@inproceedings{BarthHerkelGutwaldetal.2026, author = {Barth, Martin and Herkel, Philipp and Gutwald, Benjamin and Kr{\"u}ger, Jan Hinrich and Schrage, Tobias and Reichenstein, Tobias and Franke, J{\"o}rg}, title = {Utilization of Virtual Commissioning for Simulation-Based Energy Modeling and Dimensioning of DC-Based Production Systems}, booktitle = {2025 Winter Simulation Conference (WSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-8726-0}, doi = {https://doi.org/10.1109/WSC68292.2025.11339100}, pages = {700 -- 710}, year = {2026}, language = {en} } @inproceedings{SchrageSchudererFranke2025, author = {Schrage, Tobias and Schuderer, Peter and Franke, J{\"o}rg}, title = {Process- and Material-Specific Modeling to Use in Simulation-Based and Resource-Oriented Decision Support Systems Using the Example of Calcium Silicate Brick Production Planning}, booktitle = {Sustainable Manufacturing as a Driver for Growth: Proceedings of the 19th Global Conference on Sustainable Manufacturing, December 4-6, 2023, Buenos Aires, Argentina}, editor = {Kohl, Holger and Seliger, G{\"u}nther and Dietrich, Franz and Mur, Sebasti{\´a}n}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-77429-4}, doi = {https://doi.org/10.1007/978-3-031-77429-4_62}, pages = {562 -- 568}, year = {2025}, abstract = {As part of the fourth industrial revolution, data analysis and artificial intelligence are being integrated into production processes. In addition, energy consumption and CO2 costs are becoming decisive factors in the resource-oriented management of companies. For energy intensive and hybrid production processes a simulation-based decision support system (DSS) for production planning is validated and further developed for the sand lime brick industry to support the production planning process. The integration of empirical knowledge in the energy-intensive control of steam processes, in which quality-critical product parameters are set via thermodynamically complex relationships, is still part of current research approaches. In this paper, an approach for the mapping of an energetic system behavior in the energy-intensive and hybrid production processes will be discussed using the example of calcium silicate brick (CSB) production. Possibilities for using Discrete event simulation (DES) to increase the energy efficiency of steam processes are summarized and linked to formalized empirical knowledge in artificial Intelligence (AI) approaches.}, language = {en} } @inproceedings{SchrageBarthSchudereretal.2023, author = {Schrage, Tobias and Barth, Martin and Schuderer, Peter and Franke, J{\"o}rg}, title = {Entwicklung und Realisierung einer Modellbibliothek f{\"u}r ein Entscheidungsunterst{\"u}tzungssystem in der Kalksandsteinproduktion}, booktitle = {Simulation in Produktion und Logistik 2023}, editor = {Bergman, S{\"o}ren and Feldkamp, Niclas and Souren, Rainer and Straßburger, Steffen}, publisher = {Universit{\"a}tsverlag Ilmenau}, address = {Ilmenau}, isbn = {978-3-86360-276-5}, doi = {https://doi.org/10.22032/dbt.57787}, pages = {61 -- 70}, year = {2023}, language = {de} } @inproceedings{SchrageSchudererBarthetal.2025, author = {Schrage, Tobias and Schuderer, Peter and Barth, Martin and Franke, J{\"o}rg}, title = {Konzeptionierung und Integration eines KI-basierten Autoklavenmodells in ein simulationsbasiertes Entscheidungsunterst{\"u}tzungssystem in der Kalksandsteinproduktion}, booktitle = {Simulation in Produktion und Logistik 2025}, editor = {Rank, Sebastian and K{\"u}hn, Mathias and Schmidt, Thorsten}, publisher = {Technische Universit{\"a}t Dresden}, address = {Dresden}, isbn = {978-3-86780-809-5}, doi = {https://doi.org/10.25368/2025.288}, pages = {55-1 -- 55-10}, year = {2025}, abstract = {The calcium silicate brick (CSB) industry faces increasing pressure to improve the efficiency and sustainability of its production processes, especially due to the high energy consumption and CO₂ emissions associated with autoclaving. To support operational and strategic decision-making, a simulation-based decision support system (DSS) has been developed, utilizing a modular material flow simulation framework. However, the energy-intensive steam curing process in autoclaves remains insufficiently represented in existing simulation models. This paper presents a methodological approach for integrating an AI-based optimization agent for autoclave systems into the existing DSS used in CSB production. A review of current techniques for coupling AI assistance systems with discrete event simulation in production and logistics, within the building materials sector, provides the foundation. Building on this, the autoclaving process is modeled using a hybrid approach, combining thermodynamic domain knowledge with machine learning methods. This contribution highlights how the combination of simulation-based production planning and intelligent process modelling can drive digital transformation in energy-intensive industries.}, language = {de} }