【摘要】 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 .更多还原

【Abstract】 Being aimed at BOF (basic oxygen furnace) process which is complicated,has many factors influencing the endpoint, and is difficult to be measured continuously and accurately,the intelligent dynamic endpoint control based on the sublance information is proposed. The BOF endpoint temperature and carbon content are predicted by use of gray model and neural network compensation. On the basis of this,the RBF neural network is regarded as presetting model,and the oxygen and coolant during the reblowing period are adjusted through fuzzy adjustment.A 180t converter is simulated.The results show that the method is effective.更多还原