Abstract
This contribution presents a systematic methodology for rapid acquirement of discrete-time state space model representations of batch processes based on their historical operation data. These state space models are parsimoniously parameterized as a set of local, interdependent models. The present contribution furthermore presents how the asymptotic convergence of Iterative Learning Control is combined with the closed-loop performance of Model Predictive Control to form a robust and asymptotically stable optimal controller for ensuring reliable and reproducible operation of batch processes. This controller may also be used for Optimizing control. The modeling and control performance is demonstrated on a fed-batch protein cultivation example. The presented methodologies lend themselves directly for application as Process Analytical Technologies (PAT).
Original language | English |
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Title of host publication | 16th European Symposium on Computer Aided Process Engineering and 9th Symposium on Process Systems Engineering |
Publication date | 2006 |
Pages | 1275-1280 |
ISBN (Print) | 978-0-444-52969-5 |
Publication status | Published - 2006 |
Event | 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering: Joint Conference Event - Garmisch-Partenkirchen, Germany Duration: 9 Jul 2006 → 13 Jul 2006 Conference number: 16 and 9 |
Conference
Conference | 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering |
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Number | 16 and 9 |
Country/Territory | Germany |
City | Garmisch-Partenkirchen |
Period | 09/07/2006 → 13/07/2006 |
Series | Computer Aided Chemical Engineering |
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Volume | 21B |
ISSN | 1570-7946 |