Development of a model-based tool for the design of biotechnological processes under consideration of effects caused by heterogeneities

Publikationstyp
Doctoral Thesis
Date Issued
2025
Sprache
English
Author(s)
Moser, André  
Advisor
Pörtner, Ralf  orcid-logo
Referee
Liese, Andreas  orcid-logo
Hass, Volker C.  
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-03-27
Institute
Bioprozess- und Biosystemtechnik V-1  
TORE-DOI
10.15480/882.15766
TORE-URI
https://hdl.handle.net/11420/56958
Citation
Technische Universität Hamburg (2025)
Heterogeneous conditions caused by non-ideal mixing could cause worse process performances in large-scale reactors compared to lab-scale processes. To date, the industrial scale-up process often relies on empirical rules and costly experiments. Mathematical models and physical scale-down systems are rarely used for industrial scale up, especially for bioeconomic processes, despite their potential to address scale up challenges.
This thesis employs a physical scale-down system with two interconnected stirred tank reactors, and a novel mathematical model using a network of zones approach for the simulation of non-ideally mixed conditions. Model-based techniques are utilised to design the operational strategies in processes under controlled non-ideal conditions.
Two different research questions were formulated and answered in this thesis:
1. Is a structured, mechanistic mathematical model, calculating several interconnected ideally mixed zones, able to approximate the flow patterns in stirred tank reactors and describe the effects caused by non-ideal mixing in biotechnological processes with complex kinetics?
2. How can feeding strategies for biotechnological processes in non-ideally mixed reactors be systematically designed?
To answer the first question, a comprehensive experimental study was conducted with 19 cultivations in an ideally mixed lab-scale reactor and the non-ideally mixed scale-down system. After parameterising the mathematical model with data from 16 experiments, the model described the experimental data with high accuracy and was successfully validated with data of three experiments.
The model was also used in a modified model-based design of experiments to maximise biomass density under controlled heterogeneous conditions, predicting an experiment with high accuracy (R² = 0.94). In a second study, process differences between laboratory and pilot scale were investigated. Non-linear model-based predictive control was employed for the first time to design a process in the scale-down system, aiming to reduce differences to pilot-scale data. The mathematical model was then used to find potential explanations for these differences, identifying the different zones (and their volumes) in the large-scale system as the probable reason for the performance differences between scales.
In the future, this combination of physical and mathematical modelling techniques with model-based control methods may accelerate process development and scale-up, while increasing efficiency and reliability.
Subjects
model development | process development | model-assisted optimisation | digital twin | mathematical model | process heterogeneities
DDC Class
660.6: Biotechnology
Lizenz
https://creativecommons.org/licenses/by/4.0/
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