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:
-
- Univ. of Texas, Austin, TX (United States)
- Publication Date:
- Research Org.:
- Univ. of Texas, Austin, TX (United States); Univ. of Texas at Austin, TX (United States)
- Sponsoring Org.:
- USDOE Office of Electricity (OE); National Science Foundation (NSF)
- OSTI Identifier:
- 1605935
- Alternate Identifier(s):
- OSTI ID: 1549167; OSTI ID: 1872913
- 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; 24 POWER TRANSMISSION AND DISTRIBUTION; 97 MATHEMATICS AND COMPUTING
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. 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. 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 = {Tue Nov 28 00:00:00 EST 2017},
month = {Tue Nov 28 00:00:00 EST 2017}
}
Search WorldCat to find libraries that may hold this journalWeb of Science