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- W2010298497 abstract "Experiments were carried out with a pilot scale, wall cooled, fixed-bed reactor for benzen oxidation. The inlet concentration of benzene was varied in three ways: periodically, with PRBS, and by means of a step change. A model for the reactor is developed with use of the Karhunen-Loéve (K-L) expansion and neural networks. The K-L expansion procedure used here acts as a preprocessor to achieve good data compression while preserving as much information about the measurements as possible. A recurrent multilayer feedforward network is then used to relate the coefficients of the K-L expansion to the operating conditions. The model developed is used for on-line prediction of the axial temperatures in a fixed-bed reactor and results show that the predictions are in good agreement with measurements. Des expériences d'oxydation du benzène ont été menées à l'échelle pilote dans un réacteur à lit fixe à parois refroidies. On a fait varier la concentration à l'entrée de trois manières: périodiquement, par le PRBS et par un changement en échelon. On a mis au point un modèle de réacteur au moyen de l'expansion de Karhunen-Loéve (K-L) et des réseaux neuronaux. La méthode d'expansion K-L employée ici sert de préprocesseur pour réaliser une compression des données efficace tout en conservant le plus d'information possibles sur les mesures. Les coefficients K-L sont ensuite reliés aux conditions de fonctionnement à l'aide d'un réseau aval multicouche récurrent. Ce modèle est utilisé pour la prédiction en ligne des températures axiales dans un réacteur à lit fixe, et les résultats montrent que les prédictions sont en bon accord avec les mesures." @default.
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- W2010298497 title "A predictive neural network model based on the karhunen-loéave expansion for wall-cooled fixed-bed reactors" @default.
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- W2010298497 doi "https://doi.org/10.1002/cjce.5450740513" @default.
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