Angielski
Machine Learning for Membrane Separation Applications explores the role of Machine Learning with respect to polymeric membrane-based separation processes. The book discovers 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 the traditional techniques. Starting with highlighting the importance of Machine Learning in polymeric membranes associated separation processes, the book proceeds with depicting the role of Machine Learning in membranes design and development, fouling mitigation, and filtration systems. The book employs Machine Learning in wide variety of polymeric membranes such as nanocomposite membranes, MOF based membranes and disinfecting membranes. Machine Learning for Membrane Separation Applications serves as a useful tool for researchers in academia and industry as well as for students and teachers in membrane science and technology who are looking towards new ways to develop state of art membranes and membrane technologies for liquid and gas separations, such as wastewater treatment and CO2 mitigation. Providing the techniques of Machine Learning in polymeric membrane separation processes, this book exhibits the importance of Machine learning in this field.
