| 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. Copyright © 2006 Society of Chemical Industry |
