Abstract
Bubble frequency and bubble velocity in a fluidized bed are estimated from the cross-correlation function of pressure measurements during dynamic changes in fluidizing conditions. Estimation is based on a theoretical form of the cross-correlation function, a mathematical function of bubble frequency and velocity. The two estimation algorithms used in this work are sequential weighted least squares and a variation of Kalman filtering. The usefulness of both algorithms is demonstrated by measuring bubble frequency and bubble velocity in real time during dynamic changes in bed inventory and particle size distribution.
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