Keywords

Model predictive control, Dynamic pricing, Time scaling, Complementarity constraints

Abstract

Linear model predictive control is extended to both control and optimize a product grade schedule. The proposed methods are time-scaling of the linear dynamics based on throughput rates and grade-based objectives for product scheduling based on a mathematical program with complementarity constraints. The linear model is adjusted with a residence time approximation to time-scale the dynamics based on throughput. Although nonlinear models directly account for changing dynamics, the model form is restricted to linear differential equations to enable fast online cycle times for large-scale and real-time systems. This method of extending a linear time-invariant model for scheduling is designed for many advanced control applications that currently use linear models. Simultaneous product switching and grade target management is demonstrated on a reactor benchmark application. The objective is a continuous form of discrete ranges for product targets and economic terms that maximize overall profitability.

Original Publication Citation

Beal, Logan DR, et al. "Combined model predictive control and scheduling with dominant time constant compensation." Computers & Chemical Engineering 104 (2017): 271-282.

Document Type

Peer-Reviewed Article

Publication Date

2017-09-02

Permanent URL

http://hdl.lib.byu.edu/1877/3858

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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