Abstract
This study contributes to the comparison of partial least squares (PLS) and canonical variate analysis (CVA) for the identification of dynamic systems. Two model forms, autoregressive with exogenous inputs and state space representations, are developed with PLS and CVA being used to calculate the model parameters. The different models are compared using two case studies: a benchmark simulation of a binary distillation column and an industrial fluidised bed reactor.
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