Managing uncertainty in data-driven simulation-based optimization

Journal Article · Thu Aug 29 00:00:00 EDT 2019 · Computers and Chemical Engineering

DOI:https://doi.org/10.1016/j.compchemeng.2019.106519· OSTI ID:1642433

Hüllen, Gordon ^[1]; Zhai, Jianyuan ^[2]; Kim, Sun Hye ^[2]; Sinha, Anshuman ^[2]; Realff, Matthew J. ^[2]; Boukouvala, Fani ^[2]

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1. Georgia Inst. of Technology, Atlanta, GA (United States); Georgia Institute of Technology
2. Georgia Inst. of Technology, Atlanta, GA (United States)

Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and optimization is oftentimes a surrogate metamodel. However, several forms of uncertainty exist within the cycle that links simulation data, to metamodels, to optimization. Uncertainty may originate from parameters of the simulation, or the form and fitted parameters of the metamodel. This paper reviews different literatures that are relevant to surrogate-based optimization and proposes different strategies for handling uncertainty, by combining machine learning with stochastic programming, robust optimization, and discrepancy modeling. We show that incorporating uncertainty management within simulation-based optimization leads to more robust solutions, which protect the decision-maker from infeasible solutions. Here, we present the results of our proposed approaches through a case study for direct-air capture through temperature swing adsorption.

• Research Organization: RAPID Manufacturing Institute, New York, NY (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office
• Grant/Contract Number: EE0007888
• OSTI ID: 1642433
• Journal Information: Computers and Chemical Engineering, Journal Name: Computers and Chemical Engineering Journal Issue: C Vol. 136; ISSN 0098-1354
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Direct air capture
Neural networks
Polynomial interpolation
Simulation optimization
Surrogate modeling