Electric arc furnace (EAF) steel bottom-blowing can effectively improve the temperature and composition uniformity of the molten pool during smelting process. To explore the effect of molten-steel characteristics on bottom-blowing at various stages of smelting, we divided the smelting process of the EAF into four stages: the melting stage, the early decarburization stage, the intermediate smelting stage, and the ending smelting stage. The numerical simulation software ANSYS Fluent 18.2 was used to simulate the velocity field of molten steel under the condition of bottom-blowing stirring in different stages in EAF steelmaking process. The properties of bottom-blowing and the kinetic conditions of the steel-slag at this interface were investigated. Our results showed that at a bottom-blowing gas flow rate of 100 L/min, the average flow rates of the four stages were v1 = 0.0081 m/s, v2 = 0.0069 m/s, v3 = 0.0063 m/s, and v4 = 0.0053 m/s. The physical model verification confirmed the results, that is, the viscosity of molten steel decreased as the smelting progressed, and the flow velocity of the molten steel caused by the agitation of bottom-blowing also decreased, the effect of bottom-blowing decreased. Based on these results, a theoretical basis was provided for the development of the bottom-blowing process.

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.

Comprehensively considering the raw materials availability, production technology, and economy factor, it is unclear to choose the optimal electric arc furnace (EAF) steelmaking modes with charging scrap and hot metal (HM) in China. In this paper, the mathematical modelling and techno-economic analysis is adopted to quantitatively describe the consumption level of material and energy, as well as the economic performance in EAF steelmaking process. The calculation results reveal that considering the lowest smelting time, heat loss and total energy consumption, and better economic benefit and energy saving effect, the optimal EAF steelmaking process of 50% HM ratio with carbon powder and coal gas added is suggested, and the consumption of carbon powder and natural gas should be limited within 19.58 kg and 11.63 Nm³. This research is expected to provide some theoretical and operation guidance for low consumption and high efficiency lean production in EAF steelmaking process.

In the electric arc furnace (EAF) steelmaking process, ​accurately predicting​ the end-point temperature of molten steel (EPT-MS) ​is​ critically beneficial for ensuring efficient and stable production. ​This study proposes​ a comprehensive data mining strategy for EPT-MS prediction ​that systematically integrates anomaly detection, feature selection, and machine learning algorithms. To validate this approach, a practical dataset ​consisting of​ 1235 heats ​with​ 32 process features was utilised. Four supervised learning algorithms including logistic regression, k -nearest neighbours, decision tree, and extreme gradient boosting (XGBoost) were employed to develop prediction models. The prediction performance was rigorously evaluated by considering multiple factors, including​ data partitioning (with train/test ratios varying from 9:1 to 1:9), anomaly detection methods (auto-encoder (AE) and principal component analysis), feature selection techniques (permutation importance analysis), and algorithm performance. The results demonstrated that prediction accuracy is influenced by data quality, feature selection, and algorithm choice. Specifically, AE-based anomaly detection significantly improved data quality and enhanced prediction accuracy, while permutation importance analysis effectively reduced model complexity without sacrificing performance. Among all algorithms, XGBoost delivered the best performance, achieving hit rates exceeding 40%, 80%, and 95% within error ranges of ± 5 ∘ C , ± 10 ∘ C , and ± 15 ∘ C , respectively.