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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 , 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 Article28 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 91 506 - 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 10 113 321 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , NMPC-based workflow for simultaneous process and model development applied to a fed-batch process for recombinant C. glutamicum(Multidisciplinary Digital Publishing Institute (MDPI), 2020-10-19); ; ; ; For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products.Publicationtype: Journal Article270 159 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Estimation of process model parameters(Humana Press, 2020); ; ; ; Cell culture technology has become a substantial domain of modern biotechnology, particularly in the pharmaceutical market. Today, products manufactured from cells itself dominate the biopharmaceutical industry. In addition, a limited number of products made of in vitro cultivated cells for regenerative medicine were launched to the market. Modeling of such processes is an important task since these systems are usually nonlinear and complex. In this chapter, a framework for the estimation of process model parameters and its implementation is shown. It is aimed to support the parameter estimation task, which increases the potential of implementation and improvement of mathematical process models into the novel and existing bioprocesses. Apart from the parameter estimation, evaluation of the estimated parameters plays an essential role in order to verify these parameters and subsequently the selected model. The workflow is outlined and shown specifically on the basis of a mathematical process model describing a mammalian cell culture batch process.Publicationtype: Book Part150 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Advanced Process and Control Strategies for BioreactorsBioreactor processes have to provide an almost optimal environment to microorganisms or cells to promote growth and product formation. The design and operation of a bioreactor as the main element of fermentation is a complex task, not only with respect to a reactor's configuration and size but to the control and mode of operation. In this chapter, some fundamentals are discussed, including process and control strategies and concepts for process development. Next, examples for the use of model-based concepts for the design of experiments, feeding strategies, seed train layout, and control strategies, including simulation tools supporting biotechnological training and education, are shown.Publicationtype: Book Part151 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Modeling suspension cultures of microbial and mammalian cells with an adaptable six-compartment model(Wiley-VCH, 2017-03-04); ; ; ; Process models can be used for model-based control strategies, but model development is a time-consuming and laborious task. To reduce the modeling effort, a new structured compartment model was developed, which may easily be adapted to different cultivation processes. The proposed six-compartment model was used to describe the time courses of cultivations of bacteria, yeast, fungi, and mammalian cell lines, namely, Escherichia coli, Lactobacillus delbrueckii, Saccharomyces cerevisiae, Cyathus striatus, and a hybridoma mammalian cell line. The model can describe the time courses of important state variables and can be adapted to the cultivation processes by parameterization. This reduces the modeling effort for a new process significantly.Publicationtype: Journal Article163 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 41