Process modelling based on integration of unitwide optimal process control and plantwide scheduling

GAO Xiaoyong; JIANG Yongheng; HUANG Dexian

doi:10.11949/j.issn.0438-1157.20161200

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Process modelling based on integration of unitwide optimal process control and plantwide scheduling

更新时间：2025-11-26

- Process modelling based on integration of unitwide optimal process control and plantwide scheduling
- CIESC Journal Issue 12, Pages: 5105-5111(2016)
- 作者机构：
  
  清华大学自动化系, 清华大学信息科学与技术国家实验室,北京,100084
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161200
  CLC： P11
- Online First：05 December 2016，
  
  Published：05 December 2016
- 稿件说明：
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GAO Xiaoyong, JIANG Yongheng, HUANG Dexian. Process modelling based on integration of unitwide optimal process control and plantwide scheduling[J]. CIESC Journal, 2016, (12): 5105-5111.
DOI：

GAO Xiaoyong, JIANG Yongheng, HUANG Dexian. Process modelling based on integration of unitwide optimal process control and plantwide scheduling[J]. CIESC Journal, 2016, (12): 5105-5111. DOI： 10.11949/j.issn.0438-1157.20161200.

摘要

随着全球化市场竞争日趋激烈，炼油生产过程的系统工程方法引起学术界和工业界的普遍关注。由于炼油生产过程的复杂性，现有方法远未有效解决过程模型准确描述问题，直接导致系统工程应用效果欠佳；过程模型的准确描述成为阻碍过程系统工程成功应用的关键。从困扰过程模型准确描述的症结出发，提出了一种基于装置级优化控制与厂级调度优化集成策略的模型描述方法，意在打破当前装置级底层控制系统和上层过程系统工程应用相互孤立情况下带来的系统间互为排斥、相互抑制的困局。在炼油生产过程装置分类的基础上，基于装置级优化控制的操作运行大数据所蕴含的多个最优操作模态信息，提出分装置类别和分操作模式的多模型描述方法，解决变化原料及操作工况的影响；基于该模型描述机制，将能够为智能炼油提供基础。

Abstract

The process system engineering methods for refinery production process have drawn increasing concerns in both academic and industrial communities due to the fierce global competition. Due to the complexity of refinery production process

the effective process model is still an open problem

which hampers process system engineering application. In some senses

the process model is the fundamental basis for successful application. To break this bottleneck

an integration between unit-wide optimal process control system and plant-wide scheduling system based modelling framework is proposed. The whole refinery production processes are divided into several different classes

and each class unit has a unique and well-designed model structure. Based on the big operational data collected from the unit-wide optimal control system

the multi-mode models are obtained to take varying crudes and operating conditions into account. This modelling mechanism can provide the concrete model for smart or intelligent refinery.

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