Observability of Bacterial Growth Models in Bubble Column Bioreactors

Paola Lecca; Angela Re

doi:10.1007/978-3-030-63061-4

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Observability of Bacterial Growth Models in Bubble Column Bioreactors

Conference proceeding

Peer reviewed

Observability of Bacterial Growth Models in Bubble Column Bioreactors

Paola Lecca and Angela Re

Computational Intelligence Methods for Bioinformatics and Biostatistics CIBB 2019, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.12313, pp.309-322

Lecture Notes in Computer Science, 12313, 1

International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2019) (Bergamo, 04/09/2019–06/09/2019)

2020

DOI: https://doi.org/10.1007/978-3-030-63061-4

Handle:

https://hdl.handle.net/10863/32658

Abstract

Advection-diffusion equation

Bacterial growth

Bioreactor

Observability

Observability being the dual of controllability is an advisable property for any dynamic model of bio-based chemical production processes encompassing substrate consumption, bacterial growth and products formation. In this study we show a mathematical model of these processes and present a novel observability analysis. The invertibility properties of the observability mapping of this model in a space-time domain are analysed independently of the discretization of such domains and indicate the existence of subdomains where the measurement of the output may not be sufficiently accurate to allow reconstructing the states of the system.

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https://link.springer.com/chapter/10.1007%2F978-3-030-63061-4_27#citeasView

Details

Title: Observability of Bacterial Growth Models in Bubble Column Bioreactors
Creators: Paola Lecca
Angela Re
Publication Details: Computational Intelligence Methods for Bioinformatics and Biostatistics CIBB 2019, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.12313, pp.309-322
Editor(s): Cazzaniga P, Besozzi D, Merelli I, Manzoni L
ISBN: 978-3-030-63060-7
EISBN: 978-3-030-63061-4
ISSN: 0302-9743
EISSN: 1611-3349
Conference: International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2019) (Bergamo, 04/09/2019–06/09/2019)
Series / Volume: Lecture Notes in Computer Science
12313
Publisher: Springer, Cham
Edition: 1
Identifiers: 978-3-030-63060-7
(UNIBZ)38200196
991006491198601241
Scopus ID: 2-s2.0-85098244982
Academic Unit: Faculty of Computer Science
Language: English
Resource Type: Conference proceeding
Author Names String: Lecca P, Re A
Additional Description: Editors/Supervisors: Cazzaniga P, Besozzi D, Merelli I, Manzoni L

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