A decision tree based decomposition method for oil refinery scheduling

Xiaoyong Gao; Dexian Huang; Yongheng Jiang; Tao Chen

doi:10.1016/j.cjche.2017.10.006

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A decision tree based decomposition method for oil refinery scheduling

Selected Papers from the Chinese Process Systems Engineering Annual Meeting 2017 | Updated：2026-01-06

- A decision tree based decomposition method for oil refinery scheduling
- Chinese Journal of Chemical Engineering Vol. 26, Issue 8, Pages: 1605-1612(2018)
- Affiliations：
  
  1. 2 Department of Automation, Tsinghua University,Beijing,China,100084
  2. 3 Department of Chemical and Process Engineering, University of Surrey, Guildford,UK,GU2 7XH
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.10.006
  CLC：
- Published：2018
- Accepted：
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Xiaoyong Gao, Dexian Huang, Yongheng Jiang, Tao Chen. A decision tree based decomposition method for oil refinery scheduling[J]. Chin.J.Chem.Eng., 2018, 26(8): 1605-1612.
DOI：

Xiaoyong Gao, Dexian Huang, Yongheng Jiang, Tao Chen. A decision tree based decomposition method for oil refinery scheduling[J]. Chin.J.Chem.Eng., 2018, 26(8): 1605-1612. DOI： 10.1016/j.cjche.2017.10.006.

摘要

Abstract

Refinery scheduling attracts increasing concerns in both academic and industrial communities in recent years. However

due to the complexity of refinery processes

little has been reported for success use in real world refineries. In academic studies

refinery scheduling is usually treated as an integrated

large-scale optimization problem

though such complex optimization problems are extremely difficult to solve. In this paper

we proposed a way to exploit the prior knowledge existing in refineries

and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery

ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category (i.e. adjusting scale). Then

a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.

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Related Institution

Institute of Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology

School of Photoelectric Engineering, Changzhou Institute of Technology

Institute of Process Control Engineering, Department of Automation, Tsinghua University

Tsinghua National Laboratory for Information Science and Technology, Tsinghua University

2 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University

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