@misc{11428,
  abstract     = {{Systems that place high demands on availability are typically modular in design. However, a modular design also offers potential for optimized operation under norma requirements. In this paper we present an approach to find optimal operating points from the characteristic fields of individual modules. Our approach consists of a two-step procedure. In the first stage, Pareto sets are calculated using the NSGA-II genetic algorithm. The second stage contains a heuristic that finds situationally optimal operating points using a defined operating strategy.}},
  author       = {{Lammersen, Maximilian and Rasche, Rainer}},
  booktitle    = {{Conference proceedings of Mecatronics & AISM 2023}},
  keywords     = {{modularity, optimization, PEBB, operating strategy, genetic algorithm, Pareto}},
  location     = {{Yokohama}},
  publisher    = {{*}},
  title        = {{{Optimized Operating Points for Power Electronic Building Blocks}}},
  year         = {{2023}},
}

@misc{11377,
  abstract     = {{<jats:p>consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps &amp; cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (&lt;10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.}},
  author       = {{Hernández Rodriguez, Tanja and Sekulic, Anton and Lange-Hegermann, Markus and Frahm, Björn}},
  booktitle    = {{Processes}},
  issn         = {{2227-9717}},
  keywords     = {{Gaussian processes, Bayes optimization, Pareto optimization, multi-objective, cell culture, seed train}},
  number       = {{5}},
  publisher    = {{MDPI AG}},
  title        = {{{Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design}}},
  doi          = {{10.3390/pr10050883}},
  volume       = {{10}},
  year         = {{2022}},
}

@inbook{10193,
  abstract     = {{Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.}},
  author       = {{Hernández Rodriguez, Tanja and Sekulic, Anton and Lange-Hegermann, Markus and Frahm, Björn}},
  booktitle    = {{Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing}},
  editor       = {{Pörtner, Ralf and Möller, Johannes}},
  isbn         = {{978-3-0365-5210-1}},
  issn         = {{2227-9717}},
  keywords     = {{Gaussian processes, Bayes optimization, Pareto optimization, multi-objective, cell culture, seed train}},
  pages        = {{21--48}},
  publisher    = {{MDPI}},
  title        = {{{Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design}}},
  doi          = {{https://doi.org/10.3390/pr10050883}},
  volume       = {{special issue}},
  year         = {{2022}},
}