Identification of chemical processes using canonical variate analysis

Abstract

A method of identification of linear input-output models using canonical variate analysis (CVA) is developed for application to chemical processes. This approach yields both a process model and a nonparametric description of model uncertainty, utilizing CVA for selection of a state coordinate system that optimally relates past inputs and outputs to future outputs. Regression procedures are then used for estimation of the state-space model parameters, and the Akaike Information Criterion (AIC) is used to determine the model order. The primary computations involve singular value decompositions which are numerically stable and accurate. The effectiveness of the CVA approach is first evaluated with simulated chemical processes that exhibit most of the practical problems encountered by existing system identification methods: nonlinear dynamics, unknown model orders and time delays, nonminimum phase dynamics, partial stiffness (requiring two time-scale approaches when other identification methods are used), low input excitation in some frequency bands, and measurement and process noise. The CVA methodology is then applied to the identification of models for a pilot-scale distillation column.