Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 by the authors. This study conducts a comprehensive investigation to optimize the degradation of crystal violet (CV) dye using the Fenton process. The main objective is to improve the efficiency of the Fenton process by optimizing various physicochemical factors such as the Fe2+ concentration, H2O2 concentration, and pH of the solution. The results obtained show that the optimal dosages of Fe2+ and H2O2 giving a maximum CV degradation (99%) are 0.2 and 3.13 mM, respectively. The optimal solution pH for CV degradation is 3. The investigation of the type of acid for pH adjustment revealed that sulfuric acid is the most effective one, providing 100% yield, followed by phosphoric acid, hydrochloric acid, and nitric acid. Furthermore, the examination of sulfuric acid concentration shows that an optimal concentration of 0.1 M is the most effective for CV degradation. On the other hand, an increase in the initial concentration of the dye leads to a reduction in the hydroxyl radicals formed (HO•), which negatively impacts CV degradation. A concentration of 10 mg/L of CV gives complete degradation of dye within 30 min following the reaction. Increasing the solution temperature and stirring speed have a negative effect on dye degradation. Moreover, the combination of ultrasound with the Fenton process resulted in a slight enhancement in the CV degradation, with an optimal stirring speed of 300 rpm. Notably, the study incorporates the use of Gaussian process regression (GPR) modeling in conjunction with the Improved Grey Wolf Optimization (IGWO) algorithm to accurately predict the optimal degradation conditions. This research, through its rigorous investigation and advanced modeling techniques, offers invaluable insights and guidelines for optimizing the Fenton process in the context of CV degradation, thereby achieving the twin goals of cost reduction and environmental impact minimization.
Author(s): Mechati S, Zamouche M, Tahraoui H, Filali O, Mazouz S, Bouledjemer INE, Toumi S, Triki Z, Amrane A, Kebir M, Lefnaoui S, Zhang J
Publication type: Article
Publication status: Published
Journal: Water
Year: 2023
Volume: 15
Issue: 24
Online publication date: 14/12/2023
Acceptance date: 11/12/2023
Date deposited: 08/01/2024
ISSN (electronic): 2073-4441
Publisher: MDPI
URL: https://doi.org/10.3390/w15244274
DOI: 10.3390/w15244274
Altmetrics provided by Altmetric
