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Title: An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations

Abstract

The emphasis currently placed on enterprise-wide decision making and optimization has led to an increased need for methods of integrating nonlinear process dynamics and control information in scheduling calculations. The inevitable high dimensionality and nonlinearity of first-principles dynamic process models makes incorporating them in scheduling calculations challenging. In this work, we describe a general framework for deriving data-driven surrogate models of the closed-loop process dynamics. Focusing on Hammerstein–Wiener and finite step response (FSR) model forms, we show that these models can be (exactly) linearized and embedded in production scheduling calculations. The resulting scheduling problems are mixed-integer linear programs with a special structure, which we exploit in a novel and efficient solution strategy. A polymerization reactor case study is utilized to demonstrate the merits of this method. Finally, our framework compares favorably to existing approaches that embed dynamics in scheduling calculations, showing considerable reductions in computational effort.

Authors:
 [1];  [1];  [1];  [1]
  1. Univ. of Texas, Austin, TX (United States)
Publication Date:
Research Org.:
Univ. of Texas, Austin, TX (United States)
Sponsoring Org.:
USDOE Office of Electricity (OE); National Science Foundation (NSF)
OSTI Identifier:
1605935
Alternate Identifier(s):
OSTI ID: 1549167
Grant/Contract Number:  
OE0000841
Resource Type:
Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Volume: 110; Journal Issue: C; Journal ID: ISSN 0098-1354
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Integrated scheduling and control; Nonlinear dynamics; Surrogate models; Hammerstein–Wiener models; Finite step response models; Lagrangian relaxation

Citation Formats

Kelley, Morgan T., Pattison, Richard C., Baldick, Ross, and Baldea, Michael. An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations. United States: N. p., 2017. Web. doi:10.1016/j.compchemeng.2017.11.021.
Kelley, Morgan T., Pattison, Richard C., Baldick, Ross, & Baldea, Michael. An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations. United States. doi:https://doi.org/10.1016/j.compchemeng.2017.11.021
Kelley, Morgan T., Pattison, Richard C., Baldick, Ross, and Baldea, Michael. Tue . "An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations". United States. doi:https://doi.org/10.1016/j.compchemeng.2017.11.021. https://www.osti.gov/servlets/purl/1605935.
@article{osti_1605935,
title = {An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations},
author = {Kelley, Morgan T. and Pattison, Richard C. and Baldick, Ross and Baldea, Michael},
abstractNote = {The emphasis currently placed on enterprise-wide decision making and optimization has led to an increased need for methods of integrating nonlinear process dynamics and control information in scheduling calculations. The inevitable high dimensionality and nonlinearity of first-principles dynamic process models makes incorporating them in scheduling calculations challenging. In this work, we describe a general framework for deriving data-driven surrogate models of the closed-loop process dynamics. Focusing on Hammerstein–Wiener and finite step response (FSR) model forms, we show that these models can be (exactly) linearized and embedded in production scheduling calculations. The resulting scheduling problems are mixed-integer linear programs with a special structure, which we exploit in a novel and efficient solution strategy. A polymerization reactor case study is utilized to demonstrate the merits of this method. Finally, our framework compares favorably to existing approaches that embed dynamics in scheduling calculations, showing considerable reductions in computational effort.},
doi = {10.1016/j.compchemeng.2017.11.021},
journal = {Computers and Chemical Engineering},
number = C,
volume = 110,
place = {United States},
year = {2017},
month = {11}
}

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Cited by: 13 works
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