计算机技术与发展2024，Vol.34Issue(9)：188-194,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0155
基于MTCN-Informer的铁矿球团工艺预测模型
Prediction Model of Iron Ore Pellet Process Based on MTCN-Informer
摘要
Abstract
The prediction of finished pellet flow is the key to the production process,which determines the efficiency and output of the whole production.Iron ore pellet chain grate machine—rotary kiln is one of the important processes for producing iron ore to prepare high-quality ferroalloy.It has the characteristics of large time lag,complex parameters,complex coupling relationship,etc.,and the flow rate of finished pellets fluctuates violently,making the flow rate of pellets difficult to predict.For this reason,we use the moving average filter to smooth the fluctuating data,and the mutual information method performs feature selection on complex parameters,and then uses the Informer pellet flow prediction model based on the self-attention mechanism,which reduces the time of the traditional self-attention mechanism complexity and improves the efficiency of model training.At the same time,in view of the problem that the probabilistic sparse self-attention mechanism of the Informer model is difficult to grasp the long-term sequence fluctuations,the extended information dependence of the long-term sequence is extracted through the TCN time convolution network,and the context information is processed by combining the Informer encoding and decoding network,thereby completing accurate prediction of pellet flow.Through the experimental analysis of the actual factory data,it can be seen that compared with traditional deep learning models such as recurrent neural networks,the proposed integrated model is the best in terms of prediction accuracy and stability.关键词
球团流量预测/特征选择/时间卷积网络/编码解码网络/自注意力机制Key words
pellet flow prediction/feature selection/temporal convolutional network/encoding and decoding network/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
廖雪超,朱晨辉,赵昊裔,向桂宏,刘宗宇..基于MTCN-Informer的铁矿球团工艺预测模型[J].计算机技术与发展,2024,34(9):188-194,7.基金项目
国家自然科学基金项目(62273264) （62273264）
