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Gaussian process regression with levy flight optimization: Advanced AR66 adsorption studies

Lookup NU author(s): Dr Jie ZhangORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2024 Institution of Chemical Engineers. The coal fly ash (CFA), which is the residue generated by coal-fired power plants, was converted into a valuable zeolite material known as zeolite P (ZNa-P) through thermal and acid pretreatments followed by microwave radiation. Various analytical techniques were utilized to analyze the resulting zeolite, including X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, BET analysis, and zeta potential measurement. The efficiency of ZNa-P in eliminating anionic dyes from aqueous solutions was exhibited by successfully removing acid red dye 66 (AR66) from a solution composed of water. To optimize the removal process, Central Composite Design (CCD) was applied to investigate the impact of four main parameters: solution pH, initial dye concentration, adsorbent mass, and contact time. The generated CCD database was modeled using Gaussian process regression (GPR) with the Lévy flight distribution (LFD) optimization algorithm. The GPR model was then used to determine optimal conditions for maximum AR66 absorption (3405.3 mg/g), with a pH of 2, initial dye concentration of 1000 mg/L, adsorbent mass of 0.2 g/L, and contact time of 11 minutes. Furthermore, the GPR model exhibited significantly lower error (32.58 mg/g) in predicting the experimental values compared to the CCD model (204.92 mg/g), highlighting the efficiency and superiority of the GPR model in this study.


Publication metadata

Author(s): Harizi I, Aldahri T, Chebli D, Tahraoui H, Bouguettoucha A, Rohani S, Zhang J, Amrane A

Publication type: Article

Publication status: Published

Journal: Chemical Engineering Research and Design

Year: 2024

Volume: 207

Pages: 192-208

Print publication date: 01/07/2024

Online publication date: 01/06/2024

Acceptance date: 27/05/2024

Date deposited: 19/08/2024

ISSN (print): 0263-8762

ISSN (electronic): 1744-3563

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.cherd.2024.05.037

DOI: 10.1016/j.cherd.2024.05.037

ePrints DOI: 10.57711/76tk-av33


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Funding

Funder referenceFunder name
Algerian Ministry of Higher Education
Department of Physics, Faculty of Science, Taibah University, Madinah, Saudi Arabia
Ministry of Higher Education in Saudi Arabia
Natural Sciences and Engineering Research Council of Canada (NSERC-CRD grant)

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