A framework for modeling and optimizing dynamic systems under uncertainty. (9th June 2018)

• Record Type: Journal Article
• Authors: Nicholson, Bethany; Siirola, John
• Is Part Of: Computers & chemical engineering. Volume 114(2018)
• Page Start: 81
• Page End: 88
• Publication Date: 2018-06-09
• Subjects: Stochastic programming -- Dynamic optimization -- Optimal control -- Parameter estimation; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.11.003
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Pyomo modeling extensions provide frameworks for explicitly capturing high-level structure in optimization problems. Representing optimization problems using high-level structures leads to concise and straightforward implementations for complex problems. The composability of Pyomo extensions for dynamic optimization and stochastic programming is demonstrated. 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.