Blast Furnace Operation Research Articles

MAKING QUALITY AGGLOMERATES USING LEAN IRON ORES FOR SUSTAINABLE IRON-MAKING

For sustainable iron making and hassle-free operation of large blast furnaces (BF) and direct reduced iron (DRI) units, a more prepared burden in the form of good quality agglomerates is required. However, the situation is becoming more and more challenging owing to the faster depletion of high-grade iron ore, increased fines generation due to mechanized mining, wide variations in chemistry & mineral characteristics, high content of gangue minerals, and poor liberation characteristics. The growing demand for steel vis-à-vis iron ores has necessitated the utilizationof low-grade iron ores for steel production. The challenges lie in the economic extraction of lean ores and their conversion into quality agglomerates for use in iron making. The utilization of low-grade iron ores has assumed greater significance for the optimal utilization of natural resources and the sustainability of the steel industry. Thebeneficiated concentrate of BHQ iron ore from the Sandur Schist belt is used for the pellets. Green green pellets with 1.21 kg/pellet GCS and drop strength of 12 drop number were producedat an optimum binder of 1.2% and moisture of 9.5%. Induration of green pellets at a firing temperature of 1300 °C has resulted in adequate pellet properties of 237 kg/pellet of CCS, 91.87% TI, 7.41% RDI, and 22.56% porosity. An attempt is made to produce pellets suitable for iron making by optimizing various raw materials and operating parameters.This will reduce dependency on high-grade ores and effectively utilize low-grade iron ore to produce good-qualityiron-making pellets.

Modelling of blast furnace ironmaking process based on digital twin

AbstractThe blast furnace ironmaking process is a crucial step in the steel industry. Effectively modelling the blast furnaces is significant in ensuring smooth operation and accelerating the digitalisation transformation of blast furnaces. The authors focus on the mechanism modelling of the blast furnace operation process, using a digital twin model development platform to simulate the main reaction processes inside the blast furnaces. Under on‐site production conditions, Si, Mn and Ti contents of molten iron and the corresponding indicators for model accuracy are calculated. For Si, Mn, and Ti content, the Root Mean Square Error values are 0.0678, 0.0108 and 0.0093 for dataset 1, while 0.0933, 0.0120 and 0.0111 for dataset 2, respectively, indicating that the model has a small simulation error and high accuracy. By comparing the simulation results with accurate laboratory results, the model is validated to have satisfactory simulation reliability and compensates for the existing shortcomings in the blast furnace mechanism modelling field.

Real-time detection of coke particles in blast furnace operations using machine learning: Case of a steel plant in India

The efficiency of blast furnace operations in a steel plant relies on the quality of raw materials, including coke. The particle size distribution (PSD) of coke plays a vital role in ensuring the efficiency of the blast furnace. Conventional methods of measuring average particle size are time-consuming and labour-intensive leading to delayed operations. To address the issue, various studies have used different techniques for real-time measurement of coke particle size, including laser diffraction, acoustic sounding, X-ray computed tomography, and computer vision. While these methods have shown successful applications, there have been limitations in their lower prediction accuracy and detection speed. Therefore, our study proposes a practical approach that leverages the integration of computer vision algorithms to enhance accuracy and detection speed. The proposed methodology is compared with the conventional method of measuring particle size in the lab at a blast furnace. Our results reveal that the YOLOv8 algorithm outperforms the conventional method, providing efficient detection of 5.419 frames per second and with a mean absolute error of [Formula: see text]1.77 mm within the lab results. YOLOv8 can perform object segmentation providing much more precise polygon masks of the coke particles instead of simply providing rectangular dimensions using traditional object detection, as explored in previous studies. This approach enables real-time monitoring of PSD and timely alerts to the onsite team, significantly improving the efficiency of blast furnace operations.

Spatio-temporal and multi-mode prediction for blast furnace gas flow

The reasonable and stable distribution of blast furnace (BF) gas flow is the basis for maintaining the smooth operation of BF. Therefore, the accurate detection of the gas flow distribution is essential in the BF ironmaking process due to the direct impact on productivity, stability, and efficiency. However, there is a significant challenge to capture the complex interactions and dynamic changes of the ironmaking process by single predictive mode and two-dimensional (2D) distribution, leading to a lack of flexibility and interpretability in dealing with different abnormalities. To address this issue, a novel spatio-temporal multi-mode approach for three-dimensional (3D) BF gas flow prediction is proposed in this article. First, Pearson correlation analysis is employed to evaluate correlated variables in the spatial dimension. The precise temporal correlations among the multiple variables are matched with mutual information (MI) to extract spatio-temporal variables. Next, the spatio-temporal variables are decomposed utilizing variation mode decomposition (VMD), and the noise is removed with integrated correlation analysis and Fourier transform (FT) to identify and retain the relevant information. Finally, the MI-VMD-Informer is innovatively proposed to establish three different prediction modes based on spatio-temporal features, thus obtaining 2D and 3D gas flow distributions. The superiority of the proposed method is verified by actual BF production data.

Evaluation of Some Uncertainties in the Modeling of Blast Furnace Hydrogen Injection

While blast furnaces remain the dominant ironmaking technology, there is growing interest in using hydrogen to partially replace coke and pulverized coal to reduce CO2 emissions in this hard‐to‐decarbonize process. However, significant discrepancies in modeling the tuyere injection of hydrogen are noted in the literature, which can challenge the scale‐up from modeling predictions and pilot trials to actual blast furnace operations. To address this, the impact of uncertainties in the assumptions of the temperature in the thermal reserve zone and the ratio of H2 and CO utilization on the results of blast furnace process modeling are evaluated using a 1D steady‐state zonal model. The study indicates that variations in the assumed value of thermal reserve zone temperature significantly impact the estimation of optimal hydrogen injection rate, coke replacement ratio, and direct CO2 emissions reductions. In contrast, the same parameters are much less affected by the variations in the assumed value of the proportion between H2 and CO utilization ratios, which, however, affect the top gas composition. A need for further research to determine the range of the difference in temperatures of gas and solid in the thermal reserve zone tolerable for smooth blast furnace operation is highlighted.

Development of Low Carbon Blast Furnace Operation Technology by using Experimental Blast Furnace

Development of Low Carbon Blast Furnace Operation Technology by using Experimental Blast Furnace

Influence of Central Coke Ratio on the Internal State of Blast Furnace

Central coke charging (CCC) is a widely used burden distribution method for blast furnaces (BFs). Adjusting the central coke ratio can change the burden temperature field and affect the smooth operation of BF. This study presents a coupled physical and mathematical model, incorporating particle motion, gas flow, and heat transfer between the burden and gas in two 5500 m3 BFs. The central coke ratios of the blast furnace A (BFA) and blast furnace B (BFB) is 15% and 20%, respectively. The root positions of the cohesive zone in the BFA and BFB are in the lower part of the stack and bosh zones, respectively. In the central area of the BF, the gas flow rate, gas temperature, and burden temperature of the BFB are higher. In the edge area of the BF, the gas flow rate, gas temperature, and burden temperature of the BFA are higher. The actual top gas temperature and gas pressure verify the accuracy of the proposed model. This model investigates the influence of the central coke ratio on the position of the cohesive zone, gas flow rate, gas temperature, and burden temperature, providing a cost‐effective method for studying the effect of the burden distribution matrix on the internal state of the BF.

CFD-DEM study on the raceway transport phenomena and thermo-chemical behaviors in a three-dimensional blast furnace: Effects of process parameters

An in-depth exploration of the kinetic behaviors of the raceway can provide practical insights for optimizing blast furnace (BF) operations. In this study, the transport phenomena and thermo-chemical behaviors in the raceway of a three-dimensional BF were simulated from particulate scale by using the discrete element method-computational fluid dynamics (DEM-CFD) approach. The effects of process parameters (blast velocity, oxygen concentration, blast temperature, coke size and distribution, tuyere insertion depth, and tuyere inclination) on coke combustion characteristics (mass fraction distributions of gas species and reaction kinetics rate) and thermo-chemical behaviors (particle volume fraction, raceway size, mass loss, and coke temperature) were investigated. Meanwhile, microstructures of coke bed were analyzed, and correlations were established for raceway size prediction. The results indicate that the mass fraction distributions of O2 and CO are similar, both showing balloon-like shapes. The distributions of both the CO2 mass fraction and the kinetic rates of reactions exhibit annular shapes. The raceway size increases or even overlaps with increasing blast velocity/oxygen concentration/PSD standard deviation of coke and decreasing blast temperature/coke size. Too large or too small tuyere insertion depth or tuyere inclination is not conducive to the formation of the raceway. Besides, the mass loss, temperature, and microstructures also vary with the process parameters. RSM model can well predict the raceway depth, width, and height. This study extends the particle-scale model by considering transport phenomena towards the global perspective of a real BF, and the obtained new findings on the complex reacting behaviors in the raceway will provide better insights into the optimization of the BF process.

Development and application of closed-loop-oriented intelligent control system for blast furnace air volume addition or reduction

Adjusting the upper and lower parts is an important means to ensure stable operation of blast furnaces. It is a common operation method to adjust the smelting state of the blast furnace by adjusting the air volume. Most of the domestic blast furnace air volume increase and decrease operations are confirmed by manual experience, and it is difficult to realize intelligent automatic control. This paper presents an intelligent control system for regulating air volume in blast furnaces, based on operational logic, expert knowledge, fundamental theories, and other relevant information. The system enables automatic analysis of the blast furnace's state and intelligent reasoning of the foreman's operational methods, laying the foundation for achieving closed-loop control. The system unifies the control philosophy and stabilizes the control methods for regulating air volume in blast furnaces. This achieves standardization of operation and proceduralization of cause analysis. The system enhances adaptability by creating an information feedback mechanism, increasing acceptance among blast furnace operators, and continuously resolving various anomalies in practical applications. The system currently maintains a true hit rate of over 95%, enabling effective reduction of operational misjudgments, intelligent control of airflow adjustment, and promotion of stable and smooth blast furnace operation.

Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation

Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation

Alkaline flux dissociation in blast furnace tuyere bird’s nest: Part 1: CaO dissolution kinetics and slag physicochemical evolution in the coal-coke-slag

In the pursuit of optimizing smelting efficiency and minimizing carbon emissions in blast furnace operations, the strategic introduction of the tuyere alkaline flux injection, particularly in managing the viscosity of the coal-coke-slag in the bird’s nest region, is investigated. This study focused on exploring the dynamics of CaO dissolution within the slag of the coal-coke-slag in the bird’s nest area of a 2200 m3 blast furnace in China, particularly under conditions of instability. Quantitative analysis revealed that at 1550 °C, the CaO maximal dissolution capacity reached 62.05 wt%, with an average rate constant of dissolution observed at 4.65 × 10−4 s−1. The dissolution mechanism was characterized by interfacial dissociation, mass transfer diffusion, and the attainment of a dynamic equilibrium, predominantly influenced by the Ca2+ concentration gradient within the slag. This process facilitates the depolymerization of the slag’s network structure, heavily influenced by the aluminum oxide tetrahedron, effectively reducing slag viscosity. Further, the study quantified the optimal CaO injection content through the tuyere, suggesting a range of 4.00–5.32 kg/tHM, contingent on the use of high-quality raw fuel and advanced high wind temperature technology. This work contributes valuable theoretical insights towards enhancing the efficiency and sustainability of smelting operations, applicable to both conventional and high pellet blast furnaces, marking a significant step forward in the development of low-carbon metallurgical processes.

Improvement The Concrete Fire Resistance by Using By-Product Materials: A Review

The utilization of lime within the cement manufacturing procedure yields a considerable quantity of carbon dioxide emissions, thereby contributing to the phenomenon of the greenhouse effect and the subsequent escalation of global warming. To mitigate the emissions, it is imperative to employ diverse materials during the manufacturing process. Granulated blast furnace slag, a finely ground aggregate derived from the byproduct of blast furnace operations, has found significant application in the realm of concrete production. This material, obtained from the blast furnaces, has proven to be highly advantageous in enhancing the properties of concrete. This study aims to investigate the utilization of Ground Granulated Blast Furnace Slag (GGBS) as a supplementary material in concrete, serving as a partial replacement for cement. The utilization of ground grainy blast furnace slag and RHA has been found to enhance the properties of concrete and can be incorporated in concrete production without causing any adverse impact on the surrounding environment. This makes it a sustainable and eco-friendly solution. The strength degradation of high strength concrete, both during and post-fire exposure, may exhibit dissimilar behavior when compared to that of conventional strength concrete. The primary objective of this review is to examine the alterations in concrete properties after fire exposure, while also investigating the potential of GGBS and RHA in enhancing the fire resistance of concrete.

Blast furnace operation profile monitoring system based on DB-Mini-KMeans and multi-layer perceptron

This article proposes a system for monitoring a blast furnace operation based on DB-Mini-KMeans and multi-layer perceptron (MLP), which solves a difficult problem categorising the furnace operation types. The system uses the correlation between cooling wall temperature and furnace operation type, creates an furnace operation type recognition model with optimised K-Means and MLP algorithms and makes a comparison test with other recognition models; evaluates all kinds of operation types by combining six blast furnace production indexes, such as gas utilisation rate and airflow and establishes a monitoring system of furnace operation types in blast furnaces. The clustering performance of the system has been improved by nearly 40%, and the recognition accuracy of various operation types has reached more than 90%, covering functions such as two-dimensional schematic diagrams of furnace operation types and status warnings, which provide help and support for people to analyse furnace conditions.

Elucidating the Crystallization Behavior of CaO–SiO2–MgO–Al2O3 Slags Towards the Smooth Operation of Blast Furnace

Elucidating the Crystallization Behavior of CaO–SiO2–MgO–Al2O3 Slags Towards the Smooth Operation of Blast Furnace

Optimisation of slag composition (MgO and Basicity) to operate 20% alumina in Blast Furnace slag

Blast furnace (BF) operation depends on the smooth upward movement of gas and its pressure drop. The slag properties like viscosity affects the gas flow inside the furnace. It is well-known that Al2O3 contributes to the increase of slag viscosity. For smooth BF operation, the maximum limit of Al2O3 in the Indian BF slag is around 18.5%. Higher Al2O3 contents make tapping of the BF slag difficult. In the present work, FACTSAGE calculations were carried out to estimate the viscosity and melting temperature of 20% Al2O3 slag varying MgO% between 7 and 13 and basicity (B2, defined as wt% CaO/wt % SiO2) between 0.9 and 1.2 to find an optimum MgO% and basicity for easy tapping of the slag. The results of the calculations provide three combinations of MgO (12,12.5,13%) and B2 (0.95) of slag for operating 20% Al2O3 in slag which were validated through experimental studies involves the inclined plane test, hot stage microscopy and X-ray diffraction.

Effect of Chute on Gas Flow and Temperature of the Top of Blast Furnace

The top gas temperature is an apparent indicator of the distribution state of gas flow, and it is an important parameter to judge the furnace condition. It is related to the top equipment in addition to the charge characteristics and bottom-up generation and distribution. Therefore, the fluid-solid heat transfer model from burden surface -chute-rising ducts is established in this paper. The finite element method is used to calculate the flow and heat transfer of the gas at the top of the blast furnace, and the influence of the chute on the top gas temperature is analyzed. The accuracy of the model is verified by the actual blast furnace top temperature. The results show that the chute has a guiding effect on the gas flow. As the chute rotates, the top gas temperature fluctuates. This research is helpful for actual blast furnace operators to understand the top temperature and make the condition judgment more accurate.

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.

Study of Tuyere Combustion Flame Temperature in Vanadium and Titanium Blast Furnaces by Machine Vision and Colorimetric Thermometry

The steel industry is an important foundation of the national economy and the livelihood of the people, producing a large amount of carbon dioxide gas, accounting for about 70% of the carbon dioxide gas generated in the steel industry, which occurs during the ironmaking process. Therefore, the key technology to reduce the pollution and improve competitiveness is to increase the stability of blast furnace production and the quality of hot metal. Since the operation requirements for temperature control in the vanadium-titanium blast furnace are dramatically different compared to the traditional ones due to the low fluidity of vanadium-titanium slag, maintaining the required hot metal temperature within a narrow range with smaller fluctuations is essential. In addition, the adjustment parameters of the lower part have a significant influence on the tuyere combustion flame temperature during the daily operation of blast furnaces. At present, there is no relevant research on the online detection and analysis of vanadium-titanium blast furnace tuyere combustion flame temperature. In this study, the temperature of four tuyeres in a 500 m3 vanadium and titanium blast furnace at Jianlong Steel was detected by an online detection system. The tuyere combustion flame temperature was then calculated using colorimetric temperature measuring methodology at various times and at four distinct locations. After that, the calibration analyses, imaging parameter and the temperature tendencies in different directions of the blast furnace were investigated. This study not only offers new methods for understanding the regularity of operation and increasing the degree of visualization in vanadium and titanium smelting blast furnaces but also provides technical support for intelligent and low-carbon operation in blast furnaces.

Numerical Simulation of the Effect of Feeding Mode on the Reduction of Ore Powder in Hydrogen‐Based Flash Ironmaking Process

The efficiency and heat utilization of modern large‐scale blast furnace in China are close to limits. The potential for energy saving and consumption reduction by continuing to improve blast furnace operation is limited, so there is a need to develop nonblast furnace ironmaking technologies to solve the problems of high energy consumption and pollution. The emerging flash ironmaking technology, which is highly efficient and has low pollutant emissions, has become an option. This article uses CFD software to simulate the 3D steady‐state hydrogen‐based flash ironmaking process at a pilot scale. It is a complex system involving gas‐particle two‐phase flow, heat transfer, and chemical reactions. The article mainly studies the effects of the feeding mode and hydrogen concentration in the reducing gas on the reduction of ore particles. The results show that the residence time of the particles in the furnace is very sensitive to the feeding method, and the maximum residence time of the particles reaches about 2.8 s under the optimal feeding method. In addition, increasing the hydrogen concentration in the reducing gas decreases the particles’ residence time. However, the minimum residence time is more than 2.2 s, and the ore powder can still obtain a high reduction degree.