Multivariate Analysis and Monitoring of the Performance of Aluminum Reduction Cells

A multiblock partial least squares (PL) modeling approach is proposed in this Article for multivariate analysis and monitoring of aluminum reduction smelters and other electrochemical processes. These industries commonly operate from hundreds to a thousand of metallurgical reactors in parallel, which makes is difficult to build and maintain separate models for each unit. To cope with this problem, the proposed approach is based on the assumption that reactors sharing the same design (i.e., technology) and fed with the same lots of raw materials should also share a similar latent variable space. This allows reducing the number of latent variable models to build for process monitoring. The approach is illustrated on the basis of data collected from 31 reactors used for aluminum reduction. It was shown that well-defined regions in the raw material property and process operation spaces were associated with higher smelter performance. These could be used to establish joint multivariate specification regions for raw materials as well as for plant-wide process monitoring.