Artificial neural network for optimization of a synthesis process of γ-bi2moo6 using surface response methodology

Authors:
Guillermo González-Campos;Edith Luévano-Hipólito;Luis Martin Torres-Treviño;Azael Martinez-De La Cruz
Affiliations:
Facultad de Ingeniería Mecánica y Eléctrica, UANL, San Nicolás de los Garza, Nuevo León, México;Centro de Innovación, Investigación y Desarrollo en Ingeniería y Tecnología, UANL, Apodaca, Nuevo León, México;Centro de Innovación, Investigación y Desarrollo en Ingeniería y Tecnología, UANL, Apodaca, Nuevo León, México;Facultad de Ingeniería Mecánica y Eléctrica, UANL, San Nicolás de los Garza, Nuevo León, México,Centro de Innovación, Investigación y Desarrollo en Ingenier ...
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Year:
2012

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Abstract

In this work an artificial neural network was utilized in order to optimize the synthesis process of γ-Bi2MoO6 oxide by co-precipitation assisted with ultrasonic radiation. This oxide is recognized as an efficient photocatalyst for degradation of organic pollutants in aqueous media. For the synthesis of γ-Bi2MoO6 three variables were considered, the exposure time to ultrasonic radiation, calcination time and temperature. The efficiency of photocatalysts synthesized was evaluated in the photodegradation of rhodamine B (rhB) under sun-like irradiation. A set of experimental data were introduced into the neural network, a multilayer type perceptron with a back-propagation learning rule was used to simulate the results by modifying one of the three input variables and observing the efficiency of photocatalysts using besides a response surface methodology.