(716b) A NMPC Strategy Applied to a Continuous Direct Compaction Tablet Manufacturing.

Medina-González, S., Purdue University
Huang, Y. S., Purdue University
Bachawala, S., Purdue University
Bommireddy, Y., Purdue University
Gonzalez, M., Purdue University
Reklaitis, G. V., Purdue University
Nagy, Z. K., Purdue University
The potential improvement in process controllability, product quality homogeneity, and reduction in inventory material, along with the decrease in their associated operational/capital costs, has always motivated the development of continuous manufacturing processes in a pharmaceutical context. The switch from batch to continuous processes in the pharmaceutical industry was significantly boosted after the introduction of Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA). More recently, the QbD concept was further improved by adopting an active control strategy settling the foundation for Quality-by-Control (QbC). A variety of pharmaceutical product manufacturing modalities, including drop on-demand additive manufacturing system[1], dry granulation processes[2] and continuous direct compaction[3], have shown the benefits of using the QbC concept.

A three-layer hierarchical structure can be used to implement most integrated control strategies. In general, Level 0 consists of built-in equipment vendor provided controls and PLC’s while Level 1 uses PAT based PID controllers. Level 2 addresses the plant-wide level and implements the most advanced strategy employing optimization-based techniques such as model predictive control (MPC). The hierarchical nature of the layered control system implies that the lower layers are supervised by the upper ones offering advanced control capabilities (such as accommodating large multivariable systems and integrating multiple unit operations)[4]. Hass et al., (2017)[5] evidence the effectiveness of hybrid and/or advanced control methods (PID and MPC) to regulate the disturbances while operating a Tablet press. The same year, Mesbah et al., (2017)[6] demonstrates the benefits of implementing advanced control strategies in an integrated continuous tablet manufacturing process at the Novartis-MIT center using a computational study. Similarly, Su et al., (2017; 2019)[3-4] provides a perspective on QbC in continuous pharmaceutical manufacturing using experimental data. Remarkably, all these studies assume linear (or a linearized) systems, at the risk of the linear MPC failing to capture the process dynamics and nonlinearities. For example, the powder composition (e.g., %API and %lubricant in powder) has a significant effect over the bulk powder properties (density, flowability, etc.) and ultimately in the tablet properties (hardness, strength, etc.)[7]. However, the nonlinearities associated to the tablet properties behavior and the effect of variations in powder composition due to upstream disturbances was not explored in this work. Traditional linear/linearized MPCs risk not responding adequately to variations arising in highly nonlinear systems. Hence, the use of nonlinear model predictive control (NMPC) may be needed to effectively deliver control functionality for highly sensitive variations and nonlinear multiple input multiple output (MIMO) systems.

In this study, the benefits of using advanced control strategies such as NMPC in a highly nonlinear direct compaction line located in Purdue University’s Continuous Solids Processing Pilot Plant is investigated. The system interactions (i.e., input-output pairing) and their stability to be controlled were developed and validated using indices such as Condition Number (CN), Moriari’s resilience index (MRI) and Relative gain array (RGA). The resulting NMPC strategy was implemented in MATLAB Simulink, and the reduced order models employed for each unit operations were experimentally validated.


[1] Içten, E. Reklaitis, G., Nagy, Z., 2018. Advanced control for the continuous drop-wise additive manufacturing of pharmaceutical products. Comp. Chem. Eng., 41, 379-401.

[2] Ierapetritou, M., Muzzio, F., Reklaitis, G., 2016. Perspectives on the continuous manufacturing of powder-based pharmaceutical processes. AIChE J. 62 (6), 1846–1862.

[3] Su, Q., Moreno, M., Giridhar, A., Reklaitis, G.V., Nagy, Z.K., 2017. A systematic framework for process control design and risk analysis in continuous pharmaceutical solid-dosage manufacturing. J. Pharm. Innov. 12, 327–346.

[4] Su, Q., Ganesh, S., Moreno, M., Bommireddy, Y., Gonzalez, M., Reklaitis, G. V., & Nagy, Z. K. (2019). A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Computers & Chemical Engineering, 125, 216-231.

[5] Hass, N.T., Ierapetritou, M., Singh, R., 2017. Advanced Model Predictive Feedforward/Feedback Control

of a Tablet Press. J. Pharm. Innov. 12, 110-123.

[6] Mesbah, A., Paulson, J.A., Lakerveld, R., Braatz, R.D., 2017. Model predictive control of an integrated continuous pharmaceutical manufacturing pilot plant. Organic Process Res. Dev. 21, 844–854.

[7] Razavi, S.M., Gonzalez, M., Cuitiño, A.M., 2018. Quantification of lubrication and particle size distribution effects on tensile strength and stiffness of tablets. Powder Technology., 336, 360-374.