Optimal Petroleum Fuels Blending Under Uncertainty

Technology #17503

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FIG. 1 shows, schematically, an illustrative oil refinery system 100, in accordance with some embodiments.FIG. 2A shows an illustrative oil refinery flow chart 200A, in accordance with some embodiments.FIG. 3 shows an illustrative process 300 that may be performed by an oil refinery system to choose one or more crude oils to be procured, and/or to determine how to process one or more procured crude oils, in accordance with some embodiments.
Professor Paul Barton
Department of Chemical Engineering, MIT
External Link (yoric.mit.edu)
Yu Yang
Department of Chemical Engineering, MIT
Phebe Vayanos
Sloan School of Management, MIT
External Link (www-bcf.usc.edu)
Managed By
Christopher Noble
MIT Technology Licensing Officer - Clean and Renewable Energy
Patent Protection

Systems and Methods for Improving Petroleum Fuels Production

PCT Patent Application WO 2016-081504

Systems and Methods for Improving Petroleum Fuels Production

US Patent Pending 2016-0140448
Integrated Crude Selection and Refinery Optimization Under Uncertainty
AlChE Journal, Nov. 10, 2015, Volume 62, Issue 4, pg. 1038-1053


In the refining industry, crude oil procurement is the largest expenditure and has an enormous impact on refinery profitability. This technology can be used by refineries to optimize crude purchases and refiner operations.

Problem Addressed

Crude oil and oil markets have many uncertainties associated with the quality of the crude oil and the demands in the market. However, crude oil is processed and blended to create final products such as gasoline with strict quality constraints. Therefore, optimizing crude purchases and refining processes is essential to maximizing profits. This technology finds the global optimal solution for purchasing and blending crude oil within the affiliated uncertainties.


This method reformulates the chance-constrained problem as a tractable problem by creating a two-stage stochastic programming formulation. The first stage is selecting the best crude oil combination among several candidates and their price amounts to maximize expected gross margin across all scenarios. In the second stage, the uncertainties are realized and the optimal operations of the plant are implemented such that the market demand and quality specifications are satisfied. In order to obtain the global optimal solution for this problem, the variable discretization, feasibility and optimality-based domain reduction techniques are integrated into the non-convex generalized Benders decomposition (NGDB) methodology. The enhanced NGBD approach is able to find and verify a global optimal solution within a couple of hours compared to state-of-the-art commercial software which takes several days.


  • Optimizes crude oil purchases and blending under uncertainty
  • Produces global optimal solution in a couple of hours