Adjustable Robust Optimization for the Multi-period Planning Operations of an Integrated Refinery-Petrochemical Site under Uncertainty. (April 2022)

• Record Type: Journal Article
• Authors: Zhang, Lifeng; Yuan, Zhihong; Chen, Bingzhen
• Is Part Of: Computers & chemical engineering. Volume 160(2022)
• Publication Date: 2022-04
• Subjects: Mixed-integer nonlinear programming -- Adjustable robust optimization -- Dynamic uncertainty set -- Planning operations -- Refinery-petrochemical; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.107703
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: The formulation of a non-convex large-scale MINLP model for the multi-period planning operations of an industrial integrated refinery-petrochemical complex. The derivation of the affinely adjustable robust counterpart to model the uncertain product demand for the planning operations. The introduction of the dynamic uncertainty set to update the formulated ARO model. The evaluation of the ARO model with a fixed uncertainty set, and the ARO model with a dynamic uncertainty set. Abstract: This paper concentrates on the formulation of a large-scale nonconvex mixed-integer nonlinear programming model and the application of robust optimization for the multi-period operational planning of real-world integrated refinery-petrochemical site in China under uncertain product demands and crude oil price. To avoid excessive conservativeness resulting from classical static robust optimization, an adjustable robust counterpart incorporating resource decisions via an affinely adjustable linear decision rule is first derived. On the basis of a proposed polyhedral dynamic uncertainty set that mimics the dynamic behavior of the product demand over time, an adjustable robust counterpart with a dynamic uncertainty set is further formulated. Classical static robust optimization, adjustable robust optimization, and adjustable robust optimization with dynamic uncertainty sets are systematically compared for case studies. The results clearly illustrate the advantages of the affinely adjustable robust optimization with a dynamic uncertainty set over the classic robust optimization in decision making.