A model-based simulation and optimization tool has become standard in the drafting, design, and control of real processes, both in industrial production as well as in organizational processes. The practical benefit depends to a large extent on the availability and quality of the model predictions.
The aim of this project is to develop the methods to significantly improve both – availability and quality – of the model predictions, by interlinking the deterministic white box models with the data-driven black box approaches.
"Machine Learning processes require huge volumes of data if they are to work well. However, this is not always available in the manufacturing sector. Yet, we do have a great deal of knowledge about the processes," said project coordinator Michael Bortz. "If we take this into account and combine it with machine-learning methods, we can make model-based predictions and enable more reliable and substantially improved optimization designs. This is the basic concept explored in our project."