文献类型：期刊文献

英文题名：Data-driven robust optimization for integrated refinery planning and blending scheduling under multi-mode uncertainty

作者：Yu, Wenhao[1]; Dai, Xin[1]; Yue, Yuanhang[2]; Lin, Xinwei[1]; Yang, Minglei[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Guangdong Petrochemical Co., Ltd, Jieyang, 522031, China

年份：2026

卷号：95

起止页码：175

外文期刊名：Chinese Journal of Chemical Engineering

收录：EI(收录号：20262721049044);Scopus(收录号：2-s2.0-105043675337)

语种：英文

外文关键词：Blending - Chemical industry - Clustering algorithms - Scheduling algorithms - Uncertainty analysis

摘要：Integrating refinery planning and product blending scheduling under uncertainty is challenging due to multi-mode operating conditions and mismatched temporal scales. This work proposes a bilevel optimization framework that explicitly coordinates long-term refinery planning with short-term blending scheduling. To represent mode-dependent uncertainty in blending behavior, a data-driven robust optimization (DDRO) strategy is developed, in which mode-associated uncertainty sets are constructed from historical data using a multi-stage clustering (MSC) algorithm combined with weighted support vector machines (SVM). This enables uncertainty modeling that is adaptive to operating modes and less conservative than conventional holistic robust approaches. To solve the resulting bilevel robust model efficiently, a robust-buffer-based decomposition (RBD) algorithm is proposed, which iteratively introduces a specification buffer at the planning level to preserve scheduling feasibility under uncertainty. In the case study, the proposed multi-mode robust framework improves the net profit by approximately 1.0% compared to holistic robust model, while maintaining 100% feasibility in quality constraints and reducing computational time by more than 50% relative to the single-level formulation. These results demonstrate that the proposed framework effectively balances economic performance, robustness, and computational tractability in integrated refinery planning and scheduling. ? 2026 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd.