On the Use of Multivariate Statistical Methods to Detect, Diagnose and Mitigate Abnormal Events in Aluminium Smelters

摘要

This work aims to demonstrate how the use of latent variable methods can detect and diagnose the onset of an abnormal situation in aluminium reduction cells. Using recent data from Alcoa Fjardaal, Principal Component Analysis (PCA) was used to model typical variations occurring in 336 different pots. A drift in process operation was correctly identified sooner than with traditional statistical control technique. Additionally, concentration in low trace elements in the metal also corresponded to a drift in process operation. Such an early warning could help mitigate the impact of abnormal events.