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With the deepening of the concept of interactive reaction and the development of several studies, metallurgists are no longer simply concerned with the metallurgical properties of a single charge. Instead, they comprehensively consider the interactive reaction of composite burdens. The interactive reaction is mainly affected by the chemical composition, microstructure, reduction temperature, and other factors of the ferrous burdens. In this study, taking a sinter with different basicities and a mixed ore of sinter and lump as the research object, the effect of sinter basicity on the smelting and dripping paraments of a composite burden and the interactive reaction between different burdens was investigated using melting–dripping equipment. The results show that the dripping temperature of a single sinter increases with sinter basicity. In the integrated burdens protocol, the proportion of lump ore increased, and the interaction between the burdens was enhanced, which is mainly manifested as a reduced softening start temperature and melting start temperature of the composite burden. The air permeability of the mixed charge was improved. A change of burden structure enhances the interaction between minerals with increasing sinter basicity, resulting in a change in the liquid phase composition, which reduces the melting point of the primary slag phase, and when the basicity of the sintered is too high, it will deteriorate the gas permeability of the material column. This result is not conducive to the intensive smelting of blast furnaces. At the same time, the sintered mineral phase changes throughout the reduction process were characterized using SEM-EDS and XRD, and the main phases in the slag phase are wustite and calcium silicate. The interactive reaction between the sinter and lump produces low melting point materials, which is verified by calculating the phase diagram of CaO–SiO2–FeO. With increasing sinter basicity, the content of 2CaO·SiO2 at different breakpoints decreased. This result shows that the high melting point phase formed during the reduction process of the sinter decreases, the liquid phase formation temperature of the composite burden decreases, and the interaction between the burdens increases. Therefore, appropriately increasing the sinter basicity and increasing the lump ore proportion benefits the enhanced smelting of a blast furnace. The theoretical and experimental results obtained in this research are of great importance for improving the production application of the proportion of lump in the furnace and developing efficient and low-carbon ironmaking.

The amount of zinc-containing EAF dust has increased due to the increased proportion of galvanized steel scrap used in the electric arc furnace (EAF) steelmaking process. If the zinc in the EAF dust is not recycled, it will not only lead to a waste of valuable metal resources but also results in environmental pollution. Zinc is mainly present in the EAF dust in the form of zinc ferrite (ZnFe2O4). Zinc ferrite is a kind of spinel mineral that exhibits a crystal lattice of greater stability, which increases the difficulty of recycling valuable elements such as zinc and iron from zinc-containing EAF dust. To further clarify the carbothermic reduction process of zinc ferrite, this paper studies the kinetics of the non-isothermal carbothermal reduction of zinc ferrite and its reduction reaction mechanism. The phase transition process of the zinc ferrite carbothermal reduction reaction was analyzed via the XRD results of the reduced zinc ferrite. FeO0.85·xZnO was found at 950 °C when Fe3+ was reduced to Fe2+. The relationship between the conversion and conversion rate of the zinc ferrite carbothermal reduction process is discussed. The reduction process can be divided into three stages, and the conversion of the second stage changes greatly (0.085–0.813). Finally, the kinetics of the second stage of the carbothermic reduction of the zinc ferrite at different heating rates was evaluated through the isoconversional method and the master curve fitting method. The activation energy of the second stage is between 331.01–490.04 kJ·mol−1, and the average activation energy is 362.16 kJ·mol−1. The large change in the activation energy in the second stage indicates that the reactions in this stage are more complicated, and there are obvious differences in the activation energy between the reactions. The secondary chemical reaction is the main rate-controlling link in the second stage, and the kinetics equation of the second stage is determined.

The Chinese government made a statement at the 75th United Nations General Assembly in 2020 to increase the country’s nationally determined contributions, adopt more effective policies and measures, strive to reach the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. In recent years, with the rapid development of the iron and steel industry, the iron and steel industry has been promoted by various measures such as large-scale equipment, high-efficiency energy utilization, and reduction of pollutant emissions. Moreover, this industry has gradually made efforts to achieve low-carbon emissions. However, due to the particularity of the steel industry’s process system, the steel industry is still the main battlefield in China’s carbon emission reduction. The ironmaking process accounts for the largest proportion of energy consumption and emissions in the entire process of iron and steel smelting. Annual CO2 emissions of the iron and steel industry account for 6.7% of total global emissions, of which the energy consumption and emissions of the ironmaking system account for the total energy consumption of the entire iron and steel process, facing the important challenge of saving energy and emission reduction. To adapt to the trend and realize the transformation and upgrading of the ironmaking industry, various processes of the ironmaking industry have made great efforts in reform and innovation in recent years. This article introduces the new technology of sintering pellet quality improvement and consumption reduction from the aspects of new ironmaking technology and basic theoretical research, analysis of coke behavior in the blast furnace, blast furnace clean fuel injection technology, blast furnace longevity technology, blast furnace ironmaking data modeling technology, and metallurgical dust and mud reprocessing technology. Starting from basic research, the new ironmaking technology with the most potential is proposed. Then, under the general background of the current national carbon neutral strategy, the current international non-blast furnace ironmaking technology research progress is reviewed to provide a basis for the development of low-carbon ironmaking in China. Finally, starting from the latest micro-research methods, it introduces the current research progress in the field of ironmaking in the micro-scale, multi-scale comprehensive regulation and control of the mechanism of the blast furnace ironmaking process, and provides ideas for the future development of low-carbon ironmaking.

The hot metal temperature is a key process parameter for blast furnace (BF) ironmaking that reflects the quality of hot metal, the thermal state of BF hearth, the energy utilization efficiency of BF, and many other information. Prediction of the hot metal temperature in the next smelting cycle will be helpful in gaining a better understanding of the change trend of hot metal quality and BF smelting status in time. With this, corresponding operational measures can be conducted to maintain the BF stable and smooth state, high production, and low consumption. Nowadays, big data technology has made considerable progress toward a more accurate and faster collection, storage, transmission, query, analysis, and integration of mass data, providing a good data foundation for data-driven machine learning models. In addition, with the substantial increase in computer calculation speed and the significant development of algorithms, the prediction accuracy of deep machine learning models has noticeably improved. The development of these technologies provides feasibility for the prediction of important indicators under complex industrial conditions. Based on the data produced from a 4000-m3 BF in a large span time range (2014–2019) and daily time dimension, this paper considered hot metal temperature as the objective function. First, the characteristic parameters of hot metal temperature were processed by linear and nonlinear correlation analysis, feature selection, and normalization methods. Then, the positive and negative correlation characteristic parameters that have a significant influence on the temperature of the hot metal were obtained. Finally, prediction models of hot metal temperature were established based on two algorithms of support vector regression and extreme learning machine. Although both the algorithms can achieve effective prediction, results from support vector regression are better at an average absolute error of 4.33 °C and a hit rate of 94.0% (±10 °C).