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Manganese is an important alloying element in iron and steel. Adding the appropriate amount of manganese can enhance the properties of steel. The manganese content directly influences steel quality in the converter steelmaking process. Too little manganese results in insufficient hardness and strength of steel products, whereas excessive manganese leads to increased embrittlement and production costs. Therefore, determining the appropriate amount of manganese is crucial for improving steel quality and reducing smelting costs. The quantity of manganese added during converter steelmaking primarily depends on the predicted final manganese content. However, this content is influenced by various factors, such as the oxidation reaction process and the addition of other alloying elements. These factors exhibit nonlinear effects on the manganese content, and the factors are highly interconnected, making accurate prediction of manganese content at the end point challenging. In response to the challenges posed by noise and strong coupling in predicting manganese content at the end point of converter steelmaking, a research framework was developed to address these issues and facilitate accurate predictions. Key influencing factors in the smelting process were identified through Pearson correlation coefficient analysis and mechanistic analysis. Subsequently, the relationship between these influencing factors and end-point manganese content was modeled using the long short-term memory network (LSTM). To mitigate the effects of high-frequency noise in nonlinear and nonstationary sequences, singular spectral analysis (SSA) was employed during the prediction process. This led to the development of a method known as SSA−LSTM for predicting end-point manganese content. The effects of different test sets and the number of neurons on the prediction results were investigated using converter steelmaking production data from Hebei Jingye Iron & Steel Co., Ltd. The proposed method achieved minimal prediction error when the test set comprised 10% of the data and the number of neurons was set to 85. At these parameters, the mean absolute error of the prediction method for end-point manganese was 1.19%, with a root-mean-square error of 1.48%. These results demonstrate that the proposed method effectively addresses issues related to large noise and nonlinear data. Moreover, compared with existing time series prediction methods, the proposed method, particularly after SSA treatment, showed reduced prediction errors. This validates the effectiveness of the method and provides a basis for accurate alloy addition in actual production processes.

The blowing process in converter steelmaking at the blowing stage mainly includes oxygen supply, slag discharge, and bottom blowing. The stability of the blowing process directly affects the quality of the molten steel at the end. The traditional static control method derived from the blowing process model based on material and heat balances ignores the strong coupling relationship between raw materials and process parameters, resulting in its low reliability. Furthermore, data types for raw materials and process parameters are scalar and time series, respectively. Therefore, to extract the features of the abovementioned complex mixed data, this paper proposes a process model extraction method for converter steelmaking based on an improved autoencoder (IAE). The IAE method is based on an autoencoder that includes fully connected modules, long short-term memory network, one-dimensional convolution, and batch K-means module. In the encoder, fully connected modules, long short-term memory networks, and one-dimensional convolutional modules extract nonlinear features of scalar data, long-term dependent features of time series, and local features of time series, respectively. Hence, the hidden vector is obtained by mapping the original high-dimensional data to a low-dimensional feature space using the encoder. To update the cluster center and calculate the clustering loss, the hidden vector is input to the batch K-Means module. Thus, the decoder reconstructs the hidden vector back to the original space to yield reconstructed data, which is then used to calculate the reconstruction loss. The IAE model is trained jointly with clustering and reconstruction losses. Finally, the cluster center of the original data and cluster category of each sample are obtained. The closer the sample is to the cluster center, the better the process parameters are controlled. Additionally, samples within the same cluster category are closer during the process operation. Therefore, the oxygen supply, slagging, and bottom-blowing processes of the closest samples are considered the process models for this type of sample. The effectiveness of the IAE model is evaluated using the endpoint quality index of real data from converter steelmaking. The average hit rate for the endpoint carbon mass fraction within the error range of ±0.02% is 95.06%, the average hit rate for the endpoint temperature within the error range of ±20 ℃ is 91.48%, and the average double hit rate within the error range of ±0.02% carbon mass fraction and ±20 ℃ temperature is 90.80%. Therefore, the results show that the process model extraction method improves the endpoint hit rate.

In this article, main process data, notably time–series data such as lance position patterns, are analyzed during converter steelmaking, and methodologies in data processing and transforming are proposed. In this study, utilizing both the transformed key time–series and primary static process data, the influence of various process parameters on the end‐point parameters of converter steelmaking is analyzed. Furthermore, it establishes predictive models for the end‐point content of carbon (C) and phosphorus (P), as well as the end‐point temperature. In the findings, it is indicated that the end‐point carbon content and temperature are primarily influenced by the oxygen flow pattern, lime addition pattern, and key smelting parameters. The end‐point phosphorus content is mainly affected by the oxygen flow pattern, limestone addition pattern, and dolomite addition pattern. Regarding the prediction of end‐point carbon and phosphorus content, and end‐point temperature, compared to seven sub‐models, the hybrid model demonstrates an average accuracy improvement of 37.88%, 25.03%, and 31.51%, respectively, and the end‐point hit rate improves by 18.77%, 19.59%, and 20.41%, respectively. [ABSTRACT FROM AUTHOR]

Accurate prediction of endpoint carbon at the dynamic control stage in the converter is crucial for achieving smelting targets. Currently, there are two main methods for converter endpoint prediction: the data-driven method and the mechanism-based method. Data-driven methods exhibit high accuracy but are vulnerable to data quality variations and lack interpretability. Mechanism-based methods provide great interpretability but face challenges in precisely identifying key parameters in the mechanism formula. Inspired by the design concept of physics-informed neural networks (PINNs), an integrated data-driven and mechanism-based method for endpoint carbon prediction in BOF (dmPINNs, data-driven and mechanism-based physics-informed neural networks) is proposed, which has four parts: feature extraction, mechanism-based calculation, data-driven prediction, and integrated prediction. We identify key parameters of the mechanism formula through the neural network to obtain the specified formula for each heat and supervise the training process of the neural network through the mechanism formula to ensure interpretability. Experimental results show that, within the ±0.012% error range, the hit rate of endpoint carbon content using dmPINNs improved by 5.23% compared with the traditional data-driven method and has greater robustness with the supervision of the mechanism formula. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

The metallurgical tasks at different stages of converter blowing are different. The process operation and physical properties of molten baths are also different. It is very important to determine the flow characteristics of molten baths at different blowing stages for optimizing process operation. In this paper, a three-dimensional, full-scale model of a 120 t top–bottom combined blowing converter is established. Based on the parameters of oxygen lance position, bath temperature, bottom blowing intensity, and bath physical properties at different blowing stages, the changes in bath flow field, turbulent kinetic energy, impact depth, impact area, and wall shear force with blowing process are studied. The results show that at the initial stage of blowing, the lance position is high, the impact depth of the molten bath is 0.23 m, the impact area is 5.06 m2, the dead zone area of the longitudinal section is 0.40 m2, and the high-speed zone area is 2.73 m2. As the blowing time increases, the lance position decreases, the impact depth of the molten bath increases, the impact area decreases, and the internal velocity of the molten bath increases. In the later stage of tuyere blowing, the lance level decreases to its lowest, the impact depth increases to 0.42 m, the impact area decreases to 2.83 m2, the dead zone area of longitudinal section decreases to 0.18 m2, and the high-speed area increases to 3.34 m2. The area with the highest wall shear stress is situated within the gas–slag–metal three-phase region, where the lining experiences the most significant erosion. The fluctuation in the slag–metal interface is small, and the wall shear force is 2.80 Pa at the initial stage of blowing. From the early to late stages of blowing, the lance position decreases, the fluctuation range of the slag–metal interface increases, and the erosion of the furnace lining increases. In the later stage of blowing, the maximum wall shear force is 3.81 Pa. [ABSTRACT FROM AUTHOR]

The identification of flame status in converter steelmaking is of great significance for steel smelting and molten steel quality. It can monitor the converter smelting process, strictly control the smooth progress of the production process, and effectively avoid personal injury. However, converter steelmaking is located in a production environment with high temperatures, high smoke, and strong physical and chemical reactions. The results of traditional manual fire observation are influenced by various factors such as experience and environmental conditions, showing unstable classification results, resulting in low accuracy in identifying the flame state of converter steelmaking. In order to improve the accuracy of flame state recognition in converter steelmaking, this article fully utilizes the flame image information provided by a certain steelmaking company during the converter steelmaking blowing process to classify the converter steelmaking flame images. This article proposes a novel dense convolutional neural network (EL-DenseNet) model for flame state recognition in converter steelmaking. Firstly, the efficient channel attention mechanism (ECAM) was introduced into DenseBlock to enhance the model's attention to different channels, locate relevant useful information, and suppress useless information, thereby improving the model's ability to capture key features in flame images; Then, in the model training and validation stage, LabelSmoothing was used to replace the original cross entropy loss function, smoothing the real labels and reducing overfitting of the model to the training data. Through experiments, it has been shown that the training accuracy of the EL-DenseNet model proposed in this article is 99 % , and the testing accuracy is 96.7 % . This improves the accuracy of flame state recognition in converter steelmaking, saves manpower and material resources, and reflects the effectiveness and superiority of the model. [ABSTRACT FROM AUTHOR]

The multiphase fluid flow and heat transfer behavior in the steelmaking converter are essential for efficient and stable smelting. Herein, a mathematical model of heat transfer for the hot spot zone of the converter is established. The temperature distribution of the molten bath is clarified, and the effects of operating parameters and the exothermic rate of hot spot zone on the heat transfer behavior of the molten bath are investigated. The results show that the temperature gradient between the hot spot zone and the liquid steel as well as the circulation of liquid steel will lead to the nonuniform distribution of temperature. Both the direction of flow field and the mixing characteristics of molten bath influence the heating rate of molten bath, whereas the direction of the flow field plays a dominant role. In addition, the oxygen lance height has a significant influence on the heat transfer behavior; as the lance height increases from 1.35 to 1.65 m, the heating rate increases by 68.32%. The increase of exothermic rate will remarkably increase the temperature gradient and the heating rate increases by 65.84% and 114.28% when the exothermic rate increases by 1.5 and 2 times, respectively. [ABSTRACT FROM AUTHOR]

Currently, converter steelmaking is the main method for steelmaking, the performance of bottom blowing and bath stirring would obviously affect the efficient and stable smelting of high‐quality clean steel. At present, the uniform bottom gas distribution mode is mostly applied in converter steelmaking, which would probably lead to unstable stirring performance and inadequate mixing at the later stage of furnace campaign. Therefore, a nonuniform bottom gas distribution mode is proposed herein, which intends to improve the mixing efficiency. The physical and numerical simulations are carried out to investigate the influence of gas flowrate, nozzle configurations, and bottom gas distribution mode on bath stirring and mixing. In the results, it is shown that the large and asymmetric circulation flow is formed due to the nonuniform gas distribution for each nozzle, which enhances stirring in the horizontal direction and ultimately improves mixing efficiency. Compared with the uniform gas distribution mode, when gas distribution ratio of nonuniform bottom blowing is 3:1, the mixing time is reduced by 29%, the average velocity of liquid steel is increased from 0.0342 to 0.0404 m s−1, the ratio of the dead zone is decreased by 26.23%, and the proportion of active flow region is added by 10.49%. [ABSTRACT FROM AUTHOR]

The performance and quality of steel products are significantly impacted by the alloying element control. The efficiency of alloy utilization in the steelmaking process was directly related to element yield. This study analyses the factors that influence the yield of elements in the steelmaking process using correlation analysis. A yield prediction model was developed using a t-distributed stochastic neighbor embedding (t-SNE) algorithm, a whale optimization algorithm (WOA), and a long short-term memory (LSTM) neural network. The t-SNE algorithm was used to reduce the dimensionality of the original data, while the WOA optimization algorithm was employed to optimize the hyperparameters of the LSTM neural network. The t-SNE-WOA-LSTM model accurately predicted the yield of Mn and Si elements with hit rates of 71.67%, 96.67%, and 99.17% and 57.50%, 89.17%, and 97.50%, respectively, falling within the error range of ±1%, ±2%, and ±3% for Mn and ±1%, ±3%, and ±5% for Si. The results demonstrate that the t-SNE-WOA-LSTM model outperforms the backpropagation (BP), LSTM, and WOA-LSTM models in terms of prediction accuracy. The model was applied to actual production in a Chinese plant. The actual performance of the industrial application is within a ±3% error range, with an accuracy of 100%. Furthermore, the elemental yield predicted by the model and then added the ferroalloys resulted in a reduction in the elemental content of the product by 0.017%. The model enables accurate prediction of alloying element yields and was effectively applied in industrial production. [ABSTRACT FROM AUTHOR]

The main task in the production of steel in the basic oxygen furnace (BOF) is dephosphorization Therefore, the prediction and control of the end-point phosphorus content of molten steel is of great significance. Four machine learning regression models (Lasso, Random Forest, Xgboost, and Neural Network) were established to predict the end-point phosphorus content of molten steel in the BOF based on raw and auxiliary material data, process parameters, and production quality data. The prediction effect of the four models was further compared, and their prediction results were interpreted based on the interpretability of the models and the permutation importance method. The results showed that compared with linear regression and neural network regression model, two types of ensemble tree model have higher prediction accuracy, better stability with small data sets, and lower data preprocessing requirements. The factors influencing the end-point phosphorus (P) content in BOF were ranked in order of importance as: Tapping temperature > Turning down times > Steel scrap quantity> Operation habits of different working groups > Amount of oxygen injection> Sulfur and phosphorus content of molten iron > Addition amount of lime, limestone, and lightly burnt dolomite in the slag > Slag-splashing amount.

Scrap is one of the main raw materials in converter steelmaking. Due to the wide variety of scrap and the significant differences in its prices, the proportioning of scrap presents a challenge in converter steelmaking. This paper explores the optimal proportioning method of scrap for converters using an optimization mathematical model. Initially, the melting characteristics of scrap after being added to the converter's molten iron were investigated using finite element numerical simulations. The study found that when the weight of the scrap is fixed, both the shape and type of the scrap significantly influence the melting time. Subsequently, employing mathematical optimization methods and considering comprehensive process parameters of the 210t converter smelting, including the composition and unit price of the scrap, as well as the requirements of the converter steelmaking process for scrap, an optimization model for the proportioning of scrap in converters was established. The model was solved using computer programming languages. Through this optimization model, it is possible to obtain the lowest cost per ton of molten steel in the converter smelting process by determining the optimal proportions of various types of scrap. [ABSTRACT FROM AUTHOR]

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%.

Accurate prediction of endpoint carbon at the dynamic control stage in the converter is crucial for achieving smelting targets. Currently, there are two main methods for converter endpoint prediction: the data-driven method and the mechanism-based method. Data-driven methods exhibit high accuracy but are vulnerable to data quality variations and lack interpretability. Mechanism-based methods provide great interpretability but face challenges in precisely identifying key parameters in the mechanism formula. Inspired by the design concept of physics-informed neural networks (PINNs), an integrated data-driven and mechanism-based method for endpoint carbon prediction in BOF (dmPINNs, data-driven and mechanism-based physics-informed neural networks) is proposed, which has four parts: feature extraction, mechanism-based calculation, data-driven prediction, and integrated prediction. We identify key parameters of the mechanism formula through the neural network to obtain the specified formula for each heat and supervise the training process of the neural network through the mechanism formula to ensure interpretability. Experimental results show that, within the ±0.012% error range, the hit rate of endpoint carbon content using dmPINNs improved by 5.23% compared with the traditional data-driven method and has greater robustness with the supervision of the mechanism formula.

The metallurgical tasks at different stages of converter blowing are different. The process operation and physical properties of molten baths are also different. It is very important to determine the flow characteristics of molten baths at different blowing stages for optimizing process operation. In this paper, a three-dimensional, full-scale model of a 120 t top–bottom combined blowing converter is established. Based on the parameters of oxygen lance position, bath temperature, bottom blowing intensity, and bath physical properties at different blowing stages, the changes in bath flow field, turbulent kinetic energy, impact depth, impact area, and wall shear force with blowing process are studied. The results show that at the initial stage of blowing, the lance position is high, the impact depth of the molten bath is 0.23 m, the impact area is 5.06 m2, the dead zone area of the longitudinal section is 0.40 m2, and the high-speed zone area is 2.73 m2. As the blowing time increases, the lance position decreases, the impact depth of the molten bath increases, the impact area decreases, and the internal velocity of the molten bath increases. In the later stage of tuyere blowing, the lance level decreases to its lowest, the impact depth increases to 0.42 m, the impact area decreases to 2.83 m2, the dead zone area of longitudinal section decreases to 0.18 m2, and the high-speed area increases to 3.34 m2. The area with the highest wall shear stress is situated within the gas–slag–metal three-phase region, where the lining experiences the most significant erosion. The fluctuation in the slag–metal interface is small, and the wall shear force is 2.80 Pa at the initial stage of blowing. From the early to late stages of blowing, the lance position decreases, the fluctuation range of the slag–metal interface increases, and the erosion of the furnace lining increases. In the later stage of blowing, the maximum wall shear force is 3.81 Pa.

Converter blowing limestone powder making slag steelmaking process has the advantages of low carbon and high efficiency, and can realize the resource utilization of CO2 in the metallurgical process, which is in line with the development direction of green metallurgy. Based on a thermogravimetric-differential thermal analyzer, the kinetic mechanism of decomposition of small limestone at steelmaking temperatures was investigated by a modified double extrapolation method. The results showed that with a higher rate of heating, limestone decomposition lagged, and decomposition temperature increased. Furthermore, the smaller the limestone particle size, the lower the activation energy of decomposition. Compared with N2, air, and O2, small limestone powder used for converter blowing could complete more rapid decomposition, and the time required for decomposition shortened by about 1/3, although the decomposition temperature increased in the CO2. The limestone decomposition rate increased and then decreased at low to high CO2 partial pressures. With a limiting link, the inhibition was more significant under high CO2 partial pressure, but the reaction can be fully completed by 1000 °C. The decomposition type modeled was stochastic nucleation and subsequent growth. As the partial pressure of CO2 increased from 25% to 100%, the number of reaction stages, n, increased. [ABSTRACT FROM AUTHOR]

When combined with the high-temperature reaction characteristics of carbon dioxide—that is, the weak oxidation, endothermic, or weak exothermic effect compared with oxygen and the bubble increment effect of reacting with carbon to produce twice the gas—the carbon dioxide smelting process was targeted according to the different tasks in different periods of converter smelting. Before temperature measurement, sampling, and carbon determination using the sublance system, carbon dioxide is blown into the converter from the upper oxygen gun and the bottom blowing element of the converter. By changing the ratio of carbon dioxide and oxygen in the oxygen gun, an industrial experimental study was conducted on the effect of the carbon dioxide ratio blown from the upper oxygen gun on the endpoint of the mass fraction of phosphorus and nitrogen and the carbon–oxygen concentration product. The results show that with the gradual increase of the carbon dioxide top blowing ratio from 4.84% to 9.68% in the early and middle stages of converter smelting, the mass fraction of phosphorus at the end of the converter first decreases and then basically remains unchanged. The mass fraction of nitrogen also decreases gradually, but the decreasing range becomes smaller, and the changing trend of carbon–oxygen concentration product is the same as that of TFe in slag, which first decreases and then increases. The optimal carbon dioxide top-blowing ratio varies with the index. Furthermore, the test also shows that even if all the blown carbon dioxide reacts to generate additional carbon monoxide gas and the nitrogen is removed by the additional carbon monoxide gas generated compared with the original process, the partial pressure of nitrogen in the additional gas is much higher than the partial pressure in equilibrium with the mass fraction of nitrogen in the liquid steel. This indicates that the improvement of the denitrification effect is due to the additional gas generated by the reaction of blowing in carbon dioxide; it improves the denitrification effect of the original gas, and the generation of additional gas improves the kinetic conditions of the molten pool and facilitates the mass transfer of nitrogen. The highest decreasing ratio of the endpoint mass fraction of phosphorus, nitrogen, carbon–oxygen concentration product, and TFe in the slag of the test converter was 20.4%, 34.3%, 12.92%, and 8.89%, respectively.

The blowing flow is a key factor in molten bath stirring to affects the steel-bath interface fluctuation and chemical reaction in the top-bottom-blowing converter. The Volume of Fluid (VOF) method is widely used to capture the gas-liquid interface. However, some limitations exist in dealing with the interface curvature and normal vectors of the complex deformed slag-bath interface. The Coupled Level-Set and Volume of Fluid (CLSVOF) method uses the VOF function to achieve mass conservation and capture interface smoothly by computing the curvature and normal vector using the Level-Set function to overcome the limitations in the VOF model. In the present work, a three-dimensional (3D) transient mathematical model coupled CLSVOF method has been developed to analyze the mixing process under different injection flow rates and bottom-blowing positions. The results show that when the bottom-blowing flow rate increases from 0.252 kg/s to 0.379 kg/s, the mixing time in the molten bath gradually decreases from 74 s to 66 s. When the bottom-blowing flow rate is 0.252 kg/s, it is recommended to distribute the outer bottom-blowing position on concentric circles with Dtuy,2/D2 = 0.33. [ABSTRACT FROM AUTHOR]

During the converter steelmaking process, gas-slag-metal three-phase emulsification is realized by injecting gas to complete metallurgical tasks such as slagging, dephosphorization, decarburization, and heating. As green and intelligent development of the steel industry progresses, converter steelmaking injection technology is also constantly innovating. In this review, the types and applications of top blowing injection elements, bottom blowing injection elements, and injection the medium are reviewed. Three different types of combined blowing processes are compared. At the same time, the advantages and disadvantages of different bottom blowing elements and injection media are respectively discussed. Finally, based on the research and application status of converter injection technology, the development direction of converter steelmaking injection technology is discussed. Accelerating the innovation of converter steelmaking injection technology, especially the improvement and breakthrough of high efficiency, reductions the environmental burden, and long life technology, will play an important role in promoting the transformation and improvement of the steel industry. [ABSTRACT FROM AUTHOR]

Utilization of CO2 in steelmaking process has attracted extensive attention in recent years, not only because of its social benefits, but also its better metallurgical performance. Mixing CO2 with O2 blown by converter oxygen lance is gradually being adopted by steelmaking plants, due to its potential of reducing consumption and improving steel quality. In the present research, effect of mixing CO2 on the jet characteristics of a four-nozzle oxygen lance was studied in detail by numerical simulation, taking the combustion behavior between supersonic jets and ambient atmosphere into consideration innovatively. The simulated results showed that the combustion flame is mainly distributed in the region between multiple jets, and the high temperature flame has a noticeable influence on the low-velocity region of the jet. Due to the dilution effect of CO2, mixing CO2 into the oxygen jets will reduce the maximum temperature of the flame and slow down the combustion rate. With the increase of CO2 mixing ratio, the high-temperature zone of combustion flame moves away from the lance tip significantly. At the same distance from the nozzle, although mixing CO2 can hardly increase the velocity magnitude of the jet, but it can achieve higher dynamic pressure, indicating stronger impacting power. Then the industrial experiment of top blowing O2-CO2 was carried out in a 120-ton converter. During the blowing time of 120~300 s, the mixing ratio of CO2 was 15 vol.% for better dephosphorization, and no CO2 was mixed in the rest time of blowing. Due to the stronger stirring and better thermodynamics, the average [P] content in the final molten steel was decreased from 0.0155 wt.% to 0.0129 wt.%, achieving higher dephosphorization efficiency. [ABSTRACT FROM AUTHOR]

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The blowing flow is a key factor in molten bath stirring to affects the steel-bath interface fluctuation and chemical reaction in the top-bottom-blowing converter. The Volume of Fluid (VOF) method is widely used to capture the gas-liquid interface. However, some limitations exist in dealing with the interface curvature and normal vectors of the complex deformed slag-bath interface. The Coupled Level-Set and Volume of Fluid (CLSVOF) method uses the VOF function to achieve mass conservation and capture interface smoothly by computing the curvature and normal vector using the Level-Set function to overcome the limitations in the VOF model. In the present work, a three-dimensional (3D) transient mathematical model coupled CLSVOF method has been developed to analyze the mixing process under different injection flow rates and bottom-blowing positions. The results show that when the bottom-blowing flow rate increases from 0.252 kg/s to 0.379 kg/s, the mixing time in the molten bath gradually decreases from 74 s to 66 s. When the bottom-blowing flow rate is 0.252 kg/s, it is recommended to distribute the outer bottom-blowing position on concentric circles with Dtuy,2/D2 = 0.33.

A 3D gas–slag–metal model for a 120t converter with nozzle‐twisted lance is established, and the gas jet characteristics, slag–metal interface fluctuations, wall erosion, and changes in the molten pool flow field of the nozzle‐twisted lance of the blowing process are studied. The results reveal that, with the increase in the swirl angle, the length of the jet high‐velocity section, maximum velocity, and dynamic pressure decrease, radial distance increases, and wall shear stress first decreases and then increases. With an increase in the blowing time, the fluctuating intensity of the slag surface and the molten steel surface changes significantly, fluctuating slag surface stabilizes after 1.7 s (fluctuating intensity is 1.08–1.12), and fluctuating steel surface stabilizes after 1.3 s (fluctuating intensity is 1.23–1.33). The turbulent kinetic energy of the gas–slag–metal three‐phase interaction area increases and the wall shear stress gradually increases. [ABSTRACT FROM AUTHOR]

Highly accurate prediction of converter molten steel end temperature is important foundation of realizing intelligent smelting in converter procedure. Single model prediction of converter end temperature has problems such as weak generalization ability and difficulty in increasing accuracy. To tackle these problems, this review proposes a modeling method based on Bayesian formula that dynamically integrate multiple models with different features. First, various data patterns and features are explored using different modeling methods and respective prediction model for converter molten steel end temperature is constructed. Then, the value range of the objective is discretized and the confidence levels of the prediction results from different models are computed based on Bayesian formula. Last, the prediction values of different models are weighted according to their confidence level and a comprehensive prediction result is obtained. Testing and comparison are carried out on the integration model and the three single models (support vector regression, random forest, and BP neural network) using actual production data. Results show that the integration model based on Bayesian formula can further increase the prediction accuracy effectively on top of single model prediction accuracy. [ABSTRACT FROM AUTHOR]

Oxygen lance is the key to realize the emulsification of gas–slag–metal in the converter steelmaking process, which determines successful completion of metallurgical tasks. Herein, a 3D full‐size gas–slag–metal coupling model of the nozzle‐twisted lance in a 120t converter is established. The behavioral characteristics of gas–slag–metal of nozzle‐twisted lances (swirl angle 0°/5°/10°/15°) are studied. With the increase in the swirl angle, the axial velocity is reduced; the radial and tangential velocities are increased, and the coverage area of the slag layer, the splashing amount, and its height are reduced. In the longitudinal section, when the swirl angle increases from 0° to 15°, the areas of high‐velocity and dead zones are reduced by 15% and 36%, respectively, and the area of low‐velocity zone is increased by 17%. At different depths of molten bath, with the increase in the swirl angle, the area of high‐velocity zone first increases and then decreases; the area of low‐velocity zone increases, and the area of dead zone decreases. Finally, the industrial experiment with the nozzle‐twisted lance is carried out in a 120t converter. The phosphorus content, the carbon–oxygen product, and the total iron content in slag are all reduced. [ABSTRACT FROM AUTHOR]

Powder injection metallurgy can effectively improve the reaction efficiency, ameliorate the properties, and reduce the production cost of steel. This paper reviews recent innovations in submerged gas-powder injection technologies in the converter steelmaking, electric arc furnace (EAF) steelmaking, and ladle refining processes. Using bottom-blowing O 2 -lime powder converter steelmaking, the average weight of slag per ton of steel decreased by 11.2 kg and the average content of smelting end-point [P] decreased by 0.005% compared with conventional converter steelmaking because of the improved reaction efficiency. In side-blowing O 2 + CO 2 -lime/carbon powder EAF steelmaking, O 2 + CO 2 ‑carbon powder injection reduced the electrical energy consumption by 60 kWh/t, and the phosphorus content and losses of FeO and T.Fe were reduced by 0.0054%, 8.3%, and 5.7%, respectively. In the bottom-blowing Ar-lime powder refining ladle process, the desulfurization rates reached 69.33% and 68.97%–77.42% in laboratory experiments and industrial tests, respectively, by injecting lime powder. Based on the characteristics of metallurgy processes, by combining the endothermic and stirring-enhancing features of CO 2 gas, a new recycling process for CO 2 in steel enterprises is proposed and analyzed to extend the application of submerged gas-powder injection metallurgy. [Display omitted] [ABSTRACT FROM AUTHOR]

During the converter steelmaking process, gas-slag-metal three-phase emulsification is realized by injecting gas to complete metallurgical tasks such as slagging, dephosphorization, decarburization, and heating. As green and intelligent development of the steel industry progresses, converter steelmaking injection technology is also constantly innovating. In this review, the types and applications of top blowing injection elements, bottom blowing injection elements, and injection the medium are reviewed. Three different types of combined blowing processes are compared. At the same time, the advantages and disadvantages of different bottom blowing elements and injection media are respectively discussed. Finally, based on the research and application status of converter injection technology, the development direction of converter steelmaking injection technology is discussed. Accelerating the innovation of converter steelmaking injection technology, especially the improvement and breakthrough of high efficiency, reductions the environmental burden, and long life technology, will play an important role in promoting the transformation and improvement of the steel industry.

The oxygen supply process is a vital operation in converter steelmaking, which is to inject oxygen and kinetic energy into the liquid bath with an oxygen lance. Herein, a single-flow post-combustion oxygen lance (SF-PCL) with four primary holes and six secondary holes was designed to increase the oxygen supply intensity of a 60-ton converter in a steel plant. The attenuation and coalescence behaviors of supersonic jets for the conventional four-hole oxygen lance (COL) and the newly designed SF-PCL were studied by numerical simulation. And then, their metallurgical effects were verified by industrial practice. The simulation results indicated that the SF-PCL effectively suppresses the attenuation and coalescence of supersonic jets as well as simultaneously increases both the impact area and penetration depth. In industrial practice, compared with the COL, the newly designed SF-PCL increases the oxygen supply intensity from the original 4.89 m3 (t·min)−1 to 5.43 m3 (t·min)−1, rises the dephosphorization rate by 1.4%, shortens the blowing time by 34 s, reduces the ferrous charges consumption by 7 kg t−1 and decreases the total iron content in final slag by 0.85%. [ABSTRACT FROM AUTHOR]

An important part of the iron-and-steel production process, converter steelmaking is the most widely used and efficient method of steelmaking in the world. Under the requirements of"China Manufacturing 2025, "ensuring intelligent steelmaking, improving smelting production efficiency, and reducing production cost are major concerns that should be addressed urgently in converter steelmaking. Owing to the complex thermodynamic and dynamic reactions in the converter smelting process, sublance control and traditional flue-gas analysis models have limitations that result in low prediction accuracy of the end-point carbon in converter smelting, thereby causing the main technical bottleneck in intelligent steelmaking. Therefore, a functional digital twin model of the steelmaking process based on flue-gas analysis was proposed. First, continuously monitored real-time data were obtained by flue gas analysis to observe the carbon and oxygen reaction state of molten steel in the converter. Then, according to various stages of the converter reaction, the functional data analysis method was used to establish the functional prediction models for the early and late stages of blowing. The greatest advantage of the method is that the model can automatically adjust the coefficient function according to the measured off-gas data by using a continuous functional curve to fit the complex dynamic reaction process. Therefore, the proposed model can accurately predict not only the normal smelting process but also the decarburization and carbon drawing process for the secondary scraping slag. An industrial experiment on a 260 t converter was conducted to prove that the functional digital twin model of the converter smelting process has good self-learning and self-adaptive ability and is robust to the abnormal smelting state. Furthermore, the model can predict the carbon content of the converter dynamically in the entire process and the end-point carbon content can reach 95% at ± 0. 02%. Using the predicted value of the carbon content to control the final blowing point through the functional digital twin model can effectively prevent overblowing or underblowing. More importantly, on the premise of guaranteeing the stability of raw material composition, temperature, weight, and other parameters, the model is expected to cancel the blown-off sampling step based on sublance. This feature can reduce the production cost while improving the product quality and production efficiency for a wide range of industrial applications.

Utilization of CO2 in steelmaking process has attracted extensive attention in recent years, not only because of its social benefits, but also its better metallurgical performance. Mixing CO2 with O2 blown by converter oxygen lance is gradually being adopted by steelmaking plants, due to its potential of reducing consumption and improving steel quality. In the present research, effect of mixing CO2 on the jet characteristics of a four-nozzle oxygen lance was studied in detail by numerical simulation, taking the combustion behavior between supersonic jets and ambient atmosphere into consideration innovatively. The simulated results showed that the combustion flame is mainly distributed in the region between multiple jets, and the high temperature flame has a noticeable influence on the low-velocity region of the jet. Due to the dilution effect of CO2, mixing CO2 into the oxygen jets will reduce the maximum temperature of the flame and slow down the combustion rate. With the increase of CO2 mixing ratio, the high-temperature zone of combustion flame moves away from the lance tip significantly. At the same distance from the nozzle, although mixing CO2 can hardly increase the velocity magnitude of the jet, but it can achieve higher dynamic pressure, indicating stronger impacting power. Then the industrial experiment of top blowing O2-CO2 was carried out in a 120-ton converter. During the blowing time of 120~300 s, the mixing ratio of CO2 was 15 vol.% for better dephosphorization, and no CO2 was mixed in the rest time of blowing. Due to the stronger stirring and better thermodynamics, the average [P] content in the final molten steel was decreased from 0.0155 wt.% to 0.0129 wt.%, achieving higher dephosphorization efficiency.

At low basicity and low temperature, the dephosphorization behavior and phosphorus distribution ratio (LP) between slag and molten steel in the double slag and remaining slag process were studied with a 180 t basic oxygen furnace industrial experiment. The dephosphorization slags with different basicities were quantitatively analyzed. At the lower basicity range of 0.9–2.59, both LP and dephosphorization ratio were increased as the basicity of dephosphorization slag increased. Dephosphorization slag consisted of dark gray P-rich, light gray liquid slag, and white Fe-rich phases. With increasing basicity, not only did the morphologies of different phases in the dephosphorization slag change greatly, but the area fractions and P2O5 content of the P-rich phase also increased. The transfer route of P during dephosphorization can be deduced as hot metal → liquid slag phase + Fe-rich phase → P-rich phase. [ABSTRACT FROM AUTHOR]

Converter steelmaking is the primary steelmaking method because of its fast production rhythm and ability to handle molten iron. Despite its benefits, there are some limitations with this method that cannot be ignored. These limitations include excessive loss of iron by evaporation caused by a high-temperature fire spot zone and uneven melting of lump lime, which leads to an increase in steel consumption and a decrease in steelmaking efficiency. In view of these shortcomings, this study tested the approach of mixing a top-blown O2 lance with CO2. The mixture gas (80%O2 + 20%CO2) was used as a carrier gas to blow CaO powder from the top-blown oxygen lance into a molten bath. Based on the standard κ−ε turbulence model and the discrete particle model, the effects of flow rates of carrier gas on the distribution of gas and CaO powder jets were analyzed by numerical simulation. The simulation results show that the velocity of Cao powder increases with the increase in gas flow rate. The distribution area of Cao powder in radial section is more concentrated, and the deviation distance between Cao powder distribution and pore axis is shorter. At the same time, because of the inertia force of Cao powder, the attenuation speed of carrier gas jet in subsonic region slows down. The interaction between mixed carrier gas and powder is beneficial for the mixture gas and powder to participate in metallurgical reaction. [ABSTRACT FROM AUTHOR]

The varying characteristics of steel material consumption, oxygen consumption, and converter gas volume were studied when different ferrous materials and different proportions of limestone were used in 60 t converter. The results show that when liquid steel is used as raw material and mixture of slag-steel and lump ore as coolants, the steel material consumption per ton steel is the min-imum (only 1072.07 kg), and when liquid steel and scrap steel are used as raw materials and slag-iron by magnetic separation as coolant, the steel material consumption per ton steel reaches the maximum (1092.91 kg). Meanwhile, with the increase of the amount of limestone, the steel material consumption is increased, the oxygen consumption is slightly decreased, and the gas volume is im-proved. The results will provide a new method for optimizing the material structure and reducing the production cost in steelmaking process.

On the basis of analysis of limestone decomposing and slagging mechanism, the changing of surplus heat was studied using limestone instead of lime as the slagging agent during steelmaking process, which indicates that hot metal added ratio can reach approximate 86.1% if using lime entirely. If limestone is applied in the steelmaking process to replace all limes for slagging, the hot metal ratio can reach up to 97.0%. On this basis, the metallurgical results of using limestone instead of lime for slagging in 60 t con-verter were studied. The results show that the content of TFe in slag is 21.87% at 4 min and the basicity is 1.22 using limestone for slagging. With the blowing time increasing, the content of TFe reduces while the basicity increases to more than 3.0. Besides, the de-phosphorization process is much more stable and the dephosphorization ratio increases by 2.6%, and the average basicity of slag in the end is 3.52, meeting the demand of dephosphorization. Since the slag amount substantially reduces, which results in the decline of the consumption of steel material. The blowing time is prolonged and the temperature of liquid steel is kept constant. Furthermore, the re-sults also provide a new method and concept for optimizing the charging material structure and reducing the production cost.

Compared with the traditional limestone calcination, this paper focuses on the conditions and temperature in hot metal where limestone calcines in converter steelmaking process. Considering heat transfer, mass transfer driven by concentration gradient and mass transfer (CO2 gas flow) driven by pressure gradient, macro kinetic models are established to describe limestone decomposition rate respectively. The method of rotating cylinder sample is used in the experiment of limestone decomposition in hot metal. The image processing software Image-Pro Plus is used to analyze the cross section of limestone calcined to get the unreacted core radius of cross section of cylindrical limestone. The results show that the decomposition mechanism of limestone follows the unreacted shrinking core model, the heat conduction of lime layer is the controlling step, the heat conduction and CO2 migration through the product layer determine the rate of limestone decomposition, and the corresponding kinetic equations are established. [ABSTRACT FROM AUTHOR]

The improper disposal of large amounts of solid waste (SW) has led to serious ecological and environmental problems, especially heavy metal (HM) pollution. Converter steelmaking has the potential to co-process SW, but the distribution of heavy metals (HMs) during converter steelmaking is unclear. In this study, the effects of smelting temperature and slag alkalinity on the distribution of typical HMs in the SW of steel samples, steel slag, and the gas phase were investigated in a specially-made induction furnace. The results showed that upon increasing the smelting temperature, As (As 2 S 3) was mainly distributed in the steel sample, and the HM-containing compounds Cr 2 O 3 , CrCl 3 , ZnCl 2 , ZnS, ZnO, PbCl 2 , PbS, and PbO were mainly distributed in the gas phase. Upon increasing the alkalinity within a certain range, the distribution of HMs in steel samples and steel slag increased gradually, while their distribution in the gas phase decreased. Thermodynamic calculations, Eh-pH diagrams, XRD patterns, and XPS spectra indicated that impurity elements in the hot metal and the CaO content affected the chemical reactions by which HM-containing compounds in the steel sample formed elemental HMs and those in steel slag existed as oxides; therefore, it is necessary to choose a suitable temperature and alkalinity for slag when disposing of different types of SW. [Display omitted] • Typical heavy metal distribution characteristics in converter are verified by induction furnace simulation and theoretical calculation. • Typical heavy metal distribution mechanism in three phases affected by temperature and alkalinity is revealed. • Heavy metal-containing chloride, sulfide and oxide are existed in steel slag as oxide. • Heavy metal-containing compounds are reduced to heavy metal element into molten steel. [ABSTRACT FROM AUTHOR]

Unintentionally produced persistent organic pollutants (UP-POPs) including polychlorinated dibenzo- p-dioxins, and dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs), and polychlorinated naphthalenes (PCNs) were characterized and quantified in stack gas and fly ash from the second ventilation systems in five typical converters in five different steelmaking plants. The 2378-substituted PCDD/Fs (2378-PCDD/Fs) and dioxin-like PCB (dl-PCBs) toxic equivalents (TEQs) were 1.84-10.3 pg WHO-TEQ Nm in the stack gas and 5.59-87.6 pg WHO-TEQ g in the fly ash, and the PCN TEQs were 0.06-0.56 pg TEQ Nm in the stack gas and 0.03-0.08 pg TEQ g in the fly ash. The concentrations of UP-POPs in the present study were generally lower than those in other metallurgical processes, such as electric arc furnaces, iron ore sintering, and secondary metallurgical processes. Adding scrap metal might increase UP-POP emissions, indicating that raw material composition was a key influence on emissions. HxCDF, HpCDF, OCDF, HpCDD, and OCDD were the dominant PCDD/Fs in the stack gas and fly ash. TeCB and PeCB were dominant in the stack gas, but HxCB provided more to the total PCB concentrations in the fly ash. The lower chlorinated PCNs were dominant in all of the samples. The 2378-PCDD/F, dl-PCB, and PCN emission factors in stack gases from the steelmaking converter processes (per ton of steel produced) were 1.88-2.89, 0.14-0.76 and 229-759 μg t, respectively. [ABSTRACT FROM AUTHOR]

In order to validate the BOF process model, the heats in 170- and 330-ton converters were simulated. The different types of heats in a 170-ton converter were considered. The model validation includes the comparison of the actual and simulated end blow parameters. The behaviors of the metal and slag phase components, temperature and charge material melting, and dissolution were shown and analyzed. The end blow chemical composition of steel and the temperature were simulated quite accurately. Small deviations between actual and simulated results were found for the end blow chemical composition of slag. Using the simulation results, the oxidation-reduction behavior of phosphorus and manganese during the converter steelmaking process was explained. [ABSTRACT FROM AUTHOR]

A new model for the simulation of the converter steelmaking process was developed after the detailed study of existing thermodynamic and kinetic models and approaches. This model consists of the reaction model and models for charge materials melting and dissolution. The reaction model is based on the coupled reaction model, which includes the combination of both thermodynamics and kinetics of the involved phases. The models of charge material melting and dissolution include the definition of the driving force for the melting and dissolution, and the mass transfer coefficients in the metal and slag phases. [ABSTRACT FROM AUTHOR]