Data-driven robust optimization for crude oil blending under uncertainty

被引：29

作者：

Dai, Xin ^{[1
]}

Wang, Xiaoqiang ^{[1
]}

He, Renchu ^{[1
]}

Du, Wenli ^{[1
,2
]}

Zhong, Weimin ^{[1
,2
]}

Zhao, Liang ^{[1
,2
]}

Qian, Feng ^{[1
,2
]}

机构：

[1] East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China

[2] Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai, Peoples R China

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2020年 / 136卷

基金：

中国国家自然科学基金;

关键词：

Crude oil blending; Blending effect; Uncertainty; Data-driven robust optimization; PROGRAMMING MODEL; DECISION-MAKING; PREDICTION; OPERATIONS; FRAMEWORK; DESIGN;

D O I：

10.1016/j.compchemeng.2019.106595

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Optimization of crude oil blending helps improve the operating efficiency of refineries. However, widespread uncertainties, such as oil properties, bring difficulty in realizing this task. A data-driven robust optimization (DDRO) approach is proposed to optimize crude oil blending under uncertainty. First, the blending effect model is used to extract uncertainties of oil components from production data by recursive least squares method. Second, the uncertainty set is constructed by combining principle component analysis and robust kernel density estimation based on the historical data of blending effects. A novel data-driven robust model for recipe optimization of crude oil blending is developed by utilizing the obtained uncertainty set. The dual transformation is applied to derive the linear counterpart of the DDRO model. A case study is adopted to illustrate the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：7

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