|About this Abstract
||2016 TMS Annual Meeting & Exhibition
||Empirical Modeling of the Baking Furnace to Predict Baked Anode Properties
||Amélie Dufour, Carl Duchesne, Jayson Tessier
|On-Site Speaker (Planned)
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 method used at the partner plant. To complement the soft-sensor, this work proposes a strategy to account for the thermal history of anodes baked at eventually any position. It is shown that combining categorical variables for pit and baking positions and routinely available firing equipment data is sufficient to predict 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 density, mechanical properties and air reactivity.
||Planned: Light Metals Volume