Set Membership Estimation with Dynamic Flux Balance Models
| Autor: | Hector Budman, Xin Shen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Linear programming observability dynamic flux balance model Bioengineering TP1-1185 02 engineering and technology Measure (mathematics) Upper and lower bounds Unobservable Set (abstract data type) 03 medical and health sciences 020901 industrial engineering & automation multiparametric programming Chemical Engineering (miscellaneous) State space Applied mathematics Observability QD1-999 030304 developmental biology Mathematics 0303 health sciences set membership estimation Chemical technology Process Chemistry and Technology variable structure system Variable structure system Chemistry |
| Zdroj: | Processes Volume 9 Issue 10 Processes, Vol 9, Iss 1762, p 1762 (2021) |
| ISSN: | 2227-9717 |
| DOI: | 10.3390/pr9101762 |
| Popis: | Dynamic flux balance models (DFBM) are used in this study to infer metabolite concentrations that are difficult to measure online. The concentrations are estimated based on few available measurements. To account for uncertainty in initial conditions the DFBM is converted into a variable structure system based on a multiparametric linear programming (mpLP) where different regions of the state space are described by correspondingly different state space models. Using this variable structure system, a special set membership-based estimation approach is proposed to estimate unmeasured concentrations from few available measurements. For unobservable concentrations, upper and lower bounds are estimated. The proposed set membership estimation was applied to batch fermentation of E. coli based on DFBM. |
| Databáze: | OpenAIRE |
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