Instrumentation of a roll compactor and the evaluation of the parameter settings by neural networks

A Fitzpatrick L83 Chilsonator was instrumented in order to understand and to optimize the roll compaction process using drum-dried waxy maize starch, a plastic deforming material as a model compound. The interrelation of the four adjustable roll compactor parameter settings namely the velocity of the rolls OCS), the speed of the horizontal (HS) and of the Vertical screw (VS), and the air pressure (P-air) influenced the compact and the granule quality. The granule quality was defined by the friability and particle size distribution. As a second order polynomial was not successful to model the behaviour of the friability in function of the four roll compactor parameters, a Multilayer Feed-Forward neural network (MLF) was used. It was shown that the MLF network models the friability more accurately than a second order polynomial. The HS and the P-air mostly influenced granule quality and should be kept at a high level. The VS had no significant influence on compact quality. (C) 1997 Elsevier Science B.V.