Application of BPNN in static model of converter steelmaking based on quantumbehaved particle swarm optimization

ZHU Yaping, WANG Wenlong, XV Shenglin
（College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China）

Abstract: For the problem of low hit rate of the BOF endpoint based on static model, the factors that affect the hit rate of the BOF endpoint was firstly analyzed, topologies of the BP neural network (BPNN) were determined, the static BOF model was established. Then the quantum particle swarm optimization (QPSO) was used in the study of BP network, and the learning performance of QPSO, the basic particle swarm optimization (PSO), gradient descent was compared. Finally, experiment based on historical data of a steel plant was simulated, the hit rate of the BOF endpoint was compared under three types of BP network learning algorithm. The results indicate that the analysis improves prediction accuracy of the converter end C content and temperature.
Key words: BP neural network(BPNN); converter steelmaking; quantum particle swarm optimization(QPSO); particle swarm optimization（PSO）