Abstract
This work studies the identification of LPV (linear parameter varying) models with two scheduling variables in order to model complex industrial processes. The LPV model is parameterized as blended linear models, which is also called multi-model approach. 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 also 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.
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