Colloidal particles can attach to surfaces during transport, but the attachment depends on particle size, hydro-dynamics, solid and water chemistry, and particulate matter. The attachment is quantified in filtration theory by measuring attachment or sticking efficiency (Alpha). A comprehensive Alpha database (2538 records) was built from experiments in the literature and used to develop a machine learning (ML) model to predict Alpha. The training (r-squared: 0.86) was performed using two random forests capable of handling missing data. A holdout dataset was used to validate the training (r-squared: 0.98), and the variable importance was explored for training and validation. Finally, an additional validation dataset was built from quartz crystal microbalance experiments using surface-modified polystyrene, poly (methyl methacrylate), and polyethylene. The experiments were per -formed in the absence or presence of humic acid. Full database regression (r-squared: 0.90) predicted Alpha for the additional validation with an r-squared of 0.23. Nevertheless, when the original database and the additional validation dataset were combined into a new database, both the training (r-squared: 0.95) and validation (r-squared: 0.70) increased. The developed ML model provides a data-driven prediction of Alpha over a big database and evaluates the significance of 22 input variables.
Web of Science Categories
Engineering, Environmental; Environmental Sciences; Water Resources
Funding
Basic Science Program through the National Research Foundation (NRF) of Korea - Ministry of Education [NRF-2021R1I1A1A01054655]; NRF of Korea - Korea government (MSIT) [NRF-2020R1A2C1013851]; Hanyang University [HY-202200000000915]