Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.107477

Authors: Guotai Huang, Yuan Ma, Xiyu Gao, Anran Zhao, Peng Liu

Institution: Jilin University

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,620