  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.
