Machine Learning for Membrane Separation Applications covers the importance of polymeric membranes in separation processes and explains how machine learning is taking these processes to the next level. As polymeric membranes can be used for both gas and liquid separations, along with several other applications, they provide a bypass route to separation due to several fold benefits over traditional techniques. Sections cover the role of Machine Learning in membranes design and development, fouling mitigation, and filtration systems. Machine Learning in a wide variety of polymeric membranes, such as nanocomposite membranes, MOF based membranes, and disinfecting membranes are also covered.
This book will serve as a useful tool for researchers in academia and industry, but will also be an ideal reference for students and teachers in membrane science and technology who are looking for new ways to develop state-of-the-art membranes and membrane technologies for liquid and gas separations, such as wastewater treatment and CO2 mitigation.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Chapter 1. Membrane technology and machine learning - p. 1 7. Chapter 2. Fundamentals of machine learning - p. 25 8. Chapter 3. Machine learning applications in membrane fabrication techniques - p. 45 9. Chapter 4. Machine learning applications in membrane characterization techniques - p. 69 10. Chapter 5. Molecular dynamics simulations in membrane separations - p. 101 11. Chapter 6. Machine learning in gas separation applications - p. 129 12. Chapter 7. Machine learning in modern membrane water treatment systems - p. 153 13. Chapter 8. Machine learning in membrane fouling and aging predictions - p. 179 14. Chapter 9. Machine learning and its impact on advanced membrane materials - p. 201 15. Chapter 10. Challenges, opportunities, and future of machine learning in membrane technology - p. 227 16. Index - p. 257
Machine Learning for Membrane Separation Applications covers the importance of polymeric membranes in separation processes and explains how machine learning is taking these processes to the next level. As polymeric membranes can be used for both gas and liquid separations, along with several other applications, they provide a bypass route to separation due to several fold benefits over traditional techniques. Sections cover the role of Machine Learning in membranes design and development, fouling mitigation, and filtration systems. Machine Learning in a wide variety of polymeric membranes, such as nanocomposite membranes, MOF based membranes, and disinfecting membranes are also covered.
This book will serve as a useful tool for researchers in academia and industry, but will also be an ideal reference for students and teachers in membrane science and technology who are looking for new ways to develop state-of-the-art membranes and membrane technologies for liquid and gas separations, such as wastewater treatment and CO2 mitigation.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Chapter 1. Membrane technology and machine learning - p. 1 7. Chapter 2. Fundamentals of machine learning - p. 25 8. Chapter 3. Machine learning applications in membrane fabrication techniques - p. 45 9. Chapter 4. Machine learning applications in membrane characterization techniques - p. 69 10. Chapter 5. Molecular dynamics simulations in membrane separations - p. 101 11. Chapter 6. Machine learning in gas separation applications - p. 129 12. Chapter 7. Machine learning in modern membrane water treatment systems - p. 153 13. Chapter 8. Machine learning in membrane fouling and aging predictions - p. 179 14. Chapter 9. Machine learning and its impact on advanced membrane materials - p. 201 15. Chapter 10. Challenges, opportunities, and future of machine learning in membrane technology - p. 227 16. Index - p. 257
