关键词: 转炉炼钢, 即时学习, 灰色关联度, 长短期记忆网络

Abstract: Continuous real-time prediction of carbon and temperature in converter steelmaking process is the key to the end-point control.In view of the large difference of time series samples between furnaces and affecting the accuracy of the model prediction,a new method for predicting the end-point carbon temperature by using real-time learning strategy of quadratic similarity measurement with a Long-Short Term Memory (LSTM) network hybrid model that had feedback compensation mechanism were presented.The initial local model was established by using grey relational degree criteria,and the centroid sample was located in the local model to obtain the optimal local model.The output feedback of LSTM network was backed to the input in training,which could improve its generalization ability in the secondary training.The experimental results showed that the accuracy of temperature prediction error was 76.7% and carbon prediction error was 86.7% according to the process requirements of steelmaking.

Key words: converter steelmaking, instant learning, grey relational degree, long short-term memory network