Optimizing multi-barrier drinking water treatment through a data-driven process simulator based on machine learning

doi:10.1016/j.jclepro.2025.146987

Optimizing multi-barrier drinking water treatment through a data-driven process simulator based on machine learning

2025-11-08

0

PRE

AI

DOI：10.1016/j.jclepro.2025.146987

Original

Original request for help

Save

Abstract

En 中文

• A process simulator has been developed to optimize multi-barrier water treatment using global models and real data. • CatBoost model accurately predicts key effluent water quality parameters. • The simulator identifies optimal placement for ozone-biological activated carbon barriers. • Data-driven tool enables evidence-based treatment protocols for higher quality drinking water.

Optimizing multi-barrier drinking water treatment through a data-driven process simulator based on machine learning

Abstract

Journal

Organization