A New MINLP Continuous Time Formulation for Scheduling Optimization of Oil Refinery with Unreliable CDUs

Abdulrahman Al-Ahmari, Husam Kaid, Zhiwu Li, NaiQi Wu, Abdul-Aziz El-Tamimi, Yan Qiao and Jiafu Su

Mathematical Problems in Engineering, 2022, vol. 2022, 1-12

Abstract: Short-term scheduling of oil refinery operations is a complicated optimization problem that requires a high level of detail and needs efficient approaches and software tools. Oil refineries are naturally categorized as continuous process industries due to their production processes, which have essentially different characteristics and constraints from discrete manufacturing processes. It is a significant problem to design effective approaches for oil refinery scheduling optimization. Therefore, this paper aims to provide solutions to the above very challenging problem by proposing a novel methodology. The problem is how to optimize crude oil unloading to charging tanks and charging schedules for different types of crude oils to distillation units (CDUs). Several realistic operational features are considered, such as different arrival times for crude oils, continuous operation of CDUs, no rework of crude oils, and the ability to perform preventative maintenance on CDUs. To solve this problem effectively, a new mixed-integer nonlinear programming (MINLP) model is developed. Industrial case studies are used to demonstrate the effectiveness of the proposed formulation. The computational results show that the case studies are effectively solved with the proposed solution approach.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1298495

DOI: 10.1155/2022/1298495

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