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Thermodynamic study and the development of a support vector machine model for predicting adsorption behavior of orange peel-derived beads in wastewater treatment

Lookup NU author(s): Dr Jie ZhangORCiD

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Abstract

© 2024 Elsevier B.V.This study investigates the use of orange peels as a precursor for synthesizing sodium alginate-encapsulated beads for methylene blue (MB) removal. The prepared beads (BOP1 and BOP2) underwent characterization through FTIR, XRF, SEM and TGA. Subsequently, the impacts of various factors, including temperature, the initial pH, initial concentration, salt and humic acid, are studied. The adsorption isotherms show high adsorbed quantities of 764.92 and 659.78 mg/g for BOP1 and BOP2 respectively, while the obtained data are best described by the monolayer with two energies (MMTE) model, which is then used to perform a thermodynamic study of the MB adsorption mechanism. Additionally, the adsorption kinetics data are modeled using three models, with the PFO model identified as the most appropriate. The regenerated beads demonstrate the ability to be reused up to 7 cycles, The effects of NaCl and humic acid on MB adsorption reveal that NaCl inhibits adsorption due to competition with Na+, while humic acid has no effect. Finally, a support vector machine (SVM) model optimized by the Levy Flight Distribution Optimization (LFD) algorithm is developed and found to be capable of accurately predicting the adsorption behavior of the prepared beads. This model is then used in optimizing the process conditions for maximal MB removal. Overall, this study demonstrates that the prepared beads could be potential low-cost and environmentally friendly adsorbents for wastewater treatment applications.


Publication metadata

Author(s): Guediri A, Bouguettoucha A, Tahraoui H, Chebli D, Amrane A, Zhang J

Publication type: Article

Publication status: Published

Journal: Journal of Molecular Liquids

Year: 2024

Volume: 403

Print publication date: 01/06/2024

Online publication date: 01/05/2024

Acceptance date: 28/04/2024

ISSN (print): 0167-7322

ISSN (electronic): 1873-3166

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.molliq.2024.124860

DOI: 10.1016/j.molliq.2024.124860


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