首页|Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

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Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deter-ministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is trans-ferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

DistributionsModelOptimizationCrude oil schedulingWasserstein distanceDistributionally robust chance constraints

Xin Dai、Liang Zhao、Renchu He、Wenli Du、Weimin Zhong、Zhi Li、Feng Qian

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Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

Engineering Research Center of Process System Engineering,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Fund for Distinguished Young ScholarsInternational(Regional)Cooperation and Exchange Project

6198810162073142221781036192530561720106008

2024

10.1016/j.cjche.2023.12.007

中国化学工程学报(英文版)
中国化工学会

中国化学工程学报(英文版)

CSTPCDEI
影响因子：0.818
ISSN：1004-9541
年,卷(期)：2024.69(5)
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