Clustering Aluminum Smelting Potlines Using Fuzzy C-Means and K-Means Algorithms

Aluminum smelting potlines usually have a big number of cells, producing aluminum in a continuous and complex process. Analytical monitoring is essential to increase the industries' competitive advantage, however, during their operation, some cells share similar behaviors, therefore forming clusters of cells. These clusters rely on data patterns that are usually implicit or invisible to operation, but can be found by means data analysis. In this work we present two clustering techniques (Fuzzy C-Means and K-Means) to find and cluster the cells that present similar behaviors. The benefits of clustering are mainly in the simplification of potline analysis, since a large number of cells can be summarized in one single cluster, which can provide richer but compacted information for control and modelling.