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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)
82 Downloads (Pure)

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.

Original languageEnglish
Number of pages56
JournalEnvironmental Chemistry Letters
Early online date06 Feb 2024
DOIs
Publication statusEarly online date - 06 Feb 2024

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