Improving the quality of pig iron and steel

Yugov, P. I.; Romberg, A. L.; Du, Yang

doi:10.1007/bf02765136

Cited by 3 publications

(5 citation statements)

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“…Figure 11 shows the relation between the in uence of phases (Ca 3 ) adversely affects the rate constant and consequently decrease the sulphur removal from the metal. In this case, the tricalcium silicate is formed around the lime particle, which hinders the diffusion in the interface and delays the desulphurization.…”

Section: Calculating the Rate Constant (K)mentioning

confidence: 99%

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Use of Computational Thermodynamics in the Analysis of Hot Metal Desulphurization with Slags Based on Marble Waste and Sodalite

et al. 2016

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The steel industry is going through transformations aiming at the innovation and use of alternative raw materials. Thus, the use of industrial wastes in synthetic slag production is considered an important alternative to search for new materials and waste reuse. Therefore, the aim of the current study is to study the use of synthetic slags in hot metal desulphurization. It was proposed the use of sodalite instead of uorspar and the use of marble waste instead of lime. Marble waste was characterized by chemical analysis, X-ray diffraction (XRD) and thermogravimetric analysis. The XRD showed the presence of compounds such as CaCO 3 , MgCO 3 and SiO 2 . Subsequently, simulations using Thermo-Calc were performed in order to obtain thermodynamic data of the present phases, and to compare them to experimental data. The experimental procedures were carried out at 1400 C. In addition, hot metal tests were performed using an alumina rotor to increase the desulphurization. In hot metal desulphurization, solid CaO phase was responsible for sulfur removal. Furthermore, Ca 3 O 3 .SiO 2 and Ca 3 O 3 .Al 2 O 3 phases limited the reaction, and their concentrations were higher in the slags with marble waste and sodalite, due to the presence of SiO 2 and Al 2 O 3 in these raw materials.

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Section: Calculating the Rate Constant (K)mentioning

confidence: 99%

“…These inclusions work as crack initiating points during the deformation process, since they are more plasticity than steel [1][2][3] . Thus, it is necessary to remove it to certain types of steel.…”

Section: Introductionmentioning

confidence: 99%

Use of Computational Thermodynamics in the Analysis of Hot Metal Desulphurization with Slags Based on Marble Waste and Sodalite

et al. 2016

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“…However, the operation parameters are assigned according to the experience of operators in many steel plants that implies a high degree of randomicity in the production process. Some of the recent research findings of the current authors in this direction have been highly encouraging [1][2]7]. Artificial Neural Networks are revolutionary computing paradigms that are especially useful to address problems where solutions are not clearly formulated or where the relationships between inputs and outputs are not known.…”

Section: Introductionmentioning

confidence: 98%

“…It is necessary to desulfurize molten iron at different stages of the production process because the hot metal is usually required to contain less than 0.02% sulfur even for normal plain steel. There are different methods of desulfurizing molten iron in the production line, the main methods being the following: desulfurization of molten iron in the ladle with the use of a submersible lance; the ISID method; the Slide Gate method; the KR method; injection of magnesium through a lance with a vaporizer [1]. The KR method that is also called the mechanical stirring method has been successfully used in China for several years now.…”

Section: Introductionmentioning

confidence: 99%

A BP Neural Network Predictor Model for Desulfurizing Molten Iron

Zeng

Dan

2005

Advanced Data Mining and Applications

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Abstract. Desulfurization of molten iron is one of the stages of steel production process. A back-propagation (BP) artificial neural network (ANN) model is developed to predict the operation parameters for desulfurization process in this paper. The primary objective of the BP neural network predictor model is to assign the operation parameters on the basis of intelligent algorithm instead of the experience of operators. This paper presents a mathematical model and development methodology for predicting the three main operation parameters and optimizing the consumption of desulfurizer. Furthermore, a software package is developed based on this BP ANN predictor model. Finally, the feasibility of using neural networks to model the complex relationship between the parameters is been investigated.

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“…Contudo, esta remoção não é suficiente para os requisitos de aplicação exigidos pelo mercado. Além disso, devem ser consideradas as novas políticas das empresas em diminuir custos de produção nos altos-fornos (6) .…”

Section: Introductionunclassified

Estudo da substituição da fluorita por alumina ou sodalita e de cal por resíduo de mármore em escórias sintéticas dessulfurantes.

Grillo¹

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Improving the quality of pig iron and steel

Cited by 3 publications

References 0 publications

Use of Computational Thermodynamics in the Analysis of Hot Metal Desulphurization with Slags Based on Marble Waste and Sodalite

Use of Computational Thermodynamics in the Analysis of Hot Metal Desulphurization with Slags Based on Marble Waste and Sodalite

A BP Neural Network Predictor Model for Desulfurizing Molten Iron

Estudo da substituição da fluorita por alumina ou sodalita e de cal por resíduo de mármore em escórias sintéticas dessulfurantes.

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