Anode effect prediction based on a singular value thresholding and extreme gradient boosting approach

An anode effect often occurs during the process of alumintun electrolysis that will cause large energy consumption and low efficiency in aluminum production, thus how to identify the anode effect in advance has become an important issue. However, traditional approaches ignore the common incomplete information problem existing in the acquired data, and only consider a single predicting time, resulting in an unreliable result in anode effect prediction. In this paper, a hybrid prediction approach based on a singular value thresholding and extreme gradient boosting (SVT-XGBoost) approach is proposed to identify the anode effect in the altuninum electrolysis process. The SVT is used for data filling by the whole-features transformation, and the XGBoost is utilized for classification of the anode effect. The predicting time is set to 10min by the comparison. The experimental results show that the proposed approach has an effective ability for anode effect classification using the SVT-XGBoost compared to the previous approaches. Here, the effect of the training sample number is also investigated. The proposed approach could be applied in real-time anode effect prediction in the future.