Data-driven approaches to the modelling of bioprocesses
Bioprocess modelling presents a challenging subject, which requires a meticulous modelling strategy. During the modelling process, experimental data form a key ingredient during structure characterization and parameter estimation. Accurate system identification can only be guaranteed if the experime...
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| Published in | Transactions of the Institute of Measurement and Control Vol. 26; no. 5; pp. 349 - 372 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Thousand Oaks, CA
SAGE Publications
01.12.2004
Sage Publications Ltd |
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| Abstract | Bioprocess modelling presents a challenging subject, which requires a meticulous modelling strategy. During the modelling process, experimental data form a key ingredient during structure characterization and parameter estimation. Accurate system identification can only be guaranteed if the experimental data contain sufficient information on the process dynamics. In this respect, sufficient effort should be spent on optimal experiment design in order to maximize the information that can be extracted from data, particularly because experimental data generation for bioprocesses is usually a time-consuming, labour-intensive and costly job. This paper reviews the modelling cycle of bioprocesses, emphasizing the need for careful experimental data collection. The concepts of optimal experiment design for parameter estimation are outlined in particular. Application of this methodology is illustrated for a case study involving the optimal estimation of two model parameters describing temperature dependence of microbial growth kinetics. |
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| AbstractList | Bioprocess modelling presents a challenging subject, which requires a meticulous modelling strategy. During the modelling process, experimental data form a key ingredient during structure characterization and parameter estimation. Accurate system identification can only be guaranteed if the experimental data contain sufficient information on the process dynamics. In this respect, sufficient effort should be spent on optimal experiment design in order to maximize the information that can be extracted from data, particularly because experimental data generation for bioprocesses is usually a time-consuming, labour-intensive and costly job. This paper reviews the modelling cycle of bioprocesses, emphasizing the need for careful experimental data collection. The concepts of optimal experiment design for parameter estimation are outlined in particular. Application of this methodology is illustrated for a case study involving the optimal estimation of two model parameters describing temperature dependence of microbial growth kinetics. |
| Author | Bernaerts, Kristel Van Impe, Jan F. |
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| Cites_doi | 10.1021/ie010183d 10.1016/0005-1098(82)90107-8 10.1002/9783527620852.ch8 10.1002/(SICI)1097-0290(19971205)56:5<564::AID-BIT10>3.0.CO;2-C 10.1016/S1474-6670(17)50331-6 10.1016/S0098-1354(00)00328-8 10.1016/0025-5564(90)90055-4 10.1080/00401706.1967.10490441 10.1016/S0168-1605(99)00140-3 10.1016/S0924-2244(03)00048-7 10.1080/00401706.1970.10488698 10.1016/S0168-1605(99)00093-8 10.1021/ie970738y 10.1016/S1474-6670(17)52458-1 10.1002/aic.690350206 10.1007/BF02431915 10.1002/047134608X.W1046 10.1016/0025-5564(78)90063-9 10.1002/9780470316757 10.1080/00401706.1971.10488845 10.1021/bp00029a005 10.1109/TAC.1974.1100701 10.1016/S1474-6670(17)60544-5 10.1016/0025-5564(94)00023-S 10.1016/0043-1354(95)00105-T 10.1007/978-94-009-1141-3_21 10.1016/0043-1354(95)00106-U 10.1007/978-94-015-9111-9_10 10.1016/0168-1605(94)90157-0 10.1080/00401706.1984.10487928 10.2166/wst.1996.0575 10.1128/JB.149.1.1-5.1982 |
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| Keywords | parameter estimation optimal experiment design system identification data collection bioprocess modelling Fisher information matrix |
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