Desalination Technologies
Design and Operation
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Desalination Technologies: Design and Operation sets the scene for desalination technologies as a long-term solution to freshwater demand by analyzing the current demand for water, available water resources and future predicted demand. The book captures recent developments in thermal desalination (multistage flash desalination, multi-effect evaporation, vapor compression), membrane desalination (forward osmosis, reverse osmosis, pressure retarded, electrodialysis, membrane distillation, ultra-, nano-, and micro-filtration), and alternative processes such as freezing and ion exchange. Both dynamic and steady state models (from short cut, simple, to detail) of various desalination processes are discussed. The book is intended for (under)graduate students in chemical engineering and postgraduate researchers and industrial practitioners in desalination.
Key Features
- Provides the fundamentals of different desalination processes
- Includes desalination modeling from short and simple, to detailed and more advanced
- Discusses desalination optimization and synthesis to reduce environmental impact
- Handles thermo-physical property models and correlations
- Includes case studies to give a clearer understanding of desalination
Readership
(Under)graduate students in chemical engineering; researchers in academia and industry working on desalination
Table of Contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedications
- Authors
- Preface
- Chapter 1. Introduction
- 1.1. World water demand and crisis
- 1.2. Wastewater, reclamation and reuse, social perception
- 1.3. Sustainable water supply and management
- 1.4. Freshwater production by desalination processes
- 1.5. Market share and analysis of different desalination processes
- Chapter 2. Desalination processes
- 2.1. MEE desalination process
- 2.2. MSF desalination process
- 2.3. RO desalination process
- Chapter 3. Process modeling, simulation, optimization, and computational tools
- 3.1. Introduction
- 3.2. Modeling
- 3.3. Process simulation
- 3.4. Optimization
- 3.5. Commercial flowsheeting software used in desalination
- 3.6. Advantages of modeling and simulation
- Chapter 4. Modeling of MSF desalination process
- 4.1. Introduction
- 4.2. MSF model: type I
- 4.3. MSF model: type II
- 4.4. MSF model: type III (hybrid)
- 4.5. MSF model: type IV
- 4.6. MSF model: type V
- Nomenclature
- Chapter 5. Modeling of RO desalination process
- 5.1. Introduction
- 5.2. RO process
- 5.3. RO membrane modeling
- 5.4. RO model—Type I
- 5.5. RO model—Type II
- 5.6. RO model—Type III
- 5.7. RO model—Type IV
- 5.8. RO model—Type V
- 5.9. RO model—Type VI
- 5.10. RO model—Type VII
- 5.11. RO models for boron rejection
- Chapter 6. Modeling of MEE desalination process
- 6.1. Introduction
- 6.2. MEE process
- 6.3. MEE model Type—I
- 6.4. MEE model Type—II
- 6.5. MEE model Type – III
- 6.6. MEE model Type—IV
- Chapter 7. Optimization of MSF desalination process
- 7.1. Introduction
- 7.2. Operation of MSF processes
- 7.3. Optimization of MSF processes under fixed freshwater demand
- 7.4. Optimization of MSF processes with variable freshwater demand
- 7.5. Global optimization
- 7.6. Optimization of industrial MSF-BR process—optimization problem 7
- 7.7. MSF-BR with thermal vapor compression (MSF-BR-TVC)
- 7.8. Optimization of cogenerating plants: power and desalination plants
- Chapter 8. Optimization of RO desalination process
- 8.1. Introduction
- 8.2. Optimization problem 1
- 8.3. Optimization problem 2
- 8.4. Optimization problem 3
- 8.5. Optimization problem 4
- 8.6. Optimization problem 5
- 8.7. Optimization problem 6
- 8.8. Optimization problem 7
- 8.9. Optimization problem 8
- 8.10. Optimization problem 9
- 8.11. Optimization problem 10
- 8.12. Optimization problem 11
- 8.13. Optimization problem 12
- 8.14. Optimization problem 13
- 8.15. Optimization problem 14: boron removal
- 8.16. Optimization problem 15: boron removal
- 8.17. Optimization problem 16: boron removal
- 8.18. Optimization problem 17
- 8.19. Optimization problem 18
- 8.20. Optimization problem 19: meeting variable freshwater demand
- 8.21. Optimization problem 20: meeting variable freshwater demand
- 8.22. Optimization problem 21: internally staged design
- 8.23. Optimization problem 22: multiperiod operation
- Chapter 9. Optimization of MEE desalination process
- 9.1. Introduction
- 9.2. Optimization problem 1
- 9.3. Optimization problem 2
- 9.4. Optimization problem 3
- 9.5. Optimization problem 4
- 9.6. Optimization problem 5
- 9.7. Optimization problem 6
- 9.8. Optimization problem 7
- 9.9. Optimization problem 8
- 9.10. Optimization problem 9
- 9.11. Optimization problem 10
- 9.12. Optimization problem 11
- 9.13. Optimization problem 12
- 9.14. Optimization problem 13
- Chapter 10. Hybrid desalination processes
- 10.1. Introduction
- 10.2. Hybrid MSF-RO process 1: optimization
- 10.3. Hybrid MSF-RO process 2: optimization
- 10.4. Hybrid MSF-RO process 3: optimization
- 10.5. Hybrid MEE-TVC-RO process 1: optimization
- 10.6. Hybrid MEE-TVC-RO process 2: simulation
- 10.7. Hybrid MEE-RO process 3: optimization
- 10.8. Hybrid MEE-TVC-RO process 4: simulation
- 10.9. Hybrid MEE-TVC-RO process 5: simulation
- 10.10. Hybrid MEE-TVC-RO process 6: optimization
- 10.11. Hybrid MSF-MEE process 1: simulation
- 10.12. Hybrid MSF-MEE process 2: simulation
- 10.13. Hybrid MSF-MEE process 3: optimization
- 10.14. Hybrid Utility-MEE-TVC-RO process: optimization
- 10.15. Hybrid CHP-MEE-TVC-RO process: simulation
- 10.16. Hybrid dual-purpose power and water plant 1: simulation
- 10.17. Hybrid dual-purpose power and water plant 2: simulation
- 10.18. Hybrid forward osmosis (FO)-RO desalination process: simulation
- 10.19. Hybrid FO-MSF desalination process
- 10.20. Hybrid pressure retarded osmosis (PRO)-RO desalination process: simulation
- Chapter 11. Dynamic modeling and control of desalination processes
- 11.1. Introduction
- 11.2. Dynamic modeling of MSF process
- 11.3. MSF process control
- 11.4. Dynamic modeling of RO process
- 11.5. RO process control
- 11.6. Dynamic modeling of MEE process
- 11.7. Control of MEE process
- 11.8. Nomenclature
- 11.9. Greek letters
- Chapter 12. Use of renewable energies in desalination processes
- 12.1. Introduction
- 12.2. Freeze desalination using LNG cold energy
- 12.3. MEE-TVC-RO desalination using solar energy
- 12.4. RO desalination using solar energy
- 12.5. Combined cycle power and MSF desalination process with solar energy
- 12.6. Solar powered humidification–dehumidification based desalination
- 12.7. RO desalinization system for power and water supply using renewable energies
- 12.8. RO process for producing different grades of water using multiple renewable energy sources
- 12.9. RO desalination process using wind energy
- 12.10. MEE desalination process using thermocline energy
- 12.11. Thermosiphon powered RO desalination process
- Chapter 13. Application of artificial intelligence in desalination processes
- 13.1. Introduction
- 13.2. NN architecture
- 13.3. NN training algorithm
- 13.4. Features of MATLAB neural network toolbox
- 13.5. NN based correlation for boiling point temperature elevation (TE) in MSF process
- 13.6. NN-based correlation for estimating first and second dissociation constant of carbonic acid in seawater
- 13.7. NN-based correlation for estimating dynamic freshwater demand profile at different seasons
- 13.8. NN-based correlation for estimating dynamic water permeability constant in RO process
- 13.9. NN-based modeling of RO process
- 13.10. NN-based control of RO process
- 13.11. NN-based modeling of RO process
- 13.12. NN-based modeling of industrial MSF and RO process
- 13.13. NN-based modeling and optimization of industrial MSF
- Chapter 14. Pretreatments and posttreatments in desalination processes
- 14.1. Introduction
- 14.2. Pretreatments in MSF process
- 14.3. RO membrane fouling
- 14.4. Conventional pretreatments in RO process
- 14.5. Nonconventional pretreatments in RO process
- 14.6. Posttreatment of desalinated water
- 14.7. Environmental impact
- Index
Product details
- No. of pages: 660
- Language: English
- Copyright: © Elsevier 2022
- Published: June 24, 2022
- Imprint: Elsevier
- Paperback ISBN: 9780128137901
- eBook ISBN: 9780128137918
About the Authors
Iqbal Mujtaba
Iqbal M. Mujtaba is a Professor of Computational Process Engineering and is currently Associate Dean (Learning, Teaching and Quality) of the Faculty of Engineering and Informatics at the University of Bradford. He was the Head of the School of Engineering at the University of Bradford from 2016 to 2018. He obtained his BSc Eng and MSc Eng degrees in Chemical Engineering from Bangladesh University of Engineering and Technology (BUET) in 1983 and 1984, respectively, and obtained his PhD from Imperial College London in 1989. He is a Fellow of the IChemE, a Chartered Chemical Engineer. He was the Chair of the European Committee for Computers in Chemical Engineering Education from 2010 to 2013 and the Chair of the IChemE’s Computer Aided Process Engineering Special Interest Group from 2012 to 2019. He is currently an Associate Editor for Asia Pacific Journal of Chemical Engineering, South African Journal in Chemical Engineering, Chemical Product and Process Modelling and an Editorial Board Member of Desalination.
Professor Mujtaba leads research into dynamic modeling, simulation, optimization, and control of batch and continuous chemical processes with specific interests in distillation, industrial reactors, refinery processes, desalination, wastewater treatment, and crude oil hydrotreating focusing on energy and water. He has managed several research collaborations and consultancy projects with industry and academic institutions in the United Kingdom, Italy, Hungary, Malaysia, Thailand, India, Qatar, South Africa, Iraq, Jordan, Algeria, China, Libya, Bahrain, and Saudi Arabia. He has published more than 380 technical papers and has delivered more than 75 invited lectures/seminars/plenaries/keynotes/short courses around the world. He has supervised 37 PhD students to completion and is currently supervising 10 PhD students. He is the author/coauthor of (1) Batch Distillation: Design and Operation (textbook) published by the Imperial College Press, London, 2004, (2) “Wastewater treatment by Reverse Osmosis” published by CRC Press, 2020. He is one of the co-editors of the books (1) Application of Neural Networks and Other Learning Technologies in Process Engineering, Imperial College Press, London, 2001, (2) Composite Materials Technology: Neural Network Applications, CRC Press, 2009, (3) The Water-Food-Energy Nexus, CRC Press, 2017, (4) Water Management: Social and Technological Perspective, CRC Press, 2018.
Affiliations and Expertise
Department of Chemical Engineering, University of Bradford, Bradford, UK
Md Tanvir Sowgath
Dr. Md Tanvir Sowgath is an Associate Professor of Chemical Engineering Department, Bangladesh University of Engineering and Technology (BUET), Bangladesh, and is currently the Visiting Academic of the School of Engineering, University of Bradford, UK. He is an Associate Member of the IChemE (UK) and is a BUET technical representative for the Bangladesh Energy Regulatory Commission (2021). He was selected as technical committee member for the Bangladesh Ministry of Environment (2010, 2014), technical committee member of Sylhet Gas Field for the Bangladesh Ministry of Energy and Mineral Resource (2014e2015), Department of Public Health (2013) and Bangladesh Energy Regulatory Commission (2021). He has 12 years of teaching and research experience in the field of Chemical Engineering as an Assistant Professor and Associate Professor at BUET. He has offered a number of courses in recent years in (a) Unit operation of Chemical Process, (b) Fundamentals of Environmental Engineering, (c) Industrial Pollution Control, (d) Air Pollution Control, (e) Optimization of Chemical Process, and (f) Computer Aided Process Design.
Dr. Sowgath studied his BSc Engineering degree in Chemical Engineering at BUET and obtained his PhD degree from the University of Bradford in 2007. His PhD dissertation was entitled “Neural Network based hybrid modelling and MINLP based optimization of MSF desalination process within gPROMS.” Dr. Sowgath set the stone for graduate level research in desalination at the University of Bradford which has been carried forward by several researchers at Bradford after him.
Dr. Sowgath’s main research interests are in process modeling, dynamic simulation, steady state, and dynamic optimization of chemical processes with specific interests in Desalination, Gas Processing, and Refinery. He has supervised several undergraduate and MSc projects at BUET and has published several technical papers in journals and conference proceedings in the area of Desalination, Gas Processing, Refinery, and Fertilizer Processing. His current research focus in desalination is on fault diagnosis, scheduling, and maintenance.
Affiliations and Expertise
Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh