Hybrid Neural Network Model for RH Vacuum Refining Process Control

摘要

A hybrid neural network model, in which RH process (theoretical) model is combined organically with neural network (NN) and case-base reasoning (CBR), was established. The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel, and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction. It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.