Energy balance between multiple supersonic jets and molten bath in BOF steelmaking

Liu, Guang Qiang; Li, Jun nan; Xu, Dong; Zheng, Bing

doi:10.1080/03019233.2022.2052413

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…∆T BOF = ∆T melt + ∆T add − ∆T Ar − ∆T lining (1) where ∆T BOF is the value of the change in steel temperature during the steel discharge phase of the converter ( The main task of this stage is to remove the carbon, silicon, manganese, phosp sulfur, and oxygen contained in the iron to a certain extent under high-tempe oxidizing conditions and to raise the temperature to that of steel; this essentially in a process of oxidizing the elements in the molten pool by releasing a large amount o…”

Section: Mechanism Analysis Of Bofmentioning

confidence: 99%

“…Basic oxygen furnace (BOF) steelmaking is of great importance; it is the main method of the steelmaking process in the thermal cycle of steel. In this process, hot metal and scrap are rapidly refined into liquid steel with the required carbon content by means of a supersonic oxygen injection from the top lance and through stirring from the bottom air outlet [1]. At the same time, melting agents such as lime and dolomite are added to the vessel for processing, and impurities are removed [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

Fang

Zhang

et al. 2023

Processes

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With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tested using large amounts of field data to predict the end-point steel temperature of basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The prediction model was applied to predict the degree of influence of different operating parameters on steel temperature. A comparison of the prediction results with the production data shows that the prediction system has good prediction accuracy, with a hit rate of over 90% for steel temperature deviations within 20 °C. Compared with the traditional steel temperature management model, the prediction system in this paper has higher management efficiency and a faster response time and is more practical and generalizable in the thermal management of steel.

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Section: Mechanism Analysis Of Bofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

Fang

Zhang

et al. 2023

Processes

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“…Zhou and Ersson et al [7] found that the energy transfer for the bottom blowing is much more efficient than that of the top blowing operations. Liu et al [8] found Metals 2024, 14, 56 2 of 21 that more than 75% of the total energy is wasted in the process of jet attenuation, and the energy used for stirring the bath is less than 1.5% of the total energy. Li et al [9] presented a numerical study of transferring the characteristics of momentum/energy during oxygen jetting into the slag-metal molten bath.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Fluid Flow in the Top–Bottom Combined Blowing Converter

Liu,

Cheng,

Peng

2024

Metals

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The flow in the top–bottom combined blowing converter has an important impact on processes such as slagging, dephosphorization, decarburization, the heating of molten steel, and the homogenization of steel composition and temperature. A 1/6 reduced scale model based on a 210 t converter was used for the mathematical simulation. The validity of the model was verified by comparing the variation in cavity sizes caused by changes in the lance height and flow rate of the physical model with the numerical results. It was found that, in the bottom blowing converter, the area with higher velocity was distributed in the inverted conical plume. In top blowing, the area with higher velocity was distributed on the surface of a molten bath. The area of higher molten bath velocity in the combined blowing converter further increased. Compared with the top blowing converter, the increased percentage of the area-averaged velocity in the combined blowing converter first increased and then decreased as the distance from the bottom increased. When the top blowing flow rate changed, the combined blowing made the velocity change at the top of a molten bath smaller. The decrease in lance height significantly reduced the ratio of “inactive zone”, while the effect of the change in the flow rate was slight.

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“…They found that individual particles undergo spreading, retracting, and stabilizing processes when they impact the wall. Liu et al [8] analyzed the energy transfer efficiency of the top-blown gas to the molten pool through numerical simulations. They calculated the energy consumption of the splashing process and established the energy conservation equation in the blowing process.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of the Slag Splashing Process in A 120 Ton Top-Blown Converter

Yang

Sun

et al. 2023

Metals

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Slag splashing operations at the end of the converter blow process can improve the furnace liner life and the converter operation rate. However, the effect of factors on slag splashing at actual dimensions is yet to be fully understood. A three-dimensional transient mathematical model coupled with the response surface analysis has been established to investigate the effects of the amount of remaining slag, oxygen lance height, and top-blowing nitrogen flowrate on the slag splashing process in a 120 ton top-blown converter. The predicted splashing density is validated by the experimental data. The numerical simulation results show that the splashing density and the splashing area ratio increase with the amount of remaining slag, which has the greatest effect on slag splashing. As the oxygen lance height decreases, the splashing density and the splashing area ratio first increase and then decrease. The top-blowing nitrogen flowrate is positively correlated with the splashing area ratio. When the oxygen lance height is high, the impact of the top-blowing nitrogen flowrate on the splashing density is not significant. The splashing density increases with increasing top-blowing nitrogen flowrate as the oxygen lance height is low.

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Energy balance between multiple supersonic jets and molten bath in BOF steelmaking

Cited by 5 publications

References 27 publications

Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

Simulation of Fluid Flow in the Top–Bottom Combined Blowing Converter

Numerical Simulation of the Slag Splashing Process in A 120 Ton Top-Blown Converter

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