DocumentCode :
232065
Title :
Online monitoring of batch process using Sub-phase based Principal Component Analysis
Author :
Liu Xin ; Wang Pu ; Gao Xuejin ; Qi Yongsheng
Author_Institution :
Coll. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
fYear :
2014
fDate :
28-30 July 2014
Firstpage :
5150
Lastpage :
5155
Abstract :
Methods based on multivariate statistical projection analysis have been widely applied for batch processes monitoring. However, conventional methods are linear ones that can only model linear combinations of variables and most batch processes are non-linearity. Traditionally, in process modeling, two solutions for non-linearity have been implemented: non-linear models and local linear models. In this paper, a novel methodology named Sub-phase based Principal Component Analysis (SPPCA), which integrates methods of operation phase detection and a novel multi-way principal component analysis (MPCA), is approached. A case study from a simulated fed-batch penicillin cultivation process indicates the efficacy of approach.
Keywords :
batch processing (industrial); batch production systems; chemical products; principal component analysis; process monitoring; MPCA; SPPCA; local linear models; multivariate statistical projection analysis; multiway principal component analysis; nonlinear models; online batch process monitoring; operation phase detection; process modeling; simulated fed-batch penicillin cultivation process; sub-phase based principal component analysis; Batch production systems; Data models; Feeds; Indexes; Monitoring; Principal component analysis; Trajectory; AP clustering; Batch process monitoring; principal component analysis; sub-phase modelling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
Type :
conf
DOI :
10.1109/ChiCC.2014.6895817
Filename :
6895817
Link To Document :
بازگشت