Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
710746 | IFAC Proceedings Volumes | 2009 | 6 Pages |
Abstract
AbstractGranulation is a multivariable process characterized by several physical attributes that are essential for product performance, such as granule size and size distribution. An optimally operated granulation process will yield, in a reproducible manner, product with tightly controlled performance attributes. In this paper predictive models of the dynamics of these key variables are developed using a dynamic partial least squares approach. The method, demonstrated here on process simulation as well as on an industrial mixer-granulator process, result in accurate predictions. These models motivate the development of model predictive controllers for these processes.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
D. Ronen, C.F.W. Sanders, H.S. Tan, P.R. Mort, F.J. Doyle III,