Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept
dc.contributor | 0000-0002-3395-6266 | |
dc.contributor | 0000-0002-6062-7353 | |
dc.contributor | 0000-0002-4102-8697 | |
dc.contributor.author | Schroth, Moritz | |
dc.contributor.author | Hake, Felix | |
dc.contributor.author | Merker, Konstantin | |
dc.contributor.author | Becher, Alexander | |
dc.contributor.author | Klaeger, Tilman | |
dc.contributor.author | Huesmann, Robin | |
dc.contributor.author | Eichhorn, Detlef | |
dc.contributor.author | Oehm, Lukas | |
dc.contributor.editor | Wohlgemuth, Volker | |
dc.contributor.editor | Naumann, Stefan | |
dc.contributor.editor | Arndt, Hans-Knud | |
dc.contributor.editor | Behrens, Grit | |
dc.contributor.editor | Höb, Maximilian | |
dc.date.accessioned | 2022-09-19T09:20:42Z | |
dc.date.available | 2022-09-19T09:20:42Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-3-88579-722-7 | |
dc.identifier.issn | 1617-5468 | |
dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/39389 | |
dc.description.abstract | 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. 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 | en |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | EnviroInfo 2022 | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-328 | |
dc.subject | Operator assistance | |
dc.subject | AI | |
dc.subject | circular economy | |
dc.subject | paper production | |
dc.subject | industrial big data | |
dc.title | Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept | en |
dc.type | Text/Conference Paper | |
dc.pubPlace | Bonn | |
mci.reference.pages | 177 | |
mci.conference.location | Hamburg | |
mci.conference.date | 26.-30- September 2022 |