MACHINE LEARNING AIDED NUMERICAL AND EXPERIMENTAL INVESTIGATION OF HYDRODYNAMIC PERFORMANCE IN THE CIRCULATING FLUIDIZED BED BOILER

This research aims to demonstrate the advantages of combining machine learning algorithms with the realm of thermo-fluidic applications. The primary objective of this investigation is to pinpoint the essential hydrodynamic input parameters that can maximize the advantages of fluidization and lead to an improved design for a CFB furnace, utilizing the Apriori algorithm. Also, this algorithm is capable of identifying the right combinations of parameters that can produce maximum fluidization performance. The end results suggested by this AA are validated using computational fluid dynamics package. For this, the transient behavior of a scaled down (1:20) reactor model of a real time industrial CFB boiler is simulated using ANSYS FLUENT 18.0. In specific, the effects of fluidizing velocities, inventory heights of the bed, and particle sizes recommended by the AA are investigated. Here, the effects are assessed in terms of volume fraction distribution and axial velocity profile distribution profiles. From the results of simulations, it was clearly found that 2 m/s inlet velocity produced good circulating fluidized bed patterns on a bed inventory height of 0.5 m for a mean particle size of 200 microns. The results obtained from the simulations are once again validated visually against snapshots obtained during real-time laboratory fluidization experimental runs. Also, it is found that the manual time taken to identify the right combinations of parameters is drastically reduced by this method as against conventional optimization algorithm and trial -error methods.