Probing selective pollutant removal in nanofiltration processes: Critical insights from separation factor and removal...

The nanofiltration (NF) system applied in water and wastewater treatments generally pursues the removal-aimed selectivity, characterized by high removal of pollutants and minimal removal of non-pollutants. The NF system selectivity is traditionally quantified using the separation factor (SF), which represents a fractionation-based selectivity defined as the passage ratio of two solutes through the system. Herein, we demonstrated that SF metric might inadequately evaluate the removal-aimed selectivity in NF systems, and proposed a more relevant metric - the removal difference between pollutants and non-pollutants (ΔR). We performed experimental and modeling analysis to elucidate the impacts of membrane properties (intrinsic water/solute and solute/solute selectivity) and operating conditions (water flux and recovery) on the ΔR. The results show that ΔR offers a promising framework for evaluating the removal-aimed selectivity. Based on this metric, we suggested novel optimization strategies for membrane materials and systems, such as prioritizing membranes with high solute/solute selectivity over those with high water/solute selectivity, alongside rationally reducing water flux and optimizing water recovery. Additionally, multi-pass filtration, as a strategy to enhance solute removal, can only improve ΔR when the increase in pollutant removal is more pronounced than that of non-pollutants. This work provides a deeper understanding of selectivity in pollutant removal, offering a new perspective for the design and optimization of NF-based water treatment processes.