钢水“脱氧合金化”配料方案的优化

Optimization of“Deoxidation Alloying”Batching Scheme of Molten Steel

文章主要讨论了炼钢过程中的脱氧合金化环节的合金配料问题,通过建立多元线性规划模型实现钢水脱氧合金化过程成本的最优配比方案,并通过钢水净重预判出钢量,应用BP神经网络分析对缺失数据进预测,预测结果拟合度高达0.96。

This paper mainly discusses the problem of alloy batching in the deoxidation and alloying process in the steelmaking.By establishing a multi-linear programming model,the optimal ratio of the cost of the molten steel deoxidation and alloying process is realized,and the steel quantity is predicted by the net weight of the molten steel.BP Neural network analysis makes predictions for missing data,and the prediction results have a good fit of 0.96.