Leveraging Machine Learning to Expedite Screening of Single-atom Catalysts in Electrochemical Nitrate Reduction to Ammonia DOI

Zhongli Lu,

Jiming Liu, Houfen Li

et al.

Journal of Alloys and Compounds, Journal Year: 2024, Volume and Issue: unknown, P. 177180 - 177180

Published: Oct. 1, 2024

Language: Английский

Machine learning screening tools for the prediction of extraction yields of pharmaceutical compounds from wastewaters DOI Creative Commons
Ana Casas, Diego Rodríguez-Llorente, Guillermo Rodríguez-Llorente

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 62, P. 105379 - 105379

Published: April 30, 2024

Pharmaceutical compounds have become an increasingly important source of pollutants in wastewaters being conventional treatments ineffective removing them, so they are commonly discharged into the environment. Pharmaceuticals can be successfully removed using liquid-liquid extraction, and COSMO-RS used to predict interactions identify most promising solvents. However, COSMOtherm models cannot account for key process parameters, which reduces accuracy these computational models. Therefore, there is a need alternative approaches accurately extraction yields pharmaceuticals incorporate both processing interaction variables. This work machine learning yield eleven eight Six regression two classification were explored. The best performance was obtained with ANN regressor (test MAE: 4.510, test R2: 0.884) RF classifier accuracy: 0.938, recall: 0.974). analysis also showed features: solvent-to-feed ratio, n–octanol–water partition coefficient, hydrogen bond Van der Waals contributions excess enthalpy, pH distance nearest pKa. Machine as excellent tool screening selecting solvents conditions remove from wastewater.

Language: Английский

Citations

6
Biochar-based persulfate activation: Rate constant prediction, key variables identification, and system optimization DOI
Nurul Alvia Istiqomah, Donghwi Jung,

Jeehyeong Khim

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 65, P. 105839 - 105839

Published: July 30, 2024

Language: Английский

Citations

4
Coagulation coupled with batch biological sponge iron reactor for efficient treatment of leachate from waste transfer stations DOI
Yanyu Li,

Jiahui Xue,

Wei Zhao

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 67, P. 106115 - 106115

Published: Sept. 6, 2024

Language: Английский

Citations

4
Unlocking prediction and optimal design of CO2 methanation catalysts via active learning-enhanced interpretable ensemble learning DOI
Qingchun Yang,

Runjie Bao,

Zhao Wang

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161154 - 161154

Published: March 1, 2025

Language: Английский

Citations

0
Zero-valent iron-based materials for enhanced reductive removal of contaminants: From the trial-and-error synthesis to rational design DOI

Yinghao Shi,

Jiaming Guo,

Feilong Gao

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2024, Volume and Issue: unknown, P. 124901 - 124901

Published: Dec. 1, 2024

Language: Английский

Citations

3
Self-supported iron-doped cobalt–copper oxide heterostructures for efficient electrocatalytic denitrification DOI

Jiao Hu,

Cui Tang,

Zenghui Bi

et al.

Journal of Colloid and Interface Science, Journal Year: 2024, Volume and Issue: 675, P. 313 - 325

Published: June 27, 2024

Language: Английский

Citations

2
Leveraging Machine Learning to Expedite Screening of Single-atom Catalysts in Electrochemical Nitrate Reduction to Ammonia DOI

Zhongli Lu,

Jiming Liu, Houfen Li

et al.

Journal of Alloys and Compounds, Journal Year: 2024, Volume and Issue: unknown, P. 177180 - 177180

Published: Oct. 1, 2024

Language: Английский

Citations

1