Anode Current for Aluminum Electrolysis Cell Condition Identification Based on Improved Temporal Convolutional Network

Abstract

The stable operation of the aluminum electrolysis cell is the basis for the safe and efficient production of the electrolytic aluminum industry, and the cell condition identification technology is an important means to ensure the normal operation of the aluminum electrolysis cell. The cell condition identification technology based on anode current signal has played an increasingly important role in identify and fine control large aluminum electrolysis cells. This paper proposes an improved temporal convolutional network, which uses the time characteristics of the current signal to classify the current sequence for cell condition identification. The classification result can help people identify and monitor the conditions of the electrolysis cell. In this paper, the proposed method is verified on the real current signal, which can be used to identify various cell conditions such as anode effect and anode sliding.