Literature DB >> 27317987

Optimized continuous pharmaceutical manufacturing via model-predictive control.

Jakob Rehrl1, Julia Kruisz2, Stephan Sacher3, Johannes Khinast4, Martin Horn5.   

Abstract

This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.
Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords:  Continuous pharmaceutical manufacturing; Feeding blending unit; Model-based control; Model-predictive control

Mesh:

Year:  2016        PMID: 27317987     DOI: 10.1016/j.ijpharm.2016.06.024

Source DB:  PubMed          Journal:  Int J Pharm        ISSN: 0378-5173            Impact factor:   5.875


  2 in total

Review 1.  Developing HME-Based Drug Products Using Emerging Science: a Fast-Track Roadmap from Concept to Clinical Batch.

Authors:  Josip Matić; Amrit Paudel; Hannes Bauer; Raymar Andreina Lara Garcia; Kinga Biedrzycka; Johannes G Khinast
Journal:  AAPS PharmSciTech       Date:  2020-06-22       Impact factor: 3.246

2.  Development of a Controlled Continuous Low-Dose Feeding Process.

Authors:  Sara Fathollahi; Julia Kruisz; Stephan Sacher; Jakob Rehrl; M Sebastian Escotet-Espinoza; James DiNunzio; Benjamin J Glasser; Johannes G Khinast
Journal:  AAPS PharmSciTech       Date:  2021-10-12       Impact factor: 3.246

  2 in total

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