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.