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Deriving control parameter settings from process models to control capsule fillers integrated into continuous manufacturing

  • The aim of this work was to investigate the mean fill weight control of a continuous capsule-filling process, whether it is possible to derive controller settings from an appendant process model. To that end, a system composed out of fully automated capsule filler and an online gravimetric scale was used to control the filled weight. This setup allows to examine challenges associated with continuous manufacturing processes, such as variations in the amount of active pharmaceutical ingredient (API) in the mixture due to fluctuations of the feeders or due to altered excipient batch qualities. Two types of controllers were investigated: a feedback control and a combination of feedback and feedforward control. Although both of those are common in the industry, determining the optimal parameter settings remains an issue. In this study, we developed a method to derive the control parameters based on process models in order to obtain optimal control for each filled product. Determined via rapid automated process development (RAPD), this method is an effective and fast way of determining control parameters. The method allowed us to optimize the weight control for three pharmaceutical excipients. By conducting experiments, we verified the feasibility of the proposed method and studied the dynamics of the controlled system. Our work provides important basic data on how capsule filler can be implemented into continuous manufacturing systems.

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Metadaten
Author of HS ReutlingenCheong, Shea Fen
DOI:https://doi.org/10.1080/03639045.2019.1634093
ISSN:0363-9045
eISSN:1520-5762
Erschienen in:Drug development and industrial pharmacy
Publisher:Taylor & Francis Group
Place of publication:Abingdon
Document Type:Article
Language:English
Year of Publication:2019
Tag:continuous capsule filling; control challenges; control strategy; deriving control settings; feedback control; feedforward control; mean fill weight; process modeling; rapid automated process development
Volume:45
Issue:9
Page Number:14
First Page:1523
Last Page:1536
DDC classes:610 Medizin, Gesundheit
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen Taylor & Francis