Scheduling crude oil operations in refineries with genetic algorithm

Yan Hou, Naiqi Wu, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

With the hybrid characteristics of a refinery, it is very challenging to schedule crude oil operations. This work intends to solve this scheduling problem by decomposing it into two sub-problems hierarchically. At the upper level, a refining schedule is found, while a detailed schedule is obtained to realize it at the lower level. Given a refining schedule at the upper level, this work focuses on the detailed scheduling problem at the lower level. Based on a control-theoretic perspective, the problem is transferred to a problem of assigning charging tanks to distillers such that meta-heuristics can be applied. Then, a genetic algorithm (GA) approach is innovatively developed to solve it. An industrial case study is used to show the application of the proposed approach. It shows that the method works well and is applicable to real-life problems.

Original languageEnglish (US)
Title of host publicationICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467399753
DOIs
StatePublished - May 25 2016
Event13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016 - Mexico City, Mexico
Duration: Apr 28 2016Apr 30 2016

Publication series

NameICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control

Other

Other13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016
CountryMexico
CityMexico City
Period4/28/164/30/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Modeling and Simulation

Keywords

  • Genetic algorithm
  • crude oil operations
  • short-term scheduling

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