End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer
Continuously operated pharmaceutical manufacturing lines often consist of a wet granulation unit operation, followed by a (semi-) continuous dryer. The operating conditions of the dryer are crucial for obtaining a desired final granule moisture. Commercially available dryers lack of a thorough onlin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
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| Series: | Pharmaceutics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4923/12/5/452 |
| _version_ | 1797567971976019968 |
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| author | Jakob Rehrl Stephan Sacher Martin Horn Johannes Khinast |
| author_facet | Jakob Rehrl Stephan Sacher Martin Horn Johannes Khinast |
| author_sort | Jakob Rehrl |
| collection | DOAJ |
| description | Continuously operated pharmaceutical manufacturing lines often consist of a wet granulation unit operation, followed by a (semi-) continuous dryer. The operating conditions of the dryer are crucial for obtaining a desired final granule moisture. Commercially available dryers lack of a thorough online measurement of granule moisture during the drying process. However, this information could improve the operation of the equipment considerably, yielding a granule moisture close to the desired value (e.g., by drying time and process parameter adjustments in real-time). The paper at hand proposes a process model, which can be parameterized from a very limited number of experiments and then be used as a so-called soft sensor for predicting granule moisture. It utilizes available process measurements for the estimation of the granule moisture. The development of the model as well as parameter identification and validation experiments are provided. The proposed model paves the way for the application of sophisticated observer concepts. Possible future activities on that topic are outlined in the paper. |
| first_indexed | 2024-03-10T19:49:47Z |
| format | Article |
| id | doaj.art-90f35ffcf3d147c0889eb492e7bcaa7c |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-10T19:49:47Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-90f35ffcf3d147c0889eb492e7bcaa7c2023-11-20T00:28:05ZengMDPI AGPharmaceutics1999-49232020-05-0112545210.3390/pharmaceutics12050452End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed DryerJakob Rehrl0Stephan Sacher1Martin Horn2Johannes Khinast3Research Center Pharmaceutical Engineering GmbH, 8010 Graz, AustriaResearch Center Pharmaceutical Engineering GmbH, 8010 Graz, AustriaInstitute of Automation and Control, Graz University of Technology, 8010 Graz, AustriaResearch Center Pharmaceutical Engineering GmbH, 8010 Graz, AustriaContinuously operated pharmaceutical manufacturing lines often consist of a wet granulation unit operation, followed by a (semi-) continuous dryer. The operating conditions of the dryer are crucial for obtaining a desired final granule moisture. Commercially available dryers lack of a thorough online measurement of granule moisture during the drying process. However, this information could improve the operation of the equipment considerably, yielding a granule moisture close to the desired value (e.g., by drying time and process parameter adjustments in real-time). The paper at hand proposes a process model, which can be parameterized from a very limited number of experiments and then be used as a so-called soft sensor for predicting granule moisture. It utilizes available process measurements for the estimation of the granule moisture. The development of the model as well as parameter identification and validation experiments are provided. The proposed model paves the way for the application of sophisticated observer concepts. Possible future activities on that topic are outlined in the paper.https://www.mdpi.com/1999-4923/12/5/452continuous manufacturingsoft sensorprocess modelingcontinuous drying |
| spellingShingle | Jakob Rehrl Stephan Sacher Martin Horn Johannes Khinast End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer Pharmaceutics continuous manufacturing soft sensor process modeling continuous drying |
| title | End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer |
| title_full | End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer |
| title_fullStr | End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer |
| title_full_unstemmed | End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer |
| title_short | End-Point Prediction of Granule Moisture in a ConsiGma<sup>TM</sup>-25 Segmented Fluid Bed Dryer |
| title_sort | end point prediction of granule moisture in a consigma sup tm sup 25 segmented fluid bed dryer |
| topic | continuous manufacturing soft sensor process modeling continuous drying |
| url | https://www.mdpi.com/1999-4923/12/5/452 |
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