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

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