@misc{12402, abstract = {{In anaerobic technology, pH values are crucial for targeted volatile fatty acid production. While pH dynamics can be modeled using the Anaerobic Digestion Model No. 1 (ADM1), simulation results may be biased. To address this issue, the pH prediction routine of Visual Water, a specialized water chemistry simulator, was validated. Unlike ADM1, it accounts for ionic strength and activities while also providing an automated uncertainty analysis. The analysis revealed Visual Water simulations to better fit measured pH data from acidic solutions in a miniaturized stirred-tank reactor.}}, author = {{Kosse, Pascal and Hernández Rodriguez, Tanja and Frahm, Björn and Lübken, Manfred and Wichern, Marc}}, booktitle = {{Chemie Ingenieur Technik}}, issn = {{1522-2640}}, keywords = {{Anaerobic Digestion Model No. 1 (ADM1), Anaerobic technology, pH simulation, Uncertainty assessment, Visual Water}}, number = {{4}}, pages = {{528--534}}, publisher = {{Wiley}}, title = {{{Comparative Analysis of pH Prediction Routines in ADM1 and a Specialized Water Chemistry Simulator}}}, doi = {{10.1002/cite.202300188}}, volume = {{96}}, year = {{2024}}, } @misc{11385, abstract = {{Accurate pH calculations are essential for scientists across different disciplines to design optimal reactor solutions, but they can be arduous to calculate for complex acid-base solutions. Visual Water is a powerful software tool that provides accurate pH calculations with automated mathematical uncertainty analysis. Its workflow is presented and validated using acids and bases, showing a deviation of < 0.2 pH units between measured and calculated pH values. This highlights the software's reliability, which can help to simplify the work of non-experts in water chemistry.}}, author = {{Kosse, Pascal and Hernández Rodriguez, Tanja and Frahm, Björn and Lübken, Manfred and Wichern, Marc}}, booktitle = {{Chemie Ingenieur Technik}}, issn = {{1522-2640}}, keywords = {{Acid-base equilibria, Carboxylic acids, Dissociation constants, pH calculation software, Uncertainty assessment}}, number = {{12}}, pages = {{1960--1969}}, publisher = {{Wiley}}, title = {{{Validation and Uncertainty Assessment of a Software‐Integrated Workflow for pH Calculations}}}, doi = {{10.1002/cite.202300082}}, volume = {{95}}, year = {{2023}}, } @misc{10010, abstract = {{During product development, the customer or internal stakeholders initiate changes concerning the components or functions of a cyber-physical system (CPS). The complexity of such a CPS causes difficulties in evaluating the effects of a component change. Accordingly, product developers need an assistance system to quantify the impact of a component change on hardware, software, system functions, and production processes. Therefore, this paper focuses on concepts to evaluate the effects of component, functional, and process changes and contributes to its clarification and further understanding of the importance and requirements for such an assistance system. The literature review assesses the identified methods regarding their objectives, application objects, level of automation, and relations characteristics. However, the literature review pointed out that the change prediction method from Clarkson et al. (2004) is well-established in the literature and able to quantify the impact of a change.}}, author = {{Mordaschew, Viktoria and Herrmann, Jan-Phillip and Tackenberg, Sven}}, booktitle = {{Proceedings of the International Conference onEngineering Design (ICED23)}}, issn = {{2732-527X }}, keywords = {{Product Lifecycle Management (PLM), Change Impact, Complexity, Uncertainty}}, location = {{Bordeaux, Frankreich}}, pages = {{2655--2664}}, publisher = {{Cambridge University Press}}, title = {{{METHODS OF CHANGE IMPACT ANALYSIS FOR PRODUCT DEVELOPMENT: A SYSTEMATIC REVIEW OF THE LITERATURE}}}, doi = {{https://doi.org/10.1017/pds.2023.266 }}, year = {{2023}}, } @misc{11376, abstract = {{concentration is an important objective. The phenotype of the cells in a reactor plays an important role. Are clonal cell populations showing high cell-specific growth rates more favorable than cell lines with higher cell-specific productivities or vice versa? Five clonal Chinese hamster ovary cell populations were analyzed based on the data of a 3-month-stability study. We adapted a mechanistic cell culture model to the experimental data of one such clonally derived cell population. Uncertainties and prior knowledge concerning model parameters were considered using Bayesian parameter estimations. This model was used then to define an inoculum train protocol. Based on this, we subsequently simulated the impacts of differences in growth rates (±10%) and production rates (±10% and ±50%) on the overall cultivation time, including making the inoculum train cultures; the final production phase, the volumetric titer in that bioreactor and the ratio of both, defined as overall process productivity. We showed thus unequivocally that growth rates have a higher impact (up to three times) on overall process productivity and for product output per year, whereas cells with higher productivity can potentially generate higher product concentrations in the production vessel.}}, author = {{Hernández Rodriguez, Tanja and Morerod, Sophie and Pörtner, Ralf and Wurm, Florian M. and Frahm, Björn}}, booktitle = {{Processes}}, issn = {{2227-9717}}, keywords = {{clonal cell population, phenotypic diversity, inoculum train, uncertainty-based, cell culture model, biopharmaceutical manufacturing}}, number = {{6}}, publisher = {{MDPI AG}}, title = {{{Considerations of the Impacts of Cell-Specific Growth and Production Rate on Clone Selection—A Simulation Study}}}, doi = {{10.3390/pr9060964}}, volume = {{9}}, year = {{2021}}, } @inbook{7983, abstract = {{For the manufacturing of complex biopharmaceuticals using bioreactors with cultivated mammalian cells, high product concentration is an important objective. The phenotype of the cells in a reactor plays an important role. Are clonal cell populations showing high cell-specific growth rates more favorable than cell lines with higher cell-specific productivities or vice versa? Five clonal Chinese hamster ovary cell populations were analyzed based on the data of a 3-month-stability study. We adapted a mechanistic cell culture model to the experimental data of one such clonally derived cell population. Uncertainties and prior knowledge concerning model parameters were considered using Bayesian parameter estimations. This model was used then to define an inoculum train protocol. Based on this, we subsequently simulated the impacts of differences in growth rates (±10%) and production rates (±10% and ±50%) on the overall cultivation time, including making the inoculum train cultures; the final production phase, the volumetric titer in that bioreactor and the ratio of both, defined as overall process productivity. We showed thus unequivocally that growth rates have a higher impact (up to three times) on overall process productivity and for product output per year, whereas cells with higher productivity can potentially generate higher product concentrations in the production vessel.}}, author = {{Hernández Rodriguez, Tanja and Morerod, Sophie and Pörtner, Ralf and Wurm, Florian M. and Frahm, Björn}}, booktitle = {{Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing}}, isbn = {{978-3-0365-5210-1}}, issn = {{2227-9717 }}, keywords = {{clonal cell population, phenotypic diversity, inoculum train, uncertainty-based, cell culture model, biopharmaceutical manufacturing}}, pages = {{49--74}}, publisher = {{MDPI}}, title = {{{Considerations of the impacts of cell-specific growth and production rate on clone selection – a simulation study}}}, doi = {{10.3390/pr9060964}}, volume = {{special issue}}, year = {{2021}}, } @inbook{3349, abstract = {{Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions. For this purpose digital seed train twins have proved to be efficient, which digitally display the time-dependent behavior of important process variables based on mathematical models, strategies, and adaption procedures. This chapter will outline the needs for digitalization of seed trains, the construction of a digital seed train twin, the role of parameter estimation, and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed train for seed train digitalization.}}, author = {{Hernández Rodriguez, Tanja and Frahm, Björn}}, booktitle = {{Digital Twins Tools and Concepts for Smart Biomanufacturing}}, editor = {{Herwig, Christoph and Pörtner, Ralf and Möller, Johannes }}, isbn = {{978-3-030-71659-2}}, issn = {{1616-8542}}, keywords = {{Bayes, Digital twin, Parameter estimation, Seed train, Uncertainty}}, pages = {{97–131}}, publisher = {{Springer}}, title = {{{Digital Seed Train Twins and Statistical Methods}}}, doi = {{https://doi.org/10.1007/10_2020_137}}, volume = {{176}}, year = {{2021}}, }