@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} }