Artificial Neural Network approach to segregation characteristic of binary homogeneous mixtures in promoted gas–solid fluidized beds

Binary mixtures of size-different dolomites are fluidized in a bed where co-axial promoters are introduced. The segregation characteristic of jetsam particles has been determined for different mixtures in terms of the segregation distance by empirically correlating the results with the various system parameters viz. initial static bed height, height of a layer of particles above the bottom grid, superficial gas velocity and average particle size of the mixture with dimensional analysis for both the un-promoted and the promoted beds. Correlations have also been developed with the above system parameters from an Artificial Neural Network approach. Segregation distances for the promoted and un-promoted beds have been compared. The results through the correlations thus developed with the above system parameters from the ANN-approach and the findings with respect to the dimensional analysis approach have been compared.

The segregation characteristic of jetsam particles has been determined for different mixtures in terms of the segregation distance (YS) by empirically correlating the experimental results with the various system parameters viz. initial static bed height, height of a layer of particles above the bottom grid, superficial gas velocity and average particle size of the mixture with Artificial Neural Network approach.Figure optionsDownload as PowerPoint slide