Abstract
AbstractFactorial design (statistical approach) and artificial neural network (ANN) models have been developed for the prediction of mixing the index with four system parameters, such as static bed heights, average particle densities, average particle sizes and gas velocities, under four different experimental conditions, viz., only primary air, simultaneous primary and secondary air, disc promoter and rod promoter. The values of the mixing index obtained through the developed models are found to agree well with their experimental counterparts. It has also been found from these investigations that under simultaneous primary and secondary air supply conditions the best mixing performance is achieved, i.e. IM ≈ 1.0, as compared to rod promoter, disc promoter, and only primary air supply. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd.
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