Managing uncertainty in data-driven simulation-based optimization
Managing uncertainty in data-driven simulation-based optimization
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DOI：
10.1016/j.compchemeng.2019.106519
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Gordon Hüllen;Jianyuan Zhai;Sun Hye Kim;Anshuman Sinha;M. Realff;Fani Boukouvala
中科院分区：
文献类型：
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作者：
Gordon Hüllen;Jianyuan Zhai;Sun Hye Kim;Anshuman Sinha;M. Realff;Fani Boukouvala
使用复杂模拟数据进行优化已成为一种有吸引力的决策选项，因为它能够在搜索最佳值时嵌入对过程的高保真、非线性理解。由于缺乏易处理的代数方程，模拟和优化之间的联系往往是一个代理元模型。然而，在将仿真数据链接到元模型和优化的循环中存在几种形式的不确定性。不确定性可能源于模拟的参数，或元模型的形式和拟合参数。本文回顾了与基于代理的优化相关的不同文献，并通过将机器学习与随机规划、鲁棒优化和差异建模相结合，提出了处理不确定性的不同策略。我们表明，将不确定性管理内基于仿真的优化导致更强大的解决方案，保护决策者从不可行的解决方案。我们提出的结果，我们提出的方法，通过变温吸附直接空气捕获的案例研究。
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. We present the results of our proposed approaches through a case study for direct-air capture through temperature swing adsorption.