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Optimized continuous pharmaceutical manufacturing via model-predictive control.

Jakob Rehrl1, Julia Kruisz2, Stephan Sacher2

  • 1Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21/B/I, 8010 Graz, Austria; Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13, 8010 Graz, Austria.

International Journal of Pharmaceutics
|June 19, 2016
PubMed
Summary
This summary is machine-generated.

Model-predictive control (MPC) offers advantages over traditional PI-controllers for pharmaceutical manufacturing. This study details MPC synthesis for feeding blenders, enabling constraint handling and easier tuning.

Keywords:
Continuous pharmaceutical manufacturingFeeding blending unitModel-based controlModel-predictive control

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Pharmaceutical Manufacturing

Background:

  • Continuous pharmaceutical manufacturing requires precise control of feeding and blending processes.
  • Conventional proportional-integral (PI) controllers have limitations in handling complex process dynamics and constraints.

Purpose of the Study:

  • To demonstrate the application of model-predictive control (MPC) to a feeding blending unit.
  • To highlight the advantages of MPC over conventional PI-controllers.
  • To provide a step-by-step guide for MPC synthesis in this context.

Main Methods:

  • Development of a detailed mathematical plant model for the feeding blending unit.
  • Step-by-step synthesis of a model-predictive controller based on the derived model.
  • Simulation-based implementation and evaluation of the proposed MPC strategy.

Main Results:

  • The proposed MPC approach effectively controls the feeding blending unit.
  • MPC allows for convenient consideration of process constraints, such as mass hold-up.
  • The MPC setup is straightforward and offers easier tuning compared to conventional methods.

Conclusions:

  • Model-predictive control is a viable and advantageous strategy for feeding blending units in continuous pharmaceutical manufacturing.
  • Further investigation into state estimation and measurement equipment is needed for real-system implementation.