Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering
Lukka Thuyavan Yogarathinam, Sani I. Abba, Jamilu Usman, Dahiru U. Lawal, Isam H. Aljundi
Abstract
value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.