化工学报2018，Vol.69Issue(3)：992-997,6.DOI:10.11949/j.issn.0438-1157.20171534
基于LSTM-RNN模型的铁水硅含量预测
Research on hot metal Si-content prediction based on LSTM-RNN
摘要
Abstract
The ironmaking in blast furnace, with large delay and complex conditions, is a dynamic process. The traditional methods for prediction of silicon content in hot metal are mostly based on the statistics or the simple neural networks, leading to lower accuracy. However, a model based on the long short-term memory-recurrent neural network (LSTM-RNN) is proposed to exploit the characteristics of the mutual information before and after the time series in this paper. The independent variables are selected according to the time series trend and the correlation coefficient. After that, the silicon content is predicted according to the input variables by optimizing the parameters automatically. In order to verify the constructed model, the extremely complex production data is used to compare the LSTM-RNN and simple RNN models. Remarkably, the result shows that the prediction error of LSTM-RNN model is stable and the prediction accuracy is high.关键词
预测/动态建模/神经网络/高炉炼铁/硅含量Key words
prediction/dynamic modelling/neural network/ironmaking/silicon content分类
化学化工引用本文复制引用
李泽龙,杨春节,刘文辉,周恒,李宇轩..基于LSTM-RNN模型的铁水硅含量预测[J].化工学报,2018,69(3):992-997,6.基金项目
国家自然科学基金项目(61290321).supported by the National Nature Science Foundation of China(61290321). （61290321）
