Artificial neural network modeling of the influence of sol–gel synthesis variables on the photocatalytic activity of TiO2 nanoparticles in the removal of Acid Red 27

Artificial neural network modeling of the influence of sol–gel synthesis variables on the... In this study, titanium dioxide (TiO2) nanoparticles were prepared by the sol–gel method in different synthesis conditions. The effect of synthesis variables (including water:titanium alkoxide molar ratio, reflux temperature, reflux time, gelation pH, and stirring speed) were studied in the removal of Acid Red 27 as a model contaminant from textile industry under UV light irradiation. For the first time, we report modeling of the effects of synthesis variables on the photocatalytic activity of TiO2 nanoparticles by an artificial neural network (ANN). Five effective synthesis variables were inserted as the input of the network and reaction rate constants (k ap) were introduced as the output of the network. The results showed that the predicted data from the designed ANN model were in good agreement with the experimental data, with a correlation coefficient (R 2) of 0.9655 and mean square error of 0.00148. The designed artificial neural network provided a reliable method for modeling the photocatalytic activity of TiO2 nanoparticles prepared under different synthesis conditions. Furthermore, the relative importance of each synthesis variable was calculated based on the connection weights of ANN model. The reflux time and reflux temperature were the most significant variables in the photocatalytic activity of TiO2 nanoparticles, followed by the water:titanium alkoxide molar ratio and gelation pH. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Chemical Intermediates Springer Journals

Artificial neural network modeling of the influence of sol–gel synthesis variables on the photocatalytic activity of TiO2 nanoparticles in the removal of Acid Red 27

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Publisher
Springer Netherlands
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Chemistry; Catalysis; Physical Chemistry; Inorganic Chemistry
ISSN
0922-6168
eISSN
1568-5675
D.O.I.
10.1007/s11164-014-1753-z
Publisher site
See Article on Publisher Site

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