3 years ago

Chance-Constrained Optimization for Refinery Blend Planning under Uncertainty

Chance-Constrained Optimization for Refinery Blend Planning under Uncertainty
Phebe Vayanos, Yu Yang, Paul I. Barton
A near-global optimization approach is proposed to design blending recipes for refinery products under uncertainties. In the refining industry, the most valuable products, such as gasoline and diesel, are produced by blending several intermediate feedstocks to maximize profit and ensure that all qualities are on specification. Because of the presence of property uncertainties, optimal blending recipes under linear mixing laws should be designed by solving a linear program with joint chance constraints. However, joint chance-constrained programming is generally intractable even with Gaussian distributions, and thereby, it is usually converted to an individual chance-constrained (ICC) program to achieve a conservative approximation. To reduce this conservatism, we find a global optimal solution for the ICC program. In case studies, a multiproduct blending problem with 12 chance constraints and a crude oil procurement example with 14 chance constraints are studied to test and compare the proposed scheme with state-of-the-art optimization software to demonstrate its superior performance in terms of computational time.

Publisher URL: http://dx.doi.org/10.1021/acs.iecr.7b02434

DOI: 10.1021/acs.iecr.7b02434

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