材料期刊网

摘 要：转炉炼钢过程是一个非常复杂的物理化学变化过程，人工控制很难一次达到终点目标值，通常需要经过多次补吹才能出钢。本文通过研究影响转炉冶炼终点磷含量的主要因素，确定了影响转炉终点磷含量的参数，建立了基于Levenberg-Marquardt(LM)算法BP神经网络转炉终点磷含量的预报模型。结果表明：在预报误差目标精度为±0.002%内,命中率达到了90%。

Abstract：BOF steelmaking is a very complex physical chemistry process; it is hard to achieve the target value of end-point by manual control. Multiple reblowing operations were usually necessary to taping off. Based on analyzing the influence major factors of phosphorus end-point in converter, the dominative factors of prediction model of end-point for Conrerter smelting were fixed in this paper. A prediction model of end-point phosphorus content for BOF process is established based on Levenberg-Marquardt(LM) algorithm of BP neural network. The results show that the phosphorus content of end-point hitting rates could be reached 90% if the accuracy of target error were±0.002%.