As energy efficiency becomes increasingly important to the steel industry, the iron ore sintering process is attracting more attention since it consumes the second large amount of energy in the iron and steel making processes. The present work aims to propose a prediction model for the iron ore sintering characters. A hybrid ensemble model combined the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm is developed for regression problem. First, the factors that affect solid fuel consumption, gas fuel consumption, burn-through point (BTP), and tumbler index (TI) are ranked according to the attributes weightiness sequence by applying the RReliefF method. Second, the ELM network is selected as an ensemble predictor due to its fast learning speed and good generalization performance. Third, an improved AdaBoost.RT is established to overcome the limitation of conventional AdaBoost.RT by dynamically self-adjusting the threshold value. Then, an ensemble ELM is employed by using the improved AdaBoost.RT for better precision than individual predictor. Finally, this hybrid ensemble model is applied to predict the iron ore sintering characters by production data from No. 4 sintering machine in Baosteel. The results obtained show that the proposed model is effective and feasible for the practical sintering process. In addition, through analyzing the first superior factors, the energy efficiency and sinter quality could be obviously improved.

A transient two-dimensional mathematical model is developed to study the influence of fuel type and operation parameters on combustion and NO_x emission during the iron ore sintering process. The model was validated by comparing the model predictions with sintering pot test data. The predictions show reasonable agreement with the averaged values of the test data. In addition to the conventional sintering process, this model can also predict new processes such as flue gas recirculation, gas fuel injection, and fuel layered distribution. The simulation results show that the fuel NO_x is the main part of the NO_x emission during sintering, and thermal NO_x forms a very little part. The produced NO_x can be reduced not only by coke but also by CO around coke particles, with reduction proportions of 50% and 10%, respectively. Two types of coke A and B were compared. With Coke A as solid fuel and consumption of 3.8%, the NO_x emission was 320 ppm. Increasing the replacement of Coke A with Coke B, the NO_x emission was decreased, being decreased by 28.13% to 230 ppm with the replacement proportion of 50%. When only Coke B was used, the NO_x emissions could be lowered by 53.13% to 150 ppm. Decreasing the particle size from 1.6 mm to 1.2 mm led the NO_x emission to be increased by 10.93% from 320 ppm to 355 ppm. With Coke A as the only solid fuel, increasing the fuel ratio to 4.2% led the NO_x emission to be increased by 9.38% to 350 ppm; increasing the oxygen content of inlet air from 21% to 30% led the NO_x emission to be increased by 15.00% from 320 ppm to 368 ppm.

The distribution of burden layers in an ironmaking blast furnace strongly influences the conditions in the upper part of the process. The bed permeability largely depends on the distribution of ore and coke in the lumpy zone, which affects the radial gas flow distribution in the shaft. Along with the continuous advancement of technology, more information about the internal conditions of the blast furnace can be obtained through advanced measurement equipment, including 2D profiles and 3D surface maps of the top burden surface. However, the change of layer structure along with the burden descent cannot be directly measured. A mathematical model predicting the burden distribution and the internal layer structure during the descending process is established in this paper. The accuracy of the burden distribution model is verified by a comparison with experimental results. A sensitivity study was undertaken to clarify the role of some factors on the arising layer distribution, including the descent-rate distribution, the initial burden surface profile, and the charging direction through the charging matrix. The findings can be used as a theoretical basis to guide plant operations for optimizing the charging.

Under the global dual-carbon background, heightened public awareness of climate change and strengthened carbon taxation policies are increasing pressure on the steel industry to transition. Given the urgent need for carbon reduction, the exploration of low-carbon pathways in a blast furnace (BF) metallurgy emerges as crucial. Evaluating both asset retention and technological maturity, the development of low-carbon technologies for BFs represents the most direct and effective technical approach. This article introduces global advancements in low-carbon metallurgical technologies for BFs, showcasing international progress encompassing hydrogen enrichment, oxygen enrichment, carbon cycling technologies, biomass utilization, and carbon capture, utilization, and storage (CCUS) technologies. Hydrogen enrichment is identified as the primary technological upgrade currently, although its carbon emission reduction potential is limited to 10% to 30%, insufficient to fundamentally address high carbon emissions from BFs. Therefore, this article innovatively proposes a comprehensive low-carbon metallurgical process concept with the substitution of carbon-neutral biomass fuels at the source stage—intensification of hydrogen enrichment in the process stage—fixation of CCUS at the end stage (SS-IP-FE). This process integrates the cleanliness of biomass, the high-efficiency of hydrogen enrichment, and the thoroughness of carbon fixation through CCUS, synergistically enhancing overall effectiveness. This integrated strategy holds promise for achieving a 50% reduction in carbon emissions from BFs in the long processes. Critical elements of these core technologies are analyzed, assessing their cost-effectiveness and emission reduction potential, underscoring comprehensive low-carbon metallurgy as a pivotal direction for future steel industry development with high technological feasibility and emission reduction efficacy. The article also proposes a series of targeted recommendations, suggesting short-term focus on technological optimization, the medium-term enhancement of technology research and application, and the long-term establishment of a comprehensive low-carbon metallurgical system.