ArticleMolecules (Basel, Switzerland)2023
Response Methodology Optimization and Artificial Neural Network Modeling for the Removal of Sulfamethoxazole Using an Ozone-Electrocoagulation Hybrid Process.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Artificial Neural Network and Non-Linear Regression-Based Optimization of the Degradation of Acid Black 52 Using Atmospheric Plasma Process.ACS omega · 2025Article
- Application of Response Surface Methodology for the Optimization of Basic Red 46 Dye Degradation in an Electrocoagulation-Ozonation Hybrid System.Molecules (Basel, Switzerland) · 2025Article
- Innovative approaches to greywater micropollutant removal: AI-driven solutions and future outlook.RSC advances · 2025Review
- Recent progress in highly effective electrocoagulation-coupled systems for advanced wastewater treatment.iScience · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Removing antibiotics from water is critical to prevent the emergence and spread of antibiotic resistance, protect ecosystems, and maintain the effectiveness of these vital medications. The combination of ozone and electrocoagulation in wastewater treatment provides enhanced removal of contaminants, improved disinfection efficiency, and increased overall treatment effectiveness. In this work, the removal of sulfamethoxazole (SMX) from an aqueous solution using an ozone-electrocoagulation (O-EC) system was optimized and modeled. The experiments were designed according to the central composite design. The parameters, including current density, reaction time, pH, and ozone dose affecting the SMX removal efficiency of the OEC system, were optimized using a response surface methodology. The results show that the removal process was accurately predicted by the quadric model. The numerical optimization results show that the optimum conditions were a current density of 33.2 A/m
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Registered trials
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