(189p) Optimization of Particle Agglomeration and Breakage Operations with the Attainable Region (AR) Theory

Authors: 
Glasser, B. J., Centre of Material and Process Synthesis (COMPS)
Khumalo, N., University of the Witwatersrand
Hausberger, B., University of the Witwatersrand, Johannesburg


Tight particle size distribution control is vital to the success of any process involving particles. However, even controlling crystal size during crystallization - a process with a relatively well-known mechanism - is a daunting chore. In other operations central to the pharmaceutical and chemical industries, such as agitated drying and granulation, the task of size control is complicated due to the competition between particle agglomeration and breakage. A tool that with the ability to encompass all aspects of particle agglomeration and breakage within one framework is needed to advance the development of solid processes.

Presented here is the application of the Attainable Region (AR) approach to optimizing the simplest case of an industrial sizing process: milling. Previously, the AR approach has been used to optimize complex reactor networks and separation systems. Similarities between species in chemical reaction networks and particle sizes in agglomeration and breakage processes, suggest the AR is a capable tool for determining the optimal control strategy for a milling project. Though the optimization done in this work is for a batch ball mill and a specific material, the concepts can be applied to any system consisting of breakage, agglomeration, and classification.

We have experimentally determined the optimal control strategy for achieving a certain size distribution with the shortest amount of time and most efficient energy usage. The control strategy recommends a variation of speed throughout the process - fast speed at first followed by a lower speed - to achieve the maximum amount of material of a desired size. Benefits of this approach can be realized in any size alteration operation, as a size profile over time can be predicted. Therefore, a design methodology can be constructed without complicated mathematical tools.