(409b) Model Predictive Control on Roller Compaction for Pharmaceutical Manufacturing

Authors: 
Hsu, S., Purdue University
Reklaitis, G. V., Purdue University


When processing bulk powders with particle sizes smaller than 100 μm, problems such as poor flowability, segregation, and dust generation frequently arise. These problems can be prevented or reduced by agglomeration of powder particles, which is usually achieved by granulation processes (Shlieout et al. 2000). Granulation is the process in which primary powder particles are made to adhere to form larger, multiparticle entities called granules. It prevents segregation of the constituents of the powder mix, improves the flow properties and the compaction characteristics of the mix. The dry granulation processes such as roller compaction become attractive for pharmaceutical products, which are often moisture or heat sensitive. In addition, this technique is environmentally friendly since dust problems are minimized or avoided (Kleinebudde 2004).

Currently, the operating conditions are mostly found in experiments and only a basic regulatory control system on the gap width has been included in the roller compactor (Shlieout et al. 2002). This strategy is not suitable for continuous operation to which the pharmaceutical industry looks forward. Process control system, which is an important part of stable continuous operation, is usually designed based on a dynamic process model. However, there is no such a model existed for a roller compactor.

The possibility of model predictive control (MPC) scheme for roller compaction process is investigated in this paper. First a process dynamic model is proposed. It uses Johanson's rolling theory (Johanson 1965) to predict the stress and density profiles during the compaction and the continuity equation to describe the roll gap change. The proposed model, formulated as a differential-algebraic equation (DAE) system, considers the powder throughput as well as the roll force, so it becomes possible to design, optimize, and control the process based on the model. The simulation case studies showed the model can predict the ribbon density and gap width changes while varying roll pressure, feed speed and roll speed. The roll pressure influences the ribbon density much more than other two input variables, and the roll gap is affected by all three inputs. The interactions among the three input variables are also studied. If the ratio of feed speed to roll speed is kept constant, neither ribbon density nor gap width change, but the powder throughput.

Based on this dynamic model, a control scheme is then established. The control objective is to ensure that the product quality and the throughput follow the desired reference trajectories. The product quality is represented by the ribbon density, and the throughput is influenced by the gap width as well as the ribbon density. The roll pressure and feed speed are used for controlling these two variables. Since the linearization of the proposed model is difficult, the computation burden and stability of MPC are investigated, and model reduction based on process data is necessary to increase the computational speed. Simulation results provide the comparison of the control performance of the original model and the reduced order model.

Reference:

Johanson, J. R. (1965). "A rolling theory for granular solids." Trans. ASME: J. Appl. Mech. B., 32(4), 842-848.

Kleinebudde, P. (2004). "Roll compaction/dry granulation: pharmaceutical applications." Europ. J. Pharma. Biopharma., 58, 317-326.

Shlieout, G., Lammens, R. F., and Kleinebudde, P. (2000). "Dry granulation with a roller compactor. Part I: the functional units and operation modes." Pharma. Tech. Europ., 12(11), 24-35.

Shlieout, G., Lammens, R. F., Kleinebudde, P., and Bultmann, M. (2002). "Dry granulation with a roller compactor. Part II: Evaluating the operation modes." Pharma. Tech. Europ., 14(9), 32-39.