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

doi:10.1016/j.cjche.2023.12.007

中国化学工程学报 ›› 2024, Vol. 69 ›› Issue (5): 152-166.DOI: 10.1016/j.cjche.2023.12.007

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

Xin Dai¹, Liang Zhao¹, Renchu He¹, Wenli Du¹, Weimin Zhong¹, Zhi Li^1,2, Feng Qian¹

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2023-01-18 修回日期:2023-12-08 出版日期:2024-05-28 发布日期:2024-07-01
通讯作者: Renchu He,E-mail:renchuhe@ecust.edu.cn;Feng Qian,E-mail:fqian@ecust.edu.cn
基金资助:
The authors acknowledge the supports from National Natural Science Foundation of China (61988101, 62073142, 22178103), National Natural Science Fund for Distinguished Young Scholars (61925305) and International (Regional) Cooperation and Exchange Project (61720106008).

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

Xin Dai¹, Liang Zhao¹, Renchu He¹, Wenli Du¹, Weimin Zhong¹, Zhi Li^1,2, Feng Qian¹

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2023-01-18 Revised:2023-12-08 Online:2024-05-28 Published:2024-07-01
Contact: Renchu He,E-mail:renchuhe@ecust.edu.cn;Feng Qian,E-mail:fqian@ecust.edu.cn
Supported by:
The authors acknowledge the supports from National Natural Science Foundation of China (61988101, 62073142, 22178103), National Natural Science Fund for Distinguished Young Scholars (61925305) and International (Regional) Cooperation and Exchange Project (61720106008).

摘要/Abstract

摘要： 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 deterministic 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 transferred 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.

关键词: Distributions, Model, Optimization, Crude oil scheduling, Wasserstein distance, Distributionally robust chance constraints

Abstract: 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 deterministic 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 transferred 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.

Key words: Distributions, Model, Optimization, Crude oil scheduling, Wasserstein distance, Distributionally robust chance constraints

Xin Dai, Liang Zhao, Renchu He, Wenli Du, Weimin Zhong, Zhi Li, Feng Qian. Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty[J]. 中国化学工程学报, 2024, 69(5): 152-166.

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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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