(249p) Integrated Crude Oil Contract Selection and Scheduling
Scheduling of crude oil operations is an important and complex activity in a refinery since its costs can account for 80% of overall profit. The crude oil operations involve crude unloading, storage, blending, and processing operations. Optimal crude oil scheduling can increase profits by using cheaper crudes, minimizing crude changeovers, avoiding ship demurrage, and managing crude inventory optimally. However, mathematical modeling of the blending of different crudes in storage tanks introduces bilinear terms. The whole problem becomes a difficult, nonconvex mixed-integer nonlinear program (MINLP). Although several efforts [1-8] have been attempted to address such difficult scheduling problem, most of them assumed that crude contracts have been already selected in the planning stage, which may lead to infeasible or suboptimal crude oil scheduling operations. Therefore, it is critically important to integrate crude contracts selection and crude oil scheduling operations.
The selection and procurement of the crude oil is one of the most cost-intensive steps in a refinery. To address the fluctuations in the prices, quality and demand of the crude oil, the option of crude oil contracts is of significant use. Contracts often specify fixed amounts of crude oil that the supplier agrees to deliver at various times in the future at some agreed prices. The crude oil contracts come in various shades of prices, quality, terms, conditions, etc. Hence selection of crude oil contract is not a trivial problem. Bansal et al. proposed a relatively comprehensive classification of material supply contracts and proposed a deterministic multi-period mathematical programming model that selects optimal contracts for the minimum total procurement cost in the face of several practical considerations such as different contract types, multitier prices and discounts, logistics and inventory costs, quantity/dollar purchase commitments, spot market, product bundling, etc. However, they did not relate the supply contracts with crude oil contracts.
In this paper, we propose comprehensive classification of crude contracts and integrated nonconvex MINLP model for simultaneous optimization of crude oil contracts selection and scheduling of crude oil operations. The objective is to maximize total profit by selecting the best contracts for crude oil and optimal scheduling of crude oil operations. We enhanced the solution approach of Li et al. to solve the nonconvex MINLP model to e- global optimality. We solve a large industrial-scale case study to demonstrate the effectiveness of our model and solution algorithm. We also compare our solution approach with commercial solvers such as BARON  and ANTIGONE.
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