APPLICATION OF A NEURAL-NETWORK TO GRANULATION SCALE-UP

This paper describes a practical method for granulation scale-up by me ans of a neural network. Wet granulation was conducted using an agitat ion fluidized bed, and the scale-up characteristics were investigated using a neural network with back-propagation learning. Granule propert ies obtained by production-scale granulation under various operating c onditions were predicted. Extremely good correlation was obtained betw een the predicted data and the experimental data of agitation fluidize d bed granulation. It was found that granulation scale-up could be con ducted with high accuracy by a neural network without constructing a m athematical model with a complicated non-linear relationship using a v ast amount of experimental scale-up data.