Particulate Science and Technology, Vol.26, No.6, 574-586, 2008
Artificial Neural Network Approach to Segregation Characteristic of Binary Heterogeneous Mixtures in Promoted Gas-Solid Fluidized Beds
Binary mixtures of particles of the same size but of different densities are fluidized in a 15cm diameter column with a perforated plate distributor and two coaxial promoters. In the present work an attempt has been made to study the fluidization and the segregation characteristic of density-variant solids of the same size in terms of segregation distance. The dimensionless segregation distance has been correlated with other dimensionless groups relating to various system parameters: ratio of the density of jetsam particles to that of flotsam, initial static bed height, height of layer of particles above the bottom grid, superficial gas velocity, and average density of the mixture on the basis of the dimensional analysis approach for both un-promoted and promoted beds. Correlations have also been developed with the above system parameters by using an artificial neural network approach for different types of fluidized beds, and the findings with respect to both approaches have been compared with each other. The values of segregation distance for promoted beds have also been compared with those for the un-promoted bed in this work.
Keywords:artificial neural network approach;coaxial promoters;gas-solid fluidization;heterogeneous binary mixtures;segregation distance