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Gas hold‐up estimation in bubble columns using passive acoustic waveforms with neural networks
Author(s) -
AlMasry Waheed A,
Abdennour Adel
Publication year - 2006
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.1475
Subject(s) - waveform , artificial neural network , hydrophone , radial basis function , acoustics , computer science , bubble , phase (matter) , artificial intelligence , pattern recognition (psychology) , physics , telecommunications , radar , parallel computing , quantum mechanics
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. Copyright © 2006 Society of Chemical Industry