Forecasting industrial aging processes with machine learning methods

• Published in: Computers & chemical engineering Vol. 144; p. 107123
• Main Authors: Bogojeski, Mihail, Sauer, Simeon, Horn, Franziska, Müller, Klaus-Robert
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 04-01-2021
• Subjects: Catalyst degradation; Machine learning; Predictive maintenance; Time series prediction; Time series prediction; Catalyst degradation; Machine learning; Predictive maintenance
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2020.107123

•Machine learning models can be used to accurately forecast industrial aging processes.•Multiple machine learning models were tested using synthetic and real-world data.•Good performance of recurrent networks shows that having temporal context is crucial.•Recurrent networks maintain good performance even when dealing with smaller datasets.
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.