Control of a granulation process using a nonlinear MPC formulation

This paper presents a model predictive control (MPC) algorithm capable of controlling the evolution of particle size distributions used in aggregation/breakage processes such as granulation. High-shear wet granulation is an important aggregation/breakage process due to its ability to produce dense, spherical particles in a short amount of time. Problems typically arise in high-shear wet granulation while attempting to control final particle size distributions due to process sensitivity with respect to liquid addition. Thus, advanced control of high shear granulation processes is greatly dependent on a model capable of predicting process transients during particle growth. A population balance equation (PBE) model is implemented to capture process dynamics for the model based controller. A discrete element simulation model will act as the process to be controlled. The model determines the result of particle-particle interactions based on the physics of the process. This highly realistic model will act as a test bed for the newly developed MPC formulation which in the future can be implemented on an actual granulation process. Complexity arises in the MPC algorithm due to the fact that the model used by the controller is nonlinear, the process in question operates in batch, and the non-square nature of this problem.