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. Understanding Machine Learning Fundamentals: Membrane Insights 3. Machine learning Applications in Membrane Fabrication Techniques 4. Machine Learning Applications in Membrane Characterization Techniques 5. Molecular Dynamics Simulations in Membrane Separations 6. Machine Learning in Gas Separation Applications 7. Machine Learning in Modern Membrane Water Treatment Systems 8. Machine learning in Membrane Fouling and Aging Predictions 9. Machine Learning and Its Impact on Advanced Membrane Materials 10. Challenges, Opportunities, and Future of ML in Membrane Technology
1. Introduction to Membrane Technology and Machine Learning 2. Understanding Machine Learning Fundamentals: Membrane Insights 3. Machine learning Applications in Membrane Fabrication Techniques 4. Machine Learning Applications in Membrane Characterization Techniques 5. Molecular Dynamics Simulations in Membrane Separations 6. Machine Learning in Gas Separation Applications 7. Machine Learning in Modern Membrane Water Treatment Systems 8. Machine learning in Membrane Fouling and Aging Predictions 9. Machine Learning and Its Impact on Advanced Membrane Materials 10. Challenges, Opportunities, and Future of ML in Membrane Technology
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