Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption

[Display omitted] •Antiobiotics adsorption on carbon-based materials was modeled by machine learning.•Random forest showed best prediction accuracy than GBT and ANN.•SBET , pHsol, C0 were critical factors for TC (74%) and SMX (80%) adsorption on CBMs.•Impact tendencies of SBET, pHsol, C0 on adsorpti...

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Veröffentlicht in:	Chemical engineering journal (Lausanne, Switzerland : 1996) Jg. 406; S. 126782
Hauptverfasser:	Zhu, Xinzhe, Wan, Zhonghao, Tsang, Daniel C.W., He, Mingjing, Hou, Deyi, Su, Zhishan, Shang, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 15.02.2021
Schlagworte:	Activated carbon Antibiotics removal Engineered biochar Industrial wastewater treatment Random forest algorithm Sustainable waste management ANN BC O/C pHpzc C0 Sustainable waste management CBM WF MBC C wt. T Activated carbon Antibiotics removal GBT SBET PDP ML AC GO CN TC Engineered biochar Random forest algorithm RF pHsol (O+N)/C H/C Industrial wastewater treatment SMX
ISSN:	1385-8947, 1873-3212
Online-Zugang:	Volltext
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