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Integrated production scheduling and model predictive control of continuous processes
Author(s) -
Baldea Michael,
Du Juan,
Park Jungup,
Harjunkoski Iiro
Publication year - 2015
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.14951
Subject(s) - model predictive control , scheduling (production processes) , dynamic priority scheduling , computer science , process control , mathematical optimization , engineering , control theory (sociology) , process (computing) , mathematics , control (management) , schedule , artificial intelligence , operating system
The integration of production management and process control decisions is critical for improving economic performance of the chemical supply chain. A novel framework for integrating production scheduling and model predictive control (MPC) for continuous processes is proposed. Our framework is predicated on using a low‐dimensional time scale‐bridging model (SBM) that captures the closed‐loop process dynamics over the longer time scales that are relevant to scheduling calculations. The SBM is used as a constraint in a mixed‐integer dynamic formulation of the scheduling problem. To synchronize the scheduling and MPC calculations, a novel scheduling‐oriented MPC concept is proposed, whereby the SBM is incorporated in the expression of the controller as a (soft) dynamic constraint and allows for obtaining an explicit description of the closed‐loop process dynamics. Our framework scales favorably with system size and provides desirable closed‐loop stability and performance properties for the resulting integrated scheduling and control problem. © 2015 American Institute of Chemical Engineers AIChE J , 61: 4179–4190, 2015