@misc{11377, abstract = {{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 = {{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}}, } @misc{12812, abstract = {{Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.}}, author = {{Berns, Fabian and Lange-Hegermann, Markus and Beecks, Christian}}, booktitle = {{ Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1}}, editor = {{Panetto, H. and Madani, K. and Smirnov, A.}}, isbn = {{978-989-758-476-3}}, keywords = {{Anomaly Detection, Gaussian Processes, Explainable Machine Learning, Industry 4.0}}, location = {{Budapest, HUNGARY}}, pages = {{87--92}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0}}}, doi = {{10.5220/0010130300870092}}, year = {{2020}}, }