Designing a Robust Gasoline Blending Recipe Via Scenario-based Optimization

摘要

A scenario-based optimization approach is proposed to design a robust gasoline blending recipe under uncertainties. The proposed scheme considers the nonlinear behavior of blended octane, Monte Carlo simulation to generate a number of scenarios, and a sequential algorithm to converge to the optimal solution. This framework offers three advantages: First, incorporating the nonlinear functions into the optimization will provide a more realistic representation of the blending process. Second, the Monte Carlo sampling approach is capable of characterizing uncertainties with any probability distribution. Third, the sequential algorithm determines a near-global optimal solution in a significantly shorter time than directly using state-of-the-art optimization software. A case study with nine feedstocks and two products is presented to demonstrate the effectiveness of the proposed method.