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

Julia Picabea, Mauricio Maestri, Gabriel Salierno, Miryan Cassanello, Cataldo De Blasio, María Angélica Cardona, Daniel Hojman, Héctor Somacal

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Artikelnummer085303
TidskriftMeasurement Science and Technology
Volym33
Utgåva8
DOI
StatusPublicerad - aug. 2022
MoE-publikationstypA1 Tidskriftsartikel-refererad

Fingeravtryck

Fördjupa i forskningsämnen för ”Self-organizing maps for efficient classification of flow regimes from gamma densitometry time series in three-phase fluidized beds”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här