Journal
AICHE JOURNAL
Volume 65, Issue 6, Pages -Publisher
WILEY
DOI: 10.1002/aic.16583
Keywords
electrical capacitance tomography; fluidized bed; high-throughput experiment; machine learning; on-line monitoring
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Funding
- National Key Research and Development Program of China [2018YFB0604904]
- Newton Advanced Fellowship of the Royal Society, UK [NA140308]
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Electrical capacitance tomography has been widely used to obtain key hydrodynamic parameters of gas-solid fluidized beds, which is normally realized by first reconstructing images and then by analyzing these images. This indirect approach is time-consuming and hence difficult for on-line monitoring. Meanwhile, considering recurrence of similar flow patterns in fluidized beds, most of these calculations are repetitive and should be avoided. Here, we develop a machine learning approach to address these problems. First, superficial gas velocity linear-increasing strategy is used to perform high-throughput experiments to collect a large amount of training samples. These samples are used to train the map from normalized capacitance measurements to key parameters that obtained by an iterative image reconstruction algorithm off-line. The trained model can then be used for on-line monitoring. Preliminary tests revealed that the trained models show good prediction and generality for the estimation of the overall solid concentration and the equivalent bubble diameter.
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