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,…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Mashallah Rezakazemi received his BEng. and MEng. degrees in 2009 and 2011, respectively, both in Chemical Engineering, from the Iran University of Science and Technology (IUST), and his Ph.D. from the University of Tehran (UT) in 2015. Dr. Rezakazemi's research is in the general area of the Membrane Technology, Adsorption, Environmental Science to the service of the broad areas of learning and training. Specifically, his research in engineered and natural environmental systems involves: (i) membrane-based processes for energy-efficient desalination, CO2 capture, gas separation, and wastewater reuse, (ii) sustainable production of enriched gas stream, water and energy generation with the engineered membrane, (iii) environmental applications and implications of nanomaterials, and (iv) water and sanitation in developing countries. He has coauthored in more than 190 highly cited journal publications, conference articles and book chapters. He has received major awards (×16) and grants (×12) from various funding agencies in recognition of his research. He was awarded as country's best researcher in technical and engineering group, Ministry of Science, Research and Technology, Iran. Rezakazemi published Wiley's book "Membrane Contactor Technology: Water Treatment, Food Processing, Gas Separation, and Carbon Capture?.
Inhaltsangabe
1. Introduction to Membrane Technology and Machine Learning 2. Fundamentals of Machine Learning 3. Membrane Fabrication Techniques 4. Membrane Characterization Techniques 5. Machine Learning Algorithms and Their Applicability to Membrane Processes 6. Gas Separation with Membranes 7. Water Treatment using Membrane Technology 8. Machine Learning in Membrane Fouling and Aging Predictions 9. Advanced Membrane Materials: A Machine Learning Perspective 10. Membrane Process Simulation and Machine Learning Integration 11. Challenges and Opportunities in Merging ML with Membrane Technology 12. Real-world Case Studies: Machine Learning in Membrane Applications 13. Conclusion and the Future of ML in Membrane Technology
1. Introduction to Membrane Technology and Machine Learning 2. Fundamentals of Machine Learning 3. Membrane Fabrication Techniques 4. Membrane Characterization Techniques 5. Machine Learning Algorithms and Their Applicability to Membrane Processes 6. Gas Separation with Membranes 7. Water Treatment using Membrane Technology 8. Machine Learning in Membrane Fouling and Aging Predictions 9. Advanced Membrane Materials: A Machine Learning Perspective 10. Membrane Process Simulation and Machine Learning Integration 11. Challenges and Opportunities in Merging ML with Membrane Technology 12. Real-world Case Studies: Machine Learning in Membrane Applications 13. Conclusion and the Future of ML in Membrane Technology
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