is an absorption type. Absorption refrigeration is a heat-driven al-ternative to the work driven vapor compression refrigeration. (Sha�ur Rahman,2004 and Shone, 1987).


Figure 2.11: Basic direct contact freezing process.

Figure 2.12: Schematic diagram of indirect freezing method.

Chapter 3

Reverse osmosis methods

3.1 Introduction

Desalting of seawater, river water or brackish water by reverse osmosis (RO) isthe largest membrane separation process. This process was non-existent in the1950's; however, in the year of 2000, the production rate of potable water bythe RO membranes had exceeded 15 million m3/d. This production capacitycan only support the life of 60 million inhabitants by assuming a consumptionrate of 0.25 m3/inhabitant/day. This capacity remains to support a very smallpercentage of the entire world population, which is 6000 million inhabitants andis expected to be to 9000 million inhabitants before the year 2050. According tothis, progress and developments of RO membranes continues focusing on furtherdecrease of the unit product cost to values competitive to transportation of freshwater over long distances or even treatment of natural water sources.

3.2 Historical Background

Since the early days of civilization, humankind has adopted simple forms ofmembranes. In early agriculture communities, household sieves were inventedand developed to separate thin grain ground from coarse grain particles andshells. Consequently, cheesecloth was made from cotton �bers and used to man-ufacture cheese. Both forms of separation are based on di�erences in particlesize. However, developments in membrane technology have focused on adop-tion of other separation mechanisms, such di�erences in solution and di�usionrates of various species across the membrane material. Use of RO membranesfor water desalination started with the following two studies: Reid and Breton,1959, at the University of Florida developed cellulose acetate RO membranesand Loeb and Sourirajan, 1963, from the University of California, Los Ange-les, developed the �rst asymmetric cellulose acetate membrane, with higher saltrejection and water �ux. After that, a huge amount of research studies was con-ducted with focus on development of new membrane materials and performance

25

26 CHAPTER 3. REVERSE OSMOSIS METHODS

evaluation of these membranes. Those commercial RO membranes have beensummarized in the following points:

� In the late 1960s, the Gulf General Atomics and Aerojet General usedLoeb-Sourirajan cellulose acetate membranes for constructing spiral woundmodules.

� In 1971, DuPont introduced the Permasep B-9 permeator for brackish wa-ter desalination. The permeator contains millions of asymmetric aromaticpolyamide hollow �ne �bers.

� In late 1973, DuPont introduced the Permasep B-10 permeator. In addi-tion, asymmetric aramid �bers were used, capable of producing potablewater from seawater in a single pass.

� In the mid-1970s, cellulose triacetate hollow �ber permeators were intro-duced by Dow Chemical Company, followed by Toyobo of Japan.

� During the same period, Fluid Systems and Film Tec introduced the spiralwound polyamide thin �lm composite membranes.

� Throughout the 1980s, improvements were made to these membranes toincrease water �ux and salt rejection with both brackish water and sea-water.

� In 1991, the US army bought 8000 large FilmTec membranes to theirmobile water puri�cation units for troops in Desert Storm.

� Today the predominate membrane materials are still aramids, polyamides,and cellulose acetate and triacetate in spiral wound and hollow �ber con-�gurations.

� In 2000, the RO technology was used to treat 15 million m3/d. Thismarket is expected to continue growing during the �rst half of the 21stcentury.

� RO or ultra �ltration wastewater treatment facilities are found in manyplaces around the world.

3.3. ELEMENTS OF MEMBRANE SEPARATION 27

3.3 Elements of Membrane Separation

A number of membrane based desalination processes are used on industrial scale.These membrane-based processes are micro�ltration, ultra �ltration, nano�ltra-tion and reverse osmosis. Di�erences among these processes have been shownin Figure 3.1.

The simple classi�cation of the separation processes are:

� Reverse osmosis operates on a particle size range of 0.005 mm to 0.0001mm.

� Nano�ltration operates on a particle size range of 0.05 mm to 0.005 mm.

� Ultra �ltration operates on a particle size range of 0.15 mm to 0.05 mm.

� Micro�ltration operates on a particle size range of 1.5 mm to 0.15 mm.

There is a standard di�erence in the separation mechanism in all �ltration pro-cesses and the reverse osmosis process. In �ltration, separation is made by asieving mechanism where the membrane let smaller particles pass and retainslarger ones. On the other hand, in osmosis or reverse osmosis processes, themembrane permeates only the solvent and retains the solute. From the diagramabove, the nano�ltration, ultra �ltration and micro�ltration processes are usedto separate the suspended material only. However, the reverse osmosis processis used to separate dissolved solids. Nano�ltration is used for partial softeningof brackish water. A schematic diagram of osmosis and reverse osmosis phe-nomenon has been shown in Figure 3.2. In this consequence, the direction ofsolvent �ow is determined by its chemical reaction, which is a function of pres-sure, temperature and concentration of dissolved solids. Osmotic �ow from thepure waterside across the membrane to the salt solution side will occur untilthe equilibrium of chemical reaction is restored. Equilibrium occurs when thehydrostatic pressure di�erential resulting from the volume changes on both sidesis equal to the osmotic pressure. This is a solution property independent of themembrane. Application of an external pressure to the slat solution side equalto the osmotic pressure will also cause equilibrium. Additional pressure will risethe chemical containing of the water in the salt solution and cause a solvent�ow to the pure waterside because it now has a lower chemical reaction (UlrichMerten, 1966).


Figure 3.1: Separation process, applied pressure and size of material.

Figure 3.2: Osmosis and reverse osmosis (RO) processes.

3.4. REVERSE OSMOSIS SYSTEM 29

3.4 Reverse Osmosis System

The RO system may consist of the following basic components:

1. Feed water supply unit

2. Pretreatment system

3. High pressure pumping unit

4. Membrane element assembly unit

5. Instrumentation and control system

6. Permeate treatment and storage unit

7. Cleaning unit

The con�gurations of RO system include the single stage, two stages and two-pass system. These con�gurations have been shown in Figures 3.3, 3.4, 3.5. Thedi�erences among these three con�gurations depend on the type of feed waterand required salinity of the permeate stream. The single stage system includesa set of parallel pressure vessels. Each vessel contains a number of membranemodules in series. Each pressure vessel contains a small amount of feed water;therefore, the number of parallel vessels is set by the total capacity of the system.The two-stage system includes two sets of pressure vessels. The second stageprocesses the brine stream that left from the �rst stage. This allows maximumrecovery of permeate from brackish water. For the case of river water, the useof a single stage system together with brine recycle is su�cient to generatepermeate water with salinity below 10 ppm, two-stage system is not necessary.

Figure 3.3: Single stage RO system.


The two-pass system contains two set of pressure vessels. In addition, thesecond set of vessel processes the permeate water which left from the �rst set.This necessitates the use of an inter stage pumping unit to increase the pressureof the permeate water which left from the �rst pass and entering the secondpass. Use of the two-pass system allows production of high purity permeatewater with salinity below 10 ppm. A two-pass system can process seawater aswell as high salinity brackish water. The summary of the application range foreach con�guration has been shown in Table 3.1 (Sourirajan, 1977).

Figure 3.4: Two stage RO system.

Figure 3.5: Two pass RO system.

3.4. REVERSE OSMOSIS SYSTEM 31

Table 3.1: Characteristics of RO con�guration.Single stagesystem

Two stage system Two Pass System

Seawater(salinity

>34000ppm)

It is widely usedand the productsalinity wouldrange from lowsof 150 ppm tohighs of 500ppm. Itgenerates

drinking waterand otherhousehold

purposes. Inaddition, it can

be used inseveral industrialappications likecooling, washing

or foodprocessing.

Its use is limited,because thesalinty of thebrine stream

which left fromthe �rst stagemis large. Thiswould preventfurther recovery.This is due tothe permeationprocess thatincreases the

brine salinity andexceedes the

saturation limits,causing

membranescaling andfouling.

It garantes lowsalintiy

permeate. Thepermeate water

is used asdeminaralizerfeed in boilerhouses. In this

case thepermeate salinitycan below 10

ppm

BrackishWater(salinity

>34000ppm)

For low salinitybrackish water,single stagesystem cangenerate low

salinity permeatefor

demineralizers inboiler houses. Inthis case, therecovery ratiocon be adjustedto mantain a

permeate salinitybelow 10 ppm.

It generateslarger amounts of

permeateproduct withhigher salinitythan the singlestage system.The product

quality is suitablefor for drinking

householdapplications andother general

purposeindustrial

applications.

For higher salintybrackish water,use of the twopass system isnecessary toprovide low

salintiy productfor

damineralizers inboiler houses.

River Water(salinity

>34000ppm)

River water canbe processed in asingle stage RO

system togenerate

permeate waterwith a salinitybelow 5 ppm

Not applicable Not applicable


Table 3.2: Qualitative comparison of membrane modules [Porter, 1990].Hollow �ber Spiral wound Tubular Plate and

frame

Cost/Area Low Low High HighMembranereplacement

cost

Moderate Moderate/Low High Low

Flux Fair/Poor Good Good ExcellentPacking density Excellent Good Poor Good/FairHold-up volume Low Medium High MediumEnergy usage Low Medium High MediumAnti foulingand ease ofcleaning

Poor Good/Fair Excellent Good/Fair

3.5 RO Membrane Module Con�gurations

There are two major membrane module con�gurations used for RO system,which are hollow �ber and spiral wound. In addition, there are other con-�gurations like tubular and plate and frame that are used in the food anddairy industry. Table 3.2 has shown the distinguish features of various modules.(Sheikholeslami, 2007 and Williams, 2003)

3.5.1 Hollow Fiber Membrane Module

The �ber is asymmetric in structure and it is as thin as a human hair. Mil-lions of these �bers are formed into a bundle and folded in half to a length ofapproximately 120 cm. A perforated plastic tube, which serves as a feed waterdistributor, is inserted in the center and extends the full length of the bundle.The bundle is wrapped at both ends, which prevents the feed stream from by-passing to the brine outlet. The schematic diagram of hollow �ber membranemodule has been shown in Figure 3.6. (Watson, 2006)

3.5.2 Spiral Wound Membrane Module

In spiral wound membrane module, two �at sheets of membrane are separatedwith a permeate collector channel material to form a leaf. This assemble issealed on three sides with the fourth side left open for permeate to exit. A feedor brine spacer material sheet is added to the leaf assembly. A number of theseassemblies or leaves are wound around a central plastic permeate tube. Thistube's function is to collect the permeate water from the multiple leaf assemblies.The typical industrial spiral wound membrane element is approximately 100 or150 cm long and 10 or 20 cm in diameter, as it can be seen in Figure 3.7.

3.5. RO MEMBRANE MODULE CONFIGURATIONS 33

Figure 3.6: Hollow �ber membrane modules. (a) Assemble. (b) Fiber dimen-sions.

Figure 3.7: Spiral wound membrane module.


Figure 3.8: Tubular membrane module.

3.5.3 Tubular Membrane Module

Tubular membranes were available for laboratory use as early as the 1920'sand were �rst used in industrial applications in the 1960's. Tubular membranemodules have one or more tubes of varying diameter. The tubes themselvesare constructed of a microporous substrate material, which provides mechanicalstrength, and the membrane is cast on the inside of the tube as a �nely poroussurface layer. Feed is pumped through the membrane tubes and permeate �owsthrough the engineered pores to produce treated water. Tubular membranes areknown for their sturdy construction, long membrane life, and high �ux rates.Of all membrane types, they are more robust and can be subjected to highpressures in demanding applications. Tubular membranes are backwashed insome applications.

3.6. DESALINATION METHODS REVIEW 35

3.6 Desalination methods review

The purpose of Chapters 2 and 3 was to give a general description of the maintechniques for the desalination process. In the following table a brief summaryis given, referring to pros and cons for each method.

Table 3.3: Desalination methods pros and cons.Method Pros Cons

Single E�ectDistillation

Easy design Low performance ratio

Multiple E�ectDistillation

Brine reduction; Lowtemperature values;

Prevents energy rejection

Complex layout; Di�cultvariable control

MultistageDistillation

Optimized stages number;Low energy requirement

Complex layout; Di�cultvariable control

Direct ContactFreezing

Less corrosion and scale;Less required work; No

moving parts

Di�cult solidtransportation;

Protection against hot orcold losses

IndirectConctactFreezing

No vacuum problems Di�cult solidtransportation;

Protection against hot orcold losses; Additional

hot rejection cooler; Highrequired work

Membrane Small plant size; Lessinstallation space; Lessenergy requirement;Flexibility; Easy

modeling

Di�cult membranecleaning and replacement;Lower performances than

MSF distillation

Part II

Modeling and Analysis of a

Batch Reverse Osmosis

Desalination Plant

37

Chapter 4

Experimental results

4.1 Introduction

Most reverse osmosis (RO) and nano�ltration (NF ) processes operate as plug-�ow (PF ) systems. In this kind of processes permeate recovery, �ux and cross�ow are coupled. They are ideal for puri�cation of pre-treated source watersof uniform composition with limited permeate recovery percentages. Neverthe-less, some authors are focusing on a new semi batch technique that is emerging(Stover, 2011 and Tarquin et al., 2012). Some advantages of batch operationsare the independent manipulation of permeate recovery, �ux and cross �ow thatallow them to be optimal for high recovery and/or problematic source waters.In addition, the volume of produce concentrate is less in a batch RO process,identi�ed by high salinity of moderately soluble salts without indications ofmembrane fouling or scale deposition (Tarquin et al., 2012). However, the ap-plication of batch operation to RO and NF has been limited because it requireshigher energy and the permeate is also characterized by less quality. In theseyears, a new interesting semi-batch or continuous batch process for RO or NFplants is becoming more attractive. In these semi-batch systems, the brine feedis recirculated to the feed tank until the target permeate recovery level hasbeen reached, then the system is stopped and drained. The performances ofthese processes are better in terms of high adjustable recovery rates that resultsin decay of both concentrate production, source water pumping and pretreat-ment requirements. Moreover, some other conveniences in using this semi-batchplant are the independently adjustable cross-�ow and there is less evidence ofthe fouling and scaling phenomena. In addition, they can consume less energyand require fewer membrane elements than PF systems. In this work, a re-verse osmosis desalination plant is used in order to achieve experimental data,consisting in a batch system, characterized by the recirculation of the rejectedbrine. For simplicity, we did not consider the stopping criteria as explained inStover (2011), so that our plant is batch and not semi-batch run, for 8 hourswithout taking in consideration a target value for the permeate salinity. The

39

40 CHAPTER 4. EXPERIMENTAL RESULTS

performance of this plant are investigated studying the in�uence of some oper-ating parameters, such as operating pressure and feed salinity, on the permeatequantity and permeate salinity. To be thorough, the batch plant was then run ina continuous mode, by means of some expedients explained later in this section.Thus, some results are collected and compared to the batch mode.

Figure 4.1: Reverse osmosis process (Courtesy of Arm�eld).

4.2 Experiments setup

In Figure 4.1, it is possible to see a representation of the RO pilot plant usedfor the experiments with the list of main components in Table 4.1. It utilizesa pair of triple plunger positive displacement pumps (1) and (2) located withinthe stainless steel cabinet. These are driven by a two speed electric motor (5)through a toothed belt (6). The motor speed is selected using switch (D) onthe control console. Pumps and motor drive are mounted on a bedplate, whichin turn is mounted on vibration absorbing dampers. The pumps are connectedtogether by a �exible coupling so that both can be run continuously. A three-way ball valve (V3) situated at the rear of the unit is used to divert the �owfrom the discharge of pump no. 2 to the suction of pump no. 1, for instanceswhen only �ow from pump no. 1 is required. Feed material from the feed tank(8) is pumped up through the membrane holder module (3) which containstwo tubular membranes in series (Arm�eld FT18). Upon leaving the module,the material passes through a backpressure valve (V1) which is used to createthe desired system pressure. The material then passes through a plate heatexchanger (7) before discharging back into the feed tank (8) through pipe (18)to complete the closed loop recycle system. The permeate, using a particularmembrane in combination with a certain �ow rate and system pressure, will

4.2. EXPERIMENTS SETUP 41

Figure 4.2: Schematic diagram of Batch RO process

drain by gravity through pipe (17) into the permeate tank (9). Work done onthe �uid by the pumps causes a gradual temperature increase with time and thisis held constant by pumping the �uid through plate heat exchanger (7) where itis cooled by a �ow of cooling water. The cooling water is modulated manuallyusing valves (12) and (13) and �ow rates are monitored on variable area �owmeters (10) and (11). Figure 4.2 shows a schematic diagram of the process.

Experiments were carried out at two di�erent levels of pressure and con-sidering three di�erent initial feed salinity, in order to study the in�uence ofthese parameters on the performances of the RO process. The selected value ofpressure is kept constant manually by manipulating valve (1) during the experi-ments duration. The plant was run for 8 hours split in two days and every 15 or30 minutes measures of the permeate quantity, permeate salinity in each timestep and permeate total salinity were taken. A conductivity meter was used inorder to make the salinity measurements by using a calibration curve (Figure4.3).

At each discrete point, mass balance was made in order to obtain the salinityin the feed tank (xf ) as it was di�cult to make proper measurement with theconductivity meter due to vibrations, mixing and recirculation of the brine feedin the feed thank.

Total mass balances:

Qtf = Qt−∆t

f −Mp∆t (4.1)

Qtp = Qt−∆t

p +Mp∆t (4.2)

Salinity balances:

Qtfx

tf = Qt−∆t

f xt−∆tf −Mpxp∆t (4.3)


Table 4.1: Reverse osmosis process (Courtesy of Arm�eld).Number on Fig 4.1 Component name

1 No. 1 Circulating Pump2 No. 2 Circulating Pump3 Pci membrane systems ltd micro -240 module4 Flexible coupling5 Pump drive motor6 Pump pulley drive (under stainless steel

guard)7 Plate heat exchanger for product cooling8 Feed tank9 Permeate tank10 Flow meter (UF )11 Flow meter (RO)12 Flow meter control valve (UF )13 Flow meter control valve (RO)14 Cooling water supply inlet15 Bursting disk assembly16 System pressure sensor17 Permeate outlet from module18 Product return pipe19 Permeate tank drain20 Pump/motor damped base plate21 Control console22 Feed tank drain - not visible on diagram23 No. 2 pump drain24 Level sensor (conductivity probe)V1 Back-pressure valve (system pressure)V2 By-pass valveV3 Three-way ball valveT1-4 T1-4 Temperature sensors

4.2. EXPERIMENTS SETUP 43

Figure 4.3: Salinity-Conductivity calibration curve.

Qtpx

tpt = Qt−∆t

p xt−∆tp +Mpxp∆t (4.4)

Equations (4.1) and (4.2) result in:

Qtf +Qt

p = Qt−∆tp +Qt−∆t

f (4.5)

whilst Equations (4.3) and (4.4) give:

Qtfx

tf +Qt

pxtpt = Qt−∆t

f xt−∆tf +Qt−∆t

p xt−∆tp (4.6)

Equation (4.5) can be used to calculate Qf at any time and Equation (4.6)can be used to calculate xf at any time. In the equations, Qt

p the permeate

quantity at time t, Qtf the feed quantity at time t, Qt−∆t

f is the feed quantity

at time (t − ∆t), Qt−∆tp is the permeate quantity at time (t − ∆t), xt−∆t

f and

xt−∆tp the feed and permeate salinity at time (t − ∆t), xtpt and xtf the total

permeate and feed salinity at time t. With given feed tank quantity and qualityat time t=0 and measurements of feed tank quantity and quality at discretetime, Equations (4.1) and (4.2) will calculate quantity and quality of feed tankat each discrete time. In Table 4.2 are listed some experimental settings such asmembrane type, membrane area, membrane length, membrane diameter, initialfeed quantity, feed initial salinity, time step, feed �ow rate, and pressure dropacross the membrane (operating pressure).


Table 4.2: Experimental settingsParameter Setting value

Membrane type AFC99A [m2] 0.024L [m] 0.03dh [m] 0.01635Qf0 [m] 8x0 [m] 35-25-15

∆t [min] 30-15Mf [L/min] 18∆P [bar] 40-45

4.3 Results and discussion

4.3.1 Permeate �ux

For two di�erent operating pressures and an initial feed salinity of 25 g/L, Figure4.4 plots the permeate �ux trend and Figure 4.5 plots the corresponding feedtank salinity. The decreasing of the permeate �ux is due to increase in higherfeed salinity with time. The separation is more di�cult as the increasing inxf means increase in the osmotic pressure on the feed solution side resultingin decrease of the net driving force (∆P -∆π). In addition, it is possible tosee the in�uence of the pressure: at higher values corresponds higher permeate�ux, because the separation is more e�cient due to higher driving force andMp is directly proportional to it. This leads lo high value of xf at any timeat higher pressure (Fig. 4.5). A similar trend was found for an xf0 of 35 g/Land 15 g/L (see Appendix A). This behavior of the permeate �ux at di�erentpressures con�rm what was found in Shamel and Chung (2006) and Djebedjianet al. (2008). In Figure 4.6 it is possible to see the in�uence of the initial feedsalinity at a constant pressure of 40 bar: to higher feed salinity correspondslower collected permeate �ux because the separation becomes more di�cultcorresponding feed tank salinity as shown in Figure 4.5. Same results werecollected for an operating pressure of 45 bar (see Appendix A). The same trendswere found from Shamel and Chung (2006), Djebedjian et al. (2008) and Wilfand Klinko (1994).

4.3. RESULTS AND DISCUSSION 45

Figure 4.4: Permeate �ux trend with xf0=25 g/L.

Figure 4.5: Feed tank salinity trend with xf0=25 g/L.


Figure 4.6: Permeate �ux with P=40 bar.

4.3.2 Permeate salinity

Figure 4.7 shows instantaneous permeate salinity dynamic evolution for initialfeed salinity of 15 g/L, at two operating pressures, (i.e. 40 and 45 bar). Thepermeate salinity increases gradually almost at the same pace for both pressures,with the gradual increase of feed salinity (see Figure 4.6) until about 250 min.Beyond 250 min the rate of increase of permeate salinity at 45 bar is larger thanthat at 40 bar. Probably, this is because beyond that time the higher-pressurevalue takes over the e�ect of concentration polarization (CP , explained later inSection 5.2) and lets more salt to permeate. The same behavior was found forinitial values of salinity equal respectively to 25 g/L and 35 g/L (see AppendixA). These results con�rm what the literature reports as in Shamel and Chung,Djebedjian et al., (2008) and Voros et al., (1996). Figure 4.8 shows how higherinitial feed salinities result in higher permeate salinities. The plotted results arefor an operating pressure of 45 bar but similar trends were found for 40 bar.

4.3.2.1 E�ect of feed salinity

Figure 4.9 displays the variation of permeate salinity with the feed salinity fora constant pressure of 40 bar (similar trends were obtained at 45 bar, AppendixA). Figure 4.10 plots the functional dependency of Mp from xf at di�erentinitial conditions, xf0. In an ideal case, one would expect to obtain only one linejoining all the points. However, beyond xf=25 g/L, the rate of increase in xp ishigher for xf0=15 g/L compared to that for xf0=25 g/L. This is due to the factthat with xf0=15 g/L the CP e�ect becomes increasingly more pronounced as


Figure 4.7: Permeate salinity trend with xf0=15 g/L.

Figure 4.8: Permeate salinity trend at P=45 bar.


Figure 4.9: Permeate salinity variation with the feed salinity at P=40 bar.

feed salinity increases (see Section 5.2). This reduces the permeate �ow (Figure4.10), and leads to an increasing of the permeate salinity for the same passageof salt through the membrane (note that salt passage also gradually increaseswith increasing feed salinity for a given pressure, see discussion in Section 5.3).However, when xf0=25 g/L, the Mp is higher compared to Mp at xf=27 g/L(with xf0=15 g/L) (see Figure 4.10), which consequently decreases the value ofxp (see Figure 4.9). Similarly, the behavior of the pro�les with xf0=35 g/L andxf0=25 g/L can be explained. To the best of authors' knowledge, these typesof observations have been neither made nor reported in the literature. Section5.1 captures further the e�ect of initial feed salinity.

4.4 Constant feed salinity

As previously said, to be thorough, the batch plant was then run in a continuousmode, by means of some expedients explained later in this section. Thus, someresults are collected and compared to the batch mode.

4.4.1 Materials and methods

In the experimental set up, since it was not possible to disconnect the brinerecirculation to the feed tank, through a mass balance at discrete time interval,fresh water (Qw), was added to the feed tank to keep the salinity constant:

Qtf = Qt−∆t

f −Mp∆t (4.7)

4.4. CONSTANT FEED SALINITY 49

Figure 4.10: Permeate �ux variation with the feed salinity at P=40 bar.

Qtfd

=Qt

fxtf

xfd(4.8)

Qw = Qtfd

−Qtf (4.9)

Where xfd is the target concetration, i.e. 35 g/L.

With the calibration curve in Fig 4.11, by measuring the height, the amountof the needed volume was found. It is possible to see from Figure 4.12 that thismethod was successful since the xf trend is almost constant with the time.

4.4.2 Results and discussion

In Figures 4.13 and 4.14 results are plotted for an operating pressure of 40 barand an initial feed salinity of 35 g/L (batch system) and constant feed salinityof 35 g/L for the other experimental mode. As expected, Mp and xp remainconstant for constant feed salinity, since the plan was run for 8 hours only andit was not possible to see appreciable fouling.


Figure 4.11: Volume-Height calibration curve.

Figure 4.12: Feed tank salinity trend.

4.4. CONSTANT FEED SALINITY 51

Figure 4.13: Permeate �ux trend for batch and continuous running.

Figure 4.14: Permeate salinity trend for batch and continuous running.

Chapter 5

Assessment of RO key

parameters

5.1 Introduction

In this section, the description of the RO desalination plant goes on with thestudy and calculation of some key parameters. One of the most important isthe water permeability constant inasmuch as it can be seen in the mass balanceas it will be explained later. Therefore, a decay in the water permeabilityconstant (Kw) results in a lower permeate �ux value and consequently, in worseplant performance. The concentration polarization mechanism is also studied.It increases the salt salinity due to a boundary formed near the membranesurface when the water �ows through the membrane and the membrane rejectssalts. It results in reducing the actual product water �ow rate and salt rejectionversus theoretical estimates. Moreover, salt transport phenomenon across themembrane is analyzed by evaluating pressure and feed salinity in�uence on saltpermeability constant and salt mass �ow, compared to literature's reports.

5.2 Water permeability calculations

One of the parameters that most a�ects the performance of a RO plant, is thewater permeability constant, for its being directly proportional to the permeate�ux and inversely proportional to the driving force of the process, as it willbe explained in the following. Two di�erent methods of calculating the drivingforce of the process are considered. The methods are those used by El-Dessoukyand Ettouney (2002) and Meares (1976).

53

54 CHAPTER 5. ASSESSMENT OF RO KEY PARAMETERS

5.2.1 El-Dessouky and Ettouney model

According to the widely used solution-di�usion model, the water �ux throughthe membrane is given by:

Mp = Kw (∆P − ∆π)AC1 (5.1)

Where C1 is a conversion factor, Kw and A are the water permeability andmembrane area respectively, and ∆P the pressure di�erence between the feedand permeate sides of the membrane. Similarly, ∆π is the osmotic pressuredi�erence across the membrane. Equations (5.2) and (5.3) allow evaluating thebrine �ow rate and the brine salinity (Figure 4.2).

Mb = Mf −Mp (5.2)

xb =Mfxf −Mpxp

Mb(5.3)

Where Mb, Mf and Mp are, respectively the brine, feed and permeate �ux.After that, it is possible to calculate the permeate osmotic pressure (Equation(5.4)), the feed osmotic pressure (Equation (5.6)), and the brine osmotic pressure(Equation (5.5)), in order to obtain the net osmotic pressure di�erential acrossthe membrane (∆π) through Equation (5.8). For not too high solute salinity,the osmotic pressure is approximately a linear function of solute salinity.

πp = 0.7579xp (5.4)

πb = 0.7579xb (5.5)

πf = 0.7579xf (5.6)

π̄ = 0.5 (πf + πb) (5.7)

∆π = π̄ − πp (5.8)

Finally, it is possible to calculate the water permeability coe�cient Kw.

Kw =Mp

(∆P − ∆π)AC1(5.9)

Figure 5.1 shows the water permeability coe�cient at two di�erent levels ofpressure with an initial feed salinity of 25 g/L. It is possible to observe how itsvalue increases at higher pressures due to a higher driving force. As always,experiments conducted with an initial feed salinity of 15 g/L and 35 g/L showa similar dependence (Appendix A). Despite that, this trend does not accordwith the results of Voros et al., (1996). Figure 5.2 reports a rather importantresult. In the literature, it not possible to �nd evidence of the feed salinity role

5.2. WATER PERMEABILITY CALCULATIONS 55

on the water permeability constant. Some authors, who studied the dependenceon Kw, focused only on the pressure (Voros et al., 1996) or its decay with thetime due to the fouling phenomenon (Zhu et al., 1997) and (Al-Bastaki andAbbas, 2004). In this work, we observed that Kw values depend on feed salinity(see also Barello et al., 2013 and Section 8). A higher feed salinity correspondsto a smaller water permeability coe�cient. This trend con�rms the dependencefound between the permeate �ux and the initial feed salinity. The same trendswere found for both the tested operating pressures (Appendix A). In order tobetter clarify this dependence, Figure 5.3 plots the Kw trend as a function ofthe feed salinity. It is possible to obtain a straight line to represent such adependence that will be used in the following to model the RO process (seeSection 6).

Figure 5.1: Water permeability trend at xf0=25 g/L (Dessouky and Ettouneymodel).


Figure 5.2: Water permeability trend at P=45 bar (Dessouky and Ettouneymodel).

Figure 5.3: Water permeability trend with feed salinity at P=40 bar (Dessoukyand Ettouney model).


5.2.2 Meares model

Meares (1976) based his reverse osmosis studies on an irreversible thermody-namic treatment of the system. The model assumes turbulent �ow, osmoticpressure represented by the Van't Ho� equation, tubular con�guration, isother-mal conditions, re�ection coe�cient approximately equal to the intrinsic saltrejection Rj , and the Nerst �lm model to describe polarization phenomenon.The permeate �ux is given by:

Mp = Kw

[∆P −

(R

j

)2αR (T + 273.15)xw

]AC1 (5.10)

Where α is the number of ions produced on complete dissociation of onemolecule of electrolyte, R is the universal gas constant, T is the temperature.The wall salinity xw and the salt rejection Rj are estimated by:

xw =

exp

(MpC2ASc

3/4

ju

)Rj + (1 −Rj) exp

(MpC2ASc3/4

ju

) (5.11)

Rj =Mp

Mf(5.12)

Where C2 is a conversion factor, Sc is the Schmidt number, u is the brinevelocity and j is the Chilton-Colburn factor.

Sc =υ

D(5.13)

j = 0.0395Re−1/4 (5.14)

u =Mb

C2A(5.15)

Re =MfdhυA

(5.16)

Where ν is the viscosity, dh the hydraulic diameter of the �ow channel andD the di�usivity coe�cient. For the physical properties, correlations from Paiset al., (2007) are used.

υ =µ

ρ(5.17)

e = 1.0069 − 2.757 · 10−4 (°C) (5.18)

ρ = 498.4e+√

248400e2 + 752.4xfe (5.19)


µ = 1.234 · 10−6 exp

(0.00212xf +

1965

273.15 + T

)(5.20)

D = 6.725 · 10−6 exp

(0.154 · 10−3xf +

2513

273.15 + T

)(5.21)

In the following �gures (Figures 5.4, 5.5, 5.6 and see also, Appendix A),some results are plotted, and the trends con�rm the various dependences foundwith the El-Dessouky calculations.

Figure 5.4: Water permeability trend at xf0=15 g/L (Meares model).


Figure 5.5: Water permeability trend at P=45 bar (Meares model)

Figure 5.6: Water permeability trend with feed salinity at P=40 bar (Mearesmodel).


5.3 Concentration polarization

The term concentration polarization refers to the salinity gradient of salts onthe high-pressure side of the reverse osmosis membrane surface created by theredilution of salts left behind as water permeates through the membrane itself.The salt salinity in this boundary layer has a higher value than the salinity ofthe bulk water. This phenomenon a�ects the performance of the RO process(Meares, 1976); by increasing the osmotic pressure at the membrane surfaceleading to reduced �ux, increased salt leakage, and it favors scale development.As suggested by Kennedy et al. (1974), a material balance within the mass-transfer boundary layer near the membrane wall, between the solute carried tothe membrane by convection and the solute carried away by di�usion, gives anexpression that quanti�es concentration polarization.

CP =xw − xpxb − xp

= exp

(Mp

C2kA

)(laminar) (5.22)

Where xw is the concentration at the membrane wall, xp the permeate salin-ity, xb the brine salinity and k the mass transfer coe�cient. The mass-transfercoe�cient may be estimated using an appropriate empirical mass-transfer ex-pression. For fully developed laminar and turbulent �ow regimes, Belfort givesthe two following relations (Belfort, 1984).

Sh =kdhD

1.86

(ReScdh

L

)0.33

(turbolent) (5.23)

Where Sh, Re and Sc are the Sherwood, Reynolds (Equation 5.16) andSchmidt (Equation 5.13) numbers respectively, L is the length of the membraneand dh the hydraulic diameter of the �ow channel. Figure 5.7 shows the resultsfor an initial feed salinity of 35 g/L as a function of the operating pressure.Correspondingly, Figure 5.8 plots the variation of CP with Mp. It is possibleto observe how at higher pressures the concentration polarization phenomenonis more evident due to the increase of the driving force. In addition, in Figure5.9, the variation of CP with feed salinity for di�erent starting point is plotted.Results are plotted for an operating pressure of 45 bar, with similar trendsfound at 40 bar. In an ideal case, one would expect to obtain only one linejoining all the points. As said before in Section 3.2, with xf0=15 g/L, the CPe�ect becomes increasingly more pronounced as feed salinity increases, and thiscauses a decay in the permeate �ow (Figure 4.10). In addition, in this case, it ispossible to make an interesting observation. In the process started with xf0=25g/L, the CP value is higher compared to CP at xf=30 g/L (with xf0=15 g/L)(see Figure 5.7), which consequently decreases the value of xp (see Figure 4.9).Similarly, the behavior of the pro�les with xf0=35 g/L andxf0=25 g/L can beexplained. Again, to the best of authors' knowledge, these types of observationshave not been made and reported in literature.

5.3. CONCENTRATION POLARIZATION 61

Figure 5.7: Concentration polarization trend with xf0=35 g/L.

Figure 5.8: Concentration polarization trend with permeate �ux at xf0=35 g/L.


Figure 5.9: Feed salinity in�uence on concentration polarization at P=40 bar.

5.4 Salt transport mechanism

As suggested in El-Dessouky and Ettouney (2002), the rate of salt �ow throughthe membrane is de�ned by:

Js = Ks (x̄− xp)A (5.24)

Where Ks is the salt permeability coe�cient, xp is the permeate total dis-solved solids concentration and the average salinity is de�ned as follows:

x̄ =Mfxf +MbxbMf +Mb

(5.25)

Permeate salinity depends on the relative rates of water and salt transportthrough reverse osmosis membrane:

xp =JsMp

(5.26)

The fact that water and salt have di�erent mass transfer rates through agiven membrane creates the phenomenon of salt rejection. No membrane isideal in the sense that it absolutely rejects salts; rather, the di�erent transportrates create an apparent rejection. The salt permeability coe�cient trends areshown in Figures 5.10 and 5.11. In the former, it is possible to see the in�uenceof the initial feed salinity at a constant pressure of 40 bar. At higher values ofxf0 corresponds smaller Ks because the separation becomes more di�cult. Thisbehavior con�rms the results of Voros et al., 1996. In the latter, the dependence

5.4. SALT TRANSPORT MECHANISM 63

of pressure on the salt coe�cient is plotted where a higher operating pressureproduces higher Ks. As for the water permeability constant, it is possible toobtain a correlation that describes the dependence of the feed salinity on Ks

and that will be used in the following (Section 6). Results are plotted in Figure5.12 for a constant pressure of 40 bar. Figures 5.13 and 5.14 show the salt�ow through the membranes. The former shows the in�uence of the initial feedsalinity whilst the latter displays the in�uence of the operating pressure.

Figure 5.10: Salt permeability coe�cient trend at P=40 bar.


Figure 5.11: Salt permeability coe�cient at xf0=25 g/L.

Figure 5.12: Salt permeability trend with feed salinity at P=40 bar.

5.4. SALT TRANSPORT MECHANISM 65

Figure 5.13: Salt �ow trend at P=45 bar.

Figure 5.14: Salt �ow trend with xf0=15 g/L.

Chapter 6

RO process modeling

6.1 Introduction

In this section we propose a system of di�erential and algebraic equations tomodel the batch system, which is validated using literature values of the param-eters Kw and Ks. Results from this simulation with constant values of waterand salt permeability showed a signi�cant mismatch with the experimental re-sults. Results improved signi�cantly when feed salinity dependencies for waterand salt permeability constants were introduced in the model, taken by intrap-olation from the experimental data set. Therefore, this proves that in modelingthe desalination process, such dependance should be considered.

6.2 Equations system

With reference to Figure 4.2, the mass and the salt balances for the feed tankcan be written as follows:

d (Qf )

dt= −Mp (6.1)

d (Qfxf )

dt= −Mpxp (6.2)

In order to resolve the di�erence equations, the last one should be expandedas explained.

Qfd (xf )

dt+ xf

d (Qf )

dt= −Mpxp (6.3)

In the addends of this equation it is possible to recognize the left term ofequation (6.1), thus with this substitution equation (6.3) can be written as:

Qfd (xf )

dt+ xf (−Mp) = −Mpxp (6.4)

67

68 CHAPTER 6. RO PROCESS MODELING

d (xf )

dt=

−Mpxp + xfMp

Qf(6.5)

In this way, the feed tank is described by equations (6.1) and (6.5).As for the feed tank, it is possible to write two equations of mass and salt

balances to characterize the permeate feed tank:

d (Qp)

dt= M

p(6.6)

d (Qptxpt)

dt= Mpxp (6.7)

With the same substitutions the two equations that describe the permeatetank at the end are (6.6) and (6.8):

d (xpt)

dt=Mpxp + xptMp

Qpt(6.8)

These four di�erential equations, coupled with one of the two theories (El-Dessouky and Ettouney and Meares) previously discussed (Section 5.2.1. and5.2.2.) to describe the RO system, give a complete characterization of the plant(see Appendix B for the Matlab code).

6.2.1 Degrees of freedom

Calculating the degree of freedom of the system is one of the steps to carry outthe simulation of the process. Degrees of freedom are meant as the number ofmeasurements we have to get in order to solve the question by using the formula:

Degrees of freedom (d) = Variables number (m) � Equations umber (n)As previous said, we have to satisfy the degrees of freedom if want to solve the

question by either providing �d� number of equations or specifying �d� numberof variables. In addition, all the equation used in the calculations must beindependent.

Table 6.1 reports the number of degrees of freedom for both the models.As far as this number is 2, in Table 6.2, 6.3, 6.4 and 6.5 it is possible to seea schematic representation for both models of the variables involved in eachequation, in order to make clear which equation can be a�ected �lling these twodegrees of freedom remained.

For both models, it is possible to look for literature values of the waterpermeability constant and salt permeability constant, in order to satisfy thedegrees of freedom. Since in this way the degrees of freedom number becomesequal to zero, it is possible to solve the system of equations describing the ROplant and validate the model by comparing these results with the experimentaldata. Table 6.6 shows the parameters speci�ed for both models. Note that Kw

and Ks values (for non-fouled membrane) are taken from Voros (1996) (tubularmembrane, P=25, 30, 35 and 40 bar).

6.2. EQUATIONS SYSTEM 69

Table 6.1: Degrees of freedom.Model m n Assigned

parame-ters

d

El-Dessoukyand

Ettouney(2002)

dQf

dt ,dQpt

dt ,dxf

dt ,dxpt

dt ,Qf ,Mp,xf ,xp,Qpt,xpt,Mb,xb,πp,πb,πf ,π̄,

∆π,Kw,Mf ,∆P ,A,Ks,Js,x̄,

total number=24

(6.1),(6.5),(6.6), (6.8),(5.2),(5.3),(5.4),(5.5),(5.6), (5.7),(5.8),(5.9),(5.23),(5.24),(5.25), totalnumber=15

Mf , ∆P ,A, Q0

f ,

x0f , Q

0pt,

x0pt,

totalnum-ber=7

24-15-7=2

Meares(1976)

dQf

dt ,dQpt

dt ,dxf

dt ,dxpt

dt ,Qf ,Mp,xf ,xp,Qpt,xpt,Mb,xb,Kw,Mf ,∆P ,A,Ks,Js,x̄,Rj ,α,T ,xw,

Sc,j,u,υ,D,Re,dh,µ,ρ,f ,

total number=33

(6.1),(6.5),(6.6), (6.8),(5.2),(5.3),(5.10),(5.11),

(5.12),(5.13),(5.14),

(5.15),(5.16),

(5.17),(5.18),(5.19),(5.20),

(5.21),(5.24),

(5.25),(5.26),total

number=21

Mf , ∆P ,A, Q0

f ,

x0f , Q

0pt,

x0pt, T ,dh, α,

total nm-ber=10

33-21-10=2


Table 6.2: Parameters involved in each equation for El-Dessouly and Ettouney(2002) model (1st part).

6.1 6.5 6.6 6.8 5.2 5.3 5.4 5.5dQf

dt xdQpt

dt xdxpt

dt xdxf

dt xQf x xMp x x x x x xxf x xxp x x x xQpt x xxpt xMb x xMf x xxb x xπp xπb xπfπ̄

∆πKw

∆PAKs

x̄Js


Table 6.3: Parameters involved in each equation for El-Dessouly and Ettouney(2002) model (2nd part).

5.5 5.6 5.7 5.8 5.9 5.23 5.24 5.25dQf

dtdQpt

dtdxpt

dtdxf

dt

Qf

Mp x xxf x xxp x xQpt

xptMb xMf xxb xπp xπb x xπf x xπ̄ x x

∆π x xKw x∆P xA x xKs xx̄ x xJs x x


Table 6.4: Parameters involved in each equation for Meares (1976) model (1st

part).6.1 6.5 6.6 6.8 5.2 5.3 5.10 5.12 5.11 5.13 5.14

dQf

dt xdQpt

dt xdxpt

dt xdxf

dt xQf x xMp x x x x x x x x xxf x xxp x x xQpt x xxpt xMb x xxb x xKw xMf x x x∆P xA x xRj x x xα xT xxw x xSc x xj x xu xν xD xRe xdµρfJsKs

x̄


Table 6.5: Parameters involved in each equation for Meares (1976) model (2nd

part).5.15 5.16 5.17 5.18 5.19 5.20 5.21 5.24 5.25 5.26

dQf

dtdQpt

dtdxpt

dtdxf

dt

Qf

Mp xxf x x x xxp x xQpt

xptMb x xxb xKw

Mf x x∆PA x x xRj

αT x x xxwScju xν x xD xRe xd xµ x xρ x xf x xJs x xKs xx̄ x x


Table 6.6: Parameters values.Settings Initial conditions Literature values

Mf = 18 [L/min] Q0f = 8 [L] Kw = 2.4E-5 [m3/min/m2/bar]

∆P = 40 [bar] x0f = 35-25-15 [g/L] Kw = 0.0237 [L/min/m2]

A = 0.024 [m2] Q0pt = 1.E-10

T = 23-33 [°C] x0pt= 1.E-10

α = 2 [-]dh = 0.01635 [m]


Figures 6.1 and 6.2 show the results for both models (for x0f=35 g/L), and

it is possible to see that there is a mismatch between experimental results andcalculated results. In part it is due to some measurement errors, in part becauseconstant values for Kw and Ks are used, but this is not the case. Thus, itis necessary to incorporate in the model the dependence of the feed salinityfrom these parameters. From the experimental results (Figures 5.3, 5.6, and5.12), a linear regression is done in order to obtain two expressions of waterand salt permeability constants as a function of xf . Tables 6.7, 6.8, and 6.9report the correlations for each initial condition (x0

f ). In Figures 6.3 and 6.4

(for x0f=35 g/L), it is possible to see that, after this correction/improvement,

there is more accordance between experimental and predicted values, especiallyfor El-Dessouky and Ettouney (2002). See also Appendix A for x0

f=35g/L and

x0f=35 g/L.

Table 6.7: Water and salt permeability correlations for x0f =35 g/L(this work).

Kw

[m3/min/m2/bar

]Based on El-Dessoukyand Ettouney model

Based on Meares model

Equation Kw = −1E−6xf +6E−5 Kw = −5E−7xf +3E−5R2 0.9687 0.9927

Ks

[L/m2/min

]Equation Ks = −0.0003xf + 0.0224

R2 0.8802


Table 6.8: Water and salt permeability correlations for x0f=25 g/L (this work).

Kw

[m3/min/m2/bar




Ks

[L/m2/min

]Equation Ks = −0.0002fx+ 0.0195

R2 0.9029

Table 6.9: Water and salt permeability correlations for x0f=15 g/L (this work).

Kw

[m3/min/m2/bar




Ks

[L/m2/min

]Equation Ks = −0.0004xf + 0.0293

R2 0.9173

0 50 100 150 200 250 300 350 400 450 5000

1

2

3

4

5

6

7

8

9

10

11

t [min]

x p [g/

L]

Mares

El-Dessouki and Ettouney

Experiments

Figure 6.1: Permeate salinity trends Matlab calculations (x0f=35 g/L).


0 50 100 150 200 250 300 350 400 450 5000

1

2

3

4

5

6

7

8

x 10-3

t [min]

Mp [

L/m

in]

Mares


Experiments

Figure 6.2: Permeate �ux trends Matlab calculations (x0f=35 g/L).

0 50 100 150 200 250 300 350 400 450 5000

1

2

3

4

5

6

7

8

9

10

11

t [min]

x p [g/

L]

Mares


Experiments

Figure 6.3: Permeate salinity trends Matlab calculations (x0f=35 g/L).


0 50 100 150 200 250 300 350 400 450 5000

1

2

3

4

5

6

7

8

x 10-3

t [min]

Mp [

L/m

in]

Mares


Experiments

Figure 6.4: Permeate �ux trends Matlab calculations (x0f=36 g/L).

Part III

Water permeability arti�cial

neural network based

correlation

79

Chapter 7

Identi�cation of Kw by

Arti�cial Neural Networks

7.1 General Introduction

In the previous chapters it has been enlightened how the water permeabilityconstant, Kw, is a very important parameter in the membrane desalinationprocesses, and it can be clearly seen from the mass balance equation:

Mp = Kw (∆P − ∆π)AC1 (7.1)

It a�ects the permeate �ux, so the optimal design and operation of RO pro-cesses. Within the RO process model, calculation of Kw is therefore importantand should be evaluated in an appropriate way. For a given membrane type,Kw gradually decreases with increasing the operating pressure, feed salinityand fouling (due to build up of salt). This decay results in less �ux through themembrane and should be taken into account when designing a RO desalinationplant. The available literature models (only two) calculating the dynamic Kw

values are de�ned only for one operating pressure and one feed salinity each.The use of neural networks (ANNs), in all aspects of process engineering ac-tivities, such as modeling, design, optimization, and control, has considerablyincreased in recent years (Mujtaba and Hussain, 2001, Tanvir and Mujtaba, 2006and Aminian, 2010). In this chapter, a time dependant ANN based correlationis developed for estimating Kw for a range of salinity and operating pressureunder fouling, considering two membrane types: hollow �ber (Zhu et al., 1997)and spiral wound (and Al-Bastaki and Abbas, 2004). The ultimate objectiveis to use these correlations within a RO process modeling and optimizationframework in the future.

81

82CHAPTER 7. IDENTIFICATION OFKW BY ARTIFICIAL NEURAL NETWORKS

7.2 Membrane fouling

7.2.1 Description and mechanism

At the beginning, some attention should be paid to the de�nition of the term�membrane fouling�, which appears very often in the literature discussing waterand wastewater treatment by membrane methods. Normally, the phenomenonis characterized by a long-term �ux decline and, eventually, retention decreaseas a result of accumulation of some fouling material. Fouling may be caused byseveral compounds and these foulants may be classi�ed as (Belfort et al., 1976):

� dissolved organics, including humic substances, biological slimes and macro-molecules;

� dissolved inorganics, including inorganic precipitates such as CaSO4, CaCO3,Mg(OH)2, Fe(OH)3, and other metal hydroxides;

� particulate matter.

Jackson and Landolt (1973) studied the rate of fouling by deposition of ironhydroxide on tubular RO membranes. They found that the fouling occurred viaa two-step-growth mechanism. This fouling mechanism may be applicable toother foulants as well. Initially, in the nucleation phase, foulants are deposited inpores and surface holes of the membrane. This occurs because of the mechanicalforce acting from the convectional �ow foulants to the membrane surface andVan der Waals' force of attraction. The relative size of the foulant, the numberof pores in the membrane and surface charges a�ect the number of nucleationsites. The second step in the growing mechanism starts when su�cient foulantsare trapped on the membrane surface, and they can act as nuclei from whichgrowth proceeds by a polymerization reaction. Large fouling particles build upon the membrane surface forming a thin porous layer. The rate of growth ofthis layer depends on the nuclei concentration, the rate of polymerization reac-tion and the rate of transport of foulants to the membrane surface. Gregor andGregor (1978) explain that fouling is normally caused by materials that havelarge surface area and are hydrophobic. When a hydrophobic substance is inan aqueous solution, the total dissolution energy can be reduced by decreasingthe area exposed to the water. Therefore, this substance would adhere to themembrane surface by eliminating the repulsive interactions with the surround-ing water. Fouling materials normally have a negative charge due to hydrogenbonding and this will further increase the fouling phenomenon. Jonsson andKristen (1980), distinguished between di�erent types of fouling: irreversibleadsorption of hydrophobic substances to the membrane surface and reversiblesorption of di�erent compounds within the membrane phase. The former de-pends mainly on the transport of foulants to the membrane surface, in�uencedby the concentration in the bulk solution and the total permeate volume. There-fore, it depends strongly on the elapsed time and the membrane hydrodynamics.Further, membranes require either mechanical or chemical cleaning in order torestore the pure water permeability. The reversible sorption mechanism results

7.2. MEMBRANE FOULING 83

in smaller Kw values compared with water, because of increased frictional re-sistance and a decrease in the water content of the membrane. Due to theexistence of dynamic equilibrium between the bulk solution and the membranephase, the change in Kw is sudden and there is no time dependency as above.Water permeability constant is not only a function of the operating time, but italso depends on the membrane type (i.e. spiral wounds, hollow �ber, tubular)(Evangelista, 1985), on the water quality (salinity) (Wilf and Klinko, 1994) andon the operating pressure (M. Zhu et al., 1997).

7.2.2 Literature fouling correlations

Only two correlations in the literature (Zhu et al., 1997 and Al-Bastaki andAbbas, 2004), used to calculating Kw for RO processes under fouling havebeen developed previously and are listed in Table 7.1 with some speci�c details.These correlations take into consideration only the e�ect of fouling with time,and they are applicable for the respective membrane type only and for veryspeci�c values of pressure and feed salinity. It places limitations on such models,because the salt concentration in seawater around the world varies markedly andexperimental studies should re�ect this wide range. Figure 7.1 shows the Kw

estimated pro�les given by the two correlations. As it is possible to see, the trendis progressively decreasing with the time, because both models consider only thefouling in�uence. Therefore, it is worth observing that good correlations, whichact over a wide range of operating parameters and still consider the dynamicevolution of the fouling phenomenon, are necessary for modeling, simulation,and design of RO processes.

Table 7.1: Di�erent correlation for estimating Kw.Correlation 1: Al-Bastaki

and Abbas (2004)Zhu et al. (1997)

Correlation Kw = 0.68Kw0exp

(79

t+201

)Kw = 0.68Kw0

exp( −t

328

)Membrane

typeSpiral wound, Filmtec

BW30-400;Hollow �ber, Dupont

B-10Feed concen-

tration25.40 [g/L]; 34.80 [g/L];

Pressure 12 [bar]; 62-67 [atm]Operating

time1500 [d] 370 [d]

Notes Correlation is based onexperiments

Correlation is not basedon experiments but isused in model-based

simulation.


Figure 7.1: Water permeability trend according to literature correlations for aperiod of 370 days.

7.3 Arti�cial neural network based correlation

Arti�cial neural networks are inspired by the architecture of biological ner-vous system, which basically consists of a large combination of simple nervecells or neurons that work in parallel to facilitate rapid decisions. Analogously,ANNs are made up of a large number of primitive computational elements thatare organized in a massive parallel set. The ANN is then developed in arti-�cial synapses that connect these elements, which are characterized by a setof weights, which can typically be adjusted by a learning process. The mostimportant advantage in using this mathematical method is that ANNs do nothave to be programmed; instead, they use examples to learn how to deal withrelationships that are more complex. ANNs have proved to be highly success-ful in applications such as process control, modeling, simulation and systemidenti�cation (Bhat and McAvoy, 1990, K.J. Hunt et al., 1992, Psichogios an-dUngar, 1992). Their popularity could be attribute to the fact that ANNs cansolve many di�erent types of engineering problems with a relatively simple and�exible structure, due to their superior calculating ability. In essence, neuralnetworks consist of networks of primitive elements (neurons) that receive signals(inputs) from other neurons or from the outside. These signals are subsequentlyweighted and summed. The results (also called potentials of the neurons) arethen computed by transfer functions, which pass the output to other nodes tothe outside environment of the network. The network has a structure consist-ing of at least an input and an output layer, and possibly one or more hidden

7.3. ARTIFICIAL NEURAL NETWORK BASED CORRELATION 85

layers. Neurons in these layers are connected by means of arti�cial synapses,each of which is associated with a numerical value or weight. Once the ANN

is built, trained, validated and tested, respect to a di�erent set of inputs, it isable to produce a corresponding set of outputs according to the inner mappedrelationship. This relationship depends upon the parameters of the network,i.e. the optimal set of weights and biases (Hagan et al., 1996).

7.3.1 ANN architecture and training

An arti�cial neural network architecture design depends on the number of layersthe network has, the number of neurons in each layer, the transfer function usedin each layer, and how the layers are connected to each other. A typical ANNarchitecture is shown in Figure 7.2 and Table 7.2. Each neuron, j, in the i− thlayer is fed by a dedicated bias (bij) and is connected with the neurons of the(i�1)th layer (except the input layer) through the weights (wijk). k denotes theneuron of (i�1)th layer. The total number of neurons in layer i is nj and thetransfer function for layer i and neuron j is fij . In each layer, the value of theneuron ajj is then calculated as follows:

aij = f ij

(ni−1∑k=1

wijk · ai−1

k + bij

)(7.2)

In this work, the Levenberg-Marquardt back propagation algorithm is usedto train the network (Hagan et al., 1996). Despite the fact that it requiresmore memory space and does not converge quickly like other algorithms, itis characterized by better performances because it gets closer to the optimalsolution. An appropriately trained and validated network should be able topredict realistic outputs even when the network is presented with new inputs(test data). Although neural networks are able to interpolate data very well,they are not quite e�cient with extrapolation (Hagan et al., 1996). That is whyit is very important to choose an appropriate interval for the inputs value. Inorder to ensure that the ANN gives good results, a large number of data set isusually required during the training session. In this work, the training data setis selected in such a way that it includes the data from all regions of a desirablesimulation. The calculations continue until the error between the predicted andtarget value is close to zero. (Fig. 7.3).

7.3.2 Development of correlations

The correlation estimates Kw in terms of time (t), salinity (xf ), operating pres-sure (P ) and membrane type (M). Kw is expressed in [m/bar/min], xf ex-pressed in [g/L] and P expressed in [bar]. The input data are scaled up by themean and their standard deviation values as follows:

tscaleup =t− tmean

tstd(7.3)


Figure 7.2: A typical neural network architecture (adapted from Matlab©User's guide).

Table 7.2: Weights, biases and transfer functions for 3-layered network.1st layerWeights Bias Transfer functions

w111 w1

12 w113 w1

14

w121 w1

22 w123 w1

24

w131 w1

32 w133 w1

34

w141 w1

42 w143 w1

24

b11b12b13b14

f11 = tanhf1

2 = tanhf1

3 = tanhf1

4 = tanh

2nd layerWeights Bias Transfer functions

w211 w2

12 w213 w2

14

w221 w2

22 w223 w2

24

w231 w2

32 w233 w2

34

w241 w2

42 w243 w2

24

b21b22b23b24

f21 = tanhf2

2 = tanhf2

3 = tanhf2

4 = tanh

3nd layerWeights Bias Transfer functions

w311

w321

w331

w341

b31 f31 = 1

7.3. ARTIFICIAL NEURAL NETWORK BASED CORRELATION 87

Figure 7.3: Back propagation algorithm scheme.

xfscaleup=xf − xfmean

xfstd(7.4)

Mscaleup =M −Mmean

Mstd(7.5)

where tmean is the average of t, xfmeanthe average of xf , Pmean the average

of P and Mmean the average of M ; tstd is standard deviation of t, xfstd thestandard deviation of xf , Pstd the standard deviation of P and Mstd the stan-dard deviation of M data used to develop the correlation. There are four inputneurons in the ANN based correlations and the values are:

a11 = tscaleup; a1

2 = xfscaleup; a1

3 = Pscaleup; a14 = Mscaleup (7.6)

There is one output neuron in the ANN based correlations:

alj = Kwscaleup(7.7)

where l is the output layer.The output value is rescaled to �nd the value in original units by the following

equation:

Kw = Kwscaleup·Kwstd

+Kwmean(7.8)

For a network as shown in Figure 7.2, the correlation for the output is givenby:

a31 = f3

1

(4∑

k=1

(w3

1ka2k

)+ b31

)(7.9)


where the outputs from the second layer (a2k) are given by:

a2j = f2

j

(2∑

k=1

(w3

jka1k

)+ b21

)(7.10)

For j=1 (�rst neuron) in layer 2, Equation (7.11) can be expressed as:

a21 = f2

1

(w2

11a11 + w2

12a12 + b21

)(7.11)

In this work, transfer functions f2j = tanh and f3

1 = 1 are initially applied,and the in�uence of this choice on the network's performances is investigatedlater in the following chapter. Equation (7.12) thus becomes:

a21 = tanh

(w2

11tscaleup + w212xfscaleup

+ w213Pscaleup + w3

14Mscaleup + b21)(7.12)

The output that the network returns is:

Kwscaleup= a3

1 = w311a

21 + w3

12a22 + w3

13a23 + w3

14a24 (7.13)

7.4 Data set

The input data for the ANN are taken from two di�erent correlations publishedin literature (M. Zhu et al., 1997 and Al-Bastaki and Abbas, 2004). In the fol-lowing, the Kw values calculated from the two equations reported in Table 7.1,and fed into the ANN architecture as input data, will be addressed as correla-tions data. Both of the correlations observed an exponential decay of the waterpermeability constant over the time due to fouling (see Table 7.1 and Figure7.1). Zhu et al. (1997), studied a systematic technique for the optimal designand scheduling of �exible osmosis networks that can accommodate a given rangeof potential variations in the characteristics of the feed and system performance(varying permeability caused by fouling). They considered a hollow �ber mem-brane, a feed concentration of 34.80 [g/L], pressure in the range 62-70 [atm]and operating time 370 days. The exponential model they used for describingthe water permeability decay is however not validated with experimental. Al-Bastaki and Abbas (2004) looked the long-term performance of a medium scaleindustrial reverse osmosis water desalination plant with a spiral wound mem-brane. They used a feed concentration of 25.40 [g/L], an operating pressure of12 [bar] and studied the performances over a period of 1500 days. Their modelwas validated by comparison with some experimental data. In order to developthe correlation, both the models should refer to the same range of time. That iswhy a period of 370 days is considered for training the correlation, consideringthe lowest value between the two ranges. The data set considered in this workcorresponds to 372 points where for each time value there are two pressures,two feed salinities and two membrane types. See Appendix C for the completedata set, while a small part of it is reported in Table 7.3. In order to investigate

7.4. DATA SET 89

Table 7.3: ANN data example.t [day] xf [g/L] P [bar] M [-] Kw [m/bar/min]

0 25.4 12 1 5.675E-050 35 66 2 5.634E-052 25.4 12 1 5.653E-05

2 35 66 2 5.600E-054 25.4 12 1 5.631E-054 35 66 2 5.566E-05... ... ... ... ...366 25.4 12 1 4.404E-05

366 35 66 2 1.846E-05368 25.4 12 1 4.402E-05368 35 66 2 1.835E-05370 25.4 12 1 4.399E-05

370 35 66 2 1.824E-05

the in�uence of input parameters on the ANN performances, one hidden layerwith four neurons was used as the initial condition in the correlation. Also,a hyperbolic tangent function and a linear function were adopted between theinput & hidden layer and between the hidden & output layer. From all theinput data shown in Appendix C, the �rst two points are selected for training(bold), the next input data point for validation (italic) and the fourth one fortesting (normal) the correlations. This selection process continues sequentiallyuntil all the data points are exhausted. Thus, the total input data are dividedinto three data sets: training (50%), validation (25%) and testing (25%). Thetraining values are used to adjust the network based on the error, the valida-tion data are used to measure network generalization and to halt training whengeneralization stop improving. The test data do not have any e�ect on train-ing thus providing an independent measure of network performance during andafter training.

Chapter 8

ANN results and discussion

8.1 Introduction

In this chapter, the network is trained, validated, tested and simulated. Theperformances of the ANN correlation are recorded as number of iterations andmean square error. Moreover, by means of a statistical regression between thepredicted value of the water permeability constant Kw from the ANN correla-tion and correlations data it is possible to �nd the overall trends of the predicteddata. In addition, the ANN architecture is characterized by studying the in�u-ence of number of hidden layers and neurons for each layer. Eventually, the roleof the transfer function on the ANN performance is investigated.

8.2 The correlation

With reference to Figure 8.1 the ANN, based correlation can be expressed asfollows. The values for the hidden layer neurons are:

a21 = tanh

(w2


+ w213Pscaleup + w2

14Mscaleup + b21)(8.1)

a22 = tanh

(w2


+ w223Pscaleup + w2


a23 = tanh

(w2


+ w233Pscaleup + w2


a23 = tanh

(w2


+ w233Pscaleup + w2


and

91

92 CHAPTER 8. ANN RESULTS AND DISCUSSION

Figure 8.1: A three layer arti�cial neural network.

Kwscaleup= a3

1 = w311a

21 + w3

12a22 + w3

13a23 + w3

14a24 + b31 (8.5)

The network is trained, validated, tested and simulated using the MatlabANN toolbox, nntool. The software evaluates the weight and bias values and agraphical performance assessment of the network as shown by a typical examplein Figure 8.1. It is possible to see that in this particular case, the training took 17iterations. The result here is reasonable, because the �nal mean square error isrelatively small, the test set and validation set errors have similar characteristics,and it appears that signi�cant over-�tting has not occurred. Table 8.1 shows thescaled up input and target parameters whilst the weights and biases are shownin Table 8.2. The statistical regression between the predicted value (V ) of thewater permeability constant Kw from the ANN correlation and correlationsdata (C ) is plotted to �nd the overall trends of the predicted data. An exampleis shown in Figure 8.3. The regression plot is used to study the in�uence ofthe number of layers, number of neurons and transfer function on the ANN

performance. A perfect architecture would result in a regression value (R) of0.9961.


The arti�cial neural network described earlier was built with appropriate weightsand biases, and used to evaluate the water permeability constant for both mem-brane types. The feed salinity and pressure included the range de�ned by thetwo literature models. Figure 8.4 and 8.5 show the decay ofKw with the time forthe correlations data and the results of the ANN correlation based on the mod-els of Al-Bastaki (2004) and Zhu (1997) respectively. Input settings of xf=25.40g/L, P=12 bar, M=1 and xf=35 g/L, P=66 bar, M=2 were used for the Al-Bastaki and Zhu respectively. The results clearly show that the predictions byANN correlations �t the correlations data very well. It is important to notethat developing correlations, that take in account not only the in�uence of thetime but also that of feed salinity, pressure and membrane geometry, requiresmuch e�ort. In addition, updating of the constant coe�cients of the modelmay not be su�cient to cover all the wide range of di�erent operating param-


Figure 8.2: . Performance evaluation of the ANN training procedure.

Table 8.1: Calculated weights and biases of the arti�cial neural network.1st layerWeights Bias

w111 = −0.040 w1

12 = 0.007 w113 = −0.013 w1

14 = 0.016w1

21 = −0.171 w122 = 0.053 w1

23 = 0.037 w124 = 0.017

w131 = 0.038 w1

32 = −0.060 w133 = 0.012 w1

34 = −0.005w1

41 = −0.005 w142 = 0.002 w1

43 = −0.001 w124 = 0.005

b11 = 11.253b12 = −7.527b13 = 3.792b14 = −0.060

2nd layerWeights Bias

w211 = 0.220 w2

12 = 1.226 w213 = −1.008 w2

14 = 0.016w2

21 = −1.487 w222 = 1.138 w2

23 = −1.176 w224 = 0.017

w231 = 1.002 w2

32 = 0.958 w233 = −0.876 w2

34 = −0.005w2

41 = −0.874 w242 = 0.746 w2

43 = 0.084 w224 = 0.005

b21 = −1.884b22 = −7.527b23 = 3.792b24 = −0.060

3nd layerWeights Bias

w311 = 1.807E − 05

w321 = 2.476E − 05

w331 = 3.334E − 05

w341 = −8.283E − 05

b31 = 4.823E − 05


Figure 8.3: Linear regression of ANN predicted data with correlations

eters and the structure of the correlation may be altered altogether. However,ANN based correlations can be updated (in terms of weights and biases) easilywithout changing the structure (number of layers, number of neurons). Due toadvancement of the microcomputer, plant automation becomes a reliable meanalso for the programmed plant maintenance of process equipment. ANN basedcorrelations can be updated reliably in terms of new sets of weights and biasesfor the same architecture with new plant data by training, validation and test-ing using the plant automation software. This means that without changing thearchitecture structure already trained, with the supply of new set of input data,the old ANN is able to give good predictions the new situation too.

It is known (Mujtaba and Hussain, 2001), that ANN based correlations arepowerful tools for predicting interpolated data, but they are not able to generategood results if the actual inputs' range is not the same as those used for training,validating and testing the network. To illustrate this point, a test case was runfor Al-Bastaki and Abbas (2004) correlations for a period of 1500 days. Theresults are shown in Figure 8.6 and it is clear that the ANN based correlationgives good results only up to 370 days, i.e. the upper time limit for which thenetwork was trained. Beyond that, the ANN predictions and the correlationsdata diverge quite rapidly.

Most important contribution of this work is to be able to predict Kw forany membrane type (M=1 or M=2) for any salinity ranging from 25.4 � 34.8g/L and any operating pressure ranging from 12-66 bar using Neural Networktechnique. For membrane type M=1 and feed salinity of 25.4 g/L (as used byAl-Bastaki and Abbas, 2004), the e�ect of pressure on the Kw is evaluated and


Figure 8.4: Experimental Al-Bastaki and Abbas Kw evaluation and prediction.

Figure 8.5: Experimental Zhu Kw evaluation and prediction.


Figure 8.6: Zhu Kw experimental and ANN based correlation extrapolation.

is shown in Figure 8.6. Also for membrane type M=2 and feed salinity of 34.8g/L (as used by Zhu et al. 1997), the e�ect of pressure on the Kw is evaluatedand is shown in Figure 8.7. In both cases, the trend is con�rmed by authorsthat studied the in�uence of the operating pressure on the water permeabilityconstant (Voros et al. 1996). It can be seen that at lower concentration (Al-Bastaki and Abbas correlation, Figure 8.7) the increase in pressure producesa greater % change in Kw towards the end of the simulation time. There isno literature evidence of this behavior, and we thus suggest that this couldbe explained by considering the concentration polarization phenomenon (thisphenomenon describes the concentration gradient of salts on the high-pressureside of the reverse osmosis membrane surface created by the redilution of saltsleft behind as water permeates through the membrane itself, for more details seeSection 5.3). Its value results to be smaller at higher feed concentration (Brian,1965). Thus, at lower feed concentration (Figure 8.7) the pressure in�uence onthe Kw is more evident than at higher feed concentration (Figure 8.8), becausethe concentration polarization phenomenon is more pronounced.

For membrane type M=1 and P=12 bar (as used by Al-Bastaki and Ab-bas,2004 ), the e�ect of feed salinity is shown in Figure 8.9. Also for membranetype M=2 and P=66 bar (as used by Zhu et al. 1996), the e�ect of feed salin-ity is shown in Figure 8.10. The results show a rapid decrease in Kw withincreasing feed salinity for the Al-Bastaki and Abbas model (Figure 8.9). Aspreviously said, this dependence has often been ignored in the literature. Athigher pressures (Zhu et al. correlation � Figure 8.10), the in�uence of the feedconcentration on the Kw trend is less evident. Since there is no evidence in theliterature of this relationship, we might postulate that at higher pressures the


driving force of the mechanism dominates all other parameters in�uences.

Figure 8.7: Water permeability constant at di�erent pressures. [At feed salinityof Al-Bastaki and Abbas correlation]

Figure 8.8: Water permeability constant at di�erent pressures [At concentrationof Zhu et al. correlation]


Figure 8.9: Water permeability constant at di�erent feed salinity [At pressureof Al-Bastaki and Abbas correlation]

Figure 8.10: Water permeability constant at di�erent feed salinity [At pressureof Zhu et al. correlation]

8.4. NUMBER OF HIDDEN LAYERS 99

8.4 Number of hidden layers

This section investigates the in�uence of the number of layers on the ANN per-formance. Each hidden layer has four neurons as the input layer; whilst theoutput layer has one neuron only. The performance, shown in Table 8.2, isevaluated by monitoring the iteration time and the goal reached using Matlab.The respective regressions are shown in Figure 8.11. It can be seen that thepredicted values for 2-, 3- and 5-layer re�ect quite well the target values of thecorrelations data. For these architectures, although the number of iterations isincreasing with the number of layers, there is an improvement in the perfor-mances. As the number of layers is increased to 10 and 20 layer, the predictionsbecame rather inaccurate.

Table 8.2: Number of layers performance evaluations.2 layers 3 layers 5 layers 10 layers 20 layers

Performance 5.96E-12 5.81E-12 5.38E-12 1.13E-10 1.25E-12Epochs 16 26 53 19 3

Figure 8.11: E�ect of number of hidden layers on the ANN performance.


8.5 Number of neurons

The e�ect of modifying the number of neurons, from 2 to 20, is now consideredfor a three-layered network. The results from the ANN simulations are shownin Figure 8.12 and Table 8.3 in accordance with the study of the number of lay-ers. It can be seen that for all the di�erent ANN architectures the correlationsand the predicted data correlate quite well. There is however some oscillatorybehavior when the number of neuron is increased. Looking at the Matlab per-formances evaluations (Table 8.3), one can see that a 3 neurons con�guration isbetter than 5 and 10. In addition, the 20 neurons network shows a value nearerto the goal (set at 0) even though it takes longer iteration time to reach such atarget.

Table 8.3: Number of layers performance evaluations.2 layers 3 layers 5 layers 10 layers 20 layers

Performance 5.96E-12 5.67E-12 5.14E-12 5.86E-12 4.89E-12Epochs 39 34 99 100 42

Figure 8.12: E�ect of number of neurons on the ANN performance.

8.6. TRANSFER FUNCTIONS 101

8.6 Transfer functions

The transfer functions that Matlab proposes as connections between layers arelisted shown schematically in Table 8.4. Purelin is a linear transfer function,tansig is a hyperbolic tangent sigmoid transfer function and logsig is a log-sigmoid transfer function. The di�erences between these transfer functions areevaluated in terms of performances for a three-layered ANN with four neuronsin the hidden layer with the same transfer function in each layer. The resultsfor each transfer function are shown in Figure 8.13 and Table 8.5 the resultsfor each transfer function are reported. It can be seen that the tansig functionproduces the best predictions for this ANN architecture in terms of goal, epochsand comparison with the correlations data from the regression plotted in Figure8.14.

Table 8.4: Transfer functions list and representation.purelin tansig logsig

Table 8.5: Performance of the ANN training procedure as a function of theadopted transfer functions.

purelin tansig logsig

Performance 2.29E-11 5.17E-12 5.97E-12Epochs 5 60 58


Figure 8.13: Transfer function in�uence.

Conclusions and

Recommendations

103

Conclusions

In this section, the main reached goals and targets will be represented and sum-marized. The �rst part presented, characterized and modeled a reverse osmosisdesalination plant. Usually this kind of process is operated in continuous mode,but in recent years the batch mode is attracting more and more attention be-cause it o�ers less concentrate production, fouling and energy, and usually itrequires less membrane elements. The batch mode of operation is achieved byrecirculating the brine stream. By doing so, the feed tank salinity increases con-tinuously. Working on a RO batch desalination pilot plant, some experimentalresults are collected for di�erent feed salinity and operating pressures. In detail,permeate �ux increases for higher pressure and for lower salinity and permeatesalinity increases for higher pressure and salinity. At this point an importantobservation is made by studying the permeate salinity trends with feed salinity.It is found that the concentration polarization phenomena can in�uence thesetrends and for di�erent starting points (i.e. di�erent initial feed salinities) thevalues of the permeate salinity at a chosen feed salinity, are di�erent. In theliterature, there is no evidence of these dependencies.

Afr that, one of the most important design parameters, Kw, was evaluated asa function of changing feed salinity and pressure from the collected experimentaldata and two literature models were used. A strong pressure dependence of thewater permeability constant was observed in line with earlier observations. In-terestingly, strong salinity dependence on the water permeability constant is alsoobserved, which was always neglected or ignored in the literature from authorsthat studied this aspect of the process considering only the water permeabilitydecay with the time due to the fouling phenomena. In addition, the salt trans-port mechanism was studied in terms of mass �ow of salt that passes throughthe membrane and salt permeability constant, and interestingly it was founda strong feed salinity dependence on the latter, of which there is no evidencein literature. The batch process is then modeled by means of an di�erentialalgebraic equations system. At the beginning, constant values of water and saltpermeability are taken from literature and a signi�cant discordance is foundbetween experimental and simulated results. When feed salinity dependenciesfrom water and salt permeability constants are introduced in the model, resultsimprove signi�cantly, proving that in modeling the desalination process, suchdependence should be considered.

The second part, proposes and discusses a ANN based correlation to predict

105

106 CONCLUSIONS

the dynamic water permeability constant Kw for a RO desalination plant. Amulti-layered feed-forward network trained with back propagation method hasbeen implemented. The proposed ANN model structure (with one hidden layerand four neurons in hidden layer) is capable of predicting the correlations Kw

very close to those predicted by existing correlations in the literature. Comparedto existing correlations, the distinguishing feature of this correlation is that it isable to predict dynamic Kw values for any of the two membrane types used inthe literature and for any operating pressure and any feed salinity within a widerange. In addition, although the e�ect of pressure on Kw values is in-line withthose observed in the literature, this work adds another dimension to existingliterature that the e�ect of feed salinity on Kw values can be more pronouncedat low pressure operation (which was not reported in the past).

For a given architecture, any correlation can be updated with additionaldata from other sources or a new correlation can be developed for the newsource data. The in�uence of some network design parameters such as numbersof hidden layer, number of neurons and chosen transfer functions is studied.In detail, it is found that a small number of layers (2, 3 and 5) re�ects quitewell the target values of the correlations data. For these architectures, althoughthe number of iterations is increasing with the number of layers, there is animprovement in the performances. As the number of layers is increased to 10and 20 layer, the predictions became rather inaccurate. For the internal layerstructure, it is found that a 3 neurons con�guration is better than 5 and 10. Inaddition, the 20 neurons network shows a value nearer to the goal even though ittakes longer iteration time. In matter of transfer function, the obtained resultsbring the conclusion that tansig function gives the best predictions for this ANNarchitecture in terms of goal, epochs and comparison with the correlations data.

Recommendations

The following recommendations might be considered for any future improve-ment:

� Actual plant data may be used as model input to calculate the realisticresults from El Dessouky and Ettouney (2002) and Meares (1976) models.

� Redevelop the Matlab program by using a design simulator, for exampleAspen Plus.

� Apply cost data, for example membrane cost in the El Dessouky andEttouney (2002) and Meares (1976) models.

� Optimization of the two models could be done in further study for examplecombine with the cost analysis topic.

� About the experimental part, some other tubular membranes can be stud-ied, in order to compare then the results with the membrane AFC99 stud-ied in this dissertation.

� The ANN based correlation can be updated with new input data set, inorder to enlarge the Kw prevision range.

� The ANN based correlation can be used within a RO process modelingand optimization.

107

References

109

Journals

Al-Bastaki N., Abbas A., �Long-term performance of an industrial water desali-

nation plant�, Chemical Engineering Processing, 43 (2004) 555-558.Aminian A., �Prediction of temperature elevation for seawater in multi-stage

�ash desalination plants using radial basis function neural network�, ChemicalEngineering Journal, 162 (2010) 552-556.Belfort G., Alexandroviwics G., Marx B., �Arti�cial particulate fouling of hy-

per�ltration membranes�, Desalination, 19 (1976) 127-138.Bhat N., McAvoy T.J., �Use of neural nets for dynamic modelling and control of

chemical process system�, Computers & Chemical Engineering, 14 (1990) 573-582.Brian P.L.T., �Concentration polarization in reverse osmosis desalination with

variable �ux and incomplete salt rejection�, Industrial and Engineering Chem-istry Foundations, 4 (1965) 439-445.Christman K., �The history of chlorine�, Waterworld, 14 (1998) 66-67.Djebedjian B., Gad H., Khaled I., Rayan M.A., �Optimisation of reverse osmosis

desalination system using genetic algorithms technique�, Twelfth InternationalWater Technology Conference, IWTC12, (2008) Alexandria, Egypt.Evangelista F., �A short cut method for the design of reverse osmosis desalina-

tion plants, Industrial and Engineering Process Design and Development�, 24(1985) 221-223.Gregor H.P., Gregor C.D., �Synthetic membrane technology�, Scienti�c Ameri-can, 239 (1978) 88-101.Hunt K.J., Sbarbaro D., Zbikowski R., Gawthrop P.J., �Neural networks for

control system � A survey�, Automatica, 28 (1992) 1083-1112.Jackson M., Landolt D., �About the mechanism of formation of iron hydroxide

fouling layers on reverse osmosis membranes�, Desalination, 12 (1973) 361-378.Jonsson G., Kristen S., �Membrane �ltration of NSSC-waste liquor�, Desalina-tion, 32 (1980) 327-339.Kennedy T.J., Merson R.L., McCoy B.J., �Improving permeation �ux by pulses

reverse osmosis�, Chemical Engineering Science, 29 (1974) 1927-1931.Pais J.A.G.C.R., Manuel L. and Ferreira G.A., �Performance study of an indus-

trial RO plant for seawater desalination�, Desalination, 208 (2007) 269-276.Psichogios D.C., Ungar L.H., �A hybrid neural-network �rst principles approach

to process modeling�, American Institute of Chemical Engineering Journal, 38(1992) 1499.

111

112 JOURNALS

Shamel M.M., Chung O.T., �Drinking water from desalination of seawater: op-

timisation of reverse osmosis system operating parameters�, Jourmal of Engi-neering Science and Technology, 2, (2006) 203-211.Shone R.D.C., �The freeze desalination of mine waters�, Journal of the SouthAfrican Institute of Mining and Metallurgy, 4 (1987) 111-112.Stover R., �CCD Starts a New Generation for RO�, Desalination and WaterReuse, 1 (2011) 34 � 35.Tanvir M.S., Mujtaba I.M., �Neural Network based correlations for estimating

temperature elevation for seawater in MSF desalination process�, Desalination,195 (2006) 251-272.Tarquin, A., G. Delgado, �Concentrate Enhanced Recovery Reverse Osmosis: A

New Process for RO Concentrate and Brackish Water Treatment�, Proceedingsof the American Institute of Chemical Engineers Meeting in Pittsburg, (2012)paper number 272277.Voros N.G., Maroulis Z.B., Marinos-Kouris D., �Salt and sea water permeability

in reverse osmosis membranes�, Desalination, 104 (1996) 141-154.Wilf M., Klinko K., �Performance of commercial seawater membranes�, Desali-nation, 96 (1994) 465-476.Zhu M., El-Halwagi M.M., Al-Ahmad M., �Optimal design and scheduling of

�exible reverse osmosis networks�, Journal of Membrane Science, 129 (1997)162-174.

Books

Belfort G., Synthetic Membrane Process: Fundamentals and Water Applica-tions, Academic Press Inc., New York, (1984).El-Dessouky H.T., Ettouney H.M., Fundamentals of Salt Water Desalination,Elsevier Science B.V, Amsterdam, (2002).Ettouney H., An Overview of the Current Status of Applications of CommercialSeawater Desalination Processes, pp. 17 � 30 in Seawater Desalination: Con-ventional and Renewable Energy Processes, Cipollina A., Micale G., Rizzuti L.,Springer, Palermo, (2009).Hagan M.T., Demuth H.B., Beale M.N., Neural Network Design. Boston, PWSPublishing Company of International Thomson Publishing Inc., Boston, (1996).Hartel R.W., Evaporation and freeze concentration, pp. 341�392 in Handbookof Food Engineering, Heldman D.R., Lund D.B., Marcel Dekker, New York,(1992).Meares P., Membrane separation process, Elsevier Scienti�c, Oxford, (1976).Mujtaba I.M., Hussain M.A., Application of Neural Network and Other Learn-ing Technologies in Process Engineering, Imperial College Press, London, (2001).Pantieri L., Gordini T., L'arte di scrivere con LATEX, Gruppo Utilizzatori Ital-iani di LATEX, (2001).Popkin R., Desalination, Water for the World's Future: The State of the Art.,Praeger, New York, (1968).Porteous A., Saline Water Distillation Processes: A very general introduction,Longman Group Limited, London, (1975).Porter M., Handbook of Industrial Membrane Technology, Noyes Pubblications,Park Ridge, (1990).Rahman M. S., Ahmed M., Chen, X. D. Assessment of the innovative freezing-melting process for desalination of sea water in the Gulf countries. Final Report,Middle East Desalination Research Center, Al Khuwair, Muscat, (2003)Sheikholeslami R., Study of the Interactive E�ects of Inorganic and BiologicalFouling in RO Desalination Units Phase � A. Muscat: The middle Easr Desali-nation Research Center, (2007).Sourirajan S., Reverse Osmosis and Synthetic membranes: Theory-Technology-Engineering, Canada: National Research Council Canada, (1977).Spiegler K.S., EL-Sayed Y.M., A Desalination Primer: Introductory book forstudents and newcomers to desalination, Blaban Desalination Publications, SantaMaria Imbaro, Italy, (1994).

113

114 BOOKS

Ullmann F., Ullman's encyclopedia of industrial chemistry. 5th ed., Hans Jur-gen Arpe and Wolfgang Gerhartz, Berlin: Weingeim-VCH, (1987).Ulrich M., Desalination by Reverse Osmosis: Principles of Reverse Osmosis,Cambridge: The Massachusetts Institute of Technology, (1966).Watson I.C., Reverse Osmosis, Water Treatment Systems: Design GuidelinesManual, Muscat: Middle East Desalination Research Center, (2006).Williams M. E., A Review of Reverse Osmosis Theory, EET Corporation andWilliams Engineering Services Company Inc., Harriman TN, (2003).

Appendices

115

Appendix A

In this section are reported some further results about sections 4, 5 and 6. Theexperiments, as well as the modeling, were made with three di�erent initialfeed concentration (xf0=15, 25, 35 g/L) and two di�erent operating pressure(P=40, 45 bar). In the main body of this dissertation, in order to keep thereading �uid, we decided to put only the results for one xf0 and one P , sinceall the result con�rm the same trends. For example, in Section 4.1 results arereported for xf0=25 g/L and P=40 bar only, while in the following it is possibleto see results for xf0=15 g/L, xf0=25 g/L and P=45.

117

118 APPENDIX A

Figure A.1: Permeate �ux trend withxf0=15 g/L.

Figure A.2: Permeate �ux trend with xf0=35 g/L.

APPENDIX A 119

Figure A.3: Permeate �ux trend with P=45 bar.

Figure A.4: Feed tank salinity trend with xf0=35 g/L.

120 APPENDIX A

Figure A.5: Feed tank salinity trend with xf0=15 g/L.

Figure A.6: Permeate salinity trend with xf0=35 g/L.

APPENDIX A 121

Figure A.7: Permeate salinity trend with xf0=25 g/L

Figure A.8: Permeate salinity trend at P=40 bar.

122 APPENDIX A

Figure A.9: Permeate salinity variation with the feed salinity at P=45 bar.

Figure A.10: Permeate �ux variation with the feed salinity at P=45 bar.

APPENDIX A 123

Figure A.11: Water permeability trend at xf0=15 g/L (Dessouky and Ettouneymodel)

Figure A.12: Water permeability trend at xf0=25 g/L (Dessouky and Ettouneymodel).

124 APPENDIX A

Figure A.13: Water permeability trend at P=40 bar (Dessouky and Ettouneymodel)

Figure A.14: Water permeability trend with feed salinity at P=45 bar (Dessoukyand Ettouney model).

APPENDIX A 125

Figure A.15: Water permeability trend at xf0=25 g/L (Meares model).

Figure A.16: Water permeability trend at xf0=35 g/L (Meares model).

126 APPENDIX A

Figure A.17: Water permeability trend with P=45 bar (Meares model).

Figure A.18: Water permeability trend with feed salinity at P=45 bar (Mearesmodel).

APPENDIX A 127

Figure A.19: Concentration polarization trend with xf0 =15 g/L.

Figure A.20: Concentration polarization trend with xf0 =15 g/L.

128 APPENDIX A

Figure A.21: Concentration polarization trend with permeate �ux at xf0=25g/L.

Figure A.22: Concentration polarization trend with permeate �ux at xf0=15g/L.

APPENDIX A 129

Figure A.23: Feed salinity in�uence on concentration polarization at P=45 bar.

Figure A.24: Salt permeability coe�cient trend at P=45 bar.

130 APPENDIX A

Figure A.25: Salt permeability coe�cient at xf0=15 g/L.

Figure A.26: Salt permeability coe�cient at xf0=35 g/L.

APPENDIX A 131

Figure A.27: Salt permeability trend with feed salinity at P=40 bar.

Figure A.28: Salt �ow trend at P=40 bar.

132 APPENDIX A

Figure A.29: Salt �ow trend with xf0=25 g/L.

Figure A.30: Salt �ow trend with xf0=25 g/L.

APPENDIX A 133

Figure A.32: Permeate �ux trends Matlab calculations (xf0=25 g/L).

Figure A.31: Permeate salinity trends Matlab calculations (xf0=25 g/L).

134 APPENDIX A

Figure A.33: Permeate salinity trends with feed salinity dependence on waterand salt permeability (xf0=25 g/L).

Figure A.34: Permeate �ux trends with feed salinity dependence on water andsalt permeability (xf0=25 g/L).

APPENDIX A 135

Figure A.35: Permeate salinity trends Matlab calculations (xf0=15 g/L)

Figure A.36: Permeate �ux trends Matlab calculations (xf0=15 g/L.)

136 APPENDIX A

Figure A.37: Permeate salinity trends with feed salinity dependence on waterand salt permeability (xf0=15 g/L).

Figure A.38: Permeate �ux trends with feed salinity dependence on water andsalt permeability (xf0=25 g/L).

Appendix B

In this section the Matlab code used to model the RO desalination plant forboth models, El-Dessouky and Ettouney (2002) and Meares (1976), is reportedfor an initial feed concentration of 35 g/L and constant values for the waterand salt permeability Kw and Ksas it is possible to see in lines 23 and 24 forEl-Dessouky and Ettouney (2002) model and in lines 32 and 33 for Meares model

1 % El-Dessouky and Ettouney (2002) model

2 % K_w and K_s constant values

3

4 function main

5

6 clc

7 clear all

8 close all

9

10 global A DP M_f M_p x_p t

11

12 % INPUT

13 M_f = 18; %[l/min] feed flow rate

14 A = 0.024; %[m2] membrane area

15 DP = 40*100; %[kPa] differential pressure

16 %across the membrane

17 tf = 480; %[min] integration time

18 Q_f0 = 8; %[l] feed quantity initial value

19 x_f0 = 35; %[g/l] feed concentration initial value

20 x_pt0 = 10e-10; %[g/l] total permeate concentration

21 %initial value

22 Q_pt0 = 10e-10; %[l] total permeate quantity initial value

23

24 % LITERATURE VALUES Voros et al. (1996)

25 K_w = 2.4e-4; % [l/min/kPa/m2] water permeability constant

26 K_s = 0.0237; % [l/min/m2] salt permeability constant

27

28 % RESOLUTION

29 y0 = [Q_f0 x_f0 Q_pt0 x_pt0 ]; % ode initial values

30 ti = 0;

31 for i=5:5:tf

32 options=odeset('RelTol ', 1e-12,'AbsTol ',1e-8); % ode options

33 [t,y] = ode45(@Sisdif , [ti i], y0, options);

34 Q_f(i./5) = y(end ,1); %[l] feed quantity

35 x_f(i./5) = y(end ,2); %[g/l] feed concentration

36 Q_pt(i./5) = y(end ,3); %[l] total permeate quantity

37 x_pt(i./5) = y(end ,4); %[g/l] total permeate concentration

137

138 APPENDIX B

38 y0 = y(end ,:);

39 ti = i;

40 end

41 end

42

43

44

45 function dy = Sisdif(t,y)

46

47 global M_p x_p x_f

48

49 dy = zeros (4,1);

50

51 Q_f = y(1);

52 x_f = y(2);

53 Q_pt = y(3);

54 x_pt = y(4);

55 v=[0.006 2];

56

57 options = optimset('MaxFunEval ' ,10e10);

58

59 sol = fsolve('sisalg ',v,options);

60 M_p = sol(1)

61 x_p = sol(2)

62

63 dy(1) = -M_p;

64 dy(2) = M_p./Q_f.*(x_f -x_p);

65 dy(3) = M_p;

66 dy(4) = M_p./Q_pt .*(x_p -x_pt);

67

68 end

69

70

71

72 function k = sisalg(v)

73

74 global M_f x_f A DP K_s K_w

75

76 k = zeros (2,1);

77

78 M_p = v(1);

79 x_p = v(2);

80

81 M_b = M_f -M_p; %[l/min]

82 x_b = (x_f.*M_f -M_p.*x_p)./M_b; %[g/l]

83 pi_p = 75.79* x_p; %[kPa]

84 pi_b = 75.79* x_b; %[kPa]

85 pi_f = 75.79* x_f; %[kPa]

86 pi_av = 0.5*( pi_f+pi_b); %[kPa]

87 Dpi = pi_av -pi_p; %[kPa]

88 x_av = (M_f.*x_f+M_b.*x_b)./(M_f+M_b); %[g/l]

89 J_s = K_s.*(x_av -x_p).*A; %[g/min]

90

91 k(1)= K_w.*(DP -Dpi).*A- M_p;

92 k(2)= J_s./M_p - x_p;

93

94 end

APPENDIX B 139

1 % Meares (1976) model

2 % K_w and K_s constant values

3

4 function main

5

6 clc

7 clear all

8 close all

9

10 global A DP M_f T di alfa R

11 PM K_w K_s M_p x_p x_f iCount

12

13 % GLOBAL VALUES INITIALIZATION

14 A=0; DP=0; M_f=0; T=0; di=0; alfa =0; R=0; PM=0;

15 K_w =0; K_s=0; M_p =0; x_p=0; x_f=0; iCount =0;

16

17

18 % INPUT




22 % across the membrane


24 T = 30; % [C] system temperature

25 di = 0.0635; % [m] membrane diameter

26 alfa = 2; % [-]

27 R = 0.08205784; %[l*atm/mol/K] gas constant

28 PM = 58.443; % g/mol] salt molecular weight



31 x_pt0 = 10e-10; %[g/l] total permeate

32 %concentration initial value


34

35 % LITERATURE VALUES Voros et al. (1996)

36 K_w = 2.4e-4; % [l/min/kPa/m2] water permeability constant

37 K_s = 0.0237; % [l/min/m2] salt permeability constant

38

39 % RESOLUTION

40 y0 = [Q_f0 x_f0 Q_pt0 x_pt0 ]; % ode initial values

41 ti = 0.;

42 for i=5:5:tf







49 y0 = y(end ,:);

50 ti = i;

51 end

52 end

53

54

55


57

140 APPENDIX B

58 global A DP M_f T di alfa R

59 PM K_w K_s M_p x_p x_f iCount

60

61 dy = zeros (4,1);

62

63 Q_f = y(1);

64 x_f = y(2);

65 Q_pt = y(3);

66 x_pt = y(4);

67

68 iCount = iConto + 1;

69 if(iCount == 1)

70 v = [1 3];

71 else

72 v(1) = M_p;

73 v(2) = x_p;

74 end

75


77


79 M_p = sol(1)

80 x_p = sol(2)

81

82 dy(1) = -M_p;

83 dy(2) = M_p./Q_f.*(x_f -x_p);

84 dy(3) = M_p;

85 dy(4) = M_p./Q_pt .*(x_p -x_pt);

86

87 end

88

89

90


92

93 global A DP M_f T di alfa

94 R PM K_w K_s M_p x_p x_f iCount

95

96 k = zeros (2,1);

97

98 M_p = v(1);

99 x_p = v(2);

100

101 TK = (273.15+T);

102 cost = 1000*60;

103 appo1 = M_f - M_p;

104 appo2 = (x_f*M_f - x_p*M_p)/appo1;

105 appo3 = 1.0069 - 2.757*1.e-4*T;

106 appo4 = 498.4* appo3*sqrt (248400* appo3 ^2+752.4* x_f*appo3);

107 appo5 = 1.234*1.e-6*exp (0.00212* x_f *1965/ TK);

108 appo6 = 6.725*1.e-6*exp (0.154*1.e-3*x_f +2513/ TK);

109 appo7 = appo5 / appo4;

110 appo8 = M_f*di/A/appo7/cost;

111 appo9 = appo1/A/cost;

112 appo10 = M_p/cost/A*( appo7/appo6)^(2/3);

113 appo11 = 0.0395* appo8 ^( -1/4)*appo9;

114 appo12 = M_p/M_p;

APPENDIX B 141


116 appo14 = appo2*exp(appo10/appo11)/...

117 ...( appo12 +(1- appo12)*exp(appo13/appo11));

118 appo15 = (M_f*x_f+appo1*appo2)/(M_f+appo1);

119 appo16 = K_s*(appo15 -x_p)*A;

120

121 k(1)= appo16/M_p - x_p;

122 k(2)= A*K_w*(DP-appo12 ^2* alfa*R*TK*appo14 *100/PM)-M_p;

123

124 end

142 APPENDIX B

Now, the feed salinity dependancies are introduced, as it is possible to seein lines 83 and 84 for El-Dessouky and Ettouney (2002) model and in lines 108and 109 for Meares model.

1 % El-Dessouky and Ettouney (2002) model

2 % K_w and K_s values dependant on x_f

3

4 function main

5

6 clc

7 clear all

8 close all

9

10 global A DP M_f M_p x_p i

11

12 % INPUT




16 % across the membrane







23

24 % RESOLUTION

25 y0 = [Q_f0 x_f0 Q_pt0 x_pt0]; % ode initial values

26 ti = 0;

27 for i=5:5:tf







34 y0 = y(end ,:);

35 ti = i;

36 end

37 end

38

39

40


42

43 global M_p x_p x_f

44

45 dy = zeros (4,1);

46

47 Q_f = y(1);

48 x_f = y(2);

49 Q_pt = y(3);

50 x_pt = y(4);

51 v=[0.006 2];

52


APPENDIX B 143

54


56 M_p = sol(1)

57 x_p = sol(2)

58

59 dy(1) = -M_p;

60 dy(2) = M_p./Q_f.*(x_f -x_p);

61 dy(3) = M_p;

62 dy(4) = M_p./Q_pt .*(x_p -x_pt);

63

64 end

65

66

67


69

70 global M_f x_f A DP t i

71

72 k = zeros (2,1);

73

74 M_p = v(1);

75 x_p = v(2);

76 M_b = M_f -M_p; %[l/min]

77 x_b = (x_f.*M_f -M_p.*x_p)./M_b; % [g/l]

78 pi_p = 75.79* x_p; % [kPa]

79 pi_b = 75.79* x_b; % [kPa]

80 pi_f = 75.79* x_f; % [kPa]

81 pi_av = 0.5*( pi_f+pi_b); % [kPa]

82 Dpi = pi_av -pi_p; % [kPa]

83 x_av = (M_f.*x_f+M_b.*x_b)./(M_f+M_b); % [g/l]

84 K_w = (-1E-6* x_f+6e-5) *1000/100; % [l/min/kPa/m2]

85 K_s = ( -0.0003* x_f + 0.0224); % [l/min/kPa/m2]

86 J_s = K_s.*(x_av -x_p).*A; % [g/min]

87

88 k(1)= K_w.*(DP -Dpi).*A- M_p;

89 k(2)= J_s./M_p - x_p;

90

91 end

144 APPENDIX B

1 % Meares (1976) model

2 % K_w and K_s values dependant on x_f

3

4 function main

5

6 clc

7 clear all

8 close all

9

10 global A DP M_f T di alfa R PM M_p x_p x_f iCount

11

12 % GLOBAL VALUES INITIALIZATION

13 A=0; DP=0; M_f=0; T=0; di=0; alfa =0; R=0; PM=0;

14 K_w =0; K_s=0; M_p=0; x_p = 0; x_f=0; iCount =0;

15

16

17 % INPUT




21 %across the membrane


23 T = 30; % [C] system temperature

24 di = 0.0635; % [m] membrane diameter

25 alfa = 2; % [-]

26 R = 0.08205784; % [l*atm/mol/K] gas constant

27 PM = 58.443; % g/mol] salt molecular weight






33

34 % RESOLUTION

35 y0 = [Q_f0 x_f0 Q_pt0 x_pt0]; % ode initial values

36 ti = 0.;

37 for i=5:5:tf







44 y0 = y(end ,:);

45 ti = i;

46 end

47 end

48

49

50


52

53 global A DP M_f T di alfa R PM

54 K_w K_s M_p x_p x_f iCount

55

56 dy = zeros (4,1);

57

APPENDIX B 145

58 Q_f = y(1);

59 x_f = y(2);

60 Q_pt = y(3);

61 x_pt = y(4);

62

63 iCount = iConto + 1;

64 if(iCount == 1)

65 v = [1 3];

66 else

67 v(1) = M_p;

68 v(2) = x_p;

69 end

70


72


74 M_p = sol(1)

75 x_p = sol(2)

76

77 dy(1) = -M_p;

78 dy(2) = M_p./Q_f.*(x_f -x_p);

79 dy(3) = M_p;

80 dy(4) = M_p./Q_pt .*(x_p -x_pt);

81

82 end

83

84

85


87

88 global A DP M_f T di alfa R PM

89 K_w K_s M_p x_p x_f iCount

90

91 k = zeros (2,1);

92

93 M_p = v(1);

94 x_p = v(2);

95

96 TK = (273.15+T);

97 cost = 1000*60;

98 appo1 = M_f - M_p;

99 appo2 = (x_f*M_f - x_p*M_p)/appo1;

100 appo3 = 1.0069 - 2.757*1.e-4*T;

101 appo4 = 498.4* appo3*sqrt (248400* appo3 ^2+752.4* x_f*appo3);

102 appo5 = 1.234*1.e-6*exp (0.00212* x_f *1965/ TK);

103 appo6 = 6.725*1.e-6*exp (0.154*1.e-3*x_f +2513/ TK);

104 appo7 = appo5 / appo4;

105 appo8 = M_f*di/A/appo7/cost;

106 appo9 = appo1/A/cost;


108 appo11 = 0.0395* appo8 ^( -1/4)*appo9;

109 appo12 = M_p/M_p;


111 appo14 = appo2*exp(appo10/appo11)/...

112 ...( appo12 +(1- appo12)*exp(appo13/appo11));

113 appo15 = (M_f*x_f+appo1*appo2)/(M_f+appo1);

114 K_w = (-5E-07* x_f + 3E-05) *1000/100; % [l/min/kPa/m2]

146 APPENDIX B

115 K_s = ( -0.0003* x_f + 0.0224); % [l/min/kPa/m2]

116 appo16 = K_s*(appo15 -x_p)*A;

117

118 k(1)= appo16/M_p - x_p;

119 k(2)= A*K_w*(DP-appo12 ^2* alfa*R*TK*appo14 *100/PM)-M_p;

120

121 end

Appendix C

In this section it is possible to see the whole data set used to build the ANN

correlation discussed in Chapters 7 and 8.

147

148 APPENDIX C

APPENDIX C 149

150 APPENDIX C

APPENDIX C 151

152 APPENDIX C

APPENDIX C 153

154 APPENDIX C

APPENDIX C 155

156 APPENDIX C

APPENDIX C 157

158 APPENDIX C

Documents

REVERSE OSMOSIS DESALINATION PROCESS: WATER ...pselab.chem.polimi.it/wp-content/uploads/2014/05/Marta-Barello... · REVERSE OSMOSIS DESALINATION PROCESS: WATER PERMEABILITY ... Laura,