A novel self-supervised deep LSTM network for industrial temperature prediction in aluminum processes application.

• An enhanced LSTM architecture is proposed that the target output is extracted from the learning process. • A self-supervised model is constructed with long-short-term memory network for PT prediction. • Comprehensive experimental studies are conducted to evaluate the performance and generalization. This article studies the influence of pot temperature or electrolyte temperature in the aluminum reduction production. Specifically, these indexes reflect the distribution of the physical and energy field of the reduction cell, the current efficiency, and the lifespan of the aluminum reduction cell. Therefore, the pot temperature detection and identification are two critical and significant issues in the whole production process of aluminum electrolysis. However, due to the low measurement accuracy and high maintenance costs with the thermocouple sensor in the practical production process, the real-time measurement of pot temperature index is still a major challenge, which motivate us to develop a self-supervised soft sensor method based on deep long-short term memory (LSTM). Under the constraint of the limited samples, the proposed method achieves a competitive performance. First, the input variables are selected according to the expert experience. Then, a deep self-supervised model is built. Finally, the proposed self-supervised LSTM model is applied to real-time detection in an industrial electrolysis production case. The performance in the experiment outperforms other existing methods in terms of both accuracy and robustness aspects. [ABSTRACT FROM AUTHOR]