| Titre : |
Valorization of non-living microbial biomass for the adsorptive removal of cationic dyes from multicomponent aqueous systems : mechanistic study, DFT adsorption energy analysis, modeling, and machine learning-based optimization |
| Type de document : |
document électronique |
| Auteurs : |
Amira Othmani, Auteur ; Ammar Selatnia, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2026 |
| Importance : |
1 fichier PDF (6.6 Mo) |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Thèse de Doctorat : Génie Chimique : Alger, Ecole Nationale Polytechnique : 2026
Bibliogr. p. 175-203. - Appendices
Thèse confidentielle 3 ans jusqu'à Mars 2029 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Biosorption
Streptomyces rimosus biomass
Cationic dyes
Multicomponent aqueous systems
Density Functional Theory (DFT)
Machine learning optimization |
| Index. décimale : |
D000726 |
| Résumé : |
This research investigates the valorization of Streptomyces rimosus biomass, an industrial byproduct of antibiotic production, as an eco-friendly biosorbent for the removal of cationic dyes (Basic Blue 41, Basic Red 46, and Basic Yellow 28) from multicomponent aqueous systems. Comprehensive physicochemical characterization confirmed the presence of active functional groups responsible for high adsorption affinity. Adsorption kinetics and isotherms revealed a spontaneous, endothermic, and predominantly chemisorptive process. Density Functional Theory (DFT) analyses correlated adsorption energies and electronic descriptors with experimental performance, elucidating molecular-level interaction mechanisms. Advanced machine learning models, including a Tri-Hybrid DNN–NAS–PSO framework, provided accurate prediction and optimization of adsorption behavior. The study establishes S. Rimosus biomass as a sustainable and efficient biosorbent, offering a circular-economy approach for industrial wastewater remediation. |
Valorization of non-living microbial biomass for the adsorptive removal of cationic dyes from multicomponent aqueous systems : mechanistic study, DFT adsorption energy analysis, modeling, and machine learning-based optimization [document électronique] / Amira Othmani, Auteur ; Ammar Selatnia, Directeur de thèse . - [S.l.] : [s.n.], 2026 . - 1 fichier PDF (6.6 Mo).
Mode d'accès : accès au texte intégral par intranet.
Thèse de Doctorat : Génie Chimique : Alger, Ecole Nationale Polytechnique : 2026
Bibliogr. p. 175-203. - Appendices
Thèse confidentielle 3 ans jusqu'à Mars 2029 Langues : Anglais ( eng)
| Mots-clés : |
Biosorption
Streptomyces rimosus biomass
Cationic dyes
Multicomponent aqueous systems
Density Functional Theory (DFT)
Machine learning optimization |
| Index. décimale : |
D000726 |
| Résumé : |
This research investigates the valorization of Streptomyces rimosus biomass, an industrial byproduct of antibiotic production, as an eco-friendly biosorbent for the removal of cationic dyes (Basic Blue 41, Basic Red 46, and Basic Yellow 28) from multicomponent aqueous systems. Comprehensive physicochemical characterization confirmed the presence of active functional groups responsible for high adsorption affinity. Adsorption kinetics and isotherms revealed a spontaneous, endothermic, and predominantly chemisorptive process. Density Functional Theory (DFT) analyses correlated adsorption energies and electronic descriptors with experimental performance, elucidating molecular-level interaction mechanisms. Advanced machine learning models, including a Tri-Hybrid DNN–NAS–PSO framework, provided accurate prediction and optimization of adsorption behavior. The study establishes S. Rimosus biomass as a sustainable and efficient biosorbent, offering a circular-economy approach for industrial wastewater remediation. |
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