Journal
JOURNAL OF PROCESS CONTROL
Volume 22, Issue 7, Pages 1198-1208Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2012.05.006
Keywords
Identification; LPV model; Multi-model; Two scheduling variables; High purity distillation column
Funding
- National Natural Science Foundation of China [61174161]
- Key Research Project of Fujian Province of China [2009H0044]
- Fundamental Research Fund for the Central Universities of Xiamen University of China [201212G005]
- National Science Foundation of China [60934007]
- 973 Program of China [2012CB720500]
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In order to model complex industrial processes, this work studies the identification of linear parameter varying (LPV) models with two scheduling variables. The LPV model is parameterized as blended linear models, which is also called multi-model structure. Several weighting functions, linear, polynomial and Gaussian functions, are used and compared. The usefulness of the method is tested using a high purity distillation column model in a case study. The case study shows that a good fit of identification data is not enough to verify model quality and can even be misleading in nonlinear process identification; other measures related to process knowledge should be used in model validation. The case study also shows that commonly used LPV model based on parameter interpolation can fail for the high purity distillation column. Finally, several pitfalls in nonlinear process identification are pointed out. (C) 2012 Elsevier Ltd. All rights reserved.
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