Self-organizing maps for efficient classification of flow regimes from gamma densitometry time series in three-phase fluidized beds

The potential of artificial neural networks as a tool to classify and identify a change in the flow regime of a three-phase fluidized bed is studied. Particularly, the suitability of self-organizing maps, unsupervised neural networks that visualize the data in a lower dimension, is evaluated. Statistical features of experimental time series determined in a three-phase (granulated carbon-air-water) fluidized bed are extracted as inputs to train the self-organizing map. Photon-count time series are obtained along the fluidized bed vertical axis by gamma-densitometry at different operative conditions. Then, they are analyzed to determine the underlying flow regime indexes. When each input data is presented to the self-organizing maps, a neuron is activated, giving a visual representation of the data. The resulting models show three different regions on the map for the homogenous, transition, and heterogeneous flow regimes. Once these regions are delimited, the map can quickly classify the equipment operating conditions. The ability of the self-organizing maps to diagnose a flow transition is verified against visual observation and gas hold-up trends. The conclusions are tested for their sensitivity to alternative axial positions of the radiation source used for the densitometry.