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Forecasting industrial aging processes with machine learning methods

Authors
Bogojeski, MihailSauer, SimeonHorn, FranziskaMueller, Klaus-Robert
Issue Date
4-1월-2021
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Machine learning; Time series prediction; Predictive maintenance; Catalyst degradation
Citation
COMPUTERS & CHEMICAL ENGINEERING, v.144
Indexed
SCIE
SCOPUS
Journal Title
COMPUTERS & CHEMICAL ENGINEERING
Volume
144
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/50149
DOI
10.1016/j.compchemeng.2020.107123
ISSN
0098-1354
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.
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