Rotary kiln temperature prediction based on transfer learning and LSTM-AE with short time samples

Accurately predicting the operating temperature of rotary kilns is crucial for their efficient operation and quality control. However, the lack of sufficient data for many rotary kilns hinders the accuracy of temperature prediction. The historical temperature data from rotary kilns operating under similar conditions often contain valuable information about temperature changes. To fully utilize this valuable information, this paper proposes the establishment of the LSTM-AE model, which combines the Long Short-Term Memory network and the Auto-Encoder. Additionally, a temperature prediction method for rotary kilns under short time samples is introduced by incorporating Transfer Learning and LSTM-AE. This method effectively transfers the knowledge of temperature variations from rotary kilns with similar working conditions to the target rotary kiln. By analyzing the performance of the method and comparing it with other data-driven methods, this study demonstrates that the method effectively mitigates the impact of rotary kiln temperature delay and abrupt variability. As a result, it significantly improves the accuracy of temperature prediction in rotary kilns with short time samples. The findings of this research provide a solid foundation for the development of temperature control strategies and the quality control in rotary kilns.