Mixed-Integer Programming Approach for Short-Term Crude Oil Scheduling

Abstract

This work addresses the problem of crude oil inventory management of a real-world refinery that receives several types of oil that are delivered through a pipeline. The problem involves transfers from the pipeline to the crude tanks, settling time constraints for separating brine from the oil, interface separation between different types of oil, and charges to the crude distillation units. The short-term scheduling problem is represented as mixed-integer programming models that rely on a continuous-time formulation. The main objective is to maintain a target crude oil load while minimizing the crude tank farm operating cost subject to crude oil fluctuations in terms of quantity and quality. Property calculations yield a set of nonlinear constraints. Two formulations are developed: the first one relies on a mixed-integer nonlinear programming (MINLP) model, and the second adopts a discretization procedure for the inventory levels of the tank farm, thus generating a mixed-integer linear programming (MILP) problem. An application is developed for the scheduling of crude oil in the REVAP refinery in São José dos Campos (Brazil). It is shown that the MINLP model is able to generate a more efficient schedule for a horizon of 4 days.