Modelling and measurements in the aluminium industry ‐ Where are the obstacles?

Abstract

In this paper the necessity of obtaining experimental data with good enough quality for model verification is addressed. Relevant examples from the aluminium industry are shown to illustrate some cases where measurements and model results work hand in hand on identifying bottlenecks and improving the situation. Moreover, measurements and their interpretation are briefly touched upon, trying to enlighten a few of the challenges on data collection in industrial environments and comparison with models. Realising that measurements only uncovers parts of the real picture, an approach to estimate data interpretation errors is briefly outlined. A good model can and should rule out erroneous measurements – with the right use it can even give some guidelines on where to get good measurements.