In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future.

中文翻译：

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基于关联规则挖掘的电炉炼钢能源利用效率评价及优化能源匹配模型

在钢铁行业，评估能源利用效率（EUE）并确定最佳能源匹配模式对于解决日益严重的能源枯竭和环境问题具有重要作用。电弧炉炼钢是典型的短程粗钢生产路线，具有能源密集、冶炼节奏快、原料结构多样化的特点。本文建立了电弧炉炼钢过程的能量模型，进行能量分析和EUE评估。提出了一种基于数据清洗、特征选择和关联规则(AR)算法的指导电炉生产过程的关联规则挖掘(ARM)策略，并验证了该策略的有效性。采用无监督算法自动编码器（AE）检测并剔除异常数据，完成数据清洗，保证数据质量和准确性。当隐藏层节点数为18时，AE模型表现最佳。特征选择确定铁水（HM）比率、HM温度等10个因素作为重要数据特征，以简化模型结构。根据不同的HM配比和温度，结合k-means聚类和AR算法，提出了不同冶炼模式下电弧炉炼钢EUE的优化运行流程。结果表明，在低HM比和低HM温度的条件下，第二阶段耗电量在4853kWh~7520kWh之间、第二阶段耗氧量在1816m3~1961之间时EUE最佳。立方米，天然气消耗量在156立方米至196立方米之间。反之，在高HM比和高HM温度的条件下，EUE趋于降低，当炉壁耗氧量在4732 m3~5670 m3之间、第二阶段耗氧量在1561 立方米和 1871 立方米。相比之下，在不同的熔炼模式下，ARM得到的熔炼方案对于EUE的提升效果明显。由于EUE较高，A2B1冶炼方式的提升最为明显，从24.7%提升至53%。该研究有望为未来电炉炼钢过程节能减排提供技术思路。