Application of artificial neural networks for prediction of sinter quality based on process parameters control

Abstract

According to the characteristics of non-linearity, strong coupling and a large time delay in the sintering process, the overall analysis for the sintering process has been carried out from the process parameter control point. The sinter performance evaluation indexes and the main influential parameters were determined. The quality prediction model for the sintering process was established using back propagation (BP) neural network algorithm with momentum and variable learning rate. The simulation experimental results show that the model has a higher prediction accuracy and a stronger self-learning ability. The predictive hit rate of random samples is over 81% by adopting BP neural network with the structure of 15-24-4 and network error is 0.65×10−3, thereby verifying the accuracy and effectiveness of the quality prediction model on the basis of process parameters control.