scholarly journals Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2in Suspension and Prediction by Artificial Neural Networks

2016 ◽  
Vol 48 (8) ◽  
pp. 464-473 ◽  
Author(s):  
Messaoud Bennemla ◽  
Malika Chabani ◽  
Abdeltif Amrane
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Aqueous Solutions ◽  
Photocatalytic Degradation ◽  
Artificial Neural

Ultraviolet-activated persulfate oxidation of methyl orange: a comparison between artificial neural networks and factorial design for process modelling

2015 ◽  
Vol 14 (3) ◽  
pp. 528-535 ◽  
Author(s):  
Zacharias Frontistis ◽  
Evroula Hapeshi ◽  
Despo Fatta-Kassinos ◽  
Dionissios Mantzavinos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Methyl Orange ◽  
Aqueous Solutions ◽  
Factorial Design ◽  
Azo Dye ◽  
Process Modelling ◽  
Persulfate Oxidation ◽  
Artificial Neural ◽  
Activated Persulfate

In this work, the degradation of the azo dye methyl orange in model aqueous solutions by UVC light-induced persulfate oxidation was studied.

Applying Artificial Neural Networks in the Modelling of Copper Recovery Process by Ionic Exchange in Aqueous Solutions

2008 ◽  
Vol 59 (7) ◽  
Author(s):  
Leonard Mihaly Cozmuta ◽  
Camelia Varga ◽  
Monica Marian ◽  
Anca Mihaly Cozmuta ◽  
Tutu Hlanganani
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Aqueous Solutions ◽  
Exchange Process ◽  
Theoretical Data ◽  
Copper Recovery ◽  
Ionic Exchange ◽  
Artificial Neural

Artificial neuronal networks are widely used in different fields such as finance, medicine, engineering, geology, chemistry, physics in order to predict, classify and control the development of different processes. The paper presents experimental data on the adsorption process of copper from aqueous solutions and compares it with theoretical data obtained by mathematical modeling using artificial neural networks (ANN). The aim of the paper is to demonstrate that ANN ensure a high accuracy in the mathematical modeling of the process. We have collected experimental data by using synthetic solutions with different pH and cooper ions concentrations, which were retained on a PUROLITE S930 cationic resin. Both experimental and theoretical data obtained using ANN show the correlation between factors which influence the ionic exchange process (pH, temperature, initial copper concentration, activation energy).

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