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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Chemical engineering journal (Lausanne, Switzerland : 1996) Ročník 406; s. 126782
Hlavní autori:	Zhu, Xinzhe, Wan, Zhonghao, Tsang, Daniel C.W., He, Mingjing, Hou, Deyi, Su, Zhishan, Shang, Jin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 15.02.2021
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!