Coupled thermo-fluid stress analysis of Kambara Reactor with various anchors in the stirring of molten iron at extremely high temperatures

Huang, De-Shau; Huang, Feng-Chi

doi:10.1016/j.applthermaleng.2014.07.042

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…Schematic diagram of KR desulfurization is shown in Figure1. In process of KR desulfurization, an impeller with a protective covering of refractory materials is inserted into a certain depth below the liquid level of hot metal ladle [24], [25]. After adding desulfurization flux into the hot metal, the impeller is rotated to stir the hot metal and make its level form a whirlpool.…”

Section: A Description Of Kr Desulfurization Processmentioning

confidence: 99%

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

Yang

Zhang

et al. 2020

IEEE Access

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In the present work, the endpoint sulfur content prediction model of Kambara Reactor (KR) desulfurization in the steelmaking process is investigated. For Artificial Neural Network (ANN), the effects of different structure parameters, including the number of hidden layer neurons, activation functions and training functions, on the performance of desulfurization model are studied. The initial weights and biases of the neural network is optimized to further elevated the prediction accuracy of the model. Three models established by using Multiple Linear Regression (MLR), ANN and a hybrid algorithm (artificial neural network optimized by SAPSO, named SAPSO-ANN) are compared by the Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Relative Error (MARE). The results show that in the process of KR desulfurization, the nonlinear model of ANN and SAPSO-ANN has a higher accuracy than the linear model of MLR. Among the three models, the SAPSO-ANN model achieves the highest accuracy with R value of 0.54, RMSE of 2.61×10 −4 % and MAER of 0.47, which is selected to analyze the effect of process parameters on the desulfurization rate and design the amount of desulfurization flux in the KR desulfurization process. Experimental results show good agreements with the calculation results, indicating the practicability of the model. INDEX TERMS Artificial neural network, particle swarm optimization, multiple linear regression, prediction of endpoint sulfur content, KR desulfurization process.

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Section: A Description Of Kr Desulfurization Processmentioning

confidence: 99%

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

Yang

Zhang

et al. 2020

IEEE Access

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“…The rotary impeller method for hot metal desulphurization has been extensively studied in the last years. A wide variety of topics have been considered, among others: variable velocity stirring [7], coupled thermo-fluid stress analysis of the impellers at extremely high temperatures [8], a kinetic model that considers the contribution of dispersed particles of desulphurizing agent [2], use of residual mixtures of aluminum dross, fluorspar, and lime in the hot metal desulphurization [9], the effect of flux dispersion on the desulphurization reaction [10], effect of the flux addition method [11], and the use of residual marble, lime and fluorspar mixtures [12].…”

Section: Introductionmentioning

confidence: 99%

A Straightforward Mathematical Model of Hot Metal Desulphurization

Barrón¹,

Medina²,

Reyes³

2020

OJAppS

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A straightforward kinetic model for the hot metal desulphurization process is presented. The model contains some of the most important parameters and variables that govern the process. The inputs, outputs and parameters of a desulphurization program employed at industry are discussed. The model is able to cope with different injection policies of desulphurizers such as monoinjection, co-injection or multi-injection. When compared to the rotary impeller method, results of this model shows that the weight of lime consumed in the lance injection method is lesser than that consumed in the rotary impeller method for the same conditions of the hot metal charge.

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Computational Evaluation of Flow-Induced Abrasion of Blade and Ladle in Kanbara Reactors for Hot Metal Desulfurization

Shen³

et al. 2022

JOM

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Coupled thermo-fluid stress analysis of Kambara Reactor with various anchors in the stirring of molten iron at extremely high temperatures

Cited by 12 publications

References 10 publications

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

A Straightforward Mathematical Model of Hot Metal Desulphurization

Computational Evaluation of Flow-Induced Abrasion of Blade and Ladle in Kanbara Reactors for Hot Metal Desulfurization

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