The study of scrap melting provides data for increasing the scrap utilization rate. Here, an evaluation model is established to analyze the effect of each factor on scrap melting using statistical methods for the first time. Subsequently, the quantitative relationship between the influencing factors and melting parameters is obtained. Back propagation (BP) neural networks and multiple regression are used for predictions. For scrap melting controlled by carbon mass transfer when the bath temperature range is 1573–1723 K, the relative contribution of each parameter was mixing power > bath temperature > specific surface area > carbon content. The predicted values of the BP neural network are more accurate than those of multiple regression. The relative errors of average melting rate, average mass melting speed, and mass transfer coefficient of training sets are 14.02%, 13.95%, and 7.19%, respectively, which decrease by 22.71%, 47.22%, and 69.46%, respectively, compared with those of the regression equations after outliers are removed.

废料熔化的研究为提高废料利用率提供了数据。在这里，建立了一个评估模型来首次使用统计方法来分析各个因素对废料熔化的影响。随后，获得了影响因素和熔融参数之间的定量关系。反向传播（BP）神经网络和多元回归用于预测。对于当熔池温度范围为1573–1723 K时由碳传质控制的废料熔化，每个参数的相对贡献为混合功率>熔池温度>比表面积>碳含量。BP神经网络的预测值比多元回归的预测值更准确。平均熔化速度，平均质量熔化速度的相对误差，