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ENG470: Engineering Honours Thesis
Discipline of Engineering and Energy
ENG470: Engineering Honours Thesis
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
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ENG470: Engineering Honours Thesis
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ENG470: Engineering Honours Thesis
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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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
4.7 Hybrid System (MSF-RO) .................................................................................. 40
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
5.3 Hybrid System (MSF-RO) .................................................................................. 48
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
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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
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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
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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.
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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)
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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
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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
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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
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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
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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)
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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.
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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).
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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
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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.
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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
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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.
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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.
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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.
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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
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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)
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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.
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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)
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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)
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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
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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
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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:
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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)
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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)
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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)
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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.
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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)
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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)
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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)
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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:
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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)
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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)
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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:
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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)
4.7 Hybrid System (MSF-RO)
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)
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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.
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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).
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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
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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
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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).
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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
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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
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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.
5.3 Hybrid System (MSF-RO)
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.
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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
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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).
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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.
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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.
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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
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Figure 10: Flashing brine temperature
Figure 11: Distillate temperature
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Figure 12: Vapor temperature
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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
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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
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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
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Figure 19: Specific heat capacity of the brine and distillate
Figure 20: Specific heat capacity of the cooling brine
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Figure 21: Boiling point elevation and non-equilibrium allowance
Figure 22: Temperature drop in demister