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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Vydáno v:Chemical engineering journal (Lausanne, Switzerland : 1996) Ročník 406; s. 126782
Hlavní autoři: Zhu, Xinzhe, Wan, Zhonghao, Tsang, Daniel C.W., He, Mingjing, Hou, Deyi, Su, Zhishan, Shang, Jin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.02.2021
Témata:
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
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Shrnutí:[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 adsorption were similar for TC and SMX.•Chemical compositions and pHpzc of CBMs showed different influences on TC and SMX. Antibiotics as emerging pollutants have attracted extensive attention due to their ecotoxicity and persistence in the environment. Adsorption of antibiotics on carbon-based materials (CBMs) such as biochar and activated carbon was recognized as one of the most promising technologies for wastewater treatment. This study applied machine learning (ML) methods to develop generic prediction models of tetracycline (TC) and sulfamethoxazole (SMX) adsorption on CBMs. The results suggested that random forest outperformed gradient boosting trees and artificial neural network for both TC and SMX adsorption models. The random forest models could accurately predict the adsorption capacity of antibiotics on CBMs using material properties and adsorption conditions as model inputs. The developed ML models presented better generalization ability than traditional isotherm models under variable environmental conditions (e.g., temperature, solution pH) and adsorbent types. The relative importance analysis and partial dependence plots based on ML models were performed to compare TC and SMX adsorption on CBMs. The results indicated the critical role of specific surface area for both TC (24%) and SMX (45%) adsorption, while the other material properties (e.g., H/C, (O + N)/C, pHpzc) showed variable influences due to the differences in molecular structures, functional groups, and pKa values of TC and SMX. The accurate ML prediction models with generalization ability are useful for designing efficient CBMs with minimal experimental screening, while the relative importance and partial dependence plot analysis can guide rational applications of CBMs for antibiotics wastewater treatment.
ISSN:1385-8947
1873-3212
DOI:10.1016/j.cej.2020.126782