A Predictive Model for Sintering Ignition Temperature Based on a CNN-LSTM Neural Network with an Attention Mechanism

• Published in: Processes 2024-10, Vol.12 (10), p.2185
• Main Authors: Xiong, Da-Lin, Ning, Hou-Yin, Xie, Meng, Pan, Cong-Yuan, Chen, Liang-Jun, Yu, Zheng-Wei, Long, Hong-Ming
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Automation; Energy consumption; Feedforward control; Gas flow; Ignition temperature; Long short-term memory; Neural networks; Performance measurement; Prediction models; Predictions; Process parameters; Sintering; Software; Steel production; Temperature; Variables; Weather forecasting
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr12102185

The sintering ignition process parameters fluctuate frequently and significantly, resulting in large variations in ignition temperature, which in severe cases can exceed 200 °C. This not only increases gas consumption but also affects the quality of the sinter. Because the intelligent control model based on feedback mechanisms struggles to deal with high-frequency fluctuation conditions over time, the prediction of sintering ignition temperature using feedforward regulation is attracting increasing attention. Given the multi-variable, time-sequential and strongly coupled characteristics of the sintering ignition process, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are deeply integrated, with an attention mechanism incorporated to develop the sintering ignition temperature prediction model, enabling the accurate prediction of the ignition temperature. The research demonstrates that the combination of a CNN and the attention mechanism effectively addresses the challenges posed by the multi-variable and strongly coupled nature of sintering ignition data to predictive modeling. The LSTM network resolves the sequential data issues through its gating mechanism. As a result, the coefficient of determination (R2 ) of the CNN_LSTM-Attention model in predicting the sintering ignition temperature can reach 0.97, with a mean absolute error (MAE) as low as 10.23 °C. The predicted values closely match the actual values, achieving a hit rate of 93% within the acceptable error range. These performance metrics are significantly superior to those of the CNN-Attention and LSTM-Attention models, greatly enhancing the control accuracy of the ignition temperature.