Elsevier Machine Learning for Membrane Separation Applications

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. Provides detailed information on particular AI models for specific membrane processes Delivers hands on information on membrane materials, modifiers, design, and processes Includes state-of-the-art modern techniques for wastewater treatment CO2 mitigation