Application of eco-friendly material as an inexpensive adsorbent for methyl violet dye removal: experimental, response surface methodology and statistical physics DOI

Fatiha Bessaha,

Gania Bessaha,

Assia Benhouria

et al.

Journal of Dispersion Science and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: Dec. 4, 2024

Methyl Violet (MV) removal from aqueous solutions is studied using an Algerian Bentonite sample as a low-cost adsorbent. SEM-EDX, X-ray diffraction, and chemical composition characterized the adsorbent material. The modeling optimization study of MV adsorption artificial neural network (ANN) response surface methodology (RSM) were also examined. effects pH, contact time, dye concentration, temperature are all considered. kinetics results adjusted to best fit pseudo-second-order model. Langmuir-Freundlich Langmuir models well describe experimental data with capacity 472 mg g−1. calculated thermodynamic demonstrates that spontaneous endothermic. Desorption studies methanol indicate could successfully retain MV, even after five cycles. statistical physics theory indicates non-parallel orientation molecule's adsorption. energies varied 13.99 17.60 kJ mol−1, revealing physical systems. From these results, it can be considered raw bentonite tested herein effective in removing may used alternative high-cost commercial adsorbents.

Language: Английский

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

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 480, P. 135853 - 135853

Published: Sept. 16, 2024

Language: Английский

Citations

11
Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review DOI Creative Commons
Banza Jean Claude, Linda L. Sibali

Journal of Environmental Science and Health Part A, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Feb. 2, 2025

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 understand better current status, future possibilities, capabilities of ML supporting environmentally friendly development applications. Previous applications were classified into three categories according their objectives: material process performance prediction sustainability evaluation. helps optimize BDMs systems, predict properties performance, reverse engineering, data difficulties evaluations. Ensemble models cutting-edge Neural Networks operate satisfactorily on these datasets easily generalized. neural network poor interpretability, there not been any studies assessment that consider geo-temporal dynamics; thus, building methods is currently practical. Future research should follow workflow. Investigating potential system optimization, evaluation sustainable requires further investigation.

Language: Английский

Citations

0
Fenton Oxidation-Activated Hydrochar Derived from Factory Tea Waste with Enhanced Surface Area as a Sustainable Adsorbent for Multiple Dyes DOI
Prangan Duarah, Mihir Kumar Purkait

Colloids and Surfaces A Physicochemical and Engineering Aspects, Journal Year: 2025, Volume and Issue: unknown, P. 136635 - 136635

Published: March 1, 2025

Language: Английский

Citations

0
Application of eco-friendly material as an inexpensive adsorbent for methyl violet dye removal: experimental, response surface methodology and statistical physics DOI

Fatiha Bessaha,

Gania Bessaha,

Assia Benhouria

et al.

Journal of Dispersion Science and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: Dec. 4, 2024

Methyl Violet (MV) removal from aqueous solutions is studied using an Algerian Bentonite sample as a low-cost adsorbent. SEM-EDX, X-ray diffraction, and chemical composition characterized the adsorbent material. The modeling optimization study of MV adsorption artificial neural network (ANN) response surface methodology (RSM) were also examined. effects pH, contact time, dye concentration, temperature are all considered. kinetics results adjusted to best fit pseudo-second-order model. Langmuir-Freundlich Langmuir models well describe experimental data with capacity 472 mg g−1. calculated thermodynamic demonstrates that spontaneous endothermic. Desorption studies methanol indicate could successfully retain MV, even after five cycles. statistical physics theory indicates non-parallel orientation molecule's adsorption. energies varied 13.99 17.60 kJ mol−1, revealing physical systems. From these results, it can be considered raw bentonite tested herein effective in removing may used alternative high-cost commercial adsorbents.

Language: Английский

Citations

1