ROBUST REAL-TIME OPTIMIZATION FOR THE LINEAR OIL BLENDING

摘要

In this paper we present a robust real-time optimization method for the online linear oil blending process. The blending process consists in determining the optimal mix of components so that the final product satisfies a set of specifications. We examine different sources of uncertainty inherent to the blending process and show how to address this uncertainty applying the Robust Optimization techniques. The polytopal structure of our problem permits a simplified robust approach. Our method is intended to avoid reblending and we measure its performance in terms of the blend quality giveaway and feedstocks prices. The difference between the nominal and the robust optimal values (the price of robustness) provides a benchmark for the cost of reblending which is difficult to estimate in practice. This new information can be used to adjust the level of conservatism in the model. We analyze the feasibility of a blend to be produced from a set of feedstocks when the heel of a previous blend is used in the composition of the new blend. Additional critical information for the control system is then produced.