The identification of nonlinear models for process control using tailored "plant-friendly" input sequences

This paper considers certain practical aspects of the identification of non linear empirical models for chemical process dynamics. The primary focus is the identification of second-order Volterra models using input sequences t hat offer the following three advantages: (1) they are "plant friendly;" (2 ) they simplify the required computations; (3) they can emphasize certain m odel parameters over others. To provide a quantitative basis for discussing the first of these advantages, this paper defines a friendliness index f t hat relates to the number of changes that occur in the sequence. For conven ience, this paper also considers an additional nonlinear model structure: t he Volterra-Laguerre model. To illustrate the practical utility of the inpu t sequences considered here, second order Volterra and Volterra-Laguerre mo dels are developed that approximate the dynamics of a first-principles mode l of methyl methacrylate polymerization. (C) 2001 Elsevier Science Ltd. All rights reserved.