Abstract
Canonical correlation analysis (CCA) and projection to latent structures (PLS) are popular statistical approaches for process modeling and monitoring. CCA focuses on the correlation structure only, while PLS focuses on maximizing the covariance between process variables X and quality variables Y. In this article, a dynamic regularized latent variable regression (DrLVR) algorithm is proposed for dynamic data modeling and monitoring. DrLVR aims to maximize the projection of quality variables on the dynamic latent spaces of the process variables. A regularization term is incorporated into DrLVR to handle the collinearity issues. The dynamic monitoring scheme based on the DrLVR model is also developed. Both numerical simulations and the Tennessee Eastman process data are employed to demonstrate the effectiveness of DrLVR.
| Original language | English |
|---|---|
| Article number | 106809 |
| Journal | Computers and Chemical Engineering |
| Volume | 137 |
| DOIs | |
| Publication status | Published - 9 Jun 2020 |
| Externally published | Yes |
Funding
This work was supported in part by the Texas-Wisconsin-California Control Consortium. The first author would also like to acknowledge the financial support provided by the Chemical Engineering Department at the University of Waterloo.
Keywords
- Data analytics
- Dynamic inferential monitoring
- Dynamic inferential sensors
- Dynamic latent variable regression
- Regularization