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Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept

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dc.contributor.authorSchroth, Moritz
dc.contributor.authorHake, Felix
dc.contributor.authorMerker, Konstantin
dc.contributor.authorBecher, Alexander
dc.contributor.authorKlaeger, Tilman
dc.contributor.authorHuesmann, Robin
dc.contributor.authorEichhorn, Detlef
dc.contributor.authorOehm, Lukas
dc.contributor.editorWohlgemuth, Volker
dc.contributor.editorNaumann, Stefan
dc.contributor.editorArndt, Hans-Knud
dc.contributor.editorBehrens, Grit
dc.contributor.editorHöb, Maximilian
dc.date.accessioned2022-09-19T09:20:42Z
dc.date.available2022-09-19T09:20:42Z
dc.date.issued2022
dc.description.abstractNowadays 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 theen
dc.identifier.isbn978-3-88579-722-7
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39389
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofEnviroInfo 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-328
dc.subjectOperator assistance
dc.subjectAI
dc.subjectcircular economy
dc.subjectpaper production
dc.subjectindustrial big data
dc.titleOptimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concepten
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.citation.startPage177
gi.conference.date26.-30- September 2022
gi.conference.locationHamburg

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P328 - EnviroInfo 2022
Environmental Informatics 2022