Refinery production planning optimization under crude oil quality uncertainty

Abstract Current practice in refinery planning is to assume that the qualities of the crude oil feedstocks are known, even though they often vary. The uncertainty of the quality properties can significantly impact the profit of the refinery and needs to be considered in purchasing decisions. This work employs the product tri-section CDU model (Li et al. 2020) to build an accurate refinery model and determines the optimal crude selection by two-stage stochastic programming. The uncertainty of the crude oil quality properties is defined via the uncertainty of the TBP curves, which is described by the uncertain parameters of the beta functions approximating the TBP curves. The probabilistic scenarios are generated via random vector sampling method, leading to a relatively small number of scenarios required for the two-stage-stochastic programming model convergence. This enables us to determine the best crude oil choice, while requiring acceptable computational times, as illustrated by computational experiments.