Continuous-time modeling and global optimization approach for scheduling of crude oil operations

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Abstract

Scheduling of crude oil operations is a critical and complicated component of overall refinery operations, because crude oil costs account for about 80% of the refinery turnover. Moreover, blending with less expensive crudes can significantly increase profit margins. The mathematical modeling of blending different crudes in storage tanks results in many bilinear terms, which transforms the problem into a challenging, nonconvex, and mixed-integer nonlinear programming (MINLP) optimization model. Two primary contributions have been made. First, the authors developed a novel unit-specific event-based continuous-time MINLP formulation for this problem. Then they incorporated realistic operational features such as single buoy mooring (SBM), multiple jetties, multiparcel vessels, single-parcel vessels, crude blending, brine settling, crude segregation, and multiple tanks feeding one crude distillation unit at one time and vice versa. In addition, 15 important volume-based or weight-based crude property indices are also considered. Second, they exploited recent advances in piecewise-linear underestimation of bilinear terms within a branch-and-bound algorithm to globally optimize the MINLP problem. It is shown that the continuous-time model results in substantially fewer bilinear terms. Several examples taken from the work of Li et al. are used to illustrate that (1) better solutions are obtained and (2) ε-global optimality can be attained using the proposed branch-and-bound global optimization algorithm with piecewise-linear underestimations of the bilinear terms. © 2011 American Institute of Chemical Engineers AIChE J, 2012

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