A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units. (8th May 2018) - Dias, Lisia S.; Pattison, Richard C.; Tsay, Calvin; Baldea, Michael; Ierapetritou, Marianthi G.
Highlights: A new framework for the integration scheduling and MPC is proposed. The framework consists of a nested decision-making structure with two control loops. The outer loop is formulated as a closed-loop simulation-optimization problem. The inner loop consists of an online MPC. The framework is applied to an Air Separation Unit. Abstract: The integration of dynamic process models in scheduling calculations has recently received significant attention as a mean to improve operational performance in increasingly dynamic markets. In this work, we propose a novel framework for the integration of scheduling and model predictive control (MPC), which is applicable to industrial size problems involving fast changing market conditions. The framework consists on identifying scheduling-relevant process variables, building low-order dynamic models to capture their evolution, and integrating scheduling and MPC by, (i) solving a simulation-optimization problem to define the optimal schedule and, (ii) tracking the schedule in closed-loop using the MPC controller. The efficacy of the framework is demonstrated via a case study that considers an air separation unit operating under real-time electricity pricing. The study shows that significant cost reductions can be achieved with reasonable computational times.