Influence of Desulfurization with Fe2O3 on the Reduction of Nickel Converter Slag

Wei, Guangsheng; Wang, Yun; Zhu, Rupeng; Yang, Lingzhi

doi:10.3390/ma13102423

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…This is due to the direct contact between the lower slag and the matte layer, and some components in the slag react with matte to form SO2 bubbles, while matte particles attached to the bubbles enter the slag layer with the rise of bubbles. It would increase the copper content of the lower slag [45]. In addition, some matte particles would adhere to the bottom of the slag layer, which also leads to a significant increase in the copper content of the lower slag.…”

Section: Sedimentation Stagementioning

confidence: 99%

Matte separated behavior from slag during the cleaning process by using waste cooking oil as carbon neutral reductant

Wang

Wei

Zhou

et al. 2021

J min metall B Metall

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As a waste resource, waste cooking oil (WCO) has not been widely used. Based on the characteristics of WCO cracking, this study proposed to replace fossil-based reductant with WCO for copper slag cleaning, to solve the problem of carbon neutralization in this process. Copper slag cleaning experiments were carried out in a lab-scale electric furnace. The matte separated behavior from slag and the distribution of matte in slag were studied. The results showed that the Fe3O4 content decreases from 12.9 to 3.5 wt.% by injecting 2.2 mL of WCO into 300 g copper slag at 1250?C. The distribution of copper content in slag is gradient along the vertical direction. In the reduction stage, the excessive Fe3O4 is reduced and the fluidity of slag is improved. When the precipitation time above 60 minutes, the copper content in the middle and upper slag is reduced to 0.57 wt.%, which realizes the copper slag cleaning by using WCO.

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Section: Sedimentation Stagementioning

confidence: 99%

Matte separated behavior from slag during the cleaning process by using waste cooking oil as carbon neutral reductant

Wang

Wei

Zhou

et al. 2021

J min metall B Metall

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show abstract

“…Most studies [4][5][6][7][8] are from the perspective of metallurgical thermodynamics, studying the variation of Gibbs free energy of C, Si, Mn, P and other elements in molten steel, revealing the theory of selective oxidation in the steelmaking process, in order to control decarbonization and dephosphorization. The Static mechanism models cannot provide data feedback effectively and require long-term analysis, and more importantly, it can 't adapt to the changing conditions of the steel mill [9][10][11].…”

Section: Introductionmentioning

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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Effect of injection of different gases on removal of arsenic in form of dust from molten copper smelting slag prior to recovery process

WANG

Zhu

Dong

et al. 2023

Transactions of Nonferrous Metals Society of China

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Influence of Desulfurization with Fe2O3 on the Reduction of Nickel Converter Slag

Cited by 3 publications

References 43 publications

Matte separated behavior from slag during the cleaning process by using waste cooking oil as carbon neutral reductant

Matte separated behavior from slag during the cleaning process by using waste cooking oil as carbon neutral reductant

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

Effect of injection of different gases on removal of arsenic in form of dust from molten copper smelting slag prior to recovery process

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