A smart manufacturing strategy for multi-parametric model predictive control in air separation systems

Abstract

Recent trends in digitization and automation of information systems have led to the Industry 4.0 revolution in manufacturing systems. With the emergence of integrated "smart" systems that communicate through the cloud, collecting and manipulating the system data became a key yet, challenging component for developing optimal control strategies for these complex systems. In this work, we propose a strategy to address this problem with the case study on an Air Separation System (ASU). Our approach involves developing an ASU's controllers via high-fidelity modeling, studies in data-driven reduced-order models, and providing implementable control policies for the high-fidelity model. Connecting the high-fidelity model to a smart manufacturing platform allows integration into other smart manufacturing tools and applications. Since the high-fidelity model is computationally challenging for online optimization tasks, such as model predictive control, surrogate models are generated that represent the high-fidelity model's behavior. The derived reduced-order models are then embedded into a model predictive control formulation for the optimal control of the whole process through multiparametric programming. A multiparametric approach based on solving a small portion of the multiparametric program is proposed to reduce the computational overhead. We then close the loop by deploying the developed controllers on the high-fidelity model for tuning with prospects of employing them on the real industrial plant.