On-line system identification and control design of an extrusion cooking process: Part I. System identification

Abstract

A systems analysis of an extrusion cooking process for puffed corn snack products revealed that the specific mechanical energy ( SME) and screw speed ( SS) was a desirable pairing of measured and manipulated variables, respectively for regulating extrudate density. To facilitate the design of an SME model-based control system, a discrete-time transfer function that depicts the dynamic response of motor load ( ML) to changes in SS is required. The research literature describes several off-line techniques for developing such transfer function models but no methods for on-line system identification were found. This paper represents the first of two articles that describe our investigations into the use of on-line system identification for automatic tuning and adaptive control of a high-shear twin-screw extrusion process. This paper reports results for using various system identification schemes in combination with relay-feedback as a way to derive, in real-time, a transfer function model that accurately depicts the dynamical behavior of an extrusion cooking process. A Wenger TX-52 co-rotating twin screw extruder was subjected to relay feedback during the processing of cornmeal for a breakfast cereal formula under different moisture and screw speed conditions. The data obtained from these experiments were used to derive first-, second- and third-order discrete-time transfer functions. An analysis of the resulting transfer functions revealed that a first-order lead-lag transfer function structure adequately described the dominant dynamic behavior of the process in all cases. Next, batch and recursive implementations of least-squares, extended least-squares, output error, maximum likelihood, Box–Jenkins and predictive error algorithms were used to derive parameters for the first-order transfer function. Overall, the batch output error method provided good transfer function estimates over the range of product and process conditions studied.