以我国某钢厂120 t LF精炼炉为研究对象,通过建立由冶炼机理模型和XGBoost模型相结合的混合模型,预测LF精炼过程中的钢水成分并进行实际应用.结果表明,模型预测终点碳、硅、锰、铝等元素均处于内控范围内,并平均减少了每炉钢取样工序0.8次,提高了生产效率.
Prediction Model for LF Refined Steel Composition Based on Mechanism and XGBoost Algorithm
Taking 120 t LF refining furnace in a domestic steel mill as the research object,a mixture model combining smelting mechanism model and XGBoost model was established to predict the composition of molten steel in LF refining process,and the actual application was carried out.The results show that the predicted end-point carbon,silicon,manganese,aluminum and other elements are all within the internal control range,and the production of each furnace steel is reduced by 0.8 sampling procedures on average,and the production efficiency is improved.
LF refined steelcomposition predictionmechanism modelXGBoost model
万方数据
维普
