LAUSR

In the aluminum electrolysis, the amount of carbon consumed per unit of metal produced is always higher than the theoretical one. Raw material variability, anode properties, and reduction cell operation and performance can influence this discrepancy. Linking these factors to each individual anode could help close this gap thus improving the environmental footprint and reducing production cost. In today’s industrial environment, large volume of data, both structured and unstructured, are collected, otherwise known as big data. These capabilities could enable predicting the consumption for each anode as opposed to estimating it at the plant level as performed so far. This can be achieved using a tracking system to link the pot operation to individual baked and green anode properties. The performance of R&D Carbon Net Carbon Consumption formula was compared to a multivariate statistical approach that considers process data variables gathered from the aforementioned, linked, databases. Given the process data at hand, the prediction power of the net carbon consumption for individual anodes is significantly improved as opposed to using the existing model.