The identification of nonlinear models for process control using tailored “plant-friendly” input sequences |
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Authors: | Robert S Parker Douglas Heemstra Francis J Doyle III Ronald K Pearson Babatunde A Ogunnaike |
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Affiliation: | a School of Chemical Engineering, Purdue University, West Lafayette IN 47907, USA;b Department of Chemical Engineering, University of Delaware, Newark DE 19716, USA;c E.I. DuPont de Nemours and Co., Inc., Experimental Station E1, Wilmington, DE 19880-0101, USA;d Automatic Control Laboratory, ETH — Z, ETL K 24, CH-8092 Zürich, Switzerland |
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Abstract: | This paper considers certain practical aspects of the identification of nonlinear empirical models for chemical process dynamics. The primary focus is the identification of second-order Volterra models using input sequences that offer the following three advantages: (1) they are “plant friendly;” (2) they simplify the required computations; (3) they can emphasize certain model parameters over others. To provide a quantitative basis for discussing the first of these advantages, this paper defines a friendliness index f that relates to the number of changes that occur in the sequence. For convenience, this paper also considers an additional nonlinear model structure: the Volterra–Laguerre model. To illustrate the practical utility of the input sequences considered here, second-order Volterra and Volterra–Laguerre models are developed that approximate the dynamics of a first-principles model of methyl methacrylate polymerization. |
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Keywords: | Nonlinear identification Volterra series model Process control Input sequence design |
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