Abstract
The paper describes two parameter adaptive prediction algorithms applied to an aluminium reduction cell. The goal is to predict the cell resistance ahead of time. Detecting its rate of increase the anode-effect can be avoided by feeding alumina into the cell. Without feeding the time of anode-effect can also be predicted. Based on real-time measurements many computer simulations have been made to characterize predictor behaviour. The influence of operational events on the estimation, such as anode movement (AM) and alumina feeding (F) have also been investigated, as well as restarting the estimation after an anode-effect.
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