Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning

The optimization of water permeability and salt rejection of thin film nanocomposite (TFN) membranes is of great interests for reverse osmosis (RO) desalination. Based on literature data, machine learning was used to form prediction models of water permeability and salt pass rate for TFN RO membranes. A literature review was done to examine key parameters in membrane transport. Gradient boosting tree model was employed to learn from relevant variables such as loading, size, pore size of nanoparticles, and properties of the membranes. The results suggest that while porous nanoparticles perform better than nonporous ones, factors including loading, size and hydrophilicity are the primary factors that influence membrane performances. Ways to optimize the parameters for improved membrane performance were discussed using partial dependence plot analysis. The optimized properties were also compared with aquaporin-based membranes and implications for future development were discussed.