Keywords

scheduling, model predictive control, demand response

Abstract

Integrated scheduling and control (SC) seeks to unify the objectives of the various layers of optimization in manufacturing. This work investigates combining scheduling and control using a nonlinear discrete-time formulation, utilizing the full nonlinear process model throughout the entire horizon. This discrete-time form lends itself to optimization with time-dependent constraints and costs. An approach to combined SC is presented, along with sample pseudo-binary variable functions to ease the computational burden of this approach. An initialization strategy using feedback linearization, nonlinear model predictive control, and continuous-time scheduling optimization is presented. The formulation is applied with a generic continuous stirred tank reactor (CSTR) system in open-loop simulations over a 48-hour horizon and a sample closed-loop implementation. The value of time-based parameters is demonstrated by applying cooling constraints and dynamic energy costs of a sample diurnal cycle, enabling demand response via combined scheduling and control.

Original Publication Citation

Logan D.R. Beal, Damon Petersen, David Grimsman, Sean Warnick, John D. Hedengren. (2018). Integrated scheduling and control in discrete-time with dynamic parameters and constraints. Computers & Chemical Engineering, 115, 361-376. https://www.sciencedirect.com/science/article/pii/S0098135418303120

Document Type

Peer-Reviewed Article

Publication Date

2018-7

Permanent URL

http://hdl.lib.byu.edu/1877/4049

Publisher

Elsevier

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Chemical Engineering

University Standing at Time of Publication

Associate Professor

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