Kinetic study and modelling of cephalexin removal from aqueous solution by advanced oxidation processes through artificial neural networks

2017 ◽  
Vol 92 ◽  
pp. 72-79 ◽  
Author(s):  
Otidene R.S. da Rocha ◽  
Renato F. Dantas ◽  
Welenilton José do Nascimento Júnior ◽  
Yuji Fujiwara ◽  
Marta Maria Menezes Bezerra Duarte ◽  
...  
Keyword(s):  
Neural Networks ◽  
Aqueous Solution ◽  
Artificial Neural Networks ◽  
Kinetic Study ◽  
Advanced Oxidation Processes ◽  
Advanced Oxidation ◽  
Oxidation Processes ◽  
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.

Nonlinear Modeling for the Degradation of Aqueous Azo Dyes by Combined Advanced Oxidation Processes Using Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Masroor Mohajerani ◽  
Mehrab Mehrvar ◽  
Farhad Ein-Mozaffari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Azo Dyes ◽  
Advanced Oxidation Processes ◽  
Removal Rate ◽  
Advanced Oxidation ◽  
Ultrasonic Power ◽  
Oxidation Processes ◽  
Degradation Rates ◽  
Artificial Neural

One-hidden-layer artificial neural networks (ANNs) using a back-propagation structure have been trained on different sets of experimental data to identify and evaluate the degradation of different azo dyes (Reactive Yellow 84, Reactive Blue 19, Direct Red 23, Direct Red 28, and Acid Blue 193) by photo-Fenton process and combined ozonation and ultrasonolysis processes. Different input variables such as pH, initial concentrations of dyes and ozone, reaction time, ultrasonic power density, and initial concentrations of hydrogen peroxide and ferrous in aqueous solution were employed to model the degradation rates of azo dyes based on the decolorization efficiency and the removal rate using chemical oxygen demand (COD) and total organic carbon (TOC). A new model expression is developed to find the effect of individual parameters and their interactions on the efficiency of organic degradation by advanced oxidation processes.

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