Multivariate statistical modeling of an anode backing furnace : Modélisation statistique multivariée du four à cuisson des anodes utilisées dans la fabrication d'aluminium primaire
|Advisor:||Duchesne, Carl; Gosselin, Louis|
|Abstract:||The aluminum manufacturing process is highly influenced by the anode quality. Several factors affect the anode quality and the actual quality control strategy is inadequate to detect faulty anodes before setting them in the electrolytic cells. A soft-sensor model developed from historical carbon plant data and multivariate statistical methods was proposed in past work to obtain quick predictions of individual anode properties right after baking for quality control purposes. It could only be used for anodes baked at the coldest and hottest positions within the furnace due to the core sampling strategy used at the partner’s plant. To complement the soft-sensor, this work proposes a method for taking into account the thermal history of anodes baked at eventually any position and to allowing for the prediction of properties for all anodes. It is shown that combining categorical variables for pit and baking positions and routinely available firing equipment data is sufficient for predicting the temperature profiles of anodes baked in different positions (measured during pit surveys) and account for its impact on anode properties. Prediction results were validated using core sampling and good performance was obtained for LC, apparent and real density, compressive strength and air reactivity.|
|Document Type:||Mémoire de maîtrise|
|Open Access Date:||24 April 2018|
|Collection:||Thèses et mémoires|
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