Advertisement
Published in:
2016 | OriginalPaper | Chapter
Diagnosing Changes in Baked Anode Properties using a Multivariate Data-driven Approach
Authors : Julien Lauzon-Gauthier, Carl Duchesne, Jayson Tessier
Published in: Light Metals 2013
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
The baked anode quality control scheme used in most carbon plants consists of lab testing of anode core samples and monitoring weekly averaged properties. Both the low anode sampling rate and the averaging hide a significant amount of variability in the anode populations. Additional consideration depending on the sampling procedure needs to be taken into account while analyzing the core sample properties. In previous work, a multivariate latent variable PLS model was developed for predicting individual anode properties at the end of the baking cycle. All the data available at the Alcoa Deschambault smelter were used to build the model. This work investigates how to use this model to learn from data and, in particular, to help diagnose the root cause of variations in the electrical resistivity and LC. Changes in raw material suppliers and non-uniform temperature distribution within the baking furnace were found to contribute to the drifts.