Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining

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, characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. This paper establishes the energy model of the EAF steelmaking process to conduct 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, is proposed and verified. The unsupervised algorithm Auto-Encoder (AE) is adopted to detect and eliminate abnormal data, completing data cleaning and ensuring data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. Feature selection identifies 10 factors, such as the hot metal (HM) ratio and HM temperature, as important data features to simplify the model. Based on various ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in EAF steelmaking under different smelting modes is proposed. The results indicate that under low HM ratio and low HM temperature, optimal EUE occurs when power, oxygen, and natural gas consumption fall within specific ranges.