针对真空感应炉生产过程中温度测量成本较高及精度较差等不足，建立了基于RBF神经网络的真空感应炉终点钢水温度预报模型。
A prediction model of molten steel temperature based on RBF neural network was developed to reduce cost and improve temperature control accuracy for vacuum induction melting.
准确预报转炉冶炼终点的钢水温度与碳含量对提高转炉终点命中率具有重要意义。
Accurate prediction of the end-point temperature and carbon content of BOF molten steel is of great significance to raising the hitting rate of the end-point.
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