This paper is focused on a relatively novel eco-efficient degreasing technique, namely Fluidized Bed Degreasing (FBD), based on a fluidised bed of hard particles. An experimental campaign was aimed to investigate the relationship between FBD operational parameters and degreasing effectiveness. Consistent trends of residual oil according to FBD process parameters were found and both a related power dissipation analytical model and a neural network were developed and verified by comparison with experiments. The Multi-Layer Perceptron (MLP) neural network, trained with Back-Propagation (BP) algorithm, gave the best performance. Finally, Genetic Algorithms (GAs) were used to improve the predicting capability of the neural network solution. In detail, an experimental plan was performed to check the generalisation capability of the neural network model with GA. Copyright © 2008, Inderscience Publishers.

Barletta, M., Gisario, A., & Guarino, S. (2008). Modelling of Fluidized Bed Degreasing (FBD) process by ANNs. INTERNATIONAL JOURNAL OF SURFACE SCIENCE AND ENGINEERING, 2(3-4), 294-309 [10.1504/IJSURFSE.2008.020500].

Modelling of Fluidized Bed Degreasing (FBD) process by ANNs

BARLETTA, MASSIMILIANO;
2008

Abstract

This paper is focused on a relatively novel eco-efficient degreasing technique, namely Fluidized Bed Degreasing (FBD), based on a fluidised bed of hard particles. An experimental campaign was aimed to investigate the relationship between FBD operational parameters and degreasing effectiveness. Consistent trends of residual oil according to FBD process parameters were found and both a related power dissipation analytical model and a neural network were developed and verified by comparison with experiments. The Multi-Layer Perceptron (MLP) neural network, trained with Back-Propagation (BP) algorithm, gave the best performance. Finally, Genetic Algorithms (GAs) were used to improve the predicting capability of the neural network solution. In detail, an experimental plan was performed to check the generalisation capability of the neural network model with GA. Copyright © 2008, Inderscience Publishers.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/316503
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