Unravelling heterogenous adsorption performance of hydrochar particle and key properties in heavy metal immobilization relative to corresponding residual bulk hydrochar DOI
Wenjing Guo, Zhiyong Zhang,

Yan‐Fang Feng

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Phosphoric acid based geopolymer foam-activated carbon composite for methylene blue adsorption: isotherm, kinetics, thermodynamics, and machine learning studies DOI Creative Commons
Muhammad Irfan Khan, Suriati Sufian,

Farrukh Hassan

et al.

RSC Advances, Journal Year: 2025, Volume and Issue: 15(3), P. 1989 - 2010

Published: Jan. 1, 2025

The ACP adsorbent, a blend of activated carbon and phosphoric acid-based geopolymer foam, showed high methylene blue adsorption efficiency, aligning with the Langmuir isotherm, PSO kinetics, ANN-based predictions.

Language: Английский

Citations

3
Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization DOI Creative Commons
Chong Liu, P. Balasubramanian, Jingxian An

et al.

npj Clean Water, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 22, 2025

Language: Английский

Citations

1
Chitosan-based materials for emerging contaminants removal: Bibliometric analysis, research progress, and directions DOI
Chong Liu, Grégorio Crini, Éric Lichtfouse

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 71, P. 107327 - 107327

Published: Feb. 27, 2025

Language: Английский

Citations

1
Machine learning-driven prediction of biochar adsorption capacity for effective removal of Congo red dye DOI Creative Commons
Shubham Yadav,

P. K. Rajput,

P. Balasubramanian

et al.

Carbon Research, Journal Year: 2025, Volume and Issue: 4(1)

Published: Jan. 22, 2025

Abstract Congo red, a widely utilized dye in the textile industry, presents significant threat to living organisms due its carcinogenic properties and non-biodegradable nature. This study proposes data-driven machine-learning approach optimize biochar characteristics environmental conditions maximize adsorption capacity of for removal red dye. Therefore, six machine learning models were trained tested on dataset containing eleven input parameters (related conditions) capacity. The evaluated using performance metrics such as R-squared ( R 2 ), Mean Squared Error (MSE), Root (RMSE). With highest (0.9785) lowest RMSE (0.1357), Random Forest Regression (RF) outperformed other models. DT XGB also performed well, achieving slightly lower values 0.9741 0.9577, respectively. LR model worst, with (0.4575) (0.6821). Moreover, reliability these was validated 10-fold cross-validation method. RF once again best an value 0.9762. Feature analysis revealed that initial concentration relative dosage C 0 specific surface area BET pore volume PV ) are most factors affecting biochar, while carbon content oxygen nitrogen molar ratio [ (O + N)/C ], diameter D had minimal impact. research demonstrates can accurately predict biochar’s contaminant capacity, enhancing wastewater treatment promoting efficient, cost-effective management. Graphical

Language: Английский

Citations

0
Synthesis of Copper-Impregnated MCM-41 from Synthetic and Rice Husk-Derived Silica for Efficient Adsorption of Levofloxacin: A Machine Learning Approach DOI

Gayatri Rajput,

Vijayalakshmi Gosu, Verraboina Subbaramaiah

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 115634 - 115634

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhanced fluoride removal by modified water hyacinth: response surface methodology and machine learning approach DOI
Jagadish H. Patil, Raviraj Kusanur, Poornima G. Hiremath

et al.

Biomass Conversion and Biorefinery, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Language: Английский

Citations

0
Areca Catechu Biochar and Nano-Biochar as Adsorbents for Congo Red: Synthesis, Characterization, and Performance Evaluation DOI Creative Commons
Robiatul Adawiyah,

Nova Yuliasari,

Yulizah Hanifah

et al.

BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS, Journal Year: 2025, Volume and Issue: 20(1), P. 112 - 128

Published: Feb. 10, 2025

The presence of hazardous synthetic dyes such as Congo Red in industrial wastewater poses a significant environmental threat. This study explores the potential biochar (BC) and nano-biochar (nano-BC), derived from Areca catechu husk sustainable adsorbents for dye removal. Nano-BC was synthesised via hydrothermal carbonisation mechanical ball milling, leading to enhanced structural surface properties. X-ray Diffraction (XRD) revealed that Pinang is predominantly amorphous, while BC exhibits increased crystallinity with sharp peaks, nano-BC demonstrates highest nanostructural refinement. Fourier Transform Infra (FTIR) confirmed transformation aliphatic-rich raw biomass into aromatic-dominant structures nano-BC, showing more pronounced graphite-like features. Scanning Electron Microscope (SEM) illustrated morphological evolution, exhibiting refined, uniformly porous structures. BET analysis has significantly higher area 41.38 m²/g smaller pore size 8.4928 nm compared 22.38 15.39 nm, enhancing adsorption capacity. Furthermore, kinetics followed pseudo-second-order model, isothermal monolayer maximum capacity (Qmax = 154.526 mg/g). These findings highlight superior performance emphasising its environmentally friendly water treatment applications. Copyright © 2025 by Authors, Published BCREC Publishing Group. an open access article under CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Language: Английский

Citations

0
Leveraging Artificial Intelligence Models (GBR, SVR, and GA) for Efficient Chromium Reduction via UV/Trichlorophenol/Sulfite Reaction DOI Creative Commons
Amir H. Mohammadi,

Parsa Khakzad,

Tayebeh Rasolevandi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104599 - 104599

Published: March 1, 2025

Language: Английский

Citations

0
Predictive Capability of Dye Removal from Wastewater Using Biochar by a Rough Set Machine Learning Model DOI
P. Balasubramanian, Muhil Raj Prabhakar, Chong Liu

et al.

ACS ES&T Water, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

Language: Английский

Citations

0
Design optimization of bimetal-modified biochar for enhanced phosphate removal performance in livestock wastewater using machine learning DOI

Weilin Fu,

Xia Yao, Lisheng Zhang

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: unknown, P. 131898 - 131898

Published: Nov. 1, 2024

Language: Английский

Citations

3