Mechanistic Mathematical Models as a Basis for Digital Twins

Moser, André; Appl, Christian; Brüning, Simone; Hass, Volker C.

doi:10.1007/10_2020_152

Cited by 34 publications

(29 citation statements)

References 110 publications

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“…To the best of our knowledge, such a broad applicability for generic and specific process description was yet not established for any other modeling approach. Although it has to be noted that hybrid as well as mechanistic models reveal their benefits depending on the level of considered detail [87] , a comparable complex parameter calibration procedure as known for parametric models is not needed for our approach. Furthermore, intrinsic parameter values like the temperature as well as the pH value which are not part of mass balance conditions can be straightforwardly included in the model.…”

Section: Discussionmentioning

confidence: 99%

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

Smiatek

Clemens

Montano-Herrera

et al. 2021

Biotechnology Reports

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Highlights Novel machine learning method based on recurrent neural networks for the accurate prediction of upstream cultivation processes. Insights into the temporal evolution of main upstream parameters and outcomes. High predictive capability, straightforward implementation and broad transferability of the proposed approach. New insights into the influence of parameter changes for process outcomes. Introduction of new analysis methods like autocorrelation functions for the detailed study of experimental and computational data.

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Section: Discussionmentioning

confidence: 99%

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

Smiatek

Clemens

Montano-Herrera

et al. 2021

Biotechnology Reports

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“…A novel structured compartment model, capable of being adapted to different biotechnological expression systems (e.g., bacteria, yeast, fungi, mammalian cell lines), was used to describe yeast growth, metabolism, and biocatalysis [ 27 , 40 ]. The model was previously introduced by Brüning et al [ 27 ] and is briefly explained in the following.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, physicochemical state variables, such as DO, pH, and temperature, have a direct influence on cell metabolism, biomass growth and/or biocatalytic activity [ 27 ]. The uptake rates (rS) of the substrates (S) are rate-limiting steps, which are modeled by Monod kinetics typically used in bioprocess modeling [ 14 , 26 , 28 , 40 ]: is the half-saturation constant. The Monod-like term for the uptake rates is multiplied with the product of multiple double sigmoidal functions ( ) of the state variables ( ), which describe changes of the cell metabolism [ 27 , 41 ]: The value of a state variable is described by x .…”

Section: Methodsmentioning

confidence: 99%

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses

Moser

Kuchemüller

Deppe

et al. 2021

Bioprocess Biosyst Eng

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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, 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.

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“…The coupling between the real process and the associated DT can take place directly on the running process (process accompanying) or cyclically after the execution of the real process [6][7][8][9][10]. DTs for bioprocesses may be designed, using a shell structure [11,12] (Figure 1), including a biological, a physicochemical, a reactor, a plant and periphery as well as a control and automation sub-model. To facilitate the operation of DTs, they can be equipped with graphical user interfaces (GUIs).…”

Section: Introductionmentioning

confidence: 99%

Development of a Digital Twin for Enzymatic Hydrolysis Processes

2021

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Enzymatic hydrolysis processes can be used to produce organic nutrient media from renewable raw materials. However, many of these processes are not optimally designed, so expensive enzymes and substrates are wasted. Mathematical models and Digital Twins (DTs) are powerful tools, which can be used to optimize bioprocesses and, thus, increase the yield of the desired products. Individual enzymatic hydrolysis processes have already been modeled, but models for the combined starch hydrolysis and proteolysis, or DTs, are not available yet. Therefore, an easily adaptable, dynamic, and mechanistic mathematical model representing the kinetics of the enzymatic hydrolysis process of the combined starch hydrolysis and proteolysis was developed and parameterized using experimental data. The model can simulate the starch hydrolysis process with an agreement of over 90% and the proteolysis process with an agreement of over 85%. Subsequently, this model was implemented into an existing DT of a 20 L stirred tank reactor (STR). Since the DT cannot only map the kinetics of the enzymatic process, but also the STR with the associated periphery (pumps, heating jacket, etc.), it is ideally suited for future process control strategy development and thus for the optimization of enzymatic hydrolysis processes.

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Mechanistic Mathematical Models as a Basis for Digital Twins

Cited by 34 publications

References 110 publications

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses

Development of a Digital Twin for Enzymatic Hydrolysis Processes

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