Calibration problems with the viscosity measurement of liquid metallurgical slags

Heller, Hans P.; Schürmann, M.; Scholl, K.; Haustein, Nicole; Lychatz, Bernd; Falkus, J.

doi:10.1088/1742-6596/790/1/012010

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…When the temperature is higher than 980 • C, the Arrhenius formula was applied to express the viscosity of slag [40]. Therefore, a model of six-degree polynomial combined with Arrhenius formula has been established and the expression is as follows: (6) where η is the slag viscosity, P; T is the temperature, • C; A is a constant; E η is the activation energy; and R is the gas constant. The value of A and E η is mostly related to the composition of the slag as shown in Table 5.…”

Section: The Significant Factors and Prediction For Slag's Viscositymentioning

confidence: 99%

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Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

et al. 2020

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The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low misclassification rate. The simulation results show that temperature, SiO2, Al2O3, P2O5, and CaO have greater influences on the slag’s viscosity. The critical temperature for onset of the important influence of slag composition is 980 °C. Furthermore, it is found that SiO2 and P2O5 have positive correlations with slag’s viscosity, while temperature, Al2O3, and CaO have negative correlations. A two-equation model of six-degree polynomial combined with Arrhenius formula is also established for the purpose of providing theoretical guidance for industrial application and reutilization of slag.

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Section: The Significant Factors and Prediction For Slag's Viscositymentioning

confidence: 99%

“…It is concluded that slag with suitable viscosity is beneficial for improving the quality of molten metal and reducing the consumption of refractories as well as slag reutilization. Therefore, the viscosity of slag has been studied extensively through experimental measurements and modeling predictions [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

et al. 2020

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“…There may be 5-20% accuracy errors and 1-5% precision errors [7]. The experimental process of measuring viscosity is expensive both in terms of economics, since the equipment is expensive, in terms of time, since measuring viscosity requires homogenization of the temperature of the sample to be tested, and in terms of operator experience, since the minutiae of the experimental process can distort the final results [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Viskositas: Viscosity Prediction of Multicomponent Chemical Systems

dos¹

2022

Preprint

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Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics. As its experimental measurement is financially expensive, also in terms of time, several mathematical models were built to provide viscosity results as a function of several variables, such as chemical composition and temperature, in linear and nonlinear models. A database was built in order to produce a nonlinear model by artificial neural networks by variation of hyperparameters to provide reliable predictions of viscosity in relation to chemical systems and temperatures. The model produced named Viskositas demonstrated better statistical evaluations of mean absolute error, standard deviation and coefficient of determination (R 2 ) in relation to the test database when compared to different models from literature and 1 commercial model, offering predictions with lower errors, less variability and less generation of outliers.

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“…Las escorias contienen elementos que no se reducen o elementos oxidados, por lo que recoge algunos de los componentes indeseables en la mena y debido a su inmiscibilidad con el fundido metálico, separa del metal deseado sus componentes indeseables, ayuda a proteger al metal manteniendo su temperatura y previenen su oxidación, además de que reducen las pérdidas de calor desde la superficie y remueve inclusiones no metálicas (Kekkonen, Oghbasilasie, & Louhenkilpi, 2012). A pesar de la importancia de su función, las escorias siempre han sido un misterio para los metalúrgicos, principalmente en establecer las condiciones de la temperatura de fundición, el control de la partición de elementos mayores, menores y otros parámetros importantes del proceso (Gan & Lai, 2014;Heller et al, 2017;Raghunath, 2007).…”

Section: Introductionunclassified

Análisis de las propiedades del sistema CaO – FeO – SiO2 para su aplicación en escorias

Gamboa-Hernández

Campa-Núñez

Pedroza-Figueroa

et al. 2019

JOTI

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The study of ternary phases systems in pyrometallurgy is used for the adequate formation of slags, because they are an important foundation in establishing the conditions of operation in the separation of undesired elements. In this area, the CaO - FeO - SiO2 system is generally used for the determination of liquidus temperature and other properties mainly in the metallurgy of Pb and Fe. In this article we review the main properties of this system, such as density, surface tension and electrical conductivity. In addition, a compilation is carried out on the values obtained from the viscosity made by different authors at different compositions and temperatures, in order to verify the interaction that each component delivers to the equilibrium of the system. Through this research, the aim is to obtain the knowledge to predict the behavior of the slag that leads to making the decisions that improves the processes and this is only through reliable and consistent information.

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Calibration problems with the viscosity measurement of liquid metallurgical slags

Cited by 4 publications

References 6 publications

Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

Viskositas: Viscosity Prediction of Multicomponent Chemical Systems

Análisis de las propiedades del sistema CaO – FeO – SiO2 para su aplicación en escorias

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