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.

动态过程模型在日程安排计算中的集成最近引起了极大的关注，这是在日趋动态的市场中提高运营绩效的一种手段。在这项工作中，我们提出了一个用于调度和模型预测控制（MPC）集成的新颖框架，该框架适用于涉及快速变化的市场条件的工业规模问题。该框架包括：确定与调度相关的过程变量，建立低阶动态模型以捕获其演变，以及通过以下方式整合调度和MPC：（i）解决模拟优化问题以定义最佳调度，以及（ii）跟踪使用MPC控制器进行闭环调度。通过一个案例研究证明了该框架的有效性，该案例研究了在实时电价下运行的空气分离装置。研究表明，合理的计算时间可以显着降低成本。