In view of the high risks brought about by organic micropollutants (OMPs), nanofiltration (NF) processes have been playing a vital role in advanced water and wastewater treatment, owing to the high membrane performance in rejection of OMPs, permeation of water, and passage of mineral salts. Though numerous studies have been devoted to evaluating and technically enhancing membrane performance in removing various OMPs, the trade-off effect between water permeance and water/OMP selectivity for state-of-the-art membranes remains far from being understood. Knowledge of this effect is significant for comparing and guiding membrane development works toward cost-efficient OMP removal. In this work, we comprehensively assessed the performance of 88 NF membranes, commercialized or newly developed, based on their water permeance and OMP rejection data published in the literature. The effectiveness and underlying mechanisms of various modification methods in tailoring properties and in turn performance of the mainstream polyamide (PA) thin-film composite (TFC) membranes were quantitatively analyzed. The trade-off effect was demonstrated by the abundant data from both experimental measurements and machine learning-based prediction. On this basis, the advancement of novel membranes was benchmarked by the performance upper-bound revealed by commercial membranes and lab-made PA membranes. We also assessed the potentials of current NF membranes in selectively separating OMPs from inorganic salts and identified the future research perspectives to achieve further enhancement in OMP removal and salt/OMP selectivity of NF membranes.
Keywords: Machine learning (ML); Membrane modification; Nanofiltration (NF); Organic micropollutants (OMPs); Selectivity; Water permeance.