Economic Model Predictive Control for Cryogenic Air Separation Unit Startup

Current energy market trends incentivize frequent optimal load changes, including the startup of cryogenic air separation units (ASUs), which are large electricity consumers. In this study, we assess the potential benefits of using an economic nonlinear model predictive control (ENMPC) framework for the optimal startup of ASUs in the presence of a measured process disturbance. We also considered strategies for improving the solution computational speed of the ENMPC problem. Our case study shows substantial profit recovery by the control strategy relative to offline pre-computed optimal inputs in response to the disturbance.