Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory

arXiv (Cornell University)

Abstract

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 %.

Authors 4

1. Jan C. Schulze Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) , RWTH Aachen University , 52074 Aachen , Germany

2. Danimir T. Doncevic

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10) , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany

3. Nils Erwes Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) , RWTH Aachen University , 52074 Aachen , Germany

4. Alexander Mitsos

   Jülich Aachen Research Alliance

   Affiliation as printed

   JARA-CSD , 52056 Aachen , Germany