Phosphoric acid based geopolymer foam-activated carbon composite for methylene blue adsorption: isotherm, kinetics, thermodynamics, and machine learning studies

RSC Advances, Год журнала: 2025, Номер 15(3), С. 1989 - 2010

Опубликована: Янв. 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.

Язык: Английский

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Machine learning prediction of ammonia nitrogen adsorption on biochar with model evaluation and optimization

npj Clean Water, Год журнала: 2025, Номер 8(1)

Опубликована: Фев. 22, 2025

Язык: Английский

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Chitosan-based materials for emerging contaminants removal: Bibliometric analysis, research progress, and directions

Journal of Water Process Engineering, Год журнала: 2025, Номер 71, С. 107327 - 107327

Опубликована: Фев. 27, 2025

Язык: Английский

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Machine learning-driven prediction of biochar adsorption capacity for effective removal of Congo red dye

Carbon Research, Год журнала: 2025, Номер 4(1)

Опубликована: Янв. 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

Язык: Английский

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

Journal of environmental chemical engineering, Год журнала: 2025, Номер unknown, С. 115634 - 115634

Опубликована: Янв. 1, 2025

Язык: Английский

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Enhanced fluoride removal by modified water hyacinth: response surface methodology and machine learning approach

Biomass Conversion and Biorefinery, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

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Areca Catechu Biochar and Nano-Biochar as Adsorbents for Congo Red: Synthesis, Characterization, and Performance Evaluation

BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS, Год журнала: 2025, Номер 20(1), С. 112 - 128

Опубликована: Фев. 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).

Язык: Английский

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Leveraging Artificial Intelligence Models (GBR, SVR, and GA) for Efficient Chromium Reduction via UV/Trichlorophenol/Sulfite Reaction

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104599 - 104599

Опубликована: Март 1, 2025

Язык: Английский

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Predictive Capability of Dye Removal from Wastewater Using Biochar by a Rough Set Machine Learning Model

ACS ES&T Water, Год журнала: 2025, Номер unknown

Опубликована: Апрель 4, 2025

Язык: Английский

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

Bioresource Technology, Год журнала: 2024, Номер unknown, С. 131898 - 131898

Опубликована: Ноя. 1, 2024

Язык: Английский

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