Over the period of the last year, we have investigated aspects of process model identification and model based control for granulation processes. Significant ccomplishments from that period are reported in two journal publications which are highlighted here:
[Sanders, Hounslow, Doyle III, Powder Technology, in press, 2008]: The modeling work in this paper provides insight on improved control and design (including measurement selection) of a granulation processes. Two different control strategies (MPC and PID) are evaluated on an experimentally validated granulation model. This model is based on earlier work done at The University of Sheffield, UK and Organon, The Netherlands. The granulation kinetics were measured in a 10 liter batch granulator with an experimental design that included four process variables. The aggregation rates were extracted with a Discretized Population Balance (DPB) model. Knowledge of the kinetics was used to model a continuous (well mixed) granulator. The controller model for the Model Predictive Controller is a linearized state space model, derived from the nonlinear DPB model. It has the four process variables from the experimental design and a feed ratio as input variables. Since the DPB model describes the whole size distribution (GSD), different sets of output variables were chosen and compared. When measuring controller performance based on the full granule size distribution, a PID setup can actually produce results that fluctuate more than the open-loop response. An MPC controller improves stability on both process outputs and the full granule size distribution.
[Glaser, Sanders, Wang, Cameron, Litster, Poon, Ramachandran, Immanuel, Doyle III, Journal of Process Control, in press, 2008]: This paper details a methodology for the design of a Model Predictive Controller for a continuous granulation plant. The work is based on a nonlinear one-dimensional Population Balance Model (1D-PBM), which was parameterized using experimental step test data generated at a continuous granulation pilot plant installed at the University of Queensland, Australia. The main objective was to operate the granulator under optimal conditions while off-specification material was fed back into the granulator to increase the economy of the process. The final algorithm design combines elements of Model Predictive Control (MPC) with gain scheduling to cancel nonlinearities in the recycle flow. A model directly identified from the step test data was the basis for testing a model predictive controller. Simulations show that the efficiency and robustness of this granulation process can be improved by applying the proposed control strategy. Ongoing work focuses on the implementation of the proposed control strategy on a full scale industrial plant.
Our aims for the renewal period of this IFPRI project are reiterated here, and the main body of this document reports our progress against these aims.