A framework for modeling and optimizing dynamic systems under uncertainty
Abstract
Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE Office of Fossil Energy (FE); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy and Carbon Management (FECM)
- OSTI Identifier:
- 1414438
- Alternate Identifier(s):
- OSTI ID: 1582924
- Report Number(s):
- SAND-2017-13438J
Journal ID: ISSN 0098-1354; 659469
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computers and Chemical Engineering
- Additional Journal Information:
- Journal Volume: 114; Journal ID: ISSN 0098-1354
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Stochastic programming; Dynamic optimization; Optimal control; Parameter estimation
Citation Formats
Nicholson, Bethany, and Siirola, John. A framework for modeling and optimizing dynamic systems under uncertainty. United States: N. p., 2017.
Web. doi:10.1016/j.compchemeng.2017.11.003.
Nicholson, Bethany, & Siirola, John. A framework for modeling and optimizing dynamic systems under uncertainty. United States. https://doi.org/10.1016/j.compchemeng.2017.11.003
Nicholson, Bethany, and Siirola, John. Sat .
"A framework for modeling and optimizing dynamic systems under uncertainty". United States. https://doi.org/10.1016/j.compchemeng.2017.11.003. https://www.osti.gov/servlets/purl/1414438.
@article{osti_1414438,
title = {A framework for modeling and optimizing dynamic systems under uncertainty},
author = {Nicholson, Bethany and Siirola, John},
abstractNote = {Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.},
doi = {10.1016/j.compchemeng.2017.11.003},
journal = {Computers and Chemical Engineering},
number = ,
volume = 114,
place = {United States},
year = {Sat Nov 11 00:00:00 EST 2017},
month = {Sat Nov 11 00:00:00 EST 2017}
}
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Figures / Tables:
Table 1: Parameters for semibatch reactor model
Figures / Tables found in this record: