Anode effect prediction method in aluminum electrolysis cell production process

Abstract

The invention discloses an anode effect prediction method in an aluminum electrolysis cell production process. The method comprises steps of extracting sample data from production data of the aluminumelectrolysis cell; carrying out weighting processing on feature vectors of the sample data by using feature weights; training the sample data by adopting a support vector machine (SVM); obtaining anSVM effect forecast model and model parameters; extracting the feature vectors of the aluminum electrolysis cell to be measured and performing weighting processing; and calculating distance between the feature vectors of the aluminum electrolysis cell to be measured and a optimal hyperplane; if the distance between the feature vectors of the aluminum electrolysis cell to be measured and the optimal hyperplane is larger than or equal to a preset threshold value, the feature vectors are sent to an SVM effect forecast model, and a forecast result is obtained; and otherwise, a nearest neighbor algorithm KNN is adopted to forecast the aluminum electrolysis cell to be measured. According to the anode effect prediction method in the aluminum electrolysis cell production process, the anode effectof different electrolytic cells under various groove conditions can be effectively forecast, the operation of the stable electrolytic cell is facilitated, and the current efficiency is improved.

Description

technical field [0001] The invention belongs to the field of aluminum electrolysis, and in particular relates to a method for predicting anode effect in the production process of an aluminum electrolytic cell. Background technique [0002] The anode effect is a special phenomenon that occurs on the anode during molten salt electrolysis. When the anode effect occurs, sparks occur around the anode, the voltage of the electrolytic cell rises to tens of volts, the power consumption increases, and the quality and output of aluminum decrease, which seriously endangers the stability of the electrolytic cell and the normal operation of aluminum electrolysis production. In order to avoid the accidental occurrence of anode effect, it is of great significance to detect and predict anode effect of aluminum electrolytic cells. [0003] When the aluminum electrolytic cell is close to the anode effect, the anode current density reaches the critical current density. The magnitude of the c...

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Problems solved by technology

At present, there are still some problems in the research of anode effect prediction, such as low prediction accuracy, short advance prediction time, and low reliability of the model.

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