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Title: Scheduling of crude oil and product blending and distribution operations in a refinery
Authors: LI JIE
Keywords: Refinery, scheduling, crude oil, recipe, blending, mixed integer nonlinear programming (MINLP), process, unit slots, robustness
Issue Date: 29-Dec-2008
Citation: LI JIE (2008-12-29). Scheduling of crude oil and product blending and distribution operations in a refinery. ScholarBank@NUS Repository.
Abstract: Ever-changing crude prices, deteriorating crude qualities, and growing environmental concerns are squeezing the profit margins of modern oil refineries like never before. Optimal scheduling of various operations in a refinery offers significant potential for saving costs and increasing profits. The overall refinery operations involve three main segments, namely crude oil storage and processing, intermediate processing, and product blending and distribution. This thesis addresses the first and third important components: scheduling of crude oil, and product blending operations. First, a robust and efficient algorithm is developed to solve large, nonconvex, mixed integer nonlinear programming (MINLP) problems in crude oil scheduling. The proposed algorithm is superior to commercial solves (DICOPT, and BARON) and the existing algorithms in the literature. Although the algorithm is intended for a marine-access refinery, the algorithmic strategy is successfully applicable to in-land refineries. Third, a general synchronous slot-based MINLP formulation is developed for an integrated treatment of recipe, specifications, blending, storage, and distribution. Since commercial MINLP solvers are unsatisfactory for solving this complex MINLP, a novel and efficient procedure that solves successive MILPs instead of an MINLP, and gives excellent solutions is proposed. To improve the efficiency and obtain better solutions, unit-slot based MINLP formulation is developed. Finally, a scenario-based MINLP model is developed to obtain robust schedules for demand uncertainty during crude oil scheduling.
Appears in Collections:Ph.D Theses (Open)

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