  Nowadays cross-industry ranging challenges include the reduction of
greenhouse gas emission and enabling a circular economy. However, the
production of paper from waste paper is still a highly resource intensive task,
especially in terms of energy consumption. While paper machines produce a lot
of data, we have identified a lack of utilization of it and implement a concept
using an operator assistance system and state-of-the-art machine learning
techniques, e.g., classification, forecasting and alarm flood handling
algorithms, to support daily operator tasks. Our main objective is to provide
situation-specific knowledge to machine operators utilizing available data. We
expect this will result in better adjusted parameters and therefore a lower
footprint of the paper machines.
