Bayesian Estimators for Bioprocess Monitoring Under Uncertainty
Abstract
The simultaneous growing interest in the application of digital solutions and the use of biological organisms such as bacteria and yeasts for the production of chemicals, has increased the necessity to apply digital solutions to biological processes, with the intent of improving production while maintaining quality standards.
The class of digital tools that can help to monitor these processes in real-time is certainly of interest. Such digital tools may carefully merge all the available information to improve the monitoring. Especially, when the qualities and quantities required are either difficult or impossible to measure directly.
The focus of this thesis is therefore the investigation and application in an experimental set-up of tools, called Soft Sensors. This was done to merge available measurements together with the physical, chemical and biological knowledge described trough mathematical relations, for monitoring the glucose consumption, which was used by the bacteria to grow.
Has parts
Paper 1: Tuveri, Andrea; Pérez-García, Fernando; Lira Parada, Pedro Antonio; Imsland, Lars; Bar, Nadav. Sensor fusion based on Extended and Unscented Kalman Filter for bioprocess monitoring. Journal of Process Control 2021 ;Volum 106. s. 195-207 https://doi.org/10.1016/j.jprocont.2021.09.005 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Paper 2: Tuveri, Andrea; Eng Holck, Haakon; Nakama, Caroline Satye; Matias, José O.A.; Jäschke, Johannes; Imsland, Lars Struen; Bar, Nadav S. Bioprocess Monitoring: A Moving Horizon Estimation Experimental Application. IFAC-PapersOnLine 2022 https://doi.org/10.1016/j.ifacol.2022.07.448 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Paper 3: Tuveri, Andrea; Nakama, Caroline Satye Martins; Matias, José O.A.; Eng Holck, Haakon; Jäschke, Johannes; Imsland, Lars Struen; Bar, Nadav S. A regularized Moving Horizon Estimator for combined state and parameter estimation in a bioprocess experimental application. Computers and Chemical Engineering 2023 ;Volum 172. https://doi.org/10.1016/j.compchemeng.2023.108183 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Paper 4: Tuveri, Andrea; Nakama, Caroline Satye Martins; Imsland, Lars; Bar, Nadav. A practical implementation of Moving Horizon Estimation with Delayed Measurements in a Bioprocesses Experimental Application