Blast Furnace Ironmaking Research Articles

The operating window simulation for injecting shale gas to the blast furnace used to smelt vanadium and titanium-bearing magnetite

ABSTRACT Presently, the steel industry is confronted with the formidable challenge of reducing carbon emissions, with blast furnace ironmaking accounting for over 60% of carbon emissions in the steel production process. This research suggests a technical approach for smelting vanadium and titanium-bearing magnetite (VTM) by injecting shale gas into blast furnaces. It examines the impact of injecting shale gas on the degree of direct reduction and develops a mathematical model for the blast furnace's operating window based on energy-mass balance. The operating window for a blast furnace that was smelting VTM while injecting shale gas was established: The top gas temperature (TGT) is kept within the range of 110∼180 °C, while the raceway adiabatic flame temperature (RAFT) is maintained between 2000∼2200 °C, when shale gas is replaced for coke, the potential injection range of shale gas is 0∼90.4 kg/tHM, and the associated oxygen enrichment rate ranges from 1.3% to 12.9%; When shale gas is replaced for pulverised coal, the potential range for injecting shale gas is 0∼77.3 kg/tHM, which corresponds to an oxygen enrichment rate of 1.3% to 7.3%. The research results can offer a theoretical foundation for VTM smelting through the injection of shale gas into blast furnaces.

DEM simulation of powder phase movement in coke packed bed generated by scanning particles of a blast furnace

The rational movement and distribution of the powder phase play a significant role in promoting energy efficiency and low carbon emissions in blast furnace (BF) ironmaking. Precise description of the shape of ore is vital for simulating the solid phase motion in a BF. In this study, the shapes of actual particles were captured using a highly accurate 3D scanner, followed by a comparison of the geometric shape characterization of the regular and real particles. A detailed analysis was conducted on the characteristics of the packed bed formed by particles of various shapes and the movement process of the powder phase within the packed bed by using discrete element method (DEM). The distribution and hold-up of the powder phase in the polyhedral and spherical-polyhedral packed beds closely resembles that in the real packed bed. Given the computational time and simulation accuracy, polyhedral or spherical-polyhedral particles could potentially replace the spherical particles typically used in packed beds. This substitution is particularly relevant for applying the DEM to large-scale BF simulations.

The enrichment and transformation mechanism of Pb and Cu in suspension magnetization roasting and magnetic separation from iron tailings

Magnetic iron concentrate (MIC) and nonmagnetic tailings (NT) are obtained from magnetization roasting of iron tailings (IT). MIC containing Pb adversely affects blast furnace ironmaking, while Cu in NT poses leaching risks. This study utilizes fast pyrolysis-suspension magnetization roasting to recover iron from IT. The enrichment of Pb, Cu, and the phase transformation mechanism of Cu in the process of suspension magnetization roasting and magnetic separation were clarified. Results show 96.13 % of Cu in IT is in limonite and 47.23 % of Pb is associated with iron. At 750 °C, with 10 % dosage of biomass pyrolysis and 10 min roasting, Pb, Cu and Fe contents in MIC are 0.96, 2.14 and 3.17 times that of NT. Increasing roasting temperature enhances Cu associated with iron enrichment into the MIC, while oxidation of free copper oxide associated with iron forms magnetic copper ferrite. Increased pyrolyzed biomass leads to over-reduction of magnetite associated with Cu to FeO associated with Cu, promoting magnetic copper ferrite decomposition into FeO and free copper oxide. This research holds significant importance in controlling the quality of MIC and the storage risk of IT, and provides theoretical guidance for the regulation and recovery of valuable metals in subsequent processes.

Fault diagnosis for blast furnace ironmaking process based on randomized local fisher discriminant analysis

AbstractFault diagnosis plays a vital role in ensuring the operation safety of blast furnaces and improving the quality of molten iron in the ironmaking and steelmaking industry. The blast furnace ironmaking process (BFIP) is intrinsically nonlinear. To address the nonlinearity issue of BFIP, a novel fault diagnosis approach that combines the randomized method, local structure information, and Fisher discriminant analysis is proposed in this paper. Using a randomized feature map, the process data is first mapped onto a randomized explicit low‐dimensional feature space. Compared to kernel methods, explicit low‐dimensional random Fourier features considerably reduce the computational cost, particularly for real‐time fault diagnosis for a large training dataset or a large‐scale process. Additionally, the local structure information contained in the randomized low‐dimensional feature space is extracted. The fault diagnosis performance is improved through the exploration of the local structure of random Fourier features. Finally, the blast furnace iron‐marking process state is determined using Bayesian inference. Case studies on a real‐world BFIP are carried out to demonstrate the superior performance of the proposed method in comparison with other related methods.

Scrap Steel Recycling: A Carbon Emission Reduction Index for China

Accurately assessing carbon emissions from recycling scrap steel is essential for reducing emissions in the steel industry, especially in China, the world’s largest crude steel producer. In this study, a carbon emission reduction index was introduced to evaluate the effectiveness of recycling scrap steel in reducing emissions. The index considers the three processes used in scrap steel recycling: blast furnace ironmaking, converter steelmaking, and electric arc furnace steelmaking. This study developed an evaluation model using fuzzy analytic hierarchy process and iterative cluster analysis to determine the reduction of carbon emission. From a life cycle perspective, this study identified primary factors contributing to emissions, including fuel, raw materials, electric energy, and auxiliary materials. Then, the carbon emission reduction index for scrap recycling was developed by examining the production of one ton of steel and each additional ton of scrap steel, which can provide valuable insights into the environmental impact of scrap recycling. Finally, the study forecasts the future Carbon Emission Reduction Index for steel scrap recycling. The study indicates an increase in the carbon emission reduction index for scrap recycling prior to 2017, followed by a decrease about 11.8% from 2017 to 2018 and increases from 2018 to 2021. Finally, it dropped by 8.7% per cent in 2022. Similarly, the carbon emission reduction index for electric furnace steelmaking increased prior to 2019, then subsequently decreased. It is changing by ten per cent a year. Additionally, the scrap recycling index experienced a significant decrease of 90% in 2015, followed by a gradual increase until 2017 and then a consistent decrease every year thereafter. The index suddenly rose in 2021 and then decreased change for policy reasons. The forecast results suggest a gradual increase in the carbon emission reduction index per ton of steel scrap in the future. In conclusion, the practicable modeling methodology has the ability to assist government organizations and private enterprises in devising efficient green and low-carbon development tactics.

Effect of applying a full oxygen blast furnace on carbon emissions based on a carbon metabolism calculation model

Abstract In this study, the effects of applying a full oxygen blast furnace (FOBF) on carbon emissions were investigated by determining the ultimate minimum carbon consumption and a carbon metabolism calculation model. The results demonstrate that the minimum coke ratio of the top gas circulation-FOBF (TGR-FOBF) is significantly reduced (from 270 to 207 kg·thm−1) compared to that of the traditional ironmaking blast furnace (TBF). Owing to the complete recycling of the furnace top gas of the TGR-FOBF, the degree of direct reduction of TGR-FOBF significantly decreased to 0.14. The replacement of the TGR-FOBF with the three TBFs significantly reduced the CO2 emissions, which were the highest at 1.761 t-CO2/t-CS. Most of the CO2 emissions were generated by the direct combustion emissions (CDE) and direct process emissions (PDE), with a small amount generated by the electricity indirect emissions (EIE). The CO2 emissions generated by CDE, PDE, and EIE were 0.872 t-CO2/t-CS, 0.840 t-CO2/t-CS, and 0.049 t-CO2/t-CS, respectively. As the amount of TGR-FOBF replaced with the three TBFs increased, the CO2 emissions generated by the CDE and PDE significantly decreased. When all three TBFs were replaced with TGR–FOBF, the CO2 emissions generated by the CDE and PDE decreased to extremely low levels of 0.267 t-CO2/t-CS and 0.065 t-CO2/t-CS, respectively.

Long Short-Term Memory Parameter Optimization Based on Improved Sparrow Search Algorithm for Molten Iron Quality Prediction

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data.

Woody biomass waste derivatives in decarbonised blast furnace ironmaking process

Modern ironmaking process relies significantly on fossil-related fuels, which ultimately results in the enormous CO2 emitted into the atmosphere. Biomass of plant origin, as a carbon-neutral energy source, has been considered as an alternative to fossil-based reducing agents such as coke. This study aims to investigate the potential of three woody biomass waste derivatives produced from biomass waste pyrolysis and gasification, namely charcoal, bio-oil, and bio-syngas, as the reducing agents in blast furnace. A model based on heat and mass balance and Gibbs free energy minimisation is proposed to simulate an ironmaking process with assistance of these derivatives. The effects of specific composition of biomass waste derivatives on process operation, CO2 emissions, and coke replacement are explored. Also the effects of H2-rich gas produced from biomass waste gasification on the blast furnace operation are estimated. Results indicate that reactions of woody biomass waste derivatives in blast furnace are complex and greatly dependent on composition. When charcoal has a higher carbon content, lower CO2 concentration is found from the top gas. The higher content of hydrogen in bio-oil will inhibit further reduction in CO2 emissions. Bio-syngas with H2/CO ratio of 1.3 proves to have a remarkable potential to reduce CO2 emissions. From the aspects of available biomass waste resources across the world, woody biomass waste derivatives as reducing agents are more suitable for countries with the limited pig iron production. This study provides a reference on the future of moving forward the decarbonised ironmaking by using woody biomass waste derivatives.

Blast Furnace Slag Formation Prediction Model Using Classical Thermodynamics

Blast furnace (BF) ironmaking is the most widely used technology for hot metal production. In steelmaking industry, 80% of the steel in the world is produced in the BF‐basic oxygen furnace route. It is well known that poor quality of the raw material i.e., high gangue content in the iron ore affects the BF process performance and the cohesive zone formation, which is the most critical zone in the BF operations. Increased gangue content in the iron ore causes variation in the slag formation and slag volume thus affecting the fuel rate and BF performance. Therefore, in this study, the slag formation is studied to understand the slag properties at different locations inside the BF. The model is based on classical thermodynamics and is implemented using ChemApp linked with FactSage databases. The model aids in understanding the slag formation in the BF for various burden chemistry, fuel, and flux inputs. The final hot metal and slag composition along with the exit gas volume and composition predicted from this model are compared with industrial BF plant data operating across different conditions.

GPU-accelerated CFD-DEM modeling of gas-solid flow with complex geometry and an application to raceway dynamics in industry-scale blast furnaces

The coupling of Computational Fluid Dynamics (CFD) and Discrete Element Model (DEM) is a powerful tool for simulating dense particulate systems, yet the conventional CFD-DEM has limits for systems with large particle numbers and complex geometry. This paper reports a novel GPU-based CFD-DEM model to simulate the gas–solid flow with large particle numbers and complex geometry. A novel coupling strategy between the CFD solver and DEM solver is developed, featuring high efficiency and stability. The developed model is validated against the experimental measurements, and its efficiency is compared to the previous CFD-DEM simulations. Then, for demonstration, the model is employed to simulate the dynamic behavior of gas–solid flow in the raceway in ironmaking blast furnaces by considering complex tuyere structure details and the huge particle numbers involved. This model allows to study the effect of the tuyere angle in terms of raceway formulation and tuyere erosion. The results show that the largest and most stable raceway volume can be reached at 5° downward tuyere, although the −5° tuyere nose experiences more wear than 10° downward tuyere. The model provides a cost-effective tool to overcome the longstanding challenge of simulating dense fluid-particle systems with huge particle numbers and complex geometry.

Understanding cohesive zone behaviour of blast furnace based on computed tomography flow modelling in a fused bed of ferrous and coke particles

Performance of ironmaking blast furnace systems critically depends on the permeability of the cohesive zone which involves flow of reducing gas through a packed bed of fused ferrous and coke particles at high temperature. This study systematically investigated the effect of operating temperature on bed permeability for two different ferrous burdens - pellets, and pellets-sinter mixture supported on coke particles. To quantify the structural changes in the bed, interrupted tests at various temperatures were conducted and bed porosity was estimated using synchrotron X-ray computed tomography (CT) technique. Bed porosity showed decreasing trends with increasing temperature. A CT image based computational flow dynamics (CFD) model was developed which showed linearly decreasing trends of bed permeability parameter with increasing temperature indicating a significant loss of bed permeability in the high temperature cases. This behaviour was more pronounced for the pure pellet case compared to the mixed burden case.

A novel CFD-DEM-DPM modelling of fluid-particles-fines reacting flows

The complex fluid-particle-fine (FPf) reacting flows have been widely practised in many energy-intensive engineering processes, yet numerical methods capable of comprehensively describing the particle-scale thermochemical behaviours related to the FPf reacting flows were still lacking. In this work, a novel CFD-DEM-DPM model is developed, for the first time, to describe the fluid, particles and fines reacting flows and their interactions. Then, to demonstrate its effectiveness, it is employed to simulate the co-combustion of coke and pulverized coal in a dynamic raceway in ironmaking blast furnaces (BFs). After model validation, the complex in-furnace phenomena related to the co-combustion of coarse coke particles and fine coal particles are comprehensively captured. Particularly, instead of the pre-set raceway treatment in the previous raceway simulations, a dynamic raceway cavity is predicted explicitly driven by the fluid-particle-fine interactions and further, inside the dynamic raceway, the combustion of coal fines including moisture evaporation, devolatilization and char reactions are presented in detail. Subsequently, the impact of operations with and without coal injections are quantitatively compared, showing that both the reducing gas and reaction heat from the coal combustion contribute to an expansion of the raceway cavity. Further, the effects of pulverized coal rate on the in-furnace phenomena are studied. The present work represents a significant breakthrough in the explicit modelling of FPf reacting flows, and provides a cost-effective tool for understanding FPf-related processes and developing new technologies including carbon–neutral ironmaking technologies.

Feasibility analysis and environmental impact evaluation of biochar derived from mango pit for blast furnace injection

Biochar, produced by the pyrolysis of carbon–neutral biomass, has great potential to replace pulverized coal (PC) injection in blast furnace (BF) ironmaking. Herein, this study aimed to develop a low-ash and low-alkali metal biochar from mango pit (MP) and evaluate its environmental impact as an alternative fuel for BF ironmaking. This paper first studied the feasibility of mango pit biochar (MPB) as an alternative fuel. Compared with other preparation conditions, MPB prepared at 600 °C for 60 min (MPB-600–60) had the most similar fuel performance to PC, which was confirmed by principal component analysis (PCA). Moreover, MPB-600–60 exhibited lower ash and alkali metal content compared to PC. The co-combustion experiment demonstrated that MPB-600–60 could promote the combustion of mixed fuel, and the optimal combustibility was achieved when the addition ratio of MPB-600–60 was 80%. Comprehensive life cycle assessment (LCA) revealed that the reduction of global warming potential (GWP) amounted to 220 kg CO2 eq/tHM if MPB 100% replaced PC. These results indicated that MPB could significantly reduce the greenhouse gas emissions and the environmental impact of BF ironmaking. This study provides a novel and promising approach for developing low-carbon and clean alternative fuels in BF ironmaking.

A novel device for investigating the anti-melting loss performance of Ni60A coating on coppery tuyere

Coppery tuyere is a core component in iron-making blast furnace, mainly used for supplying hot air and injecting pulverized coal. Due to the harsh service environment, tuyeres are prone to failure by wear and melting loss. Surface coatings can significantly improve the wear resistance of copper surface, but their anti-melting loss performance is still a research blank. This paper designed a novel device for testing the anti-melting loss performance of coatings. The device included plasma melting-drop, induction preheating and infrared temperature control systems, which can simulate the process of coating being corroded by molten iron under service conditions. Anti-melting loss test results of Ni60A coating indicated that the molten iron and the γ-(Cu, Fe, Ni) solid solution in the coating can form metallurgical bonding by element diffusion, thus adhering on the coating surface. The continuous accumulation of iron will worsen the heat-transfer capability of tuyere, making it more likely to cause melting loss. Adding ceramics in the Ni60A alloy or developing coating materials with higher melting points are expected to improve the anti-melting loss performance of coatings on coppery tuyere.

Deep Learning of Partially Labeled Data for Quality Prediction Based on Stacked Target-Related Laplacian Autoencoder.

Partially labeled data, which is common in industrial processes due to the low sampling rate of quality variables, remains an important challenge in soft sensor applications. In order to exploit the information from partially labeled data, a target-related Laplacian autoencoder (TLapAE) is proposed in this work. In TLapAE, a novel target-related Laplacian regularizer is developed, which aims to extract structure-preserving and quality-related features by preserving the feature-target mapping according to the local geometrical structure of the data. In addition, stacked TLapAE (STLapAE) is further constructed to extract deep feature representations of the data by hierarchically stacking TLapAE blocks. For model training, backward propagation equations are derived based on matrix calculus techniques to update the model parameters of the proposed TLapAE. The effectiveness of the proposed STLapAE is evaluated using the butane content prediction case in a debutanizer column, the silicon content prediction case in a blast furnace (BF) ironmaking process, and the ethane concentration prediction case in an ethylene fractionator. The results show that the proposed TLapAE model has significantly improved prediction accuracy compared to soft sensors using only labeled data and other partially labeled data modeling methods.

Blast Furnace Ironmaking Process Monitoring With Time-Constrained Global and Local Nonlinear Analytic Stationary Subspace Analysis

Blast Furnace Ironmaking Process Monitoring With Time-Constrained Global and Local Nonlinear Analytic Stationary Subspace Analysis

Reinforcement Learning for Blast Furnace Ironmaking Operation With Safety and Partial Observation Considerations.

Making proper decision online in complex environment during the blast furnace (BF) operation is a key factor in achieving long-term success and profitability in the steel manufacturing industry. Regulatory lags, ore source uncertainty, and continuous decision requirement make it a challenging task. Recently, reinforcement learning (RL) has demonstrated state-of-the-art performance in various sequential decision-making problems. However, the strict safety requirements make it impossible to explore optimal decisions through online trial and error. Therefore, this article proposes a novel offline RL approach designed to ensure safety, maximize return, and address issues of partially observed states. Specifically, it utilizes an off-policy actor-critic framework to infer the optimal decision from expert operation trajectories. The "actor" in this framework is jointly trained by the supervision and evaluation signals to make decision with low risk and high return. Furthermore, we investigate a recurrent version of the actor and critic networks to better capture the complete observations, which solves the partially observed Markov decision process (POMDP) arising from sensor limitations. Verification within the BF smelting process demonstrates the improvements of the proposed algorithm in performance, i.e., safety and return.

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

Recycling Blast Furnace Dust as Raw Material in Iron-Making

A large amount of dust is formed as one of the primary by-products during the blast furnace ironmaking process. Iron and carbon are mainly compositions in the dust. Blast furnace dust (BFD) is cycled to protect the environment and recover valuable components. In this study, BFD is used pelletization process with iron ore concentration for raw materials charging to the iron-making furnace. The mixture of BFD and iron ore (30; 40; 50 by mass %) with 2 % bentonite as a binder was used for making pellets. The produced pellets were tested drop strength and fired at 1200 °C for 30 minutes in the atmosphere environment. Fired pellets were examined compressive strength and reduced at 900; 1000; 1100 °C in 30 minutes. Mechanical, porosity and reduction degree properties of reduced pellets were analysed. The results show that green strength and compressive strength are acceptable values. Good porosity is observed when using a high amount of BFD so that it gives a high degree of reduction. XRD, SEM were used for characterization. Iron whiskers were observed in the sample, which was reduced at 1000 °C. It is clearly shown that the pellets using BFD are appropriated for charging to blast furnace as raw materials.

Numerical Investigation of the Inner Profiles in Ironmaking Blast Furnace: Effect of the Ratio of Belly Diameter to Effective Height

Numerical Investigation of the Inner Profiles in Ironmaking Blast Furnace: Effect of the Ratio of Belly Diameter to Effective Height