Projects per year
Abstract
This industrial PhD thesis examines how data collected by flow-following sensor devices can be used to improve large-scale fermentation processes. The focus is on developing methods that provide a spatial and data-based quantification of flow and mixing in large-scale bioreactors, which is information that is not accessible with the currently available techniques. It is well-known that important process variables, such as pH, substrate concentration and dissolved oxygen concentration, are not homogeneously distributed in large-scale bioreactors, and that this heterogeneity in many cases has a negative impact on the production organism and in turn on the profitability of the process. Detailed understanding of flow and mixing, together with the spatial distribution of variable concentrations, enables the best possible operation of a process given the heterogeneous circumstances, and allows for more accurate modelling, which further facilitates optimization, design and control of the process.
A 580 L pilot scale stirred vessel has been extensively studied in terms of flow and mixing using the flow-following sensor devices. The examined configurations include agitation by two types of impellers under various agitation intensities. Experimental data from tracer pulse experiments and computational fluid dynamics simulations have been applied to corroborate the findings. Based on sensor device measurements from this system, an automated approach to generate compartment models which describe the flow in the vessel has been developed. The compartment model has been further expanded to automatically adapt to changes in the velocity field and volume in time, which makes it suitable for fed-batch fermentations, which is the most common type of fermentation in the industry. The capability of the automated compartment model approach is demonstrated in a 600 m3 bubble column bioreactor, in which the flow model has been coupled with a kinetic model to study concentration gradients. In addition to the developed methods, the thesis provides the necessary background to understand the strengths and weaknesses of the technology and provides an overview of the current state of sensor devices for measurement of gradients in large bioreactors.
A 580 L pilot scale stirred vessel has been extensively studied in terms of flow and mixing using the flow-following sensor devices. The examined configurations include agitation by two types of impellers under various agitation intensities. Experimental data from tracer pulse experiments and computational fluid dynamics simulations have been applied to corroborate the findings. Based on sensor device measurements from this system, an automated approach to generate compartment models which describe the flow in the vessel has been developed. The compartment model has been further expanded to automatically adapt to changes in the velocity field and volume in time, which makes it suitable for fed-batch fermentations, which is the most common type of fermentation in the industry. The capability of the automated compartment model approach is demonstrated in a 600 m3 bubble column bioreactor, in which the flow model has been coupled with a kinetic model to study concentration gradients. In addition to the developed methods, the thesis provides the necessary background to understand the strengths and weaknesses of the technology and provides an overview of the current state of sensor devices for measurement of gradients in large bioreactors.
Original language | English |
---|
Place of Publication | Kgs. Lyngby |
---|---|
Publisher | Technical University of Denmark |
Number of pages | 96 |
Publication status | Published - 2021 |
Fingerprint
Dive into the research topics of 'Development and Application of a Novel Free-floating Sensor Device for Bioprocess Optimization'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Development and Application of Novel Free-floating Sensor Device for Bioprocess Optimization
Bisgaard, J. (PhD Student), Gernaey, K. V. (Main Supervisor), Huusom, J. K. (Supervisor), Petersen, L. V. (Supervisor), Rasmussen, T. (Supervisor), Krühne, U. (Examiner), Bezzo, F. (Examiner) & Stocks, S. M. (Examiner)
01/09/2017 → 07/06/2021
Project: PhD