A Database Approach for Predicting and Monitoring Baked Anode Properties

Abstract

The baked anode quality control strategy currently used by most carbon plants based on testing anode core samples in the laboratory is inadequate for facing increased raw material variability. The low core sampling rate limited by lab capacity and the common practice of reporting averaged properties based on some anode population mask a significant amount of individual anode variability. In addition, lab results are typically available a few weeks after production and the anodes are often already set in the reduction cells preventing early remedial actions when necessary. A database approach is proposed in this work to develop a soft-sensor for predicting individual baked anode properties at the end of baking cycle. A large historical database including raw material properties, process operating parameters and anode core data was collected from a modern Alcoa plant. A multivariate latent variable PLS regression method was used for analyzing the large database and building the soft-sensor model. It is shown that the general low frequency trends in most anode physical and mechanical properties driven by raw material changes are very well captured by the model. Improvements in the data infrastructure (instrumentation, sampling frequency and location) will be necessary for predicting higher frequency variations in individual baked anode properties. This paper also demonstrates how multivariate latent variable models can be interpreted against process knowledge and used for real-time process monitoring of carbon plants, and detection of faults and abnormal operation.