Forecasting industrial aging processes with machine learning methods
DC Field | Value | Language |
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dc.contributor.author | Bogojeski, Mihail | - |
dc.contributor.author | Sauer, Simeon | - |
dc.contributor.author | Horn, Franziska | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-08-30T04:26:39Z | - |
dc.date.available | 2021-08-30T04:26:39Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2021-01-04 | - |
dc.identifier.issn | 0098-1354 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/50149 | - |
dc.description.abstract | Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic or simple empirical prediction models. In this paper, we evaluate a wider range of data-driven models, comparing some traditional stateless models (linear and kernel ridge regression, feed-forward neural networks) to more complex recurrent neural networks (echo state networks and LSTMs). We first examine how much historical data is needed to train each of the models on a synthetic dataset with known dynamics. Next, the models are tested on real-world data from a large scale chemical plant. Our results show that recurrent models produce near perfect predictions when trained on larger datasets, and maintain a good performance even when trained on smaller datasets with domain shifts, while the simpler models only performed comparably on the smaller datasets. (C) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Forecasting industrial aging processes with machine learning methods | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1016/j.compchemeng.2020.107123 | - |
dc.identifier.scopusid | 2-s2.0-85094818720 | - |
dc.identifier.wosid | 000598170700009 | - |
dc.identifier.bibliographicCitation | COMPUTERS & CHEMICAL ENGINEERING, v.144 | - |
dc.relation.isPartOf | COMPUTERS & CHEMICAL ENGINEERING | - |
dc.citation.title | COMPUTERS & CHEMICAL ENGINEERING | - |
dc.citation.volume | 144 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Time series prediction | - |
dc.subject.keywordAuthor | Predictive maintenance | - |
dc.subject.keywordAuthor | Catalyst degradation | - |
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