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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12250

Title: Gas hold-up estimation in bubble columns using passive acoustic waveforms with neural networks
Authors: Al-Masry, Abdennour, A. W.,
Keywords: Acoustic; Bubble column; Gas hold-up; Hydrophone; Neural network
Issue Date: 2006
Publisher: John Wiley & Sons, Inc
Citation: Journal of Chemical Technology and Biotechnology: Volume 81, Issue 6, Pages 951-957
Abstract: Abstract Passive acoustic waveforms produced experimentally from a bench-scale two-phase bubble column were recorded using a miniature hydrophone at three axial positions. The generated acoustic waveforms were processed and trained using artificial intelligence against global gas hold-up measurements. Two neural network architectures, the radial basis function (RBF) neural network and the recurrent Elman neural network, were employed. Both neural network techniques achieved accurate gas hold-up estimation, characterised by low mean square errors of 2.70 and 1.68% for the RBF and recurrent Elman networks respectively. The designed and trained neural networks were found to be a powerful tool for learning and replicating complex two-phase patterns. Passive acoustic waveforms were found to be a useful measuring technique for gas hold-up estimation in bubble columns under moderate operating conditions.
URI: http://hdl.handle.net/123456789/12250
ISSN: 02682575
Appears in Collections:College of Engineering

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