高炉透气性指数是一个可以快速、直观、综合反映高炉炉况的重要参数.对高炉透气性指数准确预测,可以尽早(约提前10 min)发现和避免高炉的管道、悬料、崩料、煤气流失等炉况失常现象的发生.本文提出了一种结合核主成分分析(kernel principal component analysis,KPCA)、卷积神经网络(convolutional neural net-work,CNN)和长短期记忆神经网络(long short-term memory,LSTM)的高炉透气性指数预测模型.首先,运用KPCA对原始高维输入变量进行降维,再用CNN捕捉数据的特征,最后利用LSTM对高炉透气性指数进行预测.结果表明,所构建的KPCA-CNN-LSTM高炉透气性指数预测模型较降维之前预测误差大幅减小,预测准确度大幅升高.这有利于高炉操作人员尽快掌握炉况的瞬时变化并采取有效措施恢复高炉顺行.
Prediction of blast furnace permeability index based on KPCA-CNN-LSTM model
The permeability index of blast furnace is an important parameter that can quickly,intui-tively,and comprehensively reflect the condition of the blast furnace.Accurately predicting the per-meability index of blast furnaces can detect and avoid abnormal furnace conditions such as pipeline,suspended material,collapse,and gas loss as early as possible(about 10 min in advance).This arti-cle proposes a blast furnace permeability index prediction model that combines kernel principal com-ponent analysis(KPCA),convolutional neural network(CNN),and long short-term memory(LSTM).Firstly,KPCA is used to reduce the dimensionality of the original high-dimensional input variables,followed by CNN to capture the features of the data,and finally,LSTM is used to predict the permeability index of the blast furnace.The results show that the constructed KPCA-CNN-LSTM blast furnace permeability index prediction model significantly reduces prediction errors and improves prediction accuracy compared to before dimensionality reduction.It is beneficial for blast furnace op-erators to quickly grasp the instantaneous changes in furnace conditions and take effective measures to restore smooth operation of the blast furnace.
blast furnacepermeability indexKPCACNNLSTMprediction model
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