转炉吹炼终点碳锰预测数据驱动模型
Data Driven Models of Carbon and Manganese Content Prediction at Blowing End Point in Converters
针对转炉吹炼终点碳和锰,采用大数据与机器学习算法LGBM(Light Gradient Boosting Machine,轻量梯度提升机),建立了数据驱动的转炉吹炼终点碳、锰含量预测模型,模型输入变量包括铁水成分、铁水重量、废钢重量、耗氧量和造渣剂加入量,采用学习曲线调参方法确定模型的超参数最优值。研究结果表明,在大数据的前提下,数据分布均衡有利于提高机器学习算法建立的数据驱动模型的预测效果。在(-0.025%,0.025%)、(-0.020%,…查看全部>>
For the carbon and manganese content at the end point of converter converting,data driven prediction models for carbon and manganese content at the end point of converter blowing were established by using big data and machine learning algorithm LGBM(Light Gradient Boosting Machine).The input variables of the model include the composition of hot metal,the weight of hot metal,the weight of scra,oxygen consumption and the addition amount of slagging formers.The…查看全部>>
洪科;于学渊;钟良才;史秀全;高威;赵阳;
converter blowing endpoint carbon endpoint manganese data-driven model prediction
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