Fluid Flow Characteristic of EAF Molten Steel with Different Bottom-Blowing Gas Flow Rate Distributions

Yang, Zeshi; Yang, Lingzhi; Cheng, Ting; Chen, Feng; Zheng, Fuqiang; Wang, Shuai; Guo, Yufeng

doi:10.2355/isijinternational.isijint-2019-794

Cited by 11 publications

(6 citation statements)

References 13 publications

(13 reference statements)

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“…Due to the large range of liquid velocities during flows, the turbulence formation process was included in the modeling. For this purpose, the k–ε model turbulence kinetic energy k and turbulence dissipation ε were the target parameters, as expressed by the following equations [ 21 ]:

where ρ is the gas density, σ κ and σ ε are the Prandtl turbulence numbers, k and ε are constants of 1.0 and 1.3, and G k and G b are the kinetic energy of turbulence generated by the average velocity and buoyancy, respectively.…”

Section: Methodsmentioning

confidence: 99%

Physical and Numerical Modeling of the Impeller Construction Impact on the Aluminum Degassing Process

et al. 2022

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This paper presents the results of tests on the suitability of designed heads (impellers) for aluminum refining. The research was carried out on a physical model of the URO-200, followed by numerical simulations in the FLOW 3D program. Four design variants of impellers were used in the study. The degree of dispersion of the gas phase in the model liquid was used as a criterion for evaluating the performance of each solution using different process parameters, i.e., gas flow rate and impeller speed. Afterward, numerical simulations in Flow 3D software were conducted for the best solution. These simulations confirmed the results obtained with the water model and verified them.

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Section: Methodsmentioning

confidence: 99%

Physical and Numerical Modeling of the Impeller Construction Impact on the Aluminum Degassing Process

et al. 2022

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“…Yang et al [67,68] 0. Yang et al [69] 1.09 (133-133-133) 2.07 (80-133-186) higher rates of mass transfer by increasing the stirring energy and their results summarized by the two following equations…”

Section: Melting Rate and Mass Transfer Due To Bgimentioning

confidence: 98%

“…[61] Velocities of liquid steel due to BGI are summarized in Table 9. Basically two groups have reported results: Guo et al [15] and Zhu et al [62,63,[65][66][67][68][69] in Canada and China, respectively. Guo et al reported an average velocity of 3.8 cm s À1 using a total flow rate of 60 Nl min À1 .…”

Section: Pitch Circle 3 120°5mentioning

confidence: 99%

Electric Arc Furnace Stirring: A Review

Conejo

Yan

2023

steel research int.

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“…In the middle and later stages of actual operation, the oxygen injection will be reduced. The oxygen injected at different stages of EAF smelting is closely related to the end point carbon content in the bath [36]. The carbon content in the bath can be expressed as Equations ( 9) and ( 10)…”

Section: Influence Factor Analysismentioning

confidence: 99%

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Yang

Guo

et al. 2022

Coatings

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In this manuscript, we consider the accuracy of end-point carbon content prediction affected by oxygen injection in multiple stages of electric arc furnace (EAF) melting process. Such a prediction would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. Principal component analysis (PCA) was used to normalize the 13 input variables affecting the endpoint carbon content. log-sigmoid and tan-sigmoid functions were used to verify the same sample, and it was found that the Mean squared error(MSE) of the model under logsig + logsig function was smaller, indicating that the model was more stable. At the same time, different hidden layer nodes were tried, and finally the structure of the model was determined as 13 × 10 × 8 × 1, and the activation function was logsig + logsig. Using historical smelting data to train and test the neural network model, the correlation coefficient (R) of the verified model is 0.7632, the model prediction is in the range of ±0.03%, the hit rate of the model is 64.5%, and the hit rate of the model is 42% in the range of ±0.02%. Combining the verification basis of the model with the metallurgical reaction principle of the EAF steelmaking process, a pretreatment method of phased input of total oxygen is proposed. The oxygen is divided into three stages, which are the oxygen consumption volume of 0–5 min, 5–30 min and more than 30 min and other variables are kept unchanged. The same neural network is used to train and verify the same data. After verification, the R of the oxygen staged model is 0.8274. The model prediction is in the range of ±0.03%, the hit rate of the model is 78.5%, and the hit rate of the model is 58% in the range of ±0.02%. Finally, an on-line carbon content prediction system based on artificial neural network model is developed and applied to actual production. Running results illustrated that the hit rate of end-point carbon content is 96.67%, 93.33% and 86.67%, respectively when the prediction errors are within ±0.05%, ±0.03% and ±0.01%, the improved neural network model can effectively predict the end-point carbon content, which provides a good basis for the carbon content at the end point of EAF steelmaking process.

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Fluid Flow Characteristic of EAF Molten Steel with Different Bottom-Blowing Gas Flow Rate Distributions

Cited by 11 publications

References 13 publications

Physical and Numerical Modeling of the Impeller Construction Impact on the Aluminum Degassing Process

Physical and Numerical Modeling of the Impeller Construction Impact on the Aluminum Degassing Process

Electric Arc Furnace Stirring: A Review

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

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