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。
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.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2013年第8期2918-2923,共6页
CIESC Journal
基金
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)~~
关键词
化工过程
被控变量
局部建模
实时优化
chemical processes
controlled variables
local modeling
real-time optimization
