Micropollutant removal via genetic algorithms and high throughput experimentation
Nanofiltration (NF) and reverse osmosis (RO) membranes have a high potential to remove low molecular weight trace contaminants in water that cannot be removed efficiently by conventional biological or physico-chemical treatments. However, membrane performance depends on several parameters involved in membrane synthesis. Multi-parameter optimization strategies, such as genetic algorithms (GAs) are extremely promising to minimize time and material consumption to direct membrane synthesis towards better separation properties (selectivity) of the targeted compounds combined with useful fluxes. Cellulose acetate membranes were prepared via phase inversion. The optimized parameters included compositional (polymer concentration, solvent) and also, for first time when using GA as optimization strategy in membrane synthesis, non-compositional on the level of the membrane synthesis process and post treatment (temperature, annealing time), which have a great impact on membrane performance. Dead-end filtrations were carried out to evaluate the membranes in their potential to retain ibuprofen in water by using high-throughput (HT) experimentation. Ibuprofen was selected as it is one of the smallest molecules among relevant micropollutants present in drinking water. As result, membranes with ibuprofen retention up to 96% and permeabilities in the normal range of cellulose acetate (CA)-based RO membranes were obtained. Thus prepared membranes also showed promising NaCl retention and twice the permeability compared to membranes prepared via a classical parameter-by-parameter optimization.