On-line monitoring of batch processes using Kalman filter and multivariate statistical methods

被引：0
|
作者：
Di, Liqing [1 ]
Xiong, Zhihua [1 ]
Cao, Yujin [1 ]
Yang, Xianhui [1 ]
机构：
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
基金：
美国国家科学基金会;
关键词：
batch processes; monitoring; Kalman filter; MPCA;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Multiway principal component analysis (MPCA) has been implemented to batch process monitoring widely and effectively. In general, when applying MPCA method for on-line monitoring, the unknown future data from the current time until the end of the batch have to be estimated, but it is always difficult to foresee the future behaviour precisely. In this paper, a novel method is proposed by using Kalman filter to recursively estimate the complete state of process and then using MPCA to detect abnormal batch runs. Effectiveness of the proposed method is validated on a simulated benchmark fed-batch penicillin fermentation process.
引用
收藏
页码：5511 / +
页数：2
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