Predicting of acid red 14 removals from synthetic wastewater in the advanced oxidation process using artificial neural networks and fuzzy regression

2022 ◽  
Author(s):  
Gholamreza Asadollahfardi ◽  
Malihe Afsharnasab ◽  
Mohammad Hossein Rasoulifard ◽  
Mojtaba Tayebi Jebeli
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Advanced Oxidation Process ◽  
Oxidation Process ◽  
Advanced Oxidation ◽  
Fuzzy Regression ◽  
Synthetic Wastewater ◽  
Artificial Neural

scholarly journals IMPLEMENTING ARTIFICIAL NEURAL NETWORKS MODELLING AFTER THE TREATMENT OF OIL REFINERY EFFLUENTS USING ADVANCED OXIDATION PROCESSES

2016 ◽  
Vol 10 (1) ◽  
pp. 23-32 ◽  
Author(s):  
S. C. G. Moraes ◽  
L. E. M. C. Zaidan ◽  
D. C. Napoleão ◽  
F. O. Carvalho ◽  
M. C. B. Montenegro ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Advanced Oxidation Processes ◽  
Advanced Oxidation ◽  
Oil Refinery ◽  
Oxidation Processes ◽  
Artificial Neural

scholarly journals Employment of advanced oxidation processes in the degradation of a textile dye mixture: evaluation of reaction parameters, kinetic study, toxicity and modeling by artificial neural networks

2021 ◽  
Vol 25 ◽  
pp. e12
Author(s):  
Marcos André Soares de Oliveira ◽  
Naiana Santos da Cruz Santana Neves ◽  
Rayany Magali da Rocha Santana ◽  
Alex Leandro Andrade de Lucena ◽  
Léa Elias Mendes Carneiro Zaidan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Resources ◽  
Organic Contaminants ◽  
Textile Industry ◽  
Advanced Oxidation Processes ◽  
Advanced Oxidation ◽  
Textile Dye ◽  
Oxidation Processes ◽  
Artificial Neural

Organic contaminants in industrial effluents threaten the quality of water resources, especially due to their resistance to natural degradation. The textile industry gain relevance, considering that it generates large volumes. This work aimed to evaluate the efficiency of different advanced oxidation processes (AOP) for the degradation of the mixture textile dyes in solution. After optimization of the main parameters involved in the applied processes and systems, the AOP with greater efficiency in the degradation of the compounds was the photo-Fenton/UV-C (92%) after 360 min of treatment. The experimental data showed a better fit to the Chan and Chu kinetic model and trough an evaluation using artificial neural networks it was possible to predict the maximum degradation achievable by the dye mixture. The toxicity assays, using multiple species of seeds indicated a treated solution with no toxic effects and that the applied methodology can be used without affecting the water resources.

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