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.
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González-Campos, G., Luévano-Hipólito, E., Torres-Treviño, L.M., La Cruz, A.MD. (2013). Artificial Neural Network for Optimization of a Synthesis Process of γ-Bi2MoO6 Using Surface Response Methodology. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_18
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DOI: https://doi.org/10.1007/978-3-642-37798-3_18
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