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ENG470: Engineering Honours Thesis

Discipline of Engineering and Energy


Simulation of Steady State Models Describing Hybrid

Desalination Process Using MATLAB Software

A thesis submitted to Discipline of Engineering and Energy, Murdoch University, to

fulfil the requirements for the degree of:

H1264: Bachelor of Engineering Honours [BE (Hons)]

1. Instrumentation And Control Engineering (Honours) (Major) (Primary)

2. Renewable Energy Engineering (Honours) (Major)

5th July 2019

Written By: Abdullaziz Al-Farqani

Academic Supervisor: Prof. Parisa A. Bahri

Murdoch University, Perth, Australia



Author’s Declaration

I, Abdullaziz Al-Farqani, declare that I am a current student of Murdoch University. The

thesis entitled “Simulation of Steady State Models Describing Hybrid Desalination Process

Using MATLAB Software” has been prepared for my Semester 1, 2019.

ENG470: Engineering Honours Thesis.

I declare that the report is prepared for my academic requirements. The work prepared is

authentic and was undertaken by me during my enrolment for this unit at Murdoch

University.

(Electronically Submitted, No Signature Required)

_________________________________________

Abdullaziz Al-Farqani

Student,

Discipline of Engineering and Energy

Date: 5th July 2019



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Abstract

Mathematical model development for hybrid multi-stage flash (MSF) and reverse osmosis

(RO) desalination plants has gained attention among researchers. The hybrid MSF-RO plant

has been a promising and upcoming technology in the desalination market due to its efficient

and cost-effective operation. The hybrid plant shares common intake and the final product of

the MSF and RO plants are mixed. The plant model plays a significant role in understanding

and analysing the plant performance. In this thesis, a steady state mathematical model for

hybrid MSF-RO desalination plant was developed to ensure the accuracy and reliability of

the plant model. The developed model was based on material, salt and energy balance.

Thermal and physical properties of the brine, distillate, and steam are considered. These

properties are calculated as a function of temperature and concentration. In addition, the

performance ratio of the MSF plant is determined by increasing the number of the MSF

stages. The developed model incorporates nonlinear equations which are solved using fsolve,

a nonlinear system solver, in MATLAB software. The number of the MSF stages considered

was six stages. Whereas, a single stage RO plant was treated.

The results, in this thesis, were obtained individually for the stand-alone MSF and RO plants.

The final results of both plants were then combined. The simulation of the hybrid MSF-RO

model showed sufficiently promising results. With the given input data of the feed

temperature, concentration and flowrate of both MSF and RO systems, the developed model

was used to predict the temperatures and flowrates of the brine, distillate, and cooling brine in

each stage. The results obtained were then validated through the comparison with various

researchers. Subsequently, the evaluation of the developed model indicated that the model

was accurate and can be relied upon. This is in line with the objective of creating a pathway

for future works to be carried out for optimisation and control design purposes.


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Acknowledgments

This thesis would not have been possible without the following support, advice and

encouragement throughout the process of developing this thesis.

First and foremost, my sincere appreciation goes to my academic supervisor, Professor Parisa

A. Bahri, Head of Discipline Engineering and Energy, Murdoch University, for your

patience, guidance and support as well as imparting your immensurable knowledge to me in

this field. You have made this an educational and exciting unit. Your mentorship and

invaluable advice as well as feedback have steered me to improve my analytical, creative,

technical and thinking skills. Besides the fundamentals of chemical engineering, I have also

learnt research integrity through this unit.

A special thanks to my family, especially my father and mother. Your prayers and words of

encouragement have been a constant source of support throughout my life. I would also like

to thank my wife for all your love, prayers and understanding of my goals and aspiration.

Thank you for your patience, support and sacrifice especially as I prepare for this thesis.

Thank you for being my inspiration. To my son, thank you for always cheering me up and

giving me happiness.

I would also like to express my gratitude to the company (Petroleum Development Oman) I

work for in Oman, for the financial support and for providing me the opportunity to study in

Murdoch University, Perth, Australia.

To all my friends, thank you for the enjoyable times shared despite the hard times endured

during the course of my stay here in Perth, Australia.


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vii

Table of Contents

Author’s Declaration .......................................................................................................

Abstract ....................................................................................................................... iii

Acknowledgments ........................................................................................................ iv

Table of Contents ........................................................................................................ vii

List of Figures .............................................................................................................. ix

List of Tables ............................................................................................................... x

Chapter 1: Introduction and Objectives ........................................................................... 1

1.1 Introduction ......................................................................................................... 1

1.3 Objectives of the Thesis ................................................................................... 3

1.4 Significance of the Thesis ..................................................................................... 3

1.5 Structure of the Thesis .......................................................................................... 4

Nomenclature and Symbols ........................................................................................... 6

Chapter 2: Literature Review ....................................................................................... 11

2.3 Research Background ......................................................................................... 11

2.3.1 Reverse Osmosis (RO) Plant .................................................................... 11

2.3.2 Multistage Flash (MSF) Plant ........................................................................ 11

2.3.3 Hybrid MSF-RO Plant .................................................................................. 13

2.4 Researchers’ Contribution towards the Hybrid System ........................................... 14

Chapter 3: Process Description of the Hybrid MSF-RO System ....................................... 21

3.1 Reverse Osmosis (RO) ....................................................................................... 21

3.2 Multi-stage Flash (MSF) ..................................................................................... 23

3.3 Hybrid System (MSF-RO) .................................................................................. 25


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Chapter 4: Steady State Models of the Hybrid MSF-RO System ...................................... 26

4.1 A Mathematical Model in Chemical Engineering................................................... 26

4.2 Plant Modelling ................................................................................................. 26

4.2.1 Benefits of Plant Modelling ........................................................................... 27

4.3 Methodology ..................................................................................................... 27

4.4 List of Assumptions ........................................................................................... 28

4.5 Reverse Osmosis Process Model .......................................................................... 28

4.6 Multi-stage Flash Process model .......................................................................... 32


Chapter 5: Results and Discussion ................................................................................ 41

5.1 Reverse Osmosis (RO) ....................................................................................... 41

5.2 Multi-stage Flash (MSF) ..................................................................................... 42

5.2.1 Performance Ratio (PR) ................................................................................ 42


5.4 Limitations of the Current Thesis ......................................................................... 49

Chapter 6: Conclusion and Future Work ........................................................................ 51

Bibliography .............................................................................................................. 53

Appendix ................................................................................................................... 61

Appendix A ............................................................................................................ 61


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List of Figures

Figure 1: Process Configuration of the Hybrid MSF-RO System ........................................... 21

Figure 2: Reverse Osmosis System.......................................................................................... 22

Figure 3: A schematic diagram of Multi-stage flash with brine recycle.................................. 24

Figure 4: Flashing chamber ..................................................................................................... 25

Figure 5: A schematic diagram of the hybrid MSF-RO plant ................................................. 25

Figure 6: Mass balance for stage 1 .......................................................................................... 33

Figure 7: Mass balance for any stage, except stage 1 .............................................................. 33

Figure 8: A schematic diagram of the hybrid MSF-RO plant ................................................. 40

Figure 9: Temperature of cooling brine ................................................................................... 61

Figure 10: Flashing brine temperature ..................................................................................... 62

Figure 11: Distillate temperature ............................................................................................. 62

Figure 12: Vapor temperature .................................................................................................. 63

Figure 13: Concentration of the flashing brine ........................................................................ 64

Figure 14: Flowrate of the flashing brine ................................................................................ 64

Figure 15: Distillate flowrate ................................................................................................... 65

Figure 16: Performance ratio of the MSF plant ....................................................................... 65

Figure 17: Overall heat transfer coefficient ............................................................................. 66

Figure 18: Log mean temperature difference........................................................................... 66

Figure 19: Specific heat capacity of the brine and distillate .................................................... 67

Figure 20: Specific heat capacity of the cooling brine ............................................................ 67

Figure 21: Boiling point elevation and non-equilibrium allowance ........................................ 68

Figure 22: Temperature drop in demister ................................................................................ 68


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List of Tables

Table 1: Characteristics of the Spiral Wound Membrane ........................................................ 32

Table 2: Simulation Results for a Reverse Osmosis System ................................................... 41

Table 3: Simulation Results of the Temperature(s) and Flowrate(s) Profile of the MSF

Process ..................................................................................................................................... 43

Table 4: Temperatures and Flowrates of the Brine Heater, Mixer, and Blowdown ................ 46

Table 5: Results for Thermo-dynamic Losses and Thermo-physical Properties ..................... 48

Table 6: Simulation Results of the Hybrid MSF-RO Plant ..................................................... 49

1


1

Chapter 1: Introduction and Objectives

1.1 Introduction

Potable water depletion has led to the rise of desalination plants to ensure an adequate supply

of usable and drinkable water for human needs all around the world. Shortage of rain water

and population growth are among the reasons for water depletion (Gude 2018). Freshwater

can be obtained by a process of separating undesired solid substances, salts, and minerals,

from the raw water; this process is known as desalination (Bazargan 2018). Over three

decades, the development indicator of desalination plants has shown exponential growth

throughout the world (Arafat 2017). Moreover, during the 20th century, performance

indicators of the water-desalting plants proved to be the most feasible globally (El-Dessouky

and Ettouney 2002). In terms of plant capacity, large-scale commercial plants were producing

nearly 8000 𝑚3/𝑑𝑎𝑦 in the mid-1960s, while the capacity had increased to more than

70 million 𝑚3/𝑑𝑎𝑦 in the early 1970s (Lior 2012). In recent times, there have been huge

efforts in the improvement of the desalination plants. Production of fresh water goes beyond

80 million 𝑚3/𝑑ay, and over the last 3 years, it was estimated to go above 120 million 𝑚3/

𝑑𝑎𝑦. Recently, studies have pointed out that sustainable production of water can be obtained

by higher flux hydrophobic membranes using renewable energies (Wali 2014). People are

now turning to desalination technologies due to their reliability in terms of cost-saving and

sustainable environment. Overall, desalination technologies need constant improvement to

fulfil future extensive applications (Wang et al. 2011).

There are various types of desalination technologies used in the desalination industries such

as multi-stage flash (MSF), multi-effect distillation (MED), reverse osmosis (RO), forward

osmosis (FO), and electrodialysis (ED). Two major desalination technologies are thermal and

membrane desalination technologies. Both technologies require different processes which requires


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energy to operate (Thimmaraju et al. 2018). However, MSF and RO plants are found to be the

prominent leading technologies in the desalination market (Kesieme et al. 2013). MSF

desalination process, a thermal based plant, has accounted for the largest sector of the

desalination market making it the leading source of freshwater (Ali and Kairouani 2016).

Some of the MSF advantages include simple and easy to function plants, the flow moves

from one stage to another without having moving parts such as pumps, and provides high

level of water purification (Thimmaraju et al. 2018). The efficiency of the system can also be

improved by adding more stages to increase water product capacity. Another factor that can

be considered is the performance ratio which determines the efficiency of the MSF plant. The

disadvantages on the other hand, includes high energy consumption which yield to higher

capital cost (Thimmaraju et al. 2018).

RO desalination process, membrane-based process, is another promising and widespread

technology as it is the least energy consuming process and has higher permeate flux

(Camacho et al. 2013). It is known to be mainly available in the coastal regions globally. As

there are limited natural hydrological resources, constant research and development (R&D)

emphasise on energy consumption reduction (Peñate and García-Rodríguez 2012). The RO

technology is a pressure driven membrane process. The advantages of the RO system include low

energy requirements, low operating temperature, and low water production cost. While, the

disadvantages include sensitivity to quality of feed water, more susceptible to fouling and operating

conditions of the plant (Oh, Hwang and Lee 2009).

A lot of researches have been performed to improve the operation of the stand-alone MSF

and RO systems in the most efficient and cost-effective manner. These systems are further

enhanced by integrating both thermal (MSF) and membrane (RO) processes; this is known as


3

hybrid MSF-RO process. Such hybridisation results in lower energy requirement of the RO

system and high desalting performance of the MSF process (Al-Mutaz 2005).

Among the benefits of hybridising RO process with MSF process include cost reduction on

post-treatment process and better water quality as the RO permeate concentration is mixed

with the distillate produced from the MSF system (Ericsson and Hallmans 1985).

Consequently, this results in lower product concentration compared to a stand-alone RO

system. Hence, hybridising RO process with the MSF process is found to be an economical

and promising technology. There are only few studies that have focused on the improvement

of the hybrid MSF-RO operation.

1.3 Objectives of the Thesis

The current research focuses on four key objectives. First, to develop a mathematical model

for the hybrid MSF-RO system. Then, to ensure that the developed model is sufficiently

accurate. Third, to compare the correlation and dependence between the hybrid system and

stand-alone systems for both MSF and RO will be performed. Finally, the performance for

the hybrid system will be evaluated by investigating the findings of the hybrid system

through the increase number of stage and the comparison with various researches.

1.4 Significance of the Thesis

The importance of the current research is to obtain, analyse, and validate the results from the

developed model against the results obtained from various researchers. This will further

enhance and ensure the accuracy of the process model. This thesis aims to provide significant

advantage to future researchers who intends to carry out optimisation and/or control designs


4

of the hybrid MSF- RO system. This will in turn lead to reliability and cost-effective

measures.

1.5 Structure of the Thesis

The thesis report is structured into seven chapters demonstrated below:

Chapter 1: Introduction and Objectives

This chapter introduces desalination technologies and some of the key advantages and

disadvantages. The topic also discusses different researchers’ works, the objectives of the

thesis and the significance of the thesis.

Chapter 2: Literature Review

This chapter shares the meaning of mathematical model in chemical engineering, the plant

modelling as well as the benefits of plant modelling. The section also covers researchers’

contribution towards the hybrid system.

Chapter 3: Process Description of the Hybrid MSF-RO System

This chapter introduces the process description of the hybrid MSF-RO system. This section

also describes individually the MSF and RO processes.

Chapter 4: Steady State Models of the Hybrid MSF- RO System

This chapter focuses on the steady state models of the hybrid MSF-RO. A list of assumptions

and methodology are also included in this section. Thermodynamic losses and thermo-

physical properties equations of brine, distillate and seawater are discussed as well.


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Chapter 5: Results and Discussion

This chapter mainly discusses and analyses the simulation results of the hybrid MSF-RO

process. This section evaluates the simulation results with various researchers. The results

and discussion represent the core of the developed model. In addition, the limitations of the

current thesis are also shared in this section.

Chapter 6: Conclusion and Future Work

This chapter concludes the overall findings of the thesis. Also, this section encourages future

researchers to carry out further implementation of the developed model on a higher level such

as optimisation and control design for the system improvement.


6

Nomenclature and Symbols1

Nomenclature for MSF

𝐴𝑏ℎ Total heat transfer area of brine heater

𝐴𝑟𝑗 Total heat transfer area of stage j

𝐴𝑇 Total heat transfer area

𝐵𝐷 Flow rate of brine blow down

𝐵𝑗 Flow rate of flashing brine leaving stage j

𝐵(𝑗 − 1) Flow rate of flashing brine entering stage j

𝐵𝑁 Flow rate of flashing brine leaving last stage

𝐵𝑂 Flow rate of flashing brine entering first stage

𝐵𝑃𝐸𝑗 Boiling point elevation

𝐶 Concentration (Mass fraction)

𝐶𝐵𝑗 Salt concentration of flashing brine leaving stage j

𝐶𝐵(𝑗 − 1) Salt concentration of flashing brine entering stage j

𝐶𝐵𝑁 Salt concentration of flashing brine leaving last stage

𝐶𝐵𝑂 Salt concentration of flashing brine entering first stage

𝐶𝐹 Salt concentration of feed

𝐶𝑅 Salt concentration of recycle brine

𝐶𝑊 Flow rate of reject seawater

𝐷 Flow rate of distillate

𝐷𝑖𝐻 Internal diameter of brine heater tubes

𝐷°𝐻 External diameter of brine heater tubes

𝐷𝑖𝑗 Internal diameter of tubes at stage j

1 The nomenclature and symbols are taken from (Malik, Bahri and Vu 2016)


7

𝐷°𝑗 External diameter of tubes at stage j

𝐷𝑗 Flow rate of distillate leaving stage j

𝐷(𝑗 − 1) Flow rate of distillate entering stage j

𝑓𝐻 Brine heater fouling factor

𝑓𝑗 Fouling factor at stage j

𝐹𝑚 Flow rate of makeup seawater

𝐹𝑠𝑒𝑎 Flow rate of feed seawater

𝐻𝑗 Height of brine pool at stage j

ℎ𝑗 Specific enthalpy of flashing brine leaving stage j

ℎ(𝑗 − 1) Specific enthalpy of flashing brine entering stage j

ℎ𝑚 Specific enthalpy of makeup stream

ℎ𝑅 Specific enthalpy of recycle stream

ℎ𝑤 Specific enthalpy of cooling brine entering heat recovery section

ℎ𝑣𝑗 Specific enthalpy of flashing vapour at stage j

(𝐿𝑀𝑇𝐷)𝑏ℎ Log mean temperature difference for brine heater

(𝐿𝑀𝑇𝐷)𝑗 Log mean temperature difference for stage j

𝑁𝐸𝐴𝑗 Non-equilibrium allowance in temperature for flashing brine for stage j

𝑅 Flow rate of recycle brine

𝑆𝑏ℎ Specific heat capacity of brine in brine heater

𝑆𝐵𝑗 Specific heat capacity of flashing brine leaving stage j

𝑆𝐵(𝑗 − 1) Specific heat capacity of flashing brine entering stage j

𝑆𝐷𝑗 Specific heat capacity of distillate leaving stage j

𝑆𝐷(𝑗 − 1) Specific heat capacity of distillate entering stage j

𝑆𝑟𝑐𝑗 Specific heat capacity of cooling brine leaving stage j

𝑇 Temperature


8

𝑇𝐵𝑗 Temperature of flashing brine leaving stage j

𝑇𝐵(𝑗 − 1) Temperature of flashing brine entering stage j

𝑇𝐵𝑂 Temperature of flashing brine leaving brine heater

𝑇𝐷𝑗 Temperature of distillate leaving stage j

𝑇𝐷(𝑗 − 1) Temperature of distillate entering stage j

𝑇𝐹1 Temperature of cooling brine leaving stage 1

𝑇𝐹𝑗 Temperature of cooling brine leaving stage j

𝑇𝐹(𝑗 + 1) Temperature of cooling brine entering stage j

𝑇𝑟𝑒𝑓 Reference temperature

𝑇𝑠𝑗 Temperature of flashed vapour at stage j

𝑇𝑠𝑡𝑒𝑎𝑚 Steam temperature

𝑈𝑏ℎ Overall heat transfer coefficient at the brine heater

𝑈𝑗 Overall heat transfer coefficient at the brine heater at stage j

𝑊 Flow rate of cooling brine in heat recovery section

𝑤𝑗 Width of stage j

𝑊𝑠 Flow rate of steam

Symbols for MSF

λs Latent heat of vaporisation of water in brine heater

∆𝑗 Temperature drop in demister in stage j

𝜌𝑏 Brine density

𝜌𝑤 Pure water density

Nomenclature for RO

𝐴𝑚𝑒𝑚 Area of membrane

𝐴𝑠 Salt permeability constant

𝐴𝑤 Water permeability


9

𝐶 Concentration

𝐶𝑏 Bulk concentration

𝐶𝑓 Feed concentration

𝐶𝑚𝑒𝑚 Membrane cost

𝐶𝑝 Permeate concentration

𝐶𝑝𝑣 Pressure vessel cost

𝐶𝑟 Reject concentration

𝐶𝑤 Wall concentration

𝑑 Diameter of element

𝑑𝑓 Feed spacer thickness

𝐷𝑠 Solute diffusivity

𝑓𝑐 Plant load factor

ℎ𝑠𝑝 Height of spacer channel

𝐽𝑠 Salt flux

𝐽𝑤 Water flux

𝑘 Mass transfer coefficient

𝐿𝑚 Length of membrane element

𝐿𝑝𝑣 Length of pressure vessel

𝑚 Number of membrane element in a pressure vessel

𝑁1 Number of leaves in a membrane element

𝑁𝑝𝑣 Number of pressure vessels

𝑃𝑓 Feed pressure

𝑃𝑝 Permeate pressure

𝑃𝑟 Reject pressure

𝑄𝑏 Bulk flow rate


10

𝑄𝑓 Feed flow rate

𝑄𝑝 Permeate flow rate

𝑄𝑟 Reject flow rate

𝑅𝑒 Reynolds number

𝑆𝑐 Schmidt number

𝑆𝑅 Salt rejection

𝑇 Temperature

𝑤 Membrane width

𝑉 Average axial velocity in the feed channel

Symbols for RO

μ Brine viscosity

𝜋 Osmotic pressure

𝜋𝑓 Osmotic pressure of feed

𝜋𝑝 Osmotic pressure of permeate

𝜋𝑟 Osmotic pressure of reject

∆𝜋 Osmotic pressure drop

∆𝑃 Pressure drop

∆𝑃𝑓 Pressure drop on the feed side

𝜀 Feed spacer void fraction

𝜌 Brine density

𝜌𝑤 Pure water density

Nomenclature for Hybrid MSF-RO

𝐶𝑇 Final product concentration of hybrid system


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Chapter 2: Literature Review

2.3 Research Background

Several researches have been performed to develop a model for the hybrid MSF-RO process.

The following researchers have contributed to the research of each section in one way or

another.

2.3.1 Reverse Osmosis (RO) Plant

Al-Mutaz and Al Ghunaimi (2001) researched on performance of RO process at high

temperatures. The study went through intensive trials by considering RO characteristics and

analysis made theoretically. The study determined that a high-water temperature produces

better RO operation by increasing the feed temperature by three percent with the increase in

membrane capacity. The study compared data with actual data plant figures.

Oh et al. (2009) studied the RO system for seawater desalination and came up with a simple

model based on the theoretical solution-diffusion and multiple fouling mechanism for

analysis of the performance of the RO system. The model designed also prove that

temperature influences optimisation of sea water reverse osmosis (SWRO) system. The main

aim of the develop model is to have a computer model for simulating and optimising the

process flow regardless of the types of membranes used. The findings can be further analysed

for optimisation purpose.

2.3.2 Multistage Flash (MSF) Plant

Ali and Kairouani (2016) proposed optimisation for operating parameters of the multistage

flash with brine recycle (MSF-BR) plant to increase production capacity by maximising the


12

total distillate flow rate, reduction of thermic energy consumption as well as minimise sum of

flow rates of plant’s main pumps. The authors applied MATLAB software, using response

surface methodology (RSM) in this research and the results were based on actual plant data.

The outcome of the research led to a set of Pareto2 optimal solutions. This achieved optimal

plant operation throughout the year.

Husain (2012) developed two approaches to the dynamic models. One approach was to apply

the basic dynamic phenomena known as the phenomenological models (used for

understanding the process behaviour) and the other was a black-box approach (a simple

statistical model used for control purpose). One of the model limitations was its differential

energy balance consideration of the combination of vapour space and distillate in the flash

stage. The results obtained were tested through experimental or operating data of the process

(Husain 2012).

Rosso et al. (1997) found that mathematical models provided an insightful tool which

described a steady-state model developed to analyse the MSF desalination process based on

detailed physicochemical representation of the process. Essential basic phenomena and

geometry of the stages are among features taken into consideration. The authors’ studies

were further supported by El-Dessouky, Shaban and Al-Ramadan (1995), Ali and Kairouani

(2016), as well as, Alasfour and Abdulrahim (2008). These authors supported the steady-state

mathematical model built with the intention to analyse the efficiency of the MSF

performance. The model took into account the relationships between parameters controlling

the water cost to other operating and design variables established (El-Dessouky, Shaban and

Al-Ramadan 1995). This included models which applied investigation to effects of varying

2 Pareto optimality is defined as an analytical tool which is utilised to assess social welfare and

resource allocation which does not have the ability to offer alternative objections making it an optimal

solution from many objective optimisation (Mock 2011)


13

cooling water temperatures and/or flow rates on plant performance at constant distillate

production and steam consumption (Alasfour and Abdulrahim 2009).

2.3.3 Hybrid MSF-RO Plant

Gambier and Badreddin (2003) focused on recent efforts in control theory. The authors’

efforts took into considerations not only intrinsic hybrid processes, but also continuous

processes with supervisory logic, multi-model control systems and switching control.

Introduction to the hybrid modelling and control pathed the way to development of design

tools and difficult computer controlled systems.

Talati (1994) discussed about RO and evaporation (MSF and MED) in treating desalination.

The author concentrated on the expected cost when different feedwater sources were used by

RO and MSF/MED to produce high-purity water and identified three processes in the course

of research. The results revealed that the RO process was the most cost effective, which met

the feedwater requirements as well as reject water generation (Talati 1994).

Elmesmary and Alsultan (2017) focused on the hybrid MSF and RO with the aim to improve

the effectiveness and quality of the process for economic purposes. These authors neglected

some key features of the desalination process to ascertain the outcome of their studies. The

study did not share the key features that were overlooked. This would have indirect

implications to the research findings.


14

Helal et al. (2003) on the other hand, took into consideration hybrid MSF-RO configurations

and utilised the Newton-Raphsons3 using SOLVER Tool of Microsoft Excel. Helal et al.

(2003) considered some operating variables such as steam temperature, cooling seawater flow

rate, make up flow rate and brine recycle flowrate in their studies. One unique feature of this

study was that the reject flowrate was fed into the RO process. In the current thesis, the reject

flowrate is sent back to the sea.

The hybrid system model designed incorporates nonlinear models which are accurate based

on results obtained from different researchers. No comprehensive studies have since been

carried out thus far; only semi-empirical equations calculations based on semi-empirical

equations have been reported (Al-Shayji, Al-Wadyei and Elkamel 2005).

In this thesis, a steady state mathematical model of the hybrid MSF-RO process was

developed to enhance the operation of the hybrid system and further improve the process

model efficiency.

2.4 Researchers’ Contribution towards the Hybrid System

As mentioned in chapter one, many researchers have performed researches on stand-alone

MSF and RO processes. Studies have also revealed that researchers are interested in both

dynamic and steady-state hybrid MSF-RO model. Although there has been an increase in

interest in analysing the cost and benefits of the stand-alone MSF and RO models, there are

still limited studies in improvement of the hybrid MSF-RO operation. Giving strong

3 Newton-Raphson method is a system of equations, useful for discovering quick roots of nonlinear

equations once a starting point is determined (Kiusalaas 2005).


15

consideration to the advantages of both MSF and RO, the hybrid MSF-RO is found to be a

promising technology in terms of cost reduction and final product quality. Importance should

not be confined to optimisation, instead control design should be given equal attention to

ascertain its importance when designing a hybrid MSF-RO model. The lack of research

findings in the hybrid systems inspires the need for enhancing the operation of the hybrid

system in the most efficient and cost-effective manner.

The following researchers have contributed significantly to the hybrid system. Their works

have been observed and summarised in this topic.

Elmesmary and Alsultan (2017) carried out a study on hybridisation of the MSF and RO

processes with the aim to improve the effectiveness of the final product as well as to

maximise the recovery ratio and quality of the process for cost reduction purpose. The study

emphasised more on research for the hybrid system model by describing various models

rather than using a specific software for analysing and evaluating the results. The study

concluded by sharing the authors’ opinions on the required studies for hybrid systems

improvement as MSF- Multi-Effect Distillation (MED) has similar advantages to MSF-RO.

The authors mentioned that they had intentionally ignored some of the key processes of

desalination to better demonstrate hybridisation of desalination processes. The limitations

observed by future researchers to ensure the desired outcome should not affect when

comparing their obtained outcomes with these authors study.

Helal et al. (2003) developed an exploratory study for economic evaluation of the

hybridisation of RO and MSF desalination processes. The work encompassed both

conventional brine recycle MSF and two-stage seawater reverse osmosis (SWRO)

flowsheets, as well as seven other hybrid configurations comparing the proposed designs

against the minimum water cost as its objective. The seven hybrid configurations were made


16

up of different cases such as interdependent two-stage RO and brine recycle MSF plants with

common intake-outfall facilities, and hybrid single-stage RO/brine recycle MSF plant. The

mathematical model applied to brine recycle MSF plant model for the purpose of comparing

different alternative schemes and reducing the computation time. In this study a Newton-

Raphson method was applied to obtain the minimum value of the objective function using

SOLVER tool of Microsoft Excel (Helal et al. 2003). One unique feature of this research was

that the reject flowrate leaving the MSF was fed into the RO system.

Similar to Helal et al. (2003), Hamed (2005) focused on a single stage RO process which was

coupled with the MSF process. However, Hameed (2005) further reviewed the integrated

hybrid desalinations systems by using nanofiltration (NF) membranes to reduce the

significant impact of ions from seawater. The author further concluded that the hybrid MSF-

RO produced efficiently high-water productivity. Whereas, the make-up of the MSF unit

formed from the RO reject blended with appropriate measurements of seawater was

successful (Hamed 2005). The author mentioned that further research work will be required

for NF. Similarly, Elmesmary and Alsultan (2017) agreed with Hamed (2005) and realised

the potential in combining the MSF and MED processes for further development.

Nonetheless, Elmesmary and Alsultan (2017) further added that the RO process with

nanofiltration pre-treatment should also be researched.

El-Sayed et al. (1998) focused mainly on RO by conducting a systematic approach to obtain

the gains in the RO product water flow rate as well as to test the overall performance of the

RO plant in a hybrid environment. Unlike Helal et al. (2003), El-Sayed (1998) focused more

on RO process performance of the hybrid MSF-RO. While the study obtained its objective to

confirm cost reduction by minimising the energy consumption of RO, the authors agreed that

further studies should be performed on other MSF-RO hybrid environment (El-Sayed et al.


17

1998). As their study showed corrected product water concentration was similar to feed water

temperature, there were still no concrete evidence on whether membrane capability for salt

rejection had any correlation to higher operating performance as this was not within the scope

of the research. Thus, assumptions provided in this area of study were authors’ opinions.

On the other hand, Al-Mutaz (2005) focused his study on the main strengths of both stand-

alone MSF and RO systems (high distilled performance, low energy requirement). The author

mentioned that the reject flow from the MSF plant can be applied to optimised feedwater

temperature of the RO plant. This high feed temperature was beneficial as the constant

pressure obatined from the water flux of the membrane slightly increased the temperature and

in turn increased the RO productivity. Unlike El-Sayed et al. (1998), Al-Mutaz (2005) was

able to confirm that there were correlation between lower salt rejection and feed water

temperature as it increased. The author concluded with the advantages of hybrid MSF and

RO, indicating hybrid system could lead to various positive outcomes such as extended

membrane life and improved performance, as well as low power and low production cost.

Alasfour & Abdulrahim (2009) focused on designing a rigorous steady state modelling which

would simulate the MSF with brine circulation (MSF-BR). The software used was IPSEpro®,

to facilitate a meticulous simulation of the processes using the thermo-physical properties

models, which were calculated as a function of temperature and salinity. The MSF-BR

processes was assessed based on performance prediction of the first and second law of

thermodynamics (Alasfour and Abdulrahim 2009). This study provided positive results

between the computational and actual plant data. As the results were validated, the authors’

foundation paths way for future works to be carried out more easily.

Both Marcovecchio et al. (2005) and Alasfour and Abdulrahim (2009) considered rigorous

ways to model the hybrid MSF and RO for optimisation purposes. The developed model was


18

then used to determine the optimal process design and optimum values for a defined water

production. The outcome yielded to the basic design of the hybrid system. However, it was

noted that Marcovecchio et al. (2005) used MSF-Once through (MSF-OT) whereas, Alasfour

and Abdulrahim (2009) used MSF-Brine recycle (MSF-BR).

Bandi, Uppaluri and Kumar (2016) focused on global optimisation of hybrid MSF-RO

desalination processes using differential evolution algorithm. The authors focused on five

hybrid process configurations. The first was where MSF-BR and a single stage RO system

(SRO) receives seawater directly using fluid delivery pump. The second indicated the cooling

water stream was fed as the feed stream to a single reverse osmosis (SRO) system. The third

process involved the cooling brine stream leaving the mixer one with flow rate then it splits

to three streams - the MSF recycle stream and MSF blow downstream with feed flowrate of

reject stream. The fourth hybrid indicated that the cooling water stream fed to the SRO

process and the fifth hybrid indicated the schematic process where reject water stream was

fed as feed stream to SRO. The sequential quadratic programming (SQP) algorithm in

MATLAB optimisation toolbox and its variants were deliberated in this study. The outcome

revealed that the fourth hybrid provided lowest freshwater production cost.

Moving on to Husain et al. (1994), the authors considered fifteen recovery stages and three

rejection stages in their research. The SPEEDUP package was utilised on the steady-state and

dynamic models. The FORTRAN program was designed soley for the steady-state stimilation

based on the tridiagonal matrix formula. The results were compared to vendor supplied and

actual plant data and were favourable by the authors. These authors shared the number of

stages performed in their research. This was good as it allowed researchers to enhance the

process flow when further research is performed.


19

Another study which was conducted was by Kumar, Dewal and Mukherjee (2013). The study

demonstrated that the authors were aware of the need for seawater desalination and

considered the hybridisation of the two prominent technologies of the MSF-BR and RO

processes to complement each other. The development of Supervisory Control And Data

Acquisition(SCADA), was used to monitor the hybrid MSF-BR and RO process. The control

system also considered the MSF seawater (reject cooling seawater to feed RO) to develop and

stimulate the process. Human Machine Interface(HMI) navigated the whole process of

controling and monitoring. The strenght of the researcher included detailed step by step guide

of the operational details. However, the lack of sharing the actual feed flow would make it

harder for the future researchers to progress on from this research work.

Lianying, Yangdong and Congjie (2013) performed a detailed mathematical model of the

cogeneration(combined production of minimum of two forms of energy) system to minimise

the total annual cost. This model dealt with power plant, MSF and RO which were described

as a mixed integer nonlinear programming (MINLP). The authors modified the algorithm and

anlaysed the model capabilities. Results indicated that the tri-combination(power plant, MSF

and RO) was only succeptible if water demand was above 8000 𝑚3/ℎ. Similar to this

research was research findings from Fath, Hassan and Mezher (2013) where the authors gave

emphasis to present and future propect of water production through corresponding energy

consumptions. The researchers concluded that alternate energy sources such as renewable

nuclear and desalination technologies where the potentials to overcome the water shortage

challenge. These researchers also considered alternatives to reduce energy supply to prove

their theory. One disadvantage to consider when dealing with this kind of plant was the need

to note that while water storage may be possible, storing electricity would not be pactical.

Hence, there was need for these researchers to consider the various characteristics of both

stand-alone systems (MSF and RO) for better productivity.


20

These studies have in one way or another confirmed that the hybrid system is worth

considering as they provide high productivity and low cost. These provides both prudent and

technical benefits in the long run. However, the limited studies in specifically hybrid MSF

and RO control design studies should be pursued by more researchers.


21

Chapter 3: Process Description of the Hybrid MSF-RO System

The process configuration of the hybrid MSF-RO process considered in this thesis refers to

hybridisation of the MSF-BR process with a single stage RO process (as illustrated in Figure

1). The following sections demonstrate descriptions of the hybrid MSF-RO process.

Figure 1: Process Configuration of the Hybrid MSF-RO System (Malik, Bahri and Vu 2016)

3.1 Reverse Osmosis (RO)

It is very important to understand the process description prior to developing a process

model. Figure 15 presents a schematic diagram of a single stage reverse osmosis system

(SRO). RO is a pressure-driven membrane process. The RO system is mainly composed of a

high-pressure pump (HHP), RO module, and energy recovery system (ERS). The HHP is

used as an energy recovery equipment. The HHP is also used to deliver a constant pressure

(Pf) and considerable flow (Qf) to the RO unit to achieve the separation of dissolved salts and

impurities from seawater allowing permeate flow (Qp) to pass through the membrane. As the


22

RO membrane cannot achieve 100% salt rejection, the permeate flow contains a small

amount of salts residues (Cp). While the undesired brine solution (Qr) with concentration

(Cr) is rejected. The RO module (spiral wound) is made up of a set of pressure vessels

(hollow tube), which encompass the RO membrane elements. The pressure vessel may

contain up to eight membrane elements. The type of the spiral wound element considered in

this thesis is a flat sheet, cylindrical cross flow filtration.

An important stage is that, raw feedwater is pre-treated to deflocculate the undesired

substances before being pumped into the reverse osmosis module (Bachoo and Sastry 2016).

Pre-treatment is a very important step in this desalination plant to reduce membrane

degradation rate. At the last stage, the post-treatment step is where additive chemical is added

into a storage tank for better quality of freshwater (El-Ghonemy 2017).

Figure 2: Reverse Osmosis System (Malik, Bahri and Vu 2016)


23

3.2 Multi-stage Flash (MSF)

Figure 3 demonstrates MSF with brine recycle (MSF-BR). The MSF plant mainly constitutes

of heat input section, heat recovery section and heat rejection section. The heat input section

is made up of a brine heater where the temperature of seawater is raised at maximum

temperature. The heat recovery and heat rejection sections are divided into a number of

stages. In this thesis, the number of heat recovery stages is considered five stages, whereas,

only one stage is considered for heat rejection section as illustrated in Figure 3. Additional

components such as water reject splitter, blowdown splitter, and mixer are also illustrated.

In this process, feed seawater flowrate (Fsea) is pumped into the condenser tubes of the heat

rejection section at ambient temperature. This section rejects the excessive heat from the

system for the purpose of achieving the lowest temperature of both product flowrate (D) and

flashing brine discharge flowrate (BN). In each stage, the feed seawater is preheated by

surplus energy in the condenser. As the feed seawater leaves the heat rejection section and

enters water reject separator, part of it is rejected flowrate (CW), which is then returned back

to the sea. Hence, the outlet stream leaving the water reject separator is known as makeup

flowrate (Fm). The makeup stream is then mixed with the brine recycle stream flowrate (R)

to form cooling brine flowrate (W), which enters the heat recovery section. The purpose of

the brine recycle stream is to maintain the feed seawater flowrate to an acceptable value.

Also, the brine recycle stream impacts the brine levels in each stage and reduces the residence

time in the stages; this is due to lower flashing efficiency caused by high flowrate of the brine

recycle (A. Ismail 1998). Note that, the blowdown splitter separates the flashing brine leaving

the last of the heat rejection section (BN) into two streams, the brine recycle and blowdown

flowrates (BD). The recycle brine flowrate is pumped back into the process through the

mixer, whereas, the blowdown stream is rejected.


24

The cooling brine flows through a series of condensers in each stage to condense the flashed

vapour. The temperature of feed seawater (TF) keeps increasing by absorbing the latent heat

produced by flashing brine in each stage. The cooling brine stream then enters the heat input

section, where it is heated by steam flowrate (Ws) which is fed into the brine heater, at

maximum temperature. This temperature is known as top brine temperature (TBT).

The flashing brine flowrate (B0) leaving the brine heater at TBT is directed into the first stage

of the heat recovery section. The flashing brine flashes off and produces distillate vapour (As

shown in Figure 4). Consequently, the temperature of the flashing brine keeps decreasing as

the vapour is continuously flashed in each stage. The flashed vapour flows across the

demisters or eliminators to remove the brine droplets. The distillate vapour or the pure vapour

is condensed by condenser tubes to form distilled water which is collected in the distillate

tray. This process continues all the way down the plant.

Figure 3: A schematic diagram of Multi-stage flash with brine recycle (Adapted from: Malik,

Bahri and Vu 2016)


25

Figure 4: Flashing chamber (Ali and Kairouani 2016)

3.3 Hybrid System (MSF-RO)

Figure 5 shows a simplified schematic diagram of the hybrid MSF-RO process shown above

in the first section of this chapter. The hybrid system shares common intake and the final

product of distillate flowrate (D) produced from MSF process and permeate flowrate (Qp)

produced from the RO process is combined. On other hand, the rejected flowrate (Qr) and

blowdown flowrate (BD) rejected from the RO and MSF processes, respectively, are mixed

and then pumped out of the hybrid process. The cooling seawater flowrate (CW)rejected from

the reject separator of the MSF is not utilised in the process.

It can be seen that the final concentration of the RO process (Cp) is mixed with pure distillate

produced from the MSF process, which is assumed salt free.

Figure 5: A schematic diagram of the hybrid MSF-RO plant (Adapted from: Helal et al.

2003)


26

Chapter 4: Steady State Models of the Hybrid MSF-RO System

4.1 A Mathematical Model in Chemical Engineering

In science and technology, mathematical models play an important role in the life of scientist,

engineers and researchers. The process of developing, understanding and applying

mathematical equations defines mathematical modelling (Rasmuson et al. 2014). The system

performance would rely on the firm foundation of a mathematical modelling and simulation

tools which includes analytical and numerical techniques for solving mathematical model

equations such as nonlinear algebraic equations (Chidambaram 2018). In short, the

mathematical model describes equations, function, facts and data using mathematical tools,

methods and terminology. Depending on the type of process model required, the

mathematical model would differ from one to another. A mathematical model requires

precision and unambiguity based on understanding a mutual framework. Steps would include

identifying and understanding the parameters, sorting the required data based on relevancy,

seeking for solutions to the issues faced by arranging and analysing the data based on

requirements, then to come up with a reasonable conclusion (Marion and Lawson 2015).

4.2 Plant Modelling

Plant modelling plays a significant role in the control design of desalination plants to ensure

an efficient and safe operation. It is an imperative part in process systems engineering; thus, it

is the most important and broadly used in the dynamic simulation, static simulation, process

design, and process control. The mass component, and energy balance, as well as thermo-

dynamic losses, thermo-physical properties correlations, and chemical kinetics are included

in a chemical engineering model. These can be explained by non-linear characteristics of

algebraic equations for both steady state and dynamic processes (Pantelides et al. 1988). The


27

significance of the model building is to give the analysts a transparent insight into behaviour

of plant which can subsequently be controlled and optimised (Cameron and Hangos 2001).

4.2.1 Benefits of Plant Modelling

Some benefits of plants modelling are demonstrated as follows (Cameron and Hangos 2001):

Investigate the behaviour of the plants against reality.

Enable the analyst to perform optimization, design, and control of systems.

Enable the analysts to validate data quality and quantity.

Introduce process parameters estimation

Manage process risks that may take place.

4.3 Methodology

This chapter focuses on the methods to achieve the objectives of this thesis. The steps taken

include hypothesis, collecting of data, experiment, and analyse the research objectives. The

scientific method which involves a systematic process flow has been applied to this research.

The process begins with obtaining, compiling and analysing the process details of the

selected systems and observing the works of different researchers. Then a list of assumptions

will be created for the selected systems. Temperatures of the brine, distillate and cooling

brine as well as the flow rates of the distillate and brine outlets will be predicted. A

mathematical model which describes the hybrid MSF-RO system based on mass, salt and

energy balance will be developed. The developed model comprises of models created and

used by various researchers. The model will be solved using MATLAB software. The

MATLAB solver tool used to solve the system model is fsolve; it is a nonlinear system


28

solver. The developed model incorporates a set of nonlinear algebraic equations which will

be solved simultaneously.

The accuracy of the developed model will be further evaluated and examined. Any undesired

outcomes will warrant a repeat in the process flow from the prediction stage. By iterating to

convergence, the predicted value will be tested to ensure its validity through the comparison

of works by different researchers.

4.4 List of Assumptions

A list of assumptions is made to simplify the chemical engineering problems. In this thesis,

the following assumptions are:

The flashed vapour is free from brine droplet.

Temperature profiles of the flashing brine, distillate, and cooling brine are linear.

Distillate produced in each stage is salt-free.

Thermal and physical properties for the brine, distillate, and seawater are a function of

both salinity and temperature.

Heat of mixing flashing brine is negligible.

In the brine heater, no sub-cooling of condensate.

4.5 Reverse Osmosis Process Model

First of all, the characteristics of FilmTec spiral wound reverse osmosis membrane elements

are taken from both Malik, Bahri and Vu (2016) and Lu et al. (2007) as illustrated in Table 1.

The following process model is developed for a single stage reverse osmosis system. Mass

balance, salt balance, and some algebraic equations are illustrated as follow:

https://www.sciencedirect.com/topics/chemical-engineering/reverse-osmosis


29

Mass balance:

Qf = Qp + Qr (1)

Salt balance:

Qf∗ CF = Qp∗ Cp + Qr∗ Cr (2)

The recovery ratio (%):

Qp/Qf ∗ 100 (3)

Average velocity in feed side (𝑚/𝑠):

V = Qb/(w ∗ hsp ∗ ε) (4)

Pressure drop (𝑘𝑃𝑎):

∆P = Pf − ∆Pf/2 − Pp (5)

Pressure drop on the feed side (𝑘𝑃𝑎):

∆Pf = (0.003 ∗ Qb ∗ 3600 ∗ Lpv ∗ μ)/(w ∗ d3 ) (6)

Osmotic Pressure (𝑘𝑃𝑎):

π = (0.2641 ∗ C ∗ 1000 ∗ (T + 273))/(106 − (C ∗ 1000)) (7)

Osmotic pressure difference (𝑘𝑃𝑎):

∆π = 0.5 ∗ [πf + πr] − πp (8)


30

Water flux (𝐿/𝑚2 𝑠):

Jw = Aw ∗ (∆P − ∆π) ∗ 1000 (9)

Salt flux (𝐿/𝑚2 𝑠):

Js = As ∗ (Cw − Cp) (10)

Average bulk concentration (𝑔/𝐿):

Cb = (Cf + Cr)/2 (11)

Concentration Polarization:

Cw − Cp

Cb − Cp= e

Jwρ∗k (12)

Mass transfer coefficient (𝑘𝑔/𝑚3 𝑠):

k = 0.04 ∗ Re0.75 ∗ Sc0.33 ∗ (Ds/df) (13)

Salt Rejection (%):

SR = 1 −Cp

Cf (14)

Length of the pressure vessel (m):

Lpv = m ∗ Lm (15)


31

Membrane width (m):

w = Amem/(Lm ∗ N1) (16)

Reynolds number:

Re = (ρ ∗ df ∗ V)/μ (17)

Schmidt number:

Sc = μ/(ρ ∗ Ds) (18)

In this current study, the brine viscosity (𝜇) and solute diffusivity (𝐷𝑠) are calculated as a

function of temperature and salinity:

μ = 1.234𝑥10−06 ∗ exp^(0.00212 ∗ 𝐶 + (1965/(273 + 𝑇)) ) (19)

𝐷𝑠 = 6.725𝑥10−06 ∗ exp^(0.154𝑥10−03 ∗ 𝐶 + (2513/(273 + 𝑇)) )

(20)

The unit of:

Brine viscosity is 𝑘𝑔/𝑚 𝑠

Diffusivity coefficient is 𝑚2/𝑠

However, the average brine density (kg/m3) is considered as a constant value of 1020 𝑘𝑔/

𝑚3 obtained from Lu et al. (2007)


32

Table 1: Characteristics of the Spiral Wound Membrane

Element Type SW30XLE-400

Active area (m2)

Length of the element (m)

Diameter of the element (m)

Feed space (m)

Feed flow range (m3/h)

Maximum operating pressure (kPa)

Pure water permeability constant (A, kg/m2 s Pa)

Salt permeability constant (B, kg/m2 s)

Salt rejection (%)

37.2

1.016

0.201

0.00071120

0.8–16

8300

3.5 × 10−9

3.2 × 10−5

99.700

4.6 Multi-stage Flash Process model

The developed model of the MSF is taken from Malik, Bahri and Vu (2016), Rosso et al.

(1996), and Abdul-Wahab et al. (2012). The following sub-sections demonstrate the mass,

salt, and energy balance equations. In addition, thermo-dynamic losses and thermo-physical

properties of the brine and steam are presented. The specific heat capacity of the flashing

brine, distillate, and cooling brine are shown.

5.3.1 Mass balance equations for each stage

In the heat recovery or heat rejection sections, except for stage 1, inlet flashing brine and

distillate flow rate are equal to outlet flashing brine and distillate flow rate for all other

stages.


33

Bj−1+ Dj−1

= Bj + Dj (21)

Mass balance for stage 1:

B0 = B1 + D1 (22)

Note that there is no distillate flow rate entering the first stage

(D0 = 0). As illustrated in Figure. 6

Figure 6: Mass balance for stage 1 (Abdul-Wahab, et al. 2012)

Figure 7: Mass balance for any stage, except stage 1 (Abdul-Wahab, et al. 2012)


34

5.3.2 Salt balance equation for each stage

Component balance (salt) for any stage in both heat recovery or heat rejection sections is

given as:

Bj−1. CBj−1 = Bj. CBj (23)

Note that the cooling brine flowrate (W) and the concentration of the cooling brine (CR) do

not change at any stage of the heat recovery section. This applies also to the cooling brine

flowrate (Fsea) and the feed concentration (CF) in the heat rejection section.

5.3.3 Enthalpy balance of the heat recovery and heat rejection sections

The overall enthalpy balance for any stage of heat recovery section is given as:

W. Srcj. (TFj − TFj+1)

= Dj−1. SDj−1. (TDj−1 − Tref) + Bj−1. SBj−1. (TBj−1 − Tref)

−Dj. SDj. (TDj − Tref) − Bj. SBj. (TBj − Tref) (24)

The specific heat capacity of the cooling brine (Srcj) is calculated as a function of

temperature (TFj) and concentration (CR):

Srcj = 4.185 − 5.381x10−03 ∗ CR + 6.26x10−06 ∗ CR2 − (3.055x10−05

+ 2.774x10−06 ∗ CR − 4.318x10−08 ∗ CR2) ∗ TFj

+ (8.844x10−07 + 6.527x10−10 ∗ CR − 4.003x10−10 ∗ CR2)

∗ TFj2 (25)


35

The specific heat of flashing brine (SBj) is calculated as a function of temperature (TBj)

and concentration (CBj):

SBj = (1 − Cbj ∗ (0.011311 − 1.146e − 05 ∗ TBj)) ∗ SDj (26)

The specific heat capacity of distillate (SDj) is calculated as a function of temperature

(TDj) and concentration (CB):

SDj = (1.0011833 − 6.1666652e−05 ∗ TBj + 1.3999989x10−07 ∗ TBj2 +

1.3333336x10−09 ∗ TBj3) (27)

Enthalpy balance of the flashing brine for any stage is given as:

Bj−1. hj−1 = Bj. hj + (Bj−1 − Bj). hvj (28)

Where:

hj and hvj are calculated by the following formula:

ℎ𝑗 = (4.186 − 5.381𝑥10−3 ∗ 𝐶𝐵𝑗 + 6.26𝑥10−6 ∗ 𝐶𝐵𝑗2) ∗ 𝑇𝐵𝑗− (3.055𝑥10−5 + 2.774𝑥10−6 ∗ 𝐶𝐵𝑗 − 4.318𝑥10−8 ∗ 𝐶𝐵𝑗2)∗ 𝑇𝐵𝑗2 + (8.844𝑥10−7 + 6.527𝑥10−8 ∗ 𝐶𝐵𝑗 − 4.003𝑥10−10 ∗ 𝐶𝐵𝑗2)∗ 𝑇𝐵𝑗3

(29)

hVj = 596.912 + 0.46694 ∗ TDj − 0.000460256 ∗ TDj2 (30)

The distillate temperature (TDj) can be calculate by the equilibrium equations:

TDj = TBj – (BPEj + NEAj + ∆j ) (31)

Where:

The Boiling point elevation (BPE) is calculated by:

BPEj = Cj ∗ TBj/(266919.6 − 379.669 ∗ TBj + 0.334169 ∗ TBj2) ∗

(565.757/TBj − 9.81559 + 1.54739 ∗ log(TBj) − Cj ∗ (337.178/TBj −

6.41981 + 0.922753 ∗ log(TBj) + Cj2 ∗ (32.681/TBj − 0.55368 +

0.079022 ∗ log(TBj))))

(32)


36

Where:

C =19.819 ∗ Cb

1 − Cb (33)

𝐶𝑏 = Salt Concentration (fraction)

The non-equilibrium allowance (NEA) is calculated by:

NEAj = (195.556 ∗ Hrc1.1 ∗ (

omgrc

1000)

0.5

)/(dTBj0.25 ∗ Tsj2.5) (34)

The temperature drop in demister (∆) is calculated by:

∆𝑗 = exp(1.885 − 0.02063 ∗ 𝑇𝐷𝑗) (35)

BPEj, NEAj, and ∆j are the thermodynamic losses.

Distillate and flashed steam temperature correlation is given as:

Tsj = TDj + ∆j (36)

Heat transfer model at the condenser for heat rejection section:

Fsea ∗ Srcj ∗ (TFj – TF(j + 1)) = Uj ∗ Aj ∗ (LMTD)j (37)

Where:

Uj (overall heat transfer coefficient) is now calculated as a function of (Fsea, TFj, TFj +

1, TDj, Dji, Djo, fj) and taken from Helal et al. (2003):

Uj = 1.0/(Yj + Zj) (38)

Where:

Yj represents the internal film resistance and this is defined as:


37

Yj = vj ∗ Di(0.2)/(160 + 1.92 ∗ vj ∗ TDj) (39)

vj represents the velocity of the cooling brine flowrate (Fsea) for the heat rejection section

and 𝐷𝑖 is the internal diameter of tubes at any stage j.

Zj is defined as the sum of other thermal resistance such as steam-side condensing film,

steam-side fouling, tube metal and brine-side fouling. Zj is given by

Zj = 10.24768x10−04 − 74.73939x10−07TDj + 0.999077x10−07TDj2

− 0.430046x10−09TDj−3 + 0.6206744x10−12TDj4 (40)

Note: some variables and parameters in the equations will change between heat rejection and

heat recovery sections (see Nomenclature and Symbols section).

Heat transfer model at the condenser for heat recovery section:

W ∗ Srcj ∗ (TFj – TF(j + 1)) = Uj ∗ Aj ∗ (LMTD)j (41)

Note that, Uj is defines as equation (40) except that Fsea is replaced by W and inside

diameter (𝐷𝑖).

5.3.4 Mixer and splitters equations

Mixer combines both makeup flowrate (Fm) rate and brine recycle flowrate (R) to form

cooling brine flowrate (W). The mass and salt balances around the mixer are given as:

Mass balance:

W = R + Fm (42)

Salt Balance:

W. CR = R. CBN + FmCF (43)


38

Energy balance:

W ∗ hw = R ∗ hR + Fm ∗ hm (44)

hw, hR, and hm are calculated as a function of (Tr, Cr), where Tr and Cr are the respective

stream of both temperature and concentration.

Blowdown splitter separates the flashing brine flowrate (BN) leaving the heat rejection

section into brine recycle (R) and blowdown (BD) flowrates.

Mass balance around the blowdown splitter is given as:

BN = BD + R (45)

The concentration of flashing brine entering the blowdown splitter is the same as the

concentration leaving the splitter.

On the other hand, in the reject water splitter, feed seawater flowrate (Fsea) enters the splitter

and part of the feed seawater flowrate is rejected (CW). The outlet stream leaving the splitter

is called makeup flow (Fm). Mass balance around the reject water splitter is given as:

Mass balance:

Fsea = CW + Fm (46)

Similar to the blowdown splitter, the feed concentration of the reject water splitter is the same

as the concentration of the makeup flow rate (Fm) and reject flow rate (CW).

5.3.5 Brine Heater enthalpy balance

The energy balance around the brine heater is given as:

Ws. λs = W. Sbh. (TBT − TF1) (47)


39

Where:

λs = Latent heat of vaporization of water (kJ/kg)

Sbh = Specific heat capacity of brine in brine heater (kJ/kg °C)

TBT = Top Brine Temperature (°C)

𝑇𝐹1 = Temperature of the cooling brine leaving stage 1 (°C)

𝜆𝑠 is calculated as a function of steam temperature (Tsteam):

hV = 596.912 + 0.46694.∗ Tsteam − 0.000460256 ∗ Tsteam2

(48)

hD = 1.8 ∗ (−31.92 + 1.0011833 ∗ (Tbp) − 3.0833326x10−05 ∗ (Tbp)2

+ 4.666663x10−08 ∗ (Tbp).3 + 3.333334x10−10 ∗ (Tbp)4)

(49)

λs = (hV − hD) (50)

𝑆𝑏ℎ is calculated by a function of TBT and TF1:

Sbh = 1.0011833 − 6.1666652x10−05 ∗ (.(TF1+TBT)

2) + 1.3999989x10−07.∗

(.(TF1+TBT)

2) .2+ 1.3333336x10−09.∗ (.

(TF1+TBT)

2) .3

(51)

Brine heater transfer model:

Ubh ∗ Abh ∗ (LMTD)bh = Ws λs (52)

Where:

Ubh (overall heat transfer coefficient of the brine heater) is calculated as a function of

(W, TBT, TF1, TDj, DiH , D°H , fj). Ubh is defined as same equation as Uj is calculated in heat

rejection section. Note that Fsea is replaced by W and the temperature and inside diameter of

the heat exchanger is different.

The log mean temperature difference (LMTD) of the brine heater is given as:


40

LMTD = (TBT – TF1)/log((Tsteam – TF1)/(Tsteam − TBT)) (53)

Brine heater balance: W = B0 (54)

Brine heater salt

balance: CR = CB0

(55)

The overall balance and salt balance of the MSF is given as:

Overall balance: Fm = D + BD

(56)

Overall salt Fm ∗ CF = BD ∗ CBN

(57)


The hybrid system of MSF and RO share common intake and the final product of both

systems are mixed. The final concentration (CT) represents the concentration of the final

product obtained from the average concentration of both product concentrations of stand-

alone MSF and RO. The mass and salt balances are given as:

Mass balance: Final Product = Distillate + Permeate (58)

Salt balance: Final Product ∗ CT = Permeate ∗ Cp (59)

𝑪𝒑

𝑪𝑻

(𝑪𝑾)

(𝑩𝑫)

(𝑸𝒓)

Figure 8: A schematic diagram of the hybrid MSF-RO plant (adapted from: Helal

et al. 2003)


41

Chapter 5: Results and Discussion

5.1 Reverse Osmosis (RO)

Table 2 shows simulation results for a reverse osmosis system. The input data used to solve

the process model are taken from both Malik, Bahri and Vu (2016) and Lu et al. (2007). The

feed flowrate (𝑄𝑓) and feed concentration (𝐶𝐹) are given as input data. Other process

variables have been calculated as illustrated below.

Table 2: Simulation Results for a Reverse Osmosis System

𝑄𝑓(𝑚3/ℎ) 𝑄𝑝(𝑚3/ℎ) 𝑄𝑟(𝑚3/ℎ) 𝐶𝐹(𝑔/𝐿) 𝐶𝑝(𝑔/𝐿) 𝐶𝑟(𝑔/𝐿)

1643.2 1400 243.2 40 0.12 43.93

m Pf (kPa) Cw (g/L) SR (%) Jw (𝐿/𝑚2 𝑠) Js(𝐿/𝑚2 𝑠)

5 7000 43.93 99.7 85.119 0.0013

The calculated results indicate that the permeate concentration (𝐶𝑝) contains salinity of 0.12

(𝑔/𝐿 ) as the RO membrane cannot achieve 100 % salt rejection. As calculated above, 99.7 %

salt rejection was achieved. As a result, both the reject concentration (𝐶𝑟) and the wall

concentration (𝐶𝑤) show high salinity of 43.93 ( 𝑔/𝐿 ). This is line with the FilmTec spiral

wound membrane characteristics as illustrated in Table 1 - chapter 5. The simulation results

confirm that minimal salt flux (Js) of 0.0013(𝑘𝑔/𝑚2 𝑠) was attained at an acceptable value.

On the other hand, the permeate flowrate (𝑄𝑝) was attained at 1400 (𝑚3/ℎ) with a given feed

flowrate (𝑄𝑓) of 1643.2 (𝑚3/ℎ). While, a small amount of brine flowrate (𝑄𝑟) is rejected.


42

Also, the water flux (Jw) was obtained at 85.119(𝑘𝑔/𝑚2 𝑠). Note that the feed flowrate can

be varied to observe the final results of the permeate flowrate (𝑄𝑝) and permeate

concentration (𝐶𝑝). Lu et al. (2007) has proven in their study that different feed

concentrations resulted in different product concentrations. The permeate concentration (𝐶𝑝)

depends on the relative rate of water flux and salt flux.

Based on the values of the feed flowrate and permeate flowrate, system overall recovery

found to be 85.19 % obtained from Equation (3).

5.2 Multi-stage Flash (MSF)

5.2.1 Performance Ratio (PR)

The efficiency of the MSF plant can be determined by the performance ratio. It is defined by

the amount of distillate produced by condensing one kilogram of the heated steam in the heat

input section (the brine heater) (Darwish, El-Refaee and Abdel-Jawad 1995). The main

variables that impact the PR are top brine temperature (TBT) and brine recycle flowrate as

reported by Helal, Al-Jafri and Al-Yafeai (2012) and Abdul-Wahab et al. (2012). Further, the

authors mentioned that increasing the number of stages would impact the PR of the MSF

plant.

Table 3 demonstrates simulation results of the MSF temperature profiles for brine

temperature (TBj), distillate temperature (TDj), temperature of the cooling brine (TFj),

flashing brine (Bj), distillate flow (Dj), and brine concentration (CBj). The operating data

used to solve the developed model are adapted from both Malik, Bahri and Vu (2016) and

Abdul-Wahab et al. (2012).


43

Table 3: Simulation Results of the Temperature(s) and Flowrate(s) Profile of the MSF

Process

Makeup flowrate (Fm) 3150 𝑚3/ℎ

Brine recycle flowrate (W) 12800 𝑚3/ℎ

Seawater feed to MSF (Fsea) 9700 𝑚3/ℎ

Seawater feed temperature (Tf) 26.8 °C

Seawater salt concentration (CF) 40 °C

Top brine temperature (TBT) 97.6 °C

Steam temperature (Tsteam) 112.6 °C

Distillate production (DN) 1270.68 𝑚3/ℎ

Stage No.(j) TB (j) TD (j) TF (j) B (j) D (j) CB (j)

1 94.91 92.99 91.49 12251 46.39 44.63

2 90.42 89.00 88.08 12523 74.89 44.74

3 86.42 85.00 83.58 12439 158.88 45.04

4 83.44 81.01 79.58 12359 238.88 45.33

5 82.28 78.41 76.56 12280 317.89 45.62

6 75.67 72.83 63.94 12164 434.24 46.06

As it can be seen from the above table, the temperature of the flashing brine (TBj) linearly

decreasing from stage one to the last stage. Similarly, the distillate temperature (TDj) keeps

dropping as the distillate (Dj) flows all the way down the plant. This is due to the cooling

brine (W) flowing in the condenser tubes, which cools down both temperatures in each stage.

On the other hand, the temperature of the cooling brine (TFj), in the heat recovery section,

increases as the cooling brine flows towards the brine heater. The reason for this is the

temperature of the cooling brine is absorbing the latent heat produced from the flashing brine

flowrate (Bj). Another interesting point to note is the sudden drop in both flashing brine and

distillate temperatures between stage five and stage six. This is because of the removal of the


44

surplus energy from both streams in the heat rejection section. However, the sudden drop

would not have happened if the number of stages of both heat recovery and rejection sections

increased. Thus, a gradual decrease or increase in temperature and flowrate would be

obvious. The results of this analysis are then compared with the results obtained from Abdul-

Wahab et al. (2012). Matching results were obtained. From these results, it is evident that the

developed model is able to produce robust results.

The flashing brine which enters the first stage of the heat recovery section decreases as it

continues flowing to the last stage. This is because of the vapour released from the flashing

brine in each stage. While, the distillate flowrate is accumulated as it flows from stage one to

last stage, since the vapour is continuously being condensed by the cooling brine to produced

distillate. The distillate produced from the MSF plant in this research was 434.24 𝑚3/ℎ (As

highlighted in yellow in Table 3). The final distillate reported by the researchers was

1270.68𝑚3/ℎ. Unlike Malik, Bahri and Vu (2016) and Abdul-Wahab et al. (2012) who

considered nineteen stages of the MSF plant, this research only covered six stages. Thus, the

amount of distillate produced is comparatively lower than the above researchers. This does

not mean that the results obtained in this research are invalid as the results obtained were

comparatively similar to their findings from stage one to stage six. This demonstrates the

accuracy of the developed model.

In addition to the above results, the concentration of the flashing brine was calculated based

on the mass and salt balance equations. The simulation results indicated a constant and

gradual increase of the flashing brine concentration in each stage. Although Abdul-Wahab et

al. (2012) did not run this simulation, the present research further investigated the flashing

brine concentration to prove the validity and accuracy of the process model. These results


45

indicated a similar pattern to various researchers whom have tested this field such as Rosso et

al. (1996) and Ali and Kairouani (2016). These results show consistency in line with the

works of previous researchers.

The following table (Table 4) exhibits the temperatures and flowrates of the brine heater,

mixer, and blowdown. The purpose of the brine heater is to enough temperature to heat the

cooling brine, entering the brine heater, at a maximum temperature (TBT) by using steam

flowrate. The simulated model displays the obtained values of TBT, steam flowrate, and

steam temperature. Overall these findings are in accordance with the findings reported by

Malik, Bahri and Vu (2016) and Abdul-Wahab et al. (2012). However, it should be noted

that, the TBT reported by Malik, Bahri and Vu (2016) showed a lower optimum value (90℃)

as the authors proceeded with optimisation. The authors applied both equality and inequality

constraints on optimisation variables including TBT.

Turning now to the mixer process variables, both Malik, Bahri and Vu (2016) and Abdul-

Wahab et al. (2012) studies did not report the temperature leaving the mixer and the

concentration of the cooling brine. Whereas, in the current research, these variables were

included to further explore and understand the behaviour of the process system. This will in

turn strengthen the developed model.

Similar to the TBT, Malik, Bahri and Vu (2016) also set equality constraints on the recycle

flowrate which was obtained at 9005 (𝑚3/ℎ). On the other hand, the current study showed a

higher value at 9298 (𝑚3/ℎ).

The obtained result of the performance ratio (%) of the MSF plant was 2.56 %. The increase

of the number of stages would impact the performance of the plant. For all stages, the

performance ratio increased as the number of stages increased (as illustrated in Appendix A).


46

Table 4: Temperatures and Flowrates of the Brine Heater, Mixer, and Blowdown

Brine Heater

Top brine temperature (℃) 98.02

Steam flowrate (𝑚3/ℎ) 169.6

Steam temperature (℃) 111.95

Mixer

Cooling Brine (𝑚3/ℎ) 12598

Temperature (℃) 75.36

Concentration of cooling brine (𝑔/𝐿) 44.475

Blowdown

Recycle Flowrate (𝑚3/ℎ) 9298

Flow rate of brine blow down (𝑚3/ℎ) 2864.8

Performance Ratio (%) 2.56

Table 5 demonstrates results for thermo-dynamic losses and thermos-physical properties of

the brine, steam, and water. The overall Heat transfer coefficient (U) and the log mean

temperature (LMTD) are presented as well.

The thermo-dynamic losses are reliant on temperature, concentration, and other parameters of

the MSF stages. Thermo-dynamic losses have the potential to impact the performance and

design parameters of the MSF plant. Shen (2015) reported that by increasing the number of

stages of the MSF plant, the thermo-dynamic losses decreases (Shen 2015). In Table 5, there

were marginal variations of the results obtained for the thermodynamic losses where the BPE

(j) is slightly decreasing. This confirms that the increase of the number of the MSF stages

would decrease the thermo-dynamic losses and vice-versa. On the other hand, the results in

this research showed NEA (j) and ∆ (j) increasing marginally. These results are in line with

the findings obtained by Shen (2015), who stated that thermo-dynamic losses caused by

demisters would increase with the rise of stage sequence. Since the values obtained for the

thermo-dynamic losses are very minimal, some researchers neglect these losses. Despite the


47

fact that the thermodynamic losses have the potential to affect the performance of the MSF

plant, the insignificant change of thermodynamic losses does not warrant for any concern.

In each stage, the specific heat capacity of the flashing brine (SBj) shows similar results

obtained for the specific heat capacity of the distillate (SDj). Although both concentration and

temperature change in each stage, there was a minimal change in thermo-physical properties.

Helal et al. (2003) made an assumption that the specific heat capacity of the flashing brine is

a weak function of brine concentration. Thus, the results of the current research found clear

support to the assumption made by Helal et al. (2003).

Log mean temperature difference (LMTD) defines the temperature driving force between the

hot and cold streams in tube heat exchangers (Thulukkanam 2000) . The LMTD results

display slight fluctuations between stage one and stage five. However, between stage five and

stage six, there was a drastic increase of the LMTD. This is due to the substantial difference

in the temperature leaving stage five (TDj) and the feed temperature of the cooling seawater

at stage six (TFj). Equation (38) used to determine the LMTD is given in chapter 5,

subsection 5.3. The values obtained at stage five was 6.1385 °C as compared to 22.592 °C in

stage six (as illustrated in Table 5, highlighted in yellow). These findings have a similar

pattern to Mabrouk (2013) results.

Moving on to the overall heat transfer coefficient (U), the values attained were gradually

decreasing from stage one to stage five. Whereas, the value obtained in the last stage (stage 6)

showed a greater value. To explain this, the process variables and the design parameters of

the heat recovery section (stage one to stage five) are different than that of the heat rejection

section. The process variables and design parameters of the heat recovery and heat rejection

sections are given in Chapter 5, subsection 5.3. In the same way, the findings in this thesis


48

presented similar patterns to Rosso et al. (1996) and Mabrouk (2013) results. Under these

circumstances, the developed model has been validated against the results reported by other

researchers.

Table 5: Results for Thermo-dynamic Losses and Thermo-physical Properties

Thermodynamic Losses Thermo-physical properties Other parameters

Stage

No. (j)

BPE (j)

(𝟏 ∗ 𝟏𝟎−𝟑) NEA (j) ∆ (j) SB (j) SD (j) Scr (j) LMTD(j) U(j)

1

2

3

4

5

0.6819

0.6787

0.6768

0.6755

0.6753

7.10e-04

7.49e-04

8.60e-04

0.001

0.0014

0.967

1.05

1.1403

1.238

1.306

4.1727

4.1727

4.1728

4.1729

4.1729

4.1745

4.1745

4.1746

4.1747

4.1748

3.976

3.975

3.973

3.971

3.970

2.8814

2.5418

2.9886

2.6581

6.1385

2.885e03

2.844e03

2.789e03

2.738e03

2.698e03

6 0.6707 0.0013 1.46 4.159 4.1748 3.986 22.592 3.074e03

Note: The graphs of the simulation results found in Table 3, 4 and 5 are enclosed in

Appendix A.


The following table (Table 6) illustrates the results of the final product of the hybrid MSF-

RO plant. The distillate produced from the MSF process is combined with permeate produced

from the RO process. Consequently, higher product capacity and product quality are

obtained.


49

As reported earlier, the distillate produced from the stand-alone MSF process was 434.24

𝑚3/ℎ and permeate attained from the stand-alone RO process was 1400𝑚3/ℎ. Both

processes are now integrated to increase the final freshwater product. The final product of the

hybrid system obtained was 1834.24 𝑚3/ℎ.

Table 6: Simulation Results of the Hybrid MSF-RO Plant

Hybrid MSF-RO

Final Concentration (𝒈/𝑳) Final Product (𝒎𝟑/𝒉)

0.063 1834.24

The final product concentration of the hybrid system is the average of both product

concentrations of the stand-alone MSF and RO processes. By integrating product

concentration produced from the stand-alone RO (Cp) with salt-free distillate, the salinity of

the final product became lower at 0.063 𝑔/𝐿. The results obtained ties well with Malik, Bahri

and Vu (2016) findings wherein the authors reported 0.07 𝑔/𝐿 of the final product

concentration of the hybrid system.

5.4 Limitations of the Current Thesis

There were several challenges faced throughout the duration of this research. One of the main

challenges faced is limited relevant researches performed on the hybrid MSF-RO system.

This then leads to the lack of research findings to compare and validate the current thesis.

Despite the fact that there were lack of studies on the hybrid system, the challenges were

overcome with the in-depth understanding and studies provided by the few researches who

attempted to achieve reliable data not only from theoretical studies but also from the actual

plant data.

Thus far, in this chapter, all results have been compiled, discussed, analysed and validated

against outcomes achieved by various researchers. The findings in the current thesis have


50

successfully proven that the results were either in-line or achieved better outcomes with that

of the other researchers’ findings. These positive achievements greatly influence the overall

results and findings of this thesis. All things taken into considerations, the developed

mathematical model shows promising and leading implementation of control design (which is

lacking in research work) and optimisation (a well sought-after topic for cost reduction and

product quality improvement).


51

Chapter 6: Conclusion and Future Work

In this thesis, a steady state mathematical model of the hybrid MSF-RO system has been

developed based on material, salt and energy balance. The operating data were gathered from

different researches to solve the develop model and ensure data accuracy. The mathematical

models of stand-alone systems of MSF and RO were tested individually.

For the stand-alone MSF system, the stimulation results when compared with Abdul-Wahab

et al. (2012) and Malik, Bahri and Vu (2016) findings indicate matching results. The current

research also considers actual calculations of thermodynamic losses and thermo-physical

properties for each stage, of which many researches ignored. Thermo-physical properties are

calculated as a function of temperature and concentration. While, thermodynamic losses are

calculated as a function of temperature, concentration and other parameters (refer to chapter

4). The results yield positive outcomes of the distillate temperature profile (refer to chapter

5). Moreover, the brine concentration profile has been calculated. With regards to the

performance ratio of the MSF, the results indicate that as the number of stages increases, the

performance ratio increases. Overall, these values show consistent outcomes when compared

to other researches.

Turning now to the stand-alone RO system, the stimulation results showed matching results

obtained from Malik, Bahri and Vu (2016) and Lu et al. (2007). Although the stimulation

results are matching, Lu et al (2007) considered constant values of brine viscosity and solute

diffusivity. This has been discussed in chapter 4, subsection 4.3. Whereas, in the current

thesis, these properties are calculated as a function of temperature and concentration. Another

point to consider is the characteristic of the RO membrane, where, a significantly small value

of the salt flux is observed due to high salt rejection. This outcome explains that the process

model is accurate and reliable.


52

For the hybrid MSF-RO, the result shows a reduction in permeate concentration produced by

RO system when integrated with MSF plant. Therefore, the final concentration of the hybrid

system is the average of both product concentration of stand-alone systems (MSF and RO).

Moreover, the final product flow rate is higher as the distillate and permeate flow are

combined. The final product concentration of 0.063 g/L is obtained and compared with

Malik, Bahri and Vu (2016) where the authors’ final concentration of hybrid system is 0.07

g/L.

While most studies focused on optimisation of the hybrid MSF-RO, these studies are still

lacking. On the control design, there were limited research emphasising on the control of the

hybrid system. The works presented in this thesis have proven that further research should be

performed on the hybrid MSF-RO model for the purpose of optimisation and control design.

Moreover, the performance of the hybrid system can be further explored by increasing the

number of stages. This will in turn increase the final freshwater product and the final

concentration can be further improved. The developed model in this thesis is accurate and

promising since the outcomes obtained are validated against other researchers. Ultimately,

future researchers are encouraged to improve the model and bring it to the next level.

The major purpose of conducting this research is to develop a mathematical model and

compare the obtained data through the many researches as mentioned earlier to ensure data

accuracy. The correlation and dependence between the hybrid system and stand-alone

systems (MSF and RO) were tested through the increased number of stages performed in

MSF system. The end results prove that this research has successfully completed its

objective.


53

Bibliography

Abdul-Wahab, Sabah A., K. V. Reddy, Mohammed A. Al-Weshahi, Salim Al-Hatmi, and

Yasir M. Tajeldin. 2012. “Development of a steady-state mathematical model for

multistage flash (MSF) desalination plant.” International Journal of Energy Research

710-723.

Alasfour, Fuad N., and Hassan K. Abdulrahim. 2009. “Rigorous steady state modeling of

MSF-BR desalination system.” Desalination and Water Treatment 1: 259-276.

Alasfour, Fuad N., and Hassan K. Abdulrahim. 2009. “Rigorous steady state modeling of

MSF-BR desalination system.” Desalination and Water Treatment 259-276.

Ali, Hend Ben, Ruben Ruiz-Femenia, and Ammar Ben Brahim. 2017. “Design of once-

through multistage flash process under the Generalized Disjunctive Programming

framework.” 2017 International Conference on Green Energy Conversion Systems

(GECS). Hammamet.

Ali, Ines Ben, Mehdi Turki, Jamel Belhadj , and Xavie Roboam. 2014. “Systemic design of a

reverse osmosis desalination process powered by hybrid energy system.” 2014

International Conference on Electrical Sciences and Technologies in Maghreb

(CISTEM). Tunis.

Ali, Mongi Ben, and Lakhdar Kairouani. 2016. “Multi-objective optimization of operating

parameters of a MSF-BR desalination plant using solver optimization tool of Matlab

software.” Desalination 381: 71-83.

Al-Mutaz, Ibrahim S. 2005. “Hybrid RO MSF Desalination: Present Status and Future

Perspectives .” Water Desalination and Purification Technology Outlook for the Arab

World & Non-Governmental Organizations Forum. Sharjah.

Alsadaie, Salih M., and Iqbal M. Mujtaba. 2017. “Dynamic modelling of Heat Exchanger

fouling in multistage flash (MSF) desalination.” Desalination 409: 47-65.

Alsadaie, Salih M., and Iqbal M. Mujtaba. 2016. “Generic Model Control (GMC) in

Multistage Flash (MSF) Desalination.” Journal of Process Control 44: 95-105.

Alsadaie, Salih, Mohd Fauzi Zanil, Azlan Hussain, and Iqbal M. Mujtaba. 2016.

“Development of Hybrid Fuzzy-GMC Control System for MSF Desalination

Process.” Computer Aided Chemical Engineering 38: 907-912.

Al-Shayji, K. A., A. Al-Wadyei, and A. Elkamel. 2005. “Modelling and optimization of a

multistage flash desalination process.” Engineering Optimization 37 (6): 591-607.


54

Arafat, Hassan A. 2017. Desalination Sustainability A Technical, Socioeconomic, and

Environmental Approach. Amsterdam: John Fedor.

Bachoo, Darryll Randy , and Musti K.S. Sastry. 2016. “Tensor Product Model for a Reverse

Osmosis System.” 2016 IEEE ANDESCON. Arequipa.

Bandi, Chandra Sekhar, Amit Kumar, and R. Uppaluri. 2016. “Global optimality of hybrid

MSF-RO seawater desalination processes.” Desalination 400: 47-59.

Banyasz, Csilla . 1995. Adaptive systems in control and signal processing 1995. Oxford:

Pergamon Press.

Bartman, Alex R., Aihua Zhu, Panagiotis D. Christofides, and Yoram Cohen. 2010.

“Minimizing energy consumption in reverse osmosis membrane desalination using

optimization-based control.” Proceedings of the 2010 American Control Conference.

Baltimore.

Bazargan, Alireza. 2018. A Multidisciplinary Introduction to Desalination. Alsbjergvej 10,

9260 Gistrup: River publishers.

Camacho, Lucy Mar , Ludovic Dumée, Jianhua Zhang, Jun-de Li, Mikel Duke, Juan Gomez,

and Stephen Gray. 2013. “Advances in Membrane Distillation for Water Desalination

and Purfication Applications.” Water 5: 94-196.

Cameron, Ian T., and Katalin Hangos. 2001. Process Modelling and Model Analysis. San

Diego: Academic Press.

Castillo, Oscar. 2012. Type-2 Fuzzy Logic in Intelligent Control Applications. Chennai:

Springer-Verlag.

Chidambaram, M. 2018. Mathematical Modelling and Simulation in Chemical Engineering.

New York: Cambridge University Press.

Chidambaram, M., B. Chandrasekhara Reddy, and Darwish M.K. Al-Gobaisi. 1997. “Design

of robust SISO PI controllers for a MSF desalination plant.” Desalination 109 (2):

109-119.

Chong, T. H., F. S. Wong, and A. G. Fane. 2008. “Implications of critical flux and cake

enhanced osmotic pressure (CEOP) on colloidal fouling in reverse osmosis:

Experimental observations.” Journal of Membrane Science 314 (1-2): 101-111.

Cipollina, Andrea, Giorgio Micale, and Lucio Rizzuti. 2009. Seawater Desalination:

Conventional and Renewable Energy Processes. London: Springer.

Darwish, M. A., M. M. El-Refaee, and M. Abdel-Jawad. 1995. “Developments in the multi-

stage desalting system.” Desalination 100: 35-64.


55

Du, Yawei, Lixin Xie, Yan Liu, Shaofeng Zhan, and Yingjun Xu. 2015. “Optimization of

reverse osmosis networks with split partial second pass design.” Desalination 365:

365-380.

Dutta, Suman. 2016. Optimization in Chemical Engineering. Delhi: Cambridge University

Press.

El-Dessouky, H. T., and H. M. Ettouney. 2002. Fundamentals of Salt Water Desalination.

San Diego: Elsevier Science.

El-Dessouky, Hisham, Habib I. Shaban, and Hamida Al-Ramadan. 1995. “Steady-state

analysis of multi-stage flash desalination process.” Desalination 271-287.

El-Ghonemy, A. M.K. 2017. “Performance test of a sea water multi-stage flash distillation

plant: Case study.” Alexandria Engineering Journal xxx.

Elmesmary, Mohamed M., and Abdulwahab J. Alsultan. 2017. “Hybridisation of desalination

processes.” Int. Journal of Engineering Research and Application 7 (4): 41-44.

El-Sayed, Essam, Sadeq Ebrahim, Ahmad Al-Saffar, and Mahmoud Abdel-Jawed. 1998.

“Pilot study of MSFRO hybrid systems.” Desalination 120 (1-2): 121-128.

Ericsson, Bernt, and Bengt Hallmans. 1985. “A comparative study of the economics of RO

and MSF in the Middle East.” Desalination 55: 441-459.

Gambier, A. , and E. Badreddin. 2007. “Dynamic Modeling of a Simple Reverse Osmosis

Desalination Plant for Advanced Control Purposes.” 2007 American Control

Conference. New York.

Gambier, A., and E. Badreddin. 2005. “Dynamic modeling of a single-stage MSF plant for

advanced control purposes.” Proceedings of 2005 IEEE Conference on Control

Applications, 2005. CCA 2005. Toronto.

Gambier, A., M. Fertig, and E. Badreddin. 2002. “Hybrid modelling for supervisory control

purposes for the brine heater of a multi stage flash desalination plant.” Proceedings of

the 2002 American Control Conference (IEEE Cat. No.CH37301). Anchorage.

Gernaey, Krist V., Jakob K. Huusom, and Rafiqul Gani. 2015. 12th International Symposium

on Process Systems Engineering and 25th European Symposium on Computer Aided

Process Engineering: Parts A, B and C. Copenhagen: Elsevier.

Gothandam, K. M., Shivendu Ranjan, Nandita Dasgupta, Chidambaram Ramalingam, and

Eric Lichtfouse. 2018. Nanotechnology, Food Security and Water Treatment.

Gewerbestrasse: Springer.

Gude, Veera Gnaneswar. 2018. Renewable Energy Powered Desalination Handbook:

Applications and Thermodynamics. Cambridge: Mathew Deans.


56

Haluch, Vanessa, Everton F. Zanoelo, and Christian J.L. Hermes. 2017. “Experimental

evaluation and semi-empirical modelling of a small-capacity reverse osmosis

desalination unit.” Chemical Engineering Research and Design I22: 243–253.

Hamed, Osman A. 2005. “Overview of hybrid desalination systems-current status and future

prospects.” Desalination 186: 207-214.

Hamed, Osman A. 2005. “Overview of hybrid desalination systems — current status and

future prospects.” Desalination 186 (1-3): 207-214.

Helal, A. M., A. Al-Jafri, and A. Al-Yafeai. 2012. “Enhancement of existing MSF plant

productivity through design modification and change of operating conditions.”

Desalination 307: 76-86.

Helal, A. M., A. M. El-Nashar, E. Al-Katheeri, and S. Al-Malek. 2003. “Optimal design of

hybrid RO/MSF desalination plants Part I: Modeling and algorithms.” Desalination

154 (1): 43-66.

Husain, Asghar. 2010. Desalination and Water Resources Thermal Desalination Processes -

Volume 1. Abu Dhabi: Encyclopedia of Desalination and Water Resources

(DESWARE).

Husain, Asghar. 2003. Integrated Power And Desalination Plants. Chennai: Eolss Publisher.

Husain, Asghar. 2012. “Steady-State Model.” Thermal Desalination Processes I.

Ismail, A., and E. Abu-Khousa. 1996. “Fuzzy TBT control of multi-stage flash desalination

plants.” Proceedings of the 1996 IEEE International Conferenc on Control

Application (CCA). Dearborn.

Ismail, Abdulla. 1998. “Fuzzy model reference learning control of multi-stage flash

desalination plants.” Desalination 116: 157-164.

Karimi, I. A., and R. Srinivasan. 2012. 11th International Symposium on Process Systems

Engineering - PSE2012. Amsterdam: Elsevier.

Kartik, Upendra. 2017. “slideshare.net.” 25 April. Accessed 09 13, 2018.

https://www.slideshare.net/UpendraKartik/design-of-experiments-75405493.

Kesieme, Uchenna K. , Nicholas Milne, Hal Aral, Chu Yong Cheng, and Mikel Duke. 2013.

“Economic analysis of desalination technologies in the context ofcarbon pricing,

andopportunities for membrane distillation.” Desalination 323: 66-74.

Kim, Bumjoo, Rhokyun Kwak, Hyukjin J. Kwon, Van Sang Pham, Minseok Kim, Bader Al-

Anzi, Geunbae Lim, and Jongyoon Han. 2016. “nature.” 22 August. Accessed 8 2018,

23. https://www.nature.com/articles/srep31850.


57

Kim, Jong Suk, Jun Chen, and Humberto E. Garcia. 2016. Modeling, control, and dynamic

performance analysis of a reverse osmosis desalination plant integrated within hybrid

energy systems. Idaho Falls: Idaho National Laboratory.

Kiusalaas, Jaan. 2005. “Roots of Equations.” In Numerical Methods in Engineering with

MATLAB®, 160. New York: Cambridge University Press.

https://books.google.com.au/books?id=IOvmtxAmdb4C&printsec=frontcover&dq=ne

wton+raphson+method+mathworks&hl=en&sa=X&ved=0ahUKEwiO_pLk7ZvjAhW

UWX0KHTYTB8kQ6AEIMDAB#v=onepage&q=newton%20raphson&f=false.

Krishna, P. Sai. 2009. Process Control Engineering. New Delhi: I.K. International Publishing

House Pvt. Ltd.

Kumar, Brajesh, M. L. Dewal, and S. Mukherjee. 2013. “Control and monitoring of MSF-RO

hybrid desalination process.” 2013 International Conference on Control, Automation,

Robotics and Embedded Systems (CARE). Jabalpur.

Lappalainen, Jari, Timo Korvola, and Ville Alopaeus. 2017. “Modelling and dynamic

simulation of a large MSF plant using local phase equilibrium and simultaneous mass,

momentum, and energy solver.” Computers and Chemical Engineering 97: 242-258.

Latteman, Sabine. 2010. Development of an Environmental Impact Assessment and Decision

Support System for Seawater Desalination Plants. Boca Raton: CRC Press.

Lee, Peter L. 1993. Nonlinear Process Control: Applications of Generic Model Contro.

London: Springer-Verlag.

Lee, Sangkeum, Sunghee Myung, Junhee Hong, and Dongsoo Har. 2016. “Reverse osmosis

desalination process optimized for maximum permeate production with renewable

energy.” Desalination 398: 133-143.

Lior, Noam. 2012. Advances in Water Desalination, Volume 1. New Jersey: John Wiley &

Sons.

Lu, Yan-Yue, Yang-Dong Hu, Xiu-Ling Zhang, Lian-Ying Wu, and Qing-Zhi Liu. 2007.

“Optimum design of reverse osmosis system under different feed concentration and

product specification.” Journal of Membrane Science 287 (2): 219-229.

Mabrouk, Abdel Nasser A. 2013. “Technoeconomic analysis of once through long tube MSF

process for high capacity desalination plants.” Desalination 317: 84-94.

Malik, Sidra N., Parisa A. Bahri, and Linh T.T. Vu. 2016. “Steady state optimization of

design and operation of desalinationsystems using Aspen Custom Modeler.”

Computers and Chemical Engineering 247-256.


58

Marcovecchio, Marian G., Sergio F. Mussati, Pio A. Aguirre, Nicolas J., and Scenna. 2005.

“Optimization of hybrid desalination processes including multi stage flash and reverse

osmosis systems.” Desalination 182: 111-122.

Marion, Glenn, and Daniel T. Lawson. 2015. “What is mathematical modelling?” In An

Introduction to Mathematical Modelling, by Marion Glenn, 1-32. Bioinformatics and

Statistics.

Mock, William B.T. 2011. “Pareto Optimality.” Edited by Deen K. Chatterjee. In: Chatterjee

D.K. (eds) Encyclopedia of Global Justice (Springer, Dordrecht).

doi:https://doi.org/10.1007/978-1-4020-9160-5_341.

Mujtaba, I. M., R. Srinivasan, and N. O. Elbashir. 2017. The Water-Food-Energy Nexus:

Processes, Technologies, and Challenges. Boca Raton: CRC Press.

Ogunnaike, Babatunde A., and W. Harmon Ray. 1994. Process Dynamic, Modeling, and

Control. New York: Oxford University Press.

Oh, Hyun -Je, Tae -Mun Hwang, and Sangho Lee. 2009. “A simplified simulation model of

RO systems for seawater desalination.” Desalination 238: 128–139.

Pantelides, C. C., D. Gritsis, K. R. Morison, and R.W. H. Sargent. 1988. “The mathematical

modelling of transient systems using differential-algebraic equations.” Computers &

Chemical Engineering 12 (5): 449-454.

https://www.sciencedirect.com/science/article/pii/0098135488850622.

Park, Junhyung, and Kwang Soon Lee. 2017. “A two-dimensional model for the spiral wound

reverse osmosis membrane module.” Desalination 416: 157-165.

Peñate, Baltasar , and Lourdes García-Rodríguez. 2012. “Current trends and future prospects

in the design of seawater reverse osmosis desalination technology.” Desalination 284:

1-8.

Pfafflin, James R., and Edward N. Ziegler. 2006. Encyclopedia of Environmental Science and

Engineering: A-L. Boca Raton: CRC Press.

Rahimi, Bijan, and Hui Tong Chua. 2017. Low Grade Heat Driven Multi-Effect Distillation

and Desalination. Amsterdam: John Fedor.

Rasmuson, Anders, Bengt Andersson, Louise Olsson, and Ronnie Andersson. 2014.

Mathematical Modelling in Chemical Engineering. New York: Cambridge University

Press.

Robertson, M. W., J. C. Watters, P. B. Desphande, J. Z. Assef, and I. M. Alatiqi. 1996.

“Model based control for reverse osmosis desalination processes.” Desalination 104

(1-2): 59-68.


59

Rosso, Marina, Angelo Beltramini, Marco Mazzotti, and Massimo Morbidelli. 1996.

“Modeling multistage flash desalination plants.” Desalination 108: 365-374.

Said, Said Alforjani, Masour Emtir, and Iqbal M. Mujtaba. 2013. “Flexible Design and

Operation of Multi-Stage Flash (MSF).” Processes 1: 279-295.

Samyudia, Y., and P. L. Lee. 2002. “Robust generic model control (GMC) design for

uncertain nonlinear processes.” Proceedings of the 41st IEEE Conference on Decision

and Control, 2002. Las Vegas.

Shen, S. 2015. “Thermodynamic Losses in Multi-effect Distillation.” 7th International

Conference on Cooling and Heating Technologies (ICCHT 2014). Selangor: Institute

of Physics Publishing ( IOP ). 532.

Sobana, S., and Rames C. Panda. 2011. “Identification, Modelling, and Control of

Continuous Reverse Osmosis Desalination System: A Review.” Separation Science

and Technology 46 (4): 551-560.

Talati, S. N. 1994. “Evaluation of reverse osmosis and evaporative desalination systems

under feedwater supply constraints typical in arid countries.” Desalination 97 (1-3):

353-361.

doi:https://www.sciencedirect.com/science/article/pii/0011916494000999?via%3Dihu

b.

Thimmaraju, Manish, Divya Sreepada, Gummadi Sridhar Babu, Bharath Kumar Dasari, Sai

Kiran Velpula, and Nagaraju Vallepu. 2018. “Desalination of Water.” In Desalination

and Water Treatment, 333-347. London: IntechOpen.

Thulukkanam, Kuppan. 2000. Heat Exchanger Design Handbook. New York: CRC Press.

Tiwari, G. N., and Lovedeep Sahota. 2017. Advanced Solar-Distillation Systems BAsic

Principles, Thermal Modelling, and its Application. Singapore: Springer.

Veolia. 2015. The largest hybrid desalination plant in the world. VEOLIA. 21 09. Accessed

09 04, 2018. https://www.veoliawatertechnologies.com/en/media/articles/largest-

hybrid-desalination-plant-world.

Voutchkov, Nikolay. 2017. Pretreament for Reverse Osmosis Desalination. Amsterdam:

John Fedor.

Voutchkov, Nikolay. 2018. “Thewaternetwork.com.” Accessed 09 12, 2018.

https://thewaternetwork.com/article-FfV/in-conversation-with-desalination-expert-

nikolay-voutchkov-868XkVHpIUY0dk0qSccMtA.


60

Wali, Faisal. 2014. Natureasia.com. nature MIDDLE EAST Emerging science in the Arab

world. Accessed 09 03, 2018.

https://www.natureasia.com/en/nmiddleeast/article/10.1038/nmiddleeast.2014.273.

Wang, Lawrence K., Jiaping Paul Chen, Yung-Tse Hung, and Nazih K. Shammas. 2011.

Membrane and Desalination Technologies. New York: Springer.

Woldai, Abraha. 2015. Multi-Stage Flash Desalination: Modeling, Simulation, and Adaptive

Control. Boca Raton: CRC Press.

Zheng, Hongfei. 2017. Solar Energy Desalination Technology. Amsterdam: John Fedor.


61

Appendix

Appendix A

Simulation Results of the MSF Process:

Temperatures profiles of both heat recovery and heat rejection section:

Figure 9: Temperature of cooling brine


62

Figure 10: Flashing brine temperature

Figure 11: Distillate temperature


63

Figure 12: Vapor temperature


64

Concentration profile of the flashing brine in each stage:

Figure 13: Concentration of the flashing brine

Flashing brine flowrate profile of both heat recovery and heat rejection section:

Figure 14: Flowrate of the flashing brine


65

Figure 15: Distillate flowrate

Performance ratio (PR) of the MSF plant is given as:

𝑃𝑅 (%) = 𝐷(𝑗)/𝑊𝑠

Figure 16: Performance ratio of the MSF plant


66

Thermo-dynamic losses and thermo-physical properties of the brine, water, and steam

Figure 17: Overall heat transfer coefficient

Figure 18: Log mean temperature difference


67

Figure 19: Specific heat capacity of the brine and distillate

Figure 20: Specific heat capacity of the cooling brine


68

Figure 21: Boiling point elevation and non-equilibrium allowance

Figure 22: Temperature drop in demister

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