  Achieving real-time capability is an essential prerequisite for the
industrial implementation of nonlinear model predictive control (NMPC).
Data-driven model reduction offers a way to obtain low-order control models
from complex digital twins. In particular, data-driven approaches require
little expert knowledge of the particular process and its model, and provide
reduced models of a well-defined generic structure. Herein, we apply our
recently proposed data-driven reduction strategy based on Koopman theory
[Schulze et al. (2022), Comput. Chem. Eng.] to generate a low-order control
model of an air separation unit (ASU). The reduced Koopman model combines
autoencoders and linear latent dynamics and is constructed using machine
learning. Further, we present an NMPC implementation that uses derivative
computation tailored to the fixed block structure of reduced Koopman models.
Our reduction approach with tailored NMPC implementation enables real-time NMPC
of an ASU at an average CPU time decrease by 98 %.

Systems and Control;Machine Learning;Systems and Control