Basic oxygen furnace (BOF) steelmaking is a complicated physical chemical process, in which the endpoint carbon content and temperature are two important indicators. In BOF steelmaking, the quality of raw materials varies greatly between different batches, which would lead to the inaccurate predictions for these two indicators. Additionally, there are imbalance problems in production process data of BOF steelmaking. For the time-varying problem, a novel similarity criterion based on von-Mises Fisher mixture model (VMM) is proposed in this paper and applied for sample selection of just-in-time-learning (JITL)-based endpoint carbon content and temperature prediction model. The V-shaped transfer function is utilized to develop weighted extreme learning machine (WELM) as local regression model to address the imbalance problems. The performance of the proposed methods is compared with other methods under JITL framework. The experimental results show that the proposed online model can provide a more accurate prediction.

转炉炼钢是一个复杂的物理化学过程，其终点碳含量和温度是两个重要指标。在转炉炼钢中，不同批次的原材料质量差异很大，这会导致这两个指标的预测不准确。此外，转炉炼钢生产工艺数据存在不平衡问题。针对时变问题，本文提出了一种基于von-Mises Fisher混合模型（VMM）的新相似性准则，并将其应用于基于即时学习（JITL）的终点碳含量和温度的样本选择预测模型。V形传递函数用于开发加权极限学习机（WELM）作为局部回归模型来解决不平衡问题。将所提出方法的性能与 JITL 框架下的其他方法进行了比较。实验结果表明，所提出的在线模型可以提供更准确的预测。