Physics-Based Modeling of Electric Operation, Heat Transfer, and Scrap Melting in an AC Electric Arc Furnace

Opitz, Florian; Treffinger, Peter

doi:10.1007/s11663-015-0573-x

Cited by 30 publications

(12 citation statements)

References 13 publications

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“…Therefore, to be able to solve the equation, the backward reaction rate constant is assumed to be k b = 1, leading to the simplified Eq. [7]. In addition, in Eq.…”

Section: Equilibrium Reactionsmentioning

confidence: 99%

“…Opitz and Treffinger [7] use the model of Logar et al to design a dynamic, physics-based model of a complete EAF plant which consists of four sub-systems (vessel, electric system, electrode regulation, and off-gas system). Matson and Ramirez [8,9] consider six chemical elements in the off-gas and determine the reaction rates with a chemical equilibrium algorithm by Gibbs free energy minimization.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Simulation of the Off-gas in an Electric Arc Furnace

Meier

Gandt

Echterhof

et al. 2017

Metall Mater Trans B

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The following paper describes an approach to process modeling and simulation of the gas phase in an electric arc furnace (EAF). The work presented represents the continuation of research by Logar, Dovzˇan, and Sˇkrjanc on modeling the heat and mass transfer and the thermochemistry in an EAF. Due to the lack of off-gas measurements, Logar et al. modeled a simplified gas phase under consideration of five gas components and simplified chemical reactions. The off-gas is one of the main continuously measurable EAF process values and the off-gas flow represents a heat loss up to 30 pct of the entire EAF energy input. Therefore, gas phase modeling offers further development opportunities for future EAF optimization. This paper presents the enhancement of the previous EAF gas phase modeling by the consideration of additional gas components and a more detailed heat and mass transfer modeling. In order to avoid the increase of simulation time due to more complex modeling, the EAF model has been newly implemented to use an efficient numerical solver for ordinary differential equations. Compared to the original model, the chemical components H 2 , H 2 O, and CH 4 are included in the gas phase and equilibrium reactions are implemented. The results show high levels of similarity between the measured operational data from an industrial scale EAF and the theoretical data from the simulation within a reasonable simulation time. In the future, the dynamic EAF model will be applicable for on-and offline optimizations, e.g., to analyze alternative input materials and mode of operations.

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“…Therefore, to be able to solve the equation, the backward reaction rate constant is assumed to be k b = 1, leading to the simplified Eq. [7]. In addition, in Eq.…”

Section: Equilibrium Reactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

Meier

Gandt

Echterhof

et al. 2017

Metall Mater Trans B

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“…A thorough review of CFD models for the EAF process was presented by Odenthal et al [2]. Another commonly used modeling framework is by applying linear-or nonlinear programming with physicochemical-and process-based constraints to the mass-and energy balance equations of the EAF [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The approach solves the mass and energy balance equations for every time step from beginning to the end of the process.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

Carlsson

Samuelsson

Jönsson

2019

Metals

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Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes.

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“…Modeling is a valuable tool because it enables process engineers to evaluate proposed changes without interfering with the process, which may be more or less harmful to the steel plant supply chain. These modeling approaches can be, for example, physicochemical models using established relationships such as the mass-and energy balance equations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Another approach is through Computational Fluid Dynamics (CFD) modeling [20].…”

Section: Introductionmentioning

confidence: 99%

Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

Carlsson

Samuelsson

Jönsson

2019

Metals

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The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov-Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables.

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Physics-Based Modeling of Electric Operation, Heat Transfer, and Scrap Melting in an AC Electric Arc Furnace

Cited by 30 publications

References 13 publications

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

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