Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder

The anode effect is a common failure in the aluminium electrolysis industry. If the anode effect cannot be accurately predicted, it will cause increased energy consumption, harmful gas generation and even equipment damage in the aluminium electrolysis. In this paper, an anode effect prediction framework using multi-model merging based on deep learning technology is proposed. Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics, and hidden key fault information is deeply mined. A stacked denoising autoencoder is utilized to denoise and extract features from a large number of long-period parameter data. A long short-term memory network is implemented to identify the intrinsic links between the real-time voltage and current time series and the anode effect. By setting the model time step, the anode effect can be predicted precisely in advance, and the proposed method has good robustness and generalization. Moreover, the traditional Adam algorithm is improved, which enhances the performance and convergence speed of the model. The experimental results show that the classification accuracy and F1 score of the model are 97.14% and 0.9579%, respectively. The prediction time can reach 15 min.