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

作　　者：Da-lin Xiong Xin-yu Zhang Zheng-wei Yu Xue-feng Zhang Hong-ming Long Liang-jun Chen 

机构地区：[1]Anhui Province Key Laboratory of Metallurgical Engineering&Resources Recycling,Anhui University of Technology,Ma’anshan,243002,Anhui,China [2]School of Metallurgical Engineering,Anhui University of Technology,Ma’anshan,243032,Anhui,China [3]School of Computer Science,Anhui University of Technology,Ma’anshan,243032,Anhui,China

出　　处：《Journal of Iron and Steel Research International》2025年第1期52-63,共12页钢铁研究学报(英文版)

基　　金：founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06);Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).

摘　　要：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.

关 键 词：Sinter quality Convolutional neural network Long short-term memory Image segmentation FeO prediction 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]