Machine learning prediction of biochar properties derived from food waste DOI
Ekaterina Kravchenko, Tatiana Minkina, D. A. Privizentseva

и другие.

Journal of environmental chemical engineering, Год журнала: 2025, Номер unknown, С. 116791 - 116791

Опубликована: Апрель 1, 2025

Язык: Английский

A complete review on the oxygen-containing functional groups of biochar: Formation mechanisms, detection methods, engineering, and applications DOI

Jiefeng Chen,

Junhui Zhou,

Weitao Zheng

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 946, С. 174081 - 174081

Опубликована: Июнь 20, 2024

Язык: Английский

Процитировано

31

Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives DOI

Weilin Fu,

Menghan Feng,

Changbin Guo

и другие.

Bioresource Technology, Год журнала: 2024, Номер 403, С. 130861 - 130861

Опубликована: Май 18, 2024

Язык: Английский

Процитировано

17

Engineering biochar from biomass pyrolysis for effective adsorption of heavy metal: An innovative machine learning approach DOI
Lijian Leng,

Huihui Zheng,

Tian Shen

и другие.

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 131592 - 131592

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Enhanced imidacloprid adsorption using boron-modified biochar: Insights into molecular mechanisms and environmental stability DOI
Zhengming Yang, Zhuochao Wang,

Khantaphong Charoenkal

и другие.

Chemical Engineering Journal, Год журнала: 2025, Номер unknown, С. 159729 - 159729

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Machine learning prediction of biochar physicochemical properties based on biomass characteristics and pyrolysis conditions DOI

Yuanbo Song,

Zipeng Huang,

Mengyu Jin

и другие.

Journal of Analytical and Applied Pyrolysis, Год журнала: 2024, Номер 181, С. 106596 - 106596

Опубликована: Июнь 13, 2024

Язык: Английский

Процитировано

13

Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation DOI
Chong Liu, P. Balasubramanian, Fayong Li

и другие.

Journal of Hazardous Materials, Год журнала: 2024, Номер 480, С. 135853 - 135853

Опубликована: Сен. 16, 2024

Язык: Английский

Процитировано

11

Tourmaline/ZnAL-LDH nanocomposite based photocatalytic system for efficient degradation of mixed pollutant wastewater DOI

Jiangfu Zheng,

Changzheng Fan, Xiaoming Li

и другие.

Separation and Purification Technology, Год журнала: 2024, Номер 345, С. 127306 - 127306

Опубликована: Апрель 4, 2024

Язык: Английский

Процитировано

8

Machine learning-based exploration of biochar for environmental management and remediation DOI
Burcu Oral,

Ahmet Coşgun,

M. Erdem Günay

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 360, С. 121162 - 121162

Опубликована: Май 14, 2024

Язык: Английский

Процитировано

8

Amorphous-dominated MgO hollow spheres enhanced fluoride adsorption: Mechanism analysis and machine learning prediction DOI
Lin Fan,

Dexi Wang,

Honglei Yu

и другие.

The Journal of Chemical Physics, Год журнала: 2025, Номер 162(1)

Опубликована: Янв. 2, 2025

Amorphous-dominated magnesium oxide hollow spheres (A-MgO) were prepared using a spray-drying method in this study. These exhibited excellent sphericity, large specific surface areas, and abundant porosity. A-MgO outstanding fluoride adsorption properties, with maximum capacity of 260.4 mg/g. When the pH value was less than 8, removal percentage remained more 87.4%. Moreover, above 75% even after five application cycles. In addition, research revealed that SO42-, CO32-, PO43- exerted pronounced effect on removal, whereas coexisting ions such as Br-, Cl-, NO3-, HCO3- had minimal impact process. An in-depth analysis mechanism demonstrated process by involves various synergistic mechanisms, electrostatic adsorption, ion exchange, oxygen vacancy physical pore filling. To predict performance under complex conditions, high-performance machine learning model, GBDT-S, developed hyperparameter optimization. The R2 0.99 0.80 for training testing datasets, respectively, RMSE 3.26 3.89. Interpretative SHapley Additive exPlanations technology indicated reaction time, concentration, key factors influencing percentage.

Язык: Английский

Процитировано

1

Simultaneous Mitigation of Cadmium Contamination and Greenhouse Gas Emissions in Paddy Soil by Iron-Modified Biochar DOI
Xin Wang,

Tong Zou,

Jiapan Lian

и другие.

Journal of Hazardous Materials, Год журнала: 2025, Номер 488, С. 137430 - 137430

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

1