文献类型：期刊文献

英文题名：Diesel blending under property uncertainty: A data-driven robust optimization approach

作者：Long, Jian[1];Jiang, Siyi[1];He, Renchu[1];Zhao, Liang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：306

外文期刊名：FUEL

收录：;EI(收录号：20213210747532);WOS:【SCI-EXPANDED(收录号:WOS:000702818600003)】；

基金：The work is supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (61973124, 62073142, 61873092) , International (Regional) Cooperation and Exchange Project (61720106008) .

语种：英文

外文关键词：Diesel blending; Data driven robust optimization; Robust kernel density estimation; Principal component analysis; Uncertainty set

摘要：With the increasing demand for diesel fuel and the strict diesel standards, diesel blending has become an essential technology in response to clean energy. However, in the traditional blending process, the operator does not consider the uncertainty of component oil properties during the recipe optimization, resulting in a sub-optimal or even infeasible solution. This paper proposes a data-driven robust optimization framework to address this issue. First, a hybrid machine learning method combining principal component analysis and robust kernel density estimation is used to construct uncertainty sets to capture uncertain properties. Then, a data-driven diesel blending model is formulated using the derived uncertainty set through the dual operation. Last, an actual case study is implemented to show the effectiveness of the proposed method in handling uncertainties and obtaining a good balance between robustness and optimality of the recipe optimization for diesel blending. Moreover, the parameters of the uncertainty sets are analyzed in detail for providing reasonable parameters to guide the actual diesel blending.