Batch granulation control using a simplified population balance and nonlinear model predictive control

This paper presents a simple granulation model using geometrically spaced bins in order to accommodate changes in particle size spanning many orders of magnitude. Assuming a particle size distribution measurement is available online using emerging measurement techniques, optimal modifications to the process input values are calculated using a constrained nonlinear model predictive control formulation. It is assumed that additional binder can be added at any point in time and that the mixer speed can be modified online. Given variations in initial conditions and process parameters, the shrinking horizon nonlinear moving horizon controller simulation shows improved performance as compared to open loop trajectories.