Under the background of “carbon peaking” and “carbon neutralization”, the green transformation of iron and steel enterprises is imminent. The hydrogen-rich smelting technology of blast furnaces is very important for reducing energy consumption and CO₂ emission in ironmaking systems, and it is one of the important directions of green and low-carbon development of iron and steel enterprises. In this paper, the research status of the thermal state, reduction mechanism of iron-bearing burden, coke degradation behavior, and formation of the cohesive zone in various areas of blast furnace after hydrogen-rich smelting is summarized, which can make a more clear and comprehensive understanding for the effect of H₂ on blast furnace ironmaking. Meanwhile, based on the current research situation, it is proposed that the following aspects should be further studied in the hydrogen-rich smelting of blast furnaces: (1) the utilization rate of hydrogen and degree of substitution for direct reduction, (2) combustion behavior of fuel in raceway, (3) control of gas flow distribution in the blast furnace, (4) operation optimization of the blast furnace.

In this study, a CaO–SiO₂–Al₂O₃-based continuous casting mold flux was designed using the FactSage thermodynamics software to determine the composition range of CaF₂. The viscosity characteristics of the mold flux were determined using a rotating viscometer. The results show that the constant temperature viscosity at 1300 °C decreases gradually as CaF₂ content is increased from 3% to 11% in the CaO–SiO₂–Al₂O₃-based slag. Viscosity is reduced from 0.854 to 0.241 Pa·s, viscous the flow activation energy is reduced from 157.74 to 114.34 kJ·mol⁻¹, and the break temperature is reduced from 1280 to 1180 °C. Furthermore, when the CaF₂ content is increased from 3% to 11%, the number of nonbridging fluorine bonds (Al–F structure and Si–F structure) in the melt increases to 287, the number of bridging fluorine bonds (Al–F–Al structure, Si–F–Si structure, and Si–F–Si structure) is only 17, and the network rupture of fluorine ions in the system is larger than the network formation. Consequently, both the degree of polymerization and viscosity are reduced.

The thermodynamic and kinetic behaviors of carbothermal reduction of copper slag under different basicity were studied. The results show that the reduction process of Fe and Zn in copper slag is mainly endothermic reaction. The initial reaction temperature with C is lower than that with CO. The metallization rates of Fe and Zn at various temperatures gradually increase with the increase of basicity. With increasing basicity, the reduction rate of copper slag at each heating rate and the reduction degree at 1573 K gradually increase, and the initial reaction temperature gradually decreases. The increase of basicity from 0.06 to 1.20 makes the reaction activation energy decrease by 93.54 kJ mol⁻¹. The carbothermal reduction process of copper slag at each basicity conforms to the diffusion model, and the mechanism function is f(α) = (3/2)(1−α)^4/3[(1−α)^−1/3−1]⁻¹. The increase of basicity has a positive catalytic effect on the carbothermal reduction process of copper slag.

The silicon content of hot metal is a key index for the determination of blast furnace status, and accurate prediction of the silicon content of hot metal is crucial for blast furnace ironmaking. First, 10992 sets of blast furnace data obtained from the site of an iron and steel enterprise were preprocessed. Then, 22 important feature parameters related to the silicon content of hot metal were screened by feature engineering. Finally, the hyperparameters of the Gradient Boosting Decision Tree (GBDT) algorithm model were optimized with the help of the Optuna framework, and the Optuna-GBDT model was established to predict the silicon content of hot metal. The experimental results show that compared with the Bayesian algorithm and the traditional stochastic search method, the Optuna framework can achieve better hyperparameter optimization with fewer iterations and smaller errors.The Optuna-GBDT model performs better in predicting the silicon content of hot metal compared with the optimized Random Forest (RF), Decision Tree and AdaBoost models, and the prediction results are basically in line with the actual values, with the mean absolute error (MAE) of 0.0094, the root mean square error (RMSE) of 0.0152, and the coefficient of determination (R2) of 0.975.The experimental results verified the validity and feasibility of establishing the Optuna-GBDT model to predict the silicon content of hot metal, which provides a reliable tool for iron and steel enterprises and helps to optimize the ironmaking process, improve production efficiency and product quality.

The quality of molten iron not only has a significant impact on the strength, toughness, smelting cost and service life of cast iron but also directly affects the satisfaction of users. The establishment of timely and accurate blast furnace molten iron quality prediction models is of great significance for the improvement of the production efficiency of blast furnace. In this paper, Si, S and P content in molten iron is taken as the important index to measure the quality of molten iron, and the 989 sets of production data from a No.1 blast furnace from August to October 2020 are selected as the experimental data source, predicting the quality of molten iron by the I-GWO-CNN-BiLSTM model. First of all, on the basis of the traditional data processing method, the missing data values are classified into correlation data, temporal data, periodic data and manual input data, and random forest, the Lagrangian interpolation method, the KNN algorithm and the SVD algorithm are used to complete them, so as to obtain a more practical data set. Secondly, CNN and BiLSTM models are integrated and I-GWO optimized hyperparameters are used to form the I-GWO-CNN-BiLSTM model, which is used to predict Si, S and P content in molten iron. Then, it is concluded that using the I-GWO-CNN-BiLSTM model to predict the molten iron quality can obtain high prediction accuracy, which can provide data support for the regulation of blast furnace parameters. Finally, the MCMC algorithm is used to analyze the influence of the input variables on the Si, S and P content in molten iron, which helps the steel staff control the quality of molten iron in a timely manner, which is conducive to the smooth running of blast furnace production.