针对转炉炼钢过程复杂 ,影响终点因素多 ,而且难以进行连续准确地测量 ,提出了基于副枪检测信息的智能动态终点控制方法 .采用灰色模型并通过神经网络进行补偿对转炉炼钢终点温度和碳含量进行预报 ,在此基础上 ,以RBF神经网络作为预设定模型 ,通过模糊调整确定补吹阶段需要的氧气量和加入的冷却剂量 ,并对一座180吨转炉进行仿真计算 ,结果表明了该方法的有效性 . Aiming at the complexity of converter steel making process and the factors affecting the end point, and it is difficult to measure continuously and continuously, an intelligent end-point control method based on the information of sub-gun detection is put forward.With the gray model and neural network compensation, Carbon content. On this basis, RBF neural network is used as a pre-set model to determine the amount of oxygen needed and the amount of coolant added during the blow-in stage by fuzzy adjustment. The simulation results of a 180-ton converter show that The effectiveness of this method.