Development and application of an intelligent thermal state monitoring system for sintering machine tails based on CNN-LSTM hybrid neural networks

Real-time prediction and precise control of sinter quality are pivotal for energy saving, cost reduction, quality improvement and efficiency enhancement in the ironmaking process. To advance, the accuracy and comprehensiveness of sinter quality prediction, an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory (CNN-LSTM) networks was proposed. The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature, high dust, and occlusion. The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process. Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information, a comprehensive model for sinter quality prediction was constructed, with inputs including the proportion of combustion layer, porosity rate, temperature distribution, and image features obtained from the convolutional neural network, and outputs comprising quality indicators such as underburning index, uniformity index, and FeO content of the sinter. The accuracy is notably increased, achieving a 95.8% hit rate within an error margin of +/- 1.0. After the system is applied, the average qualified rate of FeO content increases from 87.24% to 89.99%, representing an improvement of 2.75%. The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t, leading to a 6.65% reduction and underscoring significant energy saving and cost reduction effects.