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

Zhongli Lu,

Jiming Liu, Houfen Li

и другие.

Journal of Alloys and Compounds, Год журнала: 2024, Номер unknown, С. 177180 - 177180

Опубликована: Окт. 1, 2024

Язык: 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

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 62, С. 105379 - 105379

Опубликована: Апрель 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.

Язык: Английский

Процитировано

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

Jeehyeong Khim

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 65, С. 105839 - 105839

Опубликована: Июль 30, 2024

Язык: Английский

Процитировано

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

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 67, С. 106115 - 106115

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

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

Runjie Bao,

Zhao Wang

и другие.

Chemical Engineering Journal, Год журнала: 2025, Номер unknown, С. 161154 - 161154

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

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

и другие.

Applied Catalysis B Environment and Energy, Год журнала: 2024, Номер unknown, С. 124901 - 124901

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

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

Jiao Hu,

Cui Tang,

Zenghui Bi

и другие.

Journal of Colloid and Interface Science, Год журнала: 2024, Номер 675, С. 313 - 325

Опубликована: Июнь 27, 2024

Язык: Английский

Процитировано

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

Zhongli Lu,

Jiming Liu, Houfen Li

и другие.

Journal of Alloys and Compounds, Год журнала: 2024, Номер unknown, С. 177180 - 177180

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

1