Machine learning for membrane design in energy production, gas separation, and water treatment: a review
Ahmed I. Osman, Mahmoud Nasr, Mohamed Farghali, Sara S. Bakr, Abdelazeem S. Eltaweil, Ahmed K. Rashwan, Eman M. Abd El-Monaem
Abstract
Abstract Membrane filtration is a major process used in the energy, gas separation, and water treatment sectors, yet the efficiency of current membranes is limited. Here, we review the use of machine learning to improve membrane efficiency, with emphasis on reverse osmosis, nanofiltration, pervaporation, removal of pollutants, pathogens and nutrients, gas separation of carbon dioxide, oxygen and hydrogen, fuel cells, biodiesel, and biogas purification. We found that the use of machine learning brings substantial improvements in performance and efficiency, leading to specialized membranes with remarkable potential for various applications. This integration offers versatile solutions crucial for addressing global challenges in sustainable development and advancing environmental goals. Membrane gas separation techniques improve carbon capture and purification of industrial gases, aiding in the reduction of carbon dioxide emissions.