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Title: 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:
 [1];  [1]
  1. 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)
OSTI Identifier:
1414438
Report Number(s):
SAND-2017-13438J
Journal ID: ISSN 0098-1354; 659469
Grant/Contract Number:
AC04-94AL85000; NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Name: Computers and Chemical Engineering; Journal ID: ISSN 0098-1354
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. doi:10.1016/j.compchemeng.2017.11.003.
Nicholson, Bethany, and Siirola, John. 2017. "A framework for modeling and optimizing dynamic systems under uncertainty". United States. doi:10.1016/j.compchemeng.2017.11.003.
@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 = ,
place = {United States},
year = 2017,
month =
}

Journal Article:
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This content will become publicly available on November 11, 2018
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