As an industry with high energy consumption and high emission, the iron and steel industry not only drives the economic development, but also brings serious environmental pollution problems. In order to achieve green and low-carbon steel manufacturing, reducing CO 2 emissions in the blast furnace ironmaking process has become the current mainstream, of which the accurate judgment of the blast furnace status is a key to achieve it. Firstly, combining theory with production experience, this research established 6 evaluation systems of the blast furnace and extracted 22 evaluation parameters from them through mathematical statistics. After completing the data preprocessing with the help of Python, the potential elements in the initial variables were excavated and a comprehensive evaluation model of the blast furnace status was developed by Factor Analysis. Based on this, the status of the blast furnace were divided into four degrees, i.e. good, normal, poor and warning and the rationality was verified by comparison to the production logs. By means of comparing the law of data distribution under different furnace status, the optimal range of operation parameters was summarized. This study is expected to provide guidance for realizing energy conservation and consumption reduction of the blast furnace.

中文翻译：

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基于数据挖掘和机理分析的高炉状态综合评价

钢铁行业作为高能耗、高排放的行业，不仅带动了经济发展，也带来了严重的环境污染问题。为实现钢铁的绿色低碳制造，降低高炉炼铁过程中的CO 2 排放已成为当前主流，其中准确判断高炉状态是实现的关键。首先，将理论与生产经验相结合，建立了6个高炉评价体系，并通过数理统计从中提取了22个评价参数。借助 Python 完成数据预处理后，挖掘初始变量中的潜在要素，采用因子分析法建立高炉状态综合评价模型。在此基础上，将高炉状态分为良好、正常、不良和警示四个等级，并通过生产日志对比验证其合理性。通过对比不同炉况下数据分布规律，总结出最佳运行参数范围。该研究有望为实现高炉节能降耗提供指导。通过对比不同炉况下数据分布规律，总结出最佳运行参数范围。该研究有望为实现高炉节能降耗提供指导。通过对比不同炉况下数据分布规律，总结出最佳运行参数范围。该研究有望为实现高炉节能降耗提供指导。