Development of a mixing model based on LSTM for heat transition of different steel grade in continuous casting process
To further reduce the mixing length of grades transition in the slab casting process,research on accurate prediction of mixing process and transition slab division had been carried out in view of the time-varying control and its influence on the composition mixing in the continuous casting process.Through the industrial test,the influence of the amount of remaining steel in the tundish and the casting speed on the composition mixing during the transition casting process had been investigated.It has been found that with the same casting speed and section size the smaller the amount of remaining steel in the tundish is,the closer the mixed composition is to the composition of the subse-quent heat in the position of 5 m after new heat opened,which is the main influencing factor,and with the same re-sidual tundish weight and section size the smaller the casting speed is,the closer the mixed composition is to the composition of the subsequent heat in the position of 5 m after new heat opened,which is the secondary influencing factor.By collecting the production data of continuous casting process and combining the numerical simulation re-sults,a time series model based on LSTM neuron network has been established.Through the offline calculation of the model,it is found that the lower residual steel in the tundish in the early stage can accelerate the mixing process in the case of equal average tundish weight during mixing process.Deployed model online results show that the aver-age prediction deviation is 3.69％,and the accuracy meets the actual production needs.
万方数据
维普
