Schroth, MoritzHake, FelixMerker, KonstantinBecher, AlexanderKlaeger, TilmanHuesmann, RobinEichhorn, DetlefOehm, LukasWohlgemuth, VolkerNaumann, StefanArndt, Hans-KnudBehrens, GritHöb, Maximilian2022-09-192022-09-192022978-3-88579-722-7https://dl.gi.de/handle/20.500.12116/39389Nowadays 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. 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 theenOperator assistanceAIcircular economypaper productionindustrial big dataText/Conference Paper1617-5468