A framework for modeling and optimizing dynamic systems under uncertainty
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
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.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy (FE); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy and Carbon Management (FECM)
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1414438
- Alternate ID(s):
- OSTI ID: 1582924
- Report Number(s):
- SAND-2017-13438J; 659469
- Journal Information:
- Computers and Chemical Engineering, Vol. 114; ISSN 0098-1354
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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