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 %.

翻译：实现实时能力是非线性模型预测控制（NMPC）工业应用的基本前提。数据驱动模型降阶提供了一种从复杂数字孪生中获取低阶控制模型的方法。特别地，数据驱动方法对特定工艺及其模型所需的专家知识较少，并能提供具有明确通用结构的降阶模型。本文中，我们应用近期基于库普曼理论[Schulze等人(2022)，《计算机化学工程》]提出的数据驱动降阶策略，为某空分装置（ASU）生成了低阶控制模型。该降阶库普曼模型结合了自编码器与线性潜在动力学，并通过机器学习构建。此外，我们提出了一种NMPC实现方案，该方案采用针对降阶库普曼模型固定块结构定制的导数计算方法。我们的降阶方法配合定制化NMPC实现，使空分装置实时NMPC的平均CPU时间降低了98%。