Redes neuronales de base radial como modelos dinámicos para la puesta en marcha de columnas de destilación por lotes

Few rigorous models exist in literature for describing the temperature profile during start-up periods of distillation columns. In this work, a model is developed by applying several radial basis neural networks to data collected during the start-up period of a batch distillation column consisting o...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: López Sosa, Ixmit Jaryth, Pérez Pacheco, Sergio Alejandro
Formato: Online
Lenguaje:spa
Publicado: Universidad de Costa Rica 2017
Materias:
Acceso en línea:https://revistas.ucr.ac.cr/index.php/ingenieria/article/view/30456
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Few rigorous models exist in literature for describing the temperature profile during start-up periods of distillation columns. In this work, a model is developed by applying several radial basis neural networks to data collected during the start-up period of a batch distillation column consisting of ethanol and water. Neural network efficiency training is introduced through rescale entry pre-processing series. To obtain the temperature profile, data points are obtained along different points of the column, and the results are applied to multiple networks. This allows construction of the temperature profile in the column consisting of a mean square error less than the maximum established values set during the efficiency pre-processing (mse = 0.001) of the networks. This model also allows observation of transition in the column from the empty cold state to the steady state, normally a challenge in conventional models.