Machine Learning for Membrane Separation Applications

Machine Learning for Membrane Separation Applications, 1st Edition, presents a reference work on applying computational methods to polymeric membrane separation processes. The volume explains how machine learning enhances separation techniques across both gas and liquid separations. Academic researchers in chemistry and chemical engineering can utilize this reference to evaluate data-driven frameworks in separation science.

Core thematic arcs focus on the role of machine learning in membrane design and development, together with complete filtration systems. Detailed discussions examine specific materials, including nanocomposite membranes, MOF-based membranes, and disinfecting membranes. In addition, practical application coverage highlights technical approaches for CO2 mitigation.

A defining feature of the reference is its systematic focus on fouling mitigation within membrane filtration units. This orientation provides practical utility for software engineers and researchers in industry working on advanced separation tools. Industry practitioners also benefit from the structured analysis of machine learning implementations across diverse separation environments.