Closed-loop subspace identification: an orthogonal projection approach

2005 ◽  
Vol 15 (1) ◽  
pp. 53-66 ◽  
Author(s):  
Biao Huang ◽  
Steven X. Ding ◽  
S.Joe Qin
Keyword(s):  
Orthogonal Projection ◽  
Closed Loop ◽  
Subspace Identification ◽  
Projection Approach

Use of the Orthogonal Projection Approach (OPA) to Monitor Batch Processes

2003 ◽  
Vol 57 (1) ◽  
pp. 80-87 ◽  
Author(s):  
S. Gourvénec ◽  
C. Lamotte ◽  
P. Pestiaux ◽  
D. L. Massart
Keyword(s):  
Orthogonal Projection ◽  
Spectroscopic Data ◽  
Batch Processes ◽  
Multivariate Curve Resolution ◽  
Concentration Profiles ◽  
Curve Resolution ◽  
On Line ◽  
Real Concentration ◽  
Projection Approach

The orthogonal projection approach (OPA) and multivariate curve resolution (MCR) are presented as a way to monitor batch processes using spectroscopic data. Curve resolution allows one to look within a batch and predict on-line real concentration profiles of the different species appearing during reactions. Taking into account the variations of the process by using an augmented matrix of complete batches, the procedure explained here calculates some prediction coefficients that can afterwards be applied for a new batch.

Improved closed-loop subspace identification with prior information

2018 ◽  
Vol 49 (9) ◽  
pp. 1821-1835 ◽  
Author(s):  
Youqing Wang ◽  
Ling Zhang ◽  
Yali Zhao
Keyword(s):  
Prior Information ◽  
Closed Loop ◽  
Subspace Identification

Orthogonal Projection Approach Applied to Peak Purity Assessment

1996 ◽  
Vol 68 (1) ◽  
pp. 79-85 ◽  
Author(s):  
F. Cuesta Sánchez ◽  
J. Toft ◽  
B. van den Bogaert ◽  
D. L. Massart
Keyword(s):  
Orthogonal Projection ◽  
Purity Assessment ◽  
Projection Approach ◽  
Peak Purity

scholarly journals Closed-loop subspace identification taking initial state into account

2018 ◽  
Vol 51 (15) ◽  
pp. 604-609
Author(s):  
Hideyuki Tanaka ◽  
Kenji Ikeda
Keyword(s):  
Closed Loop ◽  
Subspace Identification ◽  
Initial State
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