An integrated framework for scheduling and control using fast model predictive control

Integration of scheduling and control involves extensive information exchange and simultaneous decision making in industrial practice (Engell and Harjunkoski, Comput Chem Eng. 2012;47:121-133; Baldea and Harjunkoski I, Comput Chem Eng. 2014;71:377-390). Modeling the integration of scheduling and dynamic optimization (DO) at control level using mathematical programming results in a Mixed Integer Dynamic Optimization which is computationally expensive (Flores-Tlacuahuac and Grossmann, Ind Eng Chem Res. 2006;45(20):6698-6712). In this study, we propose a framework for the integration of scheduling and control to reduce the model complexity and computation time. We identify a piece-wise affine model from the first principle model and integrate it with the scheduling level leading to a new integration. At the control level, we use fast Model Predictive Control (fast MPC) to track a dynamic reference. Fast MPC also overcomes the increasing dimensionality of multiparametric MPC in our previous study (Zhuge and Ierapetritou, AIChE J. 2014;60(9):3169-3183). Results of CSTR case studies prove that the proposed approach reduces the computing time by at least two orders of magnitude compared to the integrated solution using mp-MPC. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 3304-3319, 2015