Pseudoresistance entropy as an approach to diagnostics and control in aluminium production

Abstract

AbstractThe advent of deterministic information theory (det‐IT), which allows quantification of the T‐entropy (similar in a sense to thermodynamic entropy) of time‐series data, presents novel insights into the state behaviour of the aluminium reduction cell. The pseudoresistance signal has the potential to expose a number of abnormal/undesired process attributes (excessive magnetohydrodynamic (MHD) oscillation and short‐circuiting most importantly), and the identification of these attributes in order to improve cell diagnostics has remained an area of interest for a number of years. Some of the possible applications of process entropy techniques are explored here using specialised visualisation software. Two diagnostic metrics proposed in the literature are compared to analogous, but ultimately preferable entropic properties. Segments of a pseudoresistance trace at a frequency of 1 Hz are analysed in this paper, with recurrent process patterns identified through an elegant state‐segregation technique. It is noted that this procedure could be carried out on a continuous basis, allowing early warning of a change in process behaviour. The power and versatility of the graphical interface employed is demonstrated through spectral analysis of the same data streams, leading to further insight into the nature of MHD oscillation in particular. Present limitations on the techniques are described and future opportunities are also discussed. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd.