Data-driven two-stage distributionally robust optimization for refinery planning under uncertainty.

[Display omitted] • A data-driven TSDRO model is developed for refinery planning under uncertainty. • The uncertainty set is constructed by the Wasserstein metric and RKDE approach. • The robust counterpart of the proposed model is reformulated by duality theory. • A real-world case study of an industrial refinery is presented. • The out-of-sample performance of different methods are analyzed. This work investigates the refinery planning problem under uncertainty in product prices. A novel data-driven Wasserstein distributionally robust optimization framework is proposed for handling uncertainties in the refinery-wide planning operations. A data-driven ambiguity set is constructed based on the Wasserstein metric to model the distributional uncertainty. The robust kernel density estimation technique is adopted to establish the support set to reduce the effect of the potential outliers. Based on the derived support set and ambiguity set, a data-driven two-stage distributionally robust optimization model for refinery planning is developed. Then, the robust counterpart of the proposed model is formulated to make the problem computationally tractable. Finally, a real-world case study on a petroleum refinery is presented to illustrate the effectiveness and applicability of the proposed framework. [ABSTRACT FROM AUTHOR]