基于机器学习的钢铁企业铁水温降预测模型研究OA

Artificial intelligence technology is constantly developing and its application in metallurgical enterprises is gradually deepening,becoming an important cornerstone for intelligent manufacturing and digital transformation.The temperature drop of molten iron is a key parameter in the steelmaking process,which directly affects the thermal efficiency of steelmaking and also indirectly affects the product quality.By integrating production big data and machine learning technology(XGBoost/neural network),the end-to-end temperature drop prediction model constructed in this study,after feature engineering optimization,realizes dynamic process control,increasing the smelting efficiency by 5%,the product qualification rate by 3%,and reducing energy consumption by 2%.This model optimizes the production process,reduces production costs,and provides an effective means for the intelligent manufacturing of steel enterprises.