@misc{10787,
  abstract     = {{Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qualities and properties have yet slowed down automation, has potential for this evolution. This contribution presents optimization methods for a dynamical cross-flow filtration plant which is driven by an advanced control concept in combination with data driven product monitoring via inline near infrared spectroscopy (NIR) in order to improve energy savings and filtration performance. Using a hierarchical control and optimization structure, the non stationary batch process is steered towards a high production rate with low energy consumption for a variety of different input products.}},
  author       = {{Tebbe, Jörn and Pawlik, Thomas and Trilling-Haasler, Marc and Löbner, Jannis and Lange-Hegermann, Markus and Schneider, Jan}},
  booktitle    = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
  editor       = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}},
  isbn         = {{978-1-6654-9314-7 }},
  issn         = {{1935-4576}},
  keywords     = {{Spectroscopy, Production systems, Filtration, Velocity control, Optimization methods, Cyber-physical systems, Nonhomogeneous media}},
  location     = {{Lemgo}},
  pages        = {{1--7}},
  publisher    = {{IEEE}},
  title        = {{{Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system}}},
  doi          = {{10.1109/INDIN51400.2023.10217913}},
  year         = {{2023}},
}

@misc{10788,
  abstract     = {{For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a R2 of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.}},
  author       = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}},
  booktitle    = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
  editor       = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}},
  isbn         = {{978-1-6654-9314-7}},
  issn         = {{1935-4576}},
  keywords     = {{Spectroscopy, Smoothing methods, Systematics, Phase measurement, Linear regression, Data models, Dielectric measurement}},
  location     = {{Lemgo}},
  pages        = {{1--6}},
  publisher    = {{IEEE}},
  title        = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}},
  doi          = {{10.1109/INDIN51400.2023.10218012}},
  year         = {{2023}},
}

@inproceedings{4761,
  abstract     = {{Maintenance is an important set of various activities related to preserving from failure or decline. Improper or lack of maintenance may result in excessive component wear, production quality deterioration, or even longer downtime. However, today's production facilities strive to devote the least amount of necessary maintenance time in order to maximize production time. Therefore, new solutions for deliberate and efficient maintenance are needed. The solution proposed in this paper benefits from the newest trends and innovations in industry, namely the Asset Administration Shell (AAS) which is part of the Industrie 4.0 (I4.0) concept. The AAS shall contain the maintenance submodel which shall be used for supporting humans during the maintenance process. The submodel provides a standardized description of required tools and parts as well as step-by-step instructions which also include safety concerns and multimedia files, such as pictures and videos. In this way, maintenance can be carried out more reliably, resulting in reduced downtime. In addition, feedback from the maintenance process shall be stored in the submodel and fed through an I4.0-compliant network to other processes from different phases of the life cycle in order to improve them.}},
  author       = {{Lang, Dorota and Grunau, Sergej and Wisniewski, Lukasz and Jasperneite, Jürgen}},
  booktitle    = {{17th International Conference on Industrial Informatics (IEEE-INDIN 2019)}},
  isbn         = {{978-1-7281-2928-0 }},
  issn         = {{1935-4576}},
  keywords     = {{maintenance, Asset Administration Shell}},
  location     = {{Helsinki-Espoo, Finland}},
  pages        = {{768--773}},
  publisher    = {{IEEE}},
  title        = {{{Utilization of the Asset Administration Shell to Support Humans During the Maintenance Process}}},
  doi          = {{10.1109/indin41052.2019.8972236}},
  year         = {{2019}},
}