Recent advances in the application of advanced control methods in the chemical industry have increased interest in the identification of nonlinear dynamic models from process data. Two key issues that are relevant are model structure selection, and input sequence design. In this talk, results for finite Volterra series models will be reviewed in application to nonlinear model predictive control design. Two approaches to reducing the highly parametrized Volterra series will be covered, as will intelligent input sequence design. In particular, tailored input sequences will be derived from a weighted prediction error analysis. Furthermore, the engineering application of these sequences will be considered, resulting in the quantification of "plant-friendly" attributes.

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