Production Of High-quality Steel Research Articles

Enhanced Hydrolysis of Carbonyl Sulfide in Coking Oven Gas Utilizing an Efficient Ca-Ba-γ-Al2O3 Catalyst

China possesses a substantial capacity for coke production, resulting in the annual generation of over 100 billion standard cubic meters of the by-product coke oven gas. The comprehensive utilization of this gas has emerged as a matter of significant concern within the coking industry. The removal of carbonyl sulfide (COS) from coke oven gas is crucial for enhancing gas quality, mitigating equipment corrosion, minimizing environmental pollution, elevating the quality of recovered products, and fostering the production of high-quality steel. A novel Ca-Ba-γ-Al2O3 catalyst has been devised, employing γ-Al2O3 as the catalyst matrix and integrating calcium hydroxide (Ca(OH)2) alongside barium hydroxide octahydrate (Ba(OH)2·8H2O) as the alkaline activating components. The impact of various factors, including reaction temperature, humidity, and the number of activating components loaded, on the hydrolysis efficiency of COS has been meticulously investigated. Furthermore, the catalytic reaction mechanism has been elucidated utilizing advanced characterization techniques such as X-ray diffraction (XRD) and Brunauer–Emmett–Teller (BET) analysis. The outcomes of this research reveal that, under optimal conditions of a reaction temperature of 55 °C and a humidity of 56%, the Ca-Ba-γ-Al2O3 catalyst achieves a remarkable COS hydrolysis efficiency of 95.22%.

Accurate heat flux estimation in continuous casting Molds via MH-MCMC Bayesian Inverse Method

The present work focuses on accurately estimating heat flux variations on the hot faces of the billet and slab molds during continuous casting using the Metropolis Hastings–Markov Chain Monte Carlo (MH-MCMC) Bayesian inverse method and experimental data. Accurate estimation of heat flux variation is crucial for operational efficiency and ensuring high-quality steel products. The variation in the hot face heat flux (qn(y)) of the billet mold is estimated using the proposed MH-MCMC Bayesian inverse method and experimental data for various casting speeds. The study extends this methodology to the slab mold for estimating the variations in the heat fluxes (qnr(y), qw(x,y)) on the hot narrow and wide faces of the slab mold. The estimated heat fluxes for both molds are used in respective forward models to obtain temperatures. The temperatures obtained excellently match experimental temperatures and show superior accuracy in estimating the heat fluxes. From the present methodology, the errors between these simulated and experimental temperatures are much less compared with the literature. The predicted heat flux variations on the hot faces of the billet and slab molds are also compared with the heat flux obtained from the literature.This study provides valuable insights into accurately estimating heat fluxes in continuous casting molds (billet and slab) using the MH-MCMC Bayesian inverse method. The accurately estimated heat fluxes of the molds are essential for ensuring the quality, efficiency, and innovation of continuous casting processes.

Artificial intelligence approach to predict ladle aluminium content and to protect its refractory lining

ABSTRACT This research focuses on optimising steelmaking processes, specifically in the Basic Oxygen Furnace (BOF) and ladle operations, with an emphasis on ladle basicity and Aluminium dosage for deoxidation. The steelmaking process, conducted in the Basic Oxygen Furnace, involves oxidation, flux addition, and tapping of purified steel into ladles. Maintaining desired ladle basicity is crucial for high-quality steel production, and deoxidation with Aluminium is essential to prevent Ferro Alloy oxidation. Secondary metallurgy is performed to maintain ladle basicity and Aluminium content in the steel. The study introduces Artificial Intelligence (AI) integration, utilising Random Forest Regression model to predict Aluminium consumption. This AI-based approach enhances ladle basicity optimisation, reduces Aluminium usage, and ensures high-quality steel production. The research explores infrared technology, digital image processing, and colour visualisation for slag prevention during ladle tapping. The development of a Random Forest Regressor model surpasses empirical equations in predicting Aluminium consumption, resulting in superior ladle basicity maintenance. Comparative analysis demonstrates the accuracy of AI-aided ladles, optimising Al2O3 inclusion formation and sustaining ladle basicity. The research concludes by highlighting AI’s benefits, including improved steel quality, reduced secondary metallurgy, and enhanced operational efficiency, positioning AI as a valuable tool for cost-effective, energy-efficient, and reliable high-quality steel production.

MgAl2O4–C refractories with balanced properties for a delivery device used in the double-roll thin strip continuous casting

To realize the double-roll thin strip continuous casting process for the production of high-quality steels, it is necessary to seek a new type of refractory material tailored for delivery device employed in this process. Therefore, the present study aimed to design and prepare MgAl2O4–C refractories for this purpose as well as to study their physical and mechanical properties, thermal shock resistance, oxidation kinetics, and to perform molten steel experiments after pre-oxidation treatment. The results of the present study demonstrated that the specimens could maintain high mechanical and thermal shock properties even after pre-oxidation treatment. After pre-oxidation at 1100 °C/1 h, the cold compressive strength of the specimen reached 17.63 MPa, with a residual strength ratio (thermal shock resistance) of 77.99%. Analysis of oxidation kinetics based on the shrinking core model indicated an increase in the effective diffusion coefficient and complete oxidation time with higher pre-oxidation temperature. Pre-oxidation of MgAl2O4–C refractories has been shown to be effective in reducing carbon penetration into the molten steel from carbon-containing refractories, thus maintaining the stability of the molten steel composition. This effect was attributed to enhanced contact and wettability between the steel and refractories, an augmented thickness of the pre-oxidized decarburized layer, the formation of a dense magnesium oxide layer, and the emergence of the MgAl1·9Fe1O4 phase. Consequently, an MgAl2O4–C refractories designed for the delivery device was proposed along with anticipated applicability in the double-roll continuous casting process.

Formation Mechanism of Secondary Inclusions in Fe-36mass%Ni Alloy Using a Novel Combination Analysis Technique

Controlling the size, number, and composition of secondary inclusions is vital in the production of high-quality steels. In this study, experimental and computational investigation of the relationship between secondary inclusion formation in Fe-36mass%Ni alloy and cooling rate was carried out. Assuming the case of large ingots, solidification experiments using various cooling rates (0.17 to 128 K/min) were employed and the size, number, composition, and distribution of inclusions were analyzed by SEM-EDS automatic inclusion analysis. Like previous studies, inclusion number density increased with increasing cooling rate, while inclusion size decreased with increase of cooling rate. On the contrary, oxide inclusion area fraction was found to have little relationship with the cooling rate and was instead found related with oxygen content of the sample. As a new attempt to investigate the relationship between microsegregation and secondary inclusion formation, a combination of SEM-EDS analysis and EPMA mapping analysis was carried out. By superimposing information of microsegregation and inclusions, it was found that high-Al2O3 inclusions formed during the early stage of solidification, whereas low-Al2O3 inclusions formed during the later stage of solidification. These findings suggest that Al2O3 inclusions formed in the early stage of solidification reacted with the remaining Si-enriched liquid steel and changed into low-Al2O3 inclusions. Experimental results were also confirmed by thermodynamic calculations. Present work made it possible to understand deeper the relationship between microsegregation and secondary inclusion formation.

Two-Way PBM-Euler Model for Gas and Liquid Flow in the Ladle.

Ladle metallurgy is an important steelmaking technology in high-quality steel production. The blowing of argon at the ladle bottom has been applied in ladle metallurgy for several decades. Until now, the issue of breakage and coalescence among bubbles was still far from being solved. In order to have a deep insight into the complex process of fluid flow in the gas-stirred ladle, the Euler-Euler model and population balance model (PBM) are coupled to investigate the complex fluid flow in the gas-stirred ladle. Here, the Euler-Euler model is applied to predict the two-phase flow, and PBM is applied to predict the bubble and size distribution. The coalescence model, which considers turbulent eddy and bubble wake entrainment, is taken into account to determine the evolution of the bubble size. The numerical results show that if the mathematical model ignores the breakage of bubbles, the mathematical model gives the wrong bubble distribution. For bubble coalescence in the ladle, turbulent eddy coalescence is the main mode, and wake entrainment coalescence is the minor mode. Additionally, the number of the bubble-size group is a key parameter for describing the bubble behavior. The size group number 10 is recommended to predict the bubble-size distribution.

Методика оцінки фізико-хімічної взаємодії в системі "метал-шлак" як кооперативного іонообмінного процесу під час рафінування сталі

Dephosphorisation and desulphurisation are the most important stages of high-quality steel production, characterized by complex physical and chemical processes of interaction in the metal-slag system. The article is devoted to studying the results and evaluating the efficiency of steel refining processes at different stages of its refinement in modern Ukrainian conditions. The aim of the work is to develop a methodology for assessing the physical and chemical interaction in the metal-slag system during steel refining from the perspective of the concept of a cooperative ion exchange process using the parameters of interatomic interaction in melts. As a result of studying the regularities of sulphur and phosphorus distribution depending on the chemical composition of different steel grades from two plants and their corresponding slags, a methodology has been developed that includes the reasonable use of parameters of interatomic interaction in melts as criteria for assessing the degree of completion of ion exchange processes during steel refining. Along with the use of integral physical and chemical parameters of the chemical and charge state of metal and slag melts (chemical equivalent of metal composition ZY(e) and slag Δe(e), effective charge of element Ze(e)), the paper shows the key role of the influence of the "recharge" parameter of the element ΔZе on the process of interfacial distribution of sulfur and phosphorus. This parameter has also shown its efficiency as a criterion for assessing the deviation of the metal-slag system from equilibrium at different stages of steelmaking and finishing at the ladle furnace unit (LFU). The proposed approach makes it possible to assess the level of completion of steel refining processes in order to develop effective technological solutions. In addition, it lays the foundation for solving specific applied problems at a new level, allowing optimizing existing technologies and developing new methods and reagents for producing steels with reduced sulphur and phosphorus content.

Improving the erosion resistance of the submerged entry nozzle by applying an external electric field

In order to improve the erosion resistance of the submerged entry nozzle (SEN) in a continuous casting process, an electric field was applied between the SEN and the carbonaceous mold flux. In view of the extensive application of carbonaceous mold flux in the high-quality steel production process, the erosion behavior under carbonaceous mold flux was explored under the application of different electric fields, in terms of amplitude. The extracted results show that the erosion process between the carbonaceous mold flux and the SEN can be changed obviously by applying an external electric field. In addition, the increase of the carbon content in mold flux will promote the manifestation of erosion resistance effects. Under the control of the negative electric field, the decarburization, dissolution, adhesion, and reaction between the SEN and mold flux will be either suppressed or eliminated. Accordingly, the SEN material remains intact without any erosion and damage after the experiment. The application of electric field is anticipated to solve the problem of serious erosion in the SEN on the continuous casting process.

A detection model for corner cracks of continuous casting strand based on deep learning

ABSTRACT Continuous casting is a dominant process for steel production with high productivity, low cost and high automation, but it always suffers from corner defects in the strand. Thus, an in-situ and highly efficient detection of the strand corner crack is very urgent for high-quality steel production. In the present study, several models, namely, YOLOv5x, YOLOv5-S (YOLOv5 + ShuffleNet v2), YOLOv5-SF (YOLOv5 + ShuffleNet v2 + Focus) and YOLOv5-SFA (YOLOv5 + ShuffleNet v2 + Focus + Adam optimizer), are proposed. The experimental results show that among the four models, the mAP for YOLOv5-SFA increases fastest and the number of epochs for mAP reaches the maximum is least. The loss value is less than 0.01 and the training time is 0.369 h, which is reduced by 58.86% with the comparison of YOLOv5x. When only 100 images are used as training data, the detection accuracy is 99.64%, which increases 11.19% with comparison of YOLOv5x, and the detection time is only 0.021 s.

Phase transformations in iron-carbon alloys doped with rare earth and transition metals

Iron-based alloys began to be used so long ago that today they are classified as classical materials, since these alloys have been studied in depth. The production of various products from steel and cast iron naturally takes the first place in terms of volume, since they have a valuable set of properties: a wide range of plasticity and strength, unique technological properties, which makes it possible to apply all technological operations to them - from simple casting into earth to complex pressure treatment processes. This combination of properties provides, first of all, the composition of iron-based alloys with the main alloying element – carbon. The serious disadvantage of ferrous alloys is their low level of corrosion properties. To eliminate this drawback, both steel and cast iron are alloyed with such metals as chromium, nickel, cobalt, etc. Naturally, this leads to an increase in the mass of finished products and structures. Therefore, the search for alloying elements to improve and optimize the composition of ferrous alloys is relevant today. On the other hand, the industry of Uzbekistan produces a sufficient amount of transition, rare earth metals. Thus, the task is to get added value from the production and processing of these non-ferrous metallurgy products. The analysis of the known literature data on this topic shows that research has been carried out on the use of various transition and rare earth metals for alloying ferrous alloys. However, the available scientific results do not allow to speak about systemic knowledge about the use of rare earth and/or transition metals as effective additives for steels and cast irons. The authors studied phase equilibria, determined the compositions of the phases and the reactions occurring in the systems, constructed phase diagrams, their isothermal sections, determined the structure of iron-based alloys with additions of rare-earth and transition metals with all structural components. The prospect of using the products of nonferrous metallurgy of Uzbekistan for the production of high-quality steels and cast irons is shown.

A Review of Steel Processing Considerations for Oxide Cleanliness

Control of non-metallic inclusions is essential for the production of high-quality steel. This review summarizes processes that change inclusion compositions and concentrations during secondary steelmaking—slower changes are limited by reaction between bulk steel and slag or refractory, and faster changes involve direct additions to the steel bath. An example of the former is conversion of alumina inclusions to spinels during ladle treatment, while reoxidation and calcium treatment are typical exemplars of the fast changes. For the slower changes, inclusions approach equilibrium with the liquid steel and conceptually simple kinetic models correctly describe inclusion evolution during ladle treatment. Disequilibrium from faster changes persists for several minutes under typical ladle conditions, with small-scale inhomogeneity in the steel. Fast scanning electron microscopy with microanalysis has facilitated detailed study of these inclusion evolution processes by providing information on inclusion composition, size, and shape. Machine learning methods are likely to be increasingly important in analysis of the results. Such methods have already shown promise to improve classification of inclusions and recognizing inclusion clusters, from analyses of polished sections. Several unresolved issues that require future study are noted.

Sulphide capacity prediction of CaO–SiO2–MgO–Al2O3 slag system by using regularized extreme learning machine

ABSTRACT Desulphurization is essential in the steelmaking process for high-quality steel production, and sulphide capacity has proven to be an effective index to evaluate the desulphurization ability of molten slag or flux. Several analytical or empirical models have been proposed to calculate the sulphide capacity. However, these models usually show insufficient generalization ability when new variables/data are introduced, which limits their practical application. In this work, experimental data were collected from the literature and a regularized extreme learning machine (RELM) model was established to predict the sulphide capacity of the CaO–SiO2–MgO–Al2O3 slag system. The results demonstrated that the proposed model is robust for the prediction of sulphide capacity under different conditions. The coefficient of determination (R 2), correlation coefficient (r), root-mean-square error (RMSE) of the optimal model reached 0.9763, 0.9881, 0.113, respectively, which outperform the results of the reported models.

Specific Features of Thermal Performance of the Arc Steel Furnace in Unstable Properties Conditions of Burden Materials

In the production of steel in a state-of-the art arc furnace, there are two serious issues, resulting from a decrease in bulk density of burden materials, and an increase in non-ferrous metals concentration. The paper presents the analysis results of changes in the chemical composition of iron-containing materials related with an increase in the content of non-ferrous metals: copper, tin, lead, antimony and unspecified metals in a given steel grade and a decrease in the bulk density of burden materials. A method of reducing impurities by means of using pellets produced from natural raw materials, that are pure in terms of impurities, is presented. A method of setting up charging of burden materials with different bulk density, using the filling coefficient of the furnace operating volume, is proposed. Combined burden materials that include various types of scrap and non-ferrous metals of iron-ore pellets, that are pure in terms of impurities, will ensure high-quality steel production.

Mathematical modelling for predicting mechanical properties in rebar manufacturing

ABSTRACT The mechanical properties of steel depend strongly on chemical composition and the parameters used during thermomechanical processing. Understanding how each variable affects such properties is indispensable for obtaining high-quality steel products at a lower cost. However, the large number of variables involved in the manufacturing process makes this a difficult task. It is possible to use statistical tools combined with predictive modelling to identify the most relevant parameters and to create a mathematical function that can adequately describe the mechanical properties of the rebar from the selected input–output pairs. In the present work, information about the chemical composition and the thermomechanical processing variables were collected at steel mills and used to predict the mechanical properties of rebar using linear regression analysis and an artificial neural networks approach. The coefficient of determination between the measured and estimated values was calculated for yield strength (YS), ultimate tensile strength (UTS), UTS/YS ratio, and percent elongation. The estimation performed using the artificial neural network was better than the one calculated by linear regression analysis for all four properties studied. The results showed that an artificial neural network can be useful in evaluating and choosing the most adequate parameters to achieve the desired steel properties.

Вплив водню на якість сталевих виробів

In order to improve the quality of steel products, patterns of hydrogen dissolution in molten ironcarbon alloys and isolation during their crystallization and phase transformations are analyzed. The mechanism of hydrogen accumulation on the defects of the crystal structure of metals and alloys, the formation and propagation of cracks under the action of normal stress with the participation of a hydrogen cluster and dislocations, as well as the directions of preventing the negative effect of hydrogen are given. It has been established that the main reason for the formation of flocs is the presence of increased amounts of hydrogen in steel in tensile stress zones that occur during steel structural transformations, plastic deformation, uneven cooling, stress concentration sites, lattice defects, grain boundaries, non-metallic inclusions and segregation non homogeneities. Based on the established regularities, the modes of cooling and holding of blanks from carbon and low-alloyed steels after hot working by pressure, as well as preliminary and final anti-flocking heat treatment of steel blanks containing hydrogen, which allow to obtain high-quality steel products without flocs, are developed.

Numerical and experimental investigation of scale formation on steel tubes in a real-size reheating furnace

Precise knowledge of the scale mass formed is essential for the production of high quality steel. In this context several different models have been developed to represent the formation of the scale layer, all of these based on a purely parabolic approach. In the present work computational fluid dynamics (CFD) is used to characterize gas phase combustion and steel heating in a walking beam type reheating furnace. The developed model is based on two separate simulations. The great advantage here is that the elaborate combustion calculation is performed in a stationary simulation, while the transient heating is considered in a comparatively small domain minimizing computational effort. This allows the usage of a scale formation model without an excessive increase in computing time, with the results that this method is numerically highly efficient. In this work a user-defined function (UDF) has been applied to characterize scale formation. The model is able to calculate local scale mass based on steel temperature, time as well as the associated species concentration in the surrounding atmosphere. In addition, the model is able to depict the insulating effect due to the low thermal conductivity of the scale. The influence turned out to be minor, since the heating time is low compared to other similar furnaces. This has been evaluated by comparing the results to the model neglecting the insulating effect. The results are further compared with a analytical model based on a parabolic approach. Moreover, measurements on the real furnace have been performed. The weight of several tubes before and after heating has been recorded, as indicators of the scale mass formed. Measurements in real furnaces represent a major challenge, and the results show a very close agreement with the numerical results we have achieved.

Application of static magnetic field to casting of steel

A magnetic field was applied in the continuous caster in steelmaking. A static magnetic field has advantages in the steelmaking processes, in that the apparatus for generating a static magnetic field is simpler than alternating magnetic coils, and stabilization of the flow is enhanced by a static magnetic field because a larger force acts on a faster steel flow. Therefore, application to industrial processes has been investigated. In this presentation, two applications are discussed, as follows:1) Steel flow control in moldThe steel flow is controlled in the mold to produce high quality steel products with high productivity. An unbalanced steel flow causes surface defects. A static magnetic field is effective for stabilizing the steel flow in the mold. Instability becomes more severe with larger throughput or a thinner mold. A static magnetic field is effective for reducing this instability. Furthermore, because an insufficient steel velocity causes entrapment of bubbles and inclusions on the solidified shell, optimization of the steel flow velocity by controlling the strength of the two domain magnetic fields is important.2) Microstructure control of cast metalA static magnetic field suppresses the liquid flow, and the microstructure can be controlled by this flow control. In a Sn-Zn alloy casting test, the columnar grain ratio increased when a magnetic field stronger than 0.1 T was applied.These phenomena occurred because thermal convection was reduced by the magnetic brake, followed by less supercooling at the dendrite end. The equiaxed crystal size increases with a magnetic field. Since the magnetic field in the early stage has a larger effect, nuclear generation of equiaxed crystals is considered to be restrained by reducing the steel flow and the temperature gradient.

Microstructural adjustment of carburized steel components towards reducing the quenching-induced distortion

It is crucial to control and minimize the geometrical distortions resulted from the application of carburizing and quenching processes. This is particularly of the utmost importance for high quality steel products such as power transmission components which require high performance and dimensional precision in the range of micrometers. Carburized steel components are quenched from hardening temperature to room temperature to acquire a very hard martensitic layer (case). During the quenching process, due to the phase transformation-induced volumetric expansion in case and the interior region (core), unwanted dimensional changes may occur. In the present work, the effects of a modified hardening temperature and different soaking times on the core microstructure, the final dimensional stability and the mechanical properties are systematically investigated. Navy C-ring specimens are employed to quantify and correlate the effect of the developed microstructural constituents, magnitude of distortion, distribution of residual stresses and hardness values (macro, micro and nanohardness). Mini-tensile specimens are additionally fabricated out of the Navy C-rings’ core to evaluate the overall tensile behavior of the developed microstructures. The results show that by application of the modified hardening temperature the quenching-induced distortion can be reduced up to 27% while retaining the hardness properties of the case and core similar to that in the reference specimens which are quenched from a typical hardening temperature. The overall tensile properties of the core microstructures developed by the reference and modified heat treatments also show a nearly similar behavior.

Modeling and experiment of slag corrosion on the lightweight alumina refractory with static magnetic field facing green metallurgy

Electromagnetic field is applied widely in metallurgy and other high temperature processes, and affects the behavior of melts. The lightweight alumina based carbon free refractory is of importance for energy-saving, consumption reduction and high quality steel production, and the slag corrosion resistance is significant concerning its service life. Does electromagnetic field control the slag corrosion behavior on the lightweight alumina refractory? In this paper, a multi-field coupled model was established to describe the slag corrosion process in an electromagnetic field. The mathematical modeling in combination of experiments was applied to clarify slag corrosion behavior of lightweight alumina refractory in static magnetic field. The simulation results agree with that of the experiments, which means the proposed model is promising for slag corrosion modeling. The results show that the combination of the slag properties change, and electromagnetic damping caused by MHD (magnetohydrodynamics) effect can enhance the slag corrosion resistance by inhibiting slag penetration and promoting formation of a directional isolation layer, and be beneficial to high-quality clean steel production.

Nanocarbon containing Al2O3 – C continuous casting refractories: Effect of graphite content

Alumina-carbon refractories are broadly used in the continuous casting area in the production of steel, mainly in the devices used to control flow, because of their thermal and mechanical properties. Continuous casting refractories contain approximately 30% of residual carbon after coking to impart and improve certain refractory properties. But this leads to the threat of carbon pick up by the molten steel for high-quality steel production. In the present work, nanocarbon is used in combination with graphite to reduce the total carbon content. The changes in physical and mechanical properties like bulk density and strength with the phase and microstructural development were studied. The density and strength values are decreasing with increase in the amount of graphite in the composition. Formation of aluminum carbide in nanocarbon containing batches enhances the strength development. High surface area nanocarbon possess high reactivity than the graphite and helps in carbide phase formation and it can easily disperse in the gaps between coarse, medium and fine alumina particles, thereby reduces the porosity, and enhances density and strength development.