Accurately and continuously monitoring the hot metal temperature status of the blast furnace (BF) is a challenging job. To solve this problem, we propose a hot metal temperature prediction model based on the AdaBoost integrated algorithm using the real production data of the BF. We cleaned the raw data using the data analysis technology combined with metallurgical process theory, which mainly included data integration, outliers elimination, and missing value supplement. The redundant features were removed based on Pearson’s thermodynamic diagram analysis, and the input parameters of the model were preliminarily determined by using recursive feature elimination method. We built the hot metal temperature prediction model using the AdaBoost ensemble algorithm on a dataset with selected features as well as derived features by using K-mean clustering tags. The results show that the performance of the hot metal temperature prediction model with K-means clustering tags has been further improved, and the accurate monitoring and forecast of molten iron temperature has been achieved. The model can achieve an accuracy of more than 90% with an error of ±5°C.

中文翻译：

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基于数据挖掘的铁水温度预测

准确连续地监控​​高炉（BF）的铁水温度状态是一项艰巨的任务。为了解决这个问题，我们使用高炉的实际生产数据，基于AdaBoost集成算法提出了铁水温度预测模型。我们使用结合了冶金过程理论的数据分析技术清除了原始数据，主要包括数据集成，异常值消除和缺失值补充。根据皮尔逊的热力学图分析，去除了多余的特征，并通过递归特征消除方法初步确定了模型的输入参数。我们使用AdaBoost集成算法在具有选定特征以及通过使用K均值聚类标记得出的特征的数据集上建立了铁水温度预测模型。结果表明，具有K-均值聚类标签的铁水温度预测模型的性能得到了进一步提高，实现了对铁水温度的准确监测和预报。该模型可以达到90％以上的精度，而误差为±5°C。