Molten Iron Research Articles

Study on the Interaction Dynamics of Magnesium and Molten Cast Iron: A Chill Casting Perspective

AbstractThis study presents a reassessment of the role of magnesium in the production process of cast iron. Through detailed experimentation involving an interrupted reaction process employing a chill casting approach in a copper mold, several reaction products that emerged during the high-temperature interaction between magnesium and cast iron were thoroughly examined. Two cast irons with different chemical compositions, notably different sulfur contents, were used to describe the contribution of magnesium during desulfurization and nodularization. The designed approach successfully revealed that rapid deoxidation and desulfurization reactions of cast iron involving liquid magnesium take place before following its ignition characteristics. Furthermore, the behavior of dissolved carbon concerning the remaining magnesium and magnesium-containing reaction products in molten iron is also empirically identified. Specifically, large graphite flakes and an indication of solidification behavior shifting are circumstantially detected in the magnesium-rich regions, which, to a certain extent, can provide additional insight into the role of magnesium during graphite nucleation.

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.

Extensive iron–water exchange at Earth’s core–mantle boundary can explain seismic anomalies

Seismological observations indicate the presence of chemical heterogeneities at the lowermost mantle, just above the core–mantle boundary (CMB), sparking debate over their origins. A plausible explanation for the enigmatic seismic wave velocities observed in ultra-low-velocity zones (ULVZs) is the process of iron enrichment from the core to the silicate mantle. However, traditional models based on diffusion of atoms and penetration of molten iron fail to account for the significant iron enrichment observed in ULVZs. Here, we show that the chemical reaction between silicate bridgmanite and iron under hydrous conditions leads to profound iron enrichment within silicate, a process not seen in anhydrous conditions. Our findings suggest that the interaction between the core and mantle facilitates deep iron enrichment over a few kilometres at the bottom of the mantle when water is present. We propose that the seismic signatures observed in ULVZs indicate whole mantle convection, accompanied by deep water cycles from the crust to the core through Earth’s history.

Mathematical model of molten iron and slag flow in the hearth

The stable hearth discharge is crucial for the operation of a blast furnace (BF). This study established a mathematical model for hearth discharge based on Bernoulli's principle, which considers the blast pressure, kinetic energy of molten iron and slag, gravitational potential energy, the pressure loss of molten iron and slag flowing through the deadman and taphole. It is found that the sharp increase of molten iron and slag flow is probably due to the fracture of the mud, while the sharp decrease of molten iron and slag flow is probably due to the blockage of the taphole by coke. This paper also studies the effect of operating parameters on the hearth discharge. With the increase of taphole plug time, the production rate and slag viscosity, the tap-end time will increase. With the increase of deadman voidage, the tap-end time will be shortened. The correctness of the mathematical model was verified by the actual flow rate of slag and molten iron.

Preparation and formation mechanism of (W) WC/Fe bundle reinforced iron matrix composites

To address the issue of strength and toughness inversion in traditional metal matrix composites, which are reinforced by uniformly distributed ceramic particles, this research developed a novel space bundle configuration of reinforced iron matrix composite using tungsten wire (W) and tungsten carbide (WC). This composite was prepared using a lost foam casting process combined with in-situ reaction. Microstructural analysis revealed that WC particles are distributed along the tungsten wire, with larger particles at the center and smaller ones at the edges. The formation of the reinforcement occurs in two stages: first, a solid-liquid reaction between the solid tungsten wire and molten iron during the lost foam casting process, promoting WC formation at high temperatures and carbon potential; second, an in situ solid-solid reaction where Fe diffuses and Fe3W3C decomposes, forming a composite structure of WC and α-Fe. The composite obtained at 1100°C for 9 hours exhibited a compressive strength of 836.59 MPa and a fracture strain of 18.69%, representing increases of 48.46% and 48.69% respectively compared to grey cast iron (GCI). The (W) WC/Fe bundle reinforcement effectively enhances the strength and toughness of the composite through the high volume fraction and gradient distribution of WC particles, combined with the synergistic effect of α-Fe. This research provides a new strategy for improving the mechanical properties of composite materials and resolving the issue of strength-toughness inversion.

Rapidly Rotating Magnetohydrodynamics and the Geodynamo

The problem of the geodynamo is simple to formulate (Why does the Earth possess a magnetic field?), yet it proves surprisingly hard to address. As with most geophysical flows, the fluid flow of molten iron in the Earth's core is strongly influenced by the Coriolis effect. Because the liquid is electrically conducting, it is also strongly influenced by the Lorentz force. The balance is unusual in that, whereas each of these effects considered separately tends to impede the flow, the magnetic field in the Earth's core relaxes the effect of the rapid rotation and allows the development of a large-scale flow in the core that in turn regenerates the field. This review covers some recent developments regarding the interplay between rotation and magnetic fields and how it affects the flow in the Earth's core.

Enhancing Efficiency in Electric Arc Furnace Steelmaking: A Multi‐Objective Optimization Approach Using the Non‐Dominated Sorting Genetic Algorithm II

To realize the overall optimization of electric arc furnace (EAF) steelmaking system, a multi‐objective optimization model including smelting cost, energy consumption per ton of steel, and carbon emission per ton of steel is established. The model is optimized by multi‐objective genetic algorithm to improve the charging structure. At the same time, the data in the optimal solution set are used to analyze the influence of the change of scrap ratio on smelting cost, carbon emission per ton of steel, and smelting cycle. According to the actual working conditions and the demand of steel plant, the optimized results are selected. Compared with the actual production data, the proportion of scrap steel increases to 50.9%, the ratio of molten iron decreases to 38.8%, the smelting cost per ton of steel decreases by 12 Yuan, the energy consumption per ton of steel decreases by 4%, the carbon emission per ton of steel significantly decreases by 13%, and the smelting cycle is shortened by 2 min, but at the cost of increasing the power consumption per ton of steel. The optimized results and the analysis of the change of scrap ratio provide reference for the optimization of EAF steelmaking system.

Ironmaking process under artificial intelligence technology: A review

The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.

Composition changes and micro explosion characteristics of burning droplets formed by combustion of micrometer iron wires

To analyze the composition changes during the combustion process of iron droplets using the energy balance equation, iron wires with diameters of tens of micrometers were burned to form temperature-stable iron droplets with diameters of hundreds of micrometers. High-speed two-color imaging pyrometry was employed to measure the spatially resolved surface temperature and volume changes of the droplets under varying oxygen concentrations. Experimental results showed that the droplet's temperature stabilized between the melting points of wüstite and iron. Quantitative analysis revealed the presence of bubbles within the droplets, primarily sourced from the oxidation of carbon in the iron to carbon dioxide. Simulation results of the droplet combustion process revealed that at the experimental combustion temperatures, the transport of oxygen in molten iron oxides was the rate-limiting step. The gas responsible for micro-explosions originates from the release of dissolved excess oxygen before the solidification of molten iron oxides, with rough estimates indicating that the mass of the released gas is only 0.02% to 0.03% of the droplet mass. The impact of reaction kinetics parameters on the simulation results, as well as the role of the micro-explosion mechanism in the self-sustained combustion of iron wires, were also discussed.

Comparative Study of Heat Transfer Simulation and Effects of Different Scrap Steel Preheating Methods

The materials charged into a converter comprise molten iron and scrap steel. Adjusting the ratio by increasing scrap steel and decreasing molten iron is a steelmaking raw material strategy designed specifically for China’s unique circumstances, with the goal of lowering carbon emissions. To maintain the converter tapping temperature, scrap must be preheated to provide additional heat. Current scrap preheating predominantly utilizes horizontal tunnel furnaces, resulting in high energy consumption and low efficiency. To address these issues, a three-stage shaft furnace for scrap preheating was designed, and Fluent software was used to compare and study the preheating efficiency of the new three-stage furnace against the traditional horizontal furnace under various operational conditions. Initially, a three-dimensional transient multi-field coupling model was developed for two scrap preheating scenarios, examining the effects of both furnaces on scrap surface and core temperatures across varying preheating durations and gas velocities. Simulation results indicate that, under identical gas heat consumption conditions, scrap achieves markedly higher final temperatures in the shaft furnace compared to the horizontal furnace, with scrap surface and core temperatures increasing notably with extended preheating times and higher gas velocities, albeit with a gradual decrease in heating rate as the scrap temperature rises. At a gas velocity of 9 m/s and a preheating time of 600 s, the shaft furnace achieves the highest waste heat utilization rate for scrap, with scrap averaging 325 °C higher than in the horizontal furnace, absorbing an additional 202 MJ of heat per ton. In the horizontal preheating furnace, scrap steel exhibits a heat absorption efficiency of 35%, whereas in the vertical furnace, this efficiency increases notably to 63%. In the vertical furnace, the waste heat recovery rate of scrap steel reaches 57%.

Multi-Objective Constrained Optimization Model and Molten Iron Allocation Application Based on Hybrid Archimedes Optimization Algorithm

The challenge of distributing molten iron involves the optimal allocation of blast furnace output to various steelmaking furnaces, considering the blast furnace’s production capacity and the steelmaking converter’s consumption capacity. The primary objective is to prioritize the distribution from the blast furnace to achieve a balance between iron and steel production while ensuring that the volume of hot metal within the system remains within a safe range. To address this, a constrained multi-objective nonlinear programming model is abstracted. A linear weighting method combines multiple objectives into a single objective function, while the Lagrange multiplier method addresses constraints. The proposed hybrid Archimedes optimization algorithm effectively solves this problem, demonstrating significant improvements in time efficiency and precision compared to existing methods.

Co-wetting behavior of molten slag and iron on carbon materials in blast furnace

The interaction between molten slag, liquid iron, and carbon is one of the most typical and complex multiphase flow behaviors in the high-temperature zone of blast furnace. The wetting behavior among these phases is the most fundamental characteristic that determines their interactions. This research focuses on the co-wetting behavior of molten slag and iron on graphite, using high-temperature experiments and atomistic simulations. The results revealed that the multiphase cooperative reaction wetting process is influenced by multiple factors. The wettability of molten iron is mainly affected by its initial carbon content, while the wettability of slag is influenced by both the initial carbon content of the molten iron and its own compositional changes. The initial carbon content in molten iron affects the carburizing reaction, which in turn influences the wetting mechanism between molten iron and graphite. Additionally, it alters the reaction of FeO in the slag, thereby modifying the properties of the slag-carbon interface and affecting the overall co-wetting process. Meta-dynamic calculations showed that the reduction of molten iron oxide by dissolved carbon is significantly more effective than that by solid graphite carbon, demonstrating that the behavior of carbon between iron and slag is a key factor in the multiphase cooperative reaction process.

An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace

AbstractIn response to the problem of poor search performance and difficulty in escaping from the local optimum in the Harris hawks optimizer, an improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor (IHHO‐ELSDF) is proposed in this paper. The enhanced logarithmic spiral mechanism is adopted in the exploration phase, and its main feature is the use of an improved opposite‐learning hybrid logarithmic spiral mechanism to search for more promising regions. The dynamic factor is used to replace the escaping energy to improve the global search capability of the algorithm, and it can better balance exploration and exploitation. In addition, a random distribution strategy is proposed for the exploitation phase to avoid falling into the local optimum. Based on 23 classical test functions, the influence of the distribution probability, the three improved mechanisms, and the exploration–exploitation ratio in IHHO‐ELSDF are analyzed. Subsequently, IHHO‐ELSDF is subjected to a comparative analysis with 17 algorithms on the IEEE CEC2022 benchmark suite. These tests show that IHHO‐ELSDF outperforms most competitors in numerical optimization. Furthermore, to assess its applicability in real‐world problems, IHHO‐ELSDF is employed to optimize parameters in the wavelet neural network used for molten iron temperature prediction. The simulation results based on real production data show that the proposed prediction model achieves a high prediction precision with , , and .

Research on Molten Iron Quality Prediction Based on Machine Learning

The quality of molten iron not only has a significant impact on the strength, toughness, smelting cost and service life of cast iron but also directly affects the satisfaction of users. The establishment of timely and accurate blast furnace molten iron quality prediction models is of great significance for the improvement of the production efficiency of blast furnace. In this paper, Si, S and P content in molten iron is taken as the important index to measure the quality of molten iron, and the 989 sets of production data from a No.1 blast furnace from August to October 2020 are selected as the experimental data source, predicting the quality of molten iron by the I-GWO-CNN-BiLSTM model. First of all, on the basis of the traditional data processing method, the missing data values are classified into correlation data, temporal data, periodic data and manual input data, and random forest, the Lagrangian interpolation method, the KNN algorithm and the SVD algorithm are used to complete them, so as to obtain a more practical data set. Secondly, CNN and BiLSTM models are integrated and I-GWO optimized hyperparameters are used to form the I-GWO-CNN-BiLSTM model, which is used to predict Si, S and P content in molten iron. Then, it is concluded that using the I-GWO-CNN-BiLSTM model to predict the molten iron quality can obtain high prediction accuracy, which can provide data support for the regulation of blast furnace parameters. Finally, the MCMC algorithm is used to analyze the influence of the input variables on the Si, S and P content in molten iron, which helps the steel staff control the quality of molten iron in a timely manner, which is conducive to the smooth running of blast furnace production.

Deadman Behavior and Slag–Iron–Coke Interaction of Low Carbon and Safety Blast Furnace: A Review

The elucidation of the deadman's behavior and the interaction between slag–iron–coke within the blast furnace hearth are essential for the realization of low‐carbon and safe production. In this review, the macrostate of the deadman, the interactions between slag–iron–coke, carburizing behaviors, and renewal mechanisms are comprehensively examined. First, the formation and state of the deadman, voidage, and the distribution of coke sizes within the blast furnace hearth are characterized. The average coke particle size ranges from 20 to 30 mm, and the deadman void fraction of 30–50%. Second, the interaction between slag–iron–coke as well as the occurrence state of the mineral layer at the interface within the deadman is elucidated. The ash composition and content of coke are the key factors affecting the slag–iron–coke interaction and interface phase composition. Third, the influence exerted by critical factors such as the physical properties of the carbon source, molten iron, and temperature on the carburizing behavior are analyzed, with the renewal mechanisms of the deadman also being disclosed. Finally, three future focal areas are proposed: characterization and intelligent monitoring of deadman permeability, analysis of slag–iron–coke properties and interface mineral layers control, and in‐depth analysis of deadman renewal and carbon carburization in molten iron. It is anticipated that the studies will enhance the comprehension of deadman behavior and the interactions between slag–iron–coke, thereby fostering the blast furnace's sustainable development.

Deciphering diffuse scattering with machine learning and the equivariant foundation model: the case of molten FeO

Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom–atom pair potentials in disordered materials, has been a longstanding challenge in condensed matter physics. This perspective gives a brief overview of the traditional approaches employed over the past several decades. Namely, the use of approximate interatomic pair potentials that relate three-dimensional structural models to the measured structure factor and its’ associated pair distribution function. The use of machine learned interatomic potentials has grown in the past few years, and has been particularly successful in the cases of ionic and oxide systems. Recent advances in large scale sampling, along with a direct integration of scattering measurements into the model development, has provided improved agreement between experiments and large-scale models calculated with quantum mechanical accuracy. However, details of local polyhedral bonding and connectivity in meta-stable disordered systems still require improvement. Here we leverage MACE-MP-0; a newly introduced equivariant foundation model and validate the results against high-quality experimental scattering data for the case of molten iron(II) oxide (FeO). These preliminary results suggest that the emerging foundation model has the potential to surpass the traditional limitations of classical interatomic potentials.

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production, involves many complex factors, artificial management is difficult. Therefore, puts forward a kind of molten iron transport wisdom control system based on 5G technology, which mainly contains the intelligent identification tracking system, equipment status collection information acquisition system, locomotive vehicle terminal system, etc. Combined with the analysis of the actual application situation, the system could integrate all the processes and elements of molten iron production and transportation, realize the integration of operation and management, and also promote the improvement of the turnover efficiency of molten iron tank, reduce the demand for personnel, and reduce the labor cost.

Mineral phase reconstruction of Bayan Obo tailings by smelting reduction and crystallization control of molten slag containing niobium, rare earth and titanium

Niobium (Nb), rare earth elements (REEs), and titanium (Ti) in Bayan Obo tailings are dispersed across various fine mineral phases. To efficiently recover these valuable elements from Bayan Obo tailings, a novel processing method of complex multimetallic-associated ores, utilizing smelting reduction and crystallization control, is proposed and investigated in this study. First, by observing the crystallization behaviors of CaO-SiO2-La2O3-Nb2O5-TiO2 slag, we observed that Nb and La (lanthanum) were easily dissolved in the perovskite phase that was selected as the target phase for mineral phase reconstruction due to its good flotation characteristics. The effects of w(CaO)/w(SiO2) and the temperature control profile on the crystallization behaviors are discussed in detail. Based on the above investigation, a mineral phase reconstruction experiment performed on Bayan Obo tailings using smelting reduction and crystallization control was carried out. The results indicated that after carbothermic smelting reduction at 1,600 °C, the iron oxides in the tailings were selectively reduced to molten iron that could settle separately from the molten slag, while Nb, REEs, and Ti were enriched in the slag. After temperature-controlled crystallization of the molten slag, 94 % of the Nb and 63 % of the REEs precipitated in the perovskite, and this created a good mineral phase for the efficient flotation of Nb, REEs, and Ti. Finally, a novel processing method for Bayan Obo tailings is proposed after a feasibility analysis of reactor selection, product value, energy consumption, and eco-friendliness.

Numerical assessment of the drag force and Nusselt number during droplet impingement onto a particle

The dynamics of droplet impingement onto solid particles play a crucial role in various engineering applications, yet a fundamental understanding of the intricate momentum and heat transfer characteristics within the processes remains unclear. In this work, we numerically investigate and quantify the drag force and Nusselt (Nu) number during the process using a volume of fraction model. After model validations, it is employed to simulate the processes of molten iron ore spreading over a coke particle for demonstration. The results show that the drag force exhibits rapid initial growth, followed by significant fluctuations marked by two peaks, ultimately decreasing to a low value. The Nu number undergoes a sharp ascent to an immediate peak, followed by a two-stage decline with varying rates. Furthermore, the effect of three key operating parameters is quantified. The comparative analysis unveils that a larger droplet size significantly contributes to an augmented drag force, especially during the first peak. The Nu numbers under various droplet sizes follow a similar trajectory, rising and then decreasing until the wetter surface reaches the maximum. The larger droplets show a slower Nu number decrease. A higher initial droplet position can remarkably increase the drag force and Nu number with more rapid fluctuations. Conversely, the effect of gas velocity under the symmetrical and steady flow field is limited and can be practically disregarded. The present work reveals the fundamental characteristics of momentum and heat transfer process during droplets impact particles.

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.