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Ozonation Kinetics of Acid Red 27 Azo Dye: A novel methodology based on artificial neural networks for the determination of dynamic kinetic constants in bubble column reactors

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Ozonation Kinetics of Acid Red 27 Azo Dye: A novel methodology based on artificial neural networks for the determination of dynamic kinetic constants in bubble column reactors

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Ferre Aracil, J.; Cardona, SC.; Navarro-Laboulais, J. (2015). Ozonation Kinetics of Acid Red 27 Azo Dye: A novel methodology based on artificial neural networks for the determination of dynamic kinetic constants in bubble column reactors. Chemical Engineering Communications. 202(3):279-293. doi:10.1080/00986445.2013.841146

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Título: Ozonation Kinetics of Acid Red 27 Azo Dye: A novel methodology based on artificial neural networks for the determination of dynamic kinetic constants in bubble column reactors
Autor:
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Química y Nuclear - Departament d'Enginyeria Química i Nuclear
Universitat Politècnica de València. Instituto de Seguridad Industrial, Radiofísica y Medioambiental - Institut de Seguretat Industrial, Radiofísica i Mediambiental
Fecha difusión:
Resumen:
A procedure for the determination of initial parameter values for quadratically convergent optimization methods is proposed using artificial neural networks coupled with a non-stationary gas-liquid reaction model. The ...[+]
Palabras clave: Artificial neural network , Bubble column reactor , Kinetic rate constant estimation , Ozonation
Derechos de uso: Cerrado
Fuente:
Chemical Engineering Communications. (issn: 0098-6445 ) (eissn: 1563-5201 )
DOI: 10.1080/00986445.2013.841146
Editorial:
Taylor & Francis
Versión del editor: http://dx.doi.org/10.1080/00986445.2013.841146
Agradecimientos:
J. Ferre-Aracil acknowledges the support of the doctoral fellowship from the Universitat Politecnica de Valencia (UPV-PAID-FPI-2010-04).
Tipo: Artículo

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