Estimation of alumina content of anode cover materials using multivariate image analysis techniques

In this paper, the use of different image analysis techniques is investigated for predicting alumina content of anode cover materials used in primary aluminum smelters. This approach is proposed in to order to allow on-line estimation of alumina content for feedback control purposes, which is not currently possible due to the long time delays and limited number of samples that can be analyzed in the laboratory. Both color and textural features of various anode cover materials are extracted from digital RGB images, and partial least squares (PLS) regression models are developed for predicting alumina content from these features. Most alumina content prediction errors are within ±2σ±2σ of the X-ray fluorescence laboratory measurements, when larger variations are required by operators to make control decisions. Some challenges arising from the use of image analysis techniques for process control are also discussed.