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

Abstract

in Wiley Online Library (wileyonlinelibrary.com) 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. V C 2015 American Institute of Chemical Engineers AIChE J, 61: 3304-3319, 2015 Keywords: integration of scheduling and control, piece-wise affine approximation, fast model predictive control, Multiparametric model predictive control, mixed integer nonlinear programming

Erratum

“…We have noticed some errors in the PWA identification technique proposed in Zhuge and Ierapetritou . Specifically, the proposed PWA identification technique is not able to handle general forms of nonlinear function, as previously affirmed.…”

Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes

“…This formulation builds on previous work that demonstrates the separability of the integrated scheduling and control problem into subproblems without the need for iterations [20] and builds on previous work which demonstrates the separation into MILP and dynamic optimization problems [10,46,47]. This formulation also builds on previous work that demonstrates benefits from shifting separable computational burden into offline portions of the integrated problem [11,19,22,55].…”

“…The majority of integrated scheduling and control formulations have used cyclic schedules [7][8][9][10][11][15][16][17][18][19][20][21][22]. However, it has been suggested that a dynamic cyclic schedule may improve process economics [23].…”

Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes

“…Zhuge and Ierapetritou also present methodology to reduce the computational burden of ISC to enable closed-loop online operation for batch and continuous processes. They propose using multi-parametric model predictive control for online batch scheduling and control [48], fast model predictive control coupled with reduced order (piece-wise affine) models in scheduling and control for continuous processes [49], and decomposition into separate problems for continuous processes [50]. Chu and You demonstrate the economic benefit of closed-loop moving horizon scheduling with consideration of process dynamics in batch scheduling [29].…”

“…This enables the ISC algorithm to respond to fluctuations in market conditions as well as respond to measured process disturbances in a timely manner to ensure that production scheduling and control are updated to reflect optimal operation with current market conditions and process state. The formulation for phase 4 builds on the work of Zhuge et al [49], which justifies decomposing slot-based ISC into two subproblems: (1) NLP solution of transition times and transition control profiles and (2) MILP solution of the slot-based, continuous-time schedule. The formulation in [60] expands the work of Zhuge et al by combining a look-up transition time table with control profiles and transition times between known product steady-state conditions, calculated offline and stored in memory, with transitions from current conditions to each product.…”