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Title: Managing uncertainty in data-driven simulation-based optimization

Abstract

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.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Research Org.:
RAPID Manufacturing Institute, New York, NY (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office
OSTI Identifier:
1642433
Grant/Contract Number:  
EE0007888
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Volume: 136; Journal Issue: C; Journal ID: ISSN 0098-1354
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Surrogate modeling; Simulation optimization; Direct air capture; Neural networks; Polynomial interpolation

Citation Formats

Hüllen, Gordon, Zhai, Jianyuan, Kim, Sun Hye, Sinha, Anshuman, Realff, Matthew J., and Boukouvala, Fani. Managing uncertainty in data-driven simulation-based optimization. United States: N. p., 2019. Web. doi:10.1016/j.compchemeng.2019.106519.
Hüllen, Gordon, Zhai, Jianyuan, Kim, Sun Hye, Sinha, Anshuman, Realff, Matthew J., & Boukouvala, Fani. Managing uncertainty in data-driven simulation-based optimization. United States. https://doi.org/10.1016/j.compchemeng.2019.106519
Hüllen, Gordon, Zhai, Jianyuan, Kim, Sun Hye, Sinha, Anshuman, Realff, Matthew J., and Boukouvala, Fani. 2019. "Managing uncertainty in data-driven simulation-based optimization". United States. https://doi.org/10.1016/j.compchemeng.2019.106519. https://www.osti.gov/servlets/purl/1642433.
@article{osti_1642433,
title = {Managing uncertainty in data-driven simulation-based optimization},
author = {Hüllen, Gordon and Zhai, Jianyuan and Kim, Sun Hye and Sinha, Anshuman and Realff, Matthew J. and Boukouvala, Fani},
abstractNote = {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.},
doi = {10.1016/j.compchemeng.2019.106519},
url = {https://www.osti.gov/biblio/1642433}, journal = {Computers and Chemical Engineering},
issn = {0098-1354},
number = C,
volume = 136,
place = {United States},
year = {2019},
month = {8}
}