BOF Steelmaking Process Research Articles

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

AbstractBasic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.

Hybrid Model for Predicting Oxygen Consumption in BOF Steelmaking Process Based on Cluster Analysis

The amount of oxygen blown into the converter directly affects the productivity of the converter and the quality of the molten steel, so it is crucial to accurately predict oxygen consumption. Herein, a hybrid model for predicting oxygen consumption in basic oxygen furnace steelmaking process is established. The oxygen consumption of converter is divided into two types: the determined oxygen consumption and the estimated oxygen consumption. The determined oxygen consumption is calculated by mechanism model. And the estimated oxygen consumption is calculated by statistical model. The method of cluster analysis is also introduced into the model. After clustering the data, a statistical model for predicting the estimated oxygen consumption in each subcategory is established, which further improves the prediction accuracy of the model. After dividing the original data into three categories, the hit rate of the hybrid model is 93.3%, when the relative errors of the model are restricted within 5%. As a result, the hybrid model is fully capable of predicting oxygen consumption in converters.

High efficient vanadium extraction from vanadium slag using an enhanced acid leaching-coprecipitation process

Vanadium slag (VS) is a by-product of vanadium titanium magnetite BOF steelmaking process and becomes an important material for vanadium products preparation. Sodium roasting-water leaching is a mainstream process for industrial production, but the water leaching residue (WLR) usually remains 1.0 wt% to 3.0 wt% V2O5, which still has value for recycling extraction of vanadium. In this research, a two-stage leaching process was proposed for high efficient vanadium extraction. The first stage leaching was sodium roasting-water leaching for vS and the second stage leaching was sulfuric acid leaching for WLR. As a result, the total leaching efficiency of vanadium can reach as high as 98.52%, which was 7.72% higher than that of conventional water leaching efficiency of vanadium (90.80%). The mechanism analysis indicates that the CaNaFe2Si4O12 and NaFeSi2O6 water-insoluble phases formed and covered on (Mn,Fe)(V,Cr)2O4 spinel surface during roasting, hindering vanadium oxidation and dissolution. The structures of wrapping phases and spinel can be effectively destroyed by acid leaching, releasing vanadium in WLR. By using hydrolytic precipitation of iron ions in acid leachate, 99.78% of vanadium can be recovered as V-Fe precipitation slag, which contains 6.83 wt% V2O5 and can be cycled back to sodium roasting process. The V-Fe slag belongs to amorphous precipitation, illustrating that residual vanadium in WLR can be changed to unstable compound by acid leaching and hydrolysis precipitation, which makes vanadium easier to extract during sodium roasting-water leaching process. This research has the advantages of enhanced reuse of V resource in the WLR, achieving a high V recovery. The short process has a strong adaptability and can provide technical support for efficient V extraction from vS.

Contribution of Hot-Spot Zone in Decarburization of BOF Steel-Making: Fundamental Analysis Based upon the FactSage-Macro Program

An improved computational model to describe the decarburization process in basic oxygen furnaces for steel making is presented in this work. A dynamic model was thus developed to calculate the decarburization rate and its breakup as a contribution coming from the hot-spot zone (under jet impact) and emulsion zone (by droplet and slag reactions). In this work, multiple interconnected equilibrium/adiabatic stoichiometric-reactor-based approaches are used to describe the overall basic oxygen steel-making process. The macroprogramming facility of FactSage™ software was used to understand the thermodynamics and kinetics of basic oxygen steel-making processes. The temperature, compositions, and volumes of various phases are estimated with the use of this model. Hot-spot temperatures in the range from 2000 to 3000 °C as a benchmark was considered for calculations. The major contribution of decarburization was established to come from hot-spot reactions in the major part of the blow, except in the last part when emulsion phase reactions govern it. This development represents an original contribution to our understanding of the BOF steel-making process.

Behavior of Top-Blown Jet under a New Cyclone Oxygen Lance during BOF Steelmaking Process

An oxygen lance is the operation unit that generates supersonic oxygen jets, controls their behavior, and acts as a vital role in the steelmaking process. It is thought that airflow similar to a tornado may suppress upward splashing because of part of the jet pressure shifting from the axis of the oxygen lance to the tangential direction. Therefore, a new oxygen lance is designed to form a tornado jet, and the numerical simulation consequences are verified by the physical model. The structure of the new oxygen lance is optimized by numerical simulation results, and the comparison of simulation results before and after optimization is analyzed. On this basis, the effect of the cyclone oxygen lance on the upward splashing behavior, penetrating depth, turbulent kinetic energy, turbulent dissipation rate, and rotation of molten bath is investigated. The conclusions present that, compared with the conventional oxygen lance, the upward splashing with the cyclone oxygen lance decreases, and the penetrating depth and reaction area increase. In other words, for obtaining the same penetrating depth, the cyclone lance height can be higher than that of a conventional oxygen lance, which leads to a better protective effect on the refractories of the oxygen lance. Moreover, the average value of the turbulent kinetic energy of the cyclone nozzle is larger than that of the traditional Laval nozzle at the interface between oxygen and slag, which improves the effect of steelmaking.

Simulation study on multiphase behavior of gas-metal-slag in the 260t converter

Taking the 260t BOF steelmaking process as the research object, it is of great significance to study the Gas-metal-slag interaction with oxygen lance. In this paper, a 3D mathematical model is established by using fluent. Through changing some physical parameters of lance height and slag, the impact depth, impact cavity area and energy transfer and flow are analyzed. The numerical results show that with the increase of lance height, the diameter of impact cavity in molten steel becomes larger, but the impact depth decreases. When the lance height is 32de, the area of high speed zone in the molten steel pool is the largest, the energy in oxygen jet is mostly dissipated in the process of reaching the slag level, and the kinetic energy obtained by the slag is only about 14% of the kinetic energy of molten steel. With the increase of slag thickness, the depth and area of the cavity decrease, and the flow of molten steel is restrained. The change of slag viscosity and slag density has little effect on the flow velocity. This study can provide reference and guidance for the regulation and control of the process parameters of converter refining.

Feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

Feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

Value in use of lime in BOF steelmaking process

ABSTRACT In the BOF steelmaking process, lime quality and quantity directly affect slag quality, affecting metallurgical results, liquid metal yield, productivity, and therefore the total cost and environmental impact of the steel production. In this paper, a value in use model based on mass balance and heat balance was developed and used to evaluate the impact of lime quality on the BOF process. The model has been successfully validated using the data of the Steel Plant. Then three types of lime with different qualities were used to elaborate on the impact of lime characteristics in optimizing the steelmaking process, metallurgical benefits, overall cost impact, potential savings, and environmental benefits. The calculation results show that good-quality lime could increase the scrap ratio, reduce the lime consumption, and reduce iron loss, accordingly, improve the steel quality, increase steel yield, reduce the smelting costs, stabilize smelting operations, and reduce CO2 emissions.

Effect of blowing parameters on bath mixing efficiency during basic oxygen furnace steelmaking process

AbstractBasic Oxygen Furnace (BOF) steelmaking is the main method for steelmaking production, mixing efficiency of BOF vessel would greatly affect the uniformity of chemical composition and temperature in molten steel. Therefore, this paper conducted the effect of blowing parameters on bath mixing efficiency during BOF steelmaking process. Through measuring the mixing time of steel bath in BOF vessel, the effect of different blowing parameters was studied. Based on analysis of variance for the experimental results, the influence degree on mixing efficiency by different blowing parameters was acquired, and the optimized combination of blowing parameters was obtained. Moreover, the mixing index which indicated the mixing degree of steel bath in BOF vessel was proposed in this study, and an apparent nonlinear relation was acquired after determining relationship between the mixing index and blowing parameters. The optimal blowing parameters were applied to an 80 t industrial BOF vessel in a special steel plant, the end point carbon and oxygen equilibrium was decreased from 0.0032 to 0.0026, end point phosphorus and total Fe content in slag were reduced by 0.0012% and 2.21%, respectively. The mixing index and results presented in this work could be helpful to improve mixing efficiency in commercial BOF vessels.

Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network

A prediction model based on the principal component analysis (PCA) and back propagation (BP) neural network is proposed for BOF end-point phosphorus content, based on the characters of BOF metallurgical process and production data. PCA is used to reduce dimensionality of the factors influencing end-point phosphorus content, and eliminate the correlations among the factors, and then the obtained principal components are used as BP neural network input vectors. The combined PCA-BP neural network model is trained and tested by history data, and is further compared with multiple linear regression (MLR) model and BP neural network model. The results of the comparison show that the PCA-BP neural network model has the highest prediction accuracy and PCA improved the generalization capability. Finally, online prediction system of BOF end-point phosphorus content based on PCA and BP neural network is developed and applied in actual productive process. Field application results indicate that the hit rate of end-point phosphorus content is 96.67%, 93.33% and 86.67% respectively when prediction errors are within ±0.007%, ±0.005% and ±0.004%. The combined PCA-BP neural network model has achieved the effective prediction for end-point phosphorus content, and provided a good reference for end-point control and judgment of quick direct tapping of BOF.

Study on Slopping Prevention in the BOF Steelmaking Process

A new method of preventing slopping is proposed in this paper, by simply blowing gas at the top of the foam surface. The physical experiment results show that the foam height can be effectively dec ...

Interaction behaviour between top blown jet and molten steel during BOF steelmaking process

During oxygen steelmaking process, a systematical description of the interaction behaviour between top blown jet and molten steel is extremely important. In the present study, a cold model experiment and the corresponding numerical simulation were carried out to illustrate the highly complex interaction process inside an 80-t converter. First, the interaction process was presented in a numerical way. On this basis, the quantitative effect of pertinent operational parameters on the interaction process was investigated. To clearly describe the cavity appearance, the cavity shape index () was put forward, which is a dimensionless factor to demonstrate the cavity shape related to cavity depth and cavity width. In addition, a functional relationship correlating cavity shape index and blowing number was proposed. It can be concluded that higher blowing number (NB) results in deeper cavity depth and smaller cavity width. Moreover, the effect of cavity shape on the stirring characteristics and the mixing effect in the converter were investigated. The results show that lower and higher cavity shape index may lead to longer mixing time in smelting process. For 80-t converter, the penetration depth and impact width should be controlled approximately equal ( fluctuates around 1) to obtain better mixing effect.

Dependence of Temperature and Slag Composition on Dephosphorization at the First Deslagging in BOF Steelmaking Process

AbstractEffects of temperature and slag composition on dephosphorization in a 120 ton top-bottom combined blown converter steelmaking process by double slag method were studied. The slag properties were determined by scanning electron microscope- energy dispersive spectrometry (SEM-EDS), X-ray diffraction (XRD). The results show that the transition oxidation temperature between dephosphorization and decarbonization Tf is not the favorable temperature for the first deslagging. The optimum first deslagging temperature is confirmed to be approximately 1,673 K which is about 70 K higher than Tf. High melting temperatures phases (such as 3CaO·SiO2) in the slag with high basicity and MgO content are unfavorable to the dephosphorization. The optimum process condition for dephosphorization at the first deslagging in present work is approximately 1,673 K in temperature, 2.0 in slag basicity, 6 and 17 mass% in MgO and T.Fe content, 6 mass% ≤ MnO content.

BOF Process Control and Slopping Prediction Based on Multivariate Data Analysis

In a complex industrial batch processes such as the top-blown BOF steelmaking process, it is a complicated task to monitor and act on the progress of several important control parameters in order to avoid an undesired process event such as “slopping” and to secure a successful batch completion such as a sufficiently low steel phosphorous content. It would, therefore, be of much help to have an automated tool, which simultaneously can interpret a large number of process variables, with the function to warn of any imminent deviation from the normal batch evolution and to predict the batch end result. One way to compute, interpret, and visualize this “batch evolution” is to apply multivariate data analysis (MVDA). At SSAB Europe's steel plant in Luleå, new BOF process control devices are installed with the purpose to investigate the possibility for developing a dynamic system for slopping prediction. A main feature of this system is steelmaking vessel vibration measurements and audiometry to estimate foam height. This paper describes and discusses the usefulness of the MVDA approach for static and dynamic slopping prediction, as well as for end-of-blow phosphorous content prediction.

Numerical Simulation of the Interaction Between Supersonic Oxygen Jets and Molten Slag–Metal Bath in Steelmaking BOF Process

The impinging of multiple jets onto the molten bath in the BOF steelmaking process plays a crucial role in reactor performance but is not clearly understood. This paper presents a numerical study of the interaction between the multiple jets and slag–metal bath in a BOF by means of the three-phase volume of fluid model. The validity of the model is first examined by comparing the numerical results with experimental measurement of time-averaged cavity dimensions through a scaled-down water model. The calculated results are in reasonably good agreement with the experimental data. The mathematical model is then used to investigate the primary transport phenomena of the jets-bath interaction inside a 150-ton commercial BOF under steelmaking conditions. The numerical results show that the cavity profile and interface of slag/metal/gas remain unstable as a result of the propagation of surface waves, which, likely as a major factor, governs the generation of metal droplets and their initial spatiotemporal distribution. The total momentum transferred from the jets into the bath is consumed about a half to drive the movement of slag, rather than fully converted as the stirring power for the metal bath. Finally, the effects of operational conditions and fluid properties are quantified. It is shown that compared to viscosity and surface tension of the melts, operating pressure and lance height have a much more significant impact on the slag–metal interface behavior and cavity shape as well as the fluid dynamics in the molten bath.

The Control and Prediction of End‐Point Phosphorus Content during BOF Steelmaking Process

AbstractRemoval of phosphorus is a reaction, which plays an important role in combined converter steelmaking process, and the precise control of end‐point phosphorus content during BOF steelmaking process would greatly improve the quality of liquid steel. Therefore, the relation between dephosphorization ratio and temperature of liquid steel, FeO content of slag, slag basicity is clearly clarified through thermodynamic analysis of dephosphorization process in this paper. Besides, by means of combining the methods of multivariate regression analysis and multi‐level recursive completely, the multi‐level recursive regression model, which is used to complete the prediction of end‐point phosphorus content during BOF steelmaking process, is established based on large amount of production data. The verification of the model with the data taken from three steel plants indicates that the hit rate of the multi‐level recursive regression model is above 84% when predictive errors of the model are within ±0.005%, and it could provide a relatively good reference for real production.

Research on Using Limestone for Slagging during BOF Steelmaking Process

This paper introduced the primary research on the method of using limestone instead of lime for slagging during BOF steelmaking. The study showed that the heat is sufficient for limestone to completely replace lime on the condition that the ratio of hot metal to ferrous scrap is adjusted properly. According to thermodynamic calculations, the partial pressure of CO2at the hot metal surface at the early stage of converter steelmaking is close to 0 and nearly all the CO2can react with elements in hot metal, producing CO and reducing O2consumption. Slag with high oxidizability, high basicity and relatively low temperature can be formed quickly by using limestone, which is advantageous for dephosphorization. Industrial tests showed the endpoint Lp rises with the proportion of lime replaced by limestone increasing. CO2volume expands by 4000~30000 times when limestone is heated at extremely high speed in converter, resulting in the fragmentation of some parts of limestone and the increase of the contacting area of limestone and slag. The unreacted core model with surface exfoliated was introduced.

Prediction Model of End-point Manganese Content for BOF Steelmaking Process

Through analyzing the factors that influence end-point manganese content during BOF steelmaking process, multiple linear regression model for prediction of end-point manganese content was obtained on the basis of actual production data. Given the advantages of artificial neural network, it was used to predict end-point manganese content during BOF steelmaking process, and BP neural network model was established. By means of combining the characteristics of genetic algorithm and BP neural network completely, a combined GA-BP neural network model was established. The verification and comparison of the above three models show that the combined GA-BP neural network model has the highest prediction accuracy. The hit rate of the combined GA-BP neural network model is 90% and 84% respectively when predictive errors of the model are within ±0.03% and ±0.025%. Compared with two models aboved, the combined GA-BP neural network model could provide the most accurate prediction of end-point manganese content, and thus represents a good reference for real production.

Research on Top and Bottom Mixed Blowing CO2 in Converter Steelmaking Process

AbstractBased on the analysis of reaction mechanism between CO2 and molten pool elements at the steelmaking temperature, and on the calculation of materials and heat balance during converter steelmaking process with blowing CO2, a new technology which uses CO2‐O2 as top gas and CO2 as bottom gas in a converter was proposed and experimented in a 30 t converter. It is found that the new technology is feasible absolutely, the amounts of smoke dust and T‐Fe are reduced by 11.15% and 12.98% on average, the contents of nitrogen and phosphorus are decreased by 50% and 23.33% respectively, iron loss of slag is lowered by 3.10% and oxygen consumption is reduced remarkably. This research will provide a new blowing method for BOF steelmaking process, which can save steelmaking energy consumption and reduce smelting cost.

Predictive Model for BOF Steelmaking Using RBF Neural Network

Effective control of the endpoint steel temperature and contents of carbon, sulphur, etc. is one of the main tasks of BOF steelmaking process. This paper established a multivariable predictive model for BOF steelmaking using RBF neural network. The input data is pretreated and standardized. Receding horizon control method is used to increase the accuracy of the model. Simulation and experiment comparisons show that the model is validated and has high hit rate.