458986 Coordinating Production Scheduling and Process Operation Via Economic Model Predictive Control

Monday, November 14, 2016: 1:42 PM
Monterey II (Hotel Nikko San Francisco)
Anas Alanqar1, Helen Durand1, Fahad Albalawi2 and Panagiotis D. Christofides3, (1)Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, (2)Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, (3)Department of Chemical and Biomolecular Engineering and Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA

Supply chain optimization has been an active area of research for improving the overall profit from a chemical process [1]-[2]. Although supply chain optimization in general addresses many aspects of the production pipeline from raw material manufacturers to customers, an important aspect of the supply chain is planning and scheduling of plant production to determine what will be produced and when it will be produced. The integration of production management and control has been investigated to improve process profits [3]-[4]. Generally, only a subset of the process states are required to track a schedule; the additional states may be varied in time for economic benefit if the production schedule is simultaneously met.

In this work, we develop a Lyapunov-based economic model predictive control (LEMPC) formulation that can maintain closed-loop stability of the process states while causing states that are required to meet a schedule to track the scheduled values. By enforcing the schedule through a soft constraint, feasibility of the LEMPC is maintained at all times even in the presence of disturbances when the Lyapunov level sets used in the stability constraints of the LEMPC intersect at the time that the schedule is changed. The states that are not required to meet a schedule are permitted to vary in time to maximize the process economics. The proposed LEMPC is applied to a chemical process example in which the heating rate is to be minimized while the product concentration tracks a desired schedule. This chemical process example demonstrated that the proposed method was able to cause the product concentration to meet the desired schedule while maintaining closed-loop stability and minimizing the process energy cost.

[1] Subramanian K, Rawlings JB, Maravelias CT, Flores-Cerrillo J, Megan L. Integration of control theory and scheduling methods for supply chain management. Computers & Chemical Engineering. 2013;51:4-20.

[2] Perea-López E, Ydstie BE, Grossmann IE. A model predictive control strategy for supply chain optimization. Computers & Chemical Engineering. 2003;27:1201-1218.

[3] Baldea M, Harjunkoski I. Integrated production scheduling and process control: A systematic review. Computers & Chemical Engineering. 2014;71:377-390.

[4] Gutiérrez-Limón MA, Flores-Tlacuahuac A, Grossmann IE. A reactive optimization strategy for the simultaneous planning, scheduling and control of short-period continuous reactors. Computers & Chemical Engineering. 2016;84:507-515.

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See more of this Session: Integrated Production Scheduling and Control
See more of this Group/Topical: Computing and Systems Technology Division