Search Results (113)

Natural magnetite ore is commonly used to produce oxidized pellets as the raw material for blast furnace ironmaking. The drying of green pellets significantly affects the quality of oxidized pellets. However, the drying process in the traveling grate cannot be directly analyzed. To address this issue, in this study the influences of the drying medium temperature, medium velocity, and pellet diameter on the moisture removal, as well as the drying kinetics of the natural magnetite oxidized pellets were investigated. Orthogonal experimental results indicated that the drying medium temperature had the most significant effect on the drying rate, followed by the medium velocity, while the interaction between the pellet diameter and temperature had a minor influence. Drying kinetic model fitting revealed that the drying process followed a modified Page model (III). Model validation demonstrated that the experimental measurements closely aligned with the theoretical predictions, confirming that the Page model (III) accurately predicted the effects of the drying temperature and medium velocity on the pellet moisture content. Higher drying temperatures further improved the prediction accuracy. The findings provide valuable insights for analyzing and optimizing the drying process of the natural magnetite oxidized pellets in the industrial traveling grate systems. Full article

Iron ore sintering is a critical process in steelmaking, where the produced sinter is the main raw material for blast furnace ironmaking. The quality and yield of sinter ore directly affect the cost and efficiency of iron and steel production. Accurately predicting the burn-through point (BTP) temperature is of paramount importance for controlling quality and yield. Traditional BTP temperature prediction only utilizes data from bellows, neglecting the information contained in sinter images. This study combines color temperature information extracted from the cross-sectional frame at the discharge end with bellows data. Due to the non-stationarity of the BTP temperature, a hybrid prediction model of the BTP temperature integrating bidirectional long short-term memory and extreme gradient boosting is presented. By combining the advantages of deep learning and tree ensemble learning, a hybrid prediction model of the BTP temperature is established using the color temperature information in the cross-sectional frame at the discharge end and time-series data. Experiments were conducted with the actual running data in an iron and steel enterprise and show that the proposed method has higher accuracy than existing methods, achieving an approximately 4.3% improvement in prediction accuracy. The proposed method can provide an effective reference for decision-making and for the optimization of operating parameters in the sintering process. Full article

The U.S. ironmaking sector plays a key role in global greenhouse gas emissions, mainly due to long-standing practices such as blast furnaces (BFs) and direct reduction (DR). In this work, we develop a new mathematical approach to estimate future CO₂ emissions from the U.S. ironmaking industry through 2050. Our approach uses historical data from 2005 to 2021 and incorporates economic and energy use indicators to explore how emissions might change over time. According to our results, unless significant technological improvements and stronger energy policies are implemented, the industry is likely to continue producing large amounts of CO₂. These findings highlight the pressing need to adopt cleaner alternatives—such as hydrogen-based direct reduction—to help meet international climate goals. Supporting the transition to low-emission technologies contributes to broader efforts in sustainable industrial development and long-term climate resilience. Full article

Blast furnace slag, a high-temperature molten by-product generated during the ironmaking process in the metallurgical industry, has garnered significant attention for its resource utilization technologies. Compared to the traditional water-quenching method, dry granulation offers notable advantages. This paper systematically compares and analyzes the performance parameters of three typical dry treatment processes: mechanical crushing, air-quenching granulation, and centrifugal granulation. It reveals that the centrifugal granulation process demonstrates substantial technical superiority in key metrics, such as particle size distribution uniformity, particle morphology optimization, and heat recovery efficiency. Building on this, this study provides a comprehensive review of the current state of centrifugal granulation technology, from both experimental and simulation perspectives. Additionally, the combined processes of centrifugal granulation and air quenching can fully exploit the synergistic benefits of each technology, thereby enhancing overall efficiency. However, the wind’s cooling effect can lead to the premature solidification of molten slag when it splits into liquid filaments, resulting in slag wool. To address this, this paper proposes a centrifugal granulation device equipped with a windbreak board, which facilitates temperature zoning. This approach prevents premature solidification in the liquid filament region while ensuring the timely cooling and solidification of slag particles, offering a novel technical solution for optimizing centrifugal granulation in metallurgical solid waste resource utilization. Full article

Monitoring the blast furnace shaft static pressure is crucial for maintaining a stable ironmaking process. Traditional rule-based methods and manual inspections suffer from high labor costs and inconsistent standards. This article proposes a new unsupervised anomaly detection framework that combines adversarial autoencoder with variational mode decomposition (VMD). Firstly, using VMD combined with sample entropy calculation and clustering algorithm, the trend, period, and other components of multidimensional signals are extracted, and then these components are integrated into an improved adversarial training autoencoder to detect global and local anomalies. The proposed method has an accuracy of 0.95, a recall rate of 0.91, and an F1 score of 0.93. Which demonstrates the method effectively captures multi-scale anomalies including value bias, morphological changes, and sudden fluctuations, while providing analysts with interpretable anomaly detail diagnosis. Full article

In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly reflected by the silicon content in the hot metal. However, due to the dynamic nature and inherent delays of the ironmaking process, real-time prediction of silicon content remains a significant challenge, and traditional methods often suffer from insufficient prediction accuracy. This study presents a novel Multi-Scale Fusion Convolutional Neural Network (MSF-CNN) to accurately predict the silicon content during the blast furnace smelting process, addressing the limitations of existing data-driven approaches. The proposed MSF-CNN model extracts temporal features at two distinct scales. The first scale utilizes a Convolutional Block Attention Module, which captures local temporal dependencies by focusing on the most relevant features across adjacent time steps. The second scale employs a Multi-Head Self-Attention mechanism to model long-term temporal dependencies, overcoming the inherent delay issues in the blast furnace process. By combining these two scales, the model effectively captures both short-term and long-term temporal dependencies, thereby enhancing prediction accuracy and real-time applicability. Validation using real blast furnace data demonstrates that MSF-CNN outperforms recurrent neural network models such as Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). Compared with LSTM and the GRU, MSF-CNN reduces the Root Mean Square Error (RMSE) by approximately 22% and 21%, respectively, and improves the Hit Rate (HR) by over 3.5% and 4%, highlighting its superiority in capturing complex temporal dependencies. These results indicate that the MSF-CNN adapts better to the blast furnace’s dynamic variations and inherent delays, achieving significant improvements in prediction precision and robustness compared to state-of-the-art recurrent models. Full article

Monitoring the injection volume of clay guns is important in blast furnace ironmaking. Currently, such monitoring data are often recorded manually, which has limitations such as low reliability and high delay. To address these issues, we revisit the task from a computer vision perspective and propose an object detection method. First, we introduce an interpolation annotation technique to build a clay gun dataset. With it as a foundation, an improved object detection model called Faster R-CNN with multi-stage Positional encoding and two-branch Self Challenge (PSCfrcn) is proposed. Our model leverages multi-stage positional encoding (PE) to focus more on the relative position of local features, and a self-challenge (SC) module to mitigate the interference caused by the harsh environments. We conduct extensive experiments on the clay gun dataset from actual industrial scenarios, and use various metrics to validate the performance. Experimental results demonstrate that our method can significantly enhance the model’s discriminative power and generalization ability, which is a promising direction for this task. Full article

Charging biochar composite briquettes (BCBs) and the injection of hydrogen-rich gas into the blast furnace (BF) are two efficient methods for reducing CO₂ emission in BF ironmaking. This study investigated the reaction behavior of BCBs under a hydrogen-rich atmosphere to explore the potential combination of these two methods for enhanced CO₂ emission reduction efficiency in the BF. The employed BCB had a chemical composition of 52.57 wt.% Fe₃O₄, 24.54 wt.% FeO, 0.98 wt.% Fe, 13.16 wt.% C, and 8.75 wt.% gangue. Isothermal BCB reaction tests were conducted using a custom-design thermogravimetric device under temperatures ranging from 1173 K to 1373 K and under an atmosphere of N₂-H₂ with a H₂ content from 25 vol.% to 75 vol.%. A mathematical model was developed for the kinetics of the BCB reaction behavior under the H₂-N₂ atmosphere. Results showed that the developed model was adequate in predicting the reaction behavior of BCB. Under an atmosphere of 50 vol.% H₂-N₂, increasing the temperature from 1173 K to 1373 K resulted in a decrease in the fraction of iron-oxide oxygen removed by hydrogen from 62% to 26% and an increase in the fraction removed by biochar from 29% to 72%, indicating that hydrogen is the primary reducing agent under low temperatures, whereas, under high temperatures, biochar plays a more significant role. Under a constant temperature of 1273 K, increasing the H₂ content in the atmosphere from 25 vol.% to 75 vol.% led to an increase in the fraction of iron-oxide oxygen removed by hydrogen from 37% to 45%, and a decrease in the fraction removed by biochar from 57% to 53%, suggesting that a higher H₂ content enhances the iron oxide reduction by hydrogen but has little impact on the reduction by biochar. In the reaction process, the main products were CO and H₂O, the iron oxide reduction occurred more rapidly near the center than near the surface, whereas the gasification of biochar followed the opposite trend. The structural transformation of the BCB progressed from sinter iron oxides into the metallic iron network in the reaction. Full article

This paper reports studies on the thermal chemistry of the flash pyrolysis (heating rate of 20,000 °C/s up to 800 °C) of non-fossil fuel carbon (NFF-C) waste (or refuse-derived fuel, RDF) in the context of using this as an alternative reductant for blast furnace ironmaking. Gas chromatography–mass spectrometry (GCMS) analysis linked to the pyrolyser was used to simulate the thermal processes that take place during injection in the blast furnace raceway, where material experiences extreme temperature (ca. 1000 °C) over very short residence times (<300 ms). Species identification and qualitative analysis of evolved species generated are reported. Whilst the pyrolyser uses flash heating of a static sample, a drop tube furnace was also employed to study a sample moving rapidly through a pre-heated furnace held at 1000 °C to enable reductant burnout rates to be measured. The overarching aim of this piece of work is to study the suitability of replacing fossil fuel with non-recyclable plastic and paper as blast furnace reductants. Full article

The utilization of a high pellet ratio in blast furnace smelting represents a pivotal strategy for achieving green and low-carbon ironmaking, which can improve raw material quality, reduce energy consumption, and decrease CO₂ emissions. In this study, the impact of pellet proportion and charging sequence on the burden distribution was investigated using the discrete element method. The results revealed that the pellet mass fraction and the porosity of the ore layer gradually increase from the furnace wall toward the center under different pellet proportion conditions. As the pellet proportion increases, the radial segregation index of the pellets decreases and the porosity of the ore layer slightly increases. Furthermore, alternating charging can reduce pellet rolling, thereby lowering the flowability of the burden. The research outcomes can offer valuable insights for optimizing blast furnace charging operations when using a high pellet ratio, contributing to an improved smelting efficiency. Full article

Monitoring tapping states, which reflects the smoothness of blast furnace (BF) production, is important in the blast furnace ironmaking process. Currently, these monitoring data are often recorded manually, which has limitations such as low reliability and high delays. In this study, we propose an automatic recognition approach for tapping states based on object detection, using furnace front monitoring videos combined with learning-based image processing technology. This approach addresses crucial aspects such as automatically recognizing the start and end times of iron tapping and slag discharging, accurately calculating their duration, and logging tapping sequences for multi-taphole operations. The experimental results demonstrate that this approach can meet the requirements of accurate and real-time recognition of tapping states and calculation of key monitoring data in industrial applications. The automatic recognition system developed based on this approach has been successfully applied in engineering projects, which provides real-time guidance for comprehensive monitoring, intelligent analysis, and operational optimization in blast furnace production. Full article

The injection of hydrogen into a blast furnace is a promising technology to fulfill the low-carbon ironmaking purpose. A three-dimensional computational fluid dynamic (CFD) model is developed to investigate the effect of hydrogen injection rate and blast oxygen enrichment rate on the tuyere, raceway, and surrounding coke bed behaviors. It was found that hydrogen injection leads to a higher water vapor volume fraction in the raceway and a higher hydrogen fraction in the coke bed. The magnitude of velocity and temperature near the tuyere only increase slightly due to the cold inlet temperature of hydrogen, which also results in lower coke bed temperature. The volume-averaged temperature decreases from 2146 K to 2129 K when the injection rate increases from 0 to 1000 Nm³/h. Oxygen enrichment rate presents a highly positive correlation with temperature in the raceway and coke bed, water vapor and carbon dioxide volume fraction in the raceway, and pulverized coal burnout rate. Because more carbon participates in the raceway reaction with an increase in oxygen enrichment rate from 0% to 10%, the final carbon monoxide fraction in the coke bed increases from 0.29 to 0.40, and the final hydrogen fraction decreases from 0.15 to 0.13. With the increase in hydrogen injection, the temperature of the raceway and the coke bed decreased slightly. Pulverized coal burnout changes little with the hydrogen injection rate increasing from 500 Nm³/h to 1500 Nm³/h, which is because hydrogen combustion promotes pulverized coal at the front part of the raceway but inhibits it at the end due to the relative lack of oxygen. These results will help better understand the combustion behavior in the tuyere and raceway of the blast furnace with oxygen-rich blast and hydrogen injection. Full article

The softening–melting properties of iron-bearing materials play a crucial role in the reduction process in the lumpy zone in the blast furnace (BF) and affect the height, thickness, and shape of the cohesive zone, as well as gas permeability in the BF. A novel softening–melting method was developed based on actual BF production practices, which consistently matches the reduction index and metallization degree observed in actual BF operations compared to the conventional methods. Under the novel softening–melting testing method, the characteristic temperatures (T₄₀ and T_S) increase by about 5 °C and 49 °C, respectively, compared to the conventional method. Additionally, the permeability index (S) of the sinter in the novel method is about 707 kPa·°C lower compared to the conventional method. Clearly, the novel method results in higher softening–melting characteristic temperatures for iron-bearing materials compared to the traditional method, more closely matching actual BF conditions. This approach can provide valuable insights for improving gas permeability and enhancing the reduction process of iron-bearing materials in the BF. Full article

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. Full article

Blast furnace ironmaking plays an important role in modern industry and the development of the economy. A reasonable ingredient scheme is crucial for energy efficiency and emission reduction in blast furnace production. Determining the right blast furnace ingredients is a complicated process; therefore, this study examines the optimization of the ingredient ratio. In this paper a model of the blast furnace ingredients is established by considering cost of per ton iron, CO₂ emissions, and the theoretical coke ratio as the objective functions; ingredient parameters, process parameters, main and by-product parameters as variables; and the blast furnace smelting theory and equilibrium equation as constraints. Then, the model is solved by using an improved grey wolf optimization algorithm and an improved multi-objective grey wolf optimization algorithm. Using the data collected from the steel mill, the conclusion is that multi-objective optimization can consider the indexes of each target, so that the values of all the targets are excellent; we also compared the multi-objective solution results with the original production scheme of the steel mill, and we found that using the blast furnace ingredient scheme optimized in this study can reduce the cost of iron per ton, CO₂ emissions per ton, and the theoretical coke ratio in blast furnace production by 350 CNY/t, 1000 kg/t, and 20 kg/t, respectively, compared with the original production plan. Thus, steel mill decision makers can choose the blast furnace ingredients according to different business strategies and the actual needs of steel mills can be better met. Full article