化工学报 ›› 2013, Vol. 64 ›› Issue (8): 2918-2923.DOI: 10.3969/j.issn.0438-1157.2013.08.031

• 过程系统工程 • 上一篇    下一篇

基于被控变量在线建模的化工过程实时优化方法

叶凌箭1, 马修水1, 宋执环2   

  1. 1. 浙江大学宁波理工学院, 浙江 宁波 315100;
    2. 浙江大学控制科学与工程学系, 浙江 杭州 310027
  • 收稿日期:2012-12-11 修回日期:2013-02-24 出版日期:2013-08-05 发布日期:2013-08-05
  • 通讯作者: 马修水
  • 作者简介:叶凌箭(1984- ),男,博士,讲师。
  • 基金资助:

    浙江省自然科学基金项目(LQ13F030007);国家重点基础研究发展计划项目(2012CB720505);宁波市创新团队项目(2012B82002)。

Real-time optimization for chemical processes based on on-line modeling of controlled variables

YE Lingjian1, MA Xiushui1, SONG Zhihuan2   

  1. 1. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, Zhejiang, China;
    2. Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
  • Received:2012-12-11 Revised:2013-02-24 Online:2013-08-05 Published:2013-08-05
  • Supported by:

    supported by the Natural Science Foundation of Zhejiang Province(LQ13F030007)and the National Basic Research Program of China(2012CB720505).

摘要: 选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。

关键词: 化工过程, 被控变量, 局部建模, 实时优化

Abstract: Choosing appropriate controlled variables(CVs)is demonstrated to be effective for real-time optimization(RTO)of chemical processes,whereas existing approaches do not allow the CVs to be changed once determined,which leads to limited RTO performances.To this end,this paper presents an approach for on-line modeling CVs.The local modeling method was used to select similar samples to construct estimating models for necessary conditions of optimality(NCO),which were used as CVs to achieve better RTO performance under the functions of feedback controllers.A case study of evaporator demonstrated that the proposed method could increase the economic profit of process operation by accurate modeling for on-line NCO.

Key words: chemical processes, controlled variables, local modeling, real-time optimization

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