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Item type:Publication, Access status: without files , New type of process control system for bioprocesses with model-assisted optimization tools(Wiley, 2025-05-05); ; ; ; The development of effective control strategies for bioprocesses faces engineers with major challenges. Bioreactor systems must independently control changing conditions – a task that requires innovative automation tools such as the WinErs process control and simulation system. This integrates complex process models, enables simulations and parameter estimations, and offers control strategies for processes with multiple inputs and outputs. The WinErs Data Lake collects extensive process data, while model-assisted tools support the optimization of control variables. It provides an effective solution to bridge the gap between laboratory and industry.Publicationtype: Journal Article52 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Development of a model-based tool for the design of biotechnological processes under consideration of effects caused by heterogeneities(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.Publicationtype: Doctoral Thesis54 44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A new concept for the rapid development of digital twin core models for bioprocesses in various reactor designs(Multidisciplinary Digital Publishing Institute (MDPI), 2024-09-06); ; ; ; In this research work, a new software tool concept and its application for the rapid and flexible development of mechanistic digital twin core models for bioprocesses in various reactor designs are presented. The newly developed software tool concept automatically combines user-selected submodels into an overall digital twin core model. The main part is a biokinetic submodel, of which three were designed for enzymatic, microbial and biocatalytic processes, which can be adapted to specific processes. Furthermore, the digital twin core model contains a physico-chemical submodel (e.g., calculating pH or oxygen transfer) and a reactor submodel. The basis of the reactor submodel is an ideally mixed stirred tank reactor. The biokinetic submodel is decoupled from the reactor submodels and enables an independent parameterisation of submodels. Connecting ideally mixed stirred tank reactor models allows for the simulation of different reactor designs. The implementation of an executable digital twin core model was accelerated, creating a new software tool concept. When the concept was applied, the development time and the computing time of digital twin core models for the cultivation of <i>Saccharomyces cerevisiae</i> in two coupled stirred tank reactors as well as for enzymatic hydrolysis processes in a packed-bed reactor were reduced by 90%.Publicationtype: Journal Article26 200 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Model-assisted DoE applied to microalgae processes(Elsevier, 2022-02-02); ; ; ; This study assesses the performance of the model-assisted Design of Experiment (mDoE) software toolbox for the design of two microalgae bioprocesses. The mDoE-toolbox was applied to maximize biomass growth for Desmodesmus pseudocommunis in a photobioreactor by varying the light intensity and pH and for Chlorella vulgaris in shake flasks, by varying the light intensity and duration. For both case studies, a mathematical mechanistic model was applied. In the first study only one experiment was necessary to adapt the mathematical model and identify a combination of light intensity and pH that improved biomass yield, as confirmed experimentally. In the second study, no well-established model was available for the specific experimental arrangement. On the basis of the literature, a mathematical model was constructed and a first cycle of mDoE was performed, thus identifying the desired factor combinations. Experiments confirmed the high biomass yield but revealed shortcomings of the model. The model was improved and a second cycle of mDoE was performed. The recommended factor combinations from both cycles were comparable. The mDoE was found to be a time-saving, cost-effective and useful method enabling the identification of factor combinations leading to high biomass production for the design of two different microalgae bioprocesses with low experimental effort.Publicationtype: Journal ArticleScopus© Citations 1 90 506 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses(Springer, 2021-01-20); ; ; ; Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7, 2019) was extended and implemented into a software (“mDoE-toolbox”) to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.Publicationtype: Journal ArticleScopus© Citations 10 113 319