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XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions

Elika Karbassiyazdi, Fatemeh Fattahi, Negin Yousefi, Amirhessam Tahmassebi, Arsia Afshar Taromi, Javad Zyaie Manzari, Amir H. Gandomi, Ali Altaee, Amir Razmjou

2022Environmental Research89 citationsDOIOpen Access PDF

Topics & Concepts

AdsorptionComputer scienceProcess (computing)WastewaterActivated carbonBiochemical engineeringProcess engineeringEnvironmental scienceChemistryEnvironmental engineeringEngineeringOrganic chemistryOperating systemPer- and polyfluoroalkyl substances researchCarbon Dioxide Capture TechnologiesAir Quality and Health Impacts
XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions | Litcius