Surface response modeling of homogeneous photo Fenton Fe(III) and Fe(II) complex for sunlight degradation and mineralization of food dye

Kebir, Mohammed; Benramdhan, Imen-Kahina; Nasrallah, Noureddine; Tahraoui, Hichem; Bait, Nadia; Benaissa, Houssine; Ameraoui, Rachid; Zhang, Jie; Assadi, Aymen Amin; Mouni, Lotfi; Amrane, Abdeltif

doi:10.1016/j.catcom.2023.106780

Cited by 7 publications

(1 citation statement)

References 45 publications

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“…The accuracy of each model is evaluated using the following essential performance measures: the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). These individual metrics are computed by applying the following mathematical expressions [40][41][42][43][44]:…”

Section: Statistical Evaluation Criteriamentioning

confidence: 99%

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

Mechati,

Zamouche,

Tahraoui

et al. 2023

Water

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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.

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Section: Statistical Evaluation Criteriamentioning

confidence: 99%

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

Mechati,

Zamouche,

Tahraoui

et al. 2023

Water

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Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater

Ganthavee,

Fernando,

Trzcinski

2024

Environ. Process.

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The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better. Highlights • In-depth analysis of various artificial intelligence optimization techniques. • Prediction efficiency of artificial intelligence and machine learning algorithms. • 98% dye removal and 100% regeneration of graphite intercalation compound. • Advanced statistical analysis of targeted responses and data fitting techniques. • Analysis of uncertainties and variability using Monte Carlo simulation.

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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

Guediri,

Bouguettoucha,

Tahraoui

et al. 2024

Journal of Molecular Liquids

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Surface response modeling of homogeneous photo Fenton Fe(III) and Fe(II) complex for sunlight degradation and mineralization of food dye

Cited by 7 publications

References 45 publications

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater

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

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